Exploring the functional landscape of gene expression: directed search of large microarray compendia

MOTIVATION: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. RESULTS: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. AVAILABILITY: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL. SUPPLEMENTARY INFORMATION: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement.     

Soil fungal communities are compositionally resistant to drought manipulations – Evidence from culture-dependent and culture-independent analyses

Current environmental change predictions forecast intensified drought conditions. It is becoming increasingly evident that plant communities are sensitive to drought and that soil-inhabiting microbial communities vary along precipitation gradients. However, the drought sensitivity of microbial communities in general and that of soil fungi in particular remains unclear, even though understanding their responses to adverse environmental conditions is vital for better understanding of ecosystem service provisioning. We sampled soils at two sites with established experiments that imposed extreme, chronic drought to assess fungal community responses. We analyzed fungal communities using both culture-dependent and -independent tools and MiSeq-sequenced communities from colony forming units (CFU-PCR) on a drought simulating medium and from environmental DNA (ePCR), to compare the conclusions derived from these two methods. Our data from the two approaches consistently indicate that the composition of fungal communities is not affected by the drought treatment, whereas – based on the CFU-PCR but not ePCR data – their richness and diversity increased under drought conditions at the more mesic of the two sites. Further, based on the direct comparisons of CFU-PCR and ePCR, we estimate that more than 10% of the fungal community and more than 20% of the ascomycetes were culturable. We conclude that although recent research indicates that plant and bacterial communities respond to drought, fungal community responses are more variable, particularly in experiments that impose chronic drought under field conditions.     

Individual genome assembly from complex community short-read metagenomic datasets

Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.     

Soil fungal community changes in response to long-term fire cessation and N fertilization in tallgrass prairie

In grasslands, fire management and fertilization are established drivers of plant community change, but associated soil fungal responses are less well defined. We predicted that soil fungal communities would change seasonally, that decades of fire cessation and nitrogen (N) fertilization would alter fungal distributions, and that plant and fungal community change would be correlated. Surface soils were sampled monthly for 1 y from a 30-y fire by fertilization experiment to evaluate fungal community dynamics and assess correlation with plant community heterogeneity. ITS gene community composition was seasonally stable, excepting increased arbuscular mycorrhizal fungal summer abundance in the burned, fertilized treatment. Long-term treatments affected soil fungal and plant communities, with correlated heterogeneity patterns. Despite woody encroachment in the fire cessation treatment, soil fungal communities did not resemble those of forests. This study provides evidence supporting the strength of feedbacks between fungal and plant community change in response to long-term grassland fire and N management changes.     

Gene transfer and expression in plants: Implications and potential

This review provides a current perspective on the insertion of genes into plants. Some of the knowledge on the structure and control of plant genes gained recently from genetic engineering approaches is described, together with developments that can be expected to emerge from further exploitation of gene transfer techniques.     

Gene expression varies within and between enzootic and epizootic lineages of Batrachochytrium dendrobatidis (Bd) in the Americas

While much research focus is paid to hypervirulent fungal lineages during emerging infectious disease outbreaks, examining enzootic pathogen isolates can be equally fruitful in delineating infection dynamics and determining pathogenesis. The fungal pathogen of amphibians, Batrachochytrium dendrobatidis (Bd), exhibits markedly different patterns of disease in natural populations, where it has caused massive amphibian declines in some regions, yet persists enzootically in others. Here we compare in vitro gene expression profiles of a panel of Bd isolates representing both the enzootic Bd-Brazil lineage, and the more recently diverged, panzootic lineage, Bd-GPL. We document significantly different lineage-specific and intralineage gene expression patterns, with Bd-Brazil upregulating genes with aspartic-type peptidase activity, and Bd-GPL upregulating CBM18 chitin-binding genes, among others. We also find pronounced intralineage variation in membrane integrity and transmembrane transport ability within our Bd-GPL isolates. Finally, we highlight unexpectedly divergent expression profiles in sympatric panzootic isolates, underscoring microgeographic functional variation in a largely clonal lineage. This variation in gene expression likely plays an important role in the relative pathogenesis and host range of Bd-Brazil and Bd-GPL isolates. Together, our results demonstrate that functional genomics approaches can provide information relevant to studies of virulence evolution within the Bd clade.     

