Time-series analysis of the transcriptome and proteome of Escherichia coli upon glucose repression

Glucose consumption rate-dependent transcriptome profiling of Escherichia coli provides insight on performance as microbial factories

Background

The modification of glucose import capacity is an engineering strategy that has been shown to improve the characteristics of Escherichia coli as a microbial factory. A reduction in glucose import capacity can have a positive effect on production strain performance, however, this is not always the case. In this study, E. coli W3110 and a group of four isogenic derivative strains, harboring single or multiple deletions of genes encoding phosphoenolpyruvate:sugar phosphotransferase system (PTS)-dependent transporters as well as non-PTS transporters were characterized by determining their transcriptomic response to reduced glucose import capacity.

Results

These strains were grown in bioreactors with M9 mineral salts medium containing 20 g/L of glucose, where they displayed specific growth rates ranging from 0.67 to 0.27 h⁻¹, and specific glucose consumption rates (qs) ranging from 1.78 to 0.37 g/g h. RNA-seq analysis revealed a transcriptional response consistent with carbon source limitation among all the mutant strains, involving functions related to transport and metabolism of alternate carbon sources and characterized by a decrease in genes encoding glycolytic enzymes and an increase in gluconeogenic functions. A total of 107 and 185 genes displayed positive and negative correlations with qs, respectively. Functions displaying positive correlation included energy generation, amino acid biosynthesis, and sugar import.

Conclusion

Changes in gene expression of E. coli strains with impaired glucose import capacity could be correlated with qs values and this allowed an inference of the physiological state of each mutant. In strains with lower qs values, a gene expression pattern is consistent with energy limitation and entry into the stationary phase. This physiological state could explain why these strains display a lower capacity to produce recombinant protein, even when they show very low rates of acetate production. The comparison of the transcriptomes of the engineered strains employed as microbial factories is an effective approach for identifying favorable phenotypes with the potential to improve the synthesis of biotechnological products.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01909-y.

Pneumococcal Phasevarions Control Multiple Virulence Traits, Including Vaccine Candidate Expression

Streptococcus pneumoniae is the most common cause of bacterial illness worldwide. Current vaccines based on the polysaccharide capsule are only effective against a limited number of the >100 capsular serotypes. A universal vaccine based on conserved protein antigens requires a thorough understanding of gene expression in S. pneumoniae. All S. pneumoniae strains encode the SpnIII Restriction-Modification system. This system contains a phase-variable methyltransferase that switches specificity, and controls expression of multiple genes—a phasevarion. We examined the role of this phasevarion during pneumococcal pathobiology, and determined if phase variation resulted in differences in expression of currently investigated conserved protein antigens. Using locked strains that express a single methyltransferase specificity, we found differences in clinically relevant traits, including survival in blood, and adherence to and invasion of human cells. We also observed differences in expression of numerous proteinaceous vaccine candidates, which complicates selection of antigens for inclusion in a universal protein-based pneumococcal vaccine. This study will inform vaccine design against S. pneumoniae by ensuring only stably expressed candidates are included in a rationally designed vaccine.

IMPORTANCES. pneumoniae is the world’s foremost bacterial pathogen. S. pneumoniae encodes a phasevarion (phase-variable regulon), that results in differential expression of multiple genes. Previous work demonstrated that the pneumococcal SpnIII phasevarion switches between six different expression states, generating six unique phenotypic variants in a pneumococcal population. Here, we show that this phasevarion generates multiple phenotypic differences relevant to pathobiology. Importantly, expression of conserved protein antigens varies with phasevarion switching. As capsule expression, a major pneumococcal virulence factor, is also controlled by the phasevarion, our work will inform the selection of the best candidates to include in a rationally designed, universal pneumococcal vaccine.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.

IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Longitudinal Analysis of Gene Expression Changes During Cervical Carcinogenesis Reveals Potential Therapeutic Targets

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.

iTRAQ-Based Comparative Proteomic Analysis of Acinetobacter baylyi ADP1 Under DNA Damage in Relation to Different Carbon Sources

DNA damage response allows microorganisms to repair or bypass DNA damage and maintain the genome integrity. It has attracted increasing attention but the underlying influential factors affecting DNA damage response are still unclear. In this work, isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic analysis was used to investigate the influence of carbon sources on the translational response of Acinetobacter baylyi ADP1 to DNA damage. After cultivating in a nutrient-rich medium (LB) and defined media supplemented with four different carbon sources (acetate, citrate, pyruvate, and succinate), a total of 2807 proteins were identified. Among them, 84 proteins involved in stress response were significantly altered, indicating the strong influence of carbon source on the response of A. baylyi ADP1 to DNA damage and other stresses. As the first study on the comparative global proteomic changes in A. baylyi ADP1 under DNA damage across nutritional environments, our findings revealed that DNA damage response in A. baylyi ADP1 at the translational level is significantly altered by carbon source, providing an insight into the complex protein interactions across carbon sources and offering theoretical clues for further study to elucidate their general regulatory mechanism to adapt to different nutrient environments.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Inefficient Secretion of Anti-sigma Factor FlgM Inhibits Bacterial Motility at High Temperature

