Fatty acid-gene interactions, adipokines and obesity

It is now recognized that the low-grade inflammation observed with obesity is associated with the development of a wide range of downstream complications. As such, there is considerable interest in elucidating the regulatory mechanisms underlying the production of inflammatory molecules to improve the prevention and treatment of obesity and its co-morbidities. White adipose tissue is no longer considered a passive reservoir for storing lipids, but rather an important organ influencing energy metabolism, insulin sensitivity and inflammation by the secretion of proteins, commonly referred to as adipokines. Dysregulation of several adipokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and adiponectin, contributes to the low-grade inflammation that is a hallmark of obesity. Evidence now suggests that fatty acids represent a class of molecules that can modulate adipokine production, thereby influencing inflammatory status. Although the precise molecular mechanisms by which dietary fats regulate adipokine production remain unclear, recent findings indicate that diet-gene interactions may have an important role in the transcriptional and secretory regulation of adipokines. Single-nucleotide polymorphisms in the genes encoding TNF-α, IL-6 and adiponectin can modify circulating levels of these adipokines and, subsequently, obesity-related phenotypes. This genetic variation can also alter the influence of dietary fatty acids on adipokine production. Therefore, the current review will show that it is paramount to consider both genetic information and dietary fat intake to unravel the inter-individual variability in inflammatory response observed in intervention protocols targeting obesity.

Adipose tissue pathways involved in weight loss of cancer cachexia

Background:

The regulatory gene pathways that accompany loss of adipose tissue in cancer cachexia are unknown and were explored using pangenomic transcriptome profiling.

Methods:

Global gene expression profiles of abdominal subcutaneous adipose tissue were studied in gastrointestinal cancer patients with (n=13) or without (n=14) cachexia.

Results:

Cachexia was accompanied by preferential loss of adipose tissue and decreased fat cell volume, but not number. Adipose tissue pathways regulating energy turnover were upregulated, whereas genes in pathways related to cell and tissue structure (cellular adhesion, extracellular matrix and actin cytoskeleton) were downregulated in cachectic patients. Transcriptional response elements for hepatic nuclear factor-4 (HNF4) were overrepresented in the promoters of extracellular matrix and adhesion molecule genes, and adipose HNF4 mRNA was downregulated in cachexia.

Conclusions:

Cancer cachexia is characterised by preferential loss of adipose tissue; muscle mass is less affected. Loss of adipose tissue is secondary to a decrease in adipocyte lipid content and associates with changes in the expression of genes that regulate energy turnover, cytoskeleton and extracellular matrix, which suggest high tissue remodelling. Changes in gene expression in cachexia are reciprocal to those observed in obesity, suggesting that regulation of fat mass at least partly corresponds to two sides of the same coin.

Microarray data analysis: a practical approach for selecting differentially expressed genes

BACKGROUND

The biomedical community is rapidly developing new methods of data analysis for microarray experiments, with the goal of establishing new standards to objectively process the massive datasets produced from functional genomic experiments. Each microarray experiment measures thousands of genes simultaneously producing an unprecedented amount of biological information across increasingly numerous experiments; however, in general, only a very small percentage of the genes present on any given array are identified as differentially regulated. The challenge then is to process this information objectively and efficiently in order to obtain knowledge of the biological system under study and by which to compare information gained across multiple experiments. In this context, systematic and objective mathematical approaches, which are simple to apply across a large number of experimental designs, become fundamental to correctly handle the mass of data and to understand the true complexity of the biological systems under study.

RESULTS

The present report develops a method of extracting differentially expressed genes across any number of experimental samples by first evaluating the maximum fold change (FC) across all experimental parameters and across the entire range of absolute expression levels. The model developed works by first evaluating the FC across the entire range of absolute expression levels in any number of experimental conditions. The selection of those genes within the top X% of highest FCs observed within absolute expression bins was evaluated both with and without the use of replicates. Lastly, the FC model was validated by both real time polymerase chain reaction (RT-PCR) and variance data. Semi-quantitative RT-PCR analysis demonstrated 73% concordance with the microarray data from Mu11K Affymetrix GeneChips. Furthermore, 94.1% of those genes selected by the 5% FC model were found to lie above measurement variability using a SDwithin confidence level of 99.9%.

CONCLUSION

As evidenced by the high rate of validation, the FC model has the potential to minimize the number of required replicates in expensive microarray experiments by extracting information on gene expression patterns (e.g. characterizing biological and/or measurement variance) within an experiment. The simplicity of the overall process allows the analyst to easily select model limits which best describe the data. The genes selected by this process can be compared between experiments and are shown to objectively extract information which is biologically & statistically significant.

Genomics: food and nutrition

Nutrition is traditionally a multidisciplinary field applying principles of molecular biochemistry and statistical epidemiology to integrative metabolism and population health. Genomics, with its global perspective, is now reinventing the future of human metabolic health. Creative experimental designs are addressing metabolic questions in nutrition ranging from energy regulation to aging, and from mechanisms of absorption to the interspecies molecular crosstalk of bacteria and human cells within the intestine.