Phenotype Of Organism Research Articles

Copy number and sequence variation in rDNA of Daphnia pulex from natural populations: insights from whole-genome sequencing.

Ribosomal DNA (rDNA) has a vital role in ribosome biogenesis as it contains the genes that encode ribosomal RNA (rRNA) separated by intergenic spacers (IGSs). The rRNA genes occur in hundreds to tens of thousands of copies per haploid genome in eukaryotes and are generally highly conserved with low variation within species. Due to the repetitive nature and large size of rDNA arrays, detecting intraindividual variation can be difficult. In this study, we use whole-genome sequences of 169 Daphnia pulex individuals from 10 natural populations to measure the copy number and sequence variation in rDNA. This revealed that variation in rDNA copy number between individuals spans an order of magnitude. We further observed a substantial level of sequence variation within individual genomes. As expected, single-nucleotide polymorphisms occurred in regions of lower functional constraint such as the IGS and expansion segments of the rRNA genes. The presence of strong linkage disequilibrium among variants facilitated identification of haplotypes within each population. Although there was evidence of recombination among haplotypes from different populations, it is insufficient to eliminate linkage disequilibrium within populations. Estimating copy number and haplotype diversity within individuals revealed that the level of intraindividual sequence variation is not strongly correlated with copy number. The observed patterns of variation highlight a complex evolutionary history of rDNA in D. pulex. Future research should explore the functional implications of rDNA copy number and sequence variation on organismal phenotypes.

Glucose-Induced Cellular Morphological Changes Across Mammalian Species

All organisms encounter environmental fluctuations and must contend with these changes to maintain homeostasis. Some mammals tolerate a wide range of intracellular conditions while others do not. Altered glucose levels are an example of a physiological challenge that can be encountered by cells. We studied responses of dermal fibroblasts obtained from skin biopsies to altered glucose treatments in culture using a comparative approach. Specifically, we investigated responses of mammals known to tolerate a range of blood glucose levels (fruit-eating geladas and Egyptian rousettes) as well as a hibernator (ground squirrel), which exhibits a marked winter decrease in glucose metabolism. As a comparison, we investigated species that maintain fairly stable blood glucose (human and rat). Fibroblasts were cultured at baseline (8mM glucose), then subjected to hypoglycemic (2.5mM) and hyperglycemic (30mM) treatment over 24h. We assayed glycolytic rate in human and rat fibroblasts using a Seahorse XFp analyzer, and found no significance between oxygen consumption or glycolysis across glucose treatments (all p>0.05). To investigate underlying cell phenotypes, we used Cell Painting, employing fluorescent stains to mark organelles. Hoechst 34580 stained DNA, to quantify number and size of nuclei, offering insights into proliferation. MitoTracker Deep Red visualized mitochondria, providing information about energy generation. Alexa Fluor 488 Concanavalin A conjugate marked endoplasmic reticulum. F-actin was stained with Alexa Fluor 555 Phalloidin conjugate, detecting cytoskeletal organization and overall cell structure. Images were analyzed with CellProfiler software, which extracts ~1400 morphological cell features, including area, compactness, fluorescence intensity, and cell connectivity to quantify subtle changes in phenotype. Each extracted feature will be compared across treatments and within a comparative context. We predict that fibroblasts exposed to hyperglycemia exhibit mitochondrial fragmentation and redistribution (MitoTracker) and increased production of reactive oxygen species (assessed with MitoSOX), which is linked to the opening of mitochondrial permeability transition pores. Cell Profiler analysis will identify structural changes that could indicate negative outcomes, such as mitophagy. Hyperglycemia may also induce endoplasmic stress, increasing lipid droplets and altering shape and size of the endoplasmic reticulum. In contrast, hypoglycemia will cause mitochondrial degradation. In both hyper- and hypoglycemia, proliferation rates are predicted to decrease, resulting in fewer nuclei and reduced cell connectivity. Phenotypic differences across species, evaluated in the context of their organismal phenotype, will help explain the mechanisms of cell function in mammals under environmental stress. Funding by NSF #2022046. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Ageing as a two-phase process: theoretical framework.

