Functional Basis of Microorganism Classification

Discovery of novel treponemes associated with pododermatitis in elk (Cervus canadensis)

Pododermatitis, also known as treponeme-associated hoof disease (TAHD), presents a significant challenge to elk (Cervus canadensis) populations in the northwestern USA, with Treponema spp. consistently implicated in the lesion development. However, identifying species-specific Treponema strains from these lesions is hindered by its culture recalcitrance and limited genomic information. This study utilized shotgun sequencing, in silico genome reconstruction, and comparative genomics as a culture-independent approach to identify metagenome-assembled Treponema genomes (MATGs) from skin scraping samples collected from captive elk experimentally challenged with TAHD. The genomic analysis revealed 10 new MATGs, with 6 representing novel genomospecies associated with pododermatitis in elk and 4 corresponding to previously identified species-Treponema pedis and Treponema phagedenis. Importantly, genomic signatures of novel genomospecies identified in this study were consistently detected in biopsy samples of free-ranging elk diagnosed with TAHD, indicating a potential etiologic association. Comparative metabolic profiling of the MATGs against other Treponema genomes showed a distinct metabolic profile, suggesting potential host adaptation or geographic uniqueness of these newly identified genomospecies. The discovery of novel Treponema genomospecies enhances our understanding of the pathogenesis of pododermatitis and lays the foundation for the development of improved molecular surveillance tools to monitor and manage the disease in free-ranging elk.IMPORTANCETreponema spp. play an important role in the development of pododermatitis in free-ranging elk; however, the species-specific detection of Treponema from pododermatitis lesions is challenging due to culture recalcitrance and limited genomic information. The study utilized shotgun sequencing and in silico genome reconstruction to identify novel Treponema genomospecies from elk with pododermatitis. The discovery of the novel Treponema species opens new avenues to develop molecular diagnostic and epidemiologic tools for the surveillance of pododermatitis in elk. These findings significantly enhance our understanding of the genomic landscape of the Treponemataceae consortium while offering valuable insights into the etiology and pathogenesis of emerging pododermatitis in elk populations.

SOC bioavailability significantly correlated with the microbial activity mediated by size fractionation and soil morphology in agricultural ecosystems

Despite the fact that physical and chemical processes have been widely proposed to explicate the stabilization mechanisms of soil organic carbon (SOC), thebioavailability of SOC linked to soil physical structure, microbial community structure, and functional genes remains poorly understood. This study aims to investigate the SOC division based on bioavailability differences formed by physical isolation, and to clarify the relationships of SOC bioavailability with soil elements, pore characteristics, and microbial activity. Results revealed that soil element abundances such as SOC, TN, and DOC ranked in the same order as the soil porosity as clay > silt ≥ coarse sand > fine sand in both top and sub soil. In contrast to silt and clay, which had reduced SOC bioavailability, fine sand and coarse sand had dramatically enhanced SOC bioavailability compared to the bulk soil. The bacterial and fungal community structure was significantly influenced by particle size, porosity, and soil elements. Copiotrophic bacteria and functional genes were more prevalent in fine sand than clay, which also contained more oligotrophic bacteria. The SOC bioavailability was positively correlated with abundances of functional genes, C degradation genes, and copiotrophic bacteria, but negatively correlated with abundances of soil elements, porosity, oligotrophic bacteria, and microbial biomass (p < 0.05). This indicated that the soil physical structure divided SOC into pools with varying levels of bioavailability, with sand fractions having more bioavailable organic carbon than finer fractions. Copiotrophic Proteobacteria and oligotrophic Acidobacteria, Firmicutes, and Gemmatimonadetes made up the majority of the bacteria linked to SOC mineralization. Additionally, the fungi Mortierellomycota and Mucoromycota, which are mostly involved in SOC mineralization, may have the potential for oligotrophic metabolism. Our results indicated that particle-size fractionation could influence the SOC bioavailability by restricting SOC accessibility and microbial activity, thus having a significant impact on sustaining soil organic carbon reserves in temperate agricultural ecosystems, and provided a new research direction for organic carbon stability.

