Cours_n3_Amine_Ammar_DerivationIntegrationApproximation__Interpolation_v1_2014-02-24

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Life and death in the granuloma--- immunopathology of tuberculosis

REVIEWLife and death in the granuloma:immunopathology of tuberculosisBernadette M Saunders 1,2and Warwick J Britton 1,2During tuberculosis (TB)infection,the granuloma provides the microenvironment in which antigen-speciﬁc T cells colocate with and activate infected macrophages to inhibit the growth of Mycobacterium tuberculosis .Although the granuloma is the site for mycobacterial killing,virulent mycobacteria have developed a variety of mechanisms to resist this macrophage-mediated killing.These surviving mycobacteria become dormant,however,if host cellular immunity or the signals maintaining granuloma structure wane,or if mycobacteria resume replication,leading to reactivation of TB.This balance of life and death applies not only to the mycobacterium but also to the host macrophages that may undergo apoptosis or necrosis,leading to the characteristic caseous necrosis within the granuloma,and the potential spread of TB infection.The immunological factors controlling the development and maintenance of the granuloma will be reviewed.Immunology and Cell Biology (2007)85,103–111.doi:10.1038/sj.icb.7100027;published online 9January 2007Keywords:granuloma;tuberculosis;reactivation;cytokines;chemokinesThe formation and maintenance of granulomas are essential for the control of mycobacterial infections but,paradoxically,granulomas are also responsible for the typical immunopathology caused by these infections.There are over 70species of Mycobacteria,but Mycobacter-ium tuberculosis and Mycobacterium leprae,the causative agents of tuberculosis (TB)and leprosy,are the major human pathogens.These slow-growing mycobacteria have adapted to survival within the macrophage,and this capacity for persistence of mycobacteria in the face of a potent cellular response underlies the chronic inﬂammatory reaction of the host.Mycobacterial infection of dendritic cells (DCs)stimulates CD4and CD8T cells,which on recruitment to the sites of infection activate infected macrophages.M.tuberculosis ,however,blocks phago-lysosomal fusion and acidiﬁcation of infected phago-somes,and also partially inhibits the activation of infected macro-phages by interferon (IFN)-g ,the major effector cytokine released by Th1-like CD4T cells.As a result,some mycobacteria persist in the infected lung,leading to chronic antigenic stimulation and T-cell accumulation around macrophages.In the face of chronic cytokine stimulation,macrophages differentiate into epithelioid cells and fuse to form typical giant cells.Within the resulting granuloma,there is a balance between mycobacterial killing and survival.The local archi-tecture results in the close apposition of lymphocytes and macro-phages,and this is necessary for the activation of macrophages to kill M.tuberculosis.But the survival of some tuberculous bacilli leads to latent TB infection (LTBI),which is contained by the granulomatous process.Following acute M.tuberculosis infection,this process is adequate to control the infection in 95%of subjects,while theremainder progress to primary TB disease.There are currently two billion humans with LTBI,and reactivation of the infection occurs in 5–7%1of these subjects without,and in up to 50%with,human immunodeﬁciency virus (HIV)co-infection.Although granulomas can develop during other infections such as schistosomiasis and in non-infectious conditions,including sarcoido-sis and Crohn’s disease,this brief review will focus on recent deve-lopments in our understanding of mycobacterial granulomas.In particular,it will review the regulation of the adaptive CD4T-cell response essential for the formation of granulomas,the role of tumor necrosis factor (TNF)family members and chemokines,and the processes leading to sustained latency or reactivation of infection in humans.ADAPTIVE IMMUNITY AND GRANULOMA FORMATION Cellular requirements for granuloma formationThe activation of ab T-cell receptor (TcR)expressing CD4T cells is essential for the formation of granulomas during mycobacterial infections.2Additional types of T cells contribute to the immune response during M.tuberculosis infection in mice and humans,including ab TcR expressing CD8T cells,gd TcR expressing T cells and CD1-restricted CD4/CD8double negative T cells;however,granulomas develop during experimental M.tuberculosis infection of mice deﬁcient in these cells (Table 1).3,4For example,the initial control of infection and inﬂammatory response is normal in CD8T-cell deﬁcient mice,although later in infection there is increased bacterial load and lymphocyte accumulation in the lungs.3ThisReceived 3November 2006;revised 7November 2006;accepted 8November 2006;published online 9January 20071MycobacterialResearch Programme,Centenary Institute,Newtown,New South Wales,Australia and 2Discipline of Medicine,Central Clinical School,University of Sydney,Sydney,AustraliaCorrespondence:Dr B Saunders,Mycobacterial Research Programme,Centenary Institute,Locked Bag No 6,Newtown,2042New South Wales,Australia.E-mail:b.saunders@.auImmunology and Cell Biology (2007)85,103–111&2007Australasian Society for Immunology Inc.All rights reserved 0818-9641/07$/icbcontrasts with the marked susceptibility to M.tuberculosis of b2-microglobulin À/Àmice,which lack classical and non-classical major histocompatibility complex (MHC)class I molecules and therefore both CD8-and CD1-restricted T cells.5The increased iron stores in these mice,which is due to the lack of the b 2-microglobulin-associated iron transport molecule,HFE,also contribute to the failure to control intracellular mycobacterial infection.6The CD4T cells are stimulated by M.tuberculosis -infected DCs in the lymph nodes draining the lung,7and differentiate into Th1-like CD4T cells secreting interleukin (IL)-2,IFN-g and lymphotoxin-a (LT a ).Infection of DCs with M.tuberculosis or the vaccine strain Mycobacterium bovis bacille Calmette Guerin (BCG)results in upregu-lation of MHC class II and the costimulatory molecules,CD80and CD86,in a TLR2-dependent fashion 8,9and the secretion of IL-12and the pro-inﬂammatory cytokines.Multiple components of myco-bacteria can stimulate TLR1/2,TLR2and TLR9in vitro (reviewed in Krutzik and Modlin 10),and mice deﬁcient in both TLR2and TLR9fail to develop normal CD4T-cell responses and are profoundly suscep-tible to M.tuberculosis infection.11DCs,rather than macrophages,are the primary source of both IL-12and IL-23during mycobacterial infection,12and their secretion is enhanced by further activation of the infected DCs by IFN-g and CD40ligand-mediated interaction from CD4T cells.13This IL-12response is downregulated by endogenous IL-10,which is also released by mycobacteria-infected DCs.8Cytokine requirementsDissection of the genetic basis for Mendelian susceptibility to myco-bacterial infections (MSMD)in humans,with marked susceptibility to BCG and non-tuberculous mycobacteria such as Mycobacterium avium,along with studies in gene-deﬁcient mice,have helped deﬁne the cytokine requirements for the activation and function of CD4T cells against mycobacteria.Variants in ﬁve genes have been asso-ciated with MSMD,IFN-g receptor chains 1(R1)and IFN-g R2,Stat1in the IFN g R signalling pathway,IL-12p40and IL-12receptor R b 1.14As observed in IFN-g -deﬁcient mice,subjects with defects in the IFN-g signalling pathway are profoundly susceptible to mycobacterial infec-tions,and these present at an early age and are difﬁcult to treat.14These subjects fail to develop granulomas in tissues infected with mycobacteria,demonstrating the absolute requirement of IFN-g signalling for the activation of macrophages and granuloma forma-tion.Mice that lack IFN-g 15also fail to develop granulomas following aerosol M.tuberculosis infection,and instead of discrete granulomas composed of activated macrophages and lymphocytes,their lungs are inﬁltrated by neutrophils leading to necrosis and death necrosis.16The differentiation of Th1T cells is dependent on the transcription factor T-bet,17and mice deﬁcient in T-bet have reduced IFN-g responses andincreased susceptibility following M.tuberculosis infection.18Interest-ingly,the lungs of M.tuberculosis T-bet À/Àmice develop a striking inﬁltrate of eosinophilic macrophages and multinucleate giant cells with neutrophils rather than well-formed granulomas.Within the granuloma,IFN-g stimulates macrophages to kill mycobacteria through a variety of pathways,including activation of phagocyte oxidase and inducible nitric synthase,19to produce reactive oxygen and nitrogen metabolites,and the upregulation of the GTPase,LRG47,which stimulates phago-lysosomal fusion.20The development of mycobacteria-speciﬁc IFN-g secreting effector T cells is also dependent on the cytokine milieu during their activation by infected DCs.IL-12and IL-23are critical cytokines for the development of this Th1-like pattern of cellular immunity.They are heterodimeric cytokines composed of a shared IL-12p40chain with IL-12p35or IL-23p19chains for IL-12and IL-23,respectively,and their receptors also share a common IL-12R b 1chain.21Humans with mutations in either IL-12p40or IL-12R b 1are deﬁcient in both IL-12and IL-23signalling and have reduced IFN-g T-cell responses.22These subjects were identiﬁed because of increased susceptibility to myco-bacterial MSMD and Salmonella infections,but there are signiﬁcant differences to those with IFN-g signalling deﬁciency.The infectious syndromes are not as severe and may present later in life.Following successful therapy,they do not recur as readily,suggesting that partial immunity has developed despite IL-12/IL-23deﬁciency.23Moreover,these subjects do develop granulomas with aggregations of lympho-cytes and macrophages in infected organs,rather than the sheets of heavily infected macrophages present in IFN-g -deﬁcient subjects.This has led to the suggestion that the IL-12/IL-23axis is not as important in humans 23as in mice,where IL-12p40is essential for the development of competent antimycobacterial T-cell responses.24Some IL-12R b 1-deﬁcient individuals show evidence of an IL-12-indepen-dent pathway of IFN-g production,25and this may be sufﬁcient for macrophage activation and containment of mycobacterial infection within granulomas in some individuals.In addition,there are func-tional differences between different IL-12R b 1alleles so that the degree of signalling impairment,subsequent IFN-g deﬁciency and clinical phenotype varies between individuals.26IL-23is also important in the differentiation of a separate effector T-cell population secreting pro-inﬂammatory cytokines of the IL-17family and TNF.These IL-17-secreting T cells are implicated in the pathogenesis of autoimmune inﬂammatory processes in mice,27and the major role for IL-23was suggested to be the development of these pro-inﬂammatory T H 17cells,whereas IL-12is the critical regulatory cytokine for immunity against intracellular pathogens.Indeed,IL-12can compensate for the absence of IL-23in the control of M.tuberculosis infection;28however,this does not exclude IL-23Table 1Contribution of T-cell subsets for normal granuloma formation following aerosol M.tuberculosis infectionKO strain Survival Granuloma formationReferenceab T cell 48days Extensive inﬂammatory inﬁltration,necrotic lesions dominated by neutrophils.4CD4120days Delayed inﬂammation with few lymphocytes,predominantly macrophages localized as perivascular cufﬁng accompanied by increased neutrophils.2CD84150days Normal granuloma development initially apart from increased lymphocytes.During chronic infection,there was increased neutrophils and necrosis.60b 2m 40days Increased inﬂammatory inﬁltrate dominated by macrophages and neutrophils with caseating necrosis.Lymphocytes were restricted to the adjacent perivascular cuff.113CD1Normal Normal granuloma development.3gd T cellNormalIncreased neutrophils within granulomas,otherwise normal.114Life and death in the granuloma BM Saunders and WJ Britton104Immunology and Cell Biologyplaying a role during mycobacterial infections.Plasmid IL-23is as effective as plasmid IL-12,an adjuvant to enhance the protective effects of DNA vaccines against pulmonary TB,29and plasmid IL-23 can complement IL-12p40deﬁciency in the induction of protective IFN-g T-cell responses against TB.30In addition,it is now clear that the induction of IL-17-producing T-cell responses are not dependent on IL-23in vivo,but rather they develop in the absence of the inhibitory effects of IFN-g.Thus,BCG immunization of IFN-g or IL-12p40-deﬁcient mice leads to the emergence of mycobacteria-speciﬁc T cells secreting IL-17and TNF.30The contribution of these pro-inﬂammatory T cells to granuloma formation and their role in protective immunity remains to be clariﬁed.Regulation of inﬂammatory responsesAlthough granulomas are essential for the containment of mycobac-teria,this intense inﬂammatory response in the presence of persisting mycobacteria has the propensity for tissue damage.Therefore,regula-tion of the granulomatous process is essential.This occurs in part through mechanisms for regulating the expansion of activated T cells common to other infections;however,additional processes for regulating the granulomatous response have been deﬁned.First, IFN-g itself,while essential for granuloma formation,may also limit the uncontrolled expansion of mycobacteria-reactive T cells. In the M.avium model of pulmonary infection,IFN-gÀ/Àmice accumulate large lymphocyte inﬁltrates in the lungs,which undergo necrosis and form cavities consistent with unrestrained expansion of the T cells.31Second,endogenous IL-10,produced during mycobac-terial infections,can reduce IL-12production from mycobacteria-infected DCs,migration of DCs to draining lymph nodes and the expansion of IFN-g-secreting T cells.32In addition,IL-10‘de-activates’macrophages and reduces the impact of IFN-g on macrophage activation and resultant mycobacterial killing.This effect is most apparent during infection with less virulent mycobacteria such as M.avium;however,IL-10-deﬁcient mice,infected with M.tuberculosis, show a transient increase in IFN-g-secreting T cells and decrease in bacterial burden,consistent with a partial inhibitory effect of IL-10.33 An additional characteristic of T-betÀ/Àmice during M.tuberculosis infection was the increased production of IL-10in the lungs as well as reduced IFN-g production,and this IL-10may have contributed to the ineffectual macrophage responses in these mice.18Overexpression of huIL-10in antigen-presenting cells in the lungs of M.avium-infected mice resulted in markedly increased bacterial burdens with a marked increase in macrophages in the granulomas.34Macrophage apoptosis as well as the production of TNF,IL-12p40and nitric oxide were reduced,indicating that IL-10from macrophages and DCs exerts an inhibitory effect on the control of mycobacterial infection by suppres-sing macrophage effector mechanisms.More recently,IL-27has been found to provide another regulatory network for controlling inﬂammatory responses to mycobacteria.IL-27,which is a heterodimeric cytokine produced by DCs and macro-phages,wasﬁrst characterized as an immunoregulatory cytokine that increased the production of IFN-g from naı¨ve CD4T cells.35In vitro studies demonstrated that signalling through IL-27R on CD4T cells activated Stat1and T-bet,suggesting IL-27sensitized naı¨ve CD4 T cells to the Th1polarizing effects of IL-12.36Subsequently, M.tuberculosis infection of IL-27R(WSX-1)-deﬁcient mice revealed that IL-27played an inhibitory role in the protective immune response during theﬁrst3months of infection,when the IL-27RÀ/Àmice showed reduced bacterial burden in the lungs.37Late accumulation of IFN-g-secreting cells in the IL-27RÀ/Àmice caused severe lung pathology.This is consistent with the lethal CD4T-cell-mediated immunopathology that developed in IL-27À/Àmice infected with Toxoplasma gondii.38This downregulation of the adaptive CD4T-cell response to infection by IL-27occurs through a variety of mechan-isms,including the IL-27-mediated inhibition of IL-2productionby CD4T cells and the increased expression of SOCS-3.38IL-27also inhibits IL-17production,independent of the IFN-g-mediated suppression of IL-17.39Finally,T regulatory cells(Tregs)may also inhibit the CD4T-cell response and limit the granulomatous inﬂammatory response toM.tuberculosis.Urdahl et al.40have recently shown that Foxp3-expressing Tregs are recruited to the lung during experimental TB infection in mice along with effector T cells.Depletion of Tregs before infection resulted in increased clearance of the bacteria from the lung, indicating that the Tregs were inhibiting the T-cell-effector mechan-isms.The mechanisms of this effect of Tregs during mouse TB andtheir possible role in human TB remain to be elucidated. MICROARCHITECTURE OF THE GRANULOMAThe presence of granulomas in infected tissue is a hallmark of mycobacterial disease.This requires the coordinated recruitment of macrophages and lymphocytes across the endothelium,their migration through infected tissues and aggregation to form granulomas.This permits the close interaction of antigen-speciﬁc T cells with infected macrophages, which act to contain the bacilli and prevent further spread.The cellular and chemical factors controlling granuloma formation are considerable and incompletely understood.It is clear,however,that members of the TNF family,most notably TNF and LT a,are involvedin this process.TNF family members and granuloma developmentThe TNF superfamily currently consists of19ligands and29recep-tors.41TNF ligands are type II transmembrane proteins,that assembleas biologically active trimers.TNF receptors are type I transmembrane proteins characterized by the presence of one to six cysteine-rich domains.41TNF family members have wide-ranging involvement in immune homeostasis,activation of both macrophages and T cells,andinﬂammation.T o date the function of at least20family membershas been examined during host responses mycobacterial infections (Figure1).Five of these molecules(soluble TNF,membrane-boundTNF(MemTNF),LT a,TNFRI and CD40)are essential for normal granuloma formation and survival following M.tuberculosis infec-tion.42–46For example,in the absence of TNF,there was a delay in theinitial recruitment of monocytes into infected tissue.42,43Despite normal activation of antigen-speciﬁc T cells,42,43this was followedby a dysregulated inﬂammatory response,with the inﬂux of neutro-phils,formation of necrotic lesions,uncontrolled bacterial growthand the rapid demise of infected animals.Interestingly,mice lackingLT a,which binds to the TNFRI molecule in addition to TNFRII and HVEM,also exhibit marked susceptibility to TB infection,with a similar dysregulated inﬂammation and overwhelming bacterial growth despite the production of normal levels of TNF.45This demonstrated separate,non-compensatory functions for TNF and LT a,and this has subsequently been conﬁrmed during malaria and Listeria mono-cytogenes infections.47,48Both TNF and LT a bind TNFRI and TNFRII;however,signallingfor protective effects occurs primarily through TNFRI,as TNFRIIÀ/Àmice display only minor increased susceptibility to mycobacterial infection.49LT a also binds the HVEM receptor,but the importanceof this interaction in antibacterial immunity or inﬂammation has notbeen established.Interestingly,TNF-and LT a-deﬁcient micedisplayLife and death in the granulomaBM Saunders and WJ Britton105Immunology and Cell Biologymarked differences in their response to Mycobacterium leprae infec-tion.50While loss of either cytokine augments growth of M.leprae ,leukocyte inﬁltration in TNF À/Àmice is more extensive with some lymphocyte inﬁltration,while LT a À/Àmice exhibited less inﬂamma-tion with minimal lymphocyte inﬁltration.Further,while the chemo-kine responses in infected wild type (WT)and TNF À/Àmice were similar,LT a mice demonstrated ﬁve-to 25-fold lower levels of mRNA expression for the chemokines tested.Therefore,the inﬂammatory response of TNF-and LT a -deﬁcient mice are not identical and are inﬂuenced by the virulence of the mycobacterial pathogen.Soluble and membrane-bound forms of TNF and LT make different contributions to granuloma formation and protective immunity.Deletion of LT a removes both soluble and membrane-bound forms of LT,whereas deletion of LT b leaves the soluble LT a trimer intact.We have previously shown that mice lacking LT a ,but not LT b ,are more susceptible to TB infection,45indicating that it is the soluble LT a that coordinates granuloma formation.TNF also has membrane-bound and soluble forms.In this instance,both memTNF and soluble TNF are required for optimal resistance to mycobacterial infec-tion.44,51MemTNF alone was sufﬁcient for the establishment of normal granulomatous lesions and containment of bacterial growth for three months.44Further,these mice developed a normal antigen-speciﬁc T-cell response,but despite this,increased numbers of activated T cells accumulated in the lung during the chronic phase of infection.This resulted in progressive inﬂammation in the lung,and the memTNF mice succumbed after day 120despite control of bacterial growth.44,51Therefore,memTNF is sufﬁcient for the initial formation of granulomas,but soluble TNF is required for long-term optimal control of mycobacterial infection.Investigation of other members of the TNF superfamily (Figure 1)has demonstrated that TNFRII,CD40L,CD95,LIGHT and CD30are required for optimal immunity to mycobacterial infection.Mice lacking these TNF family molecules showed transient or partial susceptibility to mycobacterial infection.A common feature of these molecules is their expression during the immunopathological response to TB infection.For instance,CD40L is expressed on alveolar macro-phages of tuberculoid granulomas,and increased mRNA for CD40L is present in M.tuberculosis -infected macrophages.52,53CD40–CD40L interaction induces a cascade of events,including the production ofcytokines and chemokines.The absence of CD40L impaired granu-loma formation after BCG and M.avium infection,leading to increased necrosis and impaired control of bacterial growth;however,CD40L À/Àmice survived M.tuberculosis infection despite impaired granuloma formation.46,54–56Interestingly,CD40À/Àmice were more susceptible to M.tuberculosis infection than CD40L À/Àmice,exhibit-ing marked inﬂammation with increased bacterial growth.46Human studies identiﬁed variants in the CD40L gene,but none of these was associated with increased susceptibility to TB.57LIGHT,which shares common receptors with LT,is required for the optimal activation of both CD4and CD8T cells in vitro .58Never-theless,LIGHT À/Àmice infected with M.tuberculosis displayed only a transient exacerbation of bacterial load at 4weeks post infection,with no change in the granulomatous response (Saunders,unpub-lished observations,Ehlers et al .59).FAS/FASL signalling is critical for T-cell homeostasis and activation of macrophage apoptosis.Mice deﬁcient in FAS/FASL signalling controlled acute TB infection,but displayed increased pyogenic inﬂammation and bacterial growth during the chronic phase of infection.60Therefore,signalling through both TNFRI and FAS is required for optimal immunity to mycobac-terial infection and for regulation of the inﬂammatory response.Finally,antibody neutralization of the TNF family members,CD27,CD30,IBB1and OX40L,indicated that only CD30was required to control M.avium infection.61Further,CD30À/Àmice displayed reduced T-cell activation and diminished lymphocyte cufﬁng around granulomas,conﬁrming its requirement for optimal granuloma form-ation.A common feature of TNF family members,in particular TNF and LT a ,in immunity to TB infection,is their regulation of chemo-kine and chemokine receptor expression,that controls the recruitment of inﬂammatory cells.Chemokine requirements for granuloma formationThe differential regulation of chemokines and their receptors during M.tuberculosis infection in humans is increasingly recognized (Table 2);however,the function and contribution of individual chemokines or receptors to this response is still uncertain.The establishment of chemokine gradients is crucial for the recruitment of inﬂammatory cells and their aggregation to form granulomas.Multiple studies have demonstrated elevated levels of chemokines,Figure 1TNF superfamily involvement in granuloma formation and resistance to mycobacterial infection.Reported:Dark gray ligands and receptors areessential for normal granuloma formation and sustained resistance to mycobacterial infection.Pale gray ligands and receptors are required for optimal protective immunity to mycobacterial infection.Unﬁlled ligands and receptors were not required for normal granuloma formation and expression of protective immunity to mycobacterial infection.Unreported:Ligands and receptors whose function in granuloma formation and resistance to mycobacterial infection has not yet been reported.Life and death in the granuloma BM Saunders and WJ Britton106Immunology and Cell Biologyincluding MCP-1,MIP-1a,RANTES,IL-8and IP-10in the serum and bronchial alveolar lavage of TB patients,compared to healthy con-trols.62–66Human monocytes,macrophages and DCs infected with M.tuberculosis produce elevated levels of multiple chemokines, including MIP-1a,MIP-1b,MCP-1,MCP-3,RANTES,IP-10and IL-8.67–71There is also growing evidence that the expression of chemokines and the chemokine receptors CCR5and CXCR4are perturbed in HIV co-infected individuals,with increased and decreased expression depending upon the study and chemokine or receptor examined.This altered expression may be one mechanism leading to enhanced disease progression in HIV M.tuberculosis-co-infected individuals.72,73The exact requirement for individual chemokines and their recep-tors in generating protective granuloma formation during TB infec-tion remains uncertain.Studies utilizing gene-deﬁcient mice have generally indicated a high degree of redundancy in this system (Table3).Indeed,the absence of individual chemokines is adequately compensated during murine M.tuberculosis infection.As most che-mokine receptors are bound by multiple ligands,these may be more critical to coordinate cellular inﬂammation and granuloma formation. For instance,CCR1binds several ligands,including MIP-1a,MIP-1b RANTES and MCP-3,whose expression is upregulated during myco-bacterial infection.74Nevertheless,CCR1À/Àmice control M.tubercu-losis infection normally,with no abnormality in granuloma size and structure(Saunders,unpublished observations).Indeed,of the che-mokine and chemokine receptor knockout mice challenged with M.tuberculosis(Table3),75–79only CCR2was essential to control high dose M.tuberculosis infection,76,78with aberrant granuloma formation a distinguishing feature of this enhanced susceptibility.Despite this redundancy in the effect of chemokines in mice, individual chemokines may be important in the resistance of humans to TB.For example,genetic analysis revealed that a single-nucleotide polymorphism in the MCP-1gene was associated with increased risk of developing pulmonary TB.80WT individuals and homozygotes for this mutation show an inverse relationship between MCP-1and IL-12p40levels.Interestingly,MCP-1À/Àmice show equivalent control of M.tuberculosis infection to WT mice,81suggesting that there may be differences in the requirements for individual chemokines and their receptors between humans and mice.WHEN GRANULOMAS FAILGranulomas fail to form in the absence of either adaptive IFN-g-secreting CD4T-cell responses or the co-locating signals provided by TNF and its family members.This is evident in gene-deﬁcient strains of mice and subjects lacking functional IFN-g receptor/STAT1signal-ling.14But the majority of humans infected with M.tuberculosis control the initial infection through the formation of granulomas, which provide the microenvironment where speciﬁc T cells activate macrophages to contain the infection.Despite optimal activation, human macrophages fail to eradicate M.tuberculosis infection,and the dormant bacilli may persist for decades.In a minority of infected subjects,the granuloma fails for a variety of reasons,leading toTable2Chemokine expression in tissues from M.tuberculosis-infected individualsIn vivo/exvivo sample Chemokines Response ReferencePleura MIP-1a,MIP-1bMig,RANTESIP-10,MCP-1Increased expression in pleuraﬂuid from TB patientsMIP-1a,Mig,RANTES and IP-10were expressed on pleural mononuclear cells and stroma cells,whereas CXCR3and CCR5were expressed on T cells in pleura64,115–117MCP-1,MIP-1a,MIP-1b Compared to TB infection alone,MCP-1expression increased in HIV-TB co-infected individuals,whereas MIP-1a,MIP-1b and RANTES expression was decreasedBALF IP-10,IL-8MCP-1,MCP-3,MCP-4RANTESIncreased in TB patients62,65,66,118,119MIP-1aExotaxinNot increased over healthy controlsLung MCP-1,MCP-3,MCP-4,IP-10Increased in TB patientsTGF-b,IFN-g and IL-12were also increased in TB granulomas,whereas IL-4and IL-10weredecreased66,120Eotaxin Not increasedAlveolar macropha-ges CCR5RANTESMIP-1aMCP-1Upregulated following M.tuberculosis infection in PBMC and alveolar macrophages65,74,121Plasma IL-8,IP-10Elevated in TB patients and in those with concurrent HIV infection73,117,122–124 MCP-1,RANTESMIP-1a,MIP-1bRANTESIP-10and MCP-1even higher in HIV-co-infected patients than TB infected individuals aloneMCP-1Not increased in all TB patients,higher in patients with extrapulmonary TBPBMC MIP-1aRANTES Increased in M.tuberculosis-infected PBMC from TB patients.Levels were lower in HIV co-infectedindividuals65,125126127MCP-1Increased mRNA and protein in monocytes from TB patients IL-8Increased following M.tuberculosis infectionCerebral spinalﬂuid MCP-1,IL-8,MIP-1a Elevated in CSF from patients with pyogenic and tuberculous meningitis128Life and death in the granulomaBM Saunders and WJ Britton107Immunology and Cell Biology。

A gut microbiota-targeted dietary intervention for amelioration of chronic inflammation

