Genotype And Phenotype Research Articles

Genotype-to-Protein Map and Collective Adaptation in a Viral Population

Viral populations are large and highly heterogeneous. Despite the evolutionary relevance of such heterogeneity, statistical approaches to quantifying the extent to which viruses maintain a high genotypic and/or phenotypic diversity have been rarely pursued. Here, we address this issue by analyzing a nucleotide-to-protein sequence map through deep sequencing of populations of the Qβ phage adapted to high temperatures. Tens of thousands of different sequences corresponding to two fragments of the gene coding for the viral replicase were recovered. A diversity analysis of two independent populations consistently revealed that about 40% of the mutations identified caused changes in protein amino acids, leading to an almost complete exploration of the protein neighborhood of (non-silent) mutants at a distance of one. The functional form of the empirical distribution of phenotype abundance agreed with analytical calculations that assumed random mutations in the nucleotide sequence. Our results concur with the idea that viral populations maintain a high diversity as an efficient adaptive mechanism and support the hypothesis of universality for a lognormal distribution of phenotype abundances in biologically meaningful genotype–phenotype maps, highlighting the relevance of entropic effects in molecular evolution.

Genome-Wide Association Studies across Environmental and Genetic Contexts Reveal Complex Genetic Architecture of Symbiotic Extended Phenotypes.

A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.

Single-Cell Atlas of Patient-Derived Trophoblast Organoids in Ongoing Pregnancies

Trophoblast organoids (TOs) hold great promise for elucidating human placental development and function. By deriving TOs in ongoing pregnancies using chorionic villus sampling (CVS), we established a platform to study trophoblast differentiation and function in early pregnancy, including pregnancies with different fetal genetic abnormalities. We addressed cellular heterogeneity of CVS-derived TOs by providing a single-cell transcriptomic atlas and showed that CVS-TOs recapitulate key aspects of the human placenta, including syncytial fusion and hormone synthesis. This study demonstrates the utility of trophoblast organoids for investigating genetic defects in the placenta and describes an experimental platform for future personalized placental medicine approaches, including genotype–phenotype mapping.

The structure of genotype-phenotype maps makes fitness landscapes navigable.

Fitness landscapes are often described in terms of 'peaks' and 'valleys', indicating an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high dimensional, which results in counter-intuitive structural properties of genotype-phenotype maps. Here we show that these properties, such as the presence of pervasive neutral networks, make fitness landscapes navigable. For three biologically realistic genotype-phenotype map models-RNA secondary structure, protein tertiary structure and protein complexes-we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through fitness valleys. This in turn indicates that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be used under evolutionary dynamics. Our findings have broad implications for the prediction of natural evolutionary outcomes and for directed evolution.

Higher-order epistasis and phenotypic prediction

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Genotype-phenotype mapping with polyominos made from DNA origami tiles

The correlation between genetic information and characteristics of a living cell—its genotype and its phenotype—constitutes the basis of genetics. Here, we experimentally realize a primitive form of genotype-phenotype mapping with DNA origami. The DNA origami can polymerize into two-dimensional lattices (phenotype) via blunt-end stacking facilitated by edge staples at the seam of the planar DNA origami. There are 80 binding positions for edge staples, which allow us to translate an 80-bit long binary code (genotype) onto the DNA origami. The presence of an edge staple thus corresponds to a “1” and its absence to a “0.” The interactions of our DNA-based system can be reproduced by a polyomino model. Polyomino growth simulations qualitatively reproduce our experimental results. We show that not only the absolute number of base stacks but also their sequence position determine the cluster size and correlation length of the orientation of single DNA origami within the cluster. Importantly, the mutation of a few bits can result in major morphology changes of the DNA origami cluster, while more often, major sequence changes have no impact. Our experimental realization of a correlation between binary information (“genotype”) and cluster morphology (“phenotype”) thus reproduces key properties of genotype-phenotype maps known from living systems.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.

Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline in sequencing costs, multi-omics analysis has become more widely employed. While this gives us new opportunities to uncover the correlation mechanisms between single-nucleotide polymorphism (SNP), genes, and phenotypes, multi-omics still faces certain challenges, specifically: 1) When the sample size is large enough, the number of omics types is often not large enough to meet the requirements of multi-omics analysis; 2) each omics’ internal correlations are often unclear, such as the correlation between genes in genomics; 3) when analyzing a large number of traits (p), the sample size (n) is often smaller than p, n << p, hindering the application of machine learning methods in the classification of disease outcomes. Results: To solve these issues with multi-omics and build a robust classification model, we propose a graph-embedded deep neural network (G-EDNN) based on expression quantitative trait loci (eQTL) data, which achieves sparse connectivity between network layers to prevent overfitting. The correlation within each omics is also considered such that the model more closely resembles biological reality. To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. Results show that the proposed method could achieve a high classification accuracy and easy-to-interpret feature selection. This method represents an extended application of genotype–phenotype association analysis in deep learning networks.

Thermodynamics and neutral sets in the RNA sequence-structure map

The sequence-structure map of biological macromolecules, also referred to as a genotype-phenotype (GP) map, shapes evolutionary processes by determining how mutations affect molecular structures. RNA secondary structure constitutes a central model in the field, both because of the availability of efficient folding algorithms and the biological importance of RNA structures. Despite the immense knowledge about the characteristics of the RNA GP map, there is not enough understanding of how these characteristics emerge from the underlying energetics of RNA folding. In this paper, we fill this gap with a computational study of the connection between the energy gap between the lowest-energy structures, here referred to as the stability, and the characteristics of the GP map. We focus on neutral sets, i.e., sequences that share a structure, and on connected neutral components within neutral sets. We find first that both mean stability and the stability change through point mutations are linked to the size of neutral sets and neutral components and secondly that the typical stability change through mutations depends on structural characteristics. These results contribute to a deeper understanding of the RNA GP map and are relevant for evolutionary processes since stability may be under selection.

Microevolutionary variation in molar morphology of Onychomys leucogaster decoupled from genetic structure.

In neutral models of quantitative trait evolution, both genetic and phenotypic divergence scale as random walks, producing a correlation between the two measures. However, complexity in the genotype-phenotype map may alter the correlation between genotypic and phenotypic divergence, even when both are evolving neutrally or nearly so. Understanding this correlation between phenotypic and genetic variation is critical for accurately interpreting the fossil record. This study compares the geographic structure and scaling of morphological variation of the shape of the first lower molar of 77 individuals of the northern grasshopper mouse Onychomys leucogaster to genome-wide SNP variation in the same sample. We found strong genetic structure but weak or absent morphological structure indicating that the scaling of each type of variation is decoupled from one another. Low PST values relative to FST values are consistent with a lack of morphological divergence in contrast to genetic divergence between groups. This lack of phenotypic structure and the presence of notable within-sample phenotypic variance are consistent with uniform selection or constraints on molar shape across a wide geographic and environmental range. Over time, this kind of decoupling may result in patterns of phenotypic stasis masking underlying genetic patterns.

The genotype-phenotype distinction: from Mendelian genetics to 21st century biology.

The Genotype-Phenotype (G-P) distinction was proposed in the context of Mendelian genetics, in the wake of late nineteenth century studies about heredity. In this paper, we provide a conceptual analysis that highlights that the G-P distinction was grounded on three pillars: observability, transmissibility, and causality. Originally, the genotype is the non-observable and transmissible cause ofits observable and non-transmissible effect, the phenotype. We argue that the current developments of biology have called the validity of such pillars into question. First, molecular biology has unveiled the putative material substrate of the genotype (qua DNA), making it an observable object. Second, numerous findings on non-genetic heredity suggest that some phenotypic traits can be directly transmitted. Third, recent organicist approaches to biological phenomena have emphasized the reciprocal causality between parts of a biological system, which notably applies to the relation between genotypes and phenotypes. As a consequence, we submit that the G-P distinction has lost its general validity, although it can still apply to specific situations. This calls for forging new frameworks and concepts to better describe heredity and development.

