微生物学通报  2019, Vol. 46 Issue (2): 345−353

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文章信息

张荆城, 边银丙, 肖扬
ZHANG Jing-Cheng, BIAN Yin-Bing, XIAO Yang
真菌群体基因组学研究进展
Progress in fungal population genomics
微生物学通报, 2019, 46(2): 345-353
Microbiology China, 2019, 46(2): 345-353
DOI: 10.13344/j.microbiol.china.180888

文章历史

收稿日期: 2018-11-09
接受日期: 2018-12-26
网络首发日期: 2018-12-29
真菌群体基因组学研究进展
张荆城 , 边银丙 , 肖扬     
华中农业大学应用真菌研究所    湖北  武汉    430070
摘要:近年来,随着第二代高通量测序技术的出现和发展,测序成本不断降低,完成全基因组测序的真菌物种迅速增加。以大规模测序为基础的群体基因组学,也逐渐应用于解析真菌的群体结构、物种形成、种群分化和位点特异性效应。本文综述了群体基因组学在工业真菌、病原真菌、食用真菌、共生真菌及其在表型性状遗传基础解析中的研究进展,并对其今后的发展方向进行了展望。
关键词重测序    群体遗传学    基因组    进化    适应    
Progress in fungal population genomics
ZHANG Jing-Cheng , BIAN Yin-Bing , XIAO Yang     
Institute of Applied Mycology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
Abstract: In recent years, with the emergence and development of the high-throughput next generation sequencing technology, the cost of sequencing decreases continuously and the number of fungal species with complete genome sequence increases rapidly. Population genomics based on large-scale sequencing has also been gradually used in dissecting population structure, speciation, population divergence and locus-specific effects in fungi. In this review, we summarize the progress of population genomics in industrial fungi, pathogenic fungi, edible fungi, symbiotic fungi and genetic architecture dissection of phenotypic traits, and discuss future development.
Keywords: Re-sequencing    Population genetics    Genome    Evolution    Adaption    

Gulcher和Stefansson于1998年提出了群体基因组学(Population genomics)概念[1]。群体基因组学是群体遗传学的延伸,综合了基因组概念和技术与群体遗传学的理论体系[1-2]。群体基因组学可简单定义为使用分布于基因组上大量遗传标记的群体遗传学[3],它从全基因组层面上研究位点特异性效应(选择、突变和重组等)和全基因组效应(遗传漂变、瓶颈效应、基因流等),从而使我们更全面和深入地理解影响基因组和种群变异的进化过程[2, 4]

在动植物中,群体基因组学已用于分析基因组水平的遗传多样性样式、分布及连锁不平衡水平,探索物种的系统发育,群体分化与适应性进化。其中家畜和作物的驯化过程,以及重要经济性状形成的分子机制备受关注[5-8]。在细菌中,群体基因组学研究重点关注了病原菌的致病机制[9-10]。群体基因组学也应用于解析人类的系统发育历史和适应性进化[11]。真菌生长繁殖快,基因组简单,测序成本相对低,并且已有1 500种以上的真菌获得了全基因组序列(https://stateoftheworldsfungi.org/)。这些特点决定了真菌是群体基因组学研究的良好材料,目前群体基因组学已在工业真菌和病原真菌中得到较为广泛的应用。

1 工业真菌群体基因组学研究进展

作为模式物种,酿酒酵母(Saccharomyces cerevisiae)在群体基因组水平开展了大量的系统进化、种群分化和驯化研究。群体基因组学研究表明,酿酒酵母驯化菌株起源于中国,最早可能由遗传背景不同的野生菌株异型杂交产生[12-13]。酿酒酵母驯化菌株在不同工业用途的群体里发生了特异的驯化事件。例如,葡萄酒酿酒酵母和清酒酿酒酵母是单祖演化的,分别来源于单个驯化事件[14],但啤酒酿酒酵母和面包酿酒酵母却分别起源于多个祖先[12, 15-17]。研究还发现葡萄酒酿酒酵母在经历了驯化瓶颈之后,又经历了群体扩张[12, 14, 18-20]。长期的人工特异性选择改变了酿酒酵母驯化菌株的基因组,其基因组上与抗逆性、糖类转运与代谢、芳香气味等性状相关的基因拷贝数增加,但与生活史等性状相关的基因发生了衰退[13, 16]。生态因子可能是酿酒酵母驯化菌株分化的主要驱动力,研究者已发现酿酒酵母生态适应的关键基因组区域[13, 21]。例如葡萄酒酿酒酵母进化过程中的水平基因转移可能与葡萄酒发酵有关[19],参与特定调控网络的基因与Flor酿酒酵母适应性有关[22]

在酿酒酵母近缘种中,Almeida等[23]发现驯化葡萄汁酵母(S. uvarum)菌株基因组上的基因渗入与葡萄酒发酵有关,Friedrich等[24]发现克鲁弗酵母菌(Lachancea kluyveri)进化过程中的基因渗入可能通过改变其生活史来增强适应能力。

除酿酒酵母及其近缘种,米曲霉(Aspergillus oryzae)也是重要的工业菌株。Gibbons等[25]对1株野生黄曲霉(A. flavus)菌株和14株米曲霉菌株进行了基因组测序,发现所有米曲霉菌株起源于单个驯化事件,并且参与初生代谢和次生代谢的基因在曲霉驯化过程中是人工选择的主要目标。

脉孢菌属(Neurospora)具有重要的工业应用潜力。Ellison等[26]对采自于加勒比盆地的48株粗糙脉孢菌(N. crassa)的群体基因组学研究表明,粗糙脉孢菌分化为热带和亚热带亚群,并且分化的基因组区域含有与低温适应的MRH4-like RNA解旋酶基因与PAC10-like前折叠素亚基基因。Gladieux等[27]对52个N. discreta菌株的群体基因组学研究表明,异域类群二次接触时,不同程度的基因流导致了类群间基因组异质性的差异。

