Comprehensive analysis of endoplasmic reticulum stress and immune infiltration in major depressive disorder

Zhang, Jing; Xie, Shujun; Chen, Yujia; Zhou, Xin; Yang, Lingling; Li, Yan

doi:10.3389/fpsyt.2022.1008124

Cited by 7 publications

(5 citation statements)

References 91 publications

(106 reference statements)

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“…Inflammatory processes are specifically involved in MDD, and the immune profiles of MDD and control groups were analysed. The results revealed that the infiltration level of M0 macrophages was higher in MDD samples than in control samples, which was also observed by Zhang et al ( 51 ). A previous study indicated that a higher production of pro-inflammatory cytokines was detected in M0 macrophages in autologous sera from patients with MDD than in control samples ( 52 ).…”

Section: Discussionsupporting

confidence: 85%

“…The present analysis has some unique advantages. Compared with previous diagnostic model construction ( 51 ), our analysis employs a larger number of datasets and identifies two molecular subtypes associated with ERS, which may provide a more comprehensive understanding of MDD. Notably, our findings clarify several diagnostic signatures and molecular subtypes from the perspective of ERS, which can provide a deeper understanding of the molecular heterogeneity of depression; meanwhile, it is helpful to assist psychiatrists to formulate accurate and individualised treatment schemes, thus reducing the burden of depression.…”

Section: Discussionmentioning

confidence: 99%

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Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Huang

Shen

et al. 2023

Front. Psychiatry

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SubjectMajor depressive disorder (MDD) negatively affects patients’ behaviours and daily lives. Due to the high heterogeneity and complex pathological features of MDD, its diagnosis remains challenging. Evidence suggests that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of MDD; however, relevant diagnostic markers have not been well studied. This study aimed to screen for ERS genes with potential diagnostic value in MDD.MethodsGene expression data on MDD samples were downloaded from the GEO database, and ERS-related genes were obtained from the GeneCards and MSigDB databases. Differentially expressed genes (DEGs) in MDD patients and healthy subjects were identified and then integrated with ERS genes. ERS diagnostic model and nomogram were developed based on biomarkers screened using the LASSO method. The diagnostic performance of this model was evaluated. ERS-associated subtypes were identified. CIBERSORT and GSEA were used to explore the differences between the different subtypes. Finally, WGCNA was performed to identify hub genes related to the subtypes.ResultsA diagnostic model was developed based on seven ERS genes: KCNE1, PDIA4, STAU1, TMED4, MGST1, RCN1, and SHC1. The validation analysis showed that this model had a good diagnostic performance. KCNE1 expression was positively correlated with M0 macrophages and negatively correlated with resting CD4+ memory T cells. Two subtypes (SubA and SubB) were identified, and these two subtypes showed different ER score. The SubB group showed higher immune infiltration than the SubA group. Finally, NCF4, NCF2, CSF3R, and FPR2 were identified as hub genes associated with ERS molecular subtypes.ConclusionOur current study provides novel diagnostic biomarkers for MDD from an ERS perspective, and these findings further facilitate the use of precision medicine in MDD.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Huang

Shen

et al. 2023

Front. Psychiatry

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“…Secondarily, the hsa05206 pathway, has been connected to the mechanism of action of saikogenin G, a bioactive ingredient of the traditional antidepressant treatment Radix Bupleuri in Chinese medicine [71], but has not been connected to response to lithium therapy in bipolar disorder [72]. b) hsa04621 pathway (includes CARD16 gene) implicates immunological deregulation in bipolar disorder [73], schizophrenia [74], [75] depression [76] and epilepsy [77] and is a target pathway for certain antiepileptics [78]. c) hsa04750 pathway (includes ASIC3 gene), has been connected to schizophrenia [79], post-stroke depression [80] and to metabolic syndrome in bipolar disorder and schizophrenia [81].…”

Section: Kegg Analysis Results and Neuropsychiatric Diseasementioning

confidence: 99%

“…hsa04621 pathway (includes CARD16 gene) implicates immunological deregulation in bipolar disorder [73], schizophrenia [74], [75] depression [76] and epilepsy [77] and is a target pathway for certain antiepileptics [78].…”

