Biology subjects

Kalinowski, P.

Publications and source records attributed to Kalinowski, P..

3 recordsLinked to original sources

bioRxiv2024

A newly-identified IncY plasmid from multi-drug resistant Escherichia coli isolated from dairy cattle feces in Poland

Comprehensive whole-genome sequencing was performed on two multi-drug resistant Escherichia coli strains isolated from cattle manure from a typical dairy farm in Poland in 2020. The identified strains are resistant to beta-lactams, aminoglycosides, tetracyclines, trimethoprim/sulfamethoxazole, and fluoroquinolones. The complete sequences of the harbored plasmids revealed antibiotic-resistance genes (ARGs) located within many mobile genetic elements (e.g., insertional sequences or transposons), and genes facilitating conjugal transfer or promoting horizontal gene transfer. These plasmids are hitherto undescribed. Similar plasmids have been identified, but not in Poland. The identified plasmids carried resistance genes, including the tetracycline resistance gene tet(A), aph family aminoglycoside resistance genes aph(3")-lb and aph(6)-ld, {beta}-lactam resistance genes blaTEM-1, blaCTX-M-15, sulfonamide resistance gene sul2, fluoroquinolone resistance gene qnrS1, and the trimethoprim resistance gene dfrA14. The characterized resistance plasmids were categorized into the IncY incompatibility group, indicating a high possibility for dissemination among the Enterobacteriaceae. While similar plasmids (99% identity) have been found in environmental and clinical samples, none have been identified in farm animals. This findings are significant within the One Health framework, as they underline the potential for antimicrobial-resistant E. coli from livestock and food sources to be transmitted to humans and vice versa. It highlights the need for careful monitoring and strategies to limit the spread of antibiotic resistance in the One Health approach.

microbiology↗

bioRxiv2024

The IncC and IncX1 resistance plasmids present in multi-drug resistant Escherichia coli strains isolated from poultry manure in Poland

The study describes the whole-genome sequencing of two antibiotic-resistant representative Escherichia coli strains, isolated from poultry manure in 2020. The samples were obtained from a commercial chicken meat production facility in Poland. The antibiotic resistance profile was characterized by co-resistance to {beta}-lactam antibiotics, aminoglycosides, and fluoroquinolones. The three identified resistance plasmids (R-plasmids), pECmdr13.2, pECmdr13.3 and pECmdr14.1, harbored various genes conferring resistance to tetracyclines (tetR[A]) for, aminoglycoside (aph, aac and aad families), {beta}-lactam (blaCMY-2, blaTEM-176), sulfonamide (sul1, sul2), fluoroquinolone (qnrS1), and phenicol (floR). These plasmids, which have not been previously reported in Poland, were found to carry IS26 insertion elements, the intI1-integrase gene, as well as conjugal transfer genes, facilitating horizontal gene transfer. Plasmids pECmdr13.2 and pECmdr14.1 also possessed a mercury resistance gene operon related to transposon Tn1696; this promotes plasmid persistence even without antibiotic selection pressure due to co-selection mechanisms such as co-resistance. The chicken manure-derived plasmids belonged to the IncX1 (narrow host range) and IncC (broad host range) incompatibility groups. Similar plasmids have been identified in various environments, clinical isolates, and farm animals, including cattle, swine, and poultry. This study holds significant importance for the One Health approach, as it highlights the potential for antimicrobial-resistant bacteria from livestock and food sources, particularly E. coli, to transfer through the food chain to humans and vice versa. This underscores the need for vigilant monitoring of R-plasmids prevalence in the human, animal and natural environments, and to implement strategies to mitigate the spread of antibiotic resistance.

microbiology↗

bioRxiv2023

Deep learning-based cell profiling based on neuronal morphology

Treatment of neurons with {beta}-amyloid peptide (A{beta}1-42) has been widely used as a model to interrogate the cellular and molecular mechanisms underlying Alzheimers disease, and as an assay system to identify drugs that reverse or block disease phenotype. Prior studies have largely relied on high content imaging (HCI) to extract cellular features such as neurite length or branching, but these have not offered a robust/comprehensive means of relating readout to A{beta}1-42 concentrations. Here, we use a deep learning-based cell profiling technique to directly measure the impact of A{beta}1-42 on primary murine cortical neurons. The deep learning model achieved approximately 80% accuracy, compared to 54% for the cell phenotypic feature-based approach. The deep learning model could distinguish subtle neuronal morphological changes induced by a range of A{beta}1-42 concentration. When tested on a separate dataset, the accuracy remained comparable and dropped by only 2%. Our study demonstrates that deep learning-based cell profiling is superior to HCI-based feature extraction on neuronal morphology and it provides an alternative to a dose/response curve, where the modality of the response does not have to be pre-determined. Moreover, this approach could form the basis of a screening tool that can be applied to any cellular model where appropriate phenotypic markers based on genotypes and/or pathological insults are available.

bioinformatics↗