Next Article in Journal
Practical Significance of Biomarkers in Axial Spondyloarthritis: Updates on Diagnosis, Disease Activity, and Prognosis
Previous Article in Journal
Hemorrhagic Cerebral Insults and Secondary Takotsubo Syndrome: Findings in a Novel In Vitro Model Using Human Blood Samples
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 23
Issue 19
10.3390/ijms231911558
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration

by
Jing Wang
1,†,
Pan Ai
1,†,
Yi Sun
1,
Hui Shi
2,
Anshi Wu
1,* and
Changwei Wei
1,*
1
Department of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
2
Department of Clinical Psychology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2022, 23(19), 11558; https://doi.org/10.3390/ijms231911558
Submission received: 4 September 2022 / Revised: 20 September 2022 / Accepted: 21 September 2022 / Published: 30 September 2022
(This article belongs to the Section Molecular Genetics and Genomics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Temporal rhythm (TR) is involved in the pathophysiology and treatment response of major depressive disorder (MDD). However, there have been few systematic studies on the relationship between TR-related genes (TRRGs) and MDD. This study aimed to develop a novel prognostic gene signature based on the TRRGs in MDD. We extracted expression information from the Gene Expression Omnibus (GEO) database and retrieved TRRGs from GeneCards. Expressed genes (TRRDEGs) were identified differentially, and their potential biological functions were analyzed. Subsequently, association analysis and receiver operating characteristic (ROC) curves were adopted for the TRRDEGs. Further, upstream transcription factor (TF)/miRNA and potential drugs targeting MDD were predicted. Finally, the CIBERSORT algorithm was used to estimate the proportions of immune cell subsets. We identified six TRRDEGs that were primarily involved in malaria, cardiac muscle contraction, and the calcium-signaling pathway. Four genes (CHGA, CCDC47, ACKR1, and FKBP11) with an AUC of >0.70 were considered TRRDEGs hub genes for ROC curve analysis. Outcomes showed that there were a higher ratio of T cells, gamma-delta T cells, monocytes, and neutrophils, and lower degrees of CD8+ T cells, and memory resting CD4+ T cells in TRRDEGs. Four new TRRDEG signatures with excellent diagnostic performance and a relationship with the immune microenvironment were identified.

1. Introduction

Major depressive disorder (MDD) is a heterogeneous disorder characterized by diminished interest in enjoyable activities, pessimism, cognitive and sleep disturbances, and suicidal behavior [1,2]. Psychiatric disorders such as MDD are incredibly common worldwide, affecting an estimated 20–30 million people [2,3,4], and are a significant public health issue. Furthermore, worldwide disease burden is predicted to be primarily caused by MDD by 2030. Although MDD is associated with widespread individual and societal consequences, its etiology and neurobiological correlates are not well understood. Effective treatment is limited by the fact that there is still a high rate of misdiagnosis associated with MDD, which is based on subjective criteria. Hence, it is important to identify reliable diagnostic biomarkers and identify potential drug candidates to prevent or treat MDD [5].
A movement sequence is often characterized by a stereotypical rhythm or structure that can be called a “temporal rhythm”. It is a ubiquitous mechanism used by organisms to coordinate endogenous biochemical processes with the ambient environment [6]. Temporal rhythms (TR) govern almost every aspect of biology [7,8,9], and disruptions in TR are associated with human morbidities including cancer, sleep [10], mental disorders [11], and neurodegeneration [7,12]. Accumulating evidence has revealed a correlation between transcription factor (TF) and the pathogenesis of MDD. For example, patients with seasonal affective disorders experience the onset or worsening of depression during fall and winter months [13]. Accurate identification of this phenotype could be clinically valuable, as strategies targeting the circadian rhythm may potentially relieve depressive symptoms and stabilize the disease course [13]. However, the TF-associated genes that are key to the development of MDD remain unclear, and the relationship between MDD and TR-associated genes is not fully known. Therefore, identifying TR-associated genes that are associated with MDD is urgently needed so that new biomarkers and therapeutic targets can be developed for MDD treatment.
In the present study, we downloaded two reliable datasets (GSE44593 and GSE54566 [14]) from the Gene Expression Omnibus (GEO) database, and a list of TR-related genes (TRRGs) has been compiled from GeneCards. (https://www.genecards.org/, accessed on 1 March 2022) to identify reliable TF-associated differentially expressed genes (DEGs) in MDD. The CIBERSORT (https://cibersortx.stanford.edu/, accessed on 1 March 2022) [15] algorithm was used to estimate differences in the immune microenvironment between patients with MDD and controls using whole blood samples. In addition, the association between diagnostic markers and the molecular immune mechanisms underlying MDD were examined by examining infiltrating immune cells.

2. Results

2.1. Data Preprocessing and Identification of DEGs

Samples from the GSE44593 and GSE54566 datasets were combined, followed by homogenization and normalization. Boxplots were acquired (Figure 1) before and after homogenization, and based on the results, we found that the homogeneity of the two sets of samples was good after the pretreatment. After data preprocessing, the heat (Figure 2A,C) and volcano map (Figure 2B,D) of the GSE44593 and GSE54566 datasets were analyzed using R software. Among these, the GSE44593 dataset included 62 upregulated genes and 58 downregulated genes, whereas the GSE54566 dataset contained 65 upregulated and 70 downregulated genes. Subsequently, 53 common differential genes were extracted from the GSE44593 and GSE54566 datasets (Figure 3A). Next, we compiled a list of TRRGs that contained 3014 from GeneCards and displayed the two parts of genes in a Venn diagram to obtain the TRRDEGs associated with temporal rhythm in the GSE44593 and GSE54566 datasets. Six TR-related differentially regulated genes were identified as follows: Chromogranin A (CHGA), TEF, CCDC47, FK506 binding protein 11 (FKBP11), ACKR1, and ASPHD2 (Figure 3B).

2.2. Correlational Functional Analysis of TRRDEGs

GO enrichment revealed TRRDEGs to be predominantly involved in the regulation of heart contraction, heart process, muscle relaxation, and blood circulation (Figure 4A–D). Detailed outcomes are displayed in Table 1. KEGG enrichment (Table 1) showed that TRRDEGs enriched pathways mainly participated in cardiac muscle contraction and calcium signaling pathways. Supplementary Table S1 presents the results in detail. The GSEA-enriched pathways mainly participated in allograft rejection, asthma, spinal cord injury, reactome neutrophil degranulation, PID ap1 pathway, and pancreatic adenocarcinoma pathway (Figure 5 and Supplementary Table S1).

2.3. Selection of PPI Hub Genes and Construction of PPI Networks

To investigate the relationship between the six TRRDEGs, PPI analysis was performed using the STRING database. The results are presented in Figure 6. Predicted relationships between the TRRDEGs and miRNAs are displayed in Figure 7A, and the transcription factor interactions are shown in Supplementary Figure 7B. Finally, the network of TRRDEGs and drugs were analyzed, and the results are displayed in Figure 8, which revealed that CHGA and ASPH were multidrug-resistant (resistant to three or more drug classes). For example, CHGA had a drug resistance relationship with elesclomol (Cor = 0.510, p < 0.001), and ASPH had a drug resistance relationship with arsenic trioxide (Cor = 0.508, p < 0.001). This indicates that CHGA and ASPH may be potential therapeutic targets.
The association between the TRRDEG gene and miRNA network, as shown in Figure 7, predicted several possible miRNAs. Analysis of the relationship between the TRRDEGs and the transcription factor network is shown in Figure 7, and the potential transcription factors were predicted.
The association between CHAG and elesclomol was 0.510 (p < 0.01), and the association between ASPH and arsenic trioxide was 0.508 (p < 0.01). The association between ASPH and dimethylaminoparthen was 0.507 (p < 0.01), and that between ASPH and exons was 0.485 (p < 0.01). The association between CHGA and fulvestrant use was 0.477 (p < 0.01), and that between CHGA and hydrazine HCI was 0.473 (p < 0.01). Further, the association between CHGA and dexrazoxane was 0.462 (p < 0.01), between ASPH and fulvestrant use, was 0.462 (p < 0.01), between ASPH and carmustine treatment, was 0.444 (p < 0.01), between ASPH and Irofulven, was 0.438 (p < 0.01), between ASPH and pipamperone, was 0.436 (p < 0.01), between ASPH and raloxifene, was 0.421 (p < 0.01), between CHGA and raloxifene, was 0.416 (p < 0.01), between ASPH and halide use, was 0.411 (p < 0.01), between ASPH and estramustine, was 0.399 (p < 0.01), and that between ASPH and cyclophosphamide was 0.399 (p < 0.01) (Figure 8).

2.4. A Receiver Operating Characteristic (ROC) Curve and Hub Gene Expression

The expression of TRRDEGs in the GSE44593 and GSE54566 datasets was analyzed, and the diagnostic efficacy was predicted using the ROC curves. The results revealed that the expression of ACKR1, CHGA, and CCDC47 were lower in the MDD group than that in normal controls for both datasets (p < 0.05), whereas CCDC47 expression was higher in the MDD group than that in normal controls for the GSE44593 and GSE54566 datasets (p < 0.05). The ROC curve results also indicated that the diagnostic efficacy of ACKR1, CHGA, and CCDC47 was higher in the MDD group, and the area under the AUC curve was higher than 0.7. In addition, the area under the AUC curve of FKBP11 was also higher than 0.7 in both datasets (Figure 9).

2.5. Analysis of Immune Cell Infiltration and Its Association with Hub Gene Diagnostic Markers

The immune cell infiltration in the GSE44593 and GSE54566 datasets was analyzed separately. The results suggested that 15 potential immune cells were enriched in the GSE44593 dataset, for which the correlations are shown in Figure 10B. Moreover, 19 potential immune cells were enriched in the GSE54566 dataset, for which the correlations are shown in Figure 10D. MDD appears to be strongly influenced by immune cells, as evidenced by these results.

