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PubMed2025

Ancient climate changes and relaxed selection shape cave colonization in North American cavefishes.

Extreme environments serve as natural laboratories for studying evolutionary processes, with caves offering replicated instances of independent colonizations. The timing, mode and genetic underpinnings underlying cave-obligate organismal evolution remain enigmatic. We integrate phylogenomics, fossils, palaeoclimatic modelling and newly sequenced genomes to elucidate the evolutionary history and adaptive processes of cave colonization in the study group, the North American Amblyopsidae fishes. Amblyopsid fishes present a unique system for investigating cave evolution, encompassing surface, facultative cave-dwelling and cave-obligate (troglomorphic) species. Using 1105 exon markers and total-evidence dating, we reconstructed a robust phylogeny that supports the nested position of eyed, facultative cave-dwelling species within blind cavefishes. We identified three independent cave colonizations, dated to the Early Miocene (18.5 Ma), Late Miocene (10.0 Ma) and Pliocene (3.0 Ma). Evolutionary model testing supported a climate-relict hypothesis, suggesting that global cooling trends since the Early-Middle Eocene may have influenced cave colonization. Comparative genomic analyses of 487 candidate genes revealed both relaxed and intensified selection on troglomorphy-related loci. We found more loci under relaxed selection, supporting neutral mutation as a significant mechanism in cave-obligate evolution. Our findings provide empirical support for climate-driven cave colonization and offer insights into the complex interplay of selective pressures in extreme environments.

Animals↗

PubMed2026

Intersecting experimental evolution and CRISPR screens to identify novel toxin resistance loci.

Understanding toxin resistance in insects is key to appreciating niche adaptations but remains challenging due to its often-polygenic basis. A well-known example is the specialized association of Drosophila sechellia with noni fruit (Morinda citrifolia), which is toxic to other insects, including Drosophila simulans and Drosophila melanogaster. The main noni toxin is octanoic acid (OA), but the mechanisms that determine sensitivity or resistance to OA in different species remain unclear. Here, we experimentally evolved D. simulans with increased OA resistance, identifying multiple loci under selection. Cross-referencing these with a genome-wide, OA resistance CRISPR screen in a D. melanogaster cell line highlighted two proteins: Kraken, a putative detoxification enzyme expressed in digestive and renal tissues, and Alkbh7, a mitochondrial protein linked to fatty acid metabolism. Both genes show elevated expression in D. sechellia and OA-resistant D. simulans. In D. melanogaster, kraken mutants are more OA-sensitive, while Alkbh7 overexpression increased OA resistance. Mutation of these genes in D. sechellia reduced OA tolerance. Our identification of genes contributing to OA resistance in laboratory and natural contexts demonstrates how complementary selection approaches can provide insights into complex mechanisms of toxin susceptibility and adaptation. Such methods could have practical applications in the characterization of natural and artificial insecticides.

Animals↗

PubMed2025

Multiomic Analyses Reveal the Molecular Mechanisms of Arid Adaptation in a Desert Rodent Species.

Organisms living in desert habitats face multiple simultaneous pressures, such as high temperatures and arid, and the population dynamics and community diversity of small rodents are strongly affected by climate extremes. However, the potential mechanisms by which desert rodents adapt to arid remain largely unexplored. Here, we assembled a 3.18 Gb genome, including 25,812 protein-encoding genes, for Orientallactaga sibirica, which is widely distributed across both arid and semihumid environments in Eurasia. Orientallactaga sibirica has longer ears and hind limbs to enhance heat dissipation, which may be related to the positively selected genes, such as Fgf10, Fgf11, Hoxc4, Hoxd1, and Bmp4. The renal transcriptome revealed increased fat and carbohydrate metabolism for metabolic water production in O. sibirica residing in arid habitats. Pathways such as material metabolism, oxidative stress response, osmoregulation, and water and salt reabsorption were enriched in candidate genes, such as Avp, Ang, and Ace, under positive selection in O. sibirica. Moreover, amino acid replacement was observed in the protein sequences of seven candidate genes, including Aldh7a1, Lnpep, Wnk4, C1qc, and Awat2, and these specific amino acid replacements of genes such as Umod and Scnn1a were related to unique osmoregulation, osmotic protection, and water retention compensation mechanisms. Water deprivation under laboratory conditions induced the upregulation of Umod and Aldh7a1 expression, further supporting the results observed in the wild population. These findings demonstrate that the positively selected genes related to limb development and specific amino acid replacements in the genes Umod and Scnn1a for unique osmoregulation in the renal vascular system may contribute to arid adaptation in the desert rodent species O. sibirica. This study provides novel insights into the adaptive evolution of desert small mammals and can serve as a reference for future research on renal damage-related diseases, such as human kidney stones and salt-sensitive hypertension.

