Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention

Abstract

Since their endosymbiotic origin, mitochondria have lost most of their genes. Although many selective mechanisms underlying the evolution of mitochondrial genomes have been proposed, a data-driven exploration of these hypotheses is lacking, and a quantitatively supported consensus remains absent. We developed HyperTraPS, a methodology coupling stochastic modeling with Bayesian inference, to identify the ordering of evolutionary events and suggest their causes. Using 2015 complete mitochondrial genomes, we inferred evolutionary trajectories of mtDNA gene loss across the eukaryotic tree of life. We find that proteins comprising the structural cores of the electron transport chain are preferentially encoded within mitochondrial genomes across eukaryotes. A combination of high GC content and high protein hydrophobicity is required to explain patterns of mtDNA gene retention; a model that accounts for these selective pressures can also predict the success of artificial gene transfer experiments in vivo. This work provides a general method for data-driven inference of the ordering of evolutionary and progressive events, here identifying the distinct features shaping mitochondrial genomes of present-day species.

CyMIRA: The Cytonuclear Molecular Interactions Reference forArabidopsis

“…A further complication in expanding to evolutionary studies across species is that the landscape of cytonuclear integration and interactions is rapidly shifting in plants. Unlike many eukaryotes in which the gene content in cytoplasmic genomes has reached a period of long-term stasis (Johnston and Williams 2016;Janouškovec, et al 2017), flowering plants remain highly active in the process of endosymbiotic gene transfer to the nucleus (Timmis, et al 2004). For example, our CyMIRA annotations do not include OXPHOS complex II because this is entirely nuclear-encoded in A. thaliana.…”

An unusual amino acid substitution within hummingbird cytochromecoxidase alters a key proton-conducting channel

“…Proton movements coupled to electron passage through COX contribute to the proton motive force (PMF) used for ATP production and thermogenesis (15,16). While several COX subunits are nucleus-encoded and imported to mitochondria, the core, catalytic subunits of COX (subunits COI, COII, and COIII) are encoded by mitochondrial DNA (mtDNA) (17), raising the possibility that positive selection upon the mitochondrial genome may have contributed to the remarkable metabolic properties of hummingbirds. Here, we identify an amino acid substitution in COI that is universal among hummingbirds, yet exceedingly rare among other birds and vertebrates.…”

Precision identification and prediction of high mortality phenotypes and disease progression pathways in severe malaria without requiring longitudinal data

“…Here, we applied more powerful, recent tools to analyse the rich data on SM progression to facilitate robust clinical statements about disease progression dynamics and risks associated with individual patients. To this end, we combine mutual information (MI), used to learn clinical factors predictive of patient outcomes, with the recently developed HyperTraPS (hypercubic transition path sampling) algorithm [10], which we use to learn dynamic probabilistic pathways of disease progression. MI approaches are more robust regarding nonlinearities in relationships and do not suffer from the shortcomings of log odds ratios associated with linear regression.…”

“…MI approaches are more robust regarding nonlinearities in relationships and do not suffer from the shortcomings of log odds ratios associated with linear regression. HyperTraPS, which was recently applied to elucidate the dynamics of and mechanisms underlying the evolution of mtDNA genome structure [10] and efficient photosynthesis [11], allows the Bayesian inference of dynamic pathways describing the successive acquisition of features or symptoms directly from cross-sectional (or longitudinal) observations. Here we apply MI and HyperTraPS to identify prognostic factors and to infer the sequence of events from cross-sectional data in patients with severe malaria.…”