Biology subjects

Palsson, B.

Publications and source records attributed to Palsson, B..

At least 19 recordsLinked to original sources

bioRxiv2026

The first digital twin of Enterococcus faecium metabolism reproduces high-throughput phenotyping data

Enterococci are Gram-positive opportunistic pathogens responsible for a wide range of nosocomial infections. One enterococcocal species, Enterococcus faecium, is steadily increasing in prevalence and has been listed among major multidrug-resistant ESKAPE pathogens. To gain systems-level insights into its metabolism and support discovery of potential therapeutic targets, we constructed iDR478, a comprehensive manually curated genome-scale metabolic model (GEM) to serve as a digital twin for E. faecium TX0016 (strain DO). The reconstruction was based on extensive homology searches and literature evidence, and further refined and gap-filled through experimental validation. Phenotypic profiling using Biolog microarrays enabled assessment of carbon source utilization, while amino acid leave-out growth assays allowed the evaluation of auxotrophies. The final refined model accurately predicted 100% of amino acid auxotrophies and 85% of growth on sole carbon sources. Discrepancies between model predictions and experimental phenotypes identified specific knowledge gaps across metabolic pathways, including unresolved carbon source utilization phenotypes, e.g., psicose, sorbitol, and palatinose utilization. Those gaps will guide future experimental characterization. Additionally, we conducted gene essentiality analysis to evaluate the predictive capacity of iDR478. Since no experimental gene essentiality data are currently available for E. faecium, model predictions were compared against Tn-seq experimental results from E. faecalis MMH594. Under simulated unconstrained uptake conditions, iDR478 achieved 84.2% concordance with the experimental essentiality results of E. faecalis MMH594. iDR478 thus provides a framework for studying E. faecium, offers insights into its metabolic network, and serves as a source for guiding future research and identification of therapeutic targets.

systems biology↗

bioRxiv2025

Transcriptomic Profiling of scWAT Reveals Reduced Lipid-Associated Macrophage Signatures in Long-Term Endurance- and Resistance-Trained Athletes

Regular exercise training is associated with systemic and tissue-specific performance and health benefits, yet long-term molecular adaptations in subcutaneous abdominal white adipose tissue (scWAT) remain unclear. We analyzed the resting scWAT transcriptome of 89 adults (35-50 years) who were long-term (>15 years) endurance- or strength-trained, or untrained. Long-term training was linked to enrichment of ribosomal, mitochondrial, and aerobic metabolism-related pathways, while immune-related pathways were enriched in untrained individuals. A distinct sex dimorphic scWAT was identified: untrained females exhibited a more metabolically favorable, trained-like transcriptomic profile, and untrained males an untrained-like profile. In trained males, the inflammatory gene signature was lower than in untrained males. We also identified 11 genes specific to lipid-associated macrophages with lower expression in trained individuals, particularly in all endurance-trained individuals and strength-trained females. These findings highlight sex-specific, exercise-induced adaptations in scWAT relevant to metabolic health, and underscore the importance of including both sexes in scWAT research.

physiology↗

bioRxiv2025

Expanding the iModulon Knowledgebase for Bacillus subtilis: Updated Transcriptome Decomposition Reveals Novel Regulatory Mechanisms

1.The Gram-positive bacterium Bacillus subtilis is widely used as a model organism for studying cell differentiation, microbial physiology and gene regulation. Its genetic accessibility and strong protein secretion machinery make it an excellent host for industrial protein production. Transcriptomics has become an essential tool in understanding these traits and the organisms response to environmental stimuli. Recent advantages in transcriptomics have enabled genome-wide analysis of gene expression patterns under various environmental conditions and genetic perturbations, interpreting such data, however, remains challenging. Independent Component Analysis (ICA) has emerged as a powerful method to decompose bacterial transcriptomes into sets of co-regulated genes - so-called iModulons. In this study, we present an updated iModulon decomposition of Bacillus subtilis transcriptomes, generated from a compendium of 782 high-quality RNAseq samples. This dataset includes a wide range of environmental and genetic conditions. ICA decomposition revealed 142 iModulons, that captured 80% of the expression variance. We compare our version of the modularized B. subtilis transcriptome with previous decompositions and show that the updated composition enhances our understanding of B. subtilis gene regulation and enables novel insights into the organisms regulon structure and gene memberships of the WalR regulon.

systems biology↗

bioRxiv2025

Revealing Transcriptomic Responses in Escherichia coli During Early Antibiotic Exposure

