Login / Signup

Are the Organellar Genomes Useful for Fine Scale Population Structure Analysis of Endangered Plants?-A Case Study of Pulsatilla patens (L.) Mill.

Kamil SzandarSawicki JakubŁukasz PauksztoKatarzyna KrawczykMonika Szczecińska
Published in: Genes (2022)
Pulsatilla patens is a rare and endangered species in Europe and its population resources have significantly decreased over the past decades. Previous genetic studies of this species made it possible to estimate the genetic diversity of the European population and to describe the structure of chloroplast and mitochondrial genomes. The main aim of these studies was to characterize the variability of chloroplast and mitochondrial genomes in more detail at the intra-population and inter-population levels. Our study presents new organelle genome reference sequences that allow the design of novel markers that can be the starting point for testing hypotheses, past and modern biogeography of rare and endangered species P. patens, and adaptive responses of this species to changing environments. The study included sixteen individuals from five populations located in Northeastern Poland. Comparative analysis of 16 P. patens plastomes from 5 populations enabled us to identify 160 point mutations, including 64 substitutions and 96 InDels. The most numerous detected SNPs and Indels (75%) were accumulated in three intergenic spacers: ndh D- ccs A, rps 4- rps 16, and trn L(UAG)- ndh F. The mitogenome dataset, which was more than twice as large as the plastome (331 kbp vs. 151 kbp), revealed eight times fewer SNPs (8 vs. 64) and six times fewer InDels (16 vs. 96). Both chloroplast and mitochondrial genome identified the same number of haplotypes-11 out of 16 individuals, but both organellar genomes slightly differ in haplotype clustering. Despite the much lower variation, mitogenomic data provide additional resolution in the haplotype detection of P. patens , enabling molecular identification of individuals, which were unrecognizable based on the plastome dataset.
Keyphrases
  • genetic diversity
  • genome wide
  • oxidative stress
  • single cell
  • air pollution
  • machine learning
  • rna seq
  • big data
  • single molecule
  • copy number