Human ageing, along with the ageing of conventional model organisms, is depicted as a continuous and progressive decline of biological capabilities accompanied by an exponentially increasing mortality risk. However, not all organisms experience ageing identically and our understanding of the phenomenon is coloured by human-centric views. Ageing is multifaceted and influences a diverse range of species in varying ways. Some undergo swift declines post-reproduction, while others exhibit insubstantial changes throughout their existence. This vast array renders defining universally applicable "ageing attributes" a daunting task. It is nonetheless essential to recognize that not all ageing features are organism-specific. These common attributes have paved the way for identifying "hallmarks of ageing," processes that are intertwined with age, amplified during accelerated ageing, and manipulations of which can potentially modulate or even reverse the ageing process. Yet, a glaring observation is that individuals within a single population age at varying rates. To address this, demographers have coined the term 'frailty'. Concurrently, scientific advancements have ushered in the era of molecular clocks. These innovations enable a distinction between an individual's chronological age (time since birth) and biological age (physiological status and mortality risk). In 2011, the "Smurf" phenotype was unveiled in Drosophila, delineating an age-linked escalation in intestinal permeability that presages imminent mortality. It not only acts as a predictor of natural death but identifies individuals exhibiting traits normally described as age-related. Subsequent studies have revealed the phenotype in organisms like nematodes, zebrafish, and mice, invariably acting as a death predictor. Collectively, these findings have steered our conception of ageing towards a framework where ageing is not linear and continuous but marked by two distinct, necessary phases, discernible in vivo, courtesy of the Smurf phenotype. This framework includes a mathematical enunciation of longevity trends based on three experimentally measurable parameters. It facilitates a fresh perspective on the evolution of ageing as a function. In this article, we aim to delineate and explore the foundational principles of this innovative framework, emphasising its potential to reshape our understanding of ageing, challenge its conventional definitions, and recalibrate our comprehension of its evolutionary trajectory.

Pleiotropy, epistasis and the genetic architecture of quantitative traits.

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Regional random mutagenesis driven by multiple sgRNAs and diverse on-target genome editing events to identify functionally important elements in non-coding regions.

Functional regions that regulate biological phenomena are interspersed throughout eukaryotic genomes. The most definitive approach for identifying such regions is to confirm the phenotype of cells or organisms in which specific regions have been mutated or removed from the genome. This approach is invaluable for the functional analysis of genes with a defined functional element, the protein-coding sequence. By contrast, no functional analysis platforms have been established for the study of cis-elements or microRNA cluster regions consisting of multiple microRNAs with functional overlap. Whole-genome mutagenesis approaches, such as via N-ethyl-N-nitrosourea and gene trapping, have greatly contributed to elucidating the function of coding genes. These methods almost never induce deletions of genomic regions or multiple mutations within a narrow region. In other words, cis-elements and microRNA clusters cannot be effectively targeted in such a manner. Herein, we established a novel region-specific random mutagenesis method named CRISPR- and transposase-based regional mutagenesis (CTRL-mutagenesis). We demonstrate that CTRL-mutagenesis randomly induces diverse mutations within target regions in murine embryonic stem cells. Comparative analysis of mutants harbouring subtly different mutations within the same region would facilitate the further study of cis-element and microRNA clusters.

Differences on the modifying inheritance between the species; Fragaria vesca L. and Fragaria ananassa

