RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation in Escherichia coli K-12

Dietary amino acids regulate Salmonella colonization via microbiota-dependent mechanisms in the mouse gut

The gut microbiota confers host protection against pathogen colonization early after infection. Several mechanisms underlying the protection have been described, but the contributions of nutrient competition versus direct inhibition are controversial. Using an ex vivo model of Salmonella growth in the mouse cecum with its indigenous microbes, we find that nutrient limitation and typical inhibitory factors alone cannot prevent pathogen growth. However, the addition of certain amino acids markedly reverses the microbiota's ability to suppress pathogen growth. Enhanced Salmonella colonization after antibiotic treatment is ablated by exclusion of dietary protein, which requires the presence of the microbiota. Thus, dietary protein and amino acids are important regulators of colonization resistance.

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO2-to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO2.

ProPr54 web server: predicting σ54 promoters and regulon with a hybrid convolutional and recurrent deep neural network

σ54 serves as an unconventional sigma factor with a distinct mechanism of transcription initiation, which depends on the involvement of a transcription activator. This unique sigma factor σ54 is indispensable for orchestrating the transcription of genes crucial to nitrogen regulation, flagella biosynthesis, motility, chemotaxis and various other essential cellular processes. Currently, no comprehensive tools are available to determine σ54 promoters and regulon in bacterial genomes. Here, we report a σ54 promoter prediction method ProPr54, based on a convolutional neural network trained on a set of 446 validated σ54 binding sites derived from 33 bacterial species. Model performance was tested and compared with respect to bacterial intergenic regions, demonstrating robust applicability. ProPr54 exhibits high performance when tested on various bacterial species, highly surpassing other available σ54 regulon identification methods. Furthermore, analysis on bacterial genomes, which have no experimentally validated σ54 binding sites, demonstrates the generalization of the model. ProPr54 is the first reliable in silico method for predicting σ54 binding sites, making it a valuable tool to support experimental studies on σ54. In conclusion, ProPr54 offers a reliable, broadly applicable tool for predicting σ54 promoters and regulon genes in bacterial genome sequences. A web server is freely accessible at http://propr54.molgenrug.nl.

Linking molecular mechanisms to their evolutionary consequences: a primer

A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by 2 proteins, allowing us to connect any promoter genotype to 6 phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties-the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation-can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.

Identification of a comprehensive set of transcriptional regulators involved in the long-term survivability of Escherichia coli in soil

Bacteria that typically do not thrive in soil can survive therein for long periods. While much research has been conducted on the external environmental factors affecting the long-term survival of bacteria in soil, their inherent factors are poorly understood. To adapt to environmental changes, bacteria alter their gene expression patterns using transcriptional regulators such as sigma factors. Using Escherichia coli as a model bacterium, we examined the effects of each transcriptional regulator on the long-term survivability of E. coli in soil. The survivability of 294 E. coli strains deficient in transcriptional regulators in soil was measured over 6 weeks. The results showed that ten strains deficient in transcription factors significantly reduced survivability, whereas four deficient strains increased it. The functions common to several of these transcriptional regulators included carbon and nitrogen metabolism, stationary phase adaptation, and osmotic stress adaptation. These transcription factors are often global regulators and conserved among other pathogenic bacterial species. Taken together, we successfully identified a comprehensive set of transcription factors involved in the long-term survival of E. coli in soil. These findings will be useful for understanding the mechanisms underlying the adaptation of microorganisms to soil environments.

Biological Insights from RNA-RNA Interactomes in Bacteria, as Revealed by RIL-seq

Bacteria reside in constantly changing environments and require rapid and precise adjustments of gene expression to ensure survival. Small regulatory RNAs (sRNAs) are a crucial element that bacteria utilize to achieve this. sRNAs are short RNA molecules that modulate gene expression usually through base-pairing interactions with target RNAs, primarily mRNAs. These interactions can lead to either negative outcomes such as mRNA degradation or translational repression or positive outcomes such as mRNA stabilization or translation enhancement. In recent years, high-throughput approaches such as RIL-seq (RNA interaction by ligation and sequencing) revolutionized the sRNA field by enabling the identification of sRNA targets on a global scale, unveiling intricate sRNA-RNA networks. In this review, we discuss the insights gained from investigating sRNA-RNA networks in well-studied bacterial species as well as in understudied bacterial species. Having a complete understanding of sRNA-mediated regulation is critical for the development of new strategies for controlling bacterial growth and combating bacterial infections.

