A transcriptomic atlas of acute stress response to low pH in multiple Issatchenkia orientalis strains.

IMPORTANCE: Issatchenkia orientalis is a promising industrial chassis to produce biofuels and bioproducts due to its high tolerance to multiple environmental stresses such as low pH, heat, and other chemicals otherwise toxic for the most widely used microbes. Yet, little is known about specific mechanisms of such tolerance in this organism, hindering our ability to engineer this species to produce valuable biochemicals. Here, we report a comprehensive study of the mechanisms of acidic tolerance in this species via transcriptome profiling across variable pH for 12 different strains with different phenotypes. We found multiple regulatory mechanisms involved in tolerance to low pH in different strains of I. orientalis, marking potential targets for future gene editing and perturbation experiments.

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Culturing of a complex gut microbial community in mucin-hydrogel carriers reveals strain- and gene-associated spatial organization.

Microbial community function depends on both taxonomic composition and spatial organization. While composition of the human gut microbiome has been deeply characterized, less is known about the organization of microbes between regions such as lumen and mucosa and the microbial genes regulating this organization. Using a defined 117 strain community for which we generate high-quality genome assemblies, we model mucosa/lumen organization with in vitro cultures incorporating mucin hydrogel carriers as surfaces for bacterial attachment. Metagenomic tracking of carrier cultures reveals increased diversity and strain-specific spatial organization, with distinct strains enriched on carriers versus liquid supernatant, mirroring mucosa/lumen enrichment in vivo. A comprehensive search for microbial genes associated with this spatial organization identifies candidates with known adhesion-related functions, as well as novel links. These findings demonstrate that carrier cultures of defined communities effectively recapitulate fundamental aspects of gut spatial organization, enabling identification of key microbial strains and genes.

Gut Microbiome-Host Metabolome Homeostasis upon Exposure to PFOS and GenX in Male Mice.

Alterations of the normal gut microbiota can cause various human health concerns. Environmental chemicals are one of the drivers of such disturbances. The aim of our study was to examine the effects of exposure to perfluoroalkyl and polyfluoroalkyl substances (PFAS)-specifically, perfluorooctane sulfonate (PFOS) and 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propanoic acid (GenX)-on the microbiome of the small intestine and colon, as well as on liver metabolism. Male CD-1 mice were exposed to PFOS and GenX in different concentrations and compared to controls. GenX and PFOS were found to have different effects on the bacterial community in both the small intestine and colon based on 16S rRNA profiles. High GenX doses predominantly led to increases in the abundance of Clostridium sensu stricto, Alistipes, and Ruminococcus, while PFOS generally altered Lactobacillus, Limosilactobacillus, Parabacteroides, Staphylococcus, and Ligilactobacillus. These treatments were associated with alterations in several important microbial metabolic pathways in both the small intestine and colon. Untargeted LC-MS/MS metabolomic analysis of the liver, small intestine, and colon yielded a set of compounds significantly altered by PFOS and GenX. In the liver, these metabolites were associated with the important host metabolic pathways implicated in the synthesis of lipids, steroidogenesis, and in the metabolism of amino acids, nitrogen, and bile acids. Collectively, our results suggest that PFOS and GenX exposure can cause major perturbations in the gastrointestinal tract, aggravating microbiome toxicity, hepatotoxicity, and metabolic disorders.

Bottlenecks, Modularity, and the Neural Control of Behavior.

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons-that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the desired behavioral state of the animal. Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in state-dependent effects. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments.

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Complementary resource preferences spontaneously emerge in diauxic microbial communities.

Many microbes grow diauxically, utilizing the available resources one at a time rather than simultaneously. The properties of communities of microbes growing diauxically remain poorly understood, largely due to a lack of theory and models of such communities. Here, we develop and study a minimal model of diauxic microbial communities assembling in a serially diluted culture. We find that unlike co-utilizing communities, diauxic community assembly repeatably and spontaneously leads to communities with complementary resource preferences, namely communities where species prefer different resources as their top choice. Simulations and theory explain that the emergence of complementarity is driven by the disproportionate contribution of the top choice resource to the growth of a diauxic species. Additionally, we develop a geometric approach for analyzing serially diluted communities, with or without diauxie, which intuitively explains several additional emergent community properties, such as the apparent lack of species which grow fastest on a resource other than their most preferred resource. Overall, our work provides testable predictions for the assembly of natural as well as synthetic communities of diauxically shifting microbes.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome.

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

Evidence for a multi-level trophic organization of the human gut microbiome.

The human gut microbiome is a complex ecosystem, in which hundreds of microbial species and metabolites coexist, in part due to an extensive network of cross-feeding interactions. However, both the large-scale trophic organization of this ecosystem, and its effects on the underlying metabolic flow, remain unexplored. Here, using a simplified model, we provide quantitative support for a multi-level trophic organization of the human gut microbiome, where microbes consume and secrete metabolites in multiple iterative steps. Using a manually-curated set of metabolic interactions between microbes, our model suggests about four trophic levels, each characterized by a high level-to-level metabolic transfer of byproducts. It also quantitatively predicts the typical metabolic environment of the gut (fecal metabolome) in approximate agreement with the real data. To understand the consequences of this trophic organization, we quantify the metabolic flow and biomass distribution, and explore patterns of microbial and metabolic diversity in different levels. The hierarchical trophic organization suggested by our model can help mechanistically establish causal links between the abundances of microbes and metabolites in the human gut.