The gender gap in names of prokaryotes honouring persons

The aim of this study is to analyse prokaryotic names which honour persons, eponyms, from a gender perspective. Data were retrieved from the List of Prokaryotic names with Standing in Nomenclature. Excluding new combinations, the etymologies of 23 315 unique names at the rank of genus, species and subspecies were analysed. A total of 2018 (8.7 %) names honour persons (eponyms), for which the development of the female share over time was further investigated. Women started to be honoured very recently (1947) compared to men (1823). Moreover, only 14.8 % of all prokaryotic eponyms refer to females. This ratio has hardly improved since 1947, although the number of women whose contributions to microbiology could have been recognized has increased over time. In contrast, about 50 % of prokaryotic names derived from mythological characters refer to females. To reduce this gender gap, we encourage authors proposing new taxon names to honour female scientists who can serve as role models for new generations.

Learning from the unknown: exploring the range of bacterial functionality

Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

Investigating chemical and microbial functional indicators of nutrient retention capacity in greenhouse stormwater retention ponds in southwestern Ontario, Canada

The tributaries flowing through Leamington, Ontario are unique in the Canadian Lake Erie watershed due to the broad spatial extent of greenhouse operations, which more than doubled in size and density from 2011 to 2022. These greenhouse operations are considered to be potential nutrient point sources with respect to observed nutrient concentrations in tributaries adjacent to greenhouse stormwater retention ponds (GSWPs). Identifying causal factors of nutrient release, whether this be chemical or biological, within these ponds may be critical for mitigating their impact on the watershed and ultimately the receiving waters of Lake Erie. Specifically, phosphorus and nitrogen accumulation in freshwater ponds can contribute to environmental damage proximal to adjacent streams, serving as a potential catalyst for algal blooms and eutrophication. This study compared correlations between the water column N:P stoichiometry, sediment nutrient retention capacity, and drivers of microbial metabolism within GSWP sediments. Correlations between water column TN:TP ratios and sediment nutrient retention capacity were observed, suggesting an interplay between N and P in terms of nutrient limitation. Further, clear shifts were observed in the bacterial metabolic pathways analyzed through metatranscriptomics. Specifically, genes related to nitrogen fixation, nitrification and denitrification, and other metabolic processes involving sulfur and methane showed differential expression depending on the condition of the respective pond (i.e., naturalized wetland vs. dredged, eutrophic pond). Collectively, this research serves to highlight the interconnected role of chemical-biological processes particularly as they relate to significant ecosystem processes such as nutrient loading and retention dynamics in impaired freshwater systems.

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants

Background

Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline.

Results

By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships.

Conclusions

GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

Functional trait relationships demonstrate life strategies in terrestrial prokaryotes

Functional, physiological traits are the underlying drivers of niche differentiation. A common framework related to niches occupied by terrestrial prokaryotes is based on copiotrophy or oligotrophy, where resource investment is primarily in either rapid growth or stress tolerance, respectively. A quantitative trait-based approach sought relationships between taxa, traits and niche in terrestrial prokaryotes. With 175 taxa from 11 Phyla and 35 Families (n = 5 per Family), traits were considered as discrete counts of shared genome-encoded proteins. Trait composition strongly supported non-random functional distributions as preferential clustering of related taxa via unweighted pair-group method with arithmetic mean. Trait similarity between taxa increased as taxonomic rank decreased. A suite of Random Forest models identified traits significantly enriched or depleted in taxonomic groups. These traits conveyed functions related to rapid growth, nutrient acquisition and stress tolerance consistent with their presence in copiotroph-oligotroph niches. Hierarchical clustering of traits identified a clade of competitive, copiotrophic Families resilient to oxidative stress versus glycosyltransferase-enriched oligotrophic Families resistant to antimicrobials and environmental stress. However, the formation of five clades suggested a more nuanced view to describe niche differentiation in terrestrial systems is necessary. We suggest considering traits involved in both resource investment and acquisition when predicting niche.

