Luteolin Enhances Choroid Plexus 5-MTHF Brain Transport to Promote Hippocampal Neurogenesis in LOD Rats

In vivo determination of the bioavailability of folic acid through the utilization of the PBPK model in conjunction with UPLC

This paper employed a physiologically based pharmacokinetic model (PBPK) to investigate the transformations of folic acid and its metabolites in vivo. Additionally, an ultra-performance liquid chromatography (UPLC) method was developed to accurately measure the body's retention rate and conversion rate of folic acid, tetrahydrofolate, and 5-methyltetrahydrofolate. Furthermore, the bioavailability of folic acid in the body was assessed by combining this method with an evaluation technique for animal models. The study found that the gastric metabolism time was 2 h, while the small intestinal metabolism duration was 4 h. The maximum conversion rate was observed in plasma and liver after 6 h, and in the brain after 8 h. This serves as a framework for creating a model to assess the bioavailability of folic acid in living organisms, to enhance the safety and efficacy of folic acid intake.

The therapeutic potential of curculigoside in poststroke depression: a focus on hippocampal neurogenesis and mitochondrial function

OBJECTIVES

To investigate the effects and mechanism of curculigoside against poststroke depression (PSD).

METHODS

In vivo, a PSD rat model was created by combining bilateral common carotid artery occlusion and chronic unpredictable mild stress stimulations. After 4-week modeling and intragastrically administration of curculigoside, the effects of curculigoside on behavior, hippocampal neurogenesis, and hippocampal mitochondrial oxidative phosphorylation (OxPhos) were investigated. In vitro, PSD-like primary neural stem cells (NSCs) model was established by oxygen-glucose deprivation/recovery (OGD/R) combing high-corticosterone (CORT) concentration, followed by treatment with curculigoside. The investigation subsequently examined the impact of curculigoside on mitochondrial OxPhos, proliferation, and differentiation of NSCs under OGD/R + CORT conditions.

KEY FINDINGS

In vivo, PSD rats showed significantly depressive behaviors, dysfunctional neurogenesis in hippocampus, as well as decreased hippocampus adenosine triphosphate (ATP) levels, reduced electron transport chain complexes activity, and downregulates mitochondrial transcription factor A (TFAM) and PPAR-gamma coactivator 1 alpha (PGC-1α) expression in hippocampus. In vitro, OGD/R +CORT significantly injured the proliferation and differentiation, as well as impaired the mitochondrial OxPhos in NSCs. Curculigoside treatment was effective in improving these abnormal changes.

CONCLUSION

Curculigoside may repair hippocampal neurogenesis in PSD rats by enhancing hippocampal mitochondrial OxPhos, and has shown a great potential for anti-PSD.

Targeting autophagy to counteract neuroinflammation: A novel antidepressant strategy

Depression is a common disease that affects physical and mental health and imposes a considerable burden on afflicted individuals and their families worldwide. Depression is associated with a high rate of disability and suicide. It causes a severe decline in productivity and quality of life. Unfortunately, the pathophysiological mechanisms underlying depression have not been fully elucidated, and the risk of its treatment is still presented. Studies have shown that the expression of autophagic markers in the brain and peripheral inflammatory mediators are dysregulated in depression. Autophagy-related genes regulate the level of autophagy and change the inflammatory response in depression. Depression is related to several aspects of immunity. The regulation of the immune system and inflammation by autophagy may lead to the development or deterioration of mental disorders. This review highlights the role of autophagy and neuroinflammation in the pathophysiology of depression, sumaries the autophagy-targeting small moleculars, and discusses a novel therapeutic strategy based on anti-inflammatory mechanisms that target autophagy to treat the disease.

Luteolin alleviates depression-like behavior by modulating glycerophospholipid metabolism in the hippocampus and prefrontal cortex of LOD rats

BACKGROUND

Late-onset depression (LOD) is defined as primary depression that first manifests after the age of 65. Luteolin (LUT) is a natural flavonoid that has shown promising antidepressant effects and improvement in neurological function in previous studies.

AIMS

In this study, we utilized UPLC-MS/MS non-targeted metabolomics techniques, along with molecular docking technology and experimental validation, to explore the mechanism of LUT in treating LOD from a metabolomics perspective.

RESULTS

The behavioral results of our study demonstrate that LUT significantly ameliorated anxiety and depression-like behaviors while enhancing cognitive function in LOD rats. Metabolomic analysis revealed that the effects of LUT on LOD rats were primarily mediated through the glycerophospholipid metabolic pathway in the hippocampus and prefrontal cortex. The levels of key lipid metabolites, phosphatidylserine (PS), phosphatidylcholine (PC), and phosphatidylethanolamine (PE), in the glycerophospholipid metabolic pathway were significantly altered by LUT treatment, with PC and PE showing significant correlations with behavioral indices. Molecular docking analysis indicated that LUT had strong binding activity with phosphatidylserine synthase 1 (PTDSS1), phosphatidylserine synthase 2 (PTDSS2), and phosphatidylserine decarboxylase (PISD), which are involved in the transformation and synthesis of PC, PE, and PS. Lastly, our study explored the reasons for the opposing trends of PC, PE, and PS in the hippocampus and prefrontal cortex from the perspective of autophagy, which may be attributable to the bidirectional regulation of autophagy in distinct brain regions.

CONCLUSIONS

Our results revealed significant alterations in the glycerophospholipid metabolism pathways in both the hippocampus and prefrontal cortex of LOD rats. Moreover, LUT appears to regulate autophagy disorders by specifically modulating glycerophospholipid metabolism in different brain regions of LOD rats, consequently alleviating depression-like behavior in these animals.

GRP94 in cerebrospinal fluid may contribute to a potential biomarker of depression: Based on proteomics

The present study was designed to investigate potential biomarkers of depression and targets of antidepressants from the perspective of hippocampal endoplasmic reticulum stress (ERS) based on cerebrospinal fluid (CSF) proteomics. Firstly, a six-week depression model was established and treated with fluoxetine (FLX). We found antidepressant-FLX could ameliorate depression-like behaviors and cognition in depressed rats caused by chronic unpredictable mild stress (CUMS). FLX significantly increased neuronal numbers in dentate gyrus (DG) and CA3 regions of hippocampus. CSF proteome data revealed thirty-seven differentially expressed proteins (DEPs) co-regulated by CUMS and FLX, including GRP94 and EIF2α. Results of Gene Oncology (GO) annotation and KEGG pathway enrichment for DEPs mainly included PERK-mediated unfolded protein response, endoplasmic reticulum, and translational initiation. The expression levels of GRP94, p-PERK, p-EIF2α, CHOP and Caspase-12 were increased in hippocampus of CUMS rats, and FLX worked the opposite way. FLX had strong affinity and binding activity with GRP94 protein, and four key proteins on the PERK pathway (PERK, EIF2α, p-EIF2α, CHOP). We proposed that FLX may exert antidepressant effects and neuroprotective action by alleviating excessive activation of the hippocampal PERK pathway and reducing neuronal deficits in depressed rats. PERK, EIF2α, p-EIF2α, and CHOP may be potential targets for antidepressant-FLX. GRP94 in CSF may be a potential biomarker of depression and the therapeutic effects of antidepressants.

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

BACKGROUND

The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.

METHODS

The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.

RESULTS

Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.

CONCLUSION

The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.