Analysis of Differentially Expressed Genes in the Dentate Gyrus and Anterior Cingulate Cortex in a Mouse Model of Depression

Integrated Single-Cell RNA-seq and ATAC-seq Reveals Heterogeneous Differentiation of CD4+ Naive T Cell Subsets is Associated with Response to Antidepressant Treatment in Major Depressive Disorder

The mechanism involved in major depressive disorder (MDD) is well-studied but the mechanistic origin of the heterogeneous antidepressant effect remains largely unknown. Single-cell RNA-sequencing (scRNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) on peripheral blood mononuclear cells from 8 healthy individuals and 8 MDD patients before or after 12 weeks of antidepressant treatment is performed. scRNA-seq analysis reveals a lower proportion of naive T cells, particularly CD4+ naive T cells, in MDD patients compared to controls, and in nonresponders versus responders at the baseline. Flow cytometry data analysis of an independent cohort of 35 patients and 40 healthy individuals confirms the findings. Enrichment analysis of differentially expressed genes indicated obvious immune activation in responders. A specific activated CD4+ naive T population in responders characterized by enhanced mitogen-activated protein kinases (MAPK) pathway is identified. E-twenty six (ETS) is proposed as an upstream regulator of the MAPK pathway and heterogeneous differentiation in activated CD4+ naive T population is associated with the response to antidepressant treatment in MDD patients. A distinct immune feature manifested by CD4+ naive T cells during antidepressant treatment in MDD is identified. Collectively, this proposes the molecular mechanism that underlies the heterogeneous antidepressant outcomes for MDD.

Enhancing the revelation of key genes and interaction networks in non-small cell lung cancer with major depressive disorder: A bioinformatics analysis

Background and Aims

Lung cancer is ranked as the second most prevalent form of cancer worldwide. Nonsmall cell lung cancer (NSCLC) represents the predominant histological subtype. Research suggests that one-third of lung cancer patients also experiencing depression. Antidepressants play an indispensable role in the management of NSCLC. Despite significant advancements in treatment, lung cancer patients still face a high mortality rate. Major depressive disorder (MDD) and related antidepressants involved in treatment efficacy and prognosis of NSCLC. However, there has been a lack of screening and analysis regarding genes and networks associated with both NSCLC and MDD.

Methods

To investigate the correlation between MDD and NSCLC, our discovery and validation analysis included four datasets from the Gene Expression Omnibus database from NSCLC or MDD. Differential gene expression (DEGs) analysis, GO and KEGG Pathway, and protein-protein interaction network analyzes to identify hub genes, networks, and associated observations link between MDD and NSCLC.

Results

The analysis of two datasets yielded a total of 84 downregulated and 52 upregulated DEGs. Pathway enrichment analyzes indicated that co-upregulated genes were enriched in the regulation of positive regulation of cellular development, collagen-containing extracellular matrix (ECM), cytokine binding, and axon guidance. We identified 20 key genes, which were further analyzed using the MCODE plugin to identify two core subnetworks. The integration of functionally similar genes provided valuable insights into the potential involvement of these hub genes in diverse biological processes including angiogenesis humoral immune response regulation inflammatory response organization ECM network.

Conclusion

We have identified a total of 136 DEGs that participate in multiple biological signaling pathways. A total of 20 hub genes have demonstrated robust associations, potentially indicating novel diagnostic and therapeutic targets for both diseases.

Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex

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.

Zmiz1 is a novel regulator of brain development associated with autism and intellectual disability

