EBO,11,197-212,Genomics-and-Evolution-in-Traditional-Medicinal-Plants-Road-to-a-Heal.pdf_6833
LetPub-JCR2012-期刊
0239-7528 1851-2372 1224-2780 2146-3123 1860-5397 1876-2883 1757-6180 1369-703X 1934-8630 0791-7945 1996-3599 0324-1130 0525-1931 0144-8617 0008-6223 1897-5593 1664-3828 1634-0744 1842-4090 2044-4753 0920-5861 1933-6918 2045-3701 1478-811X 2228-5806 2211-1247 0008-8846 1895-1066 1733-7178 2081-9900 1868-4904 0008-8994 0272-8842 0862-5468 0173-9913 1584-8663 0352-9568 1612-1872 1752-153X 1359-7345 0275-7540 0098-6445 1385-8947 0009-2347 0255-2701 0360-7275 0263-8762 0009-2509 0930-7516 0009-2460 0009-3122 1451-9372 0009-3068 0251-0790
B POL ACAD SCI-TECH B SOC ARGENT BOT BALK J GEOM APPL BALK MED J BEILSTEIN J ORG CHEM BENEF MICROBES BIOANALYSIS BIOCHEM ENG J BIOINTERPHASES BIOL ENVIRON BUILD SIMUL-CHINA BULG CHEM COMMUN BUNSEKI KAGAKU CARBOHYD POLYM CARBON CARDIOL J CARDIORENAL MED CARNETS GEOL CARPATH J EARTH ENV CATAL SCI TECHNOL CATAL TODAY CELL ADHES MIGR CELL BIOSCI CELL COMMUN SIGNAL CELL J CELL REP CEMENT CONCRETE RES CENT EUR J CHEM CENT EUR J ENERG MAT CENT EUR J GEOSCI CENT EUR NEUROSURG CENTAURUS CERAM INT CERAM-SILIKATY CFI-CERAM FORUM INT CHALCOGENIDE LETT CHEM BIOCHEM ENG Q CHEM BIODIVERS CHEM CENT J CHEM COMMUN CHEM ECOL CHEM ENG COMMUN CHEM ENG J CHEM ENG NEWS CHEM ENG PROCESS CHEM ENG PROG CHEM ENG RES DES CHEM ENG SCI CHEM ENG TECHNOL CHEM ENG-NEW YORK CHEM HETEROCYCL COM+ CHEM IND CHEM ENG Q CHEM IND-LONDON CHEM J CHINESE U
Analysis of Genetic Diversity and Population Structure
Agricultural Sciences in China2010, 9(9): 1251-1262September 2010Received 30 October, 2009 Accepted 16 April, 2010Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of ChinaLIU Zhi-zhai 1, 2, GUO Rong-hua 2, 3, ZHAO Jiu-ran 4, CAI Yi-lin 1, W ANG Feng-ge 4, CAO Mo-ju 3, W ANG Rong-huan 2, 4, SHI Yun-su 2, SONG Yan-chun 2, WANG Tian-yu 2 and LI Y u 21Maize Research Institute, Southwest University, Chongqing 400716, P.R.China2Institue of Crop Sciences/National Key Facility for Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences,Beijing 100081, P.R.China3Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, P.R.China4Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100089, P.R.ChinaAbstractUnderstanding genetic diversity and population structure of landraces is important in utilization of these germplasm in breeding programs. In the present study, a total of 143 core maize landraces from the South Maize Region (SR) of China,which can represent the general profile of the genetic diversity in the landraces germplasm of SR, were genotyped by 54DNA microsatellite markers. Totally, 517 alleles (ranging from 4 to 22) were detected among these landraces, with an average of 9.57 alleles per locus. The total gene diversity of these core landraces was 0.61, suggesting a rather higher level of genetic diversity. Analysis of population structure based on Bayesian method obtained the samilar result as the phylogeny neighbor-joining (NJ) method. The results indicated that the whole set of 143 core landraces could be clustered into two distinct groups. All landraces from Guangdong, Hainan, and 15 landraces from Jiangxi were clustered into group 1, while those from the other regions of SR formed the group 2. The results from the analysis of genetic diversity showed that both of groups possessed a similar gene diversity, but group 1 possessed relatively lower mean alleles per locus (6.63) and distinct alleles (91) than group 2 (7.94 and 110, respectively). The relatively high richness of total alleles and distinct alleles preserved in the core landraces from SR suggested that all these germplasm could be useful resources in germplasm enhancement and maize breeding in China.Key words :maize, core landraces, genetic diversity, population structureINTRODUCTIONMaize has been grown in China for nearly 500 years since its first introduction into this second biggest pro-duction country in the world. Currently, there are six different maize growing regions throughout the coun-try according to the ecological conditions and farming systems, including three major production regions,i.e., the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, and the Southwest MaizeRegion, and three minor regions, i.e., the South Maize Region, the Northwest Maize Region, and the Qingzang Plateau Maize Region. The South Maize Region (SR)is specific because of its importance in origin of Chi-nese maize. It is hypothesized that Chinese maize is introduced mainly from two routes. One is called the land way in which maize was first brought to Tibet from India, then to Sichuan Province in southwestern China. The other way is that maize dispersed via the oceans, first shipped to the coastal areas of southeast China by boats, and then spread all round the country1252LIU Zhi-zhai et al.(Xu 2001; Zhou 2000). SR contains all of the coastal provinces and regions lie in southeastern China.In the long-term cultivation history of maize in south-ern China, numerous landraces have been formed, in which a great amount of genetic variation was observed (Li 1998). Similar to the hybrid swapping in Europe (Reif et al. 2005a), the maize landraces have been al-most replaced by hybrids since the 1950s in China (Li 1998). However, some landraces with good adapta-tions and yield performances are still grown in a few mountainous areas of this region (Liu et al.1999). Through a great effort of collection since the 1950s, 13521 accessions of maize landraces have been cur-rently preserved in China National Genebank (CNG), and a core collection of these landraces was established (Li et al. 2004). In this core collection, a total of 143 maize landrace accessions were collected from the South Maize Region (SR) (Table 1).Since simple sequence repeat ( SSR ) markers were firstly used in human genetics (Litt and Luty 1989), it now has become one of the most widely used markers in the related researches in crops (Melchinger et al. 1998; Enoki et al. 2005), especially in the molecular characterization of genetic resources, e.g., soybean [Glycine max (L.) Merr] (Xie et al. 2005), rice (Orya sativa L.) (Garris et al. 2005), and wheat (Triticum aestivum) (Chao et al. 2007). In maize (Zea mays L.), numerous studies focusing on the genetic diversity and population structure of landraces and inbred lines in many countries and regions worldwide have been pub-lished (Liu et al. 2003; Vegouroux et al. 2005; Reif et al. 2006; Wang et al. 2008). These activities of documenting genetic diversity and population structure of maize genetic resources have facilitated the under-standing of genetic bases of maize landraces, the utili-zation of these resources, and the mining of favorable alleles from landraces. Although some studies on ge-netic diversity of Chinese maize inbred lines were con-ducted (Yu et al. 2007; Wang et al. 2008), the general profile of genetic diversity in Chinese maize landraces is scarce. Especially, there are not any reports on ge-netic diversity of the maize landraces collected from SR, a possibly earliest maize growing area in China. In this paper, a total of 143 landraces from SR listed in the core collection of CNG were genotyped by using SSR markers, with the aim of revealing genetic diver-sity of the landraces from SR (Table 2) of China and examining genetic relationships and population struc-ture of these landraces.MATERIALS AND METHODSPlant materials and DNA extractionTotally, 143 landraces from SR which are listed in the core collection of CNG established by sequential strati-fication method (Liu et al. 2004) were used in the present study. Detailed information of all these landrace accessions is listed in Table 1. For each landrace, DNA sample was extracted by a CTAB method (Saghi-Maroof et al. 1984) from a bulk pool constructed by an equal-amount of leaves materials sampled from 15 random-chosen plants of each landrace according to the proce-dure of Reif et al. (2005b).SSR genotypingA total of 54 simple sequence repeat (SSR) markers covering the entire maize genome were screened to fin-gerprint all of the 143 core landrace accessions (Table 3). 5´ end of the left primer of each locus was tailed by an M13 sequence of 5´-CACGACGTTGTAAAACGAC-3´. PCR amplification was performed in a 15 L reac-tion containing 80 ng of template DNA, 7.5 mmol L-1 of each of the four dNTPs, 1×Taq polymerase buffer, 1.5 mmol L-1 MgCl2, 1 U Taq polymerase (Tiangen Biotech Co. Ltd., Beijing, China), 1.2 mol L-1 of forward primer and universal fluorescent labeled M13 primer, and 0.3 mol L-1 of M13 sequence tailed reverse primer (Schuelke 2000). The amplification was carried out in a 96-well DNA thermal cycler (GeneAmp PCR System 9700, Applied Biosystem, USA). PCR products were size-separated on an ABI Prism 3730XL DNA sequencer (HitachiHigh-Technologies Corporation, Tokyo, Japan) via the software packages of GENEMAPPER and GeneMarker ver. 6 (SoftGenetics, USA).Data analysesAverage number of alleles per locus and average num-ber of group-specific alleles per locus were identifiedAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1253Table 1 The detailed information about the landraces used in the present studyPGS revealed by Structure1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysis140-150tian 00120005AnH-06Jingde Anhui 0.0060.994Group 2170tian00120006AnH-07Jingde Anhui 0.0050.995Group 2Zixihuangyumi00120007AnH-08Zixi Anhui 0.0020.998Group 2Zixibaihuangzayumi 00120008AnH-09Zixi Anhui 0.0030.997Group 2Baiyulu 00120020AnH-10Yuexi Anhui 0.0060.994Group 2Wuhuazi 00120021AnH-11Yuexi Anhui 0.0030.997Group 2Tongbai 00120035AnH-12Tongling Anhui 0.0060.994Group 2Yangyulu 00120036AnH-13Yuexi Anhui 0.0040.996Group 2Huangli 00120037AnH-14Tunxi Anhui 0.0410.959Group 2Baiyumi 00120038AnH-15Tunxi Anhui 0.0030.997Group 2Dapigu00120039AnH-16Tunxi Anhui 0.0350.965Group 2150tianbaiyumi 00120040AnH-17Xiuning Anhui 0.0020.998Group 2Xiuning60tian 00120042AnH-18Xiuning Anhui 0.0040.996Group 2Wubaogu 00120044AnH-19ShitaiAnhui 0.0020.998Group 2Kuyumi00130001FuJ-01Shanghang Fujian 0.0050.995Group 2Zhongdouyumi 00130003FuJ-02Shanghang Fujian 0.0380.962Group 2Baixinyumi 00130004FuJ-03Liancheng Fujian 0.0040.996Group 2Hongxinyumi 00130005FuJ-04Liancheng Fujian 0.0340.966Group 2Baibaogu 00130008FuJ-05Changding Fujian 0.0030.997Group 2Huangyumi 00130011FuJ-06Jiangyang Fujian 0.0020.998Group 2Huabaomi 00130013FuJ-07Shaowu Fujian 0.0020.998Group 2Huangbaomi 00130014FuJ-08Songxi Fujian 0.0020.998Group 2Huangyumi 00130016FuJ-09Wuyishan Fujian 0.0460.954Group 2Huabaogu 00130019FuJ-10Jian’ou Fujian 0.0060.994Group 2Huangyumi 00130024FuJ-11Guangze Fujian 0.0010.999Group 2Huayumi 00130025FuJ-12Nanping Fujian 0.0040.996Group 2Huangyumi 00130026FuJ-13Nanping Fujian 0.0110.989Group 2Hongbaosu 00130027FuJ-14Longyan Fujian 0.0160.984Group 2Huangfansu 00130029FuJ-15Loangyan Fujian 0.0020.998Group 2Huangbaosu 00130031FuJ-16Zhangping Fujian 0.0060.994Group 2Huangfansu 00130033FuJ-17Zhangping Fujian0.0040.996Group 2Baolieyumi 00190001GuangD-01Guangzhou Guangdong 0.9890.011Group 1Nuomibao (I)00190005GuangD-02Shixing Guangdong 0.9740.026Group 1Nuomibao (II)00190006GuangD-03Shixing Guangdong 0.9790.021Group 1Zasehuabao 00190010GuangD-04Lechang Guangdong 0.9970.003Group 1Zihongmi 00190013GuangD-05Lechang Guangdong 0.9880.012Group 1Jiufengyumi 00190015GuangD-06Lechang Guangdong 0.9950.005Group 1Huangbaosu 00190029GuangD-07MeiGuangdong 0.9970.003Group 1Bailibao 00190032GuangD-08Xingning Guangdong 0.9980.002Group 1Nuobao00190038GuangD-09Xingning Guangdong 0.9980.002Group 1Jinlanghuang 00190048GuangD-10Jiangcheng Guangdong 0.9960.004Group 1Baimizhenzhusu 00190050GuangD-11Yangdong Guangdong 0.9940.006Group 1Huangmizhenzhusu 00190052GuangD-12Yangdong Guangdong 0.9930.007Group 1Baizhenzhu 00190061GuangD-13Yangdong Guangdong 0.9970.003Group 1Baiyumi 00190066GuangD-14Wuchuan Guangdong 0.9880.012Group 1Bendibai 00190067GuangD-15Suixi Guangdong 0.9980.002Group 1Shigubaisu 00190068GuangD-16Gaozhou Guangdong 0.9960.004Group 1Zhenzhusu 00190069GuangD-17Xinyi Guangdong 0.9960.004Group 1Nianyaxixinbai 00190070GuangD-18Huazhou Guangdong 0.9960.004Group 1Huangbaosu 00190074GuangD-19Xinxing Guangdong 0.9950.005Group 1Huangmisu 00190076GuangD-20Luoding Guangdong 0.940.060Group 1Huangmi’ai 00190078GuangD-21Luoding Guangdong 0.9980.002Group 1Bayuemai 00190084GuangD-22Liannan Guangdong 0.9910.009Group 1Baiyumi 00300001HaiN-01Haikou Hainan 0.9960.004Group 1Baiyumi 00300003HaiN-02Sanya Hainan 0.9970.003Group 1Hongyumi 00300004HaiN-03Sanya Hainan 0.9980.002Group 1Baiyumi00300011HaiN-04Tongshi Hainan 0.9990.001Group 1Zhenzhuyumi 00300013HaiN-05Tongshi Hainan 0.9980.002Group 1Zhenzhuyumi 00300015HaiN-06Qiongshan Hainan 0.9960.004Group 1Aiyumi 00300016HaiN-07Qiongshan Hainan 0.9960.004Group 1Huangyumi 00300021HaiN-08Qionghai Hainan 0.9970.003Group 1Y umi 00300025HaiN-09Qionghai Hainan 0.9870.013Group 1Accession name Entry code Analyzing code Origin (county/city)Province/Region1254LIU Zhi-zhai et al .Baiyumi00300032HaiN-10Tunchang Hainan 0.9960.004Group 1Huangyumi 00300051HaiN-11Baisha Hainan 0.9980.002Group 1Baihuangyumi 00300055HaiN-12BaishaHainan 0.9970.003Group 1Machihuangyumi 00300069HaiN-13Changjiang Hainan 0.9900.010Group 1Hongyumi00300073HaiN-14Dongfang Hainan 0.9980.002Group 1Xiaohonghuayumi 00300087HaiN-15Lingshui Hainan 0.9980.002Group 1Baiyumi00300095HaiN-16Qiongzhong Hainan 0.9950.005Group 1Y umi (Baimai)00300101HaiN-17Qiongzhong Hainan 0.9980.002Group 1Y umi (Xuemai)00300103HaiN-18Qiongzhong Hainan 0.9990.001Group 1Huangmaya 00100008JiangS-10Rugao Jiangsu 0.0040.996Group 2Bainian00100012JiangS-11Rugao Jiangsu 0.0080.992Group 2Bayebaiyumi 00100016JiangS-12Rudong Jiangsu 0.0040.996Group 2Chengtuohuang 00100021JiangS-13Qidong Jiangsu 0.0050.995Group 2Xuehuanuo 00100024JiangS-14Qidong Jiangsu 0.0020.998Group 2Laobaiyumi 00100032JiangS-15Qidong Jiangsu 0.0050.995Group 2Laobaiyumi 00100033JiangS-16Qidong Jiangsu 0.0010.999Group 2Huangwuye’er 00100035JiangS-17Hai’an Jiangsu 0.0030.997Group 2Xiangchuanhuang 00100047JiangS-18Nantong Jiangsu 0.0060.994Group 2Huangyingzi 00100094JiangS-19Xinghua Jiangsu 0.0040.996Group 2Xiaojinhuang 00100096JiangS-20Yangzhou Jiangsu 0.0010.999Group 2Liushizi00100106JiangS-21Dongtai Jiangsu 0.0030.997Group 2Kangnandabaizi 00100108JiangS-22Dongtai Jiangsu 0.0020.998Group 2Shanyumi 00140020JiangX-01Dexing Jiangxi 0.9970.003Group 1Y umi00140024JiangX-02Dexing Jiangxi 0.9970.003Group 1Tianhongyumi 00140027JiangX-03Yushan Jiangxi 0.9910.009Group 1Hongganshanyumi 00140028JiangX-04Yushan Jiangxi 0.9980.002Group 1Zaoshuyumi 00140032JiangX-05Qianshan Jiangxi 0.9970.003Group 1Y umi 00140034JiangX-06Wannian Jiangxi 0.9970.003Group 1Y umi 00140038JiangX-07De’an Jiangxi 0.9940.006Group 1Y umi00140045JiangX-08Wuning Jiangxi 0.9740.026Group 1Chihongyumi 00140049JiangX-09Wanzai Jiangxi 0.9920.008Group 1Y umi 00140052JiangX-10Wanzai Jiangxi 0.9930.007Group 1Huayumi 00140060JiangX-11Jing’an Jiangxi 0.9970.003Group 1Baiyumi 00140065JiangX-12Pingxiang Jiangxi 0.9940.006Group 1Huangyumi00140066JiangX-13Pingxiang Jiangxi 0.9680.032Group 1Nuobaosuhuang 00140068JiangX-14Ruijin Jiangxi 0.9950.005Group 1Huangyumi 00140072JiangX-15Xinfeng Jiangxi 0.9960.004Group 1Wuningyumi 00140002JiangX-16Jiujiang Jiangxi 0.0590.941Group 2Tianyumi 00140005JiangX-17Shangrao Jiangxi 0.0020.998Group 2Y umi 00140006JiangX-18Shangrao Jiangxi 0.0310.969Group 2Baiyiumi 00140012JiangX-19Maoyuan Jiangxi 0.0060.994Group 260riyumi 00140016JiangX-20Maoyuan Jiangxi 0.0020.998Group 2Shanyumi 00140019JiangX-21Dexing Jiangxi 0.0050.995Group 2Laorenya 00090002ShangH-01Chongming Shanghai 0.0050.995Group 2Jinmeihuang 00090004ShangH-02Chongming Shanghai 0.0020.998Group 2Zaobaiyumi 00090006ShangH-03Chongming Shanghai 0.0020.998Group 2Chengtuohuang 00090007ShangH-04Chongming Shanghai 0.0780.922Group 2Benyumi (Huang)00090008ShangH-05Shangshi Shanghai 0.0020.998Group 2Bendiyumi 00090010ShangH-06Shangshi Shanghai 0.0040.996Group 2Baigengyumi 00090011ShangH-07Jiading Shanghai 0.0020.998Group 2Huangnuoyumi 00090012ShangH-08Jiading Shanghai 0.0040.996Group 2Huangdubaiyumi 00090013ShangH-09Jiading Shanghai 0.0440.956Group 2Bainuoyumi 00090014ShangH-10Chuansha Shanghai 0.0010.999Group 2Laorenya 00090015ShangH-11Shangshi Shanghai 0.0100.990Group 2Xiaojinhuang 00090016ShangH-12Shangshi Shanghai 0.0050.995Group 2Gengbaidayumi 00090017ShangH-13Shangshi Shanghai 0.0020.998Group 2Nongmeiyihao 00090018ShangH-14Shangshi Shanghai 0.0540.946Group 2Chuanshazinuo 00090020ShangH-15Chuansha Shanghai 0.0550.945Group 2Baoanshanyumi 00110004ZheJ-01Jiangshan Zhejiang 0.0130.987Group 2Changtaixizi 00110005ZheJ-02Jiangshan Zhejiang 0.0020.998Group 2Shanyumibaizi 00110007ZheJ-03Jiangshan Zhejiang 0.0020.998Group 2Kaihuajinyinbao 00110017ZheJ-04Kaihua Zhejiang 0.0100.990Group 2Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group1 Group2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/RegoinAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1255Liputianzi00110038ZheJ-05Jinhua Zhejiang 0.0020.998Group 2Jinhuaqiuyumi 00110040ZheJ-06Jinhua Zhejiang 0.0050.995Group 2Pujiang80ri 00110069ZheJ-07Pujiang Zhejiang 0.0210.979Group 2Dalihuang 00110076ZheJ-08Yongkang Zhejiang 0.0140.986Group 2Ziyumi00110077ZheJ-09Yongkang Zhejiang 0.0020.998Group 2Baiyanhandipinzhong 00110078ZheJ-10Yongkang Zhejiang 0.0030.997Group 2Duosuiyumi00110081ZheJ-11Wuyi Zhejiang 0.0020.998Group 2Chun’an80huang 00110084ZheJ-12Chun’an Zhejiang 0.0020.998Group 2120ribaiyumi 00110090ZheJ-13Chun’an Zhejiang 0.0020.998Group 2Lin’anliugu 00110111ZheJ-14Lin’an Zhejiang 0.0030.997Group 2Qianhuangyumi00110114ZheJ-15Lin’an Zhejiang 0.0030.997Group 2Fenshuishuitianyumi 00110118ZheJ-16Tonglu Zhejiang 0.0410.959Group 2Kuihualiugu 00110119ZheJ-17Tonglu Zhejiang 0.0030.997Group 2Danbaihuang 00110122ZheJ-18Tonglu Zhejiang 0.0020.998Group 2Hongxinma 00110124ZheJ-19Jiande Zhejiang 0.0030.997Group 2Shanyumi 00110136ZheJ-20Suichang Zhejiang 0.0030.997Group 2Bai60ri 00110143ZheJ-21Lishui Zhejiang 0.0050.995Group 2Zeibutou 00110195ZheJ-22Xianju Zhejiang 0.0020.998Group 2Kelilao00110197ZheJ-23Pan’an Zhejiang 0.0600.940Group 21)The figures refered to the proportion of membership that each landrace possessed.Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/Regoin Table 2 Construction of two phylogenetic groups (SSR-clustered groups) and their correlation with geographical locationsGeographical location SSR-clustered groupChi-square testGroup 1Group 2Total Guangdong 2222 χ2 = 124.89Hainan 1818P < 0.0001Jiangxi 15621Anhui 1414Fujian 1717Jiangsu 1313Shanghai 1515Zhejiang 2323Total5588143by the software of Excel MicroSatellite toolkit (Park 2001). Average number of alleles per locus was calcu-lated by the formula rAA rj j¦1, with the standarddeviation of1)()(12¦ r A AA rj jV , where A j was thenumber of distinct alleles at locus j , and r was the num-ber of loci (Park 2001).Unbiased gene diversity also known as expected heterozygosity, observed heterozygosity for each lo-cus and average gene diversity across the 54 SSR loci,as well as model-based groupings inferred by Struc-ture ver. 2.2, were calculated by the softwarePowerMarker ver.3.25 (Liu et al . 2005). Unbiased gene diversity for each locus was calculated by˅˄¦ 2ˆ1122ˆi x n n h , where 2ˆˆ2ˆ2¦¦z ji ijij i X X x ,and ij X ˆwas the frequency of genotype A i A jin the sample, and n was the number of individuals sampled.The average gene diversity across 54 loci was cal-culated as described by Nei (1987) as follows:rh H rj j ¦1ˆ, with the variance ,whereThe average observed heterozygosity across the en-tire loci was calculated as described by (Hedrick 1983)as follows: r jrj obsobs n h h ¦1, with the standard deviationrn h obs obsobs 1V1256LIU Zhi-zhai et al.Phylogenetic analysis and population genetic structureRelationships among all of the 143 accessions collected from SR were evaluated by using the unweighted pair group method with neighbor-joining (NJ) based on the log transformation of the proportion of shared alleles distance (InSPAD) via PowerMarker ver. 3.25 (FukunagaTable 3 The PIC of each locus and the number of alleles detected by 54 SSRsLocus Bin Repeat motif PIC No. of alleles Description 2)bnlg1007y51) 1.02AG0.7815Probe siteumc1122 1.06GGT0.639Probe siteumc1147y41) 1.07CA0.2615Probe sitephi961001) 2.00ACCT0.298Probe siteumc1185 2.03GC0.7215ole1 (oleosin 1)phi127 2.08AGAC0.577Probe siteumc1736y21) 2.09GCA T0.677Probe sitephi453121 3.01ACC0.7111Probe sitephi374118 3.03ACC0.477Probe sitephi053k21) 3.05A TAC0.7910Probe sitenc004 4.03AG0.4812adh2 (alcohol dehydrogenase 2)bnlg490y41) 4.04T A0.5217Probe sitephi079 4.05AGATG0.495gpc1(glyceraldehyde-3-phosphate dehydrogenase 1) bnlg1784 4.07AG0.6210Probe siteumc1574 4.09GCC0.719sbp2 (SBP-domain protein 2)umc1940y51) 4.09GCA0.4713Probe siteumc1050 4.11AA T0.7810cat3 (catalase 3)nc130 5.00AGC0.5610Probe siteumc2112y31) 5.02GA0.7014Probe sitephi109188 5.03AAAG0.719Probe siteumc1860 5.04A T0.325Probe sitephi085 5.07AACGC0.537gln4 (glutamine synthetase 4)phi331888 5.07AAG0.5811Probe siteumc1153 5.09TCA0.7310Probe sitephi075 6.00CT0.758fdx1 (ferredoxin 1)bnlg249k21) 6.01AG0.7314Probe sitephi389203 6.03AGC0.416Probe sitephi299852y21) 6.07AGC0.7112Probe siteumc1545y21)7.00AAGA0.7610hsp3(heat shock protein 3)phi1127.01AG0.5310o2 (opaque endosperm 2)phi4207018.00CCG0.469Probe siteumc13598.00TC0.7814Probe siteumc11398.01GAC0.479Probe siteumc13048.02TCGA0.335Probe sitephi1158.03A TAC0.465act1(actin1)umc22128.05ACG0.455Probe siteumc11218.05AGAT0.484Probe sitephi0808.08AGGAG0.646gst1 (glutathione-S-transferase 1)phi233376y11)8.09CCG0.598Probe sitebnlg12729.00AG0.8922Probe siteumc20849.01CTAG0.498Probe sitebnlg1520k11)9.01AG0.5913Probe sitephi0659.03CACCT0.519pep1(phosphoenolpyruvate carboxylase 1)umc1492y131)9.04GCT0.2514Probe siteumc1231k41)9.05GA0.2210Probe sitephi1084119.06AGCT0.495Probe sitephi4488809.06AAG0.7610Probe siteumc16759.07CGCC0.677Probe sitephi041y61)10.00AGCC0.417Probe siteumc1432y61)10.02AG0.7512Probe siteumc136710.03CGA0.6410Probe siteumc201610.03ACAT0.517pao1 (polyamine oxidase 1)phi06210.04ACG0.337mgs1 (male-gametophyte specific 1)phi07110.04GGA0.515hsp90 (heat shock protein, 90 kDa)1) These primers were provided by Beijing Academy of Agricultural and Forestry Sciences (Beijing, China).2) Searched from Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China1257et al. 2005). The unrooted phylogenetic tree was finally schematized with the software MEGA (molecular evolu-tionary genetics analysis) ver. 3.1 (Kumar et al. 2004). Additionally, a chi-square test was used to reveal the correlation between the geographical origins and SSR-clustered groups through FREQ procedure implemented in SAS ver. 9.0 (2002, SAS Institute, Inc.).In order to reveal the population genetic structure (PGS) of 143 landrace accessions, a Bayesian approach was firstly applied to determine the number of groups (K) that these materials should be assigned by the soft-ware BAPS (Bayesian Analysis of Population Structure) ver.5.1. By using BAPS, a fixed-K clustering proce-dure was applied, and with each separate K, the num-ber of runs was set to 100, and the value of log (mL) was averaged to determine the appropriate K value (Corander et al. 2003; Corander and Tang 2007). Since the number of groups were determined, a model-based clustering analysis was used to assign all of the acces-sions into the corresponding groups by an admixture model and a correlated allele frequency via software Structure ver.2.2 (Pritchard et al. 2000; Falush et al. 2007), and for the given K value determined by BAPS, three independent runs were carried out by setting both the burn-in period and replication number 100000. The threshold probability assigned individuals into groupswas set by 0.8 (Liu et al. 2003). The PGS result carried out by Structure was visualized via Distruct program ver. 1.1 (Rosenberg 2004).RESULTSGenetic diversityA total of 517 alleles were detected by the whole set of54 SSRs covering the entire maize genome through all of the 143 maize landraces, with an average of 9.57 alleles per locus and ranged from 4 (umc1121) to 22 (bnlg1272) (Table 3). Among all the alleles detected, the number of distinct alleles accounted for 132 (25.53%), with an av-erage of 2.44 alleles per locus. The distinct alleles dif-fered significantly among the landraces from different provinces/regions, and the landraces from Guangdong, Fujian, Zhejiang, and Shanghai possessed more distinct alleles than those from the other provinces/regions, while those from southern Anhui possessed the lowest distinct alleles, only counting for 3.28% of the total (Table 4).Table 4 The genetic diversity within eight provinces/regions and groups revealed by 54 SSRsProvince/Region Sample size Allele no.1)Distinct allele no.Gene diversity (expected heterozygosity)Observed heterozygosity Anhui14 4.28 (4.19) 69 (72.4)0.51 (0.54)0.58 (0.58)Fujian17 4.93 (4.58 80 (79.3)0.56 (0.60)0.63 (0.62)Guangdong22 5.48 (4.67) 88 (80.4)0.57 (0.59)0.59 (0.58)Hainan18 4.65 (4.26) 79 (75.9)0.53 (0.57)0.55 (0.59)Jiangsu13 4.24 700.500.55Jiangxi21 4.96 (4.35) 72 (68.7)0.56 (0.60)0.68 (0.68)Shanghai15 5.07 (4.89) 90 (91.4)0.55 (0.60)0.55 (0.55)Zhejiang23 5.04 (4.24) 85 (74)0.53 (0.550.60 (0.61)Total/average1439.571320.610.60GroupGroup 155 6.63 (6.40) 91 (89.5)0.57 (0.58)0.62 (0.62)Group 2887.94 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Total/Average1439.571320.610.60Provinces/Regions within a groupGroup 1Total55 6.69 (6.40) 910.57 (0.58)0.62 (0.62)Guangdong22 5.48 (4.99) 86 (90.1)0.57 (0.60)0.59 (0.58)Hainan18 4.65 (4.38) 79 (73.9)0.53 (0.56)0.55 (0.59)Jiangxi15 4.30 680.540.69Group 2Total887.97 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Anhui14 4.28 (3.22) 69 (63.2)0.51 (0.54)0.58 (0.57)Fujian17 4.93 (3.58) 78 (76.6)0.56 (0.60)0.63 (0.61)Jiangsu13 4.24 (3.22) 71 (64.3)0.50 (0.54)0.55 (0.54)Jiangxi6 3.07 520.460.65Shanghai15 5.07 (3.20) 91 (84.1)0.55 (0.60)0.55 (0.54)Zhejiang23 5.04 (3.20) 83 (61.7)0.53 (0.54)0.60 (0.58)1258LIU Zhi-zhai et al.Among the 54 loci used in the study, 16 (or 29.63%) were dinucleotide repeat SSRs, which were defined as type class I-I, the other 38 loci were SSRs with a longer repeat motifs, and two with unknown repeat motifs, all these 38 loci were defined as the class of I-II. In addition, 15 were located within certain functional genes (defined as class II-I) and the rest were defined as class II-II. The results of comparison indicated that the av-erage number of alleles per locus captured by class I-I and II-II were 12.88 and 10.05, respectively, which were significantly higher than that by type I-II and II-I (8.18 and 8.38, respectively). The gene diversity re-vealed by class I-I (0.63) and II-I (0.63) were some-what higher than by class I-II (0.60) and II-II (0.60) (Table 5).Genetic relationships of the core landraces Overall, 143 landraces were clustered into two groups by using neighbor-joining (NJ) method based on InSPAD. All the landraces from provinces of Guangdong and Hainan and 15 of 21 from Jiangxi were clustered together to form group 1, and the other 88 landraces from the other provinces/regions formed group 2 (Fig.-B). The geographical origins of all these 143 landraces with the clustering results were schematized in Fig.-D. Revealed by the chi-square test, the phylogenetic results (SSR-clustered groups) of all the 143 landraces from provinces/regions showed a significant correlation with their geographical origin (χ2=124.89, P<0.0001, Table 2).Revealed by the phylogenetic analysis based on the InSPAD, the minimum distance was observed as 0.1671 between two landraces, i.e., Tianhongyumi (JiangX-03) and Hongganshanyumi (JiangX-04) collected from Jiangxi Province, and the maximum was between two landraces of Huangbaosu (FuJ-16) and Hongyumi (HaiN-14) collected from provinces of Fujian and Hainan, respectively, with the distance of 1.3863 (data not shown). Two landraces (JiangX-01 and JiangX-21) collected from the same location of Dexing County (Table 1) possessing the same names as Shanyumi were separated to different groups, i.e., JiangX-01 to group1, while JiangX-21 to group 2 (Table 1). Besides, JiangX-01 and JiangX-21 showed a rather distant distance of 0.9808 (data not shown). These results indicated that JiangX-01 and JiangX-21 possibly had different ances-tral origins.Population structureA Bayesian method was used to detect the number of groups (K value) of the whole set of landraces from SR with a fixed-K clustering procedure implemented in BAPS software ver. 5.1. The result showed that all of the 143 landraces could also be assigned into two groups (Fig.-A). Then, a model-based clustering method was applied to carry out the PGS of all the landraces via Structure ver. 2.2 by setting K=2. This method as-signed individuals to groups based on the membership probability, thus the threshold probability 0.80 was set for the individuals’ assignment (Liu et al. 2003). Accordingly, all of the 143 landraces were divided into two distinct model-based groups (Fig.-C). The landraces from Guangdong, Hainan, and 15 landraces from Jiangxi formed one group, while the rest 6 landraces from the marginal countries of northern Jiangxi and those from the other provinces formed an-other group (Table 1, Fig.-D). The PGS revealed by the model-based approach via Structure was perfectly consistent with the relationships resulted from the phy-logenetic analysis via PowerMarker (Table 1).DISCUSSIONThe SR includes eight provinces, i.e., southern Jiangsu and Anhui, Shanghai, Zhejiang, Fujian, Jiangxi, Guangdong, and Hainan (Fig.-C), with the annual maize growing area of about 1 million ha (less than 5% of theTable 5 The genetic diversity detected with different types of SSR markersType of locus No. of alleles Gene diversity Expected heterozygosity PIC Class I-I12.880.630.650.60 Class I-II8.180.600.580.55 Class II-I8.330.630.630.58。
与玉米间作促进苍术根际养分吸收利用的原因
中国生态农业学报(中英文) 2024年2月 第 32 卷 第 2 期Chinese Journal of Eco-Agriculture, Feb. 2024, 32(2): 309−320DOI: 10.12357/cjea.20230361曹梅玉, 张有, 闫滨滨, 万修福, 孙楷, 康传志, 王红阳, 吕朝耕, 张燕, 郭兰萍. 与玉米间作促进苍术根际养分吸收利用的原因[J]. 中国生态农业学报 (中英文), 2024, 32(2): 309−320CAO M Y, ZHANG Y, YAN B B, WAN X F, SUN K, KANG C Z, WANG H Y, LYU C G, ZHANG Y, GUO L P. Reasons for pro-moting rhizosphere nutrient absorption and utilization of Atractylodes lancea by intercropping with maize[J]. Chinese Journal of Eco-Agriculture, 2024, 32(2): 309−320与玉米间作促进苍术根际养分吸收利用的原因*曹梅玉1, 张 有2, 闫滨滨1, 万修福1, 孙 楷1, 康传志1, 王红阳1, 吕朝耕1,张 燕1**, 郭兰萍1**(1. 中国中医科学院中药资源中心/道地药材国家重点实验室培育基地 北京 100700; 2. 莱芜紫光生态园有限公司 莱芜 271100)摘 要: 与玉米间作能够缓解苍术连作障碍, 而养分条件变化是关键因素之一。
为探究苍术||玉米间作对苍术根际养分吸收利用的影响, 本研究开展了为期2年的苍术||玉米间作根际不同分隔处理的田间试验, 共设置了4种处理:苍术单作(A)、苍术||玉米间作不隔膜(AI)、苍术||玉米间作隔尼龙膜(AN)和苍术||玉米间作隔塑料膜(AP), 分别测定了苍术生物量和4种挥发油成分含量, 苍术根茎氮磷钾含量, 根际土壤pH、有机质和土壤养分因子含量。
EHP、SHIV 双重TaqMan 实时荧光定量PCR 检测方法的构建及应用
·研究论文·Chinese Journal of Animal Infectious Diseases中国动物传染病学报摘 要:本研究根据GenBank 中已有的虾肝肠胞虫(EHP )和虾血细胞虹彩病毒(SHIV )基因的保守序列,设计特异的引物和探针。
建立了快速诊断EHP 和SHIV 的双重TaqMan 实时荧光定量PCR 检测方法,并对其特异性、敏感性和稳定性进行检测。
结果表明:该方法检测限可达10 copies/μL ,其敏感性是SYBR Green real-time PCR 的10倍,普通PCR 法的100倍;对白斑综合征病毒、传染性皮下及造血组织坏死病毒、高致病性副溶血弧菌,以及桃拉综合征病毒的检测结果均为阴性,表明无交叉反应,具有良好特异性;重复性试验结果表明,该方法Ct 值的变异系数小于4%,具有良好的稳定性。
对37份已知检测结果的样品进行检测,结果符合率为100%。
本研究建立的EHP 和SHIV 检测方法具有快速、特异性强、灵敏度高等优点,适用于对虾隐性感染的早期监测。
关键词:虾肝肠胞虫;虾血细胞虹彩病毒;TaqMan 荧光定量PCR 中图分类号: S852.723文献标志码:A 文章编号:1674-6422(2022)01-0112-08Development of a Duplex TaqMan Real-time PCR Assay for Detection of EnterocyTozoon Hepatopenaei and Shrimp Hemocyte IridovirusHOUYuee 1, ZENG Junxia 1, LAN Jianyuan 1, XU Zaozhu 1, LIAO Xiuyun 2, LUO Baozheng 2(1. Zhuhai Kerric T esting T echnolongy Co. Ltd., Zhuhai 519000, China; 2. Gongbei Customs district technology center, Zhuhai 519015, China)收稿日期:2019-09-04基金项目:珠海市公共技术服务平台产学研协同创新计划项目(IETP201901011)作者简介:侯月娥,女,硕士,主要从事畜禽和水产疾病及免疫防治通信作者:罗宝正,E-mail:*************EHP 、SHIV 双重TaqMan 实时荧光定量PCR检测方法的构建及应用侯月娥1,曾俊霞1,蓝间媛1,许枣珠1,廖秀云2,罗宝正2(1.珠海科艺普检测科技有限公司,珠海51900;2.拱北海关技术中心,珠海519015)2022,30(1): 112-119Abstract: In this study, specific probes and primers were designed according to Enterocy Tozoon Hepatopenaei (EHP) and Shrimp Hemocyte Iridovirus (SHIV) gene sequences in GenBank. A d uplex TaqMan-based real-time quantitative PCR assay was then developed by optimizing the reaction conditions. The results demonstrated that this PCR assay was more sensitive for EHP and SHIV with the detection limit of 1×10 copies/μL, which was 10-fold higher than SYBR Green real-time PCR and 100-fold higher than conventional PCR. Specifi city tests showed no cross reactions with White spot syndrome virus, Infectious hypodermal and hematopoietic necrosis virus, High pathogenicity ibrio parahaemolyticus and Taura syndrome virus. Repeatability tests demonstrated that the threshold cycle of the method gave a good stability with the coeffi cient of variations were less than 4%. Then 37 samples were tested by using a duplex TaqMan real-time PCR and conventional PCR and the results reached 100% agreement. In conclusion, the duplex TaqMan-based real-time quantitative PCR assay developed here were specifi c and sensitive for rapid detection of EHP and SHIV . Therefore, it could be suitable for early duplex TaqMan-based real-time quantitative PCR assay of latent infection of shrimp.Key words: Enterocy tozoon hepatopenaei; shrimp hemocyte iridovirus; TaqMan real-time PCR; detection· 113 ·侯月娥等:EHP、SHIV 双重TaqMan 实时荧光定量PCR 检测方法的构建及应用第30卷第1期虾肝肠胞虫(Enterocytozoon hepatopenaei, EHP)是2009年于泰国养殖池塘生长迟缓的斑节对虾(Penaeus monodon)中发现的专门寄生于细胞内的原生生物[1-2]。
