J ANIM SCI-2007-Emmanuel-233-9
2017年影响因子-综合,

47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94
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ISSN 0001-0782 0001-1452 0001-1541 0001-2092 0001-2491 0001-2815 0001-2998 0001-3765 0001-4338 0001-4346 0001-4370 0001-4842 0001-4966 0001-5113 0001-5172 0001-5237 0001-527X 0001-5296 0001-5342 0001-5385 0001-5415 0001-5458 0001-5555 0001-5644 0001-5652 0001-5709 0001-5733 0001-5792 0001-5903 0001-5962 0001-5970 0001-6268 0001-6314 0001-6322 0001-6349 0001-6357 0001-6454 0001-6462 0001-6489 0001-6497 0001-6837 0001-690X 0001-6977 0001-7051 0001-70IROL 医学 病毒学 ACTA VIROLOGICA ACTA ZOOL-STOCKHOLM ACTA ZOOLOGICA 生物 动物学 GEN RELAT GRAVIT 物理 天文与天体物理 GENERAL RELATIVITY AND GRAVITATION AKTUEL UROL 医学 泌尿学与肾脏学 AKTUELLE UROLOGIE BEHAV GENET 生物 行为科学 BEHAVIOR GENETICS ADV APPL PROBAB 数学 统计学与概率论 ADVANCES IN APPLIED PROBABILITY ADV COLLOID INTERFAC ADVANCES IN COLLOID AND INTERFACE 化学 物理化学 SCIENCE ADV MATH 数学 数学 ADVANCES IN MATHEMATICS ADV PHYS 物理 物理:凝聚态物理 ADVANCES IN PHYSICS AEQUATIONES MATH MATHEMATICS APPLIED Aequationes Mathematicae MATHEMATICS, AERONAUT J 工程技术 工程:宇航 AERONAUTICAL JOURNAL AFINIDAD 化学 化学综合 AFINIDAD J NURS ADMIN 医学 护理 JOURNAL OF NURSING ADMINISTRATION AGE AGEING 医学 老年医学 AGE AND AGEING GER J AGR ECON AGRICULTURAL ECONOMICS & POLICY German Journal of Agricultural Economics AGR HIST 农林科学 科学史与科学哲学 AGRICULTURAL HISTORY AGROCHIMICA 农林科学 土壤科学 AGROCHIMICA AGRON J 农林科学 农艺学 AGRONOMY JOURNAL ALDRICHIM ACTA 化学 有机化学 ALDRICHIMICA ACTA ALGEBR LOG+ 数学 数学 Algebra and Logic ALGEBR UNIV 数学 数学 ALGEBRA UNIVERSALIS ALLG FORST JAGDZTG ALLGEMEINE FORST UND JAGDZEITUNG 农林科学 林学 AMBIX HISTORY & PHILOSOPHY OF SCIENCE Ambix AMEGHINIANA 地学 古生物学 AMEGHINIANA AM BIOL TEACH EDUCATION, SCIENTIFIC DISCIPLI AMERICAN BIOLOGY TEACHER BIOLOGY AM CERAM SOC BULL 工程技术 材料科学:硅酸盐 AMERICAN CERAMIC SOCIETY BULLETIN J AM CERAM SOC 工程技术 材料科学:硅酸盐 JOURNAL OF THE AMERICAN CERAMIC SOCIETY J AM CHEM SOC 化学 SOCIETY 化学综合 JOURNAL OF THE AMERICAN CHEMICAL J AM DENT ASSOC 医学 牙科与口腔外科 JOURNAL OF THE AMERICAN DENTAL ASSOCIATION T AM ENTOMOL SOC 生物 昆虫学 SOCIETY TRANSACTIONS OF THE AMERICAN ENTOMOLOGICAL AM FAM PHYSICIAN 医学:内科 AMERICAN FAMILY PHYSICIAN 医学 AM FERN J 生物 植物科学 AMERICAN FERN JOURNAL T AM FISH SOC 农林科学 渔业 TRANSACTIONS OF THE AMERICAN FISHERIES SOCIETY J AM GERIATR SOC 医学 老年医学 JOURNAL OF THE AMERICAN GERIATRICS SOCIETY AM HEART J 医学 心血管系统 AMERICAN HEART JOURNAL J AM HELICOPTER SOC JOURNAL OF THE AMERICAN HELICOPTER 工程技术 SOCIETY 工程:宇航 AM J AGR ECON 管理科学 农业经济与政策 AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS AM J BOT 植物科学 AMERICAN JOURNAL OF BOTANY 生物 AM J CARDIOL 医学 心血管系统 AMERICAN JOURNAL OF CARDIOLOGY AM J CLIN NUTR 医学 营养学 AMERICAN JOURNAL OF CLINICAL NUTRITION AM J CLIN PATHOL 医学 病理学 AMERICAN JOURNAL OF CLINICAL PATHOLOGY AM J ENOL VITICULT AMERICAN JOURNAL OF ENOLOGY农林科学 生物工程与应用微生物 AND VITICULTURE AM J EPIDEMIOL 医学 公共卫生、环境卫生与职业卫生 AMERICAN JOURNAL OF EPIDEMIOLOGY AM J GASTROENTEROL AMERICAN JOURNAL OF GASTROENTEROLOGY 医学 胃肠肝病学 AM J HUM GENET 生物 遗传学 AMERICAN JOURNAL OF HUMAN GENETICS AM J MATH 数学 数学 AMERICAN JOURNAL OF MATHEMATICS AM J MED 医学 医学:内科 AMERICAN JOURNAL OF MEDICINE AM J NURS 护理 AMERICAN JOURNAL OF NURSING医学
2013人文社科门类“最有学术影响力的国际期刊”目录发布

0142-6001 0146-6216 0887-6177 0003-990X 0004-0002 0004-0894 0004-4687 1352-2310 0001-8244 0005-7894 0140-525X 0005-7967 1086-3818 1366-7289 0269-9702 0301-0511 0169-3867 0006-8047 0093-934X 0141-1926 0007-0882 0144-6657 0007-1005 0007-1080 0007-1234 0007-1250 0007-1269 0144-6665 0045-3102 0142-5692 0007-1315 0007-2362 0360-1323 0007-5140 1052-150X 0007-6899 0008-1221 0706-7437 0889-4019 0009-3599 0145-2134 0009-3920 1324-9347 0305-7410 0969-5893 0272-7358 0010-0277 0269-9931 0737-0008 0264-3294 0010-0285
APPLIED LINGUISTICS APPLIED PSYCHOLOGICAL MEASUREMENT ARCHIVES OF CLINICAL NEUROPSYCHOLOGY ARCHIVES OF GENERAL PSYCHIATRY ARCHIVES OF SEXUAL BEHAVIOR AREA ASIAN SURVEY ATMOSPHERIC ENVIRONMENT BEHAVIOR GENETICS BEHAVIOR THERAPY BEHAVIORAL AND BRAIN SCIENCES, THE BEHAVIOUR RESEARCH AND THERAPY BERKELEY TECHNOLOGY LAW JOURNAL BILINGUALISM: LANGUAGE AND COGNITION BIOETHICS BIOLOGICAL PSYCHOLOGY BIOLOGY & PHILOSOPHY BOSTON UNIVERSITY LAW REVIEW BRAIN AND LANGUAGE BRITISH EDUCATIONAL RESEARCH JOURNAL BRITISH JOURNAL FOR THE PHILOSOPHY OF SCIENCE, THE BRITISH JOURNAL OF CLINICAL PSYCHOLOGY, THE BRITISH JOURNAL OF EDUCATIONAL STUDIES BRITISH JOURNAL OF INDUSTRIAL RELATIONS BRITISH JOURNAL OF POLITICAL SCIENCE BRITISH JOURNAL OF PSYCHIATRY, THE BRITISH JOURNAL OF PSYCHOLOGY BRITISH JOURNAL OF SOCIAL PSYCHOLOGY ,THE BRITISH JOURNAL OF SOCIAL WORK, THE BRITISH JOURNAL OF SOCIOLOGY OF EDUCATION BRITISH JOURNAL OF SOCIOLOGY,THE BROOKLYN LAW REVIEW BUILDING AND ENVIRONMENT BULLETIN OF THE HISTORY OF MEDICINE BUSINESS ETHICS QUARTERLY BUSINESS LAWYER ,THE CALIFORNIA LAW REVIEW CANADIAN JOURNAL OF PSYCHIATRY CAREER DEVELOPMENT QUARTERLY, THE CHICAGO-KENT LAW REVIEW CHILD ABUSE & NEGLECT CHILD DEVELOPMENT CHINA JOURNAL CHINA QUARTERLY,THE CLINICAL PSYCHOLOGY CLINICAL PSYCHOLOGY REVIEW COGNITION COGNITION & EMOTION COGNITION AND INSTRUCTION COGNITIVE NEUROPSYCHOLOGY COGNITIVE PSYCHOLOGY
大肠杆菌文献

1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK 2Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA 3Broad Institute of MIT and Harvard, Cambridge,Massachusetts, USA 4Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands 5Division ofGastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA 6Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA 7Cedars-Sinai F.Widjaja Inflammatory Bowel and Immunobiology Research Institute, Los Angeles, California, USA 8Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA9Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA10Inflammatory Bowel Disease Research Group, Addenbrooke’s Hospital, University ofCambridge, Cambridge, UK 11Department of Health Studies, University of Chicago, Chicago,Illinois, USA 12Department of Internal Medicine, Section of Digestive Diseases, Yale School of Medicine, New Haven, Connecticut, USA 13Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA 14University of Maribor,Faculty of Medicine, Center for Human Molecular Genetics and Pharmacogenomics, Maribor,Slovenia 15University Medical Center Groningen, Department of Genetics, Groningen, TheNetherlands 16Department of Pathophysiology, Gastroenterology section, KU Leuven, Leuven,Belgium 17Unit of Animal Genomics, Groupe Interdisciplinaire de Genoproteomique Appliquee (GIGA-R) and Faculty of Veterinary Medicine, University of Liege, Liege, Belgium 18Division of Gastroenterology, Centre Hospitalier Universitaire, Universite de Liege, Liege, Belgium19Department of Medical and Molecular Genetics, King’s College London School of Medicine,Guy’s Hospital, London, UK 20Division of Rheumatology Immunology and Allergy, Brigham and Women’s Hospital, Boston, Massachusetts, USA 21Program in Medical and Population Genetics,Broad Institute, Cambridge, Massachusetts, USA 22Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts, USA 23Université de Montréal and the Montreal Heart Institute,Research Center, Montréal, Québec, Canada 24Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA 25Department of Gastroenterology &Hepatology, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio 26Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA 27Peninsula College of Medicine and Dentistry, Exeter, UK 28Erasmus Hospital, Free University of Brussels,Department of Gastroenterology, Brussels, Belgium 29Massachusetts General Hospital, Harvard Medical School, Gastroenterology Unit, Boston, Massachusetts, USA 