Analysis Uncovers Gene Networks That May Underlie Obesity Risk
A computational analysis comparing the gene activity patterns and gene networks of normal-weight and overweight individuals has provided new insights into factors that could underlie overweight and obesity genetic risk.
The study, “Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index,” was published in the journal Scientific Reports.
Both genetic and environmental factors are suggested to influence overweight and obesity risk. But although certain individual genes have been associated with body mass index (BMI), the interaction of these genes and how they influence weight remains largely unknown.
Knowing which genes are active or silent as well as their gene networks — which ones interact, either working together, regulating, or suppressing each other’s function — can offer key information for understanding the mechanisms underlying genetic risk of increased body weight and obesity.
Recent studies have demonstrated that measuring the gene activity profile using a person’s blood, known as whole-blood transcriptome, is a valid tool for gathering information about the genes and biological pathways underlying obesity.
Gene activity, or expression profiling, consists of applying high-throughput molecular methods to determine the patterns of genes that are active (or expressed), ranging from highly active genes — which drive the production of a great amount of proteins — to genes that are silent, yielding few or no proteins.
Based on this knowledge, researchers at the National Institute of Nursing Research (NINR) sought to use this method to identify individual genes and gene networks that could mediate obesity risk.
They performed whole-blood transcriptome in blood samples of 90 healthy individuals, mean age 26 years, integrating this information with the clinical data of participants — including BMI, body fat, blood parameters (e.g. glucose and cholesterol levels), and blood pressure.
By comparing the pattern of gene expression between 43 normal-weight and 41 overweight individuals, researchers identified seven genes associated with BMI. RBM20, AX748233, and CASP10 were more active in subjects with normal weight, whereas SEPT12, SLC30A3, WTIP, and OR12D3 were more expressed in overweight individuals.
Then researchers used a gene network analysis to detect gene modules (groups of co-expressed genes) that could potentially interact to mediate increases in weight.
From this analysis, they identified two gene modules involved in catabolism — the biologic process of breaking down nutrients to produce the cell’s building blocks and energy — and muscle function.
The prominence of catabolism-related genes “is consistent with other studies highlighting the roles of metabolic genes in the [origin] of overweight and obesity,” researchers stated.
These groups were often regulated by zinc finger transcription factors, which bind to specific DNA sequences and play a key role in controlling which genes are turned “on” or “off.”
There were also evident changes in gene connectivity — the number of genes to which a gene is connected.
A total of 246 so-called “hub” genes — those to which many other genes are connected, likely with a more important function — were converted to non-hub genes, and 286 non-hub genes were converted to hub genes between normal and overweight individuals.
Another analysis was performed to detect more complex gene expression patterns associated with weight. Researchers looked at three-way gene interactions, where two interacting genes are under the control of a third gene, and could cause a change in expression associated with weight. The researchers identified 28 three-way gene interactions.
“This analysis suggests complicated high-order gene interactions that may contribute to the genetic mechanisms affecting human weight,” researchers said. “Together this study provides a better understanding of genetic factors in overweight and obese phenotypes through the study of networks, pathways, interactions, and regulation of weight-related genes.
“Our analyses have generated comprehensive and systematic insights into gene expression mechanisms underlying BMI and have highlighted complex high-order interactions that should be further explored in in future studies.”
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