Differential expression of small RNAs under chemical stress and fed-batch fermentation in E. coli

Non-coding regulatory sRNAs from bacteria of the Burkholderia cepacia complex

Small non-coding RNAs (sRNAs) are key regulators of post-transcriptional gene expression in bacteria. Hundreds of sRNAs have been found using in silico genome analysis and experimentally based approaches in bacteria of the Burkholderia cepacia complex (Bcc). However, and despite the hundreds of sRNAs identified so far, the number of functionally characterized sRNAs from these bacteria remains very limited. In this mini-review, we describe the general characteristics of sRNAs and the main mechanisms involved in their action as regulators of post-transcriptional gene expression, as well as the work done so far in the identification and characterization of sRNAs from Bcc. The number of functionally characterized sRNAs from Bcc is expected to increase and to add new knowledge on the biology of these bacteria, leading to novel therapeutic approaches to tackle the infections caused by these opportunistic pathogens, particularly severe among cystic fibrosis patients. KEY POINTS: •Hundreds of sRNAs have been identified in Burkholderia cepacia complex bacteria (Bcc). •A few sRNAs have been functionally characterized in Bcc. •Functionally characterized Bcc sRNAs play major roles in metabolism, biofilm formation, and virulence.

A Small RNA, SaaS, Promotes Salmonella Pathogenicity by Regulating Invasion, Intracellular Growth, and Virulence Factors

Salmonella enterica serovar Enteritidis is a common foodborne pathogen that infects both humans and animals. The S. Enteritidis virulence regulation network remains largely incomplete, and knowledge regarding the specific virulence phenotype of small RNAs (sRNAs) is limited. Here, we investigated the role of a previously identified sRNA, Salmonella adhesive-associated sRNA (SaaS), in the virulence phenotype of S. Enteritidis by constructing mutant (ΔsaaS) and complemented (ΔsaaS/psaaS) strains. SaaS did not affect S. Enteritidis; it was activated in the simulated intestinal environment (SIE), regulating the expression of virulence target genes. We discovered that it directly binds ssaV mRNA. Caco-2 and RAW 264.7 cell assays revealed that SaaS promoted S. Enteritidis invasion and damage to epithelial cells while suppressing macrophage overgrowth and destruction. Furthermore, a BALB/c mouse model demonstrated that the deletion of SaaS significantly reduced mortality and attenuated the deterioration of pathophysiology, bacterial dissemination into systemic circulation, and systemic inflammation. Our findings indicate that SaaS is required for S. Enteritidis virulence and further highlight its biological role in bacterial pathogenesis. IMPORTANCE Salmonella is a zoonotic pathogen with high virulence worldwide, and sRNAs have recently been discovered to play important roles. We explored the biological characteristics of the sRNA SaaS and developed two cell infection models and a mouse infection model. SaaS is an SIE-responsive sRNA that regulates the expression of virulence-targeted genes. Additionally, it differentially mediates invasion and intracellular growth for survival and infection of the epithelium and macrophages. We further found that SaaS enhanced bacterial virulence by promoting lethality, colonization, and inflammatory response. These findings provide a better understanding of the critical role of sRNA in bacterial virulence.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

sRNA23, a novel small RNA, regulates to the pathogenesis of Streptococcus suis serotype 2

ABBREVIATION

sRNA: small noncoding RNA; FBA: fructose diphosphate aldolase; rplB: 50S ribosomal protein L2; RACE: rapid amplification of cDNA ends; EMSA: electrophoretic mobility shift assay; THB: Todd-Hewitt broth; FBS: fetal bovine serum; BIP: 2,2'-Bipyridine.

Natural selection shapes maintenance of orthologous sRNAs in divergent host-restricted bacterial genomes

