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PubMed2026

Dissecting fluctuating selection: A unified population and quantitative genetics framework.

One of the longstanding debates in evolutionary biology is the effect of fluctuating selection on genetic changes in populations. However, the extent to which these periodic forces influence organisms at both genomic and phenotypic levels remains unclear. Despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical and theoretical findings concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate genome-wide oscillations. This study aims to elucidate how both genetic factors (e.g. heritability, number of causative loci) and ecological factors (e.g. season length, the difference in the phenotypic optima between seasons, population size dynamics) drive fluctuating selection and to identify the parameters that produce consistent oscillatory patterns. We developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. We applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations highlight the conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency trajectories, even under highly complex selection regimes. Not only does our study clarify the conditions that yield oscillatory behaviors, but these parameters can also potentially be estimated in natural populations, providing a possibility of empirically testing these models.

Fluctuating selection↗

PubMed2026

Dissecting fluctuating selection: A unified population and quantitative genetics framework.

Fluctuating selection↗

PubMed2026

Seasonal fluctuations in fitness result in severe reductions in effective population size.

Genetic evidence for fluctuating selection has begun to accumulate for different species over the past few decades, especially for the Drosophila genus where studies have reported hundreds of loci undergoing putatively adaptive oscillations across successive seasons. However, most theoretical and simulation studies of fluctuating selection have relied on abstract or weakly parameterized models, making it difficult to assess their relevance for natural populations. In this study, we simulate multilocus seasonally fluctuating selection under a recently developed model and examine its effect on the variance effective population size (Ne ) at a genome-wide scale. By recapitulating genomic, demographic, and evolutionary parameters from natural Drosophila populations in our simulations, we were able to reproduce allele frequency oscillations reported in recent studies and show that these lead to ~50% genome-wide reductions in Ne . We also demonstrate that Ne reductions are well predicted by the maximum frequency amplitude among all adaptively fluctuating loci, and that the frequency amplitudes are largely determined by the number of adaptively fluctuating loci and the strength of their epistatic interactions. Our results demonstrate that fluctuating selection can substantially reduce effective population size and underscore the importance of temporally variable selection in shaping genome-wide patterns of variation beyond classical models.

Drosophila melanogaster↗

PubMed2026

Seasonal fluctuations in fitness result in severe reductions in effective population size.

Genetic evidence for fluctuating selection has begun to accumulate for different species over the past few decades, especially for the Drosophila genus where studies have reported hundreds of loci undergoing putatively adaptive oscillations across successive seasons. However, most theoretical and simulation studies of fluctuating selection have relied on abstract or weakly parameterized models, making it difficult to assess their relevance for natural populations. In this study, we simulate multilocus seasonally fluctuating selection acting on standing genetic variation under a recently developed model and examine its effect on the variance effective population size (Ne) at a genome-wide scale. By recapitulating genomic, demographic, and evolutionary parameters from natural Drosophila populations in our simulations, we were able to reproduce allele frequency oscillations reported in recent studies and show that these lead to ∼50% genome-wide reductions in Ne. We also demonstrate that Ne reductions are well predicted by the maximum frequency amplitude among all adaptively fluctuating loci, and that the frequency amplitudes are largely determined by the number of adaptively fluctuating loci and the strength of their epistatic interactions. Our results demonstrate that fluctuating selection can substantially reduce effective population size and underscore the importance of temporally variable selection in shaping genome-wide patterns of variation beyond classical models.

Drosophila melanogaster↗

PubMed2025

Pervasive fitness trade-offs revealed by rapid adaptation to shifting population densities in large experimental populations of Drosophila melanogaster.

