ENDEAVOUR update: a web resource for gene prioritization in multiple species

Epigenome-wide methylation haplotype association analysis identified HLA-DRB1, HLA-DRB5 and HLA-DQB1 as risk factors for rheumatoid arthritis

The aim of this study was to compare nonrandom associations between physically adjacent single methylation polymorphism loci among rheumatoid arthritis (RA) and normal subjects for investigating RA-risk methylation haplotypes (meplotype). With 354 ACPA-positive RA patients and 335 normal controls selected from a case-control study based on Swedish population, we conducted the first RA epigenome-wide meplotype association study using our software EWAS2.0, mainly including (i) converted the β value to methylation genotype (menotype) data, (ii) identified methylation disequilibrium (MD) block, (iii) calculated frequent of each meplotypes in MD block and performed case-control association test and (iv) screened for RA-risk meplotypes by odd ratio (OR) and p-values. Ultimately, 545 meplotypes on 334 MD blocks were identified significantly associated with RA (p-value < .05). These meplotypes were mapped to 329 candidate genes related to RA. Subsequently, combined with gene optimization, eight RA-risk meplotypes were identified on three risk genes: HLA-DRB1, HLA-DRB5 and HLA-DQB1. Our results reported the relationship between DNA methylation pattern on HLA-DQB1 and the risk of RA for the first time, demonstrating the co-demethylation of 'cg22984282' and 'cg13423887' on HLA-DQB1 gene (meplotype UU, p-value = 2.90E - 6, OR = 1.68, 95% CI = [1.35, 2.10]) may increase the risk of RA. Our results demonstrates the potential of methylation haplotype analysis to identify RA-related genes from a new perspective and its applicability to the study of other disease.

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Exome sequencing to explore the possibility of predicting genetic susceptibility to the joint occurrence of polycystic ovary syndrome and Hashimoto's thyroiditis

A large body of evidence indicates that women with polycystic ovary syndrome (PCOS) have a higher risk of developing Hashimoto's thyroiditis (HT) than healthy individuals. Given the strong genetic impact on both diseases, common predisposing genetic factors are possibly involved but are not fully understood. Here, we performed whole-exome sequencing (WES) for 250 women with sporadic PCOS, HT, combined PCOS and HT (PCOS+HT), and healthy controls to explore the genetic background of the joint occurrence of PCOS and HT. Based on relevant comparative analyses, multivariate logistic regression prediction modeling, and the most informative feature selection using the Monte Carlo feature selection and interdependency discovery algorithm, 77 variants were selected for further validation by TaqMan genotyping in a group of 533 patients. In the allele frequency test, variants in RAB6A, GBP3, and FNDC7 genes were found to significantly (padjusted < 0.05) differentiated the PCOS+HT and PCOS groups, variant in HIF3A differentiated the PCOS+HT and HT groups, whereas variants in CDK20 and CCDC71 differentiated the PCOS+HT and both single disorder groups. TaqMan genotyping data were used to create final prediction models, which differentiated between PCOS+HT and PCOS or HT with a prediction accuracy of AUC = 0.78. Using a 70% cutoff of the prediction score improved the model parameters, increasing the AUC value to 0.87. In summary, we demonstrated the polygenic burden of both PCOS and HT, and many common and intersecting signaling pathways and biological processes whose disorders mutually predispose patients to the development of both diseases.

Disease gene prediction with privileged information and heteroscedastic dropout

Motivation

Recently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that similar genes are more likely to be associated with similar diseases. However, the genetic features these methods rely on are often hard to collect due to high experimental cost and various other technical limitations. Existing solutions of this problem significantly increase the risk of overfitting and decrease the generalizability of the models.

Results

In this work, we propose a graph neural network (GNN) version of the Learning under Privileged Information paradigm to predict new disease gene associations. Unlike previous gene prioritization approaches, our model does not require the genetic features to be the same at training and test stages. If a genetic feature is hard to measure and therefore missing at the test stage, our model could still efficiently incorporate its information during the training process. To implement this, we develop a Heteroscedastic Gaussian Dropout algorithm, where the dropout probability of the GNN model is determined by another GNN model with a mirrored GNN architecture. To evaluate our method, we compared our method with four state-of-the-art methods on the Online Mendelian Inheritance in Man dataset to prioritize candidate disease genes. Extensive evaluations show that our model could improve the prediction accuracy when all the features are available compared to other methods. More importantly, our model could make very accurate predictions when >90% of the features are missing at the test stage.

Availability and implementation

Our method is realized with Python 3.7 and Pytorch 1.5.0 and method and data are freely available at: https://github.com/juanshu30/Disease-Gene-Prioritization-with-Privileged-Information-and-Heteroscedastic-Dropout.

Biological Network Mining

In this book chapter, we introduce a pipeline to mine significant biomedical entities (or bioentities) in biological networks. Our focus is on prioritizing both bioentities themselves and the associations between bioentities in order to reveal their biological functions. We will introduce three tools BEERE, WIPER, and PAGER 2.0 that can be used together for network analysis and function interpretation: (1) BEERE is a network analysis tool for "Biomedical Entity Expansion, Ranking and Explorations," (2) WIPER is an entity-to-entity association ranking tool, and (3) PAGER 2.0 is a service for gene enrichment analysis.

