Biology subjects

Karunarathne, P.

Publications and source records attributed to Karunarathne, P..

8 recordsLinked to original sources

bioRxiv2025

Interlocus gene conversion causes mosaic divergence in tandem paralogues - simulating HMA4 evolution in Arabidopsis halleri

Following gene duplication, the evolutionary trajectories of gene copies can be shaped by various processes including mutation, selection, recombination and interlocus gene conversion (IGC). To explore their dynamics and consequences, we developed a mathematical model that simulates the early evolution of recently duplicated, tandemly arrayed gene families with positive selection on the new gene copy. We compared the results of our model to sequence variation of the three tandemly arrayed HEAVY METAL ATPase 4 (HMA4) gene copies of Arabidopsis halleri, which are known to undergo IGC. However, rate and efficacy of IGC vary within genes, resulting in a mosaic pattern of divergence. Informed by empirical data, our model captures the impact of IGC and unequal crossing-over on the diversity within each gene copy and the divergence among them. By tailoring the model to the HMA4 gene copies, we demonstrate the models flexibility and its potential to provide insights into the evolutionary dynamics driving the evolution of tandemly arrayed paralogues. This study enhances our understanding of the balance between homogenization and divergence of gene family evolution.

evolutionary biology↗

bioRxiv2025

Copy Number Variation: A Substrate for Plant Adaptation and Stress Response in Arabidopsis

Copy number variation (CNV) is a major source of genomic diversity that shapes gene family evolution and may contribute to ecological differentiation, yet its genome-wide ecological relevance remains poorly understood. Here, we analyzed CNV across four Brassicaceae species (Arabidopsis thaliana, A. lyrata, A. halleri, and Arabis alpina) to identify gene families undergoing rapid expansion or contraction. Using a birth-death model, we identified 231 rapidly evolving gene families spanning diverse functional categories. We then characterized CNV within populations of A. thaliana and A. lyrata using population-scale long-read assemblies. CNV exhibited strong heterogeneity across gene families and species, with contrasting evolutionary outcomes: A. thaliana showed greater retention of duplicated copies, whereas A. lyrata exhibited higher pseudogenization and turnover. CNV profiles were strongly structured geographically, reflecting known demographic and phylogeographic patterns in both species. Environmental association analyses revealed species-specific architectures: CNV in A. thaliana showed a diffuse, polygenic association with climate, whereas in A. lyrata associations were more strongly coupled to population structure. Despite the functional diversity of rapidly evolving families, environmentally associated CNVs were significantly enriched in defense- and stress-related functions. These results demonstrate that CNV-environment relationships emerge at the level of gene family networks and are shaped by genomic architecture and lineage history, highlighting CNV as a context-dependent driver of genome evolution and ecological differentiation. SummaryGene copy number variation--where individuals carry different numbers of copies of the same gene--is a potentially important but poorly understood source of genetic diversity. The authors studied how copy numbers evolve across four mustard family plant species and whether this variation helps plants adapt to different environments. They found that gene families involved in disease resistance and stress responses were especially prone to rapid copy number changes. These changes tracked geography and local climate, but in ways that differed between species depending on their evolutionary history. The findings highlight gene copy number variation as a key, underappreciated contributor to how plants adapt to their environments.

evolutionary biology↗

bioRxiv2025

Unlocking the Forgotten Dimension of Biodiversity: A Scalable Genetic Diversity Index for Multi-Species Analysis

Geographic patterns of community-level genetic diversity provide key information about evolutionary processes and are increasingly relevant for conservation planning under rapid environmental change. Yet, continental-scale, multi-taxon assessments of genetic diversity remain rare, largely due to difficulties in integrating heterogeneous datasets across species and markers. Here, we develop a scalable framework to quantify and map nucleotide diversity ({pi}) across co-occurring taxa using georeferenced sequence data. We compiled and harmonized genetic metadata for European vascular plants and calculated site-level{pi} for individual species. By spatially aggregating values across taxa, we derived the Genetic Diversity Index (GDI), designed to capture shared geographic structure in intraspecific variation while minimizing species-specific noise. We evaluated robustness using simulations and rarefaction procedures to assess sensitivity to sampling intensity and taxonomic composition. Using [~]630,000 sequences from 1,860 species, GDI identified consistent hotspots of genetic diversity in the Anatolian Peninsula, Southern Iberia, and the Eastern Alps. These regions coincide with areas widely recognized as long-term refugia and biogeographic transition zones. GDI showed weak relationships with species richness and phylogenetic diversity, indicating that it captures a distinct dimension of biodiversity linked to demographic persistence and evolutionary history. In addition, GDI increased significantly with late-Quaternary climate stability, supporting the expectation that regions experiencing reduced climatic fluctuation tend to retain higher levels of genetic variation. Our results demonstrate that publicly available sequence data can be synthesized into reproducible, large-scale estimates of community-level diversity. The GDI provides a practical tool for integrating genetic information into biodiversity assessments and for identifying regions where evolutionary potential may be concentrated. This framework opens new opportunities for incorporating genomic resources into conservation prioritization and macroecological research.

