Phenotypes In Model Organisms Research Articles

First person – Sarah Alghamdi

ABSTRACTFirst Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Sarah Alghamdi is first author on ‘ Contribution of model organism phenotypes to the computational identification of human disease genes’, published in DMM. Sarah is a PhD student in the lab of Robert Hoehndorf at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, investigating artificial intelligence, specifically knowledge representation and reasoning over biomedical data.

Contribution of model organism phenotypes to the computational identification of human disease genes

ABSTRACTComputing 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.

A versatile transposon-based technology to generate loss- and gain-of-function phenotypes in the mouse liver

BackgroundUnderstanding the contribution of gene function in distinct organ systems to the pathogenesis of human diseases in biomedical research requires modifying gene expression through the generation of gain- and loss-of-function phenotypes in model organisms, for instance, the mouse. However, methods to modify both germline and somatic genomes have important limitations that prevent easy, strong, and stable expression of transgenes. For instance, while the liver is remarkably easy to target, nucleic acids introduced to modify the genome of hepatocytes are rapidly lost, or the transgene expression they mediate becomes inhibited due to the action of effector pathways for the elimination of exogenous DNA. Novel methods are required to overcome these challenges, and here we develop a somatic gene delivery technology enabling long-lasting high-level transgene expression in the entire hepatocyte population of mice.ResultsWe exploit the fumarylacetoacetate hydrolase (Fah) gene correction-induced regeneration in Fah-deficient livers, to demonstrate that such approach stabilizes luciferase expression more than 5000-fold above the level detected in WT animals, following plasmid DNA introduction complemented by transposon-mediated chromosomal gene transfer. Building on this advancement, we created a versatile technology platform for performing gene function analysis in vivo in the mouse liver. Our technology allows the tag-free expression of proteins of interest and silencing of any arbitrary gene in the mouse genome. This was achieved by applying the HADHA/B endogenous bidirectional promoter capable of driving well-balanced bidirectional expression and by optimizing in vivo intronic artificial microRNA-based gene silencing. We demonstrated the particular usefulness of the technology in cancer research by creating a p53-silenced and hRas G12V-overexpressing tumor model.ConclusionsWe developed a versatile technology platform for in vivo somatic genome editing in the mouse liver, which meets multiple requirements for long-lasting high-level transgene expression. We believe that this technology will contribute to the development of a more accurate new generation of tools for gene function analysis in mice.

Bisphenol a affects neurodevelopmental gene expression, cognitive function, and neuromuscular synaptic morphology in Drosophila melanogaster

Bisphenol A (BPA) is an environmentally prevalent endocrine disrupting chemical that can impact human health and may be an environmental risk factor for neurodevelopmental disorders. BPA has been associated with behavioral impairment in children and a variety of neurodevelopmental phenotypes in model organisms. We used Drosophila melanogaster to explore the consequences of developmental BPA exposure on gene expression, cognitive function, and synapse development. Our transcriptome analysis indicated neurodevelopmentally relevant genes were predominantly downregulated by BPA. Among the misregulated genes were those with roles in learning, memory, and synapse development, as well as orthologs of human genes associated with neurodevelopmental and neuropsychiatric disorders. To examine how gene expression data corresponded to behavioral and cellular phenotypes, we first used a predator-response behavioral paradigm and found that BPA disrupts visual perception. Further analysis using conditioned courtship suppression showed that BPA impairs associative learning. Finally, we examined synapse morphology within the larval neuromuscular junction and found that BPA significantly increased the number of axonal branches. Given that our findings align with studies of BPA in mammalian model organisms, this data indicates that BPA impairs neurodevelopmental pathways that are functionally conserved from invertebrates to mammals. Further, because Drosophila do not possess classic estrogen receptors or estrogen, this research suggests that BPA can impact neurodevelopment by molecular mechanisms distinct from its role as an estrogen mimic.

