Systematic discovery of nonobvious human disease models through orthologous phenotypes

High-throughput tracking enables systematic phenotyping and drug repurposing in C. elegans disease models

There are thousands of Mendelian diseases with more being discovered weekly and the majority have no approved treatments. To address this need, we require scalable approaches that are relatively inexpensive compared to traditional drug development. In the absence of a validated drug target, phenotypic screening in model organisms provides a route for identifying candidate treatments. Success requires a screenable phenotype. However, the right phenotype and assay may not be obvious for pleiotropic neuromuscular disorders. Here, we show that high-throughput imaging and quantitative phenotyping can be conducted systematically on a panel of C. elegans disease model strains. We used CRISPR genome-editing to create 25 worm models of human Mendelian diseases and phenotyped them using a single standardised assay. All but two strains were significantly different from wild-type controls in at least one feature. The observed phenotypes were diverse, but mutations of genes predicted to have related functions led to similar behavioural differences in worms. As a proof-of-concept, we performed a drug repurposing screen of an FDA-approved compound library, and identified two compounds that rescued the behavioural phenotype of a model of UNC80 deficiency. Our results show that a single assay to measure multiple phenotypes can be applied systematically to diverse Mendelian disease models. The relatively short time and low cost associated with creating and phenotyping multiple strains suggest that high-throughput worm tracking could provide a scalable approach to drug repurposing commensurate with the number of Mendelian diseases.

The Functional Comparison of Eukaryotic Proteomes: Implications for Choosing an Appropriate Model Organism to Probe Human Biology

Phenotypic differences between species are, in significant part, determined by their proteomic diversity. The link between proteomic and phenotypic diversity can be best understood in the context of the various pathways and biological processes in which proteins participate. While the conservation pattern for individual proteins across species is expected to follow the phylogenetic relationships among the species, the diversity patterns of individual pathways may not: certain pathways may be much more conserved among distantly related species than two closely related species, owing to the ecological histories of the species. Thus, a pathway-centric analysis of proteome conservation and diversity has important implications for the appropriate choice of a model organism when investigating specific aspects of human biology. Exploiting the complete genome sequences and protein-coding gene annotations, here we perform a comprehensive gene-set-centric analysis of proteomic diversity between humans and 54 eukaryotic organisms, resulting in a catalog of organisms that are most similar to humans in terms of specific pathways, processes, expression patterns, and diseases. We corroborate our findings using species-specific mass spectrometry data.Our analysis provides a general framework to identify conserved and unique pathways in a group of organisms and a resource to prioritize appropriate model systems to study a specific biological system in a reference organism such as humans.

AtDGCR14L contributes to salt-stress tolerance via regulating pre-mRNA splicing in Arabidopsis

In plants, pre-mRNA alternative splicing has been demonstrated to be a crucial tier that regulates gene expression in response to salt stress. However, the underlying mechanisms remain elusive. Here, we studied the roles of DIGEORGE-SYNDROME CRITICAL REGION 14-like (AtDGCR14L) in regulating pre-mRNA splicing and salt stress tolerance. We discovered that Arabidopsis AtDGCR14L is required for maintaining plant salt stress tolerance and the constitutively spliced and active isoforms of important stress- and/or abscisic acid (ABA)-responsive genes. We also identified the interaction between AtDGCR14L and splicing factor U1-70k, which needs a highly conserved three amino acid (TWG) motif in DGCR14. Different from wild-type AtDGCR14L, the overexpression of the TWG-substituted AtDGCR14L mutant did not change salt stress tolerance or pre-mRNA splicing of stress/ABA-responsive genes. Additionally, SWITCH3A (SWI3A) is a core subunit of the SWI/SUCROSE NONFERMENTING (SWI/SNF) chromatin-remodeling complexes. We found that SWI3A, whose splicing depends on AtDGCR14L, actively enhances salt stress tolerance. These results revealed that AtDGCR14L may play an essential role in crosstalk between plant salt-stress response and pre-mRNA splicing mechanisms. We also unveiled the potential role of SWI3A in controlling salt stress tolerance. The TWG motif in the intrinsically disordered region of AtDGCR14L is highly conserved and crucial for DGCR14 functions.

Myeloproliferative Neoplasm-like Mutations of Calreticulin Induce Phenotypes Associated with Calreticulin Dysfunction in C. elegans

In previous research, we created a C. elegans model with homozygous mutations in calreticulin similar to those found in patients with essential thrombocythemia (ET) and primary myelofibrosis (PMF), two myeloproliferative neoplasms (MPNs). This model, lacking JAK orthologs, enabled us to examine the transcriptomic effects caused by mutant calreticulin without the influence of JAK/STAT activation, the primary pathogenic mechanism associated with calreticulin mutations known to date. Most of the gene expression changes observed seemed to be due to a partial loss of protein function, with the alteration of the extracellular matrix being particularly notable. In this study, our aim was to determine whether this model exhibited any phenotype related to these transcriptomic alterations. The results demonstrate that these strains exhibit multiple phenotypes related to the alteration of the extracellular matrix, fat levels, and fertility, which could be a possible consequence of a partial loss of calreticulin function. These phenotypes resemble some of the clinical and molecular characteristics described in patients with MPNs, but they had never before been linked to a loss of protein function in humans. Thus, these results collectively suggest that CALR mutations could have significant effects on MPNs due to loss of protein function. Delving deeper into these effects to develop innovative therapies for these patients offers considerable potential and interest, given that targeted therapies for these patients have not yielded very promising results so far.