30Viborg Regional Hospital,Medical Department, Viborg, Denmark 31Inflammatory Bowel Disease Service, Department ofGastroenterology and Hepatology, Royal Adelaide Hospital, and School of Medicine, University of Adelaide, Adelaide, Australia 32Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany 33Department of Gastroenterology and Hepatology, Flinders Medical Centre and School of Medicine, Flinders University, Adelaide, Australia 34Division ofGastroenterology, McGill University Health Centre, Royal Victoria Hospital, Montréal, Québec,Canada 35Department of Medicine II, University Hospital Munich-Grosshadern, Ludwig-Maximilians-University, Munich, Germany 36Department of Gastroenterology, Charit, Campus Mitte, UniversitŠtsmedizin Berlin, Berlin, Germany 37Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, New York, USA 38Department of Genomics, Life & Brain Center, University Hospital Bonn, Bonn, Germany 39Department ofBiosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden 40Department of Pediatrics,Cedars Sinai Medical Center, Los Angeles, California, USA 41Torbay Hospital, Department ofGastroenterology, Torbay, Devon, UK 42School of Medical Sciences, Faculty of Medical & Health Sciences, The University of Auckland, Auckland, New Zealand 43University of Groningen,University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands 44Department of Medicine, University of Otago, Christchurch, New Zealand 45Department of $watermark-text $watermark-text $watermark-textGastroenterology, Christchurch Hospital, Christchurch, New Zealand 46Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for EnvironmentalHealth, Neuherberg, Germany 47St Mark’s Hospital, Watford Road, Harrow, Middlesex, HA1 3UJ 48Nottingham Digestive Diseases Centre, Queens Medical Centre, Nottingham NG7 1AW, UK 49Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway 50Kaunas University of Medicine, Department of Gastroenterology, Kaunas, Lithuania51Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA 52Unit of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo dellaSofferenza (IRCCS-CSS) Hospital, San Giovanni Rotondo, Italy 53Ghent University Hospital,Department of Gastroenterology and Hepatology, Ghent, Belgium 54School of Medicine andPharmacology, The University of Western Australia, Fremantle, Australia 55Gastrointestinal Unit,Molecular Medicine Centre, University of Edinburgh, Western General Hospital, Edinburgh, UK 56Department of Gastroenterology, The Townsville Hospital, Townsville, Australia 57Institute of Human Genetics, Newcastle University, Newcastle upon Tyne, UK 58Department of Medicine,Ninewells Hospital and Medical School, Dundee, UK 59Genetic Medicine, MAHSC, University of Manchester, Manchester, UK 60Academic Medical Center, Department of Gastroenterology,Amsterdam, The Netherlands 61University of Maribor, Faculty for Chemistry and Chemical Engineering, Maribor, Slovenia 62King’s College London School of Medicine, Guy’s Hospital,Department of Medical and Molecular Genetics, London, UK 63Royal Hospital for Sick Children,Paediatric Gastroenterology and Nutrition, Glasgow, UK 64Guy’s & St. Thomas’ NHS Foundation Trust, St. Thomas’ Hospital, Department of Gastroenterology, London, UK 65Department ofGastroenterology, Hospital Cl’nic/Institut d’Investigaci— Biomdica August Pi i Sunyer (IDIBAPS),Barcelona, Spain 66Centro de Investigaci—n Biomdica en Red de Enfermedades Hep‡ticas y Digestivas (CIBER EHD), Barcelona, Spain 67Christian-Albrechts-University, Institute of Clinical Molecular Biology, Kiel, Germany 68Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany 69Inflammatory Bowel Diseases, Genetics and Computational Biology, Queensland Institute of Medical Research, Brisbane, Australia 70Norfolk and Norwich University Hospital 71Department of Gastroenterology, Leiden University Medical Center, Leiden,The Netherlands 72Child Life and Health, University of Edinburgh, Edinburgh, Scotland, UK 73Institute of Human Genetics and Department of Neurology, Technische Universität München,Munich, Germany 74Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, USA 75Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany 76Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, USA 78Mount Sinai Hospital Inflammatory Bowel Disease Centre, University of Toronto, Toronto, Ontario, Canada 79Azienda Ospedaliero Universitaria (AOU) Careggi, Unit of Gastroenterology SOD2, Florence, Italy 80Center for Applied Genomics,The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA 81Department of Pediatrics, Center for Pediatric Inflammatory Bowel Disease, The Children’s Hospital ofPhiladelphia, Philadelphia, Pennsylvania, USA 82Meyerhoff Inflammatory Bowel Disease Center,Department of Medicine, School of Medicine, and Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA 83Department of Gastroenterology, Royal Brisbane and Womens Hospital, and School of Medicine, University of Queensland, Brisbane, Australia 84Inflammatory Bowel Disease Research Group, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK 85Division of Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium AbstractCrohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory boweldisease (IBD), affect over 2.5 million people of European ancestry with rising prevalence in otherpopulations 1. Genome-wide association studies (GWAS) and subsequent meta-analyses of CD and UC 2,3 as separate phenotypes implicated previously unsuspected mechanisms, such as autophagy 4,$watermark-text $watermark-text $watermark-textin pathogenesis and showed that some IBD loci are shared with other inflammatory diseases 5.Here we expand knowledge of relevant pathways by undertaking a meta-analysis of CD and UC genome-wide association scans, with validation of significant findings in more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional and balancing selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe striking overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.We conducted an imputation-based association analysis using autosomal genotype level data from 15 GWAS of CD and/or UC (Supplementary Table 1, Supplementary Figure 1). We imputed 1.23 million SNPs from the HapMap3 reference set (Supplementary Methods),resulting in a high quality dataset with reduced genome-wide inflation (Supplementary Figures 2, 3) compared with previous meta-analyses of subsets of these data 2,3. The imputed GWAS data identified 25,075 SNPs that had association p < 0.01 in at least one of the CD,UC or all IBD analyses. A meta-analysis of GWAS data with Immunochip 6 validation genotypes from an independent, newly-genotyped set of 14,763 CD cases, 10,920 UC cases,and 15,977 controls was performed (Supplementary Table 1, Supplementary Figure 1).Principal components analysis resolved geographic stratification, as well as Jewish and non-Jewish ancestry (Supplementary Figure 4), and significantly reduced inflation to a level consistent with residual polygenic risk, rather than other confounding effects (from λGC =2.00 to λGC = 1.23 when analyzing all IBD samples, Supplementary Methods,Supplementary Figure 5).Our meta-analysis of the GWAS and Immunochip data identified 193 statistically independent signals of association at genome-wide significance (p < 5×10−8) in at least one of the three analyses (CD, UC, IBD). Since some of these signals (Supplementary Figure 6)probably represent associations to the same underlying functional unit, we merged thesesignals (Supplementary Methods) into 163 regions, of which 71 are reported