Deregulated Adhesion Program in Palatal Keratinocytes of Orofacial Cleft Patients

Orofacial clefts (OFCs) are the most frequent craniofacial birth defects. An orofacial cleft (OFC) occurs as a result of deviations in palatogenesis. Cell proliferation, differentiation, adhesion, migration and apoptosis are crucial in palatogenesis. We hypothesized that deregulation of these processes in oral keratinocytes contributes to OFC. We performed microarray expression analysis on palatal keratinocytes from OFC and non-OFC individuals. Principal component analysis showed a clear difference in gene expression with 24% and 17% for the first and second component, respectively. In OFC cells, 228 genes were differentially expressed (p < 0.001). Gene ontology analysis showed enrichment of genes involved in β1 integrin-mediated adhesion and migration, as well as in P-cadherin expression. A scratch assay demonstrated reduced migration of OFC keratinocytes (343.6 ± 29.62 μm) vs. non-OFC keratinocytes (503.4 ± 41.81 μm, p < 0.05). Our results indicate that adhesion and migration are deregulated in OFC keratinocytes, which might contribute to OFC pathogenesis.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information

Motivation

Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.

Results

Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.

Availability and implementation

The Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.

Supplementary information

Supplementary data are available at Bioinformatics online.

BEERE: a web server for biomedical entity expansion, ranking and explorations

BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. ‘biomedical entities’, in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity—a gene or a term—within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype–phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

Clinical whole exome sequencing in severe hypertriglyceridemia

BACKGROUND

Little data exist regarding the clinical application of whole exome sequencing (WES) for the molecular diagnosis of severe hypertriglyceridemia (HTG).

METHODS

WES was performed for 28 probands exhibiting severe HTG (≥1000 mg/dl) without any transient causes. We evaluated recessive and dominant inheritance models in known monogenic HTG genes, followed by disease-network gene prioritization and copy number variation (CNV) analyses to identify causative variants and a novel genetic mechanism for severe HTG.

RESULTS

We identified possible causative variants for severe HTG, including three novel variants, in nine probands (32%). In the recessive inheritance model, we identified two homozygous subjects with lipoprotein lipase (LPL) deficiency and one subject harboring compound heterozygous variants in both LPL and APOA5 genes (hyperchylomicronemia). In the dominant inheritance model, we identified probands harboring deleterious heterozygous variants in LPL, glucokinase regulatory protein, and solute carrier family 25 member 40 genes, possibly associated with this extreme HTG phenotype. However, gene prioritization and CNV analyses did not validate the novel genes associated with severe HTG.

CONCLUSIONS

In 28 probands with severe HTG, we identified potential causative variants within nine genes associated with rare Mendelian dyslipidemias. Clinical WES may be feasible for such extreme cases, potentially leading to appropriate therapies.

MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Background

Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization.

Results

In this study, we propose a novel method, MGOGP (Module and Gene Ontology-based Gene Prioritization), for cancer-related gene prioritization. Different from other methods, MGOGP ranks genes considering information of both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known cancer-related genes as heuristics. The performance of the proposed method is comprehensively validated by using both breast cancer and prostate cancer datasets, and by comparison with other methods. Results show that MGOGP outperforms other methods, and successfully prioritizes more genes with literature confirmed evidence.

Conclusions

This work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2216-0) contains supplementary material, which is available to authorized users.

Characterization of a new B-ALL cell line with constitutional defect of the Notch signaling pathway

Notch signaling contribution to B-cell acute lymphoblastic leukemia (B-ALL) development is still under investigation. The serendipitous onset of B-ALL in a patient affected by the germinal Notch mutation-dependent Alagille syndrome allowed us to establish a B-ALL cell line (VR-ALL) bearing a genetic loss of function in components of Notch signaling. VR-ALL is a common-type B-ALL cell line, grows in conventional culture medium supplemented with 10% serum, and gives rise, once injected into immunodeficient NOG mice, to a mouse xenograft model of B-ALL. Exome sequencing revealed deleterious mutations in some components of Notch signaling, including Jagged1, Notch1, and Notch2. In addition, VR-ALL is sensitive both in vitro and in vivo to γ-secretase inhibitors (GSIs) as well as conventional anti-leukemic drugs. For all these reasons, VR-ALL may help to gain more insights into the role of Notch signaling in B-ALL.

Genome-wide haplotype association study identify the FGFR2 gene as a risk gene for acute myeloid leukemia

Acute myeloid leukemia (AML) is a cancer of the myeloid line of blood cells, and generally considered to be caused by environment and genetic factors. In this study, we combined a genome-wide haplotype association study (GWHAS) and gene prioritization strategy to mine AML-related genetic affect factors and understand its pathogenesis. A total of 175 AML patients were downloaded from the public GEO database (GSE32462) and 218 matched Caucasian controls were from the HapMap Project. We first identified the linkage disequilibrium (LD) blocks and performed a GWHAS to scan AML-related haplotypes. Then we mapped these haplotypes to the corresponding genes as candidate. And finally, we prioritized all the AML candidate genes based on the similarity with 38 known AML susceptibility genes. The results showed that 1754 haplotypes were significant associated with AML (P<1E-5) and mapped to 591 candidate genes. After prioritizing all 591 AML candidate genes, we obtained four genes ranking at the front as AML risk genes: RUNX1, JAK1, PDGFRA, and FGFR2. Among them, RUNX1, JAK1 and PDGFRA had been confirmed as AML risk genes. In particular, we found that the gene FGFR2 was a novel AML susceptibility gene with a haplotype TT (rs7090018 and rs2912759) showed significant association with AML (P-value = 7.07E-06). In a word, our findings might provide a new perspective to understand the pathogenesis of AML.

