High-throughput DNA sequencing has proven invaluable for investigating diverse environmental and host-associated microbial communities. In this Review, we discuss emerging strategies for microbial community analysis that complement and expand traditional metagenomic profiling. These include novel DNA sequencing strategies for identifying strain-level microbial variation and community temporal dynamics; measuring additional multi'omic data types that better capture community functional activity, such as transcriptomics, proteomics, and metabolomics; and combining multiple forms of multi'omic data in an integrated framework. We highlight studies in which the multi'omics approach has led to improved mechanistic models of microbial community structure and function.

While often obvious for macroscopic organisms, determining whether a microbe is dead or alive is fraught with complications. Fields such as microbial ecology, environmental health, and medical microbiology each determine how best to assess which members of the microbial community are alive, according to their respective scientific and/or regulatory needs. Many of these fields have gone from studying communities on a bulk level to the fine-scale resolution of microbial populations within consortia. For example, advances in nucleic acid sequencing technologies and downstream bioinformatic analyses have allowed for high-resolution insight into microbial community composition and metabolic potential, yet we know very little about whether such community DNA sequences represent viable microorganisms. In this review, we describe a number of techniques, from microscopy- to molecular-based, that have been used to test for viability (live/dead determination) and/or activity in various contexts, including newer techniques that are compatible with or complementary to downstream nucleic acid sequencing. We describe the compatibility of these viability assessments with high-throughput quantification techniques, including flow cytometry and quantitative PCR (qPCR). Although bacterial viability-linked community characterizations are now feasible in many environments and thus are the focus of this critical review, further methods development is needed for complex environmental samples and to more fully capture the diversity of microbes (e.g., eukaryotic microbes and viruses) and metabolic states (e.g., spores) of microbes in natural environments.

Mass transit environments, specifically, urban subways, are distinct microbial environments with high occupant densities, diversities, and turnovers, and they are thus especially relevant to public health. Despite this, only three culture-independent subway studies have been performed, all since 2013 and all with widely differing designs and conclusions. In this study, we profiled the Boston subway system, which provides 238 million trips per year overseen by the Massachusetts Bay Transportation Authority (MBTA). This yielded the first high-precision microbial survey of a variety of surfaces, ridership environments, and microbiological functions (including tests for potential pathogenicity) in a mass transit environment. Characterizing microbial profiles for multiple transit systems will become increasingly important for biosurveillance of antibiotic resistance genes or pathogens, which can be early indicators for outbreak or sanitation events. Understanding how human contact, materials, and the environment affect microbial profiles may eventually allow us to rationally design public spaces to sustain our health in the presence of microbial reservoirs.

Host specialization by pathogens requires a repertoire of virulence factors as well as fine-tuned regulation of gene expression. The fungal wheat pathogen Zymoseptoria tritici (synonym Mycosphaerella graminicola) is a powerful model system for the discovery of genetic elements that underlie virulence and host specialization. We transcriptionally profiled the early stages of Z. tritici infection of a compatible host (wheat) and a noncompatible host (Brachypodium distachyon). The results revealed infection regulatory programs common to both hosts and genes with striking wheat-specific expression, with many of the latter showing sequence signatures of positive selection along the Z. tritici lineage. Genes specifically regulated during infection of wheat populated two large clusters of coregulated genes that may represent candidate pathogenicity islands. On evolutionarily labile, repeat-rich accessory chromosomes (ACs), we identified hundreds of highly expressed genes with signatures of evolutionary constraint and putative biological function. Phylogenetic analyses suggested that gene duplication events on these ACs were rare and largely preceded the diversification of Zymoseptoria species. Together, our data highlight the likely relevance for fungal growth and virulence of hundreds of Z. tritici genes, deepening the annotation and functional inference of the genes of this model pathogen.

Antibiotic resistance is increasingly widespread, largely due to human influence. Here, we explore the relationship between antibiotic resistance genes and the antimicrobial chemicals triclosan, triclocarban, and methyl-, ethyl-, propyl-, and butylparaben in the dust microbiome. Dust samples from a mixed-use athletic and educational facility were subjected to microbial and chemical analyses using a combination of 16S rRNA amplicon sequencing, shotgun metagenome sequencing, and liquid chromatography tandem mass spectrometry. The dust resistome was characterized by identifying antibiotic resistance genes annotated in the Comprehensive Antibiotic Resistance Database (CARD) from the metagenomes of each sample using the Short, Better Representative Extract Data set (ShortBRED). The three most highly abundant antibiotic resistance genes were tet(W), blaSRT-1, and erm(B). The complete dust resistome was then compared against the measured concentrations of antimicrobial chemicals, which for triclosan ranged from 0.5 to 1970 ng/g dust. We observed six significant positive associations between the concentration of an antimicrobial chemical and the relative abundance of an antibiotic resistance gene, including one between the ubiquitous antimicrobial triclosan and erm(X), a 23S rRNA methyltransferase implicated in resistance to several antibiotics. This study is the first to look for an association between antibiotic resistance genes and antimicrobial chemicals in dust.