R E S E A R C H A R T I C L EA gut microbiota-targeted dietary intervention for amelioration of chronic inﬂammation underlying metabolic syndromeShuiming Xiao 1,Na Fei 1,Xiaoyan Pang 1,Jian Shen 2,Linghua Wang 1,Baorang Zhang 1,Menghui Zhang 1,Xiaojun Zhang 1,Chenhong Zhang 1,Min Li 1,Lifeng Sun 1,Zhengsheng Xue 1,Jingjing Wang 1,Jie Feng 1,Feiyan Yan 1,Naisi Zhao 1,Jiaqi Liu 1,Wenmin Long 1&Liping Zhao 1,21State Key Laboratory of Microbial Metabolism,School of Life Sciences and Biotechnology,Shanghai Jiao Tong University,Shanghai,China;and 2Ministry of Education Key Laboratory of Systems Biomedicine,Shanghai Centre for Systems Biomedicine,Shanghai Jiao Tong University,Shanghai,ChinaCorrespondence:Liping Zhao,Room 3-517,Biology Building #800Dongchuan Road,Minhang Campus,Shanghai Jiao Tong University,Shanghai 200240,China.Tel.:+862134204877;fax:+862134204878;e-mail:lpzhao@Received 4June 2013;revised 14September 2013;accepted 17September 2013.Final version published online 21October 2013.DOI:10.1111/1574-6941.12228Editor:Julian MarchesiKeywordsgut microbiota;dietary intervention;chronic inﬂammation;metabolic syndrome.AbstractChronic inﬂammation induced by endotoxin from a dysbiotic gut microbiota contributes to the development of obesity-related metabolic disorders.Modiﬁ-cation of gut microbiota by a diet to balance its composition becomes a prom-ising strategy to help manage obesity.A dietary scheme based on whole grains,traditional Chinese medicinal foods,and prebiotics (WTP diet)was designed to meet human nutritional needs as well as balance the gut microbiota.Ninety-three of 123central obese volunteers (BMI ≥28kg m À2)completed a self-controlled clinical trial consisting of 9-week intervention on WTP diet followed by a 14-week maintenance period.The average weight loss reached 5.79Æ4.64kg (6.62Æ4.94%),in addition to improvement in insulin sensi-tivity,lipid proﬁles,and blood pressure.Pyrosequencing of fecal samples showed that phylotypes related to endotoxin-producing opportunistic patho-gens of Enterobacteriaceae and Desulfovibrionaceae were reduced signiﬁcantly,while those related to gut barrier-protecting bacteria of Biﬁdobacteriaceae increased.Gut permeability,measured as lactulose/mannitol ratio,was decreased compared with the baseline.Plasma endotoxin load as lipopolysac-charide-binding protein was also signiﬁcantly reduced,with concomitant decrease in tumor necrosis factor-a ,interleukin-6,and an increase in adiponec-tin.These results suggest that modulation of the gut microbiota via dietary intervention may enhance the intestinal barrier integrity,reduce circulating antigen load,and ultimately ameliorate the inﬂammation and metabolic phenotypes.IntroductionThe rapidly increasing prevalence of obesity and associated metabolic disorders has become a global public health threat (James,2008).It is widely accepted that obesity is the result of a positive long-term energy imbalance with multi-factorial etiologies involving genetic,metabolic,and envi-ronmental factors.Among the complex interactive processes,dietary pattern is considered to be of centralimportance (Bull oet al.,2007).Diet-induced obesity and metabolic abnormalities are closely associated with a chronic,low-grade,systemic inﬂammation as an importantpathological driving force (Wellen &Hotamisligil,2005;Shoelson et al.,2007).Recent evidence indicates that the alteration in compo-sition and/or activity of gut microbiota,the other genome that modulates human health,plays a pivotal role in the pathogenesis of obesity and related disorders (Mussoet al.,2011;Zhao,2013).Besides diet itself (Bull oet al.,2007),lipopolysaccharide,the cell wall component of Gram-negative bacteria living in the gut,has been dem-onstrated to induce the chronic inﬂammation in obesity,as puriﬁed lipopolysaccharide induced obesity and insulin resistance when subcutaneously infused into mice fed aFEMS Microbiol Ecol 87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley &Sons Ltd on behalf of the Federation of European Microbiological SocietiesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License,which permits use and distribution in any medium,provided the original work is properly cited,the use is non-commercial and no modiﬁcations or adaptations aremade.M I C R O B I O L O G Y E C O L O G Ynormal diet(Cani et al.,2007).Knockout of the CD14 receptor gene,a coreceptor of TLR4,abolished the obes-ity-inducing capacity of the infused lipopolysaccharide, indicating that endotoxin-provoked inﬂammation is a critical condition for development of obesity and insulin resistance.An increase in the number of endotoxin-pro-ducing bacteria and elevated endotoxin load has also been observed in various obese cohorts from epidemiological studies(Guerra et al.,2007;Lepper et al.,2007;Moreno-Navarrete et al.,2011).Due to the plasticity of the gut microbiota composition and the dominant role of diet in shaping its repertoire (Zhang et al.,2010),modulating gut microbiota by designed dietary intervention becomes a potentially promising strategy to demonstrate this chain of causation. Treatment aimed at gut microbiota by prebiotic nutrients has yielded encouraging results for the therapy of meta-bolic disorders in experimental models(Cani et al.,2008; Zhang et al.,2012).Some clinical studies using dietary intervention to manipulate gut microbiota and host metabolism have succeeded in linking the intervention to beneﬁcial phenotypic changes(Parnell&Reimer,2009). However,evidence is still needed to establish a causal link between dietary interventions,alteration of gut microbi-ota,and alleviation of inﬂammation in humans.We have designed a gut microbiota-targeted dietary intervention for central obese volunteers and showed in several case studies that this scheme can help morbidly obese volun-teers lose substantial amounts of weight and recover from metabolic disorders.Our previous study showed that one morbidly obese volunteer lost51.4of174.9kg initial weight in23weeks by this dietary intervention(Fei& Zhao,2013).We found that one endotoxin-producing bacterium,Enterobacter cloacae B29,was overgrown in the gut of this volunteer before intervention and induced obesity and insulin resistance in germfree mice(Fei& Zhao,2013).The B29-induced obese mice showed increased endotoxin load in their serum,elevated inﬂam-mation both systemically and locally in liver and fat pad, and a disrupted expression pattern of genes in lipometab-olism favoring fat synthesis and storage.To assess the possible contribution of changed gut microbiota to improve host health,we organized a self-controlled dietary intervention trial with123central obese volun-teers[body mass index(BMI)≥28kg mÀ2].The key indicators along the chain of causation as follows:gut microbiota composition(454pyrosequencing),integrity of intestinal barrier(lactulose to mannitol excretion ratio),metabolic endotoxemia(lipopolysaccharide-bind-ing protein,LBP),inﬂammatory state(pro-/anti-inﬂam-matory cytokines),insulin resistance(fasting glucose, insulin,and HOMA index),and other metabolic pheno-types were evaluated during the dietary intervention.Materials and methodsHan Chinese residents(25–55years)from Taiyuan (Shanxi Province,China)were recruited to the trial if their BMI≥28kg mÀ2,waistline≥80cm(for female) or90cm(for male),and waist–hip ratio≥0.85(for female)or0.90(for male).Subjects were excluded with alcoholism,history,or presence of gastrointestinal pathol-ogies,chronic pathologies such as diabetes(including type1and2diabetes),nephropathies,or liver cirrhosis, gastrointestinal surgery,history of administration of anti-biotics lasting more than3days in the previous 3months,psychiatric disorders,pituitary dysfunction, cancers,infectious diseases,deformity,anemia,or losing weight by surgery or drug in the past3months.The study was approved by the Ethics Committee of Chinese Clinical Trial Registry(No.ChiECRCT-000011),and written informed consent was obtained from each partici-pant before their admission to the protocol.Dietary interventionWe designed three ready-to-use food formulas based on whole grains,traditional Chinese medicinal(TCM)foods, and prebiotics(WTP diet).Formula No.1was a pre-cooked mixture of12component materials from whole grains and TCM food plants that are rich in dietaryﬁber, including adlay(Coix lachrymal-jobi L.),oat,buckwheat, white bean,yellow corn,red bean,soybean,yam,big jujube,peanut,lotus seed,and wolfberry,which was prepared in the form of canned gruel(370g wet weight per can)by a contract food manufacturer(Shanghai Meilin Meida Food Co.,Ltd.,Shanghai,China).Each can contained100g of ingredients(59g carbohydrate,15g protein,5g fat,and6gﬁber)and336kcal(70% carbohydrate,17%protein,13%fat).Formula No.2was a powder preparation for infusion(20g per bag) containing bitter melon(Momordica charantia)and oligosaccharides,which included fructo-oligosaccharide and oligoisomaltose,and totally accounted for34%of the formula No.2.Formula No.3contained soluble prebiot-ics,including guar gum,pectin,konjacﬂour,other fermentable dietaryﬁber(Fibersol2,resistant starch, hemicellulose),and oligosaccharides,and was adminis-tered in the form of powder for infusion(50g per bag). The two infusion formulas were designed to facilitate the modulation of gut microbiota with a mild antibacterial effect and gas-producing function(Fei&Zhao,2013). We adopted the self-control design and allocated all recruited volunteers into intervention group(Fig.1), which received the diet intervention consisting of an intervention(9weeks,Phase I)and a maintenance period (14weeks,Phase II).During Phase I,volunteers wereFEMS Microbiol Ecol87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley&Sons Ltd on behalf of the Federation of European Microbiological Societies358S.Xiao et al.prescribed customized menus.Three(for female)or four (for male)cans of gruel as staple food per day were rec-ommended.The suggested dose of formula No.2was 40g reconstituted with warm water in two divided doses taken orally,before breakfast and dinner.One bag of for-mula No.3was taken with more than800mL water once a week before breakfast.Appropriate amounts of vegeta-ble,fruit,and legume products could be consumed every-day according to the dietitian’s guidance to ensure complete nutrition.The diet contained1000–1600kcal, and the volunteers were allowed to consume enough of this diet to avoid hunger pangs.In Phase II,formula No. 1was not supplied,and volunteers were required to pre-pare the staple diets with high-ﬁber,low animal source foods at home by themselves under a dietitian’s guidance. The intake of meat(orﬁsh or shrimp)was<50g each day.The dosage and administration of formula No.2 were the same as in Phase I,but the dosage of formula No.3was reduced to50g every2weeks.All volunteers were asked to maintain their usual pattern of daily activi-ties during the dietary intervention,including physical activity,lifestyle,and habit;avoid to take medications that may affect gut microbiota(such as antibiotics).The volunteers were also required to keep a journal during the trial for recording the intake of the interventional foods,additional snacks and medications they took,or any other unusual events in their daily life.They submit-ted the journal to the community clinic staff for inspec-tion during their weekly return visits to make sure that they maintained their usual lifestyle except adoption of the new dietary scheme.Anthropometric data,clinical laboratory analysis,and biological samplesDuring theﬁrst visit,research staff administered a general questionnaire,collecting information on demographic characteristics,health status,disease history,gastrointesti-nal conditions,dietary habit,and physical activity.A meal-based food frequency questionnaire,where the frequency and intake of food consumption over the last 12months were recorded,was collected subsequently. Clinical data were collected at the Shanxi High-tech Medical Testing Center.At scheduled intervals,namely the baseline(À30day),the end of Phase I(9th week),and the end of Phase II(23rd week),all participants received a24-h dietary recall questionnaire and a physical examination after overnight fasting.Body weight and height were deter-mined by electronic column scales[Seca799/220,Medical Scales and Measuring Systems(Hangzhou)Co.Ltd., China].Whole blood samples were obtained for routine blood examination by an automated hematology analyzer (Sysmex K4500;Sysmex Corporation,Japan).Serum was collected to characterize biochemical and lipid proﬁles as well as ultrasensitive C-reactive protein(CRP)on an auto-matic biochemical analyzer(Sysmex Chemi-180;Sysmex Corporation).Insulin was detected by an immunoassay system(Immulite1000;Siemens Healthcare Diagnostics Inc.,Germany).The homoeostasis model assessment insu-lin resistance(HOMA1-IR)was calculated with the follow-ing formula:[fasting plasma glucose(mM)9fasting insulin(l U mLÀ1)]/22.5.HOMA2was used to determine insulin sensitivity(%S),and b-cell function(%B)calcu-lated by HOMA2calculator version2.2(Wallace et al., 2004).Plasma LBP was determined using an ELISA kit(USCN Life Science and Technology Co.,Ltd,Wuhan,China). The assay has the mean minimum detectable dose (MDD)of0.2ng mLÀ1and a measurable concentration range of0.78–50ng mLÀ1.TNF-a(MDD0.106pg mLÀ1; range0.5–32pg mLÀ1),interleukin-1b(IL-1b,MDD 0.057pg mLÀ1;range0.125–8pg mLÀ1),IL-6(MDD 0.039pg mLÀ1;range0.156–10pg mLÀ1),and adiponec-tin(MDD0.246ng mLÀ1;range3.9–250ng mLÀ1;R&D Systems,Inc.,Minneapolis,MN)were also measured using ELISA.The intra-assay and interassay coefﬁcients of variation were<5%and<10%,respectively.Plasma samples were diluted appropriately and assayed according to the manufacturer’sinstructions.Fig.1.The schematic overview of the dietary intervention.FEMS Microbiol Ecol87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley&Sons Ltd on behalf of the Federation of European Microbiological Societies Gut microbiota-targeted intervention for metabolic syndrome359Evaluation of gut permeabilityThe permeability measure was carried out following by the physical examination at the baseline,the end of Phase I and II,using lactulose/mannitol excretion ratio as marker of intestinal permeability.Volunteers drank 50mL of a solution containing5.0g lactulose and2.0g mannitol.Urine samples were collected for the following 5h,the volume voided was measured,and then aliquots were frozen atÀ80°C until analysis.Sugar concentra-tions in the urine were determined with an ion chro-matograph(DX-600;Dionex Corporation).Results were expressed as the5-h urinary excretion of each sugar and as the lactulose/mannitol(L/M)ratio.Gut microbiota proﬁlingFresh stool samples were obtained from each subject while they stayed in a hotel and immediately put on ice and transported in the shortest possible time toÀ80°C for storage until the gut microbiota analysis.Fecal micro-biota DNA was extracted using bead beating and the Inv-iMag Stool DNA Kit(KFml;Invitek GmbH,Germany). The extracted DNA from each sample was used as template to amplify the V3region of the16S rRNA gene. The products from different samples were mixed at equal ratios for pyrosequencing using the GS FLX platform (Roche).Quality control of raw data was performed as described previously(Zhang et al.,2010).All high-quality pyrose-quencing sequences were clustered using CD-HIT with 98%similarity.The most abundant sequence of each cluster was selected as a representative and aligned against the Greengenes database using the nearest alignment space termination algorithm.The resulting alignments were imported into the ARB(Ludwig et al.,2004)to gen-erate the distance matrix of these sequences for phylotype binning by DOTUR(Schloss&Handelsman,2005). Operational taxonomic units(OTUs)were deﬁned at a certain threshold,which was a criterion for species-level delineation in previous studies(Huse et al.,2007).The most abundant sequence of each OTU was selected as the representative sequence and subjected to RDP classiﬁer for taxonomical assignment with a bootstrap cutoff of 50%.The number of sequences per sample was corrected for differences in sequencing depth between samples by rareﬁcation,that is,the same number of reads is ran-domly subsampled in each sample.Secondly,the absolute number of sequences of each OTU in each sample was converted to the relative abundance to reduce the effect of differences in sequence reads.The representative sequences,together with the abundance data,were used for taxon-based analysis.Statistical analysisStatistical analysis was carried out using the SPSS Statistics 17.0Software Package(SPSS Inc.).According to the dis-tribution of the variables,data were expressed as median (interquartile range)or meanÆstandard deviation(SD)/ standard error of the mean(SEM).The signiﬁcance test was performed with the paired t-test or nonparametric-paired sample Wilcoxon signed-rank test,and correlations were determined by the Pearson and partial correlation tests.False discovery rate(FDR)was used to control the chance of making type I errors in multiple comparisons of bacterial taxa.ResultsA total of123obese volunteers(female:male=69:54) were recruited into the trial.By the end of Phase I,101 volunteers remained in the diet intervention trial.Eventu-ally,93volunteers completed the study with a retention rate of75.6%.Biological samples at baseline,end of Phase I,and completion of the trial were collected from89 volunteers(female:male=57:32),as shown in Fig.1. There were no severe adverse reactions reported during the intervention or after a2-year follow-up. Improvement in clinical parametersClinical parameters of these subjects(n=89)are summa-rized in Table1.The average weight loss was 5.20Æ3.58kg(5.95Æ3.94%of initial weight,P<0.01) during Phase I and 5.79Æ4.64kg(6.62Æ4.94%, P<0.01)by the end of Phase II.Forty-six participants (51.69%)continued to lose weight during Phase II.At the completion of the trial,18participants(20.22%)had lost more than10%of initial body weight,33participants (37.08%)lost from5%to10%,30participants(33.71%) lost from0%to5%,and eight participants(8.99%)had gained an average of0.84Æ0.70kg(Supporting infor-mation,Fig.S1).Accordingly,the average BMI was signiﬁcantly reduced from31.5(30.3–33.9kg mÀ2)to 29.8kg mÀ2(28.7–32.2kg mÀ2)at the end of Phase I (P<0.01)and to29.3kg mÀ2(28.4–31.4kg mÀ2)by completion of the trial(P<0.01).Overall,60.67%(n=54)of the participants were iden-tiﬁed with metabolic syndrome at baseline as deﬁned by the International Diabetes Federation(IDF)[central obes-ity plus any two of following abnormalities:raised trigly-cerides,reduced high-density lipoprotein(HDL) cholesterol,raised systolic or diastolic blood pressure,and raised fasting plasma glucose;central obesity was assumed if BMI>30kg mÀ2].This percentage was dramatically reduced to31.46%(n=28)and29.21%(n=26)at endFEMS Microbiol Ecol87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley&Sons Ltd on behalf of the Federation of European Microbiological Societies360S.Xiao et al.of Phase I and II,respectively.Some of the diagnostic components of metabolic syndrome,including central obesity,fasting glucose,triglycerides and blood pressure in Phase I and central obesity,triglycerides and HDL cholesterol in Phase II,were signiﬁcantly improved as a cohort (Table 1).This trend was more obvious in those participants who had components over the cutoff of IDF metabolic syndrome criterion (Table 2).For example,fasting glucose ﬂuctuated in the medical reference range in the cohort as a whole,while it restored to normal or near normal in the 12participants whose fasting glucose were >5.6mM at baseline (Table 2and Table S1).The signiﬁcant reduction (P <0.01,respectively)in fasting insulin,HOMA1-IR and HOMA2-%B index,and the increase of HOMA2-%S all implied an amelioration of insulin resistance (Table 1).Modulation of gut microbiota compositionThe barcoded 454pyrosequencing of the 16S rRNA gene V3region was used for a deep molecular inventory of the gut microbiota with an average of 3150Æ937reads per sample.A total of 156867usable unique sequences were obtained,and 3664OTUs were delineated at a 96%homology cutoff.Sequences are available at theNCBITable 1.Anthropometric and biochemical characteristics of the obese subjects at baseline,9,and 23weeks after the intervention Measurements Baseline (À30day)Phase I (9week)Phase II (23week)Medical reference range BMI (kg m )31.5(30.3–33.9)29.8**(28.7–32.2)29.3**(28.4–31.4)18–23FPG (mM) 4.90(4.64–5.28) 4.74**(4.46–5.13) 4.92††(4.63–5.36) 3.90–6.10FPI (l IU mL À1)11.9(8.5–17.1)10.7**(6.9–14.3)9.2**††(6.1–13.9)6–27HOMA1-IR 2.63(1.80–3.92) 2.40**(1.47–3.15) 1.96**†(1.25–3.09)–HOMA2-%B a 129.0(106.8–172.5)125.4(98.4–158.5)114.2**††(91.6–137.0)–HOMA2-%S a 64.0(44.7–91.1)70.9**(56.1–110.9)83.1**(54.1–121.8)–HbA1c (%)4.34(3.99–4.60) 4.83**(4.63–5.06) 4.77**(4.57–4.99) 3.8–5.8Triglycerides (mM) 1.55(1.09–2.25) 1.17**(0.86–1.80) 1.30**(0.80–1.88)0–1.7Total cholesterol (mM) 4.45Æ0.77 4.13Æ0.77** 4.38Æ0.78†† 3.00–5.17HDL-C (mM) 1.05Æ0.190.98Æ0.21** 1.09Æ0.23*††>0.91LDL-C (mM) 2.46Æ0.87 2.47Æ0.76 2.61Æ0.70*†0–4.16SBP (mmHg)127(121–135)123**(116–131)125(115–133)≤140DBP (mmHg)89(80–97)84*(79–95)89(80–95)≤90BMI,body mass index;FPG,fasting plasma glucose;FPI ,fasting plasma insulin;HOMA1-IR,homoeostasis model assessment insulin resistance;HOMA2-%S,homoeostasis model assessment insulin sensitivity;HOMA2-%B,homoeostasis model assessment b -cell function;HbA1c,glycated hemoglobin;HDL-C,high-density lipoprotein cholesterol;LDL-C,low-density lipoprotein cholesterol;SBP,systolic blood pressure;DBP,diastolic blood pressure.Results were expressed as median (interquartile range)or mean ÆSD.aHOMA2-%S and HOMA2-%B were calculated using HOMA2calculator version 2.2(Diabetes Trials Unit,University of Oxford,Oxford,UK).Signiﬁcantly different from baseline,*P <0.05,**P <0.01;Signiﬁcantly different from Phase I,†P <0.05,††P <0.01(two-tailedtest).Table 2.The alteration of metabolic syndrome components during intervention.The cutoff is according to the metabolic syndrome deﬁnition of International Diabetes Federation (IDF)No.of volunteers over the limit of IDF MS cutoff Baseline (À30day)Phase I (9week)Phase II (23week)IDF MS cutoff TG (mM;n =38) 2.49(2.01–3.41) 1.83**(1.42–2.19) 1.80**(1.30–2.37)>1.7HDL-C (mM;n =68)0.99Æ0.160.96Æ0.21 1.06Æ0.21**††–Male (n =20)0.86Æ0.140.79Æ0.150.94Æ0.20*†<1.03Female (n =48)1.05Æ0.14 1.03Æ0.19* 1.10Æ0.20††<1.29SBP (mmHg;n =37)137(133–145)130**(125–137)130**(126–140)≥130DBP (mmHg;n =54)95(90–101)90**(82–100)94**(84–100)≥85BMI,body mass index;FPG,fasting plasma glucose;TG,triglycerides;HDL-C,high-density lipoprotein cholesterol;SBP,systolic blood pressure;DBP,diastolic blood pressure.Results were expressed as median (interquartile range)or mean ÆSD.aIf body mass index is over 30kg m À2,central obesity can be assumed and waist circumference does not need to be measured.Signiﬁcantly different from baseline,*P <0.05,**P <0.01;Signiﬁcantly different from Phase I,†P <0.05,††P <0.01(two-tailed test).FEMS Microbiol Ecol 87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley &Sons Ltd on behalf of the Federation of European Microbiological SocietiesGut microbiota-targeted intervention for metabolic syndrome 361sequence read archive under accession numbers SAMN02143695–SAMN02143977.The alpha diversity decreased at the end of Phase II after the dietary interven-tion(Fig.S2).The entire microbial communities between samples have been compared using weighted(Fig.S3A) and unweighted UniFrac analysis(Fig.S3B).MANOVA showed that the gut microbiota changed signiﬁcantly after the dietary intervention[Fig.S3C(weighted)and D (unweighted)].The interindividual variability of UniFrac distance was much greater than the intra-individual varia-tion between each time point[Fig.S3E(weighted)and F (unweighted)].As revealed by taxon-based analysis,the gut microbiome of the participants was composed of four dominant phyla, Firmicutes,Bacteroidetes,Proteobacteria,and Actinobacteria. Signiﬁcant changes were observed in Actinobacteria and Proteobacteria populations due to the dietary intervention (Fig.2a).The relative abundance of Actinobacteria increased signiﬁcantly from0.70Æ0.91%(À30day)to 1.43Æ2.23%(9week;P<0.01and 2.1%FDR)and 1.35Æ2.39%(23week;P<0.05and8.9%FDR),while the number of Proteobacteria signiﬁcantly decreased from 5.29Æ5.48%(À30day)to 3.54Æ4.60%(9week; P<0.05and 6.4%FDR)and 3.25Æ4.19%(23week; P<0.01and0.9%FDR).We did not observe signiﬁcant changes in the abundance of Bacteroidetes and Firmicutes or in the ratio of these two phyla during the dietary inter-vention when the volunteers lost signiﬁcant amount of weight(Fig.2b).The abundance of the family Biﬁdobacteriaceae was signiﬁcantly increased from0.51Æ0.83%(À30day)to 1.24Æ2.20%(9week;P<0.01and 1.0%FDR)and slightly reduced to 1.09Æ2.17%(23week;Fig.2c; P<0.05and16.7%FDR).The family Enterobacteriaceae showed a decrease in abundance from 2.84Æ4.90% (À30day)to1.65Æ4.35%(9week)and0.97Æ3.50% (23week)during the trial,but the signiﬁcant difference was only observed between23week andÀ30day(Fig.2c;P<0.01and 1.5%FDR).The family Desulfovibrionaceae was signiﬁcantly reduced from 0.45Æ0.77%(À30day)to0.20Æ0.37%(9week; P<0.01and1.0%FDR),but returned to the baseline level at23week(c.0.35Æ0.62%,Fig.2c;P>0.05and 53.7%FDR).(a)(b)(c)(d)Fig.2.Dietary intervention changed intestinal microbiota.Groups of bacteria changed at the(a),(b)phylum,(c)family,and(d)genus levels. Bacteria numbers are expressed as the proportion of total intestinal microbiota,and data are meanÆSEM.*P<0.05;**P<0.01.FEMS Microbiol Ecol87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley&Sons Ltd on behalf of the Federation of European Microbiological Societies362S.Xiao et al.At the genus level,the proportion of the lipopolysac-charide-containing microbiota Escherichia /Shigella ,Klebsi-ella ,and Citrobacter ,which contain opportunistic pathogens,was reduced signiﬁcantly at week 23compared with the baseline,while the genus Biﬁdobacterium increased signiﬁcantly after the dietary intervention (Fig.2d).We observed the positive correlations between Escherichia /Shigella and systolic blood pressure;Klebsiella and fasting glucose,HbA1c,L/M ratio,etc .;Citrobacter and weight,BMI and IL-1b (Table S2).There was also a weak positive correlation (r <0.20)between Biﬁdobacteri-um and IL-1b (Table S2).Changes in biomarkers along the causal pathwayWe focused on the changes in the following biomarkers along the pathway likely connecting gut microbiota to the pathogenesis of obesity (Table 3and Fig.3):an intestinal permeability marker,L/M ratio,a gut-derived antigen load marker,LBP,inﬂammation markers including CRP,pro-inﬂammatory cytokines (TNF-a ,IL-6,and IL-1b ),anti-inﬂammatory adipokine (adiponectin),and insulin sensitivity.The L/M ratio and LBP were signiﬁcantly decreased by the end of Phase I accompanied by improvements in systemic inﬂammatory tone,character-ized by the reduction in CRP and IL-6,and the increase in adiponectin.Eventually,insulin sensitivity was increased.DiscussionModiﬁcation of diet has become an integral part of lifestyle intervention to reduce metabolic syndrome risk factors,including low-grade systemic inﬂammation (Dan-dona et al.,1998;Bull oet al.,2007).However,not all weight loss interventions lead to reduced inﬂammation (e.g.inﬂammatory markers actually increased in over-weight children after they effectively lost weight on a low-carbohydrate,high-fat diet;Alvarez et al.,2009).Weightloss achieved through diet has often been accompanied by a 7–48%reduction in CRP (Dietrich &Jialal,2005).We observed a CRP reduction of 23.17%(Phase I),com-parable to the report by Heilbronn et al.(2001)which cited a decrease in CRP of 26%in 83healthy obese women after 12weeks of energy restriction.The signiﬁ-cant reduction in IL-6and increase in adiponectin also indicated that inﬂammation was ameliorated in our volunteers.Diet-induced weight loss and alleviation of inﬂammation have been reported,but the underlying mechanism remains to be elucidated.Cani et al.(2007)proposed that high-fat diet-induced obesity is associated with gut microbiota dysbiosis,which leads to increased gut permeability,promoting metabolic endotoxemia and initiating the development of low-grade inﬂammation and insulin resistance.The gut microbiota,one of the potential sources of low-grade inﬂammation (Zhao,2013),was thus the expected target of our dietary intervention.Some clinical studies (mostly with <50participants)have tried to link dietary intervention for obesity with beneﬁcial outcomes via modulation of gut microbiota (Parnell &Reimer,2009;Diamant et al.,2011).Targeted analyses,such as FISH and qPCR,to evaluate the gut microbiota alteration in overweight or obese patients after dietary treatment have so far yielded inconsistent results (Ley et al.,2006;Duncan et al.,2008;Santacruz et al.,2009;Musso et al.,2010).Our previous study shows that there seems to be a causal pathway between endotoxin producers in the gut and obesity/insulin resistance outcomes,which can be tracked by gut barrier permeability,endotoxin load in the serum,and inﬂammatory biomarkers (Fei &Zhao,2013).In our current clinical trial,we evaluated whether this chain of causation might work in this cohort.Taxon-based com-parison at the genus level identiﬁed signiﬁcant changes in several key genera relevant to inﬂammatory and meta-bolic improvement in our participants.Most notably,OTUs in genus Biﬁdobacterium spp.were signiﬁcantly enriched after the dietary intervention.In accordance with the increase in gut barrier-protectingBiﬁdobacteriumTable 3.Inﬂammatory biomarkers,LBP,and gut permeability of the obese subjects at baseline,9,and 23weeks after the intervention MeasurementsBaseline (À30day)Phase I (9week)Phase II (23week)Medical reference range IL-1b (pg mL À1)0.07(0.03–0.12)0.07(0.05–0.15)0.06(0.04–0.12)–IL-6(pg mL À1) 2.28(1.79–3.12) 2.02*(1.62–2.62) 1.68**††(1.27–2.46)–TNF-a (pg mL À1)1.07(0.87–1.49) 1.03(0.81–1.40) 1.04*(0.82–1.50)–À1†LBP,lipopolysaccharide-binding protein;IL,interleukin;TNF-a ,tumor necrosis factor-a ;L/M ratio,lactulose/mannitol ratio.Results were expressed as median (interquartile range).Signiﬁcantly different from baseline,*P <0.05,**P <0.01;Signiﬁcantly different from Phase I,†P <0.05,††P <0.01(two-tailed test).FEMS Microbiol Ecol 87(2014)357–367ª2013The Authors.FEMS Microbiology Ecologypubished by John Wiley &Sons Ltd on behalf of the Federation of European Microbiological SocietiesGut microbiota-targeted intervention for metabolic syndrome 363。

已知的miRNA的ID中英文对照

aae Aedes aegypti 埃及伊蚊aca Anolis carolinensis 安乐蜥aga Anopheles gambiae 冈比亚按蚊aly Arabidopsis lyrata 琴叶拟南芥ame Apis mellifera 意蜂api Acyrthosiphon pisum 豌豆蚜ata Aegilops tauschii 山羊草/节节麦ath Arabidopsis thaliana 拟南芥atr Amborella trichopoda 无油樟bdi Brachypodium distachyon 二穗短柄草blv Bovine leukemia virus 牛白血病病毒bma Brugia malayi 马来丝虫bmo Bombyx mori 家蚕bna Brassica napus 油菜bra Brassica rapa 白菜/芜菁bta Bos T aurus （普通）牛cbn Caenorhabditis brenneri 线虫brennericbr Caenorhabditis briggsae 线虫briggsaecel Caenorhabditis elegans 秀丽隐杆线虫cfa Canis familiaris 家犬chi Capra hircus 山羊cin Ciona intestinalis 玻璃海鞘cqu Culex quinquefasciatus 致倦库蚊cre Chlamydomonas reinhardtii 莱茵衣藻crm Caenorhabditis remanei 线虫remanei csa Ciona savignyi 玻璃海鞘savignyicte Capitella teleta 小头虫teletadan Drosophila ananassae 果蝇ananassaeddi Dictyostelium discoideum 盘基网柄菌der Drosophila erecta 果蝇万寿菊dgr Drosophila grimshawi 果蝇grimshawi dme Drosophila melanogaster 黑腹果蝇dmo Drosophila mojavensis 果蝇mojavensis dpe Drosophila persimilis 果蝇螨dps Drosophila pseudoobscura 果蝇pseudoobscura dpu Daphnia pulex 蚤状溞dre Danio rerio 斑马鱼dse Drosophila sechellia 果蝇sechelliadsi Drosophila simulans 果蝇simulansdvi Drosophila virilis 果蝇virlisdwi Drosophila willistoni 果蝇willistonidya Drosophila yakuba 果蝇yakubaebv Epstein Barr virus EB病毒eca Equus caballus 马efu Eptesicus fuscus 大棕蝠/大棕鲇egr Echinococcus granulosus 细粒棘球绦虫emu Echinococcus multilocularis 多房棘球绦虫esi Ectocarpus siliculosus 水云siliculosusfru Fugu rubripes 红鳍东方鲀gga Gallus gallus 原鸡ggo Gorilla gorilla 大猩猩gma Glycine max 大豆gra Gossypium raimondii 雷蒙德氏棉hbr Hevea brasiliensis 橡胶树hbv Herpes B virus 乙型肝炎病毒hcmv Human cytomegalovirus 人巨细胞病毒hme Heliconius melpomene 红带袖蝶hsa Homo sapiens 智人isc Ixodes scapularis 肩突硬蜱kshv Kaposi sarcoma-associated herpesvirus 卡波济肉瘤相关疱疹病毒lgi Lottia gigantean 帽贝lja Lotus japonicas 百脉根mdm Malus domestica 苹果mdo Monodelphis domestica 短尾负鼠mdv1 Mareks disease virus 马立克氏病病毒mes Manihot esculenta 木薯星虫mghv Mouse gammaherpesvirus 68 小鼠γ疱疹病毒mml Macaca mulatta 猕猴mmu Mus musculus 小家鼠mse Manduca sexta 烟草天蛾mtr Medicago truncatula 蒺藜苜蓿nve Nematostella vectensis海葵nvi Nasonia vitripennis 金小蜂oan Ornithorhynchus anatinus 鸭嘴兽oar Ovis aries 绵羊ocu Oryctolagus cuniculus 家兔oha Ophiophagus Hannah 眼镜王蛇ola Oryzias latipes 青鳉osa Oryza sativa 水稻pma Petromyzon marinus 海七鳃鳗ppc Pristionchus pacificus 蛔虫ppe Prunus persica 桃ppt Physcomitrella patens 小立碗藓ppy Pongo pygmaeus 猩猩prd Panagrellus redivivus 全齿复活线虫/腐生线虫ptc Populus trichocarpa 毛果杨ptr Pan troglodytes 黑猩猩pxy Plutella xylostella 小菜蛾rco Ricinus communis 蓖麻rno Rattus norvegicus 褐家鼠rrv Rhesus monkey rhadinovirus 猕猴Rhadino病毒sbi Sorghum bicolor 高粱sha Sarcophilus harrisii 袋獾sja Schistosoma japonicum 日本血吸虫sko Saccoglossus kowalevskii 橡子蠕虫sly Solanum lycopersicum 番茄sma Schistosoma mansoni 曼氏血吸虫sme Schmidtea mediterranea 淡水涡虫spu Strongylocentrotus purpuratus 紫色球海胆ssc Sus scrofa 野猪str Strongyloides ratti 鼠类圆线虫stu Solanum tuberosum 马铃薯tca Tribolium castaneum 赤拟谷盗tch Tupaia chinensis 树鼩tgu T aeniopygia guttata 斑胸草雀tni Tetraodon nigroviridis 金娃娃vvi Vitis vinifera 葡萄xtr Xenopus tropicalis 热带爪蟾zma Zea mays 玉米。

Preventing bacterial DNA release and absent in melanoma 2 inflammasome activation

(2, 3). The primitive function of the Dot/Icm system is to transfer DNA by bacterial conjugation (4), but its predominant role in
infection is to translocate protein substrates across the LCV membrane into host cells. A prevalent function for known Dot/ Icm effectors is to subvert eukaryotic vesicular trafﬁcking (1, 5, 6). A large number of >250 Dot/Icm effectors are experimentally conﬁrmed (7). Few Dot/Icm effectors are genetically required for LCV avoidance of lysosomal fusion, suggesting a functional
redundancy. However, the Dot/Icm system is critical for establishment of the LCV. The LCV serves as an intracellular niche, where L. pneumophila can efﬁciently replicate, as has been observed in human U937 monocytes (8).
Legionella pneumophila, the causative agent of Legionnaires’ pneumonia, resides in a distinct vacuole structure called Legionella-containing vacuole (LCV). The LCV resists fusion with the lysosome and permits efﬁcient bacterial replication in host macrophages, which requires a Dot/Icm type IVB secretion system. Dot/Icm-translocated effector SdhA is critical for L. pneumophila intracellular growth and functions to prevent host cell death. Here, we show that the absence of SdhA resulted in elevated caspase-1 activation and IL-1β secretion as well as macrophage pyroptosis during Legionella infection. These inﬂammasome activation phenotypes were independent of the established ﬂagellin-NAIP5NLRC4 axis, but relied on the DNA-sensing AIM2 inﬂammasome. We further demonstrate that Legionella DNA was released into macrophage cytosol, and this effect was signiﬁcantly exaggerated by the absence of SdhA. SdhA bears a functional Golgi-targeting GRIP domain that is required for preventing AIM2 inﬂammasome activation. Ectopically expressed SdhA formed a unique ring-shape membrane structure, further indicating a role in membrane trafﬁcking and maintaining LCV membrane integrity. Our data together suggest a possible link, mediated by the function of SdhA, between LCV trafﬁcking/maturation and suppression of host innate immune detection.

Development and Validation of a Liquid Chromatogra

J. Chem. Chem. Eng. 5 (2011) 1-6.Development and Validation of a LiquidChromatography–Tandem Mass Spectrometry Method for Determination of Artemisinin in Rat PlasmaElhassan Gamal1,2, Yuen Kah1, Wong Jiawoei1, Chitneni Mallikarjun1,3, Al-Dahli Samer1, Khan Jiyauddin1 and Javed Qureshi31. School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden 11800, Penang, Malaysia2. Local Pharmaceutical Manufacturing Department, General Pharmacy Directorate, MOH, 11111, Khartoum-Sudan3. School of Pharmacy and Health Sciences, International Medical University, 5700, Kula Lumpur, MalaysiaReceived: September 03, 2010 / Accepted: October 11, 2010 / Published: January 10, 2011.Abstract: Artemisinin is a potent anti-malarial drug isolated from traditional Chinese medicinal herb, Artemisia annua. The objective of this study was to develop and validate a sensitive and specific LC-MS/MS method for the determination of artemisinin in rat plasma using amlodipine as Internal Standard. The method consist of a simple liquid-liquid extraction with methyl tertiary butyl ether (MTBE) with subsequent evaporation of the supernatant to dryness followed by the analysis of the reconstituted sample by LC-MS/MS with a Z-spray atmospheric pressure ionization (API) interface in the positive ion-multiple reaction monitoring mode to monitor precursor→product ions of m/z 282.70→m/z 209.0 for artemisinin and m/z 408.9→m/z 237.0 for amlodipine respectively. The method was linear (0.999) over the concentration range of 7.8–2000 ng/mL in rat plasma. The intra and inter-day accuracy were measured to be within 94-104.2% and precision (CV) were all less than 5%. The extraction recovery means for internal standard and all the artemisinin concentrations used were between 82-85%.Key words: Artemisinin, LC-MS/MS, amlodipine, plasma, accuracy and precision.1. IntroductionArtemsinin is the name given to the active principle of qinghaosu, an extract of the Chinese medicinal plant qinghaosu or green Artemisia (Artemisinin annua L.) which has been used for many years centuries in Chinese traditional medicine for treatment of fever and malaria [1]. In 1972, Chinese researchers isolated artemisinin from Artemisia annua L. sweet wormwood) and its structure was elucidate in 1979 as show in Fig. 1.The determination of artemisinin and its derivatives in biological matrices have previously been characterized using several analytical techniques suchCorresponding author: Gamal Osman Elhassan Ph.D., research field: pharmaceutical technology. E-mail: ******************.as LC, HPLC, GC-MS etc [3-8]. However, some of these methods suffer from few drawbacks. In particulars, interference with endogenous constituents in the plasma at the absorption wave length of the derivatized compounds may render these techniques unsatisfactory and few of them lacked the required sensitivity to be used for measurement of drugFig. 1 The chemical structure of artemisinin [2].ll Rights Reserved.Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method forDetermination of Artemisinin in Rat Plasma2concentration in blood sample obtained from clinical investigation [9].To increase the specificity and sensitivity of HPLC-UV method, some workers combined it with a mass spectrometry (MS) and the total system is described as LC-MS technique [10, 11]. The development of LC-tandem mass spectrometry (LC-MS/MS) has made a more specific and sensitive analysis of artemisinin and its derivatives possible [12, 13]. The objective of this study was to develop a sensitive and specific LC-MS/MS method for the determination of artemisinin in rat plasma by simple liquid-liquid extraction procedure.2. Materials and Methods2.1 MaterialsArtemisinin was purchased from Kunming Pharmaceutical Corporation (Kunming, China). Amlodipine was obtained from Sigma Chemical (Louis, USA). Acetonitrile (ACN), formic acid and methyl tertiary butyl ether (MTBE) were purchased from J.T Baker (USA).3. Methods3.1 Instrumentation and ConditionsThe instrumentation comprised of Quattro-micro tandem mass spectrometer with Z-spray atomospheric pressure ionization (API) source (Micromass, Manchester, UK) using electrospray ionization (ESI) operated at positive mode. Chromatography was performed on an Alliance 2,695 separation module (Waters, M.A, USA). The delivery system consisted of an autosampler and a column heater. The chromatographic separation was obtained using an X Terra MS C8 encapped (5 μm) (150 × 2.1 mm) analytical column (Water, USA).3.2 Sample PreparationA 250 μL aliquot of plasma was pipetted into a screw-capped culture tube, followed by 100 μL of internal standard solution (50 ng/mL). To each tube, 5 mL (MTBE) extraction solvent was then added and the mixture was vortexed for 2.5 minutes followed by centrifuging for 15 minutes at 3,500 rpm. The upper layer was transferred to a reactive vial and dried under nitrogen flow at 40 °C. The residue was then reconstituted with 250 μL of mobile phase and 20 μL was injected into the LC-MS/MS system.3.3 Assay ValidationCalibration curve at a concentration range of 7.8–2,000 ng/mL were constructed by spiking blank human plasma with a known amount of artemisinin. Plasma sample spiked with artemisinin at these concentrations 7.8, 62.5, 250, 2,000 ng/mL were used to determine the within and between-day accuracy and precision. For within-day accuracy and precision, replicates analysis (n = 6) for each concentration were performed in a single day. For between-day evaluation, analysis was carried out with a single sample of each concentration daily over 6 days, with calibration curve constructed on each day of analysis. The extraction recovery of artemisinin was estimated by comparing the peak height obtained after extraction of the samples from plasma with that of aqueous artemisinin solution of the corresponding concentration.4. Results and DiscussionBoth electrospray (TIS) and atmospheric pressure chemical ionisation (APCI) methods have been reported previously for the quantification of artemisinin derivatives in biological fluids [11, 12, 14-16]. According to the previously reported methods TIS was found to be superior to APCI for the quantification of artesunate and dihydroartemisinin (DHA) mainly because of improved linearity [16]. Therefore in this method electrospray ionization was used. When artemisinin and amlodipine were injected directly into the mass spectrometer along with mobile phase in the positive mode, the protonated molecules of artemisinin and amlodipine were set as precursorll Rights Reserved.Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method forDetermination of Artemisinin in Rat Plasma3(a)(b)Fig. 2 (a) Positive-ionization electrospray mass spectra of precursor ion for artemisinin; (b) Positive-ionization electrospray mass spectra of product ion for artemisinin.ions with m/z of 282.7 and 408.7, respectively. The product ion that gave the highest intensity was m/z of 209.0 for artemisinin and 237.7 for amlodipine. Fig 2(a) shows the spectra precursor ion, 2(b) production for artemisinin.Artemisinin and amlodipine have retention time of approximately 6.9 and 1.65 minutes, respectively (Fig.3). The peak was well resolved and free from interference from endogenous compounds in rat plasma (Fig. 4).ll Rights Reserved.Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method forDetermination of Artemisinin in Rat Plasma4Fig. 3 Plasma spiked with 500 ng/ml artemisinin and amlodipine 50 ng/mL.Fig. 4 Chromatograms for analysis of artemisinin in plasma (Rat blank plasma).Calibration curve was linear over the entire range of calibration curves with a mean correlation coefficient greater than 0.9995 (Fig. 5).The limit of quantification (LOQ) of the assay method was 7.8 ng/mL being the lowest concentration used to construct the calibration curve whereas the limit of detection (LOD) was 3.9 ng/mL at a signal to noise ratio of 3. The validation data demonstrated a good precision, accuracy and recovery. The extraction recovery means for internal standard and all artemisinin concentrations used were 75-85% (Table 1). The within-day and between-day accuracy and precision values are given in Table 2.Neither artemisinin nor the internal standard producedll Rights Reserved.Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method forDetermination of Artemisinin in Rat Plasma5Fig. 5 Mean calibration curve of artemisinin (ng/mL).Table 1 Extraction recovery.Concentration (ng/mL) Mean recovery (%) CV (%)7.81 75.081.5062.50 82.161.94250.00 82.03 2.072000.00 85.23 1.48Table 2 Within-day and between-day precision andaccuracy.Added (ng/mL)Within-day Between-day Accuracy (%) C.V (%) Accuracy (%) C.V (%)7.81 96.00 4.60 104.11 2.30 62.50 98.10 1.60 94.10 2.20 250.00 98.10 1.50 98.10 1.60 2000.00 96.10 2.50 97.10 1.80any detectable carry-over after three injections of upper limit of quantification. Blank rat plasma showed no interference with artemisinin. Interfering signals from blank plasma contributed less than 20% of the artemisinin signal at LOQ. There was no interference of artemisinin on the internal standard or vice versa. A small enhancement for artemisinin and the internal standard could be detected when references in neat injection solvent were compared with references in extracted blank biological matrix. The normalized matrix effects (artemisinin/internal standard) were close to 1 with a low variation in accordance with international guidelines. Post-column infusion experiments confirmed the absence of regions with severe matrix effects (i.e., no sharp drops or increases in the response) for blank human plasma extracted with the developed method.Xing et al. used artmether as an internal standard for the analysis of artemisinin [17]while for the analysis of artemisinin derivatives; artemisinin was used as internal standard [14]. In the present study amlodipine was found to be suitable because it could be separated chromatographically, ionized and fragmented under the conditions that optimized the intensity of artemisinin peak (Fig. 3).The analysis of artemisinin and its derivatives with mass spectrometry are most often performed with a different mode of ionization. Xing et al. used ESI inletin the positive ion-multiple reaction monitoring mode which relatively producing a higher sensitivity than in the SIM mode. Therefore, the mass spectrometry was operated at positive ion-MRM mode.4. ConclusionThe LC-MS/MS method described in this work is suitable for the determination of artemisinin in plasma. The assay procedure is simple with a relatively shortll Rights Reserved.Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method forDetermination of Artemisinin in Rat Plasma6retention time allowing sufficient sample to beprocessed to be applied to pharmacokinetic and bioavailability studies of artemisinin. The accuracy and precision of the assay method, as well as the recovery of extraction procedure were found to be satisfactory.References[1] D.L. Klayman, Qinghasou (Artemisinin): An antimalaria drug from China, Science 228 (1985) 1049-1055.[2] X.D. Luo, C.C. Shen, The chemistry, pharmacology andclinical applications of Qinghaosu (artemisinin) and it’sderivatives, Med. Res. Rev. 7 (1987) 29-52.[3] K.T. Batty, M. Ashton, K.F. Llett, G . Edwards, T.M. Davis,Selective high-performance liquid chromatography ofartesunate and α-and β-dihydroartemisinin in patients withfalciparum malaria, J. Chromatog. B 677 (2-3) (1996)345-350.[4] J. Karbwang, K. Na-Bangchang, P. Molunto, V . Banmairuroi, Determination of artemisinin and its majormetabolite, dihydroartemisinin, in plasma usinghigh-performance liquid chromatography withelectrochemical detector, J. Chromatog. B 7 (1-2) (1997)259-265.[5] K.L. Chan, K.H. Yuen, H. Takayanki, S. Jinandasa, K.K. Peh, Polymorphism of artemisinin from Artemisia annua,Phytochemistry 46 (7) (1997) 1209-1214.[6] G .Q. Li, T.O. Peggins, L.L. Fleckenstein, K. Masonic,M.H. Heiffles, T.G . Brewer, The pharmacokinetics andbiovailability of dihydroartemisinin, arteether, artemether,artesunic acid and artelinic acid in rats, J. Pharm.Pharmacol 5 (1998) 173-182.[7] B.A. Avery, K.K. Venkatesh, M.A. Avery, Rapid determination of artemisinin and related analogues usinghigh-perfomance liquid chromatography and anevaporative light scattering detector, J. Chromat. B 730 (1)(1999) 71-80.[8] S.S. Mohamed, S.A. Khalid, S.A. Ward, T.S.M. Wan,H.P.O. Tang, M. Zheng, R.K. Haynes, G . Edwards,Simultaneous determination of artemether and its majormetabolite dihydroartemisinin in plasma by gaschromatography-mass spectrometry-selected ionmonitoring, J. Chromat. B 731(1999) 251-260.[9] K.T. Batty, M. Ashton, K.F. Llett, G . Edward, T.M. Davis,The pharmacokinetics of artemisinin (ART) and artesunate (ARTS) in healthy volunteers, Am J. Trop Med. Hyg. 58(2) (1998) 125-126.[10] C. Souppart, N. Gouducheau, N. Sandenan, F. Richard,Development and validation of a high-performance liquid chromatography-mass spectrometry assay for the determination of artemisinin and its metabolite dihydraartemisinin in human plasma, J. Chromat. B 774(2002) 195-203.[11] H. Naik, D.J. Murry, L.E. Kirsch, L. Fleckenstein,Development and validation of high-performance liquid chromatography-mass spectroscopy assay for determination of artesunate and dihydrroartemisinin in human plasma, J. Chromat. B 816 (1-2) (2005) 233-242. [12] J. Xing, H. Yan, S. Zhang, G . Ren, Y . Gao, A high-performance liquid chromatography/tandem mass spectrometry method for the determination of artemisinin in rat plasma, Rapid Commun in Mass Spectro. 20 (9) (2006) 1463-1468. [13] J. Xing, H.X. Yan, R.L. Wang, L.F. Zhang, S.Q. Zhang,Liquid chromatography-tandem mass spectrometry assay for the quantitation of β-dihydroartemisinin in rat plasma, J. Chromat. B 852 (1-2) (2007) 202-207. [14] M. Rajanikanth, K.P. Madhusudanan, R.C. Gupta, An HPLC-MS method for simultaneous estimation of alpha, beta-arteether and its metabolite dihydroartemisinin, in rat plasma for application to pharmacokinetic study, J Biomed. Chromat. 17 (7) (2003) 440-446. [15] Y . Gu, Q. Li, M.V . Elendez, P. Weina, Comparison of HPLC with electrochemical detection and LC–MS/for the separation and validation of artesunate and dihydroartemisinin in animal and human plasma, J. Chromatogr B 867 (2008) 213-218. [16] W. Hanpithakpong, B. Kamanikom, A.M. Dondorp, P.Singhasivanon, N.J. White, N.P. Day, N. Lindegardh, A liquid chromatographic-tandem mass spectrometric method for determination of artesunate and its metabolite dihydroartemisinin in human plasma, J. Chromatogr. B 876 (2008) 61-68. [17] Y . Xing, H. Yan, S. Zhang, G . Ren, Y . Gao, A high-performance liquid chromatography/tandem mass spectrometry method for the determination of artemisinin rat plasma, Rapid Communication in Mass Spectrometry 20 (9) (2006) 1463-1468.ll Rights Reserved.。

