Proteome allocation is linked to transcriptional regulation through a modularized transcriptome.

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Independent component analysis reveals 49 independently modulated gene sets within the global transcriptional regulatory architecture of multidrug-resistant Acinetobacter baumannii.

Acinetobacter baumannii causes severe infections in humans, resists multiple antibiotics, and survives in stressful environmental conditions due to modulations of its complex transcriptional regulatory network (TRN). Unfortunately, our global understanding of the TRN in this emerging opportunistic pathogen is limited. Here, we apply independent component analysis, an unsupervised machine learning method, to a compendium of 139 RNA-seq data sets of three multidrug-resistant A. baumannii international clonal complex I strains (AB5075, AYE, and AB0057). This analysis allows us to define 49 independently modulated gene sets, which we call iModulons. Analysis of the identified A. baumannii iModulons reveals validating parallels to previously defined biological operons/regulons and provides a framework for defining unknown regulons. By utilizing the iModulons, we uncover potential mechanisms for a RpoS-independent general stress response, define global stress-virulence trade-offs, and identify conditions that may induce plasmid-borne multidrug resistance. The iModulons provide a model of the TRN that emphasizes the importance of transcriptional regulation of virulence phenotypes in A. baumannii. Furthermore, they suggest the possibility of future interventions to guide gene expression toward diminished pathogenic potential.IMPORTANCEThe rise in hospital outbreaks of multidrug-resistant Acinetobacter baumannii infections underscores the urgent need for alternatives to traditional broad-spectrum antibiotic therapies. The success of A. baumannii as a significant nosocomial pathogen is largely attributed to its ability to resist antibiotics and survive environmental stressors. However, there is limited literature available on the global, complex regulatory circuitry that shapes these phenotypes. Computational tools that can assist in the elucidation of A. baumannii's transcriptional regulatory network architecture can provide much-needed context for a comprehensive understanding of pathogenesis and virulence, as well as for the development of targeted therapies that modulate these pathways.

Outcome study of the Pipeline Vantage Embolization Device (second version) in unruptured (and ruptured) aneurysms (PEDVU(R) study).

BACKGROUND: The Pipeline Vantage Embolization Device (PEDV) is the fourth-generation pipeline flow diverter for intracranial aneurysm treatment. There are no outcome studies for the second PEDV version. We aimed to evaluate safety and efficacy outcomes. Primary and secondary objectives were to determine outcomes for unruptured and ruptured cohorts, respectively. METHODS: In this multicenter retrospective and prospective study, we analyzed outcome data from eight centers using core laboratory assessments. We determined 30-day and ≥3-month mortality and morbidity rates, and 6- and 18-month radiographic aneurysm occlusion rates for procedures performed during the period July 2021-March 2023. RESULTS: We included 121 consecutive patients with 131 aneurysms. The adequate occlusion rate for the unruptured cohort at short-term and medium-term follow up, and also for the ruptured cohort at short-term follow up, was >90%. Two aneurysms (1.5%) underwent retreatment. When mortality attributed to a palliative case in the unruptured cohort, or subarachnoid hemorrhage in the ruptured cohort, was excluded then the overall major adverse event rate in respective cohorts was 7.5% and 23.5%, with 0% mortality rates for each. When all event causes were included on an intention-to-treat basis, the major adverse event rates in respective cohorts were 8.3% and 40.9%, with 0.9% and 22.7% mortality rates. CONCLUSIONS: For unruptured aneurysm treatment, the second PEDV version appears to have a superior efficacy and similar safety profile to previous-generation PEDs. These are acceptable outcomes in this pragmatic and non-industry-sponsored study. Analysis of ruptured aneurysm outcomes is limited by cohort size. Further prospective studies, particularly for ruptured aneurysms, are needed.

High-resolution temporal profiling of E. coli transcriptional response.

Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress.

Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance.

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

A multi-scale expression and regulation knowledge base for Escherichia coli.

Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.

Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection.

IMPORTANCE: Pseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis thaliana. The current understanding of global transcriptional regulation in the pathogen is limited. Here, we applied iModulon analysis to a compendium of RNA-seq data to unravel its transcriptional regulatory network. We characterize each co-regulated gene set, revealing the activity of major regulators across diverse conditions. We provide new insights on the transcriptional dynamics in interactions with the plant immune system and with other bacterial species, such as AlgU-dependent regulation of flagellar genes during plant infection and downregulation of siderophore production in the presence of a siderophore cheater. This study demonstrates the novel application of iModulons in studying temporal dynamics during host-pathogen and microbe-microbe interactions, and reveals specific insights of interest.