here for the first time (Table 1, Supplementary Table 2). Figure 1A shows the relative contributions of each locus to the total variance explained in UC and CD. We have increased the total disease variance explained (variance being subject to fewer assumptions than heritability 7) from8.2% to 13.6% in CD and from 4.1% to 7.5% in UC (Supplementary Methods). Consistent with previous studies, our IBD risk loci seem to act independently, with no significantevidence of deviation from an additive combination of log odds ratios.Our combined genome-wide analysis of CD and UC enables a more comprehensive analysis of disease specificity than was previously possible. A model selection analysis(Supplementary Methods 1d) showed that 110/163 loci are associated with both disease phenotypes; 50 of these have an indistinguishable effect size in UC and CD, while 60 show evidence of heterogeneous effects (Table 1). Of the remaining loci, 30 are classified as CD-specific and 23 as UC-specific. However, 43 of these 53 show the same direction of effect in the non-associated disease (Figure 1B, overall p=2.8×10−6). Risk alleles at two CD loci,PTPN22 and NOD2, show significant (p < 0.005) protective effects in UC, exceptions that may reflect biological differences between the two diseases. This degree of sharing ofgenetic risk suggests that nearly all the biological mechanisms involved in one disease play some role in the other.The large number of IBD associations, far more than reported for any other complexdisease, increases the power of network-based analyses to prioritize genes within loci. We investigated the IBD loci using functional annotation and empirical gene network tools$watermark-text$watermark-text$watermark-text(Supplementary Table 2). Compared with previous analyses which identified candidate genes in 35% of loci 2,3 our updated GRAIL 8 -connectivity network identifies candidates in 53% of loci, including increased statistical significance for 58 of the 73 candidates from previous analyses. The new candidates come not only from genes within newly identified loci, but also integrate additional genes from previously established loci (Figure 1C). Only 29 IBD-associated SNPs are in strong linkage disequilibrium (r 2 > 0.8) with a missense variant in the 1000 Genomes Project data, which reinforces previous evidence that a large fraction of risk for complex disease is driven by non-coding variation. In contrast, 64 IBD-associated SNPs are in linkage disequilibrium with variants known to regulate gene expression (Supplementary Table 2). Overall, we highlighted a total of 300 candidate genes in 125 loci, of which 39 contained a single gene supported by two or more methods.Seventy percent (113/163) of the IBD loci are shared with other complex diseases or traits,including 66 among the 154 loci previously associated with other immune-mediated diseases 9, which is 8.6 times the number that would be expected by chance (Figure 2A, p <10−16, Supplementary Figure 7). Such enrichment cannot be attributed to the immune-mediated focus of the Immunochip, (Supplementary Methods 4a(i), Supplementary Figure 8), since the analysis is based on our combined GWAS-Immunochip data. Comparing overlaps with specific diseases is confounded by the variable power in studies of different diseases. For instance, while type 1 diabetes (T1D) shares the largest number of loci (20/39,10-fold enrichment) with IBD, this is partially driven by the large number of known T1D associations. Indeed, seven other immune-mediated diseases show stronger enrichment of overlap, with the largest being ankylosing spondylitis (8/11, 13-fold) and psoriasis (14/17,14-fold).IBD loci are also markedly enriched (4.9-fold, p < 10−4) in genes involved in primary immunodeficiencies (PIDs, Figure 2A), which are characterized by a dysfunctional immune system resulting in severe infections 10. Genes implicated in this overlap correlate with reduced levels of circulating T-cells (ADA , CD40, TAP1/2, NBS1, BLM, DNMT3B ), or of specific subsets such as Th17 (STAT3), memory (SP110), or regulatory T-cells (STAT5B ).The subset of PIDs genes leading to Mendelian susceptibility to mycobacterial disease(MSMD)10–12 is enriched still further; six of the eight known autosomal genes linked to MSMD are located within IBD loci (IL12B , IFNGR2, STAT1, IRF8, TYK2 and STAT3,46-fold enrichment, p = 1.3 × 10−6), and a seventh, IFNGR1, narrowly missed genome-wide significance (p = 6 × 10−8). Overlap with IBD is also seen in complex mycobacterial disease; we find IBD associations in 7/8 loci identified by leprosy GWAS 13, including 6cases where the same SNP is implicated. Furthermore, genetic defects in STAT314–15and CARD916, also within IBD loci, lead to PIDs involving skin infections with staphylococcus and candidiasis, respectively. The comparative effects of IBD and infectious diseasesusceptibility risk alleles on gene function and expression is summarized in Supplementary Table 3, and include both opposite (e.g. NOD2 and STAT3, Supplementary Figure 9) and similar (e.g., IFNGR2) directional effects.To extend our understanding of the fundamental biology of IBD pathogenesis we conducted searches across the IBD locus list: (i) for enrichment of specific GeneOntology (GO) terms and canonical pathways, (ii) for evidence of selective pressure acting on specific variants and pathways, and (iii) for enrichment of differentially expressed genes across immune cell types. We tested the 300 prioritized genes (see above) for enrichment in GO terms(Supplementary Methods) and identified 286 GO terms and 56 pathways demonstrating significant enrichment in genes contained within IBD loci (Supplementary Table 4,Supplementary Figure 10,11). Excluding high-level GO categories such as “immune system processes” (p = 3.5 × 10−26), the most significantly enriched term is regulation of cytokine production (p=2.7×10−24), specifically IFNG-γ, IL-12, TNF-α, and IL-10 signalling.$watermark-text$watermark-text$watermark-textLymphocyte activation was the next most significant (p=1.8 × 10−23), with activation of T-,B-, and NK-cells being the strongest contributors to this signal. Strong enrichment was also seen for response to molecules of bacterial origin (p=2.4 × 10−20), and for KEGG’s JAK-STAT signalling pathway (p = 4.8 × 10−15). We note that no enriched terms or pathways showed specific evidence of CD- or UC-specificity.As infectious organisms are known to be among the strongest agents of natural selection, we investigated whether the IBD-associated variants are subject to selective pressures (Supplementary Methods, Supplementary Table 5). Directional selection would imply that the balance between these forces shifted in one direction over the course of human history,whereas balancing selection would suggest an allele frequency dependent-scenario typified by host-microbe co-evolution, as can be observed with parasites. Two SNPs show Bonferroni-significant selection: the most significant signal, in NOD2, is under balancing selection (p = 5.2 × 10−5), and the second most significant, in the receptor TNFRSF18,showed directional selection (p = 8.9 × 10−5). The next most significant variants were in the ligand of that receptor, TNFSF18 (directional, p = 5.2 × 10−4), and IL23R (balancing, p =1.5 × 10−3). As a group, the IBD variants show significant enrichment in selection (Figure 2B) of both types (p = 5.5 × 10−6). We discovered an enrichment of balancing selection (Figure 2B) in genes annotated with the GO term “regulation of interleukin-17 production”(p = 1.4 × 10−4). The important role of IL17 in both bacterial defense and autoimmunity suggests a key role for balancing selection in maintaining the genetic relationship between inflammation and infection, and this is reinforced by a nominal enrichment of balancing selection in loci annotated with the broader GO term “defense response to bacterium” (p =0.007).We tested for enrichment of cell-type expression specificity of genes in IBD loci in 223distinct sets of sorted, mouse-derived immune cells from the Immunological Genome Consortium 17. Dendritic cells showed the strongest enrichment, followed by weaker signals that support the GO analysis, including CD4+ T, NK and NKT cells (Figure 2C). Notably,several of these cell types express genes near our IBD associations much more specifically when stimulated; our strongest signal, a lung-derived dendritic cell, had p stimulated < 1×10−6compared with p unstimulated = 0.0015, consistent with an important role for cell activation.To further our goal of identifying likely causal genes within our susceptibility loci and to elucidate networks underlying IBD pathogenesis, we screened the associated genes against 211 co-expression modules identified from weighted gene co-expression networkanalyses 18, conducted with large gene expression datasets from multiple tissues 19–21. The most significantly enriched module comprised 523 genes from omental adipose tissuecollected from morbidly obese patients 19, which was found to be 2.9-fold enriched for genes in the IBD-associated loci (p = 1.1 × 10−13, Supplementary Table 6, Supplementary Figure12). We constructed a probabilistic causal gene network using an integrative Bayesian network reconstruction algorithm 22–24 which combines expression and genotype data toinfer the direction of causality between genes with correlated expression. The intersection of this network and the genes in the IBD-enriched module defined a sub-network of genes enriched in bone marrow-derived macrophages (p < 10−16) and is suggestive of dynamic interactions relevant to IBD pathogenesis. In particular, this sub-network featured close proximity amongst genes connected to host interaction with bacteria, notably NOD2, IL10,and CARD9.A NOD2-focused inspection of the sub-network prioritizes multiple additional candidate genes within IBD-associated regions. For example, a cluster near NOD2 (Figure 2D)contains multiple IBD genes implicated in M.tb response, including SLC11A1, VDR and LGALS9. Furthermore, both SLC11A1 (also known as NRAMP1) and VDR have been$watermark-text$watermark-text$watermark-textassociated with M.tb infection by candidate gene studies 25–26, and LGALS9 modulates mycobacteriosis 27. Of interest, HCK (located in our new locus on chromosome 20 at 30.75Mb) is predicted to upregulate expression of both NOD2 and IL10, an anti-inflammatory cytokine associated with Mendelian 28 and non-Mendelian IBD 29. HCK has been linked to alternative, anti-inflammatory activation of monocytes (M2 macrophages)30;while not identified in our aforementioned analyses, these data implicate HCK as the causal gene in this new IBD locus.We report one of the largest genetic experiments involving a complex disease undertaken to date. This has increased the number of confirmed IBD susceptibility loci to 163, most of which are associated with both CD and UC, and is substantially more than reported for any other complex disease. Even this large number of loci explains only a minority of thevariance in disease risk, which suggests that other factors such as rarer genetic variation not captured by GWAS or environmental exposures make substantial contributions topathogenesis. Most of the evidence relating to possible causal genes points to an essential role for host defence against infection in IBD. In this regard the current results focus ever closer attention on the interaction between the host mucosal immune system and microbes both at the epithelial cell surface and within the gut lumen. In particular, they raise the question, in the context of this burden of IBD susceptibility genes, as to what triggers components of the commensal microbiota to switch from a symbiotic to a pathogenic relationship with the host. Collectively, our findings have begun to shed light on thesequestions and provide a rich source of clues to the pathogenic mechanisms underlying this archetypal complex disease.METHODS SUMMARY We conducted a meta-analysis of GWAS datasets after imputation to the HapMap3reference set, and aimed to replicate in the Immunochip data any SNPs with p < 0.01. We compared likelihoods of different disease models to assess whether each locus was associated with CD, UC or both. We used databases of eQTL SNPs and coding SNPs in linkage disequilibrium with our hit SNPs, as well as the network tools GRAIL andDAPPLE, and a co-expression network analysis to prioritize candidate genes in our loci.Gene Ontology, ImmGen mouse immune cell expression resource, the TreeMix selection software, and a Bayesian causal network analysis were used to functionally annotate these genes.Supplementary MaterialRefer to Web version on PubMed Central for supplementary material.AcknowledgmentsWe thank all the subjects who contributed samples and the physicians and nursing staff who helped withrecruitment globally. UK case collections were supported by the National Association for Colitis and Crohn’s disease, Wellcome Trust grant 098051 (LJ, CAA, JCB), Medical Research Council UK, the Catherine McEwan Foundation, an NHS Research Scotland career fellowship (RKR), Peninsular College of Medicine and Dentistry,Exeter, the National Institute for Health Research, through the Comprehensive Local Research Network and through Biomedical Research Centre awards to Guy’s & St. Thomas’ National Health Service Trust, King’s College London, Addenbrooke’s Hospital, University of Cambridge School of Clinical Medicine and to theUniversity of Manchester and Central Manchester Foundation Trust. The British 1958 Birth Cohort DNA collection was funded by Medical Research Council grant G0000934 and Wellcome Trust grant 068545/Z/02, and the UK National Blood Service controls by the Wellcome Trust. The Wellcome Trust Case Control Consortium projects were supported by Wellcome Trust grants 083948/Z/07/Z, 085475/B/08/Z and 085475/Z/08/Z. North American collections and data processing were supported by funds to the NIDDK IBD Genetics Consortium which is funded by the following grants: DK062431 (SRB), DK062422 (JHC), DK062420 (RHD), DK062432 (JDR), DK062423(MSS), DK062413 (DPM), DK076984 (MJD), DK084554 (MJD and DPM) and DK062429 (JHC). Additional$watermark-text$watermark-text$watermark-textfunds were provided by funding to JHC (DK062429-S1 and Crohn’s & Colitis Foundation of America, Senior Investigator Award (5-2229)), and RHD (CA141743). KYH is supported by the NIH MSTP TG T32GM07205training award. Cedars-Sinai is supported by USPHS grant PO1DK046763 and the Cedars-Sinai F. Widjaja Inflammatory Bowel and Immunobiology Research Institute Research Funds, National Center for Research Resources (NCRR) grant M01-RR00425, UCLA/Cedars-Sinai/Harbor/Drew Clinical and Translational Science Institute (CTSI) Grant [UL1 TR000124-01], the Southern California Diabetes and Endocrinology Research Grant (DERC) [DK063491], The Helmsley Foundation (DPM) and the Crohn’s and Colitis Foundation of America (DPM). RJX and ANA are funded by DK83756, AI062773, DK043351 and the Helmsley Foundation. TheNetherlands Organization for Scientific Research supported RKW with a clinical fellowship