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Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maizenitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map. [ABSTRACT FROM AUTHOR]

Differences in codon frequency between genomes, genes, or positions along a gene, modulate transcription and translation efficiency, leading to phenotypic and functional differences. Here, we present a multiscale analysis of the effects of synonymous codon recoding during heterologous gene expression in human cells, quantifying the phenotypic consequences of codon usage bias at different molecular and cellular levels, with an emphasis on translation elongation. Six synonymous versions of an antibiotic resistance gene were generated, fused to a fluorescent reporter, and independently expressed in HEK293 cells. Multiscale phenotype was analyzed by means of quantitative transcriptome and proteome assessment, as proxies for gene expression; cellular fluorescence, as a proxy for single‐cell level expression; and real‐time cell proliferation in absence or presence of antibiotic, as a proxy for the cell fitness. We show that differences in codon usage bias strongly impact the molecular and cellular phenotype: (i) they result in large differences in mRNA levels and protein levels, leading to differences of over 15 times in translation efficiency; (ii) they introduce unpredicted splicing events; (iii) they lead to reproducible phenotypic heterogeneity; and (iv) they lead to a trade‐off between the benefit of antibiotic resistance and the burden of heterologous expression. In human cells in culture, codon usage bias modulates gene expression by modifying mRNA availability and suitability for translation, leading to differences in protein levels and eventually eliciting functional phenotypic changes. [ABSTRACT FROM AUTHOR]

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Abstract Background Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into ‘resistant’ and ‘susceptible’, highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR.

Abstract Background The phenotype of an individual can be affected not only by the individual’s own genotypes, known as direct genetic effects (DGE), but also by genotypes of interacting partners, indirect genetic effects (IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genome-wide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from “peers” and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation. Results We leverage a dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated mice housed in the same cage. We develop and apply methods for igeGWAS in this context and identify 24 significant IGE loci for 17 phenotypes (FDR

Comparative ecophysiologists strive to understand physiological problems in non-model organisms, but molecular tools such as RNA interference (RNAi) are under-used in our field. Here, we provide a framework for invertebrate ecophysiologists to use RNAi to answer questions focused on physiological processes, rather than as a tool to investigate gene function. We specifically focus on non-model invertebrates, in which the use of other genetic tools (e.g. , genetic knockout lines) is less likely. We argue that because RNAi elicits a temporary manipulation of gene expression, and resources to carry out RNAi are technically and financially accessible, it is an effective tool for invertebrate ecophysiologists. We cover the terminology and basic mechanisms of RNA interference as an accessible introduction for "non-molecular" physiologists, include a suggested workflow for identifying RNAi gene targets and validating biologically relevant gene knockdowns, and present a hypothesis-testing framework for using RNAi to answer common questions in the realm of invertebrate ecophysiology. This review encourages invertebrate ecophysiologists to use these tools and workflows to explore physiological processes and bridge genotypes to phenotypes in their animal(s) of interest. [Display omitted] • RNAi is an accessible genetic tool that is underused in invertebrate physiology. • The RNAi target should be chosen to disrupt a physiological process of interest. • To appropriately interpret RNAi experiments, validation of the RNAi is required. • Careful timing of RNAi can provide strong insight into physiological processes. [ABSTRACT FROM AUTHOR]

Antibiotic resistance poses a major threat to public health. More effective ways of the antibiotic prescription are needed to delay the spread of antibiotic resistance. Employment of sequencing technologies coupled with the use of trained neural network algorithms for genotype-to-phenotype prediction will reduce the time needed for antibiotic susceptibility profile identification from days to hours.In this work, we have sequenced and phenotypically characterized 171 clinical isolates of Escherichia coli and Klebsiella pneumoniae from Norway and India. Based on the data, we have created neural networks to predict susceptibility for ampicillin, 3rd generation cephalosporins and carbapenems. All networks were trained on unassembled data, enabling prediction within minutes after the sequencing information becomes available. Moreover, they can be used both on Illumina and MinION generated data and do not require high genome coverage for phenotype prediction. We cross-checked our networks with previously published algorithms for genotype-to-phenotype prediction and their corresponding datasets. Besides, we also created an ensemble of networks trained on different datasets, which improved the cross-dataset prediction compared to a single network.Additionally, we have used data from direct sequencing of spiked blood cultures and found that AMR-Diag networks, coupled with MinION sequencing, can predict bacterial species, resistome, and phenotype as fast as 1–8 h from the sequencing start. To our knowledge, this is the first study for genotype-to-phenotype prediction: (1) employing a neural network method; (2) using data from more than one sequencing platform; and (3) utilizing sequence data from spiked blood cultures.