Trade-offs are an inherent feature of organismal biology that are expected play a fundamental role in the evolution of natural populations. Efforts to quantify trade-offs are largely confined to phenotypic measurements and the identification of negative genetic-correlations among fitness-relevant traits. Here, we use time-series genomic data collected during experimental evolution in large, genetically diverse populations of Drosophila melanogaster to directly measure the manifestation of trade-offs in response to fluctuating selection on ecological timescales. Specifically, we first conducted a lab-based selection experiment to quantify a genome-wide signal of antagonistic pleiotropy elicited in response to shifting population densities and associated with reproduction and stress tolerance selection. In doing so, we identified a putative role of two cosmopolitan inversions in these trade-offs. We then conducted an independent experiment to show that a simple manipulation of increasing population density under controlled lab-based conditions identified loci that are relevant to selection during population expansion and collapse in a complex, semi-natural setting. In concert, our results reveal how adaptation in complex, natural environments can be coarse-grained in such a manner to drive repeatable and predictable patterns of genomic variation, and further add credence to models positing a role of generic fitness trade-offs in the maintenance of variation in natural populations.

Drosophila melanogaster↗

PubMed2025

The ecology and evolution of microbial immune systems: a look on the wild vibrio side.

Natural populations of vibrio beyond the well-studied pandemic strains of Vibrio cholerae, provide a powerful model for investigating the eco-evolutionary dynamics of microbial immune systems. Their genetic diversity, ecological versatility, ease of culturability and the availability of time-series data enable detailed studies of phage-host interactions in natural contexts. This review synthesizes recent advances in vibriophage research, highlighting key findings and emerging tools. High-throughput assays and genomic tools have offered new perspectives on phage specificity, host range and the evolutionary pressures shaping these interactions. Theoretical frameworks, such as arms race and fluctuating selection dynamics, are informed by empirical data from vibrio-phage systems, with time-series sampling providing crucial insights into their temporal and spatial dynamics. A major finding is the role of mobile genetic elements (MGEs) in encoding bacterial defence systems, which shape phage-host coevolution. Discoveries like the phage satellite PICMI illustrate how MGEs facilitate the transfer of antiviral systems, influencing ecological and evolutionary dynamics. The paradox of generalist vibriophages, rare despite their broad host ranges, is also explored. By integrating experimental approaches with field observations, vibriophage research advances microbial ecology and informs sustainable applications in aquaculture and phage therapy, reinforcing vibrios as a versatile model system.This article is part of the discussion meeting issue 'The ecology and evolution of bacterial immune systems'.

Bacteriophages↗

PubMed2026

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals.

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Hordeum↗

PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗

PubMed2026

Metabolic niche differentiation and napA evolution stabilize partial denitrification in wastewater ecosystems.

Although partial denitrification (PD) is increasingly applied as a nitrite-supplying strategy for anammox-based nitrogen removal, the ecological distribution, metabolic specialization, and genomic determinants of stable nitrite accumulation remain poorly understood at the ecosystem scale. Here, we reconstructed 516 high-quality metagenome-assembled genomes (MAGs) using high-depth metagenomic sequencing of 107 wastewater treatment plants and classified denitrifiers according to their nitrite production or consumption capacities. Of these genomes, 23% (120 MAGs) were classified as partial denitrifiers, 41% (211 MAGs) as complete denitrifiers, and 36% (185 MAGs) as nitrite-reducing denitrifiers, revealing pronounced functional partitioning rather than dominance by complete denitrification pathways. Comparative genomics showed that partial denitrifiers possess metabolic architectures favoring rapid carbon oxidation and NADH generation while exhibiting constrained NADPH production and biosynthetic investment, thereby promoting nitrate-to-nitrite conversion but limiting subsequent nitrite reduction. Nitrite accumulation does not result from incomplete denitrification pathways but from metabolic niche differentiation. These metabolic trade-offs were further associated with the evolutionary divergence of the periplasmic nitrate reductase gene, napA, which displayed distinct sequence characteristics and genomic contexts between partial and complete denitrifiers. Integration of carbohydrate-active enzyme repertoires further revealed metabolic complementarity between partial denitrifiers and anammox bacteria, supporting efficient carbon handoff without direct substrate competition. From an engineering perspective, operating conditions that impose moderate electron limitation, such as low or fluctuating C/N ratios and intermittent carbon feeding, may selectively enrich partial denitrifiers and enhance a stable nitrite supply for PD-anammox systems. Together, these findings identify PD as a predictable ecological state shaped by genome-encoded metabolic specialization and provide a mechanistic basis for designing robust, low-carbon nitrogen-removal processes.