Identification and Analysis of Rice Yield-Related Candidate Genes by Walking on the Functional Network

Rice (Oryza sativa L.) is one of the most important staple foods in the world. It is possible to identify candidate genes associated with rice yield using the model of random walk with restart on a functional similarity network. We demonstrated the high performance of this approach by a five-fold cross-validation experiment, as well as the robustness of the parameter r. We also assessed the strength of associations between known seeds and candidate genes in the light of the results scores. The candidates ranking at the top of the results list were considered to be the most relevant rice yield-related genes. This study provides a valuable alternative for rice breeding and biology research. The relevant dataset and script can be downloaded at the website: http://lab.malab.cn/jj/rice.htm.

Multidimensional knowledge-based framework is an essential step in the categorization of gene sets in complex disorders

In complex disorders, collaborative role of several genes accounts for the multitude of symptoms and the discovery of molecular mechanisms requires proper understanding of pertinent genes. Majority of the recent techniques utilize either single information or consolidate the independent outlook from multiple knowledge sources for assisting the discovery of candidate genes. In any case, given that various sorts of heterogeneous sources are possibly significant for quality gene prioritization, every source bearing data not conveyed by another, we assert that a perfect strategy ought to give approaches to observe among them in a genuine integrative style that catches the degree of each, instead of utilizing a straightforward mix of sources. We propose a flexible approach that empowers multi-source information reconciliation for quality gene prioritization that augments the complementary nature of various learning sources so as to utilize the maximum information of aggregated data. To illustrate the proposed approach, we took Autism Spectrum Disorder (ASD) as a case study and validated the framework on benchmark studies. We observed that the combined ranking based on integrated knowledge reduces the false positive observations and boosts the performance when compared with individual rankings. The clinical phenotype validation for ASD shows that there is a significant linkage between top positioned genes and endophenotypes of ASD. Categorization of genes based on endophenotype associations by this method will be useful for further hypothesis generation leading to clinical and translational analysis. This approach may also be useful in other complex neurological and psychiatric disorders with a strong genetic component.

LoFtool: a gene intolerance score based on loss-of-function variants in 60 706 individuals

Motivation

Depletion of loss-of-function (LoF) mutations may provide a rank of genic functional intolerance and consequently susceptibility to disease.

Results

Here we have studied LoF mutations in 60 706 unrelated individuals and show that the most intolerant quartile of ranked genes is enriched in rare and early onset diseases and explains 87% of de novo haploinsufficient OMIM mutations, 17% more than any other gene scoring tool. We detected particular enrichment in expression of the depleted LoF genes in brain (odds ratio = 1.5; P -value = 4.2e-07). By searching for de novo haploinsufficient mutations putatively associated with neurodevelopmental disorders in four recent studies, we were able to explain 81% of them. Taken together, this study provides a novel gene intolerance ranking system, called LoFtool, which may help in ranking genes of interest based on their LoF intolerance and tissue expression.

Availability and implementation

The LoFtool gene scores are available in the Supplementary data .

Contact

joaofadista@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Dysfunction of Myosin Light-Chain 4 (MYL4) Leads to Heritable Atrial Cardiomyopathy With Electrical, Contractile, and Structural Components: Evidence From Genetically-Engineered Rats

BACKGROUND

There is increasing interest in the concept of atrial cardiomyopathy, but the underlying molecular and mechanistic determinants remain poorly defined. We identified a family with heritable atrial cardiomyopathy manifesting as progressive atrial-selective electromechanical dysfunction, tachyarrhythmias, and bradyarrhythmias requiring pacemaker implantation. Myosin light-chain 4 (MYL4), encoding the atrial-selective essential myosin light chain, was identified as a candidate gene. We used genetically modified rat models to investigate the role of MYL4 in atrial cardiomyopathy.

METHODS AND RESULTS

Exome sequencing and systematic bioinformatic analyses identified a rare missense variant of MYL4 (c.31G>A [p.E11K]) in a large multiplex atrial cardiomyopathy family pedigree. The mutation cosegregated with atrial standstill (selected as the principal presenting trait) with a logarithm of the odds score of 5.3. The phenotype of rats with MYL4 mutation knock-in confirmed the causative role of the mutation. MYL4 knockout rats showed a similar atrial cardiomyopathy phenotype, whereas rats with an adjacent 4-amino-acid deletion showed no phenotype. Both MYL4 p.E11K knock-in rats and MYL4 knockout rats showed progressive atrial electrophysiological, contractile, and fibrotic abnormalities, similar to affected patients. Biochemical analyses of MYL4 p.E11K mutation rats showed activation of proapoptotic and profibrotic signaling, along with increased atrial-cardiomyocyte terminal deoxynucleotidyl transferase dUTP nick end labeling staining, suggesting enhanced apoptotic cell death, findings that were mimicked by in vitro adenoviral transfer of the mutant gene to neonatal-rat cardiomyocytes.

