A bacterial sensor taxonomy across earth ecosystems for machine learning applications.

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase.

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

MetaMicrobesOnline: phylogenomic analysis of microbial communities.

The metaMicrobesOnline database (freely available at http://meta.MicrobesOnline.org) offers phylogenetic analysis of genes from microbial genomes and metagenomes. Gene trees are constructed for canonical gene families such as COG and Pfam. Such gene trees allow for rapid homologue analysis and subfamily comparison of genes from multiple metagenomes and comparisons with genes from microbial isolates. Additionally, the genome browser permits genome context comparisons, which may be used to determine the closest sequenced genome or suggest functionally associated genes. Lastly, the domain browser permits rapid comparison of protein domain organization within genes of interest from metagenomes and complete microbial genomes.

RegPrecise web services interface: programmatic access to the transcriptional regulatory interactions in bacteria reconstructed by comparative genomics.

Web services application programming interface (API) was developed to provide a programmatic access to the regulatory interactions accumulated in the RegPrecise database (http://regprecise.lbl.gov), a core resource on transcriptional regulation for the microbial domain of the Department of Energy (DOE) Systems Biology Knowledgebase. RegPrecise captures and visualize regulogs, sets of genes controlled by orthologous regulators in several closely related bacterial genomes, that were reconstructed by comparative genomics. The current release of RegPrecise 2.0 includes >1400 regulogs controlled either by protein transcription factors or by conserved ribonucleic acid regulatory motifs in >250 genomes from 24 taxonomic groups of bacteria. The reference regulons accumulated in RegPrecise can serve as a basis for automatic annotation of regulatory interactions in newly sequenced genomes. The developed API provides an efficient access to the RegPrecise data by a comprehensive set of 14 web service resources. The RegPrecise web services API is freely accessible at http://regprecise.lbl.gov/RegPrecise/services.jsp with no login requirements.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium.

BACKGROUND: Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. RESULTS: We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. CONCLUSIONS: The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

A universal TagModule collection for parallel genetic analysis of microorganisms.

Systems-level analyses of non-model microorganisms are limited by the existence of numerous uncharacterized genes and a corresponding over-reliance on automated computational annotations. One solution to this challenge is to disrupt gene function using DNA tag technology, which has been highly successful in parallelizing reverse genetics in Saccharomyces cerevisiae and has led to discoveries in gene function, genetic interactions and drug mechanism of action. To extend the yeast DNA tag methodology to a wide variety of microorganisms and applications, we have created a universal, sequence-verified TagModule collection. A hallmark of the 4280 TagModules is that they are cloned into a Gateway entry vector, thus facilitating rapid transfer to any compatible genetic system. Here, we describe the application of the TagModules to rapidly generate tagged mutants by transposon mutagenesis in the metal-reducing bacterium Shewanella oneidensis MR-1 and the pathogenic yeast Candida albicans. Our results demonstrate the optimal hybridization properties of the TagModule collection, the flexibility in applying the strategy to diverse microorganisms and the biological insights that can be gained from fitness profiling tagged mutant collections. The publicly available TagModule collection is a platform-independent resource for the functional genomics of a wide range of microbial systems in the post-genome era.

FastTree 2--approximately maximum-likelihood trees for large alignments.

BACKGROUND: We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. METHODOLOGY/PRINCIPAL FINDINGS: Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the "CAT" approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100-1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. CONCLUSIONS/SIGNIFICANCE: FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.

MicrobesOnline: an integrated portal for comparative and functional genomics.

Since 2003, MicrobesOnline (http://www.microbesonline.org) has been providing a community resource for comparative and functional genome analysis. The portal includes over 1000 complete genomes of bacteria, archaea and fungi and thousands of expression microarrays from diverse organisms ranging from model organisms such as Escherichia coli and Saccharomyces cerevisiae to environmental microbes such as Desulfovibrio vulgaris and Shewanella oneidensis. To assist in annotating genes and in reconstructing their evolutionary history, MicrobesOnline includes a comparative genome browser based on phylogenetic trees for every gene family as well as a species tree. To identify co-regulated genes, MicrobesOnline can search for genes based on their expression profile, and provides tools for identifying regulatory motifs and seeing if they are conserved. MicrobesOnline also includes fast phylogenetic profile searches, comparative views of metabolic pathways, operon predictions, a workbench for sequence analysis and integration with RegTransBase and other microbial genome resources. The next update of MicrobesOnline will contain significant new functionality, including comparative analysis of metagenomic sequence data. Programmatic access to the database, along with source code and documentation, is available at http://microbesonline.org/programmers.html.

Contribution of mobile genetic elements to Desulfovibrio vulgaris genome plasticity.

