Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

Jim A Mossman,David M Rand,Leann M Biancani

doi:10.1186/s12864-019-6061-y

Jim A Mossman, David M Rand + Show 1 more

Open Access

https://doi.org/10.1186/s12864-019-6061-y

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Abstract

BackgroundMitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes. The > 1 billion years of nuclear and mitochondrial gene co-evolution has necessitated coordinated expression of gene products from both genomes that maintain mitochondrial, and more generally, eukaryotic cellular function. How mitochondrial DNA (mtDNA) variation modifies host fitness has proved a challenging question but has profound implications for evolutionary and medical genetics. In Drosophila, we have previously shown that recently diverged mtDNA haplotypes within-species can have more impact on organismal phenotypes than older, deeply diverged haplotypes from different species. Here, we tested the effects of mtDNA haplotype variation on gene expression in Drosophila under standardized conditions. Using the Drosophila Genetic Reference Panel (DGRP), we constructed a panel of mitonuclear genotypes that consists of factorial variation in nuclear and mtDNA genomes, with mtDNAs originating in D. melanogaster (2x haplotypes) and D. simulans (2x haplotypes).ResultsWe show that mtDNA haplotype variation unequivocally alters nuclear gene expression in both females and males, and mitonuclear interactions are pervasive modifying factors for gene expression. There was appreciable overlap between the sexes for mtDNA-sensitive genes, and considerable transcriptional variation attributed to particular mtDNA contrasts. These genes are generally found in low-connectivity gene co-expression networks, occur in gene clusters along chromosomes, are often flanked by non-coding RNA, and are under-represented among housekeeping genes. Finally, we identify the giant (gt) transcription factor motif as a putative regulatory sequence associated with mtDNA-sensitive genes.ConclusionsThere are predictive conditions for nuclear genes that are influenced by mtDNA variation.

Highlights

Mitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes
Results mitochondrial DNA (mtDNA) effects on nuclear gene expression are numerous and roughly equal in each sex Our experimental design allowed examination of betweenmtDNA haplotype contrasts at the individual haplotype and between-species levels
These results suggest that our earlier studies’ evidence of no consistent molecular distance effects at the expression of whole organism phenotypes is recapitulated with expression of transcripts, at least to the level of divergence between the mtDNAs of D. melanogaster and D. simulans

Summary

Introduction

Mitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes. MtDNAs accumulate mutations at a high rate and these may be in the form of SNPs, or small and large scale deletions [11] How these haplotype variants, deleterious somatic point mutations, and large scale deletions affect phenotypes has been an active research area for the last 30 years [12, 13] and is motivating promising new approaches to prevent and treat inherited mtDNA-associated diseases in humans [14,15,16,17]. Mitochondrial replacement therapies are among the most promising of these therapies, but they face the challenge of identifying and circumventing unfavorable (negative) interactions between mtDNA and nDNA We refer to these types of gene x gene interactions as mitonuclear epistases (G x G). Epistatic interactions between nuclear genes are presumed to explain a significant amount of the ‘missing heritability’ in complex traits [25, 26] and diseases [27, 28], any pharmacogenomic or personalized medicine approach to disease management will require precise knowledge of how genes interact with their genetic and physical environments to accurately predict efficacy and safety

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