listeria monocytogenes egd-e的标准编号
Listeria monocytogenes EGD-e是一种实验室参考菌株,广泛用于科学研究。
EGD-e的标准编号不是一个单一数值,而是一组与该菌株相关的科学数据和信息。
以下是关于Listeria monocytogenes EGD-e的一些详细信息:
1. 基因序列信息:EGD-e的基因序列信息可以在公共数据库中找到,这些信息包括其蛋白质编码基因和非编码RNA等。
2. 耐药性基因:在EGD-e菌株中,已经鉴定出耐药性基因,例如氨基糖苷类抗性基因aacA4。
3. 毒力因子:内部蛋白A和B是Listeria monocytogenes的主要毒力因子,它们介导宿主细胞的受体依赖性进入。
EGD-e菌株中的inlA基因与其他菌株相比具有很高的核苷酸同一性。
4. 疾病关联:Listeria monocytogenes是一种令人畏惧的人类病原体,可引起食源性疾病,并在易感风险群体中发展为严重的系统性疾病。
The ecology of human development
TE Ecology of human development
9
ISP -UIO
The ecology of human development10
Ecological traps:
Calhoun: Dysfunctional losers roles - easy to get into if you break the norm of the system consequences can be social isolation.
framing, where the individuals participate in decidedly activities and has fixed roles in specific intervals of time
TE Ecology of human development
11
ISP -UIof human development
3
ISP -UIO
The ecology of human development4
Microsystem
A microsystem is a pattern of activities , roles, and interpersonal relations experienced by the developing person in a given setting with particular physical and material characteristics
批注本地保存成功开通会员云端永久保存去开通
The ecology of human development
Experience by nature and design Uri Bronfenbrenner 1979
Universities in Evolutionary Systems(系统变革中的大学)
Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewfits better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness offirms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-fits-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingfield of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and influence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-fic knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientific knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalfields(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or influence the innovation performance of a country.Figure1 provides an illustration.Thefigure demon-strates the major building blocks of a NIS in a practical policy setting.It includesfirms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for firms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to fiscal instruments that stimulate labour mobility between universities and firms.The figure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for firms.Moreover,policy has also influ-enced the use of specific ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identified and policy recommendations are derived:the so-called one-size-fits-all-approach.The focus is on determining the ingredients of an efficient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,firms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onfirms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryfields such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of thefittest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identifies three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing specific rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-nificantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘fittest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unfit’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in firms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other firms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot define an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or finality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identified ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot define the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are defined as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically significant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efficiency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to fit better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efficiency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all influence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingfixed prefer-ences and having one rational response to any fully specified decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orfirm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly influenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are influ-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established firms,start-upfirms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientific paradigms)but universities also influence the institutions of the selection envi-ronment by their strategic,scientific and entre-preneurial actions.Moreover,these actions influence–and are influenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and influence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer offices(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities offirms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinfluenced by their own scientific peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘influence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents defined byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientific disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。
Mega6_ molecular evolutionary genetics analysis version 6.0
MBEBrief CommunicationMEGA6: Molecular Evolutionary Genetics Analysis version 6.0Koichiro Tamura 1, 2, Glen Stecher 3, Daniel Peterson 3, Alan Filipski 3, and Sudhir Kumar 3,4,5*1Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, Tokyo, Japan 2 Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan3Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, Arizona, USA4School of Life Sciences, Arizona State University, Tempe, Arizona, USA5Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia*Correspondence to:Sudhir Kumar, Ph.D.Center for Evolutionary Medicine and InformaticsBiodesign Institute @ Arizona State UniversityTempe, Arizona 85287, USAEmail: s.kumar@© The Author 2013. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@MBE Advance Access published October 16, 2013 at China Agricultural University on September 22, 2014/Downloaded fromAbstractWe announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements our RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from free of charge.at China Agricultural University on September 22, 2014/Downloaded fromThe Molecular Evolutionary Genetics Analysis (MEGA) software is developed for comparative analyses of DNA and protein sequences that are aimed at inferring the molecular evolutionary patterns of genes, genomes, and species over time (Kumar, et al. 1994; Tamura, et al. 2011). MEGA is currently distributed in two editions: a graphical user interface (GUI) edition with visual tools for exploration of data and analysis results (Tamura, et al. 2011) and a command line edition (MEGA-CC) which is optimized for iterative and integrated pipeline analyses (Kumar, et al. 2012).In version 6.0, we have now added facilities for building molecular evolutionary trees scaled to time (timetrees), which are clearly needed by scientists as an increasing number of studies are reporting divergence times for species, strains, and duplicated genes (e.g., Kumar and Hedges 2011; Ward, et al. 2013). For this purpose, we have implemented the RelTime method, which can be used for large numbers of sequences that comprise contemporary datasets, is the fastest method among its peers, and is shown to perform well in computer simulations (Tamura, et al. 2012). RelTime produces estimates of relative times of divergence for all branching points (nodes) in any phylogenetic tree without requiring knowledge of the distribution of the lineage rate variation and without using clock calibrations and associated distributions. Relative time estimates produced by MEGA will be useful for determining the ordering andspacing of sequence divergence events in species and gene family trees. In fact, the (relative) branch rates produced by RelTime will enable users to determine the statistical distribution of evolutionary rates among lineages and detect rate differences between species and duplicated gene clades. In addition, relative times obtained using molecular data can be directly compared with the times from non-molecular data (e.g., fossil record) to test independent biological hypotheses. The RelTime computation in MEGA6 is highly efficient in terms of both performance and memory required. For a nucleotide alignment of 765 sequences and 2,000 base pairs (data from Tamura, et al. 2011), MEGA6 required just 43 minutes and 1 gigabyte of memory (including the calculation steps mentioned below). Both time and memory requirements increase linearly with the number of sequences in MEGA6 (Figure 1). Figure 2shows a timetree produced by MEGA6 and displayed in the Tree Explorer, which has been upgraded from previous versions of MEGA to display confidence intervals and to export relative divergence times and evolutionary rates for branches, along with absolute divergence times and confidence intervals (see below). The Tree Explorer also allows customization of the timetree display in many ways for producing publication quality images.Using calibrations to translate relative times to absolute times.The relative times produced by the RelTime method can be directly converted into absolute times when a single known divergence time at China Agricultural University on September 22, 2014 / Downloaded from(calibration point) based on fossil or other information is available. This facility is incorporated in MEGA6 where a global time factor (f), which is computed from the given calibration point, converts all estimates of relative times (NT s) to absolute times (AT s) where AT x = f×NT x for the internal node x. This approach is taken because NT s are already shown to be linearly related with the true time (Tamura, et al. 2012). However, researchers often use multiple calibration points along with information on upper and/or lower bounds on one or more calibration points. In order to consider those constraints when estimating f, we have extended the RelTime implementation to ensure that the estimate of f produces estimates of AT that satisfy the calibration constraints. In this case, if there are a range of values for f that do not violate the calibration constraints, then the midpoint of that range becomes the estimate of f. If one or more of the AT s fall outside the calibration constraints, then f is set so that their deviation from the constraints is minimized. In this case, NT s for the nodes with estimated AT s are adjusted to satisfy the calibration constraints, such that the estimated ATs for the offending nodes will lie between the minimum and maximum constraint times specified by the user. These adjustments to NT s are followed by re-optimizing all other NT s in the tree recursively using the standard RelTime algorithm. Figure 2 shows a timetree display with absolute times in the Tree Explorer, where 95% confidence intervals are shown for each nodetime (see below).Confidence intervals for time estimates.MEGA6 also provides confidence intervals for relative and absolute divergence times, which are necessary to assess the uncertainty in the estimated time and test biological hypotheses. In this formulation, variance contributed by the presence of rate variation among lineages (V R,i) is combined with the estimated variance of relative node time (V NT,i). We compute V R,i using the mean of the coefficient of variation of lineage rates over all nodes (C R). It is obtained by first computing the mean (µ) and standard deviation (σ) of the node-to-tip distance for each node in the original tree with branch lengths. Then, C R= ∑[σi/µi]2/(n-3), where n is the number of sequences. For node i, V R,i = (NT i × √C R)2. The variance of node height (V H,i) is estimated by the curvature method obtained during the maximum likelihood estimation of branch lengths, and thus relative NT s, for each node. Then, the variance of NT is V(NT i) = V NT,i + V R,i, which is used to generate a 95% confidence interval. The bounds of this interval in terms of relative time are then multiplied by the factor f to provide confidence intervals on absolute times when calibrations are provided. It is important to note that this variance does not incorporate the uncertainty specified in the calibration times by the user through the specifications of minimum and maximum bounds, because the statistical distribution of the calibration uncertainty is rarely known. Therefore, we only use the range of calibration bounds during the estimation of f that converts relative times into absolute times, as described above, but this range does not affect the size of confidence interval at China Agricultural University on September 22, 2014 / Downloaded fromin any other way. In the future, we plan to enhance the estimation of f when users provide statistical distributions specifying the calibration uncertainty (see also, Hedges and Kumar 2004).Timetree Wizard. In practice, the estimation of timetrees can be cumbersome, as one must provide a phylogeny, a sequence dataset, and calibration points with constraints. To simplify this process, we have programmed a Timetree Wizard to enable users to provide all of these inputs through an intuitive graphical interface step-by-step. Figure 3A shows a flowchart of the Timetree Wizard, where the user first provides a sequence alignment and a tree topology for use in building a timetree. MEGA6 validates these inputs by mapping (sequence) names in the topology to the names in the alignment data. If the topology contains a subset of sequences present in the alignment, MEGA automatically subsets the sequence data. Additional automatic subsetting of data is provided in the Analysis Preferences Dialog box (see below). In the next step, the user has the option to provide calibration constraints by using a new Calibration Editor in MEGA6 where calibration points are specified by (a) point-and-click on individual nodes in the tree display (Fig. 3B), (b) selecting name-pairs from drop down lists such that their most recent common ancestor on the topology refer to the desired nodes (Fig. 3C), and/or (c) uploading a text file containing calibration constraints in a simple format (Fig. 3D). If no calibration constraints are provided, then onlyrelative times and related statistical measurements will be produced by MEGa6, but users still have an option to specify them in the Tree Explorer where the timetree containing relative times is displayed.The next step in Timetree Wizard is for the user to select various analysis options in the Analysis Preferences Dialog, including the types of substitutions to consider (e.g., nucleotide, codon, or amino acid), evolutionary model describing the substitution pattern, distribution of substitution rates among sites (e.g., uniform or gamma distributed rates and the presence of invariant sites), options for excluding certain alignment positions, and stringency for merging evolutionary clock rates during timetree analysis . These options are available in a context-dependent manner based on the type of sequence data being used in the analysis (e.g., nucleotide, coding vs. non-coding, or proteins). For coding nucleotide data, the users may subset the data based on the desired codon positions or ask MEGA to automatically translate codons into amino acids and conduct analysis at the protein sequence level. The data subset options also allow for handling of gaps and missing data, where one can choose to use all the data or exclude positions that contain a few or more gaps or missing data (e.g., Partial Deletion option). The stringency for merging clock rates option indicates the statistical significance to use for deciding conditions in which the ancestor and descendant rates will be the same (rate merging), which is important to reduce the number of rate at China Agricultural University on September 22, 2014 / Downloaded fromparameters estimated and to avoid statistical over-fitting. Once these and other options are set, the RelTime computation begins.Other enhancements in MEGA. In addition to the new timetree system in MEGA6, we have made several other useful enhancements. First, we have added the Subtree-Pruning-and-Regrafting (SPR) algorithm to search for the optimal tree under the Maximum Likelihood (ML) and Maximum Parsimony (MP) criteria (Nei and Kumar 2000; Swofford 1998). In addition, the Tree-Bisection-and-Regrafting (TBR) algorithm is now included to search for the MP trees. These algorithms replace the Close-Neighbor-Interchange (CNI) approach and allow for a more exhaustive search of the tree space (Nei and Kumar 2000; Swofford 1998). These algorithms were tested on simulated datasets which were analyzed in Tamura et al. (2011). The final