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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
How Genomic and Structural Context Could Shape JAK-STAT Variant Pathogenicity
Markus Hoffmann1, Hye Kyung Lee2
1Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Centerhttps://ror.org/05vzafd60, Washington, DC, USA.
None:
The Janus kinase (JAK)-Signal Transducer and Activator of Transcription (STAT) pathway is essential for cellular signal transduction, regulating immune responses, hematopoiesis, and cell proliferation. Dysregulation of JAK-STAT signaling due to genetic variations, particularly missense mutations, has been implicated in autoimmune disorders, cancers, and hematological malignancies. This study investigates missense mutations in JAK and STAT genes, focusing on disease-associated single nucleotide polymorphisms (SNPs) and ClinVar benign variants identified in the All of Us and COSMIC databases. We analyzed the distribution of these mutations across functional domains, their structural localization, and biochemical properties. We identified mutation hotspots within specific domains, highlighting their correlation with disease phenotypes. Structural mapping revealed that disease-associated SNPs predominantly localize in linker regions and at the boundaries of secondary structures, suggesting a significant impact on folding, stability, and function of JAK and STAT proteins. Additionally, we examined the genomic context of mutations and identified vulnerable sequences; for example, 'GATC'. Furthermore, our analysis found no predominant association between potential CRISPR-Cas9 target sites and ClinVar benign/disease-associated SNPs. The analysis of amino acid sequence patterns surrounding mutations uncovered an enrichment of hydrophobic residues leucine (Leu), isoleucine (Ile), methionine (Met), and phenylalanine (Phe) in close proximity to disease-associated mutations. Our findings emphasize the importance of structural and biochemical context in determining pathogenicity. In this study, we provide a bioinformatic strategy for refining variant classification and understanding the roles of JAK-STAT pathway mutations in disease.
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