bims-mitran Biomed News
on Mitochondrial Translation
Issue of 2021‒11‒07
three papers selected by
Andreas Aufschnaiter
Stockholm University


  1. Hum Mol Genet. 2021 Oct 27. pii: ddab312. [Epub ahead of print]
      INTRODUCTION: In the era of personalized medicine with more and more patient specific targeted therapies being used, we need reliable, dynamic, faster, and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA mtDNA in metabolic regulation, aging, and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA, and thereby contributes to a range of pathophysiological alterations observed in complex diseases.METHODS: We performed an inverted mitochondrial genome wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify genetic variants associated with metabolite profiles. Because of the high coverage, next generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for identification of variants associated with the metabolome.
    RESULTS: The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio C2/C10:1 (p-value = 6.82*10-09, β = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio PC ae C42:5/PC ae C44:5 (p-value = 1.02*10-08, β = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene.
    CONCLUSION: These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.
    DOI:  https://doi.org/10.1093/hmg/ddab312
  2. Commun Biol. 2021 Nov 04. 4(1): 1262
      Mitochondrial dysfunction contributes to the pathogenesis of many neurodegenerative diseases. The mitochondrial genome encodes core respiratory chain proteins, but the vast majority of mitochondrial proteins are nuclear-encoded, making interactions between the two genomes vital for cell function. Here, we examine these relationships by comparing mitochondrial and nuclear gene expression across different regions of the human brain in healthy and disease cohorts. We find strong regional patterns that are modulated by cell-type and reflect functional specialisation. Nuclear genes causally implicated in sporadic Parkinson's and Alzheimer's disease (AD) show much stronger relationships with the mitochondrial genome than expected by chance, and mitochondrial-nuclear relationships are highly perturbed in AD cases, particularly through synaptic and lysosomal pathways, potentially implicating the regulation of energy balance and removal of dysfunction mitochondria in the etiology or progression of the disease. Finally, we present MitoNuclearCOEXPlorer, a tool to interrogate key mitochondria-nuclear relationships in multi-dimensional brain data.
    DOI:  https://doi.org/10.1038/s42003-021-02792-w