trees produced by SPR heuristic search were, on average, slightly better by the given optimality criterion than the true tree, a phenomenon explained by Nei et al. (1998). Therefore, MEGA6 heuristic searches are expected to perform well in practical data analysis.We have also upgraded MEGA source code to increase the amount of memory that MEGA can address in 64-bit computers, where it can now use up to 4 gigabyte of memory, which is twice its previous limit. Thesource code upgrade has also increased the canvas size in Tree Explorer, which can now render trees with as many as 4,000 taxa. Finally, we have implemented a usage analytics system to assess options and analyses that are the most used. At the time of installation, users have a choice to participate in this effort, where we wish to generate a better understanding of the needs of the user community for prioritizing future developments. For the future, we have already planned the release of a full 64-bit version of MEGA as well as support for partitioned ML phylogenetic analyses. An outcome of this effort is a 64-bit command-line version of MEGA6 that supports the timetree analysis, which can downloaded from /reltime and used for very large sequence datasets.AcknowledgementsWe thank Oscar Murillo for extensive help in testing the RelTime computations. We would also like to thank Sayaka Miura, Anna Freydenzon, Mike Suleski, and Abediyi Banjoko for their invaluable feedback. This work was supported from research grants from National Institutes of Health (HG002096-12 to SK and HG006039-03 to AF) and Japan Society for the Promotion of Science (JSPS) grants-in-aid for scientific research to KT. at China Agricultural University on September 22, 2014 / Downloaded fromFigure Legends.Figure 1. Time (panel A) and memory (panel B) needed for increasingly larger datasets for timetree calculations in MEGA6. Results shown are from an analysis of a nucleotide sequence alignment of 765 sequences and 2,000 base pairs. Increasingly larger number of sequences were sampled from this alignment to obtain the computational time (minutes) and computer memory (Megabytes, MB). The time taken increases polynomially with the number of sequences (4×10-05x2 + 2.64 ×10-2x; R2 = 0.99), where x is the number of sequences. However, a linear regression also fits well (0.048x; R2 = 0.93). Similarly, the memory required increases linearly with the number of sequences (1.52x, R2 = 0.99). All calculations were performed on the same computer with an Intel® Xeon® E5-2665 CPU, 128 Gigabytes of RAM, and running Windows Server 2012 64-bit edition.Figure 2. (A) Timetree inferred in MEGA6 and shown in the Tree Explorer, where it is displayed with divergence times (written in maroon font) and their respective 95% confidence intervals. A scale bar for absolute divergence times is shown. (B) An information panel that can be made visible by pressing theicon marked with an “i”. When focused on a tree node (left side), it shows the internal node identifier, and absolute or relative divergence time as appropriate; when focused on a branch (right side), it displays the local clock rate as well as the relative branch length. (C) A timetable exported using the displayed timetree, which shows the ancestor-descendant relationship along with relative node times, relative branch rates, absolute divergence times, and confidence intervals. Users can display internal node identifiers in the Tree Explorer as well as internal node names, which can be provided in the input topology file. On pressing the “Caption” in the Tree Explorer menu bar, MEGA produces the following text to inform the user about the methods, choices, and data used. Caption: The timetree shown was generated using the RelTime method. Divergence times for all branching points in the user-supplied topology were calculated using the Maximum Likelihood method based on the General Time Reversible model. Relative times were optimized and converted to absolute divergence times (shown next to branching points) based on user-supplied calibration constraints. Bars around each node represent 95% confidence intervals which were computed using the method described in Tamura et al. (2013). The estimated log likelihood value of the topology shown is -247671.60. A discrete Gamma distribution was used to model evolutionary rate differences among sites (4 categories, +G, parameter = 38.07). The tree is drawn to scale, with branch lengths measured in the relative number of substitutions per site. The analysis involved 446 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% at China Agricultural University on September 22, 2014 / Downloaded fromalignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 1048 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 (Tamura, et al. 2013). Figure 3. (A) The flowchart of the Timetree Wizard . When launching the timetree analysis, a user first provides a data file containing a sequence alignment and another file containing a phylogeny (topology).(B) The Calibration Editor is invoked when the user needs to specify calibration constraints, which contains facilities to mark calibrations on top of the user-specified topology. (C) Users may also specify calibrations selecting two sequence names whose most recent common ancestor (MRCA) points to the node to use for calibration. (D) The user may also upload constraints via formatted text files for which two types of formats are supported. In one, the calibration time constraints and the names of two taxa whose MRCA is the node to calibrate are given (panel C style). In the second, a node name in addition to the time constraints is given and this node name matches an internal node label that is included in the Newick tree file that contains the topology that is used for the timetree analysis. (E) Analysis Preferences Dialog enables the user to select methods, models and data subset options.at China Agricultural University on September 22, 2014/Downloaded fromReferencesHedges SB, Kumar S. 2004. Precision of molecular time estimates. Trends Genet. 20: 242-247.Kumar S, Hedges SB. 2011. Timetree2: Species divergence times on the iphone. Bioinformatics 27: 2023-2024.Kumar S, Stecher G, Peterson D, Tamura K. 2012. Mega-cc: Computing core of molecular evolutionarygenetics analysis program for automated and iterative data analysis. Bioinformatics 28: 2685-2686.Kumar S, Tamura K, Nei M. 1994. Mega: Molecular evolutionary genetics analysis software formicrocomputers. Comput Appl Biosci. 10: 189-191.Nei M, Kumar S. 2000. Molecular evolution and phylogenetics. Oxford ; New York: Oxford University Press. Nei M, Kumar S, Takahashi K. 1998. The optimization principle in phylogenetic analysis tends to giveincorrect topologies when the number of nucleotides or amino acids used is small. Proc Natl Acad Sci USA. 95: 12390-12397.Swofford D. 1998. Paup*: Phylogenetic analysis using parsimony (and other methods). Sunderland, MA:Sinauer Associates.Tamura K, Battistuzzi FU, Billing-Ross P, Murillo O, Filipski A, Kumar S. 2012. Estimating divergence timesin large molecular phylogenies. Proc Natl Acad Sci USA. 109: 19333-19338.Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. 2011. Mega5: Molecular evolutionarygenetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 28: 2731-2739.Tamura K, Peterson D, Stecher G, Filipski A, Kumar S. 2013. Mega6: Molecular evolutionary geneticsanalysis version 6.0. Mol Biol Evol. 30: (In press).Ward MJ, Lycett SJ, Kalish ML, Rambaut A, Leigh Brown AJ. 2013. Estimating the rate of intersubtyperecombination in early hiv-1 group m strains. J Virol. 87: 1967-1973.at China Agricultural University on September 22, 2014/Downloaded fromFIGURE 1Downloaded from/at China Agricultural University on September 22, 2014Downloaded from / at China Agricultural University on September 22, 2014Downloaded from / at China Agricultural University on September 22, 2014。
免疫组学的研究进展
免疫组学的研究进展唐康侯永利王亚珍陈丽华(中国人民解放军空军军医大学基础医学院免疫学教研室,西安 710032)中图分类号R392.9 文献标志码 A 文章编号1000-484X(2024)01-0185-07[摘要]随着高通量测序技术、生物信息学等相关领域进展以及人类对免疫系统功能认识的逐步深入,免疫组学从最初解析B细胞受体(BCR)、T细胞受体(TCR)基因序列逐渐发展为解析和绘制宿主免疫系统和抗原的互作关系以及宿主免疫系统应答机制的全景图谱,主要包括抗原表位组学、免疫基因组学、免疫蛋白质组学、抗体组学和免疫信息学等方面的研究,并基于大量免疫学研究数据建立了ImmPort、VDJdb和IEDB等免疫学数据库,加速了新抗原表位的发现和免疫应答机制等研究。
免疫组学能够揭示免疫系统与疾病的关联,促进新型疫苗和免疫治疗策略开发,将有效推动个体化医疗和精准药物治疗。
近年免疫组与暴露组等的整合以及与人工智能的融合将对全面理解免疫系统对环境因素的响应和调节机制、解析疾病发生和发展的分子机制产生重大影响。
[关键词]免疫组;免疫组学;免疫信息学;人工智能Advances in immunomics researchTANG Kang, HOU Yongli, WANG Yazhen, CHEN Lihua. Department of Immunology, School of Basic Medicine,Air Force Medical University, Xi'an 710032, China[Abstract]With the progress of high-throughput sequencing technologies and bioinformatics, and deepening understanding of immune system,immunomics has evolved from initially deciphering gene sequences of B cell receptor (BCR)and T cell receptor (TCR) to unraveling and mapping interactions between host immune system and antigens, as well as panorama of host immune system response mechanisms, which now encompasses various research areas, such as antigen epitopeomics, immunogenomics, immunopro‐teomics, antibodyomics and immunoinformatics. Based on a large amount of immunological research data, immunological databases such as ImmPort, VDJdb and IEDB have been established to accelerate discovery of new antigen epitopes and study of immune response mechanisms. Immunomics has revealed the association between immune system and diseases, promoted the development of novel vac‐cines and immunotherapeutic strategies, and effectively drove the development of personalized medicine and precision medicine. In recent years, integration of immunome with exposome and fusion it with artificial intelligence will have a significant impact on compre‐hensively understanding immune system's response and regulatory mechanisms to environmental factors, as well as deciphering molecular mechanisms underlying disease occurrence and progression.[Key words]Immunome;Immunomics;Immunoinformatics;Artificial intelligence免疫组(immunome)是宿主免疫系统与抗原的互作关系以及宿主免疫系统应答机制的全景图谱,包括免疫系统的识别对象、识别受体以及参与免疫应答过程的其他分子[1-3]。
上海交通大学SCI论文新的AB档分类目录(2014-2016)
Rank
特别 特别 特别 特别 特别 特别 特别 特别 特别 特别
NATURE REVIEWS CANCER 1474-175X NATURE REVIEWS GENETICS NATURE BIOTECHNOLOGY NATURE REVIEWS DRUG DISCOVERY NATURE REVIEWS MOLECULAR CELL NATURE MATERIALS 1471-0056 1087-0156 1474-1776 1471-0072 1476-1122 1474-1733 1748-3387 0092-8674 1471-003X 1749-4885 1061-4036 1078-8956 1548-7091 1529-2908 1535-6108 1740-1526 1755-4330 1745-2473 1465-7392 0001-8732 1097-6256 1097-2765 1552-4450 1545-9993
1083-4419
3.781
A
53 54 55 56 57 58
1364-6613 0145-9476 0907-4449
NATURE REVIEWS IMMUNOLOGY NATURE NAT NANOTECHNOL NANOTECHNOLOGY CELL NAT REV NEUROSCI NAT PHOTONICS NAT GENET NAT MED NAT METHODS NAT IMMUNOL CANCER CELL NAT REV MICROBIOL NAT CHEM NAT PHYS NAT CELL BIOL ADV PHYS NAT NEUROSCI MOL CELL NAT CHEM BIOL NAT STRUCT MOL BIOL P NATL ACAD SCI USA CELL NATURE REVIEWS NEUROSCIENCE NATURE PHOTONICS NATURE GENETICS NATURE MEDICINE NATURE METHODS NATURE IMMUNOLOGY CANCER CELL NATURE REVIEWS MICROBIOLOGY NATURE CHEMISTRY NATURE PHYSICS NATURE CELL BIOLOGY ADVANCES IN PHYSICS NATURE NEUROSCIENCE MOLECULAR CELL NATURE CHEMICAL BIOLOGY NATURE STRUCTURAL & MOLECULAR BIOLOGY PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AME
E. coli 常用菌株大全
E. coli genotypesContents[hide]•1 Nomenclature & Abbreviations•2 Methylation Issues in E. coli•3 Commonly used strainso 3.1 AG1o 3.2 AB1157o 3.3 B2155o 3.4 BL21o 3.5 BL21(AI)o 3.6 BL21(DE3)o 3.7 BL21 (DE3) pLysSo 3.8 BNN93o 3.9 BNN97o 3.10 BW26434, CGSC Strain # 7658o 3.11 C600o 3.12 C600 hflA150 (Y1073, BNN102)o 3.13 CSH50o 3.14 D1210o 3.15 DB3.1o 3.16 DH1o 3.17DH5αo 3.18DH5α Turbo (NEB)o 3.19 DH10B (Invitrogen)o 3.20 DH12S (Invitrogen)o 3.21 DM1 (Invitrogen)o 3.22 E. cloni(r) 5alpha (Lucigen)o 3.23 E. cloni(r) 10G (Lucigen)o 3.24 E. cloni(r) 10GF' (Lucigen)o 3.25 E. coli K12 ER2738 (NEB)o 3.26 ER2566 (NEB)o 3.27 ER2267 (NEB)o 3.28 HB101o 3.29 HMS174(DE3)o 3.30 High-Control(tm) BL21(DE3) (Lucigen)o 3.31 High-Control(tm) 10G (Lucigen)o 3.32 IJ1126o 3.33 IJ1127o 3.34 JM83o 3.35 JM101o 3.36 JM103o 3.37 JM105o 3.38 JM106o 3.39 JM107o 3.40 JM108o 3.41 JM109o 3.42 JM109(DE3)o 3.43 JM110o 3.44 JM2.300o 3.45 LE392o 3.46 Mach1o 3.47 MC1061o 3.48 MC4100o 3.49 MG1655o 3.50 OmniMAX2o 3.51 OverExpress(tm)C41(DE3) (Lucigen)o 3.52 OverExpress(tm)C41(DE3)pLysS (Lucigen) o 3.53 OverExpress(tm)C43(DE3) (Lucigen)o 3.54 OverExpress(tm)C43(DE3)pLysS (Lucigen) o 3.55 Rosetta(DE3)pLysSo 3.56 Rosetta-gami(DE3)pLysSo 3.57 RR1o 3.58 RV308o 3.59 SOLR (Stratagene)o 3.60 SS320 (Lucigen)o 3.61 STBL2 (Invitrogen)o 3.62 STBL3 (Invitrogen)o 3.63 STBL4o 3.64 SURE (Stratagene)o 3.65 SURE2 (Stratagene)o 3.66 TG1 (Lucigen)o 3.67 TOP10 (Invitrogen)o 3.68 Top10F' (Invitrogen)o 3.69 W3110o 3.70 WM3064o 3.71 XL1-Blue (Stratagene)o 3.72 XL1-Blue MRF' (Stratagene)o 3.73 XL2-Blue (Stratagene)o 3.74 XL2-Blue MRF' (Stratagene)o 3.75 XL1-Red (Stratagene)o 3.76 XL10-Gold (Stratagene)o 3.77 XL10-Gold KanR (Stratagene)•4 Other genotype information sources•5 ReferencesNomenclature & AbbreviationsA listed gene name means that gene carries a loss of function mutation, a Δ preceding a gene name means the gene is deleted. If a gene is not listed, it is not known to be mutated. Prophages present in wt K-12 strains (F, λ, e14, rac) are listed only if absent. E. coliB strains are naturally lon- and dcm-.•F- = Does not carry the F plasmid•F+ = Carries the F plasmid. The cell is able to mate with F- through conjugation.•F'[ ] = Carries an F plasmid that has host chromosomal genes on it froma previous recombination event. This cell can also mate with F- throughconjugation. Chromosomal genes carried in the F plasmid are listed inbrackets.•r B/K+/- = The (B/K) defines the strain lineage. The +/- indicates whether the strain has or hasn't got the restriction system.•m B/K+/- = The (B/K) defines the strain lineage. The +/- indicates whether the strain has or hasn't got the modification (methylation) system.•hsdS = Both restriction and methylation of certain sequences is deleted from the strain. If you transform DNA from such a strain into a wild type strain, it will be degraded.•hsdR = For efficient transformation of cloned unmethylated DNA from PCR amplifications•INV( ) = chromosomal inversion between locations indicated•ahpC = mutation to alkyl hydroperoxide reductase conferring disulfide reductase activity•ara-14 = cannot metabolize arabinose•araD = mutation in L-ribulose-phosphate 4-epimerase blocks arabinose metabolism•cycA = mutation in alanine transporter; cannot use alanine as a carbon source•dapD = mutation in succinyl diaminopimelate aminotransferase leads to succinate or (lysine + methionine) requirement•Δ( ) = chromosomal deletion of genes between the listed genes (may include unlisted genes!)•dam = adenine methylation at GATC sequences exist; high recombination efficiency; DNA repair turned on•dcm = cytosine methylation at second C of CCWGG sites exist. dam & dcm are the default properties and always elided, while dam- or dcm-should be declare explicitly•DE3 = Lysogen that encodes T7 RNA polymerase. Used to induce expression in T7-driven expression systems•deoR = regulatory gene that allows constitutive expression of deoxyribose synthesis genes; permits uptake of large plasmids. See Hanahan D, US Patent 4,851,348. ***This has been called into question, as the DH10B genome sequence revealed that it is deoR+. SeeDurfee08, PMID 18245285.•dnaJ = one of the chaparonins inactivated; stabilizes some mutant proteins•dut1 = dUTPase activity abolished, leading to increased dUTP concentrations, allowing uracil instead of thymine incorporation in DNA.Stable U incorporation requires ung gene mutation as well.