Historically it has been difficult to study the evolution of bacterial small RNAs (sRNAs) across distantly related species. For example, identifying homologs of sRNAs is often difficult in genomes that have undergone multiple structural rearrangements. Also, some types of regulatory sRNAs evolve at rapid rates. The high degree of genomic synteny among divergent host-restricted bacterial lineages, including intracellular symbionts, is conducive to sRNA maintenance and homolog identification. In turn, symbiont genomes can provide us with novel insights into sRNA evolution. Here, we examine the sRNA expression profile of the obligate symbiont of psyllids, Carsonella ruddii, which has one of the smallest cellular genomes described. Using RNA-seq, we identified 36 and 32 antisense sRNAs (asRNAs) expressed by Carsonella from the psyllids Bactericera cockerelli (Carsonella-BC) and Diaphorina citri (Carsonella-DC), respectively. The majority of these asRNAs were associated with genes that are involved in essential amino acid biosynthetic pathways. Eleven of the asRNAs were conserved in both Carsonella lineages and the majority were maintained by selection. Notably, five of the corresponding coding sequences are also the targets of conserved asRNAs in a distantly related insect symbiont, Buchnera. We detected differential expression of two asRNAs for genes involved in arginine and leucine biosynthesis occurring between two distinct Carsonella-BC life stages. Using asRNAs identified in Carsonella, Buchnera, and Profftella which are all endosymbionts, and Escherichia coli, we determined that regions upstream of these asRNAs encode unique conserved patterns of AT/GC richness, GC skew, and sequence motifs which may be involved in asRNA regulation.

Strategies to Increase the Production of Biosynthetic Riboflavin

Riboflavin is widely regarded as an essential nutrient that is involved in biological oxidation in vivo. In addition to preventing and treating acyl-CoA dehydrogenase deficiency in patients with keratitis, stomatitis, and glossitis, riboflavin is also closely related to the treatment of radiation mucositis and cardiovascular disease. Chemical synthesis has been the dominant method for producing riboflavin for approximately 50 years. Nevertheless, due to the intricate synthesis process, relatively high cost, and high risk of pollution, alternative methods of chemical syntheses, such as the fermentation method, began to develop and eventually became the main methods for producing riboflavin. At present, there are three types of strains used in industrial riboflavin production: Ashbya gossypii, Candida famata, and Bacillus subtilis. Additionally, many recent studies have been conducted on Escherichia coli and Lactobacillus. Fermentation increases the yield of riboflavin using genetic engineering technology to modify and induce riboflavin production in the strain, as well as to regulate the metabolic flux of the purine pathway and pentose phosphate pathway (PP pathway), thereby optimizing the culture process. This article briefly introduces recent progress in the fermentation of riboflavin.

Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis

A goal of the biotechnology industry is to be able to recognise detrimental cellular states that may lead to suboptimal or anomalous growth in a bacterial population. Our current knowledge of how different environmental treatments modulate gene regulation and bring about physiology adaptations is limited, and hence it is difficult to determine the mechanisms that lead to their effects. Patterns of gene expression, revealed using technologies such as microarrays or RNA-seq, can provide useful biomarkers of different gene regulatory states indicative of a bacterium's physiological status. It is desirable to have only a few key genes as the biomarkers to reduce the costs of determining the transcriptional state by opening the way for methods such as quantitative RT-PCR and amplicon panels. In this paper, we used unsupervised machine learning to construct a transcriptional landscape model from condition-dependent transcriptome data, from which we have identified 10 clusters of samples with differentiated gene expression profiles and linked to different cellular growth states. Using an iterative feature elimination strategy, we identified a minimal panel of 10 biomarker genes that achieved 100% cross-validation accuracy in predicting the cluster assignment. Moreover, we designed and evaluated a variety of data processing strategies to ensure our methods were able to generate meaningful transcriptional landscape models, capturing relevant biological processes. Overall, the computational strategies introduced in this study facilitate the identification of a detailed set of relevant cellular growth states, and how to sense them using a reduced biomarker panel.

Comparative transcriptomic analysis of Rickettsia conorii during in vitro infection of human and tick host cells

BACKGROUND

Pathogenic Rickettsia species belonging to the spotted fever group are arthropod-borne, obligate intracellular bacteria which exhibit preferential tropism for host microvascular endothelium in the mammalian hosts, resulting in disease manifestations attributed primarily to endothelial damage or dysfunction. Although rickettsiae are known to undergo evolution through genomic reduction, the mechanisms by which these pathogens regulate their transcriptome to ensure survival in tick vectors and maintenance by transovarial/transstadial transmission, in contrast to their ability to cause debilitating infections in human hosts remain unknown. In this study, we compare the expression profiles of rickettsial sRNAome/transcriptome and determine the transcriptional start sites (TSSs) of R. conorii transcripts during in vitro infection of human and tick host cells.