grant (90.700.281) and CW (VICI grant 918.66.620). CW is also supported by the Celiac Disease Consortium (BSIK03009). This study was also supported by the German Ministry of Education and Research through the National Genome Research Network, the Popgen biobank, through the Deutsche Forschungsgemeinschaft (DFG) cluster of excellence‘Inflammation at Interfaces’ and DFG grant no. FR 2821/2-1. S Brand was supported by (DFG BR 1912/6-1) and the Else-Kröner-Fresenius-Stiftung (Else Kröner-Exzellenzstipendium 2010_EKES.32). Italian case collections were supported by the Italian Group for IBD and the Italian Society for Paediatric Gastroenterology, Hepatology and Nutrition and funded by the Italian Ministry of Health GR-2008-1144485. Activities in Sweden were supported by the Swedish Society of Medicine, Ihre Foundation, Örebro University Hospital Research Foundation, Karolinska Institutet, the Swedish National Program for IBD Genetics, the Swedish Organization for IBD, and the Swedish Medical Research Council. DF and SV are senior clinical investigators for the Funds for Scientific Research (FWO/FNRS) Belgium. We acknowledge a grant from Viborg Regional Hospital, Denmark. VA was supported by SHS Aabenraa, Denmark. We acknowledge funding provided by the Royal Brisbane and Women’s Hospital Foundation,National Health and Medical Research Council, Australia and by the European Community (5th PCRDT). We gratefully acknowledge the following groups who provided biological samples or data for this study: theInflammatory Bowel in South Eastern Norway (IBSEN) study group, the Norwegian Bone Marrow Donor Registry (NMBDR), the Avon Longitudinal Study of Parents and Children, the Human Biological Data Interchange and Diabetes UK, and Banco Nacional de ADN, Salamanca. This research also utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the NIDDK, NIAID, NHGRI, NICHD,and JDRF and supported by U01 DK062418. The KORA study was initiated and financed by the HelmholtzZentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ.References 1. Molodecky NA, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology. 2012; 142:46–54. [PubMed: 22001864]2. Anderson CA, et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing thenumber of confirmed associations to 47. Nat Genet. 2011; 43:246–252. [PubMed: 21297633]3. Franke A, et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. 2010; 42:1118–1125. [PubMed: 21102463]4. Khor BGA, Xavier RJ. Genetics pathogenesis of inflammatory bowel disease. Nature. 2011;474:307–317. [PubMed: 21677747]5. Cho JH, Gregersen PK. Genomics and the multifactorial nature of human autoimmune disease. N Engl J Med. 2011; 365:1612–1623. [PubMed: 22029983]6. Cortes A, Brown MA. Promise and pitfalls of the Immunochip. Arthritis Res Ther. 2011; 13:101.[PubMed: 21345260]7. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Geneticinteractions create phantom heritability. Proc Natl Acad Sci USA. 2012; 109:1193–1198. [PubMed:22223662]8. Raychaudhuri S, et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 2009; 5:e1000534.10.1371/journal.pgen.1000534 [PubMed: 19557189]9. Hindorff LA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009; 106:9362–9367. [PubMed:19474294]10. International Union of Immunological Societies Expert Committee on Primary I et al. Primaryimmunodeficiencies: 2009 update. J Allergy Clin Immunol. 2009; 124:1161–1178. [PubMed:20004777]$watermark-text $watermark-text$watermark-text。
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Last year in a multipart series pub lished in Current CorrterrtR (CP ), 1 we discussed the 500 papers most cited in the Science Citation Zndex” (SCF ) from 1961 to 1982. Each part of the series covered exactly 100 articles. The fifth portion summarized the data from these studies. Overall, methods papers in the life sciences predominated. To continue the series, we have collected data on the sixth group of 100 papers shown in Table 1. They are in alphabetic order by first author. These papers all received at least 861 citations, and the most cited received 935. Each article’s 1983 citation count is in parentheses in Table 1. These additional data were not used to determine which papers would be included in this study. But they provide us with some additional current information about the use of these classics. For example, the paper by Fay Ajzenberg-Selove and Tom Lauritsen on energy levels of light nuclei received 923 citations from 1961 to 1982, but was not cited at all in the 1983 literature. And the paper by P.M. Endt and C. van der Leun, University of Utrecht, the Netherlands, which also discussed energy levels of nuclei, with special emphasis on nuclear spectroscopy, was cited 865 times from 1961 to 1982 but only once in 1983. Both papers are review articles. The latter paper is the fourth version of their review. It received the bulk of its citations over a period of only
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D. G. V. Emmanuel, A. Jafari, K. A. Beauchemin, J. A. Z. Leedle and B. N. Ametajinflammatory response in feedlot steersinduces anSaccharomyces cerevisiae and Enterococcus faecium Feeding live cultures of doi: 10.2527/jas.2006-2162007, 85:233-239.J ANIM SCI /content/85/1/233the World Wide Web at:The online version of this article, along with updated information and services, is located onFeeding live cultures of Enterococcus faecium and Saccharomyces cerevisiaeinduces an inflammatory response in feedlot steersD.G.V.Emmanuel,*A.Jafari,*1K.A.Beauchemin,†J.A.Z.Leedle,‡2and B.N.Ametaj*3*Department of Agricultural,Food and Nutritional Science,University of Alberta,Edmonton,Canada T6G 2P5;†Research Center,Agriculture and Agri-Food Canada,Lethbridge,Canada T1J 4B1;and ‡Chr.Hansen Inc.,Milwaukee,WI 53214ABSTRACT:Two experiments were conducted to in-vestigate the effects of oral supplementation of the lac-tic-acid-producing bacterium Enterococcus faecium EF212alone or in combination with Saccharomyces cer-evisiae (yeast)on mediators of the acute phase response in feedlot steers.Eight fistulated steers were used to study the effects of E.faecium alone or with yeast in a crossover design with 2Latin squares,4steers within each square,and 2periods.The length of each period was 3wk,with a 10-d adaptation and an 11-d measure-ment period.The experimental diet contained 87%steam-rolled barley,8%whole-crop barley silage,and 5%supplement (DM basis).In Exp.1,treatments were control vs.the lactic-acid-producing bacterium E.fae-cium (6×1010cfu/d).In Exp.2,treatments were control vs.E.faecium (6×1010cfu/d)and S.cerevisiae (6×1010cfu/d).The bacteria and yeast supplements were blended with calcium carbonate to supply 6×1010cfu/d when top-dressed into the diet once daily at the time Key words:acute phase protein,direct-fed microbial,feedlot steer,probiotic,yeast©2007American Society of Animal Science.All rights reserved.J.Anim.Sci.2007.85:233–239doi:10.2527/jas.2006-216INTRODUCTIONThere is growing interest in feeding direct-fed micro-bials (DFM )to cattle to improve digestion and enhance BW gain,as well as to prevent acidosis and outbreaks of foodborne pathogens.For example,Enterococcus fae-cium ,a common DFM strain was reported to reduce the risk of acidosis when fed to dairy cows (Nocek et al.,2002).In addition,E.faecium and other lactic acid-producing bacteria reduce fecal shedding of important enteropathogens like Escherichia coli O157:H7,Salmo-1Present address:Isfahan University of Technology,Isfahan,Iran 84156.2Present address:JL Microbiology Inc.,Hartland,WI.3Corresponding author:burim.ametaj@ualberta.ca Received April 5,2006.Accepted July 26,2006.233of feeding (10g/d).Steers fed the control diet received only carrier (10g/d).Blood samples were collected from the jugular vein on d 17and 21of each period,and serum amyloid A (SAA),lipopolysaccharide binding protein (LBP),haptoglobin,and alpha 1-acid glycopro-tein (α1-AGP)were measured.Supplementation of feed with E.faecium had no effect on concentrations of SAA,LBP,haptoglobin,or α1-AGP in plasma compared with those of controls.However,feeding E.faecium and yeast increased (P =0.02)plasma concentrations of SAA,LBP,and haptoglobin but had no effect on plasma α1-AGP.In conclusion,oral supplementation of E.faecium alone had no effect on the mediators of the acute phase response that were measured,whereas feeding of E.faecium and yeast induced an inflammatory response in feedlot steers fed high-grain diets.Further research is warranted to determine the mechanism(s)by which E.faecium and yeast stimulated production of acute phase proteins in feedlot steers.nella ,shigella,and clostridia (Lewenstein,et al.,1979;Zhao et al.,1998;Ohya et al.,2000).Similarly,the yeast Saccharomyces cerevisiae stimulates cellulolytic and lactate-utilizing bacteria and improves weight gain in beef cattle (Yoon and Stern,1996).Therefore,live cultures of E.faecium and S.cerevisiae might be useful to improve animal health.Although the influence of DFM and other probiotic bacteria on blood chemistry,ruminal acidosis,ruminal microflora,BW gain,digestion,and feed intake has been studied in feedlot steers (Ghorbani et al.,2002;Beauchemin et al.,2003),little information is available concerning their effect on the immune system.Several studies performed in other animal models show that live DFM are capable of modulating the innate and acquired immunity at the local and systemic level (Iso-lauri et al.,2001).For example,oral administration of E.faecium stimulated the mucosal and systemic im-Emmanuel et al. 234mune responses in young dogs with increased produc-tion of immunoglobulin A(Benyacoub et al.,2003).Sim-ilarly,a short-term oral administration of S.cerevisiae resulted in enhanced resistance of mice toward infec-tions with Klebsiella pneumoniae,Streptococcus pneu-moniae,and Streptococcus pyogenes(Bizzini and Fattal-German,1990).Activation of the immune system in conditions like inflammation,tissue injury,and infection is associated with release of acute phase proteins by the liver,known as the acute phase response(Suffredini et al.,1999). The acute phase proteins commonly studied in cattle are serum amyloid A(SAA),lipopolysaccharide binding protein(LBP),haptoglobin,and alpha1-acid glycopro-tein(α1-AGP;Ametaj et al.,2005;Gozho et al.,2005). Although the favorable effects of DFM in modulating the different aspects of metabolism and production have been studied in feedlot cattle,little attention has been paid to their immunomodulatory effects.Therefore,the objective of this study was to investigate effects of feed-ing E.faecium alone or in combination with S.cerevisiae on selected mediators of acute phase response in beef cattle fed high proportions of grain.MATERIALS AND METHODS Animals and TreatmentsAs previously reported by Beauchemin et al.(2003), 8cannulated steers(Exp.1BW=507±45kg;Exp.2 BW=538±46kg)were used in2experiments.Steers were kept in individual stalls bedded with rubber mats and cared for according to the guidelines of the Cana-dian Council on Animal Care(1993).The experimental design was a2×2Latin square with2squares,4 steers within each square,2periods,and2diets in each experiment.The squares within each experiment were conducted concurrently,and experiments were run con-secutively.The length of each period was21d,which was divided into a10-d adaptation and an11-d mea-surement.To minimize carry over effects from period to period, on the last day of periods1and2,the rumen of each steer was emptied manually,and the contents were placed into the rumen of the next steer within the square that was to receive that treatment.Thus,each steer began the period with rumen contents correspond-ing to the same treatment it was fed.In Exp.1,steers were fed a diet that was top-dressed with the control treatment(carrier)or E.faecium EF212;and in Exp.2,steers were fed a diet that was top-dressed with the control treatment(carrier)or E. faecium EF212with S.cerevisiae(yeast).The bacteria and yeast were blended with calcium carbonate(car-rier)to supply6×109cfu of bacteria or yeast/g of carrier. The diet of each steer was top-dressed with blend or carrier once daily at the time of feeding(10g/d).Both E.faecium EF212and S.cerevisiae were supplied by Chr.Hansen Inc.(Milwaukee,WI).The viability of the Table1.Ingredients and chemical composition of the total mixed diet(DM basis)Item% Ingredient1Barley silage2,37.94 Barley,steam-rolled487.13 Barley,ground0.97 Canola meal 1.52 Calcium carbonate 1.85 Trace mineral/vitamin mix50.05 Salt0.54 Chemical compositionME allowed gain,6kg/d 2.01 NE g,6Mcal/kg 1.17 NE m,6Mcal/kg 1.80 OM,%94.20 CP,%12.50 MP allowed gain,6kg/d 1.53 NDF,%22.00 Effective NDF,%8.09 ADF,%10.20 1All ingredients pelleted,excluding steam-rolled barley and silage. 2Physical effectiveness was66%,measured as the sum of the pro-portion of sample retained on the top(4.2%)and bottom(61.9%) sieves of the Pennsylvania State University particle separator.3Composition was37.1%DM,12.3%CP,45.8%NDF,28.3%ADF, and4.8%lignin based on4samples composited by period.4Processing index,calculated as the volume weight(DM basis)of the barley after processing,expressed as a percentage of its volume weight(DM basis)before processing,was79%.5Supplied per kilogram of DM of diet:15mg of Cu;63mg of Zn; 27mg of Mn;0.65mg of Io;0.2mg of Co;0.3mg of Se;4,200IU of vitamin A;415IU of vitamin D;and13IU of vitamin E.6Estimated from the NRC(1996).Animal BW used in the model was450kg.preparations was tested by Chr.Hansen Inc.before beginning the experiments.Experimental diets were formulated based on the NRC requirements(1996)to meet or exceed the CP,effectivefiber,mineral,and vitamin needs for cattle weighing450kg and gaining 1.5kg/d(Table1).A feed mixer was used for preparing the diet each day.The diet was fed once a day at0900. Feed and water were available ad libitum,and orts were approximately10%of the diet.Blood Sampling and Laboratory AnalysesBlood samples were obtained from each steer on d17 and21of each period.The reason for choosing these sampling days was the time needed for the host to re-spond to the feeding of DFM.At5h after feeding,blood samples were collected from the jugular vein into10-mL vacuum tubes containing Na-heparin(Vacutainer, Becton Dickinson,Franklin Lakes,NJ).Samples were centrifuged(5,000×g,20min,4°C)within20min, and plasma was collected,immediately placed on ice, transported to the laboratory,and frozen at−20°C un-til analysis.Concentrations of SAA in the plasma were deter-mined by commercially available bovine ELISA kits (Tridelta Development Ltd.,Greystones Co.,Wicklow, Ireland)according to the manufacturer’s instructionsDirect-fed microbials and acute phase response235and as described by McDonald et al.(1991).All samplesincluding standards were tested in duplicate.Sampleswere initially diluted1:500.Optical density values wereread on a microplate spectrophotometer(model SpectraMax190,Molecular Devices Corporation,Sunnyvale,CA)at450nm.The intra-and interassay CV were below10%.According to the manufacturer,the detection limitof the assay was0.30g/mL.Concentrations of haptoglobin in plasma were deter-mined by bovine ELISA kits(Tridelta DevelopmentLtd.),as described by Godson et al.