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Jonas, D.

Publications and source records attributed to Jonas, D..

2 recordsLinked to original sources

Genome-wide association study reveals KCNQ3 gene as a risk factor for fear-related behavior in dogs and zebrafish

Freezing behavior, characterised by attentive immobility as a reaction to a perceived threat, is widely studied in the context of fear, anxiety and stress. To uncover the genetic factors underlying this behavior, we conducted a genome-wide association study (GWAS) in kennel-housed beagle dogs. Our analysis identified a single-nucleotide polymorphism (SNP) in intron 5 of the KCNQ3 gene on chromosome 13 associated with freezing behavior in response to unfamiliar environments and people. To validate this finding, we used a zebrafish model, where CRISPR/Cas9-induced kcnq3 deficiencies led to heightened fear and arousal in two behavioral tests. KCNQ3 is implicated in several neurodevelopmental and psychiatric disorders, and our results highlight its evolutionarily conserved role in modulating fear responses. In dogs, an enriched environment can mitigate the adverse effects of KCNQ3 deficiency by reducing threat perception, highlighting the role of gene-environment interactions in shaping behavioral responses. TeaserKCNQ3 influences fear responses across species, with gene-environment interactions shaping freezing behavior in dogs.

animal behavior and cognition↗

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

microbiology↗