Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP

ChIP-mini: a low-input ChIP-exo protocol for elucidating DNA-binding protein dynamics in intracellular pathogens

Genome-wide identification of binding profiles for DNA-binding proteins from the limited number of intracellular pathogens in infection studies is crucial for understanding virulence and cellular processes but remains challenging, as the current ChIP-exo is designed for high-input bacterial cells (>1010). Here, we developed an optimized ChIP-mini method, a low-input ChIP-exo utilizing a 5,000-fold reduced number of initial bacterial cells and an analysis pipeline, to identify genome-wide binding dynamics of DNA-binding proteins in host-infected pathogens. Applying ChIP-mini to intracellular Salmonella Typhimurium, we identified 642 and 1,837 binding sites of H-NS and RpoD, respectively, elucidating changes in their binding position and binding intensity during infection. Post-infection, we observed 21 significant reductions in H-NS binding at intergenic regions, exposing the promoter region of virulence genes, such as those in Salmonella pathogenicity islands-2, 3 and effectors. Furthermore, we revealed the crucial phenomenon that novel and significantly increased RpoD bindings were found within regions exhibiting diminished H-NS binding, thereby facilitating substantial upregulation of virulence genes. These findings markedly enhance our understanding of how H-NS and RpoD simultaneously coordinate the transcription initiation of virulence genes within macrophages. Collectively, this work demonstrates a broadly adaptable tool that will enable the elucidation of DNA-binding protein dynamics in diverse intracellular pathogens during infection.

Engineering Escherichia coli to metabolize sorbitol as the sole carbon source for synthesis of recombinant L-Asparaginase-II

Sorbitol, known as D-Glucitol, is a hexose sugar alcohol that occurs naturally in various fruits, including berries, cherries, plums, pears, and apples. It is noteworthy that sorbitol can be metabolized by microbes, plants, and humans through distinct pathways. Nevertheless, in bacteria like Escherichia coli (E. coli), sorbitol is not the primary carbon source and its utilization is generally suppressed due to carbon catabolite repression. In this context, Escherichia coli has been engineered to enable the use of sorbitol as the sole carbon source for producing recombinant proteins. This modification involves a two-plasmid system where the sorbitol-6-phosphate dehydrogenase (srlD) gene is upregulated under an araBAD promoter, while the recombinant protein is expressed from a second plasmid under the tac promoter. The overexpression of srlD in the engineered E. coli strain enhances the utilization of sorbitol as the sole carbon source. When cultured in a medium supplemented solely with sorbitol, the engineered E. coli strain exhibits a 3.6 times higher specific growth rate and yields substantially higher concentration of recombinant protein compared to the wild-type strain. Additionally, the engineered strain demonstrates a higher YP/X ratio than the wild-type strain.

An Optimized Method for Reconstruction of Transcriptional Regulatory Networks in Bacteria Using ChIP-exo and RNA-seq Datasets

Transcriptional regulatory networks (TRNs) in bacteria are crucial for elucidating the mechanisms that regulate gene expression and cellular responses to environmental stimuli. These networks delineate the interactions between transcription factors (TFs) and their target genes, thereby uncovering the regulatory processes that modulate gene expression under varying environmental conditions. Analyzing TRNs offers valuable insights into bacterial adaptation, stress responses, and metabolic optimization from an evolutionary standpoint. Additionally, understanding TRNs can drive the development of novel antimicrobial therapies and the engineering of microbial strains for biofuel and bioproduct production. This protocol integrates advanced data analysis pipelines, including ChEAP, DEOCSU, and DESeq2, to analyze omics datasets that encompass genome-wide TF binding sites and transcriptome profiles derived from ChIP-exo and RNA-seq experiments. This approach minimizes both the time required and the risk of bias, making it accessible to non-expert users. Key steps in the protocol include preprocessing and peak calling from ChIP-exo data, differential expression analysis of RNA-seq data, and motif and regulon analysis. This method offers a comprehensive and efficient framework for TRN reconstruction across various bacterial strains, enhancing both the accuracy and reliability of the analysis while providing valuable insights for basic and applied research.

Insights into the regulatory mechanisms and application prospects of the transcription factor Cra

Cra (catabolite repressor/activator) is a global transcription factor (TF) that plays a pleiotropic role in controlling the transcription of several genes involved in carbon utilization and energy metabolism. Multiple studies have investigated the regulatory mechanism of Cra and its rational use for metabolic regulation, but due to the complexity of its regulation, there remain challenges in the efficient use of Cra. Here, the structure, mechanism of action, and regulatory function of Cra in carbon and nitrogen flow are reviewed. In addition, this paper highlights the application of Cra in metabolic engineering, including the promotion of metabolite biosynthesis, the regulation of stress tolerance and virulence, the use of a Cra-based biosensor, and its coupling with other transcription factors. Finally, the prospects of Cra-related regulatory strategies are discussed. This review provides guidance for the rational design and construction of Cra-based metabolic regulation systems.

Engineering Escherichia coli for l-Threonine Hyperproduction Based on Multidimensional Optimization Strategies

Exploring effective remodeling strategies to further improve the productivity of high-yield strains is the goal of biomanufacturing. However, the lack of insight into host-specific metabolic networks prevents timely identification of useful engineering targets. Here, multidimensional engineering strategies were implemented to optimize the global metabolic network for improving l-threonine production. First, the metabolic bottleneck for l-threonine synthesis was eliminated by synergistic utilization of NADH and an enhanced ATP supply. Carbon fluxes were redistributed into the TCA cycle by rationally regulating the GltA activity. Subsequently, the stress global response regulator UspA was identified to enhance l-threonine production by a transcriptomic analysis. Then, l-threonine productivity was improved by enhancing the host's stress resistance and releasing the inhibitory reaction of glucose utilization. Eventually, the l-threonine yield of THRH16 reached 170.3 g/L and 3.78 g/L/h in a 5 L bioreactor, which is the highest production index reported. This study provides rational guidance for increasing the productivity of other chemicals.