The earliest responses of pathogenic bacteria to antibiotics can affect the outcome of an infection. While long-term adaptations have been extensively studied, the immediate transcriptional changes that unfold immediately following antibiotic exposure remain poorly understood. Here, we applied iModulon analysis to time-resolved transcriptomic data from Escherichia coli exposed to subinhibitory concentrations of two antibiotics (ampicillin and ciprofloxacin), capturing transcriptional regulatory changes occurring within the first 30 minutes of exposure. This analysis reveals an integrated, three-phase response: an immediate and sustained primary response that broadly activates stress programs, a transient secondary response that restores redox balance, and a tertiary response that supports long-term survival through metabolic remodeling and antibiotic-specific defenses. These results highlight a coordinated and dynamic regulatory strategy describing how metabolic, redox, and stress responses are integrated to manage the physiological challenges of antibiotic stress. By disentangling these overlapping transcriptional regulatory programs, this work offers a genome-scale understanding of how survival mechanisms unfold during the critical moments following antibiotic exposure. The study opens new directions for investigating early survival mechanisms and the possible identification of new targets to disrupt the initial adaptation process. ImportanceInitial bacterial responses to antibiotics are important for survival and can influence the development of tolerance and resistance. Yet this period remains poorly understood, in part because the transcriptional responses that unfold within minutes of antibiotic exposure are complex and difficult to interpret. In this study, we applied novel data generation and data analytics approaches to untangle the complexity of the initial response of Escherichia coliI to two antibiotics. We reveal a three-phase process that explains how E. coli coordinates stress responses, maintains redox homeostasis, and establishes longer-term defenses. The novel transcriptomic analytics elucidate independently regulated sets of genes that constitute cellular processes. By identifying all such cellular processes that react over the initial time scale, we can deconvolute the response based on first principles of cellular physiology.

systems biology↗

bioRxiv2024

Deciphering the transcriptional regulatory network of Yarrowia lipolytica using machine learning

The transcriptional regulatory network (TRN) in Yarrowia lipolytica coordinates its cellular processes, including the response to various stimuli. The TRN has been difficult to study due to its complex nature. In industrial-size fermenters, environments are often not homogenous, resulting in Yarrowia experiencing fluctuating conditions during a fermentation. Compared with homogenous laboratory conditions, these fluctuations result in altered cellular states and behaviors due to the action of the TRN. Here, a machine learning approach was deployed to modularize the transcriptome to enable meaningful description of its changing composition. To provide a sufficiently broad dataset, a wide range of relevant fermentation conditions (nutrient limitations, growth rates, pH values, oxygen availability and CO2 stresses) were run and samples obtained for RNA-Seq generation. We thus significantly increased the number of publicly available transcriptomic dataset on Y. lipolytica W29. In total, 23 independently modulated gene sets (termed iModulons) were identified of which 9 could be linked to corresponding regulons in S. cerevisiae. Strong responses were found in relation to oxygen limitation and elevated CO2 concentrations represented by (i) altered ribosomal protein synthesis, (ii) cell cycle disturbances, (iii) respiratory gene expression, and (iv) redox homeostasis. These results provide a fine-grained systems-level understanding of the Y. lipolytica TRN in response to industrially meaningful stresses, providing engineering targets to design more robust production strains. Moreover, this study provides a guide to perform similar work with poorly characterized single-cellular eukaryotic organisms. Highlights- A large screening setup significantly expands the public RNA-Seq library on Y. lipolytica. - This work provides a systems-level understanding of the TRN in response to industrial stresses. - iModulon analysis can help accelerate bioprocess development. - Results can guide similar work on poorly characterized single-cellular eukaryotic organisms.

systems biology↗

bioRxiv2024

Data-driven modeling of E. coli transcriptional regulation

The growth of bacterial gene expression datasets has offered unprecedented coverage of achievable transcriptomes, reflecting diverse activity states of the transcription regulatory network. Machine learning methods like Independent Component Analysis (ICA) can decompose gene expression datasets into regulatory modules and condition-specific regulator activities. Here, we present a workflow to utilize inferred regulator activities to construct quantitative models of promoter regulation in E. coli. Resulting models are validated by predicting condition-specific TF effector concentrations and binding site motif strength based on differential gene expression data alone. We show how reconstructed promoter models can capture multi-scale regulation and disentangle regulator interactions, including resolving the apparent paradox where argR expression is positively correlated with its regulon despite being a repressor. We applied the workflow for all regulator-linked components extracted by ICA, demonstrating the scalability of the workflow to capture the E. coli TRN. This work suggests a path toward systematic, quantitative reconstruction of transcription regulatory networks driven by the large-scale databases that are now available for many organisms.

