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. 2025 Apr 22;42(6):2003–2017. doi: 10.1007/s10815-025-03470-0

Dissecting the genetic association between abnormal sperm parameters and depression: a transcriptome-wide analysis of 157 participants

Yinwei Chen 1, Taotao Sun 4, Penghui Yuan 4,✉, Chang Liu 2,3,✉
PMCID: PMC12229347  PMID: 40263245

Abstract

Purpose

Depression often occurs in the males with semen abnormalities. Evidence suggested a genetic correlation between depression and the pathogenesis of abnormal sperm parameters, whereas the mechanisms remained unclear.

Methods

Genomic datasets of major depressive disorder (MDD) and abnormal sperm parameters were obtained from the Gene Expression Omnibus database. After screening the datasets, differentially expressed genes (DEGs) were identified. GO and pathway enrichment analyses, a protein–protein interaction network, and receiver operator characteristic curve analysis were conducted. Then, MDD-related DEGs (MDRGs), the external validation, immunological, and translational regulation analysis were performed. Moreover, tissue expression of MDRGs was explored.

Results

A total of 249 overlapped MDRGs were discovered in the MDD and abnormal sperm parameters gene sets. MDRGs had a tight relationship with adhesion-associated and PI3 K-Akt-associated biological signaling. The protein–protein interaction module showed the enriched pathways involved in neuron differentiation and cell adhesion. Drug prediction revealed ten pharmacologic candidates. Finally, two hub MDRGs were identified and validated with good diagnostic values. Immunological and translational results showed three closely correlated kinds of CD8 + T lymphocytes, neutrophils, and macrophages, 19 transcription factor-MDRGs, and 71 miRNA-MDRGs interactions. Furthermore, expression signatures of Carnosine Dipeptidase 2 (CNDP2) and Galectin 3 Binding Protein (LGALS3BP) were displayed in cortex and testis.

Conclusion

Our study discovered the genetic profiles in abnormal sperm parameters and MDD and elucidated enriched pathways and molecular associations between hub genes and immune infiltration. These findings provide novel insights into the common pathogenesis of both diseases as well as the potential biomarkers for MDD-associated abnormal sperm parameters.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10815-025-03470-0.

Keywords: Depression, Abnormal sperm parameters, Adhesion, Drug, Immunity

Introduction

Male infertility is estimated to be a significant cause of infertility in up to 50% of infertile couples and is the sole diagnosis in 20–30% of cases overall [1, 2]. Abnormal sperm parameters are the most common clinical manifestation of male infertility [2]. Several factors, such as cryptorchidism, genetic endocrinopathy, varicocele, viral infection, obesity, smoking, and dietary factors, influence serum quality and spermatogenesis [1, 3–6]. Meanwhile, the impact of psychiatric disorders on serum quality has also gained increasing attention. Occupational and life stressors are associated with poor sperm parameters, including lower semen count, motility, morphology, and increased sperm DNA fragmentation [7–10]. Additionally, psychological stress is thought to adversely impact the hypothalamus-pituitary-gonad axis and contribute to disturbed testosterone levels [11]. Moreover, mental stress serves as an important epigenetic factor that affects the phenotype of offspring through transgenerational inheritance, especially via sperm non-coding RNAs [6, 12, 13].

Depression, commonly referred to as major depressive disorder (MDD), is one of the most common and disabling mental diseases worldwide [14, 15]. According to the Global Burden of Disease Study (2019), the age-standardized incidence rate of MDD in men globally in 2019 was 2672.5 cases per 100,000 person years [16]. MDD is characterized by changes in cognitive or physical symptoms, including low mood, sadness, melancholy, poor self-esteem, feelings of worthlessness, anhedonia, and severe fatigue [17, 18]. In addition to psychological symptoms, a number of MDD male patients presented with abnormal sperm parameters and infertility [19]. Whereas, the pathogenesis of such a phenomenon has not been clarified. The classic view suggested a vicious circle between MDD and infertile conditions, since the undesirable impact of depression on sperm quality resulted in the failure of conception, which further aggravated the severity of male infertility [20]. As a matter of fact, mental stress was not solely responsible for the reduced sperm quality, and a more solid association is underlined between MDD and abnormal sperm parameters. For example, Chen et al. discovered that depression directly damaged testicular morphology and inhibited steroid hormone synthetases in mouse models [21]. In addition, Wang et al. postulated that depressed males exhibited distinct RNA expression profiles in sperm and increased depression susceptibility to offspring via sperm microRNAs [22]. This evidence suggested a genetic correlation between MDD and spermatogenesis, whereas the detailed mechanisms have not been explored.