Unveiling the novel regulatory roles of RpoD-family sigma factors in Salmonella Typhimurium heat shock response through systems biology approaches

Three RpoD-family sigma factors, RpoD, RpoS, and RpoH, play critical roles in transcriptional regulation in Salmonella enterica serovar Typhimurium under heat shock conditions. However, the genome-wide regulatory mechanisms of these sigma factors in response to heat stress have remained elusive. In this study, we comprehensively identified 2,319, 2,226, and 213 genome-wide binding sites for RpoD, RpoS, and RpoH, respectively, under sublethal heat shock conditions (42°C). Machine learning-based transcriptome analysis was employed to infer the relative activity of iModulons, providing valuable insights into the transcriptional impact of heat shock. Integrative data analysis enabled the reconstruction of the transcriptional regulatory network of sigma factors, revealing how they modulate gene expression to adapt to heat stress, including responses to anaerobic and oxidative stresses. Notably, we observed a significant expansion of the RpoS sigmulon from 97 to 301 genes in response to heat shock, underscoring the crucial role of RpoS in regulating various metabolic processes. Moreover, we uncovered a competition mechanism between RpoD and RpoS within RpoS sigmulons, where RpoS significantly increases its binding within promoter regions shared with RpoD under heat shock conditions. These findings illuminate how three RpoD-family sigma factors coordinate multiple cellular processes to orchestrate the overall response of S. Typhimurium to heat stress.

Synthetic redesign of Escherichia coli W for faster metabolism of sugarcane molasses

BACKGROUND

Sugarcane molasses, rich in sucrose, glucose, and fructose, offers a promising carbon source for industrial fermentation due to its abundance and low cost. However, challenges arise from the simultaneous utilization of multiple sugars and carbon catabolite repression (CCR). Despite its nutritional content, sucrose metabolism in Escherichia coli, except for W strain, remains poorly understood, hindering its use in microbial fermentation. In this study, E. coli W was engineered to enhance sugar consumption rates and overcome CCR. This was achieved through the integration of a synthetically designed csc operon and the optimization of glucose and fructose co-utilization pathways. These advancements facilitate efficient utilization of sugarcane molasses for the production of 3-hydroxypropionic acid (3-HP), contributing to sustainable biochemical production processes.

RESULTS

In this study, we addressed challenges associated with sugar metabolism in E. coli W, focusing on enhancing sucrose consumption and improving glucose-fructose co-utilization. Through targeted engineering of the sucrose utilization system, we achieved accelerated sucrose consumption rates by modulating the expression of the csc operon components, cscB, cscK, cscA, and cscR. Our findings revealed that monocistronic expression of the csc genes with the deletion of cscR, led to optimal sucrose utilization without significant growth burden. Furthermore, we successfully alleviated fructose catabolite repression by modulating the binding dynamics of FruR with the fructose PTS regulon, enabling near-equivalent co-utilization of glucose and fructose. To validate the industrial applicability of our engineered strain, we pursued 3-HP production from sugarcane molasses. By integrating heterologous genes and optimizing metabolic pathways, we achieved improvements in 3-HP titers compared to previous studies. Additionally, glyceraldehyde-3-phosphate dehydrogenase (gapA) repression aids in carbon flux redistribution, enhancing molasses conversion to 3-HP.

CONCLUSIONS

Despite limitations in sucrose metabolism, the redesigned E. coli W strain, adept at utilizing sugarcane molasses, is a valuable asset for industrial fermentation. Its synthetic csc operon enhances sucrose consumption, while mitigating CCR improves glucose-fructose co-utilization. These enhancements, coupled with repression of gapA, aim to efficiently convert sugarcane molasses into 3-HP, addressing limitations in sucrose and fructose metabolism for industrial applications.

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Multiscale regulation of nutrient stress responses in Escherichia coli from chromatin structure to small regulatory RNAs

Recent research has indicated the presence of heterochromatin-like regions of extended protein occupancy and transcriptional silencing of bacterial genomes. We utilized an integrative approach to track chromatin structure and transcription in E. coli K-12 across a wide range of nutrient conditions. In the process, we identified multiple loci which act similarly to facultative heterochromatin in eukaryotes, normally silenced but permitting expression of genes under specific conditions. We also found a strong enrichment of small regulatory RNAs (sRNAs) among the set of differentially expressed transcripts during nutrient stress. Using a newly developed bioinformatic pipeline, the transcription factors regulating sRNA expression were bioinformatically predicted, with experimental follow-up revealing novel relationships for 36 sRNA-transcription factors candidates. Direct regulation of sRNA expression was confirmed by mutational analysis for five sRNAs of metabolic interest: IsrB, CsrB and CsrC, GcvB, and GadY. Our integrative analysis thus reveals additional layers of complexity in the nutrient stress response in E. coli and provides a framework for revealing similar poorly understood regulatory logic in other organisms.

Drying-wetting cycle enhances stress resistance of Escherichia coli O157:H7 in a model soil

Outbreaks of Escherichia coli (E. coli) O157:H7 in farms are often triggered by heavy rains and flooding. Most cells die with the decreasing of soil moisture, while few cells enter a dormant state and then resuscitate after rewetting. The resistance of dormant cells to stress has been extensively studied, whereas the molecular mechanisms of the cross-resistance development of the resuscitated cells are poorly known. We performed a comparative proteomic analysis on O157:H7 before and after undergoing soil dry-wet alternation. A differential expression of 820 proteins was identified in resuscitated cells compared to exponential-phase cells, as determined by proteomics analysis. The GO and KEGG pathway enrichment analyses revealed that up-regulated proteins were associated with oxidative phosphorylation, glycolysis/gluconeogenesis, the citrate cycle (TCA cycle), aminoacyl-tRNA biosynthesis, ribosome activity, and transmembrane transporters, indicating increased energy production and protein synthesis in resuscitated O157:H7. Moreover, proteins related to acid, osmotic, heat, oxidative, antibiotic stress and horizontal gene transfer efficiency were up-regulated, suggesting a potential improvement in stress resistance. Subsequent validation experiments demonstrated that the survival rates of the resuscitated cells were 476.54 and 7786.34 times higher than the exponential-phase cells, with pH levels of 1.5 and 2.5, respectively. Similarly, resuscitated cells showed higher survival rates under osmotic stress, with 7.5%, 15%, and 30% NaCl resulting in survival rates that were 460.58, 1974.55, and 3475.31 times higher. Resuscitated cells also exhibited increased resistance to heat stress, with survival rates 69.64 and 139.72 times higher at 55 °C and 90 °C, respectively. Furthermore, the horizontal gene transfer (HGT) efficiency of resuscitated cells was significantly higher (153.12-fold) compared to exponential phase cells. This study provides new insights into bacteria behavior under changing soil moisture and this may explain O157:H7 outbreaks following rainfall and flooding, as the dry-wet cycle promotes stress cross-resistance development.

