Fluoxetine is a widely used antidepressant, yet integrated analyses of its molecular mechanism remain limited. This study systematically investigated potential molecular mechanisms underlying the antidepressant effects of fluoxetine by integrating these scattered data. Using the ProMENDA database, we identified metabolites and proteins altered by fluoxetine in the brain of animal models of depression. We curated 273 differentially expressed metabolite entries and 791 differentially expressed protein entries from fluoxetine treatment and performed vote-counting, pathway enrichment, pathway crosstalk and drug-associated metabolite set enrichment analyses. Vote-counting analysis showed altered neurotransmitter levels, including increased levels of monoamines and decreased levels of neurotoxic quinolinic acid and glutamate. The results of pathway analyses based on both altered metabolites and proteins showed 121 significantly enriched pathways. Pathway crosstalk analysis identified four pathway-based modules, which were mainly involved in amino acid metabolism, neurotransmitters and multiple biological processes. Drug-associated metabolite set enrichment analysis revealed 76 significantly enriched drug-related pathways, which were mainly involved in antidepressants. This study provides a comprehensive understanding of the antidepressant effects of fluoxetine, which may provide insights for the development of novel antidepressants.

Depression is a severe mental illness that poses substantial burdens on public health. Given that depression research is still challenged by its multifaceted pathogenesis, depicting the depression associated genetic regulatory networks is essential for understanding its mechanism, optimizing diagnosis, and developing targeted therapies. However, a comprehensive panoramic view of transcriptional alterations in depression remains lacking. By leveraging the available transcriptomic studies from the National Center for Biotechnology Information (NCBI), China National Center for Bioinformatics (CNCB), European Bioinformatics Institute (EBI), and our laboratory, we compiled an extensive set of depression related datasets, encompassing 4 species, 31 types of brain and peripheral tissues, 35 categories of antidepressant interventions, and 6391 samples. Furthermore, a unified pipeline for raw data preprocessing and differential expression analysis was employed to identify differentially expressed genes (DEGs). A total of 631882 molecules entries were obtained, including 190366 entries from humans, 6612 from non-human primates, 332780 from mice, and 102124 from rats. Additionally, 15 single-cell and single-nucleus transcriptomic datasets, including 164 samples, and 78536 molecular entries were also included. Notably, we developed the TranDep database ( http://www.depression-atlas.cn/ ) for the depression research community, which provided a user-friendly web interface for browsing, and searching these molecules. To demonstrate the utility of TranDep, a case study was presented to identify robust DEGs, and explore its biological functions in the prefrontal cortex of patients with depression. Overall, TranDep is a comprehensive cross-species resource developed to provide a transcriptional atlas of depression, which may shed light on the identification and validation of diagnostic and therapeutic markers for depression.

Despite the widespread use of metabolomics and proteomics to explore the molecular landscape of depression, there is a lack of consensus regarding dysregulated molecules with replicable evidence. Thus, this study aimed to identify robust metabolomic and proteomic features in depression by integrating evidence from large-scale studies. In this study, a knowledge base-mining approach was adopted to compile a list of dysregulated molecules derived from metabolomic and proteomic studies. A vote-counting approach was performed to identify consistently altered molecules in the blood and urine samples of patients with depression. A total of 2398 molecular entries were selected, comprising 857 unique metabolites and 468 unique proteins from 143 metabolomic and 23 proteomic studies in depression. The results of vote-counting analyses revealed that 11 metabolites in blood and 5 metabolites in urine exhibited consistent disturbances across studies. Circulating levels of glutamic acid and phosphatidylcholine (32:0) were elevated in depressive patients, whereas the levels of tryptophan, kynurenic acid, kynurenine, acetylcarnitine, serotonin, creatinine, inosine, phenylalanine, and valine were lower. Urinary levels of isobutyric acid, alanine, and nicotinic acid were higher, whereas the levels of N-methylnicotinamide and tyrosine were lower. Moreover, analysis of the proteomic dataset identified only one circulating protein, ceruloplasmin, that was consistently dysregulated. Convergence comparison prioritized tryptophan as the top-ranked circulating metabolite, followed by kynurenic acid, acetylcarnitine, creatinine, serotonin, and valine. Collectively, robust evidence of metabolomic changes was observed in patients with depression, pointing to a role as potential biomarkers. Further investigation of consensus proteomic features for depression is necessitated.

BackgroundAutism spectrum disorder (ASD) is a neurological and developmental disorder commonly accompanied by gut dysbiosis and gastrointestinal symptoms. Accumulating evidence supports a crucial role of gut microbiota dysbiosis in the pathophysiological mechanisms of ASD. However, the alteration of gut microbiota shows high heterogeneity across different studies. This study aims to identify potential biomarkers in the gut microbiota of patients with ASD.MethodsWe conducted a comprehensive analysis by searching the PubMed, Web of Science, Cochrane Library, and Embase databases, for studies assessing the changes of gut microbial diversity and taxa in ASD patients and healthy controls using high-throughput sequencing. Vote counting analyses were performed to identify consistently altered gut microbes associated with ASD.ResultsSixty-four studies involving 189 differentially abundant gut microbial taxa were included. Our synthesis provided no strong evidence for a difference in α-diversity between ASD patients and healthy controls, while studies were relatively consistent in reporting differences in β-diversity. Among 189 taxa, we identified three significantly increased taxa in ASD patients: Eubacteriales, Klebsiella, and Clostridium. Additionally, there were enriched trends of Oscillospira, Dorea, and Collinsella, and depleted trends of Streptococcus, Akkermansia, Coprococcus, and Dialister. These findings suggest that the disrupted intestinal microecology and functional changes in ASD are characterized by an enrichment of pro-inflammatory genera, a reduction of specific probiotics, lactic acid-producing and utilizing bacteria, and an imbalance of anti-inflammatory butyrate-producing bacteria. Substantial heterogeneity across studies concerning demographics and methodologies was also observed.ConclusionThis systematic review contribute to a further understanding of the role of gut microbiota in ASD and support the development of microbiota-based diagnostic and therapeutic strategies for ASD.

BACKGROUND:The potential association between short-chain fatty acids (SCFAs) and aging has gained increasing research attention. Although most studies have focused on fecal SCFAs, limited attention has been directed toward SCFAs in peripheral blood. Furthermore, this relationship remains insufficiently explored in non-human primate models. METHODS:Serum samples were collected from eighty-five healthy Macaca mulatta. Serum SCFA concentrations were quantified using targeted gas chromatography-mass spectrometry. A subset of samples (19 males and 19 females) was further analyzed for reproductive and stress-related hormones using enzyme-linked immunosorbent assay kits. RESULTS:Significant age-related alterations in SCFA concentrations were identified. Isobutyric acid levels increased with age, whereas acetic acid, propionic acid, and valeric acid levels decreased. Moreover, the direction of SCFA alterations differed between males and females. Integrated analysis with hormone assays demonstrated that these sex-specific differences in SCFAs were closely associated with the activity of the hypothalamic-pituitary-gonadal axis. CONCLUSION:Serum SCFA profiles varied significantly with both age and sex. The secretion of reproductive and stress-related hormones was highly correlated with these differences. These findings provide new insights into how aging influences sex-dependent SCFA metabolism and may contribute to understanding the mechanisms underlying age- and sex-related physiological changes.

Psychiatric disorders pose substantial global burdens on public health, yet therapeutic options remain limited. Recently, gut microbiota is in the spotlight of new research on psychiatric disorders, as emerging discoveries have highlighted the importance of gut microbiome in the regulation of central nervous system via mediating the gut-brain-axis bidirectional communication. While metagenomics studies have accumulated for psychiatric disorders, few systematic efforts were dedicated to integrating these high-throughput data across diverse phenotypes, interventions, geographical regions, and biological species. To present a panoramic view of global data and provide a comprehensive resource for investigating the gut microbiota dysbiosis in psychiatric disorders, we developed the PsycGM, a manually curated and well-annotated database that provides the literature-supported associations between gut microbiota and psychiatric disorders or intervention measures. In total, PsycGM incorporated 559 studies from 31 countries worldwide, encompassing research involving humans, rats, mice, and non-human primates. PsycGM documented 8907 curated associations between 1514 gut microbial taxa and 11 psychiatric disorders, as well as 4050 associations between 869 taxa and 232 microbiota-based and non-microbiota-based interventions. Moreover, PsycGM provided a user-friendly web interface with comprehensive information, enabling browsing, retrieving and downloading of all entries. In the application of PsycGM, we panoramically depicted the intestinal microecological imbalance in depression. Additionally, we identified 9 microbial taxa consistently altered in patients with depression, with the most common dysregulations observed for Parabacteroides, Alistipes, and Faecalibacterium; in animal models of depression, consistent changes were observed in 21 microbial taxa, most frequently reported as Helicobacter, Lactobacillus, Roseburia, and the ratio of Firmicutes/Bacteroidetes. PsycGM is a comprehensive resource for future investigations on the role of gut microbiota in mental and brain health, and for therapeutic target innovations based on modifications of gut microbiota. PsycGM is freely accessed at http://psycgmomics.info .