Nowadays, high-throughput sequencing technology is a very popular experimental approach, which extensively promotes the identification of crucial regulators and elucidation of pathogenesis. Multiple sequencing results have been generously shared by researchers around the world to accelerate disease research. In the present study, we aimed to mine the genetic correlation between MDD and abnormal sperm parameters. We utilized the transcriptome data in the public repository and explored and validated major depressive disorder-related differential expressed genes (MDRGs) in human samples first. Significant biological processes and pathways, together with the drug candidates and regulatory networks, were further screened and depicted. Moreover, the subcellular localization of hub MDRGs was validated in the immunohistochemical results. Our study would pave the way to the understanding of the genetic landscape and drug target in concurrence of MDD and abnormal sperm parameters.

Materials and methods

Data collection

Transcriptome data regarding “depression” and “abnormal sperm parameters” were screened and collected from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). All GEO transcriptome data were divided into two purposes, one for data analysis and the other for data validation. In data analysis, GSE54572, transcriptome data of postmortem tissue from the anterior cingulate cortex contained 12 MDD patients and 12 healthy controls. The datasets of GSE4797, GSE6023, GSE45885, and GSE45887, transcriptome data of testis tissue, contained 74 patients with abnormal sperm parameters (azoospermia included pre/meiotic arrest, postmeiotic arrest, and Sertoli cell only-syndrome) and 21 healthy controls. To further validate stringency and authenticity, GSE54562, a dataset of MDD, and GSE26881, a dataset of abnormal sperm parameters, were both downloaded for data validation. GSE54562 contained ten MDD patients and ten controls, and GSE26881 contained nine abnormal sperm parameters patients (included oligospermia, asthenospermia, or teratospermia) and nine controls. The institutional research ethics committee at Huazhong University of Science & Technology gave its approval for the study. Helsinki Declaration was adhered to throughout all processes.

Differentially expressed gene acquisition

According to the regular normalization processes of expression matrixes, we converted the probe names, normalized expression data, and performed log2 transformation by using the R package “limma” [23]. The four datasets (GSE4797, GSE6023, GSE45885, and GSE45887) were continually merged using the strawberry-perl program (version 5.30). Batch effect between arrays was then eliminated by the R package “sva” [24]. To obtain as many DEGs as possible, we specified the filtering criteria as follows: |log2 fold-change| (|log2 FC|) > 0.1 and p-value < 0.05. MDRGs were collected after the intersection of the MDD DEGs set and abnormal sperm parameters DEGs set. The display of heatmap and Venn was achieved with the use of the R packages “heatmap” (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/heatmap.html) and “Venn diagram” [25].

Functional enrichment annotation

To provide a functional interpretation of MDRGs, we used DAVID, a bioinformatics resource system (https://david.ncifcrf.gov), to discover the significant biological pathways involved in MDRGs [26]. For Gene Ontology (GO) annotation, biological process (BP), cellular component (CC), and molecular function (MF) are the three main categories to explore the most enriched GO terms. In addition, we used the three databases (the KEGG, REACTOME, and WiKiPathway databases) to investigate the most enriched pathways. Gene number > 2 and Ease < 1 were prerequisites for filtering the significant GO and pathway terms.

Protein–protein interaction network and module annotation

Based on intrinsic algorithms, all MDRGs were uploaded in the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://string-db.org) to explore the protein interaction relationships. Active interaction sources included text mining, experiments, databases, co‑expression, neighborhood, gene fusion, and co‑occurrence. The visualization of PPI network was reprocessed in the Cytoscape software (version 3.7.1) [27]. With the use of the Molecular Complex Detection (MCODE) plugin in the Cytoscape software, the top three gene modules were screened from the PPI network according to their high MCODE scores. For MDRGs in each module, we reanalyzed the significant biological and pathway changes by utilizing the Metascape online tool (http://metascape.org). Only enriched terms with p-value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 were selected and grouped into clusters relying on the commonalities in their membership.