Fructose-1-kinase has pleiotropic roles in Escherichia coli

In Escherichia coli, the master transcription regulator catabolite repressor activator (Cra) regulates >100 genes in central metabolism. Cra binding to DNA is allosterically regulated by binding to fructose-1-phosphate (F-1-P), but the only documented source of F-1-P is from the concurrent import and phosphorylation of exogenous fructose. Thus, many have proposed that fructose-1,6-bisphosphate (F-1,6-BP) is also a physiological regulatory ligand. However, the role of F-1,6-BP has been widely debated. Here, we report that the E. coli enzyme fructose-1-kinase (FruK) can carry out its "reverse" reaction under physiological substrate concentrations to generate F-1-P from F-1,6-BP. We further show that FruK directly binds Cra with nanomolar affinity and forms higher order, heterocomplexes. Growth assays with a ΔfruK strain and fruK complementation show that FruK has a broader role in metabolism than fructose catabolism. Since fruK itself is repressed by Cra, these newly-reported events add layers to the dynamic regulation of E. coli's central metabolism that occur in response to changing nutrients. These findings might have wide-spread relevance to other γ-proteobacteria, which conserve both Cra and FruK.

Molecular insights into FucR transcription factor to control the metabolism of L-fucose in Bifidobacterium longum subsp. infantis

Bifidobacterium longum subsp. infantis commonly colonizes the human gut and is capable of metabolizing L-fucose, which is abundant in the gut. Multiple studies have focused on the mechanisms of L-fucose utilization by B. longum subsp. infantis, but the regulatory pathways governing the expression of these catabolic processes are still unclear. In this study, we have conducted a structural and functional analysis of L-fucose metabolism transcription factor FucR derived from B. longum subsp. infantis Bi-26. Our results indicated that FucR is a L-fucose-sensitive repressor with more α-helices, fewer β-sheets, and β-turns. Transcriptional analysis revealed that FucR displays weak negative self-regulation, which is counteracted in the presence of L-fucose. Isothermal titration calorimetry indicated that FucR has a 2:1 stoichiometry with L-fucose. The key amino acid residues for FucR binding L-fucose are Asp280 and Arg331, with mutation of Asp280 to Ala resulting in a decrease in the affinity between FucR and L-fucose with the Kd value from 2.58 to 11.68 μM, and mutation of Arg331 to Ala abolishes the binding ability of FucR towards L-fucose. FucR specifically recognized and bound to a 20-bp incomplete palindrome sequence (5'-ACCCCAATTACGAAAATTTTT-3'), and the affinity of the L-fucose-loaded FucR for the DNA fragment was lower than apo-FucR. The results provided new insights into the regulating L-fucose metabolism by B. longum subsp. infantis.

Proteome partitioning constraints in long-term laboratory evolution

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

Enough is enough: feedback control of specialized metabolism

Recent advances in our understanding of plant metabolism have highlighted the significance of specialized metabolites in the regulation of gene expression associated with biosynthetic networks. This opinion article focuses on the molecular mechanisms of small-molecule-mediated feedback regulation at the transcriptional level and its potential modes of action, including metabolite signal perception, the nature of the sensor, and the signaling transduction mechanisms leading to transcriptional and post-transcriptional regulation, based on evidence available from plants and other kingdoms of life. We also discuss the challenges associated with identifying the occurrences, effects, and localization of small molecule-protein interactions. Further understanding of small-molecule-controlled metabolic fluxes will enable rational design of transcriptional regulation systems in metabolic engineering to produce high-value specialized metabolites.

Effect of adaptive laboratory evolution of engineered Escherichia coli in acetate on the biosynthesis of succinic acid from glucose in two-stage cultivation

Escherichia coli MLB (MG1655 ΔpflB ΔldhA), which can hardly grow on glucose with little succinate accumulation under anaerobic conditions. Two-stage fermentation is a fermentation in which the first stage is used for cell growth and the second stage is used for product production. The ability of glucose consumption and succinate production of MLB under anaerobic conditions can be improved significantly by using acetate as the solo carbon source under aerobic condition during the two-stage fermentation. Then, the adaptive laboratory evolution (ALE) of growing on acetate was applied here. We assumed that the activities of succinate production related enzymes might be further improved in this study. E. coli MLB46-05 evolved from MLB and it had an improved growth phenotype on acetate. Interestingly, in MLB46-05, the yield and tolerance of succinic acid in the anaerobic condition of two-stage fermentation were improved significantly. According to transcriptome analysis, upregulation of the glyoxylate cycle and the activity of stress regulatory factors are the possible reasons for the elevated yield. And the increased tolerance to acetate made it more tolerant to high concentrations of glucose and succinate. Finally, strain MLB46-05 produced 111 g/L of succinic acid with a product yield of 0.74 g/g glucose. SYNOPSIS.

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.

Experimental promoter identification of a foodborne pathogen Salmonella enterica subsp. enterica serovar Typhimurium with near single base-pair resolution

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a common foodborne pathogen which is frequently used as the reference strain for Salmonella. Investigating the sigma factor network and protomers is crucial to understand the genomic and transcriptomic properties of the bacterium. Its promoters were identified using various methods such as dRNA-seq, ChIP-chip, or ChIP-Seq. However, validation using ChIP-exo, which exhibits higher-resolution performance compared to conventional ChIP, has not been conducted to date. In this study, using the representative strain S. Typhimurium LT2 (LT2), the ChIP-exo experiment was conducted to accurately determine the binding sites of catalytic RNA polymerase subunit RpoB and major sigma factors (RpoD, RpoN, RpoS, and RpoE) during exponential phase. Integrated with the results of RNA-Seq, promoters and sigmulons for the sigma factors and their association with RpoB have been discovered. Notably, the overlapping regions among binding sites of each alternative sigma factor were found. Furthermore, comparative analysis with Escherichia coli str. K-12 substr. MG1655 (MG1655) revealed conserved binding sites of RpoD and RpoN across different species. In the case of small RNAs (sRNAs), 50 sRNAs observed their expression during the exponential growth of LT2. Collectively, the integration of ChIP-exo and RNA-Seq enables genome-scale promoter mapping with high resolution and facilitates the characterization of binding events of alternative sigma factors, enabling a comprehensive understanding of the bacterial sigma factor network and condition-specific active promoters.

Fructose-1-kinase has pleiotropic roles in Escherichia coli

In Escherichia coli, the master transcription regulator Catabolite Repressor Activator (Cra) regulates >100 genes in central metabolism. Cra binding to DNA is allosterically regulated by binding to fructose-1-phosphate (F-1-P), but the only documented source of F-1-P is from the concurrent import and phosphorylation of exogenous fructose. Thus, many have proposed that fructose-1,6-bisphosphate (F-1,6-BP) is also a physiological regulatory ligand. However, the role of F-1,6-BP has been widely debated. Here, we report that the E. coli enzyme fructose-1-kinase (FruK) can carry out its "reverse" reaction under physiological substrate concentrations to generate F-1-P from F-1,6-BP. We further show that FruK directly binds Cra with nanomolar affinity and forms higher order, heterocomplexes. Growth assays with a ΔfruK strain and fruK complementation show that FruK has a broader role in metabolism than fructose catabolism. The ΔfruK strain also alters biofilm formation. Since fruK itself is repressed by Cra, these newly-reported events add layers to the dynamic regulation of E. coli central metabolism that occur in response to changing nutrients. These findings might have wide-spread relevance to other γ-proteobacteria, which conserve both Cra and FruK.