Animals↗

PubMed2026

Adaptation to Plant Defence in an Agricultural Insect Pest: Integrating Genome Scans and Gene Expression in the Soybean Aphid Reveals Multi-Genic Pathways.

In agroecosystems, intense selection pressures cause species to adapt and spread, often leading to the evolution and persistence of pests. Understanding how pests rapidly adapt can help develop sustainable strategies for their management and improve agroecosystem health. Pest adaptation involves stable variations in DNA sequence, as well as dynamic shifts in gene expression, often mediated by non-coding regulatory elements. We examined adaptation to plant defences in the soybean aphid, Aphis glycines, in which virulent aphids have overcome plant defences and avirulent aphids have not. Previous data with laboratory colonies suggested that virulent aphids have higher overall gene expression, including transposable elements, some of which influence gene regulation. However, we lack information on how genetic variation in natural populations impacts adaptation and potentially gene regulation. We integrated population genome scans of field-collected, soybean aphid populations with gene expression profiles of virulent and avirulent laboratory colonies to uncover connections between genetic differentiation and gene regulation for virulence. Genome scan methods found 2144 single nucleotide polymorphisms (SNPs) with significant genetic differentiation (i.e., outliers) in field-collected populations. These SNPs were near 1004 genes, representing 5.16% of the effective number of genes. Based on previous RNA-Seq data with laboratory colonies, we found 3160 genes and 147 long non-coding RNAs (lncRNAs) with differential expression among virulent and avirulent biotypes. By integrating both data sets, we identified 16 genes and 5 long non-coding RNAs with differential expression and that were associated with an outlier SNP (within 10 kbp). We validated SNPs with additional field collected aphids and found an aphid clone with stronger virulence than our laboratory virulent colony, surviving on 2 different aphid-resistant soybean varieties. This new virulent clone had fixed allele differences at 9 SNPs compared to our avirulent and other virulent colony. Field collected soybean aphids matching the phenotype of this new virulent clone had significant genetic differentiation with 3 outlier SNPs near genes related to zinc transport and lachesin compared to field collected avirulent aphids. Our entire data reinforced the importance of a potential multi-genetic response to overcome plant defence and generates new insights into complex genetic and regulatory mechanisms involved in insect-plant interactions.

Animals↗

PubMed2025

Reproductive Isolation due to Divergent Ecological Selection Is Accompanied by Vast Genomic Instability in Experimentally Evolved Yeast Populations.

Populations evolving independently in divergent environments accumulate genetic differences and potentially evolve reproductive isolation as a by-product of divergence. The speed and mechanisms underlying this process are difficult to investigate because we rarely get the opportunity to witness them in natural settings, and histories of selection and gene flow between populations are often unknown. Here, we experimentally evolved yeast for 1000 generations of evolution in both divergent and parallel environments. At regular time points during experimental evolution, we made crosses between parallel- and divergent-evolving populations to measure postzygotic reproductive isolation (gamete viability). We used whole genome population sequencing to determine the mutational load, the number and types of structural variation, and other genomic features of the parent, F1 and F2 intraspecific hybrids. We found evidence for large-scale phenotypic and genome-wide differentiation in response to divergent laboratory selection. Divergent-selected populations produced hybrids with reduced gamete viability-a classic signature of postzygotic reproductive isolation in the form of hybrid breakdown. Parallel-selected populations, on the other hand, remained more reproductively compatible (with exceptions). We found that F2 hybrid genomes contained vast genomic instability, that is, new structural variants (especially insertions, deletions and interchromosomal translocations) that were not observed in parent and F1 genomes, which is likely a result of chromosome missegregation and recombination errors in hybrid meiosis. Our results provide phenotypic and genomic evidence that partial reproductive isolation evolved due to adaptation to divergent environments, consistent with predictions of ecological speciation theory.