Elucidation of independently modulated genes in Streptococcus pyogenes reveals carbon sources that control its expression of hemolytic toxins.

Streptococcus pyogenes can cause a wide variety of acute infections throughout the body of its human host. An underlying transcriptional regulatory network (TRN) is responsible for altering the physiological state of the bacterium to adapt to each unique host environment. Consequently, an in-depth understanding of the comprehensive dynamics of the S. pyogenes TRN could inform new therapeutic strategies. Here, we compiled 116 existing high-quality RNA sequencing data sets of invasive S. pyogenes serotype M1 and estimated the TRN structure in a top-down fashion by performing independent component analysis (ICA). The algorithm computed 42 independently modulated sets of genes (iModulons). Four iModulons contained the nga-ifs-slo virulence-related operon, which allowed us to identify carbon sources that control its expression. In particular, dextrin utilization upregulated the nga-ifs-slo operon by activation of two-component regulatory system CovRS-related iModulons, altering bacterial hemolytic activity compared to glucose or maltose utilization. Finally, we show that the iModulon-based TRN structure can be used to simplify the interpretation of noisy bacterial transcriptome data at the infection site. IMPORTANCE S. pyogenes is a pre-eminent human bacterial pathogen that causes a wide variety of acute infections throughout the body of its host. Understanding the comprehensive dynamics of its TRN could inform new therapeutic strategies. Since at least 43 S. pyogenes transcriptional regulators are known, it is often difficult to interpret transcriptomic data from regulon annotations. This study shows the novel ICA-based framework to elucidate the underlying regulatory structure of S. pyogenes allows us to interpret the transcriptome profile using data-driven regulons (iModulons). Additionally, the observations of the iModulon architecture lead us to identify the multiple regulatory inputs governing the expression of a virulence-related operon. The iModulons identified in this study serve as a powerful guidepost to further our understanding of S. pyogenes TRN structure and dynamics.

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome.

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigated whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions could be modularized in the same way to reveal novel relationships between their compositions. We found that; 1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, 2) the modules in the proteome often represent combinations of modules from the transcriptome, 3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and 4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli.

The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

pCONUS 2 and pCONUS 2-HPC in the treatment of wide-necked intracranial aneurysms: Multicentre UK experience.

BACKGROUND/PURPOSE: pCONUS 2 and pCONUS 2-HPC are neck-bridging devices that provide coiling support in the endovascular treatment of wide-necked intracranial aneurysms. To date, limited multicentre data has been published. This study provides the first pooled data from multiple UK centres regarding outcomes for these devices covering the periprocedural period to 6-month follow-up. MATERIALS/METHODS: This retrospective, single-arm study assessed 65 patients treated over 3 years from the time of procedure to 6 months post-procedure across four UK centres. Data collected included patient demographics, aneurysm characteristics and antiplatelet regimens. Outcome measures were angiographic results and procedure-related complications from the immediate periprocedural period to 6-month follow-up. RESULTS: Fifty-four unruptured (83.1%) and 11 ruptured (16.9%) aneurysms were treated. Fifty-five aneurysms were located in the anterior circulation (87.7%). There were four device-related intraprocedural complications: three cases of asymptomatic, temporary thrombus formation and one mortality associated with branch vessel occlusion and aneurysm re-bleeding in a ruptured case. There were no post-procedural device-related complications. Satisfactory occlusion was achieved in 58/65 procedures (89.2%) at time of treatment and in 44/60 (73.3%) at 6 months. Satisfactory occlusion correlated with aneurysm size and coiling packing density. Retreatment was required for five unruptured cases (7.7%) and was straightforward with the device in situ. CONCLUSION: pCONUS 2 and pCONUS 2-HPC have good short-term safety profiles. The use of pCONUS 2-HPC in the acute treatment of ruptured aneurysms with postprocedural SAPT is feasible. The devices have an intraprocedural complication rate of 4/65 (6.2%) across multiple UK centres, including a single death (1.5%).

Adult porcine (Sus scrofa) derived inner ear cells: Characteristics in in-vitro cultures.

