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Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species’ dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ under the “Salmonella Typhimurium” page to enable browsing for interested researchers.

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date. IMPORTANCE Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons. While previous studies have relied on molecular measurements, in the manuscript we utilized an alternative technique that performs machine learning to a large data set of transcriptomic data. This approach is less reliant on hypotheses about the role of specific regulatory systems and allows for the discovery of new biological findings for already collected data. A better understanding of the structure of the M. tuberculosis TRN will have important implications in the design of improved therapeutic approaches.

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophile’s responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organism’s transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

Many genes in bacterial genomes are of unknown function, often referred to as y-genes. Recently, novel analytic methods have divided bacterial transcriptomes into independently modulated sets of genes (iModulons). Functionally annotated iModulons that contain y-genes lead to testable hypotheses to elucidate y-gene function. Inversely correlated expression of a putative transporter gene, ydhC, relative to purine biosynthetic genes, has led to the hypothesis that it encodes a purine-related transporter and revealed a LysR-family regulator, YdhB, with a predicted 23-bp palindromic binding motif. RNA-Seq analysis of a ydhB knockout mutant confirmed the YdhB-dependent activation of ydhC in the presence of adenosine. The deletion of either the ydhC or the ydhB gene led to a substantially decreased growth rate for E. coli in minimal medium with adenosine as the nitrogen source, as well as with inosine or guanosine. Taken together, we provide clear evidence that YdhB activates the expression of the ydhC gene that encodes a novel purine transporter in E. coli. We propose that the genes ydhB and ydhC be re-named as punR and punC, respectively.

The YdhB transcriptional factor, re-named here AdnB, homologous to the allantoin regulator, AllS, was shown to regulateydhCgene expression inEscherichia coli, which is divergently transcribed fromadnB,and this gene arrangement is conserved in many Protreobacteria. The predicted consensus DNA binding sequence for YdhB is also conserved in Entrobacterial genomes. RNA-seq data confirmed the activation predicted due to the binding of AdnB as shown by Chip-Exo results. Fluorescent polarization experiments revealed binding of YdhB to the predicted binding site upstream ofydhCin the presence of 0.35 mM adenine, but not in its absence. TheE. coliMG1655, strain lacking theydhBgene, showed a lower level ofydhCmRNA in cells grown in M9-glucose supplemented with 2 mM adenosine. Adenosine and adenine are products of purine metabolism and provide sources of ammonium for many organisms. They are utilized under nitrogen starvation conditions as single nitrogen sources. Deletion of either theydhCor theydhBgene leads to a substantially decreased growth rate forE. coliin minimal M9 medium with glycerol as the carbon source and adenosine or adenine as the single nitrogen source. TheydhCmutant showed increased resistance to Paromomycine, Sulfathiazole and Sulfamethohazole using Biolog plates. We provide evidence that YdhB, (a novel LysR family regulator) activates expression of theydhCgene, encoding a novel adenosine/adenine transporter inE. coli. The YdhB binding consensus for different groups of Enterobacteria was predicted.