BACKGROUND:Depression is a complex and prevalent mental disorder. Numerous studies have reported there were significant metabolomic and proteomic changes in hippocampus of depressed patients. However, few researches have systematically integrated these two omics data to identify key molecular mechanisms underlying depression. METHODS:Based on the data of Protein and Metabolite Network of Depression Database (ProMENDA), we integrate the significantly altered metabolites and proteins of hippocampus in animal models of depression. Pathway analysis was performed using IPA software to explore biological functional disturbance underlying these molecules. Finally, animal model construction, molecular biology experiments, and lentiviral transfection in vitro for gene knockout were performed to verify potential pathways. RESULTS:A total of 682 altered metabolites and 2300 altered proteins were retrieved. Pathway enrichment analysis identified 394 significantly enriched pathways, with the sirtuin signaling mediated autophagy being of particular interest. Further biological validations revealed the decrease of Sirt1, the autophagy-related genes, and autophagy markers in hippocampus of both mouse and Macaca fascicularis models of depression. Lastly, Sirt1 knockdown in primary neurons inhibited autophagy. CONCLUSION:This study expanded our understanding of multi-omics alterations in the hippocampus of depression by revealing that Sirt1 may mediate neuronal autophagy in the hippocampus of animal models of depression, which could further contribute to the pathophysiology of depression.

Major depressive disorder (MDD) is a devastating psychiatric illness with various etiologies. Both chronic stress and gut microbiome dysbiosis are implicated in the pathogenesis of MDD. However, limited research has been conducted to delineate the distinct effects of these two pathogenic factors on the brain transcriptome. We generated and compared transcriptomic features of the anterior cingulate cortex (ACC) from depressive-like mice induced by gut microbiome dysbiosis and canonical chronic stress paradigms, focusing on gene expression patterns and network characteristics. Data derived from MDD patients served as a reference standard to filter the molecular alterations associated with the disorder. Chronic stress induced a plethora of altered genes and biological functions associated with depression, prominently involving mitochondrial dysfunction. However, gut microbiota dysbiosis specifically regulated narrower range of genes and biological mechanisms, targeting aberrations in vesicular transport systems and perturbations of autophagy pathways. Network analysis revealed that hierarchical gene co-expression was specifically affected by gut microbiota dysbiosis rather than chronic stress. Further functional clustering analysis, along with the central distribution of inflammation-related differentially expressed genes, suggested an intricate interplay between disrupted autophagy processes, microglia-mediated inflammation, and synaptic dysfunctions in the network influenced by gut microbiota dysbiosis. Our findings reveal the distinctive transcriptomic alterations of brain shaped by gut microbiota and chronic stress in the development of MDD, contributing to a deeper understanding the heterogeneity of depression. Additionally, we provide a valuable data resource and bioinformatic analysis template for future studies.

Depression is a complex disorder with multiple contributing factors, and chronic stress has previously been recognized as a major causative factor, while gut microbes have also been found to be involved in depression recently. However, gene expression in depression models with different etiologies is unclear. Here, we compared the transcriptomes of the striatum in chronic social defeat stress (CSDS) model of C57BL/6J male mice and fecal microbiota transplant (FMT) model of Kumming male mice. We found that the proportion of shared differentially expressed genes (DEGs) between the two models was only 24 %. The specific DEGs of FMT model were enriched in immune and inflammatory, and are associated with changes in vascular and ciliated ependymal cells. The specific DEGs of CSDS model were enriched in neuron and synapse. The results of network analysis suggested the expression patterns and biological function of depressive-like behaviors-related modules in the two models are different. Further, the alternative splicing events of CSDS are more than FMT. Our results suggested models of depression induced by different etiologies differ significantly in gene expression and biological function. Our study also suggested us to pay attention to the characteristics of models of depression of different etiologies and provided a more comprehensive understanding of the heterogeneity of depression.

BackgroundMajor 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.MethodsWe 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.ResultsDifferential 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.ConclusionsThe 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.

