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Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
© The Author(s) 2019. Published by Oxford University Press.
Advances in understanding the biological bases of aging have intellectually revitalized the field of geriatric psychiatry and broadened its scope to include promoting successful aging and studying resilience factors in older adults. To describe the process by which this paradigm shift has occurred and illustrate its implications for treatment and research of late-life brain disorders, late-life depression is discussed as a prototype case. Prior phases of geriatric psychiatry research were focused on achieving depressive symptom relief, outlining pharmacokinetic and pharmacodynamic differences between older and younger adults, and identifying moderators of treatment response. Building on this work, current geriatric psychiatry researchers have begun to disentangle the etiologic complexity in late-life depression by focusing on the causative aging-related processes involved, identifying both neurobiological and behavioral intermediates, and finally delineating depression subtypes that are distinguishable by their underlying biology and the treatment approach required. In this review, we discuss several age-related processes that are critical to the development of late-life mood disorders, outline implications of these processes for the clinical evaluation and management of later-life psychiatric disorders, and finally put forth suggestions for better integrating aging and developmental processes into the National Institute of Mental Health's Research Domain Criteria.
© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org.
Cardiometabolic diseases are the leading cause of death worldwide and are strongly linked to both genetic and nutritional factors. The field of nutrigenomics encompasses multiple approaches aimed at understanding the effects of diet on health or disease development, including nutrigenetic studies investigating the relationship between genetic variants and diet in modulating cardiometabolic risk, as well as the effects of dietary components on multiple "omic" measures, including transcriptomics, metabolomics, proteomics, lipidomics, epigenetic modifications, and the microbiome. Here, we describe the current state of the field of nutrigenomics with respect to cardiometabolic disease research and outline a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at understanding mechanisms and developing new therapeutic options for cardiometabolic disease treatment and prevention.
© 2016 American Heart Association, Inc.
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients frequently develop glucose intolerance and post-transplant diabetes mellitus (PTDM). The clinical importance of PTDM and its detrimental impact on HSCT outcomes are under-recognized. After allo-HSCT, various mechanisms can contribute to the development of PTDM. Here we review information about hyperglycemia and PTDM after allo-HSCT as well as PTDM after solid organ transplantation and describe ways to manage hyperglycemia/PTDM after allogeneic HSCT. Taking into consideration a lack of well-established evidence in the field of allo-HSCT, more studies should be conducted in the future, which will require closer multidisciplinary collaboration between hematologists, endocrinologists and nutritionists.
BACKGROUND - Development of the intestinal subtype of gastric adenocarcinoma is marked by a progression of histopathologic lesions. Residents of the Andean regions of Colombia are at high risk for gastric cancer.
METHODS - A cohort of 976 Colombian subjects was followed over 16 years examining effects of Helicobacter pylori eradication and treatment with antioxidants on progression of lesions. We performed methylation analysis of DNA from baseline antral biopsies from 104 subjects for whom follow-up data were available for at least 12 years. Methylation was quantitated for AMPH, CDKN2A, CDH1, EN1, EMX1, NKX6-1, PCDH10, RPRM, RSPO2, SORCS3, ZIC1, and ZNF610 genes, using Pyrosequencing.
RESULTS - Levels of DNA methylation were associated with baseline diagnosis for AMPH, EMX1, RPRM, RSPO2, SORCS3, and ZNF610. After adjusting for baseline diagnosis and H. pylori infection, methylation levels of AMPH, PCDH10, RSPO2, and ZNF610 had progression coefficients that increased and P values that decreased over 6, 12, and 16 years. Methylation for SORCS3 was associated with progression at all 3 time points but without the continual strengthening of the effect. Scores for mononuclear leukocytes, polymorphonuclear leukocytes, or intraepithelial lymphocytes were unrelated to progression.
CONCLUSIONS - Methylation levels of AMPH, PCDH10, RSPO2, SORCS3, and ZNF610 predict progression of gastric lesions independent of the effect of duration of H. pylori infection, baseline diagnosis, gender of the patient, or scores for mononuclear leukocytes, polymorphonuclear leukocytes, or intraepithelial lymphocytes.
IMPACT - DNA methylation levels in AMPH, PCDH10, RSPO2, SORCS3, and ZNF610 may contribute to identification of persons with gastric lesions likely to progress.
©2015 American Association for Cancer Research.
BACKGROUND - Chronic inflammation plays a key role in cancer etiology. DNA methylation modification, one of the epigenetic mechanisms regulating gene expression, is considered a hallmark of cancer. Human and animal models have identified numerous links between DNA methylation and inflammatory biomarkers. Our objective was to prospectively and longitudinally examine associations between methylation of four inflammatory genes and cancer risk.
METHODS - We included 795 Normative Aging Study participants with blood drawn one to four times from 1999 to 2012 (median follow-up, 10.6 years). Promoter DNA methylation of IL6, ICAM-1, IFN, and TLR2 in blood leukocytes was measured using pyrosequencing at multiple CpG sites and averaged by gene for data analysis. We used Cox regression models to examine prospective associations of baseline and time-dependent methylation with cancer risk and compared mean methylation differences over time between cancer cases and cancer-free participants.
RESULTS - Baseline IFN hypermethylation was associated with all-cancer (HR, 1.49; P = 0.04) and prostate cancer incidence (HR, 1.69; P = 0.02). Baseline ICAM-1 and IL6 hypermethylation were associated with prostate cancer incidence (HR, 1.43; P = 0.02; HR, 0.70; P = 0.03, respectively). In our time-dependent analyses, IFN hypermethylation was associated with all-cancer (HR, 1.79; P = 0.007) and prostate cancer (HR, 1.57; P = 0.03) incidence; and ICAM-1 and IL6 hypermethylation were associated with prostate cancer incidence (HR, 1.39; P = 0.02; HR, 0.69; P = 0.03, respectively). We detected significant ICAM-1 hypermethylation in cancer cases (P = 0.0003) 10 to 13 years prediagnosis.
CONCLUSION - Hypermethylation of IFN and ICAM-1 may play important roles in early carcinogenesis, particularly that of prostate cancer.
IMPACT - These methylation changes could inform the development of early detection biomarkers and potential treatments of inflammation-related carcinogenesis.
©2015 American Association for Cancer Research.
Recent development of high-resolution mass spectrometry (MS) instruments enables chemical crosslinking (XL) to become a high-throughput method for obtaining structural information about proteins. Restraints derived from XL-MS experiments have been used successfully for structure refinement and protein-protein docking. However, one formidable question is under which circumstances XL-MS data might be sufficient to determine a protein's tertiary structure de novo? Answering this question will not only include understanding the impact of XL-MS data on sampling and scoring within a de novo protein structure prediction algorithm, it must also determine an optimal crosslinker type and length for protein structure determination. While a longer crosslinker will yield more restraints, the value of each restraint for protein structure prediction decreases as the restraint is consistent with a larger conformational space. In this study, the number of crosslinks and their discriminative power was systematically analyzed in silico on a set of 2055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive crosslinkers between 1 and 60Å. Depending on the protein size a heuristic was developed that determines the optimal crosslinker length. Next, simulated restraints of variable length were used to de novo predict the tertiary structure of fifteen proteins using the BCL::Fold algorithm. The results demonstrate that a distinct crosslinker length exists for which information content for de novo protein structure prediction is maximized. The sampling accuracy improves on average by 1.0 Å and up to 2.2 Å in the most prominent example. XL-MS restraints enable consistently an improved selection of native-like models with an average enrichment of 2.1.
Copyright © 2015. Published by Elsevier Inc.
We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.
©2015 American Association for Cancer Research.