Predictive Modeling of Gene Expression and Localization of DNA Binding Site Using Deep Convolutional Neural Networks

Despite the sequencing revolution, large swaths of the genomes sequenced to date lack any information about the arrangement of transcription factor binding sites on regulatory DNA. Massively Parallel Reporter Assays (MPRAs) have the potential to dramatically accelerate our genomic annotations by making it possible to measure the gene expression levels driven by thousands of mutational variants of a regulatory region. However, the interpretation of such data often assumes that each base pair in a regulatory sequence contributes independently to gene expression. To enable the analysis of this data in a manner that accounts for possible correlations between distant bases along a regulatory sequence, we developed the Deep learning Adaptable Regulatory Sequence Identifier (DARSI). This convolutional neural network leverages MPRA data to predict gene expression levels directly from raw regulatory DNA sequences. By harnessing this predictive capacity, DARSI systematically identifies transcription factor binding sites within regulatory regions at single-base pair resolution. To validate its predictions, we benchmarked DARSI against curated databases, confirming its accuracy in predicting transcription factor binding sites. Additionally, DARSI predicted novel unmapped binding sites, paving the way for future experimental efforts to confirm the existence of these binding sites and to identify the transcription factors that target those sites. Thus, by automating and improving the annotation of regulatory regions, DARSI generates experimentally actionable predictions that can feed iterations of the theory-experiment cycle aimed at reaching a predictive understanding of transcriptional control.

The emergence and evolution of gene expression in genome regions replete with regulatory motifs

Gene regulation is essential for life and controlled by regulatory DNA. Mutations can modify the activity of regulatory DNA, and also create new regulatory DNA, a process called regulatory emergence. Non-regulatory and regulatory DNA contain motifs to which transcription factors may bind. In prokaryotes, gene expression requires a stretch of DNA called a promoter, which contains two motifs called -10 and -35 boxes. However, these motifs may occur in both promoters and non-promoter DNA in multiple copies. They have been implicated in some studies to improve promoter activity, and in others to repress it. Here, we ask whether the presence of such motifs in different genetic sequences influences promoter evolution and emergence. To understand whether and how promoter motifs influence promoter emergence and evolution, we start from 50 'promoter islands', DNA sequences enriched with -10 and -35 boxes. We mutagenize these starting 'parent' sequences, and measure gene expression driven by 240,000 of the resulting mutants. We find that the probability that mutations create an active promoter varies more than 200-fold, and is not correlated with the number of promoter motifs. For parent sequences without promoter activity, mutations created over 1500 new -10 and -35 boxes at unique positions in the library, but only ~0.3% of these resulted in de-novo promoter activity. Only ~13% of all -10 and -35 boxes contribute to de-novo promoter activity. For parent sequences with promoter activity, mutations created new -10 and -35 boxes in 11 specific positions that partially overlap with preexisting ones to modulate expression. We also find that -10 and -35 boxes do not repress promoter activity. Overall, our work demonstrates how promoter motifs influence promoter emergence and evolution. It has implications for predicting and understanding regulatory evolution, de novo genes, and phenotypic evolution.

Resistance mechanism of Escherichia coli strains with different ampicillin resistance levels

Antibiotic resistance is an important problem that threatens medical treatment. Differences in the resistance levels of microorganisms cause great difficulties in understanding the mechanisms of antibiotic resistance. Therefore, the molecular reasons underlying the differences in the level of antibiotic resistance need to be clarified. For this purpose, genomic and transcriptomic analyses were performed on three Escherichia coli strains with varying degrees of adaptive resistance to ampicillin. Whole-genome sequencing of strains with different levels of resistance detected five mutations in strains with 10-fold resistance and two additional mutations in strains with 95-fold resistance. Overall, three of the seven mutations occurred as a single base change, while the other four occurred as insertions or deletions. While it was thought that 10-fold resistance was achieved by the effect of mutations in the ftsI, marAR, and rpoC genes, it was found that 95-fold resistance was achieved by the synergistic effect of five mutations and the ampC mutation. In addition, when the general transcriptomic profiles were examined, it was found that similar transcriptomic responses were elicited in strains with different levels of resistance. This study will improve our view of resistance mechanisms in bacteria with different levels of resistance and provide the basis for our understanding of the molecular mechanism of antibiotic resistance in ampicillin-resistant E. coli strains. KEY POINTS: •The mutation of the ampC promoter may act synergistically with other mutations and lead to higher resistance. •Similar transcriptomic responses to ampicillin are induced in strains with different levels of resistance. •Low antibiotic concentrations are the steps that allow rapid achievement of high antibiotic resistance.

Diversity of Transcriptional Regulatory Adaptation in E. coli

The transcriptional regulatory network (TRN) in bacteria is thought to rapidly evolve in response to selection pressures, modulating transcription factor (TF) activities and interactions. In order to probe the limits and mechanisms surrounding the short-term adaptability of the TRN, we generated, evolved, and characterized knockout (KO) strains in Escherichia coli for 11 regulators selected based on measured growth impact on glucose minimal media. All but one knockout strain (Δlrp) were able to recover growth and did so requiring few convergent mutations. We found that the TF knockout adaptations could be divided into four categories: (i) Strains (ΔargR, ΔbasR, Δlon, ΔzntR, and Δzur) that recovered growth without any regulator-specific adaptations, likely due to minimal activity of the regulator on the growth condition, (ii) Strains (ΔcytR, ΔmlrA, and ΔybaO) that recovered growth without TF-specific mutations but with differential expression of regulators with overlapping regulons to the KO'ed TF, (iii) Strains (Δcrp and Δfur) that recovered growth using convergent mutations within their regulatory networks, including regulated promoters and connected regulators, and (iv) Strains (Δlrp) that were unable to fully recover growth, seemingly due to the broad connectivity of the TF within the TRN. Analyzing growth capabilities in evolved and unevolved strains indicated that growth adaptation can restore fitness to diverse substrates often despite a lack of TF-specific mutations. This work reveals the breadth of TRN adaptive mechanisms and suggests these mechanisms can be anticipated based on the network and functional context of the perturbed TFs.

mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea . It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability

Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial TFs-DNA interactions remains an intricate challenge. A deeper understanding of DNA secondary structures could greatly enhance our knowledge of how TFs recognize and interact with DNA, thereby elucidating their biological function. In this study, we employed machine learning algorithms to predict transcription factor binding sites (TFBS) and classify them as directed-repeat (DR) or inverted-repeat (IR). To accomplish this, we divided the set of TFBS nucleotide sequences by size, ranging from 8 to 20 base pairs, and converted them into thermodynamic data known as DNA duplex stability (DDS). Our results demonstrate that the Random Forest algorithm accurately predicts TFBS with an average accuracy of over 82% and effectively distinguishes between IR and DR with an accuracy of 89%. Interestingly, upon converting the base pairs of several TFBS-IR into DDS values, we observed a symmetric profile typical of the palindromic structure associated with these architectures. This study presents a novel TFBS prediction model based on a DDS characteristic that may indicate how respective proteins interact with base pairs, thus providing insights into molecular mechanisms underlying bacterial TFs-DNA interaction.

A Cryptic Prophage Transcription Factor Drives Phenotypic Changes via Host Gene Regulation

Cryptic prophages (CPs) are elements of bacterial genomes acquired from bacteriophage that infect the host cell and ultimately become stably integrated within the host genome. While some proteins encoded by CPs can modulate host phenotypes, the potential for Transcription Factors (TFs) encoded by CPs to impact host physiology by regulating host genes has not been thoroughly investigated. In this work, we report hundreds of host genes regulated by DicC, a DNA-binding TF encoded in the Qin prophage of Esherichia coli. We identified host-encoded regulatory targets of DicC that could be linked to known phenotypes of its induction. We also demonstrate that a DicC-induced growth defect is largely independent of other Qin prophage genes. Our data suggest a greater role for cryptic prophage TFs in controlling bacterial host gene expression than previously appreciated.

RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

Promoters Constrain Evolution of Expression Levels of Essential Genes in Escherichia coli

Variability in expression levels in response to random genomic mutations varies among genes, influencing both the facilitation and constraint of phenotypic evolution in organisms. Despite its importance, both the underlying mechanisms and evolutionary origins of this variability remain largely unknown due to the mixed contributions of cis- and trans-acting elements. To address this issue, we focused on the mutational variability of cis-acting elements, that is, promoter regions, in Escherichia coli. Random mutations were introduced into the natural and synthetic promoters to generate mutant promoter libraries. By comparing the variance in promoter activity of these mutant libraries, we found no significant difference in mutational variability in promoter activity between promoter groups, suggesting the absence of a signature of natural selection for mutational robustness. In contrast, the promoters controlling essential genes exhibited a remarkable bias in mutational variability, with mutants displaying higher activities than the wild types being relatively rare compared to those with lower activities. Our evolutionary simulation on a rugged fitness landscape provided a rationale for this vulnerability. These findings suggest that past selection created nonuniform mutational variability in promoters biased toward lower activities of random mutants, which now constrains the future evolution of downstream essential genes toward higher expression levels.

Repeated glucose oscillations in high cell-density cultures influence stress-related functions of Escherichia coli

Engineering microbial cells for the commercial production of biomolecules and biochemicals requires understanding how cells respond to dynamically changing substrate (feast-famine) conditions in industrial-scale bioreactors. Scale-down methods that oscillate substrate are commonly applied to predict the industrial-scale behavior of microbes. We followed a compartment modeling approach to design a scale-down method based on the simulation of an industrial-scale bioreactor. This study uses high cell-density scale-down experiments to investigate Escherichia coli knockout strains of five major glucose-sensitive transcription factors (Cra, Crp, FliA, PrpR, and RpoS) to study their regulatory role during glucose oscillations. RNA-sequencing analysis revealed that the glucose oscillations caused the down-regulation of several stress-related functions in E. coli. An in-depth analysis of strain physiology and transcriptome revealed a distinct phenotype of the strains tested under glucose oscillations. Specifically, the knockout strains of Cra, Crp, and RpoS resulted in a more sensitive transcriptional response than the control strain, while the knockouts of FliA and PrpR responded less severely. These findings imply that the regulation orchestrated by Cra, Crp, and RpoS may be essential for robust E. coli production strains. In contrast, the regulation by FliA and PrpR may be undesirable for temporal oscillations in glucose availability.

Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach

Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with new limited data for a specific task ("transfer learning") opens roads to address new problems of biomedical information extraction. Here, to alleviate this lack of knowledge and assist literature curation, we present a new approach based on the Bidirectional Transformer for Language Understanding (BERT) architecture to classify transcriptional regulatory interactions of bacteria as a first step to extract TRNs from literature. The approach achieved a significant performance in a test dataset of sentences of Escherichia coli (F1-Score: 0.8685, Matthew's correlation coefficient: 0.8163). The examination of model predictions revealed that the model learned different ways to express the regulatory interaction. The approach was evaluated to extract a TRN of Salmonella using 264 complete articles. The evaluation showed that the approach was able to accurately extract 82% of the network and that it was able to extract interactions absent in curation data. To the best of our knowledge, the present study is the first effort to obtain a BERT-based approach to extract this specific kind of interaction. This approach is a starting point to address the limitations of reconstructing TRNs of bacteria and diseases of biological interest. Database URL: https://github.com/laigen-unam/BERT-trn-extraction.

Rhizobium etli CFN42 and Sinorhizobium meliloti 1021 bioinformatic transcriptional regulatory networks from culture and symbiosis

Rhizobium etli CFN42 proteome-transcriptome mixed data of exponential growth and nitrogen-fixing bacteroids, as well as Sinorhizobium meliloti 1021 transcriptome data of growth and nitrogen-fixing bacteroids, were integrated into transcriptional regulatory networks (TRNs). The one-step construction network consisted of a matrix-clustering analysis of matrices of the gene profile and all matrices of the transcription factors (TFs) of their genome. The networks were constructed with the prediction of regulatory network application of the RhizoBindingSites database (http://rhizobindingsites.ccg.unam.mx/). The deduced free-living Rhizobium etli network contained 1,146 genes, including 380 TFs and 12 sigma factors. In addition, the bacteroid R. etli CFN42 network contained 884 genes, where 364 were TFs, and 12 were sigma factors, whereas the deduced free-living Sinorhizobium meliloti 1021 network contained 643 genes, where 259 were TFs and seven were sigma factors, and the bacteroid Sinorhizobium meliloti 1021 network contained 357 genes, where 210 were TFs and six were sigma factors. The similarity of these deduced condition-dependent networks and the biological E. coli and B. subtilis independent condition networks segregates from the random Erdös-Rényi networks. Deduced networks showed a low average clustering coefficient. They were not scale-free, showing a gradually diminishing hierarchy of TFs in contrast to the hierarchy role of the sigma factor rpoD in the E. coli K12 network. For rhizobia networks, partitioning the genome in the chromosome, chromids, and plasmids, where essential genes are distributed, and the symbiotic ability that is mostly coded in plasmids, may alter the structure of these deduced condition-dependent networks. It provides potential TF gen-target relationship data for constructing regulons, which are the basic units of a TRN.

Unraveling the interplay between a small RNA and RNase E in bacteria

Small RNAs (sRNAs) are major regulators of gene expression in bacteria, exerting their regulation primarily via base pairing with their target transcripts and modulating translation. Accumulating evidence suggest that sRNAs can also affect the stability of their target transcripts by altering their accessibility to endoribonucleases. Yet, the effects of sRNAs on transcript stability and the mechanisms underlying them have not been studied in wide scale. Here we employ large-scale RNA-seq-based methodologies in the model bacterium Escherichia coli to quantitatively study the functional interaction between a sRNA and an endoribonuclease in regulating gene expression, using the well-established sRNA, GcvB, and the major endoribonuclease, RNase E. Studying single and double mutants of gcvB and rne and analysing their RNA-seq results by the Double Mutant Cycle approach, we infer distinct modes of the interplay between GcvB and RNase E. Transcriptome-wide mapping of RNase E cleavage sites provides further support to the results of the RNA-seq analysis, identifying cleavage sites in targets in which the functional interaction between GcvB and RNase E is evident. Together, our results indicate that the most dominant mode of GcvB-RNase E functional interaction is GcvB enhancement of RNase E cleavage, which varies in its magnitude between different targets.

Transcriptional and post-transcriptional mechanisms modulate cyclopropane fatty acid synthase through small RNAs in Escherichia coli

The small RNA (sRNA) RydC strongly activates cfa, which encodes the cyclopropane fatty acid synthase. Previous work demonstrated that RydC activation of cfa increases the conversion of unsaturated fatty acids to cyclopropanated fatty acids in membrane lipids and changes the biophysical properties of membranes, making cells more resistant to acid stress. The regulators that control RydC synthesis had not previously been identified. In this study, we identify a GntR-family transcription factor, YieP, that represses rydC transcription. YieP positively autoregulates its own transcription and indirectly regulates cfa through RydC. We further identify additional sRNA regulatory inputs that contribute to the control of RydC and cfa. The translation of yieP is repressed by the Fnr-dependent sRNA, FnrS, making FnrS an indirect activator of rydC and cfa. Conversely, RydC activity on cfa is antagonized by the OmpR-dependent sRNA OmrB. Altogether, this work illuminates a complex regulatory network involving transcriptional and post-transcriptional inputs that link the control of membrane biophysical properties to multiple environmental signals.

