Fitness Landscape Research Articles

On the issue of forecasting crop yields for the purposes of adaptive landscape agriculture

Long-term monitoring (1998–2023) of the yield of clover-timothy grass stands of the first year of use was carried out in order to find patterns of influence of landscape environmental conditions on it in various agroclimatic conditions. The research was conducted within the moraine hill located at the Gubino VNIIMZ agro-testing site in the Tver region. Soil-forming rocks are two-membered deposits consisting of an upper layer composed of relatively light rocks, underlain by moraine bouldery loam. The grass stands were exploited without fertilizers in a single-cut mode on a field divided into 120 plots. Using regression analysis, the influence of landscape and soil environment factors was determined: relief, physical and agrochemical properties of soils on grass yield, as well as the dependence of the degree of this impact on climatic conditions. It was found that the yield of perennial grasses is most strongly influenced by various fractions of the granulometric composition of soils – from stones to dust (up to 16 % of its variability) and the altitude of the location (up to 38 %), since the thermal and water-air characteristics of soils and vegetation largely depend on them. Terrain characteristics such as steepness and curvature of the surface have a minor impact on grass yield (up to 12 %). The degree of influence of agro-landscape environmental factors on the growth of grasses is largely regulated by fluctuations in weather conditions. “Climate scenarios” of a specific factor – sets of weather parameters under which its effect on the production process of a crop is manifested – in the years of sowing and mowing, as a rule, do not differ fundamentally. Knowing the nature of the influence of climatic factors allows us to more accurately predict crop yields within an agricultural landscape and, thus, optimize the location of crops on the territory of a particular farm.

Performance degradation assessment of rolling bearings for electrostatic monitoring based on IDDAE and ADPC

Abstract To improve the early degradation detection capability of the electrostatic monitoring system for rolling bearings, a performance degradation evaluation method based on improved deep denoising autoencoder (IDDAE) and adaptive density peak clustering (ADPC) is proposed in this paper. Firstly, the fusion of electrostatic charge signal features with conventional time-domain, frequency-domain and time-frequency-domain features constitutes the characteristic parameter set of the electrostatic monitoring system indicating the status of the bearings. Then, in order to improve the feature extraction ability of DAE, the deep network DDAE is constructed, and L1 regularisation and Dropout mechanism are applied to avoid overfitting in the deep network, so as to achieve non-linear mapping dimensionality reduction of high-dimensional features. Moreover, to eliminate the error caused by manually selecting the clustering centre, the parameters are adaptively determined by entropy value method and comprehensive optimisation search on the basis of DPC, thus avoiding the "chain effect" that occurs in traditional DPC when data are incorrectly aggregated due to incorrect assignment of clustering centres. Consequently, an improved ADPC algorithm is used to establish a model to measure the health status of bearings, calculate the Mahalanobis distance (MD) be-tween the test set and the cluster centre, and quantitatively characterize the degree of performance degradation of rolling bearings. Finally, combining the 3σ principle, a repair method that can satisfy online monitoring and adapt to spurious fluctuations in different situations is established on a sliding window to obtain an improved index IMD that can accurately characterise the rolling bearing degradation process. The experimental results show that the proposed method can identify early bearing degradation earlier and has better monotonicity, robustness and tendency than other performance degradation assessment methods.

Developmental bias as a cause and consequence of adaptive radiation and divergence

Efforts to reconcile development and evolution have demonstrated that development is biased, with phenotypic variation being more readily produced in certain directions. However, how this “developmental bias” can influence micro- and macroevolution is poorly understood. In this review, we demonstrate that defining features of adaptive radiations suggest a role for developmental bias in driving adaptive divergence. These features are i) common ancestry of developmental systems; ii) rapid evolution along evolutionary “lines of least resistance;” iii) the subsequent repeated and parallel evolution of ecotypes; and iv) evolutionary change “led” by biased phenotypic plasticity upon exposure to novel environments. Drawing on empirical and theoretical data, we highlight the reciprocal relationship between development and selection as a key driver of evolutionary change, with development biasing what variation is exposed to selection, and selection acting to mold these biases to align with the adaptive landscape. Our central thesis is that developmental biases are both the causes and consequences of adaptive radiation and divergence. We argue throughout that incorporating development and developmental bias into our thinking can help to explain the exaggerated rate and scale of evolutionary processes that characterize adaptive radiations, and that this can be best achieved by using an eco-evo-devo framework incorporating evolutionary biology, development, and ecology. Such a research program would demonstrate that development is not merely a force that imposes constraints on evolution, but rather directs and is directed by evolutionary forces. We round out this review by highlighting key gaps in our understanding and suggest further research programs that can help to resolve these issues.