(1996),using a poolof bovine serum as the standard.All samples includingstandards were tested in duplicate.Optical density val-ues were read on the Spectra Max190microplate spec-trophotometer at630nm.The intra-and interassay CVwere below10%,and the detection limit of the assaywas at0.05g/mL.Concentrations ofα1-AGP in plasma were measuredwith bovine radial immunodiffusion(RID)assay kits(Tridelta Development Ltd.).Single RID assays wereprepared to measure plasma concentrations ofα1-AGP.Calibrators and samples were applied to wells in5.0-L volumes.Plates were placed in humidified chambers at37°C and allowed to incubate for24h before readingthe test results.For the calibrators,a plot of the diame-ter squared on the y-axis and the concentration of theantigen on the x-axis,gave a linear function,as de-scribed previously by Mancini et al.(1965).On the basisof this linear function,sample concentrations were cal-culated.The intra-and interassay CV were below4%,and the detection limit of the assay was at50g/mL.Concentrations of LBP in the plasma were deter-mined with a commercially available multispeciesELISA kit that crossreacts with bovine LBP(Cell Sci-ences Inc.,Norwood,MA).Plasma samples were ini-tially diluted1:1,500,and samples with optical densityvalues lower than the range of the standard curve werediluted1:1,200and reassayed according to the manu-facturer’s instructions.The optical density at450nmwas measured on the Spectra Max190microplate spec-trophotometer.The intra-and interassay CV were be-low10%,and the detection limits of the assay were1.6to100ng/mL.The concentration of LBP was calculatedby extrapolating from a standard curve of knownamounts of human LBP.Statistical AnalysesData were analyzed using the MIXED procedure(SAS Inst.Inc.,Cary,NC)with thefirst autoregressivecovariance structure.For variables measured overtime,the model included treatment,day,and the2-way interaction asfixed effects.The random effectswere square,steer within square,and period.Periodwithin square was not considered in the model becauseboth squares were conducted simultaneously,and thusthe effect of period was considered to be the same forboth squares.The REML method was used to estimatethe variance components,and the Bayesian information criterion was used to determine the bestfitting model,whereas the Kenward-Roger method was used to ap-proximate the denominator degree of freedom.Data forsampling time were analyzed as repeated measures.Significance was declared at P<0.05.RESULTS AND DISCUSSION Previously,we showed the metabolic and productiveaspects of feeding E.faecium alone or combined withyeast in feedlot steers(Beauchemin et al.,2003).Otherresearchers also have investigated the productive as-pects of supplementation of E.faecium and yeast indairy cattle(Krehbiel et al.,2003;Nocek et al.,2003).To our knowledge,however,this is thefirst study toevaluate immunomodulatory effects of DFM in cattle.Results of Exp.1showed no significant overall treat-ment effects of feeding feedlot steers E.faecium onplasma concentrations of SAA,LBP,haptoglobin,andα1-AGP(Table2).No significant differences were ob-served in the concentrations of SAA,LBP,haptoglobin,andα1-AGP in blood collected on d17vs.21betweencontrols and those supplemented with E.faecium.Al-though we did notfind treatment effects in Exp.1forthe concentrations of SAA in plasma,values were con-sistent with the value of29g/mL that was reportedrecently for healthy steers(Tourlomoussis et al.,2004).The SAA values for our control steers were about40g/mL and about35g/mL in steers fed E.faecium (Table2).In contrast,results of Exp.2,in which steerswere supplemented with E.faecium and yeast,showedelevated concentrations of SAA in plasma comparedwith control steers(P=0.02;Figure1).No significantday effect or treatment×day interaction was obtainedfor concentrations of SAA in plasma in Exp.2(Figure1).Serum amyloid A is a protein produced by the liverand is associated with high-density lipoproteins in theplasma.Although the precise physiological role of SAAin the host defense mechanism is not well understood,SAA is involved in binding,neutralization,and rapidremoval of endotoxin from circulation(Baumberger etal.,1991).Production and release of SAA from liverhepatocytes is stimulated by cytokines IL-1,IL-6,andTNF-αsecreted by activated liver macrophages afterremoval of endotoxin from circulation(Watanabe et al.,2000;Elam et al.,2003).The mechanism by which addi-tion of yeast to E.faecium enhanced production of SAAby the liver is not well understood;however,some of thecontributing factors might include cytokines producedlocally by gastrointestinal immune cells or the translo-cation of yeast antigenic compounds such as glucan ormannan into the bloodstream and subsequent activa-tion of liver macrophages.Recent research indicatesthat glucan and mannan derived from S.cerevisiae in-duce production of TNF-αby monocytes(Tada et al.,2002;Majtan et al.,2005).These data are thefirst reported on concentrationsof LBP in feedlot steers.In clinically healthy Holsteindairy cows in midlactation,plasma LBP was reportedEmmanuel et al.236Table 2.Acute phase proteins in the plasma of feedlot steers with or without Enterococcus faecium (n =8;Exp.1)Day 2Treatment 11721P -valueItemCont. E.faec.Cont. E.faec.Cont. E.faec.SEM Treatment Day T ×D 3SAA,g/mL 41.035.139.036.542.933.720.90.220.900.47LBP,g/mL 21.315.712.420.530.310.87.20.570.660.30Hp,g/mL2732242702462752012170.360.710.63α1-AGP,g/mL6646826766966516682590.210.900.461Eight cannulated steers were used in a 2×2Latin square design.Steers were fed a diet that was top-dressed with either the control treatment (Cont.)or E.faecium EF212(E.faec.).The bacteria were blended with calcium carbonate (carrier)to supply 6×109cfu of bacteria per gram of carrier.The diet of each steer was top-dressed with blend or carrier,once daily at the time of feeding (10g/d).Enterococcus faecium EF212was supplied by Chr.Hansen Inc.(Milwaukee,WI).2Blood samples were obtained from each steer on d 17and 21of each period and analyzed for plasma serum amyloid A (SAA),lipopolysaccharide binding protein (LBP),haptoglobin (Hp),and alpha 1-acid glyco-protein (α1-AGP).3T ×D =Treatment ×day.to be approximately 37g/mL (Bannerman et al.,2003).The same authors reported that within 8h of adminis-tering lipopolysaccharide into the blood of dairy cows,plasma LBP increased more than 3.5-fold,reaching av-erage values of 137g/mL and remaining high during the entire 72h of the experimental period (Bannerman et al.,2003).Feedlot steers in our experiment had LBP values greater than 20g/mL 21d after feeding a diet with a high proportion of grain.Addition of E.faecium in the diet had no effect on LBP concentrations in plasma.In contrast to results of Exp.1,when E.faecium and yeast were fed inExp.2,a treatment ×day interactionFigure 1.Least squares means ±SEM (Exp.2)of plasma serum amyloid A (SAA)in control steers supplemented with 10g/d of calcium carbonate (solid bars)and steers supplemented with Enterococcus faecium EF212and Sa-charomyces cerevisiae (open bars;n =8/group)at 6×109cfu/d for 11d in a 2×2Latin square experiment (10-d adaptation and 11-d measurements).a,b Means with differ-ent superscripts differ,P =0.02.for plasma concentrations of LBP (P =0.02;Figure 2)was detected.An effect also was observed for concentra-tions of LBP between controls and steers treated with E.faecium and yeast on d 21of the experiment (P <0.05).The LBP is a liver-derived acute phase