ABSTRACT
Antidepressants remain the first-line treatment for depression. However, the factors influencing medication response are still unclear. Accumulating evidence implicates an association between alterations in gut microbiota and antidepressant response. Therefore, the aim of this study is to investigate the role of the gut microbiota-brain axis in the treatment response of venlafaxine. After chronic social defeat stress and venlafaxine treatment, mice were divided into responders and non-responders groups. We compared the composition of gut microbiota using 16 S ribosomal RNA sequencing. Meanwhile, we quantified metabolomic alterations in serum and hippocampus, as well as hippocampal neurotransmitter levels using liquid chromatography-mass spectrometry. We found that the abundances of 29 amplicon sequence variants (ASVs) were significantly altered between the responders and non-responders groups. These ASVs belonged to 8 different families, particularly Muribaculaceae. Additionally, we identified 38 and 39 differential metabolites in serum and hippocampus between the responders and non-responders groups, respectively. Lipid, amino acid, and purine metabolisms were enriched in both serum and hippocampus. In hippocampus, the concentrations of tryptophan, phenylalanine, gamma-aminobutyric acid, glutamic acid, and glutamine were increased, while the level of succinic acid was decreased in the responders group, compared with the non-responders group. Our findings suggest that the gut microbiota may play a role in the antidepressant effect of venlafaxine by modulating metabolic processes in the central and peripheral tissues. This provides a novel microbial and metabolic framework for understanding the impact of the gut microbiota-brain axis on antidepressant response.
ABSTRACT
Introduction: Wernicke encephalopathy (WE) is a potentially fatal condition caused by thiamine (vitamin B1) deficiency. Chronic alcoholism is the most common cause of WE; however, other conditions responsible for thiamine deficiency should also be considered. Case Report: We report the case of a 64-year-old woman with a history of diabetes who presented with confusion and apathy. Magnetic resonance imaging of the brain showed T2 hyperintensities involving dorsolateral medulla oblongata, tegmentum of the pons, vermis of the cerebellum, periaqueductal region, and the bilateral mammillary bodies. She had a history of intravenous glucose administration before her mental symptoms developed. On suspicion of WE, she was treated with a high dose of thiamine empirically. Her clinical condition improved rapidly in 2 weeks. Conclusion: Endogenous thiamine stores can be rapidly depleted in the case of enhanced glucose oxidation. Patients who receive glucose should also be prescribed thiamine to avoid inducing or exacerbating WE.
ABSTRACT
Major depressive disorder (MDD) is a severe mental illness characterized by a lack of objective biomarkers. Mounting evidence suggests there are extensive transcriptional molecular changes in the prefrontal cortex (PFC) of individuals with MDD. However, it remains unclear whether there are specific genes that are consistently altered and possess diagnostic power. In this study, we conducted a systematic search of PFC datasets of MDD patients from the Gene Expression Omnibus database. We calculated the differential expression of genes (DEGs) and identified robust DEGs using the RRA and MetaDE methods. Furthermore, we validated the consistently altered genes and assessed their diagnostic power through enzyme-linked immunosorbent assay experiments in our clinical blood cohort. Additionally, we evaluated the diagnostic power of hub DEGs in independent public blood datasets. We obtained eight PFC datasets, comprising 158 MDD patients and 263 healthy controls, and identified a total of 1468 unique DEGs. Through integrated analysis, we identified 290 robustly altered DEGs. Among these, seven hub DEGs (SLC1A3, PON2, AQP1, EFEMP1, GJA1, CENPD, HSD11B1) were significantly down-regulated at the protein level in our clinical blood cohort. Moreover, these hub DEGs exhibited a negative correlation with the Hamilton Depression Scale score (P < 0.05). Furthermore, these hub DEGs formed a panel with promising diagnostic power in three independent public blood datasets (average AUCs of 0.85) and our clinical blood cohort (AUC of 0.92). The biomarker panel composed of these genes demonstrated promising diagnostic efficacy for MDD and serves as a useful tool for its diagnosis.
ABSTRACT
Background: Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation. Methods: We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls. Results: Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power. Conclusions: The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.
ABSTRACT
Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .