Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

Background A non-invasive method to grade the severity of steatohepatitis and liver fibrosis is magnetic resonance imaging (MRI) based corrected T1 (cT1). We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases. Methods First, we performed a genome-wide association study (GWAS) in 14,440 Europeans in UK Biobank with liver cT1 measures. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures. Results We identified six independent genetic variants associated with liver cT1 that reached GWAS significance threshold (p<5x10-8). Four of the variants (rs75935921 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated transaminases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and BMI were causally associated with elevated cT1 whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective. Conclusion The association between two metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at risk individuals

Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poorer accuracy. Supervised machine learning methods can potentially be used in order to fully exploit the dimensionality and richness of the data. In this paper, we systematically implement and evaluate a set of supervised learning methods (L1 regression, random forest classifier) and compare them to traditional regression-based approaches for disease prediction using metabolomic data

EVALUATION OF IN VITRO ANTICANCER ACTIVITY OF SYMPLOCOS RACEMOSA BARK AGAINST HEPATOCELLULAR CARCINOMA

Objective: To investigate in vitro anticancer activity of different extracts of bark of Symplocos racemosa against hepatocellular carcinoma.Methods: Different successive extracts of Symplocos racemosa bark were prepared using hexane, chloroform, ethyl acetate, n-butanol and water and were tested in vitro for cytotoxicity using (3-4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay in rat normal liver cells (BRL-3A) and human hepatocellular carcinoma (Hep3B) cells.Results: Ethyl acetate and chloroform extract of Symplocos racemosa exhibited cytotoxicity against human hepatocellular carcinoma (Hep3B) cells in vitro with IC50 value (µg/ml) of 63.45 and 75.55 respectively and not affected the normal liver (BRL-3A) cells.Conclusion: Symplocos racemosa bark extracts showed potential cytotoxic effects on human hepatocellular carcinoma cells. The anticancer activity exhibited by ethyl acetate and chloroform extract might be due to presence of phenolics and flavonoid constituents present in the bark. Ethyl acetate extract can further be explored for possible cytotoxic activity using in vivo models of liver cancer.Â

Flipping the odds of drug development success through human genomics

Drug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies. Errors in drug target specification contribute to the extremely high rates of drug development failure. Integrating knowledge of genes that encode druggable targets with those that influence susceptibility to common disease has the potential to radically improve the probability of drug development success

Circulating Apolipoprotein E Concentration and Cardiovascular Disease Risk: Meta-analysis of Results from Three Studies

Background: The association of APOE genotype with circulating apolipoprotein E (ApoE) concentration and cardiovascular disease (CVD) risk is well established. However, the relationship of circulating ApoE concentration and CVD has received little attention. Methods and Findings: To address this, we measured circulating ApoE concentration in 9,587 individuals (with 1,413 CVD events) from three studies with incident CVD events: two population-based studies, the English Longitudinal Study of Ageing (ELSA) and the men-only Northwick Park Heart Study II (NPHSII), and a nested sub-study of the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). We examined the association of circulating ApoE with cardiovascular risk factors in the two population-based studies (ELSA and NPHSII) and the relationship between ApoE concentration and coronary heart disease and stroke in all three studies. Analyses were carried out within study, and, where appropriate, pooled effect estimates were derived using meta-analysis. In the population-based samples, circulating ApoE was associated with systolic blood pressure (correlation coefficient 0.08, p < 0.001, in both ELSA and NPHSII), total cholesterol (correlation coefficient 0.46 and 0.34 in ELSA and NPHSII, respectively; both p < 0.001), low-density lipoprotein cholesterol (correlation coefficient 0.30 and 0.14, respectively; both p < 0.001), high-density lipoprotein (correlation coefficient 0.16 and ?0.14, respectively; both p < 0.001), and triglycerides (correlation coefficient 0.43 and 0.46, respectivly; both p < 0.001). In NPHSII, ApoE concentration was additionally associated with apolipoprotein B (correlation coefficient 0.13, p = 0.001) and lipoprotein(a) (correlation coefficient ?0.11, p < 0.001). In the pooled analysis of ASCOT, ELSA, and NPHSII, there was no association of ApoE with CVD events; the odds ratio (OR) for CVD events per 1-standard-deviation higher ApoE concentration was 1.02 (95% CI 0.96, 1.09). After adjustment for cardiovascular risk factors, the OR for CVD per 1-standard-deviation higher ApoE concentration was 0.97 (95% CI 0.82, 1.15). Limitations of these analyses include a polyclonal method of ApoE measurement, rather than isoform-specific measurement, a moderate sample size (although larger than any other study to our knowledge and with a long lag between ApoE measures), and CVD events that may attenuate an effect. Conclusions: In the largest study to date on this question, we found no evidence of an association of circulating ApoE concentration with CVD events. The established association of APOE genotype with CVD events may be explained by isoform-specific functions as well as other mechanisms, rather than circulating concentrations of ApoE