CONCLUSIONS

Loss-of-function MYL4 gene variants cause progressive atrial cardiomyopathy in humans and rats. Our findings identify MYL4 as a key gene required for atrial contractile, electrical and structural integrity. These results improve our understanding of the molecular basis of atrial cardiomyopathy and introduce new models for further mechanistic analysis.

The Molecular Genetics of Autosomal Recessive Nonsyndromic Intellectual Disability: a Mutational Continuum and Future Recommendations

Intellectual disability (ID) is a clinical manifestation of the central nervous system without any major dysmorphologies of the brain. Biologically it affects learning capabilities, memory, and cognitive functioning. The basic defining features of ID are characterized by IQ<70, age of onset before 18 years, and impairment of at least two of the adaptive skills. Clinically it is classified in a syndromic (with additional abnormalities) and a nonsyndromic form (with only cognitive impairment). The study of nonsyndromic intellectual disability (NSID) can best explain the pathophysiology of cognition, intelligence and memory. Genetic analysis in autosomal recessive nonsyndrmic ID (ARNSID) has mapped 51 disease loci, 34 of which have revealed their defective genes. These genes play diverse physiological roles in various molecular processes, including methylation, proteolysis, glycosylation, signal transduction, transcription regulation, lipid metabolism, ion homeostasis, tRNA modification, ubiquitination and neuromorphogenesis. High-density SNP array and whole exome sequencing has increased the pace of gene discoveries and many new mutations are being published every month. The lack of uniform criteria has assigned multiple identifiers (or accession numbers) to the same MRT locus (e.g. MRT7 and MRT22). Here in this review we describe the molecular genetics of ARNSID, prioritize the candidate genes in uncharacterized loci, and propose a new nomenclature to reorganize the mutation data that will avoid the confusion of assigning duplicate accession numbers to the same ID locus and to make the data manageable in the future as well.

Clinical Applicability of Whole-Exome Sequencing Exemplified by a Study in Young Adults with the Advanced Cryptogenic Cholestatic Liver Diseases

BACKGROUND

The proper use of new medical tests in clinical practice requires the establishment of their value and range of diagnostic usefulness. While whole-exome sequencing (WES) has already entered the medical practice, recognizing its diagnostic usefulness in multifactorial diseases has not yet been achieved.

AIMS

The objective of this study was to establish usability of WES in determining genetic background of chronic cholestatic liver disease (CLD) in young patients.

METHODS

WES was performed on six young patients (between 17 and 22 years old) with advanced fibrosis or cirrhosis due to CLD and their immediate families. Sequencing was performed on an Ion Proton sequencer.

RESULTS

On average, 19,673 variants were identified, of which from 7 to 14 variants of an individual were nonsynonymous, homozygous, recessively inherited, and considered in silico as pathogenic. Although monogenic cause of CLD has not been determined, several heterozygous rare variants and polymorphisms were uncovered in genes previously known to be associated with CLD, including ATP8B1, ABCB11, RXRA, and ABCC4, indicative of multifactorial genetic background.

CONCLUSIONS

WES is a potentially useful diagnostic tool in determining genetic background of multifactorial diseases, but its main limitation results from the lack of opportunities for direct linkage between the uncovered genetic variants and molecular mechanisms of disease.

DDK Promotes Tumor Chemoresistance and Survival via Multiple Pathways

DBF4-dependent kinase (DDK) is a two-subunit kinase required for initiating DNA replication at individual origins and is composed of CDC7 kinase and its regulatory subunit DBF4. Both subunits are highly expressed in many diverse tumor cell lines and primary tumors, and this is correlated with poor prognosis. Inhibiting DDK causes apoptosis of tumor cells, but not normal cells, through a largely unknown mechanism. Firstly, to understand why DDK is often overexpressed in tumors, we identified gene expression signatures that correlate with DDK high- and DDK low-expressing lung adenocarcinomas. We found that increased DDK expression is highly correlated with inactivation of RB1-E2F and p53 tumor suppressor pathways. Both CDC7 and DBF4 promoters bind E2F, suggesting that increased E2F activity in RB1 mutant cancers promotes increased DDK expression. Surprisingly, increased DDK expression levels are also correlated with both increased chemoresistance and genome-wide mutation frequencies. Our data further suggest that high DDK levels directly promote elevated mutation frequencies. Secondly, we performed an RNAi screen to investigate how DDK inhibition causes apoptosis of tumor cells. We identified 23 kinases and phosphatases required for apoptosis when DDK is inhibited. These hits include checkpoint genes, G2/M cell cycle regulators, and known tumor suppressors leading to the hypothesis that inhibiting mitotic progression can protect against DDKi-induced apoptosis. Characterization of one novel hit, the LATS2 tumor suppressor, suggests that it promotes apoptosis independently of the upstream MST1/2 kinases in the Hippo signaling pathway.

Whole-genome sequencing suggests mechanisms for 22q11.2 deletion-associated Parkinson's disease

OBJECTIVES

To investigate disease risk mechanisms of early-onset Parkinson's disease (PD) associated with the recurrent 22q11.2 deletion, a genetic risk factor for early-onset PD.

METHODS

In a proof-of-principle study, we used whole-genome sequencing (WGS) to investigate sequence variants in nine adults with 22q11.2DS, three with neuropathologically confirmed early-onset PD and six without PD. Adopting an approach used recently to study schizophrenia in 22q11.2DS, here we tested candidate gene-sets relevant to PD.

