A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome-host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

The transcriptional regulator CtrA controls gene expression in Alphaproteobacteria phages: Evidence for a lytic deferment pathway

Pilitropic and flagellotropic phages adsorb to bacterial pili and flagella. These phages have long been used to investigate multiple aspects of bacterial physiology, such as the cell cycle control in the Caulobacterales. Targeting cellular appendages for adsorption effectively constrains the population of infectable hosts, suggesting that phages may have developed strategies to maximize their infective yield. Brevundimonas phage vB_BsubS-Delta is a recently characterized pilitropic phage infecting the Alphaproteobacterium Brevundimonas subvibrioides. Like other Caulobacterales, B. subvibrioides divides asymmetrically and its cell cycle is governed by multiple transcriptional regulators, including the master regulator CtrA. Genomic characterization of phage vB_BsubS-Delta identified the presence of a large intergenic region with an unusually high density of putative CtrA-binding sites. A systematic analysis of the positional distribution of predicted CtrA-binding sites in complete phage genomes reveals that the highly skewed distribution of CtrA-binding sites observed in vB_BsubS-Delta is an unequivocal genomic signature that extends to other pilli- and flagellotropic phages infecting the Alphaproteobacteria. Moreover, putative CtrA-binding sites in these phage genomes localize preferentially to promoter regions and have higher scores than those detected in other phage genomes. Phylogenetic and comparative genomics analyses show that this genomic signature has evolved independently in several phage lineages, suggesting that it provides an adaptive advantage to pili/flagellotropic phages infecting the Alphaproteobacteria. Experimental results demonstrate that CtrA binds to predicted CtrA-binding sites in promoter regions and that it regulates transcription of phage genes in unrelated Alphaproteobacteria-infecting phages. We propose that this focused distribution of CtrA-binding sites reflects a fundamental new aspect of phage infection, which we term lytic deferment. Under this novel paradigm, pili- and flagellotropic phages exploit the CtrA transduction pathway to monitor the host cell cycle state and synchronize lysis with the presence of infectable cells.

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background:

High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:

We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype.

Method:

Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge.

Results :

Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:

OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

Transcriptional rewiring of the GcrA/CcrM bacterial epigenetic regulatory system in closely related bacteria

Transcriptional rewiring is the regulation of different target genes by orthologous regulators in different organisms. While this phenomenon has been observed, it has not been extensively studied, particularly in core regulatory systems. Several global cell cycle regulators are conserved in the Alphaproteobacteria, providing an excellent model to study this phenomenon. First characterized in Caulobacter crescentus, GcrA and CcrM compose a DNA methylation-based regulatory system that helps coordinate the complex life cycle of this organism. These regulators are well-conserved across Alphaproteobacteria, but the extent to which their regulatory targets are conserved is not known. In this study, the regulatory targets of GcrA and CcrM were analyzed by SMRT-seq, RNA-seq, and ChIP-seq technologies in the Alphaproteobacterium Brevundimonas subvibrioides, and then compared to those of its close relative C. crescentus that inhabits the same environment. Although the regulators themselves are highly conserved, the genes they regulate are vastly different. GcrA directly regulates 204 genes in C. crescentus, and though B. subvibrioides has orthologs to 147 of those genes, only 48 genes retained GcrA binding in their promoter regions. Additionally, only 12 of those 48 genes demonstrated significant transcriptional change in a gcrA mutant, suggesting extensive transcriptional rewiring between these organisms. Similarly, out of hundreds of genes CcrM regulates in each of these organisms, only 2 genes were found in common. When multiple Alphaproteobacterial genomes were analyzed bioinformatically for potential GcrA regulatory targets, the regulation of genes involved in DNA replication and cell division was well conserved across the Caulobacterales but not outside this order. This work suggests that significant transcriptional rewiring can occur in cell cycle regulatory systems even over short evolutionary distances.

The degree to which genetic or physiological systems evolve over evolutionary distance is often untested. One can assume that the same system in different organisms will change very little if 1) the evolutionary distance between the organisms is small, 2) the systems perform critical functions, and 3) the organisms have been under similar selective pressures (i.e. the organisms inhabited the same ecological niche). The Alphaproteobacteria offer an excellent opportunity to test this assertion as several critical global transcriptional regulators are conserved throughout this clade. In this study, the regulons of two such global regulators, GcrA and CcrM, in two closely related Alphaproteobacteria that inhabit the same ecological niche were compared and it was found that they regulate vastly different genes. In many cases, genes were present in both organisms, but targeted by a regulator in one organism and not in the other. These results suggest that significant transcriptional rewiring can occur even in a core regulatory system over small evolutionary distances and indicate that conservation of genes and genetic regulators may not be a complete indicator of their physiological function in an organism.

Flexible comparative genomics of prokaryotic transcriptional regulatory networks

Background

Comparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.

Results

Here we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.

Conclusions

Our results demonstrate the applicability of the CGB pipeline in multiple settings. CGB’s ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

The integration of weighted gene association networks based on information entropy

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

Why Are Omics Technologies Important to Understanding the Role of Nutrition in Inflammatory Bowel Diseases?

For many years, there has been confusion about the role that nutrition plays in inflammatory bowel diseases (IBD). It is apparent that good dietary advice for one individual may prove inappropriate for another. As with many diseases, genome-wide association studies across large collaborative groups have been important in revealing the role of genetics in IBD, with more than 200 genes associated with susceptibility to the disease. These associations provide clues to explain the differences in nutrient requirements among individuals. In addition to genes directly involved in the control of inflammation, a number of the associated genes play roles in modulating the gut microbiota. Cell line models enable the generation of hypotheses as to how various bioactive dietary components might be especially beneficial for certain genetic groups. Animal models are necessary to mimic aspects of the complex aetiology of IBD, and provide an important link between tissue culture studies and human trials. Once we are sufficiently confident of our hypotheses, we can then take modified diets to an IBD population that is stratified according to genotype. Studies in IBD patients fed a Mediterranean-style diet have been important in validating our hypotheses and as a proof-of-principle for the application of these sensitive omics technologies to aiding in the control of IBD symptoms.