Temperature is one of the key cues that enable microorganisms to adjust their physiology in response to environmental changes. Here we show that motility is the major cellular function of Escherichia coli that is differentially regulated between growth at normal host temperature of 37°C and the febrile temperature of 42°C. Expression of both class II and class III flagellar genes is reduced at 42°C because of lowered level of the upstream activator FlhD. Class III genes are additionally repressed because of the destabilization and malfunction of secretion apparatus at high temperature, which prevents secretion of the anti-sigma factor FlgM. This mechanism of repression apparently accelerates loss of motility at 42°C. We hypothesize that E. coli perceives high temperature as a sign of inflammation, downregulating flagella to escape detection by the immune system of the host. Secretion-dependent coupling of gene expression to the environmental temperature is likely common among many bacteria.

•

E. coli motility is tightly turned off at febrile temperature (42°C)

•

Repression of motility is achieved at two levels of hierarchical gene regulation

•

Lowered FlhD level reduces expression of all flagellar genes

•

Impaired FlgM secretion tightens repression of class III genes

Profiling of glucose-induced transcription in Sulfolobus acidocaldarius DSM 639

Sulfolobus species can grow on a variety of organic compounds as carbon and energy sources. These species degrade glucose to pyruvate by the modified branched Entner-Doudoroff pathway. We attempted to determine the differentially expressed genes (DEGs) under sugar-limited and sugar-rich conditions. RNA sequencing (RNA-seq) was used to quantify the expression of the genes and identify those DEGs between the S. acidocaldarius cells grown under sugar-rich (YT with glucose) and sugar-limited (YT only) conditions. The functions and pathways of the DEGs were examined using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Quantitative real-time PCR (qRT-PCR) was performed to validate the DEGs. Transcriptome analysis of the DSM 639 strain grown on sugar-limited and sugar-rich media revealed that 853 genes were differentially expressed, among which 481 were upregulated and 372 were downregulated under the glucose-supplemented condition. In particular, 70 genes showed significant changes in expression levels of ≥ twofold. GO and KEGG enrichment analyses revealed that the genes encoding components of central carbon metabolism, the respiratory chain, and protein and amino acid biosynthetic machinery were upregulated under the glucose condition. RNA-seq and qRT-PCR analyses indicated that the sulfur assimilation genes (Saci_2197-2204) including phosphoadenosine phosphosulfate reductase and sulfite reductase were significantly upregulated in the presence of glucose. The present study revealed metabolic networks in S. acidocaldarius that are induced in a glucose-dependent manner, improving our understanding of biomass production under sugar-rich conditions.

Influence of Glucose Availability and CRP Acetylation on the Genome-Wide Transcriptional Response of Escherichia coli: Assessment by an Optimized Factorial Microarray Analysis

Background: While in eukaryotes acetylation/deacetylation regulation exerts multiple pleiotropic effects, in Escherichia coli it seems to be more limited and less known. Hence, we aimed to progress in the characterization of this regulation by dealing with three convergent aspects: the effector enzymes involved, the master regulator CRP, and the dependence on glucose availability.

Methods: The transcriptional response of E. coli BW25113 was analyzed across 14 relevant scenarios. These conditions arise when the wild type and four isogenic mutants (defective in deacetylase CobB, defective in N(ε)-lysine acetyl transferase PatZ, Q- and R-type mutants of protein CRP) are studied under three levels of glucose availability (glucose-limited chemostat and glucose-excess or glucose-exhausted in batch culture). The Q-type emulates a permanent stage of CRP^acetylated, whereas the R-type emulates a permanent stage of CRP^deacetylated. The data were analyzed by an optimized factorial microarray method (Q-GDEMAR).

Results: (a) By analyzing one mutant against the other, we were able to unravel the true genes that participate in the interaction between ΔcobB/ΔpatZ mutations and glucose availability; (b) Increasing stages of glucose limitation appear to be associated with the up-regulation of specific sets of target genes rather than with the loss of genes present when glucose is in excess; (c) Both CRP^deacetylated and CRP^acetylated produce extensive changes in specific subsets of genes, but their number and identity depend on the glucose availability; (d) In other sub-sets of genes, the transcriptional effect of CRP seems to be independent of its acetylation or deacetylation; (e) Some specific ontology functions can be associated with each of the different sets of genes detected herein.