Gene expression varies widely between individuals of a population, and regulatory change can underlie phenotypes of evolutionary and biomedical relevance. A key question in the field is how DNA sequence variants impact gene expression, with most mechanistic studies to date focused on the effects of genetic change on regulatory regions upstream of protein-coding sequence. By contrast, the role of RNA 3′-end processing in regulatory variation remains largely unknown, owing in part to the challenge of identifying functional elements in 3′ untranslated regions. In this work, we conducted a genomic survey of transcript ends in lymphoblastoid cells from genetically distinct human individuals. Our analysis mapped the cis-regulatory architecture of 3′ gene ends, finding that transcript end positions did not fall randomly in untranslated regions, but rather preferentially flanked the locations of 3′ regulatory elements, including miRNA sites. The usage of these transcript length forms and motifs varied across human individuals, and polymorphisms in polyadenylation signals and other 3′ motifs were significant predictors of expression levels of the genes in which they lay. Independent single-gene experiments confirmed the effects of polyadenylation variants on steady-state expression of their respective genes, and validated the regulatory function of 3′ cis-regulatory sequence elements that mediated expression of these distinct RNA length forms. Focusing on the immune regulator IRF5, we established the effect of natural variation in RNA 3′-end processing on regulatory response to antigen stimulation. Our results underscore the importance of two mechanisms at play in the genetics of 3′-end variation: the usage of distinct 3′-end processing signals and the effects of 3′ sequence elements that determine transcript fate. Our findings suggest that the strategy of integrating observed 3′-end positions with inferred 3′ regulatory motifs will prove to be a critical tool in continued efforts to interpret human genome variation.

One of the outstanding challenges in comparative genomics is to interpret the evolutionary importance of regulatory variation between species. Rigorous molecular evolution-based methods to infer evidence for natural selection from expression data are at a premium in the field, and to date, phylogenetic approaches have not been well-suited to address the question in the small sets of taxa profiled in standard surveys of gene expression. We have developed a strategy to infer evolutionary histories from expression profiles by analyzing suites of genes of common function. In a manner conceptually similar to molecular evolution models in which the evolutionary rates of DNA sequence at multiple loci follow a gamma distribution, we modeled expression of the genes of an a priori-defined pathway with rates drawn from an inverse gamma distribution. We then developed a fitting strategy to infer the parameters of this distribution from expression measurements, and to identify gene groups whose expression patterns were consistent with evolutionary constraint or rapid evolution in particular species. Simulations confirmed the power and accuracy of our inference method. As an experimental testbed for our approach, we generated and analyzed transcriptional profiles of four Saccharomyces yeasts. The results revealed pathways with signatures of constrained and accelerated regulatory evolution in individual yeasts and across the phylogeny, highlighting the prevalence of pathway-level expression change during the divergence of yeast species. We anticipate that our pathway-based phylogenetic approach will be of broad utility in the search to understand the evolutionary relevance of regulatory change.

Expression variation is widespread between species. The ability to distinguish regulatory change driven by natural selection from the consequences of neutral drift remains a major challenge in comparative genomics. In this work, we used observations of mRNA expression and promoter sequence to analyze signatures of selection on groups of functionally related genes in Saccharomycete yeasts. In a survey of gene regulons with expression divergence between Saccharomyces cerevisiae and S. paradoxus, we found that most were subject to variation in trans-regulatory factors that provided no evidence against a neutral model. However, we identified one regulon of membrane protein genes controlled by unlinked cis- and trans-acting determinants with coherent effects on gene expression, consistent with a history of directional, nonneutral evolution. For this membrane protein group, S. paradoxus alleles at regulatory loci were associated with elevated expression and altered stress responsiveness relative to other yeasts. In a phylogenetic comparison of promoter sequences of the membrane protein genes between species, the S. paradoxus lineage was distinguished by a short branch length, indicative of strong selective constraint. Likewise, sequence variants within the S. paradoxus population, but not across strains of other yeasts, were skewed toward low frequencies in promoters of genes in the membrane protein regulon, again reflecting strong purifying selection. Our results support a model in which a distinct expression program for the membrane protein genes in S. paradoxus has been preferentially maintained by negative selection as the result of an increased importance to organismal fitness. These findings illustrate the power of integrating expression- and sequence-based tests of natural selection in the study of evolutionary forces that underlie regulatory change.