Microevolutionary mechanism of high-altitude adaptation in Tibetan chicken populations from an elevation gradient.

As an indigenous breed, the Tibetan chicken is found in highland regions and shows physiological adaptations to high altitude; however, the genetic changes that determine these adaptations remain elusive. We assumed that the microevolution of the Tibetan chicken occurred from lowland to highland regions with a continuous elevation range. In this study, we analyzed the genome of 188 chickens from lowland areas to the high-altitude regions of the Tibetan plateau with four altitudinal levels. Phylogenetic analysis revealed that Tibetan chickens are significantly different from other altitude chicken populations. Reconstruction of the demographic history showed that the migration and admixture events of the Tibetan chicken occurred at different times. The genome of the Tibetan chicken was also used to analyze positive selection pressure that is associated with high-altitude adaptation, revealing the well-known candidate gene that participates in oxygen binding (HBAD), as well as other novel potential genes (e.g., HRG and ANK2) that are related to blood coagulation and cardiovascular efficiency. Our study provides novel insights regarding the evolutionary history and microevolution mechanisms of the high-altitude adaptation in the Tibetan chicken.

Ongoing Positive Selection Drives the Evolution of SARS-CoV-2 Genomes.

SARS-CoV-2 is a new RNA virus affecting humans and spreads extensively throughout the world since its first outbreak in December, 2019. Whether the transmissibility and pathogenicity of SARS-CoV-2 in humans after zoonotic transfer are actively evolving, and driven by adaptation to the new host and environments is still under debate. Understanding the evolutionary mechanism underlying epidemiological and pathological characteristics of COVID-19 is essential for predicting the epidemic trend, and providing guidance for disease control and treatments. Interrogating novel strategies for identifying natural selection using within-species polymorphisms and 3,674,076 SARS-CoV-2 genome sequences of 169 countries as of December 30, 2021, we demonstrate with population genetic evidence that during the course of SARS-CoV-2 pandemic in humans, 1) SARS-CoV-2 genomes are overall conserved under purifying selection, especially for the 14 genes related to viral RNA replication, transcription, and assembly; 2) ongoing positive selection is actively driving the evolution of 6 genes (e.g., S, ORF3a, and N) that play critical roles in molecular processes involving pathogen-host interactions, including viral invasion into and egress from host cells, and viral inhibition and evasion of host immune response, possibly leading to high transmissibility and mild symptom in SARS-CoV-2 evolution. According to an established haplotype phylogenetic relationship of 138 viral clusters, a spatial and temporal landscape of 556 critical mutations is constructed based on their divergence among viral haplotype clusters or repeatedly increase in frequency within at least 2 clusters, of which multiple mutations potentially conferring alterations in viral transmissibility, pathogenicity, and virulence of SARS-CoV-2 are highlighted, warranting attention.

Population Genetics of SARS-CoV-2: Disentangling Effects of Sampling Bias and Infection Clusters.

A novel RNA virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the ongoing outbreak of coronavirus disease 2019 (COVID-19). Population genetic analysis could be useful for investigating the origin and evolutionary dynamics of COVID-19. However, due to extensive sampling bias and existence of infection clusters during the epidemic spread, direct applications of existing approaches can lead to biased parameter estimations and data misinterpretation. In this study, we first present robust estimator for the time to the most recent common ancestor (TMRCA) and the mutation rate, and then apply the approach to analyze 12,909 genomic sequences of SARS-CoV-2. The mutation rate is inferred to be 8.69 × 10-4 per site per year with a 95% confidence interval (CI) of [8.61 × 10-4, 8.77 × 10-4], and the TMRCA of the samples inferred to be Nov 28, 2019 with a 95% CI of [Oct 20, 2019, Dec 9, 2019]. The results indicate that COVID-19 might originate earlier than and outside of Wuhan Seafood Market. We further demonstrate that genetic polymorphism patterns, including the enrichment of specific haplotypes and the temporal allele frequency trajectories generated from infection clusters, are similar to those caused by evolutionary forces such as natural selection. Our results show that population genetic methods need to be developed to efficiently detangle the effects of sampling bias and infection clusters to gain insights into the evolutionary mechanism of SARS-CoV-2. Software for implementing VirusMuT can be downloaded at https://bigd.big.ac.cn/biocode/tools/BT007081.