Integrating computational biology and forward genetics in Drosophila

Lessons from lonely flies: Molecular and neuronal mechanisms underlying social isolation

Animals respond to changes in the environment which affect their internal state by adapting their behaviors. Social isolation is a form of passive environmental stressor that alters behaviors across animal kingdom, including humans, rodents, and fruit flies. Social isolation is known to increase violence, disrupt sleep and increase depression leading to poor mental and physical health. Recent evidences from several model organisms suggest that social isolation leads to remodeling of the transcriptional and epigenetic landscape which alters behavioral outcomes. In this review, we explore how manipulating social experience of fruit fly Drosophila melanogaster can shed light on molecular and neuronal mechanisms underlying isolation driven behaviors. We discuss the recent advances made using the powerful genetic toolkit and behavioral assays in Drosophila to uncover role of neuromodulators, sensory modalities, pheromones, neuronal circuits and molecular mechanisms in mediating social isolation. The insights gained from these studies could be crucial for developing effective therapeutic interventions in future.

Activation of the ubiquitin-proteasome system contributes to oculopharyngeal muscular dystrophy through muscle atrophy

Oculopharyngeal muscular dystrophy (OPMD) is a late-onset disorder characterized by progressive weakness and degeneration of specific muscles. OPMD is due to extension of a polyalanine tract in poly(A) binding protein nuclear 1 (PABPN1). Aggregation of the mutant protein in muscle nuclei is a hallmark of the disease. Previous transcriptomic analyses revealed the consistent deregulation of the ubiquitin-proteasome system (UPS) in OPMD animal models and patients, suggesting a role of this deregulation in OPMD pathogenesis. Subsequent studies proposed that UPS contribution to OPMD involved PABPN1 aggregation. Here, we use a Drosophila model of OPMD to address the functional importance of UPS deregulation in OPMD. Through genome-wide and targeted genetic screens we identify a large number of UPS components that are involved in OPMD. Half dosage of UPS genes reduces OPMD muscle defects suggesting a pathological increase of UPS activity in the disease. Quantification of proteasome activity confirms stronger activity in OPMD muscles, associated with degradation of myofibrillar proteins. Importantly, improvement of muscle structure and function in the presence of UPS mutants does not correlate with the levels of PABPN1 aggregation, but is linked to decreased degradation of muscle proteins. Oral treatment with the proteasome inhibitor MG132 is beneficial to the OPMD Drosophila model, improving muscle function although PABPN1 aggregation is enhanced. This functional study reveals the importance of increased UPS activity that underlies muscle atrophy in OPMD. It also provides a proof-of-concept that inhibitors of proteasome activity might be an attractive pharmacological approach for OPMD.

Oculopharyngeal muscular dystrophy (OPMD) is a genetic disease characterized by progressive weakness of specific muscles, leading to swallowing difficulties (dysphagia), eyelid drooping (ptosis) and walking difficulties at later stages. No drug treatments are currently available. OPMD is due to mutations in a nuclear protein called poly(A) binding protein nuclear 1 (PABPN1) that is involved in processing of different classes of RNAs in the nucleus. We have used an animal model of OPMD that we have developed in the fly Drosophila to investigate the role in OPMD of the ubiquitin-proteasome system, a pathway specialized in protein degradation. We report an increased activity of the ubiquitin-proteasome system that is associated with degradation of muscular proteins in the OPMD Drosophila model. We propose that higher activity of the ubiquitin-proteasome system leads to muscle atrophy in OPMD. Importantly, oral treatment of this OPMD animal model with an inhibitor of proteasome activity reduces muscle defects. A number of proteasome inhibitors are approved drugs used in clinic against cancers, therefore our results provide a proof-of-concept that inhibitors of proteasome might be of interest in future treatments of OPMD.

A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary.