Functional–structural plant models (FSPMs) have been evolving for over 2 decades and their future development, to some extent, depends on the value of potential applications in crop science. To date, stabilizing crop production by identifying valuable traits for novel cultivars adapted to adverse environments is topical in crop science. Thus, this study will examine how FSPMs are able to address new challenges in crop science for sustainable crop production. FSPMs developed to simulate organogenesis, morphogenesis, and physiological activities under various environments and are amenable to downscale to the tissue, cellular, and molecular level or upscale to the whole plant and ecological level. In a modeling framework with independent and interactive modules, advanced algorithms provide morphophysiological details at various scales. FSPMs are shown to be able to: (i) provide crop ideotypes efficiently for optimizing the resource distribution and use for greater productivity and less disease risk, (ii) guide molecular design breeding via linking molecular basis to plant phenotypes as well as enrich crop models with an additional architectural dimension to assist breeding, and (iii) interact with plant phenotyping for molecular breeding in embracing three-dimensional (3D) architectural traits. This study illustrates that FSPMs have great prospects in speeding up precision breeding for specific environments due to the capacity for guiding and integrating ideotypes, phenotyping, molecular design, and linking molecular basis to target phenotypes. Consequently, the promising great applications of FSPMs in crop science will, in turn, accelerate their evolution and vice versa.

Functional–structural plant models (FSPMs) have been evolving for over 2 decades and their future development, to some extent, depends on the value of potential applications in crop science. To date, stabilizing crop production by identifying valuable traits for novel cultivars adapted to adverse environments is topical in crop science. Thus, this study will examine how FSPMs are able to address new challenges in crop science for sustainable crop production. FSPMs developed to simulate organogenesis, morphogenesis, and physiological activities under various environments and are amenable to downscale to the tissue, cellular, and molecular level or upscale to the whole plant and ecological level. In a modeling framework with independent and interactive modules, advanced algorithms provide morphophysiological details at various scales. FSPMs are shown to be able to: (i) provide crop ideotypes efficiently for optimizing the resource distribution and use for greater productivity and less disease risk, (ii) guide molecular design breeding via linking molecular basis to plant phenotypes as well as enrich crop models with an additional architectural dimension to assist breeding, and (iii) interact with plant phenotyping for molecular breeding in embracing three-dimensional (3D) architectural traits. This study illustrates that FSPMs have great prospects in speeding up precision breeding for specific environments due to the capacity for guiding and integrating ideotypes, phenotyping, molecular design, and linking molecular basis to target phenotypes. Consequently, the promising great applications of FSPMs in crop science will, in turn, accelerate their evolution and vice versa. [ABSTRACT FROM AUTHOR]

Background: Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results: We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into 'resistant' and 'susceptible', highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions: We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR. [ABSTRACT FROM AUTHOR]

The colorful phenotypes of birds have long provided rich source material for evolutionary biologists. Avian plumage, beaks, skin, and eggs—which exhibit a stunning range of cryptic and conspicuous forms—inspired early work on adaptive coloration. More recently, avian color has fueled discoveries on the physiological, developmental, and—increasingly—genetic mechanisms responsible for phenotypic variation. The relative ease with which avian color traits can be quantified has made birds an attractive system for uncovering links between phenotype and genotype. Accordingly, the field of avian coloration genetics is burgeoning. In this review, we highlight recent advances and emerging questions associated with the genetic underpinnings of bird color. We start by describing breakthroughs related to 2 pigment classes: carotenoids that produce red, yellow, and orange in most birds and psittacofulvins that produce similar colors in parrots. We then discuss structural colors, which are produced by the interaction of light with nanoscale materials and greatly extend the plumage palette. Structural color genetics remain understudied—but this paradigm is changing. We next explore how colors that arise from interactions among pigmentary and structural mechanisms may be controlled by genes that are co-expressed or co-regulated. We also identify opportunities to investigate genes mediating within-feather micropatterning and the coloration of bare parts and eggs. We conclude by spotlighting 2 research areas—mechanistic links between color vision and color production, and speciation—that have been invigorated by genetic insights, a trend likely to continue as new genomic approaches are applied to non-model species. [ABSTRACT FROM AUTHOR]

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81., (Copyright © 2024. Published by Elsevier Inc.)

