scholarly journals Gene Expression Modification by an Autosomal Inversion Associated With Three Male Mating Morphs

2021 ◽  
Vol 12 ◽  
Author(s):  
Jasmine L. Loveland ◽  
David B. Lank ◽  
Clemens Küpper
Keyword(s):  
Gene Expression ◽  
Allelic Imbalance ◽  
Phenotypic Diversity ◽  
Essential Gene ◽  
Male Mating ◽  
Linear Discriminant ◽  
Expression Of Genes ◽  
Philomachus Pugnax ◽  
Allele Specific ◽  
Genetically Determined

Chromosomal inversions are structural rearrangements that frequently provide genomic substrate for phenotypic diversity. In the ruff Philomachus pugnax, three distinct male reproductive morphs (Independents, Satellites and Faeders) are genetically determined by a 4.5 Mb autosomal inversion. Here we test how this stable inversion polymorphism affects gene expression in males during the lekking season. Gene expression may be altered through disruptions at the breakpoints and the accumulation of mutations due to suppressed recombination. We used quantitative PCR to measure expression of 11 candidate inversion genes across three different tissues (liver, adrenal glands and gonads) and tested for allelic imbalance in four inversion genes across 12 males of all three morphs (8 Independents, 2 Satellites, 2 Faeders). We quantified transcripts of CENPN, an essential gene disrupted by the inversion at the proximal breakpoint, at different exons distributed near and across the breakpoint region. Consistent with dosage dependent gene expression for the breakpoint gene CENPN, we found that expression in Independents was broadly similar for transcripts segments from inside and outside the inversion regions, whereas for Satellites and Faeders, transcript segments outside of the inversion showed at least twofold higher expression than those spanning over the breakpoint. Within the inversion, observed expression differences for inversion males across all four genes with allele-specific primers were consistent with allelic imbalance. We further analyzed gonadal expression of two inversion genes, HSD17B2 and SDR42E1, along with 12 non-inversion genes related to steroid metabolism and signaling in 25 males (13 Independents, 7 Satellites, 5 Faeders). Although we did not find clear morph differentiation for many individual genes, all three morphs could be separated based on gene expression differences when using linear discriminant analysis (LDA), regardless of genomic location (i.e., inside or outside of the inversion). This was robust to the removal of genes with the highest loadings. Pairwise correlations in the expression of genes showed significant correlations for 9–18 pairs of genes within morphs. However, between morphs, we only found a single association between genes SDR42E1 and AROM for Independents and Satellites. Our results suggest complex and wide-ranging changes in gene expression caused by structural variants.

scholarly journals Comparative Phenotypic Assessment of Cardiac Pathology, Physiology, and Gene Expression in C3H/HeJ, C57BL/6J, and B6C3F1/J Mice

2010 ◽  
Vol 38 (6) ◽  
pp. 923-942 ◽  
Author(s):  
Scott S. Auerbach ◽  
Reuben Thomas ◽  
Ruchir Shah ◽  
Hong Xu ◽  
Molly K. Vallant ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Phenotypic Characterization ◽  
Low Grade ◽  
Pathway Network ◽  
Cardiac Phenotype ◽  
Expression Of Genes ◽  
Genomic Studies ◽  
Histopathologic Analysis ◽  
Genetically Determined

Human cardiomyopathies often lead to heart failure, a major cause of morbidity and mortality in industrialized nations. Described here is a phenotypic characterization of cardiac function and genome-wide expression from C3H/HeJ, C57BL/6J, and B6C3F1/J male mice. Histopathologic analysis identified a low-grade background cardiomyopathy (murine progressive cardiomyopathy) in eight of nine male C3H/HeJ mice (age nine to ten weeks), but not in male C57BL/6J and in only of ten male B6C3F1/J mice. The C3H/HeJ mouse had an increased heart rate and a shorter RR interval compared to the B6C3F1/J and C57BL/6J mice. Cardiac genomic studies indicated the B6C3F1/J mice exhibited an intermediate gene expression phenotype relative to the 2 parental strains. Disease-centric enrichment analysis indicated a number of cardiomyopathy-associated genes were induced in B6C3F1/J and C3H/HeJ mice, including Myh7, My14, and Lmna and also indicated differential expression of genes associated with metabolic (e.g., Pdk2) and hypoxic stress (e.g. Hif1a). A novel coexpression and integrated pathway network analysis indicated Prkaa2, Pdk2, Rhoj, and Sgcb are likely to play a central role in the pathophysiology of murine progressive cardiomyopathy in C3H/HeJ mice. Our studies indicate that genetically determined baseline differences in cardiac phenotype have the potential to influence the results of cardiotoxicity studies.

scholarly journals Erratum: Corrigendum: Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance

2015 ◽  
Vol 47 (6) ◽  
pp. 690-690 ◽  
Author(s):  
James J Crowley ◽  
Vasyl Zhabotynsky ◽  
Wei Sun ◽  
Shunping Huang ◽  
Isa Kemal Pakatci ◽  
...  
Keyword(s):  
Gene Expression ◽  
Allelic Imbalance ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Allele Specific

scholarly journals Allelic imbalance metre ( A llim), a new tool for measuring allele‐specific gene expression with RNA ‐seq data

2013 ◽  
Vol 13 (4) ◽  
pp. 740-745 ◽  
Author(s):  
Ram Vinay Pandey ◽  
Susanne U. Franssen ◽  
Andreas Futschik ◽  
Christian Schlötterer
Keyword(s):  
Gene Expression ◽  
Allelic Imbalance ◽  
Specific Gene ◽  
Rna Seq ◽  
Specific Gene Expression ◽  
Allele Specific

scholarly journals Analyses of allele-specific gene expression in highly divergent mouse crosses identifies pervasive allelic imbalance