3. Discussion

Dysregulation of TR is associated with numerous neurodegenerative and mental disorders [9,16]. MDD is a severe, chronic, and highly prevalent disease with a high incidence that affects 120 million people worldwide. MDD is a heterogeneous and accurate method for diagnosis and assessment. Undoubtedly, the investigation of neurological biomarkers for diagnosing and treating MDD has the potential to improve the treatment outcomes of patients with MDD.
To date, it has been reported that TR genes are strong diagnostic and prognostic markers in cancers such as glioma [17], prostate cancer [18], and stomach adenocarcinoma [19]. However, studies that investigate whether TR genes can act as specific diagnostic biomarkers for psychiatric disorders to further explore relevant therapeutic targets for psychiatric disorders, especially MDD, are lacking. This creates an urgent need to enhance our understanding of TR in MDD through extensive validation.
In this study, we identified six potential TRRDEGs (CHGA, TEF, CCDC47, FKBP11, ACKR1, and ASPHD2) in MDD using bioinformatics analysis. Moreover, the hidden biological roles of these TRRDEGs were determined by GO and KEGG enrichment analyses, which suggested a core effect of TR on the pathophysiological mechanisms of MDD.
To illustrate the diagnostic power of the TRRDEGs in MDD, ROC curve analysis was performed, and the outcomes displayed a satisfactory diagnostic value. The four genes (CHGA, CCDC47, ACKR1, and FKBP11) showed that they may be reliable in diagnosing patients with MDD with high specificity and sensitivity. Some diagnostic biomarker signatures have been reported in previous studies. For instance, using machine learning approaches, Zhao et al. [20] found that classifiers for SVM, RF, CNN, and NB, as well as the AUC for SVM, RF, CNN, and NB were 0.84, 0.81, 0.73, and 0.83, respectively. Linna et al. [21] applied four immune-related genes (CD1C, SPP1, CD3D, and CAMKK2), and it has shown a good diagnostic value in discriminating MDD from controls based on immune-related genes, with an AUC of 0.861. In contrast, we found four TRRDEGs with an AUC of >0.7 in two datasets. Our observations indicated that the performance of the combined biomarkers was superior to that of the individual markers.
Of these four biomarkers, CHGA has been reported to have a clear relationship with MDD [22,23]. CHGA is a protein-coding gene [23] and a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins [24], which may influence the exocytotic release of neurotransmitters, including 5-hydroxytryptamine and dopamine—neurotransmitters that are involved in the sleep-wake cycle as well as implicated in depression. One study that investigated sectional CHGA levels in 40 male university students indicated an inverse association between salivary levels of CgA and the intensity of depressive symptoms [25]. Another recent retrospective study reported an inverse association between serum CgA levels and HRSD-24 score [22]. These results are similar to those of the present study. In contrast, the expression level of CCDC47 was greatly increased in patients with MDD in both datasets. CCDC47, also known as calumin, binds Ca2+ with a low affinity and high capacity. In mice, CCDC47 deficiency can result in delayed growth, atrophic neural tubes, heart defects, a shortage of blood cells in the dorsal aorta, and embryonic lethality, indicating that CCDC47 is key to early growth. Prior studies have indicated that chronic antidepressant treatment may promote behavioral benefits by alteringastrocyte intracellular Ca2+ dynamics and TrkB mRNA expression in the hippocampus [26]. These results support the results of our bioinformatics analysis and indicate that CCDC47 may be a potential biomarker of MDD.
FKBP11 is a member of the FK506 binding protein family. It participates in the regulation of mTOR—a signaling pathway in the prefrontal cortex that is compromised in MDD [27]. Therefore, we speculated that FKBP11 may play a role in MDD, partly by affecting mTOR. Another gene, ACKR1, binds more than 20 inflammatory CC and CXC chemokines and is expressed specifically in erythrocytes, venular endothelial cells, and cerebellar Purkinje neurons [28]. It is known that ACKR1 is involved in neuroinflammation in the brain [29]. Notably, inflammation is also involved in the pathogenesis of MDD, hence, we speculated that ACKR1 might participate in the progression of MDD by regulating neuroinflammation. However, the specific regulatory mechanisms of FKBP11 and ACKR1 require further investigation.
Pathway-based enrichment analysis confirmed the GO results related to malaria, cardiac muscle contraction, and the calcium signaling pathway. Although past research has shown a relationship between malaria and mental disorders [30], the role of malaria in MDD remains unclear. The current outcome indicates that these phenotypic interrelationships may share a genetic base and common pathophysiological mechanisms. Further, this finding also suggests a shared underlying pathophysiological mechanism. The high prevalence of comorbidity between depression and cardiovascular disease (CVD) is well-recognized [31,32]. However, epidemiological studies have suggested that TR disruption in specific settings could confer an increased risk of CVD [33]. Further, the findings of this study were consistent with this notion.
However, this study had several notable limitations. First, it focused entirely on the secondary mining and analysis of a previously published benchmark dataset, and the results were not verified by experimental data. This research will be used to conduct more experimental studies in the future. Second, the present results were based on a relatively small sample size. Therefore, these results need to be confirmed by a larger cohort of participants. In order to understand how MDD develops and progresses, we must identify more DEGs and explore whether they are directed at specific genes. Third, it is unknown whether the diagnosis of the four TRRDEGs reported in this study is specific only to MDD, and it is unclear whether these biomarkers can be used to differentiate patients with MDD from those with bipolar disorders (BD). Finally, further studies are needed to elucidate the mechanism and interrelationships of TRRDEGs in gene signatures.

4. Materials and Methods

4.1. Research Design

Figure 11 demonstrates the workflow of the research process.

4.2. Data Acquisition and Processing

Tow microarray datasets of MDD (GSE44593 and GSE54566 [14]) were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 22 March 2022) and preprocessed using the R package GEO query (version 3.6.5, http://r-project.org/, accessed on 22 March 2022) [34]. These two datasets were derived from Homo sapiens and contained 14 MDD brain specimens and 14 normal brain tissue specimens based on the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. Each dataset was imported, the background was corrected, and data were normalized using the Robust Multichip Average algorithm with the ‘affy’ [35] package. Inter-sample correction effects were demonstrated by box plots that were created using the ggplot2 package (R package version 3.5.2.) [36].

