27 research outputs found

    Convergence of genetic influences in comorbidity

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    Abstract Background Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data. Methods We modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data. Results After validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants. Conclusions ModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.http://deepblue.lib.umich.edu/bitstream/2027.42/112931/1/12859_2012_Article_5068.pd

    LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types

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    Abstract Background The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established. Results We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance. Conclusions Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.http://deepblue.lib.umich.edu/bitstream/2027.42/112789/1/12864_2012_Article_4373.pd

    Using l‐Carnitine as a Pharmacologic Probe of the Interpatient and Metabolic Variability of Sepsis

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162752/2/phar2448_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162752/1/phar2448.pd

    Metabolomic signatures associated with weight gain and psychosis spectrum diagnoses: A pilot study

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    Psychosis spectrum disorders (PSDs), as well as other severe mental illnesses where psychotic features may be present, like bipolar disorder, are associated with intrinsic metabolic abnormalities. Antipsychotics (APs), the cornerstone of treatment for PSDs, incur additional metabolic adversities including weight gain. Currently, major gaps exist in understanding psychosis illness biomarkers, as well as risk factors and mechanisms for AP-induced weight gain. Metabolomic profiles may identify biomarkers and provide insight into the mechanistic underpinnings of PSDs and antipsychotic-induced weight gain. In this 12-week prospective naturalistic study, we compared serum metabolomic profiles of 25 cases within approximately 1 week of starting an AP to 6 healthy controls at baseline to examine biomarkers of intrinsic metabolic dysfunction in PSDs. In 17 of the case participants with baseline and week 12 samples, we then examined changes in metabolomic profiles over 12 weeks of AP treatment to identify metabolites that may associate with AP-induced weight gain. In the cohort with pre-post data (n = 17), we also compared baseline metabolomes of participants who gained ≄5% baseline body weight to those who gained <5% to identify potential biomarkers of antipsychotic-induced weight gain. Minimally AP-exposed cases were distinguished from controls by six fatty acids when compared at baseline, namely reduced levels of palmitoleic acid, lauric acid, and heneicosylic acid, as well as elevated levels of behenic acid, arachidonic acid, and myristoleic acid (FDR < 0.05). Baseline levels of the fatty acid adrenic acid was increased in 11 individuals who experienced a clinically significant body weight gain (≄5%) following 12 weeks of AP exposure as compared to those who did not (FDR = 0.0408). Fatty acids may represent illness biomarkers of PSDs and early predictors of AP-induced weight gain. The findings may hold important clinical implications for early identification of individuals who could benefit from prevention strategies to reduce future cardiometabolic risk, and may lead to novel, targeted treatments to counteract metabolic dysfunction in PSDs

    Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations

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    Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy

    Metabolic consequences of sepsis-induced acute lung injury revealed by plasma 1H-nuclear magnetic resonance quantitative metabolomics and computational analysis

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    Metabolomics is an emerging component of systems biology that may be a viable strategy for the identification and validation of physiologically relevant biomarkers. Nuclear magnetic resonance (NMR) spectroscopy allows for establishing quantitative data sets for multiple endogenous metabolites without preconception. Sepsis-induced acute lung injury (ALI) is a complex and serious illness associated with high morbidity and mortality for which there is presently no effective pharmacotherapy. The goal of this study was to apply 1H-NMR based quantitative metabolomics with subsequent computational analysis to begin working towards elucidating the plasma metabolic changes associated with sepsis-induced ALI. To this end, this pilot study generated quantitative data sets that revealed differences between patients with ALI and healthy subjects in the level of the following metabolites: total glutathione, adenosine, phosphatidylserine, and sphingomyelin. Moreover, myoinositol levels were associated with acute physiology scores (APS) (ρ = −0.53, P = 0.05, q = 0.25) and ventilator-free days (ρ = −0.73, P = 0.005, q = 0.01). There was also an association between total glutathione and APS (ρ = 0.56, P = 0.04, q = 0.25). Computational network analysis revealed a distinct metabolic pathway for each metabolite. In summary, this pilot study demonstrated the feasibility of plasma 1H-NMR quantitative metabolomics because it yielded a physiologically relevant metabolite data set that distinguished sepsis-induced ALI from health. In addition, it justifies the continued study of this approach to determine whether sepsis-induced ALI has a distinct metabolic phenotype and whether there are predictive biomarkers of severity and outcome in these patients