RNAIII is linked with the pentose phosphate pathway through the activation of RpiRc in Staphylococcus aureus.

Staphylococcus aureus RNAIII is a dual-function regulatory RNA that controls the expression of multiple virulence genes and especially the transition from adhesion to the production of exotoxins. However, its contribution to S. aureus central metabolism remains unclear. Using MS2-affinity purification coupled with RNA sequencing, we uncovered more than 50 novel RNAIII-mRNA interactions. Among them, we demonstrate that RNAIII is a major activator of the rpiRc gene, encoding a regulator of the pentose phosphate pathway (PPP). RNAIII binds the 5' UTR of rpiRc mRNA to favor ribosome loading, leading to an increased expression of RpiRc and, subsequently, of two PPP enzymes. Finally, we show that RNAIII and RpiRc are involved in S. aureus fitness in media supplemented with various carbohydrate sources related to PPP and glycolysis. Collectively, our data depict an unprecedented phenotype associated with the RNAIII regulon, especially the direct implication of RNAIII in central metabolic activity modulation. These findings show that the contribution of RNAIII in Staphylococcus aureus adaptation goes far beyond what was initially reported. IMPORTANCE: Staphylococcus aureus is a major human pathogen involved in acute and chronic infections. Highly recalcitrant to antibiotic treatment, persistent infections are mostly associated with the loss of RNAIII expression, a master RNA regulator responsible for the switch from colonization to infection. Here, we used the MS2 affinity purification coupled with RNA sequencing approach to identify novel mRNA targets of RNAIII and uncover novel functions. We demonstrate that RNAIII is an activator of the expression of genes involved in the pentose phosphate pathway and is implicated in the adjustment of bacterial fitness as a function of carbohydrate sources. Taken together, our results demonstrate an unprecedented role of RNAIII that goes beyond the knowledge gained so far and contributes to a better understanding of the role of RNAIII in bacterial adaptation expression and the coordination of a complex regulatory network.

Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.

Staphylococcus aureus is a major human and animal pathogen, colonizing diverse ecological niches within its hosts. Predicting whether an isolate will infect a specific host and its subsequent clinical fate remains unknown. In this study, we investigated the S. aureus pangenome using a curated set of 356 strains, spanning a wide range of hosts, origins, and clinical display and antibiotic resistance profiles. We used genome-wide association study (GWAS) and random forest (RF) algorithms to discriminate strains based on their origins and clinical sources. Here, we show that the presence of sak and scn can discriminate strains based on their host specificity, while other genes such as mecA are often associated with virulent outcomes. Both GWAS and RF indicated the importance of intergenic regions (IGRs) and coding DNA sequence (CDS) but not sRNAs in forecasting an outcome. Additional transcriptomic analyses performed on the most prevalent clonal complex 8 (CC8) clonal types, in media mimicking nasal colonization or bacteremia, indicated three RNAs as potential RNA markers to forecast infection, followed by 30 others that could serve as infection severity predictors. Our report shows that genetic association and transcriptomics are complementary approaches that will be combined in a single analytical framework to improve our understanding of bacterial pathogenesis and ultimately identify potential predictive molecular markers. IMPORTANCE Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome. In addition, transcriptomic analyses performed on the most prevalent clonal types, in media mimicking either nasal colonization or bacteremia, revealed significant differences and therefore potent RNA markers. Overall, the use of both genomic and transcriptomic data in a single analytical framework can enhance our understanding of bacterial pathogenesis.

Molecular Networking for Drug Toxicities Studies: The Case of Hydroxychloroquine in COVID-19 Patients.

Using drugs to treat COVID-19 symptoms may induce adverse effects and modify patient outcomes. These adverse events may be further aggravated in obese patients, who often present different illnesses such as metabolic-associated fatty liver disease. In Rennes University Hospital, several drug such as hydroxychloroquine (HCQ) have been used in the clinical trial HARMONICOV to treat COVID-19 patients, including obese patients. The aim of this study is to determine whether HCQ metabolism and hepatotoxicity are worsened in obese patients using an in vivo/in vitro approach. Liquid chromatography high resolution mass spectrometry in combination with untargeted screening and molecular networking were employed to study drug metabolism in vivo (patient's plasma) and in vitro (HepaRG cells and RPTEC cells). In addition, HepaRG cells model were used to reproduce pathophysiological features of obese patient metabolism, i.e., in the condition of hepatic steatosis. The metabolic signature of HCQ was modified in HepaRG cells cultured under a steatosis condition and a new metabolite was detected (carboxychloroquine). The RPTEC model was found to produce only one metabolite. A higher cytotoxicity of HCQ was observed in HepaRG cells exposed to exogenous fatty acids, while neutral lipid accumulation (steatosis) was further enhanced in these cells. These in vitro data were compared with the biological parameters of 17 COVID-19 patients treated with HCQ included in the HARMONICOV cohort. Overall, our data suggest that steatosis may be a risk factor for altered drug metabolism and possibly toxicity of HCQ.