Sources and Assembly of Microbial Communities in Vineyards as a Functional Component of Winegrowing

Microbiomes are integral to viticulture and winemaking – collectively termed winegrowing – where diverse fungi and bacteria can exert positive and negative effects on grape health and wine quality. Wine is a fermented natural product, and the vineyard serves as a key point of entry for quality-modulating microbiota, particularly in wine fermentations that are conducted without the addition of exogenous yeasts. Thus, the sources and persistence of wine-relevant microbiota in vineyards critically impact its quality. Site-specific variations in microbiota within and between vineyards may contribute to regional wine characteristics. This includes distinctions in microbiomes and microbiota at the strain level, which can contribute to wine flavor and aroma, supporting the role of microbes in the accepted notion of terroir as a biological phenomenon. Little is known about the factors driving microbial biodiversity within and between vineyards, or those that influence annual assembly of the fruit microbiome. Fruit is a seasonally ephemeral, yet annually recurrent product of vineyards, and as such, understanding the sources of microbiota in vineyards is critical to the assessment of whether or not microbial terroir persists with inter-annual stability, and is a key factor in regional wine character, as stable as the geographic distances between vineyards. This review examines the potential sources and vectors of microbiota within vineyards, general rules governing plant microbiome assembly, and how these factors combine to influence plant-microbe interactions relevant to winemaking.

Snow microbiome functional analyses reveal novel aspects of microbial metabolism of complex organic compounds

Microbes active in extreme cold are not as well explored as those of other extreme environments. Studies have revealed a substantial microbial diversity and identified cold-specific microbiome molecular functions. We analyzed the metagenomes and metatranscriptomes of 20 snow samples collected in early and late spring in Svalbard, Norway using mi-faser, our read-based computational microbiome function annotation tool. Our results reveal a more diverse microbiome functional capacity and activity in the early- vs. late-spring samples. We also find that functional dissimilarity between the same-sample metagenomes and metatranscriptomes is significantly higher in early than late spring samples. These findings suggest that early spring samples may contain a larger fraction of DNA of dormant (or dead) organisms, while late spring samples reflect a new, metabolically active community. We further show that the abundance of sequencing reads mapping to the fatty acid synthesis-related microbial pathways in late spring metagenomes and metatranscriptomes is significantly correlated with the organic acid levels measured in these samples. Similarly, the organic acid levels correlate with the pathway read abundances of geraniol degradation and inversely correlate with those of styrene degradation, suggesting a possible nutrient change. Our study thus highlights the activity of microbial degradation pathways of complex organic compounds previously unreported at low temperatures.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Quantifying stream periphyton assemblage responses to nutrient amendments with a molecular approach

Nutrient (nitrogen [N] and phosphorus [P]) pollution is a pervasive water quality issue in the USA for small streams and rivers. The effect of nutrients on the biotic condition of streams is often evaluated with biological indicators such as macroinvertebrate assemblages or periphyton assemblages, particularly diatoms. Molecular approaches facilitate the use of periphyton assemblages as bioindicators because periphyton is diverse and its composition as a whole, rather than just diatoms, soft-bodied algae, or any single group, may convey additional information about responses to nutrients. To further develop the concept that a taxonomically-broad evaluation of periphyton assemblages could be useful for developing stream bioindicators, we examined microbial assemblage composition with both 16S and 18S rRNA genes, enabling us to evaluate composition in 3 domains. We measured otherwise unknown nutrient responses of different periphyton groups in situ with experiments that used glass fiber filters to allow diffusion of amended nutrients into a stream. We deployed these experimental setups in 2 streams that differ in the extent of agricultural land-use in their catchments in the southeastern USA. Experiments consisted of controls, N amendments, P amendments, and both N and P amendments. Periphyton assemblages that grew on the filters differed significantly by stream, date or season, and nutrient treatment. Assemblage differences across treatments were more consistent among Bacteria and Archaea than among eukaryotes. Effects of nutrient amendments were more pronounced in the stream with less agricultural land use and, therefore, lower nutrient loading than in the stream with more agricultural land use and higher nutrient loading. Combined N and P amendments decreased species richness and evenness for Bacteria and Archaea by ∼36 and ∼9%, respectively, compared with controls. Indicator species analysis revealed that specific clades varied in their response to treatments. Indicators based on the responses of these indicator clades were related to nutrient treatments across sites and seasons. Analyses that included all the taxa in a domain did not resolve differences in responses to N vs P. Instead, better resolution was achieved with an analysis focused on diatoms, which responded more strongly to P than N. Overall, our results showed that in situ nutrient-diffusing substrate experiments are a useful approach for describing assemblage responses to nutrients in streams. This type of molecular approach may be useful to environmental agencies and stakeholders responsible for assessing and managing stream water quality and biotic condition.