RESULTS

No mutations common to the cases with PD were found in the intact 22q11.2 region. While all were negative for rare mutations in a gene-set comprising PD disease-causing and risk genes, another candidate gene-set of 1000 genes functionally relevant to PD presented a nominally significant (P = 0.03) enrichment of rare putatively damaging missense variants in the PD cases. Polygenic score results, based on common variants associated with PD risk, were non-significantly greater in those with PD.

CONCLUSIONS

The results of this first-ever pilot study of WGS in PD suggest that the cumulative burden of genome-wide sequence variants may contribute to expression of early-onset PD in the presence of threshold-lowering dosage effects of a 22q11.2 deletion. We found no evidence that expression of PD in 22q11.2DS is mediated by a recessive locus on the intact 22q11.2 chromosome or mutations in known PD genes. These findings offer initial evidence of the potential effects of multiple within-individual rare variants on the expression of PD and the utility of next generation sequencing for studying the etiology of PD.

A large-scale benchmark of gene prioritization methods

In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.

Genetic alterations in mesiodens as revealed by targeted next-generation sequencing and gene co-occurrence network analysis

OBJECTIVE

Mesiodens is the most common type of supernumerary tooth which includes a population prevalence of 0.15%-1.9%. Alongside evidence that the condition is heritable, mutations in single genes have been reported in few human supernumerary tooth cases. Gene sequencing methods in tradition way are time-consuming and labor-intensive, whereas next-generation sequencing and bioinformatics are cost-effective for large samples and target sizes.

MATERIALS AND METHODS

We describe the application of a targeted next-generation sequencing (NGS) and bioinformatics approach to samples from 17 mesiodens patients. Subjects were diagnosed on the basis of panoramic radiograph. A total of 101 candidate genes which were captured custom genes were sequenced on the Illumina HiSeq 2500. Multistep bioinformatics processing was performed including variant identification, base calling, and in silico analysis of putative disease-causing variants.

RESULTS

Targeted capture identified 88 non-synonymous, rare, exonic variants involving 42 of the 101 candidate genes. Moreover, we investigated gene co-occurrence relationships between the genomic alterations and identified 88 significant relationships among 18 most recurrent driver alterations.

CONCLUSION

Our search for co-occurring genetic alterations revealed that such alterations interact cooperatively to drive mesiodens. We discovered a gene co-occurrence network in mesiodens patients with functionally enriched gene groups in the sonic hedgehog (SHH), bone morphogenetic proteins (BMP), and wingless integrated (WNT) signaling pathways.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Genome-wide analysis identifies 12 loci influencing human reproductive behavior

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments

MOTIVATION

Transcriptome data from the gene knockout experiment in mouse is widely used to investigate functions of genes and relationship to phenotypes. When a gene is knocked out, it is important to identify which genes are affected by the knockout gene. Existing methods, including differentially expressed gene (DEG) methods, can be used for the analysis. However, existing methods require cutoff values to select candidate genes, which can produce either too many false positives or false negatives. This hurdle can be addressed either by improving the accuracy of gene selection or by providing a method to rank candidate genes effectively, or both. Prioritization of candidate genes should consider the goals or context of the knockout experiment. As of now, there are no tools designed for both selecting and prioritizing genes from the mouse knockout data. Hence, the necessity of a new tool arises.

RESULTS

In this study, we present CLIP-GENE, a web service that selects gene markers by utilizing differentially expressed genes, mouse transcription factor (TF) network, and single nucleotide variant information. Then, protein-protein interaction network and literature information are utilized to find genes that are relevant to the phenotypic differences. One of the novel features is to allow researchers to specify their contexts or hypotheses in a set of keywords to rank genes according to the contexts that the user specify. We believe that CLIP-GENE will be useful in characterizing functions of TFs in mouse experiments.

AVAILABILITY

http://epigenomics.snu.ac.kr/CLIP-GENE REVIEWERS: This article was reviewed by Dr. Lee and Dr. Pongor.

Genome-wide haplotype association study identify TNFRSF1A, CASP7, LRP1B, CDH1 and TG genes associated with Alzheimer's disease in Caribbean Hispanic individuals

Alzheimer's disease (AD) is an acquired disorder of cognitive and behavioral impairment. It is considered to be caused by variety of factors, such as age, environment and genetic factors. In order to identify the genetic affect factors of AD, we carried out a bioinformatic approach which combined genome-wide haplotype-based association study with gene prioritization. The raw SNP genotypes data was downloaded from GEO database (GSE33528). It contains 615 AD patients and 560 controls of Caribbean Hispanic individuals. Firstly, we identified the linkage disequilibrium (LD) haplotype blocks and performed genome-wide haplotype association study to screen significant haplotypes that were associated with AD. Then we mapped these significant haplotypes to genes and obtained candidate genes set for AD. At last, we prioritized AD candidate genes based on their similarity with 36 known AD genes, so as to identify AD related genes. The results showed that 141 haplotypes on 134 LD blocks were significantly associated with AD (P<1E-4), and these significant haplotypes were mapped to 132 AD candidate genes. After prioritizing these candidate genes, we found seven AD related genes: APOE, APOC1, TNFRSF1A, LRP1B, CDH1, TG and CASP7. Among these genes, APOE and APOC1 are known AD risk genes. For the other five genes TNFRSF1A, CDH1, CASP7, LRP1B and TG, this is the first genetic association study which showed the significant association between these five genes and AD susceptibility in Caribbean Hispanic individuals. We believe that our findings can provide a new perspective to understand the genetic affect factors of AD.