IMPORTANCE

Bacteria experience many environmental stresses that challenge their membrane integrity. To withstand these challenges, bacteria sense what stress is occurring and mount a response that protects membranes. Previous work documented the important roles of small RNA (sRNA) regulators in membrane stress responses. One sRNA, RydC, helps cells cope with membrane-disrupting stresses by promoting changes in the types of lipids incorporated into membranes. In this study, we identified a regulator, YieP, that controls when RydC is produced and additional sRNA regulators that modulate YieP levels and RydC activity. These findings illuminate a complex regulatory network that helps bacteria sense and respond to membrane stress.

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

Fast growth phenotypes are achieved through optimal transcriptomic allocation, in which cells must balance tradeoffs in resource allocation between diverse functions. One such balance between stress readiness and unbridled growth in E. coli has been termed the fear versus greed (f/g) tradeoff. Two specific RNA polymerase (RNAP) mutations observed in adaptation to fast growth have been previously shown to affect the f/g tradeoff, suggesting that genetic adaptations may be primed to control f/g resource allocation. Here, we conduct a greatly expanded study of the genetic control of the f/g tradeoff across diverse conditions. We introduced 12 RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and obtained expression profiles of each. We found that these single RNAP mutation strains resulted in large shifts in the f/g tradeoff primarily in the RpoS regulon and ribosomal genes, likely through modifying RNAP-DNA interactions. Two of these mutations additionally caused condition-specific transcriptional adaptations. While this tradeoff was previously characterized by the RpoS regulon and ribosomal expression, we find that the GAD regulon plays an important role in stress readiness and ppGpp in translation activity, expanding the scope of the tradeoff. A phylogenetic analysis found the greed-related genes of the tradeoff present in numerous bacterial species. The results suggest that the f/g tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.IMPORTANCETo increase growth, E. coli must raise ribosomal content at the expense of non-growth functions. Previous studies have linked RNAP mutations to this transcriptional shift and increased growth but were focused on only two mutations found in the protein's central region. RNAP mutations, however, commonly occur over a large structural range. To explore RNAP mutations' impact, we have introduced 12 RNAP mutations found in laboratory evolution experiments and obtained expression profiles of each. The mutations nearly universally increased growth rates by adjusting said tradeoff away from non-growth functions. In addition to this shift, a few caused condition-specific adaptations. We explored the prevalence of this tradeoff across phylogeny and found it to be a widespread and conserved trend among bacteria.

Generating information-dense promoter sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs sets of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.

A library of reporters of the global regulators of gene expression in Escherichia coli

The topology of the transcription factor network (TFN) of Escherichia coli is far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs for this purpose. Each consists of a single-copy plasmid coding for green fluorescent protein (GFP) fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, as well as under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs of E. coli. Moreover, the library can be invaluable in bioindustrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.IMPORTANCECells contain thousands of genes. Many genes are involved in the control of cellular activities. Some activities require a few hundred genes to run largely synchronous transcriptional programs. To achieve this, cells have evolved global regulator (GR) proteins that can influence hundreds of genes simultaneously. We have engineered a library of Escherichia coli strains to track the levels over time of these, phenotypically critical, GRs. Each strain has a single-copy plasmid coding for a fast-maturing green fluorescent protein whose transcription is controlled by a copy of the natural GR promoter. By allowing the tracking of GR levels, with sensitivity and specificity, this library should become of wide use in scientific research on bacterial gene expression (from molecular to synthetic biology) and, later, be used in applications in therapeutics and bioindustries.

Flexible gold standards for transcription factor regulatory interactions in Escherichia coli K-12: architecture of evidence types

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.

Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration

CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in genome-wide essentiality screens, with the surprising discovery that gene-specific features substantially impact prediction. We develop a mixed-effect random forest regression model that provides better estimates of guide efficiency. We further apply methods from explainable AI to extract interpretable design rules from the model. This study provides a blueprint for predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

Multiomics Analysis Reveals the Genetic Basis of Volatile Terpenoid Formation in Oolong Tea

Oolong tea has gained great popularity in China due to its pleasant floral and fruity aromas. Although numerous studies have investigated the aroma differences across various tea cultivars, the genetic mechanism is unclear. This study performed multiomics analysis of three varieties suitable for oolong tea and three others with different processing suitability. Our analysis revealed that oolong tea varieties contained higher levels of cadinane sesquiterpenoids. PanTFBS was developed to identify variants of transcription factor binding sites (TFBSs). We found that the CsDCS gene had two TFBS variants in the promoter sequence and a single nucleotide polymorphism (SNP) in the coding sequence. Integrating data on genetic variations, gene expression, and protein-binding sites indicated that CsDCS might be a pivotal gene involved in the biosynthesis of cadinane sesquiterpenoids. These findings advance our understanding of the genetic factors involved in the aroma formation of oolong tea and offer insights into the enhancement of tea aroma.

