Biology subjects

Warringer, J.

Publications and source records attributed to Warringer, J..

18 recordsLinked to original sources

bioRxiv2025

The adaptive molecular landscape of reprogrammed telomeric sequences

Telomeric sequences vary across the tree of life and intimately co-evolve with telomere-binding protein complexes. However, the molecular mechanisms allowing organisms to adapt to new telomeric sequences are difficult to gauge from extant species. Here, we reprogrammed multiple yeast lines to human-like telomeric repeats to unveil their molecular and fitness response to novel telomeres. Initially, the exchange of telomere sequences resulted in genome instability, proteome remodelling and severe fitness decline. However, adaptive evolution experiments selected for repeated mutations that drove adaptation to the humanized telomeres. These consisted of the recurrent amplification of the telomere-binding protein TBF1, by complex aneuploidies, or in repeated mutations that attenuate the DNA damage response. Overall, our results outline a response that defines the adaptive molecular landscape to novel telomeric sequences.

evolutionary biology↗

bioRxiv2025

Divergent ecological selection causes partial reproductive isolation and vast genomic instability in yeast

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, i.e., 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.

evolutionary biology↗

bioRxiv2024

Predicting the natural yeast phenotypic landscape with machine learning

Most organisms traits result from the complex interplay of many genetic and environmental factors, making their prediction from genotypes difficult. Here, we used machine learning models to explore genotype-phenotype connections for 223 life history traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains. Firstly, we used genome-wide association studies to connect genetic variants with the phenotypes. Next, we benchmarked an automated machine learning pipeline that includes preprocessing, feature selection, and hyperparameters optimization in combination with multiple linear and complex machine learning methods. We determined gradient boosting machines as best performing in 65% of predictions and pangenome as best predictor, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The accuracy broadly varied among the phenotypes (r = 0.2-0.9), consistent with varying levels of complexity, with stress resistance being easier to predict compared to growth across carbon and nitrogen nutrients. While no specific genomic features could be linked to the predictions for most phenotypes, machine learning identifies high-impact variants with established relationships to phenotypes despite being rare in the population. Near-perfect accuracies (r>0.95) were achieved when other phenomics data were used to aid predictions, suggesting shared useful information can be conveyed across phenotypes. Overall, our study underscores the power of machine learning to interpret the functional outcome of genetic variants.

genetics↗

bioRxiv2023

Quantifying massively parallel microbial growth with spatially mediated interactions

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterized at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to an interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments, i.e. interpopulation interactions are mediated via the shared environment. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses. Author summaryImage-based platforms allow obtaining population size estimates for massively parallel growth experiments on substrate plates at relatively low cost. However, such population size data has been shown to display a high degree of spatial variability, which occurs even with isogenic populations. Here we first quantified the importance of spatial location on growth variation using a machine learning approach, and then developed spatially aware population growth models to explain the spatial structure of the growth data. Ultimately, we showed that a spatial consumer-resource model with local microhabitats connected via diffusion can fully explain the observed spatial variation in growth while allowing the inference of intrinsic growth parameters of specific populations. This result provides a method for systematic extraction of spatial growth models and paves the way for massively parallel eco-evolutionary experimentation.

ecology↗

bioRxiv2023

Genome instability footprint under rapamycin and hydroxyurea treatments

The mutational processes dictating the accumulation of mutations in genomes are shaped by genetic background, environment and their interactions. Accurate quantification of mutation rates and spectra under drugs has important implications in disease treatment. Here, we used whole-genome sequencing and time-resolved growth phenotyping of yeast mutation accumulation lines to give a detailed view of the mutagenic effects of rapamycin and hydroxyurea on the genome and cell growth. Mutation rates depended on the genetic backgrounds, but were only marginally affected by rapamycin. As a remarkable exception, rapamycin treatment was associated with frequent chromosome XII amplifications, which compensated for rapamycin induced rDNA repeat contraction on this chromosome and served to maintain rDNA content homeostasis and fitness. In hydroxyurea, a wide range of mutation rates were elevated regardless of the genetic backgrounds, with a particularly high occurrence of aneuploidy that associated with dramatic fitness loss. Hydroxyurea also induced a high T-to-G transversion rate that reversed the common G/C-to-A/T bias in yeast and gave rise to a broad range of structural variants, including mtDNA deletions. The hydroxyurea mutation footprint was consistent with the activation of error-prone DNA polymerase activities and non-homologues end joining repair pathways. Taken together, our study provides an in-depth view of mutation rates and signatures in rapamycin and hydroxyurea and their impact on cell fitness, which brings insights for assessing their chronic effects on genome integrity.

