267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation.

Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ∼10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms.

Assessing the impact of mutations found in next generation sequencing data over human signaling pathways.

Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.

Babelomics 5.0: functional interpretation for new generations of genomic data.

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.

Screening of CD96 and ASXL1 in 11 patients with Opitz C or Bohring-Opitz syndromes.

Opitz C trigonocephaly (or Opitz C syndrome, OTCS) and Bohring-Opitz syndrome (BOS or C-like syndrome) are two rare genetic disorders with phenotypic overlap. The genetic causes of these diseases are not understood. However, two genes have been associated with OTCS or BOS with dominantly inherited de novo mutations. Whereas CD96 has been related to OTCS (one case) and to BOS (one case), ASXL1 has been related to BOS only (several cases). In this study we analyze CD96 and ASXL1 in a group of 11 affected individuals, including 2 sibs, 10 of them were diagnosed with OTCS, and one had a BOS phenotype. Exome sequences were available on six patients with OTCS and three parent pairs. Thus, we could analyze the CD96 and ASXL1 sequences in these patients bioinformatically. Sanger sequencing of all exons of CD96 and ASXL1 was carried out in the remaining patients. Detailed scrutiny of the sequences and assessment of variants allowed us to exclude putative pathogenic and private mutations in all but one of the patients. In this patient (with BOS) we identified a de novo mutation in ASXL1 (c.2100dupT). By nature and location within the gene, this mutation resembles those previously described in other BOS patients and we conclude that it may be responsible for the condition. Our results indicate that in 10 of 11, the disease (OTCS or BOS) cannot be explained by small changes in CD96 or ASXL1. However, the cohort is too small to make generalizations about the genetic etiology of these diseases.

A web tool for the design and management of panels of genes for targeted enrichment and massive sequencing for clinical applications.

Disease targeted sequencing is gaining importance as a powerful and cost-effective application of high throughput sequencing technologies to the diagnosis. However, the lack of proper tools to process the data hinders its extensive adoption. Here we present TEAM, an intuitive and easy-to-use web tool that fills the gap between the predicted mutations and the final diagnostic in targeted enrichment sequencing analysis. The tool searches for known diagnostic mutations, corresponding to a disease panel, among the predicted patient's variants. Diagnostic variants for the disease are taken from four databases of disease-related variants (HGMD-public, HUMSAVAR, ClinVar and COSMIC.) If no primary diagnostic variant is found, then a list of secondary findings that can help to establish a diagnostic is produced. TEAM also provides with an interface for the definition of and customization of panels, by means of which, genes and mutations can be added or discarded to adjust panel definitions. TEAM is freely available at: http://team.babelomics.org.

A web-based interactive framework to assist in the prioritization of disease candidate genes in whole-exome sequencing studies.

Whole-exome sequencing has become a fundamental tool for the discovery of disease-related genes of familial diseases and the identification of somatic driver variants in cancer. However, finding the causal mutation among the enormous background of individual variability in a small number of samples is still a big challenge. Here we describe a web-based tool, BiERapp, which efficiently helps in the identification of causative variants in family and sporadic genetic diseases. The program reads lists of predicted variants (nucleotide substitutions and indels) in affected individuals or tumor samples and controls. In family studies, different modes of inheritance can easily be defined to filter out variants that do not segregate with the disease along the family. Moreover, BiERapp integrates additional information such as allelic frequencies in the general population and the most popular damaging scores to further narrow down the number of putative variants in successive filtering steps. BiERapp provides an interactive and user-friendly interface that implements the filtering strategy used in the context of a large-scale genomic project carried out by the Spanish Network for Research in Rare Diseases (CIBERER) in which more than 800 exomes have been analyzed. BiERapp is freely available at: http://bierapp.babelomics.org/

Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure.

Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10-25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to identify a blood-based gene variant profile using accumulation of rare variants to promote precision medicine in fertility preservation programs. A case-control (n = 118, n = 32, respectively) WES study was performed in which only non-synonymous rare variants <5% minor allele frequency (MAF; in the IGSR) and coverage ≥ 100× were considered. A profile of 66 variants of uncertain significance was used for training an unsupervised machine learning model to separate cases from controls (97.2% sensitivity, 99.2% specificity) and stratify the population into two subtypes of OF (A and B) (93.31% sensitivity, 96.67% specificity). Model testing within the IGSR female population predicted 0.5% of women as subtype A and 2.4% as subtype B. This is the first study linking OF to the accumulation of rare variants and generates a new potential taxonomy supporting application of this approach for precision medicine in fertility preservation.

