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

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.

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.

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.