A Gold Standard 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. As new methodologies emerge, a natural step is to compare their results with those from established methodologies, such as the classic methods of molecular biology used to characterize transcription factor binding sites, promoters, or transcription units. In the case of Escherichia coli K-12, the best-studied microorganism, for the last 30 years we have continuously gathered such knowledge from original scientific publications, and have organized it in two databases, RegulonDB and EcoCyc. Furthermore, since RegulonDB version 11.0 (1), we offer comprehensive datasets of binding sites from chromatin immunoprecipitation combined with sequencing (ChIP-seq), ChIP combined with exonuclease digestion and next-generation sequencing (ChIP-exo), genomic SELEX screening (gSELEX), and DNA affinity purification sequencing (DAP-seq) HT technologies, as well as additional datasets for transcription start sites, transcription units and RNA sequencing (RNA-seq) expression profiles. Here, we present for the first time an analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of E. coli K-12. An RI is formed by the transcription factor, its positive or negative effect on a promoter, a gene or transcription unit. We improved the evidence codes so that the specific methods are described, and we classified them into seven independent groups. This is the basis for our updated computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. We compare the confidence levels of the RI collection before and after adding HT evidence illustrating how knowledge will change as more HT data and methods appear in the future. Users can generate subsets filtering out the method they want to benchmark and avoid circularity, or keep for instance only the confirmed interactions. The comparison of different HT methods with the available datasets indicate that ChIP-seq recovers the highest fraction (>70%) of binding sites present in RegulonDB followed by gSELEX, DAP-seq and ChIP-exo. There is no other genomic database that offers this comprehensive high-quality anatomy of evidence supporting a corpus of transcriptional regulatory interactions.

Disruption of trehalose periplasmic recycling dysregulates cAMP-CRP signaling in Escherichia coli during stationary phase

IMPORTANCE

Survival during starvation hinges on the ability to manage intracellular energy reserves and to initiate appropriate metabolic responses to perturbations of such reserves. How Escherichia coli manage carbon storage systems under starvation stress, as well as transpose changes in intracellular metabolite levels into regulatory signals, is not well understood. Endogenous trehalose metabolism may be at the center of these processes, coupling carbon storage with carbon starvation responses. The coupled transport to the periplasm and subsequent hydrolysis of trehalose back to glucose for transport to the cytoplasm may function as a crucial metabolic signaling pathway. Although trehalose has been characterized as a stress protectant in E. coli, the disaccharide also functions as both an energy storage compound and a regulator of carbohydrate metabolism in fungi, plants, and other bacteria. Our research explores the metabolic regulatory properties of trehalose in E. coli and a potential mechanism by which the intracellular carbon pool is interconnected with regulatory circuits, enabling long-term survival.

Plant myo-inositol transport influences bacterial colonization phenotypes

Plant microbiomes are assembled and modified through a complex milieu of biotic and abiotic factors. Despite dynamic and fluctuating contributing variables, specific host metabolites are consistently identified as important mediators of microbial interactions. We combine information from a large-scale metatranscriptomic dataset from natural poplar trees and experimental genetic manipulation assays in seedlings of the model plant Arabidopsis thaliana to converge on a conserved role for transport of the plant metabolite myo-inositol in mediating host-microbe interactions. While microbial catabolism of this compound has been linked to increased host colonization, we identify bacterial phenotypes that occur in both catabolism-dependent and -independent manners, suggesting that myo-inositol may additionally serve as a eukaryotic-derived signaling molecule to modulate microbial activities. Our data suggest host control of this compound and resulting microbial behavior are important mechanisms at play surrounding the host metabolite myo-inositol.

Designing glucose utilization "highway" for recombinant biosynthesis

cAMP receptor protein (CRP) is known as a global regulatory factor mainly mediating carbon source catabolism. Herein, we successfully engineered CRP to develop microbial chassis cells with improved recombinant biosynthetic capability in minimal medium with glucose as single carbon source. The obtained best-performing cAMP-independent CRPmu9 mutant conferred both faster cell growth and a 133-fold improvement in expression level of lac promoter in presence of 2% glucose, compared with strain under regulation of CRPwild-type. Promoters free from "glucose repression" are advantageous for recombinant expression, as glucose is a frequently used inexpensive carbon source in high-cell-density fermentations. Transcriptome analysis demonstrated that the CRP mutant globally rewired cell metabolism, displaying elevated tricarboxylic acid cycle activity; reduced acetate formation; increased nucleotide biosynthesis; and improved ATP synthesis, tolerance, and stress-resistance activity. Metabolites analysis confirmed the enhancement of glucose utilization with the upregulation of glycolysis and glyoxylate-tricarboxylic acid cycle. As expected, an elevated biosynthetic capability was demonstrated with vanillin, naringenin and caffeic acid biosynthesis in strains regulated by CRPmu9. This study has expanded the significance of CRP optimization into glucose utilization and recombinant biosynthesis, beyond the conventionally designated carbon source utilization other than glucose. The Escherichiacoli cell regulated by CRPmu9 can be potentially used as a beneficial chassis for recombinant biosynthesis.

E. coli allantoinase is activated by the downstream metabolic enzyme, glycerate kinase, and stabilizes the putative allantoin transporter by direct binding

Allantoin is a good source of ammonium for many organisms, and in Escherichia coli it is utilized under anaerobic conditions. We provide evidence that allantoinase (AllB) is allosterically activated by direct binding of the allantoin catabolic enzyme, glycerate 2-kinase (GlxK) in the presence of glyoxylate. Glyoxylate is known to be an effector of the AllR repressor which regulates the allantoin utilization operons in E. coli. AllB has low affinity for allantoin, but its activation by GlxK leads to increased affinity for its substrate. We also show that the predicted allantoin transporter YbbW (re-named AllW) has allantoin specificity and the protein-protein interaction with AllB. Our results show that the AllB-dependent allantoin degradative pathway is subject to previously unrecognized regulatory mechanisms involving direct protein-protein interactions.

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.

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users' specific needs.