•endA1 = For cleaner preparations of DNA and better results in downstream applications due to the elimination of non-specific digestion by Endonuclease I•(e14) = excisable prophage like element containing mcrA gene; present in K-12 but missing in many other strains•galE = mutations are associated with high competence, increased resistance to phage P1 infection, and 2-deoxygalactose resistance.galE mutations block the production of UDP-galactose, resulting intruncation of LPS glycans to the minimal, "inner core". The exceptional competence of DH10B/TOP10 is thought to be a result of a reducedinterference from LPS in the binding and/or uptake of transforming DNA.galE15 is a point mutation resulting in a Ser123 -> Phe conversion near the enzyme's active site. See van Die, et al. PMID 6373734, Hanahan, et al. PMID 1943786, and EcoSal ISBN 1555811647. --Dcekiert 16:56,23 January 2008 (CST)•galk = mutants cannot metabolize galactose and are resistant to 2-deoxygalactose. galK16 is an IS2 insertion ~170bp downstream of the galK start codon. See EcoSal ISBN 1555811647. --Dcekiert 16:56,23 January 2008 (CST)•galU = mutants cannot metabolize galactose•gor = mutation in glutathione reductase; enhances disulphide bond formation•glnV = suppression of amber (UAG) stop codons by insertion of glutamine; required for some phage growth•gyrA96 = mutation in DNA gyrase; conveys nalidixic acid resistance •gyrA462 = mutation in DNA gyrase; conveys resistance to ccdB colicin gene product•hflA150 = protease mutation stabilizing phage cII protein; high frequency of lysogenization by λ•Δ(lac)X74 = Deletion of the entire lac operon as well as some flanking DNA (complete deletion is Δcod-mhpF; see Mol.Micro., 6:1335, andJ.Bact., 179:2573)•lacI q or lacI Q = overproduction of the lac repressor protein; -35 site in promoter upstream of lacI is mutated from GCGCAA to GTGCAA•lacI Q1 = overproduction of the lac repressor protein; contains a 15 bp deletion to create optimal -35 site in promoter upstream of lacI•lacY = deficient in lactose transport; deletion of lactose permease (M protein)•lac ZΔM15= partial deletion of the lacZ gene that allows α complementation of the β-galactosidase gene; required for blue/white selection on XGal plates. Deletes the amino portion of lacZ (aa 11-41). •LAM- or λ- = lambda lysogen deletion; approximate map location: 17.40;information from CGSC *---Karmella 13:02, 21 October 2012 (EDT): •LamR = mutation in malT1 conferring lambda resistance; synonym malT1(LamR) [1] *---Karmella 13:35, 21 October 2012 (EDT):•leuB = requires leucine•Δlon = deletion of the lon protease•malA = cannot metabolize maltose•mcrA = Mutation eliminating restriction of DNA methylated at the sequence C m CGG (possibly m CG). Carried on the e14 prophage (q.v.) •mcrB = Mutation eliminating restriction of DNA methylated at the sequence R m C•metB = requires methionine•metC = requires methionine•mrr = Mutation eliminating restriction of DNA methylated at the sequence C m AG or G m AC•mtlA = cannot metabilize mannitol•(Mu) = Mu p rophage present. Muδ means the phage is defective. •mutS - mutation inhibits DNA repair of mismatches in unmethylated newly synthesized strands•nupG = same as deoR•ompT = mutation in outer membrane protein protease VII, reducing proteolysis of expressed proteins•(P1) = Cell carries a P1 prophage. Cells express the P1 restriction system.•(P2) = Cell carries a P2 prophage. Allows selection against Red+ Gam+ λ•(φ80)= Cell carries the lambdoid prophage φ80. A defective version of this phage carrying lacZM15 deletion (as well as wild-type lacI, lacYA, and flanking sequences) is present in some strains. The φ80attachment site is just adjacent to tonB.•pLysS = contains pLysS plasmid carrying chloramphenicol resistance and phage T7 lysozyme, effective at attenuating activity of T7 RNApolymerase, for better inhibition of expression under non-inducedconditions. The sequence can be found here.•proA/B = requires proline•recA1 = For reduced occurrence of unwanted recombination in cloned DNA; cells UV sensitive, deficient in DNA repair•recA13 = as for recA1, but inserts less stable.•recBCD = Exonuclease V; mutation in RecB or RecC reduces general recombination by a factor of 100; impaired DNA repair; UV sensitive, easier propagation of inverted repeats•recJ Exonuclease involved in alternate recombination•relA = relaxed phenotype; permits RNA synthesis in absence of protein synthesis•rha = blocked rhamose metabolism•rnc = encodes RnaseIII (rnc-14 is a common null mutant)•rne = encodes RnaseE (rne-3071 is a common temperature sensitive mutant)•rpsL = mutation in ribosomal protein S12 conveying streptomycin resistance; also called strA, rpsL135(strR), strA135 [2] *---Karmella13:27, 21 October 2012 (EDT):•sbcBC = ExoI activity abolished; usually present in recBC strains;recombination proficient, stable inverted repeats•sr1 = cannot metabolize sorbitol•supE = glnV•supF = tyrT•thi = requires thiamine•thyA = requires thymidine•Tn10 = transposon normally carrying Tetracycline resistance•Tn5 = transposon normally carrying Kanamycin resistance•tonA = Mutation in outer membrane protein conveying resistance to phage T1 and phage T5•traD = Mutation eliminating transfer factor; prevents transfer of F plasmid•trxB = mutation in thioredoxin reductase; enhances disulphide bond formation in the cytoplasm•tsx = outer membrane protein mutation conveying resistance to phage T6 and colicin K•tyrT = suppression of amber (UAG) stop codons by insertion of tyrosine;needed for some phage infection such as λgt11.•ung1 = allows uracil to exist in plasmid DNA•xyl-5 = blocked xylose metabolism•Sm R = Streptomycin resistanceMethylation Issues in E. coli•Type I methylation systems:o E. coli K-12 restricts DNA which is not protected by adenine methylation at sites AA*C[N6]GTGC or GCA*C[N6]GTT, encodedby the hsdRMS genes(EcoKI). Deletions in these genes removeseither the restriction or methylation or both of these functions.o E. coli B derivative strains contain an hsdRMS system (EcoBI) restricting and protectiing the sequence TGA*[N8]TGCT orAGCA*[N8]TCA.•The mcrA gene (carried on the e14 prophage) restricts DNA which is methylated in C m CWGG or m CG sequences (methylation by the dcmgene product).•The mcrBC genes restrict R m C sequences.•The mrr gene product restricts adenine methylated sequences at CAG or GAC sites.• E. coli methylates the adenine in GATC (and the corresponding A on the opposite strand) with the dam gene product.•M.EcoKII methylates the first A at the palindromic site ATGCAT (as well as the corresponding A on the opposite strand), see (Kossykh VG (2004) J. Bact 186: 2061-2067 PMID 15028690) Note that this article has been retracted; the retraction appears to center on textual plagarism, notexperimental results. The homology to AvaIII is real. I think I believe it.tk 20:28, 9 December 2005 (EST). Rich Roberts reports: "We have tried ourselves to detect activity with this gene product and cannot detect any methyltransferase activity. In our case we used antibodies able todetect N6-methyladenine or N4 methylcytosine in DNA. The ones wehave are very sensitive and should have been able to detect 5 methylgroups in the whole E. coli chromosome. Nothing was detected in anover expressing strain."•For additional information see E. coli restriction-modification system and the NEB technical information on methylation.Commonly used strainsAG1endA1 recA1 gyrA96 thi-1 relA1 glnV44 hsdR17(r K- m K+)AB1157thr-1, araC14, leuB6(Am), Δ(gpt-proA)62, lacY1, tsx-33, qsr'-0, glnV44(AS), galK2(Oc), LAM-, Rac-0, hisG4(Oc), rfbC1, mgl-51, rpoS396(Am),rpsL31(strR), kdgK51, xylA5, mtl-1, argE3(Oc), thi-1•Bachmann BJ: Derivation and genotypes of some mutant derivatives of Escherichia coli K-12.Escherichia coli and Salmonella typhimurium. Cellular and Molecular Biology (Edited by: F C Neidhardt J L Ingraham KB Low B Magasanik M Schaechter H E Umbarger). Washington, D.C., American Society for Microbiology 1987,2:1190-1219.See CGSC#1157B2155thrB1004 pro thi strA hsdsS lacZD M15 (F`lacZD M15 lacI q traD36 proA+ proB+) D dapA::erm (Erm r) pir::RP4 [::kan (Km r) from SM10]An E. coli strain carrying the pir sequence required for maintenance of plasmids containing R6K ori. Also, this strain is auxotrophic for DAP (diaminopimelic acid - a lysine precursor). The auxotrophy helps in removal of this strain from a bi-parental mating setup after conjugation.Ref: Maintenance of broad-host-range incompatibility group P and group Q plasmids and transposition of Tn5 in Bartonella henselae following conjugal plasmid transfer from Escherichia coliDehio, C. & Meyer, M. (1997) J. Bacteriol. 179, 538–540BL21E. coli B F- dcm ompT hsdS(r B- m B-) gal [malB+]K-12(λS)•The "malB region" was transduced in from the K-12 strain W3110 to make the strain Mal+λS. See Studier et al. (2009) J. Mol. Biol. 394(4),653 for a discussion of the extent of the transfer.•Stratagene E. coli Genotype StrainsBL21(AI)F– ompT gal dcm lon hsdS B(r B- m B-) araB::T7RNAP-tetA•an E. coli B strain carrying the T7 RNA polymerase gene in the araB locus of the araBAD operon q.•Transformed plasmids containing T7 promoter driven expression are repressed until L-arabinose induction of T7 RNA polymerase.o Maximal expression is lower than that of BL21(DE3) (customer support 10/2012)•Derived from BL21.•See the product page for more information.•Brian Caliendo (Voigt lab) reported trouble getting the Datsenko and Wanner (2000) plasmid pCP20 to transform into this strain, when other strains transformed fine. Cause is unknown.BL21(DE3)F– ompT gal dcm lon hsdS B(r B- m B-) λ(DE3 [lacI lacUV5-T7 gene 1 ind1 sam7 nin5])•an E. coli B strain with DE3, a λ prophage carrying the T7 RNA polymerase gene and lacI q•Transformed plasmids containing T7 promoter driven expression are repressed until IPTG induction of T7 RNA polymerase from a lacpromoter.•Derived from B834 (Wood, 1966) by transducing to Met+.•See the original Studier paper or the summary in Methods in Enzymology for more details.•Whole genome sequence available [3]BL21 (DE3) pLysSF- ompT gal dcm lon hsdS B(r B- m B-) λ(DE3) pLy sS(cm R)•pLysS plasmid chloramphenicol resistant; grow with chloramphenicol to retain plasmid•Chloramphenicol resistant•The pLysS plasmid encodes T7 phage lysozyme, an inhibitor for T7 polymerase which reduces and almost eliminates expression fromtransformed T7 promoter containing plasmids when not induced.•see Moffatt87 for details of pLysS and pLysE plasmidsBNN93F- tonA21 thi-1 thr-1 leuB6 lacY1 glnV44 rfbC1 fhuA1 mcrB e14-(mcrA-)hsdR(r K-m K+) λ-•Some C600 strains are really BNN93BNN97•BNN93 (λgt11)o A λgt11 lysogen producing phage at 42CBW26434, CGSC Strain # 7658Δ(araD-araB)567, Δ(lacA-lacZ)514(::kan), lacIp-4000(lacI q), λ-, rpoS396(Am)?, rph-1, Δ(rhaD-rhaB)568, hsdR514•This information is from a printout sent by the E. coli Genetic Stock Center with the strain.• B.L. Wanner strain•rph-1 is a 1bp deletion that results in a frameshift over last 15 codons and has a polar effect on pyrE leading to suboptimal pyrimidine levelson minimal medium. (Jensen 1993 J Bact. 175:3401)•Δ(araD-araB)567 was formerly called ΔaraBAD AH33 by Datsenko and Wanner•Am = amber(UAG) mutation•Reference: Datsenko and Wanner, 2000, PNAS, 97:6640NOTE:•This promoter driving the expression of lacI was sequenced in this strain using a primer in mhpR (upstream of lacI) and a primer in theopposite orientation in lacI. The lac promoter was found to be identicalto wildtype. Thus, the -35 sequence was GCGCAA not GTGCAA asexpected with lacI q. Therefore this strain (or at least the versionobtained from the E. coli Genetic Stock Center) does NOT appear to be lacI q. According to Barry Wanner, this is an unexpected result. -Reshma 13:19, 5 May 2005 (EDT)•"We have now confirmed that BW25113, BW25141, and BW26434 are all lacI+, and not lacI q. We thank you for alerting us to the error withrespect to BW26434. Apparently, the lacI region was restored towild-type in a predecessor of BW25113." (from Barry WannerNovember 18, 2005)•The genotype has been corrected at the CGSCC600F- tonA21 thi-1 thr-1 leuB6 lacY1 glnV44 rfbC1 fhuA1 λ-•There are strains circulating with both e14+(mcrA+) and e14-(mcrA-) •General purpose host•See CGSC#3004•References: Appleyard, R.K. (1954) Genetics 39, 440; Hanahan, D.(1983) J. Mol. Biol. 166, 577.C600 hflA150 (Y1073, BNN102)F- thi-1 thr-1 leuB6 lacY1 tonA21 glnV44 λ- hflA150(chr::Tn10) •host for repressing plaques of λgt10 when establishing cDNA libraries •Reference Young R.A. and Davis, R. (1983) Proc. Natl. Acad. Sci. USA 80, 1194.•Tetracycline resistance from the Tn10 insertionCSH50F-λ-ara Δ(lac-pro) rpsL thi fimE::IS1•See CGSC#8085•References: Miller, J.H. 1972. Expts.in Molec.Genetics, CSH 0:14-0;Blomfeld et al., J.Bact. 173: 5298-5307, 1991.D1210HB101 lacI q lacY+DB3.1F- gyrA462 endA1 glnV44 Δ(sr1-recA) mcrB mrr hsdS20(r B-, m B-) ara14 galK2 lacY1 proA2 rpsL20(Sm r) xyl5 Δleu mtl1•useful for propagating plasmids containing the ccdB operon.•gyrA462 enables ccdB containing plasmid propagation•streptomycin resistant•appears to NOT contain lacI (based on a colony PCR) --Austin Che 16:16, 18 June 2007 (EDT)1. Bernard P and Couturier M. . pmid:1324324. PubMed HubMed[Bernard-JMolBiol-1992]2. Miki T, Park JA, Nagao K, Murayama N, and Horiuchi T. .pmid:1316444. PubMed HubMed[Miki-JMolBiol-1992]All Medline abstracts: PubMed HubMedDH1endA1 recA1 gyrA96 thi-1 glnV44 relA1 hsdR17(r K- m K+) λ-•parent of DH5α•An Hoffman-Berling 1100 strain derivative (Meselson68)•more efficient at transforming large (40-60Kb) plasmids•nalidixic acid resistant•Reference: Meselson M. and Yuan R. (1968) Nature 217:1110 PMID 4868368.DH5αF- endA1 glnV44 thi-1 recA1 relA1 gyrA96 deoR nupG Φ80d lacZΔM15Δ(lacZYA-argF)U169, hsdR17(r K- m K+), λ–•An Hoffman-Berling 1100 strain derivative (Meselson68)•Promega also lists phoA•nalidixic acid resistant•References:o FOCUS (1986) 8:2, 9.o Hanahan, D. (1985) in DNA Cloning: A Practical Approach (Glover, D.M., ed.), Vol. 1, p. 109, IRL Press, McLean, Virginia.o Grant, S.G.N. et al. (1990) Proc. Natl. Acad. Sci. USA 87: 4645-4649 PMID 2162051.o Meselson M. and Yuan R. (1968) Nature 217:1110 PMID4868368.DH5α Turbo (NEB)F´ proA+B+ lacI q∆ lacZ M15/ fhuA2 ∆(lac-proAB) glnV galR(zgb-210::Tn10)Tet S endA1 thi-1 ∆(hsdS-mcrB)5•Also known as NEB Turbo•T1 phage resistant•Rapid growth: visible colonies on agar, ~6.5 hours; shaking liquid culture OD 600 = 2.0, ~4 hours•Expresses the Lac repressor•References:o New England Biolabs, product catalogue number C2984HDH10B (Invitrogen)F-endA1 recA1 galE15 galK16 nupG rpsL ΔlacX74 Φ80lacZΔM15 araD139 Δ(ara,leu)7697 mcrA Δ(mrr-hsdRMS-mcrBC) λ-•suitable for cloning methylated cytosine or adenine containing DNA •an MC1061 derivative (Casadaban80). Prepare cells for chemical transformation with CCMB80 buffer•blue/white selection•While DH10B has been classically reported to be galU galK, the preliminary genome sequence for DH10B indicates that DH10B (and by their lineage also TOP10 and any other MC1061 derivatives) is actually galE galK galU+. Dcekiert 16:37, 23 January 2008 (CST)•Genome sequence indicates that DH10B is actually deoR+. Presumably TOP10 and MC1061 are also deoR+.•Streptomycin resistant•References:o Casdaban, M. and Cohen, S. (1980) J Mol Biol 138:179 PMID 6997493.o Grant, S.G.N. et al. (1990) Proc. Natl. Acad. Sci. USA 87:4645-4649 PMID 2162051.o E. coli Genetic Stock Center, MC1061 Recordo DH10B Genome Sequencing Project, Baylor College of Medicineo Complete sequence is available, see Durfee08, PMID 18245285. DH12S (Invitrogen)mcrA Δ(mrr-hsdRMS-mcrBC) φ80d lacZΔM15 ΔlacX74 recA1 deoR Δ(ara, leu)7697 araD139 galU galK rpsL F' [proAB+ lacI q ZΔM15]•host for phagemid and M13 vectors•useful for generating genomic libraries containing methylated cytosine or adenine residues•streptomycin resistant•References: Lin, J.J., Smith, M., Jessee, J., and Bloom, F. (1991) FOCUS 13, 96.; Lin, J.J., Smith, M., Jessee, J., and Bloom, F. (1992)BioTechniques 12, 718.DM1 (Invitrogen)F- dam-13::Tn9(Cm R) dcm- mcrB hsdR-M+ gal1 gal2 ara- lac- thr- leu- tonR tsxR Su0•Host for pBR322 and other non-pUC19 plasmids; useful for generating plasmids that can be cleaved with dam and dcm sensitive enzymes •Chloramphenicol resistant•Promega lists as F' not F-•Reference: Lorow-Murray D and Bloom F (1991) Focus 13:20E. cloni(r) 5alpha (Lucigen)fhuA2Δ(argF-lacZ)U169 phoA glnV44 Φ80Δ(lacZ)M15 gyrA96 recA1 relA1 endA1 thi-1 hsdR17•Common cloning strain.E. cloni(r) 10G (Lucigen)F- mcrAΔ(mrr-hsd RMS-mcr BC) end A1 rec A1 Φ80dlac ZΔM15 Δlac X74ara D139 Δ(ara,leu)7697 gal U gal K rps L nup G λ- ton A•Common cloning strain.•Resistant to phage T1.E. cloni(r) 10GF' (Lucigen)[F´pro A+B+ lac IqZΔM15::Tn10 (TetR)] /mcr A Δ(mrr-hsd RMS-mcr BC) end A1 rec A1 Φ80d lac ZΔM15 Δlac X74 ara D139 Δ(ara, leu)7697 gal U gal K rps Lnup Gλ ton A•Strain for cloning and single-strand DNA production.E. coli K12 ER2738 (NEB)F´proA+B+ lacIq Δ(lacZ)M15 zzf::Tn10(TetR)/ fhuA2 glnV Δ(lac-proAB) thi-1 Δ(hsdS-mcrB)5•Phage propagation strain•Also available from Lucigen Corporation.ER2566 (NEB)F- λ- fhuA2 [lon] ompT lacZ::T7 gene 1 gal sulA11 Δ(mcrC-mrr)114::IS10R(mcr-73::miniTn10-TetS)2 R(zgb-210::Tn10)(TetS) endA1 [dcm] •Host strain for the expression of a target gene cloned in the pTYBvectors.•Carry a chromosomal copy of the T7 RNA polymerase gene inserted into lacZ gene and thus under the control of the lac promoter. In theabsence of IPTG induction expression of T7 RNA polymerase issuppressed by the binding of lac I repressor to the lac promoter.