Disease gene prioritization using network and feature

Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes

Schizophrenia is a common mental disorder with high heritability and strong genetic heterogeneity. Common disease-common variants hypothesis predicts that schizophrenia is attributable in part to common genetic variants. However, recent studies have clearly demonstrated that copy number variations (CNVs) also play pivotal roles in schizophrenia susceptibility and explain a proportion of missing heritability. Though numerous CNVs have been identified, many of the regions affected by CNVs show poor overlapping among different studies, and it is not known whether the genes disrupted by CNVs contribute to the risk of schizophrenia. By using cumulative scoring, we systematically prioritized the genes affected by CNVs in schizophrenia. We identified 8 top genes that are frequently disrupted by CNVs, including NRXN1, CHRNA7, BCL9, CYFIP1, GJA8, NDE1, SNAP29, and GJA5. Integration of genes affected by CNVs with known schizophrenia susceptibility genes (from previous genetic linkage and association studies) reveals that many genes disrupted by CNVs are also associated with schizophrenia. Further protein-protein interaction (PPI) analysis indicates that protein products of genes affected by CNVs frequently interact with known schizophrenia-associated proteins. Finally, systematic integration of CNVs prioritization data with genetic association and PPI data identifies key schizophrenia candidate genes. Our results provide a global overview of genes impacted by CNVs in schizophrenia and reveal a densely interconnected molecular network of de novo CNVs in schizophrenia. Though the prioritized top genes represent promising schizophrenia risk genes, further work with different prioritization methods and independent samples is needed to confirm these findings. Nevertheless, the identified key candidate genes may have important roles in the pathogenesis of schizophrenia, and further functional characterization of these genes may provide pivotal targets for future therapeutics and diagnostics.

Effect of P2X4 and P2X7 receptor antagonism on the pressure diuresis relationship in rats

Reduced glomerular filtration, hypertension and renal microvascular injury are hallmarks of chronic kidney disease, which has a global prevalence of ~10%. We have shown previously that the Fischer (F344) rat has lower GFR than the Lewis rat, and is more susceptible to renal injury induced by hypertension. In the early stages this injury is limited to the pre-glomerular vasculature. We hypothesized that poor renal hemodynamic function and vulnerability to vascular injury are causally linked and genetically determined. In the present study, normotensive F344 rats had a blunted pressure diuresis relationship, compared with Lewis rats. A kidney microarray was then interrogated using the Endeavour enrichment tool to rank candidate genes for impaired blood pressure control. Two novel candidate genes, P2rx7 and P2rx4, were identified, having a 7− and 3− fold increased expression in F344 rats. Immunohistochemistry localized P2X₄ and P2X₇ receptor expression to the endothelium of the pre-glomerular vasculature. Expression of both receptors was also found in the renal tubule; however there was no difference in expression profile between strains. Brilliant Blue G (BBG), a relatively selective P2X₇ antagonist suitable for use in vivo, was administered to both rat strains. In Lewis rats, BBG had no effect on blood pressure, but increased renal vascular resistance, consistent with inhibition of some basal vasodilatory tone. In F344 rats BBG caused a significant reduction in blood pressure and a decrease in renal vascular resistance, suggesting that P2X₇ receptor activation may enhance vasoconstrictor tone in this rat strain. BBG also reduced the pressure diuresis threshold in F344 rats, but did not alter its slope. These preliminary findings suggest a physiological and potential pathophysiological role for P2X₇ in controlling renal and/or systemic vascular function, which could in turn affect susceptibility to hypertension-related kidney damage.

Evidence for novel genetic loci associated with metabolic traits in Yup'ik people

OBJECTIVES

To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup'ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA).

METHODS

A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ(15) N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group, and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake.

RESULTS

We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits.

CONCLUSIONS

We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.

An unbiased evaluation of gene prioritization tools

MOTIVATION

Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated.

RESULTS

Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful.

