LeDuc, C. A. | Search bioRxiv

bioRxiv2024

Automated Identification of Germline de novo Mutations in Family Trios: A Consensus-Based Informatic Approach

Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms--GATK HaplotypeCaller, DeepTrio and Velsera GRAF--exploring the assumption that a requirement of consensus can serve as an effective filter for high- quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large- scale analyses without need for manual intervention.

bioinformatics↗

bioRxiv2021

Human stem cell model of HNF1A deficiency shows uncoupled insulin to C-peptide secretion with accumulation of abnormal insulin granules

Mutations in HNF1A cause Maturity Onset Diabetes of the Young type 3 (MODY3), the most prevalent form of monogenic diabetes. We generated stem cell-derived pancreatic endocrine cells from human embryonic stem cells (hESCs) with induced hypomorphic mutations in HNF1A. Using these cells, we show that HNF1A orchestrates a transcriptional program required for distinct aspects of {beta}-cell fate and function. During islet cell differentiation, HNF1A deficiency biases islet endocrine cells towards an -cell fate associated with PAX4 down-regulation. HNF1A- deficient {beta}-cells display impaired basal and glucose stimulated-insulin secretion in association with a reduction in CACNA1A and intracellular calcium levels, and impaired insulin granule exocytosis in association with SYT13 down-regulation. Knockout of PAX4, CACNA1A and SYT13 reproduce the relevant phenotypes. Reduction of insulin secretion is associated with accumulation of enlarged secretory granules, and altered stoichiometry of secreted insulin to C-peptide. In HNF1A deficient {beta}-cells, glibenclamide, a sulfonylurea drug used in the treatment of MODY3 patients, increases intracellular calcium to levels beyond those achieved by glucose, and restores C-peptide and insulin secretion to a normal stoichiometric ratio. To study HNF1A deficiency in the context of a human disease model, we also generated stem cell-derived pancreatic endocrine cells from two MODY3 patients induced pluripotent stem cells (iPSCs). While insulin secretion defects are constitutive in cells with complete HNF1A loss of function, {beta}-cells heterozygous for hypomorphic HNF1A mutations are initially normal, but lose the ability to secrete insulin and acquire abnormal stoichiometric secretion ratios. Importantly, the defects observed in these stem cell models are also seen in circulating proportions of insulin:C-peptide in nine MODY3 patients. One sentence of summaryDeficiency of the transcription factor HNF1A biases islet endocrine cell fate towards -cells, impairs intracellular calcium homeostasis and insulin exocytosis, alters the stoichiometry of insulin to C-peptide release, and leads to an accumulation of abnormal insulin secretory granules in {beta}-cells.

cell biology↗