Grains of Description in Biological and Cultural Transmission

Abstract The question of whether cultural transmission is faithful has attracted significant debate over the last 30 years. The degree of fidelity with which an object is transmitted depends on 1) the features chosen to be relevant, and 2) the quantity of details given about those features. Once these choices have been made, an object is described at a particular grain. In the absence of conventions between different researchers and across different fields about which grain to use, transmission fidelity cannot be evaluated because it is relative to the choice of grain. In biology, because a genotype-to-phenotype mapping exists and transmission occurs from genotype to genotype, a privileged grain of description exists that circumvents this ‘grain problem.’ In contrast, in cultural evolution, the genotype–phenotype distinction cannot be drawn, rendering claims about fidelity dependent upon researchers’ choices. Thus, due to a lack of unified conventions, claims about fidelity transmission are difficult to evaluate.

Host-driven subspeciation in the hedgehog fungus, Trichophyton erinacei, an emerging cause of human dermatophytosis

Trichophyton erinacei is a main cause of dermatophytosis in hedgehogs and is increasingly reported from human infections worldwide. This pathogen was originally described in the European hedgehog ( Erinaceus europaeus) but is also frequently found in the African four-toed hedgehog ( Atelerix albiventris), a popular pet animal worldwide. Little is known about the taxonomy and population genetics of this pathogen despite its increasing importance in clinical practice. Notably, whether there are different populations or even cryptic species associated with different hosts or geographic regions is not known. To answer these questions, we collected 161 isolates, performed phylogenetic and population-genetic analyses, determined mating-type, and characterised morphology and physiology. Multigene phylogeny and microsatellite analysis supported T. erinacei as a monophyletic species, in contrast to highly incongruent single-gene phylogenies. Two main subpopulations, one specific mainly to Atelerix and second to Erinaceus hosts, were identified inside T. erinacei, and slight differences in the size of microconidia and antifungal susceptibilities were observed among them. Although the process of speciation into two lineages is ongoing in T. erinacei, there is still gene flow between these populations. Thus, we present T. erinacei as a single species, with notable intraspecies variability in genotype and phenotype. The data from wild hedgehogs indicated that sexual reproduction in T. erinacei and de novo infection of hedgehogs from soil are probably rare events and that clonal horizontal spread strongly dominates. The molecular typing approach used in this study represents a suitable tool for further epidemiological surveillance of this emerging pathogen in both animals and humans. The results of this study also highlighted the need to use a multigene phylogeny ideally in combination with other independent molecular markers to understand the species boundaries of dermatophytes.

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus.

Background: Genetic diversity provides the basic substrate for evolution. Genetic variation consists of changes ranging from single base pairs (single-nucleotide polymorphisms, or SNPs) to larger-scale structural variants, such as inversions, deletions, and duplications. SNPs have long been used as the general currency for investigations into how genetic diversity fuels evolution. However, structural variants can affect more base pairs in the genome than SNPs and can be responsible for adaptive phenotypes due to their impact on linkage and recombination. In this study, we investigate the first steps needed to explore the genetic basis of an economically important growth trait in the marine teleost finfish Chrysophrys auratus using both SNP and structural variant data. Specifically, we use feature selection methods in machine learning to explore the relative predictive power of both types of genetic variants in explaining growth and discuss the feature selection results of the evaluated methods. Methods: SNP and structural variant callers were used to generate catalogues of variant data from 32 individual fish at ages 1 and 3 years. Three feature selection algorithms (ReliefF, Chi-square, and a mutual-information-based method) were used to reduce the dataset by selecting the most informative features. Following this selection process, the subset of variants was used as features to classify fish into small, medium, or large size categories using KNN, naïve Bayes, random forest, and logistic regression. The top-scoring features in each feature selection method were subsequently mapped to annotated genomic regions in the zebrafish genome, and a permutation test was conducted to see if the number of mapped regions was greater than when random sampling was applied. Results: Without feature selection, the prediction accuracies ranged from 0 to 0.5 for both structural variants and SNPs. Following feature selection, the prediction accuracy increased only slightly to between 0 and 0.65 for structural variants and between 0 and 0.75 for SNPs. The highest prediction accuracy for the logistic regression was achieved for age 3 fish using SNPs, although generally predictions for age 1 and 3 fish were very similar (ranging from 0–0.65 for both SNPs and structural variants). The Chi-square feature selection of SNP data was the only method that had a significantly higher number of matches to annotated genomic regions of zebrafish than would be explained by chance alone. Conclusions: Predicting a complex polygenic trait such as growth using data collected from a low number of individuals remains challenging. While we demonstrate that both SNPs and structural variants provide important information to help understand the genetic basis of phenotypic traits such as fish growth, the full complexities that exist within a genome cannot be easily captured by classical machine learning techniques. When using high-dimensional data, feature selection shows some increase in the prediction accuracy of classification models and provides the potential to identify unknown genomic correlates with growth. Our results show that both SNPs and structural variants significantly impact growth, and we therefore recommend that researchers interested in the genotype–phenotype map should strive to go beyond SNPs and incorporate structural variants in their studies as well. We discuss how our machine learning models can be further expanded to serve as a test bed to inform evolutionary studies and the applied management of species.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