Uterine disorders affecting female fertility: what are the molecular functions altered in endometrium?

OBJECTIVE: To determine the molecular functions of genes exhibiting altered expression in the endometrium of women with uterine disorders affecting fertility. DESIGN: Retrospective analysis integrating case and control data from multiple cohorts with endometrium gene expression in women with uterine disorders. SETTING: Infertility research department affiliated with a university hospital. PATIENT(S): Two hundred and forty women, 121 of whom were controls, 119 of whom had endometrial adenocarcinoma (ADC), recurrent implantation failure (RIF), recurrent pregnancy loss (RPL), or stage II-IV endometriosis. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Genomewide gene expression and altered molecular functions in the endometrium of each uterine disorder. RESULT(S): Using robust analysis methods, we identified statistically significantly altered endometrial functions in all the uterine disorders. Cell cycle alterations were shared among all the pathologies investigated. Endometriosis was characterized by the down-regulation of ciliary processes. Among the endometriosis, ADC, and RIF samples, mitochondrial dysfunction and protein degradation were shared dysregulated processes. In addition, RPL had the most distinct functional profile, and 95% of affected functions were down-regulated. CONCLUSION(S): The most robust functions dysregulated in the endometrium of patients with uterine disorders across sample cohorts implicated an endometrial factor at the gene expression level. This shared endometrial factor affects endometrial receptivity processes.

Mouse Mammary Tumor Virus Signal Peptide Uses a Novel p97-Dependent and Derlin-Independent Retrotranslocation Mechanism To Escape Proteasomal Degradation.

Multiple pathogens, including viruses and bacteria, manipulate endoplasmic reticulum-associated degradation (ERAD) to avoid the host immune response and promote their replication. The betaretrovirus mouse mammary tumor virus (MMTV) encodes Rem, which is a precursor protein that is cleaved into a 98-amino-acid signal peptide (SP) and a C-terminal protein (Rem-CT). SP uses retrotranslocation for ER membrane extraction and yet avoids ERAD by an unknown mechanism to enter the nucleus and function as a Rev-like protein. To determine how SP escapes ERAD, we used a ubiquitin-activated interaction trap (UBAIT) screen to trap and identify transient protein interactions with SP, including the ERAD-associated p97 ATPase, but not E3 ligases or Derlin proteins linked to retrotranslocation, polyubiquitylation, and proteasomal degradation of extracted proteins. A dominant negative p97 ATPase inhibited both Rem and SP function. Immunoprecipitation experiments indicated that Rem, but not SP, is polyubiquitylated. Using both yeast and mammalian expression systems, linkage of a ubiquitin-like domain (UbL) to SP or Rem induced degradation by the proteasome, whereas SP was stable in the absence of the UbL. ERAD-associated Derlin proteins were not required for SP activity. Together, these results suggested that Rem uses a novel p97-dependent, Derlin-independent retrotranslocation mechanism distinct from other pathogens to avoid SP ubiquitylation and proteasomal degradation.IMPORTANCE Bacterial and viral infections produce pathogen-specific proteins that interfere with host functions, including the immune response. Mouse mammary tumor virus (MMTV) is a model system for studies of human complex retroviruses, such as HIV-1, as well as cancer induction. We have shown that MMTV encodes a regulatory protein, Rem, which is cleaved into an N-terminal signal peptide (SP) and a C-terminal protein (Rem-CT) within the endoplasmic reticulum (ER) membrane. SP function requires ER membrane extraction by retrotranslocation, which is part of a protein quality control system known as ER-associated degradation (ERAD) that is essential to cellular health. Through poorly understood mechanisms, certain pathogen-derived proteins are retrotranslocated but not degraded. We demonstrate here that MMTV SP retrotranslocation from the ER membrane avoids degradation through a unique process involving interaction with cellular p97 ATPase and failure to acquire cellular proteasome-targeting sequences.