Two-way learning with one-way supervision for gene expression data.

BACKGROUND: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material. RESULTS: The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively. CONCLUSIONS: Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Individual saturated and monounsaturated fatty acids trigger distinct transcriptional networks in differentiated 3T3-L1 preadipocytes.

BACKGROUND/AIMS: Saturated fatty acids (SFA) are widely thought to induce inflammation in adipose tissue (AT), while monounsaturated fatty acids (MUFA) are purported to have the opposite effect; however, it is unclear if individual SFA and MUFA behave similarly. Our goal was to examine adipocyte transcriptional networks regulated by individual SFA (palmitic acid, PA; stearic acid, SA) and MUFA (palmitoleic acid, PMA; oleic acid, OA). METHODS: Differentiated preadipocytes were treated with either 250 µM PA, SA, PMA, or OA for 48 h. Gene expression was analyzed using microarrays and real-time RT-PCR. Data were compared with those of a previous study reporting AT gene expression in humans following the consumption of SFA- or MUFA-enriched diets. RESULTS: Individual fatty acid treatments had significant effects on adipocyte gene expression. Functional analyses revealed that PA induced the TLR signalling pathway, while PMA had the opposite effect. SA and OA had similar effects, with increases in key metabolic pathways including mTOR and PPAR signalling and a reduction in TLR signalling. Ccl5 was validated as a candidate gene that may mediate the differential inflammatory effects of SFA and MUFA in AT. CONCLUSIONS: Individual SFA and MUFA trigger distinct transcriptional responses in differentiated preadipocytes, with inflammatory and metabolic pathways particularly sensitive to these fatty acids.

Caloric restriction induces changes in insulin and body weight measurements that are inversely associated with subsequent weight regain.

BACKGROUND: Successful weight maintenance following weight loss is challenging for many people. Identifying predictors of longer-term success will help target clinical resources more effectively. To date, focus has been predominantly on the identification of predictors of weight loss. The goal of the current study was to determine if changes in anthropometric and clinical parameters during acute weight loss are associated with subsequent weight regain. METHODOLOGY: The study consisted of an 8-week low calorie diet (LCD) followed by a 6-month weight maintenance phase. Anthropometric and clinical parameters were analyzed before and after the LCD in the 285 participants (112 men, 173 women) who regained weight during the weight maintenance phase. Mixed model ANOVA, Spearman correlation, and linear regression were used to study the relationships between clinical measurements and weight regain. PRINCIPAL FINDINGS: Gender differences were observed for body weight and several clinical parameters at both baseline and during the LCD-induced weight loss phase. LCD-induced changes in BMI (Spearman's ρ = 0.22, p = 0.0002) were inversely associated with weight regain in both men and women. LCD-induced changes in fasting insulin (ρ = 0.18, p = 0.0043) and HOMA-IR (ρ = 0.19, p = 0.0023) were also associated independently with weight regain in both genders. The aforementioned associations remained statistically significant in regression models taking account of variables known to independently influence body weight. CONCLUSIONS/SIGNIFICANCE: LCD-induced changes in BMI, fasting insulin, and HOMA-IR are inversely associated with weight regain in the 6-month period following weight loss.