Conclusions: CRP cannot be thought of only as an effector of catabolite repression, because it acts along all the glucose conditions tested (excess, limited, and exhausted), exerting both positive and negative effects through different sets of genes. Acetylation of CRP does not seem to be a binary form of regulation, as there is not a univocal relationship between its activation/inhibitory effect and its acetylation/deacetylation stage. All the combinatorial possibilities are observed. During the exponential growth phase, CRP also exerts a very significant transcriptional effect, mainly on flagellar assembly and chemotaxis (FDR = 7.2 × 10⁻⁴⁴).

iDREM: Interactive visualization of dynamic regulatory networks

The Dynamic Regulatory Events Miner (DREM) software reconstructs dynamic regulatory networks by integrating static protein-DNA interaction data with time series gene expression data. In recent years, several additional types of high-throughput time series data have been profiled when studying biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Combining all available time series and static datasets in a unified model remains an important challenge and goal. To address this challenge we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and combines them with existing interaction data to reconstruct networks that can lead to novel hypotheses on the function and timing of regulators. Users can interactively visualize and query the resulting model. We showcase the functionality of the new tool by applying it to microglia developmental data from multiple labs.

Carbohydrate Utilization in Bacteria: Making the Most Out of Sugars with the Help of Small Regulatory RNAs

Survival of bacteria in ever-changing habitats with fluctuating nutrient supplies requires rapid adaptation of their metabolic capabilities. To this end, carbohydrate metabolism is governed by complex regulatory networks including posttranscriptional mechanisms that involve small regulatory RNAs (sRNAs) and RNA-binding proteins. sRNAs limit the response to substrate availability and set the threshold or time required for induction and repression of carbohydrate utilization systems. Carbon catabolite repression (CCR) also involves sRNAs. In Enterobacteriaceae, sRNA Spot 42 cooperates with the transcriptional regulator cyclic AMP (cAMP)-receptor protein (CRP) to repress secondary carbohydrate utilization genes when a preferred sugar is consumed. In pseudomonads, CCR operates entirely at the posttranscriptional level, involving RNA-binding protein Hfq and decoy sRNA CrcZ. Moreover, sRNAs coordinate fluxes through central carbohydrate metabolic pathways with carbohydrate availability. In Gram-negative bacteria, the interplay between RNA-binding protein CsrA and its cognate sRNAs regulates glycolysis and gluconeogenesis in response to signals derived from metabolism. Spot 42 and cAMP-CRP jointly downregulate tricarboxylic acid cycle activity when glycolytic carbon sources are ample. In addition, bacteria use sRNAs to reprogram carbohydrate metabolism in response to anaerobiosis and iron limitation. Finally, sRNAs also provide homeostasis of essential anabolic pathways, as exemplified by the hexosamine pathway providing cell envelope precursors. In this review, we discuss the manifold roles of bacterial sRNAs in regulation of carbon source uptake and utilization, substrate prioritization, and metabolism.

Integrative FourD omics approach profiles the target network of the carbon storage regulatory system

Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets.

All Three Endogenous Quinone Species of Escherichia coli Are Involved in Controlling the Activity of the Aerobic/Anaerobic Response Regulator ArcA

The enteron Escherichia coli is equipped with a branched electron transfer chain that mediates chemiosmotic electron transfer, that drives ATP synthesis. The components of this electron transfer chain couple the oxidation of available electron donors from cellular metabolism (e.g., NADH, succinate, lactate, formate, etc.) to the reduction of electron acceptors like oxygen, nitrate, fumarate, di-methyl-sulfoxide, etc. Three different quinones, i.e., ubiquinone, demethyl-menaquinone and menaquinone, couple the transfer of electrons between the dehydrogenases and reductases/oxidases that constitute this electron transfer chain, whereas, the two-component regulation system ArcB/A regulates gene expression, to allow the organism to adapt itself to the ambient conditions of available electron donors and acceptors. Here, we report that E. coli can grow and adjust well to transitions in the availability of oxygen, with any of the three quinones as its single quinone. In all three ‘single-quinone’ E. coli strains transitions in the activity of ArcB are observed, as evidenced by changes in the level of phosphorylation of the response regulator ArcA, upon depletion/readmission of oxygen. These results lead us to conclude that all quinol species of E. coli can reduce (i.e., activate) the sensor ArcB and all three quinones oxidize (i.e., de-activate) it. These results also confirm our earlier conclusion that demethyl-menaquinone can function in aerobic respiration.