Identification of p53-target genes in Danio rerio

To orchestrate the genomic response to cellular stress signals, p53 recognizes and binds to DNA containing specific and well-characterized p53-responsive elements (REs). Differences in RE sequences can strongly affect the p53 transactivation capacity and occur even between closely related species. Therefore, the identification and characterization of a species-specific p53 Binding sistes (BS) consensus sequence and of the associated target genes may help to provide new insights into the evolution of the p53 regulatory networks across different species. Although p53 functions were studied in a wide range of species, little is known about the p53-mediated transcriptional signature in Danio rerio. Here, we designed and biochemically validated a computational approach to identify novel p53 target genes in Danio rerio genome. Screening all the Danio rerio genome by pattern-matching-based analysis, we found p53 RE-like patterns proximal to 979 annotated Danio rerio genes. Prioritization analysis identified a subset of 134 candidate pattern-related genes, 31 of which have been investigated in further biochemical assays. Our study identified runx1, axin1, traf4a, hspa8, col4a5, necab2, and dnajc9 genes as novel direct p53 targets and 12 additional p53-controlled genes in Danio rerio genome. The proposed combinatorial approach resulted to be highly sensitive and robust for identifying new p53 target genes also in additional animal species.

Linkage and association analysis of circulating vitamin D and parathyroid hormone identifies novel loci in Alaska Native Yup'ik people

BACKGROUND

Vitamin D deficiency is a well-documented public health issue with both genetic and environmental determinants. Populations living at far northern latitudes are vulnerable to vitamin D deficiency and its health sequelae, although consumption of traditional native dietary pattern rich in fish and marine mammals may buffer the effects of reduced sunlight exposure. To date, few studies have investigated the genetics of vitamin D metabolism in circumpolar populations or considered genediet interactions with fish and n-3 fatty acid intake.

METHODS

We searched for genomic regions exhibiting linkage and association with circulating levels of vitamin D and parathyroid hormone (PTH) in 982 Yup'ik individuals from the Center for Alaska Native Health Research Study. We also investigated potential interactions between genetic variants and a biomarker of traditional dietary intake, the δ15N value.

RESULTS

We identified several novel regions linked with circulating vitamin D and PTH as well as replicated a previous linkage finding on 2p16.2 for vitamin D. Bioinformatic analysis revealed multiple candidate genes for both PTH and vitamin D, including CUBN, MGAT3, and NFKBIA. Targeted association analysis identified NEBL as a candidate gene for vitamin D and FNDC3B for PTH. We observed significant associations between a variant in MXD1 and vitamin D only when an interaction with the δ15N value was included. Finally, we integrated pathway level information to illustrate the biological validity of the proposed candidate genes.

CONCLUSION

We provide evidence of linkage between several biologically plausible genomic regions and vitamin D metabolism in a circumpolar population. Additionally, these findings suggest that a traditional dietary pattern may modulate genetic effects on circulating vitamin D.

An integrative in silico system for predicting dysregulated genes in the human epileptic focus: Application to SLC transporters

OBJECTIVE

Many different gene families are currently being investigated for their potential role in epilepsy and in the response to antiepileptic drugs. A common research challenge is identifying the members of a gene family that are most significantly dysregulated within the human epileptic focus, before taking them forward for resource-intensive functional studies. Published data about transcriptomic changes within the human epileptic focus remains incomplete. A need exists for an accurate in silico system for the prediction of dysregulated genes within the epileptic focus. We present such a bioinformatic system. We demonstrate the validity of our approach by applying it to the solute carrier (SLC) gene family. There are >400 known SLCs. SLCs have never been systematically studied in epilepsy.

METHODS

Using our in silico system, we predicted the SLCs likely to be dysregulated in the epileptic focus. We validated our in silico predictions by identifying ex vivo the SLCs dysregulated in epileptic foci, and determining the overlap between our in silico and ex vivo results. For the ex vivo analysis, we used a custom oligonucleotide microarray containing exon probes for all known SLCs to analyze 24 hippocampal samples obtained from surgery for pharmacoresistant mesial temporal lobe epilepsy and 24 hippocampal samples from normal postmortem controls.

RESULTS

There was a highly significant (p < 9.99 × 10(-7) ) overlap between the genes identified by our in silico and ex vivo strategies. The SLCs identified were either metal ion exchangers or neurotransmitter transporters, which are likely to play a part in epilepsy by influencing neuronal excitability.

SIGNIFICANCE

The identified SLCs are most likely to mediate pharmacoresistance in epilepsy by enhancing the intrinsic severity of epilepsy, but further functional work will be needed to fully evaluate their role. Our successful in silico strategy can be adapted in order to prioritize genes relevant to epilepsy from other gene families.

Dysfunction of the Voltage-Gated K+ Channel β2 Subunit in a Familial Case of Brugada Syndrome

BACKGROUND

The Brugada syndrome is an inherited cardiac arrhythmia associated with high risk of sudden death. Although 20% of patients with Brugada syndrome carry mutations in SCN5A, the molecular mechanisms underlying this condition are still largely unknown.