The ICL1 and MLS1 Genes, Integral to the Glyoxylate Cycle, are Essential and Specific for Caloric Restriction-Mediated Extension of Lifespan in Budding Yeast

The regulation of complex energy metabolism is intricately linked to cellular energy demands. Caloric restriction (CR) plays a pivotal role in modulating the expression of genes associated with key metabolic pathways, including glycolysis, the tricarboxylic acid (TCA) cycle, and the glyoxylate cycle. In this study, the chronological lifespan (CLS) of 35 viable single-gene deletion mutants under both non-restricted and CR conditions, focusing on genes related to these metabolic pathways is evaluated. CR is found to increase CLS predominantly in mutants associated with the glycolysis and TCA cycle. However, this beneficial effect of CR is not observed in mutants of the glyoxylate cycle, particularly those lacking genes for critical enzymes like isocitrate lyase 1 (icl1Δ) and malate synthase 1 (mls1Δ). This analysis revealed an increase in isocitrate lyase activity, a key enzyme of the glyoxylate cycle, under CR, unlike the activity of isocitrate dehydrogenase, which remains unchanged and is specific to the TCA cycle. Interestingly, rapamycin, a compound known for extending lifespan, does not increase the activity of the glyoxylate cycle enzyme. This suggests that CR affects lifespan through a distinct metabolic mechanism.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

WormBase 2024: status and transitioning to Alliance infrastructure

WormBase has been the major repository and knowledgebase of information about the genome and genetics of Caenorhabditis elegans and other nematodes of experimental interest for over 2 decades. We have 3 goals: to keep current with the fast-paced C. elegans research, to provide better integration with other resources, and to be sustainable. Here, we discuss the current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance). As an Alliance member, WormBase will continue to interact with the C. elegans community, develop new features as needed, and curate key information from the literature and large-scale projects.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Joint representation of molecular networks from multiple species improves gene classification

Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

The role of androgens and estrogens in social interactions and social cognition

Gonadal hormones are becoming increasingly recognized for their effects on cognition. Estrogens, in particular, have received attention for their effects on learning and memory that rely upon the functioning of various brain regions. However, the impacts of androgens on cognition are relatively under investigated. Testosterone, as well as estrogens, have been shown to play a role in the modulation of different aspects of social cognition. This review explores the impact of testosterone and other androgens on various facets of social cognition including social recognition, social learning, social approach/avoidance, and aggression. We highlight the relevance of considering not only the actions of the most commonly studied steroids (i.e., testosterone, 17β-estradiol, and dihydrotestosterone), but also that of their metabolites and precursors, which interact with a plethora of different receptors and signalling molecules, ultimately modulating behaviour. We point out that it is also essential to investigate the effects of androgens, their precursors and metabolites in females, as prior studies have mostly focused on males. Overall, a comprehensive analysis of the impact of steroids such as androgens on behaviour is fundamental for a full understanding of the neural mechanisms underlying social cognition, including that of humans.

Use of Arabidopsis thaliana as a model to understand specific carcinogenic events: Comparison of the molecular machinery associated with cancer-hallmarks in plants and humans

Model organisms are fundamental in cancer research given that they rise the possibility to characterize in a quantitative-objective fashion the organisms as a whole in ways that are infeasible in humans. From this perspective, model organisms with short generation times and established protocols for genetic manipulation allow the understanding of basic biology principles that might guide carcinogenic onset. The cancer-hallmarks (CHs) approach, a modular perspective for cancer understanding, stands that underlying the variability among different cancer types, critical events support the carcinogenic origin and progression. Thus, CHs as interconnected genetic circuitry, have a causal effect over cancer biogenesis and might represent a comparison scaffold among model organisms to identify and characterize evolutionarily conserved modules to understand cancer. Nevertheless, the identification of novel cancer regulators by comparative genomics approaches relies on selecting specific biological processes or related signaling cascades that limit the type of detected regulators, even more, holistic analysis from a systemic perspective is absent. Similarly, although the plant Arabidopsis thaliana has been used as a model organism to dissect specific disease-associated mechanisms, given the evolutionary distance between plants and humans, a general concern about the utility of using A. thaliana as a cancer model persists. In the present research, we take advantage of the CHs paradigm as a framework to establish a functional systemic comparison between plants and humans, that allowed the identification not only of specific novel key genetic regulators, but also, biological processes, metabolic systems, and genetic modules that might contribute to the neoplastic transformation. We propose five cancer-hallmarks that overlapped in conserved mechanisms and processes between Arabidopsis and human and thus, represent mechanisms which study can be prioritized in A. thaliana as an alternative model for cancer research. Additionally, derived from network analyses and machine learning strategies, a new set of potential candidate genes that might contribute to neoplastic transformation is described. These findings postulate A. thaliana as a suitable model to dissect, not all, but specific cancer properties, highlighting the importance of using alternative complementary models to understand carcinogenesis.

