Biology subjects

Palsson, B. O.

Publications and source records attributed to Palsson, B. O..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Machine Learning Uncovers the Transcriptional Regulatory Network for the Production Host Streptomyces albidoflavus J1074

Streptomyces albidoflavus is a popular and genetically tractable platform strain used for natural product discovery and production via the expression of heterologous biosynthetic gene clusters (BGCs). However, its transcriptional regulatory network (TRN) and its impact on secondary metabolism is poorly understood. Here we characterized its TRN by applying an independent component analysis to a compendium of 218 high quality RNA-seq transcriptomes from both in-house and public sources spanning 88 unique growth conditions. We obtained 78 independently modulated sets of genes (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through analyses of condition-dependent TRN activity states, we (i) describe how the TRN adapts to different growth conditions, (ii) conduct a cross-species iModulon comparison, uncovering shared features and unique characteristics of the TRN across lineages, (iii) detail the transcriptional activation of several endogenous BGCs, including surugamide, minimycin and paulomycin, and (iv) infer potential functions of 40% of the uncharacterized genes in the S. albidoflavus genome. Our findings provide a comprehensive and quantitative understanding of the TRN of S. albidoflavus, providing a knowledge base for further exploration and experimental validation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/574332v3_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1a7d0aorg.highwire.dtl.DTLVardef@10750eborg.highwire.dtl.DTLVardef@1518644org.highwire.dtl.DTLVardef@145f5b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of bottom up parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard macroscopic kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and microscopic rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates. Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured using in vitro assays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.

systems biology↗

bioRxiv2023

Understanding and exploitation of host stress responses to protein production using novel transcriptomic analytics

Predictable expression of heterologous genes in a production host is a fundamental challenge in biotechnology. While traditional methods focus on manipulating expression and the property of the heterologous gene, a systems biology approach can complement with designs to improve the host itself. Previously, Independent Component Analysis (ICA) of the RNAseq data helped reveal independently modulated gene sets (iModulons) in bacteria. This was later applied to identify common stress responses related to heterologous gene expression for Escherichia coli. In this study, we expand this analysis with additional non-enzymatic proteins and apply our findings to design novel protein production optimization. By leveraging the Precise-1K transcriptomics knowledge base, we identify three iModulons as novel transcriptional responses to protein production stress; Cold Shock, gcvB sRNA, and the uncharacterized UC-9 iModulons. By studying the gene membership in the UC-9 iModulon, we discover effective novel design targets for improving protein production. This study demonstrates the value of big data analytics and systems understanding of host responses for designing novel strategies to optimize protein production.

systems biology↗

bioRxiv2023

Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses

Microbial strains used in large-scale biomanufacturing of melatonin often experience stresses like reactive oxygen species (ROS), SOS response, and acid stress, which can reduce productivity. This study leveraged a data-driven workflow to identify mutations that could improve robustness to these stresses for an industrially important melatonin production strain. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 available conditions. oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute to fitness in ROS stress. Across these genes, 259 total mutations were inspected and 10 were chosen for reintroduction based on mutation clustering and transcriptional signals as evidence of fitness impact. Strains engineered with mutations in oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress, and reduced SOS response suggesting improved genetic stability. Additionally, new evidence was generated towards understand the function of ygfZ, a gene of relatively uncertain function. This meta-analysis approach utilized interoperable multi-omics datasets to identify targeted mutations conferring industrially-relevant phenotypes, describing an approach for data-driven strain engineering to optimize microbial cell factories. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/558093v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1c8b4f6org.highwire.dtl.DTLVardef@1e08eacorg.highwire.dtl.DTLVardef@1b6fcb7org.highwire.dtl.DTLVardef@89554f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Using the E. coli Alleleome in Strain Design

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the sequence solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Both ALE and WT contributed highly enriched variants, where the enrichment and benefits depended on the conditions, genes, and gene product regions. The results also revealed that variants not originating from the initial experiment could potentially confer substantially greater benefits. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, representing what could be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/558058v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1a8e843org.highwire.dtl.DTLVardef@28a134org.highwire.dtl.DTLVardef@16f6849org.highwire.dtl.DTLVardef@127b43c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

A data-driven approach for timescale decomposition of biochemical reaction networks

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.

systems biology↗

bioRxiv2023

Interpreting roles of mutations associated with the emergence of S. aureus USA300 strains using transcriptional regulatory network reconstruction