Neurodevelopmental disorders (NDDs) are a class of pathologies arising from perturbations in brain circuit formation and maturation with complex etiological triggers often classified as environmental and genetic. Neuropsychiatric conditions such as autism spectrum disorders (ASD), intellectual disability (ID), and attention deficit hyperactivity disorders (ADHD) are common NDDs characterized by their hereditary underpinnings and inherent heterogeneity. Genetic risk factors for NDDs are increasingly being identified in non-coding regions and proteins bound to them, including transcriptional regulators and chromatin remodelers. Importantly, de novo mutations are emerging as important contributors to NDDs and neuropsychiatric disorders. Recently, de novo mutations in transcriptional co-factor Zmiz1 or its regulatory regions have been identified in unrelated patients with syndromic ID and ASD. However, the role of Zmiz1 in brain development is unknown. Here, using publicly available databases and a Zmiz1 mutant mouse model, we reveal that Zmiz1 is highly expressed during embryonic brain development in mice and humans, and though broadly expressed across the brain, Zmiz1 is enriched in areas prominently impacted in ID and ASD such as cortex, hippocampus, and cerebellum. We investigated the relationship between Zmiz1 structure and pathogenicity of protein variants, the epigenetic marks associated with Zmiz1 regulation, and protein interactions and signaling pathways regulated by Zmiz1. Our analysis reveals that Zmiz1 regulates multiple developmental processes, including neurogenesis, neuron connectivity, and synaptic signaling. This work paves the way for future studies on the functions of Zmiz1 and highlights the importance of combining analysis of mouse models and human data.

Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression

Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate.

Dissection of cellular disruptions in autism spectrum disorder comorbidities

Up to 80% of children with autism spectrum disorder have at least one other neuropsychiatric comorbidity. The causes of such disorders are highly genetic, yet many studies fail to take analysis further than risk gene discovery to see cellular and mechanistic changes occurring. Therefore, the goal of this study was to unveil novel gene expression signatures involved in important neurodevelopmental processes that, when disrupted, lead to each of the autism comorbidities of interest. We achieved this by analysing a single-nuclei RNA sequencing dataset with prefrontal cortex samples from autism spectrum disorder plus comorbidities for differentially expressed genes. The highest number of alterations was seen in excitatory neurons, which also showed differential population and cell-cell interactions across disorders and an increase in expression of genes involved in neurodevelopmental pathways. Interestingly, the group without comorbidities displayed an increase in neuron-neuron interactions yet a decrease in population number, suggesting a major rewiring of neuronal connections. Further analysis of the topmost significant genes from this cell type in developing prefrontal cortex datasets revealed interesting expression trajectories corresponding to important time points during corticogenesis. This further identified four novel candidate genes that show a potential link to developmental pathways that may contribute to autism and its comorbidities when dysregulated. The study provides a better understanding of co-occurring conditions at a transcriptomic and cell-type level and thereby aid future research in providing earlier diagnosis, care and intervention.

Neuroinflammation Profiling of Brain Cytokines Following Repeated Blast Exposure

Due to use of explosive devices and heavy weapons systems in modern conflicts, the effect of BW on the brain and body is of increasing concern. These exposures have been commonly linked with neurodegenerative diseases and psychiatric disorders in veteran populations. A likely neurobiological link between exposure to blasts and the development of neurobehavioral disorders, such as depression and PTSD, could be neuroinflammation triggered by the blast wave. In this study, we exposed rats to single or repeated BW (up to four exposures-one per day) at varied intensities (13, 16, and 19 psi) to mimic the types of blast exposures that service members may experience in training and combat. We then measured a panel of neuroinflammatory markers in the brain tissue with a multiplex cytokine/chemokine assay to understand the pathophysiological process(es) associated with single and repeated blast exposures. We found that single and repeated blast exposures promoted neuroinflammatory changes in the brain that are similar to those characterized in several neurological disorders; these effects were most robust after 13 and 16 psi single and repeated blast exposures, and they exceeded those recorded after 19 psi repeated blast exposures. Tumor necrosis factor-alpha and IL-10 were changed by 13 and 16 psi single and repeated blast exposures. In conclusion, based upon the growing prominence of negative psychological health outcomes in veterans and soldiers with a history of blast exposures, identifying the molecular etiology of these disorders, such as blast-induced neuroinflammation, is necessary for rationally establishing countermeasures and treatment regimens.

Potential Mechanisms of Shu Gan Jie Yu Capsule in the Treatment of Mild to Moderate Depression Based on Systemic Pharmacology and Current Evidence

Background

Shu Gan Jie Yu (SGJY) capsule has a good effect on relieving depressive symptoms in China. However, the mechanism of action is still unclear. Therefore, systemic pharmacology and molecular docking approaches were used to clarify its corresponding antidepressant mechanisms.