In vivo quantitative high-throughput screening for drug discovery and comparative toxicology

Quantitative high-throughput screening (qHTS) pharmacologically evaluates chemical libraries for therapeutic uses, toxicological risk and, increasingly, for academic probe discovery. Phenotypic high-throughput screening assays interrogate molecular pathways, often relying on cell culture systems, historically less focused on multicellular organisms. Caenorhabditis elegans has served as a eukaryotic model organism for human biology by virtue of genetic conservation and experimental tractability. Here, a paradigm enabling C. elegans qHTS using 384-well microtiter plate laser-scanning cytometry is described, in which GFP-expressing organisms revealing phenotype-modifying structure-activity relationships guide subsequent life-stage and proteomic analyses, and Escherichia coli bacterial ghosts, a non-replicating nutrient source, allow compound exposures over two life cycles, mitigating bacterial overgrowth complications. We demonstrate the method with libraries of anti-infective agents, or substances of toxicological concern. Each was tested in seven-point titration to assess the feasibility of nematode-based in vivo qHTS, and examples of follow-up strategies were provided to study organism-based chemotype selectivity and subsequent network perturbations with a physiological impact. We anticipate that this qHTS approach will enable analysis of C. elegans orthologous phenotypes of human pathologies to facilitate drug library profiling for a range of therapeutic indications.

The functional role of Ire1 in regulating autophagy and proteasomal degradation under prolonged proteotoxic stress

Inhibition of endoribonuclease/kinase Ire1 has shown beneficial effects in many proteotoxicity-induced pathology models. The mechanism by which this occurs has not been elucidated completely. Using a proteotoxic yeast model of Huntington's disease, we show that the deletion of Ire1 led to lower protein aggregation at longer time points. The rate of protein degradation was higher in ΔIre1 cells. We monitored the two major protein degradation mechanisms in the cell. The increase in expression of Rpn4, coding for the transcription factor controlling proteasome biogenesis, was higher in ΔIre1 cells. The chymotrypsin-like proteasomal activity was also significantly enhanced in these cells at later time points of aggregation. The gene and protein expression levels of the autophagy gene Atg8 were higher in ΔIre1 than in wild-type cells. Significant increase in autophagy flux was also seen in ΔIre1 cells at later time points of aggregation. The results suggest that the deletion of Ire1 activates UPR-independent arms of the proteostasis network, especially under conditions of aggravated stress. Thus, the inhibition of Ire1 may regulate UPR-independent cellular stress-response pathways under prolonged stress.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

Promoting validation and cross-phylogenetic integration in model organism research

Model organism (MO) research provides a basic understanding of biology and disease due to the evolutionary conservation of the molecular and cellular language of life. MOs have been used to identify and understand the function of orthologous genes, proteins, cells and tissues involved in biological processes, to develop and evaluate techniques and methods, and to perform whole-organism-based chemical screens to test drug efficacy and toxicity. However, a growing richness of datasets and the rising power of computation raise an important question: How do we maximize the value of MOs? In-depth discussions in over 50 virtual presentations organized by the National Institutes of Health across more than 10 weeks yielded important suggestions for improving the rigor, validation, reproducibility and translatability of MO research. The effort clarified challenges and opportunities for developing and integrating tools and resources. Maintenance of critical existing infrastructure and the implementation of suggested improvements will play important roles in maintaining productivity and facilitating the validation of animal models of human biology and disease.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Humanized yeast to model human biology, disease and evolution

For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.

Rational Design and Identification of Harmine-Inspired, N-Heterocyclic DYRK1A Inhibitors Employing a Functional Genomic In Vivo Drosophila Model System

Deregulation of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) plays a significant role in developmental brain defects, early-onset neurodegeneration, neuronal cell loss, dementia, and several types of cancer. Herein, we report the discovery of three new classes of N-heterocyclic DYRK1A inhibitors based on the potent, yet toxic kinase inhibitors, harmine and harmol. An initial in vitro evaluation of the small molecule library assembled revealed that the core heterocyclic motifs benzofuranones, oxindoles, and pyrrolones, showed statistically significant DYRK1A inhibition. Further, the utilization of a low cost, high-throughput functional genomic in vivo model system to identify small molecule inhibitors that normalize DYRK1A overexpression phenotypes is described. This in vivo assay substantiated the in vitro results, and the resulting correspondence validates generated classes as architectural motifs that serve as potential DYRK1A inhibitors. Further expansion and analysis of these core compound structures will allow discovery of safe, more effective chemical inhibitors of DYRK1A to ameliorate phenotypes caused by DYRK1A overexpression.