The risk factors and causes of intracerebral hemorrhage (ICH) and the degree of functional recovery after ICH are distinct between young and elderly patients. The increasing incidence of ICH in young adults has become a concern; however, research on the molecules and pathways involved ICH in subjects of different ages is lacking. In this study, tandem mass tag (TMT)-based proteomics was utilized to examine the protein expression profiles of perihematomal tissue from young and aged mice 24 h after collagenase-induced ICH. Among the 5,129 quantified proteins, ICH induced 108 and 143 differentially expressed proteins (DEPs) in young and aged mice, respectively; specifically, there were 54 common DEPs, 54 unique DEPs in young mice and 89 unique DEPs in aged mice. In contrast, aging altered the expression of 58 proteins in the brain, resulting in 39 upregulated DEPs and 19 downregulated DEPs. Bioinformatics analysis indicated that ICH activated different proteins in complement pathways, coagulation cascades, the acute phase response, and the iron homeostasis signaling pathway in mice of both age groups. Protein–protein interaction (PPI) analysis and ingenuity pathway analysis (IPA) demonstrated that the unique DEPs in the young and aged mice were related to lipid metabolism and carbohydrate metabolism, respectively. Deeper paired-comparison analysis demonstrated that apolipoprotein M exhibited the most significant change in expression as a result of both aging and ICH. These results help illustrate age-related protein expression changes in the acute phase of ICH.

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.

IntroductionSchizophrenia is a complex psychiatric disorder, of which molecular pathogenesis remains largely unknown. Accumulating evidence suggest that gut microbiota may affect brain function via the complex gut-brain axis, which may be a potential contributor to schizophrenia. However, the alteration of gut microbiota showed high heterogeneity across different studies. Therefore, this study aims to identify the consistently altered gut microbial taxa associated with schizophrenia.MethodsWe conducted a systematic search and synthesis of the up-to-date human gut microbiome studies on schizophrenia, and performed vote counting analyses to identify consistently changed microbiota. Further, we investigated the effects of potential confounders on the alteration of gut microbiota.ResultsWe obtained 30 available clinical studies, and found that there was no strong evidence to support significant differences in α-diversity and β-diversity between schizophrenic patients and healthy controls. Among 428 differential gut microbial taxa collected from original studies, we found that 8 gut microbial taxa were consistently up-regulated in schizophrenic patients, including Proteobacteria, Gammaproteobacteria, Lactobacillaceae, Enterobacteriaceae, Lactobacillus, Succinivibrio, Prevotella and Acidaminococcus. While 5 taxa were consistently down-regulated in schizophrenia, including Fusicatenibacter, Faecalibacterium, Roseburia, Coprococcus and Anaerostipes.DiscussionThese findings suggested that gut microbial changes in patients with schizophrenia were characterized by the depletion of anti-inflammatory butyrate-producing genera, and the enrichment of certain opportunistic bacteria genera and probiotics. This study contributes to further understanding the role of gut microbiota in schizophrenia, and developing microbiota-based diagnosis and therapy for schizophrenia.

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.

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.

Aging is a systemic physiological degenerative process, with alterations in gut microbiota and host metabolism. However, due to the interference of multiple confounding factors, aging-associated molecular characteristics have not been elucidated completely. Therefore, based on 16S ribosomal RNA (rRNA) gene sequencing and non-targeted metabolomic detection, our study systematically analyzed the composition and function of the gut microbiome, serum, and fecal metabolome of 36 male rhesus monkeys spanning from 3 to 26 years old, which completely covers juvenile, adult, and old stages. We observed significant correlations between 41 gut genera and age. Moreover, 86 fecal and 49 serum metabolites exhibited significant age-related correlations, primarily categorized into lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, and organoheterocyclic compounds. Further results suggested that aging is associated with significant downregulation of various amino acids constituting proteins, elevation of lipids, particularly saturated fatty acids, and steroids. Additionally, age-dependent changes were observed in multiple immune-regulatory molecules, antioxidant stress metabolites, and neurotransmitters. Notably, multiple age-dependent genera showed strong correlations in these changes. Together, our results provided new evidence for changing characteristics of gut microbes and host metabolism during aging. However, more research is needed in the future to verify our findings.

Depression is a serious mental illness, but the molecular mechanisms of depression remain unclear. Previous research has reported metabolomic changes in the blood of patients with depression, while integrated analysis based on these altered metabolites was still lacking. The objective of this study was to integrate metabolomic changes to reveal the underlying molecular alternations of depression. We retrieved altered metabolites in the blood of patients with depression from the MENDA database. Pathway analysis was conducted to explore enriched pathways based on candidate metabolites. Pathway crosstalk analysis was performed to explore potential correlations of these enriched pathways, based on their shared candidate metabolites. Moreover, potential interactions of candidate metabolites with other biomolecules such as proteins were assessed by network analysis. A total of 854 differential metabolite entries were retrieved in peripheral blood of patients with depression, including 555 unique candidate metabolites. Pathway analysis identified 215 significantly enriched pathways, then pathway crosstalk analysis revealed that these pathways were clustered into four modules, including amino acid metabolism, nucleotide metabolism, energy metabolism and others. Additionally, eight molecular networks were identified in the molecular network analysis. The main functions of these networks involved amino acid metabolism, molecular transport, inflammatory responses and others. Based on integrated analysis, our study revealed pathway-based modules and molecular networks associated with depression. These results will contribute to the underlying knowledge of the molecular mechanisms in depression.

