The use of non-drug intervention for calcium deficiency has attracted attention in recent years. Although calcium carbonate is the preferred raw material for calcium supplementation, there are few reports on the mechanism of the combined action of chondroitin sulfate and calcium to alleviate osteoporosis from the perspective of gut microbiota and metabolomics. In this study, a rat model of osteoporosis was established by feeding a low-calcium diet. The intestinal microbiota abundance, fecal and plasma metabolite expression levels of rats fed a basal diet, a low-calcium diet, a low-calcium diet plus calcium carbonate, and a low-calcium diet plus chondroitin sulfate were compared. The results showed that compared with the low calcium group, the calcium content and bone mineral density of femur were significantly increased in the calcium carbonate and chondroitin sulfate groups. 16 S rRNA sequencing and metabolomics analysis showed that chondroitin sulfate intervention could reduce short-chain fatty acid synthesis of intestinal flora, slow down inflammatory response, inhibit osteoclast differentiation, promote calcium absorption and antioxidant mechanism, and alleviate osteoporosis in low-calcium feeding rats. Correlation analysis showed that the selected intestinal flora was significantly correlated with metabolites enriched in feces and plasma. This study provides scientific evidence of the potential impact of chondroitin sulfate as a dietary supplement for patients with osteoporosis.

RNA-binding proteins (RBPs) are proteins that physically and functionally bind to RNA to regulate the RNA metabolism such as alternative splicing, polyadenylation, transport, maintenance of stability, localization, and translation. There is accumulating evidence that dysregulated RBPs play an essential role in the pathogenesis of malignant tumors including a variety of types of sarcomas. On the other hand, prognosis of patients with sarcoma, especially with sarcoma in advanced stages, is very poor, and almost no effective standard treatment has been established for most of types of sarcomas so far, highlighting the urgent need for identifying novel therapeutic targets based on the deep understanding of pathogenesis. Therefore, defining the network of interactions between RBPs and disease-related RNA targets will contribute to a better understanding of sarcomagenesis and identification of a novel therapeutic target for sarcomas.

PURPOSE: The prevalence of hyperlipidemia and related illnesses is on its rise, and atorvastatin is the frequently used hypolipidemic agent. However, there is still uncertainty about the mechanisms, especially the relationship between the lipid-lowering effect, intestinal microbiome, and metabolic profiles. We aim to intensively explain the mechanism of the hypolipidemic effect of atorvastatin through multi-omics perspective of intestinal microbiome and metabolomics. METHODS: Multi-omics methods play an increasingly important role in the analysis of intestinal triggers and evaluation of metabolic disorders such as obesity, hyperlipidemia, and diabetes. Therefore, we were prompted to explore intestinal triggers, underlying biomarkers, and potential intervention targets of atorvastatin in the treatment of dyslipidemia through multi-omics. To achieve this, SPF Wistar rats were fed a high-fat diet or normal diet for 8 weeks. Atorvastatin was then administered to high-fat diet-fed rats. RESULTS: By altering intestinal microbiome, a high-fat diet can affect feces and plasma metabolic profiles. Treatment with atorvastatin possibly increases the abundance of Bacteroides, thereby improving "propanoate metabolism" and "glycine, serine and threonine metabolism" in feces and plasma, and contributing to blood lipid reduction. CONCLUSION: Our study elucidated the intestinal triggers and metabolites of high-fat diet-induced dyslipidemia from the perspective of intestinal microbiome and metabolomics. It equally identified potential intervention targets of atorvastatin. This further explains the mechanism of the hypolipidemic effect of atorvastatin from a multi-omics perspective.

Type 2 diabetes mellitus (T2DM) is a burdensome global disease. In-depth understanding of its mechanism will help to optimize diagnosis and treatment, which reduces the burden. Multi-omics research has unparalleled advantages in contributing to the overall understanding of the mechanism of this chronic metabolic disease. In the past two decades, the study of multi-omics on T2DM-related intestinal flora perturbation and plasma dyslipidemia has shown tremendous potential and is expected to achieve major breakthroughs. The regulation of intestinal flora in diabetic patients has been confirmed by multiple studies. The use of metagenomics, 16S RNA sequencing, and metabolomics has comprehensively identified the overall changes in the intestinal flora and the metabolic disturbances that could directly or indirectly participate in the intestinal flora-host interactions. Lipidomics combined with other "omics" has characterized lipid metabolism disorders in T2DM. The combined application and cross-validation of multi-omics can screen for dysregulation in T2DM, which will provide immense opportunities to understand the mechanisms behind T2DM.

PURPOSE: The purpose of this study was to explore the difference and association between intestinal microbiota and plasma metabolomics between type 2 diabetes mellitus (T2DM) and normal group and to identify potential microbiota biomarkers that contribute the most to the difference in metabolites. METHODS: Six male ZDF model (fa/fa) rats were fed by a Purina #5008 Lab Diet (crude protein 23.5%, crude fat 6.5%) for 3 weeks and their age-matched 6 ZDF control (fa/+) rats were fed by normal rodent diet. Their stool and blood samples were collected at 12 weeks. To analyze the microbial populations in these samples, we used a 16S rRNA gene sequencing approach. Liquid chromatography-mass spectrometry (LC-MS) followed by multivariate statistical analysis was applied to the plasma metabolites profiling. Correlation analysis of them was calculated by Pearson statistical method. RESULTS: Twelve potential biomarkers of intestinal microbial flora and 357 differential metabolites were found in ZDF fa/fa rats, among which there are three flora that contributed the most to the perturbation of metabolites, including genus Phocea, Pseudoflavonifractor and species Lactobacillus intestinalis. CONCLUSION: Our study demonstrates the alterations of the abundance and diversity of the intestinal microbiota and the perturbation of metabolites in ZDF rats (fa/fa). We found three potential biomarkers of intestinal microbiota that may lead to perturbation in plasma metabolites. This may prompt new pathogenesis of obesity-related T2DM, but we also need to study further about the causal relationship between intestinal microbe and T2DM, so as to find the target of T2DM treatment or preventive measures.