Multiscale genetic architecture of donor-recipient differences reveals intronic LIMS1 mismatches associated with kidney transplant survival

Donor-recipient (D-R) mismatches outside of human leukocyte antigens (HLAs) contribute to kidney allograft loss, but the mechanisms remain unclear, specifically for intronic mismatches. We quantified non-HLA mismatches at variant-, gene-, and genome-wide scales from single nucleotide polymorphism (SNP) data of D-Rs from 2 well-phenotyped transplant cohorts: Genomics of Chronic Allograft Rejection (GoCAR; n = 385) and Clinical Trials in Organ Transplantation-01/17 (CTOT-01/17; n = 146). Unbiased gene-level screening in GoCAR uncovered the LIMS1 locus as the top-ranked gene where D-R mismatches associated with death-censored graft loss (DCGL). A previously unreported, intronic, LIMS1 haplotype of 30 SNPs independently associated with DCGL in both cohorts. Haplotype mismatches showed a dosage effect, and minor-allele introduction to major-allele-carrying recipients showed greater hazard of DCGL. The LIMS1 haplotype and the previously reported LIMS1 SNP rs893403 are expression quantitative trait loci (eQTL) in immune cells for GCC2 (not LIMS1), which encodes a protein involved in mannose-6-phosphase receptor (M6PR) recycling. Peripheral blood and T cell transcriptome analyses associated the GCC2 gene and LIMS1 SNPs with the TGF-β1/SMAD pathway, suggesting a regulatory effect. In vitro GCC2 modulation impacted M6PR-dependent regulation of active TGF-β1 and downstream signaling in T cells. Together, our data link LIMS1 locus D-R mismatches to DCGL via GCC2 eQTLs that modulate TGF-β1-dependent effects on T cells.

Differential adenine methylation analysis reveals increased variability in 6mA in the absence of methyl-directed mismatch repair

IMPORTANCE

Methylation greatly influences the bacterial genome by guiding DNA repair and regulating pathogenic and stress-response phenotypes. But, the rate of epigenetic changes and their consequences on molecular phenotypes are underexplored. Through a detailed characterization of genome-wide adenine methylation in a commonly used laboratory strain of Escherichia coli, we reveal that mismatch repair deficient populations experience an increase in epimutations resulting in a genome-wide reduction of 6mA methylation in a manner consistent with genetic drift. Our findings highlight how methylation patterns evolve and the constraints on epigenetic evolution due to post-replicative DNA repair, contributing to a deeper understanding of bacterial genome evolution and how epimutations may introduce semi-permanent variation that can influence adaptation.

The PapB/FocB family protein TosR acts as a positive regulator of flagellar expression and is required for optimal virulence of uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) is a major causative agent of urinary tract infections. The bacteria internalize into the uroepithelial cells, where aggregate and form microcolonies. UPEC fimbriae and flagella are important for the formation of microcolonies in uroepithelial cells. PapB/FocB family proteins are small DNA-binding transcriptional regulators consisting of approximately 100 amino acids that have been reported to regulate the expression of various fimbriae, including P, F1C, and type 1 fimbriae, and adhesins. In this study, we show that TosR, a member of the PapB/FocB family is the activator of flagellar expression. The tosR mutant had similar expression levels of type 1, P and F1C fimbriae as the parent strain, but flagellar production was markedly lower than in the parent strain. Flagellin is a major component of flagella. The gene encoding flagellin, fliC, is transcriptionally activated by the sigma factor FliA. The fliA expression is induced by the flagellar master regulator FlhDC. The flhD and flhC genes form an operon. The promoter activity of fliC, fliA and flhD in the tosR mutant was significantly lower than in the parent strain. The purified recombinant TosR does not bind to fliC and fliA but to the upstream region of the flhD gene. TosR is known to bind to an AT-rich DNA sequence consisting of 29 nucleotides. The characteristic AT-rich sequence exists 550-578 bases upstream of the flhD gene. The DNA fragment lacking this sequence did not bind TosR. Furthermore, loss of the tosR gene reduced motility and the aggregation ability of UPEC in urothelial cells. These results indicate that TosR is a transcriptional activator that increases expression of the flhDC operon genes, contributing to flagellar expression and optimal virulence.

