Challenges in using transcriptome data to study the c-di-GMP signaling network in Pseudomonas aeruginosa clinical isolates.

In the Pseudomonas aeruginosa type strain PA14, 40 genes are known to encode for diguanylate cyclases (DGCs) and/or phosphodiesterases (PDEs), which modulate the intracellular pool of the nucleotide second messenger c-di-GMP. While in general, high levels of c-di-GMP drive the switch from highly motile phenotypes towards a sessile lifestyle, the different c-di-GMP modulating enzymes are responsible for smaller and in parts nonoverlapping phenotypes. In this study, we sought to utilize previously recorded P. aeruginosa gene expression datasets on 414 clinical isolates to uncover transcriptional changes as a result of a high expression of genes encoding DGCs. This approach might provide a unique opportunity to bypass the problem that for many c-di-GMP modulating enzymes it is not known under which conditions their expression is activated. However, while we demonstrate that the selection of subgroups of clinical isolates with high versus low expression of sigma factor encoding genes served the identification of their downstream regulons, we were unable to confirm the predicted DGC regulons, because the high c-di-GMP associated phenotypes were rapidly lost in the clinical isolates,. Further studies are needed to determine the specific mechanisms underlying the loss of cyclase activity upon prolonged cultivation of clinical P. aeruginosa isolates.

Distinct Long- and Short-Term Adaptive Mechanisms in Pseudomonas aeruginosa.

Heterogeneous environments such as the chronically infected cystic fibrosis lung drive the diversification of Pseudomonas aeruginosa populations into, e.g., mucoid, alginate-overproducing isolates or small-colony variants (SCVs). In this study, we performed extensive genome and transcriptome profiling on a clinical SCV isolate that exhibited high cyclic diguanylate (c-di-GMP) levels and a mucoid phenotype. We observed a delayed, stepwise decrease of the high levels of c-di-GMP as well as alginate gene expression upon passaging the SCV under noninducing, rich medium growth conditions over 7 days. Upon prolonged passaging, this lagging reduction of the high c-di-GMP levels under noninducing planktonic conditions (reminiscent of a hysteretic response) was followed by a phenotypic switch to a large-colony morphology, which could be linked to mutations in the Gac/Rsm signaling pathway. Complementation of the Gac/Rsm signaling-negative large-colony variants with a functional GacSA system restored the SCV colony morphotype but was not able to restore the high c-di-GMP levels of the SCV. Our data thus suggest that expression of the SCV colony morphotype and modulation of c-di-GMP levels are genetically separable and follow different evolutionary paths. The delayed switching of c-di-GMP levels in response to fluctuating environmental conditions might provide a unique opportunity to include a time dimension to close the gap between short-term phenotypic and long-term genetic adaptation to biofilm-associated growth conditions. IMPORTANCE Extreme environments, such as those encountered during an infection process in the human host, make effective bacterial adaptation inevitable. While bacteria adapt individually by activating stress responses, long-term adaptation of bacterial communities to challenging conditions can be achieved via genetic fixation of favorable traits. In this study, we describe a two-pronged bacterial stress resistance strategy in the opportunistic pathogen Pseudomonas aeruginosa. We show that the production of adjusted elevated c-di-GMP levels, which drive protected biofilm-associated phenotypes in vivo, resembles a stable hysteretic response which prevents unwanted frequent switching. Cellular hysteresis might provide a link between individual adaptability and evolutionary adaptation to ensure the evolutionary persistence of host-adapted stress response strategies.

The Core Proteome of Biofilm-Grown Clinical Pseudomonas aeruginosa Isolates.

Comparative genomics has greatly facilitated the identification of shared as well as unique features among individual cells or tissues, and thus offers the potential to find disease markers. While proteomics is recognized for its potential to generate quantitative maps of protein expression, comparative proteomics in bacteria has been largely restricted to the comparison of single cell lines or mutant strains. In this study, we used a data independent acquisition (DIA) technique, which enables global protein quantification of large sample cohorts, to record the proteome profiles of overall 27 whole genome sequenced and transcriptionally profiled clinical isolates of the opportunistic pathogen Pseudomonas aeruginosa. Analysis of the proteome profiles across the 27 clinical isolates grown under planktonic and biofilm growth conditions led to the identification of a core biofilm-associated protein profile. Furthermore, we found that protein-to-mRNA ratios between different P. aeruginosa strains are well correlated, indicating conserved patterns of post-transcriptional regulation. Uncovering core regulatory pathways, which drive biofilm formation and associated antibiotic tolerance in bacterial pathogens, promise to give clues to interactions between bacterial species and their environment and could provide useful targets for new clinical interventions to combat biofilm-associated infections.

Environment-driven changes of mRNA and protein levels in Pseudomonas aeruginosa.

