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An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.
Hum Genet
January 2025
TCS Research, Tata Consultancy Services, Hyderabad, India.
Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy.
View Article and Find Full Text PDFThe Morphologic and Molecular Heterogeneity of Fumarate Hydratase-deficient Leiomyomas: Integrative Molecular Profiling of Uterine Smooth Muscle Tumors With Histologic Feature Correlation.
Int J Gynecol Pathol
January 2025
Departments of Pathology.
The morphologic features of uterine smooth muscle tumors (USMTs) are subject to interobserver variability and are complicated by consideration of features of fumarate hydratase deficiency (FHd) and other morphologic subtypes, with difficult cases occasionally diagnosed as smooth muscle tumor of uncertain malignant potential (STUMP). We compare immunohistochemical findings and detailed morphologic analysis of 45 USMTs by 4 fellowship-trained gynecologic pathologists with comprehensive molecular analysis, focusing on FHd leiomyomas (n=15), compared to a variety of other USMTs with overlapping morphologic features, including 9 STUMPs, 8 usual-type leiomyomas (ULM), 11 apoplectic leiomyomas, and 2 leiomyomas with bizarre nuclei (LMBN). FHd leiomyomas, defined by immunohistochemical (IHC) loss of FH and/or 2SC accumulation, showed FH mutations and/or FH copy loss in all cases, with concurrent TP53 mutations in 2 tumors.
View Article and Find Full Text PDFUnlabelled: Autism Spectrum Disorder (ASD) is characterized by restricted and repetitive behaviors and social differences, both of which may manifest, in part, from underlying differences in corticostriatal circuits and reinforcement learning. Here, we investigated reinforcement learning in mice with mutations in either or , both high-confidence ASD risk genes associated with major syndromic forms of ASD. Using an odor-based two-alternative forced choice (2AFC) task, we tested adolescent mice of both sexes and found male and heterozygote (Het) mice showed enhanced learning performance compared to their wild type (WT) siblings.
View Article and Find Full Text PDFZebrafish models of genetic epilepsy benefit from the ability to assess disease-relevant knock-out alleles with numerous tools, including genetically encoded calcium indicators (GECIs) and hypopigmentation alleles to improve visualization. However, there may be unintended effects of these manipulations on the phenotypes under investigation. There is also debate regarding the use of stable loss-of-function (LoF) alleles in zebrafish, due to genetic compensation (GC).
View Article and Find Full Text PDFExtracellular domain of TREM2 possess two distinct ligand recognition sites: Insights from machine-learning guided docking and all-atoms molecular dynamics simulations.
Heliyon
January 2025
Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India.
The myeloid-specific triggering receptors expressed on myeloid cells 2 (TREM2) is a group of class I receptors expressed in brain microglia plays a decisive role in neurodegenerative diseases such as Alzheimer's disease (AD) and Nasu Hakola disease (NHD). The extracellular domain (ECD) of TREM2 interacts with a wide-range of ligands, yet the molecular mechanism underlying recognition of such ligands to this class I receptor remains underexplored. Herein, we undertook a systematic investigation for exploring the mode of ligand recognition in immunoglobulin-like ectodomain by employing both knowledge-based and machine-learning guided molecular docking approach followed by the state-of-the-art all atoms molecular dynamics (MD) simulations.
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