Drug candidate prediction

To acquire the drug candidates implicated in the pathogenesis of MDD and abnormal sperm parameters, we utilized the accumulated data on drug-protein interactions in the Drug Gene Interaction database (https://www.dgidb.org/). All drug-protein interactions were downloaded and reprocessed in the Cytoscape software, and top ten drug candidates were screened based on their connectivity to MDRGs. Two-dimension structure of each drug candidate was collected from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/).

Significant MDD-related DEG evaluation

To narrow down the gene number of important MDRGs, we utilized the cytoHubba plugin in the Cytoscape software and analyzed the connectivity ranking of each MDRG in six embedded algorithms (MCC, DMNC, Degree, EPC, BottleNeck, and Betweenness). The MDRGs at the top of the algorithmic ranking were regarded as significant MDRGs.

Receiver operator characteristic analysis

With the use of the R package “timeROC” [28], ROC analysis was carried out to assess the diagnostic potential of variables and the reliability of diagnostic models. For each significant MDRG, we first calculated the area under the curve (AUC) in the MDD (GSE54572) and abnormal sperm parameters (GSE4797, GSE6023, GSE45885, and GSE45887) datasets. Then, we calculated their AUCs in two independent validation sets, a MDD dataset (GSE54572) and an abnormal sperm parameter dataset (GSE26881). We removed MDRGs with low AUC values, and MDRGs with AUC > 0.65 in above all datasets were kept as hub MDRGs.

Immune infiltration analysis

To clarify the role of immune cells in the disease progression of MDD and abnormal sperm parameters, single-sample Gene Set Enrichment Analysis (ssGSEA) [29] was performed to calculate the level of immune cells according to the expression of immune-related markers in transcriptome data. We mined the differences in the levels of immune cells via Wilcoxon’s test, including between the MDD and control samples and between the abnormal sperm parameters and control samples. In addition, the correlation between the immune cells and hub MDRGs was analyzed using Spearman’s test. p-value < 0.05 was the prerequisite for filtering enriched immune cells.

Transcriptional and post-transcriptional network construction

To mine the transcriptional and post-transcriptional regulation of hub MDRGs, we collected interacted transcription factor (TF) with MDRGs in the JASPAR database (https://jaspar.genereg.net/) and interacted miRNA in the miRTarBase database (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php). We comprehensively summarized the interaction information and drew the regulatory network with the help of the NetworkAnalyst online tool (https://www.networkanalyst.ca/NetworkAnalyst/) and Cytoscape software.

Tissue expression analysis

To display the expression condition of hub MDRGs in human tissues, the immunohistochemical data were downloaded from the Human Protein Atlas database (HPA) (https://www.proteinatlas.org/). Due to the neural and spermatogenic regulation of hub MDRGs, we displayed the localization characteristics of hub MDRGs in the cerebral cortex and testis, respectively.

Results

Overlapped MDD-related DEG identification

A schematic diagram of the research design is depicted in Fig. 1. Sperm result and testicular pathology of the enrolled patients with abnormal sperm parameters are shown in Supplemental Table 1. Moreover, the detailed information of included datasets and participants was displayed in Supplemental Table 2. After the DEGs analysis, we found there were 523 upregulated DEGs and 539 downregulated DEGs between the MDD (n = 12) and healthy control groups (n = 12) (Fig. 2A). In addition, 2842 upregulated DEGs and 2582 downregulated DEGs were discovered between the abnormal sperm parameters (n = 74) and healthy control groups (n = 21) (Fig. 2B). Subsequently, we made the intersection of MDD DEGs and abnormal sperm parameters DEGs to harvest the crosslink genes functioning in the initiation of MDD and MI. Finally, there were 249 MDRGs in both the MDD DEGs and abnormal sperm parameters DEGs sets (Fig. 2C).

Fig. 1.

Fig. 1

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The flowchart of the study design. MDD, major depression disorder; ASP, abnormal sperm parameters; GSE, GEO Series; MDRGs, major depression disorder-related differentially expressed genes; GO, Gene Ontology; PPI, protein–protein interaction; ROC, receiver operating characteristic

Fig. 2.

