Human Intellectual Disability Genes Form Conserved Functional Modules in Drosophila

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.     

Gene Networks Underlying Convergent and Pleiotropic Phenotypes in a Large and Systematically-Phenotyped Cohort with Heterogeneous Developmental Disorders

Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.     

Towards an Informative Mutant Phenotype for Every Bacterial Gene

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, in Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.     

Massively Parallel Sequencing of Patients with Intellectual Disability,Congenital Anomalies and/or Autism Spectrum Disorders with a Targeted Gene Panel

Developmental delay and/or intellectual disability (DD/ID) affects 1–3% of all children. At least half of these are thought to have a genetic etiology. Recent studies have shown that massively parallel sequencing (MPS) using a targeted gene panel is particularly suited for diagnostic testing for genetically heterogeneous conditions. We report on our experiences with using massively parallel sequencing of a targeted gene panel of 355 genes for investigating the genetic etiology of eight patients with a wide range of phenotypes including DD/ID, congenital anomalies and/or autism spectrum disorder. Targeted sequence enrichment was performed using the Agilent SureSelect Target Enrichment Kit and sequenced on the Illumina HiSeq2000 using paired-end reads. For all eight patients, 81–84% of the targeted regions achieved read depths of at least 20×, with average read depths overlapping targets ranging from 322× to 798×. Causative variants were successfully identified in two of the eight patients: a nonsense mutation in the ATRX gene and a canonical splice site mutation in the L1CAM gene. In a third patient, a canonical splice site variant in the USP9X gene could likely explain all or some of her clinical phenotypes. These results confirm the value of targeted MPS for investigating DD/ID in children for diagnostic purposes. However, targeted gene MPS was less likely to provide a genetic diagnosis for children whose phenotype includes autism.     

Exome Sequencing Identifies Three Novel Candidate Genes Implicated in Intellectual Disability

Intellectual disability (ID) is a major health problem mostly with an unknown etiology. Recently exome sequencing of individuals with ID identified novel genes implicated in the disease. Therefore the purpose of the present study was to identify the genetic cause of ID in one syndromic and two non-syndromic Pakistani families. Whole exome of three ID probands was sequenced. Missense variations in two plausible novel genes implicated in autosomal recessive ID were identified: lysine (K)-specific methyltransferase 2B (KMT2B), zinc finger protein 589 (ZNF589), as well as hedgehog acyltransferase (HHAT) with a de novo mutation with autosomal dominant mode of inheritance. The KMT2B recessive variant is the first report of recessive Kleefstra syndrome-like phenotype. Identification of plausible causative mutations for two recessive and a dominant type of ID, in genes not previously implicated in disease, underscores the large genetic heterogeneity of ID. These results also support the viewpoint that large number of ID genes converge on limited number of common networks i.e. ZNF589 belongs to KRAB-domain zinc-finger proteins previously implicated in ID, HHAT is predicted to affect sonic hedgehog, which is involved in several disorders with ID, KMT2B associated with syndromic ID fits the epigenetic module underlying the Kleefstra syndromic spectrum. The association of these novel genes in three different Pakistani ID families highlights the importance of screening these genes in more families with similar phenotypes from different populations to confirm the involvement of these genes in pathogenesis of ID.     

Genetic insights into the functional elements of language

Language disorders cover a wide range of conditions with heterologous and overlapping phenotypes and complex etiologies harboring both genetic and environmental influences. Genetic approaches including the identification of genes linked to speech and language phenotypes and the characterization of normal and aberrant functions of these genes have, in recent years, unraveled complex details of molecular and cognitive mechanisms and provided valuable insight into the biological foundations of language. Consistent with this approach, we have reviewed the functional aspects of allelic variants of genes which are currently known to be either causally associated with disorders of speech and language or impact upon the spectrum of normal language ability. We have also reviewed candidate genes associated with heritable speech and language disorders. In addition, we have evaluated language phenotypes and associated genetic components in developmental syndromes that, together with a spectrum of altered language abilities, manifest various phenotypes and offer details of multifactorial determinants of language function. Data from this review have revealed a predominance of regulatory networks involved in the control of differentiation and functioning of neurons, neuronal tracks and connections among brain structures associated with both cognitive and language faculties. Our findings, furthermore, have highlighted several multifactorial determinants in overlapping speech and language phenotypes. Collectively this analysis has revealed an interconnected developmental network and a close association of the language faculty with cognitive functions, a finding that has the potential to provide insight into linguistic hypotheses defining in particular, the contribution of genetic elements to and the modular nature of the language faculty.     

