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Artigo em Inglês | MEDLINE | ID: mdl-33318029


BACKGROUND: Evidence for aspirin's chemopreventative properties on colorectal cancer (CRC) is substantial, but its mechanism of action is not well-understood. We combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease CRC risk. METHODS: Human colorectal adenoma cells (RG/C2) were treated with aspirin (24 hours) and a stable isotope labeling with amino acids in cell culture (SILAC) based proteomics approach identified altered protein expression. Protein quantitative trait loci (pQTLs) from INTERVAL (N = 3,301) and expression QTLs (eQTLs) from the eQTLGen Consortium (N = 31,684) were used as genetic proxies for protein and mRNA expression levels. Two-sample MR of mRNA/protein expression on CRC risk was performed using eQTL/pQTL data combined with CRC genetic summary data from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). RESULTS: Altered expression was detected for 125/5886 proteins. Of these, aspirin decreased MCM6, RRM2, and ARFIP2 expression, and MR analysis showed that a standard deviation increase in mRNA/protein expression was associated with increased CRC risk (OR: 1.08, 95% CI, 1.03-1.13; OR: 3.33, 95% CI, 2.46-4.50; and OR: 1.15, 95% CI, 1.02-1.29, respectively). CONCLUSIONS: MCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation, indicating a possible role in aspirin's reduction of metastasis. IMPACT: Our approach has shown how laboratory experiments and population-based approaches can combine to identify aspirin-targeted proteins possibly affecting CRC risk.

AMIA Jt Summits Transl Sci Proc ; 2020: 430-439, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32477664


Understanding the different effects of chemical substances on human proteins is fundamental for designing new drugs. It is also important for elucidating the different mechanisms of action of drugs that can cause side-effects. In this context, computational methods for predicting chemical-protein interactions can provide valuable insights on the relation between therapeutic chemical substances and proteins. Their predictions therefore can help in multiple tasks such as drug repurposing, identifying new drug side-effects, etc. Despite their useful predictions, these methods are unable to predict the different implications - such as change in protein expression, abundance, etc, - of chemical - protein interactions. Therefore, In this work, we study the modelling of chemical-protein interactions' effects on proteins activity using computational approaches. We hereby propose using 3D tensors to model chemicals, their target proteins and the effects associated to their interactions. We then use multi-part embedding tensor factorisation to predict the different effects of chemicals on human proteins. We assess the predictive accuracy of our proposed method using a benchmark dataset that we built. We then show by computational experimental evaluation that our approach outperforms other tensor factorisation methods in the task of predicting effects of chemicals on human proteins.

Brief Bioinform ; 2020 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-32065227


Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph's inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug-target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.

Bioinformatics ; 36(2): 603-610, 2020 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-31368482


MOTIVATION: Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates. RESULTS: We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision-recall curves in standard benchmark tests. AVAILABILITY AND IMPLEMENTATION: The data, predictions and models are available at: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reconhecimento Automatizado de Padrão , Proteínas , Simulação por Computador , Interações Medicamentosas , Bases de Conhecimento
JAMA Oncol ; 3(5): 636-651, 2017 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-28241208


Importance: The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. Objective: To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. Data Sources: Genomewide association studies (GWAS) published up to January 15, 2015. Study Selection: GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. Data Extraction and Synthesis: Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. Main Outcomes and Measures: Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. Results: Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). Conclusions and Relevance: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Predisposição Genética para Doença/genética , Análise da Randomização Mendeliana/métodos , Neoplasias/genética , Homeostase do Telômero/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , Doenças Cardiovasculares/genética , Feminino , Estudo de Associação Genômica Ampla , Mutação em Linhagem Germinativa , Humanos , Masculino , Pessoa de Meia-Idade , Polimorfismo de Nucleotídeo Único , Medição de Risco/métodos , Telômero/genética