Regulation of phosphate starvation-specific responses in Escherichia coli

Toxic agents added into the medium of rapidly growing Escherichia coli induce specific stress responses through the activation of specialized transcription factors. Each transcription factor and downstream regulon (e.g. SoxR) are linked to a unique stress (e.g. superoxide stress). Cells starved of phosphate induce several specific stress regulons during the transition to stationary phase when the growth rate is steadily declining. Whereas the regulatory cascades leading to the expression of specific stress regulons are well known in rapidly growing cells stressed by toxic products, they are poorly understood in cells starved of phosphate. The intent of this review is to both describe the unique mechanisms of activation of specialized transcription factors and discuss signalling cascades leading to the induction of specific stress regulons in phosphate-starved cells. Finally, I discuss unique defence mechanisms that could be induced in cells starved of ammonium and glucose.

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

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

An integrated transcriptomics-functional genomics approach reveals a small RNA that modulates Bacteroides thetaiotaomicron sensitivity to tetracyclines

Gene expression plasticity allows bacteria to adapt to diverse environments, tie their metabolism to available nutrients, and cope with stress. This is particularly relevant in a niche as dynamic and hostile as the human intestinal tract, yet transcriptional networks remain largely unknown in gut Bacteroides spp. Here, we map transcriptional units and profile their expression levels in Bacteroides thetaiotaomicron over a suite of 15 defined experimental conditions that are relevant in vivo , such as variation of temperature, pH, and oxygen tension, exposure to antibiotic stress, and growth on simple carbohydrates or on host mucin-derived glycans. Thereby, we infer stress- and carbon source-specific transcriptional regulons, including conditional expression of capsular polysaccharides and polysaccharide utilization loci, and expand the annotation of small regulatory RNAs (sRNAs) in this organism. Integrating this comprehensive expression atlas with transposon mutant fitness data, we identify conditionally important sRNAs. One example is MasB, whose inactivation led to increased bacterial tolerance of tetracyclines. Using MS2 affinity purification coupled with RNA sequencing, we predict targets of this sRNA and discuss their potential role in the context of the MasB-associated phenotype. Together, this transcriptomic compendium in combination with functional sRNA genomics-publicly available through a new iteration of the 'Theta-Base' web browser (www.helmholtz-hiri.de/en/datasets/bacteroides-v2)-constitutes a valuable resource for the microbiome and sRNA research communities alike.

Understanding the Genome-Wide Transcription Response To Various cAMP Levels in Bacteria Using Phenomenological Models

Attempts to understand gene regulation by global transcription factors have largely been limited to expression studies under binary conditions of presence and absence of the transcription factor. Studies addressing genome-wide transcriptional responses to changing transcription factor concentration at high resolution are lacking. Here, we create a data set containing the entire Escherichia coli transcriptome in Luria-Bertani (LB) broth as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill's model to accurately summarize individual gene responses into three intuitively understandable parameters, Emax, n, and k, reflecting the sensitivity, nonlinearity, and midpoint of the dynamic range. Our data show that most cAMP-regulated genes have an n of >2, with their k values centered around the wild-type concentration of cAMP. Additionally, cAMP receptor protein (CRP) affinity to a promoter is correlated with Emax but not k, hinting that a high-affinity CRP promoter need not ensure transcriptional activation at lower cAMP concentrations and instead affects the magnitude of the response. Finally, genes belonging to different functional classes are tuned to have different k, n, and Emax values. We demonstrate that phenomenological models are a better alternative for studying gene expression trends than classical clustering methods, with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network. IMPORTANCE Different genes may follow different trends in response to various transcription factor concentrations. In this study, we ask two questions: (i) what are the trends that different genes follow in response to changing transcription factor concentrations and (ii) what methods can be used to extract information from the gene trends so obtained. We demonstrate a method to analyze transcription factor concentration-dependent genome-wide expression data using phenomenological models. Conventional clustering methods and principal-component analysis (PCA) can be used to summarize trends in data but have limited interpretability. The use of phenomenological models greatly enhances the interpretability and thus utility of conventional clustering. Transformation of dose-response data into phenomenological constants opens up avenues to ask and answer many different kinds of question. We show that the phenomenological constants obtained from the model fits can be used to generate insights about network topology and allows integration of other experimental data such as chromatin immunoprecipitation sequencing (ChIP-seq) to understand the system in greater detail.

Characterization of an Entner-Doudoroff pathway-activated Escherichia coli

BACKGROUND

Escherichia coli have both the Embden-Meyerhof-Parnas pathway (EMPP) and Entner-Doudoroff pathway (EDP) for glucose breakdown, while the EDP primarily remains inactive for glucose metabolism. However, EDP is a more favorable route than EMPP for the production of certain products.

RESULTS

EDP was activated by deleting the pfkAB genes in conjunction with subsequent adaptive laboratory evolution (ALE). The evolved strains acquired mutations in transcriptional regulatory genes for glycolytic process (crp, galR, and gntR) and in glycolysis-related genes (gnd, ptsG, and talB). The genotypic, transcriptomic and phenotypic analyses of those mutations deepen our understanding of their beneficial effects on cellulosic biomass bio-conversion. On top of these scientific understandings, we further engineered the strain to produce higher level of lycopene and 3-hydroxypropionic acid.

CONCLUSIONS

These results indicate that the E. coli strain has innate capability to use EDP in lieu of EMPP for glucose metabolism, and this versatility can be harnessed to further engineer E. coli for specific biotechnological applications.

Evaluation of Salmonella Typhimurium Lacking fruR, ssrAB, or hfq as a Prophylactic Vaccine against Salmonella Lethal Infection

Non-typhoidal Salmonella (NTS) is one of the primary causes of foodborne gastroenteritis; occasionally, it causes invasive infection in humans. Because of its broad host range, covering diverse livestock species, foods of animal origin pose a critical threat of NTS contamination. However, there is currently no licensed vaccine against NTS infection. FruR, also known as Cra (catabolite repressor/activator), was initially identified as the transcriptional repressor of the fructose (fru) operon, and then found to activate or repress the transcription of many different genes associated with carbon and energy metabolism. In view of its role as a global regulator, we constructed a live attenuated vaccine candidate, ΔfruR, and evaluated its prophylactic effect against NTS infection in mice. A Salmonella Typhimurium mutant strain lacking fruR was defective in survival inside macrophages and exhibited attenuated virulence in infected mice. Immunization with the ΔfruR mutant stimulated the production of antibodies, including the IgG, IgM, and IgG subclasses, and afforded a protection of 100% to mice against the challenge of lethal infection with a virulent Salmonella strain. The prophylactic effect obtained after ΔfruR immunization was also validated by the absence of signs of hepatosplenomegaly, as these mice had comparable liver and spleen weights in comparison with healthy mice. These results suggest that the ΔfruR mutant strain can be further exploited as a promising vaccine candidate against Salmonella lethal infection.