Microglia express distinct M1 and M2 phenotypic markers in nervous system in mice

Microglia Express Distinct M1and M2Phenotypic Markers in the Postnatal and Adult Central Nervous System in Male and Female MiceJessica M.Crain,1,2Maria Nikodemova,3*and Jyoti J.Watters 1,2,31Program in Cellular and Molecular Biology,University of Wisconsin,Madison,Wisconsin 2Center for Women’s Health Research,University of Wisconsin,Madison,Wisconsin 3Department of Comparative Biosciences,University of Wisconsin,Madison,WisconsinAlthough microglial activation is associated with all CNS disorders,many of which are sexually dimorphic or age-dependent,little is known about whether microglial basal gene expression is altered with age in the healthy CNS or whether it is sex dependent.Analysis of microglia from the brains of 3-day (P3)-to 12-month-old male and female C57Bl/6mice revealed distinct gene expression proﬁles during postnatal development that differ signiﬁ-cantly from those in adulthood.Microglia at P3are char-acterized by relatively high iNOS,TNF a and arginase-I mRNA levels,whereas P21microglia have increased expression of CD11b,TLR4,and FcR g I.Adult microglia (2–4months)are characterized by low proinﬂammatory cytokine expression,which increases by 12months of age.Age-dependent differences in gene expression sug-gest that microglia likely undergo phenotypic changes during ontogenesis,although in the healthy brain they did not express exclusively either M1or M2phenotypic markers at any time.Interestingly,microglia were sexually dimorphic only at P3,when females had higher expres-sion of inﬂammatory cytokines than males,although there were no sex differences in estrogen receptor expression at this or any other time evaluated pared with microglia in vivo ,primary microglia prepared from P3mice had considerably altered gene expression,with higher levels of TNF a ,CD11b,arginase-I,and VEGF ,sug-gesting that culturing may signiﬁcantly alter microglial properties.In conclusion,age-and sex-speciﬁc variances in basal gene expression may allow differential microglial responses to the same stimulus at different ages,perhaps contributing to altered CNS vulnerabilities and/or diseasecourses.VC 2013Wiley Periodicals,Inc.Key words:microglia;development;aging;sexualdimorphism;M1/M2phenotypeMicroglia,the resident innate immune cells in the central nervous system (CNS),are associated with the pathogenesis of virtually all CNS disorders or injuries.One important characteristic of these cells is high morpho-logical and functional plasticity.They acquire an activated,amoeboid morphology in response to invading pathogens and/or CNS damage.At the same time,they increase their production of a wide array of chemokines,cytokines,ni-tric oxide,and reactive oxygen species that mediate neu-roinﬂammation (Hoek et al.,2000;Streit et al.,2005;Graeber et al.,2011).In contrast,microglia in the healthy adult CNS are characterized by a quiescent morphology with numerous thin,ramiﬁed processes.Although com-monly considered “resting,”emerging evidence suggests that quiescent microglia are highly motile and are actively involved in many physiological processes that include making dynamic contacts with neurons (Wake et al.,2009;Graeber,2010;Parkhurst and Gan,2010;Ketten-mann et al.,2011;Paolicelli et al.,2011;Tremblay and Majewska,2011;Tremblay et al.,2011).However,the gene expression proﬁles of “resting”microglia in the healthy CNS are not well characterized,and even less is known about whether microglia undergo changes in gene expression that accompany their functional alterations from postnatal development to aging.In the postnatal brain,microglia are important for synaptic pruning (Paoli-celli et al.,2011),and they have an activated,amoeboid morphology with high phagocytic activity (Schwarz et al.,2012).Microglial changes toward an amoeboid morphol-ogy are also associated with aging (Conde and Streit,2006;von Bernhardi et al.,2010),suggesting that their gene expression proﬁles may also be altered at these times.Therefore,for the present study,we hypothesized that,in the normal CNS,microglia undergo age-dependent geneJ.M.Crain and M.Nikodemova contributed equally to this work.Contract grant sponsor:NINDS;Contract grant number:R01NS049033;Contract grant sponsor:NIH;Contract grant number:R25GM083252(to J.M.C.)*Correspondence to:Maria Nikodemova,PhD,Department of Compar-ative Biosciences,University of Wisconsin,2015Linden Drive,Madison,WI 53706.E-mail:nikodemova@Received 2January 2013;Revised 20February 2013;Accepted 29March 2013Published online 17May 2013in Wiley Online Library().DOI:10.1002/jnr.23242VC 2013Wiley Periodicals,Inc.Journal of Neuroscience Research 91:1143–1151(2013)expression changes that reﬂect the morphologic and func-tional plasticity that they exhibit during development and aging.We focused on key genes associated with the classi-cal proinﬂammatory(M1)and alternative anti-inﬂamma-tory(M2)phenotypes,hypothesizing that microglia will express more M1markers in the postnatal and aging CNS when they display an activated morphology,whereas,in the young adult CNS,microglia will be polarized toward the M2phenotype.Another aspect of microglial biology that is rarely studied is whether microglial gene expression is sex de-pendent(Sierra et al.,2007).Many neurodegenerative diseases characterized by neuroinﬂammation are sexually dimorphic.For example,women are at higher risk for developing Alzheimer’s disease and multiple sclerosis, whereas men are more likely to develop amyotrophic lat-eral sclerosis and Parkinson’s disease(Payami et al.,1996; Logroscino et al.,2010;Wirdefeldt et al.,2011;Voskuhl and Gold,2012).Although the causes of these sex differ-ences remain poorly understood,potential sexual dimor-phisms in microglia may play a role.Estrogen receptors (ER)in the CNS mediate the effects of estrogens in females as well as the effects of testosterone in males, which is converted in the brain to estrogen by aromatase (Balthazart and Ball,1998).ERs underlie sex-dependent differences in neurons(Bloch et al.,1992;Simerly et al., 1997;Shughrue et al.,2002;Flores et al.,2003)and sup-press inﬂammatory responses of microglia and macro-phages(Vegeto et al.,2006;Smith et al.,2011;Arevalo et al.,2012).Therefore,we hypothesized that microglial inﬂammatory gene expression would be sex dependent and that alterations in ER expression would accompany these changes.A major goal of this study was to deter-mine the age at which potential sexual dimorphisms in microglial gene expression would be evident.To address our hypotheses,we evaluated ER and M1 and M2marker gene expression in microglia from healthy C57Bl/6male and female mice ranging in age from3days to12months.Primary microglial cultures derived from neonatal animals are an invaluable tool to study many aspects of microglial activities.Therefore,we also com-pared their gene expression proﬁles with those of microglia in vivo from neonates of the same age,to determine whether and how in vitro culturing alters their properties.MATERIALS AND METHODSAnimalsC57Bl/6mice were purchased from Charles River.All animals were maintained in an AALAC-accredited animal facil-ity with a12-hr light/dark cycle regime and access to food and water ad libitum.The7–8-week-old and4-month-old females were virgins.The12-month-old mice were retired breeders, with females not having borne a litter for at least2months prior to their use to minimize the possibility that hormones associated with pregnancy/lactation would interfere with microglial activ-ities.All experiments were approved by the University of Wis-consin Madison Institutional Animal Care and Use Committee.We examined microglial gene expression at different ages,selected based on important developmental milestones. Postnatal day3(P3)is a time following the testosterone surge in males(that occurs on the day of birth)that is responsible for masculinization of the still developing CNS.In addition,pri-mary microglial cultures are usually prepared from mice of this age.P21is a time proximal to weaning and represents an im-portant transition before the onset of puberty that begins during the fourth week of age in this mouse strain(Witham et al., 2012).Seven-to eight-week-old mice are young adults that have acquired full reproductive capacity,and4-month-old mice represent sexually mature adults.These adult ages are also the most commonly used ages in most studies.Finally,12-month-old mice represent older animals at a time when both male and female reproductive potential and gonadal hormone levels are beginning to decline.C57Bl/6mice usually do not produce litters after1year of age(Liu et al.,2013).CD11b1Cell IsolationCD11b1cells were isolated as previously described (Crain et al.,2009;Nikodemova and Watters,2012). Brieﬂy,mice were euthanized and perfused with cold phos-phate-buffered saline(PBS).Whole brains(including cere-bellum and brainstem)were dissected and dissociated into a single-cell suspension using the Neural Tissue Dissociation Kit containing papain(Miltenyi Biotec,Bergisch Gladbach, Germany).Myelin was removed by centrifugation in0.9M sucrose in Hank’s buffered salt solution.Cells were stained with phycoerythrin(PE)-conjugated anti-CD11b antibodies, followed by magnetic bead-conjugated secondary antibodies against PE.Magnetically tagged CD11b1cells were then separated using MS columns according to the manufacturer’s protocol(Miltenyi Biotec).Reagents were used at4 C,and the cells were kept on ice during the isolation process.The average purity of isolated cells having the characteristics of microglia was>97%as determined byﬂow cytometry for CD11b/CD45staining(Crain et al.,2009;Nikodemova and Watters,2012).We recently showed that the isolation efﬁciencies of microglia expressing low and high CD11b levels were equal;therefore,microglial isolation is not expected to be affected by variations in CD11b expression at different time points(Nikodemova and Watters,2012). GenotypingThe sex of the3-day-old mice was veriﬁed by genotyp-ing for the sex-determining region Y(SRY)gene,which is located on the Y chromosome,as previously described(Crain et al.,2009).Genomic DNA was isolated by digestion of a small section of tail and then used in PCR for SRY.Primary Microglial CulturesPrimary neonatal microglial cultures were prepared as previously described,from approximately50%female and50% male litters(Nikodemova et al.,2007).Brieﬂy,3-day-old C57Bl/6mice were euthanized,and brains were dissected and cleaned of meninges and visible blood vessels and then dissoci-ated by incubation in0.25%trypsin and DNase I,followed by1144Crain et al.Journal of Neuroscience Researchtrituration with a Pasteur pipette.Cells were plated in T75ﬂasks containing Dulbecco’s modiﬁed Eagle’s medium supple-mented with10%fetal bovine serum and penicillin/streptomy-cin.Microglia were harvested by shaking10–14days later and cultured for2days in the medium described above.The purity of microglial cultures was>96%as assessed by CD11b1stain-ing,as described previously(Nikodemova et al.,2007).RNA Extraction and Quantitative PCRRNA was extracted from either primary microglial cul-tures or freshly isolated microglia using Tri reagent(Sigma-Aldrich,St.Louis,MO).cDNA was synthesized from1l g total RNA and MMLV reverse transcriptase(Invitrogen,Carlsbad, CA)as previously described(Crain et al.,2009).Quantitative PCR was performed on an ABI7300system using Power SYBR green(Applied Biosystems,Carlsbad,CA).The primer sequences are given in Table I and were designed to span introns whenever possible.Primer efﬁciency was tested by serial dilu-tion.ER b expression was tested by using three independent primer sets whose efﬁciency was veriﬁed with cDNA from ovary as a positive control.The Ct values for ER b were 20in the ovaries(highly expressed),26in whole brain tissue homoge-nates,and>33in isolated microglia(deﬁned as undetectable).Ct values from duplicate measurements of each sample were averaged,and relative expression levels were determined by the DD Ct method.The expression of each gene was nor-malized to the levels of18s and/or b-actin within each sample as previously described(Crain et al.,2009).Statistical AnalysisData are expressed as mean6SEM of n58–14mice in each group.Results for primary microglial cultures are from three independent culture preparations.Statistical analyses were performed in Sigma Stat3.1software.One-way ANOVA was used to determine statistically signiﬁcant differences in gene expression over time in the same sex,and two-way ANOVA followed by the Holm-Sidak test was used to determine differ-ences in age-dependent gene expression between females and males.Statistical signiﬁcance was set at P<0.05.Levels of gene expression are displayed relative to3-day-old males,which allowed comparison over time and between sexes.In some cases,a Student’s t-test was used to determine differences between primary cultures and P3microglia or differences in expression between males and females at the same time point, as indicated in Results.Gene expression in microglial cultures was compared with both P3male and female microglia.RESULTSAge-and Sex-Dependent M1Gene Expression We examined basal expression levels of key proin-ﬂammatory genes typically associated with the M1pheno-type:iNOS,TNF a,IL-1b,and IL-6in na€ıve mice at P3, P21,7–8weeks,4months,and12months of age.Nota-bly,the expression of each gene displayed a unique time course,suggesting their independent regulation with age. The levels of mRNA for each gene are expressed relative to3-day-old males.iNOS mRNA levels were highest at P3,followed by a signiﬁcant70%downregulation by P21(Fig.1A).In adult mice,iNOS expression was only10–20%of that seen at P3(two-way ANOVA,P<0.001).We did not detect any sex differences in the expression of iNOS at any age.TNF a was highly expressed at P3,but it was signiﬁ-cantly lower between P21and4months of age(Fig.1B).A second peak of TNF a mRNA levels occurred at12months in both sexes.A two-way ANOVA revealed signiﬁcant age-dependent changes in TNF a expression(P<0.001), without signiﬁcant interaction with sex(P<0.2).Although a two-way ANOVA analysis did not reveal signiﬁcant sex-dependent TNF a expression,females had60%higher TNF a mRNA levels at P3than males,a difference that was statistically signiﬁcant by Student’s t-test(P<0.007).In males,there were no signiﬁcant age-dependent changes in the expression of IL-1b(Fig.1C),and,in females,IL-1b decreased by50%at7weeks of age com-pared with P3.By12months,IL1-b appeared to be up-regulated in both sexes,but this increase did not reach statistical signiﬁcance as determined by one-way or two-way ANOVA.A sex difference was observed in IL-1bTABLE I.Primer Sequences Used for qPCRGene Forward primer Reverse primerVEGF TTGAGACCCTGGTGGACATCT CACACAGGACGGCTTGAAGA ER a TGCGCAAGTGTTACGAAGTGG TCATGTCTCCTGAAGCACCCA ER b GCTGGCTGACAAGGAACTGGT CGAGGTCTGGAGCAAAGATGA Arginase-I AGCCAATGAAGAGCTGGCTGGT AACTGCCAGACTGTGGTCTCCA IL-10GCCTTATCGGAAATGATCCA TCTCACCCAGGGAATTCAAA iNOS TGACGCTCGGAACTGTAGCAC TGATGGCCGACCTGATGTT TNF a TGTAGCCCACGTCGTAGCAA AGGTACAACCCATCGGCTGG IL-6ACTTCCATCCAGTTGCCTTC GTCTCCTCTCCGGACTTGTG IL-1b TGTGCAAGTGTCTGAAGCAGC TGGAAGCAGCCCTTCATCTT TLR4GAGGCAGCAGGTGGAATTGTAT TTCGAGGCTTTTCCATCCAA TLR2CGAGTGGTGCAAGTACGAACTG TGGTGTTCATTATCTTGCGCAG FcR g I TGCTACTTTGGGTTCCAGTCGGT TACTGACCCATGGAGGCTGCA CD11b AAGGATTCAGCAAGCCAGAA GGAGGGATGAGAGTCCACAT 18S CGGGTGCTCTTAGCTGAGTGTCCCG CTCGGGCCTGCTTTGAACAC b-Atin ACCCTAAGGCCAACCGTGAA AGAGCATAGCCCTCGTAGATGGMicroglial Gene Expression in Healthy Brain1145 Journal of Neuroscience ResearchmRNA levels at P3,when expression was signiﬁcantly higher in females than in males (t -test,P <0.001).Contrary to other proinﬂammatory genes that have high expression levels at P3,IL-6expression was lowest at P3compared with adult animals,which had three to four times greater IL-6mRNA levels (two-way ANOVA,P <0.002).Interestingly,whereas females had higher IL-6mRNA levels at P3(t -test,P <0.001),this sexual dimorphism did not persist in adulthood (Fig.1D).Age-and Sex-Dependent M2Gene ExpressionWe investigated the expression of genes often used to indicate the M2phenotype:the anti-inﬂammatory cytokine IL-10,arginase-I,and the growth factor VEGF.We detected no signiﬁcant age-dependent changes in the expression of IL-10in males (one-way ANOVA,P 50.12;Fig.2A);however,females showed decreased expression at 7weeks of age (one-way ANOVA,P <0.04).Females alsohad almost twofold higher levels of IL-10mRNA at P3than males (two-way ANOVA,P <0.03).The time course of arginase-I was very similar to that of iNOS.The highest expression was observed at P3,followed by downregulation by P21to 30%of P3levels (Fig.2B).In adulthood,the levels of arginase-I mRNA were <10%of P3expression (two-way ANOVA,P <0.001).No differences were observed between males and females.The expression of VEGF,a growth factor that supports neuronal survival,was unchanged at all time points evaluated (Fig.2C),and no differences between males and females were observed.Age-and Sex-Dependent Expression of Membrane ProteinsToll-like receptors (TLRs)play an important role in the activation of innate immune cells,including microglia.We analyzed the expression of TLR4and TLR2becauseFig.1.Basal expression of proinﬂammatory genes in microglia.The expression of iNOS (A )and TNF a (B ),but not of IL-1b (C ),was signiﬁcantly higher in microglia isolated from whole brains of 3-day-old mice.On the contrary,IL-6(D )expression was lowest at P3compared with other ages.Females had higher expression of TNF a ,IL-1b ,and IL-6than males at P3.Gene expression in primary micro-glia was signiﬁcantly affected by culturing in vitro .Gene expressiondata are expressed as fold change relative to 3-day-old males.+Signiﬁ-cant age-dependent differences vs.3-day-old males.*Signiﬁcant age-dependent differences vs.3-day-old females.#Signiﬁcant differences between males and females of the same age.“a”indicates signiﬁcant difference in gene expression in primary microglia vs.3-day-old males.One symbol,P <0.05;two symbols,P <0.01;three symbols,P <0.001;N.D.,not determined.1146Crain et al.Journal of Neuroscience Researchthey are associated with CNS disorders such as ischemia,infections,multiple sclerosis,and others,and males and females are differentially predisposed to these disorders.The highest expression of TLR4was observed at P21in both sexes (Fig.3A;two-way ANOVA,P <0.02).TLR2expression also exhibited age-dependent changes (two-way ANOVA,P <0.016),the lowest mRNA levels being observed at 7weeks of age (Fig.3B).There were no statis-tically signiﬁcant sex-dependent differences in the expres-sion of TLR2or TLR4.Fc receptors mediate antibody-dependent phagocy-tosis,and morphological studies indicate differences in the prevalence of amoeboid microglia in postnatal males and females (Schwarz et al.,2012).We evaluated the expres-sion of FcR g I that binds IgG,the most common class of antibodies (Fig.3C).FcR g I mRNA levels were signiﬁ-cantly upregulated at P21compared with all other ages (two-way ANOVA,P <0.002),but there were no differ-ences between males and females.Finally,we examined the expression of CD11b,an integrin involved in cell adhesion,phagocytosis,chemo-taxis,and inﬂammation.CD11b is often upregulated upon microglial activation.CD11b mRNA levels were lowest at P3,followed by the highest expression levels at P21(one-way ANOVA,P <0.001,for males;P <0.01,for females;Fig.3D).Although CD11b expression was downregulated after P21,it still remained higher than at P3.We found no signiﬁcant differences in CD11b expression between males and females at any age.Age-and Sex-Dependent Expression of ERsWe also evaluated ER a and ER b expression in microglia,given the sexual dimorphisms in several neuro-logic disorders.ER a mRNA expression was very low at P3but increased at P21.Its expression further increased by 7–8weeks of age,after which time its levels remained constant until 12months of age in both sexes (Fig.4).Compared with P3,ER a mRNA levels were approxi-mately fourfold higher at 21days and six-to sevenfold higher at the other time points.Importantly,no differen-ces were observed in microglial ER a expression between males and females at any age.ER b mRNA expression was not detectable at any age evaluated,in either male or female mice,suggesting that this gene is not expressed in microglia from healthy animals.Basal Gene Expression in Primary MicrogliaBecause mixed-sex primary microglial cultures are commonly used to study microglia in vitro ,we compared gene expression in cultured cells to that of microglia freshly isolated from animals of the same age (P3)from which the primary cultures had been prepared.We found that gene expression in primary microglia was signiﬁcantly different from that of male and female P3microglia in vivo .Moreover,primary microglial gene proﬁles did not match the proﬁle of microglia at any age evaluated here.TNF a mRNA levels were highly upregulated in neonatal microglial cultures compared with male butnotFig.2.Basal expression of anti-inﬂammatory and trophic factor genes in microglia.P3females had higher microglial expression of IL-10(A )than males.Arginase-I (B )expression was highest at P3both in males and in females compared with other ages.There were no sex-or age-dependent changes in VEGF (C )expression.Primary microglia cultures had signiﬁ-cantly lower expression of IL-10compared with males or females in vivo ,whereas VEGF was signiﬁcantly upregulated compared with any age or sex in vivo .Gene expression data are expressed as fold change relative to 3-day-old males.+Signiﬁcant age-dependent differences vs.3-day-old males.*Sig-niﬁcant age-dependent differences vs.3-day-old females.#Signiﬁcant differ-ences between males and females of the same age.“a”indicates signiﬁcant difference in gene expression in primary microglia vs.3-day-old males.One symbol,P <0.05;two symbols,P <0.01;three symbols,P <0.001.Microglial Gene Expression in Healthy Brain 1147Journal of Neuroscience Researchfemale P3microglia (Fig.1B),whereas iNOS expression was signiﬁcantly downregulated,with levels comparable to those observed in male and female adult microglia (Fig.1A).IL-10mRNA levels were also signiﬁcantly lower in primary cultures compared with male and female micro-glia of any age (Fig.2A).Arginase-I mRNA showed lev-els comparable to levels in male and female P3microglia (Fig.2B).Interestingly,the expression of VEGF was increased by sevenfold in primary cultures compared with freshly isolated male and female microglia from mice of any age.CD11b mRNA levels in primary cultures were 16–18times higher than at P3in males and females (Fig.3),whereas the expression of TLR2,TLR4,and FcR g I was comparable to that of P3mice.Finally,ER a mRNA levels in primary microglial cells were signiﬁcantly down-regulated compared with male and female microglia in vivo from any age,whereas ER b mRNA levels remained undetectable (Fig.4).DISCUSSIONMicroglia possess great morphological and functional plas-ticity that allow their rapid response to speciﬁc physiolog-ical or pathological signals.However,it is not known whether microglial properties differ in the healthy CNS of postnatal,adult,and aged mice,since no studies have systematically evaluated microglia over time.Our data demonstrate that basal microglial gene expression signiﬁcantly varies in the postnatal and the adult brain,perhaps allowing microglial acquisition of speciﬁc age-dependent phenotypes.Interestingly,however,microglia in the healthy CNS are not fully committed to either an inﬂammatory or an anti-inﬂammatory phenotype at any age but rather display some M1and M2markers with variable age-dependent expression levels.At P3,microglia were characterized by high expres-sion of iNOS,TNF a ,and arginase-I mRNAlevelsFig. 3.Basal expression of membrane proteins in microglia.P21microglia were characterized by elevated expression of TLR4(A ),FcR g I (C ),and CD11b (D ),but not of TLR2(B ),compared with other ages.Interestingly,primary microglial cells had elevated CD11b levels compared with male or female P3pups in vivo .Gene expression data are expressed as fold change relative to 3-day-old males.+Signiﬁcant age-dependent differences vs.3-day-old males.*Signiﬁ-cant age-dependent differences vs.3-day-old females.#Signiﬁcant differences between males and females of the same age.“a”indicates signiﬁcant difference in gene expression in primary microglia vs.3-day-old males.One symbol,P <0.05;two symbols,P <0.01;three symbols,P <0.001.1148Crain et al.Journal of Neuroscience Researchrelative to other ages.Thus,during the early postnatal pe-riod,microglia express concomitant M1(iNOS,TNF a )and M2(arginase-I)markers,suggesting either that they acquire a unique phenotype related to speciﬁc develop-mental needs at this age or that there are several microglial subpopulations that may be region speciﬁc.The latter is supported by the presence of at least three different microglial morphologies found in many CNS regions at this age,with the amoeboid morphology being the most prevalent (Schwarz et al.,2012).Both TNF a and nitric oxide (produced by iNOS)exert pleiotropic effects.In addition to their well-known role in inﬂammation,both are involved in neuronal apoptosis and synaptic plasticity and pruning,frequent processes during early CNS devel-opment in which microglia are actively involved (McCoy and Tansey,2008;Zhou and Zhu,2009).The signiﬁ-cance of arginase-I expression at this age is not yet clear.Both iNOS and arginase-I use arginine as a substrate for their enzymatic activities,thus competing for arginine availability.Some studies suggest that arginase-I may function as a modulator of iNOS activity to prevent over-production of nitric oxide in immune cells (Chang et al.,1998;Mori,2007).On the other hand,in macrophages,arginase-I activity is important for extracellular matrix production,facilitating wound healing (Bansal and Ochoa,2003).At P3the brain is still developing and growing,so it is possible that microglia participate in extracellular matrix building through arginase-I activities.At P21,iNOS,TNF a ,and arginase-I are downre-gulated,whereas IL-6,CD11b,TLR4,and FcR g ImRNAs are signiﬁcantly increased,suggesting that micro-glia at P21are phenotypically and functionally distinct from both P3and adult microglia.The functional signiﬁ-cance of elevated CD11b and TLR4expression at P21is not yet clear and warrants further study.Fc receptors mediate antibody-dependent phagocytosis and are impor-tant modulators of microglial activities.Although increased IgG and Fc receptor levels are evident in the aged CNS and during neurodegenerative disease in ani-mal models and humans (Lira et al.,2011;Lunnon et al.,2011;Cribbs et al.,2012),the role of microglial Fc recep-tors during CNS development is unknown.Our data sug-gest that they may play a role in the postnatal period,when increased phagocytosis may be necessary for clear-ing debris from neuronal remodeling processes.Between 2and 4months of age,microglia express low levels of the M1markers iNOS and TNF a mRNA.IL-10and arginase-I expressions,markers of two different M2subtypes,are also low,suggesting that microglia in the healthy adult brain are not polarized to either the M1or M2phenotype.However,by 12months of age,microglial TNF a mRNA had increased to the levels found during early postnatal development.IL-1b mRNA was also increased at 12months,although not signiﬁcantly.Similar results have been reported from other studies (Godbout et al.,2005;Sierra et al.,2007)and suggest that,at this older age,microglia may be polarizing toward the M1phenotype.The only signiﬁcant sexual dimorphisms we observed in microglial gene expression were in the early postnatal period (P3).Microglia from female mice had higher mRNA levels for TNF a ,IL-1b ,IL-6,and IL-10than those from males.The testosterone surge occurring in male mice shortly after birth may underlie this sex-related differences,as androgens reportedly reduce the expression of proinﬂammatory cytokines in macrophages (Brown et al.,2007;Vignozzi et al.,2012),and they are converted to estrogens in the CNS which also exert anti-inﬂammatory effects.ER a levels were lowest at P3rela-tive to other ages,and no differences between males and females were found at this age or at any other tested.In addition,we did not detect ER b mRNA in microglia at all,consistent with a report by Sierra et al.(2008).Previ-ous studies have shown effects of ER b in activated micro-glial cell lines and primary cultures and in ischemically injured nonhuman primates (Mor et al.,1999;Baker et al.,2004;Takahashi et al.,2004;Lewis et al.,2008),but no reports to our knowledge have demonstrated effects of ER b activation on inﬂammatory gene expres-sion in quiescent microglia.Together these data suggest that ER b is not expressed in microglia in the healthy CNS and that neither ER a nor ER b underlies the sexual dimor-phisms observed in early postnatal microglial gene expres-sion.However,a recent study by Sato et al.(2004)showed that some effects of male sex hormones in the CNS are mediated via androgen receptors,so they may be responsi-ble for some sex-dependent differences in microglial gene expression,although androgen receptors have not been detected in microglia (Sierra et al.,2008).Regardless,theFig.4.Basal expression of estrogen receptors in microglia.The expres-sion levels of ER a and ER b were evaluated by qRT-PCR and are expressed as fold change relative to 3-day-old males.P3male and female microglia had the lowest expression of ER a compared with other ages.No sex-dependent differences were detected in ER a expression at any age.Primary microglia had downregulated ER a levels compared with P3male and female microglia in vivo .ER b was unde-tectable in all ages.+Signiﬁcant age-dependent differences vs.3-day-old males.*Signiﬁcant age-dependent differences vs.3-day-old females.#Signiﬁcant differences between males and females of the same age.“a”indicates signiﬁcant difference in gene expression in primary microglia vs.3-day-old males.Two symbols,P <0.01;three symbols,P <0.001.Microglial Gene Expression in Healthy Brain 1149Journal of Neuroscience Research。

