Decreased VLDL-Apo B 100 fractional synthesis rate despite hypertriglyceridemia in subjects with type 2 diabetes and nephropathy

Subjects with Type 2 Diabetes Mellitus (T2DM) and diabetic nephropathy (DN) often exhibit hypertriglyceridemia. The mechanism(s) of such an increase are poorly known. OBJECTIVE: We investigated VLDL-Apo B 100 kinetics in T2DM subjects with and without DN, and in healthy controls. DESIGN: Stable isotope 13C-leucine infusion, and modelling analysis of tracer-to-tracee ratio dynamics in the protein product pool in the 6-8 hr period following tracer infusion, were employed. SETTING: Male subjects affected by T2DM, either with (n=9) or without (n=5) DN, and healthy male controls (n=6), were studied under spontaneous glycemic levels in the post-absorptive state. RESULTS: In the T2DM patients with DN, plasma triglyceride (TG) (2.2\ub10.8 mmol/L, Mean\ub1SD) and VLDL-Apo B 100 (17.4\ub110.4 mg/dl) concentrations, and VLDL-Apo B 100 pool (0.56\ub10.29 g), were 3e60-80% greater (p<0.05 or less) than those of the T2DM subjects without DN (TG: 1.4\ub10.5 mmol/L; VLDL-Apo B 100: 9.9\ub12.5 mg/dl; VLDL-Apo B 100 pool: 0.36\ub10.09 g), and 3e80-110% greater (p<0.04 or less) than those of nondiabetic controls (TG: 1.2\ub10.4 mmol/L; VLDL-Apo B 100: 8.2\ub11.7 mg/dl; VLDL-Apo B 100: 0.32\ub10.09 g). In sharp contrast however, in the subjects with T2DM and DN, VLDL-Apo B 100 FSR was 6550% lower (4.8\ub12.2 pools/day) than that of either the T2DM subjects without DN (9.9\ub14.3 pools/day, p<0.025) or the control subjects (12.5\ub19.1 pools/day, p<0.04). CONCLUSIONS: The hypertriglyceridemia of T2DM patients with DN is not due to hepatic VLDL-Apo B 100 overproduction, which is decreased, but it should be attributed to decreased apolipoprotein removal

Ongoing β-Cell Turnover in Adult Nonhuman Primates Is Not Adaptively Increased in Streptozotocin-Induced Diabetes

OBJECTIVE: \u3b2-Cell turnover and its potential to permit \u3b2-cell regeneration in adult primates are unknown. Our aims were 1) to measure \u3b2-cell turnover in adult nonhuman primates; 2) to establish the relative contribution of \u3b2-cell replication and formation of new \u3b2-cells from other precursors (defined thus as \u3b2-cell neogenesis); and 3) to establish whether there is an adaptive increase in \u3b2-cell formation (attempted regeneration) in streptozotocin (STZ)-induced diabetes in adult nonhuman primates. RESEARCH DESIGN AND METHODS: Adult (aged 7 years) vervet monkeys were administered STZ (45-55 mg/kg, n = 7) or saline (n = 9). Pancreas was obtained from each animal twice, first by open surgical biopsy and then by euthanasia. \u3b2-Cell turnover was evaluated by applying a mathematic model to measured replication and apoptosis rates. RESULTS: \u3b2-Cell turnover is present in adult nonhuman primates (3.3 \ub1 0.9 mg/month), mostly (~80%) derived from \u3b2-cell neogenesis. \u3b2-Cell formation was minimal in STZ-induced diabetes. Despite marked hyperglycemia, \u3b2-cell apoptosis was not increased in monkeys administered STZ. CONCLUSIONS: There is ongoing \u3b2-cell turnover in adult nonhuman primates that cannot be accounted for by \u3b2-cell replication. There is no evidence of \u3b2-cell regeneration in monkeys administered STZ. Hyperglycemia does not induce \u3b2-cell apoptosis in nonhuman primates in vivo

The Transcriptional Response in Human Umbilical Vein Endothelial Cells Exposed to Insulin: A Dynamic Gene Expression Approach

BACKGROUND: In diabetes chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance. METHODOLOGY AND PRINCIPAL FINDINGS: The transcriptional response of endothelial cells in the 440 minutes following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway-based enrichment analysis revealed "Electron Transport Chain" significantly enriched. Results were validated on genes belonging to "Electron Transport Chain" pathway, using quantitative RT-PCR. CONCLUSIONS: As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

Background: Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined. Methodology/Principal Findings: To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20 % of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time. Conclusion/Significance: The new method allows identifying characteristic dynamic responses to insulin stimulus, commo

Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment

MOTIVATION: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. METHODS: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. RESULTS: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks