Discovering variation of secondary metabolite diversity and its relationship with disease resistance in Cornus florida L.

Abstract

Understanding intraspecific relationships between genetic and functional diversity is a major goal in the field of evolutionary biology and is important for conserving biodiversity. Linking intraspecific molecular patterns of plants to ecological pressures and trait variation remains difficult due to environment‐driven plasticity. Next‐generation sequencing, untargeted liquid chromatography–mass spectrometry (LC‐MS) profiling, and interdisciplinary approaches integrating population genomics, metabolomics, and community ecology permit novel strategies to tackle this problem. We analyzed six natural populations of the disease‐threatened Cornus florida L. from distinct ecological regions using genotype‐by‐sequencing markers and LC‐MS‐based untargeted metabolite profiling. We tested the hypothesis that higher genetic diversity in C. florida yielded higher chemical diversity and less disease susceptibility (screening hypothesis), and we also determined whether genetically similar subpopulations were similar in chemical composition. Most importantly, we identified metabolites that were associated with candidate loci or were predictive biomarkers of healthy or diseased plants after controlling for environment. Subpopulation clustering patterns based on genetic or chemical distances were largely congruent. While differences in genetic diversity were small among subpopulations, we did observe notable similarities in patterns between subpopulation averages of rarefied‐allelic and chemical richness. More specifically, we found that the most abundant compound of a correlated group of putative terpenoid glycosides and derivatives was correlated with tree health when considering chemodiversity. Random forest biomarker and genomewide association tests suggested that this putative iridoid glucoside and other closely associated chemical features were correlated to SNPs under selection.

Chemical and Antifungal Variability of Several Accessions of Azadirachta indica A. Juss. from Six Locations Across the Colombian Caribbean Coast: Identification of Antifungal Azadirone Limonoids

“…Hence, metabolite variations according to growing places could be considered different to those related to antifungal activity in these A. indica specimens. This fact is in agreement with the environment-driven plasticity of intraspecific metabolite patterns due to ecological pressures and trait variations [32].…”

Plant adaptation to climate change—Where are we?

“…Landscape genomics has emerged as a powerful approach for testing the relationship between genomic variation and environmental heterogeneity among natural populations (e.g., Rellstab et al, 2015;Li et al, 2017;Pais et al, 2017Pais et al, , 2018Pais et al, , 2020Dalongeville et al, 2018). Two strategies have been widely used to identify candidate genes involved in environmental adaptation in landscape genomic studies.…”

“…Two strategies have been widely used to identify candidate genes involved in environmental adaptation in landscape genomic studies. One involves scanning the genome of two or many populations in different habitats to detect outlier loci showing excess of population differentiation (F ST ; genome scan method; e.g., Storz, 2005;Foll & Gaggiotti, 2008;Excoffier et al, 2009;Wittkopp & Kalay, 2011;Pais et al, 2017Pais et al, , 2018Pais et al, , 2020Gould et al, 2018), and the other is genome-wide association study between molecular markers and environmental variables, which are treated as phenotypes (GWAS method; e.g., Rellstab et al, 2015;Li et al, 2017;Frachon et al, 2018;Razgour et al, 2019). These two methods require different sampling strategies.…”

In memory of Professor Tang Yan‐Cheng: New perspectives in systematic and evolutionary biology

“…Modern plant systematics has expanded in scope from characterizing species diversity and relationships to the population level to understanding genetic and genomic variation and population structure at fine scales, as well as to identify the genetic loci associated with local adaptation along spatial and ecological gradients (e.g., climate, soil, and disease). For example, Pais and colleagues present advances made in understanding a non-model tree species, Cornus florida L., by integrating landscape ecology, phylogeography, population genomics, and metabolomics, using genome-wide association studies (GWAS) and traditional community ecology methods (Pais et al, 2017(Pais et al, , 2018(Pais et al, , 2020. Pais et al (2017) used this integrative approach and Genotyping By Sequencing (GBS) to identify more than 50 loci under selection for adaptation to local environments (differing in temperature, precipitation, and soil nutrients in the southern Appalachian Mountains, Piedmont Plateau, and coastal plain of North Carolina).…”

“…In Cornus florida L. (colloquially, "flowering dogwood"), some loci under selection were determined to be associated with dogwood anthracnose disease, which has seriously threatened natural populations. By integrating nontargeted liquid chromatography-mass spectrometry (LC-MS) profiling and GWAS, Pais et al (2018) discovered that some loci under selection were associated with the production of certain groups of metabolites. One such locus represents an Ltype lectin domain containing receptor kinase that is known to be important in providing immunity to disease in plants (Singh & Zimmerli, 2013).…”