2 植物病原真菌群体基因组学研究进展

植物病原真菌对粮食安全有重要影响,是一类重要的群体基因组学研究对象。小麦叶枯菌(Zymoseptoria tritici)专一侵染小麦,而该属其他病原菌可侵染多个寄主。Stukenbrock等[28]开展了小麦叶枯菌及其属内其他3个物种的比较群体基因组学研究,发现27个与寄主专化型和物种形成有关基因在物种间显著分化。Hartmann等[29]对小麦叶枯菌的群体基因组学研究表明,约5% (1 Mb)的小麦叶枯菌基因组经历了近期正向选择,与寄主适应和非生物环境适应有关的基因是正向选择的靶标。

棉花黄萎病菌(Verticillium dahlia)生理小种间的比较群体基因组学研究表明,1号生理小种存在一个特有的50 kb序列,该序列含有编码效应子基因Ave1。同源性分析显示Ave1可能起源于植物,经水平基因转移到棉花黄萎病菌[30]

对布氏白粉菌(Blumeria graminis) 46株不同专化型菌株的比较群体基因组学分析表明,能侵染小黑麦和小麦的B. graminis f. sp. triticale是由小麦专化型(B. graminis f. sp. tritici)和黑麦专化型(B. graminis f. sp. secalis)杂交形成的。不同专化型致病菌株杂交,是对新寄主的适应性机制[31]

Badouin等[32]对花粉黑穗病菌(Microbotryum lychnidis-dioicae,MvSl)和(Microbotryum silenes-dioicae,MvSd)进行的群体基因组学研究表明,MvSd和MvSl分别有大约1%和17%的基因组受选择性清除(Selective sweeps)影响,选择性清除可能参与了花粉黑穗病菌对寄主植物的适应性进化。

稻瘟菌(Magnaporthe oryzae)可侵染多种单子叶植物,对稻瘟菌的群体基因组学研究表明,稻瘟菌分化形成的每个类群只侵染有限的寄主物种[33],而且部分稻瘟菌间存在基因交流[33-34]。Islam等[35]将导致孟加拉国麦瘟病暴发的病原菌与20个稻瘟菌进行了群体基因组学研究,发现其可能是来源于南美且侵染小麦的稻瘟菌类群。在侵染水稻的稻瘟菌群体中,研究发现稻瘟菌形成了3个分化支、6个类群,并且分化于约1 000年前[33, 36]。分化支1的菌株含有两种交配型,而分化支2和分化支3的菌株分别只含有交配型Mat1-2和Mat1-1[36]。分化支2特异性缺失一个富含半胱氨酸的致病因子编码基因,该基因可抑制由BAX介导的本氏烟草细胞坏死[36]

对大麦云纹病菌(Rhynchosporium commune)的群体基因组学研究表明,3个亚群含有特异的选择性清除,空间异质性的生物和非生物选择压力主要决定了大麦云纹病菌的演化轨迹[37]。对禾谷镰刀菌(Fusarium graminearum)的群体基因组学研究表明,群体特异性选择压力导致了121个与宿主入侵、拮抗或环境适应的基因在不同亚群间分化[38]。Derbyshire等[39]对20个核盘菌(Sclerotinia sclerotiorum)进行了群体基因组学研究,鉴定到了参与转录调控的选择性清除。

对咖啡叶锈病菌(Hemileia vastatrix)的群体基因组学研究表明,咖啡叶锈病菌分化为侵染四倍体咖啡的C3亚群和侵染二倍体咖啡的C1、C2亚群。C3基因组上的遗传重组与致病性有关,C2和C3之间的基因渗入表明毒力因子可能在不同的亚群间快速交换[40]

3 人体病原真菌群体基因组学研究进展

在人体病原真菌中,群体基因组学研究重点关注了隐球菌属(Cryptococcus)和念珠菌属(Candida)。

Engelthaler等[41]对格特隐球菌(C. gattii)进行了群体基因组学研究,鉴定到了可能与栖息地适应、毒力和病理相关的基因。同时发现北美太平洋西北地区的格特隐球菌来源于南美,其基因组上存在新型隐球菌(C. neoformans var. grubii)的基因渗入。Billmyre等[42]探究了格特隐球菌VGII亚群的起源,发现VGIIb可能来源于澳大利亚,VGIIa由毒力较小的无性系经有丝分裂产生,在形成过程中可能发生了表型突变,但VGIIc经有性生殖方式产生。Farrer等[43]在VGII亚群里鉴定到了一个跨大陆分布的新无性系群体VGIIx,同时还发现亚群VGII和VGIII之间存在线粒体重组,多药物转运体基因在格特隐球菌基因组上经历了正向选择。Farrer等[44]对格特隐球菌VGIIa、VGIIb、VGIIc、VGIIIa和VGIIIb的比较群体基因组学研究表明,859个基因经历了正向选择或宽松的纯化选择。

Vanhove等[45]对47个临床和环境来源的赞比亚新型隐球菌(C. neoformans)进行了群体基因组学研究,发现3/4的临床菌株属于VNI亚群,亚群VNB分化为2个类群。Desjardins等[46]对387个新型隐球菌进行了群体基因组学研究,也发现亚群VNB形成两个没有重组的类群VNBI和VNBII,而且VNBI在进化过程中经历了瓶颈效应。对交配型位点的进化分析表明,交配型MATα和MATa的进化历程不同。肌醇转运蛋白和分解代谢基因经历了正向选择,人脑中富含肌醇,适合新型隐球菌产生毒力[47-48]。Rhodes等[49]对188个新型隐球菌的群体基因组学研究表明,类群间存在重组,之前被认为仅特定存在于非洲的VNB亚群菌株也出现于南美洲。

对白色念珠菌(C. albicans)的群体基因组学研究表明,可能与白色念珠菌寄主适应性有关的细胞壁基因和细胞表面基因在基因组上快速进化[50-51]。杂合性丢失(Loss-of-heterozygosity)现象在白色念珠菌基因组上广泛存在[50-53],并且与抗药性普遍关联[50]。白色念珠菌主要表现为无性系群体结构,Wang等[52]和Ropars等[53]发现了白色念珠菌间的基因交流,证明了自然状态下白色念珠菌的准性生殖。