protein that is implicated in modulating host responses to endo-toxin from gram-negative bacteria.The protein inter-acts with circulatory endotoxin to form complexes that bind to CD14,which facilitates binding and activation of TLR4/MD-2complex on cells of the monocytic lineage and neutrophils,resulting in their activation (Fitzger-ald et al.,2004).This triggers release of cytokines,which areresponsible for initiating the acute phaseFigure 2.Least squares means ±SEM (Exp.2)of plasma lipopolysaccharide binding protein (LBP)in control steers supplemented with carrier (solid bars)and steers supple-mented with Enterococcus faecium EF212and Sacharomyces cerevisiae (open bars;n =8/group)at 6×109cfu/d for 11d in a 2×2Latin square experiment (10-d adaptation and 11-d measurements).a,b Means with different super-scripts differ,P =0.02.Direct-fed microbials and acute phase response237Figure3.Least squares means±SEM(Exp.2)of plasma haptoglobin in control steers supplemented with carrier (solid bars)and steers supplemented with Enterococcus faecium EF212and Sacharomyces cerevisiae(open bars;n= 8/group)at6×109cfu/d for11d in a2×2Latin square experiment(10-d adaptation and11-d measurements). a,b Means with different superscripts differ,P=0.01. response(Moshage,1997).Lipopolysaccharide binding protein also facilitates transferring of endotoxin to lipo-proteins and its rapid removal from circulation by the liver(Kitchens and Thompson,2003).Increased plasma concentrations of LBP in our steers support the hypoth-esis that feeding yeast may increase translocation of endotoxin,or yeast-derived antigenic compounds like glucans and mannans,or both.In support of this hy-pothesis are results showing that enhanced production of TNF-αby monocytes stimulated with the S.cerevisiae membrane-product mannan required presence of LBP (Tada et al.,2002).Tourlomoussis et al.(2004)reported haptoglobin con-centration of110g/mL in plasma of healthy beef cat-tle;however,cattle under different pathological condi-tions had average plasma haptoglobin values of approx-imately270g/mL.Results of Exp.1showed haptoglobin concentrations of about270g/mL in con-trol steers and about225g/mL in steers supplemented with E.faecium(Table2).Elevated plasma haptoglobin values suggest translocation of bacteria into the blood-stream of feedlot steers fed high proportions of grain.In our study,E.faecium supplementation reduced plasma haptoglobin values.Further,Gozho et al.(2005)re-ported increased concentrations of endotoxin in the ru-men of male Jersey cattle fed high-grain diets and that this was associated with elevated concentrations of hap-toglobin in plasma.Endotoxin is a cell-wall component of gram-negative bacteria and,when released in great amounts,has been documented to affect gut mucosal barrier functions and subsequent translocation of bac-teria and bacterial products(Deitch,1990). Treatment affected plasma concentration of hapto-globin in steers fed E.faecium and yeast(P<0.01; Figure3);however,no effect of day or treatment×dayFigure4.Least squares means±SEM(Exp.2)of alpha1-acid glycoprotein(α1-AGP)in control steers supple-mented with carrier(solid bars)and steers supplemented with Enterococcus faecium EF212and Sacharomyces cerevis-iae(open bars;n=8/group)at6×109cfu/d for11d in a2×2Latin square experiment(10-d adaptation and11-d measurements).interaction was observed(Figure3).Typically,concen-trations of haptoglobin in plasma are low but increase when there is an inflammatory response and transloca-tion of bacteria into the bloodstream(Deignan et al., 2000).By binding to hemoglobin,haptoglobin prevents utilization of iron in the hemoglobin by bacteria translo-cated into the bloodstream(Wassell,2000).Thus,the greater plasma haptoglobin concentration in steers fed yeast might be due to increased translocation of bacte-ria into the bloodstream.The mechanism by which yeast increases translocation of bacteria is not well un-derstood and remains to be elucidated.This is thefirst report on concentrations ofα1-AGP in plasma of feedlot steers.In healthy dairy cows,Ta-mura et al.(1989)obtained plasma values ofα1-AGP of approximately283g/mL.In addition,cows suffering from traumatic pericarditis,arthritis,mastitis,pneu-monia,and mesenteric liponecrosis hadα1-AGP values at or greater than450g/mL(Tamura et al.,1989). When steers were supplemented with E.faecium alone (Exp.1),results showed values of plasmaα1-AGP greater than600g/mL(Table2).Elevated concentra-tions ofα1-AGP in control and experimental animals in our experiment suggest that feeding high proportions of grain solicits an inflammatory condition in feedlot steers.Concentrations of the acute phase proteinα1-AGP also were elevated(greater than600g/mL)in plasma of all steers in Exp.2,and again,no differences were found between controls and steers fed E.faecium and yeasts(Figure4).Elevated concentrations of this acute phase protein are again indicative of an inflammatory response in feedlot steers fed E.faecium and yeast.As previously stated(Beauchemin et al.,2003),in Exp.1, 6of the8steers in period1and5of the8steers inEmmanuel et al. 238period2experienced subclinical ruminal acidosis.In Exp.2,5steers experienced subclinical ruminal acido-sis in period1and4in period2(Beauchemin et al., 2003).Prolonged exposure of the ruminal epithelium to high acid concentrations(i.e.,acidosis)can result in inflammation of the rumen wall(i.e.,rumenitis)and then to hyperkeratosis and parakeratosis(Fell and Weekes,1975).Alpha1-acid glycoprotein is produced by the liver to control inappropriate or extended activation of the immune system(Fournier et al.,2000).In a previous publication involving metabolic and production aspects of the same experiments,we re-ported that steers supplemented with E.faecium had almost4-fold greater total number of coliform bacteria in the feces than those of controls(16×106vs.3.8×106cfu/g)(Beauchemin et al.,2003).This effect was negated,however,when yeast was provided.Interest-ingly,data reported in this paper showed that supple-menting diets of the same feedlot steers with E.faecium alone did not elicit an acute phase response;however, supplementing E.faecium and yeast was associated with increased concentrations of acute phase proteins in the plasma.The reason for elevated production of acute phase proteins when E.faecium and yeast were fed to steers is not well understood at present and may be due to lysis of coliform bacteria when yeast is added. Yeast is known to support gram-positive bacteria, which in turn might produce bacteriocins,antibiotic-like substances that can kill certain gram-negative bac-teria(Nes and Holo,2000).Dead gram-negative bacte-ria release endotoxin,and the latter may transfer into the bloodstream and stimulate production of cytokines and increase gut permeability to gastrointestinalflora (Deitch,1990).Enhanced production of proinflamma-tory cytokines like IL-1,TNF-α,and IL-6has been re-ported following intake of probiotics(Wold,2001).The proinflammatory cytokines stimulate hepatocytes to se-crete acute phase proteins(Gruys et al.,2005).In conclusion,results reported in this study show for thefirst time that feeding live probiotic bacteria such as E.faecium to feedlot steers under high-grain diet for a period of11d had no effects on acute phase proteins measured(i.e.,SAA,LBP,haptoglobin,andα1-AGP). On the other hand,feeding a combination of E.faecium and S.cerevisiae increased concentrations of SAA,LBP, and haptoglobin in the plasma of experimental animals. 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