bioRxivEvolutionary BiologyPreprint
Genomic underpinnings of convergent adaptation to high altitudes for alpine plants
X. Zhang, T. Kuang, W. Dong, Z. Qian, H. Zhang, J. B. Landis, T. Feng, L. Li, Y. Sun, J. Huang, T. Deng, H. Wang, H. Sun
Abstract
Evolutionary convergence is one of the most striking examples of adaptation driven by natural selection. However, genomic evidence for convergent adaptation to extreme environments remains scarce. The Himalaya-Hengduan Mountains represent the worlds most species-rich temperate alpine biota, providing an ideal "natural laboratory" for studying convergent adaptation to high altitudes. Here, we generate reference genomes for two alpine plants, Saussurea obvallata (Asteraceae) and Rheum alexandrae (Polygonaceae), with 37,938 and 61,463 annotated protein-coding genes. By integrating an additional five alpine genomes, we investigate genomic signatures of convergent adaptation to the hostile environments of high altitudes. We show that alpine genomes tend to mitigate their genetic load by contracting genes functioning in the immune system to survive such harsh environments with few pathogens present. We detect signatures of convergent positive selection on a set of genes involved in reproduction and development and reveal that molecular convergence has acted on genes involved in self-incompatibility, cell wall modification, DNA repair and stress resistance, which underlie adaptation to extremely cold, high UV radiation and hypoxia environments. Using gene expression profiles, we further demonstrate that genes associated with cuticular wax and flavonoid biosynthetic pathways exhibit higher expression levels in leafy bracts, shedding lights on the genetic mechanisms of the adaptive greenhouse morphology. Our integrative data provide genomic insights into the convergent evolution at higher-taxonomic levels, aiding in deep understanding of genetic adaptation to complex environments.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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