By modifying variability, let’s mean that a certain feature of the organism undergoes changes and does not maintain uniformity during its appearance (ie during the transfer of information from the genetic structure to the phenotypic one). In this paper, it is shown how the modifying inheritance is presented in the two types of the strawberry plant, in the one that grows in nature, Fragaria vesca L., and in the one cultivated in greenhouses, Fragaria ananassa. The number of leaf teeth, of these species, has served as the analyzed feature, on which our search for this type of inheritance is based. The paper sheds light on the great role of the environment in elaborating the inheritance of organisms by causing modifying changes in their phenotype in function of the status of the environmental factors in which this organism grows and develops. When these phenotypic modifications manage to become heritable, then it is possible to deal with the action of the epigenetic effect in inheritance. For this reason, studies of modifying inheritance in the phenotype of organisms are necessary. From the comparison of the results obtained, in terms of the modification changes observed between these two species, the null Hypothesis (Ho) is rejected; so, the observed changes have a real basis and are not a random finding. Thus it becomes clear to us that the modifying changes in the inheritance of the plant grown in the natural environment (with significant changes in environmental factors) are greater than those of the species grown in strict conditions – in greenhouses (without large variations in environmental factors). Of course, these changes have an impact on the nutritional and taste values of their fruits, as they affect the nutrient processing laboratory, the leaves

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

Using a Novel Multiplexed Algal Cytological Imaging (MACI) Assay and Machine Learning as a Way to Characterize Complex Phenotypes in Plant-Type Organisms.

High-throughput phenotypic profiling assays, popular for their ability to characterize alternations in single-cell morphological feature data, have been useful in recent years for predicting cellular targets and mechanisms of action (MoAs) for different chemicals and novel drugs. However, this approach has not been extensively used in environmental toxicology due to the lack of studies and established methods for performing this kind of assay in environmentally relevant species. Here, we developed a multiplexed algal cytological imaging (MACI) assay, based on the subcellular structures of the unicellular microalgae, Raphidocelis subcapitata, a toxicology and ecological model species. Several different herbicides and antibiotics with unique MoAs were exposed to R. subcapitata cells, and MACI was used to characterize cellular impacts by measuring subtle changes in their morphological features, including metrics of area, shape, quantity, fluorescence intensity, and granularity of individual subcellular components. This study demonstrates that MACI offers a quick and effective framework for characterizing complex phenotypic responses to environmental chemicals that can be used for determining their MoAs and identifying their cellular targets in plant-type organisms.

Conservation Mitonuclear Replacement: Facilitated mitochondrial adaptation for a changing world.

Most species will not be able to migrate fast enough to cope with climate change, nor evolve quickly enough with current levels of genetic variation. Exacerbating the problem are anthropogenic influences on adaptive potential, including the prevention of gene flow through habitat fragmentation and the erosion of genetic diversity in small, bottlenecked populations. Facilitated adaptation, or assisted evolution, offers a way to augment adaptive genetic variation via artificial selection, induced hybridization, or genetic engineering. One key source of genetic variation, particularly for climatic adaptation, are the core metabolic genes encoded by the mitochondrial genome. These genes influence environmental tolerance to heat, drought, and hypoxia, but must interact intimately and co-evolve with a suite of important nuclear genes. These coadapted mitonuclear genes form some of the important reproductive barriers between species. Mitochondrial genomes can and do introgress between species in an adaptive manner, and they may co-introgress with nuclear genes important for maintaining mitonuclear compatibility. Managers should consider the relevance of mitonuclear genetic variability in conservation decision-making, including as a tool for facilitating adaptation. I propose a novel technique dubbed Conservation Mitonuclear Replacement (CmNR), which entails replacing the core metabolic machinery of a threatened species-the mitochondrial genome and key nuclear loci-with those from a closely related species or a divergent population, which may be better-adapted to climatic changes or carry a lower genetic load. The most feasible route to CmNR is to combine CRISPR-based nuclear genetic editing with mitochondrial replacement and assisted reproductive technologies. This method preserves much of an organism's phenotype and could allow populations to persist in the wild when no other suitable conservation options exist. The technique could be particularly important on mountaintops, where rising temperatures threaten an alarming number of species with almost certain extinction in the next century.

