Effective biomarkers and therapeutic targets of nerve-immunity interaction in the treatment of depression: an integrated investigation of the miRNA-mRNA regulatory networks

Wu, Zixuan; Cai, Zhixiang; Shi, Hongshuo; Huang, Xuyan; Cai, Minjie; Yuan, Kai; Huang, Peidong; Shi, Guoqi; Yan, Tao; Li, Zhichao

doi:10.18632/aging.204030

Cited by 19 publications

(8 citation statements)

References 68 publications

(65 reference statements)

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“…Especially their ability to overcome the blood-brain barrier, broaden new horizons for the studies on the interaction between central nervous system and peripheral tissues, for example, bone. Currently, it has been reported that the expression profile of miRNAs was significantly altered in patients with neuropsychiatric disorders(Rao et al, 2013; Wang, 2021; Wu et al, 2022; Żurawek & Turecki, 2021). And some of these miRNAs contained in EVs play a vital role in central regulation in bone metaboliasm.…”

Section: Discussionmentioning

confidence: 99%

Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast

Zhang,

Li,

Huang

et al. 2024

Preprint

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It has been well-validated that chronic psychological stress leads to bone loss, but the underlying mechanism remains unclarified. In this study, we established and analyzed the chronic unpredictable mild stress (CUMS) mice to investigate the miRNA-related pathogenic mechanism involved in psychological stress-induced osteoporosis. Our result found that these CUMS mice exhibited osteoporosis phenotype that mainly attributed to the abnormal activities of osteoclasts. Subsequently, miRNA sequencing and other analysis showed that miR-335-3p, which is normally highly expressed in the brain, was significantly down-regulated in the nucleus ambiguous (NAC), serum, and bone of the CUMS mice. Additionally,in vitrostudies detected that miR-335-3p is important for osteoclast differentiation, with its direct targeting site in Fos. Further studies demonstrated Fos was upregulated in CUMS osteoclast, and the inhibition of Fos suppressed the accelerated osteoclastic differentiation, as well as the expression of osteoclastic genes, such as Nfatc1, Acp5, Mmp9, in miR-335-3p restrained osteoclasts. In conclusion, this work indicated that psychological stress may down-regulate the miR-335-3p expression, which resulted in the accumulation of Fos and the up-regulation of NFACT1 signaling pathway in osteoclasts, leading to its accelerated differentiation and abnormal activity. These results decipher a previously unrecognized paradigm that miRNA can act as a link between psychological stress and bone metabolism.Impact statementmiR-335-3p, which targets FOS and inhibits its activation of NFATC1 signaling, is an important regulator for osteoclast function and responsible for the psychological stress induced osteoporosis

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Section: Discussionmentioning

confidence: 99%

Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast

Zhang,

Li,

Huang

et al. 2024

Preprint

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“…For example, a research team ever accumulated evidence of the "immune metabolism" subtype of MDD by clustering specific behavioral symptoms that reflected the balance between energy intake and consumption. [15] Herein, 10 hub genes of AS and MDD were identified and obtained by GSEA enrichment analysis involving nuclear transcribed m RNA catabolic process, protein targeting, ribosomal subunit, translational initiation, and viral gene expression. It could be noted from the analysis of the proportion of immune cells in AS and MDD datasets that Neutrophils, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated and Tregs accounted for a high proportion.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a research team ever accumulated evidence of the “immune metabolism” subtype of MDD by clustering specific behavioral symptoms that reflected the balance between energy intake and consumption. [ 15 ]…”

Section: Discussionmentioning

confidence: 99%

Genetic association between ankylosing spondylitis and major depressive disorders: Shared pathways, protein networks and the key gene

Dong

Lü

et al. 2023

Medicine

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The prevalence of ankylosing spondylitis (AS) and major depressive disorders (MDD) becomes increasingly pronounced, exerting a significant impact on the life quality of contemporary people. Although there is mounting evidence of a link between AS and major depression disorders, the specific interactions between the two have not been thoroughly investigated. To this end, this study aimed to check whether the gene expression profiles of patients with AS and major depression disorders overlapped, and whether there were any functional links between the identified genes via protein-protein interactions. Herein, the relationship between the 4 datasets (GSE73754, GSE98793, GSE25101, and GSE54564) chosen from the Gene Expression Omnibus for evaluation and validation was investigated using gene characterization and functional enrichment. Then, following Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes that explore the biological processes of common genes and demonstrate the interrelationships between common genes, hub genes were obtained using the STRING database and the application cytoHubba plugin of Cytoscape software. The correlation between the gene and 22 types of immuno-infiltrating cells was explored, and the key gene as well as the diagnostic efficiency of the key gene was obtained through verification. A total of 204 shared genes were discovered, the majority of which were functionally enriched in Ribosome, Coronavirus disease COVID19, Starch and sucrose metabolism, and Galactose metabolism. Then, efforts were made to go through STRING. Immuno-infiltration studies revealed that Neutrophils, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, and T cells regulatory were associated with the pathogenesis of AS and MDD. Additionally, the receiver operating characteristic curve revealed that the key gene MRPL13 played diagnostic roles in AS and MDD after intersecting 10 hub genes with 37 differential expression genes from the 2 validation datasets. The obtained results suggest an overlapping genetic structure between AS and major depression disorders. MRPL13 may provide key insights into the relationship between AS and MDD.

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“…Also, hsa04613 has been separately connected [84] to depression and to fatigue (in patients receiving chemotherapy for cancer) [85]; fatigue is also a characteristic symptom of depression. hsa05203 has been separately connected to Post Traumatic stress Disorder, a major predictor for depression [86], epigenetic contributions to human behavior [87], risk for first episode psychosis [88] and schizophrenia [89] and hsa05322 has been separately associated with oxidative stress and cognitive function in schizophrenia [90] and antipsychotics-induced parkinsonism [91].…”

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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Effective biomarkers and therapeutic targets of nerve-immunity interaction in the treatment of depression: an integrated investigation of the miRNA-mRNA regulatory networks

Cited by 19 publications

References 68 publications

Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast

Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast

Genetic association between ankylosing spondylitis and major depressive disorders: Shared pathways, protein networks and the key gene

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

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