bims-humivi Biomed News
on Human mito-nuclear genetic interplay
Issue of 2026–05–03
two papers selected by
Mariangela Santorsola, Università di Pavia
  1. Nuclear genetic modulation of tissue-specific mitochondrial RNA processing contributes to common disease risk.
  2. Dango: Predicting higher-order genetic interactions.
  1. Nat Commun. 2026 Apr 30.
    Nuclear genetic modulation of tissue-specific mitochondrial RNA processing contributes to common disease risk.
    Eleonora Centanini, Oliver Pain, Patrick F Chinnery, Alan Hodgkinson.
      Mitochondrial dysfunction is widely implicated in human disease, yet whether it plays a causal role and why effects are tissue-specific remain unclear. Here, we analyse over 15,000 RNA-sequencing datasets from 49 tissue types integrated with germline genetic data to investigate the impact of mitochondrial DNA (mtDNA) transcription on disease risk. We identify 25 nuclear genetic variants associated with mtDNA transcript abundance, revealing gene- and tissue-specific regulatory architectures. We then develop tissue-specific genetic scores to predict mtDNA transcript levels and validate them in independent datasets. Applying these scores to 377,439 UK Biobank participants reveals significant associations between predicted mtDNA transcript abundance and multiple common diseases and quantitative traits, many showing marked tissue specificity, including associations with hypertension and Parkinson's disease in biologically relevant tissues. These findings provide genetic evidence that variation in mtDNA transcriptional processes contributes to complex disease biology and highlight mitochondrial RNA processing as a compelling therapeutic target.
    DOI:  https://doi.org/10.1038/s41467-026-72649-5
  2. Cell Syst. 2026 Apr 24. pii: S2405-4712(26)00075-X. [Epub ahead of print] 101593
    Dango: Predicting higher-order genetic interactions.
    Ruochi Zhang, Mihir Bafna, Jianzhu Ma, Jian Ma.
      Higher-order genetic interactions have profound implications for understanding the molecular mechanisms of phenotypic variation, yet they remain poorly characterized. Most studies focus on pairwise interactions because high-throughput screens over the vast combinatorial space are challenging. Here, we develop Dango, a computational method based on a self-attention hypergraph neural network, to predict higher-order genetic interactions among groups of genes. As a proof of concept, we provide predictions for over 400 million trigenic interactions in the yeast S. cerevisiae, greatly expanding their quantitative landscape. Dango accurately predicts trigenic interactions and reveals biological functions related to cell growth. We further incorporate protein embeddings and uncertainty estimation to improve biological relevance and interpretability. Moreover, predicted interactions serve as genetic markers for growth responses across diverse conditions. Together, Dango enables a more complete map of complex genetic interactions that shape phenotypic diversity. A record of this paper's transparent peer review process is included in the supplemental information.
    Keywords:  artificial intelligence; higher-order genetic interaction; hypergraph neural network; model uncertainty estimation; systems biology
    DOI:  https://doi.org/10.1016/j.cels.2026.101593
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