Fig. 2

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Functional annotation of overlapped MDRGs in the MDD dataset (GSE54572) and abnormal sperm parameters dataset (GSE4797, GSE6023, GSE45885, and GSE45887). A Heatmap displays DEGs in the MDD and control groups. B Heatmap displays DEGs in the abnormal sperm parameters and control groups. C Venn diagram displays overlapped MDRGs in both the MDD DEGs and abnormal sperm parameters DEGs after the intersection. D GO functional annotation of MDRGs. E Pathway enrichment annotation of MDRGs in the KEGG database, REACTOME database, and WikiPathways database. MDD, major depression disorder; ASP, abnormal sperm parameters; GSE, GEO Series; MDRGs, major depression disorder-related differentially expressed genes; DEGs, differentially expressed genes

Functional annotation of MDD-related DEGs

To interpret the potential biological meaning underlying the MDRGs, we first examined the significantly enriched terms in the GO database. As shown in Fig. 2D, the biological process part was predominantly enriched with DNA replication, dendrite development, cell projection assembly, and spermatogenesis. The cellular component part was markedly focused on nucleoplasm, postsynaptic density, and focal adhesion. The molecular function part had clear pertinence in metal ion binding, transcription factor binding, and protein binding involved in cell–cell adhesion.

In the next step, we carried out pathway enrichment annotation in three pathway databases (the KEGG, REACTOME, and WiKiPathway databases). Figure 2E revealed that the overlapped MDRGs were tightly related to adhesion-associated (focal adhesion and cell adhesion molecules), PI3 K-Akt-associated (PI3 K-Akt signaling, PIP3 activates AKT signaling, PI3 K/AKT signaling in cancer, PI5P, PP2 A, and IER3 regulate PI3 K/AKT signaling, negative regulation of the PI3 K/AKT network), and rRNA processing-associated (major pathway of rRNA processing in the nucleolus and cytosol and rRNA processing in the nucleus and cytosol). Taken together, adhesion-associated and PI3 K-Akt-associated biological signaling may have an influence on the pathological interaction in MI-associated MDD.

Interpretation of protein–protein interaction module

To analyze the intrinsic linkage between MDRGs, we collected the interaction signatures of MDRGs in the STRING database. As shown in Fig. 3A, there were 241 nodes and 1422 interactions in the interaction circle. After performing MCODE analysis, we selected the top three protein interaction modules with MCODE score > 4 (Fig. 3B), suggesting that cluster proteins may have more protein similarities or be involved in a pathway. Then, we explored significant pathway enrichment in each module. For Module One, the enriched pathways contained metabolism of RNA and regulation of miRNA transcription (Fig. 3C). Pathways in Module Two revolved around cell morphogenesis involved in neuron differentiation, ribosome biogenesis, and RHO GTPase Effectors (Fig. 3D). Pathways in Module Three were closely related to cell junction organization, positive regulation of cell–cell adhesion, and chemical synaptic transmission (Fig. 3E). These results highlight the importance of neuron differentiation and cell adhesion in molecular mechanisms of MDD and MI.

Fig. 3.

Fig. 3

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PPI network and module analysis. A PPI network of MDRGs. B Top three modules generated from the PPI network with high scores. GO term interaction and functional annotation of Module One (C), Module Two (D), and Module Three (E). Dot color represents different GO terms, and dot size represents the number of consensus genes in one GO term. PPI, protein–protein interaction; MDRGs, major depression disorder-related differentially expressed genes; GO, Gene Ontology

Drug prediction

In the next step, we focused on the drug candidate which may attenuate the progression of MI-associated MDD. Based on the predictive algorithm in the DGIdb, we harvested 1477 interactions between MDRGs and drug candidates. Among these interactions, we selected the top ten drug candidates (kenpaullone, dovitinib, alsterpaullone, PF- 00562271, RG- 1530, ilorasertib, R- 406, CYC- 116, cenisertib, and TAE- 684) with high connectivity scores. These ten candidates had a strong interaction with Fms Related Receptor Tyrosine Kinase 1 (FLT1), Interleukin 1 Receptor Associated Kinase 1 (IRAK1), 3-Phosphoinositide Dependent Protein Kinase 1 (PDPK1), SMAD Family Member 3 (SMAD3), and so on with experimental evidence. The mean query scores of each drug candidate were all more than 0.1 in Fig. 4A.

Fig. 4.