在念珠菌属的其他物种中,Carreté等[54]开展了光滑念珠菌(C. glabrata)的群体基因组学研究,发现光滑念珠菌中存在重组和交配现象,其细胞壁蛋白基因有着广泛的变异。Douglass等[55]对20个临床克鲁斯氏念珠菌(C. krusei)和12个环境库德毕赤酵母(Pichia kudriavzevii)的群体基因组学研究表明,它们是同一物种,且临床和环境来源菌株没有分化。

4 食用菌群体基因组学研究进展

食用菌是一类对人类有重要食用和经济价值的大型真菌,群体基因组学在食用菌中仅有少量的报道。

Branco等[56]对采自于美国加州海岸和山区的28株短柄粘盖牛肝菌(Suillus brevipes)菌株进行了全基因组重测序,发现海岸群体和山区群体仅在少部分基因组区域存在分化,其中一个极度分化的区域含有能够提高植物和酵母耐盐性的膜Na+/H+离子交换器基因。该研究小组进一步引入了27个北美短柄粘盖牛肝菌菌株,研究了洲际水平上牛肝菌气候适应性的遗传基础。基因型-环境关联分析与受选择区域分析挖掘出的基因主要与物质跨膜运输和解旋酶活性相关,这些基因可能参与了短柄粘盖牛肝菌的低温胁迫应答[57]

Xiao等[58]对60株中国野生及栽培香菇(Lentinula edodes)菌株进行了全基因组重测序,发现栽培菌株和野生菌株具有不同的基因库,说明栽培菌株可能并不起源于中国境内的野生菌株。84个候选基因驱动了群体分化,其中18个基因与逆境响应有关,例如Pbs2-like MAPKK蛋白编码基因、DnaJ编码基因和Cfs环丙烷脂肪酸合成酶基因。基因组特异性单核苷酸多态位点(Single nucleotide polymorphism,SNP)所在基因的GO (Gene ontology)富集分析表明,栽培菌株相关基因显著富集于逆境响应生物学过程。由于香菇子实体形成是一个逆境响应过程,因此推断环境因子(尤其是温度)在选择作用下驱动了中国香菇群体的分化。

5 共生真菌群体基因组学研究进展

共生真菌具有十分重要的生态价值,在修复土壤、环境监测、保持和提高植物多样性等方面有广阔的应用前景,目前共生真菌的群体基因组学研究尚处于起步阶段。

Wyss等[59]首次对模式丛枝菌根真菌根内球囊霉(Rhizophagus irregularis)的20个菌株进行了群体基因组学研究。所有菌株内多等位基因(Poly-allelic) SNP密度高于参考基因组,且菌株内等位基因频率分布情况偏离二倍体、四倍体或标准双核体的分布模式,推断根内球囊霉菌株内的遗传变异可能由异核性、拷贝数变异、非整倍性等因素共同造成。基于多等位基因SNP与基于单等位基因(Mono-allelic) SNP的系统发育分析的结果较为一致,说明菌株内的遗传变异在群体内得以保留。Savary等[60]对81株根内球囊霉菌株进行了群体基因组学研究。基于6 888个变异位点的信息,根内球囊霉菌株形成4个亚群。4个亚群间的分布跨越了很大的地理距离,说明根内球囊霉的分化与地理位置无关。

6 群体基因组学在真菌表型性状遗传基础解析中的研究进展

全基因组关联分析(Genome-wide association study,GWAS)和QTL作图(Quantitative trait locus mapping,QTL mapping)可用于解析数量性状的遗传基础。GWAS利用自然群体长期进化所形成的遗传变异,结合群体基因组学的GWAS分析在病原真菌与工业真菌中开展了大量研究。而QTL作图利用杂交的后代中分离的遗传变异,分析时需要构建专门的作图群体,目前结合群体基因组学的QTL作图分析仅在少数真菌物种中开展了相关研究。

在病原真菌中,Dalman等[61]研究了多年异担子菌(Heterobasidion annosum)致病力的遗传基础。在7个重叠群上找到了12个与致病力关联SNP,2个重叠群与之前QTL作图得到的毒力基因区域位置相近。酿酒酵母可引起人体粘膜和系统性感染。Muller等[62]收集了44个临床菌株和44个非临床菌株进行GWAS的研究,结果表明假菌丝形成、细胞壁维护和细胞解毒相关基因与酿酒酵母致病性有关。Gao等[63]选用191个小麦颖枯病菌(Parastagonospora nodorum)进行GWAS分析,检测出一个新的位点与毒力相关。Talas等[64]对220个禾谷镰刀菌进行GWAS分析,发现多个SNP与毒力因子、毒素合成和杀菌剂敏感性显著关联。Hartmann等[65]选用106株小麦叶枯菌进行GWAS分析,鉴定到了25个位点与病原菌繁殖相关。一个编码高分泌小蛋白的基因Zt_8_609与毒力有关,小麦叶枯菌通过删除该基因的部分片段来获得毒力。Desjardins等[46]对新型隐球菌进行了GWAS分析,鉴定出参与酵母-菌丝状态转化的基因RZE1与新型隐球菌毒力相关,转录因子BZP4上的一个功能缺失突变与临床菌株黑化作用显著降低相关。

在工业真菌中,Palma-Guerrero等[66]首次在粗糙脉孢菌中进行了GWAS分析。检测到一个cse1基因(编码神经元钙感受器类似物)与发芽无性孢子的交流效率显著关联。Jeffares等[67]分析了161个栗酒裂殖酵母(Schizosaccharomyces pombe)菌株的基因组和表型变异,检测到1 239个SNP标记及180个插入/缺失(Insertion/deletion,InDel)标记与89个性状间存在显著相关性。InDel对表型的影响大于SNP,每个性状关联到的最显著位点平均解释了22%的表型变异。Sardi等[68]利用156个酿酒酵母菌株的基因组信息,研究其对植物纤维素水解产物毒素的抗性机制。鉴定出38个SNP与性状关联,候选基因参与氧化还原反应,蛋白折叠或修饰,DNA代谢和修复等过程。Peter等[12]对酿酒酵母的GWAS分析结果表明,拷贝数变异(Copy number variation,CNV)对表型的影响大于SNP。