The genome of Desulfovibrio vulgaris strain DePue, a sulfate-reducing Deltaproteobacterium isolated from heavy metal-impacted lake sediment, was completely sequenced and compared with the type strain D. vulgaris Hildenborough. The two genomes share a high degree of relatedness and synteny, but harbour distinct prophage and signatures of past phage encounters. In addition to a highly variable phage contribution, the genome of strain DePue contains a cluster of open-reading frames not found in strain Hildenborough coding for the production and export of a capsule exopolysaccharide, possibly of relevance to heavy metal resistance. Comparative whole-genome microarray analysis on four additional D. vulgaris strains established greater interstrain variation within regions associated with phage insertion and exopolysaccharide biosynthesis.

FastTree: computing large minimum evolution trees with profiles instead of a distance matrix.

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N(2)) space and O(N(2)L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

Metabolic flux analysis of Shewanella spp. reveals evolutionary robustness in central carbon metabolism.

Shewanella spp. are a group of facultative anaerobic bacteria widely distributed in marine and freshwater environments. In this study, we profiled the central metabolic fluxes of eight recently sequenced Shewanella species grown under the same condition in minimal medium with [3-13C] lactate. Although the tested Shewanella species had slightly different growth rates (0.23-0.29 h(-1)) and produced different amounts of acetate and pyruvate during early exponential growth (pseudo-steady state), the relative intracellular metabolic flux distributions were remarkably similar. This result indicates that Shewanella species share similar regulation in regard to central carbon metabolic fluxes under steady growth conditions: the maintenance of metabolic robustness is not only evident in a single species under genetic perturbations (Fischer and Sauer, 2005; Nat Genet 37(6):636-640), but also observed through evolutionary related microbial species. This remarkable conservation of relative flux profiles through phylogenetic differences prompts us to introduce the concept of metabotype as an alternative scheme to classify microbial fluxomics. On the other hand, Shewanella spp. display flexibility in the relative flux profiles when switching their metabolism from consuming lactate to consuming pyruvate and acetate.

FastBLAST: homology relationships for millions of proteins.

BACKGROUND: All-versus-all BLAST, which searches for homologous pairs of sequences in a database of proteins, is used to identify potential orthologs, to find new protein families, and to provide rapid access to these homology relationships. As DNA sequencing accelerates and data sets grow, all-versus-all BLAST has become computationally demanding. METHODOLOGY/PRINCIPAL FINDINGS: We present FastBLAST, a heuristic replacement for all-versus-all BLAST that relies on alignments of proteins to known families, obtained from tools such as PSI-BLAST and HMMer. FastBLAST avoids most of the work of all-versus-all BLAST by taking advantage of these alignments and by clustering similar sequences. FastBLAST runs in two stages: the first stage identifies additional families and aligns them, and the second stage quickly identifies the homologs of a query sequence, based on the alignments of the families, before generating pairwise alignments. On 6.53 million proteins from the non-redundant Genbank database ("NR"), FastBLAST identifies new families 25 times faster than all-versus-all BLAST. Once the first stage is completed, FastBLAST identifies homologs for the average query in less than 5 seconds (8.6 times faster than BLAST) and gives nearly identical results. For hits above 70 bits, FastBLAST identifies 98% of the top 3,250 hits per query. CONCLUSIONS/SIGNIFICANCE: FastBLAST enables research groups that do not have supercomputers to analyze large protein sequence data sets. FastBLAST is open source software and is available at http://microbesonline.org/fastblast.

Environmental genomics reveals a single-species ecosystem deep within Earth.

DNA from low-biodiversity fracture water collected at 2.8-kilometer depth in a South African gold mine was sequenced and assembled into a single, complete genome. This bacterium, Candidatus Desulforudis audaxviator, composes >99.9% of the microorganisms inhabiting the fluid phase of this particular fracture. Its genome indicates a motile, sporulating, sulfate-reducing, chemoautotrophic thermophile that can fix its own nitrogen and carbon by using machinery shared with archaea. Candidatus Desulforudis audaxviator is capable of an independent life-style well suited to long-term isolation from the photosphere deep within Earth's crust and offers an example of a natural ecosystem that appears to have its biological component entirely encoded within a single genome.

Horizontal gene transfer and the evolution of transcriptional regulation in Escherichia coli.

BACKGROUND: Most bacterial genes were acquired by horizontal gene transfer from other bacteria instead of being inherited by continuous vertical descent from an ancient ancestor. To understand how the regulation of these acquired genes evolved, we examined the evolutionary histories of transcription factors and of regulatory interactions from the model bacterium Escherichia coli K12. RESULTS: Although most transcription factors have paralogs, these usually arose by horizontal gene transfer rather than by duplication within the E. coli lineage, as previously believed. In general, most neighbor regulators - regulators that are adjacent to genes that they regulate - were acquired by horizontal gene transfer, whereas most global regulators evolved vertically within the gamma-Proteobacteria. Neighbor regulators were often acquired together with the adjacent operon that they regulate, and so the proximity might be maintained by repeated transfers (like 'selfish operons'). Many of the as yet uncharacterized (putative) regulators have also been acquired together with adjacent genes, and so we predict that these are neighbor regulators as well. When we analyzed the histories of regulatory interactions, we found that the evolution of regulation by duplication was rare, and surprisingly, many of the regulatory interactions that are shared between paralogs result from convergent evolution. Another surprise was that horizontally transferred genes are more likely than other genes to be regulated by multiple regulators, and most of this complex regulation probably evolved after the transfer. CONCLUSION: Our findings highlight the rapid evolution of niche-specific gene regulation in bacteria.

Orthologous transcription factors in bacteria have different functions and regulate different genes.

Transcription factors (TFs) form large paralogous gene families and have complex evolutionary histories. Here, we ask whether putative orthologs of TFs, from bidirectional best BLAST hits (BBHs), are evolutionary orthologs with conserved functions. We show that BBHs of TFs from distantly related bacteria are usually not evolutionary orthologs. Furthermore, the false orthologs usually respond to different signals and regulate distinct pathways, while the few BBHs that are evolutionary orthologs do have conserved functions. To test the conservation of regulatory interactions, we analyze expression patterns. We find that regulatory relationships between TFs and their regulated genes are usually not conserved for BBHs in Escherichia coli K12 and Bacillus subtilis. Even in the much more closely related bacteria Vibrio cholerae and Shewanella oneidensis MR-1, predicting regulation from E. coli BBHs has high error rates. Using gene-regulon correlations, we identify genes whose expression pattern differs between E. coli and S. oneidensis. Using literature searches and sequence analysis, we show that these changes in expression patterns reflect changes in gene regulation, even for evolutionary orthologs. We conclude that the evolution of bacterial regulation should be analyzed with phylogenetic trees, rather than BBHs, and that bacterial regulatory networks evolve more rapidly than previously thought.

Cell-wide responses to low-oxygen exposure in Desulfovibrio vulgaris Hildenborough.

The responses of the anaerobic, sulfate-reducing organism Desulfovibrio vulgaris Hildenborough to low-oxygen exposure (0.1% O(2)) were monitored via transcriptomics and proteomics. Exposure to 0.1% O(2) caused a decrease in the growth rate without affecting viability. Concerted upregulation of the predicted peroxide stress response regulon (PerR) genes was observed in response to the 0.1% O(2) exposure. Several of the candidates also showed increases in protein abundance. Among the remaining small number of transcript changes was the upregulation of the predicted transmembrane tetraheme cytochrome c(3) complex. Other known oxidative stress response candidates remained unchanged during the low-O(2) exposure. To fully understand the results of the 0.1% O(2) exposure, transcriptomics and proteomics data were collected for exposure to air using a similar experimental protocol. In contrast to the 0.1% O(2) exposure, air exposure was detrimental to both the growth rate and viability and caused dramatic changes at both the transcriptome and proteome levels. Interestingly, the transcripts of the predicted PerR regulon genes were downregulated during air exposure. Our results highlight the differences in the cell-wide responses to low and high O(2) levels in D. vulgaris and suggest that while exposure to air is highly detrimental to D. vulgaris, this bacterium can successfully cope with periodic exposure to low O(2) levels in its environment.

A phylogenomic gene cluster resource: the Phylogenetically Inferred Groups (PhIGs) database.

BACKGROUND: We present here the PhIGs database, a phylogenomic resource for sequenced genomes. Although many methods exist for clustering gene families, very few attempt to create truly orthologous clusters sharing descent from a single ancestral gene across a range of evolutionary depths. Although these non-phylogenetic gene family clusters have been used broadly for gene annotation, errors are known to be introduced by the artifactual association of slowly evolving paralogs and lack of annotation for those more rapidly evolving. A full phylogenetic framework is necessary for accurate inference of function and for many studies that address pattern and mechanism of the evolution of the genome. The automated generation of evolutionary gene clusters, creation of gene trees, determination of orthology and paralogy relationships, and the correlation of this information with gene annotations, expression information, and genomic context is an important resource to the scientific community. DISCUSSION: The PhIGs database currently contains 23 completely sequenced genomes of fungi and metazoans, containing 409,653 genes that have been grouped into 42,645 gene clusters. Each gene cluster is built such that the gene sequence distances are consistent with the known organismal relationships and in so doing, maximizing the likelihood for the clusters to represent truly orthologous genes. The PhIGs website contains tools that allow the study of genes within their phylogenetic framework through keyword searches on annotations, such as GO and InterPro assignments, and sequence similarity searches by BLAST and HMM. In addition to displaying the evolutionary relationships of the genes in each cluster, the website also allows users to view the relative physical positions of homologous genes in specified sets of genomes. SUMMARY: Accurate analyses of genes and genomes can only be done within their full phylogenetic context. The PhIGs database and corresponding website http://phigs.org address this problem for the scientific community. Our goal is to expand the content as more genomes are sequenced and use this framework to incorporate more analyses.