SUMMARYA central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena—from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.

Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries

The clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings. We evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact. As of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings. We demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.

Fast free-energy-based neutral set size estimates for the RNA genotype-phenotype map.

The genotype–phenotype (GP) map of RNA secondary structure links each RNA sequence to its corresponding secondary structure. Previous research has shown that the large-scale structural properties of GP maps, such as the size of neutral sets in genotype space, can influence evolutionary outcomes. In order to use neutral set sizes, efficient and accurate computational methods are needed to compute them. Here, we propose a new method, which is based on free energy estimates and is much faster than existing sample-based methods. Moreover, this approach can give insight into the reasons behind neutral set size variations, for example, why structures with fewer stacks tend to have larger neutral set sizes. In addition, we generalize neutral set size calculations from the previously studied many-to-one framework, where each sequence folds into a single energetically preferred structure, to a fuller many-to-many framework, where several low-energy structures are included. We find that structures with high neutral sets in one framework also tend to have large neutral sets in the other framework for a range of parameters and thus the choice of GP map does not fundamentally affect which structures have the largest neutral set sizes.

Abstract B027: Modeling the emergence of therapy resistance with fitness seascapes

Abstract One approach to the study of therapy resistance is through an evolutionary lens, where novel sub-types emerge through a random process and are selected for by an increased fitness. Computational models are used to understand the parameter spaces that facilitate resistance, optimize therapy regimens, and predict the evolutionary trajectory. Fitness landscapes are structures that map genotype to fitness and are commonly used in computational models of evolution. Most fitness landscape models assume the environment is constant. However, this obscures evolutionary trade-offs that depend on environmental change, i.e., where a resistant mutant trades a lower growth rate in the absence of drug in exchange for drug resistance. Furthermore, a disease agent population in a patient will never experience a constant drug concentration; the drug concentration will vary according to pharmacokinetic effects and spatial relationships to blood vessels. Here, we explored fitness seascapes as an extension to fitness landscapes. Fitness seascapes model a variable genotype-phenotype mapping. In this work, we used fitness seascapes that map genotypes to drug dose-response curves. In contrast to fitness landscapes, the fitness rank-order of genotypes changes as a function of drug concentration, representing evolutionary tradeoffs. Using a novel computational platform, we showed that modeling evolutionary tradeoffs with fitness seascapes dramatically impacted the evolutionary trajectory of a population subject to changing drug concentrations. We also showed how mutant selection windows, a commonly studied structure in evolutionary medicine, are embedded in fitness seascapes. Using an empirical fitness seascape measured in genetically engineered S. cerevisiae, we then explored the utility of fitness seascapes in two in silico experiments. In the first experiment, we subjected an evolving population to varying rates of change of drug concentration, finding that populations experiencing a lower rate of change were more likely to become resistant, while those experiencing a higher rate of change were more likely to go extinct. This aligns with similar previously published in vitro experiments. Next, we simulated patients undergoing therapy with varying levels of drug regimen nonadherence using a pharmacokinetic model. We found that a higher rate of drug regimen nonadherence was associated with a higher rate of resistance, while a higher rate of adherence was associated with a higher rate of elimination of the disease agent. Future work may reveal patterns of nonadherence that promote resistance. Here, we have demonstrated the utility of fitness seascapes in studying resistance. Our results highlight the importance of evolutionary trade-offs when modeling the emergence of resistance under varying drug concentrations. These results have implications for diseases across kingdoms, including viral and bacterial infections and cancer. In the future, fitness seascapes may be used to optimize drug dosing regimens or understand differences in metabolism in silico. Citation Format: Eshan S. King, Emily Dolson, Jacob G. Scott. Modeling the emergence of therapy resistance with fitness seascapes [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B027.