Inactivation of ackA and pta Genes Reduces GlpT Expression and Susceptibility to Fosfomycin in Escherichia coli

Fosfomycin is used to treat a variety of bacterial infections, including urinary tract infections caused by Escherichia coli. In recent years, quinolone-resistant and extended-spectrum β-lactamase (ESBL)-producing bacteria have been increasing. Because fosfomycin is effective against many of these drug-resistant bacteria, the clinical importance of fosfomycin is increasing. Against this background, information on the mechanisms of resistance and the antimicrobial activity of this drug is desired to enhance the usefulness of fosfomycin therapy. In this study, we aimed to explore novel factors affecting the antimicrobial activity of fosfomycin. Here, we found that ackA and pta contribute to fosfomycin activity against E. coli. ackA and pta mutant E. coli had reduced fosfomycin uptake capacity and became less sensitive to this drug. In addition, ackA and pta mutants had decreased expression of glpT that encodes one of the fosfomycin transporters. Expression of glpT is enhanced by a nucleoid-associated protein, Fis. We found that mutations in ackA and pta also caused a decrease in fis expression. Thus, we interpret the decrease in glpT expression in ackA and pta defective strains to be due to a decrease in Fis levels in these mutants. Furthermore, ackA and pta are conserved in multidrug-resistant E. coli isolated from patients with pyelonephritis and enterohemorrhagic E. coli, and deletion of ackA and pta from these strains resulted in decreased susceptibility to fosfomycin. These results suggest that ackA and pta in E. coli contribute to fosfomycin activity and that mutation of these genes may pose a risk of reducing the effect of fosfomycin. IMPORTANCE The spread of drug-resistant bacteria is a major threat in the field of medicine. Although fosfomycin is an old type of antimicrobial agent, it has recently come back into the limelight because of its effectiveness against many drug-resistant bacteria, including quinolone-resistant and ESBL-producing bacteria. Since fosfomycin is taken up into the bacteria by GlpT and UhpT transporters, its antimicrobial activity fluctuates with changes in GlpT and UhpT function and expression. In this study, we found that inactivation of the ackA and pta genes responsible for the acetic acid metabolism system reduced GlpT expression and fosfomycin activity. In other words, this study shows a new genetic mutation that leads to fosfomycin resistance in bacteria. The results of this study will lead to further understanding of the mechanism of fosfomycin resistance and the creation of new ideas to enhance fosfomycin therapy.

Deep Profiling of the Proteome Dynamics of Pseudomonas aeruginosa Reference Strain PAO1 under Different Growth Conditions

As one of the most common bacterial pathogens causing nosocomial infections, Pseudomonas aeruginosa is highly adaptable to survive under various conditions. Here, we profiled the abundance dynamics of 3489 proteins across different growth stages in the P. aeruginosa reference strain PAO1 using data-independent acquisition-based quantitative proteomics. The proteins differentially expressed during the planktonic growth exhibit several distinct patterns of expression profiles and are relevant to various biological processes, highlighting the continuous adaptation of the PAO1 proteome during the transition from the acceleration phase to the stationary phase. By contrasting the protein expressions in a biofilm to planktonic cells, the known roles of T6SS, phenazine biosynthesis, quorum sensing, and c-di-GMP signaling in the biofilm formation process were confirmed. Additionally, we also discovered several new functional proteins that may play roles in the biofilm formation process. Lastly, we demonstrated the general concordance of protein expressions within operons across various growth states, which permits the study of coexpression protein units, and reversely, the study of regulatory components in the operon structure. Taken together, we present a high-quality and valuable resource on the proteomic dynamics of the P. aeruginosa reference strain PAO1, with the potential of advancing our understanding of the overall physiology of Pseudomonas bacteria.

Regulation of adhesin synthesis in Aggregatibacter actinomycetemcomitans

Aggregatibacter actinomycetemcomitans is a gram-negative bacterium associated with periodontal disease and a variety of disseminated extra-oral infections. Tissue colonization is mediated by fimbriae and non-fimbriae adhesins resulting in the formation of a sessile bacterial community or biofilm, which confers enhanced resistance to antibiotics and mechanical removal. The environmental changes experienced by A. actinomycetemcomitans during infection are detected and processed by undefined signaling pathways that alter gene expression. In this study, we have characterized the promoter region of the extracellular matrix protein adhesin A (EmaA), which is an important surface adhesin in biofilm biogenesis and disease initiation using a series of deletion constructs consisting of the emaA intergenic region and a promotor-less lacZ sequence. Two regions of the promoter sequence were found to regulate gene transcription and in silico analysis indicated the presence of multiple transcriptional regulatory binding sequences. Analysis of four regulatory elements, CpxR, ArcA, OxyR, and DeoR, was undertaken in this study. Inactivation of arcA, the regulator moiety of the ArcAB two-component signaling pathway involved in redox homeostasis, resulted in a decrease in EmaA synthesis and biofilm formation. Analysis of the promoter sequences of other adhesins identified binding sequences for the same regulatory proteins, which suggests that these proteins are involved in the coordinate regulation of adhesins required for colonization and pathogenesis.

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.

