Introduction: Obesity is a multifactorial condition that results from the interactions among genetic, dietary, environmental, and lifestyle factors. In our study, we have employed a novel integrative approach to identify mechanisms involved in human disease. Method: In contrast to previous methodologies employed for integration of heterogeneous OMIC data, we based the integration on genomic positions of alterations in human disease. A data search for various types of studies on obesity (genome-wide association, meta-analysis, transcriptomic, proteomic studies and epigenetic studies) was conducted in literature sources and OMIC data repositories, using GWAS Central and Medline database with search string (obesity) AND (transcriptome OR proteome OR genome-wide OR microarray OR profiling OR epigenetics). Additionally, Gene Expression Omnibus (GEO) repository, Array Express and Stanford Microarray Database were searched to discover suitable sources of data for inclusion in our initial dataset. Results and Discussion: As a result of the employed high through put technology, 71 high scoring regions were identified. We identified 8 high scoring gene regions (ATP5O, ALK7, CR1, CR2, S100, GAPDH, TLR1 and TLR6) that have not yet been associated to obesity. Interestingly, all of these genes were identified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to be implicated in the energy metabolism and the immune response, which are known to be involved in obesity. Conclusion: In our study, we have performed a novel integrative approach to identify candidate regions and genes involved in human disease. The results showed that none of the high scoring genes that were identified were yet associated with obesity per se, but that they were found to be implicated in the immune response or the energy metabolism. Further research will be needed to validate the found gene regions for obesity.

Objective: OMICs research has become of great interest over the last years and enabled the research community to acquisition an increasing amount of data. In a prior study by our group, we have employed a novel positional integrative approach. For this second study we utilized the same integration method but weighed each individual data source trying to verify our already found regions and/or identify new gene regions of interest for obesity. Method:In contrast to previous methodologies employed for integration of heterogeneous OMIC data, we based the integration on genomic positions of alterations in human disease and employed an additional weighing step. A data search for various types of studies on Obesity (genome-wide association, meta-analysis, transcriptomic, proteomic studies and epigenetic studies) was conducted to establish the initial data set. For this study, five different weighing settings were used, to individually double the input of genomic data, transcriptomic data, proteomic data, microRNA data and epigenetic data in comparison to all the other data sources. Results and discussion:The analysis identified ten gene regions (ATP5O, ALK7, GAPDH, IFFO1, NCAPD2, DDX50, MAOB, ATM, STOX1 and PRRC2A). ATP5O, ALK7 and GADPH were also identified in our prior study as the highest scoring gene regions and prove to be consistent in our data set. Even though, the ten high ranked and discussed genes could not be directly linked to obesity nine of them are associated with Type 2 Diabetes or a neurodegenerative disease. Both disorders show a higher prevalence in obese individuals compared to lean. Conclusion:In this study, we applied a new method of positional integrational analysis of different OMIC-layers and an additional validation step through weighing. our study provides a basis for further research to elucidate underlying mechanisms of these associations and identify new targets for preventive and therapeutic interventions.

Background: In recent years, more and more evidence has accumulated to elucidate the modulating roles of microRNA in immune and inflammatory system. We conducted a novel positional omics integration study to identify microRNAs that could shed further light on the possible links between microRNAs, adipose tissue immunity/inflammation and obesity.