Addressing ethical challenges in the Genetics Substudy of the National Eye Survey of Trinidad and Tobago (GSNESTT).

BACKGROUND: The conduct of international collaborative genomics research raises distinct ethical challenges that require special consideration, especially if conducted in settings that are research-naïve or resource-limited. Although there is considerable literature on these issues, there is a dearth of literature chronicling approaches taken to address these issues in the field. Additionally no previous ethical guidelines have been developed to support similar research in Trinidad and Tobago. METHODS: A literature review was undertaken to identify strategies used to address common ethical issues relevant to human genetics and genomics research in research-naïve or resource-limited settings. Strategies identified were combined with novel approaches to develop a culturally appropriate, multifaceted strategy to address potential challenges in the Genetics Substudy of the National Eye Survey of Trinidad and Tobago (GSNESTT). RESULTS: Regarding the protection of study participants, we report a decision to exclude children as participants; the use of a Community Engagement and Sensitization Strategy to increase the genetic literacy of the target population; the involvement of local expertise to ensure cultural sensitivity and to address potential comprehension barriers in informed consent; and an audit of the informed consent process to ensure valid consent. Concerning the regulation of the research, we report on ethics approvals from relevant authorities; a Materials Transfer Agreement to guide sample ownership and export; and a Sample Governance Committee to oversee data use and data access. Finally regarding the protection of the interests of scientists from the host country, we report on capacity building efforts to ensure that local scientists have access to data collected through the project and appropriate recognition of their contributions in future publications. CONCLUSION: This paper outlines an ethical framework for the conduct of population-based genetics and genomics research in Trinidad and Tobago; highlights common issues arising in the field and strategies to address these

Genetic drug target validation using Mendelian randomisation

Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses

Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

The PROGRESS series (www.progress-partnership.org) sets out a framework of four interlinked prognosis research themes and provides examples from several disease fields to show why evidence from prognosis research is crucial to inform all points in the translation of biomedical and health related research into better patient outcomes. Recommendations are made in each of the four papers to improve current research standards What is prognosis research? Prognosis research seeks to understand and improve future outcomes in people with a given disease or health condition. However, there is increasing evidence that prognosis research standards need to be improved Why is prognosis research important? More people now live with disease and conditions that impair health than at any other time in history; prognosis research provides crucial evidence for translating findings from the laboratory to humans, and from clinical research to clinical practice This first article introduces the framework of four interlinked prognosis research themes and then focuses on the first of the themes - fundamental prognosis research, studies that aim to describe and explain future outcomes in relation to current diagnostic and treatment practices, often in relation to quality of care Fundamental prognosis research provides evidence informing healthcare and public health policy, the design and interpretation of randomised trials, and the impact of diagnostic tests on future outcome. It can inform new definitions of disease, may identify unanticipated benefits or harms of interventions, and clarify where new interventions are required to improve prognosis