Identifying the Novel Gut Microbial Metabolite Contributing to Metabolic Syndrome in Children Based on Integrative Analyses of Microbiome-Metabolome Signatures

The pathogenesis of gut microbiota and their metabolites in the development of metabolic syndrome (MS) remains unclear. This study aimed to evaluate the signatures of gut microbiota and metabolites as well as their functions in obese children with MS. A case-control study was conducted based on 23 MS children and 31 obese controls. The gut microbiome and metabolome were measured using 16S rRNA gene amplicon sequencing and liquid chromatography-mass spectrometry. An integrative analysis was conducted, combining the results of the gut microbiome and metabolome with extensive clinical indicators. The biological functions of the candidate microbial metabolites were validated in vitro. We identified 9 microbiota and 26 metabolites that were significantly different from the MS and the control group. The clinical indicators of MS were correlated with the altered microbiota Lachnoclostridium, Dialister, and Bacteroides, as well as with the altered metabolites all-trans-13,14-dihydroretinol, DL-dipalmitoylphosphatidylcholine (DPPC), LPC 24: 1, PC (14:1e/10:0), and 4-phenyl-3-buten-2-one, etc. The association network analysis further identified three MS-linked metabolites, including all-trans-13,14-dihydroretinol, DPPC, and 4-phenyl-3-buten-2-one, that were significantly correlated with the altered microbiota. Bio-functional validation showed that all-trans-13, 14-dihydroretinol could significantly upregulate the expression of lipid synthesis genes and inflammatory genes. This study identified a new biomarker that may contribute to MS development. These findings provided new insights regarding the development of efficient therapeutic strategies for MS. IMPORTANCE Metabolic syndrome (MS) has become a health concern worldwide. Gut microbiota and metabolites play an important role in human health. We first endeavored to comprehensively analyze the microbiome and metabolome signatures in obese children and found the novel microbial metabolites in MS. We further validated the biological functions of the metabolites in vitro and illustrated the effects of the microbial metabolites on lipid synthesis and inflammation. The microbial metabolite all-trans-13, 14-dihydroretinol may be a new biomarker in the pathogenesis of MS, especially in obese children. These findings were not available in previous studies, and they provide new insights regarding the management of metabolic syndrome.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

Gut Microbiota, Glucose, Lipid, and Water-Electrolyte Metabolism in Children With Nonalcoholic Fatty Liver Disease

There is evidence that nonalcoholic fatty liver disease (NAFLD) is affected by gut microbiota, glucose, and lipid. However, the function of water-electrolyte metabolism remains undefined in children with NAFLD. Therefore, the aim of this case-control study was to better understand these interactions. The sample consisted of 75 children, aged between 7 and 16, of whom 25 had nonalcoholic fatty liver (NAFL), 25 had nonalcoholic steatohepatitis (NASH), and 25 were obese and without NAFLD. These groups were matched by age, sex, and body mass index. Data were collected between June, 2019 and December, 2019 at the Hunan Children’s Hospital, in China. Microbiome composition in fecal samples was assessed using 16S ribosomal RNA amplicon sequencing. In the clinical indices, 12 glucose and lipid metabolism indices were included, and six water-electrolyte metabolism indices were included. The results indicated that microbiomes of NAFLD children had lower alpha diversity but higher beta diversity index than the other two groups. Specifically, anti-inflammatory and probiotics abundance (e.g., Faecalibacterium, Akkermansia, and Bifidobacterium_adolescentis) was significantly decreased in NAFLD, whereas the abundance of harmful bacteria (e.g., Staphylococcaceae) was increased. Moreover, the abundance of butyrate-producing bacteria (e.g., Faecalibacterium, Roseburia_inulinivorans, Roseburia_intestinalis, and Coprococcus_comes) was significantly decreased in NASH. The abundance of these bacteria were associated with glucose, lipid, and water-electrolyte metabolism (e.g., glucose, triglyceride, cholesterol, inorganic salt, total body water, etc.), implying that the NAFLD and its severity were associated with glucose, lipid, and water-electrolyte metabolism dysbiosis. Therefore, these findings suggest that the gut microbiome, especially butyrate-producing bacteria, play an important role in the development of NAFLD in children.

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.