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PubMed2026

Reducing haystacks to needles - ViralClust: A Nextflow pipeline to cluster viral sequences.

BACKGROUND: The rapid accumulation of viral genome sequences presents major challenges for downstream analysis tools, including tools for multiple sequence alignments, phylogeny, and genome/alignment visualization, due to computational constraints and sampling biases caused by outbreak-driven over-representation. Selecting representative genomes through clustering offers a principled alternative to random subsampling, yet choosing appropriate clustering strategies remains non-trivial and context-dependent. RESULTS: Here, we present ViralClust, a modular Nextflow pipeline for bias-aware representative selection from large viral genome datasets. ViralClust integrates five distinct clustering algorithms (CD-HIT-EST, SUMACLUST, VSEARCH, MMSeqs2, and HDBSCAN) within a unified workflow, enabling direct comparison of clustering outcomes and flexible adaptation to diverse biological questions, considering a balanced phylogenetic distribution of the selected sequences. We evaluated ViralClust on six RNA and DNA virus datasets ranging from 632 to 156,586 sequences and spanning genome lengths from 890 to 197,185 nucleotides. Across all datasets, clustering reduced dataset size by ~95 % or more while preserving genetic diversity across species, genera, and families, and effectively mitigating biases introduced by outbreaks, partial genomes, and sequence orientation artifacts. CONCLUSIONS: By supporting whole-genome clustering and scalable representative selection, ViralClust enables efficient and reproducible downstream analyses that would otherwise be computationally infeasible. Rather than offering a prescriptive, guided analysis engine, our framework functions as a flexible comparative collection of complementary strategies, allowing users to empirically evaluate trade-offs and choose the ideal method tailored to their specific analytical endpoints.

Bioinformatics↗

PubMed2025

Adaptation in a keystone grazer under novel predation pressure.

Understanding how species adapt to environmental change is necessary to protect biodiversity and ecosystem services. Growing evidence suggests species can adapt rapidly to novel selection pressures like predation from invasive species, but the repeatability and predictability of selection remain poorly understood in wild populations. We tested how a keystone aquatic herbivore, Daphnia pulicaria, evolved in response to predation pressure by the introduced zooplanktivore Bythotrephes longimanus. Using high-resolution 210Pb-dated sediment cores from 12 lakes in Ontario (Canada), which primarily differed in invasion status by Bythotrephes, we compared Daphnia population genetic structure over time using whole-genome sequencing of individual resting embryos. We found strong genetic differentiation between populations approximately 70 years before versus 30 years after reported Bythotrephes invasion, with no difference over this period in uninvaded lakes. Compared with uninvaded lakes, we identified, on average, 64 times more loci were putatively under selection in the invaded lakes. Differentiated loci were mainly associated with known reproductive and stress responses, and mean body size consistently increased by 14.1% over time in invaded lakes. These results suggest Daphnia populations were repeatedly acquiring heritable genetic adaptations to escape gape-limited predation. More generally, our results suggest some aspects of environmental change predictably shape genome evolution.

Animals↗

PubMed2025

Eco-Evolutionary Genomics Reveal Mountain Range-Specific Adaptation and Intraspecific Variation in Vulnerability to Climate Change of Alpine Endemics.

Alpine plants restricted to rocky habitats exhibit intraspecific diversification due to range fragmentation during Holocene warming, complicating predictions of their climate vulnerability. A lack of understanding of eco-evolutionary mechanisms driving their response to climate change results in ineffective conservation efforts. To uncover the genomic basis of their diversification and explain spatial patterns of their vulnerability, we combine landscape genomics and species distribution modelling. Our model, the Campanula lehmanniana complex, occurs in three distinct central Asian mountain ranges, considered both a biodiversity hotspot and a vascular plant diversity darkspot. Genome-environment association confirmed the adaptive basis of intraspecific diversification, driven by numerous loci of small effect. Genomic and ecological data indicate mountain range-specific climate sensitivity driven by altitude, temperature and precipitation. The cold-dry adapted group from Zeravshan-Hissar Mts will face niche decline but show a higher degree of preadaptation to future climate, while the temperate-humid group from Tian Shan shows an opposite response, with a higher risk of maladaptation despite predicted niche expansion. Maladapted populations at northern margins may require an influx of adaptive variation to cope with predicted changes. However, limited landscape connectivity between island-like habitats, combined with long migration distances required to minimise genotype-environment disruption, highlights the role of human-assisted migration in enabling evolutionary rescue. These results underscore the need to facilitate gene flow from pre- to maladapted populations and the importance of population-specific approaches to inform effective conservation strategies in heterogeneous mountain ecosystems. The results may be relevant to numerous Central Asian mountain species that show similar phylogeographic patterns.