CONTACT

yves.moreau@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable psychiatric disorder characterized by hyperactivity, inattention and increased impulsivity. In recent years, a large number of genetic studies for ADHD have been published and related genetic data has been accumulated dramatically. To provide researchers a comprehensive ADHD genetic resource, we previously developed the first genetic database for ADHD (ADHDgene). The abundant genetic data provides novel candidates for further study. Meanwhile, it also brings new challenge for selecting promising candidate genes for replication and verification research. In this study, we surveyed the computational tools for candidate gene prioritization and selected five tools, which integrate multiple data sources for gene prioritization, to prioritize ADHD candidate genes in ADHDgene. The prioritization analysis resulted in 16 prioritized candidate genes, which are mainly involved in several major neurotransmitter systems or in nervous system development pathways. Among these genes, nervous system development related genes, especially SNAP25, STX1A and the gene-gene interactions related with each of them deserve further investigations. Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

Computational strategies for the genome-wide identification of cis-regulatory elements and transcriptional targets

Transcription factors (TFs) are key proteins that decode the information in our genome to express a precise and unique set of proteins and RNA molecules in each cell type in our body. These factors play a pivotal role in all biological processes, including the determination of a cell's fate during development and the maintenance of a cell's physiological function. To achieve this, a TF binds to specific DNA sequences in the noncoding part of the genome, recruits chromatin modifiers and cofactors, and directs the transcription initiation rate of its "target genes." Therefore, a key challenge in deciphering a transcriptional switch is to identify the direct target genes of the master regulators that control the switch, the cis-regulatory elements implementing (auto-)regulatory loops, and the target genes of all the TFs in the downstream regulatory network. A better knowledge of a TF's targetome during specification and differentiation of a particular cell type will generate mechanistic insight into its developmental program. Here, I review computational strategies and methods to predict transcriptional targets by genome-wide searches for TF binding sites using position weight matrices, motif clusters, phylogenetic footprinting, chromatin binding and accessibility data, enhancer classification, motif enrichment, and gene expression signatures.

Developmental genetics and new sequencing technologies: the rise of nonmodel organisms

Much of developmental biology in the past decades has been driven by forward genetic studies in a few model organisms. We review recent work with relatives of these species, motivated by a desire to understand the evolutionary and ecological context for morphological innovation. Unfortunately, despite a number of shining examples, progress in nonmodel systems has often been slow. The current revolution in DNA sequencing has, however, enormous potential in extending the reach of genetics. We discuss how developmental biology will benefit from these advances, particularly by increasing the universe of study species.

Functional genomic screen and network analysis reveal novel modifiers of tauopathy dissociated from tau phosphorylation

A functional genetic screen using loss-of-function and gain-of-function alleles was performed to identify modifiers of tau-induced neurotoxicity using the 2N/4R (full-length) isoform of wild-type human tau expressed in the fly retina. We previously reported eye pigment mutations, which create dysfunctional lysosomes, as potent modifiers; here, we report 37 additional genes identified from ∼1900 genes screened, including the kinases shaggy/GSK-3beta, par-1/MARK, CamKI and Mekk1. Tau acts synergistically with Mekk1 and p38 to down-regulate extracellular regulated kinase activity, with a corresponding decrease in AT8 immunoreactivity (pS202/T205), suggesting that tau can participate in signaling pathways to regulate its own kinases. Modifiers showed poor correlation with tau phosphorylation (using the AT8, 12E8 and AT270 epitopes); moreover, tested suppressors of wild-type tau were equally effective in suppressing toxicity of a phosphorylation-resistant S11A tau construct, demonstrating that changes in tau phosphorylation state are not required to suppress or enhance its toxicity. Genes related to autophagy, the cell cycle, RNA-associated proteins and chromatin-binding proteins constitute a large percentage of identified modifiers. Other functional categories identified include mitochondrial proteins, lipid trafficking, Golgi proteins, kinesins and dynein and the Hsp70/Hsp90-organizing protein (Hop). Network analysis uncovered several other genes highly associated with the functional modifiers, including genes related to the PI3K, Notch, BMP/TGF-β and Hedgehog pathways, and nuclear trafficking. Activity of GSK-3β is strongly upregulated due to TDP-43 expression, and reduced GSK-3β dosage is also a common suppressor of Aβ42 and TDP-43 toxicity. These findings suggest therapeutic targets other than mitigation of tau phosphorylation.