The Role of Gut Microbiota in Obesity

Abstract Obesity is a disease which is currently one of the most serious problems affecting approximately 650 million people worldwide. Improper lifestyle is considered the primary cause of the disease; however, many other factors contribute to the problem. In recent years, attention has been drawn to the role of gut microbiota in developing and controlling obesity and overweight. Microorganisms in the gastrointestinal tract are responsible for the fermentation of certain nutrients, causing efficient digestion, stimulation of intestinal transit, vitamin production, and modulation of the host’s immune system. Numerous studies have demonstrated that gut microbiota composition differs between obese individuals and those with a normal body mass index (BMI). It has also been shown that altering gut microbiota can influence the phenotype of the host organism, promoting metabolic changes, including BMI reduction. Recent studies aimed at using probiotics to modify gut microbiota composition to reduce body weight are still inconclusive.

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

HPA flexibility and FKBP5: promising physiological targets for conservation.

Hypothalamic-pituitary-adrenal axis (HPA) flexibility is an emerging concept recognizing that individuals that will cope best with stressors will probably be those using their hormones in the most adaptive way. The HPA flexibility concept considers glucocorticoids as molecules that convey information about the environment from the brain to the body so that the organismal phenotype comes to complement prevailing conditions. In this context, FKBP5 protein appears to set the extent to which circulating glucocorticoid concentrations can vary within and across stressors. Thus, FKBP5 expression, and the HPA flexibility it causes, seem to represent an individual's ability to regulate its hormones to orchestrate organismal responses to stressors. As FKBP5 expression can also be easily measured in blood, it could be a worthy target of conservation-oriented research attention. We first review the known and likely roles of HPA flexibility and FKBP5 in wildlife. We then describe putative genetic, environmental and epigenetic causes of variation in HPA flexibility and FKBP5 expression among and within individuals. Finally, we hypothesize how HPA flexibility and FKBP5 expression should affect organismal fitness and hence population viability in response to human-induced rapid environmental changes, particularly urbanization. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.

A common cellular response to broad splicing perturbations is characterized by metabolic transcript downregulation driven by the Mdm2-p53 axis.

Disruptions in core cellular processes elicit stress responses that drive cell-state changes leading to organismal phenotypes. Perturbations in the splicing machinery cause widespread mis-splicing, resulting in p53-dependent cell-state changes that give rise to cell-type-specific phenotypes and disease. However, a unified framework for how cells respond to splicing perturbations, and how this response manifests itself in nuanced disease phenotypes, has yet to be established. Here, we show that a p53-stabilizing Mdm2 alternative splicing event and the resulting widespread downregulation of metabolic transcripts are common events that arise in response to various splicing perturbations in both cellular and organismal models. Together, our results classify a common cellular response to splicing perturbations, put forth a new mechanism behind the cell-type-specific phenotypes that arise when splicing is broadly disrupted, and lend insight into the pleiotropic nature of the effects of p53 stabilization in disease.

Exploring Adaptive Phenotypes for the Human Calcium-Sensing Receptor Polymorphism R990G.

Rainforest hunter-gatherers from Southeast Asia are characterized by specific morphological features including a particularly dark skin color (D), short stature (S), woolly hair (W), and the presence of steatopygia (S)-fat accumulation localized in the hips (DSWS phenotype). Based on previous evidence in the Andamanese population, we first characterized signatures of adaptive natural selection around the calcium-sensing receptor gene in Southeast Asian rainforest groups presenting the DSWS phenotype and identified the R990G substitution (rs1042636) as a putative adaptive variant for experimental follow-up. Although the calcium-sensing receptor has a critical role in calcium homeostasis by directly regulating the parathyroid hormone secretion, it is expressed in different tissues and has been described to be involved in many biological functions. Previous works have also characterized the R990G substitution as an activating polymorphism of the calcium-sensing receptor associated with hypocalcemia. Therefore, we generated a knock-in mouse for this substitution and investigated organismal phenotypes that could have become adaptive in rainforest hunter-gatherers from Southeast Asia. Interestingly, we found that mouse homozygous for the derived allele show not only lower serum calcium concentration but also greater body weight and fat accumulation, probably because of enhanced preadipocyte differentiation and lipolysis impairment resulting from the calcium-sensing receptor activation mediated by R990G. We speculate that such differential features in humans could have facilitated the survival of hunter-gatherer groups during periods of nutritional stress in the challenging conditions of the Southeast Asian tropical rainforests.

