Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study

The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host–metagenome–phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH₄) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian^® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH₄. Heritability of CH₄ was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH₄ ranged from −0.76 to 0.65 in the non-recursive bivariate model and from −0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH₄ and the microbiota compositions are partially controlled by the host genotype.     

土壤宏基因组中抗草甘膦新基因的克隆及其功能验证

【目的】从土壤宏基因组中克隆新的抗草甘膦新基因,并对其功能进行验证,为培育抗除草剂转基因新品种奠定基础。【方法】利用被草甘膦污染的土壤建立宏基因组文库,经筛选从中成功克隆了一个新的具有草甘膦抗性的5-烯醇丙酮莽草酸-3-磷酸合成酶(EPSPS)基因(命名为soilA)。构建了Prrn启动子驱动soilA基因的原核表达载体pSYTH-soilA,将其导入大肠杆菌进行原核草甘膦抗性试验,构建了35S启动子驱动soilA基因的植物表达载体pC330E,利用农杆菌介导法转化烟草,并对经PCR和胶体金试纸条鉴定的转基因烟草植株进行草甘膦耐受能力检测。【结果】序列分析表明,所获得的soilA基因长1 347bp,编码448个氨基酸,经BLAST分析,结果表明其属于ClassⅡ型EPSPS基因家族。原核表达soilA可使受体菌具有耐受1 200mmol/L草甘膦。构建soilA植物表达载体pC330E,通过农杆菌介导法将其导入烟草,经PCR和胶体金试纸条检测共获得107株阳性烟草植株,经过喷洒试验获得了3株高耐受草甘膦植株,这3株转基因烟草最高可耐受200mmol/L草甘膦。【结论】新克隆的编码EPSPS的soilA基因能够提高受体材料的草甘膦耐受能力。     

基于高通量测序技术的冠心病患者肠道菌群多样性研究

为了分析冠心病人群肠道菌群结构和多样性特征,探究冠心病人群与健康人群肠道菌群结构差异。研究共采集50例冠心病患者和35例健康人粪便样本,提取粪便样本总DNA;根据细菌16SrDNA V3~V5区设计引物进行扩增,利用Illumina Miseq平台进行高通量测序;测序结果经过Reads拼接,OTUs(operational taxonomic units)聚类,物种注释,α多样分析,主成分分析,最终得到样品物种信息。在冠心病组和健康组中,拟杆菌门(Bacteroidetes)和厚壁菌门(Firmicutes)的丰度最高,其中拟杆菌门分别占到46.94%和61.96%,厚壁菌门分别占到41.51%和30.58%;丰度较高的是变形菌门(Proteobacteria)、梭杆菌门(Fusobacteria)、放线菌门(Actinobacteria)。此外,两组菌群结构差异性表现为:冠心病组的拟杆菌门明显低于对照组(P=0.0150.05);而冠心病组的厚壁菌门显著高于对照组(P=0.0090.01)。α多样性分析结果显示,两组人群各样本在细菌种群多样性方面没有明显差异。OTUs比对结果表示,Veillonella、Veillonella dispar、Scardovia、Mogibacterium致病菌在冠心病组显著增高,而Bacteroidetes门下Parabacteroides、Bacteroides uniformis、Bacteroides caccae等有益菌则显著降低。主成份分析显示,综合PC1、PC2、PC3因素冠心病组和对照组样本被较好的区分开,表明健康组和冠心病组菌群结构存在显著差异。这些结果表明,冠心病患者与健康人群在肠道菌群多样性方面没有明显差异,但在菌群结构上存在较大差异;同时冠心病人肠道菌群致病菌数量多于健康人群,而拟杆菌类有益菌低于健康人群。     

一个东北农田黑土样品宏病毒组的初步分析

病毒在地球上无处不在,几乎能感染任何生物,包括动物、植物、真菌以及细菌等,因而在生物地球化学元素和能量的循环过程中发挥重要作用。了解土壤病毒基因信息有助于我们深入理解病毒在生态系统中所扮演的角色。本研究选取一个东北农田黑土样品中的病毒为研究对象,基于病毒宏基因组测序技术获得土壤病毒基因序列,并利用生物信息分析方法揭示土壤病毒多样性。同时,结合个性化分析宏病毒组基因序列、进行功能基因分析、宿主预测和单病毒基因组组装和注释。研究发现该农田土壤检测到的病毒主要归属于有尾噬菌体目（Caudovirales,59.38%）和疱疹病毒目（Herpesvirales,2.56%）等2个病毒目中的29个病毒科,其中以长尾噬菌体科（Siphoviridae）、微小噬菌体科（Microviridae）的病毒数量最多,分别占44.48%和20.53%。基因功能分析表明土壤病毒可能参与土壤中的酶催化、生物代谢（如氮化合物代谢、分解代谢、多生物代谢、细胞代谢、初级代谢、含碱基小分子代谢以及有机物代谢等）等过程。宿主预测分析揭示检测到的病毒宿主分属5个菌门中的35个菌属。本研究结果丰富了土壤病毒的基因数据库,为土壤病毒分离提供参考,并为进一步理解土壤病毒生态学意义提供数据支撑。     

Technical note: overcoming host contamination in bovine vaginal metagenomic samples with nanopore adaptive sequencing

Animal metagenomic studies, in which host-associated microbiomes are profiled, are an increasingly important contribution to our understanding of the physiological functions, health and susceptibility to diseases of livestock. One of the major challenges in these studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of metagenomic profiling. This is the first study comparing the effectiveness of different sequencing methods for profiling bovine vaginal metagenomic samples. We compared the new method of Oxford Nanopore Technologies (ONT) adaptive sequencing, which can be used to target or eliminate defined genetic sequences, to standard ONT sequencing, Illumina 16S rDNA amplicon sequencing, and Illumina shotgun sequencing. The efficiency of each method in recovering the metagenomic data and recalling the metagenomic profiles was assessed. ONT adaptive sequencing yielded a higher amount of metagenomic data than the other methods per 1 Gb of sequence data. The increased sequencing efficiency of ONT adaptive sequencing consequently reduced the amount of raw data needed to provide sufficient coverage for the metagenomic samples with high host-to-microbe DNA ratio. Additionally, the long reads generated by ONT adaptive sequencing retained the continuity of read information, which benefited the in-depth annotations for both taxonomical and functional profiles of the metagenome. The different methods resulted in the identification of different taxa. Genera Clostridium, which was identified at low abundances and categorized under Order “Unclassified Clostridiales” when using the 16S rDNA amplicon sequencing method, was identified to be the dominant genera in the sample when sequenced with the three other methods. Additionally, higher numbers of annotated genes were identified with ONT adaptive sequencing, which also produced high coverage on most of the commonly annotated genes. This study illustrates the advantages of ONT adaptive sequencing in improving the amount of metagenomic data derived from microbiome samples with high host-to-microbe DNA ratio and the advantage of long reads in preserving intact information for accurate annotations.     

瘤胃微生物基因组文库中BAC末端序列分析

基于前期从瘤胃微生物基因组文库中筛选的功能酶克隆菌,利用T7和pIBRP引物,测定功能酶克隆菌的BAC末端序列,并利用NCBI Blast系统对BAC末端序列进行分析.结果表明:共得到26条BAC末端序列,经Blast比对分析,与已知编码基因的匹配度低,而大部分BAC末端序列与阪崎肠杆菌和环境中的微生物匹配度高,说明瘤胃中的微生物与其它环境中的微生物具有一定的相似性,进一步丰富了对瘤胃微生物的认识.     

