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CrpP enzymes have been recently described as a novel ciprofloxacin-resistance mechanism. We investigated by whole genome sequencing the presence of crpP-genes and other mechanisms involved in quinolone resistance in MDR/XDR-Pseudomonas aeruginosa isolates (n = 55) with both ceftolozane-tazobactam susceptible or resistant profiles recovered from intensive care unit patients during the STEP (Portugal) and SUPERIOR (Spain) surveillance studies. Ciprofloxacin resistance was associated with mutations in the gyrA and parC genes. Additionally, plasmid-mediated genes (qnrS2 and aac(6′)-Ib-cr) were eventually detected. Ten chromosomal crpP-like genes contained in related pathogenicity genomic islands and 6 different CrpP (CrpP1-CrpP6) proteins were found in 65% (36/55) of the isolates. Dissemination of CrpP variants was observed among non-related clones of both countries, including the CC175 (Spain) high-risk clone and CC348 (Portugal) clone. Interestingly, 5 of 6 variants (CrpP1-CrpP5) carried missense mutations in an amino acid position (Gly7) previously defined as essential conferring ciprofloxacin resistance, and decreased ciprofloxacin susceptibility was only associated with the novel CrpP6 protein. In our collection, ciprofloxacin resistance was mainly due to chromosomal mutations in the gyrA and parC genes. However, crpP genes carrying mutations essential for protein function (G7, I26) and associated with a restored ciprofloxacin susceptibility were predominant. Despite the presence of crpP genes is not always associated with ciprofloxacin resistance, the risk of emergence of novel CrpP variants with a higher ability to affect quinolones is increasing. Furthermore, the spread of crpP genes in highly mobilizable genomic islands among related and non-related P. aeruginosa clones alert the dispersion of MDR pathogens in hospital settings.

The STEP surveillance study was designed to increase knowledge about distribution of multidrug-resistant (MDR) Enterobacterales and Pseudomonas aeruginosa in Portugal, focusing on the intensive care unit (ICU). Antimicrobial susceptibility of common agents was also evaluated and compared with that of one of the latest therapeutic introductions, ceftolozane-tazobactam (C/T). Clinical isolates of Enterobacterales (n=426) and P. aeruginosa (n=396) from patients admitted in Portuguese ICUs were included. Activity of C/T and comparators was investigated using standard broth microdilution. Isolates were recovered from urinary tract (UTI, 36.9%), intra-abdominal (IAI, 24.2%) and lower respiratory tract (LRTI, 38.9%) infections. In P. aeruginosa, overall distribution of MDR/extremely-drug resistant (XDR)/pan-drug resistant (PDR) isolates accounted for 21.2%, 23.2% and 0.8%, respectively. C/T was the most potent agent tested against P. aeruginosa and MDR/XDR/PDR phenotypes. In Escherichia coli, extended-spectrum beta-lactamases (ESBL) and carbapenemase (CP) phenotypes accounted for 16.6% and 1.7%, respectively, whereas in Klebsiella spp., ESBL and CP-phenotypes represented 28.5% and 17.9%, respectively. Overall, susceptibility of C/T against Enterobacterales was 86.9%. C/T was the least affected agent in E. coli (99.4% susceptibility), whereas its activity was moderate in Klebsiella spp. (71.5%) and Enterobacter spp. (70.4%), due in part to a high rate of ESBL and CP-phenotypes. In Enterobacterales, blaKPC was the most prevalent CP gene (63.0%), followed by blaOXA-48 (33.3%) and blaVIM (3.7%). These microbiological results reinforce C/T as a therapeutic option in ICU patients with UTI, IAI or LRTI due to P. aeruginosa or Enterobacterales isolates, but not for CP producers.

Streptococcus pneumoniae is a strictly fermentative human pathogen that relies on carbohydrate metabolism to generate energy for growth. The nasopharynx colonized by the bacterium is poor in free sugars, but mucosa lining glycans can provide a source of sugar. In blood and inflamed tissues glucose is the prevailing sugar. As a result during progression from colonization to disease S. pneumoniae has to cope with a pronounced shift in carbohydrate nature and availability. Thus, we set out to assess the pneumococcal response to sugars found in glycans and the influence of glucose (Glc) on this response at the transcriptional, physiological, and metabolic levels. Galactose (Gal), N-acetylglucosamine (GlcNAc), and mannose (Man) affected the expression of 8 to 14% of the genes covering cellular functions including central carbon metabolism and virulence. The pattern of end-products as monitored by in vivo (13)C-NMR is in good agreement with the fermentation profiles during growth, while the pools of phosphorylated metabolites are consistent with the type of fermentation observed (homolactic vs. mixed) and regulation at the metabolic level. Furthermore, the accumulation of α-Gal6P and Man6P indicate metabolic bottlenecks in the metabolism of Gal and Man, respectively. Glc added to cells actively metabolizing other sugar(s) was readily consumed and elicited a metabolic shift toward a homolactic profile. The transcriptional response to Glc was large (over 5% of the genome). In central carbon metabolism (most represented category), Glc exerted mostly negative regulation. The smallest response to Glc was observed on a sugar mix, suggesting that exposure to varied sugars improves the fitness of S. pneumoniae. The expression of virulence factors was negatively controlled by Glc in a sugar-dependent manner. Overall, our results shed new light on the link between carbohydrate metabolism, adaptation to host niches and virulence.