Multistability and regime shifts in microbial communities explained by competition for essential nutrients.

Microbial communities routinely have several possible species compositions or community states observed for the same environmental parameters. Changes in these parameters can trigger abrupt and persistent transitions (regime shifts) between such community states. Yet little is known about the main determinants and mechanisms of multistability in microbial communities. Here, we introduce and study a consumer-resource model in which microbes compete for two types of essential nutrients each represented by multiple different metabolites. We adapt game-theoretical methods of the stable matching problem to identify all possible species compositions of such microbial communities. We then classify them by their resilience against three types of perturbations: fluctuations in nutrient supply, invasions by new species, and small changes of abundances of existing ones. We observe multistability and explore an intricate network of regime shifts between stable states in our model. Our results suggest that multistability requires microbial species to have different stoichiometries of essential nutrients. We also find that a balanced nutrient supply promotes multistability and species diversity, yet make individual community states less stable.

Multiple stable states in microbial communities explained by the stable marriage problem.

Experimental studies of microbial communities routinely reveal that they have multiple stable states. While each of these states is generally resilient, certain perturbations such as antibiotics, probiotics, and diet shifts, result in transitions to other states. Can we reliably both predict such stable states as well as direct and control transitions between them? Here we present a new conceptual model-inspired by the stable marriage problem in game theory and economics-in which microbial communities naturally exhibit multiple stable states, each state with a different species' abundance profile. Our model's core ingredient is that microbes utilize nutrients one at a time while competing with each other. Using only two ranked tables, one with microbes' nutrient preferences and one with their competitive abilities, we can determine all possible stable states as well as predict inter-state transitions, triggered by the removal or addition of a specific nutrient or microbe. Further, using an example of seven Bacteroides species common to the human gut utilizing nine polysaccharides, we predict that mutual complementarity in nutrient preferences enables these species to coexist at high abundances.

Links of gut microbiota composition with alcohol dependence syndrome and alcoholic liver disease.

BACKGROUND: Alcohol abuse has deleterious effects on human health by disrupting the functions of many organs and systems. Gut microbiota has been implicated in the pathogenesis of alcohol-related liver diseases, with its composition manifesting expressed dysbiosis in patients suffering from alcoholic dependence. Due to its inherent plasticity, gut microbiota is an important target for prevention and treatment of these diseases. Identification of the impact of alcohol abuse with associated psychiatric symptoms on the gut community structure is confounded by the liver dysfunction. In order to differentiate the effects of these two factors, we conducted a comparative "shotgun" metagenomic survey of 99 patients with the alcohol dependence syndrome represented by two cohorts-with and without liver cirrhosis. The taxonomic and functional composition of the gut microbiota was subjected to a multifactor analysis including comparison with the external control group. RESULTS: Alcoholic dependence and liver cirrhosis were associated with profound shifts in gut community structures and metabolic potential across the patients. The specific effects on species-level community composition were remarkably different between cohorts with and without liver cirrhosis. In both cases, the commensal microbiota was found to be depleted. Alcoholic dependence was inversely associated with the levels of butyrate-producing species from the Clostridiales order, while the cirrhosis-with multiple members of the Bacteroidales order. The opportunist pathogens linked to alcoholic dependence included pro-inflammatory Enterobacteriaceae, while the hallmarks of cirrhosis included an increase of oral microbes in the gut and more frequent occurrence of abnormal community structures. Interestingly, each of the two factors was associated with the expressed enrichment in many Bifidobacterium and Lactobacillus-but the exact set of the species was different between alcoholic dependence and liver cirrhosis. At the level of functional potential, the patients showed different patterns of increase in functions related to alcohol metabolism and virulence factors, as well as pathways related to inflammation. CONCLUSIONS: Multiple shifts in the community structure and metabolic potential suggest strong negative influence of alcohol dependence and associated liver dysfunction on gut microbiota. The identified differences in patterns of impact between these two factors are important for planning of personalized treatment and prevention of these pathologies via microbiota modulation. Particularly, the expansion of Bifidobacterium and Lactobacillus suggests that probiotic interventions for patients with alcohol-related disorders using representatives of the same taxa should be considered with caution. Taxonomic and functional analysis shows an increased propensity of the gut microbiota to synthesis of the toxic acetaldehyde, suggesting higher risk of colorectal cancer and other pathologies in alcoholics.

Data on gut metagenomes of the patients with alcoholic dependence syndrome and alcoholic liver cirrhosis.

Alcoholism is associated with significant changes in gut microbiota composition. Metagenomic sequencing allows to assess the altered abundance levels of bacterial taxa and genes in a culture-independent way. We collected 99 stool samples from the patients with alcoholic dependence syndrome (n=72) and alcoholic liver cirrhosis (n=27). Each of the samples was surveyed using "shotgun" (whole-genome) sequencing on SOLiD platform. The reads are deposited in the ENA (project ID: PRJEB18041).

MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data.

MOTIVATION: High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features. RESULTS: We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from novel environmental niches. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/ctlab/metafast The code is written in Java and is platform independent (tested on Linux and Windows x86_64). CONTACT: ulyantsev@rain.ifmo.ru SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.

BACKGROUND: A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study. RESULTS: We developed a method for calculating pairwise dissimilarity (beta-diversity) of "shotgun" metagenomes based on short k-mer spectra (5 ≤ k ≤ 11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified. CONCLUSIONS: Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.