Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

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.

A comprehensive review of genomics, transcriptomics, proteomics, and metabolomic insights into the differentiation of Pseudomonas aeruginosa from the planktonic to biofilm state: A multi-omics approach

Biofilm formation by Pseudomonas aeruginosa is primarily responsible for chronic wound and lung infections in humans. These infections are persistent owing to the biofilm's high tolerance to antimicrobials and constantly changing environmental factors. Understanding the mechanism governing biofilm formation can help to develop therapeutics explicitly directed against the molecular markers responsible for this process. After numerous years of research, many genes responsible for both in vitro and in vivo biofilm development remain unidentified. However, there is no "all in one" complete in vivo or in vitro biofilm model. Recent findings imply that the shift from planktonic bacteria to biofilms is a complicated and interrelated differentiation process. Research on the applications of omics technologies in P. aeruginosa biofilm development is ongoing, and these approaches hold great promise for expanding our knowledge of the mechanisms of biofilm formation. This review discusses the different factors that affect biofilm formation and compares P. aeruginosa biofilm formation using the omics approaches targeting essential biological macromolecules, such as DNA, RNA, Protein, and metabolome. Furthermore, we have outlined the application of currently available omics tools, such as genomics, proteomics, metabolomics, transcriptomics, and integrated multi-omics methodologies, to understand the differential gene expression (biofilm vs. planktonic bacteria) of P. aeruginosa biofilms.

Independent component analysis of Corynebacterium glutamicum transcriptomes reveals its transcriptional regulatory network

Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C. glutamicum gene expression profile which includes 263 samples from 29 independent projects. We obtained 87 robust independent regulatory modules (iModulons). These iModulons explain 76.7% of the variance in the expression profile and constitute the quantitative transcriptional regulatory network of C. glutamicum. By analyzing the constituent genes in iModulons, we identified potential targets for 20 transcription factors. We also captured the changes in iModulon activities under different growth rates and dissolved oxygen concentrations, demonstrating the ability of iModulons to comprehensively interpret transcriptional responses to environmental changes. In summary, this study provides a genome-scale quantitative transcriptional regulatory network for C. glutamicum and informs future research on complex changes in the transcriptome.

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.

Transcriptome-wide marker gene expression analysis of stress-responsive sulfate-reducing bacteria

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation.

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.

Systems biology of competency in Vibrio natriegens is revealed by applying novel data analytics to the transcriptome

Vibrio natriegens regulates natural competence through the TfoX and QstR transcription factors, which are involved in external DNA capture and transport. However, the extensive genetic and transcriptional regulatory basis for competency remains unknown. We used a machine-learning approach to decompose Vibrio natriegens's transcriptome into 45 groups of independently modulated sets of genes (iModulons). Our findings show that competency is associated with the repression of two housekeeping iModulons (iron metabolism and translation) and the activation of six iModulons; including TfoX and QstR, a novel iModulon of unknown function, and three housekeeping iModulons (representing motility, polycations, and reactive oxygen species [ROS] responses). Phenotypic screening of 83 gene deletion strains demonstrates that loss of iModulon function reduces or eliminates competency. This database-iModulon-discovery cycle unveils the transcriptomic basis for competency and its relationship to housekeeping functions. These results provide the genetic basis for systems biology of competency in this organism.

Imaging biofilms using fluorescence in situ hybridization: seeing is believing

Biofilms are complex structures with an intricate relationship between the resident microorganisms, the extracellular matrix, and the surrounding environment. Interest in biofilms is growing exponentially given its ubiquity in so diverse fields such as healthcare, environmental and industry. Molecular techniques (e.g., next-generation sequencing, RNA-seq) have been used to study biofilm properties. However, these techniques disrupt the spatial structure of biofilms; therefore, they do not allow to observe the location/position of biofilm components (e.g., cells, genes, metabolites), which is particularly relevant to explore and study the interactions and functions of microorganisms. Fluorescence in situ hybridization (FISH) has been arguably the most widely used method for an in situ analysis of spatial distribution of biofilms. In this review, an overview on different FISH variants already applied on biofilm studies (e.g., CLASI-FISH, BONCAT-FISH, HiPR-FISH, seq-FISH) will be explored. In combination with confocal laser scanning microscopy, these variants emerged as a powerful approach to visualize, quantify and locate microorganisms, genes, and metabolites inside biofilms. Finally, we discuss new possible research directions for the development of robust and accurate FISH-based approaches that will allow to dig deeper into the biofilm structure and function.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fit phenotypes are achieved through optimal transcriptomic allocation. Here, we performed a high-resolution, multi-scale study of the transcriptomic tradeoff between two key fitness phenotypes, stress response (fear) and growth (greed), in Escherichia coli. We introduced twelve RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and found that single mutations resulted in large shifts in the fear vs. greed tradeoff, likely through destabilizing the rpoB-rpoC interface. RpoS and GAD regulons drive the fear response while ribosomal proteins and the ppGpp regulon underlie greed. Growth rate selection pressure during ALE results in endpoint strains that often have RNAP mutations, with synergistic mutations reflective of particular conditions. A phylogenetic analysis found the tradeoff in numerous bacteria species. The results suggest that the fear vs. greed tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.

Compendium-Wide Analysis of Pseudomonas aeruginosa Core and Accessory Genes Reveals Transcriptional Patterns across Strains PAO1 and PA14

Pseudomonas aeruginosa is an opportunistic pathogen that causes difficult-to-treat infections. Two well-studied divergent P. aeruginosa strain types, PAO1 and PA14, have significant genomic heterogeneity, including diverse accessory genes present in only some strains. Genome content comparisons find core genes that are conserved across both PAO1 and PA14 strains and accessory genes that are present in only a subset of PAO1 and PA14 strains. Here, we use recently assembled transcriptome compendia of publicly available P. aeruginosa RNA sequencing (RNA-seq) samples to create two smaller compendia consisting of only strain PAO1 or strain PA14 samples with each aligned to their cognate reference genome. We confirmed strain annotations and identified other samples for inclusion by assessing each sample's median expression of PAO1-only or PA14-only accessory genes. We then compared the patterns of core gene expression in each strain. To do so, we developed a method by which we analyzed genes in terms of which genes showed similar expression patterns across strain types. We found that some core genes had consistent correlated expression patterns across both compendia, while others were less stable in an interstrain comparison. For each accessory gene, we also determined core genes with correlated expression patterns. We found that stable core genes had fewer coexpressed neighbors that were accessory genes. Overall, this approach for analyzing expression patterns across strain types can be extended to other groups of genes, like phage genes, or applied for analyzing patterns beyond groups of strains, such as samples with different traits, to reveal a deeper understanding of regulation. IMPORTANCE Pseudomonas aeruginosa is a ubiquitous pathogen. There is much diversity among P. aeruginosa strains, including two divergent but well-studied strains, PAO1 and PA14. Understanding how these different strain-level traits manifest is important for identifying targets that regulate different traits of interest. With the availability of thousands of PAO1 and PA14 samples, we created two strain-specific RNA-seq compendia where each one contains hundreds of samples from PAO1 or PA14 strains and used them to compare the expression patterns of core genes that are conserved in both strain types and to determine which core genes have expression patterns that are similar to those of accessory genes that are unique to one strain or the other using an approach that we developed. We found a subset of core genes with different transcriptional patterns across PAO1 and PA14 strains and identified those core genes with expression patterns similar to those of strain-specific accessory genes.

Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia

Thousands of Pseudomonas aeruginosa RNA sequencing (RNA-seq) gene expression profiles are publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In this work, the transcriptional profiles from hundreds of studies performed by over 75 research groups were reanalyzed in aggregate to create a powerful tool for hypothesis generation and testing. Raw sequence data were uniformly processed using the Salmon pseudoaligner, and this read mapping method was validated by comparison to a direct alignment method. We developed filtering criteria to exclude samples with aberrant levels of housekeeping gene expression or an unexpected number of genes with no reported values and normalized the filtered compendia using the ratio-of-medians method. The filtering and normalization steps greatly improved gene expression correlations for genes within the same operon or regulon across the 2,333 samples. Since the RNA-seq data were generated using diverse strains, we report the effects of mapping samples to noncognate reference genomes by separately analyzing all samples mapped to cDNA reference genomes for strains PAO1 and PA14, two divergent strains that were used to generate most of the samples. Finally, we developed an algorithm to incorporate new data as they are deposited into the SRA. Our processing and quality control methods provide a scalable framework for taking advantage of the troves of biological information hibernating in the depths of microbial gene expression data and yield useful tools for P. aeruginosa RNA-seq data to be leveraged for diverse research goals. IMPORTANCE Pseudomonas aeruginosa is a causative agent of a wide range of infections, including chronic infections associated with cystic fibrosis. These P. aeruginosa infections are difficult to treat and often have negative outcomes. To aid in the study of this problematic pathogen, we mapped, filtered for quality, and normalized thousands of P. aeruginosa RNA-seq gene expression profiles that were publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The resulting compendia facilitate analyses across experiments, strains, and conditions. Ultimately, the workflow that we present could be applied to analyses of other microbial species.