RESULTS

We performed deep sequencing on total RNA from Amblyomma americanum AAE2 cells and human microvascular endothelial cells (HMECs) infected with R. conorii. Strand-specific RNA sequencing of R. conorii transcripts revealed the expression 32 small RNAs (Rc_sR's), which were preferentially expressed above the limit of detection during tick cell infection, and confirmed the expression of Rc_sR61, sR71, and sR74 by quantitative RT-PCR. Intriguingly, a total of 305 and 132 R. conorii coding genes were differentially upregulated (> 2-fold) in AAE2 cells and HMECs, respectively. Further, enrichment for primary transcripts by treatment with Terminator 5'-Phosphate-dependent Exonuclease resulted in the identification of 3903 and 2555 transcription start sites (TSSs), including 214 and 181 primary TSSs in R. conorii during the infection to tick and human host cells, respectively. Seventy-five coding genes exhibited different TSSs depending on the host environment. Finally, we also observed differential expression of 6S RNA during host-pathogen and vector-pathogen interactions in vitro, implicating an important role for this noncoding RNA in the regulation of rickettsial transcriptome depending on the supportive host niche.

CONCLUSIONS

In sum, the findings of this study authenticate the presence of novel Rc_sR's in R. conorii, reveal the first evidence for differential expression of coding transcripts and utilization of alternate transcriptional start sites depending on the host niche, and implicate a role for 6S RNA in the regulation of coding transcriptome during tripartite host-pathogen-vector interactions.

Resistance Profiles of Salmonella Isolates Exposed to Stresses and the Expression of Small Non-coding RNAs

Salmonella can resist various stresses and survive during food processing, storage, and distribution, resulting in potential health risks to consumers. Therefore, evaluation of bacterial survival profiles under various environmental stresses is necessary. In this study, the resistance profiles of five Salmonella isolates [serotypes with Agona, Infantis, Typhimurium, Enteritidis, and a standard strain (ATCC 13076, Enteritidis serotype)] to acidic, hyperosmotic, and oxidative stresses were examined, and the relative expressions of non-coding small RNAs were also evaluated, including CyaR, MicC, MicA, InvR, RybB, and DsrA, induced by specific stresses. The results indicated that although all tested strains displayed a certain resistance to stresses, there was great diversity in stress resistance among the strains. According to the reduction numbers of cells exposed to stress for 3 h, S. Enteritidis showed the highest resistance to acidic and hyperosmotic stresses, whereas ATCC 13076 showed the greatest resistance to oxidative stress, with less than 0.1 Log CFU/ml of cell reduction. Greater resistance of cells to acidic, hyperosmotic, and oxidative stresses was observed within 1 h, after 2 h, and from 1 to 2 h, respectively. The relative expression of sRNAs depended on the isolate for each stress; acidic exposure for the tested isolates induced high expression levels of DsrA, MicC, InvR, RybB, MicA, and CyaR. The sRNA RybB, associated with sigma E and outer membrane protein in bacteria, showed a fold change of greater than 7 in S. Enteritidis exposed to the tested stresses. CyaR and InvR involved in general stress responses and stress adaptation were also induced to show high expression levels of Salmonella exposed to hyperosmotic stress. Overall, these findings demonstrated that the behaviors of Salmonella under specific stresses varied according to strain and were likely not related to other profiles. The finding also provided insights into the survival of Salmonella subjected to short-term stresses and for controlling Salmonella in the food industry.

Systems Analyses Reveal the Resilience of Escherichia coli Physiology during Accumulation and Export of the Nonnative Organic Acid Citramalate

Citramalate is an attractive biotechnology target because it is a precursor of methylmethacrylate, which is used to manufacture Perspex and other high-value products. Engineered E. coli strains are able to produce high titers of citramalate, despite having to express a foreign enzyme and tolerate the presence of a nonnative biochemical. A systems analysis of the citramalate fermentation was undertaken to uncover the reasons underpinning its productivity. This showed that E. coli readily adjusts to the redirection of metabolic resources toward recombinant protein and citramalate production and suggests that E. coli is an excellent chassis for manufacturing similar small, polar, foreign molecules.

