Biology subjects

Kowalewski, J.

Publications and source records attributed to Kowalewski, J..

5 recordsLinked to original sources

bioRxiv2025

Structural and molecular basis of resistance to aminoglycoside antibiotics by APH(3')-IIb from Pseudomonas aeruginosa

Pseudomonas aeruginosa is one of the six bacteria of greatest concern identified by the WHO for its resistance to antibiotics. In the case of aminoglycosides, used in combination with other antibiotics in the treatment of severe infections, resistance is mainly due to modification of the antibiotic by bacterial enzymes. In this study, we have functionally and structurally characterized one such enzyme from P. aeruginosa, aminoglycoside 3-O-phosphotransferase IIb, APH(3)-IIb. The results will provide a better understanding of this resistance mechanism and enable us to envisage solutions for blocking it and restoring the efficacy of aminoglycosides.

biochemistry↗

bioRxiv2025

Whole-miRNome sequencing (WMS) - a panel for targeted sequencing of all human miRNA genes

There is a growing interest in the genetic variation of noncoding genomic elements, including miRNAs, and several mutations in miRNA genes implicated in human diseases, including cancer, have already been detected. However, the lack of dedicated analytical tools severely hampers progress in this area. In this study, we developed whole-miRNome sequencing (WMS), which enables targeted sequencing of all human miRNA genes (n[~]2000) and 28 miRNA biogenesis genes. Herein, by sequencing almost 600 samples, including [~]300 tumor/normal pairs of samples from different cancer types, we identified [~]2, 000 mutations, including 1, 435 cancer somatic mutations, with 879 occurring in miRNA genes. These mutations were located in all parts of the genes, including seed or cleavage sites essential for the functioning of miRNA genes. The high reliability of the mutations was confirmed through various approaches, including different sequencing methods. The analysis identified several miRNA genes with functional enrichment of cancer mutations, including MIR3928, specifically mutated in basal cell carcinoma (BCC), indicating its potential role in this cancer. WMS also allowed the identification of multiple copy number alterations, hotspots of which often encompassed miRNA genes. WMS provides highly effective, low-cost sequencing of all miRNA genes in different types of samples, including highly degraded FFPE samples.

genomics↗

bioRxiv2024

Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Preventing beneficial insects like honey bees (Apis mellifera) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >180 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory show a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available. SIGNIFICANCE STATEMENTWith honey bee populations declining partly due to pesticide exposure, we aimed to find smells that could keep bees away from pesticide-treated crops. We overcome challenges studying the complex bee olfactory system by developing an AI model trained on existing bee behavior data to predict chemicals bees would find aversive. The predictive model screened millions of compounds, identifying more than 130 potential repellents. Behavior testing in the lab and in field tests confirmed the effectiveness of the bee repellents. This method could lead to bee-safe pesticide formulations, potentially protecting pollinator populations while maintaining crop protection.

animal behavior and cognition↗

bioRxiv2023

Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents

The rational discovery of behaviorally active odorants is impeded by limited information on how the olfactory system generates percept or valence for a volatile chemical. In previous studies, we showed that chemical informatics could be used to predict ligands for a large repertoire of odorant receptors in Drosophila (Boyle et al., 2013). However, it remained difficult to predict behavioral valence of volatiles since the activities of a large ensembles of odor receptors encode odor information, and little is known about the complex information processing circuitry. This is a systems-level challenge well-suited for Machine-learning approaches which we have used to model olfaction in two organisms with completely unrelated olfactory receptor proteins: humans ([~]400 GPCRs) and insects ([~]100 ion-channels). We use chemical structure-based Machine Learning models for prediction of valence in insects and for 146 human odor characters. Using these predictive models, we evaluate a vast chemical space of >10 million compounds in silico. Validations of human and insect behaviors yield very high success rates. The discovery of desirable fragrances for humans that are highly repulsive to insects offers a powerful integrated approach to discover new insect repellents.

neuroscience↗

bioRxiv2023

wTSA-CRAFT : an open-access web server for rapid analysis of thermal shift assay experiments

The automated data processing provided by the TSA-CRAFT tool enables now to reach high throughput speed analysis of thermal shift assays. While the software is powerful and freely available, it still requires installation process and command line efforts that could be discouraging. To simplify the procedure, we decided to make it available and easy to use by implementing it with a graphical interface via a web server, enabling a cross-platform usage from any web browsers. We developed a web server embedded version of the TSA-CRAFT tool, enabling a user-friendly graphical interface for formatting and submission of the input file and visualization of the selected thermal denaturation profiles. We describe a typical case study of buffer condition optimization of the biologically relevant APH(3)-IIb bacterial protein in a 96 deep-well thermal shift analysis screening. wTSA-CRAFT is freely accessible for non-commercial usage at https://bioserv.cbs.cnrs.fr/TSA_CRAFT.

bioinformatics↗