Prediction of gene-phenotype associations in humans, mice, and plants using phenologs

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.

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.

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.

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.

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.

Plant Biosystems Design Research Roadmap 1.0

Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Background

Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions.

Results

According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse.

Conclusion

Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

Transcriptomic investigation reveals toxic damage due to tilmicosin and potential resistance against tilmicosin in primary chicken myocardial cells

Tilmicosin is widely used to treat respiratory infections in animals and has been reported to induce cardiac damage and even sudden death. However, its exact mechanisms, especially in chickens, remain unclear. This study confirmed the dose-dependent damaging effect of tilmicosin on primary chicken myocardial cells. Primary chicken myocardial cells treated with tilmicosin (0.5 μg/mL) for 0 h, 12 h, and 48 h were subjected to RNA sequencing and bioinformatics analysis. Transcriptomic analysis revealed that cytokine-cytokine receptor interactions, calcium signaling pathway, peroxisomes, phagosomes, mitogen-activated protein kinase (MAPK) signaling pathway, and oxidative phosphorylation were significantly and differentially affected after 12 h or 48 h of tilmicosin treatment. Further evidence demonstrated consistently increased proinflammatory factors, peroxidation, and ferroptosis, and intracellular ion imbalance was caused by tilmicosin for 12 h, but this imbalance had recovered at 48 h. Meanwhile, intracellular resistance to tilmicosin-induced toxicity involved the active regulation of cyclooxygenase-1 and ATPase H⁺/K⁺-transporting beta subunit at 48 h, sustained activation of MAPK12, and downregulation of dual specificity phosphatase 10 at 12 h. In summary, this study suggests that tilmicosin exerts its cardiotoxicity in primary chicken myocardial cells through multiple mechanisms and finds several intracellular molecular targets to resist the toxicity.

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.

Querying of Disparate Association and Interaction Data in Biomedical Applications

In biomedical applications, network models are commonly used to represent interactions and higher-level associations among biological entities. Integrated analyses of these interaction and association data has proven useful in extracting knowledge, and generating novel hypotheses for biomedical research. However, since most datasets provide their own schema and query interface, opportunities for exploratory and integrative querying of disparate data are currently limited. In this study, we utilize RDF-based representations of biomedical interaction and association data to develop a querying framework that enables flexible specification and efficient processing of graph template matching queries. The proposed framework enables integrative querying of biomedical databases to discover complex patterns of associations among a diverse range of biological entities, including biomolecules, biological processes, organisms, and phenotypes. Our experimental results on the UniProt dataset show that the proposed framework can be used to efficiently process complex queries, and identify biologically relevant patterns of associations that cannot be readily obtained by querying each dataset independently.

From phenologs to silent suppressors: Identifying potential therapeutic targets for human disease

Orthologous phenotypes, or phenologs, are seemingly unrelated phenotypes generated by mutations in a conserved set of genes. Phenologs have been widely observed and accepted by those who study model organisms, and allow one to study a set of genes in a model organism to learn more about the function of those genes in other organisms, including humans. At the cellular and molecular level, these conserved genes likely function in a very similar mode, but are doing so in different tissues or cell types and can result in different phenotypic effects. For example, the RAS-RAF-MEK-MAPK pathway in animals is a highly conserved signaling pathway that animals adopted for numerous biological processes, such as vulval induction in Caenorhabditis elegans and cell proliferation in mammalian cells; but this same gene set has been co-opted to function in a variety of cellular contexts. In this review, I give a few examples of how suppressor screens in model organisms (with a emphasis on C. elegans) can identify new genes that function in a conserved pathway in many other organisms. I also demonstrate how the identification of such genes can lead to important insights into mammalian biology. From such screens, an occasional silent suppressor that does not cause a phenotype on its own is found; such suppressors thus make for good candidates as therapeutic targets.

A specific deficit of auditory processing in children with Rolandic Epilepsy and their relatives

Previous research shows that children with Rolandic Epilepsy have deficits of auditory processing. We wanted to confirm the nature of this deficit and whether it aggregates in families. We compared 40 children with Rolandic Epilepsy and 32 unaffected siblings with 99 typically developing children and 71 parents of RE children with 31 healthy adults on a battery of auditory processing tests. We also examined ear advantage in children with RE, their siblings and parents using population norms and measured non-word reading performance. We found a specific deficit for competing words in patients, their siblings and their parents, suggesting that this particular impairment of auditory processing present in children with RE, is heritable and likely to be persistent. Importantly, scores on this subtest in patients and siblings were significantly correlated with non-word reading performance. We saw increased rates of atypical left ear advantage in patients and siblings but no evidence of this in parents. We present these findings as evidence of familial incidence of dichotic listening and ear advantage abnormalities in relatives of children with Rolandic Epilepsy.