Anammox↗

PubMed2025

Genome-wide Parallelism Underlies Rapid Freshwater Adaptation Fueled by Standing Genetic Variation in a Wild Fish.

A fundamental focus of ecological and evolutionary biology is determining how natural populations adapt to environmental changes. Rapid parallel phenotypic evolution can be leveraged to uncover the genetics of adaptation. Using population genomic approaches, we investigated the genetic architecture underlying rapid parallel freshwater adaptation of Neosalanx brevirostris by comparing four freshwater-resident populations with their common ancestral anadromous population. We demonstrated that the rapid parallel adaptation to freshwater followed a complex polygenic architecture and was characterized by genomic-level parallelism, which proceeded predominantly through repeated selection on the preexisting standing genetic variations. Frequencies of the genome-wide adaptive standing variations were moderate in the ancestral anadromous population, which had pre-adapted to fluctuating salinities. Relatively large allele frequency shifts were observed at some adaptive single-nucleotide polymorphisms (SNPs) during parallel adaptation to freshwater environments, with a large fraction of freshwater-favored alleles being fixed or nearly fixed. These adaptive SNPs were involved in multiple biological functions associated with osmoregulation, immunoregulation, locomotion, metabolism, etc., which were highly consistent with the polygenic architecture of adaptive divergence between the two ecotypes involving multiple complex physiological and behavioral traits. This work provides insight into the mechanisms by which natural populations rapidly evolve to changes in the environment and highlights the importance of standing genetic variation for the evolutionary potential of populations facing global environmental changes.

Animals↗

PubMed2025

Ancient Introgression Explains Mitochondrial Genome Capture and Mitonuclear Discordance Among South American Collared Tropidurus Lizards.

Mitonuclear discordance-evolutionary discrepancies between mitochondrial and nuclear DNA phylogenies-can arise from various factors, including introgression, incomplete lineage sorting, recent or ancient demographic fluctuations, sex-biased dispersal asymmetries, among others. Understanding this phenomenon is crucial for accurately reconstructing evolutionary histories, as failing to account for discordance can lead to misinterpretations of species boundaries, phylogenetic relationships, and historical biogeographic patterns. We investigate the evolutionary drivers of mitonuclear discordance in the Tropidurus spinulosus species group, which contains nine species of lizards inhabiting open tropical and subtropical environments in South America. Using a combination of population genetic and phylogenomic approaches applied to mitochondrial and nuclear data, we identified different instances of gene flow that occurred in ancestral lineages of extant species. Our results point to a complex evolutionary history marked by prolonged isolation between species, demographic fluctuations, and potential episodes of secondary contact with genetic admixture. These conditions likely facilitated mitochondrial genome capture while diluting signals of nuclear introgression. Furthermore, we found no strong evidence supporting incomplete lineage sorting or natural selection as primary drivers of the observed mitonuclear discordance. Therefore, the unveiled patterns are most consistent with neutral demographic processes, coupled with ancient mitochondrial introgression, as the main factors underlying the mismatch between nuclear and mitochondrial phylogenies in this system. Future research could further explore the role of other demographic processes, such as asymmetric sex-biased dispersal, in shaping these complex evolutionary patterns.

Animals↗

PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗

PubMed2025

Fluctuating DNA methylation tracks cancer evolution at clinical scale.

Cancer development and response to treatment are evolutionary processes1,2, but characterizing evolutionary dynamics at a clinically meaningful scale has remained challenging3. Here we develop a new methodology called EVOFLUx, based on natural DNA methylation barcodes fluctuating over time4, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterized lymphoid cancer samples spanning a broad spectrum of diseases and show that initial tumour growth rate, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe faster initial tumour growth in more aggressive disease subtypes, and that evolutionary histories are strong independent prognostic factors in two series of chronic lymphocytic leukaemia. Using EVOFLUx for phylogenetic analyses of aggressive Richter-transformed chronic lymphocytic leukaemia samples detected that the seed of the transformed clone existed decades before presentation. Orthogonal verification of EVOFLUx inferences is provided using additional genetic data, including long-read nanopore sequencing, and clinical variables. Collectively, we show how widely available, low-cost bulk DNA methylation data precisely measure cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.

