Noncoding deletions reveal a gene that is critical for intestinal function.

Large-scale genome sequencing is poised to provide a substantial increase in the rate of discovery of disease-associated mutations, but the functional interpretation of such mutations remains challenging. Here we show that deletions of a sequence on human chromosome 16 that we term the intestine-critical region (ICR) cause intractable congenital diarrhoea in infants1,2. Reporter assays in transgenic mice show that the ICR contains a regulatory sequence that activates transcription during the development of the gastrointestinal system. Targeted deletion of the ICR in mice caused symptoms that recapitulated the human condition. Transcriptome analysis revealed that an unannotated open reading frame (Percc1) flanks the regulatory sequence, and the expression of this gene was lost in the developing gut of mice that lacked the ICR. Percc1-knockout mice displayed phenotypes similar to those observed upon ICR deletion in mice and patients, whereas an ICR-driven Percc1 transgene was sufficient to rescue the phenotypes found in mice that lacked the ICR. Together, our results identify a gene that is critical for intestinal function and underscore the need for targeted in vivo studies to interpret the growing number of clinical genetic findings that do not affect known protein-coding genes.

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect.

BACKGROUND: A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient's disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses. RESULTS: VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness-aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status-high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards' diverse gene-to-gene relationships, including SuperPaths-integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of "disease SuperPaths", generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease-disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources. CONCLUSIONS: MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.

VarElect: the phenotype-based variation prioritizer of the GeneCards Suite.

BACKGROUND: Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates. RESULTS: We describe a novel tool, VarElect ( http://ve.genecards.org ), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards' powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards' diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal ("MiniCards") and hyperlinks to the parent databases. CONCLUSIONS: We demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient's disease. VarElect's capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.

TECPR2 mutations cause a new subtype of familial dysautonomia like hereditary sensory autonomic neuropathy with intellectual disability.

BACKGROUND: TECPR2 was first described as a disease causing gene when the c.3416delT frameshift mutation was found in five Jewish Bukharian patients with similar features. It was suggested to constitute a new subtype of complex hereditary spastic paraparesis (SPG49). RESULTS: We report here 3 additional patients from unrelated non-Bukharian families, harboring two novel mutations (c.1319delT, c.C566T) in this gene. Accumulating clinical data clarifies that in addition to intellectual disability and evolving spasticity the main disabling feature of this unique disorder is autonomic-sensory neuropathy accompanied by chronic respiratory disease and paroxysmal autonomic events. CONCLUSION: We suggest that the disease should therefore be classified as a new subtype of hereditary sensory-autonomic neuropathy. The discovery of additional mutations in non-Bukharian patients implies that this disease might be more common than previously appreciated and should therefore be considered in undiagnosed cases of intellectual disability with autonomic features and respiratory symptoms regardless of demographic origin.

Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios.

PURPOSE: Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene-disease associations. METHODS: We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients. RESULTS: We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10(-8)). This enrichment is only partially explained by mutations found in known disease-causing genes. CONCLUSION: This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

Deficiency of asparagine synthetase causes congenital microcephaly and a progressive form of encephalopathy.

We analyzed four families that presented with a similar condition characterized by congenital microcephaly, intellectual disability, progressive cerebral atrophy, and intractable seizures. We show that recessive mutations in the ASNS gene are responsible for this syndrome. Two of the identified missense mutations dramatically reduce ASNS protein abundance, suggesting that the mutations cause loss of function. Hypomorphic Asns mutant mice have structural brain abnormalities, including enlarged ventricles and reduced cortical thickness, and show deficits in learning and memory mimicking aspects of the patient phenotype. ASNS encodes asparagine synthetase, which catalyzes the synthesis of asparagine from glutamine and aspartate. The neurological impairment resulting from ASNS deficiency may be explained by asparagine depletion in the brain or by accumulation of aspartate/glutamate leading to enhanced excitability and neuronal damage. Our study thus indicates that asparagine synthesis is essential for the development and function of the brain but not for that of other organs.

TECPR2: a new autophagy link for neurodegeneration.

Autophagy dysfunction has been implicated in a group of progressive neurodegenerative diseases, and has been reported to play a major role in the pathogenesis of these disorders. We have recently reported a recessive mutation in TECPR2, an autophagy-implicated WD repeat-containing protein, in five individuals with a novel form of monogenic hereditary spastic paraparesis (HSP). We found that diseased skin fibroblasts had a decreased accumulation of the autophagy-initiation protein MAP1LC3B/LC3B, and an attenuated delivery of both LC3B and the cargo-recruiting protein SQSTM1/p62 to the lysosome where they are subject to degradation. The discovered TECPR2 mutation reveals for the first time a role for aberrant autophagy in a major class of Mendelian neurodegenerative diseases, and suggests mechanisms by which impaired autophagy may impinge on a broader scope of neurodegeneration.

Mutation in TECPR2 reveals a role for autophagy in hereditary spastic paraparesis.

We studied five individuals from three Jewish Bukharian families affected by an apparently autosomal-recessive form of hereditary spastic paraparesis accompanied by severe intellectual disability, fluctuating central hypoventilation, gastresophageal reflux disease, wake apnea, areflexia, and unique dysmorphic features. Exome sequencing identified one homozygous variant shared among all affected individuals and absent in controls: a 1 bp frameshift TECPR2 deletion leading to a premature stop codon and predicting significant degradation of the protein. TECPR2 has been reported as a positive regulator of autophagy. We thus examined the autophagy-related fate of two key autophagic proteins, SQSTM1 (p62) and MAP1LC3B (LC3), in skin fibroblasts of an affected individual, as compared to a healthy control, and found that both protein levels were decreased and that there was a more pronounced decrease in the lipidated form of LC3 (LC3II). siRNA knockdown of TECPR2 showed similar changes, consistent with aberrant autophagy. Our results are strengthened by the fact that autophagy dysfunction has been implicated in a number of other neurodegenerative diseases. The discovered TECPR2 mutation implicates autophagy, a central intracellular mechanism, in spastic paraparesis.

In-silico human genomics with GeneCards.

Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.