Analysis of human mitochondrial genome co-occurrence networks of Asian population at varying altitudes

Abstract

Networks have been established as an extremely powerful framework to understand and predict the behavior of many large-scale complex systems. We studied network motifs, the basic structural elements of networks, to describe the possible role of co-occurrence of genomic variations behind high altitude adaptation in the Asian human population. Mitochondrial DNA (mtDNA) variations have been acclaimed as one of the key players in understanding the biological mechanisms behind adaptation to extreme conditions. To explore the cumulative effects of variations in the mitochondrial genome with the variation in the altitude, we investigated human mt-DNA sequences from the NCBI database at different altitudes under the co-occurrence motifs framework. Analysis of the co-occurrence motifs using similarity clustering revealed a clear distinction between lower and higher altitude regions. In addition, the previously known high altitude markers 3394 and 7697 (which are definitive sites of haplogroup M9a1a1c1b) were found to co-occur within their own gene complexes indicating the impact of intra-genic constraint on co-evolution of nucleotides. Furthermore, an ancestral ‘RSRS50’ variant 10,398 was found to co-occur only at higher altitudes supporting the fact that a separate route of colonization at these altitudes might have taken place. Overall, our analysis revealed the presence of co-occurrence interactions specific to high altitude at a whole mitochondrial genome level. This study, combined with the classical haplogroups analysis is useful in understanding the role of co-occurrence of mitochondrial variations in high altitude adaptation.

Role of mitochondrial genetic interactions in determining adaptation to high altitude human population

“…In the Tibetan co-mutation network, the nodes in R5 category were 3010 ( 16S_rRNA ), 8414 ( ATP8 ), 14,668 ( ND6 ) and 12,361 ( ND5 ). Variable site 3010 was shown to be a high-altitude marker in Tibetan population 59 and also reported from network motifs with variable sites 8414 and 14668 37 while, 12,361 was shown to be associated with nonalcoholic fatty liver disease 60 . It is noteworthy that in the Tibetan co-mutation network, there were no nodes in the R6 category, and in the Andean co-mutation network, there were no nodes in the R5 category, while in the Ethiopian co-mutation network nodes were present in both the R5 and R6 categories.…”

“…Although, for these co-mutation networks, it is a subject of further investigation that whether the two nodes connected through more than one step also share the information of change in allele frequency or not. In terms of co-mutation, this provides evidence for the fixation and inheritance of variations as a single cohort, and intragenic constraints 37 in the human mitochondrial genome. A high <C> also implies www.nature.com/scientificreports/ that any given variable site prefers to co-mutate with all the other genes throughout the mitochondrial genome except for tRNA genes.…”

“…There exist other approaches based on pair-wise interaction such that two variations significantly interacting through logic regression 31 , predictive rule inference 32 , and shrunken methodology 33 . Networks of variable sites were analyzed to identify genes of angiogenesis associated with breast cancer 34 , time-dependent weight dynamics in chickens 35 , feed efficiency in duroc and landrace pigs 36 , altitude-dependent interactions of mitochondrial genes in Asian population 37 .…”

Role of mitochondrial genetic interactions in determining adaptation to high altitude in human population around the globe

“…In the Tibetan co-mutation network, the nodes in R5 category were 3010 (16S rRNA), 8414 (ATP8), 14668 (ND6) and 12361 (ND5). Variable site 3010 was shown to be a high-altitude marker in Tibetan population [50] and also form co-occurrence motifs with variable sites 8414 and 14668 [31] while, 12361 was shown to be associated with nonalcoholic fatty liver disease [51]. It is noteworthy that in the Tibetan co-mutation network there were no nodes in R6 category, and in the Andean co-mutation network there were no nodes in R5 category while in the Ethiopian co-mutation network nodes were present in both the R5 and R6 categories.…”

“…There exist other approaches based on pair-wise interaction such that two variations significantly interacting through logic regression [25], predictive rule inference [26], and shrunken methodology [27]. Networks of variable sites were used to identify angiogenesis genes associated with breast cancer [28], time-dependent weight dynamics in chickens [29], feed efficiency in duroc and landrace pigs [30], altitude-dependent interactions of mitochondrial genes in Asian population [31], etc.…”