Climate Change↗

PubMed2026

The Polish Konik Horse: A Multidisciplinary Review of Its Origin, Genetics, Ecology, Health, Behaviour and Reproductive Biology.

The Polish Konik horse (PKH) is one of Europe's best-known native conservation breeds. Traditionally associated with the extinct Eurasian tarpan and conservation grazing, the breed has recently become the subject of multidisciplinary research encompassing genetics, ecology, health, behaviour and reproduction. This narrative review summarises current knowledge on the biological characteristics and contemporary scientific significance of the PKH. Literature published between 2005 and 2026 was identified through searches of PubMed, Scopus, Web of Science and Google Scholar and narratively synthesised. Available evidence suggests that, despite severe historical bottlenecks, the PKH has retained considerable genetic diversity and its characteristic maternal and paternal founder-line structure. Recent molecular studies have revised traditional concepts of the breed's origin, while ecological research supports its important role in conservation grazing and wetland restoration. Behavioural and reproductive studies indicate stable temperament, high reproductive efficiency and adaptation to extensive management systems. However, current knowledge is derived predominantly from observational studies, with relatively few comparative investigations and limited genomic and longitudinal data. The PKH represents a valuable model for research on conservation genetics, environmental adaptation, animal welfare, reproductive biology and ecosystem management. Further interdisciplinary studies are needed to strengthen the evidence base for conservation and breeding strategies.

Polish Konik horse↗

PubMed2026

Adaptation to seasonal drought in Arabis alpina is linked to the demographic history and climatic changes since the last glacial maximum.

Understanding how species adapt to new environments is a central goal in evolutionary biology, and a topical question in climate change research. Here, we sequenced the genomes of 426 individuals of the perennial, Arctic-alpine herb Arabis alpina to study demography and adaptation, with a focus on populations in Northern Spain, that experience warm and dry summers. Our inference supports a scenario in which A. alpina colonized Northern Spain in a range expansion event that started near the Alps around 216 thousand years ago (kya). During the last glacial episode (115 to 12 kya), this expansion proceeded westward, and effective population sizes were large across Europe, likely due to a larger suitable habitat for A. alpina. These ancient demographic events gave rise to a highly diverged genetic lineage in Northern Spain. In the present interglacial (between 12 kya and present), populations became increasingly fragmented, and lost genetic diversity across Europe. Furthermore, we detected signatures of selection at genes associated with responses to abiotic stress, including drought stress, and regulation of growth, for instance at SC5D and NAC055, which reflects the climatic changes since the last glacial period. Notably, an ancient polymorphism at the gene FRL1 emerged as a candidate for conferring variation in flowering behavior, and for contributing to adaptation to drought. Our study suggests that the combination of ancestral variation in flowering behavior, and positive selection on new mutations involved in drought responses, underlies the evolution of a new trait syndrome, and adaptation to climate change.

Droughts↗

PubMed2026

Integrative quantum and systems biology of cancer: From molecular fluctuations to ecological outcomes.

This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.

Neoplasms↗

PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗

PubMed2025

Locality-aware pooling enhances protein language model performance across varied applications.