Dynamic Transcriptome Landscape of Maize Embryo and

Dynamic Transcriptome Landscape of Maize Embryo and Endosperm Development1[W][OPEN]Jian Chen2,Biao Zeng2,Mei Zhang,Shaojun Xie,Gaokui Wang,Andrew Hauck,and Jinsheng Lai*State Key Laboratory of Agro-biotechnology and National Maize Improvement Center,Department of Plant Genetics and Breeding,China Agricultural University,Beijing100193,People’s Republic of ChinaMaize(Zea mays)is an excellent cereal model for research on seed development because of its relatively large size for both embryo and endosperm.Despite the importance of seed in agriculture,the genome-wide transcriptome pattern throughout seed development has not been well ing high-throughput RNA sequencing,we developed a spatio-temporal transcriptome atlas of B73maize seed development based on53samples from fertilization to maturity for embryo, endosperm,and whole seed tissues.A total of26,105genes were found to be involved in programming seed development,in-cluding1,614transcription factors.Global comparisons of gene expression highlighted the fundamental transcriptomic repro-gramming and the phases of development.Coexpression analysis provided further insight into the dynamic reprogramming of the transcriptome by revealing functional transitions during bined with the published nonseed high-throughput RNA sequencing data,we identiﬁed91transcription factors and1,167other seed-speciﬁc genes,which should help elucidate key mechanisms and regulatory networks that underlie seed development.In addition,correlation of gene expression with the pattern of DNA methylation revealed that hypomethylation of the gene body region should be an important factor for the expressional activation of seed-speciﬁc genes,especially for extremely highly expressed genes such as zeins.This study provides a valuable resource for understanding the genetic control of seed development of monocotyledon plants.Maize(Zea mays)is one of the most important crops and provides resources for food,feed,and biofuel (Godfray et al.,2010).It has also been used as a model system to study diverse biological phenomena,such as transposons,heterosis,imprinting,and genetic diversity (Bennetzen and Hake,2009).The seed is a key organ of maize that consists of the embryo,endosperm,and seed coat.Maize seed development initiates from a double fertilization event in which two pollen sperm fuse with the egg and central cells of the female gametophyte to produce the progenitors of the embryo and endosperm, respectively(Dumas and Mogensen,1993;Chaudhury et al.,2001).The mature embryo inherits the genetic information for the next plant generation(Scanlon and Takacs,2009),whereas the endosperm,which is storage tissue for the embryo,persists throughout seed devel-opment and functions as the site of starch and protein synthesis(Sabelli and Larkins,2009).Elucidation of the genetic regulatory mechanisms involved in maize seed development will facilitate the design of strategies to improve yield and quality,and provide insight that is applicable to other monocotyledon plants.A key means to explore the mechanisms of seed de-velopment is to identify gene activities and functions. Genetic studies have uncovered a number of genes that play major roles in governing embryogenesis and ac-cumulation of endosperm storage compounds,such as Viviparous1,KNOTTED1,Indeterminate gametophyte1, Shrunken1(Sh1),Opaque2(O2),and Defective kernel1 (Chourey and Nelson,1976;McCarty et al.,1991;Smith et al.,1995;Vicente-Carbajosa et al.,1997;Lid et al., 2002;Evans,2007).Furthermore,the activity of some genes has also been extensively studied.Typical exam-ples are zein genes that encode primary storage proteins in endosperm.Woo et al.(2001)examined zein gene expression and showed that they were the most highly expressed genes in endosperm based on EST data,where-as their dynamic expression patterns were revealed in a later study(Feng et al.,2009).Nevertheless,informa-tion on the global gene expression network throughout seed development is still very limited.The transcriptome is the overall set of transcripts, which varies based on cell or tissue type,develop-mental stage,and physiological condition.Analysis of transcriptome dynamics aids in implying the function of unannotated genes,identifying genes that act as critical network hubs,and interpreting the cellular pro-cesses associated with development.In Arabidopsis (Arabidopsis thaliana),the genes expressed in devel-oping seed and its subregions at several develop-ment stages have been analyzed with Affymetrix GeneChips(Le et al.,2010;Belmonte et al.,2013).In1This work was supported by the National High Technology Re-search and Development Program of China(863Project,grant no. 2012AA10A305to J.L.)and the National Natural Science Foundation of China(grant no.31225020to J.L.).2These authors contributed equally to the article.*Address correspondence to jlai@.The author responsible for distribution of materials integral to the ﬁndings presented in this article in accordance with the policy de-scribed in the Instructions for Authors()is: Jinsheng Lai(jlai@).[W]The online version of this article contains Web-only data.[OPEN]Articles can be viewed online without a subscription./cgi/doi/10.1104/pp.114.240689maize,microarray-based atlases of global transcription have provided insight into the programs controlling development of different organ systems (Sekhon et al.,2011).Compared with microarray,high-throughput RNA sequencing (RNA-seq)is a powerful tool to com-prehensively investigate the transcriptome at a much lower cost,but with higher sensitivity and accuracy (Wang et al.,2009b).Several studies have taken ad-vantage of the RNA-seq strategy to interpret the dy-namic reprogramming of the transcriptome during leaf,shoot apical meristem,and embryonic leaf develop-ment in maize (Li et al.,2010;Takacs et al.,2012;Liu et al.,2013).To date,only two studies have focused on identi-fying important regulators and processes required for embryo,endosperm,and/or whole seed development in maize based on a genome-wide transcriptional pro-ﬁle produced by RNA-seq (Liu et al.,2008;Teoh et al.,2013).However,these studies were limited by the low number of samples used and they did not provide an extensive,global view of transcriptome dynamics over the majority of seed development stages.Here,we pres-ent a comprehensive transcriptome study of maize em-bryo,endosperm,and whole seed tissue from fertilization to maturity using RNA-seq,which serves as a valu-able resource for analyzing gene function on a global scale and elucidating the developmental processes of maize seed.RESULTSGeneration and Analysis of the RNA-seq Data SetTo systematically investigate the dynamics of the maize seed transcriptome over development,we generated RNA-seq libraries of B73seed tissues from different developmental stages,including 15embryo,17endo-sperm,and 21whole seed samples (Fig.1).Utilizing paired-end Illumina sequencing technology,we gen-erated around 1.9billion high-quality reads,80.2%of which could be uniquely mapped to the B73referencegenome (Schnable et al.,2009;Supplemental Table S1).The genic distribution of reads was 66.6%exonic,25.6%splice junction,and 2.6%intronic,leaving about 5%from unannotated genomic regions,demonstrating that most of the detected genes have been annotated.Uniquely mapped reads were used to estimate nor-malized transcription level as reads per kilobase per million (RPKM).To reduce the in ﬂuence of transcrip-tion noise,genes from the B73ﬁltered gene set (FGS)were included for analysis only if their RPKM values were $1.Considering that our purpose was not to identify minor differential expression of genes between two time points of development,but to provide an atlas of gene expression pro ﬁle across tissues using time se-ries biological samples,we only randomly selected 12samples to have a biological replicate (Supplemental Fig.S1)to assess our data parisons of bio-logical replicates showed that their expression values were highly correlated (average R 2=0.96).For the samples with biological replicates,we took the average RPKM as the expression quantity.To further evaluate the quality of our expression data,we compared the transcript abundance patterns of a number of selected genes with previously measured expression pro ﬁles (Supplemental Fig.S2).For example,LEAFY COTYLE-DON1(LEC1),which functions in embryogenesis,was mainly expressed in the early stage of the embryo (Lotan et al.,1998).Globulin2(Glb2)had high expres-sion late in embryogenesis,in accordance with its func-tion as an important storage protein in the embryo (Kriz,1989).Similarly,O2,a transcription factor (TF)that regulates zein synthesis (Vicente-Carbajosa et al.,1997),and Fertilization independent endosperm1(Fie1),a repressor of endosperm development in the absence of fertilization (Danilevskaya et al.,2003),were almost ex-clusively expressed in the endosperm.Expression pat-terns of these selected genes were identi ﬁed exclusively in their known tissue of activity,indicating that the embryo and endosperm samples were processed well.In total,we detected 26,105genes expressed in at least 1of the 53samples (Supplemental Data Set S1).The distribution of these genes is revealed by aVennFigure 1.Overview of the time series maize seed samples used for RNA-seq analysis.The photographs show the changes in maize embryo,endosperm,and whole seed during development.The 53samples shown here were used to generate RNA-seq libraries.Bar =5mm.Transcriptome Dynamics of Maize Seeddiagram (Fig.2A),which shows that 20,360genes were common among all three tissue types.The number of genes detected in endosperm tissue during the devel-opmental stages was lower and much more variable compared with embryo or whole seed tissue,and a greater number of genes were expressed in the tissue during the early and late phases.Several thousand fewer expressed genes were detected 14d after polli-nation (DAP)in the endosperm compared with 6or 8DAP (Fig.2B;Supplemental Data Set S2).In addition,the median expression level in the embryo was roughly 2-fold greater than that of the endosperm from 10to 30DAP (Fig.2C).Of the 1,506genes unique to the whole seed samples,451were present in RNA-seq data of 14and/or 25DAP pericarp tissue (Morohashi et al.,2012).Considering that the maternal tissue is the vast majority of the content of the early seed (Márton et al.,2005;Pennington et al.,2008),we inferred that most of these genes might be expressed exclusively in maternal tissue such as the pericarp or nucellus.Moreover,1,062genes in the embryo and endosperm with low expression were not detected in whole seed samples (Fig.2C).Division of Development Phases by Global Gene Expression PatternsTo gain insight into the relationships among the dif-ferent transcriptomes,we performed principal compo-nent analysis (PCA)on the complete data set,which can graphically display the transcriptional signatures and developmental similarity.The ﬁrst component(40.7%variance explained)separated samples based on tissue identity and clearly distinguished embryo from endosperm samples,with whole seed samples located in between (Fig.3A).The second component (24.4%variance explained)discriminated early,mid-dle,and late stages of development for all three tissues (Fig.3A).The wider area occupied by endosperm sam-ples than embryo demonstrates stronger transcriptome reprogramming in developing endosperm,which is mainly attributable to drastic changes in the early and late stages.Moreover,whole seed samples of 0to 8DAP and 30to 38DAP clustered closely to the embryo,but 10to 28DAP samples were close to the endosperm.Cluster analysis of the time series data for the tissues grouped samples well along the axis of developmental time (Fig.3,B –D).Embryo samples from 10to 20DAP and 22to 38DAP were the primary clusters,which correspond to morphogenesis and maturation phases of development (Fig.3B).This is consistent with the embryo undergoing active DNA synthesis,cell division,and differentiation,and then switching to synthesis of storage reserve and desiccation (Vernoud et al.,2005).Expression differences in endosperm samples resulted in three primary clusters,which correspond to early,middle,and late phases of development (Fig.3C).The earliest time point (6and 8DAP)is an active period of cell division and cell elongation that terminates at about 20to 25DAP (Duvick,1961).The samples of 10to 24DAP formed one subgroup and 26to 34DAP formed another subgroup,suggesting that they mark the period forming the main cell types and maturation of endo-sperm,respectively (Fig.3C).The two subgroupsformFigure 2.Analysis of global gene expres-sion among different samples.A,Venn diagram of the 26,105genes detected among embryo,endosperm,and whole seed.B,Number of genes expressed in each of the samples.C,Comparison of expression levels of genes detected in embryo and endosperm tissues.Chen et al.a larger cluster for active accumulation of storage com-pounds during 10to 34DAP.The distinct cluster of 36and 38DAP is in accordance with the end of storage compound accumulation in the endosperm and the ac-tivation of biological processes involved in dormancy and dehydration.In whole seed tissue,a primary clus-ter was formed from the earliest time points with 0to 4DAP and 6to 8DAP samples as subgroups,separating the nucellus degradation as well as endosperm syncy-tial and cellularization phases from the rapid expansion of the endosperm and development of embryonic tissues (Fig.3D).After 10DAP,the embryo and endosperm dominate the formation of seed,as shown in Figure 1and morphological observation (Pennington et al.,2008).As effected by both embryo and endosperm,10to 28DAP whole seed samples clustered together and 30to 38DAP formed another group (Fig.3D).These results con ﬁrmthat the expression data successfully captured the char-acteristic seed development phases and should there-fore contain valuable insights about corresponding changes in the transcriptome.Integration of Gene Activity and Cellular Function across Development PhasesThe PCA and hierarchical clustering analysis graph-ically display the relationship among different sam-ples,but do not indicate the detailed cellular ing the k-means clustering algorithm,we classi ﬁed the detected genes into 16,14,and 10coexpression modules for embryo,endosperm,and whole seed,re-spectively,each of which contains genes that harbor similar expression patterns (Fig.4).We then used Map-Man annotation to assign genes to functionalcategoriesFigure 3.Global transcriptome relationships among different stages and tissues.A,PCA of the RNA-seq data for the 53seed samples shows ﬁve distinct groups:I for embryo (light red),II for endosperm (light blue),and III to V for early (III),middle (IV),and late (V)whole seed (light purple).B to D,Cluster dendrogram showing global transcriptome relationships among time series samples of embryo (B),endosperm (C),and whole seed (D).The y axis measures the degree of variance (see the “Materials and Methods”).The bottom row indicates the developmental phases according to the cluster dendrogram of the time series data.au,Approximately unbiased.Transcriptome Dynamics of Maize Seed(Supplemental Fig.S3).Thus,we can aggregate genes over continuous time points and obtain a view of func-tional transitions along seed development.According to the cluster analysis results,most mod-ules of the embryo can be divided into middle (10–20DAP)and late (22–38DAP)stages (Fig.4A).The middle stage,best represented by modules C1to C7,is typi ﬁed by the overrepresentation of glycolysis,tri-carboxylic acid cycle,mitochondrial electron transport,redox,RNA regulation,DNA and protein synthesis,cell organization,and division-related genes.This is consis-tent with the high requirement of energy during em-bryo formation.The late stage represented by C8to C12exhibited up-regulation of the cell wall,hormone me-tabolism (ethylene and jasmonate),stress,storage pro-teins,and transport-related genes,which coincides with the maturation of the embryo.The modules C13to C16included genes that were broadly expressed across the time points sampled and were related to hormone me-tabolism (brassinosteroid),cold stress,RNA process-ing and regulation,amino acid activation,and protein targeting.All of the 14coexpression modules of endosperm can be roughly divided into early (6–8DAP),middle (10–34DAP),and late (36–38DAP)stages (Fig.4B).The early stage (represented by modules C1to C4)isexempli ﬁed by high expression of hormone metabo-lism (gibberellin),cell wall,cell organization and cycle,amino acid metabolism,DNA,and protein synthesis –related genes,which is consistent with differentiation,mitosis,and endoreduplication.Genes in the tricar-boxylic acid cycle and mitochondrial electron transport are also overrepresented and related to energy de-mands at that time.The middle stage (best represented by C5to C8,in which different modules have distinct pro ﬁles)is the active storage accumulation phase and exhibits high expression of carbohydrate metabolism genes,as expected.Increased expression of protein degradation-related genes around 26to 34DAP in C7and C8coincides with the process of endosperm matu-ration.Genes involved in protein degradation,second-ary metabolism,oxidative pentose phosphate,receptor kinase signaling,and transport were up-regulated in the late stage in modules C9to C14during the con-cluding phase of endosperm maturation.Ten DAP and later time points of whole seed sam-ples re ﬂect the additive combination of embryo and endosperm expression.Genes that are active early in development (0–8DAP)in clusters C1to C4are ex-pected to be related to maternal tissue,which is the bulk of the seed at that time.A group of genes are highly expressed in C1at 0DAP,but theirexpressionFigure 4.Coexpression modules.A to C,Expression patterns of coexpression modules of embryo (A),endosperm (B),and whole seed (C),ordered according to the sample time points of their peak expression.For each gene,the RPKM value normalized by the maximum value of all RPKM values of the gene over all time points is shown.Chen et al.drops rapidly by2DAP,suggesting that they have functional roles that precede pollination.This group includes photosynthesis light reaction members and some TFs involved in RNA regulation.Genes related to cell wall and protein degradation,signaling,nucle-otide metabolism,DNA synthesis,cell organization, and mitochondrial electron transport are overrepre-sented in C2to C4,which has increased expression after pollination,in accordance with the degradation of nucellus tissue and development of embryonic tissues. The expression patterns and functional categories of the1,506genes detected only in whole seed samples are shown in Supplemental Figure S4.Because these genes tend to be expressed at high levels mainly before 8DAP,they are presumed to have functions in early seed development.Together,these data show that the transition of major biochemical processes along the developmental time axis of the seed is produced partly by highly coordi-nated transcript dynamics.TF Expression during Seed DevelopmentOf the2,297identiﬁed maize TFs(Zhang et al.,2011), 1,614(70%)are included in our analysis(Supplemental Data Set S3),which accounts for6.18%of the total number of genes detected in seed tissue.The num-ber of TFs detected in the different samples is shown in Supplemental Figure S5A.Their proportion to the total genes expressed in each tissue time point was always greater in embryo than endosperm samples (Supplemental Fig.S5B).Shannon entropy has been used to determine the speciﬁcity of gene expression, with lower values indicating a more time-speciﬁc pro-ﬁle(Makarevitch et al.,2013).The Shannon entropy of TFs was signiﬁcantly lower than all other genes in both embryo(P=2.3310210)and endosperm(P,1.53 1023),indicating that TFs tended to be expressed more time speciﬁcally than other genes(Supplemental Fig.S5, C and D).The number of TFs from each family used in the seed development program,along with the proportion of members present in the coexpression modules rel-ative to the total members of the family expressed in the tissue,is shown in Supplemental Figure S6. Enrichment of these TF families in the coexpression modules based on observed numbers was evaluated with Fisher’s exact test.Signiﬁcant TF family enrich-ment was identiﬁed for speciﬁc coexpression modules. For example,12auxin-response factor TFs(38.7%) were expressed in embryo module C2during mor-phogenesis and one-half of the detected members of the WRKY family were active in endosperm module C12late in endosperm development.The WRKY family has been reported to be mainly involved in the physiological programs of pathogen defense and se-nescence(Eulgem et al.,2000;Pandey and Somssich, 2009).Twenty-one MIKC family TFs(56.8%)were pres-ent in whole seed module C2,implying an important role in regulating genes involved in response to fertil-ization.The developmental speciﬁcity of the detected TFs makes them excellent candidates for reverse ge-netics approaches to investigate their role in grain production.Tissue-Speciﬁc Genes of SeedIdentiﬁcation of uncharacterized tissue-speciﬁc genes can help to explain their function and understand the underlying control of tissue or organ identity.To gen-erate a comprehensive catalog of seed-speciﬁc genes, results from this study were compared with25pub-lished nonseed RNA-seq data sets(Jia et al.,2009;Wang et al.,2009a;Li et al.,2010;Davidson et al.,2011;Bolduc et al.,2012),including root,shoot,shoot apical meristem, leaf,cob,tassel,and immature ear(Supplemental Table S2).In total,we identiﬁed1,258seed-speciﬁc genes,in-cluding91TFs from a variety of families(Supplemental Data Set S4).To gain further insight into the spatial expression trend in the developing seed,we divided these genes into four groups:embryo speciﬁc,endo-sperm speciﬁc,expressed in both embryo and endo-sperm,and other as only expressed in whole seed (Table I).The dynamic expression patterns of these genes reﬂect their roles in corresponding development stages(Supplemental Fig.S7).The largest numbers of seed-speciﬁc genes were observed in the endosperm, consistent with a study in maize using microarrays (Sekhon et al.,2011),perhaps reﬂecting the speciﬁc function of endosperm.We compared the distribution of tissue-speciﬁc genes and TFs in embryo and endosperm coexpression mod-ules to identify important phases in the underlying transcription network.Coexpression modules with an enrichment of tissue-speciﬁc genes or TFs may provide insight about uncharacterized genes and preparation for subsequent developmental processes.A feature of gene activity is shown in Figure5.Fisher’s exact test (P,0.05)was used to determine modules with sig-niﬁcant enrichment of tissue-speciﬁc genes and TFs in the embryo and endosperm.In the embryo,TFs and tissue-speciﬁc genes were signiﬁcantly enriched in the late phase,suggesting a speciﬁc process during matu-ration.In the endosperm,we observed that TFs and tissue-speciﬁc genes were overrepresented in the mid-dle phase,which conforms to the role of endosperm inTable I.Total number of detected seed-speciﬁc genes and TFsData are presented as n.Tissue Type Speciﬁc Genes Speciﬁc TFs Embryo24923Endosperm74259Embryo and endosperm2196Other a483Total1,25891a These genes only detected expression in whole seeds.Transcriptome Dynamics of Maize Seedstorage compound accumulation and to speci ﬁc pro-gress at this phase.To gain further insight into the functional signi ﬁ-cance of tissue-speci ﬁc genes,overrepresented gene ontology (GO)terms were examined using the WEGO online tool (Ye et al.,2006;Supplemental Fig.S8).All overrepresented GO terms were observed for themiddle embryo development phase,including the bio-synthetic process,cellular metabolic process,macro-molecule,and nitrogen compound metabolic process.Similarly,overrepresented GO terms for the endosperm were mostly observed in the early phase,including the macromolecule,nitrogen compound metabolic pro-cess,DNA binding,and transcription regulator.GenesFigure 5.Distribution and enrichment of genes,tissue-speciﬁc genes,TFs,and tissue-speciﬁc TFs in coexpression modules of embryo and endosperm.A and B,Bars indicate the percentage of all detected genes (green),tissue-speciﬁc genes (blue),TFs (red),and tissue-speciﬁc TFs (purple)observed in a coexpression module (C)or in the development phase (Total)relative to the total number of each group detected across samples for embryo (A)and endosperm (B).The number of genes represented by the percentage is shown on the right y axis.Enrichment for tissue-speciﬁc genes and TFs was evaluated with Fisher’s exact test based on the number of genes observed in each coexpression module,whereas enrichment for tissue-speciﬁc TFs was evaluated based on the number of TFs observed in each coexpression module.Asterisks represent signiﬁcant enrichment at a false dis-covery rate #0.05.Chen et al.involved in the nutrient reservoir class were enriched in the middle phase.The oxidoreductase class was overrepresented in late phase,and is known to be in-volved in maturation (Zhu and Scandalios,1994).The Expression of Zein Genes in EndospermZeins are the most important storage proteins in maize endosperm and are an important factor in seed quality.According to Xu and Messing (2008),there are 41a ,1b ,3g ,and 2d zein genes.In order to explore their expression pattern,we ﬁrst con ﬁrmed the gene models by mapping publicly available full-length com-plementary DNAs of zein subfamily genes to B73bac-terial arti ﬁcial chromosomes,and then mapped these back to the reference genome.Because some of these zein genes were not assembled in the current B73ref-erence genome or were only annotated in the working gene set,we con ﬁrmed a ﬁnal set of 35zein genes in the FGS of the B73annotation,including 30a ,1b ,3g ,and 1d zein genes (Supplemental Table S3).About three-quarters (26)of these were in the list of the 100most highly expressed genes in the endosperm,based on mean expression across all endosperm samples (Supplemental Table S4).The distribution of these most highly expressed genes clearly showed that the 26zeins and 4starch synthesis genes were actively expressed in the middle phase of endosperm devel-opment,characteristic of storage compound accumu-lation (Fig.6A).Previous research has shown that the zein genes con-stitute approximately 40%to 50%of the total tran-scripts in the endosperm (Marks et al.,1985;Woo et al.,2001),but these results are based on EST data from a single tissue or pooled tissues and only a few zein genes were assessed.Thus,we reevaluated the transcriptomic contribution of zein genes across endosperm develop-ment using our RNA-seq data,which is able to overcome the high structural similarity among them,especially in the a family (Xu and Messing,2008).Zein genes stably accounted for about 65%of transcripts from 10to 34DAP,with 19-kD a zeins (approximately 42%),22-kD a zeins (approximately 8%),and g zeins (approximately 10%)representing the most abundant transcripts (Fig.6B).The expression of different members within agivenFigure 6.Analysis of highly expressed genes in the endosperm.A,The distribution of the 100most highly expressed genes in the endosperm ordered by mean expression in different modules.B,The dynamic transcript levels of different zein gene family members in the endosperm as reﬂected by their percentage among all detected gene transcript levels.C,Heat map showing RPKM values of 35zein genes in the different development stages of the endosperm.+,Having intact coding regions;2,with premature_stop;N,no;Y ,yes.Transcriptome Dynamics of Maize Seed。

EMMREML软件说明说明书

Package‘EMMREML’October12,2022Type PackageVersion3.1Date2015-07-20Title Fitting Mixed Models with Known Covariance StructuresAuthor Deniz Akdemir,Okeke Uche GodfreyMaintainer Deniz Akdemir<****************************>Depends Matrix,statsDescription The main functions are'emmreml',and'emmremlMultiKernel'.'emm-reml'solves a mixed model with known covariance structure using the'EMMA'algo-rithm.'emmremlMultiKernel'is a wrapper for'emmreml'to handle multiple random compo-nents with known covariance structures.The function'emmremlMultivariate'solves a multivari-ate gaussian mixed model with known covariance structure using the'ECM'algorithm. License GPL-2NeedsCompilation noRepository CRANDate/Publication2015-07-2205:52:07R topics documented:EMMREML (2)emmreml (2)emmremlMultiKernel (4)emmremlMultivariate (6)Index91EMMREML Fitting mixed models with known covariance structures.DescriptionThe main functions are emmreml,and emmremlMultiKernel.emmreml solves a mixed model with known covariance structure using the EMMA algorithm in Kang et.al.(2008).emmremlMulti-Kernel is a wrapper for emmreml to handle multiple random components with known covariance structures.The function emmremlMultivariate solves a multivariate gaussian mixed model with known covariance structure using the ECM algorithm in Zhou and Stephens(2012).DetailsPackage:EMMREMLType:PackageVersion: 3.1Date:2015-07-20License:GPL-2Author(s)Deniz Akdemir,Okeke Uche GodfreyMaintainer:Deniz Akdemir<****************************>ReferencesEfﬁcient control of population structure in model organism association mapping.Kang,Hyun Min and Zaitlen,Noah A and Wade,Claire M and Kirby,Andrew and Heckerman,David and Daly, Mark J and Eskin,Eleazar.Genetics,2008.Genome-wide efﬁcient mixed-model analysis for association studies.Zhou,Xiang and Stephens, Matthew.Nature genetics,2012.emmreml Solver for Gaussian mixed model with known covariance structure.DescriptionThis function estimates the parameters of the modely=Xβ+Zu+ewhere y is the n vector of response variable,X is a nxq known design matrix ofﬁxed effects,Z isa nxl known design matrix of random effects,βis qx1vector ofﬁxed effects coefﬁcients and u ande are independent variables with N l(0,σ2u K)and N n(0,σ2e I n)correspondingly.It also producesthe BLUPs and some other useful statistics like large sample estimates of variances and PEV.Usageemmreml(y,X,Z,K,varbetahat=FALSE,varuhat=FALSE,PEVuhat=FALSE,test=FALSE)Argumentsy nx1numeric vectorX nxq matrixZ nxl matrixK lxl matrix of known relationshipsvarbetahat TRUE or FALSEvaruhat TRUE or FALSEPEVuhat TRUE or FALSEtest TRUE or FALSEValueVu Estimate ofσ2uVe Estimate ofσ2ebetahat BLUEs forβuhat BLUPs for uXsqtestbetaχ2test statistics for testing whether theﬁxed effect coefﬁcients are equal to zero.pvalbeta pvalues obtained from large sample theory for theﬁxed effects.We report the pvalues adjusted by the"padjust"function for allﬁxed effect coefﬁcients.Xsqtestuχ2test statistic values for testing whether the BLUPs are equal to zero.pvalu pvalues obtained from large sample theory for the BLUPs.We report the pvalues adjusted by the"padjust"function.varuhat Large sample variance for the BLUPs.varbetahat Large sample variance for theβ’s.PEVuhat Prediction error variance estimates for the BLUPs.loglik loglikelihood for the model.Examplesn=200M1<-matrix(rnorm(n*300),nrow=n)K1<-cov(t(M1))K1=K1/mean(diag(K1))covY<-2*K1+1*diag(n)Y<-10+crossprod(chol(covY),rnorm(n))#training setTrainset<-sample(1:n,150)funout<-emmreml(y=Y[Trainset],X=matrix(rep(1,n)[Trainset],ncol=1),Z=diag(n)[Trainset,],K=K1)cor(Y[-Trainset],funout$uhat[-Trainset])emmremlMultiKernel Function toﬁt Gaussian mixed model with multiple mixed effects withknown covariances.DescriptionThis function is a wrapper for the emmreml toﬁt Gaussian mixed model with multiple mixed effects with known covariances.The modelﬁtted is y=Xβ+Z1u1+Z2u2+...Z k u k+e where y is the n vector of response variable,X is a nxq known design matrix ofﬁxed ef-fects,Z j is a nxl j known design matrix of random effects for j=1,2,...,k,βis nx1vec-tor ofﬁxed effects coefﬁcients and U=(u t1,u t2,...,u tk )t and e are independent variables withN L(0,blockdiag(σ2u1K1,σ2u2K2,...,σ2ukK k))and N n(0,σ2e I n)correspondingly.The function pro-duces the BLUPs for the L=l1+l2+...+l k dimensional random effect U.The variance parameters for random effects are estimated as(ˆw1,ˆw2,...,ˆw k)∗ˆσ2u where w=(w1,w2,...,w k)are the kernel weights.The function also provides some useful statistics like large sample estimates of variances and PEV.UsageemmremlMultiKernel(y,X,Zlist,Klist,varbetahat=FALSE,varuhat=FALSE,PEVuhat=FALSE,test=FALSE)Argumentsy nx1numeric vectorX nxq matrixZlist list of random effects design matrices of dimensions nxl1,...,nxl kKlist list of known relationship matrices of dimensions l1xl1,...,l k xl kvarbetahat TRUE or FALSEvaruhat TRUE or FALSEPEVuhat TRUE or FALSEtest TRUE or FALSEValueVu Estimate ofσ2uVe Estimate ofσ2ebetahat BLUEs forβuhat BLUPs for uweights Estimates of kernel weightsXsqtestbeta Aχ2test statistic based for testing whether theﬁxed effect coefﬁcients are equal to zero.pvalbeta pvalues obtained from large sample theory for theﬁxed effects.We report the pvalues adjusted by the"padjust"function for allﬁxed effect coefﬁcients.Xsqtestu Aχ2test statistic based for testing whether the BLUPs are equal to zero.pvalu pvalues obtained from large sample theory for the BLUPs.We report the pvalues adjusted by the"padjust"function.varuhat Large sample variance for the BLUPs.varbetahat Large sample variance for theβ’s.PEVuhat Prediction error variance estimates for the BLUPs.loglik loglikelihood for the model.Examples####example#Data from Gaussian process with three#(total four,including residuals)independent#sources of variationn=80M1<-matrix(rnorm(n*10),nrow=n)M2<-matrix(rnorm(n*20),nrow=n)M3<-matrix(rnorm(n*5),nrow=n)#Relationship matricesK1<-cov(t(M1))K2<-cov(t(M2))K3<-cov(t(M3))K1=K1/mean(diag(K1))K2=K2/mean(diag(K2))K3=K3/mean(diag(K3))#Generate datacovY<-2*(.2*K1+.7*K2+.1*K3)+diag(n)Y<-10+crossprod(chol(covY),rnorm(n))#training setTrainsamp<-sample(1:80,60)funout<-emmremlMultiKernel(y=Y[Trainsamp],X=matrix(rep(1,n)[Trainsamp],ncol=1),Zlist=list(diag(n)[Trainsamp,],diag(n)[Trainsamp,],diag(n)[Trainsamp,]),Klist=list(K1,K2,K3),varbetahat=FALSE,varuhat=FALSE,PEVuhat=FALSE,test=FALSE)#weightsfunout$weights#Correlation of predictions with true values in test setuhatmat<-matrix(funout$uhat,ncol=3)uhatvec<-rowSums(uhatmat)cor(Y[-Trainsamp],uhatvec[-Trainsamp])emmremlMultivariate Function toﬁt multivariate Gaussian mixed model with with knowncovariance structure.DescriptionThis function estimates the parameters of the modelY=BX+GZ+Ewhere Y is the dxn matrix of response variable,X is a qxn known design matrix ofﬁxed effects, Z is a lxn known design matrix of random effects,B is dxq matrix ofﬁxed effects coefﬁcients and G and E are independent matrix variate variables with N dxl(0,V G,K)and N dxn(0,V E,I n) correspondingly.It also produces the BLUPs for the random effects G and some other statistics. UsageemmremlMultivariate(Y,X,Z,K,varBhat=FALSE,varGhat=FALSE,PEVGhat=FALSE,test=FALSE,tolpar=1e-06,tolparinv=1e-06)ArgumentsY dxn matrix of response variableX qxn known design matrix ofﬁxed effectsZ lxn known design matrix of random effectsK lxl matrix of known relationshipsvarBhat TRUE or FALSEvarGhat TRUE or FALSEPEVGhat TRUE or FALSEtest TRUE or FALSEtolpar tolerance parameter for convergencetolparinv tolerance parameter for matrix inverseValueVg Estimate of V GVe Estimate of V EBhat BLUEs for BGpred BLUPs for GXsqtestBχ2test statistics for testing whether theﬁxed effect coefﬁcients are equal to zero.pvalB pvalues obtained from large sample theory for theﬁxed effects.We report the pvalues adjusted by the"padjust"function for allﬁxed effect coefﬁcients.XsqtestGχ2test statistic values for testing whether the BLUPs are equal to zero.pvalG pvalues obtained from large sample theory for the BLUPs.We report the pvalues adjusted by the"padjust"function.varGhat Large sample variance for BLUPs.varBhat Large sample variance for the elements of B.PEVGhat Prediction error variance estimates for the BLUPs.Examplesl=20n<-15m<-40M<-matrix(rbinom(m*l,2,.2),nrow=l)rownames(M)<-paste("l",1:nrow(M))beta1<-rnorm(m)*exp(rbinom(m,5,.2))beta2<-rnorm(m)*exp(rbinom(m,5,.1))beta3<-rnorm(m)*exp(rbinom(m,5,.1))+beta2g1<-M%*%beta1g2<-M%*%beta2g3<-M%*%beta3e1<-sd(g1)*rnorm(l)e2<-(-e1*2*sd(g2)/sd(g1)+.25*sd(g2)/sd(g1)*rnorm(l))e3<-1*(e1*.25*sd(g2)/sd(g1)+.25*sd(g2)/sd(g1)*rnorm(l))y1<-10+g1+e1y2<--50+g2+e2y3<--5+g3+e3Y<-rbind(t(y1),t(y2),t(y3))colnames(Y)<-rownames(M)cov(t(Y))Y[1:3,1:5]K<-cov(t(M))K<-K/mean(diag(K))rownames(K)<-colnames(K)<-rownames(M)X<-matrix(1,nrow=1,ncol=l)colnames(X)<-rownames(M)Z<-diag(l)rownames(Z)<-colnames(Z)<-rownames(M)SampleTrain<-sample(rownames(Z),n)Ztrain<-Z[rownames(Z)%in%SampleTrain,]Ztest<-Z[!(rownames(Z)%in%SampleTrain),]##For a quick answer,tolpar is set to1e-4.Correct this in practice.outfunc<-emmremlMultivariate(Y=Y%*%t(Ztrain),X=X%*%t(Ztrain),Z=t(Ztrain),K=K,tolpar=1e-4,varBhat=FALSE,varGhat=FALSE,PEVGhat=FALSE,test=FALSE)Yhattest<-outfunc$Gpred%*%t(Ztest)cor(cbind(Ztest%*%Y[1,],Ztest%*%outfunc$Gpred[1,],Ztest%*%Y[2,],Ztest%*%outfunc$Gpred[2,],Ztest%*%Y[3,],Ztest%*%outfunc$Gpred[3,])) outfuncRidgeReg<-emmremlMultivariate(Y=Y%*%t(Ztrain),X=X%*%t(Ztrain),Z=t(Ztrain%*%M), K=diag(m),tolpar=1e-5,varBhat=FALSE,varGhat=FALSE,PEVGhat=FALSE,test=FALSE)Gpred2<-outfuncRidgeReg$Gpred%*%t(M)cor(Ztest%*%Y[1,],Ztest%*%Gpred2[1,])cor(Ztest%*%Y[2,],Ztest%*%Gpred2[2,])cor(Ztest%*%Y[3,],Ztest%*%Gpred2[3,])IndexEMMREML,2emmreml,2emmremlMultiKernel,4 emmremlMultivariate,69。

碧云天生物技术产品说明书.pdf_1694035340.240097

碧云天生物技术/Beyotime Biotechnology订货热线：400-168-3301或800-8283301订货e-mail：******************技术咨询：*****************网址：碧云天网站微信公众号hVSMC-hTERT-GFP (人永生化主动脉平滑肌细胞)产品编号产品名称包装C6430 hVSMC-hTERT-GFP (人永生化主动脉平滑肌细胞) 1支/瓶产品简介：Organism Tissue Morphology Culture Properties Homo sapiens (Human) Aorta/smooth muscle Fibroblast Adherent本细胞株详细信息如下:General InformationCell Line Name hVSMC-hTERT-GFP (Human Immortalized Aortic Smooth Muscle Cells)Synonyms －Organism Homo sapiens (Human)Tissue Aorta/smooth muscleCell Type －Morphology FibroblastDisease －Strain －Biosafety Level* －Age at Sampling －Gender －Genetics －Ethnicity －Applications －Category －* Biosafety classification is based on U.S. Public Health Service Guidelines, it is the responsibility of the customer to ensure that their facilities comply with biosafety regulations for their own country.CharacteristicsKaryotype －Virus Susceptibility －Derivation －Clinical Data －Antigen Expression －Receptor Expression －Oncogene －Genes Expressed Carcinoembryonic antigen (CEA) 0.7 ng/10 6 cells/10 days; keratin; transforming growth factor beta,myc +; myb + ; ras +; fos +; sis +; p53 +; abl -; ros -; src -,HLA A2, B8, B17; blood type A; Rh+,The cells are positive for keratin by immunoperoxidase staining.,The line is positive for expression of c-myc, K-ras, H-ras, N-ras, myb, sis and fos oncogenes.Gene expressiondatabases －Metastasis －Tumorigenic －Effects －Comments This cell line was hTERT immortalized. Stably expresses GFP utilizing blasticidin as the antibiotic selection agent. If GFP expression is weak, blasticidin (1-2µg/ml, should be tested before adding) should be added directly to the cells in culture.2 / 6 C6430 hVSMC-hTERT-GFP (人永生化主动脉平滑肌细胞) 400-1683301/800-8283301 碧云天/BeyotimeCulture Method Doubling Time － Methods for Passages Wash by PBS once then 0.05% trypsin-EDTA solution and incubate at room temperature (or at 37ºC),observe cells under an inverted microscope until cell layer is dispersed (usually within 1 to 5 minutes) Medium DMEM/F-12 (1:1)+10% FBS+bFGF (1ng/ml) Special Remarks －Medium Renewal － Subcultivation Ratio － Growth Condition 95% air+ 5% CO 2, 37ºC Freeze medium DMEM (high glucose)+20% FBS+10% DMSO ，也可以订购碧云天的细胞冻存液(C0210)。

Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease trans

Reproduction numbers and sub-threshold endemicequilibria for compartmental models of disease transmissionP.van den Driesschea,1,James Watmough b,*,2aDepartment of Mathematics and Statistics,University of Victoria,Victoria,BC,Canada V8W 3P4b Department of Mathematics and Statistics,University of New Brunswick,Fredericton,NB,Canada E3B 5A3Received 26April 2001;received in revised form 27June 2001;accepted 27June 2001Dedicated to the memory of John JacquezAbstractA precise deﬁnition of the basic reproduction number,R 0,is presented for a general compartmental disease transmission model based on a system of ordinary diﬀerential equations.It is shown that,if R 0<1,then the disease free equilibrium is locally asymptotically stable;whereas if R 0>1,then it is unstable.Thus,R 0is a threshold parameter for the model.An analysis of the local centre manifold yields a simple criterion for the existence and stability of super-and sub-threshold endemic equilibria for R 0near one.This criterion,together with the deﬁnition of R 0,is illustrated by treatment,multigroup,staged progression,multistrain and vector–host models and can be applied to more complex models.The results are signiﬁcant for disease control.Ó2002Elsevier Science Inc.All rights reserved.Keywords:Basic reproduction number;Sub-threshold equilibrium;Disease transmission model;Disease control1.IntroductionOne of the most important concerns about any infectious disease is its ability to invade a population.Many epidemiological models have a disease free equilibrium (DFE)at whichtheMathematical Biosciences 180(2002)29–48/locate/mbs*Corresponding author.Tel.:+1-5064587323;fax:+1-5064534705.E-mail addresses:pvdd@math.uvic.ca (P.van den Driessche),watmough@unb.ca (J.Watmough).URL:http://www.math.unb.ca/$watmough.1Research supported in part by an NSERC Research Grant,the University of Victoria Committee on faculty research and travel and MITACS.2Research supported by an NSERC Postdoctoral Fellowship tenured at the University of Victoria.0025-5564/02/$-see front matter Ó2002Elsevier Science Inc.All rights reserved.PII:S0025-5564(02)00108-630P.van den Driessche,J.Watmough/Mathematical Biosciences180(2002)29–48population remains in the absence of disease.These models usually have a threshold parameter, known as the basic reproduction number,R0,such that if R0<1,then the DFE is locally as-ymptotically stable,and the disease cannot invade the population,but if R0>1,then the DFE is unstable and invasion is always possible(see the survey paper by Hethcote[1]).Diekmann et al.[2]deﬁne R0as the spectral radius of the next generation matrix.We write down in detail a general compartmental disease transmission model suited to heterogeneous populations that can be modelled by a system of ordinary diﬀerential equations.We derive an expression for the next generation matrix for this model and examine the threshold R0¼1in detail.The model is suited to a heterogeneous population in which the vital and epidemiological parameters for an individual may depend on such factors as the stage of the disease,spatial position,age or behaviour.However,we assume that the population can be broken into homo-geneous subpopulations,or compartments,such that individuals in a given compartment are indistinguishable from one another.That is,the parameters may vary from compartment to compartment,but are identical for all individuals within a given compartment.We also assume that the parameters do not depend on the length of time an individual has spent in a compart-ment.The model is based on a system of ordinary equations describing the evolution of the number of individuals in each compartment.In addition to showing that R0is a threshold parameter for the local stability of the DFE, we apply centre manifold theory to determine the existence and stability of endemic equilib-ria near the threshold.We show that some models may have unstable endemic equilibria near the DFE for R0<1.This suggests that even though the DFE is locally stable,the disease may persist.The model is developed in Section2.The basic reproduction number is deﬁned and shown to bea threshold parameter in Section3,and the deﬁnition is illustrated by several examples in Section4.The analysis of the centre manifold is presented in Section5.The epidemiological ramiﬁcations of the results are presented in Section6.2.A general compartmental epidemic model for a heterogeneous populationConsider a heterogeneous population whose individuals are distinguishable by age,behaviour, spatial position and/or stage of disease,but can be grouped into n homogeneous compartments.A general epidemic model for such a population is developed in this section.Let x¼ðx1;...;x nÞt, with each x i P0,be the number of individuals in each compartment.For clarity we sort the compartments so that theﬁrst m compartments correspond to infected individuals.The distinc-tion between infected and uninfected compartments must be determined from the epidemiological interpretation of the model and cannot be deduced from the structure of the equations alone,as we shall discuss below.It is plausible that more than one interpretation is possible for some models.A simple epidemic model illustrating this is given in Section4.1.The basic reproduction number can not be determined from the structure of the mathematical model alone,but depends on the deﬁnition of infected and uninfected compartments.We deﬁne X s to be the set of all disease free states.That isX s¼f x P0j x i¼0;i¼1;...;m g:In order to compute R0,it is important to distinguish new infections from all other changes inpopulation.Let F iðxÞbe the rate of appearance of new infections in compartment i,Vþi ðxÞbe therate of transfer of individuals into compartment i by all other means,and VÀi ðxÞbe the rate oftransfer of individuals out of compartment i.It is assumed that each function is continuously diﬀerentiable at least twice in each variable.The disease transmission model consists of non-negative initial conditions together with the following system of equations:_x i¼f iðxÞ¼F iðxÞÀV iðxÞ;i¼1;...;n;ð1Þwhere V i¼VÀi ÀVþiand the functions satisfy assumptions(A1)–(A5)described below.Sinceeach function represents a directed transfer of individuals,they are all non-negative.Thus,(A1)if x P0,then F i;Vþi ;VÀiP0for i¼1;...;n.If a compartment is empty,then there can be no transfer of individuals out of the compartment by death,infection,nor any other means.Thus,(A2)if x i¼0then VÀi ¼0.In particular,if x2X s then VÀi¼0for i¼1;...;m.Consider the disease transmission model given by(1)with f iðxÞ,i¼1;...;n,satisfying con-ditions(A1)and(A2).If x i¼0,then f iðxÞP0and hence,the non-negative cone(x i P0, i¼1;...;n)is forward invariant.By Theorems1.1.8and1.1.9of Wiggins[3,p.37]for each non-negative initial condition there is a unique,non-negative solution.The next condition arises from the simple fact that the incidence of infection for uninfected compartments is zero.(A3)F i¼0if i>m.To ensure that the disease free subspace is invariant,we assume that if the population is free of disease then the population will remain free of disease.That is,there is no(density independent) immigration of infectives.This condition is stated as follows:(A4)if x2X s then F iðxÞ¼0and VþiðxÞ¼0for i¼1;...;m.The remaining condition is based on the derivatives of f near a DFE.For our purposes,we deﬁne a DFE of(1)to be a(locally asymptotically)stable equilibrium solution of the disease free model,i.e.,(1)restricted to X s.Note that we need not assume that the model has a unique DFE. Consider a population near the DFE x0.If the population remains near the DFE(i.e.,if the introduction of a few infective individuals does not result in an epidemic)then the population will return to the DFE according to the linearized system_x¼Dfðx0ÞðxÀx0Þ;ð2Þwhere Dfðx0Þis the derivative½o f i=o x j evaluated at the DFE,x0(i.e.,the Jacobian matrix).Here, and in what follows,some derivatives are one sided,since x0is on the domain boundary.We restrict our attention to systems in which the DFE is stable in the absence of new infection.That is, (A5)If FðxÞis set to zero,then all eigenvalues of Dfðx0Þhave negative real parts.P.van den Driessche,J.Watmough/Mathematical Biosciences180(2002)29–4831The conditions listed above allow us to partition the matrix Df ðx 0Þas shown by the following lemma.Lemma 1.If x 0is a DFE of (1)and f i ðx Þsatisﬁes (A1)–(A5),then the derivatives D F ðx 0Þand D V ðx 0Þare partitioned asD F ðx 0Þ¼F 000 ;D V ðx 0Þ¼V 0J 3J 4;where F and V are the m Âm matrices deﬁned byF ¼o F i o x j ðx 0Þ !and V ¼o V i o x jðx 0Þ !with 16i ;j 6m :Further ,F is non-negative ,V is a non-singular M-matrix and all eigenvalues of J 4have positive real part .Proof.Let x 02X s be a DFE.By (A3)and (A4),ðo F i =o x j Þðx 0Þ¼0if either i >m or j >m .Similarly,by (A2)and (A4),if x 2X s then V i ðx Þ¼0for i 6m .Hence,ðo V i =o x j Þðx 0Þ¼0for i 6m and j >m .This shows the stated partition and zero blocks.The non-negativity of F follows from (A1)and (A4).Let f e j g be the Euclidean basis vectors.That is,e j is the j th column of the n Ân identity matrix.Then,for j ¼1;...;m ,o V i o x jðx 0Þ¼lim h !0þV i ðx 0þhe j ÞÀV i ðx 0Þh :To show that V is a non-singular M-matrix,note that if x 0is a DFE,then by (A2)and (A4),V i ðx 0Þ¼0for i ¼1;...;m ,and if i ¼j ,then the i th component of x 0þhe j ¼0and V i ðx 0þhe j Þ60,by (A1)and (A2).Hence,o V i =o x j 0for i m and j ¼i and V has the Z sign pattern (see Appendix A).Additionally,by (A5),all eigenvalues of V have positive real parts.These two conditions imply that V is a non-singular M-matrix [4,p.135(G 20)].Condition (A5)also implies that the eigenvalues of J 4have positive real part.Ã3.The basic reproduction numberThe basic reproduction number,denoted R 0,is ‘the expected number of secondary cases produced,in a completely susceptible population,by a typical infective individual’[2];see also [5,p.17].If R 0<1,then on average an infected individual produces less than one new infected individual over the course of its infectious period,and the infection cannot grow.Conversely,if R 0>1,then each infected individual produces,on average,more than one new infection,and the disease can invade the population.For the case of a single infected compartment,R 0is simply the product of the infection rate and the mean duration of the infection.However,for more complicated models with several infected compartments this simple heuristic deﬁnition of R 0is32P.van den Driessche,J.Watmough /Mathematical Biosciences 180(2002)29–48insuﬃcient.A more general basic reproduction number can be deﬁned as the number of new infections produced by a typical infective individual in a population at a DFE.To determine the fate of a‘typical’infective individual introduced into the population,we consider the dynamics of the linearized system(2)with reinfection turned oﬀ.That is,the system _x¼ÀD Vðx0ÞðxÀx0Þ:ð3ÞBy(A5),the DFE is locally asymptotically stable in this system.Thus,(3)can be used to de-termine the fate of a small number of infected individuals introduced to a disease free population.Let wi ð0Þbe the number of infected individuals initially in compartment i and letwðtÞ¼w1ðtÞ;...;w mðtÞðÞt be the number of these initially infected individuals remaining in the infected compartments after t time units.That is the vector w is theﬁrst m components of x.The partitioning of D Vðx0Þimplies that wðtÞsatisﬁes w0ðtÞ¼ÀV wðtÞ,which has the unique solution wðtÞ¼eÀVt wð0Þ.By Lemma1,V is a non-singular M-matrix and is,therefore,invertible and all of its eigenvalues have positive real parts.Thus,integrating F wðtÞfrom zero to inﬁnity gives the expected number of new infections produced by the initially infected individuals as the vector FVÀ1wð0Þ.Since F is non-negative and V is a non-singular M-matrix,VÀ1is non-negative[4,p.137 (N38)],as is FVÀ1.To interpret the entries of FVÀ1and develop a meaningful deﬁnition of R0,consider the fate of an infected individual introduced into compartment k of a disease free population.The(j;k)entry of VÀ1is the average length of time this individual spends in compartment j during its lifetime, assuming that the population remains near the DFE and barring reinfection.The(i;j)entry of F is the rate at which infected individuals in compartment j produce new infections in compartment i. Hence,the(i;k)entry of the product FVÀ1is the expected number of new infections in com-partment i produced by the infected individual originally introduced into compartment k.Fol-lowing Diekmann et al.[2],we call FVÀ1the next generation matrix for the model and set R0¼qðFVÀ1Þ;ð4Þwhere qðAÞdenotes the spectral radius of a matrix A.The DFE,x0,is locally asymptotically stable if all the eigenvalues of the matrix Dfðx0Þhave negative real parts and unstable if any eigenvalue of Dfðx0Þhas a positive real part.By Lemma1, the eigenvalues of Dfðx0Þcan be partitioned into two sets corresponding to the infected and uninfected compartments.These two sets are the eigenvalues of FÀV and those ofÀJ4.Again by Lemma1,the eigenvalues ofÀJ4all have negative real part,thus the stability of the DFE is determined by the eigenvalues of FÀV.The following theorem states that R0is a threshold parameter for the stability of the DFE.Theorem2.Consider the disease transmission model given by(1)with fðxÞsatisfying conditions (A1)–(A5).If x0is a DFE of the model,then x0is locally asymptotically stable if R0<1,but un-stable if R0>1,where R0is deﬁned by(4).Proof.Let J1¼FÀV.Since V is a non-singular M-matrix and F is non-negative,ÀJ1¼VÀF has the Z sign pattern(see Appendix A).Thus,sðJ1Þ<0()ÀJ1is a non-singular M-matrix;P.van den Driessche,J.Watmough/Mathematical Biosciences180(2002)29–483334P.van den Driessche,J.Watmough/Mathematical Biosciences180(2002)29–48where sðJ1Þdenotes the maximum real part of all the eigenvalues of the matrix J1(the spectral abscissa of J1).Since FVÀ1is non-negative,ÀJ1VÀ1¼IÀFVÀ1also has the Z sign pattern.Ap-plying Lemma5of Appendix A,with H¼V and B¼ÀJ1¼VÀF,we have ÀJ1is a non-singular M-matrix()IÀFVÀ1is a non-singular M-matrix:Finally,since FVÀ1is non-negative,all eigenvalues of FVÀ1have magnitude less than or equal to qðFVÀ1Þ.Thus,IÀFVÀ1is a non-singular M-matrix;()qðFVÀ1Þ<1:Hence,sðJ1Þ<0if and only if R0<1.Similarly,it follows thatsðJ1Þ¼0()ÀJ1is a singular M-matrix;()IÀFVÀ1is a singular M-matrix;()qðFVÀ1Þ¼1:The second equivalence follows from Lemma6of Appendix A,with H¼V and K¼F.The remainder of the equivalences follow as with the non-singular case.Hence,sðJ1Þ¼0if and only if R0¼1.It follows that sðJ1Þ>0if and only if R0>1.ÃA similar result can be found in the recent book by Diekmann and Heesterbeek[6,Theorem6.13].This result is known for the special case in which J1is irreducible and V is a positive di-agonal matrix[7–10].The special case in which V has positive diagonal and negative subdiagonal elements is proven in Hyman et al.[11,Appendix B];however,our approach is much simpler(see Section4.3).4.Examples4.1.Treatment modelThe decomposition of fðxÞinto the components F and V is illustrated using a simple treat-ment model.The model is based on the tuberculosis model of Castillo-Chavez and Feng[12,Eq.(1.1)],but also includes treatment failure used in their more elaborate two-strain model[12,Eq.(2.1)].A similar tuberculosis model with two treated compartments is proposed by Blower et al.[13].The population is divided into four compartments,namely,individuals susceptible to tu-berculosis(S),exposed individuals(E),infectious individuals(I)and treated individuals(T).The dynamics are illustrated in Fig.1.Susceptible and treated individuals enter the exposed com-partment at rates b1I=N and b2I=N,respectively,where N¼EþIþSþT.Exposed individuals progress to the infectious compartment at the rate m.All newborns are susceptible,and all indi-viduals die at the rate d>0.Thus,the core of the model is an SEI model using standard inci-dence.The treatment rates are r1for exposed individuals and r2for infectious individuals. However,only a fraction q of the treatments of infectious individuals are successful.Unsuc-cessfully treated infectious individuals re-enter the exposed compartment(p¼1Àq).The diseasetransmission model consists of the following diﬀerential equations together with non-negative initial conditions:_E¼b1SI=Nþb2TI=NÀðdþmþr1ÞEþpr2I;ð5aÞ_I¼m EÀðdþr2ÞI;ð5bÞ_S¼bðNÞÀdSÀb1SI=N;ð5cÞ_T¼ÀdTþr1Eþqr2IÀb2TI=N:ð5dÞProgression from E to I and failure of treatment are not considered to be new infections,but rather the progression of an infected individual through the various compartments.Hence,F¼b1SI=Nþb2TI=NB B@1C CA and V¼ðdþmþr1ÞEÀpr2IÀm Eþðdþr2ÞIÀbðNÞþdSþb1SI=NdTÀr1EÀqr2Iþb2TI=NB B@1C CA:ð6ÞThe infected compartments are E and I,giving m¼2.An equilibrium solution with E¼I¼0has the form x0¼ð0;0;S0;0Þt,where S0is any positive solution of bðS0Þ¼dS0.This will be a DFE if and only if b0ðS0Þ<d.Without loss of generality,assume S0¼1is a DFE.Then,F¼0b100;V¼dþmþr1Àpr2Àm dþr2;givingVÀ1¼1ðdþmþr1Þðdþr2ÞÀm pr2dþr2pr2m dþmþr1and R0¼b1m=ððdþmþr1Þðdþr2ÞÀm pr2Þ.A heuristic derivation of the(2;1)entry of VÀ1and R0are as follows:a fraction h1¼m=ðdþmþr1Þof exposed individuals progress to compartment I,a fraction h2¼pr2=ðdþr2Þof infectious individuals re-enter compartment E.Hence,a fractionh1of exposed individuals pass through compartment I at least once,a fraction h21h2passthroughat least twice,and a fraction h k 1h k À12pass through at least k times,spending an average of s ¼1=ðd þr 2Þtime units in compartment I on each pass.Thus,an individual introduced into com-partment E spends,on average,s ðh 1þh 21h 2þÁÁÁÞ¼s h 1=ð1Àh 1h 2Þ¼m =ððd þm þr 1Þðd þr 2ÞÀm pr 2Þtime units in compartment I over its expected lifetime.Multiplying this by b 1gives R 0.The model without treatment (r 1¼r 2¼0)is an SEI model with R 0¼b 1m =ðd ðd þm ÞÞ.The interpretation of R 0for this case is simpler.Only a fraction m =ðd þm Þof exposed individuals progress from compartment E to compartment I ,and individuals entering compartment I spend,on average,1=d time units there.Although conditions (A1)–(A5)do not restrict the decomposition of f i ðx Þto a single choice for F i ,only one such choice is epidemiologically correct.Diﬀerent choices for the function F lead to diﬀerent values for the spectral radius of FV À1,as shown in Table 1.In column (a),treatment failure is considered to be a new infection and in column (b),both treatment failure and pro-gression to infectiousness are considered new infections.In each case the condition q ðFV À1Þ<1yields the same portion of parameter space.Thus,q ðFV À1Þis a threshold parameter in both cases.The diﬀerence between the numbers lies in the epidemiological interpretation rather than the mathematical analysis.For example,in column (a),the infection rate is b 1þpr 2and an exposed individual is expected to spend m =ððd þm þr 1Þðd þr 2ÞÞtime units in compartment I .However,this reasoning is biologically ﬂawed since treatment failure does not give rise to a newly infected individual.Table 1Decomposition of f leading to alternative thresholds(a)(b)Fb 1SI =N þb 2TI =N þpr 2I 0000B B @1C C A b 1SI =N þb 2TI =N þpr 2I m E 000B B @1C C A Vðd þm þr 1ÞE Àm E þðd þr 2ÞI Àb ðN ÞþdS þb 1SI =N dT Àr 1E Àqr 2I þb 2TI =N 0B B @1C C A ðd þm þr 1ÞE ðd þr 2ÞI Àb ðN ÞþdS þb 1SI =N dT Àr 1E Àqr 2I þb 2TI =N 0B B @1C C A F0b 1þpr 200 0b 1þpr 2m 0 V d þm þr 10Àm d þr 2d þm þr 100d þr 2 q (FV À1)b 1m þpr 2mðd þm þr 1Þðd þr 2Þﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃb 1m þpr 2mðd þm þr 1Þðd þr 2Þs 36P.van den Driessche,J.Watmough /Mathematical Biosciences 180(2002)29–484.2.Multigroup modelIn the epidemiological literature,the term‘multigroup’usually refers to the division of a het-erogeneous population into several homogeneous groups based on individual behaviour(e.g., [14]).Each group is then subdivided into epidemiological compartments.The majority of mul-tigroup models in the literature are used for sexually transmitted diseases,such as HIV/AIDS or gonorrhea,where behaviour is an important factor in the probability of contracting the disease [7,8,14,15].As an example,we use an m-group SIRS-vaccination model of Hethcote[7,14]with a generalized incidence term.The sample model includes several SI multigroup models of HIV/ AIDS as special cases[8,15].The model equations are as follows:_I i ¼X mj¼1b ijðxÞS i I jÀðd iþc iþ iÞI i;ð7aÞ_S i ¼ð1Àp iÞb iÀðd iþh iÞS iþr i R iÀX mj¼1b ijðxÞS i I j;ð7bÞ_Ri¼p i b iþc i I iþh i S iÀðd iþr iÞR i;ð7cÞfor i¼1;...;m,where x¼ðI1;...;I m;S1;...;S m;R1;...;R mÞt.Susceptible and removed individu-als die at the rate d i>0,whereas infected individuals die at the faster rate d iþ i.Infected in-dividuals recover with temporary immunity from re-infection at the rate c i,and immunity lasts an expected1=r i time units.All newborns are susceptible,and a constant fraction b i are born into each group.A fraction p i of newborns are vaccinated at birth.Thereafter,susceptible individuals are vaccinated at the rate h i.The incidence,b ijðxÞdepends on individual behaviour,which determines the amount of mixing between the diﬀerent groups(see,e.g.,Jacquez et al.[16]). The DFE for this model isx0¼ð0;...;0;S01;...;S0m;R01;...;R0mÞt;whereS0 i ¼b i d ið1Àp iÞþr iðÞd iðd iþh iþr iÞ;R0 i ¼b iðh iþd i p iÞd iðd iþh iþr iÞ:Linearizing(7a)about x¼x0givesF¼S0i b ijðx0ÞÂÃandV¼½ðd iþc iþ iÞd ij ;where d ij is one if i¼j,but zero otherwise.Thus,FVÀ1¼S0i b ijðx0Þ=ðd iÂþc iþ iÞÃ:P.van den Driessche,J.Watmough/Mathematical Biosciences180(2002)29–4837For the special case with b ij separable,that is,b ijðxÞ¼a iðxÞk jðxÞ,F has rank one,and the basic reproduction number isR0¼X mi¼1S0ia iðx0Þk iðx0Þd iþc iþ i:ð8ÞThat is,the basic reproduction number of the disease is the sum of the‘reproduction numbers’for each group.4.3.Staged progression modelThe staged progression model[11,Section3and Appendix B]has a single uninfected com-partment,and infected individuals progress through several stages of the disease with changing infectivity.The model is applicable to many diseases,particularly HIV/AIDS,where transmission probabilities vary as the viral load in an infected individual changes.The model equations are as follows(see Fig.2):_I 1¼X mÀ1k¼1b k SI k=NÀðm1þd1ÞI1;ð9aÞ_Ii¼m iÀ1I iÀ1Àðm iþd iÞI i;i¼2;...;mÀ1;ð9bÞ_Im¼m mÀ1I mÀ1Àd m I m;ð9cÞ_S¼bÀbSÀX mÀ1k¼1b k SI k=N:ð9dÞThe model assumes standard incidence,death rates d i>0in each infectious stage,and theﬁnal stage has a zero infectivity due to morbidity.Infected individuals spend,on average,1=m i time units in stage i.The unique DFE has I i¼0,i¼1;...;m and S¼1.For simplicity,deﬁne m m¼0. Then F¼½F ij and V¼½V ij ,whereF ij¼b j i¼1;j6mÀ1;0otherwise;&ð10ÞV ij¼m iþd i j¼i;Àm j i¼1þj;0otherwise:8<:ð11ÞLet a ij be the(i;j)entry of VÀ1.Thena ij¼0i<j;1=ðm iþd iÞi¼j;Q iÀ1k¼jm kQ ik¼jðm kþd kÞj<i:8>>><>>>:ð12ÞThus,R0¼b1m1þd1þb2m1ðm1þd1Þðm2þd2Þþb3m1m2ðm1þd1Þðm2þd2Þðm3þd3ÞþÁÁÁþb mÀ1m1...m mÀ2ðm1þd1Þ...ðm mÀ1þd mÀ1Þ:ð13ÞThe i th term in R0represents the number of new infections produced by a typical individual during the time it spends in the i th infectious stage.More speciﬁcally,m iÀ1=ðm iÀ1þd iÀ1Þis the fraction of individuals reaching stage iÀ1that progress to stage i,and1=ðm iþd iÞis the average time an individual entering stage i spends in stage i.Hence,the i th term in R0is the product of the infectivity of individuals in stage i,the fraction of initially infected individuals surviving at least to stage i,and the average infectious period of an individual in stage i.4.4.Multistrain modelThe recent emergence of resistant viral and bacterial strains,and the eﬀect of treatment on their proliferation is becoming increasingly important[12,13].One framework for studying such sys-tems is the multistrain model shown in Fig.3,which is a caricature of the more detailed treatment model of Castillo-Chavez and Feng[12,Section2]for tuberculosis and the coupled two-strain vector–host model of Feng and Velasco-Hern a ndez[17]for Dengue fever.The model has only a single susceptible compartment,but has two infectious compartments corresponding to the two infectious agents.Each strain is modelled as a simple SIS system.However,strain one may ‘super-infect’an individual infected with strain two,giving rise to a new infection incompartment。

脑神经胶质瘤替莫唑胺耐药circRNA-miRNA-mRNA调控网络的构建

因此，本研究以人脑神经胶质瘤替莫唑胺耐药细胞株 U87/TR 以及其亲本细胞株 U87 为细胞模型，通过高通量 circRNA 芯片比较其 circRNAs 表达谱差异，并初步筛选关键 circRNAs，并预测 circRNAmiRNA-mRNA 调控网络，为进一步深入研究 cir‑ cRNA 在脑神经胶质瘤替莫唑胺耐药中的作用及其分子机制提供依据。本研究符合《世界医学协会赫尔辛基宣言》相关要求。 1 材料与方法 1.1 主要材料试剂人脑神经胶质瘤替莫唑胺耐药 U87/TR 细胞株以及其亲本 U87 细胞株购买于上海弘顺生物科技有限公司（规格 BW124577）。RP‑ MI-1640 培养液、胎牛血清、胰蛋白酶购自美国 GIB‑ CO 公司。RNeasy Mini Kit 购自德国 Qiagen 公司， RT-PCR 试剂盒等购自美国 Sigma 公司。circRNA 芯片的制备与杂交由上海吉凯生物科技公司完成。 1.2 方法 1.2.1 circRNA 芯片检测及数据分析用 RNeasy Mini Ki（t Qiagen，Hilden，德国）提取人脑神经胶质瘤替莫唑胺耐药 U87/TR 细胞株以及其亲本 U87 细胞
Changsha, Hunan 421001, China.
Abstract： Objective To screen the differential expression profile of Circular RNA (circRNA) in brain glioma temozolomide-resis‑ tant cell line U87/TR and parental cell line U87, and to construct a key Circular RNA (circRNA) - microRNA (miRNA) - messenger RNA(mRNA) regulatory network.Methods From January to June 2020, circRNAs chip was used to detect the differential expression profile of circRNAs in glioma-resistant temozolomide cells and verified by RT-PCR. miRanda database was used to predict the cir‑ cRNA-miRNA target binding relationship. miRanda v5, TargetScan, and miBase databases were used to predict the target genes. Cyto‑ scape software was used to construct the circRNA-miRNA-mRNA regulatory network. Results The results of circRNA microarray showed that U87/TR cells had 313 significantly differentially expressed circRNAs compared with U87 cells (P<0.05), of which 145 were significantly up-regulated and 168 were significantly down-regulated, RT-PCR verification results were consistent with the results of the chip. The prediction results of miRNA target sites showed that the key up-regulation of hsa_circRNA_009054, hsa_circRNA_ 104338 (n=3, t=12.09, 9.52, P=0.007, 0.011) and down-regulation of hsa_circRNA_016459, hsa_circRNA_101975 (n=3, t=13.86, 7.75, P=0.005, 0.016) can target multiple miRNAs such as hsa-miR-33a-5p, hsa-miR-15b-3phsa-miR-193b-5p and hsa-miR-23a-5p, the key circRNA-miRNA-mRNA regulatory network included 4 key circRNAs, 20 key miRNAs, and 278 key mRNAs. Conclusion circRNA is abnormally expressed in temozolomide-resistant cells of cerebral glioma. Key circRNAs such as hsa_circRNA_009054, hsa_circRNA_104338 and hsa_circRNA_016459, hsa_circRNA_101975 and other key circRNAs may participate in the drug resis‑ tance process of temozolomide in brain glioma through the circRNA-miRNA-mRNA regulatory network. Key words： Glioma; Temozolomide; Drug resistance, neoplasm; circRNA differential expression profile; circRNA-miRNAmRNA regulatory network