Reproductive Isolation↗

PubMed2026

Genetic mutations driving ciprofloxacin resistance in laboratory-evolved Salmonella Typhimurium.

Ciprofloxacin resistance in Salmonella Typhimurium is a significant public health concern, and the mechanisms by which the resistance evolves are poorly defined. Here, by serial passaging under antibiotic selection, we isolated ciprofloxacin-resistant S. Typhimurium mutants and subjected them to whole-genome sequencing to reveal the major mutations associated with resistance. The Low CipR mutant acquired four chromosomal mutations in ramR, icdA, lipB, and gyrA, and the High CipR mutant gained additional mutations in gyrB, yaiC, and corA. Functional characterization determined that mutations in ramR resulted in efflux pump upregulation, while disruptions in the TCA cycle caused by mutations in icdA and lipB led to metabolic alterations. These changes indirectly enhanced resistance by increasing the expression of the global regulator MarA and reducing OmpF-dependent membrane permeability. Despite the observation of the G105A substitution in GyrA, enzymatic assays confirmed the failure to support resistance to ciprofloxacin, possibly because the structural alteration remained minimal. GyrB488-489dup was associated with maintained supercoiling under ciprofloxacin and enhanced fluoroquinolone resistance, suggesting a major role in resistance evolution. Other mutations in yaiC impaired biofilm and, in corA, intracellular accumulation of magnesium, possibly stabilizing the bacterial cell envelope under antibiotic pressure. The findings provide novel explanations for the multifaceted mechanisms leading to ciprofloxacin resistance in Salmonella and suggest targets to combat antimicrobial resistance.IMPORTANCEAntibiotic resistance in Salmonella Typhimurium is an increasing public health concern, yet the genetic changes that allow bacteria to become resistant are not fully understood. In this study, we evolved ciprofloxacin-resistant Salmonella in the laboratory and identified the mutations that arise during resistance development. We found that resistance does not result from a single change but from multiple adaptations affecting drug efflux, metabolism, and the antibiotic target. Some mutations increased the activity of pumps that remove antibiotics from the cell, while others altered bacterial metabolism and reduced membrane permeability, making it harder for the drug to enter. A duplication in the DNA gyrase subunit GyrB played a particularly important role in maintaining DNA function under antibiotic stress. Together, these results reveal how diverse genetic changes cooperate to generate ciprofloxacin resistance and provide insights that may help guide strategies to combat drug-resistant Salmonella infections.

DNA gyrase↗

PubMed2026

Recurrent reversible mutations at gaf1 driving metastable TORC1 inhibitor resistance in fission yeast.