MOTIVATION: Protein language models (PLMs) are amongst the most exciting recent advances for characterizing protein sequences, and have enabled a diverse set of applications, including structure determination, functional property prediction, and mutation impact assessment, all from single protein sequences alone. State-of-the-art PLMs leverage transformer architectures originally developed for natural language processing, and are pre-trained on large protein databases to generate contextualized representations of individual amino acids. To harness the power of these PLMs to predict protein-level properties, these per-residue embeddings are typically "pooled" to fixed-size vectors that are further utilized in downstream prediction networks. Common pooling strategies include Cls-Pooling and Avg-Pooling, but neither of these approaches can capture the local substructures and long-range interactions observed in proteins. RESULTS: We propose the use of attention pooling, which can naturally capture these important features of proteins. To make the expensive attention operator (quadratic in the length of the input protein) feasible in practice, we introduce bag-of-mer pooling, or BoM-Pooling, a locality-aware hierarchical pooling technique that combines windowed average pooling with attention pooling. We empirically demonstrate that both full attention pooling and BoM-Pooling outperform previous pooling strategies on three important, diverse tasks: (i) predicting the activities of two proteins as they are varied; (ii) detecting remote homologs; and (iii) predicting signaling protein interactions with peptides. Overall, our work highlights the advantages of biologically inspired pooling techniques in protein sequence modeling and is a step toward more effective adaptations of language models in biological settings. AVAILABILITY AND IMPLEMENTATION: https://github.com/Singh-Lab/bom-pooling.

Natural Language Processing↗

PubMed2026

Genomic analysis of breed composition and population structure in Montana composite cattle.

The Montana composite was developed in Brazil from crosses between Bos indicus and Bos taurus and structured into four biological types: Zebu (N), adapted taurine (A), British taurine (B), and continental taurine (C). This study aimed to characterize the genetic diversity and population structure of the Montana composite using genomic data through principal component analysis (PCA), admixture analysis, and Wright's FST statistic. The PCA revealed a clear separation between Bos indicus and Bos taurus groups, with Montana animals distributed in an intermediate position. The first two principal components explained 69.48% and 3.45% of the total variation, respectively. Supervised admixture estimates indicated a predominance of taurine contribution, with type A accounting for 34.47%, 52.64%, and 51.71% at K = 4, 9, and 11, respectively. Increasing the ancestry resolution refined the contribution of individual founder breeds without changing the overall predominance of taurine ancestry. Comparisons between breed proportions obtained from pedigree and genomic data revealed significant differences, for most biological types and ancestry models (P < 0.001), indicating that realized breed composition deviates from theoretical expectations. Estimates of genetic differentiation confirmed greater divergence between Zebu and taurine groups, as well as reduced distances among populations sharing common ancestry. Specific relationships were identified between the composite and some of its founder breeds, particularly Belmont Red, Senepol, and Tuli. Overall, the results demonstrate that the Montana composite has a complex genomic structure, with genomic ancestry varying according to the resolution adopted and differing from pedigree-based expectations.

Animals↗

PubMed2024

PSEUDO-RESPONSE REGULATOR 3b and transcription factor ABF3 modulate abscisic acid-dependent drought stress response in soybean.

The circadian system plays a pivotal role in facilitating the ability of crop plants to respond and adapt to fluctuations in their immediate environment effectively. Despite the increasing comprehension of PSEUDO-RESPONSE REGULATORs and their involvement in the regulation of diverse biological processes, including circadian rhythms, photoperiodic control of flowering, and responses to abiotic stress, the transcriptional networks associated with these factors in soybean (Glycine max (L.) Merr.) remain incompletely characterized. In this study, we provide empirical evidence highlighting the significance of GmPRR3b as a crucial mediator in regulating the circadian clock, drought stress response, and abscisic acid (ABA) signaling pathway in soybeans. A comprehensive analysis of DNA affinity purification sequencing and transcriptome data identified 795 putative target genes directly regulated by GmPRR3b. Among them, a total of 570 exhibited a significant correlation with the response to drought, and eight genes were involved in both the biosynthesis and signaling pathways of ABA. Notably, GmPRR3b played a pivotal role in the negative regulation of the drought response in soybeans by suppressing the expression of abscisic acid-responsive element-binding factor 3 (GmABF3). Additionally, the overexpression of GmABF3 exhibited an increased ability to tolerate drought conditions, and it also restored the hypersensitive phenotype of the GmPRR3b overexpressor. Consistently, studies on the manipulation of GmPRR3b gene expression and genome editing in plants revealed contrasting reactions to drought stress. The findings of our study collectively provide compelling evidence that emphasizes the significant contribution of the GmPRR3b-GmABF3 module in enhancing drought tolerance in soybean plants. Moreover, the transcriptional network of GmPRR3b provides valuable insights into the intricate interactions between this gene and the fundamental biological processes associated with plant adaptation to diverse environmental conditions.