Section: Discussionmentioning

confidence: 99%

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Thomaidis

Michos

Papadimitriou³

et al. 2022

Preprint

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Objective: Complex machine learning classification algorithms using transcriptome data from post-mortem cerebellar tissue of bipolar patients and unaffected controls, have been recently included in pipelines for patient control classification and identification of characteristic biomarkers. Transcriptomic profile differences between patients and controls, can provide useful information about the role of the cerebellum in the pathogenesis of bipolar disorder and mood deregulation and in normal mood regulation and physiology. User-friendly, fully automated machine learning algorithms, using data extracted from established repositories, could achieve extremely high classification scores and disease- related predictive biomarker identification, in very short time frames and scaled down to small datasets, thus facilitating research on mood disorders. Method: An application of a fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper- parameter values, for classification between patients and controls and the production of models for biosignature selection, is presented. Transcriptome data used for the analysis were downloaded from the BioDataome preprocessed datasets database. The Dataome dataset, derived from the parent Gene Expression Omnibus GSE35974 (2013) and GSE35978 datasets, which have been originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and unaffected controls, (from the Stanley Medical Research Institute Neuropathology Consortium and Array collections), using Affymetrix Human Gene 1.0 ST Array. Patient and control groups were closely matched for age and sex . Results: Bipolar patients have been identified from controls based on the cerebellar transcriptomic profile with AUC 0.929 and Average Precision 0.955. Patients and Controls have been classified in two separated groups with no close to the boundary cases. Using 6 of the characteristic features discovered during the selection process, 99,6% classification accuracy was achieved. The three biomarkers contributing most to the predictive power of the model (92,7%), are also deregulated in temporal lobe epilepsy. Conclusion: The cerebellar transcriptome of bipolar patients has a discrete profile and can be used for further exploration of the role of this area in health and disease. 93% AUC and 96% Precision were achieved during classification between unaffected controls and patients with Bipolar Disorder.

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“…Psychiatric disorders like major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are associated with widespread molecular alterations in the brain, including alterations in neurotransmitter systems (serotonin, dopamine, norepinephrine, glutamate, GABA) (1-7), deficits in neuronal homeostasis (8,9), neuroinflammation (10)(11)(12), and oxidative and endoplasmic reticulum stress (13)(14)(15)(16)(17). They are also associated with brain circuit dysfunction at multiple scales, including large-scale brain networks like the default mode network (18,19), excitatory-inhibitory balance (20)(21)(22), and cortical microcircuits (23)(24)(25)(26)(27).…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic pathology of neocortical microcircuit cell types across psychiatric disorders

Arbabi,

Newton,

et al. 2023

Preprint

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Background: Psychiatric disorders like major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are characterized by altered cognition and mood, brain functions that depend on information processing by cortical microcircuits. We hypothesized that psychiatric disorders would display cell type-specific transcriptional alterations in neuronal subpopulations that make up cortical microcircuits: excitatory pyramidal (PYR) neurons and vasoactive intestinal peptide- (VIP), somatostatin- (SST), and parvalbumin- (PVALB) expressing inhibitory interneurons. Methods: We performed cell type-specific molecular profiling of subgenual anterior cingulate cortex, a region implicated in mood and cognitive control, using laser capture microdissection followed by RNA sequencing (LCM-seq). We sequenced libraries from 130 whole cells pooled per neuronal subtype (VIP, SST, PVALB, superficial and deep PYR) in 76 subjects from the University of Pittsburgh Brain Tissue Donation Program, evenly split between MDD, BD and SCZ subjects and healthy controls. Results: We identified hundreds of differentially expressed (DE) genes and biological pathways across disorders and neuronal subtypes, with the vast majority in inhibitory neuron types, particularly PVALB. DE genes were distinct across cell types, but partially shared across disorders, with nearly all shared genes involved in the formation and maintenance of neuronal circuits. Coordinated alterations in biological pathways were observed between select pairs of microcircuit cell types and partially shared across disorders. Finally, DE genes coincided with known risk variants from psychiatric genome-wide association studies, indicating cell type-specific convergence between genetic and transcriptomic risk for psychiatric disorders. Conclusions: We present the first cell type-specific dataset of cortical microcircuit gene expression across multiple psychiatric disorders. Each neuronal subtype displayed unique dysregulation signatures, some shared across cell types and disorders. Inhibitory interneurons showed more dysregulation than excitatory pyramidal neurons. Our study suggests transdiagnostic cortical microcircuit pathology in SCZ, BD, and MDD and sets the stage for larger-scale studies investigating how cell circuit-based changes contribute to shared psychiatric risk.

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Comprehensive analysis of endoplasmic reticulum stress and immune infiltration in major depressive disorder

Cited by 7 publications

References 91 publications

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

Identification of a diagnostic model and molecular subtypes of major depressive disorder based on endoplasmic reticulum stress-related genes

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Transcriptomic pathology of neocortical microcircuit cell types across psychiatric disorders

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