bioinformatics↗

bioRxiv2024

RBC-GEM: a Knowledge Base for Systems Biology of Human Red Blood Cell Metabolism

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, the most extensive and meticulously curated metabolic reconstruction of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 28 studies published over the past two decades resulting in a RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. This reconstruction of the RBC metabolic network is a knowledge base consisting of 718 genes encoding proteins acting on 1,590 unique metabolites through 2,554 biochemical reactions: a 700% size expansion over its predecessor. This reconstruction as an up-to-date curated knowledge base can be used for contextualization of data and for the construction of a computational whole-cell model of a human RBC. Author SummaryHuman red blood cells (RBCs) have been studied for decades because of their unique physiology, essential oxygen delivery functions, and general accessibility. RBCs are the simplest yet most numerous of human cell types due to the loss of cellular organelles during their development process. This process has evolved to maximize hemoglobin content per cell to facilitate RBCs main function in gas transport. RBCs are integral to a variety of medical applications, such as blood storage for transfusion. Recent advancements in high-throughput data collection have greatly expanded our understanding of RBC metabolism, highlighting important roles and functions for RBCs in maintaining homeostasis in the organism in addition to oxygen transport. Here we provide a knowledge base for the human RBC as a genome-scale metabolic reconstruction. Our results highlight the complexity of RBC metabolism, supported by recent advancements in high-throughput data collection methods for detecting low-abundance proteins in RBCs. We make knowledge about the RBC findable, accessible, interoperable, and reusable (FAIR). As RBC research is likely to see many translational medical advancements, a knowledge base for the contextualization of RBC data will serve as an essential resource for further research and medical application development.

systems biology↗

bioRxiv2024

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation of Escherichia coli

Adaptive laboratory evolution (ALE) is able to generate microbial strains which exhibit extreme phenotypes, revealing fundamental biological adaptation mechanisms. Here, we use ALE to evolve Escherichia coli strains that grow at temperatures as high as 45.3{degrees}C, a temperature lethal to wild type cells. The strains adopted a hypermutator phenotype and employed multiple systems-level adaptations that made global analysis of the DNA mutations difficult. Given the challenge at the genomic level, we were motivated to uncover high temperature tolerance adaptation mechanisms at the transcriptomic level. We employed independently modulated gene set (iModulon) analysis to reveal five transcriptional mechanisms underlying growth at high temperatures. These mechanisms were connected to acquired mutations, changes in transcriptome composition, sensory inputs, phenotypes, and protein structures. They are: (i) downregulation of general stress responses while upregulating the specific heat stress responses; (ii) upregulation of flagellar basal bodies without upregulating motility, and upregulation fimbriae; (iii) shift toward anaerobic metabolism, (iv) shift in regulation of iron uptake away from siderophore production, and (v) upregulation of yjfIJKL, a novel heat tolerance operon which we characterized using AlphaFold. iModulons associated with these five mechanisms explain nearly half of all variance in the gene expression in the adapted strains. These thermotolerance strategies reveal that optimal coordination of known stress responses and metabolism can be achieved with a small number of regulatory mutations, and may suggest a new role for large protein export systems. ALE with transcriptomic characterization is a productive approach for elucidating and interpreting adaptation to otherwise lethal stresses.

systems biology↗

bioRxiv2023

Genomic insights into Lactobacillaceae: Analyzing the Alleleome of core pangenomes for enhanced understanding of strain diversity and revealing Phylogroup-specific unique variants

The Lactobacillaceae familys significance in food and health, combined with available strain-specific genomes, enables genome assessment through pangenome analysis. The Alleleome of the core pangenomes of the Lactobacillaceae family, which identifies natural sequence variations, was reconstructed from the amino acid and nucleotide sequences of the core genes across 2,447 strains of 26 species. It comprised 3.71 million amino acid variants in 29,448 core genes across the family. The alleleome analysis of the Lactobacillaceae family revealed key findings: 1) In the core pangenome, amino acid substitutions prevailed over rare insertions and deletions, 2) Purifying negative selection primarily influenced core gene variations in the family, with diversifying selection noted in L. helveticus. L. plantarums core alleleome was investigated due to its industrial importance. In L. plantarum, the defining characteristics of its core alleleome included: 1) It is highly conserved; 2) Among 235 isolation sources, the primary categories displaying variant prevalence were fermented food, feces, and unidentified sources; 3) It is predominantly characterized by conservative and moderately conservative mutations; and 4) Phylogroup-specific core variant gene analysis identified unique variants (DltX, FabZ1, Pts23B, CspP) in phylogroups I and B which could be used as identifier or validation markers of strain or phylogroup.