Methods

Traditional Chinese Medicine Database and Analysis Platform (TCMSP), the Encyclopedia of Traditional Chinese Medicine (ETCM), and Swiss Target Prediction servers were used to screen and predict the bioactive components of the SGJY capsule and their antidepressive targets. Mild to moderate depression (MMD) related genes were obtained from GeneCards and DisGeNET databases. A network of bioactive components-therapeutic targets of the SGJY capsule was established by STRING 11.5 and Cytoscape 3.9.0 software. Gene function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by utilizing Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform. Active components were taken to dock with the hypothetical proteins by iGEMDOCK and SwissDock, and the docking details were visually displayed by UCSF Chimera software. Then, the related research literature of the SGJY capsule was reviewed, summarized, sorted, and analyzed, including experimental evidence and clinical experience.

Results

Seven active components and 45 intersection targets were included in the study. PPI network had genuinely uncovered the potential therapeutic targets, such as AKT1, HSP90AA1, ESR1, EGFR, and PTGS2. KEGG pathway analysis showed that the mechanism of the SGJY capsule on MMD was mainly involved in the PI3K-Akt signaling pathway.

Conclusions

In this study, we have successfully predicted the biochemically active constituents, potential therapeutic targets, and comprehensively predicted the related drug-gene interaction of the SGJY capsule for treating MMD and provided a basis for subsequent experiments.

Rosmarinic acid relieves LPS-induced sickness and depressive-like behaviors in mice by activating the BDNF/Nrf2 signaling and autophagy pathway

Neuroinflammation is one of the main causes of sickness and depressive-like behavior. Rosmarinic acid (RA) has been shown to have a significant anti-neuroinflammatory effect. However, the protective effects and the underlying mechanism of RA on sickness and depressive-like behavior under conditions of neuroinflammation are still unclear.　In the present study,　we investigated the effects and　the underlying mechanism of RA on lipopolysaccharide (LPS)-treated mice with sickness behavior. The behavioral effects of LPS treatment and RA administration were assessed using behavioral tests including a sucrose preference test and an open field test. The neuroprotective effects of RA in conditions of neuroinflammatory injury were determined by HE staining, Nissl staining, and immunofluorescent staining. Moreover, its underlying mechanism was analyzed by using real-time PCR analysis, western blot, and immunofluorescent analysis. The results indicated that RA dramatically mitigated sickness behaviors and histologic brain damage in mice exposed to LPS. In addition, RA administration markedly promoted the expression of brain-derived neurotrophic factor (BDNF)/erythroid 2-related factor 2 (Nrf2), the key regulatory proteins for Nrf2 activation (p21 and p62), the downstream antioxidant enzymes (HO-1, NQO1, GCLC), the autophagy-related proteins (LC3II and Beclin1), and mitochondrial respiratory enzyme genes (ME1, IDH1, 6-PGDH), while reducing the expression of pro-inflammatory genes (CD44, iNOS, TNFα, IL-1β). Moreover, the double-label immunofluorescent analysis revealed that RA increased the fluorescence intensity of LC3 mostly co-localized with neurons and co-expressed with Nrf2. Taken together, our research found that RA could effectively alleviate sickness behaviors and nerve injury caused by neuroinflammation, and its protective effects were mediated by the Nrf2 signaling pathway, which reduced cellular oxidative stress, inflammation, mitochondrial respiratory function damage, and autophagy imbalance. Therefore, RA has the potential to prevent or treat sickness and depressive-like behaviors under conditions of neuroinflammation.

Age-Associated Molecular Changes in Human Hippocampus Subfields as Determined by Quantitative Proteomics

The hippocampus demonstrates age-associated changes in functions, neuronal circuitry, and plasticity during various developmental stages. On the contrary, there is a significant knowledge gap on age-associated proteomic alterations in the hippocampus subfields. Using tandem mass tag-based high-resolution mass spectrometry and quantitative proteomics, we report here age-associated changes in the human hippocampus at the subregional level. We used formalin-fixed paraffin-embedded hippocampal tissue sections from a total of 12 healthy individuals, with 3 individuals from each of the 4 different age groups, specifically, 1-10, 21-30, 31-40, and 81-90 years. We found that lysosome and oxidative phosphorylation were the pathways enriched in the 81- to 90-year age group. On the contrray, nervous system development, synaptic plasticity and transmission, messenger RNA (mRNA) splicing, and electron transport chain (ETC) complex-I activity were the enriched biological processes observed in the younger age groups. In a hippocampus subfield context, our topline findings on age-associated proteome changes include altered expression of proteins associated with adult neurogenesis with age in the dentate gyrus and increased expression of immune response-associated proteins with age in certain cornu ammonis sectors of the hippocampus. Signal peptide analysis predicted hippocampal proteins with secretory potential. While these new findings warrant replication in larger study samples, the current data contribute to (1) our understanding of the molecular basis of proteomic changes across various age groups in hippocampus subfields in healthy individuals, and (2) the design and interpretation of future research on the age-associated neurodegenerative disorders.

