Genome sequencing of the nine-spined stickleback (Pungitius pungitius) provides insights into chromosome evolution

Abstract

The Gasterostidae fish family hosts several species that are important models for ecoevolutionary, genetic and genomic research. In particular, a wealth of genetic and genomic data have been generated for the three-spined stickleback (Gasterosteus aculeatus), the 'ecology's supermodel', while the genomic resources for the nine-spined stickleback (Pungitius pungitius) have remained relatively scarce. Here, we report a high-quality chromosome-level genome assembly of P. pungitius consisting of 5,303 contigs (N50 = 1.2 Mbp) with a total size of 521Mbp. These contigs were mapped to 21 linkage groups using a high-density linkage map, yielding a final assembly with 98.5% BUSCO completeness. A total of 25,062 protein-coding genes were annotated, and ca. 23% of the assembly was found to consist of repetitive elements.A comprehensive analysis of repetitive elements uncovered centromeric-specific tandem repeats and provided insights into the evolution of retrotransposons. A multigene phylogenetic analysis inferred a divergence time of about 26 million years (MYA) between nine-and three-spined sticklebacks, which is far older than the commonly assumed estimate of 13 MYA. Compared to the three-spined stickleback, we identified an additional duplication of several genes in the hemoglobin cluster. Sequencing data from populations adapted to different environments indicated potential copy number variations in hemoglobin genes. Furthermore, genome-wide synteny comparisons between three-and nine-spined sticklebacks identified chromosomal rearrangements underlying the karyotypic differences between the two species. The high-quality chromosome-scale assembly of the nine-spined stickleback genome obtained with long-read sequencing technology provides a crucial resource for comparative and population genomic investigations of stickleback fishes and teleosts.

Variation in the Number and Position of rDNA Loci Contributes to the Diversification and Speciation in Nigella (Ranunculaceae)

“…Ns-Sat1, Ns-Sat2 and Nd-Sat1 are all 178 bp long AT-rich satellite repeats. Due to their localization patterns and their length similarity with described centromeric satellites in other species such as Arabidopsis ( Copenhaver et al, 1999 ), human ( Choo et al, 1991 ) and the fish Pungitius pungitius ( Varadharajan et al, 2019 ) it is tempting to speculate that these sequences indeed represent centromeric repeats of Nigella , although a functional proof is still missing. The 178 bp satellite unit is consistent with the 150–180 bp length DNA required to wrap around a single nucleosome ( Henikoff et al, 2001 ).…”

Variation in the number and position of rDNA loci is one of the driving forces in the diversification and speciation in Nigella (Ranunculaceae)

“…Ns-Sat1, Ns-Sat2 and Nd-Sat1 are all 178 bp long AT-rich satellite repeats. Due to their localization patterns and their length similarity with described centromeric satellites in other species such as Arabidopsis (Copenhaver et al 1999), human (Choo et al 1991) and the sh Pungitius pungitius (Varadharajan et al 2019) it is tempting to speculate that these sequences indeed represent centromeric repeats of Nigella, although a functional proof is still missing. The 178 bp satellite unit is consistent with the 150-180 bp length DNA required to wrap around a single nucleosome (Henikoff et al 2001).…”

Effects of marker type and filtering criteria onQST-FSTcomparisons

“…An assembled and annotated reference genome (110× coverage) for P. pungitius from a Pyöreälampi individual [40] was used for obtaining the genetic data. SNP data and in silico microsatellite data were obtained by whole genome resequencing of 10 female individuals from each of the above-mentioned four populations.…”

“…[28,52]), we explored how the F ST in our data was influenced by variation in recombination rate. To this end, the local recombination rate was estimated using a sex-averaged genetic map (an improved [40] version of that reported in [53]). First, non-monotonic regions were manually removed and outlier markers discarded by performing loess regression ( α = 0.2, degree = 1), interpolating genetic distance from physical positions and removing any points that lie further than 2 cM from the line.…”