Abstract
Cold climates represent one of the major environmental challenges that anatomically modern humans faced during their dispersal out of Africa. The related adaptive traits have been achieved by modulation of thermogenesis and thermoregulation processes where nuclear (nuc) and mitochondrial (mt) genes play a major role. In human populations, mitonuclear genetic interactions are the result of both the peculiar genetic history of each human group and the different environments they have long occupied. This study aims to investigate mitonuclear genetic interactions by considering all the mitochondrial genes and 28 nuclear genes involved in brown adipose tissue metabolism, which have been previously hypothesized to be crucial for cold adaptation. For this purpose, we focused on three human populations (i.e., Finnish, British, and Central Italian people) of European ancestry from different biogeographical and climatic areas, and we used a machine learning approach to identify relevant nucDNA–mtDNA interactions that characterized each population. The obtained results are twofold: (i) at the methodological level, we demonstrated that a machine learning approach is able to detect patterns of genetic structure among human groups from different latitudes both at single genes and by considering combinations of mtDNA and nucDNA loci; (ii) at the biological level, the analysis identified population-specific nuclear genes and variants that likely play a relevant biological role in association with a mitochondrial gene (such as the “obesity gene” FTO in Finnish people). Further studies are needed to fully elucidate the evolutionary dynamics (e.g., migration, admixture, and/or local adaptation) that shaped these nucDNA–mtDNA interactions and their functional role.
Highlights
The mitonuclear interaction can be considered the most successful mutualism in the history of life according to the complex dynamics of conflicts and cooperation that has been established between the mitochondrial and nuclear genomic backgrounds (Rand and Mossman, 2020)
Figure 1) with the machine learning (ML) random forest (RF) algorithm based on the single nucleotide variants (SNVs) frequency scores for mitochondrial DNA (mtDNA) genes, cold adaptation associated nucDNA genes, and their pair combinations
Here we applied a method of ML to investigate and integrate genomic information coming from both nuclear and mitochondrial DNA
Summary
The mitonuclear interaction can be considered the most successful mutualism in the history of life according to the complex dynamics of conflicts and cooperation that has been established between the mitochondrial and nuclear genomic backgrounds (Rand and Mossman, 2020). Mitochondria and nucleus communicate to ensure optimal cellular function, and a part of the mitochondrial proteins are encoded in the nucleus. This communication occurs at many levels (Mottis et al, 2019). Mitochondria actively influence and physically interact with other cellular components, such as the lysosomes, the endoplasmic reticulum, and cytosolic pathways, creating a mitocellular communication network based on a variety of signals. Small molecules (e.g., AMP, NAD+, ROS, oxygen, and other metabolites) act as mitochondrial messengers, and they signal mitochondrial activity to other cellular components. Mitochondria communicate with distant tissues through circulating molecules, called mitokines (e.g., FGF21 and GDF15), which are nuclearencoded signaling molecules secreted by cells that experienced mitochondrial stress
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