Fig. 4

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Drug prediction, connectivity ranking analysis and external validation of MDRGs. A Predictive top ten drug candidates for MDD with abnormal sperm parameters, and query score represents the mean query score of each drug-gene pair in the DGIdb. B Connectivity ranking analysis of significant overlapped MDRGs (MCC, DMNC, Degree, EPC, BottleNeck, and Betweenness). C External validation of two hub MDRGs (CNDP2 and LGALS3BP) by ROC analysis in the MDD dataset, abnormal sperm parameters dataset, and two independent validation sets (AUC > 0.65). MDRGs, major depression disorder-related differentially expressed genes; MDD, major depression disorder; abnormal sperm parameters, abnormal sperm parameters; DGIdb, Drug Gene Interaction database; ROC, receiver operating characteristic; AUC, area under the curve

External validation of hub MDD-related DEGs

To obtain the MDRGs with more important values, we calculated the connectivity rank of each MDRG by exploring six connectivity ranking algorithms. As shown in Fig. 4B, 23 MDRGs were identified at the top level. We first mined the diagnostic value of these 23 MDRGs in the MDD dataset and abnormal sperm parameters dataset to see if the patient and control populations could be accurately distinguished. Next, to enhance the authority and stringency of our data, we confirmed the diagnostic value of the MDRGs in additional two independent datasets. Supplementary Fig. 1 displayed the 502 upregulated DEGs and 649 downregulated DEGs in the independent MDD dataset and 214 upregulated DEGs and 774 downregulated DEGs in the independent abnormal sperm parameters dataset. In Fig. 4C, we found that two hub MDRGs (CNDP2 and LGALS3BP) had good performance in the ROC results of initial and validation datasets (area under the curve > 0.65). Therefore, we recommended the genes, Carnosine Dipeptidase 2 (CNDP2) and Galectin 3 Binding Protein (LGALS3BP), should be regarded as hub MDRGs, which had the potential as biomarkers for abnormal sperm parameters-associated MDD.

Immune infiltration cells

Due to the immune cells residing in the testicular interstitial space and the cortex of the brain [30, 31], we investigated the immune cells infiltrated in the MDD and abnormal sperm parameters samples. After performing ssGSEA analysis, we found that the levels of CD8 + T cells and NK cells in the MDD group were significantly different from those in the control group (Fig. 5A). Similarly, there were evident differences in the levels of neutrophils, macrophages, and Treg cells between the abnormal sperm parameters and control groups (Fig. 5B). Subsequently, we focused on the expression relevance of the above significant immune cells and hub MDRGs. As shown in Fig. 5C, for MDD samples, hub MDRG (LGALS3BP) was negatively related to the level of CD8 + T cells (r = − 0.6, p = 0.0023). For abnormal sperm parameters samples, hub MDRGs (CNDP2 and LGALS3BP) had the same remarkable trend as the level of neutrophils and macrophages. The correlation results of CNDP2 were as follows: neutrophils: r = 0.34, p = 0.00084, and macrophages: r = 0.31, p = 0.002. Additionally, the correlation results of LGALS3BP were as follows: neutrophils: r = 0.55, p = 1.7 e−8, and macrophages: r = 0.47, p = 1.9 e−6. The results implied that the immune cells of CD8 + T cells, neutrophils, and macrophages may participate in the immunologic regulation of the progression of MDD and abnormal sperm parameters.

Fig. 5.

Fig. 5

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Immune infiltration analysis of two hub MDRGs. A Significant immune cell types between the MDD and control groups. B Significant immune cell types between the abnormal sperm parameters and control groups. C The scatter plot displays the correlation of hub MDRGs (CNDP2 and LGALS3BP) and significant immune cell types (CD8 + T cell, neutrophil, and macrophage). MDRGs, major depression disorder-related differentially expressed genes; MDD, major depression disorder; abnormal sperm parameters, abnormal sperm parameters

Transcriptional and post-transcriptional network

Considering the enriched functional annotation of transcription factor (TF) binding and metabolism of RNA and regulation of miRNA transcription, we mined the relevance of TF and miRNA with hub MDRGs and further constructed the transcriptional and post-transcriptional network. In Fig. 6A, the interaction network contained 19 TF-MDRG interactions and 71 miRNA-MDRG interactions. Notably, the TFs of Signal Transducer And Activator Of Transcription 3 (STAT3) and Forkhead Box C1 (FOXC1) could both bind to CNDP2 and LGALS3BP, and the miRNA of has-mir- 493 - 3p could have an effect on the post-transcription level of these two MDRGs.