Macrophages,Immunity,andMetabolicDisease

ImmunityReview Macrophages,Immunity,and Metabolic DiseaseJoanne C.McNelis1and Jerrold M.Olefsky1,*1Department of Medicine,University of California,San Diego,9500Gilman Drive,La Jolla,CA92093,USA*Correspondence:jolefsky@/10.1016/j.immuni.2014.05.010Chronic,low-grade adipose tissue inﬂammation is a key etiological mechanism linking the increasing inci-dence of type2diabetes(T2D)and obesity.It is well recognized that the immune system and metabolism are highly integrated,and macrophages,in particular,have been identiﬁed as critical effector cells in the initiation of inﬂammation and insulin resistance.Recent advances have been made in the understanding of macrophage recruitment and retention to adipose tissue and the participation of other immune cell pop-ulations in the regulation of this inﬂammatory process.Here we discuss the pathophysiological link between macrophages,obesity,and insulin resistance,highlighting the dynamic immune cell regulation of adipose tissue inﬂammation.We also describe the mechanisms by which inﬂammation causes insulin resistance and the new therapeutic targets that have emerged.IntroductionType2diabetes(T2D)has become a global epidemic,with huge social and economic costs.The World Health Organization esti-mates that3.4million deaths per year worldwide are attributable to T2D,a number that is predicted to increase in the next decade (http://www.who.int/mediacentre/factsheets/fs312/en/). Approximately$175billion is spent on diabetes-related health-care annually in the United States alone(Centers for Disease Control and Prevention,2011).The majority of cases of diabetes (80%)are attributable to the parallel increasing rates of obesity (/About_us/News_Landing_Page/ Diabetes-and-obesity-rates-soar/)and thus extensive research efforts have been made to elucidate the mechanistic links be-tween these two conditions.Nutrient excess and adiposity acti-vate several metabolic pathways implicated in the development of insulin resistance,including inﬂammatory signaling,lipotoxic-ity,aberrant adipokine secretion(Sartipy and Loskutoff,2003; Steppan et al.,2001;Yamauchi et al.,2001),adipose tissue hypoxia(Cramer et al.,2003),endoplasmic reticulum(ER)stress (Ozcan et al.,2004;Urano et al.,2000),and mitochondrial dysfunction(Furukawa et al.,2004).A detailed description of all of these processes is beyond the scope of this piece and there are excellent recent reviews on these subjects(Samuel and Shulman,2012;Hotamisligil,2010;Johnson and Olefsky,2013; Lee and Ozcan,2014).Here,we will focus on obesity-associated chronic inﬂammation,which we believe is a key,unifying com-ponent of insulin resistance.Indeed,several of the metabolic processes mentioned above,such as ER stress,hypoxia,and lipotoxicity,can all converge on the development of metabolic inﬂammation.Obesity-associated metabolic inﬂammation is unlike the paradigm of classical inﬂammation—an acute inﬂammatory response,deﬁned by the characteristic signs of redness, swelling,and pain.Instead,it is a form of‘‘sterile inﬂammation’’produced in response to metabolic(rather than infectious)stimuli and is chronically sustained at a subacute level without adequate resolution.Theﬁrst evidence for a pathophysiological link between obesity,inﬂammation,and insulin resistance was provided more than a century ago,when it was observed that the anti-inﬂammatory drug salicylate,the principle metabolite in aspirin, had beneﬁcial effects on glucose control in diabetic patients (Williamson,1901).This concept was revisited in1993when Hotamisligil et al.(1993)demonstrated that tumor necrosis factor-a(TNF-a)(a proinﬂammatory cytokine)is secreted,in increased amounts,from the adipose tissue of obese rodents and is a potent negative regulator of insulin signaling.The complexity of this inﬂammatory response was realized,some 10years later,when two groups independently demonstrated that obesity is associated with the accumulation of macro-phages in adipose tissue,which were found to be the principal source of inﬂammatory mediators,including TNF-a,expressed by this metabolic tissue(Weisberg et al.,2003;Xu et al.,2003).A number of reports have now demonstrated the key importance of macrophage-elicited metabolic inﬂammation in insulin resis-tance.During obesity this immune cell population differs,not only in number,but also in inﬂammatory phenotype and tissue localization.In this review we will focus on the pathophysiolog-ical connections between obesity,macrophages,and insulin resistance.In particular,we will describe the mechanisms by which macrophages are recruited to metabolic tissues,mediate inﬂammation,and impact insulin signaling.We will also discuss current anti-inﬂammatory therapeutic strategies for the treat-ment of type2diabetes(T2D).Inﬂammatory SignalingThe secretion of inﬂammatory cytokines and chemokines by adipose tissue macrophages(ATMs)extends beyond TNF-a and includes interleukin-6(IL-6),IL-1b,monocyte chemotactic protein1(MCP-1,CCL2),and macrophage inhibitory factor (MIF)(Olefsky and Glass,2010).Production of these inﬂam-matory factors is under the transcriptional control of two key intracellular inﬂammatory pathways,c-Jun N-terminal kinase (JNK)-activator protein1(AP1)and IKappa B kinase beta(IKK)-nuclear factor kappa-light-chain-enhancer of activated B cells (NF-k B),which differ in their upstream signaling components but converge on the induction of overlapping inﬂammatory genes.These two inﬂammatory pathways are initiated byalmost36Immunity41,July17,2014ª2014Elsevier Inc.all of the mediators implicated in the development of insulin resistance,including oxidative and ER stress,saturated fatty acids,and inﬂammatory cytokines,highlighting their importance in the pathogenesis of disease (Solinas and Karin,2010).IKK b -NF-k B signaling is initiated by activation of IKK b and sub-sequent phosphorylation of the inhibitor of NF-k B (I k B).In the noninﬂammatory state,I k B retains NF-k B in an inhibitory cyto-plasmic complex.After inﬂammatory stimuli,I k B is phospho-rylated,dissociates from NF-k B,and undergoes degradation.This permits the translocation of free NF-k B to the nucleus,where it binds to cognate DNA response elements,leading to transactivation of inﬂammatory genes.Similarly,activation of JNK-AP-1signaling by inﬂammatory mediators leads to phos-phorylation and activation of JNK,which then phosphorylates the N terminus of c-Jun.This initiates a switch of c-Jun dimers for c-Jun-c-Fos heterodimers,which ultimately stimulate tran-scription of inﬂammatory target genes.Both JNK1and IKK signaling are upregulated in adipose (Weisberg et al.,2003;Xu et al.,2003),skeletal muscle (Bandyopadhyay et al.,2005),and liver (Cai et al.,2005)from insulin-resistant rodents and humans.Obesity activates JNK and NF-k B signaling by several mecha-nisms.For example,IL-1and TNF-a instigate inﬂammatory signaling through classical activation of their cell surface recep-tors (Olefsky and Glass,2010).Alternatively,the inﬂammatory process can be initiated by activation of pattern recognition receptors (PRRs),which include Toll-like receptors (TLRs)and NOD-like receptors (NLRs).PRRs sense exogenous pathogen-associated molecular patterns (PAMPs),including microbial derived LPS,peptidoglycan,and bacterial DNA,as well as endo-degradationNF-κBAP-1Inflammatory gene expressionInsulin resistanceFigure 1.Inﬂammatory Signaling Pathways Implicated in the Development of Insulin ResistanceActivation of TLR2,TLR4,and/or tumor necrosis factor receptor (TNFR)leads to the activation of NF-k B and JNK signaling.The serine kinases IKK b and JNK phosphorylate IRS-1and IRS-2,inhibit-ing downstream insulin signaling.In addition,the activation of IKK b leads to the phosphorylation and degradation of the inhibitor of NF-k B,I k B,which permits the translocation of NF-k B to the nucleus.Similarly,the activation of JNK leads to the formation of the AP-1transcription factor.Nuclear NF-k B and AP-1transactivate inﬂamma-tory genes,which can contribute to insulin resis-tance in a paracrine manner.Abbreviations are as follows:PI3K,phosphoinositide 3-kinase;RIP,receptor interacting protein;Myd88,myeloid dif-ferentiation primary response gene-88;SFA,saturated fatty acid;TRADD,TNF receptor-asso-ciated death domain;TRAF2,TNF receptor-associated factor-2;TRIF,TIR domain containing adaptor protein inducing IFN-g .Adapted from Osborn and Olefsky (2012).genous damage-associated molecular patterns (DAMPs),such as saturated fatty acids (Nguyen et al.,2007),ATP,and heat shock proteins.Of the TLRs,TLR4has been shown to play a particularly impor-tant role in initiating saturated fatty acid-mediated macrophage inﬂammation.Indeed,hematopoietic cell-speciﬁc dele-tion of TLR4protects mice from high fat diet (HFD)-induced insu-lin resistance (Orr et al.,2012;Saberi et al.,2009).Obesity-associated PAMPs and DAMPs have also been shown to activate the nucleotide-binding domain and leucine-rich-repeat-containing (NLR)protein NLRP3inﬂammasome,a multiprotein complex comprised of a PRR (NLRP3),a protease (caspase 1),and an adaptor protein.Several studies have shown that obesity is associated with the activation of the inﬂamma-some in adipose tissue (Stienstra et al.,2012;Vandanmagsar et al.,2011).Upon activation of the inﬂammasome,caspase 1initiates the maturation of pro-IL-1b and pro-IL-18.Consistent with the proinﬂammatory effects of these cytokines,genetic ablation of components of the NLRP3inﬂammasome amelio-rates insulin resistance (Stienstra et al.,2011;Vandanmagsar et al.,2011).In addition to receptor-mediated pathways,inﬂam-matory signaling can be stimulated by cellular stresses such as reactive oxygen species (ROS),ER stress,hypoxia,and lipotox-icity,which can all be enhanced in the obese insulin-resistant state (Cramer et al.,2003;Furukawa et al.,2004;Samuel and Shulman,2012;Urano et al.,2000;Lee et al.,2014).Mechanisms of Insulin Resistance in InﬂammationNumerous studies have shown that metabolic inﬂammation mediates insulin resistance through the inhibition of insulin signaling.Insulin (I)binding to its receptor (R)initiates a compli-cated signaling cascade (Figure 1).In brief,IR activation stimulates the recruitment and phosphorylation of several IR substrates including insulin receptor substrate 1-4(IRS-1-4),src homology 2containing protein (SHC),and growth factorImmunity 41,July 17,2014ª2014Elsevier Inc.37ImmunityReviewreceptor-bound protein2(Grb-2),which leads to the activation of two downstream signaling pathways.The phosphatidylinosi-tol3-kinase pathway(PI3K)-protein kinase B(PKB)pathway plays a major role in eliciting the effects of insulin on metabolism, increasing skeletal muscle and adipocyte glucose uptake, glycogen synthesis,and lipogenesis,while suppressing hepatic glucose production.Activation of the Ras-mitogen activated protein kinase(MAPK)pathway mediates the effect of insulin on mitogenesis and cell growth.Inﬂammatory signaling can interfere with insulin action through several transcriptional and posttranscriptional mechanisms. First,stress-activated serine kinases,such as JNK and IKK b, phosphorylate IRs and IRS proteins at inhibitory sites,attenu-ating downstream insulin signaling(Gao et al.,2002;Ozes et al.,2001).Accordingly,abrogation of inﬂammatory signaling with salicylates,which inhibit IKK b,prevents inhibitory IRS-1 phosphorylation,restoring insulin sensitivity(Gao et al.,2003). Second,the transcription factors NF-k B and AP-1regulate the expression of several metabolic genes that inﬂuence insulin sensitivity.For example,inﬂammatory mediators induce the expression of suppressor of cytokine signaling(SOCS)proteins which bind to the insulin receptor and impair its ability to phos-phorylate IRS-1and IRS-2proteins(Emanuelli et al.,2000; Kawazoe et al.,2001;Ueki et al.,2004).Conversely,NF-k B represses the expression of several components of the insulin signaling pathway including glucose transporter type4 (GLUT4)(Stephens and Pekala,1991),IRS-1,and AKT(Ruan et al.,2002).Third,JNK signaling can regulate cytokine expres-sion posttranscriptionally by causing stabilization of mRNAs that encode inﬂammatory cytokines(Chen et al.,2000).Finally,a relatively recent discovery is that inﬂammatory signals may also inﬂuence insulin sensitivity by regulating microRNA(miRNA) expression.For example,TLR4signaling represses the expres-sion of miR-223,which negatively regulates inﬂammatory gene expression(Chen et al.,2012;Haneklaus et al.,2013).Several miRNAs are dysregulated in obesity and this topic has been the subject of several recent reviews(Haneklaus et al.,2013;Qu-iat and Olson,2013).Inﬂammation can also affect insulin action indirectly by modu-lating various metabolic pathways,resulting in the production of ‘‘second messengers,’’such as fatty acids,that promote insulin resistance.For example,TNF-a stimulates adipocyte lipolysis contributing to elevated serum free fatty acid(FFA)concentra-tions,which can lead to decreased insulin sensitivity.Addition-ally,inﬂammatory signaling induces the expression of genes involved in lipid processing,including the enzymes that synthe-size ceramide,a sphingolipid that inhibits insulin activation of AKT(Holland et al.,2011;Schubert et al.,2000).Indeed,mice lacking TLR4are protected from ceramide accumulation and in-sulin resistance after the infusion of saturated fatty acids (Holland et al.,2011),and treating HFD mice with myriocin,an in-hibitor of ceramide production,improves glucose tolerance (Ussher et al.,2010).Inﬂammatory mediators also stimulate de novo hepatic lipogenesis,contributing to steatosis and elevated serum lipid levels.Treatment of mice with TNF-a or IL-1b in-creases the activity of acetyl-CoA carboxylase,the rate-limiting step in lipid synthesis(Feingold and Grunfeld,1992).Similarly, transgenic overexpression of IKK-b in hepatocytes stimulates de novo hepatic lipogenesis(van Diepen et al.,2011).NF-k B and AP-1also induce the expression of inﬂammatory cytokines,which can then act in an autocrine or paracrine manner,initiating a feed-forward loop to exacerbate insulin resistance.In addition,it is thought that if the magnitude of cyto-kine production is great enough,they can‘‘leak’’out of the adi-pose tissue and potentiate insulin resistance in an endocrine fashion in peripheral tissues such as muscle and liver(Osborn and Olefsky,2012).In line with this concept,elevated concentra-tions of TNF-a,IL-6,and MCP-1have been observed in the serum of individuals with diabetes and prospective studies have shown that circulating inﬂammatory markers are indicative of future disease risk.However,further studies are required to determine whether circulating cytokines are sufﬁcient to induce insulin resistance or whether they are merely a marker of tissue inﬂammation.Obesity and Adipose Tissue MacrophagesAdipose tissue macrophages(ATMs)can span the spectrum from an anti-inﬂammatory to a proinﬂammatory phenotype. The nomenclature to deﬁne different macrophage populations is variable and somewhat confusing,as described in the accom-panying review(Murray et al.,2014,this issue).Here we refer to anti-inﬂammatory macrophages as M2-like or alternatively acti-vated macrophages(AAMs),and proinﬂammatory macrophages as M1-like or classically activated macrophages(CAMs)(Olefsky and Glass,2010).AAMs predominantly make up the tissue-resident macrophages dispersed throughout lean adipose and support adipose homeostasis(Odegaard et al.,2007). Conversely,during obesity,the balance is tilted toward the recruitment of CAMs,which are primarily found in a ring-like conﬁguration around large dying adipocytes,termed crown-like structures(CLSs)(Lumeng et al.,2008).These two macro-phage populations are phenotypically and functionally distinct. M2macrophages express CD11b,F4/80,CD301,and CD206 and promote local insulin sensitivity through production of anti-inﬂammatory cytokines,such as IL-10(Olefsky and Glass, 2010).In contrast,M1macrophages express CD11c in addition to CD11b and F4/80and secrete inﬂammatory factors including TNF-a,IL-1b,IL-6,leukotriene B4(LTB4),and nitric oxide(NO) (Lumeng et al.,2007).The recruitment,differentiation,and/or survival of these macrophage subpopulations are contingent on the local signals produced within adipose tissue.The alternative activation of tissue-resident macrophages is mediated by the type2cytokine IL-4,which is expressed at high amounts in lean adipose tissue (Wu et al.,2011).IL-4induces the expression of peroxisome pro-liferator activated receptor gamma(PPAR g)(Huang et al.,1999) and peroxisome proliferator activated receptor delta(PPAR d) (Kang et al.,2008),which are required for maintenance of the alternatively activated state(Desvergne,2008;Odegaard et al., 2007).Conversely,in the obese state,inﬂammatory mediators released from adipose tissue,such as saturated fatty acids,cy-tokines,LTB4,and interferon-g(IFN-g),induce the recruitment of monocytes and/or their differentiation into M1-like macro-phages.Macrophage polarization states are also associated with dif-ferential activation of intrinsic biochemical pathways,including those of glucose,lipid,amino acid,and iron metabolism.For example,M1macrophages rely on glycolysis and oxidative38Immunity41,July17,2014ª2014Elsevier Inc.Immunity Reviewphosphorylation of pyruvate,whereas M2macrophages exhibit high rates of fatty acid oxidation(Biswas and Mantovani, 2012).Modiﬁcations to macrophage metabolic homeostasis result in altered energy supply and the production of lipid-and amino acid-derived mediators,which enable the macrophage to promote or resolve inﬂammation and contribute to the mainte-nance of the polarization state.Excellent reviews on this topic have recently been published(Biswas and Mantovani,2012;Re-calcati et al.,2012).Although the classiﬁcation of these two distinct ATM popula-tions is useful for experimental purposes,it is important to appre-ciate that it is an oversimpliﬁcation.In vivo,macrophages are a heterogeneous population and can display phenotypes across the spectrum from anti-to proinﬂammatory.Furthermore, ATMs display plasticity and can alter or‘‘switch’’phenotypes in response to changes in the local microenvironment(Li et al., 2010).Mechanisms of Inﬂammation-Induced Insulin Resistance:Lessons from Animal ModelsThe most compelling evidence for a mechanistic link between inﬂammation and insulin resistance has been provided by mu-rine studies that,by a variety of models,have repeatedly demonstrated the etiological role of M1macrophages in insulin resistance(see Table S1available online).Although murine models strongly suggest a role for inﬂammation in the patho-genesis of insulin resistance in human obesity,theﬁdelity with which these mouse models translate to man is not proven and there are several differences in immune response mechanisms between mice and men.Deﬁnitive anti-inﬂammatory pharmaco-logical studies will be needed to solidify the applicability of mouse to human disease and this is described in more detail later in this review(see Anti-inﬂammatory Therapeutic Strate-gies).Nevertheless,several lines of evidence indicate that inﬂammation is causally linked to insulin resistance in mice. First,the ablation of inﬂammatory CD11c+myeloid cells(Pat-souris et al.,2008)or depletion of ATMs by intraperitoneal administration of clodronate liposomes(Bu et al.,2013;Feng et al.,2011)improves glucose tolerance in obese insulin-resis-tant mice,conﬁrming the requirement of this immune cell popu-lation in the etiology of insulin resistance.In addition,studies have shown that the polarization state of ATMs is a key determi-nant of the adipose tissue inﬂammatory milieu and insulin sensi-tivity.Accordingly,mice with a myeloid-speciﬁc deletion of the transcriptional regulators PPAR g(Hevener et al.,2007;Ode-gaard et al.,2007)and PPAR d(Desvergne,2008;Kang et al., 2008;Odegaard et al.,2008),which are critical for the mainte-nance of the AAM state,display reduced adipose AAMs and are predisposed to HFD-induced adipose tissue inﬂammation, glucose intolerance,and insulin resistance.Finally,ablation of JNK(Han et al.,2013;Sabio et al.,2008;Solinas et al.,2007; Vallerie et al.,2008;Zhang et al.,2011)or IKK b(Arkan et al., 2005)protects mice from HFD-induced adipose tissue inﬂam-mation,conﬁrming the importance of these inﬂammatory signaling pathways.In these studies,the gene-targeted mice re-tained systemic insulin sensitivity,demonstrating that inhibition of inﬂammatory signals in macrophages is sufﬁcient to mitigate obesity-induced insulin resistance not only in adipose tissue, but also in muscle and liver.ATM RecruitmentAlthough macrophages are a key effector cell in the propagation of inﬂammation,it is clear that adipocytes are an important initi-ator of the inﬂammatory response.Adipocytes are not simply a storage depot for excess energy but are dynamic endocrine cells that produce and secrete both proinﬂammatory and anti-inﬂam-matory bioactive molecules,depending on microenvironmental cues.Secretion of these factors can regulate the recruitment and activation of immune cell populations.During the develop-ment of obesity,nutrient excess tips the balance toward the development of a more inﬂammatory adipocyte state,including the secretion of potent chemoattractants such as MCP-1and LTB4.These chemoattractants provide a chemotactic gradient for the recruitment of monocytes to adipose tissue,where they subsequently mature into ATMs.In addition,once recruited, proinﬂammatory macrophages themselves secrete additional chemokines,initiating a feed-forward loop and potentiating the inﬂammatory response.Of the known adipocyte-derived chemokines,MCP-1and its receptor chemokine(C-C motif)receptor2(CCR2)have been intensively studied.Several reports have shown that MCP-1is secreted in parallel with increasing adiposity in both mice and humans(Chen et al.,2005;Christiansen et al.,2005;Kim et al., 2006).In murine models of obesity,adipose tissue expression of MCP-1is rapidly induced after the initiation of HFD feeding and serum MCP-1concentrations are signiﬁcantly elevated after4weeks of this regime(Chen et al.,2005).In support of the MCP-1-CCR2system playing a role in ATM recruitment, CCR2-and MCP-1-deﬁcient mice exhibit reduced ATM content, insulin resistance,and hyperinsulinemia(Gutierrez et al.,2011; Weisberg et al.,2006),and overexpression of adipocyte MCP-1was sufﬁcient to induce adipose inﬂammation and insulin resistance in lean mice(Kamei et al.,2006).Furthermore, treatment of mice with a pharmacological antagonist of CCR2 lowered ATM content and improved insulin sensitivity without altering body mass(Sullivan et al.,2013;Tamura et al.,2010). However,other studies have shown that CCR2-deﬁcient mice are not protected from HFD-induced insulin resistance and macrophage accumulation(Chen et al.,2005;Gutierrez et al., 2011).The reasons for these discordantﬁndings are unclear, but the complexity and redundancy of chemokine signaling in different genetic backgrounds may play a role.The chemoattractant LTB4and its speciﬁc receptor BLT1 have also been implicated in macrophage recruitment to in-ﬂamed adipose tissue.LTB4is synthesized from arachadonic acid by the5-lipoxygenase(5-LOX)pathway(Spite et al., 2011).The expression and activity of key components of this pathway are increased in adipocytes and M1macrophages in obesity(Mothe-Satney et al.,2012).Consistent with this,LTB4 concentration is elevated in the adipose tissue and serum of murine models of obesity,in correlation with adipocyte size (Mothe-Satney et al.,2012).Supporting a pathological role for this increase,genetic deletion or pharmacological inhibition of 5-LOX(Mothe-Satney et al.,2012)or5-LO activating protein (FLAP)(Horrillo et al.,2010)protects mice from HFD-induced macrophage accumulation and associated insulin resistance. Targeting the LTB4-BLT1axis more speciﬁcally,recent studies show that genetic depletion of BLT1protects mice from obesity-induced inﬂammation and insulin resistance(Spite Immunity41,July17,2014ª2014Elsevier Inc.39Immunity Reviewet al.,2011),making this receptor an attractive potential target for drug discovery.Neuronal guidance molecules,factors typically studied for their role in embryonic axon development,were recently found to participate in the regulation of immune cell function.So far, four families of neural guidance cues have been implicated in the regulation of immune cell migration:the netrins,slits, ephrins,and semaphorins(Funk and Orr,2013;Wanschel et al.,2013).One such molecule,Semaphorin3E(Sema3E), can act as an adipocyte-derived chemokine to induce macro-phage recruitment to adipose tissue via its receptor PlexinD1, expressed on ATMs.Shimizu et al.(2013)observed that HFD feeding selectively increased Sema3E expression in visceral adipose tissue,accompanied by a parallel increase in serum Sema3E levels.Overexpression of Sema3E in adipocytes induced adipose tissue inﬂammation and insulin resistance in chow-fed mice,whereas genetic deletion of Sema3E or the sequestration of serum Sema3E with a soluble form of PlexinD1markedly improved these parameters.Sema3E is also elevated in the serum of diabetic humans,suggesting that this pathway may play a role in human disease(Schmidt and Moore,2013).ATM RetentionThe majority of studies on ATM accumulation have focused on the recruitment of monocytes to inﬂamed adipocytes,but macrophage emigration from adipose tissue might also be impaired in the obese state.The resolution of inﬂammation is a highly orchestrated process involving several cell types and mediators.The egress of macrophages out of inﬂamed tissue to local lymphoid tissues is an integral part of this process and is due to the concerted effect of chemo-repulsive forces from inﬂamed tissue and chemo-attractive signals from local lymph nodes(Bellingan et al.,1996;Randolph,2008).In addition to classical chemokines,neural guidance molecules also regulate this process(van Gils et al.,2012).The concept that macrophage emigration might be impaired in obese adipose tissue stems from the study of macrophage retention in atherosclerotic plaques.In murine models of athero-sclerosis,lowering of serum cholesterol concentrations or trans-plantation of the aortic arch from atherosclerotic LDL receptor KO mice to WT mice reestablishes macrophage egress to lymph nodes,reducing artery wall inﬂammation and plaque instability (Feig et al.,2011).These studies have led to the identiﬁcation of key pathways that regulate this process.For example,the chemokine receptor CCR7,which is expressed on macro-phages,promotes the recruitment of inﬂammatory macro-phages toward chemokine(C-C motif)ligand19(CCL19)and CCL21,secreted from lymphoid tissues.Upregulation of CCR7 by atheroma macrophages is necessary for the resolution of inﬂammation induced by the correction of dyslipidemia(Wan et al.,2013).There may also be signals that emanate from adipose tissue that prevent macrophage egress.For example,Netrin-1, secreted by macrophages in mouse atheroma,acts in an auto-crine/paracrine manner to retard the egress of macrophages that express the Netrin-1receptor rin-1is particularly interesting because,unlike other chemokines,it blocks macro-phage movement by inhibiting actin reorganization,making cells refractory to further chemokine stimuli.It is likely that expression of Netrin-1by adipocytes or ATMs potentiates the inﬂammatory phenotype of obese adipose tissue by inhibiting the process of resolution.Inﬂammation in Other Tissue TypesGiven the obvious connection between obesity and adiposity, studies have naturally focused on obesity-driven inﬂammation in adipose tissue.However,obesity can also causes inﬂamma-tion in other metabolic tissues such as liver,pancreatic islets, and perhaps also muscle.The liver is the major source of endogenous glucose produc-tion,which in the normal state is inhibited by the postprandial rise in insulin elevations.When the liver is insulin resistant, this inhibitory effect is impaired while the stimulatory effect of in-sulin on lipogenesis remains intact,contributing to the develop-ment of hyperglycemia and hepatic steatosis.Many studies have shown that obesity induces hepatic inﬂammation(Lanthier et al., 2011;Osborn and Olefsky,2012)associated with a substantial increase in liver macrophages(Johnson and Olefsky,2013;Ob-stfeld et al.,2010).As in adipose,liver macrophages comprise two populations-resident macrophages,termed Kupffer cells (KCs)and recruited hepatic macrophages(RHMs).KCs are long lived and relatively abundant in the liver,representing about 20%–25%of nonparenchymal cell population,in the nonin-ﬂamed state(Tang et al.,2013).KCs play an important role in tissue homeostasis,clearing foreign and harmful particles,for which their location in the liver sinusoids makes them well posi-tioned.In contrast,recruited macrophages are short lived and enter the liver in increased numbers during obesity,due to the secretion of chemokines,particularly MCP-1(Obstfeld et al., 2010;Oh et al.,2012).Chemical ablation of phagocytic cells in the liver(including KCs and RHMs)protects mice from HFD-induced insulin resistance,demonstrating the importance of these cells in the development of metabolic dysfunction(Lanthier et al.,2011;Neyrinck et al.,2009).In addition,genetic models have been used to establish a role for hepatic inﬂammation in insulin sensitivity.Depletion or overexpression of IKK b,specif-ically within hepatocytes,has shown that hepatic inﬂammation can regulate local insulin sensitivity,but not peripheral insulin sensitivity,in muscle and fat(Arkan et al.,2005;Cai et al., 2005).In obesity,the situation in liver is similar to that in adipose tissue with increased recruitment and activation of liver macro-phages,increased inﬂammatory signaling,and local production of inﬂammatory cytokines and chemokines.It is likely that the inﬂammatory cytokines exert paracrine effects to cause hepatic insulin resistance,similar to the situation in adipose tissue(see Figure2).Skeletal muscle is the primary site of glucose uptake,account-ing for around80%of insulin-stimulated glucose disposal(Os-born and Olefsky,2012).Therefore,decreased muscle insulin sensitivity in obesity has a profound effect on hyperglycemia in insulin-resistant individuals.Several studies have shown that obesity is associated with increased muscle inﬂammatory gene expression,along with macrophage inﬁltration in both mice and humans(Fink et al.,2013,2014;Hevener et al.,2007; Nguyen et al.,2007).These macrophages are largely localized to the small intermuscular adipose depots(termed marbling) that arise within skeletal muscle in obesity(Fink et al.,2014).40Immunity41,July17,2014ª2014Elsevier Inc.Immunity Review。

Actin, myosin, cytokeratins and spectrin are components of the

Tissue and Cell37(2005)293–308Actin,myosin,cytokeratins and spectrin are components of theguinea pig sperm nuclear matrixJuan Ocampo a,1,Ricardo Mondrag´o n b,Ana Lilia Roa-Espitia a,Natalia Chiquete-F´e lix a,Zaira O.Salgado a,Adela M´u jica a,∗a Departamento de Biolog´ıa Celular,Centro de Investigaci´o n y Estudios Avanzados del Instituto Polit´e cnico Nacional.Apdo.Postal14740,07000M´e xico,D.F.,M´e xicob Departamento de Bioqu´ımica,Centro de Investigaci´o n y Estudios Avanzados del Instituto Polit´e cnico Nacional.Apdo.Postal14740,07000M´e xico,D.F.,M´e xicoReceived25November2004;received in revised form17March2005;accepted23March2005Available online24June2005AbstractThe nuclear matrix(NM)of somatic cells is an internal nuclear framework structure,with a structural function and participation in DNA replication and transcription.The NM has been described in mouse,hamster and human spermatozoa.In this study,an NM structural component of the guinea pig sperm nucleus was obtained by removing nuclear proteins and DNA from DTT-CTAB nuclei.Removal was achieved with high ionic strength salt and microccocal nuclease treatments including a heparin treatment to cause a slight swelling of the nucleus and facilitate material extraction.Actin,myosin,cytokeratins and spectrin were detected associated to NM by indirect immunoﬂuorescence,immunogold staining and Western blotting analysis using speciﬁc antibodies.The presence of NM in guinea pig sperm nucleus is shown for theﬁrst time and some of its components are identiﬁed.This is also theﬁrst report on cytokeratins and myosin presence in guinea pig sperm.A retarding effect of nuclear decondensation caused by heparin is induced after phalloidin and/or diacetyl-monoxime(a myosin ATPase activity inhibitor) treatment,suggesting a role for F-actin and myosin in the maintenance of nuclear stability in sperm.The actin role was supported by the decondensing effect that citochalasin D and gelsolin had on sperm nuclei.©2005Elsevier Ltd.All rights reserved.Keywords:Protamines;Heparin;Nuclear decondensation;Phalloidin;Diacetyl-monoxime;Cytochalasin D;Gelsolin1.IntroductionThe nuclear matrix(NM)is deﬁned as the biochemical and structural residual entity obtained from cell nuclei after sequential detergent,salt and nuclease extraction treatments (Verheijen et al.,1988;Cook,1988;Jackson et al.,1988).The NM has been isolated from a wide range of organisms and cell types:from primitive eukaryots,cells of metazoans and metaphytes,and even neoplastic cells and cell lines(Fey and ∗Corresponding author.Tel.:+5550613992;fax:+5557477081.E-mail addresses:jolesv@.mx(J.Ocampo),adelam@cell.cinvestav.mx(A.M´u jica).1Present address:Departamento de Ciencias Biol´o gicas,Facultad de Estudios Superiores Cuautitl´a n(Universidad Nacional Aut´o noma de M´e xico),Km.2.5Carretera Cuautitl´a n-Teoloyucan,Cuautitl´a n Izcalli,Edo. M´e x.CP.54700,M´e xico.Penman,1988;Getzenberg et al.,1991;Pienta and Hoover, 1994).In diverse preparations,the NM is identiﬁed as a structural internal framework that extends through the entire nucleus and is constituted by a large number of small rounded bodies named interchromatinic granules,which are joined to thick and thinﬁlaments(Verheijen et al.,1988;He et al., 1990;Nickerson et al.,1995).The NM has a structural func-tion,it confers internal rigidity to the nucleus and avoids its deformation in response to cytoskeletal tensions(Georgatos, 1994;Penman,1994).Also,the participation of the NM has been demonstrated in DNA replication and transcription,in heteronuclear RNA processing,and in the cellular response to steroid hormone activity(He et al.,1990).On the other hand,the mammalian sperm nucleus exhibits a complex structure that seems to reﬂect a speciﬁc DNA orga-nization in which protamines are involved(Jager et al.,1990).0040-8166/$–see front matter©2005Elsevier Ltd.All rights reserved. doi:10.1016/j.tice.2005.03.003294J.Ocampo et al./Tissue and Cell37(2005)293–308These small basic proteins progressively replace histones dur-ing spermatogenesis(Pongsawasdi and Svasti,1976).Sperm chromatin is stabilized and packaged by disulﬁde bonds formed between protamines.The DNA–protamine complex is condensed up to an almost crystalline form that is biochem-ically inert(Ward and Coffey,1991;Ward,1994;Santi et al., 1995).At fertilization,during the male pronucleus formation, a reverse process occurs and chromatin acquires a somatic organization;disulﬁde bonds are reduced,and the protamines are hydrolyzed and replaced by egg histones(Yanagimachi, 1994).These changes permit subsequent sperm chromosome formation and their interaction with the egg chromosomes, at the male and female pronuclear fusion(Ward and Coffey, 1991;Santi et al.,1995).In the mouse sperm nucleus,the NM forms part of a struc-ture named the perinuclear matrix.It consists of a perinuclear theca(PT)and aﬁlamentous internal matrix(Bellv´e et al., 1992).NM presence has also been suggested in the hamster sperm nucleus with a structural function,forming the frame-work for the chromatin loop domains organization(Ward and Coffey,1991).In addition,an NM has been described in human spermatozoa(Santi et al.,1995;Markova,2004).On the other hand,absence of an NM structure has been claimed in guinea pig sperm(S´a nchez-V´a zquez et al.,1998).The aim of this work was to deﬁne if an NM structure is present in guinea pig sperm nuclei and to search for the presence of actin and other cytoskeletal and related proteins as part of the NM components.Our results showed that guinea pig sperm nuclei possess an NM containing actin,myosin,cytokeratins and spectrin molecules.Additionally,phalloidin,an F-actin stabilizing drug,and diacetyl-monoxime(DAM),a myosin ATPase-activity inhibitor,delayed nuclear decondensation by heparin. Furthermore,cytochalasin D and gelsolin,two compounds that alter actinﬁlaments,when assayed on DTT-CTAB nuclei, produced a nuclei size increment.2.Materials and methods2.1.MaterialsAnti-cytokeratin monoclonal antibodies and polyclonal antibodies against vimentin and spectrin were obtained from Sigma Chemical Co.(St.Louis,MO,USA).Poly-clonal antibody anti-myosin was acquired from ICN Biomedicals(Aurbra,OH,USA).Anti-actin monoclonal antibody was kindly provided by Dr.Manuel Hern´a ndez (Department of Cell Biology,CINVESTA V-IPN,M´e xico). Polyclonal anti-TRITC(tetramethyl-rhodamine isothio-cianate;Hern´a ndez-Gonz´a lez et al.,2000)and anti-DNase antibodies were raised in our laboratory.TRITC-labeled goat anti-rabbit,rabbit anti-sheep,rabbit anti-mouse IgG antibodies and horse-radish peroxidase(HRP)-labeled rabbit anti-mouse and goat anti-rabbit antibodies were bought from HyClone Labs(UT,USA).Gold-labeled(5or20nm particles)goat anti-rabbit,rabbit anti-sheep and rabbit anti-mouse IgG antibodies were bought from EY Laboratories (San Mateo,CA,USA).Chemilumiscence(ECL)kit and immunogold silver staining(IGSS)quality gelatin were purchased from Amersham Life Science(Buckinghamshire, UK).Medium grade LR-White resin kit was bought from London Resin,Ltd.(Hampshire,UK).Hoescht33258, phalloidin(from Amanita phalloides),TRITC-phalloidin, d,l-dithiothreitol(DTT),cetyltrimethylammonium bro-mide(CTAB),trizma base,phenylmethylsulfonyl-ﬂuoride (PMSF),para-hydroxymercury-benzoic acid(p HMB), para-amino-benzamidine(p ABA),benzamidine,heparin, microccocal nuclease,silver nitrate,sodium thiosulfate, salmon protamines,Harris hematoxylin,Tween20,Triton X-100and cytochalasin D were bought from Sigma Chemical Co.Gelsolin was puriﬁed in our laboratory from ram blood serum(Cabello-Ag¨u eros et al.,2003).Protease inhibitor Complete TM cocktail tablets were bought from Roche Diag-nostics and Molecular Biochemicals(Mannheim,Germany). SDS,bis-acrylamide,acrylamide,TEMED,ammonium persulfate,amido black and molecular weight markers were bought from Bio-Rad Laboratories(USA).Brij36-T was obtained from Canamex,S.A.(M´e xico).Diacetyl-monoxime was purchased from ICN Biomedicals(Aurbra, OH,USA).N,N-dimethylformamide was obtained from Merck(Darmstadt,Germany).Fat-free milk was purchased from Baden,S.A.(Hidalgo,M´e xico).All other chemicals were of analytical grade and obtained from JT,Baker S.A. (M´e xico).2.2.Guinea pig sperm cells and sperm nuclei isolationCauda epididymis and ductus deferens spermatozoa were obtained byﬂushing the lumen of both structures with 154mM NaCl at37◦C,as reported by Trejo and M´u jica (1990).Pooled spermatozoa were centrifuged and washed twice with154mM NaCl by resuspension/centrifugation at 600×g for3min.Sperm concentration was determined in a Neubauer chamber(M´u jica and Valdes Ru´ız,1983).Sper-matozoa were resuspended at1×108cells per milliliter in 50mM Tris–HCl,pH9,added with a commercial mixture of protease inhibitors(Tris-Complete;see the following).The sperm suspension was treated with10%Brij36-T at1.2%,ﬁnal concentration(Ju´a rez-Mosqueda and M´u jica,1999)and incubated for5min on ice,for plasma membrane,nuclear membrane and acrosome solubilization.The cells were col-lected by centrifuging at600×g for3min and washed three times with Tris-Complete.For nuclei isolation,spermato-zoa resuspended in Tris-Complete were treated with fresh 90mM DTT in50mM Tris–HCl,pH9(DTT25.4mM,ﬁnal concentration)and incubated for15min on ice.Afterwards, sample was added with CTAB at2.22%,ﬁnal concentra-tion(Hern´a ndez-Montes et al.,1973).The obtained sperm nuclei(DTT-CTAB nuclei)were immediately centrifuged and washed with Tris-Complete as above.Nuclear purity was determined in a Zeiss optical microscope,model Axioscop2.J.Ocampo et al./Tissue and Cell37(2005)293–308295A working solution of commercial protease inhibitors (Tris-Complete)was prepared by dissolving a Complete TM tablet in25ml of50mM Tris–HCl,according to the manu-facturer’s instructions.2.3.Sperm nuclei decondensation by heparinDTT-CTAB nuclei resuspended in Tris-Complete were added with1500USP of heparin(per108nuclei in1ml) and incubated in a water bath at37◦C.At0.0,0.25,0.5,1,2, 5and10min of incubation,aliquot samples were withdrawn andﬁxed(v/v)with3%formaldehyde in PBS(140mM NaCl, 2.7mM KCl,1.5mM KH2PO4,8.1mM Na2HPO4,pH7.2) for1h.Afterﬁxation,nuclei were collected by centrifuging at600×g for3min.Nuclei-containing pellets were resus-pended in50mM NH4Cl and incubated for10min at room temperature.Afterwards,nuclei were washed twice with PBS and once in distilled water by resuspension/centrifugation as above.Sample smears were prepared on glass slides;addi-tionally,drops of the nuclear suspension were placed on collodion-carbon-coated grids.To observe decondensation: (a)the nuclei smears were stained with50␮l of Hoescht 33258solution(5␮g/ml PBS)per slide and incubated for 10min in the dark(moisty chamber).Samples were observed in a Zeiss optical microscope model Axioscop2,equipped with epiﬂuorescence optics(using a365nmﬁlter)and dig-ital camera,model Axiocam H2C.Micrographs were pro-cessed with Axiovision3.1software(Zeiss).(b)Samples of nuclei on grids were stained with0.02%phosphotungstic acid aqueous solution(Ju´a rez-Mosqueda and M´u jica,1999) for transmission electron microscopy(TEM).Samples were examined and micrographed with a Jeol JEM-2000EX elec-tron microscope,at80kV.2.4.Nuclear matrix obtentionDTT-CTAB isolated nuclei,resuspended in1ml of Tris-Complete,at a concentration of1×108were added with1ml of1M NaCl and incubated for30min on ice.The process was repeated once.After each treatment,nuclei were centrifuged (600×g,3min)and resuspended with1ml Tris-Complete. Both NaCl supernatants were saved and precipitated with cold acetone(v/v)and maintained at−20◦C during18h for protein precipitation.NaCl-treated nuclei were resuspended in1ml Tris-Complete and added with1500USP heparin in100␮l Tris-Complete and incubated at37◦C for1min. Afterwards,the nuclear suspension was diluted with2ml Tris-Complete and centrifuged(600×g,3min).Supernatant was added(v/v)with acetone as indicated above.Nuclei were resuspended in1ml Tris-Complete plus40UI micrococcal nuclease(in100␮l PBS)and2mM CaCl2(ﬁnal concen-tration)and incubated for1h on ice.Nuclei were collected at600×g for3min,and the supernatant was processed as above.Nuclease treatment was repeated once.Residual struc-tures(sperm nuclear matrices)wereﬁxed in Karnowskyﬁxa-tive(Karnowsky,1965)(v/v)for1h and processed for TEM,or in3%formaldehyde(v/v)as indicated above.Smears on glass slides were prepared for indirect immunoﬂuorescence, and samples for electrophoresis and Western blotting(see the following).2.5.Indirect immunoﬂuorescence and TRITC-phalloidin stainingNM smears on glass slides were incubated with the appropriateﬁrst antibodies using a previous protocol (Moreno-Fierros et al.,1992).First antibodies,appropriately diluted with blocking solution(5%bovine serum albumine in PBS)against actin(without dilution),cytokeratins,vimentin, spectrin(1:100dilution)and myosin(1:10dilution)were used.TRITC-labeled appropriate secondary antibodies were used,diluted1:5000in blocking solution.Controls incubated only with the secondary antibody were performed.Addi-tionally,some samples were stained with TRITC-phalloidin to visualize F-actin(Moreno-Fierros et al.,1992).All samples were observed with a Zeiss optical microscope(see above).2.6.SDS–PAGE,AUGE and Western blotting2.6.1.SDS–polyacrylamide gel electrophoresis(SDS–PAGE)Whole spermatozoa or DTT-CTAB nuclei or NM sam-ples(from1×108spermatozoa)were resuspended in1ml Tris-Complete and treated with25.4mM DTT(ﬁnal concen-tration).For whole sperm sample,Tris-Complete was added with4mM PMSF,4mM p HMB,2mM p ABA,and20mM benzamidine(ﬁnal concentrations).After15min incubation, the sample was solubilized with2.2%SDS(ﬁnal concentra-tion)(Hern´a ndez-Montes et al.,1973)and boiled for5min; then it was precipitated with cold acetone(v/v)and main-tained at−20◦C,for18h.Acetone-precipitated proteins from either entire sperm,DTT-CTAB nuclei,nuclear matri-ces or from the different supernatants indicated above,were collected at1500×g for10min.Pellets were dissolved in 50␮l Tris-Complete and12.5␮l sample buffer(Laemmli, 1970),then boiled for3min.Samples were electrophoresed in7.5,10or12%polyacrylamide-SDS gels(Laemmli,1970). Gels were stained with silver nitrate using a modiﬁed previ-ously described method(Morrissey,1981):gels wereﬁxed in ethanol:acetic acid:water(3:1:6)mixture for30min,and post-ﬁxed for30min in the following solution:30%ethanol, pH6.0,adjusted with0.4M sodium acetate,0.5%glutaralde-hyde and0.1%sodium thiosulphate.Gels were washed with deionized water for2h,with changes each10–15min and then incubated for30min in3%methanol,0.1%silver nitrate and0.025%formaldehyde ter,gels were washed with deionized water for10–15s and revealed with2.5% sodium carbonate solution added with0.025%formaldehyde. The revealing solution was changed each5min,until ade-quate protein staining was obtained.The revealing reaction was stopped with5%acetic acid solution.296J.Ocampo et al./Tissue and Cell37(2005)293–3082.6.2.Acid urea gel electrophoresis(AUGE)The AUGE electrophoresis method,described by Panyim and Chalkley(1969),for histone extraction and visualization was used here for protamines,with slight modiﬁcations.The NM sample(from1×108nuclei)was resuspended in2ml of 0.25M HCl solution and left to stand for30min on ice.It was then centrifuged at600×g for3min.The supernatant was added(v/v)with20%trichloroacetic acid,incubated30min on ice and centrifuged at800×g for10min.The pellet was resuspended with50␮l of lysis buffer(8M urea–0.9M acetic acid)and electrophoresed in15%polyacrylamide gel,with 6.25M urea and7%acetic acid(ﬁnal concentrations),with-out stacking gel.The sample was run at100V for two hours, from anode(+)to cathode(−).Puriﬁed salmon protamines were used as molecular weight marker.Band proteins were revealed using0.1%amido black solution.2.6.3.Western blottingProteins from SDS–PAGE gels were transferred to nitrocellulose membranes(Towbin et al.,1979)and then immunostained by a previously described protocol(Moreno-Fierros et al.,1992).First antibodies,appropriately diluted with blocking solution(1%Triton X-100,plus5%fat-free milk in PBS):against actin(without dilution),cytokeratins, vimentin,spectrin(1:100dilution)and myosin(1:10dilution) were used and appropriate secondary antibodies labeled with HRP were employed,diluted1:10,000with blocking solu-tion.HRP was developed by chemiluminiscence ECL kit. Controls incubated only with the secondary antibody were performed.2.7.Immunogold localization of cytoskeletal and related proteins in NM and whole spermatozoaNM samples wereﬁxed in Karnowsky(1965)ﬁxative for 20min at room temperature and then incubated with appro-priateﬁrst antibodies(diluted as above with5%IGSS gelatin in PBS)using a previous protocol(Ursitti and Wade,1993). Control samples were performed with a non-related anti-body(anti-DNase)asﬁrst antibody.Appropriate secondary antibodies coupled to5or20nm colloidal gold particles were used.Samples incubated with the anti-TRITC anti-bodies were previously incubated with TRITC-phalloidin for30min.All samples were stained with0.02%phospho-tungstic acid and examined and micrographed in the TEM, as indicated above.Whole spermatozoa wereﬁxed in4%paraformaldehyde for1h at room temperature.Samples were washed with PBS and dehydrated in increasing ethanol concentrations from40 to95%.Cells were embedded in50%LR-White resin diluted in ethanol,then in100%LR-White resin,samples were kept overnight at4◦C.Afterward,samples were transferred into gelatin capsules,added with100%LR-White resin and poly-merized under UV light at4◦C during24h.For immunostain-ing,thin sections obtained in a Reichert Jung ultramicrotome were mounted on formvar-carbon-coated nickel grids and sequentiallyﬂoated on PBSMT(PBS added with1%free-fat milk plus0.05%Tween20).Grids were incubated with the ﬁrst antibodies(diluted in PBSMT)during2h at room tem-perature and then for12h,at4◦C.Grids were thoroughly washed with PBSMT and incubated with the appropriate secondary antibodies(diluted in PBSMT)coupled to5or 20nm gold particles.Control samples incubated only with the secondary antibody were performed.Positive controls were performed using skeletal muscle sections for detection of actin,cytokeratins,myosin and spectrin.Samples incu-bated with anti-TRITCﬁrst antibody had been preincubated with TRITC-phalloidin for30min.All sections were stained with2%uranyl acetate and examined and micrographed in a TEM as mentioned above.2.8.Heparin decondensation of phalloidin and/or diacetyl-monoxime(DAM)pretreated nucleiDTT-CTAB nuclei,1×108per milliliter in Tris–HCl, pH9,were treated with50mM phalloidin,and/or11mM DAM,ﬁnal concentrations(stocks solutions1M phalloidin and100mM DAM,both in PBS)for30min at room tem-perature.Afterwards,1500USP heparin in100␮l Tris–HCl were added and aliquot samples were withdrawn at15,30 and60s andﬁxed(v/v)with3%formaldehyde in PBS for 1h.Afterﬁxation,nuclei were collected by centrifugation at 600×g for3min.Pellets were resuspended in50mM NH4Cl and incubated for10min at room temperature.Subsequently, nuclei were washed twice with PBS,and once with distilled water by resuspension/centrifugation as above.Smears were prepared from each sample on glass slides and stained with Harris hematoxylin(Luna,1963)and observed and micro-graphed in a optical microscope as mentioned above.2.9.Sperm nuclei treatment with cytochalasin DDTT-CTAB nuclei were obtained as indicated in Section2.2;but in absence of protease inhibitors.For cytochalasinD treatment,isolated DTT-CTAB nuclei were resuspended in154mM NaCl solution,at concentration of5×106cells per milliliter and treated with70␮M cytochalasin D,ﬁnal concentration,in dimethylformamide.Nuclei incubations in NaCl or in NaCl with dimethylformamide(cytochalasin dilu-ent)were used as controls.At selected times,aliquots were withdrawn andﬁxed(v/v)with3%formaldehyde for1h. Smears were prepared and observed in a Zeiss routine micro-scope and images were recorded on Kodak Tri-Xﬁlm,400 ASA.2.10.Sperm nuclei treatment with gelsolinCauda epididymis and ductus deferens spermatozoa were washed in154mM NaCl(see above),resuspended in NaCl 1×108cells per milliliter and treated with10%Brij36-T at 1.2%(ﬁnal concentration)for5min on ice.Four drops(25␮l each)were withdrawn and place on a piece of paraﬁlm overJ.Ocampo et al./Tissue and Cell37(2005)293–308297a Petri-dish and incubated on ice.The sperm suspension was sequentially treated with(ﬁnal concentrations):25mM DTT for15min and1.2%CTAB for10min,on ice.Afterwards, two of the samples(drops)were added with8␮l gelsolin solu-tion(2␮M,ﬁnal concentration)and the other two with8␮l 50mM Tris–4mM EGTA,pH9(gelsolin diluent)and mixed. After10min incubation,samples were placed on formvar-carbon-coated grids for10min;sample excess was removed with Whatman paper.Samples wereﬁxed in Karnowskyﬁx-ative for1h on ice,afterwards theﬁxative was removed and the samples were incubated in50mM NH4Cl,for10min. NH4Cl was removed and the grids were washed three times with PBS,and two times with distilled water.Samples were contrasted for3min with aqueous0.02%phosphotungstic acid and examined with electron microscope as above.This treatment was performed in protease inhibitors absence. 2.11.Morphometric analysisThe morphometric analysis was performed with the Sigma Scan Pro3.0software(Jandel Scientiﬁc)in a PC(850Mhz ADM processor,128Mb RAM).Recorded images were scanned with a ScanJet6100C/T(Hewlett Packard).Nucleus length(in raw pixels)was measured with the image measure-ment software from scanned negatives at250×.A minimum of25nuclei from each group at each time(phalloidin/heparin, phalloidin-DAM/heparin,DAM/heparin and heparin alone), for phalloidin-DAM-heparin assay.For cytochalasin assay, 50nuclei were measured from each group at each time(NaCl,dymethyl-formamide,and cytochalasin).For both experi-ments,data were expressed as the mean length±standard deviation,to which Student’s t test(two tail)was applied, using Sigma Plot4.0software(SPSS).3.Results3.1.Morphology of sperm nuclei before and after decondensation by heparin treatmentA pure sample of membrane-less nuclei was obtained by using selective solubilization of plasma membrane,acro-some and nuclear membrane by Brij36-T detergent treatment (Ju´a rez-Mosqueda and M´u jica,1999),followed by DTT-CTAB treatment(Hern´a ndez-Montes et al.,1973;Hern´a ndez et al.,1994).Nuclei were observed by phase contrast microscopy as ovoidal structures,with a dark small zone at the implantation fossa(Fig.1a,arrow).Heparin treatment of DTT-CTAB nuclei at a concentration of1500USP produced an increase in nucleus size,which correlated with the incubation time.Nuclear morphology drastically changed,in relation to the control,at1and2min of heparin treatment(Fig.1b),both poles were swollen and at the middle of the nucleus a belt-like structure,formed by two parallel bands,was observed(arrow).Concomitant with the morphological change,heparin pro-duced a decrease in nucleus-relative DNA,in Hoescht-33258 stained preparations(Fig.1a and b )observed with aepi-Fig.1.Heparin decondensation effect on DTT-CTAB nuclei preparation.Morphology.Nuclei from mature spermatozoa,obtained after sequential Brij36-T and DTT-CTAB treatments were incubated for0(a and a )and2min(b and b )with1500USP heparin at37◦C and formaldehyde-ﬁxed.(a and b)Correspond to phase contrast micrographs and(a and b )areﬂuorescence images of Hoescht33251-stained samples.Nucleus size and morphology changed in a time-dependent manner;both poles swelled,held by a belt-like structure(Fig.1b arrow).Hoescht stain DNA decreased in parallel with nucleus size increment. Thick arrow indicatesﬂagellum implantation fossa.Magniﬁcation250×.298J.Ocampo et al./Tissue and Cell 37(2005)293–308Fig.2.Electron microscopy of heparin-treated DTT-CTAB nuclei.Sperm samples were treated with Brij 36T and DTT-CTAB as in Fig.1.Whole mount preparations were stained with 0.02%aqueous phosphotungstic acid.(a)Control nuclei without heparin.Nuclei were observed as rounded structures lacking both nuclear membrane and perinuclear theca.(b)Nuclei treated with heparin for 2min.At this treatment time was observed an increase in the polar zones higher than in the equatorial zone,in which a three-lamellar belt-like structure was present.Bar 2␮m.ﬂuorescence microscope.On the other hand,when nuclei decondensation was performed in presence of Tris-Complete (protease inhibitor mixture),nuclear swelling was retarded;under this condition,1min treatment with heparin was equivalent to 15s of treatment without inhibitors (see the following).DTT-CTAB nuclei were heparin-treated in absence of Tris-Complete.At electron microscopy,control nuclei were observed as rounded structures of heterochromatin,lacking both nuclear membrane and PT (Fig.2a).After 15and 30s of heparin treatment,nuclei increased up to twice in size (data not shown).At 1and 2min of heparin addition,the increase in nucleus size was more pronounced.Furthermore,an equatorial band became apparent showing a three-lamellar organization:an electron-lucent region,between two electron-dense zones (Fig.2b).At longer incubation time (5–10min),the tight equatorial belt disappeared and,ﬁnally,the whole nucleus became disorganized (data not shown).3.2.Nuclear matrix isolationTreatment to obtain nuclear matrices was always per-formed in sample suspensions and in the presence of Tris-Complete.DNA and protamines were removed from the nucleus in order to identify a residual skeletal structure.Following DTT-CTAB sperm treatment,nuclei were imme-diately incubated in 0.5M NaCl (ﬁnal concentration).NaCl treatment extracted abundant quantities of proteins (Fig.3,lane 1).A second treatment with 0.5M NaCl,only extracted scarce proteins,both in number and quantity (Fig.3,lane 2).Nucleus morphology under this condition is shown in ter,nuclei were treated for 1min with heparin and only two protein bands were revealed in the supernatant (Fig.3,lane 3);nuclear morphology is shown in Fig.4b.The rounded nuclear shape was retained,but it was less electron-dense than DTT-CTAB nuclei (Fig.2a)and around 30%larger in size.Two sequential microccocal nuclease nuclear treatments extracted a high number of proteins (Fig.3,lanes 4and 5).Bydeﬁnition,the residual structure is the NM and it appeared as a round-shaped framework of ﬁbrogranular aspect (Fig.4c).Fig.4d has a higher magniﬁcation of Fig.4c,to show struc-tural details.This skeletal structure,solubilized by DTT-SDS treatment and resolved in SDS–PAGE showed a large number of bands;several of them were also observed in the nuclease supernatants,and a few were enriched in the NM fraction (Fig.3,lane 6).Additional observations about the resulting NM fractions are:(1)they retained both the rounded aspect and their size after NaCl treatment,(2)when heparin treatment was omit-ted,the nuclease treatment extracted less protein quantity (data not shown)and the residual structure differed from that obtained after whole treatment.It was more electron-dense and showed a granular surface (Fig.4e);(3)microccocal nuclease treatment did not produce changes in the nuclear size,(4)the use of the protease inhibitor mixture was essen-tial to obtain a well-structured NM;the residual material was completely disorganized when obtained in the absence of inhibitors (Fig.4f);(5)additionally,the NM was highly elec-trodense,it was not necessary to stain it with phosphotungstic acid for visualization;(6)protamines were apparently absent from the NM,since in urea-solubilized samples only bands of high molecular weight were observed (Fig.5,lane 2),and not those expected for protamines (5–8kDa;Fuentes-Mascorro et al.,2000;Fig.5,lane 1);(8)DNA was also apparently absent from the NM isolated structure;since DTT-SDS sol-ubilization showed a very ﬂuid sample and not the viscous typical appearance for DNA.3.3.Actin,myosin,cytokeratins and spectrin are components of the guinea pig sperm NM3.3.1.Detection by indirect immunoﬂuorescence and by TRITC-phalloidin stainingThe protein distribution in NM preparations was examined by using monospeciﬁc polyclonal antibodies.A monoclonal was used for cytokeratins.Staining conditions were the same。