There is a need for an animal model that closely parallels human cochlea gestational development. This study aims to document porcine inner ear anatomy, and in vitro porcine derived inner ear cell culture characteristics. Twenty-four temporal bone were harvested from 12 adult pigs (Sus scrofa). Six were formalin fixed and their maximal diameters were measured. The cochlea duct length was determined by the insertion length of a Nucleus 22 cochlear implant in two bones. Four formalin fixed bones were sectioned for histology. Cochlear and vestibular tissues were harvested from non-fixed bones, cultured and characterized at different passages (P). Gene and protein expression of multipotent stem/progenitor (Nestin and Sox2), inner ear hair (Myosin VIIa, Prestin) and supporting (Cytokeratin 18 and Vimentin) cell markers were determined. The porcine cochlea was a 3.5 turn spiral. There was a separate vestibular compartment. The cochlear mean maximal diameter and height was 7.99 and 3.77 mm, respectively. Sphere forming cells were identified on phase-contrast microscopy. The relative mRNA expression levels of KRT18, MYO7A and SLC26A5 were significantly positively correlated in cochlear cultures; and MYO7A and SLC26A5; SOX2 and KRT18; NES and SLC26A5 genes were positively correlated in vestibular cultures (p = .037, Spearman correlation [τ] = .900). Inner ear sensory and stem cell characteristics persist in passaged porcine inner ear cells. Further work is required to establish the usefulness of porcine inner ear cell cultures to the study of human inner ear disorders.

The pattern of primary headache in the North India population: a hospital-based study.

BACKGROUND: The aim of the study was to investigate the clinical profile, disease burden, quality of life, and treatment patterns of various headache subtypes. METHOD: In this prospective observational study, 815 patients presenting with chief complaints of headache between January 2020 to September 2021 were registered. After a detailed history, clinical examination, and subtyping, they were assessed at baseline with well-validated scales for severity (Visual Analogue Scale-VAS), disability burden (Migraine Disability Assessment- MIDAS), Humanistic burden (Headache Impact Test-HIT-6), and quality of life (World health organization-quality of life-WHO-QoL-8) scores. After initiating adequate management, parameters were reassessed at 3 and 6 months. RESULTS: 549 (67.7%) patients had migraine (395-episodic migraine, 144-chronic migraine), 266 (32.2%) patients had tension-type headache (TTH). Loss of sleep, prolonged working hours, and stress were common triggers. Disease burden, severity, and poor life quality was quite high in migraine patients (76.5% with moderate to severe disability, 61.7% with severe headache at onset, and 72% with poor life quality). All parameters had statistically significant improvement with preventive medication and lifestyle changes. CONCLUSION: In our study, we found migraine was the most common primary headache followed by TTH. Migraine patients had more severity, disease burdens, and inferior quality of life at onset compared to other headaches. With early and proper diagnosis as well as preventive treatment (including lifestyle modifications), all parameters could be reversed positively in a brief time. This is the first study on headache burden and its effect on the quality of life in the north Indian population.

Coordination of CcpA and CodY Regulators in Staphylococcus aureus USA300 Strains.

The complex cross talk between metabolism and gene regulatory networks makes it difficult to untangle individual constituents and study their precise roles and interactions. To address this issue, we modularized the transcriptional regulatory network (TRN) of the Staphylococcus aureus USA300 strain by applying independent component analysis (ICA) to 385 RNA sequencing samples. We then combined the modular TRN model with a metabolic model to study the regulation of carbon and amino acid metabolism. Our analysis showed that regulation of central carbon metabolism by CcpA and amino acid biosynthesis by CodY are closely coordinated. In general, S. aureus increases the expression of CodY-regulated genes in the presence of preferred carbon sources such as glucose. This transcriptional coordination was corroborated by metabolic model simulations that also showed increased amino acid biosynthesis in the presence of glucose. Further, we found that CodY and CcpA cooperatively regulate the expression of ribosome hibernation-promoting factor, thus linking metabolic cues with translation. In line with this hypothesis, expression of CodY-regulated genes is tightly correlated with expression of genes encoding ribosomal proteins. Together, we propose a coarse-grained model where expression of S. aureus genes encoding enzymes that control carbon flux and nitrogen flux through the system is coregulated with expression of translation machinery to modularly control protein synthesis. While this work focuses on three key regulators, the full TRN model we present contains 76 total independently modulated sets of genes, each with the potential to uncover other complex regulatory structures and interactions. IMPORTANCE Staphylococcus aureus is a versatile pathogen with an expanding antibiotic resistance profile. The biology underlying its clinical success emerges from an interplay of many systems such as metabolism and gene regulatory networks. This work brings together models for these two systems to establish fundamental principles governing the regulation of S. aureus central metabolism and protein synthesis. Studies of these fundamental biological principles are often confined to model organisms such as Escherichia coli. However, expanding these models to pathogens can provide a framework from which complex and clinically important phenotypes such as virulence and antibiotic resistance can be better understood. Additionally, the expanded gene regulatory network model presented here can deconvolute the biology underlying other important phenotypes in this pathogen.

