Biology subjects

Raghupathy, N.

Publications and source records attributed to Raghupathy, N..

7 recordsLinked to original sources

bioRxiv2024

Anapc5 and Anapc7 as genetic modifiers of KIF18A function in fertility and mitotic progression

The kinesin family member 18A (KIF18A) is an essential regulator of microtubule dynamics and chromosome alignment during mitosis. Functional dependency on KIF18A varies by cell type and genetic context but the heritable factors that influence this dependency remain unknown. To address this, we took advantage of the variable penetrance observed in different mouse strain backgrounds to screen for loci that modulate germ cell depletion in the absence of KIF18A. We found a significant association at a Chr5 locus where anaphase promoting complex subunits 5 (Anapc5) and 7 (Anapc7) were the top candidate genes. We found that both genes were differentially expressed in a sensitive strain background when compared to resistant strain background at key timepoints in gonadal development. We also identified a novel retroviral insertion in Anapc7 that may in part explain the observed expression differences. In cell line models, we found that depletion of KIF18A induced mitotic arrest, which was partially rescued by co-depletion of ANAPC7 (APC7) and exacerbated by co-depletion of ANAPC5 (APC5). These findings suggest that differential expression and activity of Anapc5 and Anapc7 may influence sensitivity to KIF18A depletion in germ cells and CIN cells, with potential implications for optimizing antineoplastic therapies.

cell biology↗

bioRxiv2022

Variation in histone configurations correlates with gene expression across nine inbred strains of mice

It is well established that epigenetic features, such as histone modifications and DNA methylation, are associated with variation in gene expression across cell types. Less well known is the extent to which epigenetic states vary across genetically diverse individuals, and whether such variation corresponds to inter-individual variation in gene expression. To investigate genetically driven variation in epigenetics, we conducted a survey of epigenetic modifications and gene expression in hepatocytes of nine inbred mouse strains. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), and DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represented a distinct combination of the four histone modifications. We found that chromatin states varied widely across the nine strains and that epigenetic state was strongly correlated with local gene expression. We replicated this correspondence between chromatin state and gene expression in an independent population of Diversity Outbred mice in which we imputed local chromatin state. In contrast, we found that DNA methylation did not vary across the inbred strains and was not correlated with variation in gene expression in DO mice. This work suggests that chromatin state is highly influenced by local genotype and may be a primary mode through which expression quantitative trait loci (eQTLs) are mediated. Through examples, we demonstrate that naturally occurring chromatin state variation, in conjunction with gene expression, can aid in functional annotation of the mouse genome. Finally, we provide a data resource that documents variation in chromatin state in hepatocytes across genetically distinct mice.

genomics↗

bioRxiv2020

Genotype-free individual genome reconstruction of Multiparental Population Models by RNA sequencing data

AbstractMulti-parent populations (MPPs), model organisms derived from two or more inbred founder strains, are widely used in biomedical and agricultural research. Gene expression profiling by direct RNA sequencing (RNA-Seq) is commonly applied to MPPs to investigate gene expression regulation and to identify candidate genes. In genetically diverse populations, including MPPs, quantification of gene expression is improved when the RNA-Seq reads are aligned to individualized transcriptomes that incorporate known polymorphic loci. However, the process of constructing and analyzing individual genomes can be computationally demanding and error prone. We propose a new approach, genome reconstruction by RNA-Seq (GBRS), that relies on simultaneous alignment of RNA-Seq reads to founder strain transcriptomes to reconstruct the diploid genome and quantify total and allele-specific gene expression in MPPs. We demonstrate that GBRS performs as well as methods that rely on high-density genotyping arrays. When used in conjunction with other genotyping methods, GBRS provides quality control for detecting sample mix-ups or contamination. GBRS software is freely available at https://github.com/churchill-lab/gbrs.

bioinformatics↗

bioRxiv2019

The genome of C57BL/6J \"Eve\", the mother of the laboratory mouse genome reference strain

Isogenic laboratory mouse strains are used to enhance reproducibility as individuals within a strain are essentially genetically identical. For the most widely used isogenic strain, C57BL/6, there is also a wealth of genetic, phenotypic, and genomic data, including one of the highest quality reference genomes (GRCm38.p6). However, laboratory mouse strains are living reagents and hence genetic drift occurs and is an unavoidable source of accumulating genetic variability that can have an impact on reproducibility over time. Nearly 20 years after the first release of the mouse reference genome, individuals from the strain it represents (C57BL/6J) are at least 26 inbreeding generations removed from the individuals used to generate the mouse reference genome. Moreover, C57BL/6J is now maintained through the periodic reintroduction of mice from cryopreserved embryo stocks that are derived from a single breeder pair, aptly named C57BL/6J Adam and Eve. To more accurately represent the genome of todays C57BL/6J mice, we have generated a de novo assembly of the C57BL/6J Eve genome (B6Eve) using high coverage, long-read sequencing, optical mapping, and short-read data. Using these data, we addressed recurring variants observed in previous mouse studies. We have also identified structural variations that impact coding sequences, closed gaps in the mouse reference assembly, some of which are in genes, and we have identified previously unannotated coding sequences through long read sequencing of cDNAs. This B6Eve assembly explains discrepant observations that have been associated with GRCm38-based analyses, and has provided data towards a reference genome that is more representative of the C57BL/6J mice that are in use today.

genomics↗

bioRxiv2018

Reference trait analysis reveals correlations between gene expression and quantitative traits in disjoint samples

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTLs. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative \"reference\" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint sub-samples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest in order to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the dataset and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait datasets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of the reference trait method for identifying relations between complex traits and their molecular substrates.\n\nAUTHOR SUMMARYSystems genetics exploits natural genetic variation and high-throughput measurements of molecular intermediates to dissect genetic contributions to complex traits. An important goal of this strategy is to correlate molecular features, such as transcript or protein abundance, with complex traits. For practical, technical, or financial reasons, it may be impossible to measure complex traits and molecular intermediates on the same individuals. Instead, in some cases these two sets of traits may be measured on independent cohorts. We outline a method, reference trait analysis, for identifying molecular correlates of complex traits in this scenario. We show that our method powerfully identifies complex trait correlates across a wide range of parameters that are biologically plausible and experimentally practical. Furthermore, we show that reference trait analysis can identify transcripts correlated to a complex trait more accurately than approaches such as TWAS that use genetic variation to predict gene expression. Reference trait analysis will contribute to furthering our understanding of variation in complex traits by identifying molecular correlates of complex traits that are measured in different individuals.

genomics↗

bioRxiv2018

Accurate estimation of single cell allele-specific gene expression using all reads and combining information across cells

Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We propose a new method for ASE analysis from single cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells.

bioinformatics↗

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↗