The human pathogen Streptococcus pneumoniae is a strictly fermentative organism that relies on glycolytic metabolism to obtain energy. In the human nasopharynx S. pneumoniae encounters glycoconjugates composed of a variety of monosaccharides, which can potentially be used as nutrients once depolymerized by glycosidases. Therefore, it is reasonable to hypothesise that the pneumococcus would rely on these glycan-derived sugars to grow. Here, we identified the sugar-specific catabolic pathways used by S. pneumoniae during growth on mucin. Transcriptome analysis of cells grown on mucin showed specific upregulation of genes likely to be involved in deglycosylation, transport and catabolism of galactose, mannose and N acetylglucosamine. In contrast to growth on mannose and N-acetylglucosamine, S. pneumoniae grown on galactose re-route their metabolic pathway from homolactic fermentation to a truly mixed acid fermentation regime. By measuring intracellular metabolites, enzymatic activities and mutant analysis, we provide an accurate map of the biochemical pathways for galactose, mannose and N-acetylglucosamine catabolism in S. pneumoniae. Intranasal mouse infection models of pneumococcal colonisation and disease showed that only mutants in galactose catabolic genes were attenuated. Our data pinpoint galactose as a key nutrient for growth in the respiratory tract and highlights the importance of central carbon metabolism for pneumococcal pathogenesis.

Background Existing tools to model cell growth curves do not offer a flexible integrative approach to manage large datasets and automatically estimate parameters. Due to the increase of experimental time-series from microbiology and oncology, the need for a software that allows researchers to easily organize experimental data and simultaneously extract relevant parameters in an efficient way is crucial. Results BGFit provides a web-based unified platform, where a rich set of dynamic models can be fitted to experimental time-series data, further allowing to efficiently manage the results in a structured and hierarchical way. The data managing system allows to organize projects, experiments and measurements data and also to define teams with different editing and viewing permission. Several dynamic and algebraic models are already implemented, such as polynomial regression, Gompertz, Baranyi, Logistic and Live Cell Fraction models and the user can add easily new models thus expanding current ones. Conclusions BGFit allows users to easily manage their data and models in an integrated way, even if they are not familiar with databases or existing computational tools for parameter estimation. BGFit is designed with a flexible architecture that focus on extensibility and leverages free software with existing tools and methods, allowing to compare and evaluate different data modeling techniques. The application is described in the context of bacterial and tumor cells growth data fitting, but it is also applicable to any type of two-dimensional data, e.g. physical chemistry and macroeconomic time series, being fully scalable to high number of projects, data and model complexity.

Background Efflux pump activity has been associated with multidrug resistance phenotypes in bacteria, compromising the effectiveness of antimicrobial therapy. The development of methods for the early detection and quantification of drug transport across the bacterial cell wall is a tool essential to understand and overcome this type of drug resistance mechanism. This approach was developed to study the transport of the efflux pump substrate ethidium bromide (EtBr) across the cell envelope of Escherichia coli K-12 and derivatives, differing in the expression of their efflux systems. Results EtBr transport across the cell envelope of E. coli K-12 and derivatives was analysed by a semi-automated fluorometric method. Accumulation and efflux of EtBr was studied under limiting energy supply (absence of glucose and low temperature) and in the presence and absence of the efflux pump inhibitor, chlorpromazine. The bulk fluorescence variations were also observed by single-cell flow cytometry analysis, revealing that once inside the cells, leakage of EtBr does not occur and that efflux is mediated by active transport. The importance of AcrAB-TolC, the main efflux system of E. coli, in the extrusion of EtBr was evidenced by comparing strains with different levels of AcrAB expression. An experimental model was developed to describe the transport kinetics in the three strains. The model integrates passive entry (influx) and active efflux of EtBr, and discriminates different degrees of efflux between the studied strains that vary in the activity of their efflux systems, as evident from the calculated efflux rates: "Equation missing" = 0.0173 ± 0.0057 min-1; "Equation missing" = 0.0106 ± 0.0033 min-1; and "Equation missing" = 0.0230 ± 0.0075 min-1. Conclusion The combined use of a semi-automated fluorometric method and an experimental model allowed quantifying EtBr transport in E. coli strains that differ in their overall efflux activity. This methodology can be used for the early detection of differences in the drug efflux capacity in bacteria accounting for antibiotic resistance, as well as for expedite screening of new drug efflux inhibitors libraries and transport studies across the bacterial cell wall.