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

A new resource for characterizing X-linked genes in Drosophila melanogaster: systematic coverage and subdivision of the X chromosome with nested, Y-linked duplications

Interchromosomal duplications are especially important for the study of X-linked genes. Males inheriting a mutation in a vital X-linked gene cannot survive unless there is a wild-type copy of the gene duplicated elsewhere in the genome. Rescuing the lethality of an X-linked mutation with a duplication allows the mutation to be used experimentally in complementation tests and other genetic crosses and it maps the mutated gene to a defined chromosomal region. Duplications can also be used to screen for dosage-dependent enhancers and suppressors of mutant phenotypes as a way to identify genes involved in the same biological process. We describe an ongoing project in Drosophila melanogaster to generate comprehensive coverage and extensive breakpoint subdivision of the X chromosome with megabase-scale X segments borne on Y chromosomes. The in vivo method involves the creation of X inversions on attached-XY chromosomes by FLP-FRT site-specific recombination technology followed by irradiation to induce large internal X deletions. The resulting chromosomes consist of the X tip, a medial X segment placed near the tip by an inversion, and a full Y. A nested set of medial duplicated segments is derived from each inversion precursor. We have constructed a set of inversions on attached-XY chromosomes that enable us to isolate nested duplicated segments from all X regions. To date, our screens have provided a minimum of 78% X coverage with duplication breakpoints spaced a median of nine genes apart. These duplication chromosomes will be valuable resources for rescuing and mapping X-linked mutations and identifying dosage-dependent modifiers of mutant phenotypes.

A genome-wide gene function prediction resource for Drosophila melanogaster

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Large-scale benchmark of Endeavour using MetaCore maps

SUMMARY

Endeavour is a tool that detects the most promising genes within large lists of candidates with respect to a biological process of interest and by combining several genomic data sources. We have benchmarked Endeavour using 450 pathway maps and 826 disease marker sets from MetaCore of GeneGo, Inc. containing a total of 9911 and 12 432 genes, respectively. We obtained an area under the receiver operating characteristic curves of 0.97 for pathway and of 0.91 for disease gene sets. These results indicate that Endeavour can be used to efficiently prioritize candidate genes for pathways and diseases.

AVAILABILITY

Endeavour is available at http://www.esat.kuleuven.be/endeavour

Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function

BACKGROUND

Discovering the functions of all genes is a central goal of contemporary biomedical research. Despite considerable effort, we are still far from achieving this goal in any metazoan organism. Collectively, the growing body of high-throughput functional genomics data provides evidence of gene function, but remains difficult to interpret.

RESULTS

We constructed the first network of functional relationships for Drosophila melanogaster by integrating most of the available, comprehensive sets of genetic interaction, protein-protein interaction, and microarray expression data. The complete integrated network covers 85% of the currently known genes, which we refined to a high confidence network that includes 20,000 functional relationships among 5,021 genes. An analysis of the network revealed a remarkable concordance with prior knowledge. Using the network, we were able to infer a set of high-confidence Gene Ontology biological process annotations on 483 of the roughly 5,000 previously unannotated genes. We also show that this approach is a means of inferring annotations on a class of genes that cannot be annotated based solely on sequence similarity. Lastly, we demonstrate the utility of the network through reanalyzing gene expression data to both discover clusters of coregulated genes and compile a list of candidate genes related to specific biological processes.

CONCLUSIONS

Here we present the the first genome-wide functional gene network in D. melanogaster. The network enables the exploration, mining, and reanalysis of experimental data, as well as the interpretation of new data. The inferred annotations provide testable hypotheses of previously uncharacterized genes.

Explorations in topology-delving underneath the surface of genetic interaction maps

High throughput assays, as well as advances in computational approaches, have recently allowed the acquisition of vast amounts of genetic interaction (GI) data in several organisms. Since GIs are a functional measure that reports on the effect of a mutation in one gene on the phenotype of a mutation in another, they can serve as a powerful tool to study both the function of individual genes and the wiring of biological networks. Therefore, these data hold much promise for advancing our understanding of cellular systems. In this review we focus on the methodologies currently available for using and interpreting large datasets of GIs for functional gene groups (GI maps), and elaborate on the challenges ahead. In addition, we discuss potential applications for the study of evolution and disease mechanisms, and highlight the need for comprehensive integrative analysis to extract the wealth of information found in these maps.