Background: The gut-brain axis has been shown to play an important role in depression. However, few studies have examined proteomic changes in the intestine of the nonhuman primate model of depression. Methods: We investigated the intestinal proteome of macaques (Macaca fascicularis) with depression-like (DL) behaviors by data-independent acquisition techniques. We also performed integration analyses of proteomic changes, previous metabolomic and microbiotic data. Moreover, we confirmed the gene expressions of key proteins. Results: Sixty-five differentially expressed proteins (DEPs) were identified, of which fifty-four DEPs were downregulated and the others were altered conversely in DL macaques compared with the control group. Pathway analysis indicated that mitochondrial function and energy metabolism were representative functions of DEPs. The key DEPs were significantly associated with glycerophospholipid metabolism and imbalances of gut microbe. We confirmed that key molecules (NDUFB4, UQCR10, PISD) were significantly inhibited, which may disturb the energy transformation of the electron respiratory chain and the homeostasis of the mitochondrial membrane. Limitations: Further research is warranted to determine the effects of depression on other peripheral organs. Conclusions: These findings suggest the functional disorder of intestinal mitochondria in DL macaques. The disturbances of glycerophospholipid metabolism and gut microbiota may exacerbate disruptions of energy metabolism. Taking together, our study provides new clues to the relationship between depression and intestinal proteome.

This study aimed to investigate the risk factors of patients with postpartum hemorrhage (PPH) after cesarean delivery (CD) and to develop a risk-factor model for PPH after CD. Patients were selected from seven affiliated medical institutions of Chongqing Medical University from January 1st, 2015, to January 1st, 2020. Continuous and categorical variables were obtained from the hospital’s electronic medical record systems. Independent risk factors were identified by univariate analysis, least absolute shrinkage and selection operator and logistic regression. Furthermore, logistic, extreme gradient boosting, random forest, classification and regression trees, as well as an artificial neural network, were used to build the risk-factor model. A total of 701 PPH cases after CD and 2797 cases of CD without PPH met the inclusion criteria. Univariate analysis screened 28 differential indices. Multi-variable analysis screened 10 risk factors, including placenta previa, gestational age, prothrombin time, thrombin time, fibrinogen, anemia before delivery, placenta accreta, uterine atony, placental abruption and pregnancy with uterine fibroids. Areas under the curve by random forest for the training and test sets were 0.957 and 0.893, respectively. The F1 scores in the random forest training and test sets were 0.708. In conclusion, the risk factors for PPH after CD were identified, and a relatively stable risk-factor model was built.

Major depressive disorder is caused by gene-environment interactions and the gut microbiota plays a pivotal role in the development of depression. However, the underlying mechanisms remain elusive. Herein, the differentially expressed hippocampal long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) between mice inoculated with gut microbiota from major depressive disorder patients or healthy controls were detected, to identify the effects of gut microbiota-dysbiosis on gene regulation patterns at the transcriptome level, and in further to explore the microbial-regulated pathological mechanisms of depression. As a result, 200 mRNAs, 358 lncRNAs, and 4 miRNAs were differentially expressed between the two groups. Functional analysis of these differential mRNAs indicated dysregulated inflammatory response to be the primary pathological change. Intersecting these differential mRNAs with targets of differentially expressed miRNAs identified 47 intersected mRNAs, which were mainly related to neurodevelopment. Additionally, a microbial-regulated lncRNA-miRNA-mRNA network based on RNA-RNA interactions was constructed. Subsequently, according to the competitive endogenous RNAs (ceRNA) hypothesis and the biological functions of these intersected genes, two neurodevelopmental ceRNA sub-networks implicating in depression were identified, one including two lncRNAs (4930417H01Rik and AI480526), one miRNA (mmu-miR-883b-3p) and two mRNAs (Adcy1 and Nr4a2), and the other including six lncRNAs (5930412G12Rik, 6430628N08Rik, A530013C23Rik, A930007I19Rik, Gm15489, and Gm16251), one miRNA (mmu-miR-377-3p) and three mRNAs (Six4, Stx16, and Ube3a), and these molecules could be recognized as potential genetic and epigenetic biomarkers in microbial-associated depression. This study provides new understanding of the pathogenesis of depression induced by gut microbiota-dysbiosis and may act as a theoretical basis for the development of gut microbiota-based antidepressants.