PhenoExam: gene set analyses through integration of different phenotype databases.

BACKGROUND: Gene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. RESULTS: PhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. CONCLUSIONS: We developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G' and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes. Github R package URL is https://github.com/alexcis95/PhenoExam . Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/ .

Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data.

Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.

Variants of genes encoding TNF receptors and ligands and proteins regulating TNF activation in familial multiple sclerosis.

INTRODUCTION: Numerous genetic variants have been associated with susceptibility to multiple sclerosis (MS). Variants located in genes involved in specific pathways, such as those affecting TNF-α, can contribute to the risk of MS. The purpose of this study was to determine whether variants of these genes are associated with greater risk of MS. METHODS: We used whole-exome sequencing to study genes coding for TNF-α receptors and ligands, and proteins promoting TNF-α expression in 116 individuals from 19 families including at least two MS patients. We compared patients with MS, patients with other autoimmune diseases, and healthy individuals. RESULTS: Greater polymorphism was observed in several genes in families with familial MS compared to the general population; this may reflect greater susceptibility to autoimmune diseases. Pedigree analysis also revealed that LT-α variants rs1041981 and rs2229094 and LT-ß variant rs4647197 were associated with MS and that LT-ß variant rs4647183 was associated with other autoimmune diseases. The association between autoimmune disease and TNFAIP2 variant rs1132339 is particularly noteworthy, as is the fact that TNFAIP6 variant rs1046668 appears to follow a recessive inheritance pattern. CONCLUSIONS: Our findings support the idea that the risk of familial MS is associated with variants of signaling pathways, including those involving TNF-α.

A Novel Approach for the Identification of Pharmacogenetic Variants in MT-RNR1 through Next-Generation Sequencing Off-Target Data.

Specific genetic variants in the mitochondrially encoded 12S ribosomal RNA gene (MT-RNR1) cause aminoglycoside-induced irreversible hearing loss. Mitochondrial DNA is usually not included in targeted sequencing experiments; however, off-target data may deliver this information. Here, we extract MT-RNR1 genetic variation, including the most relevant ototoxicity variant m.1555A>G, using the off-target reads of 473 research samples, sequenced through a capture-based, custom-targeted panel and whole exome sequencing (WES), and of 1245 diagnostic samples with clinical WES. Sanger sequencing and fluorescence-based genotyping were used for genotype validation. There was a correlation between off-target reads and mitochondrial coverage (rcustomPanel = 0.39, p = 2 × 10-13 and rWES = 0.67, p = 7 × 10-21). The median read depth of MT-RNR1 m.1555 was similar to the average mitochondrial genome coverage, with saliva and blood samples giving comparable results. The genotypes from 415 samples, including three m.1555G carriers, were concordant with fluorescence-based genotyping data. In clinical WES, median MT-RNR1 coverage was 56×, with 90% of samples having ≥20 reads at m.1555 position, and one m.1494T and three m.1555G carriers were identified with no evidence for heteroplasmy. Altogether, this study shows that obtaining MT-RNR1 genotypes through off-target reads is an efficient strategy that can impulse preemptive pharmacogenetic screening of this mitochondrial gene.

Exonic variants of genes related to the vitamin D signaling pathway in the families of familial multiple sclerosis using whole-exome next generation sequencing.

INTRODUCTION: Vitamin D (VD) deficiency has been associated with multiple sclerosis (MS) and other autoimmune diseases (AIDs). However, the effect of the genetics of VD on the risk of MS is subject to debate. This study focuses on genes linked to the VD signaling pathway in families with MS. The evaluation of gene variants in all the members of families could contribute to an additional knowledge on the information obtained from case-control studies that use nonrelated healthy people. MATERIAL AND METHODS: We studied 94 individuals from 15 families including at least two patients with MS. We performed whole-exome next generation sequencing on all individuals and analyzed variants of the DHCR7, CYP2R1, CYP3A4, CYP27A1, GC, CYP27B1, LRP2, CUBN, DAB2, FCGR, RXR, VDR, CYP24A1, and PDIA3 genes. We also studied PTH, FGF23, METTL1, METTL21B, and the role of the linkage disequilibrium block on the long arm of chromosome 12, through analysis of the CDK4, TSFM, AGAP2, and AVIL genes. We compared patients with MS, other AIDs and unaffected members from different family types. RESULTS: The study described the variants in the VD signaling pathway that appear in families with at least two patients with MS. Some infrequent variants were detected in these families, but no significant difference was observed between patients with MS and/or other AIDs and unaffected family members in the frequency of these variants. Variants previously associated with MS in the literature were not observed in these families or were distributed similarly in patients and unaffected family members. CONCLUSION: The study of genes involved in the VD signaling pathway in families that include more than one patient with MS did not identify any variants that could explain the presence of the disease, suggesting that VD metabolism could probably play a role in MS more as an environmental factor rather than as a genetic factor. Our study also supports the analysis of cases and unaffected individuals within families in order to determine the influence of genetic factors.

Whole-exome sequencing analysis in twin sibling males with an anterior cruciate ligament rupture.