Mice are the most widely used animal model to study genotype to phenotype relationships. Inbred mice are genetically identical, which eliminates genetic heterogeneity and makes them particularly useful for genetic studies. Many different strains have been bred over decades and a vast amount of phenotypic data has been generated. In addition, recently whole genome sequencing-based genome-wide genotype data for many widely used inbred strains has been released. Here, we present an approach for in silico fine-mapping that uses genotypic data of 37 inbred mouse strains together with phenotypic data provided by the user to propose candidate variants and genes for the phenotype under study. Public genome-wide genotype data covering more than 74 million variant sites is queried efficiently in real-time to provide those variants that are compatible with the observed phenotype differences between strains. Variants can be filtered by molecular consequences and by corresponding molecular impact. Candidate gene lists can be generated from variant lists on the fly. Fine-mapping together with annotation or filtering of results is provided in a Bioconductor package called MouseFM. In order to characterize candidate variant lists under various settings, MouseFM was applied to two expression data sets across 20 inbred mouse strains, one from neutrophils and one from CD4+ T cells. Fine-mapping was assessed for about 10,000 genes, respectively, and identified candidate variants and haplotypes for many expression quantitative trait loci (eQTLs) reported previously based on these data. For albinism, MouseFM reports only one variant allele of moderate or high molecular impact that only albino mice share: a missense variant in the Tyr gene, reported previously to be causal for this phenotype. Performing in silico fine-mapping for interfrontal bone formation in mice using four strains with and five strains without interfrontal bone results in 12 genes. Of these, three are related to skull shaping abnormality. Finally performing fine-mapping for dystrophic cardiac calcification by comparing 9 strains showing the phenotype with eight strains lacking it, we identify only one moderate impact variant in the known causal gene Abcc6. In summary, this illustrates the benefit of using MouseFM for candidate variant and gene identification.

Mice are the most widely used animal model to study genotype to phenotype relationships. Inbred mice are genetically identical, which eliminates genetic heterogeneity and makes them particularly useful for genetic studies. Many different strains have been bred over decades and a vast amount of phenotypic data has been generated. In addition, recently whole genome sequencing-based genome-wide genotype data for many widely used inbred strains has been released. Here, we present an approach for in silico finemapping that uses genotypic data of 37 inbred mouse strains together with phenotypic data provided by the user to propose candidate variants and genes for the phenotype under study. Public genome-wide genotype data covering more than 74 million variant sites is queried efficiently in real-time to provide those variants that are compatible with the observed phenotype differences between strains. Variants can be filtered by molecular consequences and by corresponding molecular impact. Candidate gene lists can be generated from variant lists on the fly. Fine-mapping together with annotation or filtering of results is provided in a Bioconductor package called MouseFM. In order to characterize candidate variant lists under various settings, MouseFM was applied to two expression data sets across 20 inbred mouse strains, one from neutrophils and one from CD4+ T cells. Fine-mapping was assessed for about 10,000 genes, respectively, and identified candidate variants and haplotypes for many expression quantitative trait loci (eQTLs) reported previously based on these data. For albinism, MouseFM reports only one variant allele of moderate or high molecular impact that only albino mice share: a missense variant in the Tyr gene, reported previously to be causal for this phenotype. Performing in silico fine-mapping for interfrontal bone formation in mice using four strains with and five strains without interfrontal bone results in 12 genes. Of these, three are related to skull shaping abnormality. Finally performing fine-mapping for dystrophic cardiac calcification by comparing 9 strains showing the phenotype with eight strains lacking it, we identify only one moderate impact variant in the known causal gene Abcc6. In summary, this illustrates the benefit of using MouseFM for candidate variant and gene identification. [ABSTRACT FROM AUTHOR]

The discovery in the early 1990s of the expansion of unstable simple sequence repeats as the causative mutation for a number of inherited human disorders, including Huntington's disease (HD), opened up a new era of human genetics and provided explanations for some old problems. In particular, an inverse association between the number of repeats inherited and age at onset, and unprecedented levels of germline instability, biased toward further expansion, provided an explanation for the wide symptomatic variability and anticipation observed in HD and many of these disorders. The repeats were also revealed to be somatically unstable in a process that is expansion-biased, age-dependent and tissue-specific, features that are now increasingly recognised as contributory to the age-dependence, progressive nature and tissue specificity of the symptoms of HD, and at least some related disorders. With much of the data deriving from affected individuals, and model systems, somatic expansions have been revealed to arise in a cell division-independent manner in critical target tissues via a mechanism involving key components of the DNA mismatch repair pathway. These insights have opened new approaches to thinking about how the disease could be treated by suppressing somatic expansion and revealed novel protein targets for intervention. Exciting times lie ahead in turning these insights into novel therapies for HD and related disorders. [ABSTRACT FROM AUTHOR]

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

The animal mitochondrial genome, although small, can have a big impact on health and disease. Non-pathogenic sequence variation among mitochondrial DNA (mtDNA) haplotypes influences traits including fertility, healthspan and lifespan, whereas pathogenic mutations are linked to incurable mitochondrial diseases and other complex conditions like ageing, diabetes, cancer and neurodegeneration. However, we know very little about how mtDNA genetic variation contributes to phenotypic differences. Infrequent recombination, the multicopy nature and nucleic acid-impenetrable membranes present significant challenges that hamper our ability to precisely map mtDNA variants responsible for traits, and to genetically modify mtDNA so that we can isolate specific mutants and characterize their biochemical and physiological consequences. Here, we summarize the past struggles and efforts in developing systems to map and edit mtDNA. We also assess the future of performing forward and reverse genetic studies on animal mitochondrial genomes. This article is part of the theme issue 'Linking the mitochondrial genotype to phenotype: a complex endeavour'. [ABSTRACT FROM AUTHOR]

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits. [ABSTRACT FROM AUTHOR]

Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits. [ABSTRACT FROM AUTHOR]

Predicting phenotypes based on genotypes and understanding the effects of complex multilocus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density. [ABSTRACT FROM AUTHOR]

Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.