Humans↗

PubMed2026

Lineage dynamics of invasive Escherichia coli isolates in the Netherlands from 1975 to 2021: a retrospective longitudinal genomic analysis.

BACKGROUND: Escherichia coli is a common cause of invasive infections such as bloodstream and cerebrospinal fluid infections in neonates. Strains positive for the K1 capsule are considered the most common cause of such neonatal invasive infections. This assumption of K1 dominance, and indeed the population genomics of E coli causing invasive infections in general is largely unstudied. We aimed to provide a comprehensive characterisation of this pathogen population using a longitudinal isolate collection. METHODS: In this analysis we report the findings of the SENTINEL study, a longitudinal genomic analysis of 1790 invasive E coli isolates collected mainly from newborns in the Netherlands between 1975 and 2021 by the Netherlands Reference Laboratory for Bacterial Meningitis, Amsterdam University Medical Centre, Amsterdam, Netherlands. The dataset included all bacterial strains cultured from cerebrospinal fluid or blood in cases of (clinical) bacterial meningitis (1976 to 1980). In 1981 the criteria were expanded to include neonates (aged ≤4 weeks) with E coli sepsis, and from July, 2016 all infants younger than 1 year with E coli sepsis were included. All isolates were sequenced using either the HiSeq 2500 or HiSeq 4000 platforms (Illumina, San Diego, CA, USA). We confirmed species and identified sequence types (STs), detected antimicrobial resistance genes, virulence genes, and the presence of K1 capsule, and characterised the dynamics of these factors over time. FINDINGS: Our data show a highly dynamic bacterial population that is entirely unaffected by antimicrobial resistance determinants. Key pathogen population fluctuations include the complete disappearance of the dominant lineage ST567 and the swapping of dominant ST95 clones from a single serotype O18:H7 clone to two distinct serotype O1:H7 clones, with changes in virulence factors including major fimbrial adhesins. These findings, combined with only 58·8% (1053 of 1790) prevalence in K1-expressing isolates in the entire study population, point to host-pathogen interaction and immune selection pressures as key drivers of bacterial population dynamics in this largely antimicrobial-naive population. INTERPRETATION: Our data show the vital need for ongoing genomic surveillance of microbial pathogen populations to guide appropriate intervention strategies. Additionally, genomic insights of a pathogen population from one specific disease syndrome or patient population cannot always be generalised across other cohorts. FUNDING: Wellcome Antimicrobial and Antimicrobial Resistance Doctoral Training Programme and the National Institute for Health and Care Research Birmingham Biomedical Research Centre.

Netherlands↗

PubMed2026

Landscape Genomics Reveals Divergent Adaptation Modes and Predicts Climate Vulnerability in Xinjiang Indigenous Sheep.

Climate change increasingly endangers precious indigenous sheep germplasm resources distributed across diverse Chinese landscapes, and systematically decoding their polygenic climate-adaptive genetic mechanisms is essential for targeted breed conservation and long-term sustainable pastoral production. Whole-genome resequencing data from 93 individuals covering six representative local sheep breeds were analyzed in this work. After filtering highly collinear climate variables, three mature landscape genomic approaches were jointly applied to identify environment-linked gene variants, while two predictive metrics across ten CMIP6 future climate scenarios quantified each breed's long-term adaptive risks. Six temperature- and water-related environmental factors jointly drove sheep population genetic differentiation, with temperature fluctuation indices showing markedly stronger explanatory power. Detected adaptive genes were significantly enriched in ion transport, energy metabolism and cellular stress response pathways. Future projections indicated western breeds (Bayinbuluke, Cele Black, Xiahe) face severe maladaptation risks under high-emission SSP370 scenarios by 2100, whereas central and eastern breeds possess much broader climate tolerance. This study systematically reveals the core genomic basis of ovine climate adaptation and quantifies distinct breed-specific climate vulnerability, providing solid reliable theoretical support for precision germplasm conservation and selective breeding of climate-resilient sheep varieties.

adaptive loci↗

PubMed2026

Climate and soil shape Daqu wheat quality and seed microbiome via rhizosphere taxa and microbial assembly.

