Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent
MOLECULAR NUTRITION & FOOD RESEARCH
Authors: Smith, Caren E.; Follis, Jack L.; Dashti, Hassan S.; Tanaka, Toshiko; Graff, Mariaelisa; Fretts, Amanda M.; Kilpelainen, Tuomas O.; Wojczynski, Mary K.; Richardson, Kris; Nalls, Mike A.; Schulz, Christina-Alexandra; Liu, Yongmei; Frazier-Wood, Alexis C.; van Eekelen, Esther; Wang, Carol; de Vries, Paul S.; Mikkila, Vera; Rohde, Rebecca; Psaty, Bruce M.; Hansen, Torben; Feitosa, Mary F.; Lai, Chao-Qiang; Houston, Denise K.; Ferruci, Luigi; Ericson, Ulrika; Wang, Zhe; de Mutsert, Renee; Oddy, Wendy H.; de Jonge, Ester A. L.; Seppala, Ilkka; Justice, Anne E.; Lemaitre, Rozenn N.; Sorensen, Thorkild I. A.; Province, Michael A.; Parnell, Laurence D.; Garcia, Melissa E.; Bandinelli, Stefania; Orho-Melander, Marju; Rich, Stephen S.; Rosendaal, Frits R.; Pennell, Craig E.; Kiefte-de Jong, Jessica C.; Kahonen, Mika; Young, Kristin L.; Pedersen, Oluf; Aslibekyan, Stella; Rotter, Jerome I.; Mook-Kanamori, Dennis O.; Zillikens, M. Carola; Raitakari, Olli T.; North, Kari E.; Overvad, Kim; Arnett, Donna K.; Hofman, Albert; Lehtimaeki, Terho; Tjonneland, Anne; Uitterlinden, Andre G.; Rivadeneira, Fernando; Franco, Oscar H.; German, J. Bruce; Siscovick, David S.; Cupples, L. Adrienne; Ordovas, Jose M.
Abstract
Scope: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption. Methods and results: A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10(-7)), and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 x 10(-8)) such that each serving of low-fat dairy was associated with 0.225 kg m(-2) lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure. Conclusion: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.
Systemic biological study for identification of miR-299-5p target genes in cancer
META GENE
Authors: Khoei, Saeideh Gholamzadeh; Manoochehri, Hamed; Saidijam, Massoud
Abstract
Background and aim: MicroRNAs have an essential role in cancer development and progression. They have high potential for using as diagnostic biomarkers and also for therapeutic purposes. MiR-299-5p is frequently dysregulated in variety of cancers. However, there is no precise information about its mechanism of action. So, in this study the key target genes of miR-299-5p were identified through a systems biology approach to perceive the miR-299-5p mechanism of action in carcinogenesis. Methods: TCGA Pan-Cancer (PANCAN) cohort data was used for miR-229-5p Survival analysis. MiRwalk database was used to predict the target genes of miR-299-5p. The co-predicted targets using three or more different software were chosen. Further analysis was carried out using DAVID database for functional annotation and KEGG pathway analysis. Subsequently, STRING online tool and Cytoscape software were used for protein-protein interaction (PPI) network analysis and module selection. Results: MiR-229-5p can significantly predict overall survival of cancer patients. Among 5475 predicted targets for miR-229-5p, 1583 of them were co-predicted by three or more software. The co-predicted targets were mainly enriched in biological processes such as the regulation of the single-organism cellular processes. Four important pathways including Focal adhesion, P53 signaling pathway, renal cell carcinoma, and O-glycan biosynthesis were identified. Eight significant modules with 13 essential hub genes, including RAP1A, XIAP, BIRC3, CCND2, PAK2, PXN, PPP2CB, PPP1R12A, CDKN1A, CYCS, SERPINE1, SIAH1, and HIF1A were recognized after network analysis. Conclusion: In this study we screened many false positive target genes predicted for miR-299-5. Finally, 13 essential hub genes were introduced. These genes potentially are potent cancer biomarkers and suitable therapeutic targets in targeted cancer therapies.
COA
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