Sirt1 deficiency upregulates glutathione metabolism to prevent hepatocellular carcinoma initiation in mice.

Sirtuin-1 (SIRT1) is involved in various metabolic pathways, including fatty acid synthesis and gluconeogenesis in the liver. However, its role in initiation and progression of liver cancer remains unclear. Studying Sirt1 liver-specific knockout (LKO) mice in combination with diethylnitrosamine (DEN) treatment, we demonstrated that loss of Sirt1 rendered mice resistant to DEN-induced hepatocellular carcinoma (HCC) development. RNA-seq revealed that livers from LKO mice exhibited an enrichment in glutathione metabolism eight months after DEN challenge. Sirt1 deficiency elevated the expression of glutathione-s-transferase family genes by increasing the level of Nrf2, a key regulator of glutathione metabolism. Hence, LKO livers displayed a reductive environment with an increased ratio of GSH to GSSG and an elevated GSH level. Furthermore, using CRISPR knockout techniques, we confirmed that the impairment of HCC formation in LKO mice is mainly dependent on NRF2 signaling. Meanwhile, HCC induced by DEN could be blocked by the administration of N-acetyl cysteine (NAC) when administered one month after DEN challenge. However, NAC treatment starting five months after DEN injection was not able to prevent tumor development. In conclusion, our findings indicate that a reductive environment orchestrated by glutathione metabolism at an early stage can prevent the initiation of HCC.

The metabolic footprint during adipocyte commitment highlights ceramide modulation as an adequate approach for obesity treatment.

BACKGROUND: Metabolic modulation is capable of maintaining cell potency, regulating niche homeostasis, or determining cell fate. However, little is known regarding the metabolic landscape during early adipogenesis or whether metabolic modulation could be a potential approach for obesity treatment. METHODS: The metabolic footprint during adipocyte commitment was evaluated by metabolomics analysis in mouse embryonic fibroblasts (MEFs). The role of apoptosis induced by ceramide and how ceramide is regulated were evaluated by omics analysis in vitro, human database and the adipocyte-specific Sirt1 knockout mouse. FINDINGS: The metabolic footprint showed that a complicated diversity of metabolism was enriched as early as 3 h and tended to fluctuate throughout differentiation. Subsequently, the scale of these perturbed metabolic patterns was reduced to reach a balanced state. Of high relevance is the presence of apoptosis induced by ceramide accumulation, which is associated with metabolic dynamics. Interestingly, apoptotic cells were not merely a byproduct of adipogenesis but rather promoted the release of lipid components to facilitate adipogenesis. Mechanistically, ceramide accumulation stemming from hydrolysis and the de novo pathway during early adipogenesis is regulated by Sirt1 upon epigenetic alterations of constitutive Histone H3K4 methylation and H3K9 acetylation. INTERPRETATION: The metabolic footprint during adipocyte commitment highlights that apoptosis induced by ceramide is essential for adipogenesis, which is reversed by suppression of Sirt1. Therefore, Sirt1 may constitute a target to treat obesity or other ceramide-associated metabolic syndromes. FUNDING: This project was supported by grants from the University of Macau (SRG2015-00008-FHS, MYRG2016-00054-FHS and MYRG2017-00096-FHS to RHW; CPG2019-00019-FHS to CXD) and from the National Natural Science Foundation of China (81672603 and 81401978) to QC.

The Mealiness and Quality of Herbal Medicine: Licorice for Example.

BACKGROUND: The morphological identification is an effective and simple quality evaluation method in Chinese drugs, and the traits of mealiness and color were widely used in the commercial market of Chinese drugs. OBJECTIVE: The objective of this study was to explore the correlation between mealiness of herbal drugs and its quality; licorice was selected as an example. MATERIALS AND METHODS: The mealiness of licorice was graded by its weight; meanwhile, the content of glycyrrhizic acid and liquiritin was determined by high-performance liquid chromatography-diode-array detection method; the content of polysaccharides, soluble sugars, pectin, total starch, amylose, and amylopectin was measured by colorimetric method; and the number and diameter of starch granule were observed by microscope. RESULTS: The results showed that the mealiness of licorice which collected from wild and cultivated plants is positively correlated with the content of glycyrrhizic acid, liquiritin, the ratio of amylose to total starch, and the number of starch granules whose diameter was over 5 µm. However, the mealiness is negatively correlated with the total starch. Further, the formation mechanism of starch granule was discussed. CONCLUSION: It is for the first time to report the positive correlation between the mealiness and the starch granule size, the ratio of amylose to total starch, which can provide rationality for the quality evaluation using the character of mealiness in herbal medicine. SUMMARY: It is a convenient method to justify the quality of herbal medicine. To explore the correlation between mealiness of herbal drugs and its quality, licorice was selected as an example. The result indicated that the effective constituent is correlated with mealiness of licorice. Abbreviations Used: TCM: Traditional Chinese Medicine.

Blind image quality assessment via deep learning.

This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model's effectiveness, efficiency, and robustness.