Abnormal Reginal Homogeneity in Left Anterior Cingulum Cortex and Precentral Gyrus as a Potential Neuroimaging Biomarker for First-Episode Major Depressive Disorder

OBJECTIVE

There is no objective method to diagnose major depressive disorder (MDD). This study explored the neuroimaging biomarkers using the support vector machine (SVM) method for the diagnosis of MDD.

METHODS

52 MDD patients and 45 healthy controls (HCs) were involved in resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Imaging data were analyzed with the regional homogeneity (ReHo) and SVM methods.

RESULTS

Compared with HCs, MDD patients showed increased ReHo in the left anterior cingulum cortex (ACC) and decreased ReHo in the left precentral gyrus (PG). No correlations were detected between the ReHo values and the Hamilton Rating Scale for Depression (HRSD) scores. The SVM results showed a diagnostic accuracy of 98.96% (96/97). Increased ReHo in the left ACC, and decreased ReHo in the left PG were illustrated, along with a sensitivity of 98.07%(51/52) and a specificity of100% (45/45).

CONCLUSION

Our results suggest that abnormal regional neural activity in the left ACC and PG may play a key role in the pathophysiological process of first-episode MDD. Moreover, the combination of ReHo values in the left ACC and precentral gyrusmay serve as a neuroimaging biomarker for first-episode MDD.

Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning

Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases. However, constructing a classification model with superior performance for differentiating SCZ, BP, and MDD samples is still a great challenge. In this study, the transcriptomic data was applied for discovering key genes and constructing a classification model. In this dataset, there were 268 samples including four groups (67 SCZ patients, 40 BP patients, 57 MDD patients, and 104 healthy controls), which were applied for constructing a classification model. First, 269 probes of differentially expressed genes (DEGs) among four sample groups were identified by the feature selection method. Second, these DEGs were validated by the literature review including disease relevance with the psychiatric disorders of these DEGs, the hub genes in the PPI (protein–protein interaction) network, and GO (gene ontology) terms and pathways. Third, a classification model was constructed using the identified DEGs by machine learning method to classify different groups. The ROC (receiver operator characteristic) curve and AUC (area under the curve) value were used to assess the classification capacity of the model. In summary, this classification model might provide clues for the diagnoses of these psychiatric disorders.

Shared and distinct functional connectivity of hippocampal subregions in schizophrenia, bipolar disorder, and major depressive disorder

Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) share etiological and pathophysiological characteristics. Although neuroimaging studies have reported hippocampal alterations in SZ, BD, and MDD, little is known about how different hippocampal subregions are affected in these conditions because such subregions, namely, the cornu ammonis (CA), dentate gyrus (DG), and subiculum (SUB), have different structural foundations and perform different functions. Here, we hypothesize that different hippocampal subregions may reflect some intrinsic features among the major psychiatric disorders, such as SZ, BD, and MDD. By investigating resting functional connectivity (FC) of each hippocampal subregion among 117 SZ, 103 BD, 96 MDD, and 159 healthy controls, we found similarly and distinctly changed FC of hippocampal subregions in the three disorders. The abnormal functions of middle frontal gyrus might be the core feature of the psychopathological mechanisms of SZ, BD, and MDD. Anterior cingulate cortex and inferior orbital frontal gyrus might be the shared abnormalities of SZ and BD, and inferior orbital frontal gyrus is also positively correlated with depression and anxiety symptoms in SZ and BD. Caudate might be the unique feature of SZ and showed a positive correlation with the cognitive function in SZ. Middle temporal gyrus and supplemental motor area are the differentiating features of BD. Our study provides evidence for the different functions of different hippocampal subregions in psychiatric pathology.