Bu, C. H. | Search bioRxiv

bioRxiv2021

De novo germline mutation in the Dual Specificity Phosphatase 10 gene accelerates autoimmune diabetes in Non-Obese Diabetic (NOD) mice

Here we report the isolation by selective breeding of two sublines of Non-Obese Diabetic (NOD) mice exhibiting a significant difference in the incidence of autoimmune type 1 diabetes (T1D). Whole genome sequencing of the NOD/NckH (high T1D incidence) and NOD/NckL (low T1D incidence) revealed the presence of a limited number of variants specific to each subline. Treating the age of T1D onset as a quantitative trait and using automated meiotic mapping (AMM), enhanced susceptibility in the NOD/NckH subline was unambiguously attributed to a recessive allele of Dusp10 which encodes a dual specificity phosphatase. The causative effect of the mutation was verified with a high level of confidence by targeting Dusp10 with CRISPR/Cas9 in NOD/NckL mice: in these animals a higher incidence of diabetes was observed. Expression of wild-type Dusp10 correlated with higher levels of surface PD-L1 in the islets of NOD/NckL mice.

immunology↗

bioRxiv2020

Candidate Explorer: a tool for discovery, evaluation, and display of mutations causing significant immune phenotypes

When applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

genetics↗