4.3. Identification of Differentially Expressed Genes (DEGs)

Analysis of differentially expressed genes (DEGs) between patients with and without MDD was performed using R software via the limma package [37]. The heatmap package was employed to construct the expression heat map of the differential distribution of DEGs. Further, volcano plots presented the differential expression, which was produced with the ggplot2 package in R v3.5.2. Significant DEGs were identified using the cut-off criterion of a p-value of < 0.05 and log fold change (|logFC|) ≥ 0.2.

4.4. Data Acquisition and Temporal Rhythm-Related Differentially Expressed Genes (TRRDEGs)

TRRGs were downloaded from GeneCards [38] and converted into a gene list. Thereafter, the temporal rhythm-related differentially expressed genes (TRRDEGs) of the DEGs were identified using a Venn diagram.

4.5. Functional Analysis

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment for the TRRDEGs were performed by comparing the cluster function of the cluster profile R package [39] (v3.10.1), and a p-value of ≤0.05 was considered significant. Gene set enrichment analysis (GSEA) was performed for pathways enriched by GO and KEGG.

4.6. Identification of Protein-Protein Interaction (PPI) Networks of TRRDEGs

The STRING [40] (https://string-db.org/, accessed on 5 April 2022) database was used to construct PPI networks of TRRDEGs to forecast protein functional relationships and protein-protein interactions. A network model visualized using Cytoscape [41] (v3.7.2) was built by selecting genes with a score of ≥0.4. Hub genes were screened from the PPI network using the cytoHubba [42] plugin.

4.7. Construction of miRNA and TRRDEGs Networks

We used the R package miRNAtap (https://bioconductor.org/packages/release/bioc/html/miRNAtap.html, accessed on 5 April 2022) to predict the possible TRRDEGs miRNAs. The multiMiR [43] package was adopted to select the miRtarbase [44] database information. Next, we chose the luciferase reporter assay with the most rigorous experimental grade to verify the results. The related TRRDEGs miRNAs were predicted through visualization by CyTargetLinker plugin of Cytoscape [41] software (version 3.8.2).

4.8. Correlation Analysis of TRRDEGs and Transcription Factors (TFs)

The NetworkAnalyst database [45] (http://www.networkanalyst.ca, accessed on 10 April 2022) was used to analyze the interactions between candidate transcription factors (TFs) and TRRDEGs genes. We used Gene Regulatory Networks to select TF-gene interactions based on TF and gene target data downloaded from the ENCODE ChIP-seq data. The prediction criteria were as follows: the BETA Minus algorithm predicted a peak density signal of <500 and a prediction score of <1. Target genes were predicted using the MCODE plugin and Cytohbba plugin of Cytoscape [41] software.

4.9. Relationship between Target Genes and Drug Response

Processed data, including the RNA (RNA-se data) and chemical compound activity (DTP NCI-60 data), were obtained from CellMiner [46] (https://discover.nci.nih.gov/cellminer/home.do, accessed on 10 April 2022). Next, a drug sensitivity analysis was performed using the R limma and R impute package22.

4.10. Assessment of Immune Cell Infiltration

CIBERSORT [15] (https://cibersortx.stanford.edu/, accessed on 5 April 2022), which is a bioinformatics algorithm that precisely calculates immune cell compositions based on gene expression profiles, was adopted to estimate different immune cell subtypes. Correlation analysis was performed using the R package e1071 [47], parallel package, and preprocess core package. We also plotted a bar plot and the correlation heat map.

4.11. Statistical Analysis

All analyses were performed using the R statistical software (version 4.0.2). A Student’s t-test was used when constant variables between groups were normally distributed, while the Mann-Whitney U test or Wilcoxon signed-rank test was used when constant variables were not normally distributed. Categorical variables were compared using the chi-square test or Fisher exact test. Pearson correlation coefficients were calculated for correlation analysis between different genes. All p-values were two-sided, and p-values of ≤0.05 were considered statistically significant.

5. Conclusions

In summary, this study involved a novel approach to identify six TRRDEGs that were primarily involved in malaria, cardiac muscle contraction, and the calcium-signaling pathway. Furthermore, four genes (CHGA, CCDC4, ACKR1, and FKBP11) with an AUC of >0.70 were considered TRR-DEGs hub genes for ROC curve analysis. Moreover, outcomes showed that there was a higher ratio of T cells, gamma-delta T cells, monocytes, and neutrophils, lower degrees of CD8+ T cells, and memory resting CD4+ T cells in TRRDEGs. Thus, this led to the suggestion that TR is involved in MDD. The integrated functional annotations showed that these genes may be involved in immune or inflammatory responses or signaling pathways that contribute to MDD pathogenesis, which paves the foundation for identifying novel biomarkers and treatment targets for mood disorders.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms231911558/s1.