结合群体基因组学的QTL作图分析在酿酒酵母中开展了大量研究,解析了酿酒酵母发酵过程中挥发性化合物形成[69]、氟哌啶醇耐药性[70]、氮吸收[71]等性状的遗传基础。

在小麦叶枯菌中,QTL作图分析用于解析毒力性状[72]、黑化作用[73]、杀菌剂敏感性[74]与热适应[75]的遗传基础。小麦叶枯菌的7号染色体上存在一个与宿主专化型有关的大效应QTL[72]。12个QTLs与黑化作用显著相关,含有包括参与黑色素合成的聚酮合酶基因PKS1在内的16个候选基因[73]。3个QTLs与杀菌剂敏感性相关,其中2个QTLs含有新的杀菌剂敏感性基因,另外一个QTL含有编码参与麦角固醇生物合成的蛋白基因ERG6[74]PKS1基因与杀菌剂敏感性相关,表明其一因多效性。4个一因多效性的QTLs与热敏感性相关,其中一个QTL含有6个候选基因,包括编码一个丝裂原活化蛋白激酶激酶且与酿酒酵母低温抗性相关的基因PBS2[75]

7 真菌群体基因组的发展方向 7.1 多组学融合

随着千种真菌基因组测序计划的开展(http://1000.fungalgenomes.org),越来越多的真菌种类获得了全基因组序列,为在更多真菌种类中开展群体基因组学研究奠定了基础。同时,三维基因组学、表观组学、转录组学、翻译组学、蛋白质组学、代谢组学和表型组学也日益发展。将群体基因组学与它们结合起来,对数据进行多组学整合分析,可以解析不同组学水平变异间的相互关系,有利于从不同层面上揭示真菌群体在进化过程中发生的变化,解析复杂表型性状形成和调控的分子机理。

7.2 与泛基因组学结合

目前群体基因组学研究主要以重测序为研究手段,存在着一些不足。首先,基于重测序的研究在遗传变异检出时依赖于短序列比对到参考基因组上时的高度相似性,因此会不可避免地遗漏高度多态基因组区域的遗传变异信息,尤其当所研究物种基因组上存在丰富的变异与转座子活性时[76-77]。此外,参考基因组不能代表一个物种所有的遗传信息。一些功能上重要的基因有可能在参考基因组上缺失,却位于该物种其他个体的基因组上[77-79]。泛基因组学研究能有效地解决这些问题。泛基因组学对一个物种的不同个体进行深度测序和从头组装,从而区分物种的核心基因组(Core genome)、非必需基因组(Dispensable genome)与个体特异性基因[80]。在真菌中,泛基因组学已在酿酒酵母[12, 81]、格特隐球菌[43]、根内球囊霉[82]和小麦叶枯菌[83]等物种中得到了应用。结合泛基因组学的群体基因组学能更准确地揭示物种基因组变异及其功能。

7.3 新测序方法的引入

对于不可培养真菌,其基因组测序困难,限制了群体基因组学在这些真菌物种中的应用。单细胞测序是指对单个细胞进行物理分离、全基因组扩增以及测序的技术,能有效地解决不可培养真菌测序的难题[84-85]。此外,单细胞测序能有效区分细胞与细胞间的遗传变异,结合群体基因组学可以揭示真菌细胞水平上的基因组遗传变异对生物学功能的影响。但单细胞测序费用昂贵,技术难度大,还需要进一步优化。

目前的群体基因组学研究主要依赖于二代短序列测序技术[86],引入三代测序可以获得更好的研究结果。三代测序是单分子测序技术,具有较长读长的特点,在高GC含量区域、重复序列等复杂结构测序上具有优势,结合使用二代测序和三代测序进行混合组装可以获得质量更好的参考基因组[87-88]。三代测序也能准确鉴定低频SNP[89],弥补了二代测序技术的缺点。

7.4 大数据分析平台的构建

群体基因组学研究会产生海量的测序数据。对缺乏生物信息学基础的研究者来说,分析处理大数据时会面临较大困难,从而不得不求助于专业的生物信息分析员,进而造成研究费用增加和数据处理不彻底等问题[7]。有鉴于此,进一步开发用户界面友好、高效、大规模和整体综合的自动分析流程能减轻研究者的工作负担[90]。因此,部分测序公司开发出了功能强大的生物信息分析云平台,极大方便了群体基因组学研究者。

7.5 候选基因功能的验证

利用群体基因组学方法鉴定到候选基因,需要对其进行功能验证。目前的遗传操作方法包括RNA干扰[91-92]、基因过表达[93-94]和以CRISPR-Cas9为代表的基因编辑技术[95-97],这些方法已经成功应用于多个真菌物种的基因功能研究。因此,对真菌群体基因组学研究挖掘出的候选基因也可以进行验证,从而阐明候选基因的生物学功能。