Using Caenorhabditis elegans as a Model for Mechanistic Insights of Craniofacial Development

Caenorhabditis elegans has served as a powerful model for understanding the molecular and cell biology of clinically important human proteins due to the conservation of genes that are associated with human disorders. It is well established that evolution has conserved critical domains of proteins and their cellular functions even though the phenotypic output for analogous mutations can be distinct among organisms. To that end, the genes that are associated with human craniosynostosis such as TWIST1, TCF12, and FGFR2 have homologs in C. elegans hlh-8, hlh-2, and egl-15, respectively. Whereas mutations in these human genes lead to bone defects in the skull, mutations in the C. elegans genes lead to defects primarily in nonstriated muscles that are responsible for laying eggs and controlling defecation. Even though the phenotypes are distinct in nature, the ability to quantify them in C. elegans can give a sense of the severity to provide a genotype-phenotype correlation. With the advent of CRISPR/Cas-9 genome editing in C. elegans, it is possible to model specific patient mutations that affect conserved amino acids in C. elegans proteins. These mutant strains can then be evaluated for their phenotypes in both homozygous and heterozygous animals. The assays that can be used to measure these phenotypes are described in this chapter.

What is a phenotype? History and new developments of the concept

Even though the word "phenotype", as well as the expression "genotype-phenotype relationship", are a part of the everyday language of biologists, they remain abstract notions that are sometimes misunderstood or misused. In this article, I begin with a review of  the genesis of the concept of phenotype and of the meaning of the genotype-phenotype "relationship" from a historical perspective. I then illustrate how the development of new approaches for exploring the living world has enabled us to phenotype organisms at multiple levels, with traits that can either be measures or parameters of functions, leading to a virtually unlimited amount of phenotypic data. Thus, pleiotropy becomes a central issue in the study of the genotype-phenotype relationship. Finally, I provide a few examples showing that important genetic and evolutionary features clearly differ with the phenotypic level considered. The way genotypic variation propagates across the phenotypic levels to shape fitness variation is an essential research program in biology.

GRASP55 restricts early-stage autophagy and regulates spatial organization of the early secretory network

There is great interest in understanding the cellular mechanisms controlling autophagy, a tightly regulated catabolic and stress-response pathway. Prior work has uncovered links between autophagy and the Golgi reassembly stacking protein of 55 kDa (GRASP55), but their precise interrelationship remains unclear. Intriguingly, both autophagy and GRASP55 have been functionally and spatially linked to the endoplasmic reticulum (ER)­­-Golgi interface, broaching this compartment as a site where GRASP55 and autophagy may intersect. Here, we uncover that loss of GRASP55 enhances LC3 puncta formation, indicating that GRASP55 restricts autophagosome formation. Additionally, using proximity-dependent biotinylation, we identify a GRASP55 proximal interactome highly associated with the ER-Golgi interface. Both nutrient starvation and loss of GRASP55 are associated with coalescence of early secretory pathway markers. In light of these findings, we propose that GRASP55 regulates spatial organization of the ER-Golgi interface, which suppresses early autophagosome formation.

Summary: The Golgi protein GRASP55 restricts early-stage autophagy and regulates spatial organization of the early secretory network. We also identify a GRASP55 proximal interactome enriched at the ER-Golgi interface.

Single-nuclei chromatin profiling of ventral midbrain reveals cell identity transcription factors and cell-type-specific gene regulatory variation

BACKGROUND

Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility, such as Parkinson's disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression in a cell-type-specific manner depending on the chromatin structure and accessibility.

RESULTS

We report 20,658 single-nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell-type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell-type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons.

CONCLUSIONS

Our data set provides an extensive resource to study gene regulation in mesencephalon and provides insights into control of cell identity in the midbrain and identifies cell-type-specific regulatory variation possibly underlying phenotypic and behavioural differences between mouse strains.

Discovery of new vascular disrupting agents based on evolutionarily conserved drug action, pesticide resistance mutations, and humanized yeast

Thiabendazole (TBZ) is an FDA-approved benzimidazole widely used for its antifungal and antihelminthic properties. We showed previously that TBZ is also a potent vascular disrupting agent and inhibits angiogenesis at the tissue level by dissociating vascular endothelial cells in newly formed blood vessels. Here, we uncover TBZ's molecular target and mechanism of action. Using human cell culture, molecular modeling, and humanized yeast, we find that TBZ selectively targets only 1 of 9 human β-tubulin isotypes (TUBB8) to specifically disrupt endothelial cell microtubules. By leveraging epidemiological pesticide resistance data and mining chemical features of commercially used benzimidazoles, we discover that a broader class of benzimidazole compounds, in extensive use for 50 years, also potently disrupt immature blood vessels and inhibit angiogenesis. Thus, besides identifying the molecular mechanism of benzimidazole-mediated vascular disruption, this study presents evidence relevant to the widespread use of these compounds while offering potential new clinical applications.