Analysis of 1,000+ Type-Strain Genomes Substantially Improves Taxonomic Classification of Alphaproteobacteria

The class Alphaproteobacteria is comprised of a diverse assemblage of Gram-negative bacteria that includes organisms of varying morphologies, physiologies and habitat preferences many of which are of clinical and ecological importance. Alphaproteobacteria classification has proved to be difficult, not least when taxonomic decisions rested heavily on a limited number of phenotypic features and interpretation of poorly resolved 16S rRNA gene trees. Despite progress in recent years regarding the classification of bacteria assigned to the class, there remains a need to further clarify taxonomic relationships. Here, draft genome sequences of a collection of genomes of more than 1000 Alphaproteobacteria and outgroup type strains were used to infer phylogenetic trees from genome-scale data using the principles drawn from phylogenetic systematics. The majority of taxa were found to be monophyletic but several orders, families and genera, including taxa recognized as problematic long ago but also quite recent taxa, as well as a few species were shown to be in need of revision. According proposals are made for the recognition of new orders, families and genera, as well as the transfer of a variety of species to other genera and of a variety of genera to other families. In addition, emended descriptions are given for many species mainly involving information on DNA G+C content and (approximate) genome size, both of which are confirmed as valuable taxonomic markers. Similarly, analysis of the gene content was shown to provide valuable taxonomic insights in the class. Significant incongruities between 16S rRNA gene and whole genome trees were not found in the class. The incongruities that became obvious when comparing the results of the present study with existing classifications appeared to be caused mainly by insufficiently resolved 16S rRNA gene trees or incomplete taxon sampling. Another probable cause of misclassifications in the past is the partially low overall fit of phenotypic characters to the sequence-based tree. Even though a significant degree of phylogenetic conservation was detected in all characters investigated, the overall fit to the tree varied considerably.

Genomic Analysis of Bacillus megaterium NCT-2 Reveals Its Genetic Basis for the Bioremediation of Secondary Salinization Soil

Bacillus megaterium NCT-2 is a nitrate-uptake bacterial, which shows high bioremediation capacity in secondary salinization soil, including nitrate-reducing capacity, phosphate solubilization, and salinity adaptation. To gain insights into the bioremediation capacity at the genetic level, the complete genome sequence was obtained by using a multiplatform strategy involving HiSeq and PacBio sequencing. The NCT-2 genome consists of a circular chromosome of 5.19 Mbp and ten indigenous plasmids, totaling 5.88 Mbp with an average GC content of 37.87%. The chromosome encodes 5,606 genes, 142 tRNAs, and 53 rRNAs. Genes involved in the features of the bioremediation in secondary salinization soil and plant growth promotion were identified in the genome, such as nitrogen metabolism, phosphate uptake, the synthesis of organic acids and phosphatase for phosphate-solubilizing ability, and Trp-dependent IAA synthetic system. Furthermore, strain NCT-2 has great ability of adaption to environments due to the genes involved in cation transporters, osmotic stress, and oxidative stress. This study sheds light on understanding the molecular basis of using B. megaterium NCT-2 in bioremediation of the secondary salinization soils.

Fingerprinting cities: differentiating subway microbiome functionality

BACKGROUND

Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.

RESULTS

As a part of the 2018 CAMDA challenge, we functionally profiled the available ~ 400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo.

CONCLUSIONS

We demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~ 400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

HFSP: high speed homology-driven function annotation of proteins

Motivation

The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

Results

Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

Supplementary information

Supplementary data are available at Bioinformatics online.

fusionDB: assessing microbial diversity and environmental preferences via functional similarity networks

Microbial functional diversification is driven by environmental factors, i.e. microorganisms inhabiting the same environmental niche tend to be more functionally similar than those from different environments. In some cases, even closely phylogenetically related microbes differ more across environments than across taxa. While microbial similarities are often reported in terms of taxonomic relationships, no existing databases directly link microbial functions to the environment. We previously developed a method for comparing microbial functional similarities on the basis of proteins translated from their sequenced genomes. Here, we describe fusionDB, a novel database that uses our functional data to represent 1374 taxonomically distinct bacteria annotated with available metadata: habitat/niche, preferred temperature, and oxygen use. Each microbe is encoded as a set of functions represented by its proteome and individual microbes are connected via common functions. Users can search fusionDB via combinations of organism names and metadata. Moreover, the web interface allows mapping new microbial genomes to the functional spectrum of reference bacteria, rendering interactive similarity networks that highlight shared functionality. fusionDB provides a fast means of comparing microbes, identifying potential horizontal gene transfer events, and highlighting key environment-specific functionality.

