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- De Marinis, Yang, et al.
(author)
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Regulation of Nuclear Receptor Interacting Protein 1 (NRIP1) Gene Expression in Response to Weight Loss and Exercise in Humans
- 2017
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In: Obesity. - : Wiley. - 1930-7381. ; 25:8, s. 1400-1409
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Journal article (peer-reviewed)abstract
- Objective: Nuclear receptor interacting protein 1 (NRIP1) is an important energy regulator, but few studies have addressed its role in humans. This study investigated adipose tissue and skeletal muscle NRIP1 gene expression and serum levels in response to weight loss and exercise in humans. Methods: NRIP1 expression was measured by microarray and serum NRIP1 by ELISA and Western blotting. Skeletal muscle transcriptomes were analyzed from Gene Expression Omnibus databases. Network-based proximity analysis was performed on the proximity of NRIP1 interacting genes in the human interactome. Results: In patients with obesity, adipose tissue NRIP1 mRNA expression increased during weight loss and weight maintenance and showed strong associations with metabolic markers and anthropometric parameters. Serum NRIP1 protein levels also increased after weight loss. In skeletal muscle, imposed rest increased NRIP1 expression by 80%, and strength training increased expression by ∼25% compared to baseline. Following rest, NRIP1 expression became sensitive to insulin stimulation. After re-training, NRIP1 expression decreased. Interactome analysis showed significant proximity of NRIP1 interacting partners to the obesity network/module. Conclusions: NRIP1 gene expression and serum levels are strongly associated with metabolic states such as obesity, weight loss, different types of exercise, and peripheral tissue insulin resistance, potentially as a mediator of sedentary effects.
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- Liu, Xueming, et al.
(author)
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Robustness and lethality in multilayer biological molecular networks
- 2020
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Journal article (peer-reviewed)abstract
- Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein–protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.
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- Ripatti, Samuli, et al.
(author)
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A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses
- 2010
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In: The Lancet. - 1474-547X. ; 376:9750, s. 1393-1400
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Journal article (peer-reviewed)abstract
- Background Comparison of patients with coronary heart disease and controls in genome-wide association studies has revealed several single nucleotide polymorphisms (SNPs) associated with coronary heart disease. We aimed to establish the external validity of these findings and to obtain more precise risk estimates using a prospective cohort design. Methods We tested 13 recently discovered SNPs for association with coronary heart disease in a case-control design including participants differing from those in the discovery samples (3829 participants with prevalent coronary heart disease and 48 897 controls free of the disease) and a prospective cohort design including 30 725 participants free of cardiovascular disease from Finland and Sweden. We modelled the 13 SNPs as a multilocus genetic risk score and used Cox proportional hazards models to estimate the association of genetic risk score with incident coronary heart disease. For case-control analyses we analysed associations between individual SNPs and quintiles of genetic risk score using logistic regression. Findings In prospective cohort analyses, 1264 participants had a first coronary heart disease event during a median 10.7 years' follow-up (IQR 6.7-13.6). Genetic risk score was associated with a first coronary heart disease event. When compared with the bottom quintile of genetic risk score, participants in the top quintile were at 1.66-times increased risk of coronary heart disease in a model adjusting for traditional risk factors (95% CI 1.35-2.04, p value for linear trend=7.3x10(-10)). Adjustment for family history did not change these estimates. Genetic risk score did not improve C index over traditional risk factors and family history (p=0.19), nor did it have a significant effect on net reclassification improvement (2.2%, p=0.18); however, it did have a small effect on integrated discrimination index (0.004, p=0.0006). Results of the case-control analyses were similar to those of the prospective cohort analyses. Interpretation Using a genetic risk score based on 13 SNPs associated with coronary heart disease, we can identify the 20% of individuals of European ancestry who are at roughly 70% increased risk of a first coronary heart disease event. The potential clinical use of this panel of SNPs remains to be defined.
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- Sharma, Amitabh, et al.
(author)
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Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes
- 2018
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In: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 4:1
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Journal article (peer-reviewed)abstract
- Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways’ relationship to T2D.
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- Taneera, Jalal, et al.
(author)
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A Systems Genetics Approach Identifies Genes and Pathways for Type 2 Diabetes in Human Islets
- 2012
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In: Cell Metabolism. - : Elsevier BV. - 1550-4131 .- 1932-7420. ; 16:1, s. 122-134
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Journal article (peer-reviewed)abstract
- Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors. Using 48 genes located near T2D risk variants, we identified gene coexpression and protein-protein interaction networks that were strongly associated with islet insulin secretion and HbA(1c). We integrated our data to form a rank list of putative T2D genes, of which CHL1, LRFN2, RASGRP1, and PPM1K were validated in INS-1 cells to influence insulin secretion, whereas GPR120 affected apoptosis in islets. Expression variation of the top 20 genes explained 24% of the variance in HbA(1c) with no claim of the direction. The data present a global map of genes associated with islet dysfunction and demonstrate the value of systems genetics for the identification of genes potentially involved in T2D.
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