Regulation of the Antimicrobial Response by NLR Proteins

Regulation of the AntimicrobialResponse by NLR ProteinsEran Elinav,1,3Till Strowig,1,3Jorge Henao-Mejia,1,3and Richard A.Flavell1,2,*1Department of Immunobiology,Yale University School of Medicine,New Haven,CT06520,USA2Howard Hughes Medical Institute3These authors contributed equally to this work*Correspondence:richard.ﬂavell@DOI10.1016/j.immuni.2011.05.007Nucleotide-binding,oligomerization domain(NOD)-like receptor(NLR)proteins are a family of innate immune receptors that play a pivotal role in microbial sensing,leading to the initiation of antimicrobial immune responses.Dysregulation of the function of multiple NLR family members has been linked,both in mice and humans,to a propensity for infection and autoinﬂammatory disease.Despite our increased under-standing of NLR function and interactions,many aspects related to mechanisms of sensing,downstream signaling,and in vivo functions remain elusive.In this review,we focus on key members of the NLR family, describing their activation by diverse microbes,downstream effector functions,and interactions with each other and with other innate sensor protein families.Also discussed is the role of microbial sensing by NLR receptors leading to activation of the adaptive immune arm that collaborates in the antimicrobial defense.IntroductionNucleotide-binding,oligomerization domain-like receptor(NLR) proteins are a family of proteins with diverse functions in the immune system,characterized by a shared domain architecture that includes a nucleotide-binding domain(NBD)and a leucine-rich repeat(LRR)domain.The latter,which is shared with other innate immune proteins such as the Toll-like receptor(TLR) family,is thought to play a role in recognition and autoregulation of pathogen-and danger-associated molecular patterns(PAMPs and DAMPs,respectively).The NBD can bind nucleotides and is possibly involved in the induction of conformational changes and self-oligomerization that are necessary for NLR function.On the basis of the presence of additional domains,NLRs were grouped into subfamilies(Table1)(Ting et al.,2008).Typical domains present in NLRs are the caspase activation and recruitment domains(CARDs)and the pyrin domains(PYDs).These domains are involved in homeotypic protein interactions and allow the recruitment of downstream effector molecules.In this review,we will highlight the function of several members of the NLR family that are involved in the regulation of the antimi-crobial immune response and focus on concepts of pathogen recognition as well as their interplay with other innate immune receptors.NOD1and NOD2Two widely studied members of the NLRC(NOD-like receptor containing a CARD domain)family are NOD1and NOD2.The discovery that mutations in NOD2are strongly associated with Crohn’s disease,an autoinﬂammatory disorder that is thought to be driven by aberrant immune response against intestinal microbes(reviewed in Cho,2008),highlighted the importance of NLRs in the regulation of antimicrobial responses.NOD1 and NOD2are cytosolic receptors that recognize distinct building blocks of peptidoglycan(PGN),a polymer consisting of glycan chains crosslinked to each other via short peptides (Fritz et al.,2006).In Gram-positive bacteria,it is the major building block of the cell wall.Although it is present only in smaller amounts in Gram-negative bacteria,its general abun-dance and its highly conserved structure make PGN a prime target for recognition by pattern recognition receptors(PRRs). NOD2recognizes a minimal motif of muramyl dipeptide(MDP) that is found in all PGNs.In contrast,NOD1recognizes muro-peptides(iE-DAPs)that are found in the PGN of Gram-negative and only some Gram-positive bacteria(Figure1).Because PGN structures are actively remodeled during bacterial cell growth and division,the constant release of NOD1and NOD2 ligands from bacteria allows the innate immune system to survey its surrounding for the presence of bacteria.Interestingly,recent studies have identiﬁed additional agonists for NOD2,N-glycolyl muramyl dipeptide from mycobacteria(Coulombe et al.,2009) and viral ssRNA(Sabbah et al.,2009)(discussed below)demon-strating that NOD1and NOD2initiate innate responses upon recognition of a larger variety of pathogen-derived molecules. The mechanisms by which these agonists cross the host’s cell membrane to stimulate NOD1and NOD2remain not fully under-stood.This is important given that only a minority of bacteria replicates in the cytoplasm,where they can be directly sensed, yet bacteria localized in phagosomes and outside the cell can effectively activate both NOD1and NOD2.Several transport systems,including pannexin,PepT1,and PepT2,as well as endocytosis,have been demonstrated to enable hydrophilic muramyl peptides to cross to the cytoplasm(Lee et al.,2009; Marina-Garcı´a et al.,2009;Vavricka et al.,2004).Signaling downstream of NOD1and NOD2was thought to mainly result in the activation of NF-k B signaling(Fritz et al., 2006;Kanneganti et al.,2007)(Figure1).Indeed,recognition of PGN ligands by NOD1and NOD2leads to a conformational change that activates receptor-interacting serine-threonine kinase2(RIP2)via cellular inhibitors of apoptosis1and2 (cIAP1and2)(Bertrand et al.,2009),subsequently leading to ubiquitination of NF-k B essential modulator(NEMO)and the activation of the proinﬂammatory NF-k B pathway.In parallel, Immunity34,May27,2011ª2011Elsevier Inc.665Table1.The NLR FamilySubfamily Human Mouse N Terminus Other Names666Immunity34,May27,2011ª2011Elsevier Inc.MDP recognition can also lead to the activation of the mitogen-activated protein kinase (MAPK)pathway via RIP2,which con-tributes to cytokine production.Importantly,numerous studies have highlighted the interaction with other signaling pathways that stimulate NF-k B,including TLRs (Lee and Kim,2007).Depending on the cell type,NOD agonists modulate the magni-tude of TLR ligand-induced cytokine production.In dendritic cells,NOD1and NOD2agonists can synergize with TLR ligands leading to enhanced production of proinﬂammatory cytokines (Fritz et al.,2005).In contrast,in splenocytes MDP treatment leads to a hyporesponsive state and decreased TLR ligand-induced NF-k B signaling (Watanabe et al.,2004),indicating that the interaction between the different PRR pathways is strongly dependent on the cellular context as well as the inﬂam-matory setting.Both NOD1and NOD2are highly expressed in antigen-presenting cells (APCs)such as monocytes,macrophages,and dendritic cells (Fritz et al.,2006;Kanneganti et al.,2007).Several recent studies identiﬁed NOD2expression also in other hemato-poietic lineages (Petterson et al.,2011;Shaw et al.,2009).In addition,NOD1is expressed in many epithelial cell subsets,whereas NOD2seems to be more restricted to specialized cell types such as Paneth cells in the small intestine.NOD2expres-sion is potently induced by TLR ligands including LPS and by inﬂammatory mediators such as TNF-a ,IFN-g ,and IL-17in other nonhematopoietic tissues.Furthermore,a recent study demon-strated that NOD2expression in the intestine is regulated by signals from the microbiota,given that germfree mice had lower NOD2expression that was reversible upon monocolonization with commensal bacteria (Petnicki-Ocwieja et al.,2009).Role of NOD1in the Antimicrobial ResponseA role for NOD1as a modulator of the in vivo antimicrobial response was described ﬁrst in infection with the pathogen Helicobacter pylori ,in which Nod1À/Àmice featured enhanced susceptibility to infection with this pathogen (Viala et al.,2004).This phenotype correlated with an impaired acute inﬂammatory response,probably due to decreased production of chemokines by gastric epithelial cells as well as to impaired innate immune cell recruitment.Subsequent studies established roles for NOD1in the induction of cytokines,antimicrobial peptides,and type I interferons during H.pylori infection (Watanabe et al.,2010a ).In the later study,Strober and colleagues found an unexpected signaling pathway in epithelial cells leading to the induction of IFN-b via RIP2and TNF-receptor-associated factor 3(TRAF3)(Watanabe et al.,2010b ).NOD2-mediated immune control of the infection further required members of the canonical type Iinterferon cascade consisting of IFN-regulatory factor 7(IRF7),and the IFN-stimulated gene factor 3(ISGF3)complex.Similar to studies with TLR-deﬁcient mice,Nod1À/Àmice also had reduced T helper 1(Th1)cell responses upon H.pylori infection,suggesting that the two pathways may cooperate in the induction of adaptive immune responses.NOD1has also been involved in the recognition of other patho-gens including Clostridium difﬁcile (Hasegawa et al.,2011),Legionella pneumophila (Berrington et al.,2010a;Frutuoso et al.,2010),Listeria monocytogenes (Boneca et al.,2007),Staphylo-coccus aureus (Travassos et al.,2004),and Pseudomonas aeruginosa (Travassos et al.,2005).In many of these studies,either an impaired recruitment or function of neutrophils was noted during the inﬂammatory process.An interesting ﬁnding was re-ported,in which impaired neutrophil function in Nod1À/Àmice was observed already in the steady state that could have con-tributed to the results of the other studies (Clarke et al.,2010).It was hypothesized that PGNs derived from commensal bacteria circulating in the serum prime neutrophils at distant sites through NOD1signaling.Accordingly,broad-spectrum antibiotic-treat-ment that leads to suppression of certain commensal microﬂora communities resulted in similarly diminished neutrophil-mediated antimicrobial responses,whereas injection of NOD1ligands was able to restore the normal response.NOD1was also demonstrated to provide protection against infection with the intracellular parasite Trypanosome cruzi ,the etiological agent of Chagas disease (Silva et al.,2010).Nod1À/Àmice featured worsened disease,normal production of cyto-kines,and levels of parasitemia similar to those of Myd88À/Àmice,which,in turn,featured a near complete lack of cytokine induction.The exacerbated disease in Nod1À/Àmice was suggested to be the result of impaired ability of macrophages to kill intracellular parasites.These results indicate that,in some infections,TLRs and NOD1may orchestrate different aspects of the immune response upon infection.Additional in vivo studies will be needed to understand the role of NOD1during systemic immune responses,which may lead to new ther-apeutic approaches.Role of NOD2in the Antimicrobial ResponseIn vitro studies have implicated NOD2in a number of bacterial infectious models including Listeria monocytogenes (Kobayashi et al.,2005),Staphylococcus aureus (Deshmukh et al.,2009),Chlamydophila pneumoniae (Shimada et al.,2009),Strepto-coccus pneumoniae (Opitz et al.,2004),and Mycobacterium tuberculosis (Divangahi et al.,2008).Interestingly,NOD2was found to be required for immune control of L.monocytogenesTable 1.Continued aCurrently disputed as to whether it contains a CARD,PYD,or another N terminus binding domain.Immunity 34,May 27,2011ª2011Elsevier Inc.667after oral,but not intravenous and intraperitoneal infection,indi-cating that NOD2might have nonredundant functions in intes-tinal antimicrobial responses,whereas other PRRs such as TLRs could be sufﬁcient for the host antimicrobial response when infected via different routes.A recent study highlighted collaboration between NOD1and NOD2in the Salmonella typhi-murium colitis model (Geddes et al.,2010).Although mice deﬁ-cient in either NOD1or NOD2had normal susceptibility for infec-tion,mice deﬁcient in both NOD1and NOD2featured decreased inﬂammation but increased bacterial colonization of the intes-tine.Notably,NOD1and NOD2expression in both hematopoi-etic and nonhematopoietic cells contributed to resistance against infection.Although this was in an apparent conﬂict with a previous study demonstrating that Ripk2À/Àmice have compa-rable disease to wild-type (WT)controls (Bruno et al.,2009),the authors found that RIP2(encoded by Ripk2)was only involved in S.typhimurium -induced colitis with bacteria expressing prefer-entially a particular type III secretion system (Salmonella patho-genicity island [SPI]-2).This suggests that pathogen-sensing requirements are not only distinct for different microbes,but may differ among subtypes within the same species.The traditional view that NOD2only recognizes MDP was recently challenged as Nod2À/Àcells were found to be impaired in the expression of type I interferons upon stimulation with viral single-stranded RNA (ssRNA)(Sabbah et al.,2009).Interestingly,this pathway is independent of RIP2and does not require the CARD domain,but is rather dependent on the mitochondrial antiviral signaling protein (MAVS)and interferon regulatory factor 3(IRF3)(Figure 1).Strikingly,Nod2À/Àmice have an enhanced susceptibility to infection with respiratory syncytial virus (RSV)and decreased production of IFN-b after inﬂuenza virus infection (Figure 2).The authors were able to demonstrate direct binding of NOD2to viral ssRNA,but not host mRNA,by PCR methods.Therefore,NOD2has the ability to recognize a larger variety of structural elements than previously appreciated.Induction of Autophagy by NOD1and NOD2Autophagy is a lysosomal degradation pathway that was origi-nally shown to be important during development and in meta-bolic states of stress such as starvation,but was subsequentlydemonstrated to be central in antimicrobial immunity (Mu¨nz,2009).Recently,it has been reported that NOD1and NOD2areFigure 1.Microbial Activation of NLRs(A)Activation of NOD1and NOD2results,depending on the recognized ligand,in transcription of genes encoding chemokines,cytokines,antimicrobial peptides,and type I interferons.MDPs and iE-DAPs are derived from extracellular,intracytosolic,or intravesicular bacteria,whose recognition can stimulate activation of the NF-kB and MAPK pathways.NOD1-mediated recognition of H.pylori and NOD2-mediated recognition of ssRNA stimulate type I interferon transcription via IRF7and IRF3,respectively.NOD1and NOD2recruit ATG16L to sites of bacterial phagocytosis to initiate autophagy.(B)Activation of NLRC4and NLRP3results in assembly of inﬂammasomes that activate caspase-1.This activation requires two signals.Signal I induces transcription of pro-IL-1b and pro-IL-18;signal II is provided by PAMPs and DAMPs.In the case of NLRC4,recognition of cytoplasmatic ﬂagellin leads to pyropotosis,a specialized form of cell death,via an ASC-independent mechanism.NLRC4also induces processing of pro-IL-1b and pro-IL-18via ASC-dependent mechanisms.NLRP3senses either directly microbial molecules or indirectly signals associated with cellular perturbations such as increased ROS production,release of lysosomal proteases into the cytoplasm upon ‘‘frustrated’’phagocytosis,and potassium efﬂux.Assembly of the NLRP3inﬂammasome leads to processing of pro-IL-1b and pro-IL-18by caspase-1.668Immunity 34,May 27,2011ª2011Elsevier Inc.able to regulate autophagy,as stimulation with NOD1and NOD2 agonists leads to the NOD-dependent recruitment of ATG16L to the plasma membrane at sites of Salmonella typhimurium and Shigellaﬂexneri intrusion(Figure2)(Cooney et al.,2010;Travas-sos et al.,2010).This recruitment resulted in efﬁcient degrada-tion of bacteria in autophagosomes and subsequent processing of bacterial antigens for presentation on MHC class II molecules. In these studies,conﬂicting results were reported on RIP2 dependency of this pathway,which could potentially be attrib-uted,at least partially,to the different model systems(human versus mouse)and cell types(dendritic cells versusﬁbroblasts). Further studies are needed to determine the details of this pathway.Mutations in NOD2that are associated with Crohn’s disease resulted in its impaired abilities to recruit ATG16L to the plasma membrane,induce autophagy,and promote antigen presentation.Importantly,thisﬁnding was able to link mutations that have been associated with Crohn’s disease,i.e.,NOD2as well as ATG16L mutations,into a single pathway(Cho,2008).It will be of extraordinary interest to understand the molecular details of this pathway,which may lead to aberrant bacterial pro-cessing and defective presentation of antigens to CD4+T cells, resulting in an inﬂammatory cascade characteristic of Crohn’s disease.Regulation of the Antimicrobial Adaptive Immune Response by NOD1and NOD2Beyond NOD1and NOD2’s roles in modulation of innate immu-nity,they also direct adaptive immune responses.Such effects were already demonstrated in early studies identifying MDP as the minimal adjuvant components of complete Freund’s adju-vant(Strominger,2007).Indeed,NOD1and NOD2were shown to be directly involved in the priming of adaptive immunity when cognate ligands were used as adjuvant(Kobayashi et al., 2005;Werts et al.,2007).Notably,nonhematopoietic cells required NOD1,indicting an important role for this molecule in epithelial and stromal cells upstream of APCs(Werts et al., BAFigure2.Crosstalk between NLRs and Other Innate Immune Pathways(A)Inﬂuenza virus infection.Extracellular PRRs(TLR4)recognizes microbial products,whereas inﬂuenza virus is detected by TLR7.This leads to transcriptional activation of genes encoding proinﬂammatory cytokines(pro-IL-1b,pro-IL-18,TNF-a,and IL-6)in an NF-kB-dependent manner(signal I).Inﬂuenza virus M2ionic channel mediates the activation of the NLRP3inﬂammasome that leads to pro-IL-1b and pro-IL-18processing and secretion(signal II).NOD2recognizes inﬂuenza virus ssRNA stimulating type I interferon transcription via IRF3.(B)Salmonella infection.Salmonella is recognized by extracellular PRRs(TLR2,TLR4,and TLR5)resulting in transcriptional activation of genes encoding pro-inﬂammatory cytokines(pro-IL-1b,pro-IL-18,TNF-a,and IL-6)in an NF-kB-dependent manner(signal I).Intracellular Salmonella activate NLRP3and NLRC4 inﬂammasomes,which leads to pro-IL-1b,pro-IL-18processing into their biological active forms.NLRC4responds toﬂagellin injected by the T3SS,whereas NLRP3is activated through an undeﬁned T3SS-independent signal(signal II).NLRP3inﬂammasome assembly is completely dependent on ASC,whereas NLRC4 may assemble an inﬂammasome without ASC.NLRC4inﬂammasome activation also results in pyroptosis and release of bacteria into the extracellular space. Activation of NOD1and NOD2by Salmonella leads to chemokine secretion and neutrophil recruitment to the site of infection.NOD1and NOD2also target ATG16L to sites of bacterial phagocytosis to initiate autophagy.Immunity34,May27,2011ª2011Elsevier Inc.6692007).In addition to NOD2’s role in the regulation of the adaptive immune response via APCs and the microenvironment,it was found to directly act in T cells as part of a modulatory network that controls the antimicrobial response.There is an enhanced susceptibility of Nod2À/Àmice to Toxoplasma gondii infection, which was attributed to a diminished T cell IFN-g production (Shaw et al.,2009).This cell-intrinsic effect was suggested to involve a defect in signaling downstream of the costimulatory molecule CD28.Studies in other T cell-dependent models, including graft-versus-host disease(GVHD)(Penack et al., 2009)and an in vivo Th2cell model(Magalhaes et al.,2008), did not report NOD2-dependent intrinsic defects in T cell func-tion,suggesting that NOD2functions in a nonredundant manner in T cells under speciﬁc conditions,possibly depending on the level of CD28requirement.Inﬂammasome Forming NLR ProteinsIn contrast to the NOD proteins,several other members of the NLR protein family(see Table1)may form multiprotein com-plexes,named‘‘inﬂammasomes.’’This results in activation of inﬂammatory caspases,cysteine proteases that are synthesized as inactive zymogens.Upon activation,caspases trigger cellular programs that lead to inﬂammation or cell death.Caspase-1is the most prominent member of the proinﬂammatory group of caspases that also includes caspase-4,caspase-5,caspase-11, and caspase-12.Activation of pro-caspase-1is essential for the processing of pro-IL-1b and pro-IL-18and for the secretion of their mature active forms.Caspase-1’s catalytic activity is tightly regulated by the inﬂammasomes,in a signal-dependent manner. Inﬂammasomes require two signals to accomplish their biolog-ical function.Signal I initiates transcriptional activation of inﬂam-masome components and is often provided through TLR and NF-k B signaling,whereas signal II is required to initiate inﬂamma-some assembly.Known inﬂammasomes are composed of one of several NLR and PYHIN proteins,including NLRP1,NLRP3,NLRC4,and AIM2that function as sensors of endogenous or exogenous PAMPs or DAMPs(Sutterwala et al.,2007b).After sensing the relevant signal,inﬂammasomes are assembled through homo-philic CARD-CARD and PYD-PYD interactions between NLRs, apoptosis-associated speck-like protein containing a CARD (ASC),and pro-caspase-1(Agostini et al.,2004;Martinon et al.,2002).Inﬂammasome components and the activation of downstream pathways depend on the nature of the stimuli,the sensor protein and,to some extent,on the presence of absence of ASC.Although there is conclusive evidence that NLRP1, NLRP3,and NLRC4regulate proinﬂammatory responses through caspase-1activation,domain structure conservation through the NLRP family suggests that other uncharacterized members might also regulate inﬂammasome assembly.NLRC4NLRC4contains an N-terminal CARD,a central NOD domain, and a C-terminal LRR.NLRC4is expressed mainly in lymphoid tissues,more speciﬁcally in myeloid cells,but also in the gastro-intestinal tract(Hu et al.,2010).Activation of the NLRC4inﬂam-masome leads to caspase-1activation,release of IL-1b,and a rapid form of cell death called pyroptosis(Sutterwala and Flavell,2009)(Figure1).Because NLRC4contains a CARD domain,it can interact directly with pro-caspase-1;therefore, the role of ASC in NLRC4-mediated responses has remained elusive.Infection of ASC-deﬁcient macrophages with NLRC4-activating bacteria results in defective caspase-1activation and IL-1b secretion(Mariathasan et al.,2004;Sutterwala et al., 2007a;Suzuki et al.,2007),yet normal pyroptosis,suggesting that pathways downstream NLRC4are independently regulated through the presence of ASC.In concordance,Broz and colleagues demonstrated that NLRC4-Inﬂammasomes con-taining ASC form a single large‘‘focus’’in which Caspase-1 undergoes autoproteolysis and processes IL-1b/IL-18.In contrast,NLRC4-ASC-independent inﬂammasomes activate Caspase-1without autoproteolysis and do not form any large structures in the cytosol.Moreover,Caspase-1mutants that were unable to undergo autoproteolysis promoted rapid cell death and processed IL-1b/IL-18inefﬁciently,which suggests that NLRC4forms spatially and functionally distinct inﬂamma-somes complexes in response to bacterial pathogens(Broz et al.,2010b).Interestingly,NLRC4and ASC probably do not interact directly as NLRC4does not contain a PYD;therefore, it is possible that additional PYD-containing proteins are required for NLRC4-ASC-dependent processing of IL-1b in response to pathogens.Bacterial Activation of the NLRC4InﬂammasomeThe NLRC4inﬂammasome activators are mainly Gram-negative bacteria that contain bacterial type III(T3SS)or type IV(T4SS) secretion systems.These include Salmonella(Mariathasan et al.,2004),Legionella(Zamboni et al.,2006),Shigella(Suzuki et al.,2007),Pseudomonas(Sutterwala et al.,2007a),and Yersinia (Brodsky et al.,2010).The microbial moleculeﬂagellin is re-quired to induce NLRC4-mediated caspase-1activation during Legionella,Salmonella,and low burden infection of Pseudo-monas(Franchi et al.,2006;Franchi et al.,2007;Miao et al., 2006;Miao et al.,2008;Molofsky et al.,2006).L.pneumophila and S.typhimurium mutant strains lackingﬂagellin or expressing point mutations in its gene are defective in their ability to induce caspase-1activation(Franchi et al.,2006;Miao et al.,2006). Moreover,delivery ofﬂagellin molecules to the cytosol through transfection or retroviral transduction results in caspase-1activa-tion in an NLRC4-dependent manner(Franchi et al.,2006;Light-ﬁeld et al.,2008;Miao et al.,2006).In contrast,extracellular ﬂagellin is unable to induce the activation of the NLRC4inﬂamma-some,suggesting that additional factors are required to enable ﬂagellin transport into the cytosol(Franchi et al.,2006;Miao et al.,2006).Because NLRC4activation is dependent on func-tional bacterial secretion systems(T3SS,T4SS)(Franchi et al., 2006;Sutterwala et al.,2007a;Suzuki et al.,2007;Zamboni et al.,2006),it has been proposed that the T3SS can serve as a route forﬂagellin monomers to gain entry into the cytosol,leading to caspase-1activation(Sun et al.,2007).L.pneumophila is unique in that the activation of the NLRC4inﬂammasome by the C-terminal portion of itsﬂagellin requires the presence of a second NLRP,NAIP5(Coers et al.,2007;Zamboni et al.,2006).NLRC4 and NAIP5had been suggested to physically interact to regulate caspase-1activation;however,the exact role of NAIP5in this process remains uncertain.The NLRC4inﬂammasome can also be activated in aﬂagellin-independent manner.The nonﬂagellated bacterium S.ﬂexneri670Immunity34,May27,2011ª2011Elsevier Inc.and the P.aeruginosa mutant PAK DﬂiC(ﬂagellin-deﬁcient)have been shown to activate capase-1through NLRC4(Sutterwala et al.,2007a;Suzuki et al.,2007).These observations suggest that additional microbial molecules can trigger assembly of the NLRC4inﬂammasome.Indeed,Miao and colleagues recently demonstrated that NLRC4detects the rod protein of the T3SS of multiple Gram-negative bacteria through a sequence motif that is also found inﬂagellin(Miao et al.,2010b).NLRC4Interaction with Other ProinﬂammatorySignaling PathwaysNLRC4plays a critical role during pulmonary and peritoneal P.aeruginosa infection(Franchi et al.,2007;Sutterwala et al., 2007a).However,NLRC4contribution during other bacterial infections is less clearly deﬁned,suggesting that it may have redundant or additive roles during host immune responses. S.typhimurium-infected NLRC4-deﬁcient macrophages feature marked defects in caspase-1activation and pyroptosis(Maria-thasan et al.,2004).In contrast,in vivo studies suggested that the absence of caspase-1but not of NLRC4results in enhanced susceptibility to S.typhimurium infection(Lara-Tejero et al., 2006).The discrepancy between the in vitro and in vivo results suggests that additional pathways contribute to Salmonella-induced caspase-1activation.In fact,although NLRC4is acti-vated by Salmonellaﬂagellin,NLRP3responds to another,unde-ﬁned,Salmonella-T3SS-independent signal(Figure2)(Broz et al.,2010a).Accordingly,mice lacking both NLRs were more susceptible to S.typhimurium infection(Broz et al.,2010a).Simi-larly,Yersinia-T3SS triggers ASC-dependent caspase-1activa-tion through the NLRP3and NLRC4inﬂammasomes(Brodsky et al.,2010).Interestingly,NLRP3was not essential for the in vivo bacterial clearance in this study(Brodsky et al.,2010). In vivo restriction of L.pneumophila is dependent on complex interactions between the MyD88,NOD1and NOD2,and NLRC4 pathways.Rip2-deﬁcient mice infected with aﬂagellin-deﬁcient L.pneumophila(DﬂaA)were able to clear the bacteria,indicating that MyD88signaling,independently of RIP2and NLRC4,is able to protect the host against L.pneumophila infection(Archer et al.,2010).Concurrently,WT and DﬂaA L.pneumophila grew to comparable levels in the lung of Myd88À/Àmice,which suggests that replication of L.pneumophila was not restricted severely by the NLRC4pathway.However,Myd88À/Àmice survived a high-dose challenge of WT L.pneumophila but succumbed to equal doses of DﬂaA strain;therefore,NLRC4 provides a level of host protection under high bacterial burden that is independent of MyD88(Archer et al.,2010).In this context,signaling through RIP2and MyD88cooperate to attract neutrophils to the site of L.pneumophila infection;this can be of particular importance in light of recent evidence indicating that NLRC4-dependent,ASC-independent pyroptosis results in bacterial release from macrophages,exposing bacteria to uptake and killing by neutrophils(Miao et al.,2010a).Finally,a crosstalk between NLRC4and TLR5(ﬂagellin-extracellular receptor)has been recently identiﬁed.TLR5has a protective role in S.typhimurium and P.aeruginosa infection in mice(Hawn et al.,2003;Morris et al.,2009).Likewise,humans carrying a dominant-negative allele of TLR5are more suscep-tible to L.pneumophila infection,suggesting that TLR5and NLRC4have nonredundant roles in these infections(Hawn et al.,2003).In contrast,ﬂagellin and OVA immunization elicits a normal humoral immune response in single Nlrc4À/Àor Tlr5À/Àmice but not in Nlrc4À/ÀTlr5À/Àmice(Vijay-Kumar et al.,2010). NLRP3NLRP3,like most other members of the NLRP subfamily(with the exception of NLRP10),consists of a carboxy-terminal LRR domain,a central NOD domain,and an amino-terminal PYD, mainly interacting with ASC(Schroder and Tschopp,2010). NLRP3is mainly expressed in multiple cells of the hematopoietic system,of the lymphocytic and myelogenic lineages,and in other cell types such as skin keratinocytes,transitional epithe-lium of the urinary tract,and osteoblasts.Noninfectious triggers for NLRP3inﬂammasome formation include crystal activators such as monosodium urate(MSU),calcium pyrophosphate dihydrate(CPPD),asbestos,silica,and alum,protein aggregates such asﬁbrillar b-amyloid,haptens such as trinitrophenylchlor-ide,and ultraviolet irradiation.A detailed description of these DAMPs is provided elsewhere(Schroder and Tschopp,2010). In general,inﬂammasome activation is believed to involve two steps,theﬁrst involving transcription of the‘‘building blocks,’’e.g.,pro-IL-1b,pro-IL-18,and pro-caspase-1,as well as expression of NLRP3itself in the case of NLRP3inﬂammasome activation.The second step,through poorly understood interac-tions of activators with the relevant NLRP,leads to inﬂamma-some assembly that in turn drives cleavage and release of active IL-1b and IL-18.This process is best studied in the NLRP3 inﬂammasome.Theﬁrst priming event of NLRP3inﬂammasome activation involves intricate interaction with other PRR such as TLR and NOD2,resulting in NF-kB-driven transcriptional activa-tion.TLR-induced priming can be performed through both the MyD88and TRIF pathways,given that only deﬁciency of both adaptor proteins results in abrogated priming(Bauernfeind et al.,2009).Interestingly,cytokines including TNF-a,IL-1a, and IL-1b have been suggested to be capable to provide this priming signal for NLRP3assembly,providing an alternative mechanism for inﬂammasome activation in‘‘sterile inﬂamma-tion’’in the absence of microbial interactions(Franchi et al., 2009a).In vivo,some of the NLRP3activators can induce inﬂam-masome formation in the absence of a priming signal.Whether such signal is provided constitutively in the in vivo setting remains to be determined.The precise mechanism whereby NLRP3senses the presence of its activators remains elusive.Although physical interaction of microbial activators with NLRP3may occur in some cases (Marina-Garcı´a et al.,2008),a more general mechanism is thought to involve a cellular mediator(s)that interacts with the structurally diverse DAMPs and PAMPs to provide signals for NLRP3inﬂammasome formation.Similar indirect innate trig-gering systems have been described in plants,in which path-ogen-induced alterations in endogenous proteins are sensed by NLR-like R proteins that,in turn,trigger canonical resistance mechanisms against these pathogens(Mackey et al.,2002). Three nonexclusive models for indirect NLRP3activation have been proposed and were reviewed in(Schroder and Tschopp, 2010)(Figure1).Theﬁrst involves ATP-induced activation of the nonselective cation channel P2X7,which in turn activates the larger pore-forming Pannexin-1,leading to potassium efﬂux and potentially cytoplasmic migration of PAMPs and DAMPs, Immunity34,May27,2011ª2011Elsevier Inc.671。

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ReviewEmerging biomarkers and intervention targets forimmune-modulation of atherosclerosis e A review of the experimental evidenceHarry Björkbacka *,Gunilla Nordin Fredrikson,Jan NilssonDepartment of Clinical Sciences,Skåne University Hospital Malmö,Lund University,Swedena r t i c l e i n f oArticle history:Received 16July 2012Received in revised form 30October 2012Accepted 31October 2012Available online 6November 2012Keywords:BiomarkersImmune modulation Regulatory T cells Monocytes In ﬂammation Autoimmunitya b s t r a c tThe role of in ﬂammation in atherosclerosis and plaque vulnerability is well recognized.However,it is only during recent years it has become evident that this in ﬂammation is modulated by immune responses against plaque antigens such as oxidized LDL.Interestingly,both protective and pathogenic immune responses exist and experimental data from animal studies suggest that modulation of these immune responses represents a promising new target for treatment of cardiovascular disease.It has been proposed that during early stages of the disease,autoimmune responses against plaque antigens are controlled by regulatory T cells that inhibit the activity of auto-reactive Th1effector T cells by release of anti-in ﬂammatory cytokines such as IL-10and TGF-b .As the disease progresses this control is gradually lost and immune responses towards plaque antigens switch towards activation of Th1effector T cells and release of pro-in ﬂammatory cytokines such as interferon-g ,TNF-a and IL-1b .Several novel immune-modulatory therapies that promote or mimic tolerogenic immune responses against plaque antigens have demonstrated athero-protective effects in experimental models and a ﬁrst generation of such immune-modulatory therapies are now in early or about to enter into clinical testing.A challenge in the clinical development of these therapies is that our knowledge of the role of the immune system in atherosclerosis largely rests on data from animal models of the disease.It is therefore critical that more attention is given to the characterization and evaluation of immune biomarkers for cardiovascular risk.Ó2012Elsevier Ireland Ltd.All rights reserved.Contents 1.Atherosclerosis and the immune system ...............................................................................................102.T cells modulate inflammation in atherosclerosis ........................................................................................103.Emerging therapies promoting regulatory T cell responses ...............................................................................104.Clinical studies of regulatory T cells and cardiovascular disease ...........................................................................125.B cells and antibodies have multifaceted roles ..........................................................................................126.Therapeutic opportunities to target chemokine receptors and monocytes ...................................................................137.Anti-inflammatory drugs and atherosclerosis ...........................................................................................148.Challenges facing the translation of experimental immune-modulation of atherosclerosis into clinical therapy .................................149.Conclusions and perspectives .........................................................................................................15References . (15)Current therapies for prevention of cardiovascular disease rest almost exclusively on risk factor intervention.This approach hasproven very successful and experience from randomized clinical trials has shown that up to 40%of cardiovascular events can be prevented by optimal medical risk factor intervention [1].However,in spite of these encouraging results the majority of treated patients still receive insuf ﬁcient protection.It is likely that to further improve the ef ﬁcacy of cardiovascular prevention new treatments directly targeting the disease process in the arterial wall need to*Corresponding author.Experimental Cardiovascular Research,CRC 91:12,Lund University,Skåne University Hospital Malmö,Jan Waldenströms gata 35,SE-20502Malmö,Sweden.Tel.:þ46(0)40391205;fax:þ46(0)40391212.E-mail address:harry.bjorkbacka@med.lu.se (H.Björkbacka).Contents lists available at SciVerse ScienceDirectAtherosclerosisjournal homepa ge:www.elsevie/locate/atherosclerosis0021-9150/$e see front matter Ó2012Elsevier Ireland Ltd.All rights reserved./10.1016/j.atherosclerosis.2012.10.074Atherosclerosis 227(2013)9e 17be developed.Such treatments will need to speciﬁcally target atherosclerotic plaque inﬂammation.In this review we will discuss evidence suggesting that this can be achieved by modulating immune responses against plaque antigens as well as the need of developing validated biomarkers that can be used to measure such immune responses.The concept of the immune system as an attractive target for future cardiovascular therapies is primarily based on experimental studies demonstrating that inhibition of inﬂammatory mediators and induction of speciﬁc immune responses can reduce athero-sclerosis burden[2e5].The proposed pleiotropic and anti-inﬂammatory effects of statins and the usefulness of the inﬂam-matory marker high-sensitivity C-reactive protein(hsCRP)in risk prediction in humans reinforce this notion[6,7].On the other hand, the failures of non-steroidal anti-inﬂammatory drugs(NSAIDs)and COX-2speciﬁc inhibitors have taught us that general anti-inﬂammatory treatment may not be a viable option for the treat-ment of atherosclerosis and the targeting of more speciﬁc immune responses will be needed[8,9].Therefore,CRP,predominantly secreted by the liver and adipose tissue in response to inﬂamma-tory stress,is a relatively crude marker for the evaluation of speciﬁc immune responses in the vascular wall.It has become increasingly clear that to truly understand the role of inﬂammation in athero-sclerosis CRP will be insufﬁcient and novel biomarkers assessing the complex role of the immune system in the disease need to be developed.There is today convincing evidence for presence of immune responses against plaque antigens in atherosclerosis and it has been proposed that disease progression occurs as a result ofa loss of immune tolerance against these antigens in the plaque[10].However,atherosclerosis is probably not an autoimmune disease in the classical sense but rather a state of local immune dysfunction resulting in an imbalance between naturally occurring autoimmunity and the regulatory immune cells that should control this autoimmunity.The circumstances that women,which gener-ally are more prone to develop autoimmune diseases,suffer from cardiovascular disease at an older age than men also suggest that atherosclerosis is not a true autoimmune disease.1.Atherosclerosis and the immune systemAn impressive list,encompassing almost all aspects of the immune system,can be compiled when trying to summarize immune mechanisms that contribute to atherosclerosis develop-ment in mice(Fig.1).Depletion of monocytes,the precursors of macrophage foam cells in plaques,can to a large extent abrogate atherosclerosis development in mice[11,12].Interestingly,this effect is more prominent at early stages of the disease while depleting monocyte/macrophages from advanced lesions does not alter pla-que size or composition[13].Dendritic cells have been implicated in the formation of the earliest detectable plaques[14,15].Neutrophils are found in increased numbers in hypercholesterolemic mice and neutrophil depletion effectively reduces atherosclerosis if neutrophil-depleting antibodies are administered during early atherosclerosis development[16,17].A concept has been put forth where neutrophils pave the way for monocyte inﬁltration during early atherosclerosis development[18].Platelets also assist in the recruitment of immune cells to the atherosclerotic plaque by facili-tating interactions between endothelial cells and monocytes, neutrophils,dendritic cells and T cells[19].T cell responses have generally been regarded as pro-atherogenic,although protective T cell responses also exist.In this context it is interesting to note that cardiovascular complications are more common in human immu-nodeﬁciency virus-infected individuals than in age-matched unin-fected individuals and that antiretroviral therapy,which increases T cell counts,reduces the cardiovascular risk in treated subjects[20].2.T cells modulate inﬂammation in atherosclerosisInitial studies in severely immune-deﬁcient mice provided inconclusive and partly contradictiveﬁndings regarding the role of the immune system in atherosclerosis[21e23].It was later revealed that this was explained by the fact that both protective and atherogenic immune responses exist[24].There is now convincing evidence that type1T helper(Th1)cells promote disease and deletion of the transcription factor T-bet,which is required for Th1lineage commitment,reduces atherosclerosis [25,26].The role of type2T helper(Th2)cells in atherosclerosis is unclear as several Th2cytokines have been assigned protective roles,whereas deletion of IL-4,the proteotypic cytokine for Th2cells,reduces atherosclerosis development in some studies [27e29].The role of Th17in atherosclerosis is also debated as conﬂicting reports exist[30e32].In contrast to CD4þT helper cells, CD8þcytolytic T lymphocytes have not been extensively studied, even though CD8þT cells are abundant in atherosclerotic plaques and they have been shown to be activated in hypercholesterolemic ApoeÀ/Àmice[33].Activation of CD8þT cells has also been associ-ated with increased plaque formation in these mice[34].In apparent contrast with these observations,atherosclerosis protec-tion achieved by immunization with apolipoprotein B(apoB) peptide has been suggested to involve CD8þT cells[35].On the other hand,Tap1-deﬁciency that leads to severely diminished CD8þT cell populations does not alter atherosclerosis development in ApoeÀ/Àmice[36].Regulatory T cells expressing the transcription factor FoxP3,on the other hand,are clearly limiting atherosclerosis development[37].The development and activation of natural killer T(NK T)cells,a subset that expresses surface markers characteristic of both natural killer cells and conventional T cells,depends on the interaction of their T cell receptor with lipids and glycolipids pre-sented on CD1d,an MHC-class I-type molecule.Deletion of CD1d, which also eliminates NK T cells,reduces atherosclerosis develop-ment,whereas administration of the exogenous activator a GalCer augments atherosclerosis[38,39].The role of different immune cells in atherosclerosis is summarized in Fig.1.3.Emerging therapies promoting regulatory T cell responsesThe atherosclerosis quenching properties of regulatory T cells have attracted much attention in recent years and have spurred the development of therapies that inhibit atherosclerosis in mice by promoting regulatory T cells.Regulatory T cells prevent autoim-munity by controlling the activity of potentially auto-reactive T cells that have escaped deletion in the thymus.These natural regulatory T cells are characterized by expression of CD25and the transcrip-tion factor FoxP3,which is considered the master regulator of the regulatory T cell transcription program.A wealth of data supports a protective role for regulatory T cells in atherosclerosis.Mice lacking the co-stimulatory molecules CD80/86,CD28or ICOS have reduced numbers of regulatory T cells and consequently develop atherosclerosis more readily[37,40].Furthermore,depletion of regulatory T cells with an anti-CD25antibody or by immunizations targeting FoxP3also signiﬁcantly increases the formation of atherosclerotic plaques[37,41].Regulatory T cells generated in the periphery are characterized by expression of IL-10(Tr1cells)or TGF-b(Th3cells).Adoptive transfer of a clone of ovalbumin-speciﬁc Tr1cells together with its cognate antigen inhibits plaque devel-opment in mice and inhibition of Th3cells through deletion of the receptor for TGF-b on T cells enhances disease progression[42,43]. Thus,inhibition of atherosclerosis has been associated with induction of several types of regulatory T cells including natural regulatory T cells in response to anti-CD3and anti-CD45RB treat-ment[44,45],Th3cells through oral immunization with oxidizedH.Björkbacka et al./Atherosclerosis227(2013)9e17 10Fig.1.The role of different immune cell populations in experimental atherosclerosis in mice and in prediction of cardiovascular disease risk in humans.Red arrows indicate a role in facilitating atherosclerosis development in mice or positive association in humans.Blue arrows indicate an inhibition of atherosclerosis development in mice or negative association in humans.(For interpretation of the references to colour in this ﬁgure legend,the reader is referred to the web version of this article.)H.Björkbacka et al./Atherosclerosis 227(2013)9e 1711LDL[46]and Tr1cell through nasal immunization with an apoB peptide fused with the cholera toxin B subunit[47].Both experimental and clinical evidence support the existence of autoimmune responses against LDL modiﬁed by oxidation[10]. As much as10%of the T cells in atherosclerotic plaques are oxidized LDL-speciﬁc[48].Interestingly,the existence of T effector cells auto-reactive for native apoB has recently also been reported and neutralization of these cells by immunization against their speciﬁc T cell receptor was shown to result in decreased atherosclerosis [49].The existence of T cells that are auto-reactive for native or modiﬁed lipoproteins is not unexpected because the immune system is forced to allow the generation of some cells with limited auto-reactivity to avoid narrowing the capacity for immunological diversity required for an effective host defence.Such auto-reactive T effector cells will normally be controlled by regulatory T cells with similar antigen speciﬁcity.However,it has been proposed that the balance between plaque antigen speciﬁc T effector cells and regu-latory T cells is shifted in atherosclerosis allowing plaque inﬂam-mation to progress[10].Accordingly,several novel strategies to promote peripheral tolerance against self-antigens in mice by modulating regulatory T cells have emerged and constitute an attractive novel approach to treat atherosclerosis.Particularly,the possibility to induce antigen-speciﬁc tolerance has advantages over more general anti-inﬂammatory therapies that also may compro-mise defence against invading pathogens.Autoimmunity against self-antigens,such as oxidized LDL and heat shock proteins(HSPs), plays an important role in the development of atherosclerosis and immunization with HSPs or apoB peptides are associated with a decrease in atherosclerosis development[50,51].Atherosclerosis protection achieved by immunization with apoB peptide and Alum adjuvant has been credited to regulatory T cell dependent mecha-nisms[52,53].Similarly,oral administration of oxidized LDL or HSP60is associated with increased number of regulatory T cells and reduced atherosclerosis[46,54].Mucosal delivery of apoB peptides has also been shown to reduce atherosclerosis and to be associated with an increase in Tr1-type regulatory T cells[47].Furthermore, subcutaneous infusion of low doses of apoB peptide has been shown to inhibit atherosclerosis development and was found to promote antigen-speciﬁc regulatory T cells[55].Dendritic cells can also be used to confer tolerance to self-antigens.Intravenous administration of dendritic cells pulsed with the complete apoB protein and IL-10reduces atherosclerosis by a mechanism involving activation of regulatory T cells[56].Taken together,these data imply that immunizations with self-antigens boost existing regulatory T cell immunity and that breaking the tolerance to self-antigens may be an important contributor to atherosclerosis development[49,57].Theﬁrst of these therapies are now close to entering clinical testing,emphasizing the need for a better under-standing of the role of regulatory T cells in cardiovascular disease in humans.Based on the experimental evidence both oxidized LDL and more well deﬁned LDL antigens,such as different apoB peptides,represent possible components of future atherosclerosis vaccines for human use(Fig.2).However,apoB peptides have the advantage of synthetic manufacturing under controlled conditions and a lower risk for contaminations.4.Clinical studies of regulatory T cells and cardiovascular diseaseDespite strong experimental evidence supporting a protective role of regulatory T cells in atherosclerosis our understanding of their clinical signiﬁcance remains limited.Decreased levels of circulating regulatory T cells have been reported in patients with acute coronary syndrome[58e61],but prospective studies analyzing the association of regulatory T cells with cardiovascular risk have been lacking.Recently,however,Wigren and coworkers [62]reported that low baseline levels of regulatory T cells,deﬁned as CD4þFoxP3þT cells,were associated with an increased risk for development of acute myocardial infarction during a15-year follow-up of700subjects taking part in the cardiovascular sub-study of the MalmöDiet and Cancer study.The hazard ratio for suffering a coronary event in the lowest tertile of CD4þFoxP3þT cells was1.9compared to the highest tertile and this increase in risk was independent of other cardiovascular risk factors.It was also reported that low levels of CD4þFoxP3þT cells were associated with an increased release of pro-inﬂammatory cytokines from activated peripheral blood mononuclear leukocytes.Although these obser-vations are encouraging because they provide theﬁrst prospective evidence for a role of regulatory T cells in coronary artery disease they need to be interpreted with some caution due to the technical difﬁculties in deﬁning human regulatory T cells.In mice regulatory T cells are easily identiﬁed based on expression of the surface markers CD4and CD25which characterizes a largely homogenous regulatory population of cells also expressing the transcription factor FoxP3that is required for regulatory T cell development and function.However,the use of CD4and CD25is inadequate in humans because conventional CD4þT cells also express CD25in response to activation.One approach has been to identify CD4þCD25high cells as these have been shown to have regulatory properties[63],but the limitation with this strategy is that it only identiﬁes CD45ROþmemory regulatory T cells and not CD45RAþnaïve regulatory T cells[64].To overcome this problem the combination of CD25high and CD127low has been used which identiﬁes a relatively pure population of FoxP3expressing cells in humans[65],but this still fails to discriminate between regulatory T cells and conventional T cells that in response to activation up-regulate CD25and down-regulate CD127[64].Accordingly,it is much more complex to study the association between regulatory T cells and disease in humans than in mice.The recent observation that regulatory T cells may differentiate into pro-inﬂammatory Th17cells further contributes to this challenge[66].5.B cells and antibodies have multifaceted rolesInitial studies suggested that B cells have an overall protective role in atherosclerosis as B-cell deﬁcient mice display increased atherosclerosis compared to control mice and transfer of B cells to atherosclerotic splenectomized mice reduces atherosclerosis [67,68].However,more recently the protective role of B cells in atherosclerosis has been challenged as B cell depletion with anti-CD20antibodies decreases atherosclerosis[69,70].These discrep-ancies may in part be due to differential effects of B cell subsets. Depletion of B2B cells ameliorates atherosclerosis,whereas B1a cells are protective presumably through the secretion of natural IgM antibodies that bind to oxidized LDL and apoptotic cells[70e 73].Natural IgM may limit inﬂammation by facilitating the removal of damaged cells and lipoproteins,and high levels of these autoantibodies have been associated with a lower cardiovascular risk in several clinical studies[74].The observation that immuni-zation with phosphorylcholine,the target for natural IgM,reduces atherosclerosis suggests that these antibodies represent an inter-esting novel intervention goal[75].Oxidized LDL is also targeted by IgG autoantibodies.The role of these antibodies in the development of atherosclerosis has been controversial because their associations with atherosclerosis severity and cardiovascular risk have been inconsistent in clinical studies[76].This inconsistency may in part be due to difﬁculties in standardizing the oxidized LDL antigen used in the antibody ELISAs because studies based on deﬁned apo B peptide antigens have provided more consistentﬁndings.Interestingly,high levels of IgGH.Björkbacka et al./Atherosclerosis227(2013)9e17 12autoantibodies against apoB peptides have generally been associ-ated with less severe atherosclerosis and a lower cardiovascular risk [77,78]suggesting that they may have a protective role.This notion has received support from experimental studies demon-strating that treatment of hypercholesterolemic mice with a recombinant human IgG antibody recognizing the MDA-modi ﬁed 661-680amino acid sequence of human apo B inhibits the devel-opment of atherosclerosis and potentiates plaque regression induced by cholesterol lowering [79,80].The possible athero-protective effect of these antibodies in humans is presently being investigated in the GLACIER trial (Goal of oxidised Ldl and ACtivated macrophage Inhibition by Exposure to a Recombinant antibody, ).6.Therapeutic opportunities to target chemokine receptors and monocytesIn mice,monocytes are divided into a Ly-6C high CX3CR1low CCR2þsubset that is actively recruited into in ﬂamed tissue and a Ly-6C low CX3CR1high CCR2Àsubset that home into non-in ﬂamed tissues [81].The Ly-6C high monocytes are increased in blood during hypercholesterolaemia and are preferentially recruited to athero-sclerotic plaques by the use of CCR2and CX3CR1receptors [82,83].In contrast,Ly-6C low monocytes have been shown to enter plaques less frequently than Ly-6C high monocytes,but they are,on the other hand,more prone to develop into plaque resident cells expressing CD11c [83].Despite a high expression of CX3CR1,the Ly-6C low monocytes depend mainly on CCR5to enter plaques [83].Inter-estingly,some of the same receptors that regulate plaque entry may also contribute to atherosclerosis by altering the homeostasis ofmonocyte subsets.The mobilization of classical Ly-6C high mono-cytes from bone marrow is severely impaired in the absence of CCR2,resulting in reduced numbers of this monocyte subset in blood,which could contribute to reduced atherosclerosis [84].Absence of CX3CR1affects survival of Ly-6C low monocytes,as CX3CR1seems to mediate growth factor like signals,resulting in reduced Ly-6C low monocyte levels [85].Notably,when the three chemokine pathways CCR2,CX3CR1and CCR5are simultaneously targeted,atherosclerosis in mice is almost completely abrogated [11].During atherosclerosis development in mice,the spleen supplements the haematopoietic function of the bone marrow by producing Ly-6C high monocytes that readily accumulate in the aorta [86].Interestingly,Dutta et al.have recently shown that the accu-mulation of Ly-6C high monocytes is accelerated in the aorta of mice subjected to a myocardial infarction by coronary ligation [87].These data indicate that myocardial infarction might be followed by a period of increased plaque growth and vulnerability fuelled by increased monocyte in ﬂammation,which may explain the high risk of recurring events in acute coronary syndrome patients,if mono-cyte behaviour is similar in humans.In humans at least three monocyte subsets can be de ﬁned by their expression of CD14and CD16[88].The classical (CD14þþCD16À)monocytes express CCR2,whereas non-classical (CD14þCD16þþ)monocytes and intermediate (CD14þþCD16þ)monocytes express higher levels of CX3CR1than the classical monocytes.Recently,Berg and co-workers reported that increased levels of classical CD14þþCD16Àmonocytes at baseline were asso-ciated with an increased risk for suffering cardiovascular events during a 15-year follow-up of 700subjects from the MalmöDiet and Cancer Study population cohort [89].The hazard ratioforFig.2.Possible immune targets for treatment of atherosclerosis in humans and immune targets important for monitoring disease and ef ﬁcacy of immune-modulatory therapies.H.Björkbacka et al./Atherosclerosis 227(2013)9e 1713suffering a cardiovascular event in the highest tertile of classical monocytes was 1.66compared to the lowest tertile even after adjustment for common risk factors.The classical monocytes did not,however,associate with the extent of atherosclerosis, measured as intima-media thickness(IMT),at baseline.In contrast, the percentage of monocytes expressing CD16was negatively associated to the extent of carotid atherosclerosis at baseline.This association was independent of other common risk factors.These data might indicate that different monocyte subsets have different biological functions in cardiovascular disease in humans. CD14þþCD16Àclassical monocytes might cause inﬂammation that weakens theﬁbrous cap covering plaques and thus be associated with increased risk of clinical events,whereas CD16-expressing monocytes might play a role in determining the size of the pla-que,perhaps even having a protective,or reparative,rather than plaque promoting function.Despite strong evidence in mice,Berg et al.found no association between CX3CR1and CCR2expression on monocytes and cardio-vascular risk[89].CCR5expression on non-classical CD14þCD16þþmonocytes was,however,negatively associated to carotid IMT. Although the association of chemokine receptor expression on monocytes to cardiovascular risk is poorly understood,the poten-tial to treat human disease by targeting chemokine receptors is emphasized by the fact that polymorphisms in the CX3CR1gene have been found to protect against atherosclerosis[90e92]. Evidence also indicates that a naturally occurring frame-shift mutation in the human CCR5gene is associated with lower carotid intima-media thickness in the common carotid artery and reduced cardiovascular disease risk[93].Also,in mice,pharmaco-logical intervention of several chemokine receptors has been proven to reduce atherosclerosis[94].In addition,it has been speculated that several approved chemokine receptor antagonists, such as CCR5antagonists approved for HIV therapy,could also limit plaque development[95].At present,clinical studies targeting atherosclerosis with these compounds have,however,not been initiated.7.Anti-inﬂammatory drugs and atherosclerosisThe fact that atherosclerosis is an inﬂammatory disease taken together with observations that inﬂammatory biomarkers such as CRP predicts risk for cardiovascular events[96]suggests the possibility of using anti-inﬂammatory drugs in prevention and treatment of the disease.However,in conﬂict with this notion the use of non-steroidal anti-inﬂammatory drugs(NSAID)has been found to be associated with increased cardiovascular risk[8,9]. Statins,on the other hand,have been shown to have an anti-inﬂammatory effect both systemically[97]as well as in athero-sclerotic plaques[98]which may explain part of their protective action.Novel approaches to target inﬂammation in atherosclerosis includes the Cardiovascular Inﬂammation Reduction Trial(CIRT)in which7000stable coronary artery disease patients with persistent elevation of CRP are allocated to either placebo or low-dose methotrexate[99]and the Cankinumab Anti-inﬂammatory Thrombosis Outcomes Study(CANTOS)in which17,200stable post-myocardial infarction patients with persistent elevation of CRP are allocated to either placebo or three different doses of the IL-1b neutralizing antibody Cankinumab[100].The lipoprotein phospholipase A2(Lp-PLA2)inhibitor darapladib represents another novel and interesting possibility to speciﬁcally inhibit plaque inﬂammation.Oxidized phospholipids in LDL are hydro-lysed by Lp-PLA2to generate pro-inﬂammatory lysophosphati-dylcholines(lysoPCs)[101]and recent studies have revealed very strong association between the levels of lysoPC and pro-inﬂammatory cytokines in human atherosclerotic plaques[102].Plasma levels of Lp-PLA2and its activity have also been shown to be independent predictors of cardiovascular risk in several epidemi-ological studies[103].Darapladib is currently being studied in two large phase III trials:STABILITY(Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial),involving15,828 patients with coronary heart disease[103]and SOLID-TIMI52(the Stabilization of Plaques Using Darapladib e Thrombolysis in Myocardial Infarction52Trial)which is estimated to include11,500 patients with acute coronary syndromes.8.Challenges facing the translation of experimentalimmune-modulation of atherosclerosis into clinical therapy Current therapies focussing on risk factor reduction such as lipid-lowering treatment have been shown to reduce the risk of ischaemic cardiovascular events by up to40%in randomized clin-ical trials[1].However,it is difﬁcult to achieve additional risk reduction by treating risk factors alone leaving the majority of treated subjects without sufﬁcient protection emphasizing the need for development of novel therapies directly targeting the atherosclerotic disease process in the plaque.Such therapies should preferentially act through speciﬁc inhibition of plaque inﬂamma-tion.Down-regulation of pro-inﬂammatory autoimmune response against plaque antigens represents a potential approach to achieve this.As discussed above several tolerogenic plaque-antigen vaccines have been shown to be effective in animal models of atherosclerosis.However,the challenge of translating these results into clinically effective therapies should not be underestimated. Most of the available knowledge of the role of immunity in atherosclerosis is based on studies performed in mice and our understanding of the importance of these disease mechanisms in humans remains limited.There is an urgent need for studies identifying and validating immune biomarkers for cardiovascular risk in man as well as to develop biomarkers that can be used to monitor the effect of atherosclerosis vaccines in clinical trials.Such biomarkers are likely to include plaque antigen-speciﬁc autoanti-bodies,antibodies against vaccine antigens and detailed charac-terization of circulating immune cells believed to be involved in the disease process.To obtain sufﬁcient sensitivity these biomarkers also need to be antigen speciﬁc.Although the key antigens in human atherosclerosis need to be moreﬁrmly established it will be relatively straight-forward to develop and standardize assays for determination of autoantibodies against these antigens.The same will most likely be true for vaccine antigens.However,the experi-ence from animal studies suggests that the mechanism of action of athero-protective immune therapies involves modulation of cellular immunity that may be equally or more important than modulation of humoral immunity.Characterization of different T cells,such as Th1and regulatory T cells usingﬂow cytometry is relatively simple in the young genetically identical mice with limited exposure to pathogens used in experimental atheroscle-rosis studies.However,this is considerably more difﬁcult in a clin-ical setting where the different T cell populations are much more heterogeneous.Such analysis should preferably also be done in an antigen-speciﬁc way, e.g.it should be possible to restrict the analysis to T cells speciﬁc for a certain atherosclerosis or vaccine antigens.Possible approaches to achieve this include ex vivo antigen challenge and the use of synthetic tetramer HLA-antigen constructs that binds only to T cells speciﬁc for that antigen. Important immune targets for monitoring disease and the efﬁcacy of immune-modulatory therapies are summarized in Fig.2.Another important limitation of the animal studies is that they with few exceptions have demonstrated effect of vaccines on early development of atherosclerosis rather than on the more clinically relevant advanced plaques.It is not unlikely that strategies forH.Björkbacka et al./Atherosclerosis227(2013)9e17 14。

cma自噬途径

cma自噬途径【原创版】目录1.CMA 自噬途径的概述2.CMA 自噬途径的过程3.CMA 自噬途径的关键分子4.CMA 自噬途径的功能和应用正文1.CMA 自噬途径的概述CMA（Chaperone-Mediated Autophagy）自噬途径，即 chaperone 介导的自噬，是一种细胞内的复杂生物过程。

CMA 自噬途径是细胞对内部物质进行周转的重要方式之一，它通过降解细胞内的损坏蛋白质、细胞器以及其他有害物质，来维持细胞内的稳态。

2.CMA 自噬途径的过程CMA 自噬途径主要分为以下几个步骤：（1）chaperone 的识别和结合：分子伴侣蛋白（chaperone）能够识别并结合到细胞内的受损蛋白质或其他有害物质上。

（2）chaperone-substrate 的复合物的形成：chaperone 与受损蛋白质或其他有害物质结合后，形成 chaperone-substrate 的复合物。

（3）复合物转运至溶酶体：chaperone-substrate 的复合物在细胞内的运输过程中，会遇到一种称为“自噬相关蛋白”（autophagy-related protein，简称 ARP）的分子，ARP 能够识别并结合到 chaperone 上，引导复合物转运至溶酶体。

（4）溶酶体内的降解：当复合物到达溶酶体后，溶酶体内的酸性环境以及水解酶会分解掉复合物中的受损蛋白质或其他有害物质，完成降解过程。

3.CMA 自噬途径的关键分子CMA 自噬途径涉及到多个关键分子，包括：（1）chaperone：是 CMA 自噬途径中的重要组成部分，能够识别并结合到受损蛋白质或其他有害物质上。

常见的 chaperone 有 HSP60、HSP70 等。

（2）ARP：自噬相关蛋白，能够识别并结合到 chaperone 上，引导复合物转运至溶酶体。

（3）溶酶体：是 CMA 自噬途径的最后一个环节，是降解复合物的场所。

4.CMA 自噬途径的功能和应用CMA 自噬途径在细胞内具有重要的生物学功能，包括：（1）维持细胞内稳态：通过降解细胞内的损坏蛋白质、细胞器以及其他有害物质，来维持细胞内的稳态。

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