•Deficient in both lon and ompT proteases.ER2267 (NEB)F´ proA+B+ lacIq Δ(lacZ)M15 zzf::mini-Tn10 (KanR)/ Δ(argF-lacZ)U169glnV44 e14-(McrA-) rfbD1? recA1 relA1? endA1 spoT1? thi-1Δ(mcrC-mrr)114::IS10•Commonly used for titering M13 phage because of the strain's F' plasmid, which carries KanR, and its slow growth, which promotes easy visualization of plaques.HB101F- mcrB mrr hsdS20(r B- m B-) recA13 leuB6 ara-14 proA2 lacY1 galK2 xyl-5mtl-1 rpsL20(Sm R) glnV44 λ-Please note that different sources have different genotypes so treat this information with caution.•From a GIBCO BRL list of competent cells.•Hybrid of E. coli K12 and E. coli B (but 98% K strain AB266 according to Smith et al.)•Host for pBR322 and many plasmids•Sigma lists the deletion Δ(gpt,proA). Check this.•Promega does not list F-, mcrB, or mrr•Streptomycin resistant•References:o Boyer, H.W. and Roulland-Dussoix, D. (1969) J. Mol. Biol. 41, 459.o Smith, M., Lorow, D., and Jessee, J. (1989) FOCUS 11, 56 - pdf version from Invitrogeno Lacks S and Greenberg JR (1977) J Mol Biol 114:153.HMS174(DE3)F- recA1 hsdR(rK12- mK12+) (DE3) (Rif R)•HMS174 strains provide the recA mutation in a K-12 background. Like BLR, these strains may stabilize certain target genes whose productsmay cause the loss of the DE3 prophage.•DE3 indicates that the host is a lysogen of lDE3, and therefore carries a chromosomal copy of the T7 RNA polymerase gene under control of the lacUV5 promoter. Such strains are suitable for production of proteinfrom target genes cloned in pET vectors by induction with IPTG.High-Control(tm) BL21(DE3) (Lucigen)F– ompT gal dcm hsdS B(r B- m B-) (DE3)/Mini-F lacI q1(Gent r)•The HI-Control BL21(DE3) cells contain a single-copy BAC plasmid harboring a specially engineered version of the lacI q1 repressor allele.The lacI q1 allele expresses ~170-fold more lac repressor protein thanthe wild-type lacI gene.•The increased pool of lac repressor in HI-Control BL21(DE3) cells maintains tight control over the expression of T7 RNA polymerase from the lacUV5 promoter, reducing leaky expression of genes cloned undera T7 promoter.•an E. coli B strain with DE3, a λ prophage carrying the T7 RNA polymerase gene and lacI q•Transformed plasmids containing T7 promoter driven expression are repressed until IPTG induction of T7 RNA polymerase from a lacpromoter.High-Control(tm) 10G (Lucigen)F- mcrAΔ(mrr-hsd RMS-mcr BC) end A1 rec A1 Φ80dlac ZΔM15 Δlac X74ara D139 Δ(ara,leu)7697 gal U gal K rps L nup G λ- ton A/Mini-F lacI q1(Gent r) •The HI-Control 10G cells contain a single-copy BAC plasmid harboringa specially engineered version of the lacI q1 repressor allele. The lacI q1allele expresses ~170-fold more lac repressor protein than the wild-type lacI gene.•For stable cloning of T7 protein expression plasmids.•Resistant to phage T1.IJ1126E. coli K-12 recB21 recC22 sbcA5 endA gal thi Su+ Δ(mcrC-mrr)102::Tn10 See Endy:IJ1126IJ1127IJ1126 lacUV5 lacZ::T7 gene1-KnrSee Endy:IJ1127JM83rpsL ara Δ(lac-proAB) Φ80dlacZΔM15•Sigma lists thi. Check this.•streptomycin resistantJM101glnV44 thi-1 Δ(lac-proAB) F'[lacI q ZΔM15 traD36 proAB+]•host for M13mp vectors•recA+, r K+•original blue/white cloning strain•has all wt restriction systems•References: Messing, J. et al. (1981) Nucleic Acids Res. 9, 309;Yanisch-Perron, C., Vieira, J., and Messing, J. (1985) Gene 33, 103. JM103endA1 glnV44 sbcBC rpsL thi-1 Δ(lac-proAB) F'[traD36 proAB+ lacI qlacZΔM15]•streptomycin resistant•References: Hanahan, D. (1983) J. Mol. Biol. 166:557-80.•NEB says this strain encodes a prophage encoded EcoP1 endonuclease.•Sigma lists (P1) (r K-m K+ rP1+ mP1+)JM105endA1 glnV44 sbcB15 rpsL thi-1 Δ(lac-proAB) [F' traD36 proAB+ lacI qlacZΔM15] hsdR4(r K-m K+)•Sigma lists sbcC•streptomycin resistant•References: Yanisch-Perron, C., Vieira, J., and Messing, J. (1985) Gene 33, 103.JM106endA1 glnV44 thi-1 relA1 gyrA96 Δ(lac-proAB) F- hsdR17(r K-m K+) •References: Yanisch-Perron, C., Vieira, J., and Messing, J. (1985) Gene 33, 103.JM107endA1 glnV44 thi-1 relA1 gyrA96 Δ(lac-proAB) [F' traD36 proAB+ lacI qlacZΔM15] hsdR17(R K- m K+) λ-•host for M13mp vectors•recA+, r K+•Sigma lists e14- (McrA-)•nalidixic acid resistant•References: Yanisch-Perron, C., Vieira, J., and Messing, J. (1985) Gene 33, 103.。
入侵植物喜旱莲子草_生物学_生态学及管理_潘晓云
植 物 分 类 学 报 45 (6): 884–900(2007)doi:10.1360/aps06134 Acta Phytotaxonomica Sinica ———————————2006-08-28收稿, 2007-03-12收修改稿。
基金项目: 国家自然科学基金(30400052); 国家重点基础研究发展计划资助(2006CB403305) (Supported by the National Natural Science Foundation of China, Grant No. 30400052; National Basic Research Program of China, Grant No. 2006CB403305 )。
* 通讯作者(Author for correspondence. E-mail: jkchen@; Tel.: 86-21-65642468; Fax: 86-21-65642468)。
入侵植物喜旱莲子草——生物学、生态学及管理1潘晓云 1耿宇鹏 2Alejandro SOSA 1张文驹 1李 博 1陈家宽*1(生物多样性和生态工程教育部重点实验室, 复旦大学生物多样性科学研究所, 长江河口湿地生态系统野外科学 观测研究站 上海 200433)2(南美生物防治实验室 布宜诺斯艾利斯 1686)Invasive Alternanthera philoxeroides : biology, ecology andmanagement1PAN Xiao-Yun 1GENG Yu-Peng 2Alejandro SOSA 1ZHANG Wen-Ju 1LI Bo 1CHEN Jia-Kuan * 1(Ministry of Education Key Laboratory for Biodiversity Science & Ecological Engineering , Institute of Biodiversity Science ,Fudan University , Coastal Ecosystems Research Station of Yangtze River Estuary , Shanghai 200433, China) 2(South American Biological Control Laboratory , USDA-ARS , Hurlingham-Buenos Aires 1686, Argentina) Abstract In this review, we present a detailed account of Alternanthera philoxeroides (alligatorweed), including A. philoxeroides description, intraspecific variation from native to introduced regions, its life history strategies, invasion mechanisms, and management strategies. Alternanthera philoxeroides is a herbaceous amphibious weed of Amaranthaceae, native to South America, distributed from Buenos Aires Province (39° S) to south Brazil. It was first described by Martius in 1826, and consists of several taxa in both its native and non-native ranges. Current knowledge indicates that two forms of alligator weed exist in Argentina: A. philoxeroides f. philoxeroides in the southern range and A. philoxeroides f. angustifolia in the northern range. In Argentina, both forms set fruits and produce viable seeds. Alternanthera philoxeroides is now found as a serious weed from tropical to warm temperate regions, including the USA, China, India, South-East Asia, Australia and New Zealand. It is thought to have been brought to China during the 1930s, and later widely cultivated and spread in southern China as fodder during 1950s. The invasions of alligatorweed in China have caused considerable concerns, and now it is one of the 12 most harmful alien invasive species in China. Alligatorweed is found on stationary and slow moving water bodies, creeks, channels, riverbanks and associated areas that are occasionally flooded. It can also be found in terrestrial habitats as a pasture weed within urban environments. Alligatorweed does not produce viable seed in China and reproduces vegetatively from vegetative fragments (stems, rhizome or root tubes), which can be transported by water movement, boats, machinery and vehicles, and in hay. Movement between river catchments is common because of the human activities. Alligatorweed forms a floating mass which spreads out over the water. Its growth disrupts the ecology of banks and shallows and crowds out other plant species, restricts water flow, increases sedimentation, aggravates flooding, limits access and use by man and provides a favorable breeding area for disease vectors. We need better understanding of the biology and ecology of alligatorweed to assess the efficiency of control methods in any theoretical framework. According to the6期潘晓云等: 入侵植物喜旱莲子草——生物学、生态学及管理885 knowledge of the life history strategy of alligatorweed, we suggest that metapopulation theory is a good tool to improve management efficiency from watershed and regional perspectives. Key words alligatorweed (Alternanthera philoxeroides), biological invasion, dispersal, disturbance, intraspecific variation, life history, metapopulation, watershed management.摘要喜旱莲子草Alternanthera philoxeroides原产于南美洲, 属于苋科Amaranthaceae莲子草属Alternanthera。
非等位基因
非等位基因概述非等位基因是指同一基因座上的不同等位基因。
等位基因是指在某个给定的基因座上,可以存在多种不同的变体。
每个个体继承了一对等位基因,一对等位基因可能会导致不同的表型表达。
非等位基因的存在使得遗传学研究更加复杂,因为不同的等位基因会对个体的表型产生不同的影响。
背景在生物学中,基因座是指染色体上一个特定的位置,该位置上的基因决定了某个特征的表达方式。
每个基因座上可以有多种不同的等位基因。
等位基因是指在某个特定基因座上的不同基因变体。
每个个体都会继承一对等位基因,通过这对等位基因的不同组合,决定了个体的表型。
然而,并非所有基因座上的等位基因都具有相同的表现型。
非等位基因的影响非等位基因的存在导致不同等位基因会对个体表型产生不同的影响。
有些非等位基因会表现出显性效应,也就是说,当个体继承了一个突变的等位基因时,即使同时继承了一个正常的等位基因,但显性效应会使得突变的等位基因的表型表达得到体现。
相反,有些非等位基因会表现出隐性效应,当个体继承了两个突变的等位基因时,才会表现出突变的表型。
除了显性和隐性效应之外,非等位基因还可能发生两种其他类型的表型效应。
一种是共显效应,当个体继承了两个不同的突变等位基因时,在表型表达上会表现出一种新的特征,这个特征并不是单个突变等位基因所能导致的。
另一种是部分显性效应,当个体继承了两个不同的突变等位基因时,表型表达将介于两个单独突变等位基因的表型之间。
重组和非等位基因重组是指两个不同的染色体交换部分基因序列的过程。
在重组的过程中,非等位基因可能会发生改变,导致新的等位基因组合形成。
这一过程使得非等位基因的表型效应更加复杂,因为新的等位基因可能将不同基因座的效应组合起来。
非等位基因的重要性非等位基因对生物的适应性和多样性起着重要作用。
通过对等位基因的各种组合的研究,人们可以更好地理解基因与表型之间的关系,并揭示遗传变异对物种适应环境的重要性。
总结非等位基因是指同一基因座上的不同等位基因。
益生菌肠道微生物的基因组学英文论文及翻译
The genomics of probiotic intestinal microorganismsSeppo Salminen1 , Jussi Nurmi2 and Miguel Gueimonde1(1) Functional Foods Forum, University of Turku, FIN-20014 Turku, Finland(2) Department of Biotechnology, University of Turku, FIN-20014 Turku, FinlandSeppo SalminenEmail: *********************Published online: 29 June 2005AbstractAn intestinal population of beneficial commensal microorganisms helps maintain human health, and some of these bacteria have been found to significantly reduce the risk of gut-associated disease and to alleviate disease symptoms. The genomic characterization of probiotic bacteria and other commensal intestinal bacteria that is now under way will help to deepen our understanding of their beneficial effects.While the sequencing of the human genome [1, 2] has increased ourunderstanding of the role of genetic factors in health and disease, each human being harbors many more genes than those in their own genome. These belong to our commensal and symbiotic intestinal microorganisms - our intestinal 'microbiome' - which play an important role in maintaining human health and well-being. A more appropriate image of ourselves would be drawn if the genomes of our intestinal microbiota were taken into account. The microbiome may contain more than 100 times the number of genes in the human genome [3] and provides many functions that humans have thus not needed to develop themselves. The indigenous intestinal microbiota provides a barrier against pathogenic bacteria and other harmful food components [4–6]. It has also been shown to have a direct impact on the morphology of the gut [7], and many intestinal diseases can be linked to disturbances in the intestinal microbial population [8].The indigenous microbiota of an infant's gastrointestinal tract is originally created through contact with the diverse microbiota of the parents and the immediate environment. During breast feeding, initial microbial colonization is enhanced by galacto-oligosaccharides in breast milk and contact with the skin microbiota of the mother. This early colonization process directs the microbial succession until weaning and forms the basis for a healthy microbiota. The viable microbes in the adultintestine outnumber the cells in the human body tenfold, and the composition of this microbial population throughout life is unique to each human being. During adulthood and aging the composition and diversity of the microbiota can vary as a result of disease and the genetic background of the individual.Current research into the intestinal microbiome is focused on obtaining genomic data from important intestinal commensals and from probiotics, microorganisms that appear to actively promote health. This genomic information indicates that gut commensals not only derive food and other growth factors from the intestinal contents but also influence their human hosts by providing maturational signals for the developing infant and child, as well as providing signals that can lead to an alteration in the barrier mechanisms of the gut. It has been reported that colonization by particular bacteria has a major role in rapidly providing humans with energy from their food [9]. For example, the intestinal commensal Bacteroides thetaiotaomicron has been shown to have a major role in this process, and whole-genome transcriptional profiling of the bacterium has shown that specific diets can be associated with selective upregulation of bacterial genes that facilitate delivery of products of carbohydrate breakdown to the host's energy metabolism [10, 11]. Key microbial groups in the intestinal microbiota are highly flexible in adapting to changes in diet, and thus detailed prediction of their actions and effects may be difficult. Although genomic studies have revealed important details about the impact of the intestinal microbiota on specific processes [3, 11–14], the effects of species composition and microbial diversity and their potential compensatory functions are still not understood.Probiotics and healthA probiotic has been defined by a working group of the International Life Sciences Institute Europe (ILSI Europe) as "a viable microbial food supplement which beneficially influences the health of the host" [15]. Probiotics are usually members of the healthy gut microbiota and their addition can assist in returning a disturbed microbiota to its normal beneficial composition. The ILSI definition implies that safety and efficacy must be scientifically demonstrated for each new probiotic strain and product. Criteria for selecting probiotics that are specific for a desired target have been developed, but general criteria that must be satisfied include the ability to adhere to intestinal mucosa and tolerance of acid and bile. Such criteria have proved useful but cumbersome in current selection processes, as there are several adherence mechanisms and they influence gene upregulation differently in the host. Therefore, two different adhesion studies need to be conducted on each strain and theirpredictive value for specific functions is not always good or optimal. Demonstration of the effects of probiotics on health includes research on mechanisms and clinical intervention studies with human subjects belonging to target groups.The revelation of the human genome sequence has increased our understanding of the genetic deviations that lead to or predispose to gastrointestinal disease as well as to diseases associated with the gut, such as food allergies. In 1995, the first genome of a free-living organism, the bacterium Haemophilus influenzae, was sequenced [16]. Since then, over 200 bacterial genome sequences, mainly of pathogenic microorganisms, have been completed. The first genome of a mammalian lactic-acid bacterium, that of Lactococcus lactis, a microorganism of great industrial interest, was completed in 2001 [17]. More recently, the genomes of numerous other lactic-acid bacteria [18], bifidobacteria [12] and other intestinal microorganisms [13, 19, 20] have been sequenced, and others are under way [21]. Table 1lists the probiotic bacteria that have been sequenced. These great breakthroughs have demonstrated that evolution has adapted both microbes and humans to their current state of cohabitation, or even symbiosis, which is beneficial to both parties and facilitates a healthy and relatively stable but adaptable gut environment.Table 1Lessons from genomesLactic-acid bacteria and bifidobacteria can act as biomarkers of gut health by giving early warning of aberrations that represent a risk of specific gut diseases. Only a few members of the genera Lactobacillus and Bifidobacterium, two genera that provide many probiotics, have been completely sequenced. The key issue for the microbiota, for probiotics, and for their human hosts is the flexibility of the microorganisms in coping with a changeable local environment and microenvironments.This flexibility is emphasized in the completed genomes of intestinal and probiotic microorganisms. The complete genome sequence of the probiotic Lactobacillus acidophilus NCFM has recently been published by Altermann et al. [22]. The genome is relatively small and the bacterium appears to be unable to synthesize several amino acids, vitamins and cofactors. Italso encodes a number of permeases, glycolases and peptidases for rapid uptake and utilization of sugars and amino acids from the human intestine, especially the upper gastrointestinal tract. The authors also report a number of cell-surface proteins, such as mucus- and fibronectin-binding proteins, that enable this strain to adhere to the intestinal epithelium and to exchange signals with the intestinal immune system. Flexibility is guaranteed by a number of regulatory systems, including several transcriptional regulators, six PurR-type repressors and ninetwo-component systems, and by a variety of sugar transporters. The genome of another probiotic, Lactobacillus johnsonii [23], also lacks some genes involved in the synthesis of amino acids, purine nucleotides and numerous cofactors, but contains numerous peptidases, amino-acid permeases and other transporters, indicating a strong dependence on the host.The presence of bile-salt hydrolases and transporters in these bacteria indicates an adaptation to the upper gastrointestinal tract [23], enabling the bacteria to survive the acidic and bile-rich environments of the stomach and small intestine. In this regard, bile-salt hydrolases have been found in most of the sequenced genomes of bifidobacteria and lactic-acid bacteria [24], and these enzymes can have a significant impact on bacterial survival. Another lactic-acid bacterium, Lactobacillus plantarum WCFS1, also contains a large number of genes related to carbohydrate transport and utilization, and has genes for the production of exopolysaccharides and antimicrobial agents [18], indicating a good adaptation to a variety of environments, including the human small intestine [14]. In general, flexibility and adaptability are reflected by a large number of regulatory and transport functions.Microorganisms that inhabit the human colon, such as B. thetaiotaomicron and Bifidobacterium longum [12], have a great number of genes devoted to oligosaccharide transport and metabolism, indicating adaptation to life in the large intestine and differentiating them from, for example, L. johnsonii [23]. Genomic research has also provided initial information on the relationship between components of the diet and intestinal microorganisms. The genome of B. longum [12] suggests the ability to scan for nutrient availability in the lower gastrointestinal tract in human infants. This strain is adapted to utilizing the oligosaccharides in human milk along with intestinal mucins that are available in the colon of breast-fed infants. On the other hand, the genome of L. acidophilus has a gene cluster related to the metabolism of fructo-oligosaccharides, carbohydrates that are commonly used as prebiotics, or substrates to肠道微生物益生菌的基因组学塞波萨米宁,尤西鲁米和米格尔哥尔摩得(1)功能性食品论坛,图尔库大学,FIN-20014芬兰图尔库(2)土尔库大学生物技术系,FIN-20014芬兰图尔库塞波萨米宁电子邮件:seppo.salminen utu.fi线上发表于2005年6月29日摘要肠道有益的共生微生物有助于维护人体健康,一些这些细菌被发现显着降低肠道疾病的风险和减轻疾病的症状。