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.

Responses of carbapenemase-producing and non-producing carbapenem-resistant Pseudomonas aeruginosa strains to meropenem revealed by quantitative tandem mass spectrometry proteomics

Pseudomonas aeruginosa is an opportunistic pathogen with increasing incidence of multidrug-resistant strains, including resistance to last-resort antibiotics, such as carbapenems. Resistances are often due to complex interplays of natural and acquired resistance mechanisms that are enhanced by its large regulatory network. This study describes the proteomic responses of two carbapenem-resistant P. aeruginosa strains of high-risk clones ST235 and ST395 to subminimal inhibitory concentrations (sub-MICs) of meropenem by identifying differentially regulated proteins and pathways. Strain CCUG 51971 carries a VIM-4 metallo-β-lactamase or 'classical' carbapenemase; strain CCUG 70744 carries no known acquired carbapenem-resistance genes and exhibits 'non-classical' carbapenem-resistance. Strains were cultivated with different sub-MICs of meropenem and analyzed, using quantitative shotgun proteomics based on tandem mass tag (TMT) isobaric labeling, nano-liquid chromatography tandem-mass spectrometry and complete genome sequences. Exposure of strains to sub-MICs of meropenem resulted in hundreds of differentially regulated proteins, including β-lactamases, proteins associated with transport, peptidoglycan metabolism, cell wall organization, and regulatory proteins. Strain CCUG 51971 showed upregulation of intrinsic β-lactamases and VIM-4 carbapenemase, while CCUG 70744 exhibited a combination of upregulated intrinsic β-lactamases, efflux pumps, penicillin-binding proteins and downregulation of porins. All components of the H1 type VI secretion system were upregulated in strain CCUG 51971. Multiple metabolic pathways were affected in both strains. Sub-MICs of meropenem cause marked changes in the proteomes of carbapenem-resistant strains of P. aeruginosa exhibiting different resistance mechanisms, involving a wide range of proteins, many uncharacterized, which might play a role in the susceptibility of P. aeruginosa to meropenem.

Two novel genes identified by large-scale transcriptomic analysis are essential for biofilm and rugose colony development of Vibrio vulnificus

Many pathogenic bacteria form biofilms to survive under environmental stresses and host immune defenses. Differential expression (DE) analysis of the genes in biofilm and planktonic cells under a single condition, however, has limitations to identify the genes essential for biofilm formation. Independent component analysis (ICA), a machine learning algorithm, was adopted to comprehensively identify the biofilm genes of Vibrio vulnificus, a fulminating human pathogen, in this study. ICA analyzed the large-scale transcriptome data of V. vulnificus cells under various biofilm and planktonic conditions and then identified a total of 72 sets of independently co-regulated genes, iModulons. Among the three iModulons specifically activated in biofilm cells, BrpT-iModulon mainly consisted of known genes of the regulon of BrpT, a transcriptional regulator controlling biofilm formation of V. vulnificus. Interestingly, the BrpT-iModulon additionally contained two novel genes, VV1_3061 and VV2_1694, designated as cabH and brpN, respectively. cabH and brpN were shared in other Vibrio species and not yet identified by DE analyses. Genetic and biochemical analyses revealed that cabH and brpN are directly up-regulated by BrpT. The deletion of cabH and brpN impaired the robust biofilm and rugose colony formation. CabH, structurally similar to the previously known calcium-binding matrix protein CabA, was essential for attachment to the surface. BrpN, carrying an acyltransferase-3 domain as observed in BrpL, played an important role in exopolysaccharide production. Altogether, ICA identified two novel genes, cabH and brpN, which are regulated by BrpT and essential for the development of robust biofilms and rugose colonies of V. vulnificus.

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.

Using genome-wide expression compendia to study microorganisms

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.