Localized TWIST1 and TWIST2 basic domain substitutions cause four distinct human diseases that can be modeled in Caenorhabditis elegans

Twist transcription factors, members of the basic helix-loop-helix family, play crucial roles in mesoderm development in all animals. Humans have two paralogous genes, TWIST1 and TWIST2, and mutations in each gene have been identified in specific craniofacial disorders. Here, we describe a new clinical entity, Sweeney-Cox syndrome, associated with distinct de novo amino acid substitutions (p.Glu117Val and p.Glu117Gly) at a highly conserved glutamic acid residue located in the basic DNA binding domain of TWIST1, in two subjects with frontonasal dysplasia and additional malformations. Although about one hundred different TWIST1 mutations have been reported in patients with the dominant haploinsufficiency Saethre-Chotzen syndrome (typically associated with craniosynostosis), substitutions uniquely affecting the Glu117 codon were not observed previously. Recently, subjects with Barber-Say and Ablepharon-Macrostomia syndromes were found to harbor heterozygous missense substitutions in the paralogous glutamic acid residue in TWIST2 (p.Glu75Ala, p.Glu75Gln and p.Glu75Lys). To study systematically the effects of these substitutions in individual cells of the developing mesoderm, we engineered all five disease-associated alleles into the equivalent Glu29 residue encoded by hlh-8, the single Twist homolog present in Caenorhabditis elegans. This allelic series revealed that different substitutions exhibit graded severity, in terms of both gene expression and cellular phenotype, which we incorporate into a model explaining the various human disease phenotypes. The genetic analysis favors a predominantly dominant-negative mechanism for the action of amino acid substitutions at this highly conserved glutamic acid residue and illustrates the value of systematic mutagenesis of C. elegans for focused investigation of human disease processes.

Epistasis in Neuropsychiatric Disorders

The contribution of epistasis to human disease remains unclear. However, several studies have now identified epistatic interactions between common variants that increase the risk of a neuropsychiatric disorder, while there is growing evidence that genetic interactions contribute to the pathogenicity of rare, multigenic copy-number variants (CNVs) that have been observed in patients. This review discusses the current evidence for epistatic events and genetic interactions in neuropsychiatric disorders, how paradigm shifts in the phenotypic classification of patients would empower the search for epistatic effects, and how network and cellular models might be employed to further elucidate relevant epistatic interactions.

Drosophila and genome-wide association studies: a review and resource for the functional dissection of human complex traits

Human genome-wide association studies (GWAS) have successfully identified thousands of susceptibility loci for common diseases with complex genetic etiologies. Although the susceptibility variants identified by GWAS usually have only modest effects on individual disease risk, they contribute to a substantial burden of trait variation in the overall population. GWAS also offer valuable clues to disease mechanisms that have long proven to be elusive. These insights could lead the way to breakthrough treatments; however, several challenges hinder progress, making innovative approaches to accelerate the follow-up of results from GWAS an urgent priority. Here, we discuss the largely untapped potential of the fruit fly, Drosophila melanogaster, for functional investigation of findings from human GWAS. We highlight selected examples where strong genomic conservation with humans along with the rapid and powerful genetic tools available for flies have already facilitated fine mapping of association signals, elucidated gene mechanisms, and revealed novel disease-relevant biology. We emphasize current research opportunities in this rapidly advancing field, and present bioinformatic analyses that systematically explore the applicability of Drosophila for interrogation of susceptibility signals implicated in more than 1000 human traits, based on all GWAS completed to date. Thus, our discussion is targeted at both human geneticists seeking innovative strategies for experimental validation of findings from GWAS, as well as the Drosophila research community, by whom ongoing investigations of the implicated genes will powerfully inform our understanding of human disease.

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

A microRNA-328 binding site in PAX6 is associated with centrotemporal spikes of rolandic epilepsy

Objective

Rolandic epilepsy is a common genetic focal epilepsy of childhood characterized by centrotemporal sharp waves on electroencephalogram. In previous genome‐wide analysis, we had reported linkage of centrotemporal sharp waves to chromosome 11p13, and fine mapping with 44 SNPs identified the ELP4‐PAX6 locus in two independent US and Canadian case–control samples. Here, we aimed to find a causative variant for centrotemporal sharp waves using a larger sample and higher resolution genotyping array.

Methods

We fine‐mapped the ELP4‐PAX6 locus in 186 individuals from rolandic epilepsy families and 1000 population controls of European origin using the Illumina HumanCoreExome‐12 v1.0 BeadChip. Controls were matched to cases on ethnicity using principal component analysis. We used generalized estimating equations to assess association, followed up with a bioinformatics survey and literature search to evaluate functional significance.

Results

Homozygosity at the T allele of SNP rs662702 in the 3′ untranslated region of PAX6 conferred increased risk of CTS: Odds ratio = 12.29 (95% CI: 3.20–47.22), P = 2.6 × 10⁻⁴ and is seen in 3.9% of cases but only 0.3% of controls.

Interpretation

The minor T allele of SNP rs662702 disrupts regulation by microRNA‐328, which is known to result in increased PAX6 expression in vitro. This study provides, for the first time, evidence of a noncoding genomic variant contributing to the etiology of a common human epilepsy via a posttranscriptional regulatory mechanism.

Applications of comparative evolution to human disease genetics

Direct comparison of human diseases with model phenotypes allows exploration of key areas of human biology which are often inaccessible for practical or ethical reasons. We review recent developments in comparative evolutionary approaches for finding models for genetic disease, including high-throughput generation of gene/phenotype relationship data, the linking of orthologous genes and phenotypes across species, and statistical methods for linking human diseases to model phenotypes.

Utilization of information from gene networks towards a better understanding of functional similarities between complex traits: a dairy cattle model

Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore — trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.

Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures

Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to generate adaptive mechanisms, what has implications for epilepsy management and drug discovery.

Drosophila and experimental neurology in the post-genomic era

For decades, the fruit fly, Drosophila melanogaster, has been among the premiere genetic model systems for probing fundamental neurobiology, including elucidation of mechanisms responsible for human neurologic disorders. Flies continue to offer virtually unparalleled versatility and speed for genetic manipulation, strong genomic conservation, and a nervous system that recapitulates a range of cellular and network properties relevant to human disease. I focus here on four critical challenges emerging from recent advances in our understanding of the genomic basis of human neurologic disorders where innovative experimental strategies are urgently needed: (1) pinpointing causal genes from associated genomic loci; (2) confirming the functional impact of allelic variants; (3) elucidating nervous system roles for novel or poorly studied genes; and (4) probing network interactions within implicated regulatory pathways. Drosophila genetic approaches are ideally suited to address each of these potential translational roadblocks, and will therefore contribute to mechanistic insights and potential breakthrough therapies for complex genetic disorders in the coming years. Strategic collaboration between neurologists, human geneticists, and the Drosophila research community holds great promise to accelerate progress in the post-genomic era.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic

The InParanoid database (http://InParanoid.sbc.su.se) provides a user interface to orthologs inferred by the InParanoid algorithm. As there are now international efforts to curate and standardize complete proteomes, we have switched to using these resources rather than gathering and curating the proteomes ourselves. InParanoid release 8 is based on the 66 reference proteomes that the ‘Quest for Orthologs’ community has agreed on using, plus 207 additional proteomes from the UniProt complete proteomes—in total 273 species. These represent 246 eukaryotes, 20 bacteria and seven archaea. Compared to the previous release, this increases the number of species by 173% and the number of pairwise species comparisons by 650%. In turn, the number of ortholog groups has increased by 423%. We present the contents and usages of InParanoid 8, and a detailed analysis of how the proteome content has changed since the previous release.

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology.

Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders

Major neuropsychiatric disorders are highly heritable, with mounting evidence suggesting that these disorders share overlapping sets of molecular and cellular underpinnings. In the current article we systematically test the degree of genetic commonality across six major neuropsychiatric disorders-attention deficit hyperactivity disorder (ADHD), anxiety disorders (Anx), autistic spectrum disorders (ASD), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ). We curated a well-vetted list of genes based on large-scale human genetic studies based on the NHGRI catalog of published genome-wide association studies (GWAS). A total of 180 genes were accepted into the analysis on the basis of low but liberal GWAS p-values (<10(-5)). 22% of genes overlapped two or more disorders. The most widely shared subset of genes-common to five of six disorders-included ANK3, AS3MT, CACNA1C, CACNB2, CNNM2, CSMD1, DPCR1, ITIH3, NT5C2, PPP1R11, SYNE1, TCF4, TENM4, TRIM26, and ZNRD1. Using a suite of neuroinformatic resources, we showed that many of the shared genes are implicated in the postsynaptic density (PSD), expressed in immune tissues and co-expressed in developing human brain. Using a translational cross-species approach, we detected two distinct genetic components that were both shared by each of the six disorders; the 1st component is involved in CNS development, neural projections and synaptic transmission, while the 2nd is implicated in various cytoplasmic organelles and cellular processes. Combined, these genetic components account for 20-30% of the genetic load. The remaining risk is conferred by distinct, disorder-specific variants. Our systematic comparative analysis of shared and unique genetic factors highlights key gene sets and molecular processes that may ultimately translate into improved diagnosis and treatment of these debilitating disorders.