Productivity of bacterial cell factories is frequently compromised by stresses imposed by recombinant protein synthesis and carbon-to-product conversion, but little is known about these bioprocesses at a systems level. Production of the unnatural metabolite citramalate in Escherichia coli requires the expression of a single gene coding for citramalate synthase. Multiomic analyses of a fermentation producing 25  g liter⁻¹ citramalate were undertaken to uncover the reasons for its productivity. Metabolite, transcript, protein, and lipid profiles of high-cell-density, fed-batch fermentations of E. coli expressing either citramalate synthase or an inactivated enzyme were similar. Both fermentations showed downregulation of flagellar genes and upregulation of chaperones IbpA and IbpB, indicating that these responses were due to recombinant protein synthesis and not citramalate production. Citramalate production did not perturb metabolite pools, except for an increased intracellular pyruvate pool. Gene expression changes in response to citramalate were limited; none of the general stress response regulons were activated. Modeling of transcription factor activities suggested that citramalate invoked a GadW-mediated acid response, and changes in GadY and RprA regulatory small RNA (sRNA) expression supported this. Although changes in membrane lipid composition were observed, none were unique to citramalate production. This systems analysis of the citramalate fermentation shows that E. coli has capacity to readily adjust to the redirection of resources toward recombinant protein and citramalate production, suggesting that it is an excellent chassis choice for manufacturing organic acids.

IMPORTANCE Citramalate is an attractive biotechnology target because it is a precursor of methylmethacrylate, which is used to manufacture Perspex and other high-value products. Engineered E. coli strains are able to produce high titers of citramalate, despite having to express a foreign enzyme and tolerate the presence of a nonnative biochemical. A systems analysis of the citramalate fermentation was undertaken to uncover the reasons underpinning its productivity. This showed that E. coli readily adjusts to the redirection of metabolic resources toward recombinant protein and citramalate production and suggests that E. coli is an excellent chassis for manufacturing similar small, polar, foreign molecules.

Systematic analysis of the effects of different nitrogen source and ICDH knockout on glycolate synthesis in Escherichia coli

BACKGROUND

Glycolate is an important α-hydroxy carboxylic acid widely used in industrial and consumer applications. The production of glycolate from glucose in Escherichia coli is generally carried out by glycolysis and glyoxylate shunt pathways, followed by reduction to glycolate. Glycolate accumulation was significantly affected by nitrogen sources and isocitrate dehydrogenase (ICDH), which influenced carbon flux distribution between the tricarboxylic acid (TCA) cycle and the glyoxylate shunt, however, the mechanism was unclear.

RESULTS

Herein, we used RNA-Seq to explore the effects of nitrogen sources and ICDH knockout on glycolate production. The Mgly534 strain and the Mgly624 strain (with the ICDH deletion in Mgly534), displaying different phenotypes on organic nitrogen sources, were also adopted for the exploration. Though the growth of Mgly534 was improved on organic nitrogen sources, glycolate production decreased and acetate accumulated, while Mgly624 achieved a balance between cell growth and glycolate production, reaching 0.81 g glycolate/OD (2.6-fold higher than Mgly534). To further study Mgly624, the significant changed genes related to N-regulation, oxidative stress response and iron transport were analyzed. Glutamate and serine were found to increase the biomass and productivity respectively. Meanwhile, overexpressing the arginine transport gene argT accelerated the cell growth rate and increased the biomass. Further, the presence of Fe2+ also speeded up the cells growth and compensated for the lack of reducing equivalents.

CONCLUSION

Our studies identified that ICDH knockout strain was more suitable for glycolate production. RNA-Seq provided a better understanding of the ICDH knockout on cellular physiology and glycolate production.

Transcriptional noise and exaptation as sources for bacterial sRNAs

Understanding how new genes originate and integrate into cellular networks is key to understanding evolution. Bacteria present unique opportunities for both the natural history and experimental study of gene origins, due to their large effective population sizes, rapid generation times, and ease of genetic manipulation. Bacterial small non-coding RNAs (sRNAs), in particular, many of which operate through a simple antisense regulatory logic, may serve as tractable models for exploring processes of gene origin and adaptation. Understanding how and on what timescales these regulatory molecules arise has important implications for understanding the evolution of bacterial regulatory networks, in particular, for the design of comparative studies of sRNA function. Here, we introduce relevant concepts from evolutionary biology and review recent work that has begun to shed light on the timescales and processes through which non-functional transcriptional noise is co-opted to provide regulatory functions. We explore possible scenarios for sRNA origin, focusing on the co-option, or exaptation, of existing genomic structures which may provide protected spaces for sRNA evolution.

sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs

Small RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability, or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our small RNA target prediction organizing tool (SPOT), which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.

