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A hierarchical Bayesian framework for inferring mitochondrial clonal selection from single-cell data.

Qianqian Song1, Xiaobo Zhou2, Aoqi Wang3

  • 1West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China.

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Summary
This summary is machine-generated.

MitoBayes reveals how mitochondrial DNA mutations create cell clones driving diseases like Alzheimer's and cancer. This framework links mitochondrial genetic diversity to disease, offering new therapeutic targets.

Keywords:
genotype–phenotype relationshipshierarchical bayesian modelingmitochondrial clonal selectionmitochondrial genetic heterogeneitymitochondrial-driven pathogenesisselection pressure estimation

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Area of Science:

  • Genomics
  • Computational Biology
  • Cell Biology

Background:

  • Mitochondrial genetic heterogeneity, driven by somatic mitochondrial DNA (mtDNA) mutations, generates intracellular clonal populations.
  • The selective dynamics of these mitochondrial clones in disease are not well understood.
  • Understanding these dynamics is crucial for disease mechanisms and therapeutic development.

Purpose of the Study:

  • To develop a computational framework, MitoBayes, for inferring clone-specific selection pressures from mitochondrial genetic heterogeneity.
  • To analyze disease-specific patterns of mitochondrial clonal selection in Alzheimer's disease (AD), non-small-cell lung cancer (NSCLC), and small-cell lung cancer (SCLC).
  • To establish the clinical relevance of mitochondrial clonal selection in cancer patient outcomes.

Main Methods:

  • Developed a hierarchical Bayesian framework (MitoBayes) to model mitochondrial clonal lineage structure, allele frequency variation, and phenotypic burdens.
  • Benchmarked MitoBayes for accuracy in recovering selection coefficients across various scenarios of genetic heterogeneity and data sparsity.
  • Applied MitoBayes to single-cell atlases of AD, NSCLC, and SCLC, and performed pan-cancer survival analyses.

Main Results:

  • MitoBayes accurately recovers selection coefficients, demonstrating its robustness.
  • Identified distinct, disease-specific patterns of mitochondrial clonal selection in AD, NSCLC, and SCLC.
  • Found selective expansion of clones linked to PVALB interneuron disruption in AD, immune cell remodeling in NSCLC, and MT-ATP6 clone enrichment in SCLC, associated with platinum resistance.
  • Demonstrated clinical relevance of MT-ATP6 activity in pan-cancer survival and identified a m.2356C>G clone in HCC linked to POLR2A activation and adverse prognosis.

Conclusions:

  • MitoBayes is a robust statistical framework for linking mitochondrial genetic diversity to disease phenotypes.
  • Mitochondrial clonal selection is a mechanistically and clinically actionable target for therapeutic and diagnostic development.
  • Specific mitochondrial clones and their associated pathways (e.g., MT-ATP6, mitochondria-nucleus signaling) play significant roles in disease progression and treatment resistance.