A mannose-sensing AraC-type transcriptional activator regulates cell-cell aggregation of Vibrio cholerae

In addition to catalyzing coupled transport and phosphorylation of carbohydrates, the phosphoenolpyruvate:carbohydrate phosphotransferase system (PTS) regulates various physiological processes in most bacteria. Therefore, the transcription of genes encoding the PTS is precisely regulated by transcriptional regulators depending on substrate availability. As the distribution of the mannose-specific PTS (PTSMan) is limited to animal-associated bacteria, it has been suggested to play an important role in host-bacteria interactions. In Vibrio cholerae, mannose is known to inhibit biofilm formation. During host infection, the transcription level of the V. cholerae gene encoding the putative PTSMan (hereafter referred to as manP) significantly increases, and mutations in this gene increase host survival rate. Herein, we show that an AraC-type transcriptional regulator (hereafter referred to as ManR) acts as a transcriptional activator of the mannose operon and is responsible for V. cholerae growth and biofilm inhibition on a mannose or fructose-supplemented medium. ManR activates mannose operon transcription by facilitating RNA polymerase binding to the promoter in response to mannose 6-phosphate and, to a lesser extent, to fructose 1-phosphate. When manP or manR is impaired, the mannose-induced inhibition of biofilm formation was reversed and intestinal colonization was significantly reduced in a Drosophila melanogaster infection model. Our results show that ManR recognizes mannose and fructose in the environment and facilitates V. cholerae survival in the host.

Global pleiotropic effects in adaptively evolved Escherichia coli lacking CRP reveal molecular mechanisms that define the growth physiology

Evolution facilitates emergence of fitter phenotypes by efficient allocation of cellular resources in conjunction with beneficial mutations. However, system-wide pleiotropic effects that redress the perturbations to the apex node of the transcriptional regulatory networks remain unclear. Here, we elucidate that absence of global transcriptional regulator CRP in Escherichia coli results in alterations in key metabolic pathways under glucose respiratory conditions, favouring stress- or hedging-related functions over growth-enhancing functions. Further, we disentangle the growth-mediated effects from the CRP regulation-specific effects on these metabolic pathways. We quantitatively illustrate that the loss of CRP perturbs proteome efficiency, as evident from metabolic as well as ribosomal proteome fractions, that corroborated with intracellular metabolite profiles. To address how E. coli copes with such systemic defect, we evolved Δcrp mutant in the presence of glucose. Besides acquiring mutations in the promoter of glucose transporter ptsG, the evolved populations recovered the metabolic pathways to their pre-perturbed state coupled with metabolite re-adjustments, which altogether enabled increased growth. By contrast to Δcrp mutant, the evolved strains remodelled their proteome efficiency towards biomass synthesis, albeit at the expense of carbon efficiency. Overall, we comprehensively illustrate the genetic and metabolic basis of pleiotropic effects, fundamental for understanding the growth physiology.

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

Bacteria and archaea, single-cell organisms collectively known as prokaryotes, live in all imaginable environments and comprise the majority of living organisms on this planet. Prokaryotes play a critical role in the homeostasis of multicellular organisms (such as animals and plants) and ecosystems. In addition, bacteria can be pathogenic and cause a variety of diseases in these same hosts and ecosystems. In short, understanding the biology and molecular functions of bacteria and archaea and devising mechanisms to engineer and optimize their properties are critical scientific endeavors with significant implications in healthcare, agriculture, manufacturing, and climate science among others. One major molecular difference between unicellular and multicellular organisms is the way they express genes–multicellular organisms make individual RNA molecules for each gene while, prokaryotes express operons (i.e., a group of genes coding functionally related proteins) in contiguous polycistronic RNA molecules. Understanding which genes exist within operons is critical for elucidating basic biology and for engineering organisms. In this work, we use a combination of statistical and machine learning-based methods to use next-generation sequencing data to predict operon structure across a range of prokaryotes. Our method provides an easily implemented, robust, accurate, and flexible way to determine operon structure in an organism-agnostic manner using readily available data.

Feedback regulation and coordination of the main metabolism for bacterial growth and metabolic engineering for amino acid fermentation

Living organisms such as bacteria are often exposed to continuous changes in the nutrient availability in nature. Therefore, bacteria must constantly monitor the environmental condition, and adjust the metabolism quickly adapting to the change in the growth condition. For this, bacteria must orchestrate (coordinate and integrate) the complex and dynamically changing information on the environmental condition. In particular, the central carbon metabolism (CCM), monomer synthesis, and macromolecular synthesis must be coordinately regulated for the efficient growth. It is a grand challenge in bioscience, biotechnology, and synthetic biology to understand how living organisms coordinate the metabolic regulation systems. Here, we consider the integrated sensing of carbon sources by the phosphotransferase system (PTS), and the feed-forward/feedback regulation systems incorporated in the CCM in relation to the pool sizes of flux-sensing metabolites and αketoacids. We also consider the metabolic regulation of amino acid biosynthesis (as well as purine and pyrimidine biosyntheses) paying attention to the feedback control systems consisting of (fast) enzyme level regulation with (slow) transcriptional regulation. The metabolic engineering for the efficient amino acid production by bacteria such as Escherichia coli and Corynebacterium glutamicum is also discussed (in relation to the regulation mechanisms). The amino acid synthesis is important for determining the rate of ribosome biosynthesis. Thus, the growth rate control (growth law) is further discussed on the relationship between (p)ppGpp level and the ribosomal protein synthesis.

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Synthetic biosensor accelerates evolution by rewiring carbon metabolism toward a specific metabolite

Proper carbon flux distribution between cell growth and production of a target compound is important for biochemical production because improper flux reallocation inhibits cell growth, thus adversely affecting production yield. Here, using a synthetic biosensor to couple production of a specific metabolite with cell growth, we spontaneously evolve cells under the selective condition toward the acquisition of genotypes that optimally reallocate cellular resources. Using 3-hydroxypropionic acid (3-HP) production from glycerol in Escherichia coli as a model system, we determine that mutations in the conserved regions of proteins involved in global transcriptional regulation alter the expression of several genes associated with central carbon metabolism. These changes rewire central carbon flux toward the 3-HP production pathway, increasing 3-HP yield and reducing acetate accumulation by alleviating overflow metabolism. Our study provides a perspective on adaptive laboratory evolution (ALE) using synthetic biosensors, thereby supporting future efforts in metabolic pathway optimization.