Author Contributions

Conceptualization, J.W., P.A., Y.S., H.S., A.W. and C.W.; methodology, J.W. and Y.S.; software, J.W.; validation, C.W. and A.W.; formal analysis, J.W. and P.A.; writing—original draft preparation, J.W. and P.A.; writing—review and editing, Y.S. and H.S.; C.W. and A.W. provided guidance, discussions, review and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

There were no specific grants from public, commercial, or not-for-profit funding agencies for this research.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank Xin Wen for statistical advice and Vikas Narang for assistance in editing.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cai, H.; Xie, X.M.; Zhang, Q.; Cui, X.; Lin, J.X.; Sim, K.; Ungvari, G.S.; Zhang, L.; Xiang, Y.T. Prevalence of Suicidality in Major Depressive Disorder: A Systematic Review and Meta-Analysis of Comparative Studies. Front. Psychiatry 2021, 12, 690130. [Google Scholar] [CrossRef] [PubMed]
  2. Dong, M.; Wang, S.B.; Li, Y.; Xu, D.D.; Ungvari, G.S.; Ng, C.H.; Chow, I.H.I.; Xiang, Y.T. Prevalence of suicidal behaviors in patients with major depressive disorder in China: A comprehensive meta-analysis. J. Affect. Disord. 2018, 225, 32–39. [Google Scholar] [CrossRef] [PubMed]
  3. Citrome, L.; Jain, R.; Tung, A.; Landsman-Blumberg, P.B.; Kramer, K.; Ali, S. Prevalence, treatment patterns, and stay characteristics associated with hospitalizations for major depressive disorder. J. Affect. Disord. 2019, 249, 378–384. [Google Scholar] [CrossRef]
  4. Dang, H.; Li, J.; Liu, C.; Xu, F. Chromogranin A provides additional prognostic information in children with severe hand, foot, and mouth disease: A prospective observational study. Int. J. Infect. Dis. 2020, 93, 367–374. [Google Scholar] [CrossRef]
  5. Strawbridge, R.; Young, A.H.; Cleare, A.J. Biomarkers for Depression: Recent Insights, Current Challenges and Future Prospects. Focus. Am. Psychiatr. Publ. 2018, 16, 194–209. [Google Scholar] [CrossRef] [PubMed]
  6. Ma, T.J.; Zhang, Z.W.; Lu, Y.L.; Zhang, Y.Y.; Tao, D.C.; Liu, Y.Q.; Ma, Y.X. CLOCK and BMAL1 stabilize and activate RHOA to promote F-actin formation in cancer cells. Exp. Mol. Med. 2018, 50, 1–15. [Google Scholar] [CrossRef]
  7. Nelson, R.J.; Bumgarner, J.R.; Liu, J.A.; Love, J.A.; Melendez-Fernandez, O.H.; Becker-Krail, D.D.; Walker, W.H., II; Walton, J.C.; DeVries, A.C.; Prendergast, B.J. Time of day as a critical variable in biology. BMC Biol. 2022, 20, 142. [Google Scholar] [CrossRef]
  8. Fishbein, A.B.; Knutson, K.L.; Zee, P.C. Circadian disruption and human health. J. Clin. Investig. 2021, 131, 48286. [Google Scholar] [CrossRef]
  9. Yalcin, M.; Mundorf, A.; Thiel, F.; Amatriain-Fernandez, S.; Kalthoff, I.S.; Beucke, J.C.; Budde, H.; Garthus-Niegel, S.; Peterburs, J.; Relogio, A. It’s About Time: The Circadian Network as Time-Keeper for Cognitive Functioning, Locomotor Activity and Mental Health. Front. Physiol. 2022, 13, 873237. [Google Scholar] [CrossRef]
  10. Ruben, M.D.; Hogenesch, J.B.; Smith, D.F. Sleep and Circadian Medicine: Time of Day in the Neurologic Clinic. Neurol. Clin. 2019, 37, 615–629. [Google Scholar] [CrossRef]
  11. Walker, W.H., II; Walton, J.C.; DeVries, A.C.; Nelson, R.J. Circadian rhythm disruption and mental health. Transl. Psychiatry 2020, 10, 28. [Google Scholar] [CrossRef]
  12. Li, J.Z.; Bunney, B.G.; Meng, F.; Hagenauer, M.H.; Walsh, D.M.; Vawter, M.P.; Evans, S.J.; Choudary, P.V.; Cartagena, P.; Barchas, J.D.; et al. Circadian patterns of gene expression in the human brain and disruption in major depressive disorder. Proc. Natl. Acad. Sci. USA 2013, 110, 9950–9955. [Google Scholar] [CrossRef]
  13. Emens, J.S.; Berman, A.M.; Thosar, S.S.; Butler, M.P.; Roberts, S.A.; Clemons, N.A.; Herzig, M.X.; McHill, A.W.; Morimoto, M.; Bowles, N.P.; et al. Circadian rhythm in negative affect: Implications for mood disorders. Psychiatry Res. 2020, 293, 113337. [Google Scholar] [CrossRef]
  14. Chang, L.-C.; Jamain, S.; Lin, C.-W.; Rujescu, D.; Tseng, G.C.; Sibille, E. A Conserved BDNF, Glutamate- and GABA-Enriched Gene Module Related to Human Depression Identified by Coexpression Meta-Analysis and DNA Variant Genome-Wide Association Studies. PLoS ONE 2014, 9, e90980. [Google Scholar] [CrossRef]
  15. Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.G.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015, 12, 453. [Google Scholar] [CrossRef]
  16. Logan, R.W.; McClung, C.A. Rhythms of life: Circadian disruption and brain disorders across the lifespan. Nat. Rev. Neurosci. 2019, 20, 49–65. [Google Scholar] [CrossRef]
  17. Wang, Z.; Su, G.; Dai, Z.; Meng, M.; Zhang, H.; Fan, F.; Liu, Z.; Zhang, L.; Weygant, N.; He, F.; et al. Circadian clock genes promote glioma progression by affecting tumour immune infiltration and tumour cell proliferation. Cell Prolif. 2021, 54, e12988. [Google Scholar] [CrossRef]
