Comparative genome-wide polymorphic microsatellite markers in Antarctic penguins through next generation sequencing

Genetic diversity and structure of captive gentoo penguin populations in Japan

Until the last decade, gentoo penguins were usually split into two subspecies, northern gentoo penguins (Pygoscelis papua papua) breeding in the Falkland Islands, South Georgia, and other subantarctic islands and southern gentoo penguins (P. papua ellsworthi) breeding in the South Sandwich, South Orkney and South Shetland islands, and Antarctic Peninsula. Recent genetics research, however, suggests that the population at South Georgia is much more closely related to those further south and should be included in P. papua ellsworthi. In Japanese zoos and aquariums, captive breeding of gentoo penguins is conducted separately in three populations: "Captive-South Georgia," originating from South Georgia, "Captive-South Shetlands," originating from South Shetlands, and "Captive-Unknown," originating from at least one founder of unknown subspecies. The aims of the present study were to investigate the genetic diversity and differentiation of these captive populations using microsatellite analysis. Genetic diversity in each captive population was similar to that found in the wild, although they had much lower contemporary effective population sizes. Pairwise genetic differentiation indexes (F_ST ) among the three captive populations were as follows: 0.0309 ("Captive-South Georgia" and "Captive-Unknown"), 0.1094 ("Captive-South Georgia" and "Captive-South Shetlands"), and 0.1214 ("Captive-South Shetlands" and "Captive-Unknown"). Using Bayesian clustering, there was relatively high genetic differentiation between the "Captive-South Shetlands" group, which formed a distinct cluster, and individuals of the "Captive-Unknown" group, which were assigned to clusters in common with "Captive-South Georgia." The results from the present study are useful for future management of captive gentoo penguin populations in Japan.

Application of PCR-HRM method for microsatellite polymorphism genotyping in the LDHA gene of pigeons (Columba livia)

High-resolution melting (HRM) is a post-PCR method that allows to discriminate genotypes based on fluorescence changes during the melting phase. HRM is used to detect mutations or polymorphisms (e.g. microsatellites, SNPs, indels). Here, the (TTTAT)_3-5 microsatellite polymorphism within intron 6 of the LDHA gene in pigeons was analysed using the HRM method. Individuals (123 homing pigeons) were genotyped using conventional PCR. Birds were classified into groups based on genotype type and the results were tested by qPCR-HRM and verified using sequencing. Based on the evaluated protocol, five genotypes were identified that vary in the number of TTTAT repeat units (3/3, 4/4, 3/4, 4/5, and 5/5). Sequencing have confirmed the results obtained with qPCR-HRM and verified that HRM is a suitable method for identification of three-allele microsatellite polymorphisms. It can be concluded that the high-resolution melting (HRM) method can be effectively used for rapid (one-step) discrimination of the (TTTAT)_3-5 microsatellite polymorphism in the pigeon’s LDHA gene.

Age-associated variation in the gut microbiota of chinstrap penguins (Pygoscelis antarctica) reveals differences in food metabolism

Age is known to affect the gut microbiota in various animals; however, this relationship is poorly understood in seabirds. We investigated the temporal succession of gut microbiota in captive chinstrap penguins of different ages using high-throughput sequencing. The gut microbiota exhibited a significant age succession pattern, reaching maturity in adults and then declining with increasing age. Only 15 amplicon sequence variants were shared among the gut microbiota in chinstrap penguins at all studied ages, and these contributed to most of the age-related variations in total gut microbiota. Co-occurrence networks found that these key bacteria belonged to the genera Acinetobacter, Clostridium sensu stricto, and Fusobacterium, and more species interactions were found within the same taxonomy. Functional prediction indicated that most of the metabolic functions were more abundant in the gut microbiota in adult chinstrap penguins, except for carbohydrate metabolism, which was significantly more abundant in older individuals.

High-coverage genomes to elucidate the evolution of penguins

BACKGROUND

Penguins (Sphenisciformes) are a remarkable order of flightless wing-propelled diving seabirds distributed widely across the southern hemisphere. They share a volant common ancestor with Procellariiformes close to the Cretaceous-Paleogene boundary (66 million years ago) and subsequently lost the ability to fly but enhanced their diving capabilities. With ∼20 species among 6 genera, penguins range from the tropical Galápagos Islands to the oceanic temperate forests of New Zealand, the rocky coastlines of the sub-Antarctic islands, and the sea ice around Antarctica. To inhabit such diverse and extreme environments, penguins evolved many physiological and morphological adaptations. However, they are also highly sensitive to climate change. Therefore, penguins provide an exciting target system for understanding the evolutionary processes of speciation, adaptation, and demography. Genomic data are an emerging resource for addressing questions about such processes.