Small RNAs (sRNAs) posttranscriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA target prediction organizing tool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (number of correctly predicted targets/number of total known targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulate cfa mRNA, encoding cyclopropane fatty acid synthase. SPOT identified cfa along with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition to cfa mRNA, RydC also regulates trpE and pheA mRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs.

IMPORTANCE Small RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability, or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our small RNA target prediction organizing tool (SPOT), which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.

Transcriptional Landscape of a bla KPC-2 Plasmid and Response to Imipenem Exposure in Escherichia coli TOP10

The diffusion of KPC-2 carbapenemase is closely related to the spread of Klebsiella pneumoniae of the clonal-group 258 and linked to IncFII_K plasmids. Little is known about the biology of multi-drug resistant plasmids and the reasons of their successful dissemination. Using E. coli TOP10 strain harboring a multi-replicon IncFII_K-IncFIB bla_KPC−2-gene carrying plasmid pBIC1a from K. pneumoniae ST-258 clinical isolate BIC-1, we aimed to identify basal gene expression and the effects of imipenem exposure using whole transcriptome approach by RNA sequencing (RNA-Seq). Independently of the antibiotic pressure, most of the plasmid-backbone genes were expressed at low levels. The most expressed pBIC1a genes were involved in antibiotic resistance (bla_KPC−2, bla_TEM and aph(3′)-I), in plasmid replication and conjugation, or associated to mobile elements. After antibiotic exposure, 34% of E. coli (pBIC1a) genome was differentially expressed. Induction of oxidative stress response was evidenced, with numerous upregulated genes of the SoxRS/OxyR oxydative stress regulons, the Fur regulon (for iron uptake machinery), and IscR regulon (for iron sulfur cluster synthesis). Nine genes carried by pBIC1a were up-regulated, including the murein DD-endopeptidase mepM and the copper resistance operon. Despite the presence of a carbapenemase, we observed a major impact on E. coli (pBIC1a) whole transcriptome after imipenem exposure, but no effect on the level of transcription of antimicrobial resistance genes. We describe adaptive responses of E. coli to imipenem-induced stress, and identified plasmid-encoded genes that could be involved in resistance to stressful environments.

Regulatory RNA in Mycobacterium tuberculosis, back to basics

Since the turn of the millenium, RNA-based control of gene expression has added an extra dimension to the central dogma of molecular biology. Still, the roles of Mycobacterium tuberculosis regulatory RNAs and the proteins that facilitate their functions remain elusive, although there can be no doubt that RNA biology plays a central role in the baterium's adaptation to its many host environments. In this review, we have presented examples from model organisms and from M. tuberculosis to showcase the abundance and versatility of regulatory RNA, in order to emphasise the importance of these 'fine-tuners' of gene expression.

Small regulatory bacterial RNAs regulating the envelope stress response

Most bacteria encode a large repertoire of RNA-based regulatory mechanisms. Recent discoveries have revealed that the expression of many genes is controlled by a plethora of base-pairing noncoding small regulatory RNAs (sRNAs), regulatory RNA-binding proteins and RNA-degrading enzymes. Some of these RNA-based regulated processes respond to stress conditions and are involved in the maintenance of cellular homeostasis. They achieve it by either direct posttranscriptional repression of several mRNAs, including blocking access to ribosome and/or directing them to RNA degradation when the synthesis of their cognate proteins is unwanted, or by enhanced translation of some key stress-regulated transcriptional factors. Noncoding RNAs that regulate the gene expression by binding to regulatory proteins/transcriptional factors often act negatively by sequestration, preventing target recognition. Expression of many sRNAs is positively regulated by stress-responsive sigma factors like RpoE and RpoS, and two-component systems like PhoP/Q, Cpx and Rcs. Some of these regulatory RNAs act via a feedback mechanism on their own regulators, which is best reflected by recent discoveries, concerning the regulation of cell membrane composition by sRNAs in Escherichia coli and Salmonella, which are highlighted here.

Genome-wide Escherichia coli stress response and improved tolerance towards industrially relevant chemicals

Background

Economically viable biobased production of bulk chemicals and biofuels typically requires high product titers. During microbial bioconversion this often leads to product toxicity, and tolerance is therefore a critical element in the engineering of production strains.