Caenorhabditis elegans,a pluricellular model organism to screen new genes involved in mitochondrial genome maintenance

The inheritance of functional mitochondria depends on faithful replication and transmission of mitochondrial DNA (mtDNA). A large and heterogeneous group of human disorders is associated with mitochondrial genome quantitative and qualitative anomalies. Several nuclear genes have been shown to account for these severe OXPHOS disorders. However, in several cases, the disease-causing mutations still remain unknown.Caenorhabditis elegans has been largely used for studying various biological functions because this multicellular organism has short life cycle and is easy to grow in the laboratory. Mitochondrial functions are relatively well conserved between human and C. elegans, and heteroplasmy exists in this organism as in human. C. elegans therefore represents a useful tool for studying mtDNA maintenance. Suppression by RNA interference of genes involved in mtDNA replication such as polg-1, encoding the mitochondrial DNA polymerase, results in reduced mtDNA copy number but in a normal phenotype of the F1 worms. By combining RNAi of genes involved in mtDNA maintenance and EtBr exposure, we were able to reveal a strong and specific phenotype (developmental larval arrest) associated to a severe decrease of mtDNA copy number. Moreover, we tested and validated the screen efficiency for human orthologous genes encoding mitochondrial nucleoid proteins. This allowed us to identify several genes that seem to be closely related to mtDNA maintenance in C. elegans.This work reports a first step in the further development of a large-scale screening in C. elegans that should allow to identify new genes of mtDNA maintenance whose human orthologs will obviously constitute new candidate genes for patients with quantitative or qualitative mtDNA anomalies.     

A peep into mitochondrial disorder:multifaceted from mitochondrial DNAmutations to nuclear gene modulation

Mitochondrial genome is responsible for multiple human diseases in a maternal inherited pattern, yet phenotypes of patients in a same pedigree frequently vary largely. Genes involving in epigenetic modification, RNA processing, and other biological pathways, rather than “threshold effect” and environmental factors, provide more specific explanation to the aberrant phenotype. Thus, the double hit theory, mutations both in mitochondrial DNA and modifying genes aggravating the symptom, throws new light on mitochondrial dysfunction processes. In addition, mitochondrial retrograde signaling pathway that leads to reconfiguration of cell metabolism to adapt defects in mitochondria may as well play an active role. Here we review selected examples of modifier genes and mitochondrial retrograde signaling in mitochondrial disorders, which refine our understanding and will guide the rational design of clinical therapies.     

New connections across pathways and cellular processes: industrialized mutant screening reveals novel associations between diverse phenotypes in Arabidopsis

In traditional mutant screening approaches, genetic variants are tested for one or a small number of phenotypes. Once bona fide variants are identified, they are typically subjected to a limited number of secondary phenotypic screens. Although this approach is excellent at finding genes involved in specific biological processes, the lack of wide and systematic interrogation of phenotype limits the ability to detect broader syndromes and connections between genes and phenotypes. It could also prevent detection of the primary phenotype of a mutant. As part of a systems biology approach to understand plastid function, large numbers of Arabidopsis thaliana homozygous T-DNA lines are being screened with parallel morphological, physiological, and chemical phenotypic assays (www.plastid.msu.edu). To refine our approaches and validate the use of this high-throughput screening approach for understanding gene function and functional networks, approximately 100 wild-type plants and 13 known mutants representing a variety of phenotypes were analyzed by a broad range of assays including metabolite profiling, morphological analysis, and chlorophyll fluorescence kinetics. Data analysis using a variety of statistical approaches showed that such industrial approaches can reliably identify plant mutant phenotypes. More significantly, the study uncovered previously unreported phenotypes for these well-characterized mutants and unexpected associations between different physiological processes, demonstrating that this approach has strong advantages over traditional mutant screening approaches. Analysis of wild-type plants revealed hundreds of statistically robust phenotypic correlations, including metabolites that are not known to share direct biosynthetic origins, raising the possibility that these metabolic pathways have closer relationships than is commonly suspected.     

Homozygosity mapping and candidate prioritization identify mutations, missed by whole-exome sequencing, in SMOC2, causing major dental developmental defects

Inherited dental malformations constitute a clinically and genetically heterogeneous group of disorders. Here, we report on a severe developmental dental defect that results in a dentin dysplasia phenotype with major microdontia, oligodontia, and shape abnormalities in a highly consanguineous family. Homozygosity mapping revealed a unique zone on 6q27-ter. The two affected children were found to carry a homozygous mutation in SMOC2. Knockdown of smoc2 in zebrafish showed pharyngeal teeth that had abnormalities reminiscent of the human phenotype. Moreover, smoc2 depletion in zebrafish affected the expression of three major odontogenesis genes: dlx2, bmp2, and pitx2.     