RESULTS

Here we present a novel dataset of 19 high-coverage genomes that, together with 2 previously published genomes, encompass all extant penguin species. We also present a well-supported phylogeny to clarify the relationships among penguins. In contrast to recent studies, our results demonstrate that the genus Aptenodytes is basal and sister to all other extant penguin genera, providing intriguing new insights into the adaptation of penguins to Antarctica. As such, our dataset provides a novel resource for understanding the evolutionary history of penguins as a clade, as well as the fine-scale relationships of individual penguin lineages. Against this background, we introduce a major consortium of international scientists dedicated to studying these genomes. Moreover, we highlight emerging issues regarding ensuring legal and respectful indigenous consultation, particularly for genomic data originating from New Zealand Taonga species.

CONCLUSIONS

We believe that our dataset and project will be important for understanding evolution, increasing cultural heritage and guiding the conservation of this iconic southern hemisphere species assemblage.

Genome-wide mining of microsatellites in king cobra (Ophiophagus hannah) and cross-species development of tetranucleotide SSR markers in Chinese cobra (Naja atra)

The complete genome sequence provides the opportunity for genome-wide and coding region analysis of SSRs in the king cobra and for cross-species identification of microsatellite markers in the Chinese cobra. In the Ophiophagus hannah genome, tetranucleotide repeats (38.03%) were the most abundant category, followed by dinucleotides (23.03%), pentanucleotides (13.07%), mononucleotides (11.78%), trinucleotides (11.49%) and hexanucleotides (2.6%). Twenty predominant motifs in the O. hannah genome were (A)n (C)n, (AC)n, (AG)n, (AT)n, (AGG)n, (AAT)n, (AAG)n, (AAC)n, (ATG)n, (ATAG)n, (AAGG)n, (ATCT)n, (CCTT)n, (ATTT)n, (AAAT)n, (AATAG)n, (ATTCT)n, (ATATGT)n, (AGATAT)n. In total, 4344 SSRs were found in coding sequences (CDSs). Tetranucleotides (52.79%) were the most abundant microsatellite type in CDS, followed by trinucleotides (28.50%), dinucleotides (11.02%), pentanucleotides (4.42%), mononucleotides (1.77%), and hexanucleotides (1.50%). A total of 984 CDSs containing microsatellites were assigned 11152 Gene Ontology (GO) functional terms. Gene Ontology (GO) analysis demonstrated that cellular process, cell and binding were the most frequent GO terms in biological process, cellular component and molecular function, respectively. Thirty-two novel highly polymorphic (PIC > 0.5) SSR markers for Naja atra were developed from cross-species amplification based on the tetranucleotide microsatellite sequences in the king cobra genome. The number of alleles (N_A) per locus had between 3 and 11 alleles with an average of 6.5, the polymorphism information content (PIC) value ranged from 0.521 to 0.858 (average = 0.707), the observed heterozygosity (Ho) of 32 microsatellite loci ranged from 0.292 to 0.875 (mean = 0.678), the expected heterozygosity (H_E) ranged from 0.561 to 0.889 (average = 0.761), and 3 microsatellite loci exhibited statistically significant departure from Hardy-Weinberg equilibrium (HWE) after Bonferroni correction (p < 0.003).

Chinstrap penguin population genetic structure: one or more populations along the Southern Ocean?

Background

Historical factors, demography, reproduction and dispersal are crucial in determining the genetic structure of seabirds. In the Antarctic marine environment, penguins are a major component of the avian biomass, dominant predators and important bioindicators of ecological change. Populations of chinstrap penguins have decreased in nearly all their breeding sites, and their range is expanding throughout the Antarctic Peninsula. Population genetic structure of this species has been studied in some colonies, but not between breeding colonies in the Antarctic Peninsula or at the species’ easternmost breeding colony (Bouvetøya).

Results

Connectivity, sex-biased dispersal, diversity, genetic structure and demographic history were studied using 12 microsatellite loci and a mitochondrial DNA region (HVRI) in 12 breeding colonies in the South Shetland Islands (SSI) and the Western Antarctic Peninsula (WAP), and one previously unstudied sub-Antarctic island, 3600 km away from the WAP (Bouvetøya). High genetic diversity, evidence of female bias-dispersal and a sign of population expansion after the last glacial maximum around 10,000 mya were detected. Limited population genetic structure and lack of isolation by distance throughout the region were found, along with no differentiation between the WAP and Bouvetøya (overall microsatellite F_ST = 0.002, p = 0.273; mtDNA F_ST = − 0.004, p = 0.766), indicating long distance dispersal. Therefore, genetic assignment tests could not assign individuals to their population(s) of origin. The most differentiated location was Georges Point, one of the southernmost breeding colonies of this species in the WAP.

Conclusions

The subtle differentiation found may be explained by some combination of low natal philopatric behavior, high rates of dispersal and/or generally high mobility among colonies of chinstrap penguins compared to other Pygoscelis species.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1207-0) contains supplementary material, which is available to authorized users.