Background: Accurate models are crucial to estimate the phenotypes from high throughput genomic data. While the genetic and phenotypic data are sensitive, secure models are essential to protect the private information. Therefore, construct an accurate and secure model is significant in secure inference of phenotypes., Methods: We propose a secure inference protocol on homomorphically encrypted genotype data with encrypted linear models. Firstly, scale the genotype data by feature importance with Xgboost or Adaboost then train linear models to predict the phenotypes in plaintext. Secondly, encrypt the model parameters and test data with CKKS scheme for secure inference. Thirdly, predict the phenotypes under CKKS homomorphically encryption computation. Finally, decrypt the encrypted predictions by client to compute the 1-NRMSE/AUC for model evaluation., Results: 5 phenotypes of 3000 samples with 20390 variants are used to validate the performance of the secure inference protocol. The protocol achieves 0.9548, 0.9639, 0.9673 (1-NRMSE) for 3 continuous phenotypes and 0.9943, 0.99290 (AUC) for 2 category phenotypes in test data. Moreover, the protocol shows robust in 100 times of random sampling. Furthermore, the protocol achieves 0.9725 (the average accuracy) in an encrypted test set with 198 samples, and it only takes 4.32s for the overall inference. These help the protocol rank top one in the iDASH-2022 track2 challenge., Conclusion: We propose an accurate and secure protocol to predict the phenotype from genotype and it takes seconds to obtain hundreds of predictions for all phenotypes., Competing Interests: Competing interests The authors declare that there is no conflict of interest.

Plant glandular secreting trichomes are epidermal protuberances that produce structurally diverse specialized metabolites, including medically important compounds. Trichomes of many plants in the nightshade family (Solanaceae) produce O-acylsugars, and in cultivated and wild tomatoes these are mixtures of aliphatic esters of sucrose and glucose of varying structures and quantities documented to contribute to insect defense. We characterized the first two enzymes of acylsucrose biosynthesis in the cultivated tomato Solanum lycopersicum. These are type I/IV trichome-expressed BAHD acyltransferases encoded by Solyc12g006330-or S. lycopersicum acylsucrose acyltransferase 1 (Sl-ASAT1)-and Solyc04g012020 (Sl-ASAT2). These enzymes were used--in concert with two previously identified BAHD acyltransferases--to reconstruct the entire cultivated tomato acylsucrose biosynthetic pathway in vitro using sucrose and acyl-CoA substrates. Comparative genomics and biochemical analysis of ASAT enzymes were combined with in vitro mutagenesis to identify amino acids that influence CoA ester substrate specificity and contribute to differences in types of acylsucroses that accumulate in cultivated andwild tomato species. This work demonstrates the feasibility of the metabolic engineering of these insecticidal metabolites in plants and microbes. [ABSTRACT FROM AUTHOR]

Differences in codon frequency between genomes, genes, or positions along a gene, modulate transcription and translation efficiency, leading to phenotypic and functional differences. Here, we present a multiscale analysis of the effects of synonymous codon recoding during heterologous gene expression in human cells, quantifying the phenotypic consequences of codon usage bias at different molecular and cellular levels, with an emphasis on translation elongation. Six synonymous versions of an antibiotic resistance gene were generated, fused to a fluorescent reporter, and independently expressed in HEK293 cells. Multiscale phenotype was analyzed by means of quantitative transcriptome and proteome assessment, as proxies for gene expression; cellular fluorescence, as a proxy for single-cell level expression; and real-time cell proliferation in absence or presence of antibiotic, as a proxy for the cell fitness. We show that differences in codon usage bias strongly impact the molecular and cellular phenotype: (i) they result in large differences in mRNA levels and protein levels, leading to differences of over 15 times in translation efficiency; (ii) they introduce unpredicted splicing events; (iii) they lead to reproducible phenotypic heterogeneity; and (iv) they lead to a trade-off between the benefit of antibiotic resistance and the burden of heterologous expression. In human cells in culture, codon usage bias modulates gene expression by modifying mRNA availability and suitability for translation, leading to differences in protein levels and eventually eliciting functional phenotypic changes., (© 2023 The Protein Society.)