Caenorhabditis elegans for rare disease modeling and drug discovery: strategies and strengths

Although nearly 10% of Americans suffer from a rare disease, clinical progress in individual rare diseases is severely compromised by lack of attention and research resources compared to common diseases. It is thus imperative to investigate these diseases at their most basic level to build a foundation and provide the opportunity for understanding their mechanisms and phenotypes, as well as potential treatments. One strategy for effectively and efficiently studying rare diseases is using genetically tractable organisms to model the disease and learn about the essential cellular processes affected. Beyond investigating dysfunctional cellular processes, modeling rare diseases in simple organisms presents the opportunity to screen for pharmacological or genetic factors capable of ameliorating disease phenotypes. Among the small model organisms that excel in rare disease modeling is the nematode Caenorhabditis elegans. With a staggering breadth of research tools, C. elegans provides an ideal system in which to study human disease. Molecular and cellular processes can be easily elucidated, assayed and altered in ways that can be directly translated to humans. When paired with other model organisms and collaborative efforts with clinicians, the power of these C. elegans studies cannot be overstated. This Review highlights studies that have used C. elegans in diverse ways to understand rare diseases and aid in the development of treatments. With continuing and advancing technologies, the capabilities of this small round worm will continue to yield meaningful and clinically relevant information for human health.

Comparative Transcriptomics Reveals Distinct Patterns of Gene Expression Conservation through Vertebrate Embryogenesis

Despite life's diversity, studies of variation often remind us of our shared evolutionary past. Abundant genome sequencing and analyses of gene regulatory networks illustrate that genes and entire pathways are conserved, reused, and elaborated in the evolution of diversity. Predating these discoveries, 19th-century embryologists observed that though morphology at birth varies tremendously, certain stages of vertebrate embryogenesis appear remarkably similar across vertebrates. In the mid to late 20th century, anatomical variability of early and late-stage embryos and conservation of mid-stages embryos (the "phylotypic" stage) was named the hourglass model of diversification. This model has found mixed support in recent analyses comparing gene expression across species possibly owing to differences in species, embryonic stages, and gene sets compared. We compare 186 microarray and RNA-seq data sets covering embryogenesis in six vertebrate species. We use an unbiased clustering approach to group stages of embryogenesis by transcriptomic similarity and ask whether gene expression similarity of clustered embryonic stages deviates from a null expectation. We characterize expression conservation patterns of each gene at each evolutionary node after correcting for phylogenetic nonindependence. We find significant enrichment of genes exhibiting early conservation, hourglass, late conservation patterns in both microarray and RNA-seq data sets. Enrichment of genes showing patterned conservation through embryogenesis indicates diversification of embryogenesis may be temporally constrained. However, the circumstances under which each pattern emerges remain unknown and require both broad evolutionary sampling and systematic examination of embryogenesis across species.

Model organisms contribute to diagnosis and discovery in the undiagnosed diseases network: current state and a future vision

Decreased sequencing costs have led to an explosion of genetic and genomic data. These data have revealed thousands of candidate human disease variants. Establishing which variants cause phenotypes and diseases, however, has remained challenging. Significant progress has been made, including advances by the National Institutes of Health (NIH)-funded Undiagnosed Diseases Network (UDN). However, 6000-13,000 additional disease genes remain to be identified. The continued discovery of rare diseases and their genetic underpinnings provides benefits to affected patients, of whom there are more than 400 million worldwide, and also advances understanding the mechanisms of more common diseases. Platforms employing model organisms enable discovery of novel gene-disease relationships, help establish variant pathogenicity, and often lead to the exploration of underlying mechanisms of pathophysiology that suggest new therapies. The Model Organism Screening Center (MOSC) of the UDN is a unique resource dedicated to utilizing informatics and functional studies in model organisms, including worm (Caenorhabditis elegans), fly (Drosophila melanogaster), and zebrafish (Danio rerio), to aid in diagnosis. The MOSC has directly contributed to the diagnosis of challenging cases, including multiple patients with complex, multi-organ phenotypes. In addition, the MOSC provides a framework for how basic scientists and clinicians can collaborate to drive diagnoses. Customized experimental plans take into account patient presentations, specific genes and variant(s), and appropriateness of each model organism for analysis. The MOSC also generates bioinformatic and experimental tools and reagents for the wider scientific community. Two elements of the MOSC that have been instrumental in its success are (1) multidisciplinary teams with expertise in variant bioinformatics and in human and model organism genetics, and (2) mechanisms for ongoing communication with clinical teams. Here we provide a position statement regarding the central role of model organisms for continued discovery of disease genes, and we advocate for the continuation and expansion of MOSC-type research entities as a Model Organisms Network (MON) to be funded through grant applications submitted to the NIH, family groups focused on specific rare diseases, other philanthropic organizations, industry partnerships, and other sources of support.

