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BackgroundAlthough it has long been proposed that genetic factors contribute to adaptation to high altitude, such factors remain largely unverified. Recent advances in high-throughput sequencing have made it feasible to analyze genome-wide patterns of genetic variation in human populations. Since traditionally such studies surveyed only a small fraction of the genome, interpretation of the results was limited.ResultsWe report here the results of the first whole genome resequencing-based analysis identifying genes that likely modulate high altitude adaptation in native Ethiopians residing at 3,500 m above sea level on Bale Plateau or Chennek field in Ethiopia. Using cross-population tests of selection, we identify regions with a significant loss of diversity, indicative of a selective sweep. We focus on a 208 kbp gene-rich region on chromosome 19, which is significant in both of the Ethiopian subpopulations sampled. This region contains eight protein-coding genes and spans 135 SNPs. To elucidate its potential role in hypoxia tolerance, we experimentally tested whether individual genes from the region affect hypoxia tolerance in Drosophila. Three genes significantly impact survival rates in low oxygen: cic, an ortholog of human CIC, Hsl, an ortholog of human LIPE, and Paf-AHα, an ortholog of human PAFAH1B3.ConclusionsOur study reveals evolutionarily conserved genes that modulate hypoxia tolerance. In addition, we show that many of our results would likely be unattainable using data from exome sequencing or microarray studies. This highlights the importance of whole genome sequencing for investigating adaptation by natural selection.

The hypoxic conditions at high altitudes present a challenge for survival, causing pressure for adaptation. Interestingly, many high-altitude denizens (particularly in the Andes) are maladapted, with a condition known as chronic mountain sickness (CMS) or Monge disease. To decode the genetic basis of this disease, we sequenced and compared the whole genomes of 20 Andean subjects (10 with CMS and 10 without). We discovered 11 regions genome-wide with significant differences in haplotype frequencies consistent with selective sweeps. In these regions, two genes (an erythropoiesis regulator, SENP1, and an oncogene, ANP32D) had a higher transcriptional response to hypoxia in individuals with CMS relative to those without. We further found that downregulating the orthologs of these genes in flies dramatically enhanced survival rates under hypoxia, demonstrating that suppression of SENP1 and ANP32D plays an essential role in hypoxia tolerance. Our study provides an unbiased framework to identify and validate the genetic basis of adaptation to high altitudes and identifies potentially targetable mechanisms for CMS treatment.

In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization. [ABSTRACT FROM AUTHOR]