Evolution can be seen as the accumulations of species’ adaptations to external environments (Dobzhansky & Gould, 1982), thus understanding the mechanisms underlying the organisms’ adaptations, especially at the molecular level, has been one of the core issues of evolutionary biology. Moreover, such understanding can also provide an efficient framework to reveal the relationship between genotype and phenotype (i.e., forward genetics; Figure 1). Through years of field and laboratory observations and measurements, a large number of cases of phenotypic adaptations, both morphological and physiological, have been identified in vertebrates, such as the limb evolution and their corresponding adaptations to specific locomotion types in bats and whales (Liang et al, 2013), the evolution of antimicrobial peptides and adaptations to amphibious skin structures in frogs (Rollins-Smith, 2009), and the most classic evolution of the beak sizes/shapes of Darwin’s finches and the adaptation to specific ecological niches (Lamichhaney et al, 2015). These case studies provide rich materials for the study of adaptive evolution and the underlying molecular mechanisms.
With the recent technical advances, large quantities of DNA or RNA can be sequenced in a much more efficient way. In addition to the de novo whole-genome sequencing, the recent advances in sequencing technologies (i.e., resequencing, RNA-seq, and restriction site-associated DNA sequencing) and computational power further enable the genomic era. It has been proposed that the new genomic data can provide great insights for a diverse set of questions in evolutionary biology, such as questions on phylogenetic relationships and the tree of life, genome-size evolution, historical demography and current population structure, adaptive potential, hybridization and speciation, and the genetic bases of phenotypic traits (Allendorf et al, 2010; Brandies et al, 2019; Liedtke et al, 2018; Supple & Shapiro, 2018). Under this view, more and more vertebrate genomes, transcriptomes and other omics data are accumulated and now available, even for some non-model species (https://www.ncbi.nlm.nih.gov/genome). However, the availability of genomes is still heavily taxon-biased. Until now, majority of the available vertebrate genomes are from mammals and birds (Allendorf, 2017; Genome 10K Community of Scientists, 2009; Ostrander et al, 2019; Supple & Shapiro, 2018; Zhang, 2015), and for other diverse vertebrate groups, only very few genomes were available comparatively. For amphibians specifically, of the 8 043 currently recognized amphibian species (https://amphibiaweb.org/index.html, 2019; Che & Wang, 2016), no more than 20 of them have published or released genomes available (Table 1). Since amphibians possess many unique characteristics for the study of genome evolution and molecular adaptations (see below), such lack of genomic information really limits our understandings of amphibian evolution, as well as conservation (Funk et al, 2018; Shaffer et al, 2015).
Order Family Species Common name Genome size (Gb) Scaffold N50 (Mb) Contig N50 (Kb) Assembly level Sequencing technology Reference Link Gymnophiona Siphonopidae Microcaecilia unicolor Common caecilians 4.68 376.14 3 660 Chromosome PacBio N/A https://vgp.github.io/genomeark/Microcaecilia_unicolor/ Gymnophiona Dermophiidae Geotrypetes seraphini Gaboon caecilian 3.78 272.61 20 660 Chromosome PacBio N/A https://vgp.github.io/genomeark/Geotrypetes_seraphini/ Gymnophiona Rhinatrematidae Rhinatrema bivittatum Two-lined caecilian 5.32 486.88 0.350 Chromosome PacBio N/A https://www.ncbi.nlm.nih.gov/assembly/GCF_901001135.1/ Caudata Ambystomatidae Ambystoma mexicanum Axolotl 32.4 1410 210 Chromosome PacBio Nowoshilow et al, 2018 https://www.ncbi.nlm.nih.gov/assembly/GCA_002915635.2/ Caudata Salamandridae Pleurodeles waltl Iberian ribbed newt 19.38 N/A N/A Chromosome Illumina Elewa et al, 2017 https://www.ncbi.nlm.nih.gov/bioproject/353981 Anura Hylidae Dendropsophus ebraccatus Hourglass treefrog 2.24 60.91 8860 Chromosome PacBio N/A https://vgp.github.io/genomeark/Dendropsophus_ebraccatus Anura Megophryidae Leptobrachium leishanense Leishan spiny toad 3.55 394.69 1 900 Chromosome PacBio RS II Li et al, 2019a https://www.ncbi.nlm.nih.gov/genome/46619?genome_assembly_id=743320 Anura Megophryidae Vibrissaphora ailaonica Ailao spiny toad 3.53 412.42 820 Chromosome PacBio Li et al, 2019b http://gigadb.org/dataset/100624 Anura Ranidae Lithobates catesbeianus American bullfrog 5.8 0.05 5 Scaffold Illumina Hammond et al, 2017 https://www.ncbi.nlm.nih.gov/genome/23031?genome_assembly_id=353399 Anura Ranidae Rana temporaria Common frog 4.18 0.05 2.889 Scaffold Illumina HiSeq N/A https://www.ncbi.nlm.nih.gov/assembly/GCA_009802015.1 Anura Pyxicephalidae Pyxicephalus adspersus African bullfrog 1.56 157.52 30 Chromosome Illumina Denton et al, 2018a https://www.ncbi.nlm.nih.gov/assembly/GCA_004786255.1/ Anura Bufonidae Rhinella marina Marine toad 2.55 N/A 160 Contig PacBio RS II Edwards et al, 2018 https://www.ncbi.nlm.nih.gov/assembly/GCA_900303285.1/ Anura Scaphiopodidae Scaphiopus couchii Couch's spadefoot toad 0.48 N/A 0.362 Scaffold Illumina Seidl et al, 2019 https://www.ncbi.nlm.nih.gov/genome/85144?genome_assembly_id=723961 Anura Scaphiopodidae Scaphiopus holbrookii Eastern spadefoot toad 0.71 N/A 0.514 Scaffold Illumina Seidl et al, 2019 https://www.ncbi.nlm.nih.gov/genome/69097?genome_assembly_id=723960 Anura Scaphiopodidae Spea bombifrons Plains spadefoot toad 0.77 N/A 0.522 Scaffold Illumina Seidl et al, 2019 https://www.ncbi.nlm.nih.gov/assembly/GCA_009364475.1/ Anura Scaphiopodidae Spea multiplicata Mexican spadefoot toad 1.07 0.07 30 Scaffold PacBio Seidl et al, 2019 https://www.ncbi.nlm.nih.gov/assembly/GCA_009364415.1/ Anura Dicroglossinae Nanorana parkeri Tibetan frog 2.05 1.06 30 Scaffold Illumina Sun et al, 2015 https://www.ncbi.nlm.nih.gov/assembly/GCF_000935625.1/ Anura Pipidae Xenopus laevis African clawed frog 2.72 136.57 20 Chromosome Illumina Session et al, 2016 https://www.ncbi.nlm.nih.gov/assembly/GCF_001663975.1/ Anura Pipidae Xenopus tropicalis Tropical clawed frog 1.45 153.96 14 630 Chromosome PacBio; Illumina HiSeq Session et al, 2016 https://www.ncbi.nlm.nih.gov/assembly/GCF_000004195.4/ Anura Dendrobatidae Oophaga pumilio Strawberry poison frog 5.5 0.072 0.385 Scaffold Illumina HiSeq Rogers et al, 2018 https://www.ncbi.nlm.nih.gov/genome/86474 N/A: Not available.
Table 1. List of amphibian species with available reference genome
One major reason leading to such “genome deficient” in amphibians may come from the methodological challenges for assembling very large, repetitive genomes (Elliott & Gregory, 2015). It has been reported that the estimated average genome sizes of Anurans (frogs), Gymnophionas (caecilians), and Caudatas (salamanders) were 4.1, 5.6, and 32 gigabases (Gb), respectively (Liedtke et al, 2018). Such large genomes of amphibians post major challenges to both the sequencing and the assembling processes of the genomic data. However, with the fast development of sequencing technology, particularly the “third” (i.e., PacBio’s single molecule real-time system) and even “fourth” generation of sequencing techniques (i.e., Oxford Nanopore PromethION system) (Deamer et al, 2016; Glenn, 2011), as well as the efficient assembly methods (Ruan & Li, 2020), such difficulties could now be overcome to a large extent. As results, more and more high-quality genome assemblies of amphibians are emerging in the recent years (Li et al, 2019b; Nowoshilow et al, 2018; Smith et al, 2019).
For the approaches used to determine the genetic mechanisms underlying trait evolutions, two main approaches were used in both comparative (interspecific) and population (intraspecific) genomic approaches utilize commonly used methods (i.e., dN/dS for comparative and FST for population genomics) through genome-wide scans for loci under positive selection in particular lineages or populations (Lee & Coop, 2019; Zhang et al, 2014). Generally, comparative genomics can reveal the evolutionary patterns of genes within a large time scale and determine whether the genes experienced rapid or slow evolution, while the population genomics can identify genes associated with local adaptation of one or more populations in a relatively short time scale. One main logic behind the two approaches is to search for outlier genes that show signals of strong selection and are significantly separated from the background genes. At present, there have been lots of example studies having applied such analytical logic. For instance, the recent analyses of dozens of ruminant genomes makes it possible to decipher the genetic underpinnings of multiple phenotypes of this taxa, like the evolution of headgear and multichambered stomach, and thus makes this taxa a good model for further genomic analyses, like studying adaptive evolutionary mechanisms of them (Wang et al, 2019).
Under these analytical frameworks, the genomic analyses that have been used commonly in mammals and other well-studied groups are gradually applied into amphibians, leading to important findings on the macro- and adaptive evolution of amphibians at the molecular level (Nowoshilow et al, 2018; Sun et al, 2015, 2018; Wang et al, 2018; Yang et al, 2012). However, because of the specific characteristics of amphibian genomes (i.e., high repeatability and incomplete annotations of genomic elements), simply copying the analytical methods of the past may not be sufficient to reveal the evolutionary mechanisms of adaptive evolution of these unique organisms. With the accumulation of omics data, evolutionary herpetologists need to beware of analytical methods used and be able to interpret the results from genomic dataset.
There have been several reviews that focus on the applications of omics data to better understand amphibian conservation, ecology, and evolution (Funk et al, 2018; Shaffer et al, 2015; Storfer et al, 2009), yet none of them focuses on the evolutionary patterns and mechanisms of amphibians’ adaptive evolution, or how to apply the accumulated omics data to study amphibians effectively. In this review, we first give a brief overview of recent progresses of studies on the adaptive evolution of amphibians, and then we focus our discussions by giving perspectives on the future directions for studies on adaptive evolution of amphibians, including the potential contributions of the repetitive elements on the genome evolution of amphibians.
Perspectives on studying molecular adaptations of amphibians in the genomic era
- Received Date: 2020-02-16
- Accepted Date: 2020-04-23
- Molecular adaptation /
- Gene subnetwork /
- Phenotypic evolution /
- Transposable element /
Abstract: Understanding the genetic mechanisms underlying particular adaptations/phenotypes of organisms is one of the core issues of evolutionary biology. The use of genomic data has greatly advanced our understandings on this issue, as well as other aspects of evolutionary biology, including molecular adaptation, speciation, and even conservation of endangered species. Despite the well-recognized advantages, usages of genomic data are still limited to non-mammal vertebrate groups, partly due to the difficulties in assembling large or highly heterozygous genomes. Although this is particularly the case for amphibians, nonetheless, several comparative and population genomic analyses have shed lights into the speciation and adaptation processes of amphibians in a complex landscape, giving a promising hope for a wider application of genomics in the previously believed challenging groups of organisms. At the same time, these pioneer studies also allow us to realize numerous challenges in studying the molecular adaptations and/or phenotypic evolutionary mechanisms of amphibians. In this review, we first summarize the recent progresses in the study of adaptive evolution of amphibians based on genomic data, and then we give perspectives regarding how to effectively identify key pathways underlying the evolution of complex traits in the genomic era, as well as directions for future research.
|Citation:||Yan-Bo Sun, Yi Zhang, Kai Wang. Perspectives on studying molecular adaptations of amphibians in the genomic era[J]. Zoological Research. doi: 10.24272/j.issn.2095-8137.2020.046|