genomics↗

bioRxiv2022

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Adaptive Laboratory Evolution (ALE) of microbes can improve the efficiency of sustainable industrial processes important to the global economy, but chance and genetic background effects often lead to suboptimal outcomes. Here we report an ALE platform to circumvent these flaws through parallelized clonal evolution at an unprecedented scale. Using this platform, we clonally evolved 104 yeast populations in parallel from many strains for eight desired wine production traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum and increased the chances of evolving improved wine yeasts unencumbered by unwanted side effects. ALE gains often coincided with distinct aneuploidies and the emergence of semi-predictable side effects that were characteristic of each selection niche. Many high performing ALE strains retained their desired traits upon transfer to industrial conditions and produced high quality wine. Overall, our ALE platform brings evolutionary engineering into the realm of high throughput science and opens opportunities for rapidly optimizing microbes for use in many industrial sectors which otherwise could take many years to accomplish.

bioengineering↗

bioRxiv2022

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins that enhance their evolvability has therefore attracted substantial interest. However, our current understanding of how individual genes influence the speed of adaptation is limited, partly because accurately tracking adaptation for many experimental cell populations in parallel is challenging. Here we use a high throughput artificial laboratory evolution (ALE) platform to track the adaptation of >18.000 cell populations corresponding to single gene deletion strains in the haploid yeast deletion collection. We report that the fitness of gene knockout near-perfectly (R2=0.91) predicts their adaptation dynamics under arsenic exposure, leaving virtually no role for dedicated evolvability functions in the corresponding proteins. We tracked the adaptation of another >23.000 yeast gene knockout populations to a diverse range of selection pressures and generalised the almost perfect (R2=0.72 to 0.98) capacity of initial fitness to predict the rate of adaptation. Finally, we reconstruct mutations in the genes FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wildtype cells, in gene deletions covering a broad fitness range. We show that the predictability of arsenic adaptation can be understood almost entirely as a global epistasis phenomenon where excluding arsenic from cells, through these mutations, is more beneficial in cells with low arsenic fitness regardless of what causes the arsenic defects. The lack of genes with a meaningful effect on the adaptation dynamics of clonally reproducing cell populations diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

evolutionary biology↗

bioRxiv2021

Predictive evolution of metabolic phenotypes using model-designed selection niches

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

systems biology↗

bioRxiv2021

Machine-learning prediction of resistance to sub-inhibitory antimicrobial concentrations from Escherichia coli genomes