Glycine max↗

PubMed2026

Adaptive evolution of polyploid crops.

Crop evolution represents a fundamental biological process through which plants respond to selection in different environments. This encompasses mechanisms operating at multiple scales of biological organization, including genetic and epigenetic regulation and higher-order interactions among molecular complexes. This Review synthesizes how polyploidy shapes crop evolution by generating duplicated genes, driving genome reorganization, altering dosage relationships and promoting regulatory divergence, which together influence crop metabolism, physiology, development and environmental responses. We focus mainly on the mechanisms underlying adaptation in polyploid crops, including the consequences of gene and genome duplication, genome reorganization and subfunctionalization. We also examine how hybridization, phenotypic plasticity and crop-microbiome interactions intersect with polyploidy to expand or constrain adaptive potential. Together, these processes affect crop survival, fitness and breeding value under changing environments. We suggest that future research connect polyploid genome architecture with experimentally validated signatures of selection and field performance to make better use of polyploidy-derived variation in crop improvement.

Polyploidy↗

PubMed2026

Genomic exploration of Bacillus paralicheniformis TB197: an agrobiotechnological tool from the Sonoran Desert.

Climate change and the harmful effects of extensive agrochemical use for plant nutrition and pest control on soils, the environment, and human health are driving the search for sustainable alternatives that reduce their use while increasing plant resilience. In regenerative agriculture, microorganisms have become valuable tools, acting as biological control agents or biostimulants, such as plant growth-promoting rhizobacteria, and/or to enhance plant performance under abiotic stress. The genus Bacillus is well known for its versatile interactions with plants. Specifically, Bacillus paralicheniformis TB197 has demonstrated high efficacy in controlling phytopathogenic nematodes and adapting to diverse soil and crop conditions. Based on these traits, we explored the agricultural potential of this strain through genomic analysis and in vitro and in vivo assays. Gene analysis identified functions related to three main areas: (i) stress resistance and plant colonization, (ii) plant growth promotion, and (iii) phytopathogen control. The strain showed high tolerance to salinity and temperature, promoted plant growth, and exhibited strong antifungal activity. These findings highlight the potential of the TB197 strain as a promising candidate for developing next-generation bioinoculants.IMPORTANCEThe use of beneficial microorganisms is a pivotal strategy for mitigating the environmental impacts of intensive agriculture while preserving crop productivity. Bacillus paralicheniformis TB197 is a native desert soil bacterium with genetic traits associated with stress tolerance, plant growth promotion, and suppression of plant pathogens. In this study, we employed a multifaceted approach integrating genomic analysis and functional assays to demonstrate the strain's multifunctional potential as an agricultural bioinoculant. The results of the study demonstrate that a singular bacterial strain can integrate multiple beneficial functions relevant to sustainable agriculture. This work contributes to the field of applied microbiology by expanding the understanding of how environmentally adapted bacteria can serve as biological alternatives to chemical inputs in agroecosystems.

Bacillus↗

PubMed2026

Extracellular Vesicles From Glioblastoma Cells Reflect 2D vs. 3D Culture Adaptation and Resistance to Temozolomide.