2015 ◽  
Vol 47 (4) ◽  
pp. 353-360 ◽  
Author(s):  
James J Crowley ◽  
Vasyl Zhabotynsky ◽  
Wei Sun ◽  
Shunping Huang ◽  
Isa Kemal Pakatci ◽  
...  
Keyword(s):  
Gene Expression ◽  
Allelic Imbalance ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Allele Specific

scholarly journals Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

2019 ◽  
Author(s):  
Zeynep Kalender Atak ◽  
Ibrahim Ihsan Taskiran ◽  
Christopher Flerin ◽  
David Mauduit ◽  
Liesbeth Minnoye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Deep Learning ◽  
Allelic Imbalance ◽  
Chromatin Accessibility ◽  
Genome Variation ◽  
Explainable Ai ◽  
Allele Specific ◽  
The Impact ◽  
Deep Learning Model

Brief AbstractPrioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

scholarly journals Unexpected variability of allelic imbalance estimates from RNA sequencing

2020 ◽  
Author(s):  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
Saumya Gupta ◽  
Andrey A. Mironov ◽  
Shamil Sunyaev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Allelic Imbalance ◽  
Experimental Methods ◽  
Specific Gene ◽  
Differential Analysis ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific ◽  
Noise Models

RNA sequencing and other experimental methods that produce large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques have not been fully understood. We assessed the reproducibility of allele-specific expression measurements by conducting replicate sequencing experiments from the same RNA sample. Surprisingly, variation in the estimates of allelic imbalance (AI) between technical replicates was up to 7-fold higher than expected from commonly applied noise models. We show that AI overdispersion varies substantially between replicates and between experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression

scholarly journals Genomic analysis of allele-specific expression in the mouse liver

2015 ◽  
Author(s):  
Ashutosh K Pandey ◽  
Robert W Williams
Keyword(s):  
Gene Expression ◽  
Phenotypic Diversity ◽  
Direct Method ◽  
Eqtl Analysis ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
F1 Hybrid ◽  
Cis Acting ◽  
Functional Variants ◽  
Allele Specific

Genetic differences in gene expression contribute significantly to phenotypic diversity and differences in disease susceptibility. In fact, the great majority of causal variants highlighted by genome-wide association are in non-coding regions that modulate expression. In order to quantify the extent of allelic differences in expression, we analyzed liver transcriptomes of isogenic F1 hybrid mice. Allele-specific expression (ASE) effects are pervasive and are detected in over 50% of assayed genes. Genes with strong ASE do not differ from those with no ASE with respect to their length or promoter complexity. However, they have a higher density of sequence variants, higher functional redundancy, and lower evolutionary conservation compared to genes with no ASE. Fifty percent of genes with no ASE are categorized as house-keeping genes. In contrast, the high ASE set may be critical in phenotype canalization. There is significant overlap between genes that exhibit ASE and those that exhibit strong cis expression quantitative trait loci (cis eQTLs) identified using large genetic expression data sets. Eighty percent of genes with cis eQTLs also have strong ASE effects. Conversely, 40% of genes with ASE effects are associated with strong cis eQTLs. Cis-acting variation detected at the protein level is also detected at the transcript level, but the converse is not true. ASE is a highly sensitive and direct method to quantify cis-acting variation in gene expression and complements and extends classic cis eQTL analysis. ASE differences can be combined with coding variants to produce a key resource of functional variants for precision medicine and genome-to-phenome mapping.

scholarly journals Gene expression noise produces cell-to-cell heterogeneity in eukaryotic homologous recombination rate

2019 ◽  
Author(s):  
Jian Liu ◽  
Jean-Marie François ◽  
Jean-Pascal Capp
Keyword(s):  
Gene Expression ◽  
Dna Damage ◽  
Homologous Recombination ◽  
Genome Stability ◽  
Phenotypic Diversity ◽  
Cell Heterogeneity ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Expression Of Genes ◽  
Evolutionary Consequences

AbstractVariation in gene expression among genetically identical individual cells (called gene expression noise) directly contributes to phenotypic diversity. Whether such variation can impact genome stability and lead to variation in genotype remains poorly explored. We addressed this question by investigating whether noise in the expression of genes affecting homologous recombination (HR) activity either directly (RAD52) or indirectly (RAD27) confers cell-to-cell heterogeneity in HR rate inSaccharomyces cerevisiae. Using cell sorting to isolate subpopulations with various expression levels, we show that spontaneous HR rate is highly heterogeneous from cell-to-cell in clonal populations depending on the cellular amount of proteins affecting HR activity. Phleomycin-induced HR is even more heterogeneous, showing thatRAD27expression noise strongly affects the rate of recombination from cell-to-cell. Strong variations in HR rate between subpopulations are not correlated to strong changes in cell cycle stage. Moreover, this heterogeneity occurs even when simultaneously sorting cells at equal expression level of another gene involved in DNA damage response (BMH1) that is upregulated by DNA damage, showing that the initiating DNA damage is not responsible for the observed heterogeneity in HR rate. Thus gene expression noise seems mainly responsible for this phenomenon. Finally, HR rate non-linearly scales with Rad27 levels showing that total amount of HR cannot be explained solely by the time- or population-averaged Rad27 expression. Altogether, our data reveal interplay between heterogeneity at the gene expression and genetic levels in the production of phenotypic diversity with evolutionary consequences from microbial to cancer cell populations.

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