Identification of a transcription factor, PunR, that regulates the purine and purine nucleoside transporter punC in E. coli

Many genes in bacterial genomes are of unknown function, often referred to as y-genes. Recently, the analytic methods have divided bacterial transcriptomes into independently modulated sets of genes (iModulons). Functionally annotated iModulons that contain y-genes lead to testable hypotheses to elucidate y-gene function. The inversely correlated expression of a putative transporter gene, ydhC, relative to purine biosynthetic genes, has led to the hypothesis that it encodes a purine-related transporter and revealed a LysR-family regulator, YdhB, with a predicted 23-bp palindromic binding motif. RNA-Seq analysis of a ydhB knockout mutant confirmed the YdhB-dependent activation of ydhC in the presence of adenosine. The deletion of either the ydhC or the ydhB gene led to a substantially decreased growth rate for E. coli in minimal medium with adenosine, inosine, or guanosine as the nitrogen source. Taken together, we provide clear evidence that YdhB activates the expression of the ydhC gene that encodes a purine transporter in E. coli. We propose that the genes ydhB and ydhC be re-named as punR and punC, respectively.

Rodionova et al. find that the putative transporter gene, ydhC and its regulator ydhB are involved in purine transportation and that the expression of the ydhC gene is activated by the YdhB in E. coli. The authors suggest renaming the regulator PunR and the transporter PunC, respectively.

The impact of carbon and nitrogen catabolite repression in microorganisms

Organisms have cellular machinery that is focused on optimum utilization of resources to maximize growth and survival depending on various environmental and developmental factors. Catabolite repression is a strategy utilized by various species of bacteria and fungi to accommodate changes in the environment such as the depletion of resources, or an abundance of less-favored nutrient sources. Catabolite repression allows for the rapid use of certain substrates like glucose over other carbon sources. Effective handling of carbon and nitrogen catabolite repression in microorganisms is crucial to outcompete others in nutrient limiting conditions. Investigations into genes and proteins linked to preferential uptake of different nutrients under various environmental conditions can aid in identifying regulatory mechanisms that are crucial for optimum growth and survival of microorganisms. The exact time and way bacteria and fungi switch their utilization of certain nutrients is of great interest for scientific, industrial, and clinical reasons. Catabolite repression is of great significance for industrial applications that rely on microorganisms for the generation of valuable bio-products. The impact catabolite repression has on virulence of pathogenic bacteria and fungi and disease progression in hosts makes it important area of interest in medical research for the prevention of diseases and developing new treatment strategies. Regulatory networks under catabolite repression exemplify the flexibility and the tremendous diversity that is found in microorganisms and provides an impetus for newer insights into these networks.

Hierarchy of transcription factor network in Escherichia coli K-12: H-NS-mediated silencing and Anti-silencing by global regulators

Transcriptional regulation for genome expression determines growth and adaptation of single-cell bacteria that are directly exposed to environment. The transcriptional apparatus in Escherichia coli K-12 is composed of RNA polymerase core enzyme and two groups of its regulatory proteins, seven species of promoter-recognition subunit sigma and about 300 species of transcription factors. The identification of regulatory targets for all these regulatory proteins is critical toward understanding the genome regulation as a whole. For this purpose, we performed a systematic search in vitro of the whole set of binding sites for each factor by gSELEX system. This review summarizes the accumulated knowledge of regulatory targets for more than 150 TFs from E. coli K-12. Overall TFs could be classified into four families: nucleoid-associated bifunctional TFs; global regulators; local regulators; and single-target regulators, in which the regulatory functions remain uncharacterized for the nucleoid-associated TFs. Here we overview the regulatory targets of two nucleoid-associated TFs, H-NS and its paralog StpA, both together playing the silencing role of a set of non-essential genes. Participation of LeuO and other global regulators have been indicated for the anti-silencing. Finally, we propose the hierarchy of TF network as a key framework of the bacterial genome regulation.

A Small Regulatory RNA Generated from the malK 5' Untranslated Region Targets Gluconeogenesis in Vibrio Species

Vsr217 is a small RNA from Vibrio tasmaniensis LGP32, a pathogen associated with mortality events affecting juvenile oysters. The vsr217 gene is located within the 5' untranslated region (UTR) of malK, encoding the ATPase component of the maltose importer, and is conserved within the genus Vibrio. In the presence of maltose, vsr217 is regulated by MalT, the positive regulator of the maltose regulon. vsr217 is required in cis for the full expression of malK. In addition, Vsr217 acts in trans to downregulate the expression of fbp encoding fructose-1,6-bisphosphatase, an enzyme involved in gluconeogenesis. Thus, in the presence of maltose, the induction of Vsr217 is expected to promote glycolysis by negatively regulating the expression of a key enzyme of gluconeogenesis. IMPORTANCE Juvenile pacific oysters have been subject in recent years to summer mortality episodes with deep economic consequences. The pathogen Vibrio tasmaniensis has been associated with such mortality events. For bacterial pathogens, survival within the host requires profound metabolic adaptations according to available resources. All kinds of regulatory elements, including noncoding RNAs, orchestrate this response. Oysters are rich in glycogen, a precursor of maltose, and we previously reported that V. tasmaniensis maltose-regulated genes are strongly induced during oyster infection. Here, we report the dual mechanism by which a small regulatory RNA, generated from the 5' untranslated region of a gene belonging to the maltose regulon, acts both in cis and trans. In cis, it stimulates growth on maltose, and in trans, it downregulates the expression of a gene associated with gluconeogenesis, thus coordinating maltose utilization with central carbon metabolism.

Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli

Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.

Escherichia coli segments its controls on carbon-dependent gene expression into global and specific regulations

How bacteria adjust gene expression to cope with variable environments remains open to question. Here, we investigated the way global gene expression changes in E. coli correlated with the metabolism of seven carbon substrates chosen to trigger a large panel of metabolic pathways. Coarse-grained analysis of gene co-expression identified a novel regulation pattern: we established that the gene expression trend following immediately the reduction of growth rate (GR) was correlated to its initial expression level. Subsequent fine-grained analysis of co-expression demonstrated that the Crp regulator, coupled with a change in GR, governed the response of most GR-dependent genes. By contrast, the Cra, Mlc and Fur regulators governed the expression of genes responding to non-glycolytic substrates, glycolytic substrates or phosphotransferase system transported sugars following an idiosyncratic way. This work allowed us to expand additional genes in the panel of gene complement regulated by each regulator and to elucidate the regulatory functions of each regulator comprehensively. Interestingly, the bulk of genes controlled by Cra and Mlc were, respectively, co-regulated by Crp- or GR-related effect and our quantitative analysis showed that each factor took turns to work as the primary one or contributed equally depending on the conditions.