Use of Targeted Exome Sequencing for Molecular Diagnosis of Skeletal Disorders

Genetic disorders of the skeleton comprise a large group of more than 450 clinically distinct and genetically heterogeneous diseases associated with mutations in more than 300 genes. Achieving a definitive diagnosis is complicated due to the genetic heterogeneity of these disorders, their individual rarity and their diverse radiographic presentations. We used targeted exome sequencing and designed a 1.4Mb panel for simultaneous testing of more than 4,800 exons in 309 genes involved in skeletal disorders. DNA from 69 individuals from 66 families with a known or suspected clinical diagnosis of a skeletal disorder was analyzed. Of 36 cases with a specific clinical hypothesis with a known genetic basis, mutations were identified for eight cases (22%). Of 20 cases with a suspected skeletal disorder but without a specific diagnosis, four causative mutations were identified. Also included were 11 cases with a specific skeletal disorder but for which there was at the time no known associated gene. For these cases, one mutation was identified in a known skeletal disease genes, and re-evaluation of the clinical phenotype in this case changed the diagnoses from osteodysplasia syndrome to Apert syndrome. These results suggest that the NGS panel provides a fast, accurate and cost-effective molecular diagnostic tool for identifying mutations in a highly genetically heterogeneous set of disorders such as genetic skeletal disorders. The data also stress the importance of a thorough clinical evaluation before DNA sequencing. The strategy should be applicable to other groups of disorders in which the molecular basis is largely known.     

Mutations in DDX3X Are a Common Cause of Unexplained Intellectual Disability with Gender-Specific Effects on Wnt Signaling

Intellectual disability (ID) affects approximately 1%–3% of humans with a gender bias toward males. Previous studies have identified mutations in more than 100 genes on the X chromosome in males with ID, but there is less evidence for de novo mutations on the X chromosome causing ID in females. In this study we present 35 unique deleterious de novo mutations in DDX3X identified by whole exome sequencing in 38 females with ID and various other features including hypotonia, movement disorders, behavior problems, corpus callosum hypoplasia, and epilepsy. Based on our findings, mutations in DDX3X are one of the more common causes of ID, accounting for 1%–3% of unexplained ID in females. Although no de novo DDX3X mutations were identified in males, we present three families with segregating missense mutations in DDX3X, suggestive of an X-linked recessive inheritance pattern. In these families, all males with the DDX3X variant had ID, whereas carrier females were unaffected. To explore the pathogenic mechanisms accounting for the differences in disease transmission and phenotype between affected females and affected males with DDX3X missense variants, we used canonical Wnt defects in zebrafish as a surrogate measure of DDX3X function in vivo. We demonstrate a consistent loss-of-function effect of all tested de novo mutations on the Wnt pathway, and we further show a differential effect by gender. The differential activity possibly reflects a dose-dependent effect of DDX3X expression in the context of functional mosaic females versus one-copy males, which reflects the complex biological nature of DDX3X mutations.     

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool

To date, genome-wide association studies have identified thousands of statistically-significant associations between genetic variants, and phenotypes related to a myriad of traits and diseases. A key goal for human-genetics research is to translate these associations into functional mechanisms. Popular gene-set analysis tools, like MAGMA, map variants to genes they might affect, and then integrate genome-wide association study data (that is, variant-level associations for a phenotype) to score genes for association with a phenotype. Gene scores are subsequently used in competitive gene-set analyses to identify biological processes that are enriched for phenotype association. By default, variants are mapped to genes in their proximity. However, many variants that affect phenotypes are thought to act at regulatory elements, which can be hundreds of kilobases away from their target genes. Thus, we explored the idea of augmenting a proximity-based mapping scheme with publicly-available datasets of regulatory interactions. We used MAGMA to analyze genome-wide association study data for ten different phenotypes, and evaluated the effects of augmentation by comparing numbers, and identities, of genes and gene sets detected as statistically significant between mappings. We detected several pitfalls and confounders of such “augmented analyses”, and introduced ways to control for them. Using these controls, we demonstrated that augmentation with datasets of regulatory interactions only occasionally strengthened the enrichment for phenotype association amongst (biologically-relevant) gene sets for different phenotypes. Still, in such cases, genes and regulatory elements responsible for the improvement could be pinpointed. For instance, using brain regulatory-interactions for augmentation, we were able to implicate two acetylcholine receptor subunits involved in post-synaptic chemical transmission, namely CHRNB2 and CHRNE, in schizophrenia. Collectively, our study presents a critical approach for integrating regulatory interactions into gene-set analyses for genome-wide association study data, by introducing various controls to distinguish genuine results from spurious discoveries.     

Disease Gene Characterization through Large-Scale Co-Expression Analysis

Background

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).

Results

Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.

Discussion

UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.     

Co-clustering phenome�Cgenome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.