Systems biology approaches address the challenge of translating sequence information into function. In this study, we described the Pseudomonas aeruginosa PA14 proteomic landscape and quantified environment-driven changes in protein levels by the use of LC-MS techniques. Previously recorded mRNA data allowed a comparison of RNA to protein ratios for each individual gene and, thus, to explore the relationship between an mRNA being differentially expressed between environmental conditions and the mRNA-protein correlation for that gene. We developed a Random Forest-based predictor for protein levels and found that the mRNA to protein correlation was higher for genes/proteins that undergo dynamic changes. One example of a discrepancy between protein and predicted protein levels was observed for a phage-related gene cluster, which was translated into low protein levels under standard growth conditions. However, under SOS-inducing conditions more protein was produced and the prediction of protein levels based on mRNA abundancy became more accurate. In conclusion, our systems biology approach sheds light on complex mRNA to protein level relationships and uncovered condition-dependent post-transcriptional regulatory events.

Quantitative Secretomics Reveals Extrinsic Signals Involved in Human Pluripotent Stem Cell Cardiomyogenesis.

Human pluripotent stem cells can be differentiated in vitro into cardiomyocytes (CMs) but the molecular mechanisms behind this process are still not fully understood. In particular, the identification of morphogens remained elusive because the mass spectrometry-based identification of secreted proteins from cell culture supernatants is impeded by high levels of albumin present in common differentiation media. An albumin-free cardiomyogenic differentiation medium is established in this study and applied for secretomics at seven different time points during in vitro differentiation. By this analysis 4832 proteins are identified with 1802 being significantly altered during differentiation and 431 of these are annotated as secreted. Numerous extrinsic components of Wnt, TGFß, Activin A, Nodal, BMP, or FGF signaling pathways are quantitatively assessed during differentiation. Notably, the abundance of pathway agonists is generally lower compared to the respective antagonists but their curves of progression over timer were rather similar. It is hypothesized that TGFß, Activin A, and Nodal signaling are turned down shortly upon the initiation of cardiac differentiation whereas BMP signaling is switched on. Wnt and FGF signaling peaks between d0 and d3 of differentiation, and interestingly, Activin A and TGFß signaling seem to be reactivated at the cardiac progenitor stages and/or in early CMs.

Glucosyltransferase-dependent and -independent effects of TcdB on the proteome of HEp-2 cells.

Toxin B (TcdB) of the nosocomial pathogen C. difficile has been reported to exhibit a glucosyltransferase-dependent and -independent effect on treated HEp-2 cells at toxin concentration above 0.3 nM. In order to investigate and further characterize both effects epithelial cells were treated with wild type TcdB and glucosyltransferase-deficient TcdBNXN and their proteomes were analyzed by LC-MS. Triplex SILAC labeling was used for quantification. Identification of 5212 and quantification of 4712 protein groups was achieved. Out of these 257 were affected by TcdB treatment, 92 by TcdBNXN treatment and 49 by both. TcdB mainly led to changes in proteins that are related to "GTPase mediated signaling" and the "cytoskeleton" while "chromatin" and "cell cycle" related proteins were altered by both, TcdB and TcdBNXN . The obtained dataset of HEp-2 cell proteome helps us to better understand glucosyltransferase-dependent and -independent mechanisms of TcdB and TcdBNXN , particularly those involved in pyknotic cell death. All proteomics data have been deposited in the ProteomeXchange with the dataset identifier PXD006658 (https://proteomecentral.proteomexchange.org/dataset/PXD006658).

Toxin A of the nosocomial pathogen Clostridium difficile induces primary effects in the proteome of HEp-2 cells.

PURPOSE: This study was carried out to investigate the impact of high concentrations of Clostridium difficile toxin A (TcdA) on the proteome of human cells. It should also be examined whether a catalytically deficient mutant (TcdANXN ) has an effect on target cells. EXPERIMENTAL DESIGN: Proteome changes were investigated after treatment of HEp-2 cells with 20 nM TcdA for 8 h using a triplex SILAC labeling method and shotgun proteomics. Proteins from differently labeled and treated cells were combined for analysis using an HPLC coupled to an Orbitrap mass spectrometer. RESULTS: Nearly 4000 proteins were identified in each replicate and 3500 could be quantified by SILAC triplicate analysis. 51 proteins exhibited an altered abundance with 29 up-regulated and 22 down-regulated proteins. In contrast, TcdANXN had no provable impact on the protein profile of HEp-2 cells. Data analysis of regulated proteins revealed that mainly plasma membrane, cell death, cell proliferation and actin cytoskeleton proteins were affected by TcdA treatment. CONCLUSIONS AND CLINICAL RELEVANCE: This proteome analysis showed novel insights of TcdA impact onepithelial cells. Comparison with long-term treatment studies reveals distinctions in affected cellular processes that will improve the understanding of TcdA functions and might help to find new tools for diagnosis and treatment of CDI.