参考文献
[1]
Gulcher J, Stefansson K. Population genomics: laying the groundwork for genetic disease modeling and targeting[J]. Clinical Chemistry and Laboratory Medicine, 1998, 36(8): 523-527.
[2]
Luikart G, England PR, Tallmon D, et al. The power and promise of population genomics: from genotyping to genome typing[J]. Nature Reviews Genetics, 2003, 4(12): 981-994. DOI:10.1038/nrg1226
[3]
Stinchcombe JR, Hoekstra HE. Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits[J]. Heredity, 2008, 100(2): 158-170. DOI:10.1038/sj.hdy.6800937
[4]
Black Ⅳ WC, Baer CF, Antolin MF, et al. Population genomics: genome-wide sampling of insect populations[J]. Annual Review of Entomology, 2001, 46: 441-469. DOI:10.1146/annurev.ento.46.1.441
[5]
Liang SY, Zhou ZK, Hou SS. The research progress of farm animal genomics based on sequencing technologies[J]. Hereditas, 2017, 39(4): 276-292. (in Chinese)
梁素芸, 周正奎, 侯水生. 基于测序技术的畜禽基因组学研究进展[J]. 遗传, 2017, 39(4): 276-292.
[6]
Mei CG, Wang HC, Zan LS, et al. Research progress on animal genome research based on high-throughput sequencing technology[J]. Journal of Northwest A & F University (Natural Science Edition), 2016, 44(3): 43-51. (in Chinese)
梅楚刚, 王洪程, 昝林森, 等. 基于高通量测序的动物基因组研究进展[J]. 西北农林科技大学学报:自然科学版, 2016, 44(3): 43-51.
[7]
Wang YS. Research progress of plant population genomics based on high-throughput sequencing[J]. Hereditas, 2016, 38(8): 688-699. (in Chinese)
王云生. 基于高通量测序的植物群体基因组学研究进展[J]. 遗传, 2016, 38(8): 688-699.
[8]
Ellegren H. Genome sequencing and population genomics in non-model organisms[J]. Trends in Ecology & Evolution, 2014, 29(1): 51-63.
[9]
Sheppard SK, Guttman DS, Fitzgerald JR. Population genomics of bacterial host adaptation[J]. Nature Reviews Genetics, 2018, 19(9): 549-565. DOI:10.1038/s41576-018-0032-z
[10]
Sun ZH. Application of population genomics in the research of lactic acid bacteria[J]. Journal of Chinese Institute of Food Science and Technology, 2017, 17(8): 12-18. (in Chinese)
孙志宏. 群体基因组学在乳酸菌研究中的应用[J]. 中国食品学报, 2017, 17(8): 12-18.
[11]
Lachance J, Tishkoff SA. Population genomics of Human adaptation[J]. Annual Review of Ecology, Evolution, and Systematics, 2013, 44: 123-143. DOI:10.1146/annurev-ecolsys-110512-135833
[12]
Peter J, de Chiara M, Friedrich A, et al. Genome evolution across 1, 011Saccharomyces cerevisiae isolates[J]. Nature, 2018, 556(7701): 339-344. DOI:10.1038/s41586-018-0030-5
[13]
Duan SF, Han PJ, Wang QM, et al. The origin and adaptive evolution of domesticated populations of yeast from Far East Asia[J]. Nature Communications, 2018, 9(1): 2690.
[14]
Marsit S, Leducq JB, Durand É, et al. Evolutionary biology through the lens of budding yeast comparative genomics[J]. Nature Reviews Genetics, 2017, 18(10): 581-598. DOI:10.1038/nrg.2017.49
[15]
Gonçalves M, Pontes A, Almeida P, et al. Distinct domestication trajectories in top-fermenting beer yeasts and wine yeasts[J]. Current Biology, 2016, 26(20): 2750-2761. DOI:10.1016/j.cub.2016.08.040
[16]
Gallone B, Steensels J, Prahl T, et al. Domestication and divergence of Saccharomyces cerevisiae beer yeasts[J]. Cell, 2016, 166(6): 1397-1410. DOI:10.1016/j.cell.2016.08.020
[17]
Strope PK, Skelly DA, Kozmin SG, et al. The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen[J]. Genome Research, 2015, 25(5): 762-774. DOI:10.1101/gr.185538.114
[18]
Schacherer J, Shapiro JA, Ruderfer DM, et al. Comprehensive polymorphism survey elucidates population structure of Saccharomyces cerevisiae[J]. Nature, 2009, 458(7236): 342-345. DOI:10.1038/nature07670
[19]
Almeida P, Barbosa R, Zalar P, et al. A population genomics insight into the Mediterranean origins of wine yeast domestication[J]. Molecular Ecology, 2015, 24(21): 5412-5427. DOI:10.1111/mec.2015.24.issue-21
[20]
Liti G, Carter DM, Moses AM, et al. Population genomics of domestic and wild yeasts[J]. Nature, 2009, 458(7236): 337-341. DOI:10.1038/nature07743
[21]
Legras JL, Galeote V, Bigey F, et al. Adaptation of S. cerevisiae to fermented food environments reveals remarkable genome plasticity and the footprints of domestication[J]. Molecular Biology and Evolution, 2018, 35(7): 1712-1727. DOI:10.1093/molbev/msy066
[22]
Coi AL, Bigey F, Mallet S, et al. Genomic signatures of adaptation to wine biological ageing conditions in biofilm-forming flor yeasts[J]. Molecular Ecology, 2017, 26(7): 2150-2166. DOI:10.1111/mec.2017.26.issue-7
[23]
Almeida P, Gonçalves C, Teixeira S, et al. A Gondwanan imprint on global diversity and domestication of wine and cider yeast Saccharomyces uvarum[J]. Nature Communications, 2014, 5: 4044. DOI:10.1038/ncomms5044
[24]
Friedrich A, Jung P, Reisser C, et al. Population genomics reveals chromosome-scale heterogeneous evolution in a protoploid yeast[J]. Molecular Biology and Evolution, 2015, 32(1): 184-192.
[25]
Gibbons JG, Salichos L, Slot JC, et al. The evolutionary imprint of domestication on genome variation and function of the filamentous fungus Aspergillus oryzae[J]. Current Biology, 2012, 22(15): 1403-1409. DOI:10.1016/j.cub.2012.05.033
[26]
Ellison CE, Hall C, Kowbel D, et al. Population genomics and local adaptation in wild isolates of a model microbial eukaryote[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(7): 2831-2836. DOI:10.1073/pnas.1014971108