CircRNA-Mediated Regulation of Angiogenesis: A New Chapter in Cancer Biology

Angiogenesis is necessary for carcinoma progression and is regulated by a variety of pro- and anti-angiogenesis factors. CircRNAs are RNA molecules that do not have a 5'-cap or a 3'-polyA tail and are involved in a variety of biological functions. While circRNA-mediated regulation of tumor angiogenesis has received much attention, the detailed biological regulatory mechanism remains unclear. In this review, we investigated circRNAs in tumor angiogenesis from multiple perspectives, including its upstream and downstream factors. We believe that circRNAs have natural advantages and great potential for the diagnosis and treatment of tumors, which deserves further exploration.

Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease

Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.

Molecular Mechanism of Tumor Cell Immune Escape Mediated by CD24/Siglec-10

Tumor immune escape is an important part of tumorigenesis and development. Tumor cells can develop a variety of immunosuppressive mechanisms to combat tumor immunity. Exploring tumor cells that escape immune surveillance through the molecular mechanism of related immunosuppression in-depth is helpful to develop the treatment strategies of targeted tumor immune escape. The latest studies show that CD24 on the surface of tumor cells interacts with Siglec-10 on the surface of immune cells to promote the immune escape of tumor cells. It is necessary to comment on the molecular mechanism of inhibiting the activation of immune cells through the interaction between CD24 on tumor cells and Siglec-10 on immune cells, and a treatment strategy of tumors through targeting CD24 on the surface of tumor cells or Siglec-10 on immune cells.

A Yeast-Based Model for Hereditary Motor and Sensory Neuropathies: A Simple System for Complex, Heterogeneous Diseases

Charcot–Marie–Tooth (CMT) disease encompasses a group of rare disorders that are characterized by similar clinical manifestations and a high genetic heterogeneity. Such excessive diversity presents many problems. Firstly, it makes a proper genetic diagnosis much more difficult and, even when using the most advanced tools, does not guarantee that the cause of the disease will be revealed. Secondly, the molecular mechanisms underlying the observed symptoms are extremely diverse and are probably different for most of the disease subtypes. Finally, there is no possibility of finding one efficient cure for all, or even the majority of CMT diseases. Every subtype of CMT needs an individual approach backed up by its own research field. Thus, it is little surprise that our knowledge of CMT disease as a whole is selective and therapeutic approaches are limited. There is an urgent need to develop new CMT models to fill the gaps. In this review, we discuss the advantages and disadvantages of yeast as a model system in which to study CMT diseases. We show how this single-cell organism may be used to discriminate between pathogenic variants, to uncover the mechanism of pathogenesis, and to discover new therapies for CMT disease.

Circular RNA and tumor microenvironment

Circular RNAs (circRNAs) are small non-coding RNAs with a unique ring structure and play important roles as gene regulators. Disturbed expressions of circRNAs is closely related to varieties of pathological processes. The roles of circRNAs in cancers have gained increasing concerns. The communications between the cancer cells and tumor microenvironment (TME) play complicated roles to affect the malignant behaviors of cancers, which potentially present new therapeutic targets. Herein, we reviewed the roles of circRNAs in the TME.

Posttranslational Arginylation Enzyme Arginyltransferase1 Shows Genetic Interactions With Specific Cellular Pathways in vivo

Arginyltransferase1 (ATE1) is a conserved enzyme in eukaryotes mediating posttranslational arginylation, the addition of an extra arginine to an existing protein. In mammals, the dysregulations of the ATE1 gene (ate1) is shown to be involved in cardiovascular abnormalities, cancer, and aging-related diseases. Although biochemical evidence suggested that arginylation may be involved in stress response and/or protein degradation, the physiological role of ATE1 in vivo has never been systematically determined. This gap of knowledge leads to difficulties for interpreting the involvements of ATE1 in diseases pathogenesis. Since ate1 is highly conserved between human and the unicellular organism Schizosaccharomyces pombe (S. pombe), we take advantage of the gene-knockout library of S. pombe, to investigate the genetic interactions between ate1 and other genes in a systematic and unbiased manner. By this approach, we found that ate1 has a surprisingly small and focused impact size. Among the 3659 tested genes, which covers nearly 75% of the genome of S. pombe, less than 5% of them displayed significant genetic interactions with ate1. Furthermore, these ate1-interacting partners can be grouped into a few discrete clustered categories based on their functions or their physical interactions. These categories include translation/transcription regulation, biosynthesis/metabolism of biomolecules (including histidine), cell morphology and cellular dynamics, response to oxidative or metabolic stress, ribosomal structure and function, and mitochondrial function. Unexpectedly, inconsistent to popular belief, very few genes in the global ubiquitination or degradation pathways showed interactions with ate1. Our results suggested that ATE1 specifically regulates a handful of cellular processes in vivo, which will provide critical mechanistic leads for studying the involvements of ATE1 in normal physiologies as well as in diseased conditions.