genomics↗

bioRxiv2023

Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.

systems biology↗

bioRxiv2023

BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as; i) data selection, ii) functional annotation, iii) phylogenetic analysis, iv) genome mining, and v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge in the ever-growing genomic datasets of biotechnologically relevant bacterial species. BGCFlow is available for downloading at https://github.com/NBChub/bgcflow.

bioinformatics↗

bioRxiv2023

Global pathogenomic analysis identifies known and novel genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. Applied to 12 species, 27,155 genomes, and 69 drugs, we 1) found AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrated that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identified 142 novel AMR gene candidates. Validation of two candidates in E. coli BW25113 revealed cases of conditional resistance:{Delta} cycA conferred ciprofloxacin resistance in minimal media with D-serine, and frdD V111D conferred ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

bioinformatics↗

bioRxiv2022

Metabolic homeostasis and growth in abiotic cells

Metabolism constitutes the core chemistry of life. How it began on the early Earth and whether it had a cellular origin is still uncertain. A leading hypothesis for lifes origins postulates that metabolism arose from geochemical CO2-fixing pathways, driven by inorganic catalysts and energy sources, long before enzymes or genes existed. The acetyl-CoA pathway and the reductive tricarboxylic acid cycle are considered ancient reaction networks that hold relics of early carbon-fixing pathways. Although transition metals can promote many steps of these pathways, whether they form a functional metabolic network in abiotic cells has not been shown. Here, we formulate a nonenzymatic carbonfixing network from these pathways and determine its functional feasibility in abiotic cells by imposing the fundamental physico-chemical constraints of the early Earth. Using first principles, we show that abiotic cells could have sustainable steady carbon-fixing cycles that perform a systemic function over a relatively narrow range of conditions. Furthermore, we find that in all feasible steady states, the operation of the cycle elevates the osmotic pressure, leading to volume expansion. These results suggest that achieving homeostatic metabolic states under prebiotic conditions was possible, but challenging, and volume growth was a fundamental property of early metabolism.

evolutionary biology↗

bioRxiv2022

Laboratory-acquired mutations fall outside the wild-type alleleome of Escherichia coli

Inexpensive DNA sequencing has led to a rapidly increasing number of whole genome sequences in the public domain. Natural sequence variation can now be assessed across a large number of sequenced strains of a bacterial species, resulting in the definition of the wild-type alleleome (the collection of alleles for every gene found in the species). Concurrently, laboratory evolution emerged as a new approach to address biological questions and to develop new phenotypic traits, and a large number of laboratory acquired mutations can be found in databases. The availability of this large-scale sequence variation data now allows for a detailed comparison of mutations fixed in natural versus laboratory evolutions. Such comparison shows that laboratory-acquired mutations are rarely found in the wild-type alleleome of Escherichia coli. The E. coli alleleome is highly conserved as most of the sequence variation is concentrated in about 2% of the coding region. We find that there are typically two alternate amino acids coded for in the variable locations, and switches between the two are found in the data sets. Finally, we find that adaptive laboratory mutations, unlike wild-type mutations, do not utilize the redundancy built into the genetic code: they are less likely to be synonymous and rely on changing a single nucleotide in a codon. However, the uniqueness of mutations fixed in laboratory evolutions bodes well for synthetic biology by revealing novel exploitable sequence space untouched by natural evolution.

bioengineering↗

bioRxiv2022

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital acquired infections. The pathogenicity and virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze pathogenicity and antibiotic resistance of P. aeruginosa. Our analysis revealed: 1) 116 iModulons, 81 of which show strong association with known regulators; 2) novel roles of two-component systems in regulating antibiotics efflux pumps; 3) substrate-efflux pump associations; 4) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; 5) differential activation of Cell Division iModulon resulting from exposure to different beta-lactam antibiotics; and 6) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa pathogenicity. SignificanceLarge data sets and machine learning are impacting a growing number of areas of research in the life sciences. Once the compendia of bacterial transcriptomes reached a critical size, we could use source signal extraction algorithms to find lists of co-regulated genes (called iModulons) associated with a transcription factor (TF) to them. The gene composition of iModulons and their condition-dependent activity levels constitute a quantitative description of the composition of bacterial transcriptomes. This study shows how this approach can be used to reveal the responses of P. aeruginosa to antibiotics and thus yield a deep regulatory understanding of pathogenicity properties. This study motivates the execution of similar studies for the other ESKAPEEs to yield a broad understanding of the role of TRNs in antibiotic responses to these urgent threat bacterial pathogens.

