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Published year: 20174
Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles.
LHA - Leipzig Health Atlas

Abstract (Expand)

INTRODUCTION: Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. … These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms. METHODS: Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies. RESULTS AND CONCLUSIONS: The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified.

Authors: A. Arakelyan, L. Nersisyan, D. Poghosyan, L. Khondkaryan, A. Hakobyan, H. Loffler-Wirth, E. Melanitou, H. Binder

Date Published: 4th Nov 2017

Publication Type: Not specified

IRS1 DNA promoter methylation and expression in human adipose tissue are related to fat distribution and metabolic traits.
LHA - Leipzig Health Atlas

Abstract (Expand)

The SNP variant rs2943650 near IRS1 gene locus was previously associated with decreased body fat and IRS1 gene expression as well as an adverse metabolic profile in humans. Here, we hypothesize that … these effects may be mediated by an interplay with epigenetic alterations. We measured IRS1 promoter DNA methylation and mRNA expression in paired human subcutaneous and omental visceral adipose tissue samples (SAT and OVAT) from 146 and 41 individuals, respectively. Genotyping of rs2943650 was performed in all individuals (N = 146). We observed a significantly higher IRS1 promoter DNA methylation in OVAT compared to SAT (N = 146, P = 8.0 x 10(-6)), while expression levels show the opposite effect direction (N = 41, P = 0.011). OVAT and SAT methylation correlated negatively with IRS1 gene expression in obese subjects (N = 16, P = 0.007 and P = 0.010). The major T-allele is related to increased DNA methylation in OVAT (N = 146, P = 0.019). Finally, DNA methylation and gene expression in OVAT correlated with anthropometric traits (waist- circumference waist-to-hip ratio) and parameters of glucose metabolism in obese individuals. Our data suggest that the association between rs2943650 near the IRS1 gene locus with clinically relevant variables may at least be modulated by changes in DNA methylation that translates into altered IRS1 gene expression.

Authors: K. Rohde, M. Klos, L. Hopp, X. Liu, M. Keller, M. Stumvoll, A. Dietrich, M. R. Schon, D. Gartner, T. Lohmann, M. Dressler, P. Kovacs, H. Binder, M. Bluher, Y. Bottcher

Date Published: 28th Sep 2017

Publication Type: Not specified

Genome-wide DNA promoter methylation and transcriptome analysis in human adipose tissue unravels novel candidate genes for obesity.
LHA - Leipzig Health Atlas

Abstract (Expand)

OBJECTIVE/METHODS: DNA methylation plays an important role in obesity and related metabolic complications. We examined genome-wide DNA promoter methylation along with mRNA profiles in paired samples … of human subcutaneous adipose tissue (SAT) and omental visceral adipose tissue (OVAT) from non-obese vs. obese individuals. RESULTS: We identified negatively correlated methylation and expression of several obesity-associated genes in our discovery dataset and in silico replicated ETV6 in two independent cohorts. Further, we identified six adipose tissue depot-specific genes (HAND2, HOXC6, PPARG, SORBS2, CD36, and CLDN1). The effects were further supported in additional independent cohorts. Our top hits might play a role in adipogenesis and differentiation, obesity, lipid metabolism, and adipose tissue expandability. Finally, we show that in vitro methylation of SORBS2 directly represses gene expression. CONCLUSIONS: Taken together, our data show distinct tissue specific epigenetic alterations which associate with obesity.

Authors: M. Keller, L. Hopp, X. Liu, T. Wohland, K. Rohde, R. Cancello, M. Klos, K. Bacos, M. Kern, F. Eichelmann, A. Dietrich, M. R. Schon, D. Gartner, T. Lohmann, M. Dressler, M. Stumvoll, P. Kovacs, A. M. DiBlasio, C. Ling, H. Binder, M. Bluher, Y. Bottcher

Date Published: 27th Jan 2017

Publication Type: Not specified

Human Diseases: obesity

Personalized disease phenotypes from massive omics data.
LHA - Leipzig Health Atlas

Abstract (Expand)

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual … on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such "big data." Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Authors: Hans Binder, Lydia Hopp, K. Lembcke, Henry Löffler-Wirth

Date Published: 2017

Publication Type: Not specified

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