Glioblastoma (GBM) is an aggressive brain tumor marked by extensive heterogeneity, resistance to therapy, and dismal prognosis. Extracellular vesicles (EVs) have emerged as key players in GBM biology, mediating intercellular communication and therapy adaptation. However, the exact functions and molecular impact of EVs in GBM remain incompletely understood. In this study, we performed a comparative proteomic analysis of U87MG GBM cells grown in two-dimensional (2D) monolayers and three-dimensional (3D) spheroids following temozolomide (TMZ) treatment, alongside characterization of EVs derived from both culture systems. 3D-spheroids secreted more EVs of smaller size and exhibited a more TMZ-resistant, stem-like proteome under TMZ-induced genotoxic stress. In contrast, 2D cell cultures demonstrated greater proteome remodeling, with EVs enriched in protein families involved in DNA repair, oxidative stress adaptation, and methylation processes. Notably, several methyltransferases were decreased intracellularly but selectively retained in EVs, suggesting active sorting to influence the tumor microenvironment or modulate epigenetic states in recipient cells. EVs also carried adhesion molecules and signaling proteins linked to migration, invasion, and Wnt pathway activation, as well as metabolic enzymes connecting serine metabolism and redox control to TMZ resistance. Mapping EV and cellular proteomes onto The Cancer Genome Atlas (TCGA) dataset identified prognostic protein families associated with either poor or favorable patient outcomes. Our data demonstrate that EV cargo composition mirrors TMZ-induced phenotypic adaptation and reveals molecular mechanisms underlying therapeutic resistance. These EV-associated signatures may serve as clinically actionable biomarkers for patient stratification and offer potential targets to overcome chemoresistance in GBM.

Humans↗

PubMed2026

BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.

MOTIVATION: Hypergraph-based models for brain disorder prediction mainly adopt imaging-derived hypergraphs as propagation backbones. However, the entanglement of topology construction and feature propagation leaves regional representations weakly constrained by underlying biological organization, making them vulnerable to subject-specific variation and noise, particularly in heterogeneous multimodal settings. RESULTS: We present BriGHT, a Brain transcriptome-reGularized Hypergraph framework for mulTimodal disorder prediction. BriGHT employs a transcriptome-derived structural reference as a soft anchoring prior to regularize neuroimaging ROI embeddings, stabilizing representation geometry while preserving disease-relevant subject-specific variation. BriGHT further incorporates a reliability-aware fusion module to estimate subject-specific modality reliability from prediction confidence, cross-modal consistency, and decision certainty, enabling adaptive integration under heterogeneous modality quality. Experiments on three neuroimaging cohorts (ADNI, ADHD-200, REST-meta-MDD) and four modalities (VBM, fMRI, FDG, AV45) demonstrate that BriGHT consistently outperforms competing graph/hypergraph learning methods across six brain disorder prediction tasks. Perturbation analyses show that BriGHT benefits from the spatial correspondence between transcriptomic modules and imaging ROIs, rather than from arbitrary hypergraph regularization alone. Ablation and meta-analytic interpretability analyses support the contribution of transcriptomic anchoring and adaptive fusion to robust and biologically meaningful brain disorder prediction. AVAILABILITY: The software is publicly available at: https://github.com/Yaolab-fantastic/BriGHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2025

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform genomic foundation models on mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

Journal Article↗

PubMed2026

Genome-Wide Characterization of the Apple HD-Zip IV Gene Family and Functional Validation of MdHDZIV3 Under PEG-Induced Osmotic Stress.