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in healthcare settings, the presence of sub-inhibitory antimicrobial residues in the environment and in the clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. We used machine-learning to predict the population doubling time and growth yield of 1,432 genetically diverse E. coli expanding under exposure to three sub-inhibitory concentrations of six classes of antimicrobials from single nucleotide genetic variants, accessory gene variation and the presence of known AMR genes. We could predict cell yields in the held-out test data with an average correlation (Spearmans {rho}) of 0.63 (0.32 - 0.90 across concentrations) and cell doubling time with an average correlation of 0.47 (0.32 - 0.74 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This points to the remaining missing heritability of growth under antimicrobials exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual SNPs and genes. Predictions based on whole genome information were generally superior to those based only on known AMR genes, and also accurate for AMR resistance at therapeutic concentrations. We also pinpointed genes and SNPs determining the predicted growth and thereby recapitulated the known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing growth effects for known resistance genes for each strain. Our results underscore the potential of predictive modelling of growth patterns from genomic data under sub-inhibitory concentrations of antimicrobials, although the remaining missing heritability poses an issue for achieving the accuracy and precision required for clinical use. ImportancePredicting bacterial growth from genome sequences is important not only for a rapid characterization of strains in clinical diagnostic applications but for the identification of novel targets for drug discovery. Previous studies examined the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study has so far examined the prediction power of genome sequences for bacterial growth in natural strains. In this study, we used a high throughput phenotypic assay to measure bacterial growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under non-therapeutic sub-inhibitory concentrations of antimicrobials that are common in nonclinical settings. Our results revealed a moderate to strong correlation between predicted and actual values for different antimicrobials concentrations. Furthermore, the quantified effect of resistance genes on bacterial growth indicate these genes are still effective at sublethal antimicrobial concentrations.

microbiology↗

bioRxiv2020

Aborting meiosis overcomes hybrid sterility

Hybrids between species or diverged lineages contain fundamentally novel genetic combinations but an impaired meiosis often makes them evolutionary dead ends. Here, we explored to what extent and how an aborted meiosis followed by a return-to-growth (RTG) promotes recombination across a panel of 20 yeast diploid backgrounds with different genomic structures and levels of sterility. Genome analyses of 284 clones revealed that RTG promoted recombination and generated extensive regions of loss-of-heterozygosity in sterile hybrids with either a defective meiosis or a heavily rearranged karyotype, whereas RTG recombination was reduced by high sequence divergence between parental subgenomes. The RTG recombination preferentially occurred in regions with local sequence homology and in meiotic recombination hotspots. The loss-of-heterozygosity had a profound impact on sexual and asexual fitness, and enabled genetic mapping of phenotypic differences in sterile lineages where linkage or association analyses failed. We propose that RTG gives sterile hybrids access to a natural route for genome recombination and adaptation. One sentence summaryAborting meiosis followed by a return to mitotic growth promotes evolution by genome wide-recombination in sterile yeast hybrids.

genetics↗

bioRxiv2020

Control of mitochondrial superoxide production includes programmed mtDNA deletion and restoration

Deletion of mitochondrial DNA in eukaryotes is mainly attributed to rare accidental events associated with mitochondrial replication or repair of double-strand breaks. We report the discovery that yeast cells arrest harmful intramitochondrial superoxide production by shutting down respiration through genetically controlled deletion of mitochondrial oxidative phosphorylation genes. We show that the regulatory circuitry underlying this editing critically involves the antioxidant enzyme superoxide dismutase 2 and two-way mitochondrial-nuclear communication. While mitochondrial DNA homeostasis is rapidly restored after cessation of a short-term superoxide stress, long-term stress causes maladaptive persistence of the deletion process, leading to complete annihilation of the cellular pool of intact mitochondrial genomes and irrevocable loss of respiratory ability. Our results may therefore be of etiological as well as therapeutic importance with regard to age-related mitochondrial impairment and disease. One-Sentence SummaryGenetically controlled editing of mitochondrial DNA is an integral part of the yeasts defenses against oxidative damage.

cell biology↗

bioRxiv2020

Mutagenic mechanisms of cancer-associated DNA polymerase ε alleles

A single amino acid residue change in the exonuclease domain of human DNA polymerase {varepsilon}, P286R, is associated with the development of colorectal cancers, and has been shown to impart a mutagenic phenotype. Perhaps unexpectedly, the corresponding Pol {varepsilon} allele in the yeast Saccharomyces cerevisiae (pol2-P301R), was found to drive greater mutagenesis than exonuclease-deficient Pol {varepsilon} (pol2-4), a phenotype sometimes termed ultra-mutagenesis. By studying the impact on mutation frequency, type, replication-strand bias, and sequence context, we show that ultra-mutagenesis is commonly observed in cells carrying a range of cancer-associated Pol {varepsilon} exonuclease domain alleles. Similarities between mutations generated by these alleles and those generated in pol2-4 cells indicate a shared mechanism of mutagenesis that yields a mutation pattern similar to cancer Signature 14. Comparison of POL2 ultra-mutator with pol2-M644G, a mutant in the polymerase domain decreasing Pol {varepsilon} fidelity, revealed unexpected analogies in the sequence context and strand bias of mutations. Analysis of mutational patterns unique to exonuclease domain mutant cells suggests that backtracking of the polymerase, when the mismatched primer end cannot be accommodated in the proofreading domain, results in the observed increase in insertions and T>A mutations in specific sequence contexts.

