Shih, C.-h. | Search bioRxiv

bioRxiv2026

Deficiency of miR130a leads to fat hypertrophy, hepatic steatosis, insulin resistance and glucose intolerance in mice

Insulin resistance, excessive and ectopic fat accumulation, chronic low-grade inflammation, and pancreatic beta-cell failure are pathological features of type 2 diabetes mellitus.MiR-130a has been demonstrated to suppress the mRNA levels of PPAR{gamma}, NF-{kappa}B, and TNF- in vitro. PPAR{gamma} is a master regulator of systemic fat and glucose metabolism. NF-{kappa}B and TNF- are pivotal modulators of inflammation. Therefore, we aimed to examine the systemic effect of miR130a on fat metabolism, glucose/insulin homeostasis, and inflammation in mice. We found that mirR130a-deficient mice exhibited larger white fat mass with hypertrophic adipocytes, increased lipogenic gene expression in fat, and elevated serum leptin levels than controls. The white fat pads of mirR130a-deficient mice showed significant macrophage infiltration with enhanced expression of pro-inflammatory genes. In addition, mirR130a-deficient mice had more severe hepatic steatosis and higher hepatic triglycerides content than controls. Similarly, mirR130a-deficient mice had increased macrophage infiltration and lipogenic and inflammatory gene expression in the liver. Consistently, we found that Lepob/ob mice expressed markedly decreased miR130a expression in the liver and white fat compared to controls. Importantly, mirR130a-deficient mice displayed impaired glucose tolerance and worsened insulin resistance, accompanied with reduced serum adiponectin levels. Furthermore, insulin secretion is reduced in mirR130a-deficient mice compared to controls. In conclusion, knockout of miR130a in mice results in fat hypertrophy, hepatic steatosis, increased macrophage infiltration in liver and fat, glucose intolerance, and insulin resistance. These data indicate miR130a exert systemic anti-diabetic effects.

molecular biology↗

bioRxiv2017

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature, thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have training data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of protein length and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multi-protein synthetic circuits, RNA circuits or the cell free production of small molecule products.

synthetic biology↗