附石生物营养质量对干旱胁迫的响应及机制

在全球气候变暖的大背景下,干旱事件的发生频率显著提高。附石生物是河流生态系统最重要的初级生产者之一,它作为食物网物质循环的基础,为上层消费者提供碳源。干旱造成水体面积的缩小势必会对附石生物的生境产生影响,而干旱对附石生物的营养质量产生的潜在影响目前鲜有研究。基于脂肪酸和化学计量学两种食物营养质量指示方法,利用中宇宙控制实验模拟野外干旱情景,本研究明确了干旱对附石生物营养质量的影响,并从宏观的附石生物膜的藻类群落结构和微观的宏基因组技术两个层面阐明了影响机制。研究结果表明,干旱后多不饱和脂肪酸(Polyunsaturated fatty acid,PUFA)中的二十二碳六烯酸(Docosahexaenoic acid,DHA)、花生四烯酸(Arachidonic acid,ARA)和二十碳五烯酸(Eicosapentaenoic acid,EPA)等含量减少;附石生物中磷(P)含量显著减少(p<0.05),C/P、N/P比值增加。硅藻是附石生物膜中的优势类群,是优质脂肪酸的生产者,富含EPA、DHA,干旱后硅藻密度和丰度显著减少(p<0.05)。基于宏基因组KEGG功能注释结果,附石生物脂质代谢通路中脂肪酸生物合成显著减少(p<0.05),脂肪酸降解显著增加(p<0.05),花生四烯酸代谢等通路减少,这导致EPA、DHA含量的下降。该研究指出干旱导致河流生态系统基础资源营养质量下降,进而可能对整个河流生态系统食物网中消费者的生长和繁殖产生影响。该研究从基础资源碳质量新视角下为研究水生态系统食物网的碳源及传递提供了重要依据。     

两类抗生素药剂对‘纽荷尔’脐橙黄龙病菌的抑制作用及根际细菌群落结构的影响

为探究磺胺二甲氧嘧啶钠盐（sulfadimethoxine sodium salt, SDM）和氨苄青霉素（ampicillin, AMP）对柑橘黄龙病（citrus Huanglongbing, HLB）的防效以及对柑橘根际细菌群落结构的影响, 本试验以‘纽荷尔’脐橙为研究对象, 采用0.5 g/L SDM和1 g/L AMP进行灌根, 使用TaqMan qPCR技术监测病树叶内黄龙病菌菌量的动态变化, 并利用宏基因组技术分析根际细菌群落结构的变化。结果显示, AMP连续处理后病树叶片内黄龙病菌菌含量在一定程度上受到抑制, 初步判断AMP对黄龙病有一定的抑制效果, SDM处理对黄龙病菌菌含量抑制效果较差;2类抗生素处理组中根际主要优势类群结构和相对丰度在门、属分类水平上均发生改变, 解磷细菌中慢生根瘤菌属Bradyrhizobium细菌的相对丰度在处理后有明显变化, pstA、pstC等部分磷循环功能基因相对丰度发生改变, 表明2类抗生素药剂均影响了‘纽荷尔’脐橙病树根际细菌群落结构。     

Beyond microbial diversity for predicting soil functions: A mini review

Since the advent of sequencing technologies,the determination of microbial diversity to predict microbial functions,which are the major determinants of soil functions,has become a major topic of interest,as evidenced by the 900 publications dealing with soil metagenome published up to 2017.However,the detection of a gene in soil does not mean that the relative function is expressed,and the presence of a particular taxon does not mean that the relative functions determined in pure culture also occur in the studied soil.Another critical step is to link microbial community composition or function to the product analyzed to determine flux rates.Indeed,flux rates might not only be highly dynamic,but several metabolites can depend on different reactions,which makes the link to one process of interest difficult or even impossible.This review also discusses biases caused by sampling,storage of samples,DNA extraction and purification,sequencing(amplicon-vs.metagenome sequencing),and bioinformatic data analysis.Insights and the limits of predicting microbial interactions by network inference methods are critically discussed,and finally,future directions for a better understanding of soil functions by using measurements of microbial diversity are presented.     

稻田土壤DNA的提取及nifA基因的PCR扩增

为弄清茅贡米土壤微生物群落结构和生物固氮等代谢活动机制,采用试剂盒、SDS高盐和土壤预处理+SDS高盐3种土壤DNA提取方法,以从贵州茅贡米基地采集的土样提取的宏基因组DNA为模板,进行nifA基因的PCR扩增,比较了3种土壤DNA提取方法的效果.结果表明,试剂盒提取的DNA扩增到约200 bp和900 bp的nifA...