The homeodomain-leucine zipper IV (HD-Zip IV) transcription factor subfamily plays essential roles in epidermal development, cuticle formation, lipid metabolism, and environmental adaptation in plants. Despite its biological importance, the HD-Zip IV family has not been systematically characterized in apple (Malus domestica). Here, we identified 17 apple HD-Zip IV genes and named them MdHDZIV1-MdHDZIV17 based on their locations on the chromosomes. The 17 genes showed a nonuniform distribution on eight chromosomes, while the occurrence of both tandem and segmental duplications indicated that family expansion involved more than one duplication mechanism. All MdHDZIV proteins contained the conserved HD, LZ, START, and SAD domains but lacked the MEKHLA domain, consistent with typical HD-Zip IV structural features. Phylogenetic analysis classified MdHDZIV proteins into five groups together with HD-Zip IV members from Arabidopsis thaliana and rice, indicating evolutionary conservation of this subfamily. Collinearity and Ka/Ks analyses revealed that duplicated MdHDZIV gene pairs were mainly subjected to purifying selection. Promoter scanning revealed diverse cis-regulatory motifs associated with hormonal signaling, environmental stress, light response, and epidermal regulation, including ABRE, ARE, W-box, MYC, G-box, and L1-box motifs. Integration of transcriptomic profiling with qRT-PCR validation revealed pronounced tissue-dependent differences in the expression of MdHDZIV genes in leaf, fruit skin, and branch bark. Under PEG6000-induced osmotic stress and NaCl-induced salt stress, 10 candidate MdHDZIV genes displayed gene-specific and stress type-specific expression patterns, with MdHDZIV3 showing strong induction under PEG6000 treatment. Functional validation in apple calli showed that MdHDZIV3 overexpression enhanced PEG tolerance, increased fresh weight, elevated SOD and POD activities, and reduced MDA accumulation under osmotic stress. These findings provide a genome-wide framework for understanding the apple HD-Zip IV gene family.

abiotic stress↗

PubMed2025

Experimental Validation of Genome-Environment Associations in Arabidopsis.

Identifying the genetic basis of local adaptation is a key goal in evolutionary biology. Allele frequency clines along environmental gradients, known as genotype-environment associations (GEA), are often used to detect potential loci causing local adaptation but are rarely followed by experimental validation. Here, we tested loci identified in three moisture-related GEA studies on Arabidopsis. We studied 42 GEA-identified genes using t-DNA knockout lines under drought and tested effects on flowering time, an adaptive trait, and genotype-by-environment (GxE) interactions for performance and fitness. In total, 16/42 genes had significant effects on traits involved in local adaptation or performance responses to the environment. We found that wrky38 mutants had significant GxE effects for fitness; lsd1 plants had a significant GxE effect for flowering time, and 11 genes showed flowering time effects with no drought interaction. However, most GEA candidates did not exhibit GxE. In the follow-up experiments, wrky38 caused decreased stomatal conductance and specific leaf area under drought, indicating potentially adaptive drought avoidance. Additionally, GEA identified natural putative LoF variants of WRKY38 associated with dry environments, as well as alleles associated with variation in LSD1 expression. While only a few GEA-identified genes were validated for GxE interactions for fitness, we likely overlooked some genes because experiments might not well represent natural environments and t-DNA insertions might not well represent natural alleles. Nevertheless, GEAs apparently identified some genes contributing to local adaptation. GEA and follow-up experiments are straightforward to implement in model systems and demonstrate prospects for GEA discovery of new local adaptations.

Arabidopsis↗

PubMed2025

Constraints in temperature adaptation reinforce differences in thermal niche between mesophilic and psychrotolerant Bacillus cereus group species.

Experimental evolution has demonstrated that mesophilic microbes readily adapt to increases in temperature. However, many microbes are psychrotolerant and resistant to cold, which is associated with physiological specializations, suggesting constraints in thermal adaptation. We hypothesized that constraints would limit adaption differently in a mesophilic species (Bacillus thuringiensis) compared with its psychrotolerant relative B. mycoides-with adaptation at cooler temperatures and adaptation at higher temperatures being constrained in each species, respectively. To test this hypothesis, we imposed 140 generations of selection at temperatures at and below the optimum for productivity for both species. The fitness and thermal performance of evolved bacteria showed ancestral thermal niche plays a role in thermal adaptation over this time scale, in support of our hypothesis of adaptive constraints. Temperature-dependent trade-offs appeared common in B. mycoides, with fitness gains associated with decreases in operational niche width; fitness gains at one temperature caused a decrease in the range of temperatures that the bacterium showed appreciable growth. Genome resequencing showed that variation in mutation supply and selection strength could not explain temperature-dependent responses to selection. Importantly, metabolic theory only held true for mesophilic B. thuringiensis, showing abundant but less studied psychrotolerant species could follow different adaptive trajectories.

Bacillus thuringiensis↗