molecular biology↗

bioRxiv2020

Genomic epidemiology and evolution of Escherichia coli in wild animals

Escherichia coli is a common bacterial species in the gastrointestinal tracts of warm-blooded animals and humans. Pathogenic and antimicrobial resistance in E. coli may emerge via host switching from animal reservoirs. Despite its potential clinical importance, knowledge of the population structure of commensal E. coli within wild hosts and the epidemiological links between E. coli in non-human hosts and E. coli in humans is still scarce. In this study, we analysed the whole genome sequencing data of a collection of 119 commensal E. coli recovered from the guts of 68 mammal and bird species in Mexico and Venezuela in the 1990s. We observed low concordance between the population structures of E. coli colonizing wild animals and the phylogeny, taxonomy and ecological and physiological attributes of the host species, with distantly related E. coli often colonizing the same or similar host species and distantly related host species often hosting closely related E. coli. We found no evidence for recent transmission of E. coli genomes from wild animals to either domesticated animals or humans. However, multiple livestock- and human-related virulence factor genes were present in E. coli of wild animals, including virulence factors characteristic for Shiga toxin-producing E. coli (STEC) and atypical enteropathogenic E. coli (aEPEC), where several isolates from wild hosts harboured the locus of enterocyte effacement (LEE) pathogenicity island. Moreover, E. coli in wild animal hosts often harboured known antibiotic resistance determinants, including against ciprofloxacin, aminoglycosides, tetracyclines and beta-lactams, with some determinants present in multiple, distantly related E. coli lineages colonizing very different host animals. We conclude that although the genome pools of E. coli colonizing wild animal and human gut are well separated, they share virulence and antibiotic resistance genes and E. coli underscoring that wild animals could serve as reservoirs for E. coli pathogenicity in human and livestock infections. ImportanceEscherichia coli is a clinically importance bacterial species implicated in human and livestock associated infections worldwide. The bacterium is known to reside in the guts of humans, livestock and wild animals. Although wild animals are recognized to serve as potential reservoirs for pathogenic E. coli strains, the knowledge of the population structure of E. coli in wild hosts is still scarce. In this study we used the fine resolution of whole genome sequencing to provide novel insights into the evolution of E. coli genomes within a broad range of wild animal species (including mammals and birds), the co-evolution of E. coli strains with their hosts and the genetics of pathogenicity of E. coli strains in wild hosts. Our results provide evidence for the clinical importance of wild animals as reservoirs for pathogenic strains and necessitate the inclusion of non-human hosts in the surveillance programs for E. coli infections.

microbiology↗

bioRxiv2020

Domestication reprogrammed the budding yeast life cycle

Domestication of plants and animals is the foundation for feeding the world population. We report that domestication of the model yeast S. cerevisiae reprogrammed its life cycle entirely. We tracked growth, gamete formation and cell survival across many environments for nearly 1000 genome sequenced isolates and found a remarkable dichotomy between domesticated and wild yeasts. Wild yeasts near uniformly trigger meiosis and sporulate when encountering nutrient depletions, whereas domestication relaxed selection on sexual reproduction and favoured survival as quiescent cells. Domestication also systematically enhanced fermentative over respiratory traits while decreasing stress tolerance. We show that this yeast domestication syndrome was driven by aneuploidies and gene function losses that emerged independently in multiple domesticated lineages during the species recent evolutionary history. We found domestication to be the most dramatic event in budding yeast evolution, raising questions on how much domestication has distorted our understanding of this key model species.

evolutionary biology↗

bioRxiv2019

Intragenic repeat expansions control yeast chronological aging

Aging varies among individuals due to both genetics and environment but the underlying molecular mechanisms remain largely unknown. Using a highly recombined Saccharomyces cerevisiae population, we found 30 distinct Quantitative Trait Loci (QTLs) that control chronological life span (CLS) in calorie rich and calorie restricted environments, and under rapamycin exposure. Calorie restriction and rapamycin extended life span in virtually all genotypes, but through different genetic variants. We tracked the two major QTLs to massive expansions of intragenic tandem repeats in the cell wall glycoproteins FLO11 and HPF1, which caused a dramatic life span shortening. Life span impairment by N-terminal HPF1 repeat expansion was partially buffered by rapamycin but not by calorie restriction. The HPF1 repeat expansion shifted yeast cells from a sedentary to a buoyant state, thereby increasing their exposure to surrounding oxygen. The higher oxygenation perturbed methionine, lipid, and purine metabolism, which likely explains the life span shortening. We conclude that fast evolving intragenic repeat expansions can fundamentally change the relationship between cells and their environment with profound effects on cellular life style and longevity.

genomics↗

bioRxiv2018

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The diagnosis is typically made by measuring a few known determinants previously identified from whole genome sequencing, and thus is restricted to existing information on biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average F1 score of 0.88 on held-out data (range 0.66-0.96). While the best models most frequently employed all inputs, an average F1 score of 0.73 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and failed to improve prediction for ten out of 11 antibiotics. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data are informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.\n\nSummaryOne of the major health threats of 21st century is emergence of antibiotic resistance. To manage its economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.93 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

bioinformatics↗

bioRxiv2018

Conjugation factors controlling F-plasmid antibiotic resistance transmission

The rapid horizontal transmission of many antibiotic resistance genes between bacterial host cells on conjugative plasmids is a major cause of the accelerating antibiotic resistance crisis. Preventing understanding and targeting conjugation, there currently are no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation. We introduce a novel experimental framework to screen for conjugation based horizontal transmission of antibiotic resistance between >60.000 pairs of cell populations in parallel. Plasmid-carrying donor strains are constructed in high throughput. We then mix the resistance plasmid carrying donors with recipients in a design where only transconjugants can reproduce, measure growth in dense intervals and extract transmission times as the growth lag. As proof-of-principle, we exhaustively explored chromosomal genes controlling F plasmid donation within E. coli populations, by screening the Keio deletion collection at high replication. We recover all six known chromosomal gene mutants affecting conjugation and identify >50 novel factors, all of which diminish antibiotic resistance transmission. We verify 10 of the novel genes effects in a liquid mating assay. The new framework holds great potential for exhaustive disclosing of candidate targets for helper drugs that delay resistance development in patients and societies and improves the longevity of current and future antibiotics.

microbiology↗

bioRxiv2017

Patterns of selection reveal shared molecular targets over short and long evolutionary timescales

Standing and de novo genetic variants can both drive adaptation to environmental changes, but their relative contributions and interplay remain poorly understood. Here we investigated the dynamics of drug adaptation in yeast populations with different levels of standing variation by experimental evolution coupled with time-resolved sequencing and phenotyping. We found a doubling of standing variation alone boost the adaptation by 64.1% and 51.5% in hydroxyuea and rapamycin respectively. The causative standing and de novo variants were selected on shared targets of RNR4 in hydroxyurea and TOR1, TOR2 in rapamycin. The standing and de novo TOR variants map to different functional domains and act via distinct mechanisms. Interestingly, standing TOR variants from two domesticated strains exhibited opposite resistance effects, reflecting lineage-specific functional divergence. This study provides a dynamic view on how standing and de novo variants interactively drive adaptation and deepens our understanding of clonally evolving diseases.

genetics↗