Results

Here, a systems biology approach was employed to understand the chemical stress response of Escherichia coli, including a genome-wide screen for mutants with increased fitness during chemical stress. Twelve chemicals with significant production potential were selected, consisting of organic solvent-like chemicals (butanol, hydroxy-γ-butyrolactone, 1,4-butanediol, furfural), organic acids (acetate, itaconic acid, levulinic acid, succinic acid), amino acids (serine, threonine) and membrane-intercalating chemicals (decanoic acid, geraniol). The transcriptional response towards these chemicals revealed large overlaps of transcription changes within and between chemical groups, with functions such as energy metabolism, stress response, membrane modification, transporters and iron metabolism being affected. Regulon enrichment analysis identified key regulators likely mediating the transcriptional response, including CRP, RpoS, OmpR, ArcA, Fur and GadX. These regulators, the genes within their regulons and the above mentioned cellular functions therefore constitute potential targets for increasing E. coli chemical tolerance. Fitness determination of genome-wide transposon mutants (Tn-seq) subjected to the same chemical stress identified 294 enriched and 336 depleted mutants and experimental validation revealed up to 60 % increase in mutant growth rates. Mutants enriched in several conditions contained, among others, insertions in genes of the Mar-Sox-Rob regulon as well as transcription and translation related gene functions.

Conclusions

The combination of the transcriptional response and mutant screening provides general targets that can increase tolerance towards not only single, but multiple chemicals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0577-5) contains supplementary material, which is available to authorized users.

A feed-forward loop between SroC and MgrR small RNAs modulates the expression of eptB and the susceptibility to polymyxin B in Salmonella Typhimurium

Base-pairing small RNAs (sRNAs) regulate gene expression commonly by direct interaction with cognate mRNAs. Nevertheless, recent studies have expanded this knowledge with the discovery of the RNA 'sponges' which are able to interact and repress the functions of classical base-pairing sRNAs. In this work, we present evidence indicating that the sponge RNA SroC from Salmonella enterica serovar Typhimurium base pairs with the MgrR sRNA, thereby antagonizing its regulatory effects on both gene expression and resistance to the antimicrobial peptide polymyxin B (PMB). By a predictive algorithm, we determined putative SroC-MgrR base-pairing regions flanking the interaction area between MgrR and its target mRNA, eptB, encoding a LPS-modifying enzyme. With a two-plasmid system and compensatory mutations, we confirmed that SroC directly interacts and down-regulates the levels of MgrR, thus relieving the MgrR-mediated repression of eptB mRNA. Since it was previously shown that an Escherichia coli strain carrying an mgrR deletion is more resistant to PMB, we assessed the significance of SroC in the susceptibility of S. Typhimurium to PMB. Whereas the sroC deletion increased the sensitivity to PMB, as compared to the wild-type, the resistance phenotypes between the ΔmgrR and ΔsroCΔmgrR strains were comparable, evidencing that mgrR mutation is epistatic to the sroC mutation. Together, these results indicate that both SroC and MgrR sRNAs compose a coherent feed-forward loop controlling the eptB expression and hence the LPS modification in S. Typhimurium.

A new custom microarray for sRNA profiling in Escherichia coli

Bacterial small RNAs (sRNAs) play essential roles in the post-transcriptional control of gene expression. To improve their detection by conventional microarrays, we designed a custom microarray containing a group of probes targeting known and some putative Escherichia coli sRNAs. To assess its potential in detection of sRNAs, RNA profiling experiments were performed with total RNA extracted from E. coli MG1655 cells exponentially grown in rich (Luria-Bertani) and minimal (M9/glucose) media. We found that many sRNAs could yield reasonably strong and statistically significant signals corresponding to nearly all sRNAs annotated in the EcoCyc database. Besides differential expression of two sRNAs (GcvB and RydB), expression of other sRNAs was less affected by the composition of the growth media. Other examples of the differentially expressed sRNAs were revealed by comparing gene expression of the wild-type strain and its isogenic mutant lacking functional poly(A) polymerase I (pcnB). Further, northern blot analysis was employed to validate these data and to assess the existence of new putative sRNAs. Our results suggest that the use of custom microarrays with improved capacities for detection of sRNAs can offer an attractive opportunity for efficient gene expression profiling of sRNAs and their target mRNAs at the whole transcriptome level.