It has long been recognized that the modification of penicillin-binding proteins (PBPs) to reduce their affinity for β-lactams is an important mechanism (target modification) by which Gram-positive cocci acquire antibiotic resistance. Among Gram-negative rods (GNR), however, this mechanism has been considered unusual, and restricted to clinically irrelevant laboratory mutants for most species. Using as a model Pseudomonas aeruginosa, high up on the list of pathogens causing life-threatening infections in hospitalized patients worldwide, we show that PBPs may also play a major role in β-lactam resistance in GNR, but through a totally distinct mechanism. Through a detailed genetic investigation, including whole-genome analysis approaches, we demonstrate that high-level (clinical) β-lactam resistance in vitro, in vivo, and in the clinical setting is driven by the inactivation of the dacB-encoded nonessential PBP4, which behaves as a trap target for β-lactams. The inactivation of this PBP is shown to determine a highly efficient and complex β-lactam resistance response, triggering overproduction of the chromosomal β-lactamase AmpC and the specific activation of the CreBC (BlrAB) two-component regulator, which in turn plays a major role in resistance. These findings are a major step forward in our understanding of β-lactam resistance biology, and, more importantly, they open up new perspectives on potential antibiotic targets for the treatment of infectious diseases.
Individual-based modelling of biofilms accounts for the fact that individual organisms of the same species may well be in a different physiological state as a result of environmental gradients, lag times in responding to change, or noise in gene expression, which we have become increasingly aware of with the advent of single-cell microbiology. But progress in developing and using individual-based modelling has been hampered by different groups writing their own code and the lack of an available standard model. We therefore set out to merge most features of previous models and incorporate various improvements in order to provide a common basis for further developments. Four improvements stand out: the biofilm pressure field allows for shrinking or consolidating biofilms; the continuous-in-time extracellular polymeric substances excretion leads to more realistic fluid behaviour of the extracellular matrix, avoiding artefacts; the stochastic chemostat mode allows comparison of spatially uniform and heterogeneous systems; and the separation of growth kinetics from the individual cell allows condition-dependent switching of metabolism. As an illustration of the model's use, we used the latter feature to study how environmentally fluctuating oxygen availability affects the diversity and composition of a community of denitrifying bacteria that induce the denitrification pathway under anoxic or low oxygen conditions. We tested the hypothesis that the existence of these diverse strategies of denitrification can be explained solely by assuming that faster response incurs higher costs. We found that if the ability to switch metabolic pathways quickly incurs no costs the fastest responder is always the best. However, if there is a trade-off where faster switching incurs higher costs, then there is a strategy with optimal response time for any frequency of environmental fluctuations, suggesting that different types of denitrifying strategies win in different environments. In a single environment, biodiversity of denitrifiers is higher in biofilms than chemostats, higher with than without costs and higher at intermediate frequency of change. The highly modular nature of the new computational model made this case study straightforward to implement, and reflects the sort of novel studies that can easily be executed with the new model.
In this study, we evaluated how gene expression differs in mature Pseudomonas aeruginosa biofilms as opposed to planktonic cells by the use of RNA sequencing technology that gives rise to both quantitative and qualitative information on the transcriptome. Although a large proportion of genes were consistently regulated in both the stationary phase and biofilm cultures as opposed to the late exponential growth phase cultures, the global biofilm gene expression pattern was clearly distinct indicating that biofilms are not just surface attached cells in stationary phase. A large amount of the genes found to be biofilm specific were involved in adaptation to microaerophilic growth conditions, repression of type three secretion and production of extracellular matrix components. Additionally, we found many small RNAs to be differentially regulated most of them similarly in stationary phase cultures and biofilms. A qualitative analysis of the RNA-seq data revealed more than 3000 putative transcriptional start sites (TSS). By the use of rapid amplification of cDNA ends (5′-RACE) we confirmed the presence of three different TSS associated with the pqsABCDE operon, two in the promoter of pqsA and one upstream of the second gene, pqsB. Taken together, this study reports the first transcriptome study on P. aeruginosa that employs RNA sequencing technology and provides insights into the quantitative and qualitative transcriptome including the expression of small RNAs in P. aeruginosa biofilms.
The genetic adaptation of pathogens in host tissue plays a key role in the establishment of chronic infections. While whole genome sequencing has opened up the analysis of genetic changes occurring during long-term infections, the identification and characterization of adaptive traits is often obscured by a lack of knowledge of the underlying molecular processes. Our research addresses the role of Pseudomonas aeruginosa small colony variant (SCV) morphotypes in long-term infections. In the lungs of cystic fibrosis patients, the appearance of SCVs correlates with a prolonged persistence of infection and poor lung function. Formation of P. aeruginosa SCVs is linked to increased levels of the second messenger c-di-GMP. Our previous work identified the YfiBNR system as a key regulator of the SCV phenotype. The effector of this tripartite signaling module is the membrane bound diguanylate cyclase YfiN. Through a combination of genetic and biochemical analyses we first outline the mechanistic principles of YfiN regulation in detail. In particular, we identify a number of activating mutations in all three components of the Yfi regulatory system. YfiBNR is shown to function via tightly controlled competition between allosteric binding sites on the three Yfi proteins; a novel regulatory mechanism that is apparently widespread among periplasmic signaling systems in bacteria. We then show that during long-term lung infections of CF patients, activating mutations invade the population, driving SCV formation in vivo . The identification of mutational “scars” in the yfi genes of clinical isolates suggests that Yfi activity is both under positive and negative selection in vivo and that continuous adaptation of the c-di-GMP network contributes to the in vivo fitness of P. aeruginosa during chronic lung infections. These experiments uncover an important new principle of in vivo persistence, and identify the c-di-GMP network as a valid target for novel anti-infectives directed against chronic infections.
Sigma factors are essential global regulators of transcription initiation in bacteria which confer promoter recognition specificity to the RNA polymerase core enzyme. They provide effective mechanisms for simultaneously regulating expression of large numbers of genes in response to challenging conditions, and their presence has been linked to bacterial virulence and pathogenicity. In this study, we constructed nine his-tagged sigma factor expressing and/or deletion mutant strains in the opportunistic pathogen Pseudomonas aeruginosa. To uncover the direct and indirect sigma factor regulons, we performed mRNA profiling, as well as chromatin immunoprecipitation coupled to high-throughput sequencing. We furthermore elucidated the de novo binding motif of each sigma factor, and validated the RNA- and ChIP-seq results by global motif searches in the proximity of transcriptional start sites (TSS). Our integrated approach revealed a highly modular network architecture which is composed of insulated functional sigma factor modules. Analysis of the interconnectivity of the various sigma factor networks uncovered a limited, but highly function-specific, crosstalk which orchestrates complex cellular processes. Our data indicate that the modular structure of sigma factor networks enables P. aeruginosa to function adequately in its environment and at the same time is exploited to build up higher-level functions by specific interconnections that are dominated by a participation of RpoN.
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