Biology subjects

Kolenda, R.

Publications and source records attributed to Kolenda, R..

3 recordsLinked to original sources

bioRxiv2023

Profiling of the Helicobacter pylori redox switch HP1021 regulon using a multi-omics approach

The gastric human pathogen Helicobacter pylori has developed mechanisms to combat stress factors, including reactive oxygen species (ROS), which are present in the stomachs harsh environment. Here, we present a comprehensive study on the redox switch protein HP1021 regulon combining transcriptomic, proteomic and DNA-protein interactions analyses. Our results indicated that HP1021 decides about H. pylori response to oxidative stress. HP1021 regulon included 498 genes, of which 411 responded to oxidative stress. HP1021 controlled typical ROS response pathways (katA, rocF) and less canonical ones, particularly DNA uptake and central carbohydrate metabolism. We identified HP1021 as the first molecular regulator of competence in H. pylori, as HP1021-dependent repression of the comB DNA uptake genes was relieved under oxidative conditions, increasing natural competence. Furthermore, HP1021 controlled glucose consumption by directly regulating the gluP transporter and had an important impact on maintaining the energetic balance in the cell.

molecular biology↗

bioRxiv2022

Strain and serovar variants of Salmonella enterica exhibit diverse tolerance to food chain-related stress

Non-Typhoidal Salmonella (NTS) continues to be a leading cause of foodborne illness worldwide. Food manufacturers implement hurdle technology by combining more than one approach to control food safety and quality, including preservatives such as organic acids, refrigeration, and heating. We assessed the variation in survival in stresses of genotypically diverse isolates of Salmonella enterica to identify genotypes with potential elevated risk to sub-optimal processing or cooking. Sub-lethal heat treatment, survival in desiccated conditions and growth in the presence of NaCl or organic acids were investigated. S. Gallinarum strain 287/91 was most sensitive to all stress conditions. While none of the strains replicated in a food matrix at 4{degrees}C, S. Infantis strain S1326/28 retained the greatest viability, and six strains exhibited a significantly reduced viability. A S. Kedougou strain exhibited the greatest resistance to incubation at 60{degrees}C in a food matrix that was significantly greater than S. Typhimurium U288, S Heidelberg, S. Kentucky, S. Schwarzengrund and S. Gallinarum strains. Two isolates of monophasic S. Typhimurium, S04698-09 and B54 Col9 exhibited the greatest tolerance to desiccation that was significantly more than for the S. Kentucky and S. Typhimurium U288 strains. In general, the presence of 12mM acetic acid or 14mM citric acid resulted in a similar pattern of decreased growth in broth, but this was not observed for S. Enteritidis, and S. Typhimurium strains ST4/74 and U288 S01960-05. Acetic acid had a moderately greater effect on growth despite the lower concentration tested. A similar pattern of decreased growth was observed in the presence of 6% NaCl, with the notable exception that S. Typhimurium strain U288 S01960-05 exhibited enhanced growth in elevated NaCl concentrations. An understanding of the molecular basis of phenotypic variation in response to stress has the potential to improve process validation during food challenge tests, improve processing, and result in more reliable risk assessments in the food industry.

microbiology↗

bioRxiv2022

The impact of negative data sampling on antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared to traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

bioinformatics↗