Single-cell transcriptomics reveal potent extrafollicular B cell response linked with granzyme K+ CD8 T cell activation in lupus kidney

ObjectivesB and T cells constitute the majority of infiltrating lymphocytes in the kidney and represent the local perpetrators in lupus nephritis (LN), but the underlying pathogenic mechanisms are not well...

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties.

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

Phytoclimatological study in the high plains of Sétif: application of the bioclimatic classification system (Algeria)

The Global Bioclimatic Classification System (WBCS) by Rivas-Martínez is an effective approach to reflect and study the role of climate in determining the potential distribution of natural vegetation at various temporal and spatial scales. In particular, the diversity of physical characteristics in the Setifian high plains leads to the complexity of large-scale phytogeographic belts. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), have been created for the study area based on Rivas-Martínez's global bioclimatic classification system for current and future climate scenarios, using the parallel climate model from CMIP5 (Coupled Model Intercomparison Project) under the intermediate greenhouse gas emission scenario (RCP4.5). Precipitation and temperature data from the "WorldClim" dataset were used as the data source. These climatic variables were reduced to a spatial resolution of 1 km. The objective is to understand how the climate is evolving in this region, what the consequences are, and what the future holds for these natural ecosystems. A phytosociological study complemented by satellite image analysis reveals a high diversity of pre-forestry formations. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and the magnitude of change, 2) identify and locate the new projected isobioclimates, and 3) explore the compositional changes in vegetation types among analogous isobioclimatic zones. The ordination of the relationships between vegetation and bioclimates shows a strong correlation between Rivas-Martinez indices and the distribution and composition of vegetation. Changes in ombrotype, thermotype, bioclimate, ombrotype, and isobioclimate differed between the current and future series. The resulting map presents 9 isobioclimatic units, which are advantageous for confirming the diagnosis of bioclimatic characteristics and describing the relationships between climatic variables and the corresponding distributions of natural vegetation. We identified eight different isobioclimates for the current period (1979-2013) and seven for the future period (2050), three thermotype horizons for both the current and future periods, and finally, four ombrotype horizons for the period (1979-2013), while two ombrohorizons represent the period (2050). This approach is useful for explaining territorial diversity in environmental applications related to ecological adaptation assessment, nature conservation, landscape regionalization, and planning.

On Recombination.

The predominant explanations for including chromosomal recombination during meiosis are that it serves as a mechanism for repair or as a mechanism for increased adaptability. However, neither gives a clear immediate selective advantage to the reproducing organism itself. This letter revisits the idea that sex emerged and is maintained because it enables a simple form of fitness landscape smoothing to explain why recombination evolved. Although recombination was originally included in the idea, as with the other explanations, no immediate benefit was identified. That a benefit exists if the dividing cell(s) form a simple colony of the resulting haploids for some time after reproduction is explored here and shown to further increase the benefits of the landscape smoothing process.

GRNAde: Geometric Deep Learning for 3D RNA inverse design.

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Open source code: github.com/chaitjo/geometric-rna-design.

A general multi-scale description of metastable adaptive motion across fitness valleys

We consider a stochastic individual-based model of adaptive dynamics on a finite trait graph G=(V,E)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G=(V,E)$$\\end{document}. The evolution is driven by a linear birth rate, a density dependent logistic death rate and the possibility of mutations along the directed edges in E. We study the limit of small mutation rates for a simultaneously diverging population size. Closing the gap between Bovier et al. (Ann Appl Probab 29(6):3541–358, 2019) and Coquille et al. (Electron J Probab 26:1–37, 2021) we give a precise description of transitions between evolutionary stable conditions (ESC), where multiple mutations are needed to cross a valley in the fitness landscape. The system shows a metastable behaviour on several divergent time scales, corresponding to the widths of these fitness valleys. We develop the framework of a meta graph that is constituted of ESCs and possible metastable transitions between them. This allows for a concise description of the multi-scale jump chain arising from concatenating several jumps. Finally, for each of the various time scales, we prove the convergence of the population process to a Markov jump process visiting only ESCs of sufficiently high stability.