Global coordination of metabolic pathways in Escherichia coli by active and passive regulation

Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.

Global Transcriptional Regulators Fine-Tune the Translational and Metabolic Efficiency for Optimal Growth of Escherichia coli

Global transcriptional regulators coordinate complex genetic interactions that bestow better adaptability for an organism against external and internal perturbations. These transcriptional regulators are known to control an enormous array of genes with diverse functionalities. However, regulator-driven molecular mechanisms that underpin precisely tuned translational and metabolic processes conducive for rapid exponential growth remain obscure. Here, we comprehensively reveal the fundamental role of global transcriptional regulators FNR, ArcA, and IHF in sustaining translational and metabolic efficiency under glucose fermentative conditions in Escherichia coli By integrating high-throughput gene expression profiles and absolute intracellular metabolite concentrations, we illustrate that these regulators are crucial in maintaining nitrogen homeostasis, govern expression of otherwise unnecessary or hedging genes, and exert tight control on metabolic bottleneck steps. Furthermore, we characterize changes in expression and activity profiles of other coregulators associated with these dysregulated metabolic pathways, determining the regulatory interactions within the transcriptional regulatory network. Such systematic findings emphasize their importance in optimizing the proteome allocation toward metabolic enzymes as well as ribosomes, facilitating condition-specific phenotypic outcomes. Consequentially, we reveal that disruption of this inherent trade-off between ribosome and metabolic proteome economy due to the loss of regulators resulted in lowered growth rates. Moreover, our findings reinforce that the accumulations of intracellular metabolites in the event of proteome repartitions negatively affects the glucose uptake. Overall, by extending the three-partition proteome allocation theory concordant with multi-omics measurements, we elucidate the physiological consequences of loss of global regulators on central carbon metabolism restraining the organism to attain maximal biomass synthesis.IMPORTANCE Cellular proteome allocation in response to environmental or internal perturbations governs their eventual phenotypic outcome. This entails striking an effective balance between amino acid biosynthesis by metabolic proteins and its consumption by ribosomes. However, the global transcriptional regulator-driven molecular mechanisms that underpin their coordination remains unexplored. Here, we emphasize that global transcriptional regulators, known to control expression of a myriad of genes, are fundamental for precisely tuning the translational and metabolic efficiencies that define the growth optimality. Towards this, we systematically characterized the single deletion effect of FNR, ArcA, and IHF regulators of Escherichia coli on exponential growth under anaerobic glucose fermentative conditions. Their absence disrupts the stringency of proteome allocation, which manifests as impairment in key metabolic processes and an accumulation of intracellular metabolites. Furthermore, by incorporating an extension to the empirical growth laws, we quantitatively demonstrate the general design principles underlying the existence of these regulators in E. coli.

Vibrio cholerae FruR facilitates binding of RNA polymerase to the fru promoter in the presence of fructose 1-phosphate

In most bacteria, efficient use of carbohydrates is primarily mediated by the phosphoenolpyruvate (PEP):carbohydrate phosphotransferase system (PTS), which concomitantly phosphorylates the substrates during import. Therefore, transcription of the PTS-encoding genes is precisely regulated by transcriptional regulators, depending on the availability of the substrate. Fructose is transported mainly through the fructose-specific PTS (PTS^Fru) and simultaneously converted into fructose 1-phosphate (F1P). In Gammaproteobacteria such as Escherichia coli and Pseudomonas putida, transcription of the fru operon encoding two PTS^Fru components, FruA and FruB, and the 1-phosphofructokinase FruK is repressed by FruR in the absence of the inducer F1P. Here, we show that, contrary to the case in other Gammaproteobacteria, FruR acts as a transcriptional activator of the fru operon and is indispensable for the growth of Vibrio cholerae on fructose. Several lines of evidence suggest that binding of the FruR-F1P complex to an operator which is located between the –35 and –10 promoter elements changes the DNA structure to facilitate RNA polymerase binding to the promoter. We discuss the mechanism by which the highly conserved FruR regulates the expression of its target operon encoding the highly conserved PTS^Fru and FruK in a completely opposite direction among closely related families of bacteria.

Systems evaluation reveals novel transporter YohJK renders 3-hydroxypropionate tolerance in Escherichia coli

Previously, we have reported that 3-hydroxypropionate (3-HP) tolerance in Escherichia coli W is improved by deletion of yieP, a less-studied transcription factor. Here, through systems analyses along with physiological and functional studies, we suggest that the yieP deletion improves 3-HP tolerance by upregulation of yohJK, encoding putative 3-HP transporter(s). The tolerance improvement by yieP deletion was highly specific to 3-HP, among various C2-C4 organic acids. Mapping of YieP binding sites (ChIP-exo) coupled with transcriptomic profiling (RNA-seq) advocated seven potential genes/operons for further functional analysis. Among them, the yohJK operon, encoding for novel transmembrane proteins, was the most responsible for the improved 3-HP tolerance; deletion of yohJK reduced 3-HP tolerance regardless of yieP deletion, and their subsequent complementation fully restored the tolerance in both the wild-type and yieP deletion mutant. When determined by 3-HP-responsive biosensor, a drastic reduction of intracellular 3-HP was observed upon yieP deletion or yohJK overexpression, suggesting that yohJK encodes for novel 3-HP exporter(s).

Redefining fundamental concepts of transcription initiation in bacteria

Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.

Synthetic reconstruction of extreme high hydrostatic pressure resistance in Escherichia coli

Although high hydrostatic pressure (HHP) is an interesting parameter to be applied in bioprocessing, its potential is currently limited by the lack of bacterial chassis capable of surviving and maintaining homeostasis under pressure. While several efforts have been made to genetically engineer microorganisms able to grow at sublethal pressures, there is little information for designing backgrounds that survive more extreme pressures. In this investigation, we analyzed the genome of an extreme HHP-resistant mutant of E. coli MG1655 (designated as DVL1), from which we identified four mutations (in the cra, cyaA, aceA and rpoD loci) causally linked to increased HHP resistance. Analysing the functional effect of these mutations we found that the coupled effect of downregulation of cAMP/CRP, Cra and the glyoxylate shunt activity, together with the upregulation of RpoH and RpoS activity, could mechanistically explain the increased HHP resistance of the mutant. Using combinations of three mutations, we could synthetically engineer E. coli strains able to comfortably survive pressures of 600-800 MPa, which could serve as genetic backgrounds for HHP-based biotechnological applications.