Metastable phenotypic inheritance is often attributed to epigenetic mechanisms, but reversible genetic alterations can produce similar instability. Here, we investigated the basis of unstable resistance to TORC1 inhibitor (rapamycin plus caffeine) in Schizosaccharomyces pombe. Six independent, metastable resistant mutants were isolated. Genetic mapping positioned the causal lesion to a single Mendelian locus, which sequencing identified as gaf1, encoding a GATA transcription factor and a key negative regulator of growth downstream of TORC1. In each mutant, distinct loss-of-function mutations (insertions, deletions, or point mutations) were found in gaf1 in the resistant state, and these mutations precisely reverted to the wild-type sequence upon loss of resistance. Restoring the wild-type gaf1 allele abolished resistance, indicating that reversible genetic disruption of gaf1 is both necessary and sufficient for the metastable phenotype. Furthermore, strong resistance in several strains from a genome-wide deletion library was due to secondary, inactivating mutations in gaf1, underscoring its role as a recurrent adaptive target under rapamycin plus caffeine treatment. Mechanistically, gaf1 inactivation established a distinct basal transcriptome and pronounced derepression of translation and metabolic programs upon drug treatment. While rapamycin plus caffeine triggered extensive chromatin remodeling and H3K9 methylation contributed partially to resistance, these epigenetic changes were most consistent with a downstream modifying layer. Our study shows that metastable drug resistance in fission yeast is predominantly associated with recurrent, reversible genetic inactivation of the central transcriptional regulator gaf1, demonstrating how rapidly reversible genetic switches can drive adaptive evolution.IMPORTANCEDistinguishing between genetic and epigenetic inheritance is fundamental to understanding how cells adapt to environmental stress. In the fission yeast Schizosaccharomyces pombe, rapid and reversible drug resistance is often assumed to be driven by epigenetic switches that change gene activity without altering DNA. However, our study reveals that this instability can be caused by physical mutations in a single gene, gaf1, which acts as a genetic toggle. These mutations appear under drug pressure and precisely revert to the original sequence when the drug is removed. We also demonstrate that these spontaneous mutations can contaminate standard laboratory yeast collections, leading to potential misinterpretation of experimental data. These findings broaden our understanding of unstable inheritance and show that DNA sequences can be far more dynamic than previously recognized during rapid evolution and the development of drug resistance.

TORC1 signaling↗

PubMed2026

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.

Plant diseases destroy 20-40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

convolutional neural networks↗

PubMed2026

Intersecting experimental evolution and CRISPR screens to identify novel toxin resistance loci.

Understanding toxin resistance in insects is key to appreciate niche adaptations but remains challenging due to its often-polygenic basis. A well-known example is the specialized association of Drosophila sechellia with noni fruit ( Morinda citrifolia ), which is toxic to most other insects, including the closely-related Drosophila simulans and Drosophila melanogaster . Toxicity of noni is due to its high concentration of octanoic acid (OA), but the mechanisms that determine sensitivity or resistance to OA in different species remain poorly understood. Here, we experimentally-evolved D. simulans with increased OA resistance, identifying multiple loci under selection. Cross-referencing these with a genome-wide, OA-resistance CRISPR screen in a D. melanogaster cell line highlighted two proteins: Kraken, a putative detoxification enzyme expressed in digestive and renal tissues, and Alkbh7, a mitochondrial protein linked to fatty acid metabolism. Both genes show elevated expression in D. sechellia and OA-resistant D. simulans . In D. melanogaster , kraken mutants are more OA-sensitive, while Alkbh7 overexpression increased OA resistance. Importantly, mutation of these genes in D. sechellia reduced OA tolerance. Our identification of genes underlying OA resistance in laboratory and natural contexts demonstrates how complementary, cross-species selection approaches can provide insights into complex mechanisms of toxin susceptibility and adaptation; such methods could also have practical applications in the characterization of natural and artificial insecticides.

Journal Article↗

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

PubMed2023

The genetic basis of adaptation to copper pollution in Drosophila melanogaster.

Introduction: Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods: We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organism Drosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion: Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations near CG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in the Drosophila Synthetic Population Resource (DSPR) and the Drosophila Genetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition to CG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance.

Drosophila↗

PubMed2026

Intra-colony divergence and global allele sharing reflect purifying selection and recombination at the Botryllus histocompatibility factor locus.

