Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions

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...

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Main Authors: Alena Kalyakulina, Vincenzo Iannuzzi, Marco Sazzini, Paolo Garagnani, Sarika Jalan, Claudio Franceschi, Mikhail Ivanchenko, Cristina Giuliani
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Physiology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2020.575968/full
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language English
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author Alena Kalyakulina
Vincenzo Iannuzzi
Vincenzo Iannuzzi
Marco Sazzini
Paolo Garagnani
Sarika Jalan
Sarika Jalan
Claudio Franceschi
Mikhail Ivanchenko
Mikhail Ivanchenko
Cristina Giuliani
Cristina Giuliani
spellingShingle Alena Kalyakulina
Vincenzo Iannuzzi
Vincenzo Iannuzzi
Marco Sazzini
Paolo Garagnani
Sarika Jalan
Sarika Jalan
Claudio Franceschi
Mikhail Ivanchenko
Mikhail Ivanchenko
Cristina Giuliani
Cristina Giuliani
Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
Frontiers in Physiology
mitonuclear interactions
human populations
cold adaptation
machine learning
human ecology
human evolution
author_facet Alena Kalyakulina
Vincenzo Iannuzzi
Vincenzo Iannuzzi
Marco Sazzini
Paolo Garagnani
Sarika Jalan
Sarika Jalan
Claudio Franceschi
Mikhail Ivanchenko
Mikhail Ivanchenko
Cristina Giuliani
Cristina Giuliani
author_sort Alena Kalyakulina
title Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
title_short Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
title_full Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
title_fullStr Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
title_full_unstemmed Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions
title_sort investigating mitonuclear genetic interactions through machine learning: a case study on cold adaptation genes in human populations from different european climate regions
publisher Frontiers Media S.A.
series Frontiers in Physiology
issn 1664-042X
publishDate 2020-11-01
description 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.
topic mitonuclear interactions
human populations
cold adaptation
machine learning
human ecology
human evolution
url https://www.frontiersin.org/articles/10.3389/fphys.2020.575968/full
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spelling doaj-6adb381179e34b708e12964fff5d20822020-11-25T04:08:42ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-11-011110.3389/fphys.2020.575968575968Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate RegionsAlena Kalyakulina0Vincenzo Iannuzzi1Vincenzo Iannuzzi2Marco Sazzini3Paolo Garagnani4Sarika Jalan5Sarika Jalan6Claudio Franceschi7Mikhail Ivanchenko8Mikhail Ivanchenko9Cristina Giuliani10Cristina Giuliani11Department of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaAlma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, Bologna, ItalyLaboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, ItalyLaboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, ItalyDepartment of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, ItalyComplex Systems Laboratory, Discipline of Physics, Indian Institute of Technology Indore, Indore, IndiaCenter for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon, South KoreaLaboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaDepartment of Applied Mathematics, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaLaboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaLaboratory of Molecular Anthropology and Centre for Genome Biology, Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, ItalySchool of Anthropology and Museum Ethnography, University of Oxford, Oxford, United KingdomCold 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.https://www.frontiersin.org/articles/10.3389/fphys.2020.575968/fullmitonuclear interactionshuman populationscold adaptationmachine learninghuman ecologyhuman evolution