[27]
Gladieux P, Wilson BA, Perraudeau F, et al. Genomic sequencing reveals historical, demographic and selective factors associated with the diversification of the fire-associated fungus Neurospora discreta[J]. Molecular Ecology, 2015, 24(22): 5657-5675. DOI:10.1111/mec.13417
[28]
Stukenbrock EH, Bataillon T, Dutheil JY, et al. The making of a new pathogen: insights from comparative population genomics of the domesticated wheat pathogen Mycosphaerella graminicola and its wild sister species[J]. Genome Research, 2011, 21(12): 2157-2166. DOI:10.1101/gr.118851.110
[29]
Hartmann FE, McDonald BA, Croll D. Genome-wide evidence for divergent selection between populations of a major agricultural pathogen[J]. Molecular Ecology, 2018, 27(12): 2725-2741. DOI:10.1111/mec.2018.27.issue-12
[30]
de Jonge R, van Esse HP, Maruthachalam K, et al. Tomato immune receptor Ve1 recognizes effector of multiple fungal pathogens uncovered by genome and RNA sequencing[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(13): 5110-5115. DOI:10.1073/pnas.1119623109
[31]
Menardo F, Praz CR, Wyder S, et al. Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species[J]. Nature Genetics, 2016, 48(2): 201-205. DOI:10.1038/ng.3485
[32]
Badouin H, Gladieux P, Gouzy J, et al. Widespread selective sweeps throughout the genome of model plant pathogenic fungi and identification of effector candidates[J]. Molecular Ecology, 2017, 26(7): 2041-2062. DOI:10.1111/mec.2017.26.issue-7
[33]
Gladieux P, Condon B, Ravel S, et al. Gene flow between divergent cereal- and grass-specific lineages of the rice blast fungus Magnaporthe oryzae[J]. mBio, 2018, 9(1): e01219-17.
[34]
Gladieux P, Ravel S, Rieux A, et al. Coexistence of multiple endemic and pandemic lineages of the rice blast pathogen[J]. mBio, 2018, 9(2): e01806-17.
[35]
Islam MT, Croll D, Gladieux P, et al. Emergence of wheat blast in Bangladesh was caused by a South American lineage of Magnaporthe oryzae[J]. BMC Biology, 2016, 14(1): 84. DOI:10.1186/s12915-016-0309-7
[36]
Zhong ZH, Chen ML, Lin LY, et al. Population genomic analysis of the rice blast fungus reveals specific events associated with expansion of three main clades[J]. The ISME Journal, 2018, 12(8): 1867-1878. DOI:10.1038/s41396-018-0100-6
[37]
Mohd-Assaad N, McDonald BA, Croll D. Genome-wide detection of genes under positive selection in worldwide populations of the barley scald pathogen[J]. Genome Biology and Evolution, 2018, 10(5): 1315-1332. DOI:10.1093/gbe/evy087
[38]
Kelly AC, Ward TJ. Population genomics of Fusarium graminearum reveals signatures of divergent evolution within a major cereal pathogen[J]. PLoS One, 2018, 13(3): e0194616. DOI:10.1371/journal.pone.0194616
[39]
Derbyshire M, Denton-Giles M, Hane JK, et al. Selective sweeps in populations of the broad host range plant pathogenic fungus Sclerotinia sclerotiorum[J]. bioRxiv, 2018, 352930.
[40]
Silva DN, Várzea V, Paulo OS, et al. Population genomic footprints of host adaptation, introgression and recombination in coffee leaf rust[J]. Molecular Plant Pathology, 2018, 19(7): 1742-1753. DOI:10.1111/mpp.2018.19.issue-7
[41]
Engelthaler DM, Hicks ND, Gillece JD, et al. Cryptococcus gattii in North American Pacific Northwest: whole-population genome analysis provides insights into species evolution and dispersal[J]. mBio, 2014, 5(4): e01464-14.
[42]
Billmyre RB, Croll D, Li WJ, et al. Highly recombinant VGII Cryptococcus gattii population develops clonal outbreak clusters through both sexual macroevolution and asexual microevolution[J]. mBio, 2014, 5(4): e01494-14.
[43]
Farrer RA, Desjardins CA, Sakthikumar S, et al. Genome evolution and innovation across the four major lineages of Cryptococcus gattii[J]. mBio, 2015, 6(5): e00868-15.
[44]
Farrer RA, Voelz K, Henk DA, et al. Microevolutionary traits and comparative population genomics of the emerging pathogenic fungus Cryptococcus gattii[J]. Philosophical transactions of the Royal Society B: Biological Sciences, 2016, 371(1709): 20160021. DOI:10.1098/rstb.2016.0021
[45]
Vanhove M, Beale MA, Rhodes J, et al. Genomic epidemiology of Cryptococcus yeasts identifies adaptation to environmental niches underpinning infection across an African HIV/AIDS cohort[J]. Molecular Ecology, 2017, 26(7): 1991-2005. DOI:10.1111/mec.2017.26.issue-7
[46]
Desjardins CA, Giamberardino C, Sykes SM, et al. Population genomics and the evolution of virulence in the fungal pathogen Cryptococcus neoformans[J]. Genome Research, 2017, 27(7): 1207-1219. DOI:10.1101/gr.218727.116
[47]
Fisher SK, Novak JE, Agranoff BW. Inositol and higher inositol phosphates in neural tissues: homeostasis, metabolism and functional significance[J]. Journal of Neurochemistry, 2002, 82(4): 736-754. DOI:10.1046/j.1471-4159.2002.01041.x
[48]
Shea JM, Henry JL, del Poeta M. Lipid metabolism in Cryptococcus neoformans[J]. FEMS Yeast Research, 2006, 6(4): 469-479. DOI:10.1111/fyr.2006.6.issue-4
[49]
Rhodes J, Desjardins CA, Sykes SM, et al. Tracing Genetic exchange and biogeography of Cryptococcus neoformans var. grubii at the global population level[J]. Genetics, 2017, 207(1): 327-346. DOI:10.1534/genetics.117.203836
[50]
Ford CB, Funt JM, Abbey D, et al. The evolution of drug resistance in clinical isolates of Candida albicans[J]. eLife, 2015, 4: e00662. DOI:10.7554/eLife.00662
[51]
Hirakawa MP, Martinez DA, Sakthikumar S, et al. Genetic and phenotypic intra-species variation in Candida albicans[J]. Genome Research, 2015, 25(3): 413-425.
[52]
Wang JM, Bennett RJ, Anderson MZ. The genome of the human pathogen Candida albicans is shaped by mutation and cryptic sexual recombination[J]. mBio, 2018, 9(5): e01205-18.
[53]
Ropars J, Maufrais C, Diogo D, et al. Gene flow contributes to diversification of the major fungal pathogen Candida albicans[J]. Nature Communications, 2018, 9(1): 2253.
[54]
Carreté L, Ksiezopolska E, Pegueroles C, et al. Patterns of genomic variation in the opportunistic pathogen Candida glabrata suggest the existence of mating and a secondary association with humans[J]. Current Biology, 2018, 28(1): 15-27. DOI:10.1016/j.cub.2017.11.027
[55]
Douglass AP, Offei B, Braun-Galleani S, et al. Population genomics shows no distinction between pathogenic Candida krusei and environmental Pichia kudriavzevii: one species, four names[J]. PLoS Pathogens, 2018, 14(7): e1007138. DOI:10.1371/journal.ppat.1007138
[56]
Branco S, Gladieux P, Ellison CE, et al. Genetic isolation between two recently diverged populations of a symbiotic fungus[J]. Molecular Ecology, 2015, 24(11): 2747-2758. DOI:10.1111/mec.13132
[57]
Branco S, Bi K, Liao HL, et al. Continental-level population differentiation and environmental adaptation in the mushroom Suillus brevipes[J]. Molecular Ecology, 2017, 26(7): 2063-2076. DOI:10.1111/mec.2017.26.issue-7
[58]
Xiao Y, Chen XJ, Liu J, et al. Population genomic analysis uncovers environmental stress-driven selection and adaptation of Lentinula edodes population in China[J]. Scientific Reports, 2016, 6: 36789. DOI:10.1038/srep36789
[59]
Wyss T, Masclaux FG, Rosikiewicz P, et al. Population genomics reveals that within-fungus polymorphism is common and maintained in populations of the mycorrhizal fungus Rhizophagus irregularis[J]. The ISME Journal, 2016, 10(10): 2514-2526. DOI:10.1038/ismej.2016.29
[60]
Savary R, Masclaux FG, Wyss T, et al. A population genomics approach shows widespread geographical distribution of cryptic genomic forms of the symbiotic fungus Rhizophagus irregularis[J]. The ISME Journal, 2018, 12(1): 17-30. DOI:10.1038/ismej.2017.153
[61]
Dalman K, Himmelstrand K, Olson Å, et al. A genome-wide association study identifies genomic regions for virulence in the non-model organism Heterobasidion annosum s.s[J]. PLoS One, 2013, 8(1): e53525. DOI:10.1371/journal.pone.0053525
[62]
Muller LAH, Lucas JE, Georgianna DR, et al. Genome-wide association analysis of clinical vs. nonclinical origin provides insights into Saccharomyces cerevisiae pathogenesis[J]. Molecular Ecology, 2011, 20(19): 4085-4097. DOI:10.1111/mec.2011.20.issue-19
[63]
Gao YY, Liu ZH, Faris JD, et al. Validation of genome-wide association studies as a tool to identify virulence factors in Parastagonospora nodorum[J]. Phytopathology, 2016, 106(10): 1177-1185. DOI:10.1094/PHYTO-02-16-0113-FI
[64]
Talas F, Kalih R, Miedaner T, et al. Genome-wide association study identifies novel candidate genes for aggressiveness, deoxynivalenol production, and azole sensitivity in natural field populations of Fusarium graminearum[J]. Molecular Plant-Microbe Interactions, 2016, 29(5): 417-430. DOI:10.1094/MPMI-09-15-0218-R
[65]
Hartmann FE, Sánchez-Vallet A, McDonald BA, et al. A fungal wheat pathogen evolved host specialization by extensive chromosomal rearrangements[J]. The ISME Journal, 2017, 11(5): 1189-1204. DOI:10.1038/ismej.2016.196
[66]
Palma-Guerrero J, Hall CR, Kowbel D, et al. Genome wide association identifies novel loci involved in fungal communication[J]. PLoS Genetics, 2013, 9(8): e1003669. DOI:10.1371/journal.pgen.1003669
[67]
Jeffares DC, Rallis C, Rieux A, et al. The genomic and phenotypic diversity of Schizosaccharomyces pombe[J]. Nature Genetics, 2015, 47(3): 235-241. DOI:10.1038/ng.3215
[68]
Sardi M, Paithane V, Place M, et al. Genome-wide association across Saccharomyces cerevisiae strains reveals substantial variation in underlying gene requirements for toxin tolerance[J]. PLoS Genetics, 2018, 14(2): e1007217. DOI:10.1371/journal.pgen.1007217
[69]
Eder M, Sanchez I, Brice C, et al. QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[J]. BMC Genomics, 2018, 19(1): 166. DOI:10.1186/s12864-018-4562-8
[70]
Wang X, Kruglyak L. Genetic basis of haloperidol resistance in Saccharomyces cerevisiae is complex and dose dependent[J]. PLoS Genetics, 2014, 10(12): e1004894. DOI:10.1371/journal.pgen.1004894
[71]
Cubillos FA, Brice C, Molinet J, et al. Identification of nitrogen consumption genetic variants in yeast through QTL mapping and bulk segregant RNA-seq analyses[J]. G3: Genes, Genomes, Genetics, 2017, 7(6): 1693-1705.
[72]
Stewart EL, Croll D, Lendenmann MH, et al. Quantitative trait locus mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici[J]. Molecular Plant Pathology, 2018, 19(1): 201-216.
[73]