  18. Feng, D.; Xiong, Q.; Zhang, F.; Shi, X.; Xu, H.; Wei, W.; Ai, J.; Yang, L. Identification of a Novel Nomogram to Predict Progression Based on the Circadian Clock and Insights into the Tumor Immune Microenvironment in Prostate Cancer. Front. Immunol. 2022, 13, 777724. [Google Scholar] [CrossRef]
  19. Huang, Z.; He, A.; Wang, J.; Lu, H.; Zhang, R.; Wu, L.; Feng, Q. The circadian clock is associated with prognosis and immune infiltration in stomach adenocarcinoma. Aging 2021, 13, 16637–16655. [Google Scholar] [CrossRef]
  20. Zhao, S.; Bao, Z.; Zhao, X.; Xu, M.; Li, M.D.; Yang, Z. Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods. Front. Neurosci. 2021, 15, 645998. [Google Scholar] [CrossRef]
  21. Ning, L.; Yang, Z.; Chen, J.; Hu, Z.; Jiang, W.; Guo, L.; Xu, Y.; Li, H.; Xu, F.; Deng, D. A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder. BMC Immunol. 2022, 23, 6. [Google Scholar] [CrossRef]
  22. Yao, Q.; Tong, Y.; Peng, R.; Liu, Z.; Li, Y. Associations of serum chromogranin A with depressive symptoms in men with unipolar depressive disorder. Gen. Hosp. Psychiatry 2020, 66, 120–124. [Google Scholar] [CrossRef]
  23. Chen, X.; Meng, S.; Li, S.; Zhang, L.; Wu, L.; Zhu, H.; Zhang, Y. Role of 5-Hydroxytryptamine and Intestinal Flora on Depressive-Like Behavior Induced by Lead Exposure in Rats. Biomed. Res. Int. 2021, 2021, 5516604. [Google Scholar] [CrossRef]
  24. Mir, S.A.; Biswas, N.; Cheung, W.; Wan, J.; Webster, N.; Macedo, E.; O’Connor, D.T.; Vaingankar, S.M. Chromogranin A pathway: From pathogenic molecule to renal disease. J. Hypertens. 2020, 38, 456–466. [Google Scholar] [CrossRef]
  25. Matsumoto, T.; Asakura, H.; Hayashi, T. Increased salivary chromogranin A in women with severe negative mood states in the premenstrual phase. J. Psychosom. Obs. Gynaecol. 2012, 33, 120–128. [Google Scholar] [CrossRef]
  26. Ferreira, F.R.; Cupido, A.; Catalin, B.; Silva, W.A., Jr.; Kirchhoff, F.; Del-Bel, E.A.; Guimaraes, F.S. Astrocyte Intracellular Ca(2+) and TrkB Signaling in the Hippocampus Could Be Involved in the Beneficial Behavioral Effects of Antidepressant Treatment. Neurotox. Res. 2021, 39, 860–871. [Google Scholar] [CrossRef]
  27. Jernigan, C.S.; Goswami, D.B.; Austin, M.C.; Iyo, A.H.; Chandran, A.; Stockmeier, C.A.; Karolewicz, B. The mTOR signaling pathway in the prefrontal cortex is compromised in major depressive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 2011, 35, 1774–1779. [Google Scholar] [CrossRef]
  28. Pruenster, M.; Mudde, L.; Bombosi, P.; Dimitrova, S.; Zsak, M.; Middleton, J.; Richmond, A.; Graham, G.; Segerer, S.; Nibbs, R.; et al. The Duffy antigen receptor for chemokines transports chemokines and supports their promigratory activity. Nat. Immunol. 2009, 10, 101–108. [Google Scholar] [CrossRef]
  29. Marchetti, L.; Francisco, D.; Soldati, S.; Jahromi, N.H.; Barcos, S.; Gruber, I.; Pareja, J.R.; Thiriot, A.; von Andrian, U.; Deutsch, U.; et al. ACKR1 favors transcellular over paracellular T-cell diapedesis across the blood-brain barrier in neuroinflammation in vitro. Eur. J. Immunol. 2021, 52, 161–177. [Google Scholar] [CrossRef]
  30. Jenkins, R.; Ong’Echa, M.; Othieno, C.; Ongeri, L.; Sifuna, P.; Omollo, R.; Leonard, B.; Ogutu, B. Malaria, mental disorders, immunity and their inter-relationships—A cross sectional study in a household population in a health and demographic surveillance site in Kenya. eBioMedicine 2019, 39, 369–376. [Google Scholar] [CrossRef] [Green Version]
  31. Hare, D.L.; Toukhsati, S.R.; Johansson, P.; Jaarsma, T. Depression and cardiovascular disease: A clinical review. Eur. Heart J. 2014, 35, 1365–1372. [Google Scholar] [CrossRef]
  32. Zhang, F.; Cao, H.; Baranova, A. Shared Genetic Liability and Causal Associations between Major Depressive Disorder and Cardiovascular Diseases. Front. Cardiovasc. Med. 2021, 8, 735136. [Google Scholar] [CrossRef]
  33. Xu, W.; Jain, M.K.; Zhang, L. Molecular link between circadian clocks and cardiac function: A network of core clock, slave clock, and effectors. Curr. Opin. Pharm. 2021, 57, 28–40. [Google Scholar] [CrossRef]
  34. Sean, D.; Meltzer, P.S. GEOquery: A bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 2007, 23, 1846–1847. [Google Scholar]
  35. Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. Affy—Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20, 307–315. [Google Scholar] [CrossRef]
  36. Villanueva, R.A.M.; Chen, Z.J. ggplot2: Elegant Graphics for Data Analysis, 2nd edition. Meas-Interdiscip. Res. 2019, 17, 160–167. [Google Scholar] [CrossRef]
  37. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.F.; Law, C.W.; Shi, W.; Smyth, G.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