Molecular parallelism in the evolution of a master sex-determining role for the anti-Mullerian hormone receptor 2 gene (amhr2) in Midas cichlids.

The evolution of sex chromosomes and their differentiation from autosomes is a major event during genome evolution that happened many times in several lineages. The repeated evolution and lability of sex-determination mechanisms in fishes makes this a well-suited system to test for general patterns in evolution. According to current theory, differentiation is triggered by the suppression of recombination following the evolution of a new master sex-determining gene. However, the molecular mechanisms that establish recombination suppression are known from few examples, owing to the intrinsic difficulties of assembling sex-determining regions (SDRs). The development of forward-genetics and long-read sequencing have generated a wealth of data questioning central aspects of the current theory. Here, we demonstrate that sex in Midas cichlids is determined by an XY system, and identify and assemble the SDR by combining forward-genetics, long-read sequencing and optical mapping. We show how long-reads aid in the detection of artefacts in genotype-phenotype mapping that arise from incomplete genome assemblies. The male-specific region is restricted to a 100-kb segment on chromosome 4 that harbours transposable elements and a Y-specific duplicate of the anti-Mullerian receptor 2gene, which has evolved master sex-determining functions repeatedly. Our data suggest that amhr2Y originated by an interchromosomal translocation from chromosome 20 to 4 pre-dating the split of Midas and Flier cichlids. In the latter, it is pseudogenized and translocated to another chromosome. Duplication of anti-Mullerian genes is a common route to establishing new sex determiners, highlighting the role of molecular parallelism in the evolution of sex determination.

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps—including biophysically interpretable models—from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

Multi-Omics Reveals Different Strategies in the Immune and Metabolic Systems of High-Yielding Strains of Laying Hens

Lohmann Brown (LB) and Lohmann Selected Leghorn (LSL) are two commercially important laying hen strains due to their high egg production and excellent commercial suitability. The present study integrated multiple data sets along the genotype-phenotype map to better understand how the genetic background of the two strains influences their molecular pathways. In total, 71 individuals were analyzed (LB, n = 36; LSL, n = 35). Data sets include gut miRNA and mRNA transcriptome data, microbiota composition, immune cells, inositol phosphate metabolites, minerals, and hormones from different organs of the two hen strains. All complex data sets were pre-processed, normalized, and compatible with the mixOmics platform. The most discriminant features between two laying strains included 20 miRNAs, 20 mRNAs, 16 immune cells, 10 microbes, 11 phenotypic traits, and 16 metabolites. The expression of specific miRNAs and the abundance of immune cell types were related to the enrichment of immune pathways in the LSL strain. In contrast, more microbial taxa specific to the LB strain were identified, and the abundance of certain microbes strongly correlated with host gut transcripts enriched in immunological and metabolic pathways. Our findings indicate that both strains employ distinct inherent strategies to acquire and maintain their immune and metabolic systems under high-performance conditions. In addition, the study provides a new perspective on a view of the functional biodiversity that emerges during strain selection and contributes to the understanding of the role of host–gut interaction, including immune phenotype, microbiota, gut transcriptome, and metabolome.