Comprehensive genotyping and clinical characterisation reveal 27 novel NKX2-1 mutations and expand the phenotypic spectrum

BACKGROUND

NKX2-1 encodes a transcription factor with large impact on the development of brain, lung and thyroid. Germline mutations of NKX2-1 can lead to dysfunction and malformations of these organs. Starting from the largest coherent collection of patients with a suspected phenotype to date, we systematically evaluated frequency, quality and spectrum of phenotypic consequences of NKX2-1 mutations.

METHODS

After identifying mutations by Sanger sequencing and array CGH, we comprehensively reanalysed the phenotype of affected patients and their relatives. We employed electrophoretic mobility shift assay (EMSA) to detect alterations of NKX2-1 DNA binding. Gene expression was monitored by means of in situ hybridisation and compared with the expression level of MBIP, a candidate gene presumably involved in the disorders and closely located in close genomic proximity to NKX2-1.

RESULTS

Within 101 index patients, we detected 17 point mutations and 10 deletions. Neurological symptoms were the most consistent finding (100%), followed by lung affection (78%) and thyroidal dysfunction (75%). Novel symptoms associated with NKX2-1 mutations comprise abnormal height, bouts of fever and cardiac septum defects. In contrast to previous reports, our data suggest that missense mutations in the homeodomain of NKX2-1 not necessarily modify its DNA binding capacity and that this specific type of mutations may be associated with mild pulmonary phenotypes such as asthma. Two deletions did not include NKX2-1, but MBIP, whose expression spatially and temporarily coincides with NKX2-1 in early murine development.

CONCLUSIONS

The high incidence of NKX2-1 mutations strongly recommends the routine screen for mutations in patients with corresponding symptoms. However, this analysis should not be confined to the exonic sequence alone, but should take advantage of affordable NGS technology to expand the target to adjacent regulatory sequences and the NKX2-1 interactome in order to maximise the yield of this diagnostic effort.

Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish

Comprehensive functional annotation of vertebrate genomes is fundamental to biological discovery. Reverse genetic screening has been highly useful for determination of gene function, but is untenable as a systematic approach in vertebrate model organisms given the number of surveyable genes and observable phenotypes. Unbiased prediction of gene-phenotype relationships offers a strategy to direct finite experimental resources towards likely phenotypes, thus maximizing de novo discovery of gene functions. Here we prioritized genes for phenotypic assay in zebrafish through machine learning, predicting the effect of loss of function of each of 15,106 zebrafish genes on 338 distinct embryonic anatomical processes. Focusing on cardiovascular phenotypes, the learning procedure predicted known knockdown and mutant phenotypes with high precision. In proof-of-concept studies we validated 16 high-confidence cardiac predictions using targeted morpholino knockdown and initial blinded phenotyping in embryonic zebrafish, confirming a significant enrichment for cardiac phenotypes as compared with morpholino controls. Subsequent detailed analyses of cardiac function confirmed these results, identifying novel physiological defects for 11 tested genes. Among these we identified tmem88a, a recently described attenuator of Wnt signaling, as a discrete regulator of the patterning of intercellular coupling in the zebrafish cardiac epithelium. Thus, we show that systematic prioritization in zebrafish can accelerate the pace of developmental gene function discovery.

Contemporary, yeast-based approaches to understanding human genetic variation

Determining how genetic variation contributes to human health and disease is a critical challenge. As one of the most genetically tractable model organisms, yeast has played a central role in meeting this challenge. The advent of new technologies, including high-throughput DNA sequencing and synthesis, proteomics, and computational methods, has vastly increased the power of yeast-based approaches to determine the consequences of human genetic variation. Recent successes include systematic exploration of the effects of gene dosage, large-scale analysis of the effect of coding variation on gene function, and the use of humanized yeast to model disease. By virtue of its manipulability, small genome size, and genetic tractability, yeast is poised to help us understand human genetic variation.