Biology subjects

Munger, S. C.

Publications and source records attributed to Munger, S. C..

3 recordsLinked to original sources

bioRxiv2018

The RNA-Binding Protein DND1 Acts Sequentially as a Negative Regulator of Pluripotency and a Positive Regulator of Epigenetic Modifiers Required for Germ Cell Reprogramming

The adult spermatogonial stem cell population arises from pluripotent primordial germ cells (PGCs) that enter the fetal testis around embryonic day 10.5 (E10.5). These cells undergo rapid mitotic proliferation, then enter a prolonged period of cell cycle arrest (G1/G0) during which they transition to pro-spermatogonia. In mice homozygous for the Ter mutation in the RNA-binding protein DND1 (DND1Ter/Ter), many germ cells fail to enter G1/G0, and give rise to teratomas, tumors in which many embryonic cell types are represented. To investigate the origin of these tumors, we sequenced the transcriptome of male germ cells in DND1Ter/Ter mutants at E12.5, E13.5, and E14.5, just prior to the formation of teratomas, and correlated this information with direct targets of DND1 identified by DO-RIP-Seq. Consistent with previous results, we found that DND1 controls the down regulation of many genes associated with pluripotency and active cell cycle, including elements of the mTor, Hippo and Bmp/Nodal signaling pathways. However, DND1 targets also include genes associated with male differentiation including a large group of chromatin regulators activated in wild type but not mutant germ cells during the transition between E13.5 and E14.5. These results suggest multiple functions of DND1, and link DND1 to the initiation of epigenetic modifications in male germ cells.

cell biology↗

bioRxiv2017

Hierarchical Analysis of Multi-mapping RNA-Seq Reads Improves the Accuracy of Allele-specific Expression

Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. Direct RNA sequencing (RNA-Seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. However, estimating ASE is challenging due to ambiguities in read alignment. Current approaches do not account for the hierarchy of multiple read alignments to genes, isoforms, and alleles. We have developed EMASE (Expectation-Maximization for Allele Specific Expression), an integrated approach to estimate total gene expression, ASE, and isoform usage based on hierarchical allocation of multi-mapping reads. In simulations, EMASE outperforms standard ASE estimation methods. We apply EMASE to RNA-Seq data from F1 hybrid mice where we observe widespread ASE associated with cis-acting polymorphisms and a small number of parent-of-origin effects at known imprinted genes. The EMASE software is freely available under GNU license at https://github.com/churchill-lab/emase and it can be adapted to other sequencing applications.

bioinformatics↗

bioRxiv2017

Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in Diversity Outbred mice

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. Multi-parent populations provide a resource for developing methods to understand these relationships. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Using weighted gene co-expression network analysis (WGCNA), we summarized transcript data into functional modules which we then used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects. We were also able to identify alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

genetics↗