Harmine Acts as an Indirect Inhibitor of Intracellular Protein Aggregation

Protein aggregation and oxidative stress are two pathological hallmarks of a number of protein misfolding diseases, including Huntington's disease (HD). Whether protein aggregation precedes elevation of oxidative stress or follows it remains ambiguous. We have investigated the role of harmine, a beta-carboline alkaloid, in aggregation of a mutant huntingtin fragment (103Q-htt) in a yeast model of HD. We observed that harmine was able to decrease intracellular aggregation of 103Q-htt, and this reduction was higher than that observed with trehalose, a conventional protein stabilizer. The presence of harmine also decreased prion formation. Decreased protein aggregation was accompanied by reduction in oxidative stress. However, harmine had no effect on aggregation of the mutant huntingtin fragment in vitro. Thus, based on experimental data, we conclude that the antioxidant harmine lowers aggregation-induced elevation in oxidative stress, which slows down intracellular protein aggregation.

Genetic interaction screen for severe neurodevelopmental disorders reveals a functional link between Ube3a and Mef2 in Drosophila melanogaster

Neurodevelopmental disorders (NDDs) are clinically and genetically extremely heterogeneous with shared phenotypes often associated with genes from the same networks. Mutations in TCF4, MEF2C, UBE3A, ZEB2 or ATRX cause phenotypically overlapping, syndromic forms of NDDs with severe intellectual disability, epilepsy and microcephaly. To characterize potential functional links between these genes/proteins, we screened for genetic interactions in Drosophila melanogaster. We induced ubiquitous or tissue specific knockdown or overexpression of each single orthologous gene (Da, Mef2, Ube3a, Zfh1, XNP) and in pairwise combinations. Subsequently, we assessed parameters such as lethality, wing and eye morphology, neuromuscular junction morphology, bang sensitivity and climbing behaviour in comparison between single and pairwise dosage manipulations. We found most stringent evidence for genetic interaction between Ube3a and Mef2 as simultaneous dosage manipulation in different tissues including glia, wing and eye resulted in multiple phenotype modifications. We subsequently found evidence for physical interaction between UBE3A and MEF2C also in human cells. Systematic pairwise assessment of the Drosophila orthologues of five genes implicated in clinically overlapping, severe NDDs and subsequent confirmation in a human cell line revealed interactions between UBE3A/Ube3a and MEF2C/Mef2, thus contributing to the characterization of the underlying molecular commonalities.

Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction

Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the entity-quality (EQ) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically rich format, as well as numerical vector representations generated using natural language processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.

Finding relationships among biological entities

Confusion over the concepts of “relationships” and “similarities” lies at the heart of many battles over the direction and intent of research projects. Here is a short story that demonstrates the difference between the two concepts: You look up at the clouds, and you begin to see the shape of a lion. The cloud has a tail, like a lion’s tale, and a fluffy head, like a lion’s mane. With a little imagination the mouth of the lion seems to roar down from the sky. You have succeeded in finding similarities between the cloud and a lion. If you look at a cloud and you imagine a tea kettle producing a head of steam and you recognize that the physical forces that create a cloud and the physical forces that produced steam from a heated kettle are the same, then you have found a relationship. Most popular classification algorithms operate by grouping together data objects that have similar properties or values. In so doing, they may miss finding the true relationships among objects. Traditionally, relationships among data objects are discovered by an intellectual process. In this chapter, we will discuss the scientific gains that come when we classify biological entities by relationships, not by their similarities.

Interrogating the ccm-3 Gene Network

Cerebral cavernous malformations (CCMs) are neurovascular lesions caused by mutations in one of three genes (CCM1-3). Loss of CCM3 causes the poorest prognosis, and little is known about how it regulates vascular integrity. The C. elegans ccm-3 gene regulates the development of biological tubes that resemble mammalian vasculature, and in a genome-wide reverse genetic screen, we identified more than 500 possible CCM-3 pathway genes. With a phenolog-like approach, we generated a human CCM signaling network and identified 29 genes in common, of which 14 are required for excretory canal extension and membrane integrity, similar to ccm-3. Notably, depletion of the MO25 ortholog mop-25.2 causes severe defects in tube integrity by preventing CCM-3 localization to apical membranes. Furthermore, loss of MO25 phenocopies CCM3 ablation by causing stress fiber formation in endothelial cells. This work deepens our understanding of how CCM3 regulates vascular integrity and may help identify therapeutic targets for treating CCM3 patients.

