Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

A systems approach identifies time-dependent associations of multimorbidities with pancreatic cancer risk.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed in late adulthood; therefore, many patients suffer or have suffered from other diseases. Identifying disease patterns associated with PDAC risk may enable a better characterization of high-risk patients. METHODS: Multimorbidity patterns (MPs) were assessed from 17 self-reported conditions using hierarchical clustering, principal component, and factor analyses in 1705 PDAC cases and 1084 controls from a European population. Their association with PDAC was evaluated using adjusted logistic regression models. Time since diagnosis of morbidities to PDAC diagnosis/recruitment was stratified into recent (<3 years) and long term (≥3 years). The MPs and PDAC genetic networks were explored with DisGeNET bioinformatics-tool which focuses on gene-diseases associations available in curated databases. RESULTS: Three MPs were observed: gastric (heartburn, acid regurgitation, Helicobacter pylori infection, and ulcer), metabolic syndrome (obesity, type-2 diabetes, hypercholesterolemia, and hypertension), and atopic (nasal allergies, skin allergies, and asthma). Strong associations with PDAC were observed for ≥2 recently diagnosed gastric conditions [odds ratio (OR), 6.13; 95% confidence interval CI 3.01-12.5)] and for ≥3 recently diagnosed metabolic syndrome conditions (OR, 1.61; 95% CI 1.11-2.35). Atopic conditions were negatively associated with PDAC (high adherence score OR for tertile III, 0.45; 95% CI, 0.36-0.55). Combining type-2 diabetes with gastric MP resulted in higher PDAC risk for recent (OR, 7.89; 95% CI 3.9-16.1) and long-term diagnosed conditions (OR, 1.86; 95% CI 1.29-2.67). A common genetic basis between MPs and PDAC was observed in the bioinformatics analysis. CONCLUSIONS: Specific multimorbidities aggregate and associate with PDAC in a time-dependent manner. A better characterization of a high-risk population for PDAC may help in the early diagnosis of this cancer. The common genetic basis between MP and PDAC points to a mechanistic link between these conditions.

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns.

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools.

A knowledge-driven approach to extract disease-related biomarkers from the literature.

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

Expression of epithelial cadherin in the human male reproductive tract and gametes and evidence of its participation in fertilization.

Epithelial cadherin (E-cadherin) has been involved in several calcium-dependent cell-cell adhesion events; however, its participation in gamete interaction has not been fully investigated. Our results have demonstrated expression of E-cadherin mRNA in the human male reproductive tract showing higher levels in the caput, corpus and cauda epididymis than in the testis. The mature 122 kDa E-cadherin was detected in epididymal protein extracts and was localized in the epithelial cells from the three epididymal regions. Moreover, the 86 kDa E-cadherin ectodomain was found in cauda epididymal and seminal plasma. Western immunoblotting of human sperm protein extracts allowed the identification of four E-cadherin forms (122, 105, 97 and 86 kDa). The protein was localized in the acrosomal region of intact spermatozoa, remained associated with the head of acrosome-reacted cells and was also detected on the oocyte surface. A similar localization was determined for other proteins of the adhesion complex (beta-catenin and actin). Spermatozoa incubated with anti-E-cadherin antibodies showed impaired binding to homologous zona pellucida (ZP); in addition, presence of these antibodies inhibited the penetration of human spermatozoa to ZP-free hamster oocytes. The results presented here describe the expression of E-cadherin in the male reproductive tract and gametes and strongly suggest its involvement in adhesion events during human fertilization. The identification of proteins involved in gamete interaction will contribute to the understanding of the molecular basis of fertilization and help in the diagnosis and treatment of infertility.

Expression of human proacrosin in Escherichia coli and binding to zona pellucida.

Proacrosin is a multifunctional protein present in the sperm acrosome. This study characterizes the expression of human proacrosin in bacteria and assesses zona pellucida binding activity. The cDNA encoding human proacrosin was subcloned in pGEX-3X and pET-22b vectors. In the pGEX system, expression of the full-length fusion protein was not detected. In the pET system, an expression product with an apparent molecular size similar to that expected for the proenzyme (Rec-40, 42-44 kDa) was recognized by a monoclonal antibody to human acrosin, AcrC5F10. A 32-34-kDa protein (Rec-30), not recognized by AcrC5F10 on Western blots, was the major expression product. Proteins of 21 (Rec-20) and 18 (Rec-10) kDa were recovered as insoluble expression products as were Rec-40 and Rec-30, and truncated products from the C terminus were detected in the soluble fraction. Rec-40 and Rec-30 coexisted at any culture time tested. Immune serum raised against Rec-30 (AntiRec-30) stained the acrosomal region of permeabilized human spermatozoa and recognized the recombinant proteins and proacrosin from human sperm extracts. Amino acid sequence analysis indicated that Rec-30, Rec-20, and Rec-10 are N-terminal fragments of proacrosin. The recombinant proteins Rec-40, -30, -20, and -10 were found to interact with homologous (125)I-zona pellucida glycoproteins.