METHODS AND RESULTS

We combined whole-exome sequencing and linkage analysis to identify the genetic variant likely causing Brugada syndrome in a pedigree for which SCN5A mutations had been excluded. This approach identified 6 genetic variants cosegregating with the Brugada electrocardiographic pattern within the pedigree. In silico gene prioritization pointed to 1 variant residing in KCNAB2, which encodes the voltage-gated K(+) channel β2-subunit (Kvβ2-R12Q). Kvβ2 is widely expressed in the human heart and has been shown to interact with the fast transient outward K(+) channel subunit Kv4.3, increasing its current density. By targeted sequencing of the KCNAB2 gene in 167 unrelated patients with Brugada syndrome, we found 2 additional rare missense variants (L13F and V114I). We then investigated the physiological effects of the 3 KCNAB2 variants by using cellular electrophysiology and biochemistry. Patch-clamp experiments performed in COS-7 cells expressing both Kv4.3 and Kvβ2 revealed a significant increase in the current density in presence of the R12Q and L13F Kvβ2 mutants. Although biotinylation assays showed no differences in the expression of Kv4.3, the total and submembrane expression of Kvβ2-R12Q were significantly increased in comparison with wild-type Kvβ2.

CONCLUSIONS

Altogether, our results indicate that Kvβ2 dysfunction can contribute to the Brugada electrocardiographic pattern.

Mapping genetic determinants of host susceptibility to Pseudomonas aeruginosa lung infection in mice

Background

P. aeruginosa is one of the top three causes of opportunistic human bacterial infections. The remarkable variability in the clinical outcomes of this infection is thought to be associated with genetic predisposition. However, the genes underlying host susceptibility to P. aeruginosa infection are still largely unknown.

Results

As a step towards mapping these genes, we applied a genome wide linkage analysis approach to a mouse model. A large F2 intercross population, obtained by mating P. aeruginosa-resistant C3H/HeOuJ, and susceptible A/J mice, was used for quantitative trait locus (QTL) mapping. The F2 progenies were challenged with a P. aeruginosa clinical strain and monitored for the survival time up to 7 days post-infection, as a disease phenotype associated trait. Selected phenotypic extremes of the F2 distribution were genotyped with high-density single nucleotide polymorphic (SNP) markers, and subsequently QTL analysis was performed. A significant locus was mapped on chromosome 6 and was named P. aeruginosa infection resistance locus 1 (Pairl1). The most promising candidate genes, including Dok1, Tacr1, Cd207, Clec4f, Gp9, Gata2, Foxp1, are related to pathogen sensing, neutrophils and macrophages recruitment and inflammatory processes.

Conclusions

We propose a set of genes involved in the pathogenesis of P. aeruginosa infection that may be explored to complement human studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2676-4) contains supplementary material, which is available to authorized users.

Candidate gene prioritization with Endeavour

Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

SoftPanel: a website for grouping diseases and related disorders for generation of customized panels

BACKGROUND

Targeted next-generation sequencing is playing an increasingly important role in biological research and clinical diagnosis by allowing researchers to sequence high priority genes at much higher depths and at a fraction of the cost of whole genome or exome sequencing. However, in designing the panel of genes to be sequenced, investigators need to consider the tradeoff between the better sensitivity of a broad panel and the higher specificity of a potentially more relevant panel. Although tools to prioritize candidate disease genes have been developed, the great majority of these require prior knowledge and a set of seed genes as input, which is only possible for diseases with a known genetic etiology.

RESULTS

To meet the demands of both researchers and clinicians, we have developed a user-friendly website called SoftPanel. This website is intended to serve users by allowing them to input a single disorder or a disorder group and generate a panel of genes predicted to underlie the disorder of interest. Various methods of retrieval including a keyword search, browsing of an arborized list of International Classification of Diseases, 10th revision (ICD-10) codes or using disorder phenotypic similarities can be combined to define a group of disorders and the genes known to be associated with them. Moreover, SoftPanel enables users to expand or refine a gene list by utilizing several biological data resources. In addition to providing users with the facility to create a "hard" panel that contains an exact gene list for targeted sequencing, SoftPanel also enables generation of a "soft" panel of genes, which may be used to further filter a significantly altered set of genes identified through whole genome or whole exome sequencing. The service and data provided by SoftPanel can be accessed at http://www.isb.pku.edu.cn/SoftPanel/ . A tutorial page is included for trying out sample data and interpreting results.

CONCLUSION

SoftPanel provides a convenient and powerful tool for creating a targeted panel of potential disease genes while supporting different forms of input. SoftPanel may be utilized in both genomics research and personalized medicine.

Beegle: from literature mining to disease-gene discovery

Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle, an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes. Beegle is publicly available at http://beegle.esat.kuleuven.be/.

