2b-RAD Genotyping of the Seagrass Cymodocea nodosa Along a Latitudinal Cline Identifies Candidate Genes for Environmental Adaptation

Abstract

Plant populations distributed along broad latitudinal gradients often show patterns of clinal variation in genotype and phenotype. Differences in photoperiod and temperature cues across latitudes influence major phenological events, such as timing of flowering or seed dormancy. Here, we used an array of 4,941 SNPs derived from 2b-RAD genotyping to characterize population differentiation and levels of genetic and genotypic diversity of three populations of the seagrass Cymodocea nodosa along a latitudinal gradient extending across the Atlantic-Mediterranean boundary (i.e., Gran Canaria-Canary Islands, Faro-Portugal, and Ebro Delta-Spain). Our main goal was to search for potential outlier loci that could underlie adaptive differentiation of populations across the latitudinal distribution of the species. We hypothesized that such polymorphisms could be related to variation in photoperiod-temperature regime occurring across latitudes. The three populations were clearly differentiated and exhibited diverse levels of clonality and genetic diversity. Cymodocea nodosa from the Mediterranean displayed the highest genotypic richness, while the Portuguese population had the highest clonality values. Gran Canaria exhibited the lowest genetic diversity (as observed heterozygosity). Nine SNPs were reliably identified as outliers across the three sites by two different methods (i.e., BayeScan and pcadapt), and three SNPs could be associated to specific protein-coding genes by screening available C. nodosa transcriptomes. Two SNPs-carrying contigs encoded for transcription factors, while the other one encoded for an enzyme specifically involved in the regulation of flowering time, namely Lysine-specific histone demethylase 1 homolog 2. When analyzing biological processes enriched within the whole dataset of outlier SNPs identified by at least one method, "regulation of transcription" and "signalling" were among the most represented. Our results highlight the fundamental importance signal integration and gene-regulatory networks, as well as epigenetic regulation via DNA (de)methylation, could have for enabling adaptation of seagrass populations along environmental gradients.

Keywords: 2b-RAD; SNPs; flowering; latitude; outlier loci; seagrass.

FIGURE 1

Sampling scheme and environmental characteristics of the selected sites. (A) Sampling locations of C. nodosa: GC—Las Palmas de Gran Canaria (Lat: 27°N, Canary Islands); FA—Faro (Lat: 36°N, Portugal); EB—Ebro Delta (Lat: 40°N, Spain). (B) Annual patterns of photoperiod for the different sampling sites. Daylight hours were derived from annual data and averaged for every 15 days (https://www.timeanddate.com). (C) Long term monthly means of Sea Surface Temperature (SST) registered on the Atlantic and Mediterranean waters from NOAA derived from data for years 1971–2000 (https://psl.noaa.gov/data/gridded/data.noaa.ersst.v4.html).

FIGURE 2

Distribution of transitions and transversions in the SNPs dataset. Percentages (%) of transitions and transversions of the whole set of 7,562 SNPs identified for 63 individuals is depicted using an alluvial diagram.

FIGURE 3

Population structure analysis and clonal diversity. (A) Principal Component Analysis (PCA) displaying the three C. nodosa populations collected along a latitudinal gradient of distribution (i.e., Gran Canaria, Faro and Ebro). (B) Admixture plot of the C. nodosa populations at K = 4. (C) Number of distinct Multi Locus Lineages (MLLs) identified at each sampling site. (D) Weir and Cockerham F_ST weighted estimate between populations.

FIGURE 4

Functional annotation of 83 outlier loci displaying a positive match with transcribed regions. Functional annotation of SNPs with a positive blast hit against C. nodosa transcriptomes is depicted using a sunburst diagram. Each sequence has been assigned to a MapMan BIN category and sub-category based on its biological role or enzymatic activity. The definition of the BINs is included in the figure legend. A detailed description of BIN sub-categories can be retrieved from Supplementary Table S12.