Lendenmann MH, Croll D, Stewart EL, et al. Quantitative Trait locus mapping of melanization in the plant pathogenic fungus Zymoseptoria tritici[J]. G3: Genes, Genomes, Genetics, 2014, 4(12): 2519-2533.
[74]
Lendenmann MH, Croll D, McDonald BA. QTL mapping of fungicide sensitivity reveals novel genes and pleiotropy with melanization in the pathogen Zymoseptoria tritici[J]. Fungal Genetics and Biology, 2015, 80: 53-67. DOI:10.1016/j.fgb.2015.05.001
[75]
Lendenmann MH, Croll D, Palma-Guerrero J, et al. QTL mapping of temperature sensitivity reveals candidate genes for thermal adaptation and growth morphology in the plant pathogenic fungus Zymoseptoria tritici[J]. Heredity, 2016, 116(4): 384-394. DOI:10.1038/hdy.2015.111
[76]
Wendel JF, Jackson SA, Meyers BC, et al. Evolution of plant genome architecture[J]. Genome Biology, 2016, 17(1): 37.
[77]
Zhao Q, Feng Q, Lu HY, et al. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice[J]. Nature Genetics, 2018, 50(2): 278-284. DOI:10.1038/s41588-018-0041-z
[78]
Montenegro JD, Golicz AA, Bayer PE, et al. The pangenome of hexaploid bread wheat[J]. The Plant Journal, 2017, 90(5): 1007-1013. DOI:10.1111/tpj.2017.90.issue-5
[79]
Li MZ, Chen L, Tian SL, et al. Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies[J]. Genome Research, 2016, 27(5): 865.
[80]
Vernikos G, Medini D, Riley DR, et al. Ten years of pan-genome analyses[J]. Current Opinion in Microbiology, 2015, 23: 148-154. DOI:10.1016/j.mib.2014.11.016
[81]
Dunn B, Richter C, Kvitek DJ, et al. Analysis of the Saccharomyces cerevisiae pan-genome reveals a pool of copy number variants distributed in diverse yeast strains from differing industrial environments[J]. Genome Research, 2012, 22(5): 908-924. DOI:10.1101/gr.130310.111
[82]
Chen ECH, Morin E, Beaudet D, et al. High intraspecific genome diversity in the model arbuscular mycorrhizal symbiont Rhizophagus irregularis[J]. New Phytologist, 2018, 220(4): 1161-1171. DOI:10.1111/nph.14989
[83]
Plissonneau C, Hartmann FE, Croll D. Pangenome analyses of the wheat pathogen Zymoseptoria tritici reveal the structural basis of a highly plastic eukaryotic genome[J]. BMC Biology, 2018, 16(1): 5. DOI:10.1186/s12915-017-0457-4
[84]
Turley CB, Obeid J, Larsen R, et al. Leveraging a statewide clinical data warehouse to expand boundaries of the learning health system[J]. Egems, 2016, 4(1): 1245.
[85]
Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science[J]. Nature Reviews Genetics, 2016, 17(3): 175-188. DOI:10.1038/nrg.2015.16
[86]
Grünwald NJ, McDonald BA, Milgroom MG. Population genomics of fungal and oomycete pathogens[J]. Annual Review of Phytopathology, 2016, 54: 323-346. DOI:10.1146/annurev-phyto-080614-115913
[87]
Cao CX, Han W, Zhang HP. Application of third generation sequencing technology to microbial research[J]. Microbiology China, 2016, 43(10): 2269-2276. (in Chinese)
曹晨霞, 韩琬, 张和平. 第三代测序技术在微生物研究中的应用[J]. 微生物学通报, 2016, 43(10): 2269-2276.
[88]
Ma DN, Zhang XT, Wei LF, et al. Benchmarking hybrid correction and assembly using short illumina reads and long PacBio reads[J]. Genomics and Applied Biology, 2018, 37(4): 1547-1555. (in Chinese)
马东娜, 张兴坦, 魏柳锋, 等. 基因组二代测序数据与三代测序数据的混合校正和组装[J]. 基因组学与应用生物学, 2018, 37(4): 1547-1555.
[89]
Smith CC, Wang Q, Chin CS, et al. Validation of ITD mutations in FLT3 as a therapeutic target in human acute myeloid leukaemia[J]. Nature, 2012, 485(7397): 260-263. DOI:10.1038/nature11016
[90]
Li WK, Li FY, Zhang SY, et al. Automatic analysis pipeline of next-generation sequencing data[J]. Hereditas, 2014, 36(6): 618-624. (in Chinese)
李文轲, 李丰余, 张思瑶, 等. 基因组二代测序数据的自动化分析流程[J]. 遗传, 2014, 36(6): 618-624.
[91]
Mu DS, Shi L, Ren A, et al. The development and application of a multiple gene co-silencing system using endogenous URA3 as a reporter gene in Ganoderma lucidum[J]. PLoS One, 2012, 7(8): e43737. DOI:10.1371/journal.pone.0043737
[92]
Carreras-Villaseñor N, Esquivel-Naranjo EU, Villalobos-Escobedo JM, et al. The RNAi machinery regulates growth and development in the filamentous fungus Trichoderma atroviride[J]. Molecular Microbiology, 2013, 89(1): 96-112. DOI:10.1111/mmi.2013.89.issue-1
[93]
Zhang DH, Li N, Yu XY, et al. Overexpression of the homologous lanosterol synthase gene in ganoderic acid biosynthesis in Ganoderma lingzhi[J]. Phytochemistry, 2017, 134: 46-53. DOI:10.1016/j.phytochem.2016.11.006
[94]
Hong EJ, Kim NK, Lee D, et al. Overexpression of the laeA gene leads to increased production of cyclopiazonic acid in Aspergillus fumisynnematus[J]. Fungal Biology, 2015, 119(11): 973-983. DOI:10.1016/j.funbio.2015.06.006
[95]
Dicarlo JE, Norville JE, Mali P, et al. Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems[J]. Nucleic Acids Research, 2013, 41(7): 4336-4343. DOI:10.1093/nar/gkt135
[96]
Sugano SS, Suzuki H, Shimokita E, et al. Genome editing in the mushroom-forming basidiomycete Coprinopsis cinerea, optimized by a high-throughput transformation system[J]. Scientific Reports, 2017, 7(1): 1260. DOI:10.1038/s41598-017-00883-5
[97]
Liu R, Chen L, Jiang YP, et al. Efficient genome editing in filamentous fungus Trichoderma reesei using the CRISPR/Cas9 system[J]. Cell Discovery, 2015, 1: 15007.