A Single Amino Acid Deletion (ΔF1502) in the S6 Segment of CaV2.1 Domain III Associated with Congenital Ataxia Increases Channel Activity and Promotes Ca2+ Influx

Mutations in the CACNA1A gene, encoding the pore-forming Ca_V2.1 (P/Q-type) channel α_1A subunit, result in heterogeneous human neurological disorders, including familial and sporadic hemiplegic migraine along with episodic and progressive forms of ataxia. Hemiplegic Migraine (HM) mutations induce gain-of-channel function, mainly by shifting channel activation to lower voltages, whereas ataxia mutations mostly produce loss-of-channel function. However, some HM-linked gain-of-function mutations are also associated to congenital ataxia and/or cerebellar atrophy, including the deletion of a highly conserved phenylalanine located at the S6 pore region of α_1A domain III (ΔF1502). Functional studies of ΔF1502 Ca_V2.1 channels, expressed in Xenopus oocytes, using the non-physiological Ba²⁺ as the charge carrier have only revealed discrete alterations in channel function of unclear pathophysiological relevance. Here, we report a second case of congenital ataxia linked to the ΔF1502 α_1A mutation, detected by whole-exome sequencing, and analyze its functional consequences on Ca_V2.1 human channels heterologously expressed in mammalian tsA-201 HEK cells, using the physiological permeant ion Ca²⁺. ΔF1502 strongly decreases the voltage threshold for channel activation (by ~ 21 mV), allowing significantly higher Ca²⁺ current densities in a range of depolarized voltages with physiological relevance in neurons, even though maximal Ca²⁺ current density through ΔF1502 Ca_V2.1 channels is 60% lower than through wild-type channels. ΔF1502 accelerates activation kinetics and slows deactivation kinetics of Ca_V2.1 within a wide range of voltage depolarization. ΔF1502 also slowed Ca_V2.1 inactivation kinetic and shifted the inactivation curve to hyperpolarized potentials (by ~ 28 mV). ΔF1502 effects on Ca_V2.1 activation and deactivation properties seem to be of high physiological relevance. Thus, ΔF1502 strongly promotes Ca²⁺ influx in response to either single or trains of action potential-like waveforms of different durations. Our observations support a causative role of gain-of-function Ca_V2.1 mutations in congenital ataxia, a neurodevelopmental disorder at the severe-most end of CACNA1A-associated phenotypic spectrum.

Ehlers-Danlos Syndrome, Hypermobility Type, Is Linked to Chromosome 8p22-8p21.1 in an Extended Belgian Family

Joint hypermobility is a common, mostly benign, finding in the general population. In a subset of individuals, however, it causes a range of clinical problems, mainly affecting the musculoskeletal system. Joint hypermobility often appears as a familial trait and is shared by several heritable connective tissue disorders, including the hypermobility subtype of the Ehlers-Danlos syndrome (EDS-HT) or benign joint hypermobility syndrome (BJHS). These hereditary conditions provide unique models for the study of the genetic basis of joint hypermobility. Nevertheless, these studies are largely hampered by the great variability in clinical presentation and the often vague mode of inheritance in many families. Here, we performed a genome-wide linkage scan in a unique three-generation family with an autosomal dominant EDS-HT phenotype and identified a linkage interval on chromosome 8p22-8p21.1, with a maximum two-point LOD score of 4.73. Subsequent whole exome sequencing revealed the presence of a unique missense variant in the LZTS1 gene, located within the candidate region. Subsequent analysis of 230 EDS-HT/BJHS patients resulted in the identification of three additional rare variants. This is the first reported genome-wide linkage analysis in an EDS-HT family, thereby providing an opportunity to identify a new disease gene for this condition.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine.

Identifying new targets in leukemogenesis using computational approaches

There is a need to identify novel targets in Acute Lymphoblastic Leukemia (ALL), a hematopoietic cancer affecting children, to improve our understanding of disease biology and that can be used for developing new therapeutics. Hence, the aim of our study was to find new genes as targets using in silico studies; for this we retrieved the top 10% overexpressed genes from Oncomine public domain microarray expression database; 530 overexpressed genes were short-listed from Oncomine database. Then, using prioritization tools such as ENDEAVOUR, DIR and TOPPGene online tools, we found fifty-four genes common to the three prioritization tools which formed our candidate leukemogenic genes for this study. As per the protocol we selected thirty training genes from PubMed. The prioritized and training genes were then used to construct STRING functional association network, which was further analyzed using cytoHubba hub analysis tool to investigate new genes which could form drug targets in leukemia. Analysis of the STRING protein network built from these prioritized and training genes led to identification of two hub genes, SMAD2 and CDK9, which were not implicated in leukemogenesis earlier. Filtering out from several hundred genes in the network we also found MEN1, HDAC1 and LCK genes, which re-emphasized the important role of these genes in leukemogenesis. This is the first report on these five additional signature genes in leukemogenesis. We propose these as new targets for developing novel therapeutics and also as biomarkers in leukemogenesis, which could be important for prognosis and diagnosis.

Identifying candidate disease genes using a trace norm constrained bipartite raking model

Computational prediction of genes that play roles in human diseases remains an important but challenging task. In this work, we formulate candidate gene prediction as a bipartite ranking problem combining a task-wise ordered observation model with a latent multitask regression function using the matrix-variate Gaussian process (MV-GP). We then use a trace-norm constrained variational inference approach to obtain the bipartite ranking model variables and the parameters of the underlying multitask regression model. We use this model to predict candidate genes from two gene-disease association data sets and show that our model outperforms current state-of-the-art methods. Finally, we demonstrate the practical utility of our method by successfully recovering well characterized gene-disease associations hidden in our training data.