A Novel Accurate Peak Extraction Algorithm of Mass Spectrometry Based on Iterative Adaptive Curve Fitting.

In the analysis of mass spectrometry, the peak identification from the overlapped region is necessary yet difficult. Although various methods have been developed to identify these peaks, especially the continuous wavelet transformation, their applications are still limited and it is hard to deal with the complex overlapped peaks. In this study, a novel peak extraction algorithm of mass spectrometry based on iterative adaptive curve fitting is proposed to address these challenges. It fully utilizes the global optimization characteristics of adaptive curve fitting. Initial peak parameters are obtained using a window searching method, and the residuals between the adaptive fitting peak and the original data indicate the fit's effectiveness and provide information about the peaks in overlap. Using this information, we performed iterative adaptive fitting, continuously updating the overlapped peaks until the residuals met the completion criteria. All of the peaks within the overlapped region can be successfully extracted by the final fitting. The proposed method is evaluated by the simulated data, the real signal from a public data set, and the spectra of two different mass spectrometry instruments. The results demonstrate that this method can more effectively extract peaks with severe overlap and multiple overlapped peaks, resist noise interference, and offer the potential to process peaks with a high dynamic range. More importantly, the proposed method accurately identifies overlapped peaks in the actual spectra from various mass spectrometry instruments, which helps the qualitative and quantitative analyses to a great extent.

Challenges and Solutions for Leave-One-Out Biosensor Design in the Context of a Rugged Fitness Landscape

The leave-one-out (LOO) green fluorescent protein (GFP) approach to biosensor design combines computational protein design with split protein reconstitution. LOO-GFPs reversibly fold and gain fluorescence upon encountering the target peptide, which can be redefined by computational design of the LOO site. Such an approach can be used to create reusable biosensors for the early detection of emerging biological threats. Enlightening biophysical inferences for nine LOO-GFP biosensor libraries are presented, with target sequences from dengue, influenza, or HIV, replacing beta strands 7, 8, or 11. An initially low hit rate was traced to components of the energy function, manifesting in the over-rewarding of over-tight side chain packing. Also, screening by colony picking required a low library complexity, but designing a biosensor against a peptide of at least 12 residues requires a high-complexity library. This double-bind was solved using a “piecemeal” iterative design strategy. Also, designed LOO-GFPs fluoresced in the unbound state due to unwanted dimerization, but this was solved by fusing a fully functional prototype LOO-GFP to a fiber-forming protein, Drosophila ultrabithorax, creating a biosensor fiber. One influenza hemagglutinin biosensor is characterized here in detail, showing a shifted excitation/emission spectrum, a micromolar affinity for the target peptide, and an unexpected photo-switching ability.

Biogeography confounds the signal of cospeciation in Batesian mimicry.