  38. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–1.30.33. [Google Scholar] [CrossRef]
  39. Yu, G.C.; Wang, L.G.; Han, Y.Y.; He, Q.Y. clusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters. Omics 2012, 16, 284–287. [Google Scholar] [CrossRef]
  40. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef]
  41. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  42. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8, 11. [Google Scholar] [CrossRef]
  43. Ru, Y.; Kechris, K.J.; Tabakoff, B.; Hoffman, P.; Radcliffe, R.A.; Bowler, R.; Mahaffey, S.; Rossi, S.; Calin, G.A.; Bemis, L.; et al. The multiMiR R package and database: Integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 2014, 42, e133. [Google Scholar] [CrossRef] [PubMed]
  44. Huang, H.Y.; Lin, Y.C.; Cui, S.; Huang, Y.; Tang, Y.; Xu, J.; Bao, J.; Li, Y.; Wen, J.; Zuo, H.; et al. miRTarBase update 2022: An informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2022, 50, D222–D230. [Google Scholar] [CrossRef] [PubMed]
  45. Zhou, G.; Soufan, O.; Ewald, J.; Hancock, R.E.W.; Basu, N.; Xia, J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019, 47, W234–W241. [Google Scholar] [CrossRef]
  46. Reinhold, W.C.; Sunshine, M.; Liu, H.; Varma, S.; Kohn, K.W.; Morris, J.; Doroshow, J.; Pommier, Y. CellMiner: A web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res. 2012, 72, 3499–3511. [Google Scholar] [CrossRef] [Green Version]
  47. Meyer, E.D.D.; Hornik, K.; Weingessel, A.; Leisch, F. E1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071). TU Wien R package version. Compr. R Arch. Netw. 2021, 17–19. [Google Scholar]
Ijms 23 11558 g001 550
Figure 1. Boxplot before and after sample calibration for the GSE44593 dataset and the GSE54566 dataset. (A,B) Diagrams showing the data before and after sample correction to remove the inter-batch difference (C,D).
Figure 1. Boxplot before and after sample calibration for the GSE44593 dataset and the GSE54566 dataset. (A,B) Diagrams showing the data before and after sample correction to remove the inter-batch difference (C,D).
Ijms 23 11558 g001
Ijms 23 11558 g002 550
Figure 2. Results of DEGs. (A) Heatmap of GSE44593. (B) Heatmap of GSE74089. (C) Volcano plot of GSE44593. Red represents up-regulation of differential genes, green represents down-regulation of DEGs, and gray represents indifference. (D) Volcano plot of GSE54566.
Figure 2. Results of DEGs. (A) Heatmap of GSE44593. (B) Heatmap of GSE74089. (C) Volcano plot of GSE44593. Red represents up-regulation of differential genes, green represents down-regulation of DEGs, and gray represents indifference. (D) Volcano plot of GSE54566.
Ijms 23 11558 g002
Ijms 23 11558 g003 550
Figure 3. Venn diagrams displaying the overlap between significant DEGs and TR-related genes. (A) Common genes in GSE44593 and GSE54566. (B) TRRDEGs were analyzed by developing Venn diagrams of DEGs. Common genes in DEGs and the significant differentially expressed TR-related genes were analyzed by developing Venn diagrams of TRRDEGs.
Figure 3. Venn diagrams displaying the overlap between significant DEGs and TR-related genes. (A) Common genes in GSE44593 and GSE54566. (B) TRRDEGs were analyzed by developing Venn diagrams of DEGs. Common genes in DEGs and the significant differentially expressed TR-related genes were analyzed by developing Venn diagrams of TRRDEGs.
Ijms 23 11558 g003
Ijms 23 11558 g004 550
Figure 4. Functional correlation analysis of TRRDEGs. (A) Enrichment analysis of GO biological functions. (B) Enrichment analysis of GO-CC (cellular component, CC) functions, the color of the dots indicates the logFC of the gene. (C) Enrichment analysis of GO-MF (molecular function, MF) functions. (D) Enrichment analysis of the KEGG pathway.
Figure 4. Functional correlation analysis of TRRDEGs. (A) Enrichment analysis of GO biological functions. (B) Enrichment analysis of GO-CC (cellular component, CC) functions, the color of the dots indicates the logFC of the gene. (C) Enrichment analysis of GO-MF (molecular function, MF) functions. (D) Enrichment analysis of the KEGG pathway.
Ijms 23 11558 g004
Ijms 23 11558 g005 550
Figure 5. The results of the GSEA analysis. (A). KEGG_ALLOGRAFT_REJECTION, p value using the Kolmogorov-Smirnov test. (B). KEGG_ASTHMA. (C). WP_SPINAL_CORD_INJURY. (D). REACTOME_NEUTROPHIL_DEGRANULATION. (E). PID_AP1_PATHWAY. (F). WP_PANCREATIC_ADENOCARCINOMA_PATHWAY.
Figure 5. The results of the GSEA analysis. (A). KEGG_ALLOGRAFT_REJECTION, p value using the Kolmogorov-Smirnov test. (B). KEGG_ASTHMA. (C). WP_SPINAL_CORD_INJURY. (D). REACTOME_NEUTROPHIL_DEGRANULATION. (E). PID_AP1_PATHWAY. (F). WP_PANCREATIC_ADENOCARCINOMA_PATHWAY.
Ijms 23 11558 g005
Ijms 23 11558 g006 550
