Plateau Grass and Greenhouse Flower? Distinct Genetic Basis of Closely Related Toad Tadpoles Respectively Adapted to High Altitude and Karst Caves

Abstract

Genetic adaptation to extremes is a fascinating topic. Nevertheless, few studies have explored the genetic adaptation of closely related species respectively inhabiting distinct extremes. With deep transcriptome sequencing, we attempt to detect the genetic architectures of tadpoles of five closely related toad species adapted to the Tibetan Plateau, middle-altitude mountains and karst caves. Molecular evolution analyses indicated that not only the number of fast evolving genes (FEGs), but also the functioning coverage of FEGs, increased with elevation. Enrichment analyses correspondingly revealed that the highland species had most of the FEGs involved in high-elevation adaptation, for example, amino acid substitutions of XRCC6 in its binding domains might improve the capacity of DNA repair of the toad. Yet, few FEGs and positively selected genes (PSGs) involved in high-elevation adaptation were identified in the cave species, and none of which potentially contributed to cave adaptation. Accordingly, it is speculated that in the closely related toad tadpoles, genetic selection pressures increased with elevation, and cave adaptation was most likely derived from other factors (e.g., gene loss, pseudogenization or deletion), which could not be detected by our analyses. The findings supply a foundation for understanding the genetic adaptations of amphibians inhabiting extremes.

Multi-Omics Approaches Revealed the Associations of Host Metabolism and Gut Microbiome With Phylogeny and Environmental Adaptation in Mountain Dragons

“…However, the accumulation of stochastic genetic changes in the genome constitutes an obstacle to the screening of environment-related variations, especially for interspecies studies. Additionally, genetic variations may not always explain adaptation processes intuitively due to the limited understanding of their cellular functions ( Chang et al, 2020 ), especially when adaptive traits are determined by multiple genetic loci or mutations are located in non-coding regions. Moreover, not all environmental adaptive traits are caused by changes in DNA sequences; for example, epigenetics, in response to external or environmental factors, can also shape cellular and physiological phenotypic traits by changing cellular gene expression patterns ( Bird, 2007 ; Vogt, 2017 ).…”

Commensal microbiota and host metabolic divergence are associated with the adaptation of Diploderma vela to spatially heterogeneous environments

“…However, the adaptation enabled by plasticity may not has a genetic basis (Zhu et al 2016). And the genetic variations may not always explain the adaptation processes intuitively due to the limited understanding of their cellular function (Chang et al 2020), especially when the adaptive traits are determined by multiple genetic loci. Metabolites are the end products of cellular processes and their variation is expected to reflect the final effect of molecular signal transduction (Nicholson & Lindon 2008).…”

“…local adaptation) and non-genetic approaches (i.e. adaptive plasticity), have a fundamental role in maintaining genetic or functional diversity and responding to climate change (Davis & Shaw 2001;Charmantier et al 2008). Revealing the underlying molecular basis can not only provides mechanistic insights into the evolution and adaptation (Schluter 1996), but also enable more reliable prediction, at population level, on species persistence and distribution at the context of global climate change (Kellermann et al 2009;Valladares et al 2014).…”

Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage

“…The following supporting information can be downloaded at: , Figure S1: Putative AMP filters used for amphibians and insects; Figure S2: Runtime and memory of each dataset through rAMPage; Figure S3: Score, length, and charge distribution of filtered putative AMPs; Figure S4: Amino acid composition of filtered putative AMPs; Figure S5: Antimicrobial susceptibility and hemolysis testing of 21 putative AMPs; Figure S6: Multiple sequence alignments of moderately to highly active AMPs; Figure S7: Multiple sequence alignments of moderately to highly active AMP precursors; Figure S8: Distribution of alignment of filtered putative AMPs to mature reference AMPs; Figure S9: Distribution of reference mature AMPs; Figure S10: Approach for peptides with multiple cleavage sites; Table S1: Peptide naming convention; Table S2: Subset of 21 putative AMPs synthesized and validated against E. coli and S. aureus; Table S3: Annotation of moderately to highly active putative mature AMPs; Table S4: Major options for rAMPage; Table S5: Sensitivity of all putative AMP filter combinations; Table S6: Amphibian RNA-seq datasets; Table S7: Insect RNA-seq datasets; Table S8: Breakdown of AMP sequences in AMP databases; Table S9: Shell scripting dependencies of rAMPage; Table S10: Bioinformatic tool dependencies of rAMPage; Table S11: Command and parameters for each step of rAMPage. References [ 11 , 27 , 29 , 30 , 41 , 59 , 60 , 61 , 62 , 63 , 64 , 73 , 74 , 75 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ...…”