p38-regulated FOXC1 stability is required for colorectal cancer metastasis

Aberrant expression of forkhead box C1 (FOXC1) promotes tumor metastasis in multiple human malignant tumors. However, the upstream modulating mode and downstream molecular mechanism of FOXC1 in metastasis of colorectal cancer (CRC) remain unclear. Herein we describe a systematic analysis of FOXC1 expression and prognosis in CRC performed on our clinical data and public databases, which indicated that FOXC1 upregulation in CRC samples was significantly associated with poor prognosis. FOXC1 knockdown inhibited migration and invasion, whereas FOXC1 overexpression caused the opposite phenotype in vitro and in vivo. Furthermore, MMP10, SOX4 and SOX13 were verified as the target genes of FOXC1 for promoting CRC metastasis. MMP10 was demonstrated as the direct target and mediator of FOXC1. Interestingly, Ser241 and Ser272 of FOXC1 were identified as the key sites to interact with p38 and phosphorylation, which were critically required for maintaining the stability of FOXC1 protein. Moreover, FOXC1 was dephosphorylated by protein phosphatase 2A and phosphorylated by p38, which maintained FOXC1 protein stability through inhibiting ubiquitination. Expression of p38 was correlated with FOXC1 and MMP10 expression, indirectly indicating that FOXC1 was regulated by p38 MAPK. Therefore, FOXC1 is strongly suggested as a pro-metastatic gene in CRC by transcriptionally activating MMP10, SOX4 and SOX13; p38 interacts with and phosphorylates the Ser241 and ser272 sites of FOXC1 to maintain its stability by inhibiting ubiquitination and degradation. In conclusion, the protein stability of FOXC1 mediated by p38 contributes to the metastatic effect in CRC. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

BACKGROUND

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.

METHODS

Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.

RESULTS

Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.

CONCLUSIONS

Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Using zebrafish to study skeletal genomics

While genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of skeletal diseases, animal models are required to identify causal mechanisms and to translate underlying biology into new therapies. Despite large-scale knockout mouse phenotyping efforts, the skeletal functions of most genes residing at GWAS-identified loci remain unknown, highlighting a need for complementary model systems to accelerate gene discovery. Over the past several decades, zebrafish (Danio rerio) has emerged as a powerful system for modeling the genetics of human diseases. In this review, our goal is to outline evidence supporting the utility of zebrafish for accelerating our understanding of human skeletal genomics, as well as gaps in knowledge that need to be filled for this purpose. We do this by providing a basic foundation of the zebrafish skeletal morphophysiology and phenotypes, and surveying evidence of skeletal gene homology and the use of zebrafish for post-GWAS analysis in other tissues and organs. We also outline challenges in translating zebrafish mutant phenotypes. Finally, we conclude with recommendations of future directions and how to leverage the large body of tools and knowledge of skeletal genetics in zebrafish for the needs of human skeletal genomic exploration. Due to their amenability to rapid genetic approaches, as well as the large number of conserved genetic and phenotypic features, there is a strong rationale supporting the use of zebrafish for human skeletal genomic studies.

Integrating yeast chemical genomics and mammalian cell pathway analysis

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human genetics research. Although a number of in silico algorithms have been developed to predict the pathogenicity of variants identified in these datasets, functional studies are critical to determining how specific genomic variants affect protein function, especially for missense variants. In the Undiagnosed Diseases Network (UDN) and other rare disease research consortia, model organisms (MO) including Drosophila, C. elegans, zebrafish, and mice are actively used to assess the function of putative human disease-causing variants. This protocol describes a method for the functional assessment of rare human variants used in the Model Organisms Screening Center Drosophila Core of the UDN. The workflow begins with gathering human and MO information from multiple public databases, using the MARRVEL web resource to assess whether the variant is likely to contribute to a patient's condition as well as design effective experiments based on available knowledge and resources. Next, genetic tools (e.g., T2A-GAL4 and UAS-human cDNA lines) are generated to assess the functions of variants of interest in Drosophila. Upon development of these reagents, two-pronged functional assays based on rescue and overexpression experiments can be performed to assess variant function. In the rescue branch, the endogenous fly genes are "humanized" by replacing the orthologous Drosophila gene with reference or variant human transgenes. In the overexpression branch, the reference and variant human proteins are exogenously driven in a variety of tissues. In both cases, any scorable phenotype (e.g., lethality, eye morphology, electrophysiology) can be used as a read-out, irrespective of the disease of interest. Differences observed between reference and variant alleles suggest a variant-specific effect, and thus likely pathogenicity. This protocol allows rapid, in vivo assessments of putative human disease-causing variants of genes with known and unknown functions.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.

The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics

The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http://browser.planteome.org/amigo) and our data repository.