Since the inception of the field of evolution, mimicry has yielded insights into foundational evolutionary processes, including adaptive peak shifts, speciation, and the emergence and maintenance of phenotypic polymorphisms.1,2,3 In recent years, the coevolutionary processes generating mimicry have gained increasing attention from researchers. Despite significant advances in understanding Batesian and Müllerian mimicry in Lepidopteran systems, few other mimetic systems have received similar detailed research. Here, we present a Batesian mimicry complex involving flightless, armored Pachyrhynchus weevils and their winged Doliops longhorn beetle mimics and examine their coevolutionary patterns within the Philippine archipelagos. Pachyrhynchus weevils are primarily found in the Philippines, where distinct species radiations have occurred on different islands, each with unique color patterns serving as a warning to predators. This defensive trait and mimicry between unrelated species were first described by Wallace in 1889. Notably, the distantly related longhorn beetle Doliops, despite being soft-bodied and ostensibly palatable, mimics the heavily armored, flightless Pachyrhynchus. To address mimicry in this system, we reconstructed the phylogeny of Doliops using a probe set consisting of 38,000 ultraconserved elements. Our study examines the following questions central to understanding the Pachyrhynchus-Doliops mimicry system: (1) to what extent are coevolutionary interactions conserved (i.e., lineage-constrained) and (2) are the codiversification patterns primarily driven by biotic or abiotic factors?4,5,6 To assess color mimicry and cospeciation, we examined the evolution of nanostructure-based warning colors and the effect of island biogeography on cospeciation. Our findings demonstrate the beetle's ability to repeatedly evolve multiple solutions to similar evolutionary challenges, evolving similar color patterns using different types of photonic crystals with varying degrees of order. We revealed that the observed pattern of cospeciation is driven mainly by abiotic factors from their biogeographic history. Unlike the patterns of coevolution seen between angiosperms and insect lineages,7 most ecological interactions do not persist longer than a few million years, leading to patterns of modularity rather than ecological nestedness.4,6,7.

Emergent time scales of epistasis in protein evolution

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.

Importance of higher-order epistasis in large protein sequence-function relationships.

Epistasis complicates our understanding of protein sequence-function relationships and impedes our ability to build accurate predictive models for novel genotypes. Although pairwise epistasis has been extensively studied in proteins, the significance of higher-order epistasis for protein sequence-function relationships remains contentious, largely due to challenges in fitting higher-order epistatatic interactions for full-length proteins. Here, we introduce a novel transformer-based approach. The key feature of our method is that we can adjust the order of interactions fit by the model by changing the number of attention layers while also accounting for any global nonlinearity induced by the experimental conditions. This allows us to test if inclusion of higher-order interactions leads to enhanced model performance. Applying our method to 10 large protein sequence-function datasets, we found that the importance of higher-order epistasis differs substantially between proteins, accounting for up to 60% of the total variance attributed to epistasis. We also found that including higher-order epistasis is particularly important for generalizing locally sampled fitness data to distant regions of sequence space and for modeling an additional multipeak fitness landscape derived from combining mutagenesis data from 4 orthologous green fluorescencent proteins. Our findings suggest that higher-order epistasis often does play an important role in protein sequence-function relationships, and thus should be properly incorporated during protein engineering and evolutionary data analysis.

Genetic history and biological adaptive landscape of the Tujia people inferred from shared haplotypes and alleles

BackgroundHigh-quality genomic datasets from under-representative populations are essential for population genetic analysis and medical relevance. Although the Tujia are the most populous ethnic minority in southwestern China, previous genetic studies have been fragmented and only partially reveal their genetic diversity landscape. The understanding of their fine-scale genetic structure and potentially differentiated biological adaptive features remains nascent.ObjectivesThis study aims to explore the demographic history and genetic architecture related to the natural selection of the Tujia people, focusing on a meta-Tujia population from the central regions of the Yangtze River Basin.ResultsPopulation genetic analyses conducted on the meta-Tujia people indicate that they occupy an intermediate position in the East Asian North-South genetic cline. A close genetic affinity was identified between the Tujia people and neighboring Sinitic-speaking populations. Admixture models suggest that the Tujia can be modeled as a mixture of northern and southern ancestries. Estimates of f3/f4 statistics confirmed the presence of ancestral links to ancient Yellow River Basin millet farmers and the BaBanQinCen-related groups. Furthermore, population-specific natural selection signatures were explored, revealing highly differentiated functional variants between the Tujia and southern indigenous populations, including genes associated with hair morphology (e.g., EDAR) and skin pigmentation (e.g., SLC24A5). Additionally, both shared and unique selection signatures were identified among ethnically diverse but geographically adjacent populations, highlighting their extensive admixture and the biological adaptations introduced by this admixture.ConclusionsThe study unveils significant population movements and genetic admixture among the Tujia and other ethno-linguistically diverse East Asian groups, elucidating the differentiated adaptation processes across geographically diverse populations from the current genetic landscape.

Cooperative coevolution for non-separable large-scale black-box optimization: Convergence analyses and distributed accelerations

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous-game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition, there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the recent multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional test functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores.

FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.

Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy high-throughput experiments. We demonstrate how FLIGHTED can improve model performance on two categories of experiments: single-step selection assays, such as phage display and SELEX, and a novel high-throughput assay called DHARMA that ties activity to base editing. We then compare the performance of standard machine-learning models on fitness landscapes generated with and without FLIGHTED. Accounting for noise significantly improves model performance, especially of CNN architectures, and changes relative rankings on numerous common benchmarks. Based on our new benchmarking with FLIGHTED, data size, not model scale, currently appears to be limiting the performance of protein fitness models, and the choice of top model architecture matters more than the protein language model embedding. Collectively, our results indicate that FLIGHTED can be applied to any high-throughput assay and any machine learning model, making it straightforward for protein designers to account for experimental noise when modeling protein fitness.

Enriching productive mutational paths accelerates enzyme evolution.

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase-an enzymatic model system for proton transfer from carbon-that accelerates the proton abstraction step >108-fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme's fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.

Promoters Constrain Evolution of Expression Levels of Essential Genes in Escherichia coli.

Variability in expression levels in response to random genomic mutations varies among genes, influencing both the facilitation and constraint of phenotypic evolution in organisms. Despite its importance, both the underlying mechanisms and evolutionary origins of this variability remain largely unknown due to the mixed contributions of cis- and trans-acting elements. To address this issue, we focused on the mutational variability of cis-acting elements, that is, promoter regions, in Escherichia coli. Random mutations were introduced into the natural and synthetic promoters to generate mutant promoter libraries. By comparing the variance in promoter activity of these mutant libraries, we found no significant difference in mutational variability in promoter activity between promoter groups, suggesting the absence of a signature of natural selection for mutational robustness. In contrast, the promoters controlling essential genes exhibited a remarkable bias in mutational variability, with mutants displaying higher activities than the wild types being relatively rare compared to those with lower activities. Our evolutionary simulation on a rugged fitness landscape provided a rationale for this vulnerability. These findings suggest that past selection created nonuniform mutational variability in promoters biased toward lower activities of random mutants, which now constrains the future evolution of downstream essential genes toward higher expression levels.

Host and antibiotic jointly select for greater virulence in Staphylococcus aureus.

Widespread antibiotic usage has resulted in the rapid evolution of drug-resistant bacterial pathogens and poses significant threats to public health. Resolving how pathogens respond to antibiotics under different contexts is critical for understanding disease emergence and evolution going forward. The impact of antibiotics has been demonstrated most directly through in vitro pathogen passaging experiments. Independent from antibiotic selection, interactions with hosts have also altered the evolutionary trajectories and fitness landscapes of pathogens, shaping infectious disease outcomes. However, it is unclear how interactions between hosts and antibiotics impact the evolution of pathogen virulence. Here, we evolved and re-sequenced Staphylococcus aureus , a major bacterial pathogen, varying exposure to host and antibiotics to tease apart the contributions of these selective pressures on pathogen adaptation. After 12 passages, S. aureus evolving in Caenorhabditis elegans nematodes exposed to a sub-minimum inhibitory concentration of antibiotic (oxacillin) became highly virulent, regardless of whether the ancestral pathogen was methicillin-resistant (MRSA) or methicillin-sensitive (MSSA). Host and antibiotic exposure selected for reduced drug susceptibility in MSSA lineages while increasing MRSA total growth outside hosts. We identified mutations in genes involved in complex regulatory networks linking virulence and metabolism, including codY , agr , and gdpP , suggesting that rapid adaptation to infect hosts may have pleiotropic effects. In particular, MSSA populations under selection from host and antibiotic accumulated mutations in the global regulator gene codY , which controls biofilm formation in S. aureus. These populations had indeed evolved more robust biofilms-a trait linked to both virulence and antibiotic resistance-suggesting evolution of one trait can confer multiple adaptive benefits. Despite evolving in similar environments, MRSA and MSSA populations proceeded on divergent evolutionary paths, with MSSA populations exhibiting more similarities across replicate populations. Our results underscore the importance of considering multiple and concurrent selective pressures as drivers of pervasive pathogen traits.