The grain quality and seed microbiome of Daqu wheat are fundamental determinants of Daqu fermentation performance; however, the mechanisms by which cultivation environments influence these traits via rhizosphere microbial communities remain unclear. Bacterial and fungal communities across the bulk soil-rhizosphere-seed continuum of three wheat cultivars grown in four ecoregions were characterized using absolute quantitative amplicon sequencing. The rhizosphere microbiome was treated as a central intermediary, while the response variables were seed microbial diversity and grain-quality traits, including starch content, protein content, and grain hardness. Twelve physicochemical properties of soil and 11 climatic factors were integrated into a multidimensional association framework. Environmental conditions exerted stronger influences on both seed quality traits and microbial diversity than cultivar identity. Distinct regional signatures were also evident in rhizosphere microbiomes, with environmental gradients explaining community variation more effectively than geographic distance. Bacterial communities exhibited greater sensitivity to environmental fluctuations than fungi. Mantel analyses identified available nitrogen, precipitation, and atmospheric pressure as significant drivers of core rhizosphere taxa (P < 0.05). iCAMP revealed that stochastic processes predominantly governed rhizosphere bacterial assembly, whereas stochastic and deterministic mechanisms jointly shaped fungal assembly. Partial least squares path modeling further uncovered a rhizosphere-mediated environment-seed cascade, wherein sunlight intensity and duration, atmospheric pressure, and soil nitrogen directly or indirectly affected seed wet gluten content, grain hardness, and seed microbial diversity through their influences on rhizosphere microbiota. Rhizosphere bacterial diversity was negatively associated with seed bacterial diversity (path coefficient = -0.118, P < 0.05), indicating that rhizosphere communities may shape seed endophytic bacterial assemblages via environmental filtering and competitive interactions. Collectively, these findings elucidate how environments shape the quality and seed microbiomes of Daqu wheat, providing scientific guidance for optimal site selection and the standardized production of high-quality brewing wheat for industrial Baijiu.

Triticum↗

PubMed2026

Experimental evolution reveals contrasting adaptive landscapes in lab and field environments.

Experimental evolution is widely used to infer microbial responses to environmental change, yet most laboratory studies impose constant, well-mixed conditions that differ fundamentally from fluctuating, spatially structured field environments. We compared genomic evolution in the leaf litter-associated bacterium Curtobacterium strain MMLR14_002 under control and warming treatments in laboratory culture and in a complementary field experiment. Laboratory-derived isolates accumulated more mutations per genome and exhibited stronger locus-level parallelism, with mutations recurring in a small number of coding loci. Field-derived isolates accumulated fewer mutations per genome, and these mutations rarely occurred in the same coding loci across replicate populations. Instead, field isolates exhibited a higher proportion of intergenic mutations, with mutations recurring in the same intergenic regions across independent field deployments. When coding mutations were detected in the field, they were distributed across functionally diffuse targets and more often involved metabolic pathways than the core cellular processes repeatedly targeted during laboratory evolution. Warming itself did not consistently influence mutation accumulation or the genomic distribution of mutations; instead, laboratory and field contexts primarily shaped the accumulation, targets, and repeatability of genomic change. These results suggest that laboratory thermal evolution identifies adaptive routes favored under sustained selection but may overestimate coding-level parallelism under heterogeneous field conditions. Bridging laboratory and field evolution will likely require experimental designs that incorporate temporal variability and spatial heterogeneity characteristic of natural systems.IMPORTANCEA central goal of experimental evolution is to infer how microbes evolve in nature from laboratory studies. Here, we evaluate this assumption by comparing genomic evolution of a leaf litter-associated Curtobacterium strain in laboratory and field warming experiments to identify broad patterns rather than isolate the contribution of any single environmental factor. We find that the strong parallelism at coding loci observed under laboratory conditions is reduced in the field, while mutations recurring in the same intergenic regions across field deployments suggest that parallel evolution in nature may more often involve regulatory noncoding regions rather than coding targets. These results show that environmental context reshapes adaptive landscapes and may limit the parallelism of coding-level genomic responses inferred from homogeneous laboratory conditions.

experimental evolution↗

PubMed2026

AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.

Humans↗