Exclusivity offers a sound yet practical species criterion for bacteria despite abundant gene flow

BACKGROUND

The question of whether bacterial species objectively exist has long divided microbiologists. A major source of contention stems from the fact that bacteria regularly engage in horizontal gene transfer (HGT), making it difficult to ascertain relatedness and draw boundaries between taxa. A natural way to define taxa is based on exclusivity of relatedness, which applies when members of a taxon are more closely related to each other than they are to any outsider. It is largely unknown whether exclusive bacterial taxa exist when averaging over the genome or are rare due to rampant hybridization.

RESULTS

Here, we analyze a collection of 701 genomes representing a wide variety of environmental isolates from the family Streptomycetaceae, whose members are competent at HGT. We find that the presence/absence of auxiliary genes in the pan-genome displays a hierarchical (tree-like) structure that correlates significantly with the genealogy of the core-genome. Moreover, we identified the existence of many exclusive taxa, although individual genes often contradict these taxa. These conclusions were supported by repeating the analysis on 1,586 genomes belonging to the genus Bacillus. However, despite confirming the existence of exclusive groups (taxa), we were unable to identify an objective threshold at which to assign the rank of species.

CONCLUSIONS

The existence of bacterial taxa is justified by considering average relatedness across the entire genome, as captured by exclusivity, but is rejected if one requires unanimous agreement of all parts of the genome. We propose using exclusivity to delimit taxa and conventional genome similarity thresholds to assign bacterial taxa to the species rank. This approach recognizes species that are phylogenetically meaningful, while also establishing some degree of comparability across species-ranked taxa in different bacterial clades.

Linking pangenomes and metagenomes: the Prochlorococcus metapangenome

Pangenomes offer detailed characterizations of core and accessory genes found in a set of closely related microbial genomes, generally by clustering genes based on sequence homology. In comparison, metagenomes facilitate highly resolved investigations of the relative distribution of microbial genomes and individual genes across environments through read recruitment analyses. Combining these complementary approaches can yield unique insights into the functional basis of microbial niche partitioning and fitness, however, advanced software solutions are lacking. Here we present an integrated analysis and visualization strategy that provides an interactive and reproducible framework to generate pangenomes and to study them in conjunction with metagenomes. To investigate its utility, we applied this strategy to a Prochlorococcus pangenome in the context of a large-scale marine metagenomic survey. The resulting Prochlorococcus metapangenome revealed remarkable differential abundance patterns between very closely related isolates that belonged to the same phylogenetic cluster and that differed by only a small number of gene clusters in the pangenome. While the relationships between these genomes based on gene clusters correlated with their environmental distribution patterns, phylogenetic analyses using marker genes or concatenated single-copy core genes did not recapitulate these patterns. The metapangenome also revealed a small set of core genes that mostly occurred in hypervariable genomic islands of the Prochlorococcus populations, which systematically lacked read recruitment from surface ocean metagenomes. Notably, these core gene clusters were all linked to sugar metabolism, suggesting potential benefits to Prochlorococcus from a high sequence diversity of sugar metabolism genes. The rapidly growing number of microbial genomes and increasing availability of environmental metagenomes provide new opportunities to investigate the functioning and the ecology of microbial populations, and metapangenomes can provide unique insights for any taxon and biome for which genomic and sufficiently deep metagenomic data are available.

COMPUTATIONAL APPROACHES TO STUDY MICROBES AND MICROBIOMES

Technological advances are making large-scale measurements of microbial communities commonplace. These newly acquired datasets are allowing researchers to ask and answer questions about the composition of microbial communities, the roles of members in these communities, and how genes and molecular pathways are regulated in individual community members and communities as a whole to effectively respond to diverse and changing environments. In addition to providing a more comprehensive survey of the microbial world, this new information allows for the development of computational approaches to model the processes underlying microbial systems. We anticipate that the field of computational microbiology will continue to grow rapidly in the coming years. In this manuscript we highlight both areas of particular interest in microbiology as well as computational approaches that begin to address these challenges.

Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires

BACKGROUND

The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context.

RESULTS

We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides.

CONCLUSIONS

We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.