Urochordates, the closest relatives of vertebrates, lack adaptive immunity. However, some taxa, such as the colonial species Botryllus schlosseri, provide a unique model for studying innate self/non-self recognition through natural allogeneic transplantation responses. In this species, interactions between colonies are controlled by a highly polymorphic locus, with the Botryllus histocompatibility factor (BHF) being the only gene known to predict tissue fusion or rejection outcomes with complete accuracy. Here, we analyzed full-length BHF alleles from 19 laboratory-born and wild colonies and found that highly divergent alleles tend to coexist within individuals, whereas identical alleles can be shared across continental-scale distances. Despite extensive length variation, evidence of intragenic recombination, and pronounced nucleotide diversity, BHF exhibits limited protein divergence, with 33 alleles encoding only 17 distinct protein variants. Contrary to expectations for polymorphic recognition genes, no evidence of balancing or directional selection was detected. Instead, signatures of purifying selection were observed. We propose that this contrast between nucleotide and protein diversity arises from the combined effects of recombination, human-mediated gene flow, and linkage to nearby loci under balancing selection, while functional constraints maintain protein stability. These findings suggest that extensive protein diversification may not be a universal driver of allorecognition gene evolution.

Animals↗

PubMed2026

Spatiotemporal patterns of Rift Valley fever virus in Africa: a retrospective genomic epidemiology and phylodynamic modelling study.

BACKGROUND: Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen causing outbreaks in humans and ruminants across Africa and the Arabian Peninsula. Originally restricted to the Great Rift Valley, RVFV has expanded geographically, prompting its classification by WHO as a pathogen of pandemic potential. We investigated the evolutionary and spatial dynamics of RVFV across Africa. METHODS: We used genomic data generated at the International Livestock Research Institute Nairobi genomic laboratory (BioProject PRJNA1106221) and combined with publicly available datasets retrieved from the National Center for Biotechnology (NCBI) GenBank nucleotide database. In retrieving RVFV genome sequences from the NCBI GenBank, we applied the search terms "Rift Valley fever virus segment L AND 6404[SLEN]", "Rift Valley fever virus segment M AND 3885[SLEN]", and "Rift Valley fever virus segment S AND 1520:1690[SLEN]" for L (Large), M (Medium), and S (Small) segments, respectively. For sequences without additional spatiotemporal information, we searched PubMed to extract the associated sequence metadata. We performed molecular clock analysis, phylogenetic inference, phylodynamic modelling (continuous phylogeographic reconstruction), and landscape phylogeography on the three RVFV genome segments (L, M, and S). We aimed to assess evolutionary rates, dispersal patterns, and environmental drivers. Focus was placed on lineage C, the most widely distributed variant. FINDINGS: The global dataset used in this study consisted of large (n=236), medium (n=237), and small (n=247), which were further filtered to exclude potential reassortants and vaccine strains. Genome sequences retrieved from NCBI GenBank database comprised large (n=180), medium (n=184), and small (n=202). The genome sequences from retrospective human and livestock isolates comprised large (n=56), medium (n=53), and small (n=45) collected in Burundi (2018), Kenya (2007, 2018, 2019, 2021, and 2022), and Rwanda (2018 and 2022). Our dataset revealed that RVFV exhibited low overall genetic diversity. Lineage C, however, showed evidence of active evolution, with substitution rates ranging from 3·58 × 10-4 to 9·76 × 10-4 substitutions per site per year. This lineage probably originated in Zimbabwe in the mid-1970s and has since expanded across eastern and southern Africa. Phylogeographic reconstructions revealed rapid spread, with diffusion coefficients exceeding 50 000 km2 per year. INTERPRETATION: Lineage C appears capable of establishing endemic transmission in new regions, with ongoing diversification observed during interepidemic periods. These observations reinforce the value of continuous genomic surveillance, particularly during cryptic transmission phases when adaptive mutations might emerge. Although further evidence is needed, observed trends in climate variability and land-use change point to the potential benefit of targeted surveillance in settings that could be at increased risk, including urban centres and wetlands. FUNDING: This work was supported by the German Federal Ministry for Economic Cooperation and Development, the Rockefeller Foundation, and the Africa Centres for Disease Control and Prevention.

Rift Valley fever virus↗