Objective

Adipose tissue is the primary site for lipid deposition that protects the organisms in cases of nutrient excess during obesogenic diets. The histone deacetylase Sirtuin 1 (SIRT1) inhibits adipocyte differentiation by targeting the transcription factor peroxisome proliferator activated-receptor gamma (PPARγ).

Methods

To assess the specific role of SIRT1 in adipocytes, we generated Sirt1 adipocyte-specific knockout mice (ATKO) driven by aP2 promoter onto C57BL/6 background. Sirt1 (flx/flx) aP2Cre (+) (ATKO) and Sirt1 (flx/flx) aP2Cre (-) (WT) mice were fed high-fat diet for 5 weeks (short-term) or 15 weeks (chronic-term). Metabolic studies were combined with gene expression analysis and phosphorylation/acetylation patterns in adipose tissue.

Results

On standard chow, ATKO mice exhibit low-grade chronic inflammation in adipose tissue, along with glucose intolerance and insulin resistance compared with control fed mice. On short-term HFD, ATKO mice become more glucose intolerant, hyperinsulinemic, insulin resistant and display increased inflammation. During chronic HFD, WT mice developed a metabolic dysfunction, higher than ATKO mice, and thereby, knockout mice are more glucose tolerant, insulin sensitive and less inflamed relative to control mice. SIRT1 attenuates adipogenesis through PPARγ repressive acetylation and, in the ATKO mice adipocyte PPARγ was hyperacetylated. This high acetylation was associated with a decrease in Ser273-PPARγ phosphorylation. Dephosphorylated PPARγ is constitutively active and results in higher expression of genes associated with increased insulin sensitivity.

Conclusion

Together, these data establish that SIRT1 downregulation in adipose tissue plays a previously unknown role in long-term inflammation resolution mediated by PPARγ activation. Therefore, in the context of obesity, the development of new therapeutics that activate PPARγ by targeting SIRT1 may provide novel approaches to the treatment of T2DM.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.