BICRA, a SWI/SNF Complex Member, Is Associated with BAF-Disorder Related Phenotypes in Humans and Model Organisms

SWI/SNF-related intellectual disability disorders (SSRIDDs) are rare neurodevelopmental disorders characterized by developmental disability, coarse facial features, and fifth digit/nail hypoplasia that are caused by pathogenic variants in genes that encode for members of the SWI/SNF (or BAF) family of chromatin remodeling complexes. We have identified 12 individuals with rare variants (10 loss-of-function, 2 missense) in the BICRA (BRD4 interacting chromatin remodeling complex-associated protein) gene, also known as GLTSCR1, which encodes a subunit of the non-canonical BAF (ncBAF) complex. These individuals exhibited neurodevelopmental phenotypes that include developmental delay, intellectual disability, autism spectrum disorder, and behavioral abnormalities as well as dysmorphic features. Notably, the majority of individuals lack the fifth digit/nail hypoplasia phenotype, a hallmark of most SSRIDDs. To confirm the role of BICRA in the development of these phenotypes, we performed functional characterization of the zebrafish and Drosophila orthologs of BICRA. In zebrafish, a mutation of bicra that mimics one of the loss-of-function variants leads to craniofacial defects possibly akin to the dysmorphic facial features seen in individuals harboring putatively pathogenic BICRA variants. We further show that Bicra physically binds to other non-canonical ncBAF complex members, including the BRD9/7 ortholog, CG7154, and is the defining member of the ncBAF complex in flies. Like other SWI/SNF complex members, loss of Bicra function in flies acts as a dominant enhancer of position effect variegation but in a more context-specific manner. We conclude that haploinsufficiency of BICRA leads to a unique SSRIDD in humans whose phenotypes overlap with those previously reported.

Cas9 Cuts and Consequences; Detecting, Predicting, and Mitigating CRISPR/Cas9 On- and Off-Target Damage: Techniques for Detecting, Predicting, and Mitigating the On- and off-target Effects of Cas9 Editing.

Large deletions and genomic re-arrangements are increasingly recognized as common products of double-strand break repair at Clustered Regularly Interspaced, Short Palindromic Repeats - CRISPR associated protein 9 (CRISPR/Cas9) on-target sites. Together with well-known off-target editing products from Cas9 target misrecognition, these are important limitations, that need to be addressed. Rigorous assessment of Cas9-editing is necessary to ensure validity of observed phenotypes in Cas9-edited cell-lines and model organisms. Here the mechanisms of Cas9 specificity, and strategies to assess and mitigate unwanted effects of Cas9 editing are reviewed; covering guide-RNA design, RNA modifications, Cas9 modifications, control of Cas9 activity; computational prediction for off-targets, and experimental methods for detecting Cas9 cleavage. Although recognition of the prevalence of on- and off-target effects of Cas9 editing has increased in recent years, broader uptake across the gene editing community will be important in determining the specificity of Cas9 across diverse applications and organisms.

O-glycan initiation directs distinct biological pathways and controls epithelial differentiation.

Post‐translational modifications (PTMs) greatly expand the function and potential for regulation of protein activity, and O‐glycosylation is among the most abundant and diverse PTMs. Initiation of O‐GalNAc glycosylation is regulated by 20 distinct GalNAc‐transferases (GalNAc‐Ts), and deficiencies in individual GalNAc‐Ts are associated with human disease, causing subtle but distinct phenotypes in model organisms. Here, we generate a set of isogenic keratinocyte cell lines lacking either of the three dominant and differentially expressed GalNAc‐Ts. Through the ability of keratinocytes to form epithelia, we investigate the phenotypic consequences of the loss of individual GalNAc‐Ts. Moreover, we probe the cellular responses through global transcriptomic, differential glycoproteomic, and differential phosphoproteomic analyses. We demonstrate that loss of individual GalNAc‐T isoforms causes distinct epithelial phenotypes through their effect on specific biological pathways; GalNAc‐T1 targets are associated with components of the endomembrane system, GalNAc‐T2 targets with cell–ECM adhesion, and GalNAc‐T3 targets with epithelial differentiation. Thus, GalNAc‐T isoforms serve specific roles during human epithelial tissue formation.

An Automated Method To Predict Mouse Gene and Protein Sequences Using Variant Data.

With recent advances in sequencing technologies, the scientific community has begun to probe the potential genetic bases behind complex phenotypes in humans and model organisms. In many cases, the genomes of genetically distinct strains of model organisms, such as the mouse (Mus musculus), have not been fully sequenced. Here, we report on a tool designed to use single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data to predict gene, mRNA, and protein sequences for up to 36 genetically distinct mouse strains. By automated querying of freely accessible databases through a graphical interface, the software requires no data and little computational experience. As a proof of concept, we predicted the gene and amino acid sequence of the aryl hydrocarbon receptor (Ahr) for all inbred mouse strains of which variant data were currently available through Mouse Genome Project. Predicted sequences were compared with fully sequenced genomes to show that the tool is effective in predicting gene and protein sequences.

Phenomap - Challenges and Successes in Bringing Together Multiple Data Projects to Build New Visualizations of Phenotypic Information and Specimen Records

Connecting biodiversity data across databases is not as easy as one might think. Different databases use different identifiers and taxonomies and connecting these data often results in loss of information and precision. Here we present some of the challenges we faced with integrating multiple biodiversity data sets, including specimen data from the scientific collections, during a hackathon hosted by the Phenoscape project in December of 2017. The hackathon brought together a diverse group of participants, including biologists and software developers, to explore ways of using the computable phenotype data in the Phenoscape Knowledgebase (KB) (Edmunds et al. 2015). The KB contains ontology-annotated data that links evolutionary phenotypes from the comparative literature to model organism phenotypes enabling, e.g., the retrieval of candidate genes for evolutionary phenotypes and the generation of synthetic supermatrices of presence/absence characters. During this hackathon, our team explored how to link phenotype data in the KB to museum specimen data in iDigBio (Matsunaga et al. 2013) with the hope of creating visualizations including world maps showing species distributions with different character states and their phylogenetic relationships. We visualized lineage relationships by querying the Open Tree of Life (OT) (Hinchliff et al. 2015) website using data integrated by another group at the hackathon that linked KB and OT taxonomic identifiers. Phenoscape uses terms from anatomy, quality, and taxonomy ontologies to annotate characters and taxonomic information from the phylogenetic literature along with specimen information. When populating the KB, specimen identifiers such as occurrence identifiers, collector’s number, and catalog numbers were preserved if present in the literature. We found that these identifiers, although standard in the biodiversity domain, were mostly insufficient to uniquely identify the source specimen in iDigBio. As an alternative, we instead mapped all the occurrences of taxa using string matches of the genus and species from Vertebrate Taxonomy Ontology identifiers. Without specimen identifiers that are consistent across databases, we lost the ability to explore spatial and temporal variation of characters within genera and were only able to explore phenotypes and geographic distributions among genera. We look forward to discussing these issues with the collections community represented at this meeting by the Society for the Preservation of Natural History Collections (SPNHC). We developed an R Shiny application that integrates characters and taxa from Phenoscape with specimen records from iDigBio and phylogenies from OT, to visualize phenotypic characters and taxon distributions in three interactive panels. The app allows a user to visualize OT phylogenies and place presence/absence character data on the tree. Specifically, users can: select taxa or specific characters to visualize their geographic distributions, navigate a phylogeny browser which displays character and specimen data available for taxa under consideration, and view a heatmap of characters available for character and taxon combinations. Because of our challenges joining data, our distribution map leaves users with the impression that all individuals in a genus exhibit a character whereas the KB was populated with data describing individuals. We hope that with improved data standards and their use by more people, constructing applications like ours will become easier.

An ancestry-based approach for detecting interactions.

Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies. In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6). We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes.