Many-to-many mapping: A simulation study of how the number of traits and tasks affect the evolution of form and function

Many-to-many mapping of form-to-function posits that multiple morphological and physiological traits affect the performance of multiple tasks in an organism, and that redundancy and multitasking occur simultaneously to shape the evolution of an organism’s phenotype. Many-to-many mapping is expected to be ubiquitous in nature, yet little is known about how it influences the evolution of organismal phenotype. The F-matrix is a powerful tool to study these issues because it describes how multiple traits affect multiple tasks. We undertook a simulation study using the F-matrix to test how the number of traits and the number of tasks affect trait integration and evolvability, as well as the relationships among tasks. We found that as the number of traits and/or tasks increases, the relationships between the tasks and the integration between the traits become weaker, and that the evolvability of the traits increases, all resulting in a system that is freer to evolve. We also found that as the number of traits increases, performance tradeoffs tend to become weaker, but only to a point. Our work shows that it is important to consider not only multiple traits, but also the multitude of tasks that those traits carry out when studying form-function relationships. We suggest that evolution of these relationships follows functional lines of least resistance, which are less defined in more complex systems, resulting in a mechanism for diversification.

Opportunities and Challenges in Applying AI to Evolutionary Morphology

Synopsis Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitized specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long-standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorizing them into 3 stages based on their order of appearance: (1) machine learning, (2) deep learning, and (3) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilized and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments has the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “big data” that aligns the study of phenotypes with genomics and other areas of bioinformatics.

Targeting the key players of phenotypic plasticity in cancer cells by phytochemicals.

Plasticity of phenotypic traits refers to an organism's ability to change in response to environmental stimuli. As a result, the response may alter an organism's physiological state, morphology, behavior, and phenotype. Phenotypic plasticity in cancer cells describes the considerable ability of cancer cells to transform phenotypes through non-genetic molecular signaling activities that promote therapy evasion and tumor metastasis via amplifying cancer heterogeneity. As a result of metastable phenotypic state transitions, cancer cells can tolerate chemotherapy or develop transient adaptive resistance. Therefore, new findings have paved the road in identifying factors and agents that inhibit or suppress phenotypic plasticity. It has also investigated novel multitargeted agents that may promise new effective strategies in cancer treatment. Despite the efficiency of conventional chemotherapeutic agents, drug toxicity, development of resistance, and high-cost limit their use in cancer therapy. Recent research has shown that small molecules derived from natural sources are capable of suppressing cancer by focusing on the plasticity of phenotypic responses. This systematic, comprehensive, and critical review analyzes the current state of knowledge regarding the ability of phytocompounds to target phenotypic plasticity at both preclinical and clinical levels. Current challenges/pitfalls, limitations, and future perspectives are also discussed.

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).

Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax-the wavelength of maximum absorbance-which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites. The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism's ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.

Drift on holey landscapes as a dominant evolutionary process.

An organism's phenotype has been shaped by evolution but the specific processes have to be indirectly inferred for most species. For example, correlations among traits imply the historical action of correlated selection and, more generally, the expression and distribution of traits is expected to be reflective of the adaptive landscapes that have shaped a population. However, our expectations about how quantitative traits-like most behaviors, physiological processes, and life-history traits-should be distributed under different evolutionary processes are not clear. Here, we show that genetic variation in quantitative traits is not distributed as would be expected under dominant evolutionary models. Instead, we found that genetic variation in quantitative traits across six phyla and 60 species (including both Plantae and Animalia) is consistent with evolution across high-dimensional "holey landscapes." This suggests that the leading conceptualizations and modeling of the evolution of trait integration fail to capture how phenotypes are shaped and that traits are integrated in a manner contrary to predictions of dominant evolutionary theory. Our results demonstrate that our understanding of how evolution has shaped phenotypes remains incomplete and these results provide a starting point for reassessing the relevance of existing evolutionary models.