Sexual dimorphism in myocardial acylcarnitine and triglyceride metabolism

Figure S1. Concentrations of non-esterified fatty acid moieties in NOD and Wistar rats by sex. (PPTX 163 kb

Genomic and metabolic disposition of non-obese Type 2 Diabetic rats to increased myocardial fatty acid metabolism

Lipotoxicity of the heart has been implicated as a leading cause of morbidity in Type 2 Diabetes Mellitus (T2DM). While numerous reports have demonstrated increased myocardial fatty acid (FA) utilization in obese T2DM animal models, this diabetic phenotype has yet to be demonstrated in non-obese animal models of T2DM. Therefore, the present study investigates functional, metabolic, and genomic differences in myocardial FA metabolism in non-obese type 2 diabetic rats. The study utilized Goto-Kakizaki (GK) rats at the age of 24 weeks. Each rat was imaged with small animal positron emission tomography (PET) to estimate myocardial blood flow (MBF) and myocardial FA metabolism. Echocardiograms (ECHOs) were performed to assess cardiac function. Levels of triglycerides (TG) and non-esterified fatty acids (NEFA) were measured in both plasma and cardiac tissues. Finally, expression profiles for 168 genes that have been implicated in diabetes and FA metabolism were measured using quantitative PCR (qPCR) arrays. GK rats exhibited increased NEFA and TG in both plasma and cardiac tissue. Quantitative PET imaging suggests that GK rats have increased FA metabolism. ECHO data indicates that GK rats have a significant increase in left ventricle mass index (LVMI) and decrease in peak early diastolic mitral annular velocity (E’) compared to Wistar rats, suggesting structural remodeling and impaired diastolic function. Of the 84 genes in each the diabetes and FA metabolism arrays, 17 genes in the diabetes array and 41 genes in the FA metabolism array were significantly up-regulated in GK rats. Our data suggest that GK rats’ exhibit increased genomic disposition to FA and TG metabolism independent of obesity

Discovery and targeting of a noncanonical mechanism of sarcoma resistance to ADI-PEG20 mediated by the microenvironment

PURPOSE: Many cancers lack argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme of arginine biosynthesis. This deficiency causes arginine auxotrophy, targetable by extracellular arginine-degrading enzymes such as ADI-PEG20. Long-term tumor resistance has thus far been attributed solely to ASS1 reexpression. This study examines the role of ASS1 silencing on tumor growth and initiation and identifies a noncanonical mechanism of resistance, aiming to improve clinical responses to ADI-PEG20. EXPERIMENTAL DESIGN: Tumor initiation and growth rates were measured for a spontaneous Ass1 knockout (KO) murine sarcoma model. Tumor cell lines were generated, and resistance to arginine deprivation therapy was studied in vitro and in vivo. RESULTS: Conditional Ass1 KO affected neither tumor initiation nor growth rates in a sarcoma model, contradicting the prevalent idea that ASS1 silencing confers a proliferative advantage. Ass1 KO cells grew robustly through arginine starvation in vivo, while ADI-PEG20 remained completely lethal in vitro, evidence that pointed toward a novel mechanism of resistance mediated by the microenvironment. Coculture with Ass1-competent fibroblasts rescued growth through macropinocytosis of vesicles and/or cell fragments, followed by recycling of protein-bound arginine through autophagy/lysosomal degradation. Inhibition of either macropinocytosis or autophagy/lysosomal degradation abrogated this growth support effect in vitro and in vivo. CONCLUSIONS: Noncanonical, ASS1-independent tumor resistance to ADI-PEG20 is driven by the microenvironment. This mechanism can be targeted by either the macropinocytosis inhibitor imipramine or the autophagy inhibitor chloroquine. These safe, widely available drugs should be added to current clinical trials to overcome microenvironmental arginine support of tumors and improve patient outcomes

An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: functional, metabolic, and differential network analysis.

Estrogen exerts diverse biological effects in multiple tissues in both animals and humans. Much of the accumulated knowledge on the role of estrogen receptor (ER) in the heart has been obtained from studies using ovariectomized mice, whole body ER gene knock-out animal models, ex vivo heart studies, or from isolated cardiac myocytes. In light of the wide systemic influence of ER signaling in regulating a host of biological functions in multiple tissues, it is difficult to infer the direct role of ER on the heart. Therefore, we developed a mouse model with a cardiomyocyte-specific deletion of the ERα allele (cs-ERα-/-). Male and female cs-ERα-/- mice with age/sex-matched wild type controls were examined for differences in cardiac structure and function by echocardiogram and differential gene expression microarray analysis. Our study revealed sex-differences in structural parameters in the hearts of cs-ERα-/- mice, with minimal functional differences. Analysis of microarray data revealed differential variations in the expression of 208 genes affecting multiple transcriptional networks. Furthermore, we report sex-specific differences in the expression of 56 genes. Overall, we developed a mouse model with cardiac-specific deletion of ERα to characterize the role of ERα in the heart independent of systemic effects. Our results suggest that ERα is involved in controlling the expression of diverse genes and networks in the cardiomyocyte in a sex-dependent manner

Additional file 5: of Sexual dimorphism in myocardial acylcarnitine and triglyceride metabolism

Table S2. Summary statistics and ANOVA and principal component analysis results for metabolites. (XLSX 43 kb

Differential expression of genes involved in fatty acid metabolism.

<p>RNA from (N=4/group) rats were isolated and expression analyzed as mentioned earlier. The fold changes of genes grouped based on their function; Fatty acid transport (<b>A</b>), Fatty acid catabolism (<b>B</b>), Ketogenesis and TG metabolism (<b>C</b>), are presented here. All results are presented fold change (mean ± 1SD) relative to Wistar rats. *P<0.05, #P<0.01 ‡P<0.001 denotes significance (Students’t-Test).</p

Clustering and functional grouping of genes involved in fatty acid metabolism.

<p>(<b>A</b>-<b>D</b>) Heat map and Gene correlation clusters of genes involved in FA Metabolism including; fatty acid catabolism(<b>A</b>), fatty acid transport (<b>B</b>), triglyceride biosynthesis (<b>C</b>), and ketogenesis (<b>D</b>). Gene correlation clusters were calculated using the Spearman Correlation Distances and complete linkage for hierarchical clustering. Data for individual biological replicates are shown at the 95% or above confidence level. (<b>E</b>) Pie chart illustrating functional distribution of genes involved in FA metabolism in heart and varies between GK and Wistar rats. (N=4/group). .</p

PET measures of myocardial fatty acid metabolism and blood flow.

<p>(<b>A</b>) Extrinsic myocardial fatty acid oxidation rate (MFAO) (<b>B</b>) Extrinsic myocardial fatty acid esterification rate (MFAE), (<b>C</b>) Extrinsic myocardial fatty acid utilization rate (MFAU), (<b>D</b>) myocardial Extraction Fraction (EF) in GK and control rats. *denotes that GK rats are significantly different (P<0.05) than Wistars for that measurement. All results are presented as mean ± 1 SEM with N=4/group.</p

Clustering and functional grouping of genes involved in diabetes.

<p>(<b>A</b>) Heat map and Gene correlation clusters of genes involved in diabetes including; transcription factors, metabolic enzymes, Receptors and secreted factors. Gene correlation clusters were calculated using the Spearman Correlation Distances and complete linkage for hierarchical clustering. Data for individual biological replicates are shown at the 95% or above confidence level. (<b>B</b>) Pie chart illustrating functional distribution of genes involved in diabetes and varies between GK and Wistar rats. (N=4/group).</p

Network analysis of genes significant for sex: List of significant genes (root list) varying by sex was uploaded on to GeneGo tool and network was built.

<p>Receptor networks (A) was drawn using build network for your data option. Except receptors and receptor ligands, others were hidden from this network in order to visualize the alteration in receptor networks.. Transcription factor network (B) was drawn using analyze network function for transcription factors. In this analysis for every transcription factor with direct ligand (s) in the root list, the algorithm generates a sub-network consisting of all shortest paths from that transcription factor to the closest receptor with direct target (s) in the root list. The figure shows the differentially expressed genes in the network (Blue for down-regulated and Red for up-regulated).</p