Pan-Genome Analysis of Transcriptional Regulation in Six Salmonella enterica Serovar Typhimurium Strains Reveals Their Different Regulatory Structures.

Establishing transcriptional regulatory networks (TRNs) in bacteria has been limited to well-characterized model strains. Using machine learning methods, we established the transcriptional regulatory networks of six Salmonella enterica serovar Typhimurium strains from their transcriptomes. By decomposing a compendia of RNA sequencing (RNA-seq) data with independent component analysis, we obtained 400 independently modulated sets of genes, called iModulons. We (i) performed pan-genome analysis of the phylogroup structure of S. Typhimurium and analyzed the iModulons against this background, (ii) revealed different genetic signatures in pathogenicity islands that explained phenotypes, (iii) discovered three transport iModulons linked to antibiotic resistance, (iv) described concerted responses to cationic antimicrobial peptides, and (v) uncovered new regulons. Thus, by combining pan-genome and transcriptomic analytics, we revealed variations in TRNs across six strains of serovar Typhimurium. IMPORTANCE Salmonella enterica serovar Typhimurium is a pathogen involved in human nontyphoidal infections. Treating S. Typhimurium infections is difficult due to the species's dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN) that is different across strains. In this study, we describe the use of independent component analysis to characterize the differential TRNs across the S. Typhimurium pan-genome using a compendium of high-quality RNA-seq data. This approach provided unprecedented insights into the differences between regulation of key cellular functions and pathogenicity in the different strains. The study provides an impetus to initiate a large-scale effort to reveal the TRN differences between the major phylogroups of the pathogenic bacteria, which could fundamentally impact personalizing treatments of bacterial pathogens.

Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes.

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

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 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 the biofilm production and antibiotic resistance of P. aeruginosa. Our analysis revealed: (i) 116 iModulons, 81 of which show strong association with known regulators; (ii) novel roles of regulators in modulating antibiotics efflux pumps; (iii) substrate-efflux pump associations; (iv) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; (v) differential activation of 'Cell Division' iModulon resulting from exposure to different beta-lactam antibiotics and (vi) 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 virulence.

Membrane transporter identification and modulation via adaptive laboratory evolution.

Membrane transport proteins are potential targets for medical and biotechnological applications. However, more than 30% of reported membrane transporter families are either poorly characterized or lack adequate functional annotation. Here, adaptive laboratory evolution was leveraged to identify membrane transporters for a set of four amino acids as well as specific mutations that modulate the activities of these transporters. Specifically, Escherichia coli was adaptively evolved under increasing concentrations of L-histidine, L-phenylalanine, L-threonine, and L-methionine separately with multiple replicate evolutions. Evolved populations and isolated clones displayed growth rates comparable to the unstressed ancestral strain at elevated concentrations (four-to six-fold increases) of the targeted amino acids. Whole genome sequencing of the evolved strains revealed a diverse number of key mutations, including SNPs, small deletions, and copy number variants targeting the transporters leuE for histidine, yddG for phenylalanine, yedA for methionine, and brnQ and rhtC for threonine. Reverse engineering of the mutations in the ancestral strain established mutation causality of the specific mutations for the tolerant phenotypes. The functional roles of yedA and brnQ in the transport of methionine and threonine, respectively, are novel assignments and their functional roles were validated using a flow cytometry cellular accumulation assay. To demonstrate how the identified transporters can be leveraged for production, an L-phenylalanine overproduction strain was shown to be a superior producer when the identified yddG exporter was overexpressed. Overall, the results revealed the striking efficiency of laboratory evolution to identify transporters and specific mutational mechanisms to modulate their activities, thereby demonstrating promising applicability in transporter discovery efforts and strain engineering.

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network.

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.