Figure 6. The PPI network of TRRDEGs and the prediction of the hub genes. (A) The PPI network of TRRDEGs. (B) Prediction of hub genes; a darker shade of red indicates a higher accuracy of prediction, ranging from yellow to red.
Figure 6. The PPI network of TRRDEGs and the prediction of the hub genes. (A) The PPI network of TRRDEGs. (B) Prediction of hub genes; a darker shade of red indicates a higher accuracy of prediction, ranging from yellow to red.
Ijms 23 11558 g006
Ijms 23 11558 g007 550
Figure 7. PPI analysis of target gene. (A). Target gene-miRNA network analysis. Purple represents the predicted miRNA and yellow represents the target gene. (B). Target gene-transcription factor; and pink represents the target gene analysis. Green represents the predicted gene-transcription factors.
Figure 7. PPI analysis of target gene. (A). Target gene-miRNA network analysis. Purple represents the predicted miRNA and yellow represents the target gene. (B). Target gene-transcription factor; and pink represents the target gene analysis. Green represents the predicted gene-transcription factors.
Ijms 23 11558 g007
Ijms 23 11558 g008 550
Figure 8. The target gene-drug network analysis was sorted according to the correlation score.
Figure 8. The target gene-drug network analysis was sorted according to the correlation score.
Ijms 23 11558 g008
Ijms 23 11558 g009 550
Figure 9. Expression analysis and the ROC curve predicted diagnostic efficacy of TRRDEGs molecules in the GSE44593 and GSE54566 datasets. (A–F) Group comparison of TRRDEGs molecules. (G–L) ROC curve diagnostic efficacy of TRRDEGs molecules. * represents p < 0.05.
Figure 9. Expression analysis and the ROC curve predicted diagnostic efficacy of TRRDEGs molecules in the GSE44593 and GSE54566 datasets. (A–F) Group comparison of TRRDEGs molecules. (G–L) ROC curve diagnostic efficacy of TRRDEGs molecules. * represents p < 0.05.
Ijms 23 11558 g009
Ijms 23 11558 g010 550
Figure 10. Evaluation and visualization of immune cell infiltration in the GSE44593 and GSE54566 datasets. (A) Heat map of enriched immune cell distribution in the GSE44593 dataset. (B) Correlation analysis of enriched immune cells in the GSE44593 dataset. (C) Heat map of enriched immune cell distribution in the GSE54566 dataset. (D) Correlation analysis of enriched immune cells in the GSE54566 dataset.
Figure 10. Evaluation and visualization of immune cell infiltration in the GSE44593 and GSE54566 datasets. (A) Heat map of enriched immune cell distribution in the GSE44593 dataset. (B) Correlation analysis of enriched immune cells in the GSE44593 dataset. (C) Heat map of enriched immune cell distribution in the GSE54566 dataset. (D) Correlation analysis of enriched immune cells in the GSE54566 dataset.
Ijms 23 11558 g010
Ijms 23 11558 g011 550
Figure 11. Demonstrates the workflow of the research process.
Figure 11. Demonstrates the workflow of the research process.
Ijms 23 11558 g011
Table
Table 1. GO/KEGG analysis.
Table 1. GO/KEGG analysis.
OntologyIDDescriptionGeneRatiop-Value
BPGO:0008016regulation of heart contraction2/60.003
BPGO:0060047heart contraction2/60.003
BPGO:0003015heart process2/60.003
BPGO:1901077regulation of relaxation of muscle1/60.004
BPGO:1903522regulation of blood circulation2/60.004
CCGO:0071782endoplasmic reticulum tubular network1/60.006
CCGO:0042629mast cell granule1/60.007
CCGO:0033017sarcoplasmic reticulum membrane1/60.012
CCGO:0016529sarcoplasmic reticulum1/60.021
CCGO:0016528sarcoplasm1/60.024
MFGO:0019957C-C chemokine binding1/50.007
MFGO:0003755peptidyl-prolyl cis-trans isomerase activity1/50.012
MFGO:0016859cis-trans isomerase activity1/50.013
MFGO:0008307structural constituent of muscle1/50.013
MFGO:0051213dioxygenase activity1/50.025
KEGGhsa05144Malaria1/20.012
KEGGhsa04260Cardiac muscle contraction1/20.021
KEGGhsa04020Calcium signaling pathway1/20.049
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wang, J.; Ai, P.; Sun, Y.; Shi, H.; Wu, A.; Wei, C. Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration. Int. J. Mol. Sci. 2022, 23, 11558. https://doi.org/10.3390/ijms231911558

AMA Style

Wang J, Ai P, Sun Y, Shi H, Wu A, Wei C. Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration. International Journal of Molecular Sciences. 2022; 23(19):11558. https://doi.org/10.3390/ijms231911558

Chicago/Turabian Style

Wang, Jing, Pan Ai, Yi Sun, Hui Shi, Anshi Wu, and Changwei Wei. 2022. "Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration" International Journal of Molecular Sciences 23, no. 19: 11558. https://doi.org/10.3390/ijms231911558

APA Style

Wang, J., Ai, P., Sun, Y., Shi, H., Wu, A., & Wei, C. (2022). Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration. International Journal of Molecular Sciences, 23(19), 11558. https://doi.org/10.3390/ijms231911558

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop