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BackgroundThe highly heterogeneous pathogenesis of depression and limited response to current antidepressants call for more objective evidence for depression subtypes. Reactive and endogenous depression are two etiologically distinct subtypes associated with different treatment responses. This study aims to explore the potential biomarkers that differentiate reactive and endogenous depressions.MethodsThe clinical manifestations and biological indicators of 64 unmedicated mild-to-moderate depression patients (32 reactive depression patients and 32 endogenous depression patients) and 21 healthy subjects were observed. The 24-item Hamilton rating scale for depression (HAMD-24) was used to evaluate the severity of depression. Serum levels of depression-related biological indicators were measured by using the enzyme-linked immunosorbent assay.ResultsThe NLRP3 level of reactive depression was significantly lower than those of endogenous depression and healthy controls. There was a significant negative correlation between the BDNF level and the HAMD-24 total scores for patients with reactive depression.ConclusionOur findings suggested the serum NLRP3 and BDNF levels could be potential biomarkers for detecting and evaluating the severity of reactive depression.

Obesity is a global epidemic elevating the risk of various metabolic disorders. As there is a lack of effective drugs to treat obesity, we combined bioinformatics and reverse network pharmacology in this study to identify effective herbs to treat obesity. We identified 1011 differentially expressed genes (DEGs) of adipose tissue after weight loss by analyzing five expression profiles (GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710) from the Gene Expression Omnibus (GEO) database. We identified 27 hub genes from the protein–protein interaction (PPI) network by performing MCODE using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hub genes have roles in the extracellular matrix–receptor interaction, cholesterol metabolism, PI3K-Akt signaling pathway, etc. Ten herbs (Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus) targeting the nine hub genes (COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1) using six ingredients were identified as the key herbs. Quercetin and (-)-epigallocatechin-3-gallate were determined to be the key ingredients. Lastly, Ingredients-Targets, Herbs-Ingredients-Targets, and Herbs-Taste-Meridian Tropism networks were constructed using Cytoscape to elucidate this complex relationship. This study could help identify promising therapeutic targets and drugs to treat obesity.

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