Familial predisposition is among the major genetic risk factors for non-contact musculoskeletal tissue injuries. Personal genome sequence shows that different polymorphism profiles may account for the number and the degree of injuries and the recovery time. Genotyping studies allow investigation into genome factors with potential impact on pathogenesis of non-contact ligament injuries. We have studied a family with twin sibling males surgically diagnosed of an anterior cruciate ligament non-contact rupture and non-affected progenitors (father and mother) were subjected to whole exome sequencing (WES) analysis. WES analysis previously carried out on 16 individuals, without ACL injury medical records, were also included in this study for single nucleotide variants (SNVs) and small insertions and deletions detection (indels), variant filtering and to prioritize variants relative to the disease. WES analysis to identify SNVs and indels was performed using open web-based bioinformatics tools. A set of 11 new variants shared by family members can be associated to ACL non-contact injury, including SerpinA11, ARSI, NOCHT4, EPB41, FDFT1, POMC, KIF26A, OLFML2B, ATG7, FAH and WDR6. All of them, except ATG7 and WDR6, have shown a damaging predictive pattern by combinatorial standard predictive scores. In combination to the identified SNVs of EPB41 and SerpinA11 genes, ACTL7A gene showed a predicted deleterious variant reinforcing the idea these variants impact on of fibroblast-like cells deformability and ECM misbalance, Differential gene expression and RNA sequencing analysis will help to understand the combined participation of these protein coding genes in ACL non-contact injuries.

More DNA-Aptamers for Small Drugs: A Capture-SELEX Coupled with Surface Plasmon Resonance and High-Throughput Sequencing.

To address limitations in the production of DNA aptamers against small molecules, we introduce a DNA-based capture-SELEX (systematic evolution of ligands by exponential enrichment) protocol with long and continuous randomized library for more flexibility, coupled with in-stream direct-specificity monitoring via SPR and high throughput sequencing (HTS). Applying this capture-SELEX on tobramycin shows that target-specificity arises at cycle number 8, which is confirmed by sequence convergence in HTS analysis. Interestingly, HTS also shows that the most enriched sequences are already visible after only two capture-SELEX cycles. The best aptamers displayed K(D) of approximately 200 nM, similar to RNA and DNA-based aptamers previously selected for tobramycin. The lowest concentration of tobramycin detected on label-free SPR experiments with the selected aptamers is 20-fold smaller than the clinical range limit, demonstrating suitability for small-drug biosensing.

NOTCH pathway inactivation promotes bladder cancer progression.

NOTCH signaling suppresses tumor growth and proliferation in several types of stratified epithelia. Here, we show that missense mutations in NOTCH1 and NOTCH2 found in human bladder cancers result in loss of function. In murine models, genetic ablation of the NOTCH pathway accelerated bladder tumorigenesis and promoted the formation of squamous cell carcinomas, with areas of mesenchymal features. Using bladder cancer cells, we determined that the NOTCH pathway stabilizes the epithelial phenotype through its effector HES1 and, consequently, loss of NOTCH activity favors the process of epithelial-mesenchymal transition. Evaluation of human bladder cancer samples revealed that tumors with low levels of HES1 present mesenchymal features and are more aggressive. Together, our results indicate that NOTCH serves as a tumor suppressor in the bladder and that loss of this pathway promotes mesenchymal and invasive features.

FireDB: a compendium of biological and pharmacologically relevant ligands.

FireDB (http://firedb.bioinfo.cnio.es) is a curated inventory of catalytic and biologically relevant small ligand-binding residues culled from the protein structures in the Protein Data Bank. Here we present the important new additions since the publication of FireDB in 2007. The database now contains an extensive list of manually curated biologically relevant compounds. Biologically relevant compounds are informative because of their role in protein function, but they are only a small fraction of the entire ligand set. For the remaining ligands, the FireDB provides cross-references to the annotations from publicly available biological, chemical and pharmacological compound databases. FireDB now has external references for 95% of contacting small ligands, making FireDB a more complete database and providing the scientific community with easy access to the pharmacological annotations of PDB ligands. In addition to the manual curation of ligands, FireDB also provides insights into the biological relevance of individual binding sites. Here, biological relevance is calculated from the multiple sequence alignments of related binding sites that are generated from all-against-all comparison of each FireDB binding site. The database can be accessed by RESTful web services and is available for download via MySQL.

APPRIS: annotation of principal and alternative splice isoforms.

Here, we present APPRIS (http://appris.bioinfo.cnio.es), a database that houses annotations of human splice isoforms. APPRIS has been designed to provide value to manual annotations of the human genome by adding reliable protein structural and functional data and information from cross-species conservation. The visual representation of the annotations provided by APPRIS for each gene allows annotators and researchers alike to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, APPRIS also selects a single reference sequence for each gene, here termed the principal isoform, based on the annotations of structure, function and conservation for each transcript. APPRIS identifies a principal isoform for 85% of the protein-coding genes in the GENCODE 7 release for ENSEMBL. Analysis of the APPRIS data shows that at least 70% of the alternative (non-principal) variants would lose important functional or structural information relative to the principal isoform.

firestar--advances in the prediction of functionally important residues.

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.