Expression of LjENOD40 genes in response to symbiotic and non-symbiotic signals: LjENOD40-1 and LjENOD40-2 are differentially regulated in Lotus japonicus

Nitrogen fixation in nodules provides leguminous plants with an ability to grow in nitrogen-starved soil. Infection of the host plants by microsymbionts triggers various physiological and morphological changes during nodule formation. In Lotus japonicus, expression of early nodulin (ENOD) genes is triggered by perception of bacterial signal molecules, nodulation factors (Nod factors). We examined the expression patterns of ENOD40 genes during the nodule formation process. Two ENOD40 genes of L. japonicus were specifically expressed in the nodule formation process, but they showed different expression patterns upon infection. Each ENOD40 gene demonstrates an individual specificity and regulation with regard to rhizobial infection.     

纳帕海高原湿地真菌群落多样性和组成的分布

【背景】位于滇西北的纳帕海高原湿地,是我国唯一的低纬度、高海拔、季节性半封闭型高原湿地。真菌在湿地生态系统的维持和稳定中发挥着特殊作用,然而关于纳帕海高原湿地真菌群落多样性和组成的研究目前仍无报道。【目的】对纳帕海高原湿地不同季节和土壤类型真菌群落多样性和组成及与环境因子的关系开展系统研究分析,促进对高原湿地微生物多样性的深入认识。【方法】采用荧光定量PCR和高通量测序技术,分析了纳帕海高原湿地不同季节和土壤类型中真菌的数量、群落多样性和组成及其与环境因子的关系。【结果】真菌数量级的变化对于人为干扰下的湿地土壤退化是敏感的响应指标。在真菌群落组成中,约有60%以上未确定的分类信息,40%有确定分类信息的包括6个门17个纲37个目53个科63个属,大部分分类信息集中在Ascomycota门,相对优势属为Gibberella。通过分类水平、OTU水平和β多样性分析比较,在纳帕海高原湿地整体真菌群落多样性和组成受季节变化影响不显著,但不同土壤类型的变化呈显著差异,推测是由于不同采样区植物根际效应和种类的影响。CCA (Canonical correlation analysis)分析表明,在不同采样区受不同土壤理化因子的影响。【结论】揭示了纳帕海高原湿地土壤真菌群落多样性和组成的区域特征,从微生物学角度进一步提出了对纳帕海高原湿地环境保护和恢复的重要性。     

Optimising the identification of causal variants across varying genetic architectures in crops

Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome‐wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real‐world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade‐offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes for moderately complex traits while a Bayesian approach adopted from genomic prediction provides the most favourable outcomes for extremely complex traits. We assert that by estimating the complexity of genetic architectures for target traits and selecting an appropriate statistical approach for the degree of complexity detected, researchers can substantially improve the ability to dissect the genetic factors controlling complex traits such as flowering time, plant height and yield component.     

大数据时代土壤微生物地理学研究综述

土壤蕴含极为丰富的微生物多样性,它们在物质分解、元素生物地球化学循环、植物生产力和生物健康中扮演着关键角色。理解土壤微生物的生物地理分布格局、形成机制与群落构建规则,有助于预测在全球变化背景下土壤微生物组的功能演变及其对陆地生态系统的调控影响。自21世纪以来,土壤微生物生物地理学在各种大型国际微生物计划的推动下逐步形成了分子生物学技术耦合大数据分析的模式,实现了多种尺度上的关联研究。阐述了土壤微生物在分布格局和群落构建规则方面的研究进展,重点介绍了分子生物学技术和大数据分析在土壤微生物生物地理研究中的应用,对土壤微生物生物地理学未来在微生物分类分辨率、模型验证与构建和功能基因地理学的发展方向进行了展望。     

High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.