牛病毒性腹泻病毒牦牛分离株E0基因的克隆表达
摘
要 : 青 海牦牛 牛病毒 性 腹 泻病 毒 ( VD 将 B V)青 海 泽库 ( QHZ 株 的 E K) 0基 因亚 克 隆入 原核 表 达 载
体 p T 3 ( ) 构 建 了重组表 达 载体 p T 3 ( )E , E 一2 a , E 一2 a 一 0 然后 用重 组质 粒 转化 Roet( E ) 受 态细胞 , 利 sta D 3 感 并 用 I T 诱 导蛋 白表达 。表 达的 蛋 白用 Hi B n P G s a d镍 柱进行 亲合 层析 纯化 , senbo 鉴 定 表 达蛋 白。结 — Wetr lt
少[ 。本 研 究 以青 海 发 病 牦 牛 中分 离 出 的 B V 6 ] VD 流 行株 为试验 材料 , 隆表达 了 B V QHZ 克 VD K株 的
E 0基 因 , 进 一 步 研 究 B DV E 为 V 0蛋 白 的 生 物 学 活
r s B V) 也称 牛病 毒性 腹 泻一 膜病 病 毒 , 猪 u , VD , 黏 与
1 2 1 B V 0基 因 生 物 信 息 学 分 析 采 用 . . VD E
内外 针 对 牦 牛 源 B V O基 因 的 研 究 报 道 较 VD E
收 稿 日期 : 0 20 —8 2 1 - 22 基 金项 目 : 技 部 基 础 研 究 专 项 ( )F 1 2 O 科 2 ( Y2 O O ) O8
1 7
s uf 5 0基因片段 1 L, e2 , 0 酶切处理的 p T E - 二 级结 构 、 水 性 , 将 其 抗 原 性 与 NA 亲 并 DL、 2 V aeb f r . I E C4 3a 2 载体 5 . T N l ae / , 菌水 65 。将 L, 4D A gs x 无 p i 1L . L 毒株 进行 比较 。 l2 2 E _ . O基 因 的 扩 增 引 物 序 列 为 : F 5 P1 r _
GDF11_生物学功能的研究进展
CHINA MEDICINE AND PHARMACY Vol.14 No.8 April 202449[基金项目] 四川省绵阳市卫生健康委员会科研课题(201929);四川省绵阳市第三人民医院科研立项课题(202209)。
▲通讯作者GDF11生物学功能的研究进展敬媛媛 胥勋梅 张栋珉 田博文▲四川省绵阳市第三人民医院 四川省精神卫生中心,四川绵阳 621000[摘要]生长分化因子11(GDF11)是转化生长因子-β超家族的成员之一,近年因其在调节各种组织器官的发育和分化中的多种功能而备受关注。
GDF11具有广泛的生物学效应,包括在临床应用中逆转衰老、逆转与年龄相关的病理变化和调节损伤后器官再生的能力等,如逆转年龄相关的心肌肥厚、改善衰老骨骼肌代谢、促进神经血管再生、减少肝脏脂肪变性。
但是也有研究报道GDF11对心脏、骨骼肌、肝脏等并不存在有益作用。
目前的研究显示GDF11在各个器官系统中的生物学功能存在争议。
本文回顾近年来GDF11在各个系统及疾病中的作用,旨在对当前相关研究进行总结归纳,为以GDF11为靶点相关疾病的防治及预后提供理论依据。
[关键词] GDF11;生物学功能;心脏;骨骼肌;大脑;肝脏[中图分类号] R363 [文献标识码] A [文章编号] 2095-0616(2024)08-0049-04DOI:10.20116/j.issn2095-0616.2024.08.12Research progress in the biological function of GDF11JING Yuanyuan XU Xunmei ZHANG Dongmin TIAN BowenThe Third Hospital of Mianyang, Sichuan Provincial Center for Mental Health, Sichuan, Mianyang 621000, China[Abstract] Growth differentiation factor 11 (GDF11) is a member of the transforming growth factor-β superfamily that has received much attention in recent years due to its multiple functions in regulating the development and differentiation of various tissues and organs. Its extensive biological effects include reversing aging in clinical applications, as well as reversing age-related pathological changes and regulating organ regeneration after injury, such as reversing age-related myocardial hypertrophy, improving aging skeletal muscle metabolism, promoting neurovascular regeneration, and reducing liver steatosis. However, some studies have reported that GDF11 does not have beneficial effects on the heart, skeletal muscles, liver, etc. So current research shows that the biological function of GDF11 in various organ systems is controversial. This article reviews the role of GDF11 in various systems and diseases in recent years, aiming to summarize current relevant research and provide a theoretical basis for the prevention, treatment, and prognosis of diseases related to GDF11 as the target.[Key words] GDF11; Biological function; Heart; Skeletal muscles; Brain; Liver由于复杂的生物化学变化,器官和组织在衰老过程中会发生许多变化。
embl-ebi的发展史
embl-ebi的发展史EMBL-EBI(欧洲生物信息研究所)是欧洲分子生物学实验室(EMBL)旗下的一个研究机构,致力于生物信息学和计算生物学的研究与发展。
自从其成立以来,EMBL-EBI在生物信息学领域取得了巨大的成就,为全球的生命科学研究和医学进步做出了重要贡献。
EMBL-EBI的发展可以追溯到1980年代,当时生物学家和计算机科学家开始意识到,随着生物学研究的不断进展,大量的生物学数据需要进行存储、管理和分析。
为了满足这一需求,EMBL-EBI应运而生。
成立之初,EMBL-EBI的主要任务是建立一个集中存储和管理生物学数据的数据库。
这个数据库不仅包括基因序列和蛋白质序列,还包括生物学实验数据、三维结构数据以及生物学文献等。
随着时间的推移,EMBL-EBI逐渐壮大并发展出了一系列重要的数据库和工具。
其中最著名的数据库之一是欧洲核酸数据库(ENA),它是全球最大的基因和核酸序列数据库之一。
ENA不仅存储了大量的基因和核酸序列数据,还提供了一系列的分析工具和服务,帮助科学家们进行基因和核酸序列的比对、注释和分析。
另一个重要的数据库是蛋白质家族数据库(Pfam),它收集了全球范围内的蛋白质家族信息,并为科学家们提供了一套丰富的工具和资源,用于研究蛋白质结构、功能和进化。
此外,EMBL-EBI还开发了一系列其他的数据库和工具,包括基因组数据库、代谢组学数据库、系统生物学数据库等,为科学家们提供了丰富的数据资源和分析工具,促进了生命科学研究的进展。
除了数据库和工具的开发,EMBL-EBI还致力于推动生物信息学和计算生物学的研究和教育。
该机构定期举办各种培训课程和研讨会,吸引了大量的国际学者和研究人员参与其中。
此外,EMBL-EBI还与其他研究机构和学术机构合作,开展各种合作研究项目,推动生物信息学的发展和应用。
近年来,随着技术的不断进步和生物学研究的深入,生物信息学和计算生物学的重要性越来越凸显。
EMBL-EBI作为全球领先的生物信息学研究机构之一,继续致力于推动生物信息学的研究和应用。
- 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
IntroductionThere are over 300,000 species of extant seed plants around the globe.1About 60% of plants have found medicinal use in the post-Neolithic human history. Nowadays, people col-lect plants for medicinal use not only from the wild but also through artificial cultivation, which is an indispensable part of human civilization. There are over 10,000 medicinal plant species in China, accounting for ∼87% of the Chinese mate-ria medica (CMM).2 Medicinal plants are also essential raw materials of many chemical drugs, eg, the blockbuster drugs for antimalarial and anticancer therapy. Currently, more than one-third of clinical drugs are derived from botanical extracts and/or their derivatives. Unfortunately, most medicinal plants have not been domesticated, and currently there is no toolkit to improve their medicinal attributes for better clini-cal efficacy. Immoderate harvesting has led to supply crisis of phytomedicine, exemplified by the taxane-producing Taxus plants.3On the other hand, successful domestication and improvement are not realistic without deeper insights into the evolutionary pattern of medicinal plant genomes. Artificial selection can be regarded as an accelerated and targeted natu-ral selection. Studies of medicinal plant genome evolution are crucial not only for the understanding of the ubiquitous mechanisms of plant evolution and phylogeny but also for plant-based drug discovery and development, as well as the sustainable utilization of plant pharmaceutical resources. This review gives a preliminary examination of the recent devel-opments in medicinal plant genome evolution research and summarizes the benefits, gaps, and prospects of the current research topics.Evolution of Genome, Gene, and GenotypeGenome sequencing. The genomic studies of medicinal plants lag behind those of model plants and important crop plants. The genome sequences encompass essential infor-mation of plant origin, evolution, development, physiology, inheritable traits, epigenomic regulation, etc., which are the premise and foundation of deciphering genome diversity and chemodiversity (especially various secondary metabolites with potential bioactivities) at the molecular level. High-throughput sequencing of medicinal plants could not only shed light on the biosynthetic pathways of medicinal compounds, especially secondary metabolites,4 and their regulation mechanisms but also play a major role in the molecular breeding of high-yield-ing medicinal cultivars and molecular farming of transgenic medicinal strains.Genomics and Evolution in Traditional Medicinal Plants:Road to a Healthier LifeDa-cheng Hao1 and Pei-Gen Xiao21Biotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian, P. R. China. 2Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing, P. R. China.AbstrAct: Medicinal plants have long been utilized in traditional medicine and ethnomedicine worldwide. This review presents a glimpse of the current status of and future trends in medicinal plant genomics, evolution, and phylogeny. These dynamic fields are at the intersection of phytochemistry and plant biology and are concerned with the evolution mechanisms and systematics of medicinal plant genomes, origin and evolution of the plant genotype and metabolic phenotype, interaction between medicinal plant genomes and their environment, the correlation between genomic diversity and metabolite diver-sity, and so on. Use of the emerging high-end genomic technologies can be expanded from crop plants to traditional medicinal plants, in order to expedite medicinal plant breeding and transform them into living factories of medicinal compounds. The utility of molecular phylogeny and phylogenomics in pre-dicting chemodiversity and bioprospecting is also highlighted within the context of natural-product-based drug discovery and development. Representative case studies of medicinal plant genome, phylogeny, and evolution are summarized to exemplify the expansion of knowledge pedigree and the paradigm shift to the omics-based approaches, which update our awareness about plant genome evolution and enable the molecular breeding of medicinal plants and the sustainable utilization of plant pharmaceutical resources.KEywords: genome evolution, medicinal plants, phylogeny, phylogenomics, drug discovery and developmentCiTaTion: Hao and Xiao. Genomics and Evolution in traditional medicinal Plants: road to a Healthier life. Evolutionary Bioinformatics 2015:11 197–212 doi: 10.4137/EBo.s31326. TYPE: reviewRECEivEd: July 08, 2015. RESubMiTTEd: august 24, 2015. aCCEPTEd foR PubLiCaTion: august 31, 2015.aCadEMiC EdiToR: Jike cui, associate EditorPEER REviEw: five peer reviewers contributed to the peer review report. reviewers’ reports totaled 1,675 words, excluding any confidential comments to the academic editor. fundinG: This work is supported by the Scientific Research Foundation for ROCS, ministry of Education, china, and natural science fund of liaoning Province. the authors confirm that the funder had no influence over the study design, content of the article, or selection of this journal.CoMPETinG inTERESTS: Authors disclose no potential conflicts of interest.CoRRESPondEnCE: hao@CoPYRiGHT: © the authors, publisher and licensee libertas academica limited. this is an open-access article distributed under the terms of the creative commons cc-By-nc 3.0 license.P aper subject to independent expert blind peer review. all editorial decisions madeby independent academic editor. upon submission manuscript was subject to anti-plagiarism scanning. Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. this journal is a member of the committee on Publication Ethics (coPE). Provenance: the authors were invited to submit this paper.P ublished by libertas academica. learn more about this journal.Hao and XiaoA few principles should be considered when selecting medicinal plants for whole-genome sequencing projects: first, source plants of well-known and expensive CMMs or important chemical drugs that are in heavy demand, eg, Panax ginseng 5,6 and Artemisia annua 7; second, representa-tive plants whose pharmaceutical components are relatively unambiguous and that have typical secondary metabolism pathways, eg, Salvia medicinal plants 8,9; third, characteristic plants that are in a large medicinal genus/family, such as Glycyrrhiza uralensis (Chinese liquorice; Fabaceae)10,11 and Lycium chinense (Chinese boxthorn; Solanaceae)12; fourth, medicinal plants that are potential model plants and have considerable biological data; and last, medicinal plants whose genetic backgrounds are known, with reasonably small diploid genome and relatively straightforward genome structure, should be given priority.As there is a lack of comprehensive molecular genetic studies on most medicinal plants, it is vital to have some preliminary genome evaluations done before whole-genome sequencing. First, DNA barcoding techniques 13 could be used to authenticate the candidate species; second, karyotypes should be determined by observing metaphase chromosomes; last, flow cytometry and pulsed-field gel electrophoresis (PFGE)9,14 could be used to determine the ploidy level and genome size. For example, flow cytometry was used to deter-mine the genome size of four Panax species 15 with Oryza sativa as the internal standard. P. notoginseng (San Qi in traditional Chinese medicine) has the largest genome (2454.38 Mb), followed by P. pseudoginseng (2432.72 Mb), P. vietnamensis (2018.02 Mb), and P. stipuleanatus (1947.06 Mb), but their genomes are smaller than the P. ginseng genome (∼3.2 Gb). A more reliable approach for species identification without the reference genome is the genome survey via the whole-genome shotgun sequencing.16 Such non-deep sequencing (30×coverage), followed by the bioinformatics analysis, is highly valuable in assessing the genome size, heterozygosity, repeat sequence, GC content, etc, facilitating decision mak-ing on the whole-genome sequencing approaches. In addi-tion, RAD-Seq (restriction-site associated DNA sequencing; Fig. 1)17 could be chosen to construct a RAD library and perform the low-coverage genome sequencing of reduced rep-resentation, which is an effective approach for assessing the heterozygosity of the candidate genome.The whole-genome sequencing platform is chosen based on the budgetary resources and the preliminary evaluation of candidate genomes.2 The GS FLX or Illumina HiSeq 2500 platform might be suitable for a small, simple genome. How-ever, the majority of the plant genomes are complex, which means they are diploid/polyploidy genomes with .50% repeat sequences and .0.5% heterozygosity. Two or more sequenc-ing platforms could be combined for shotgun and paired-end sequencing, while large insert libraries, eg, BAC (bacterial artificial chromosome),9 YAC (yeast artificial chromosome),18 and Fosmid,14 can be constructed for sequencing; then a sophisticated bioinformatics software 19–23 can be used for sequence quality control and assembly. For instance, GS FLX and shotgun sequencing can be used for the initial genome assembly to generate 454 contigs, and then the paired-end sequencing data from the Illumina HiSeq or SOLiD platform used to determine the order and orientation of 454 contigs,Genomic DNARestriction digestion, 200–400bp RAD tag sequencesIllumina HiSeq PE125 sequencingData filtering, QCUnknown reference genomeRAD tag clustering, local assembly Alignment with referenceSNP (and InDel, SV) detection, annotation, statisticsProgeny genotyping Genetic mapGenetic map construction,assessmentQTL mapping (if phenotype data available)Evolutionary tree construction Population PCA Population genetic structurePopulation evolutionLinkage disequilibrium Population history,effective sizePopulation migrationSelective elimination GO and KEGG enrichment of candidate genes Known reference genomefigure 1. technology roadmap of raD-seq and its utility in population evolution and genetic map.abbreviations: PE, paired end; Qc, quality control; inDel, insertion and deletion; sv, splice variant; Pca, principal component analysis; Qtl, quantitative trait loci.Genomics and evolution in traditional medicinal plantsthus generating scaffolds. Next, Illumina HiSeq or SOLiD data are used to fill the gap between some contigs. These steps streamline the genome sequencing pipeline as a whole.The genetic map and physical map are fundamental tools for the assembly of the complex plant genomes and func-tional genomics research. The genetic linkage map of Bupleu-rum chinense (Bei Chai Hu in traditional Chinese medicine, TCM) was constructed