systems biology↗

bioRxiv2022

Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity

Respiration requires organisms to have an electron transport system (ETS) for the generation of proton motive force across the membrane that drives ATP synthase. Although the molecular details of the ETS are well studied and constitute textbook material, few studies have appeared to elucidate its systems biology. The most thermodynamically efficient ETS consists of two enzymes, an NADH: quinone oxidoreductase (NqRED) and a dioxygen reductase (O2RED), which facilitate the shuttling of electrons from NADH to oxygen. However, evolution has produced variations within ETS which modulate the overall energy efficiency of the system even within the same organism 1-3. The system-level impact of these variations and their individual physiological optimality remain poorly determined. To mimic varying ETS efficiency we generated four Escherichia coli deletion strains (named ETS-1H, 2H, 3H, and 4H) harboring unbranched ETS variants that pump 1, 2, 3, or 4 proton(s) per electron respectively. We then used a combination of synergistic methods (laboratory evolution, multi-omic analyses, and computation of proteome allocation) to characterize these ETS variants. We found that: (a) all four ETS variants evolved to a similar optimized growth rate, (b) the evolution of ETS variants was enabled by specific rewiring of major energy-generating pathways that couple to the ETS to optimize their ATP production capability, (c) proteome allocation per ATP generated was the same for all the variants, (d) the aero-type, that designates the overall ATP generation strategy 4 of a variant, remained conserved during its laboratory evolution, with the exception of the ETS-4H variant, and (e) integrated computational analysis of then data supported a proton-to-ATP ratio of 10 protons per 3 ATP for ATP synthase for all four ETS variants. We thus have defined the Aero-Type System (ATS) as a generalization of the aerobic bioenergetics, which is descriptive of the metabolic systems biology of respiration and demonstrates its plasticity.

systems biology↗

bioRxiv2022

Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

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.

systems biology↗

bioRxiv2021

A systems approach discovers the role and characteristics of seven LysR type transcription factors in Escherichia coli

Although Escherichia coli K-12 strains represent perhaps the best known model bacteria, we do not know the identity or functions of all of their transcription factors (TFs). It is now possible to systematically discover the physiological function of TFs in E. coli BW25113 using a set of synergistic methods; including ChIP-exo, growth phenotyping, conserved gene clustering, and transcriptome analysis. Among 47 LysR-type TFs (LTFs) found on the E. coli K-12 genome, many regulate nitrogen source utilization or amino acid metabolism. However, 19 LTFs remain unknown. In this study, we elucidated the regulation of seven of these 19 LTFs: YbdO, YbeF, YgfI, YiaU, YneJ, YcaN, YbhD. We show that: 1) YbdO regulation has an effect on bacterial growth at low pH with citrate supplementation. YbdO is a repressor of the ybdNM operon and is implicated in the regulation of citrate lyase genes (citCDEFG); 2) YgfI activates the dhaKLM operon that encodes the phosphotransferase system involved in glycerol and dihydroxyacetone utilization; 3) YiaU regulates the yiaT gene encoding an outer membrane protein, and waaPSBOJYZU operon is also important in determining cell density at the stationary phase; 4) YneJ, re-named here as PtrR, directly regulates the expression of the succinate-semialdehyde dehydrogenase, Sad (also known as YneI), and is a predicted regulator of fnrS (a small RNA molecule). PtrR is important for bacterial growth in the presence of L-glutamate and putrescine as nitrogen sources; and 5) YbhD and YcaN regulate adjacent y-genes on the genome and YbeF is involved in flagella gene regulation. We have thus established the functions for four LTFs and identified the target genes for three LTFs. IMPORTANCEThe reconstruction of the transcriptional regulatory network (TRN) is important for gram-negative bacteria such as E. coli. LysR-type TFs are abundant in Enterobacteria, but many LTF functions still remain unknown. Here we report putative functions of uncharacterized TFs based on multi-omics data related to L-threonine, L-glutamate, and putrescine utilization. Amino acids (AAs) and polyamines are important sources of nitrogen for many microorganisms, but the increase in one amino acid or putrescine concentration in a minimal medium also induces stress. Although polyamine metabolism has been studied, the TRN that controls the putrescine (Ptr) and AA utilization at minimal medium conditions are still poorly understood. The function of previously uncharacterized transcriptional regulators YbdO, YgfI, and YneJ (PtrR) were identified in Escherichia coli. PtrR is important for Ptr and L-glutamate utilization, while YgfI transcriptional regulation was found to be important for growth on L-threonine and glycerol as a carbon source.

systems biology↗