The Staphylococcus aureus clonal complex 8 (CC8) is made up of several subtypes with varying levels of clinical burden; from community-associated methicillin resistant S. aureus (CA-MRSA) USA300 strains to hospital-associated (HA-MRSA) USA500 strains and ancestral methicillin susceptible (MSSA) strains. This phenotypic distribution within a single clonal complex makes CC8 an ideal clade to study the emergence of mutations important for antibiotic resistance and community spread. Gene level analysis comparing USA300 against MSSA and HA-MRSA strains have revealed key horizontally acquired genes important for its rapid spread in the community. However, efforts to define the contributions of point mutations and indels have been confounded by strong linkage disequilibrium resulting from clonal propagation. To break down this confounding effect, we combined genetic association testing with a model of the transcriptional regulatory network (TRN) to find candidate mutations that may have led to changes in gene regulation. First, we used a De Bruijn graph genome-wide association study (DBGWAS) to enrich mutations unique to the USA300 lineages within CC8. Next, we reconstructed the TRN by using Independent Component Analysis on 670 RNA sequencing samples from USA300 and non-USA300 CC8 strains which predicted several genes with strain-specific altered expression patterns. Examination of the regulatory region of one of the genes enriched by both approaches, isdH, revealed a 38 base pair deletion containing a Fur binding site and a conserved Single Nucleotide Polymorphism (SNP) which likely led to the altered expression levels in USA300 strains. Taken together, our results demonstrate the utility of reconstructed TRNs to address the limits of genetic approaches when studying emerging pathogenic strains.

systems biology↗

bioRxiv2023

Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

ILimosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied independent component analysis to 117 high-quality RNA-seq datasets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. This study uncovers the fundamental properties of L. reuteris TRN, deepens our understanding of its arginine metabolism, and the co-regulation of riboflavin metabolism and fatty acid biosynthesis. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and the role of isoprenoid biosynthesis in L. reuteris adaptive response to environmental changes. Through the integration of transcriptomics and machine learning, we provide a systems-level understanding of L. reuteris response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/547516v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@118ec4corg.highwire.dtl.DTLVardef@141b0b7org.highwire.dtl.DTLVardef@1b8cf6forg.highwire.dtl.DTLVardef@3ab181_HPS_FORMAT_FIGEXP M_FIG C_FIG Comprehensive iModulon Workflow Overview. Our innovative workflow is grounded in the analysis of the LactoPRECISE compendium, a curated dataset containing 117 internally sequenced RNA-seq samples derived from a diversity of 50 unique conditions, encompassing an extensive range of 13 distinct condition types. We employ the power of Independent Component Analysis (ICA), a cutting-edge machine learning algorithm, to discern the underlying structure of iModulons within this wealth of data. In the subsequent stage of our workflow, the discovered iModulons undergo detailed scrutiny to uncover media-specific regulatory mechanisms governing metabolism, illuminate the context-dependent intricacies of gene expression, and predict pathways leading to the biosynthesis of probiotic secondary metabolites. Our workflow offers an invaluable and innovative lens through which to view probiotic strain design while simultaneously highlighting transformative approaches to data analytics in the field.

bioinformatics↗

bioRxiv2023

StressME: unified computing framework of Escherichia coli metabolism, gene expression, and stress responses

Generalist microbes have adapted to a multitude of environmental stresses through their integrated stress response system. Individual stress responses have been quantified by E. coli metabolism and expression (ME) models under thermal, oxidative and acid stress, respectively. However, the systematic quantification of crosstalk among these stress responses remains lacking. Here, we present StressME: the unified stress response model of E. coli combining thermal (FoldME), oxidative (OxidizeME) and acid (AcidifyME) stress responses. StressME is the most up to date ME model for E. coli and it reproduces all published single-stress ME models. Additionally, it includes refined rate constants to improve prediction accuracy for wild-type and stress-evolved strains. StressME revealed certain optimal proteome allocation strategies associated with cross-stress responses. These stress-optimal proteomes were shaped by trade-offs between protective vs. metabolic enzymes; cytoplasmic vs. periplasmic chaperones; and expression of stress-specific proteins. As StressME is tuned to compute metabolic and gene expression responses under mild acid, oxidative, and thermal stresses, it is useful for engineering and health applications. The modular design of our open-source package also facilitates model expansion (e.g., to new stress mechanisms) by the computational biology community. Author summaryA fundamental understanding of multi-stress adaptation in E.coli has potential industrial relevance. While individual stress responses have been quantified through the protein regulatory network in E.coli, the systematic quantification of the crosstalk among stress responses remains lacking. Here, we develop a new modeling pipeline by which thermal, oxidative and acid stress response can be coupled to each other, and the metabolic activities, protein and metabolic flux redistribution due to cross stress can be quantified. We optimize the effective rate constants in the integrated model. We then confirm the model robustness by validating against the published data under single stress. Finally, we use the model to characterize the cross-adaptation between protective and catalytic proteins as well as between chaperones present in different cellular compartments. We find effective cross-protection against cross stress by adapting the E.coli cells to the thermal stress first. We also indicate the presence of trade-offs by which the cell may refuse to give up more protein allocation away from one stress response to the other, because doing so would decrease stress tolerance further. The single stress plug-in design makes the model build-up pipeline flexible and expandable, allowing incorporation of more stressors into the model architecture for industrial applications.