using 28 ISSR (inter-simple sequence repeat) and 44 SSR (microsatellite) markers 24; 29 ISSRs and 170 SRAPs (sequence-related amplified polymorphisms) were mapped to 25 linkage groups of Siraitia grosvenorii (Luo Han Guo in TCM).25 These preliminary results are useful in meta-bolic gene mapping, map-based cloning, and marker-assisted selection of medicinal traits. The high-throughput physi-cal map could be anchored via the BAC-pool sequencing,26 which, along with its integration with high-density genetic maps, could benefit from next-generation sequencing (NGS) and high-throughput array platforms.27 The development of dense genetic maps of medicinal plants is still challenging, as the parental lines and their progenies with the unambiguous genetic link are not available for most medicinal plants.chloroplast genome evolution. Chloroplast (cp) is responsible for photosynthesis, and its genome sequences have versatile utility in evolution, adaptation, and robust growth of most medicinal plants. The substitution rate of the cp nucleotide sequence is 3–4 times faster than that of the mitochondria (mt) sequence,5 implicating more uses of the former in inferring both interspecific and intraspecific evolutionary relationships.5,28–33P. ginseng is a “crown” TCM plant and frequently used in health-promoting food and clinical therapy. NGS technol-ogy provides insight into the evolution and polymorphism of P. ginseng cp genome.5 The cp genome length of Chinese P. ginseng cultivars Damaya (DMY), Ermaya (EMY), and Gaolishen (GLS) was 156,354 bp, while it was 156,355 bp for wild ginseng (YSS), which are smaller than Omani lime (C. aurantiifolia ;159,893 bp)29 and 12 Gossypium cp genomes (159,959–160,433 bp)32 but bigger than Rhazya stricta cp genome (154,841 bp).34 Gene content, GC content, and gene order in DMY are quite similar to those of other strains, and nucleotide sequence diversity of the inverted repeat region (IR) is lower than that of large single-copy region (LSC) and small single-copy region (SSC). The high-resolution reads were mapped to the genome sequences to investigate the dif-ferences of the minor allele, which showed that the cp genome attained heterogeneity during domestication; 208 minor allele sites with minor allele frequencies (MAFs) of $0.05 were identified. The polymorphism site numbers per kb of the cp genome of DMY, EMY, GLS, and YSS were 0.74, 0.59, 0.97, and 1.23, respectively. All minor allele sites were in the LSC and IR regions, and the four strains showed the same variation types (substitution base or indel) at all identified polymorphism sites. The minor allele sites of the cp genome underwent puri-fying selection to adapt to the changing environment during domestication. The study of the cp genome of medicinal plants with particular focus on minor allele sites would be valuable in probing the dynamics of the cp genomes and authenticating different strains and cultivars.The genus Citrus contains many economically important fruits that are grown worldwide for their high nutritional and medicinal value. Due to frequent hybridizations among spe-cies and cultivars, the exact number of natural species and the evolutionary relationships within this genus are blurred. It is essential to compare the Citrus cp genomes and to develop suitable genetic markers for both basic research and practical use. A reference-assisted approach was adopted to assemble the complete cp genome of Omani lime,29 whose organization and gene content are similar to those of most rosids lineages characterized to date. By comparing with the sweet orange (C. sinensis ), 3 intergenic regions and 94 SSRs were identified as potentially informative markers for resolving interspecific relationships, which can be harnessed to better understand the origin of domesticated Citrus and foster germplasm conserva-tion. A comparison among 72 species belonging to 10 fami-lies of representative rosids lineages also provides new insights into their cp genome evolution.The monocot family Orchidaceae, which is evolutionarily more ancient than asterids and rosids, is one of the largest angio-sperm families, including many medicinal, horticultural, and ornamental species. Orchid phytometabolites display antinoci-ceptive,35 antiangiogenic,36 and antimycobacterial 37 activities, among others. In south Asia, orchid bulb is used for the treat-ment of asthma, bronchitis, throat infections, dermatological infections, and also as a blood purifier.38 Sequencing the com-plete cp genomes of the medicinal plant Dendrobium officinale (Tie Pi Shi Hu in TCM) and the ornamental orchid Cypripe-dium macranthos revealed their gene content and order, as well as potential RNA editing sites.39 The cp genomes of these two species and those of five known photosynthetic orchids are similar in structure as well as gene order and content, but the organization of the IR/SSC junction and ndh genes is dis-tinct. IRs flanking the SSC region underwent expansion or contraction in different Orchidaceae species. Fifteen highly divergent protein-coding genes were identified, which are use-ful in phylogenetic inference of orchids. Phylogenomic analy-sis of cp can be used to resolve the interspecific relationship, which cannot be inferred by a few cp markers. Bamboo leaves are used as a component in TCM for the anti-inflammatory function.40 Medicinal bamboo cupping therapy is applied to reduce fibromyalgia symptoms.41 Bamboo extracts exhibit antioxidant effects 42 and are used to treat chronic fever and infectious diseases.43 The whole cp genome datasets of 22 tem-perate bamboos considerably increased resolution along the backbone of tribe Arundinarieae (temperate woody bamboo) and afforded solid support for most relationships regardless of the very short internodes and long branches in the tree.33 An additional cp phylogenomic study, involving the full cp genome sequences of eight Olyreae (herbaceous bamboo) andHao and Xiao10 Arundinarieae species, strengthened the soundness of the above study and recovered monophyletic relationship between Bambuseae (tropical woody bamboo) and Olyreae.44The monocot genus Fritillaria (Liliaceae) consists of nearly 140 species of bulbous perennial plants and includes the taxa of both horticultural and medicinal importance. The bulbs of plants belonging to the Fritillaria cirrhosa group have been used as antitussive and expectorant herbs in TCM for thousands of years.45 The anticancer activity and cardiovascular effects of Fritillaria phytometabolites are well documented.10 Fritillaria species have attracted attention also because of their remarkably large genome sizes, with all values recorded to date above 30 Gb.46 A phylogenetic reconstruction including most currently recognized species diversity of the genus was per-formed.46 Three regions of the cp genome were sequenced in 92 species (∼66% of the genus) and in representatives of nine other genera of Liliaceae. Eleven low-copy nuclear genes were screened in selected species, but they had limited utility in phylogenetic reconstruction. Phylogenetic analysis of a com-bined plastid dataset supported the monophyly of the major-ity of presently identified subgenera. However, the subgenus Fritillaria , which is by far the largest and includes the most important species used in TCM, is found to be polyphyletic. Clade containing the source plants of Chuan Bei Mu, Hubei Bei Mu, and Anhui Bei Mu might be treated as a separate subgenus.47 The Japanese endemic subgenus Japonica , which contains the species with the largest recorded genome size for any diploid plant, is sister to the largely Middle Eastern and Central Asian subgenus Rhinopetalum , which is significantly incongruent with the nuclear ITS tree. Convergent or paral-lel evolution of phenotypic traits may be a common cause of incongruence between morphology-based classifications and the results of molecular phylogeny. While the relationships between most major Fritillaria lineages can be resolved, these results also highlight the need for data from more indepen-dently evolving loci, which is quite perplexing given the huge nuclear genomes found in these plants.Medicinal plant diversity, comprising genetic diversity, medicinal species diversity, ecological system diversity, and so on,48 results from the intricate interactions between the plant and its environment, and thus is profoundly influenced by the ecological complex and the relevant versatile ecologi-cal processes. The effects of the evolutionary processes have to be taken into full consideration when explaining the link between climatic/ecological factors and medicinal plant diver-sity, especially that in a region where there is strong, uneven differentiation of species. A distinguished example is the “sky islands” of southwest China,49 where the extraordinarily rich resources of medicinal plants rose and thrived during the Qua-ternary Period. To date, many medicinal tribes and genera, eg, Pedicularis ,50 Clematis ,51 Aconitum ,52,53 and Delphinium ,54 are still in the process of rapid radiation and dynamic dif-ferentiation. The cp genome sequence can be regarded as the super-barcode of the organelle scale, and thus can be usedto probe the intraspecific variations 55 and phylogeographic patterns of the same species in disparate geographic locations (eg, geoherb or Daodi medicinal materials).56 The applica-tion of cp genome sequencing at the population level may provide clues for the timing and degree of intraspecific differentiation. Deriving the inter-population relationship from cp dataset can be considered as the more detailed phylo-genetic reconstruction.Mitochondria genome evolution. Some fundamental evolution concepts, such as lateral gene transfer, are bolstered by the inquiry of the origin of mt, while plants are especially useful in elucidating the mechanisms of cytonuclear coevolu-tion. Although the gene order of the mt genome might evolve relatively fast in land plants, the substitution rate of its nucle-otide sequence is merely 1/100 of that of animal sequence.48 Therefore, the mt genome sequence is less useful than the cp genome in inferring the phylogenetic relationship of medicinal species.57 Notwithstanding, analysis of the genome sequence is still able to contribute to the knowledge on the evolution of the mt genome. Moreover, the terpene synthase has been found in mt,58 highlighting its utility in secondary metabolism.Rhazya stricta (Apocynaceae) is native to arid regions in South Asia and the Middle East and is used extensively in folk medicine. Analyses of the complete cp and mt genomes and a nuclear (nr) transcriptome of Rhazya shed light on intercom-partmental transfers between genomes and the patterns of evo-lution among eight asterid mt genomes.34 The Rhazya genome is highly conserved, with gene content and order identical to the ancestral organization of angiosperms. The 548,608 bp mt genome contains recombination-derived repeats that generate a compound organization; transferred DNA from the cp and nr genomes, and bidirectional DNA transfers between the mt and the nucleus are also disclosed. The mt genes sdh3 and rps14 have been transferred to the nucleus and have acquired targeting transit peptides. Two copies of rps14 are present in the nucleus; only one has the mt targeting transit peptide and may be functional. Phylogenetic analyses suggest that Rhazya has experienced a single transfer of this gene to the nucleus, followed by a duplication event. The phylogenetic distribution of gene losses and the high level of sequence divergence in tar-geting transit peptides suggest multiple, independent transfers of both sdh3 and rps14 across asterids. Comparative analyses of mt genomes of eight asterids indicates a complicated evolu-tionary history in this thriving eudicot clade with substantial diversity in genome organization and size, repeat, gene and intron content, and the amount of alien DNA from the cp and nr genomes. The genomic data enable a rigorous inspection of the gene transfer events.Nuclear genome evolution. The whole cp genome data-set is not enough to elucidate the phylogenetic relationship of groups undergoing rapid radiation, eg, Zingiberales.59 The cp genome is equivalent to one gene locus, thus it only represents one fulfillment to the coalescent random processes and cannot be used with confidence to reconstruct the evolution history ofGenomics and evolution in traditional medicinal plantsthe populations. Most genetic history of any medicinal plant hides in the nr genome.High-throughput sequencing and the relevant bioinfor-matics advances have revolutionized contemporary thinking on nuclear genome/transcriptome evolution and provided basic data for further breeding endeavor. Coix (Poaceae), a closely related genus of Sorghum and Zea , has 9–11 species with different ploidy levels. The exclusively cultivated C. lacryma-jobi (2n = 20) is widely used in East and Southeast Asia as food and traditional medicine. C. aquatica has three fertile cytotypes (2n = 10, 20, and 40) and one sterile cyto-type (2n = 30), C. aquatica HG, which is found in Guangxi, China.60 Low-coverage genome sequencing (genome survey) showed that ∼76% of the C. lacryma-jobi genome and 73% of the C. aquatica HG genome are repetitive sequences, among which the long terminal repeat (LTR) retrotransposable elements dominate, but the proportions of many repeat sequences vary greatly between the two species, suggesting their evolution-ary divergence. A novel 102 bp variant of centromeric satellite repeat CentX and two other satellites are exclusively found in C. aquatica HG. Fluorescence in situ hybridization (FISH) analysis and fine karyotyping showed that C. lacryma-jobi is likely a diploidized paleotetraploid species and C. aquatica HG is possibly from a recent hybridization. These Coix taxa share more coexisting repeat families and higher sequence similarity with Sorghum than with Zea , which agrees with the phylogenetic relationship.Whole-genome sequencing has been implemented in the representative species of some plant families/genera (Fig. 2), eg, Capsicum annuum ,19,20 Coffea canephora ,21 Brassica napus ,22 Phalaenopsis equestris ,23 etc. The genome sequences of the cultivated pepper Zunla-1 (Capsicum annuum ) and its wild pro-genitor Chiltepin (Capsicum annuum var. glabriusculum ) were compared to provide insights into Capsicum domestication and specialization. The pepper genome expanded ∼0.3 Mya by a rapid amplification of retrotransposon elements, resulting in a genome containing ∼81% repetitive sequences and 34,476 protein-coding genes. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular signature of artificial selection, providing a list of candidate domestication genes.19 Dosage compensation effect of tandem duplication genes might contribute to the pungency divergence in pepper.19 The Capsicum reference genome, along with tomato and potato genomes, provides critical informa-tion for the study of the evolution of other Solanaceae species, including the well-known Atropa medicinal plants.One of the milestone breakthroughs is the success-ful sequencing and assembly of the complex heterozygous genome. The heterozygous genome of C. canephora has been deciphered,21 which displays a conserved chromosomal gene order among asteroid angiosperms. Although it shows no sign of the whole-genome triplication identified in Solanaceae spe-cies, the genome includes several species-specific gene family expansions, eg, N -methyltransferases (NMTs) involved in caf-feine biosynthesis, defense-related genes, and alkaloid and fla-vonoid enzymes involved in secondary metabolite production. Caffeine NMTs expanded through sequential tandem dupli-cations independently and are distinct from those of cacaoMimulus Utricularia TomatoPotato no further polyploidization PepinoEggplant no further polyploidization Chili pepper no further polyploidization Tobacco no further polyploidization Coffee no further polyploidization Grape no further polyploidization ArabidopsisPapaya no further polyploidization Cacao no further polyploidization Medicago SoybeanStrawberry no further polyploidization Peach no further polyploidization PoplarTomatilloPetuniaSweet potato OliveRosidsCore eudicotsAsteridsPeppermintfigure 2. Examples of the phylogeny and genome duplication history of core eudicots.notes: arrowheads indicate hexaploidization; triangles indicate tetraploidization. the current evidence does not suggest further polyploidization after speciation in the genomes of potato, eggplant, chili pepper, tobacco, coffee, grape, papaya, cacao, strawberry, and peach. few genomic data are available in pepino, tomatillo, and many other species.。