Population Genomics Reveals Gene Flow and Adaptive Signature in Invasive Weed Mikania micrantha

Abstract

A long-standing and unresolved issue in invasion biology concerns the rapid adaptation of invaders to nonindigenous environments. Mikania micrantha is a notorious invasive weed that causes substantial economic losses and negative ecological consequences in southern China. However, the contributions of gene flow, environmental variables, and functional genes, all generally recognized as important factors driving invasive success, to its successful invasion of southern China are not fully understood. Here, we utilized a genotyping-by-sequencing approach to sequence 306 M. micrantha individuals from 21 invasive populations. Based on the obtained genome-wide single nucleotide polymorphism (SNP) data, we observed that all the populations possessed similar high levels of genetic diversity that were not constrained by longitude and latitude. Mikania micrantha was introduced multiple times and subsequently experienced rapid-range expansion with recurrent high gene flow. Using FST outliers, a latent factor mixed model, and the Bayesian method, we identified 38 outlier SNPs associated with environmental variables. The analysis of these outlier SNPs revealed that soil composition, temperature, precipitation, and ecological variables were important determinants affecting the invasive adaptation of M. micrantha. Candidate genes with outlier signatures were related to abiotic stress response. Gene family clustering analysis revealed 683 gene families unique to M. micrantha which may have significant implications for the growth, metabolism, and defense responses of M. micrantha. Forty-one genes showing significant positive selection signatures were identified. These genes mainly function in binding, DNA replication and repair, signature transduction, transcription, and cellular components. Collectively, these findings highlight the contribution of gene flow to the invasion and spread of M. micrantha and indicate the roles of adaptive loci and functional genes in invasive adaptation.

Genomic characterisation and dissection of the onset of resistance to acetyl CoA carboxylase-inhibiting herbicides in a large collection of Digitaria insularis from Brazil

“…The population genetic differentiation coefficient (Fst) confirmed a very low genetic differentiation between D. insularis populations (0.003 ≤ F ST ≤ 0.064), suggesting that gene flow between populations was common. The interpopulation gene flow estimates (Nm = 3.65–83.08) were approximately two-fold higher than the values obtained in invasive weed Mikania micrantha ( Ruan et al, 2021 ). Such low level of population differentiation and high level of gene flow among populations are indicative of recent expansion of the species as an agricultural weed in Brazil.…”

“…The in silico analysis of the top outlier loci revealed hits with adaptive genes such as DUF579, DUF2404 and GRAS family transcription factor involved in growth and development and/or abiotic stress responses ( Lee et al, 2012 ; Wang et al, 2022 ). In invasive weed species, adaptive signatures throughout the genomes have been identified using GBS markers and SNPs active in geographic differentiation detected more often pathways related to growth and defence responses ( Martin et al, 2016 ; van Boheemen et al, 2017 ; Ruan et al, 2021 ). The identification of similar functional genes in the present study therefore suggests that the populations have been adapting to changes in the environments while going through population expansion.…”

Landscape characteristics determine the gene exchange pattern of Opisthopappus species in the Taihang Mountains

“…2.6 F ST outliers filtered and selected SNPs identified F ST outliers generally show the selected genes or loci among populations. To identify the selected SNPs in the twoOpisthopappus species, BAYESCAN 2.1 software was used to filter the F ST outliers (Fischer et al, 2011;Foll et al, 2010;Ruan et al, 2021). Prior odds of the selection model were set at 10,000 to reduce false-positive results under a variety of demographic events.…”

“…The SNP associations with landscape factors were assessed using Samβada v.0.9.0 and latent factor mixed model (LFMM) software (Chien et al, 2020;Feng et al, 2015;Ruan et al, 2021). Samβada builds logistic regressions to estimate an individual's probability of presenting a particular molecular marker depending on the landscape factors that characterize its sampling site (Li et al, 2019;Vargas-Mendoza et al, 2016).…”

“…< 5 were kept(Mahtani- Williams et al, 2020;Papadopulos et al, 2014;Ruan et al, 2021) (Support files 2).The principal component analysis was performed with Origin 2022 software (https://www.originlab.com/) to determine important landscape factors that contributed to the O. longilobus and O. taihangensis . Subsequently, the screened factors were also mapped by Origin 2022 software (Fig.2).…”