This year's Database Issue of Nucleic Acids Research contains 152 papers that include descriptions of 54 new databases and update papers on 98 databases, of which 16 have not been previously featured in NAR. As always, these databases cover a broad range of molecular biology subjects, including genome structure, gene expression and its regulation, proteins, protein domains, and protein–protein interactions. Following the recent trend, an increasing number of new and established databases deal with the issues of human health, from cancer-causing mutations to drugs and drug targets. In accordance with this trend, three recently compiled databases that have been selected by NAR reviewers and editors as ‘breakthrough’ contributions, denovo-db, the Monarch Initiative, and Open Targets, cover human de novo gene variants, disease-related phenotypes in model organisms, and a bioinformatics platform for therapeutic target identification and validation, respectively. We expect these databases to attract the attention of numerous researchers working in various areas of genetics and genomics. Looking back at the past 12 years, we present here the ‘golden set’ of databases that have consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database. The Database Issue is freely available online at the https://academic.oup.com/nar web site. An updated version of the NAR Molecular Biology Database Collection is available at http://www.oxfordjournals.org/nar/database/a/.

Mitochondrial Function and Mitophagy in the Elderly: Effects of Exercise

Aging is a natural, multifactorial and multiorganic phenomenon wherein there are gradual physiological and pathological changes over time. Aging has been associated with a decrease of autophagy capacity and mitochondrial functions, such as biogenesis, dynamics, and mitophagy. These processes are essential for the maintenance of mitochondrial structural integrity and, therefore, for cell life, since mitochondrial dysfunction leads to an impairment of energy metabolism and increased production of reactive oxygen species, which consequently trigger mechanisms of cellular senescence and apoptotic cell death. Moreover, reduced mitochondrial function can contribute to age-associated disease phenotypes in model organisms and humans. Literature data show beneficial effects of exercise on the impairment of mitochondrial biogenesis and dynamics and on the decrease in the mitophagic capacity associated to aging. Thus, exercise could have effects on the major cell signaling pathways that are involved in the mitochondria quality and quantity control in the elderly. Although it is known that several exercise protocols are able to modify the activity and turnover of mitochondria, further studies are necessary in order to better identify the mechanisms of interaction between mitochondrial functions, aging, and physical activity, as well as to analyze possible factors influencing these processes.

A Novel Test for Detecting SNP-SNP Interactions in Case-Only Trio Studies.

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of case-only tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

Purpose:Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles.Genet Med 18 6, 608–617.Methods:Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease–gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein–protein association neighbors.Genet Med 18 6, 608–617.Results:Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease–gene associations and ranked the correct seeded variant in up to 87% when detectable disease–gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation.Genet Med 18 6, 608–617.Conclusion:Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608–617.

Similarity-based search of model organism, disease and drug effect phenotypes.

BackgroundSemantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.ResultsWe have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet.ConclusionsPhenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.

OrthoDB v8: update of the hierarchical catalog of orthologs and the underlying free software.

Orthology, refining the concept of homology, is the cornerstone of evolutionary comparative studies. With the ever-increasing availability of genomic data, inference of orthology has become instrumental for generating hypotheses about gene functions crucial to many studies. This update of the OrthoDB hierarchical catalog of orthologs (http://www.orthodb.org) covers 3027 complete genomes, including the most comprehensive set of 87 arthropods, 61 vertebrates, 227 fungi and 2627 bacteria (sampling the most complete and representative genomes from over 11,000 available). In addition to the most extensive integration of functional annotations from UniProt, InterPro, GO, OMIM, model organism phenotypes and COG functional categories, OrthoDB uniquely provides evolutionary annotations including rates of ortholog sequence divergence, copy-number profiles, sibling groups and gene architectures. We re-designed the entirety of the OrthoDB website from the underlying technology to the user interface, enabling the user to specify species of interest and to select the relevant orthology level by the NCBI taxonomy. The text searches allow use of complex logic with various identifiers of genes, proteins, domains, ontologies or annotation keywords and phrases. Gene copy-number profiles can also be queried. This release comes with the freely available underlying ortholog clustering pipeline (http://www.orthodb.org/software).

Clinical interpretation of CNVs with cross-species phenotype data

BackgroundClinical evaluation of CNVs identified via techniques such as array comparative genome hybridisation (aCGH) involves the inspection of lists of known and unknown duplications and deletions with the goal of...

Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data.

Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data.

The influence of disease categories on gene candidate predictions from model organism phenotypes.

BackgroundThe molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component.ResultsIn this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model.ConclusionsIn conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.

Epistasis and quantitative traits: using model organisms to study gene-gene interactions.

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.