genetics↗

bioRxiv2025

ClusterApp to visualize, organize, and navigate metabolomics data

BackgroundClustering analysis is a foundational step in exploratory data analysis workflows, with dimensionality reduction methods commonly used to visualize multidimensional data in lower-dimensional spaces and infer sample clustering. Principal Component Analysis (PCA) is widely applied in metabolomics but is often suboptimal for clustering visualization. Metabolomics data often require specialized manipulations such as blank removal, quality control adjustments, and data transformations that demand efficient visualization tools. However, the lack of user-friendly tools for clustering without computational expertise presents a challenge for metabolomics researchers. ClusterApp addresses this gap as a web application that performs Principal Coordinate Analysis (PCoA), expanding clustering alternatives in metabolomics. Built on a QIIME 2 Docker image, it enables PCoA computation and Emperor plot visualization. The app supports data input from GNPS, GNPS2, or user-provided spreadsheets. Freely available, ClusterApp can be locally installed as a Docker image or integrated into Jupyter notebooks, offering accessibility and flexibility to diverse users. ResultsTo demonstrate the data preprocessing techniques available in ClusterApp, we analyzed two Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) metabolomics datasets: one exploring metabolomic differences in mouse tissue samples and another investigating coral life history stages. Among the dissimilarity measures available, the Bray-Curtis measure effectively highlighted key metabolomic variations and patterns across both datasets. Targeted filtering significantly enhanced data reliability by retaining biologically relevant features, 10,617 in the coral dataset and 7,341 in the mouse dataset while eliminating noise. The combination of Total Ion Current (TIC) normalization and auto-scaling improved clustering resolution, revealing distinct separations in tissue types and life stages. ClusterApps flexible features, such as customizable blank removal and group selection, provided tailored analyses, enhancing visualization and interpretation of metabolomic profiles. ConclusionClusterApp addresses the need for accessible, dynamic tools for exploratory data analysis in metabolomics. By coupling data transformation capabilities with PCoA on multiple dissimilarity matrices, it provides a versatile solution for clustering analysis. Its web interface and Docker-based deployment offer flexibility, accommodating a wide range of use cases through graphical or programmatic interactions. ClusterApp empowers researchers to uncover meaningful patterns and relationships in metabolomics data without requiring cumbersome data manipulation or advanced bioinformatics expertise.

bioinformatics↗

bioRxiv2023

Recurrent hybridization and gene flow shaped Norway and Siberian spruce evolutionary history over multiple glacial cycles

Over the last decades, extensive genome-wide resequencing studies have highlighted the extent of hybridization and introgression between closely related species. Animal and plant species went through cycles of contractions and expansions as a result of glacial cycles. These repeated sequences of reproductive isolation and admixture at continental scales have led to the accumulation over time of an ancient, deep-seated and complex genetic structure. This structure was blurred by extensive gene flow, or reinforced by strong local adaptation. This already multi-layered structure has often been further enhanced by hybridization. We investigated this complexity in Norway spruce (Picea abies) and Siberian spruce (P. obovata), two closely related species dominating Eurasian boreal forests and forming a vast hybrid zone. Here, we genotyped 542 individuals of both species and their hybrids at 480K SNPs. Individuals came from 55 populations, extending from western Europe to Siberia with a focus on the main hybrid zone. Despite extensive gene flow and a clear Isolation-by-Distance pattern at the continental scale, distinct genetic clusters emerged, indicating barriers and corridors to migration. Coalescent-based demographic inferences revealed that Norway and Siberian spruce repeatedly hybridized during the Pleistocene with introgression pattern varying depending on the latitude. In northern ranges, P. obovata expanded into P. abies while P. abies expanded into P. obovata in the southern parts. Two cryptic refugia located in the large hybrid zone played a critical role in shaping the current distribution of the two species. Our study highlights the importance of considering the whole species complex instead of separate entities to shed light on their complex demographic histories.

evolutionary biology↗

bioRxiv2023

Hybridization mediated range expansion and climate change resilience in two conifers

Current global climate change is expected to affect biodiversity negatively at all scales leading to mass biodiversity loss. Many studies have shown that the distribution of allele frequencies across a species range is often influenced by specific genetic loci associated with local environmental variables. This association reflects local adaptation and allele changes at those loci could thereby contribute to the evolutionary response to climate change. However, predicting how species will adapt to climate change from this type of data alone remains challenging. In the present study, we combined exome capture sequences and environmental niche reconstruction, to test multiple methods for assessing local adaptation and climate resilience in two widely distributed conifers, Norway spruce and Siberian spruce. Both species are keystone species of the boreal forest and share a vast hybrid zone. We show that local adaptation in conifers can be detected through allele frequency variation, population-level ecological preferences, and historical niche movement. Moreover, we integrated genetic and ecological information into genetic offset predictive models to show that hybridization plays a central role in expanding the niche breadth of the two conifer species and may help both species to cope better with future changing climates. This joint genetic and ecological analysis also identified genetically isolated populations that are at risk under current climate change.

evolutionary biology↗

bioRxiv2022

A new framework for detecting copy number variants from single nucleotide polymorphism data: 'rCNV', a versatile R package for paralogs and CNVs detection

Studies show that copy number variants (CNVs), due to their ubiquitous presence in eukaryotes, contribute to phenotypic variation, environmental adaptation, and fuel species divergence at a previously unknown rate. However, the detection of CNVs in genomes, especially in non-model organisms is challenging due to the need for costly genomic resources and complex computational infrastructure. Therefore, to provide researchers with a low-cost and easily accessible resource, we developed a robust statistical framework and an R software package to detect CNVs using allelic-read depth from SNPs data. The core of the framework exploits i) the allelic-read depth ratio distribution in heterozygotes for individual SNPs and testing it against an expected distribution under a binomial sampling, and ii) SNPs showing an apparent excess of heterozygotes under Hardy-Weinberg equilibrium, to detect alleles in putatively multi-copy regions. The use of multiple statistical tests to find the deviation in allelic-read depth ratio distribution makes our method sensitive to sampling and aware of reference biases thereby minimizing false detection of CNVs. Our framework is well-catered for high throughput short-reads data, hence, most GBS technologies (e.g., RADseq, Exome-capture, WGS). As such, it allows calling CNVs from genomes of varying complexity. The framework is implemented in the R package "rCNV" which effortlessly automates the analysis. We trained our models on simulated data and tested on four datasets obtained from different sequencing technologies (i.e., RADseq: Chinook salmon - Oncorhynchus tshawytscha, American lobster - Homarus americanus, Exome-capture: Norway Spruce - Picea abies, and WGS: Malaria mosquito -Anopheles gambiae).

evolutionary biology↗

bioRxiv2020

phyloregion: R package for biogeographic regionalization and spatial conservation

O_LIBiogeographical regionalization is the classification of regions in terms of their biotas and is key to understanding biodiversity patterns across the world. Previously, it was only possible to perform analysis of biogeographic regionalization on small datasets, often using tools that are difficult to replicate. C_LIO_LIHere, we present phyloregion, a package for the analysis of biogeographic regionalization and spatial conservation in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever-increasing size and complexity. C_LIO_LICompared to available packages, phyloregion is three to four orders of magnitude faster and memory efficient for cluster analysis, determining optimal number of clusters, evolutionary distinctiveness of regions, as well as analysis of more standard conservation measures of phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment. C_LIO_LIA case study of zoogeographic regionalization for 9574 species of squamate reptiles (amphisbaenians, lizards, and snakes) across the globe, reveals their evolutionary affinities, using visualization tools that allow rapid identification of patterns and underlying processes with user-friendly colours-for example- indicating the levels of differentiation of the taxa in different regions. C_LIO_LIUltimately, phyloregion would facilitate rapid biogeographic analyses that accommodates the ongoing mass-production of species occurrence records and phylogenetic datasets at any scale and for any taxonomic group into completely reproducible R workflows. C_LI

bioinformatics↗