A compendium of human genes regulating feeding behavior and body weight, its functional characterization and identification of GWAS genes involved in brain-specific PPI network

Abstract

BackgroundObesity is heritable. It predisposes to many diseases. The objectives of this study were to create a compendium of genes relevant to feeding behavior (FB) and/or body weight (BW) regulation; to construct and to analyze networks formed by associations between genes/proteins; and to identify the most significant genes, biological processes/pathways, and tissues/organs involved in BW regulation.ResultsThe compendium of genes controlling FB or BW includes 578 human genes. Candidate genes were identified from various sources, including previously published original research and review articles, GWAS meta-analyses, and OMIM (Online Mendelian Inheritance in Man). All genes were ranked according to knowledge about their biological role in body weight regulation and classified according to expression patterns or functional characteristics. Substantial and overrepresented numbers of genes from the compendium encoded cell surface receptors, signaling molecules (hormones, neuropeptides, cytokines), transcription factors, signal transduction proteins, cilium and BBSome components, and lipid binding proteins or were present in the brain-specific list of tissue-enriched genes identified with TSEA tool. We identified 27 pathways from KEGG, REACTOME and BIOCARTA whose genes were overrepresented in the compendium. Networks formed by physical interactions or homological relationships between proteins or interactions between proteins involved in biochemical/signaling pathways were reconstructed and analyzed. Subnetworks and clusters identified by the MCODE tool included genes/proteins associated with cilium morphogenesis, signal transduction proteins (particularly, G protein–coupled receptors, kinases or proteins involved in response to insulin stimulus) and transcription regulation (particularly nuclear receptors). We ranked GWAS genes according to the number of neighbors in three networks and revealed 22 GWAS genes involved in the brain-specific PPI network. On the base of the most reliable PPIs functioning in the brain tissue, new regulatory schemes interpreting relevance to BW regulation are proposed for three GWAS genes (ETV5, LRP1B, and NDUFS3). ConclusionsA compendium comprising 578 human genes controlling FB or BW was designed, and the most significant functional groups of genes, biological processes/pathways, and tissues/organs involved in BW regulation were revealed. We ranked genes from the GWAS meta-analysis set according to the number and quality of associations in the networks and then according to their involvement in the brain-specific PPI network and proposed new regulatory schemes involving three GWAS genes (ETV5, LRP1B, and NDUFS3) in BW regulation. The compendium is expected to be useful for pathology risk estimation and for design of new pharmacological approaches in the treatment of human obesity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0466-2) contains supplementary material, which is available to authorized users.

Periodontal complications with obesity

“…Two underlying, currently indisputable, laws of obesity suggest that, first, for obesity to develop, energy intake must exceed energy expenditure and, second, obese individuals have a higher level of energy expenditure than lean individuals and therefore require higher energy intake to maintain their higher body mass. 85 As simple as "energy in versus energy out" may sound, resulting in the stereotypical solution of "eat less and exercise more", the list 89,90 However, the recent rapid increase in the prevalence of obesity cannot be accounted for simply by genetic change, suggesting that environmental determinants are also important. 83 Environmental influences on overweight and obesity are primarily related to food intake and physical activity behaviors.…”

Obesidad y genética

“…Ignati eva y col en el año 2016 (4) , con estudios amplios del genoma (GWAS) logró identi fi car 578 genes humanos que intervienen en el control de los hábitos alimenti cios y en la regulación del peso corporal. Nuevamente, en ninguna de las variantes de estos se encontró que su presencia individual tenga poder predicti vo signifi cati vo.…”

“…Se trata de genes involucrados en el sistema hipotalá-mico de regulación del apetito, algunos de los cuales, como la leptina, cumplen además otras funciones (4,10) . La leptina y su receptor se expresan también en el sistema inmune, influyendo en el estado inflamatorio que se encuentra en la obesidad y que predispone no solo a la diabetes, enfermedad cardiovascular y al síndrome metabólico, sino también a enfermedades alérgicas y autoinmunes (10) .…”

Online resources on gene networks containing human and animal data

“…Реконструкция генных сетей обеспечивает методическую основу для исследования закономерностей организации и механизмов генетического контроля функционирования живых систем (Neph et al, 2012;Stergachis et al, 2014), их эволюции (Mustafin et al, 2017), роли генов в развитии патологий Ignatieva et al, 2016;Saik et al, 2016), а также для разработки математических моделей биологических процессов (Подколодный и др., 2016; Podkolodnaya et al, 2017).…”

“…Высокопроизводительные экспериментальные методы, применяемые в современных исследованиях, позволяют получать и анализировать все взаимодействия между определенными типами моле-кулярно-генетических объектов, идентифицированных в конкретном типе клеток (например, ДНК-белковые и белок-белковые взаимодействия). Массовое накопление подобных данных стало предпосылкой для выделения в рамках теории генных сетей таких сущностей, как: 1) сети транскрипционной регуляции (transcriptional regulatory networks) (Reece-Hoyes et al, 2005;Neph et al, 2012); 2) сети взаимодействий между миРНК и соответствующи-ми генами-мишенями (miRNA regulatory networks) (Plaisier et al, 2012;Obermayer, Levine, 2014); а также 3) сети белок-белковых взаимодействий (protein-protein interaction (PPI) networks) (Lei et al, 2013;Ignatieva et al, 2016).…”