Tyrosine phosphorylation controlled poly(A) polymerase I activity regulates general stress response in bacteria

RNA 3'-end polyadenylation that marks transcripts for degradation is implicated in general stress response in Escherichia coli Yet, the mechanism and regulation of poly(A) polymerase I (PAPI) in stress response are obscure. We show that pcnB (that encodes PAPI)-null mutation widely stabilises stress response mRNAs and imparts cellular tolerance to multiple stresses, whereas PAPI ectopic expression renders cells stress-sensitive. We demonstrate that there is a substantial loss of PAPI activity on stress exposure that functionally phenocopies pcnB-null mutation stabilising target mRNAs. We identify PAPI tyrosine phosphorylation at the 202 residue (Y202) that is enormously enhanced on stress exposure. This phosphorylation inhibits PAPI polyadenylation activity under stress. Consequentially, PAPI phosphodeficient mutation (tyrosine 202 to phenylalanine, Y202F) fails to stimulate mRNA expression rendering cells stress-sensitive. Bacterial tyrosine kinase Wzc phosphorylates PAPI-Y202 residue, and that wzc-null mutation renders cells stress-sensitive. Accordingly, wzc-null mutation has no effect on stress sensitivity in the presence of pcnB-null or pcnB-Y202F mutation. We also establish that PAPI phosphorylation-dependent stress tolerance mechanism is distinct and operates downstream of the primary stress regulator RpoS.

Comparative Analysis of Transcriptomic Response of Escherichia coli K-12 MG1655 to Nine Representative Classes of Antibiotics

The use of antibiotics leads to strong stresses to bacteria, leading to profound impact on cellular physiology. Elucidating how bacteria respond to antibiotic stresses not only helps us to decipher bacteria's strategies to resistant antibiotics but also assists in proposing targets for antibiotic development. In this work, a comprehensive comparative transcriptomic analysis on how Escherichia coli responds to nine representative classes of antibiotics (tetracycline, mitomycin C, imipenem, ceftazidime, kanamycin, ciprofloxacin, polymyxin E, erythromycin, and chloramphenicol) was performed, aimed at determining and comparing the responses of this model organism to antibiotics at the transcriptional level. On average, 39.71% of genes were differentially regulated by antibiotics at concentrations that inhibit 50% growth. Kanamycin leads to the strongest transcriptomic response (76.4% of genes regulated), whereas polymyxin E led to minimal transcriptomic response (4.7% of genes regulated). Further GO, KEGG, and EcoCyc enrichment analysis found significant transcriptomic changes in carbon metabolism, amino acid metabolism, nutrient assimilation, transport, stress response, nucleotide metabolism, protein biosynthesis, cell wall biosynthesis, energy conservation, mobility, and cell-environmental communications. Analysis of coregulated genes led to the finding of significant reduction of sulfur metabolism by all antibiotics, and analysis of transcription factor-coding genes suggested clustered regulatory patterns implying coregulation. In-depth analysis of regulated pathways revealed shared and unique strategies of E. coli resisting antibiotics, leading to the proposal of four different strategies (the pessimistic, the ignorant, the defensive, and the invasive). In conclusion, this work provides a comprehensive analysis of E. coli's transcriptomic response to antibiotics, which paves the road for further physiological investigation. IMPORTANCE Antibiotics are among the most important inventions in the history of humankind. They are the ultimate reason why bacterial infections are no longer the number one threat to people's lives. However, the wide application of antibiotics in the last half a century has led to aggravating antibiotic resistance, weakening the efficacy of antibiotics. To better comprehend the ways bacteria deal with antibiotics that may eventually turn into resistance mechanisms, and to identify good targets for potential antibiotics, knowledge on how bacteria regulate their physiology in response to different classes of antibiotics is needed. This work aimed to fill this knowledge gap by identifying changes of bacterial functions at the transcription level and suggesting strategies of bacteria to resist antibiotics.

Deduction of signaling mechanisms from cellular responses to multiple cues

Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to deduce the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to deduce molecular mechanisms from cell-level data.

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains

Sigma70 factor plays a crucial role in prokaryotes and regulates the transcription of most of the housekeeping genes. One of the major challenges is to predict the sigma70 promoter or sigma70 factor binding site with high precision. In this study, we trained and evaluate our models on a dataset consists of 741 sigma70 promoters and 1,400 non-promoters. We have generated a wide range of features around 8,000, which includes Dinucleotide Auto-Correlation, Dinucleotide Cross-Correlation, Dinucleotide Auto Cross-Correlation, Moran Auto-Correlation, Normalized Moreau-Broto Auto-Correlation, Parallel Correlation Pseudo Tri-Nucleotide Composition, etc. Our SVM based model achieved maximum accuracy 97.38% with AUROC 0.99 on training dataset, using 200 most relevant features. In order to check the robustness of the model, we have tested our model on the independent dataset made by using RegulonDB10.8, which included 1,134 sigma70 and 638 non-promoters, and able to achieve accuracy of 90.41% with AUROC of 0.95. Our model successfully predicted constitutive promoters with accuracy of 81.46% on an independent dataset. We have developed a method, Sigma70Pred, which is available as webserver and standalone packages at https://webs.iiitd.edu.in/raghava/sigma70pred/. The services are freely accessible.