systems biology↗

bioRxiv2023

CyuR is a Dual Regulator for L-Cysteine Dependent Antimicrobial Resistance in Escherichia coli

Hydrogen sulfide (H2S), mainly produced from L-cysteine (Cys), renders bacteria highly resistant to oxidative stress. This mitigation of oxidative stress was suggested to be an important survival mechanism to achieve antimicrobial resistance (AMR) in many pathogenic bacteria. CyuR (known as DecR or YbaO) is a recently characterized Cys-dependent transcription regulator, responsible for the activation of the cyuAP operon and generation of hydrogen sulfide from Cys. Despite its potential importance, the regulatory network of CyuR remains poorly understood. In this study, we investigated the roles of the CyuR regulon in a Cys-dependent AMR mechanism in E. coli strains. We found: 1) Cys metabolism has a significant role in AMR and its effect is conserved in many E. coli strains, including clinical isolates; 2) CyuR negatively controls the expression of mdlAB encoding a transporter that exports antibiotics such as cefazolin and vancomycin; 3) CyuR binds to a DNA sequence motif GAAwAAATTGTxGxxATTTsyCC in the absence of Cys, confirmed by an in vitro binding assay; and 4) CyuR may regulate 25 additional genes as suggested by in silico motif scanning and transcriptome sequencing. Collectively, our findings expanded the understanding of the biological roles of CyuR relevant to antibiotic resistance associated with Cys.

systems biology↗

bioRxiv2023

Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3,591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heaps coefficient {lambda} (a measure of pangenome openness) was found to have an average value of 0.27 (ranging from 0.07-0.37), 3) the pangenome openness was correlated with the abundance and genomic location of transposons and mobilomes, 4) the pangenome for each species was divided into core, accessory, and rare genomes, that highlight the species-specific properties (such as motility and restriction-modification systems), 5) the pangenome of Lactiplantibacillus plantarum (which contained the highest number of genomes found amongst the 26 species studied) contained nine distinct phylogroups, and 6) genome mining revealed a richness of detected biosynthetic gene clusters, with functions ranging from antimicrobial and probiotic to food preservation, but [~]93% were of unknown function. This study provides the first in-depth comparative pangenomics analysis of the Lactobacillaceae family.

systems biology↗

bioRxiv2023

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

The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agriculturally-significant interactions, a comprehensive understanding of the TRN of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we 1) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, 2) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and 3) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae. ImportancePseudomonas 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.

systems biology↗

bioRxiv2023

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.

bioengineering↗

bioRxiv2022

Lab 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 reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

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. The underlying transcriptional regulatory network (TRN) is responsible for altering the physiological state of the bacterium to adapt to each host environment. Consequently, an in-depth understanding the comprehensive dynamics of its TRN could inform new therapeutic strategies. Here, we compiled 116 existing high-quality RNA-seq data sets of 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 nga-ifs-slo virulence-related operon, which allowed us to identify carbon sources that control its expression. In particular, dextrin utilization upregulated nga-ifs-slo operon by activation of two-component regulatory system CovRS-related iModulons, and changed bacterial hemolytic activity compared to glucose or maltose utilization. Finally, we show that the iModulon-based TRN structure can be used to simplify interpretation of noisy bacterial transcriptome at the infection site.

microbiology↗

bioRxiv2022

Coordination of CcpA and CodY regulators in Staphylococcus aureus USA300 strains

The complex crosstalk 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 carbons 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. ImportanceStaphylococcus 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 herein can deconvolute the biology underlying other important phenotypes in this pathogen.

microbiology↗

bioRxiv2022

Quantitative sequence basis for the E. coli transcriptional regulatory network

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Inherently the DNA sequence is the primary determinant of the TRN; however, it is well established that the presence of a DNA binding motif does not guarantee a functional regulatory protein binding site. Thus, the extent to which the TRN architecture can be predicted by the genome DNA sequence alone remains unclear. Here, we developed machine learning models that predict the TRN structure of E. coli based on genome sequence. Models were constructed successfully (cross-validation AUROC >= 0.8) for 84% (57/68) of valid E. coli regulons identified from top-down analysis of RNA-seq data. We found that: 1) While regulatory motif strength is the most important sequence feature for determining regulon membership, additional features such as DNA shape substantially influence membership; 2) complex regulons involving multiple interacting regulators could be unraveled by machine learning; 3) investigating regulons where initial ML models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, while regulon structure can appear to be variable across estimation methods and strains, we found that strong regulatory sequence features underlie both the genes that appear most consistently in regulons across estimation methods as well as the core regulon genes in the Fur pan-regulon. This work develops a quantitative understanding of the sequence basis of the TRN and suggests a path towards computationally-guided control of transcriptional regulation for synthetic biology applications.

bioinformatics↗

bioRxiv2022

Machine-learning from Pseudomonas putida 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, 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 1,993 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 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

systems biology↗