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Transcriptome Signatures Predict Phenotypic Variations of . | LitMetric

AI Article Synopsis

  • - Health care facilities are threatened by a new human fungal pathogen that is resistant to many antifungal drugs and lacks effective diagnostic tools, leading to concerns about its genetic and phenotypic diversity across different regions.
  • - The study focuses on the gene expression differences in clinical isolates with varying antifungal resistance, using antifungal susceptibility screening and comparative transcriptional profiling to highlight significant variations.
  • - Findings indicate that even small changes in gene expression can influence drug response, and large-scale transcriptional profiling could assist in predicting the behavior of patient isolates, ultimately aiding in selecting more effective antifungal treatments.

Article Abstract

Health care facilities are facing serious threats by the recently emerging human fungal pathogen owing to its pronounced antifungal multidrug resistance and poor diagnostic tools. Distinct clades evolved seemingly simultaneously at independent geographical locations and display both genetic and phenotypic diversity. Although comparative genomics and phenotypic profiling studies are increasing, we still lack mechanistic knowledge about the species diversification and clinical heterogeneity. Since gene expression variability impacts phenotypic plasticity, we aimed to characterize transcriptomic signatures of patient isolates with distinct antifungal susceptibility profiles in this study. First, we employed an antifungal susceptibility screening of clinical isolates to identify divergent intra-clade responses to antifungal treatments. Interestingly, comparative transcriptional profiling reveals large gene expression differences between clade I isolates and one clade II strain, irrespective of their antifungal susceptibilities. However, comparisons at the clade levels demonstrate that minor changes in gene expression suffice to drive divergent drug responses. Finally, we functionally validate transcriptional signatures reflecting phenotypic divergence of clinical isolates. Thus, our results suggest that large-scale transcriptional profiling allows for predicting phenotypic diversities of patient isolates, which may help choosing suitable antifungal therapies of multidrug-resistant

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8079977PMC
http://dx.doi.org/10.3389/fcimb.2021.662563DOI Listing

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