Small RNAs in vancomycin-resistant Enterococcus faecium involved in daptomycin response and resistance.

Vancomycin-resistant Enterococcus faecium is a leading cause of hospital-acquired infections and outbreaks. Regulatory RNAs (sRNAs) are major players in adaptive responses, including antibiotic resistance. They were extensively studied in gram-negative bacteria, but less information is available for gram-positive pathogens. No sRNAs are described in E. faecium. We sought to identify a set of sRNAs expressed in vancomycin-resistant E. faecium Aus0004 strain to assess their roles in daptomycin response and resistance. Genomic and transcriptomic analyses revealed a set of 61 sRNA candidates, including 10 that were further tested and validated by Northern and qPCR. RNA-seq was performed with and without subinhibitory concentrations (SICs) of daptomycin, an antibiotic used to treat enterococcal infections. After daptomycin SIC exposure, the expression of 260 coding and srna genes was altered, with 80 upregulated and 180 downregulated, including 51% involved in carbohydrate and transport metabolisms. Daptomycin SIC exposure significantly affected the expression of seven sRNAs, including one experimentally confirmed, sRNA_0160. We studied sRNA expression in isogenic mutants with increasing levels of daptomycin resistance and observed that expression of several sRNAs, including sRNA_0160, was modified in the stepwise mutants. This first genome-wide sRNA identification in E. faecium suggests that some sRNAs are linked to antibiotic stress response and resistance.

sRNA and cis-antisense sRNA identification in Staphylococcus aureus highlights an unusual sRNA gene cluster with one encoding a secreted peptide.

The human pathogen Staphylococcus aureus expresses a set of transcriptional factors and small RNAs (sRNAs) to adapt to environmental variations. Recent harmonization of staphylococcal sRNA data allowed us to search for novel sRNAs using DETR'PROK, a computational pipeline for identifying sRNA in prokaryotes. We performed RNA-Seq on Newman strain and identified a set of 48 sRNA candidates. To avoid bioinformatic artefacts, we applied a series of cut-offs and tested experimentally each selected intergenic region. This narrowed the field to 24 expressed sRNAs, of which 21 were new and designated with Srn identifiers. Further examination of these loci revealed that one exhibited an unusual condensed sRNA cluster of about 650 nucleotides. We determined the transcriptional start sites within this region and demonstrated the presence of three contiguous sRNA genes (srn_9342, srn_9344 and srn_9345) expressed from the positive strand, and two others (srn_9343 and srn_9346) transcribed from the opposite one. Using comparative genomics, we showed that genetic organization of the srn_9342-9346 locus is specific to Newman and that its expression is growth-phase dependent and subjected to nutrient deprivation and oxidative stress. Finally, we demonstrated that srn_9343 encodes a secreted peptide that could belong to a novel S. aureus toxin-antitoxin system.

A new mathematical model of bacterial interactions in two-species oral biofilms.

Periodontitis are bacterial inflammatory diseases, where the bacterial biofilms present on the tooth-supporting tissues switch from a healthy state towards a pathogenic state. Among bacterial species involved in the disease, Porphyromonas gingivalis has been shown to induce dysbiosis, and to induce virulence of otherwise healthy bacteria like Streptococcus gordonii. During biofilm development, primary colonizers such as S. gordonii first attach to the surface and allow the subsequent adhesion of periodontal pathogens such as P. gingivalis. Interactions between those two bacteria have been extensively studied during the adhesion step of the biofilm. The aim of the study was to understand interactions of both species during the growing phase of the biofilm, for which little knowledge is available, using a mathematical model. This two-species biofilm model was based on a substrate-dependent growth, implemented with damage parameters, and validated thanks to data obtained on experimental biofilms. Three different hypothesis of interactions were proposed and assayed using this model: independence, competition between both bacteria species, or induction of toxicity by one species for the other species. Adequacy between experimental and simulated biofilms were found with the last hypothetic mathematical model. This new mathematical model of two species bacteria biofilms, dependent on different substrates for growing, can be applied to any bacteria species, environmental conditions, or steps of biofilm development. It will be of great interest for exploring bacterial interactions in biofilm conditions.