Ratajczak, F. | Search bioRxiv

bioRxiv2024

Interplay between mitochondrial and nuclear DNA in gene expression regulation

Using data from 48 tissues and 684 European individuals in the GTEx dataset, we investigate the role of mitochondrial DNA (mtDNA) and its encoded genes in gene regulation. We perform a comprehensive multi-tissue eQTL analysis on mtDNA encoded protein-coding genes, identifying 111 mtDNA-cis-eQTLs (FDR<5%) and 260 nucDNA trans-eQTLs (P<5x10-8). We further identify 108 mtDNA associations with 68 nucDNA encoded genes (FDR<5%). Our results are not driven by nuclear mitochondrial sequences (NUMTs) or minority cell types within tissues. Incorporating mtDNA trans-eQTLs in gene expression networks improves prediction of nucDNA genes with mitochondrial function. nucDNA trans-eQTLs for mtDNA genes are enriched in genes involved in mitochondrial pathways and GWAS hits for complex traits and diseases, implicating dual regulation by both genomes in maintaining mitochondrial function and organismal health. Our multi-tissue map of dual-genome gene regulation is an essential step towards understanding mito-nuclear cross-talk in health and disease.

genomics↗

bioRxiv2023

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. We have developed a positive-unlabeled graph representation-learning ensemble-approach to predict core genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validation, we demonstrate that our most confident predictions validate at rates on par with the Mendelian disorder genes, and all candidates exhibit core-gene properties like transcriptional deregulation in diseases and loss-of-function intolerance. Predicted candidates are enriched for drug targets and druggable proteins and, in contrast to Mendelian disorder genes, also for druggable but yet untargeted gene products. Model interpretation suggests key molecular mechanisms and physical interactions for core gene predictions. Our results demonstrate the potential of graph representation learning and pave the way for studying core gene properties and future drug development.

genetics↗