Fig. 6.

Fig. 6

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Transcriptional and post-transcriptional interaction and tissue expression evaluation. A Transcriptional (transcriptional factor) and post-transcriptional (miRNA) interaction network of hub MDRGs in the JASPAR and miRTarBase database. B Immunohistochemistry evaluation of the expression of the two MDRGs in the HPA database (including cerebral cortex and testis) (scale bars = 200 and 100 μm). MDRGs, major depression disorder-related differentially expressed genes; HPA, Human Protein Atlas

Cellular expression signature of hub MDD-related DEGs

As shown in Fig. 6B, both hub MDRGs were expressed to some extent in the testis and the cerebral cortex. In the immunohistochemical data of cerebral cortex, CNDP2 was expressed at moderate levels in the endothelial and glial cells, while it was expressed at low levels in the neuronal and neuropil cells. Consistently, LGALS3BP was hard detected in the neuronal and neuropil cells, and it was expressed at low levels in the endothelial and glial cells. In the data of testis, there was a negative expression of CNDP2 in spermatogonia cells, spermatocytes, round/early spermatids, and elongated/late spermatids, but there was a positive area in Sertoli cells and Leydig cells. However, LGALS3BP was expressed in seminiferous ducts and Leydig cells at moderate levels. These data indicate that there was a low expression of CNDP2 and LGALS3BP in neuronal and spermatogenic cells in normal tissues, and their elevated expression may propel the development of neural system disorders and male fertility disorders.

Discussion

Accumulative researches have confirmed the high prevalence of abnormal sperm parameters and infertility in male MDD patients [19, 32, 33]. These patients inevitably suffered varying degrees of sadness, helplessness, poor self-esteem, and disharmony in their relationships with partners, family, and community [34, 35], which might further aggravate the severity of the disease and create a vicious cycle. Therefore, characterizing the pathogenesis of abnormal sperm parameters in MDD patients which might provide novel therapeutic targets is in great demand. Recent study showed that an altered genetic expression profile in the MDD males’ spermatozoa could be delivered to offspring and increase their susceptibility to depression. This phenomenon indicated that a genetic profile exists in the crosslink of the two diseases. Herein, our study summarized and validated the hub MDRGs and valuable biological pathways underlying the pathogenesis for the first time. In addition, their immunological and transcriptional signature associated with hub MDRGs was assessed and displayed based on multi-omics analyses.

In the DEG analysis of MDD and abnormal sperm parameters, the number of DEGs in the gene sets of abnormal sperm parameters DEGs was far greater than that in MDD DEGs. This difference can reach more than five times. Interestingly, overlapped MDRGs made up half of the gene set of MDD DEGs. In the results of functional annotation, these MDRGs were identified to be closely involved in adhesion-associated and PI3 K-Akt-associated biological signaling. In fact, a histological study on the cortex demonstrated that compared with controls, cell adhesion molecules markedly increased in the dorsolateral prefrontal cortex of patients with depression [36]. Healthy cell adhesion status is essential to the normal function of the Sertoli-Sertoli and Sertoli-spermatid interface at the site of the blood-testis barrier [37]. Moreover, intercellular adhesion molecules are mainly involved in the transepithelial migration of spermatogenic cells during the epithelial cycle in the seminiferous epithelium, especially for the transit of preleptotene spermatocytes [37]. It is reasonable to imply that the disorder in MDRGs expression negatively affects the hippocampal neural survival and the permeability of the blood-testis barrier.

PI3 K-Akt-associated signaling is another significant signaling enriched herein. Wu et al. [38] indicated that PI3 K-Akt-GSK3β-mediated neuroplasticity is an important stimulus in neuroplastic damage of depression, including disturbance of neurotransmitter synthesis, transmission and release, and abnormal synapses. The improvement of depression phenotypes by multiple drugs (baicalin, quercitrin, Paeonia lactiflora Pall) in animal models involves significant changes in PI3 K-Akt signaling, together with the improvement of the release of inflammatory and apoptotic factors [39–41]. Similarly, PI3 K and Akt are highly important in spermatogenesis. They are regarded to predominantly participate in the maintenance and differentiation of spermatogonial stem cells and proliferation of spermatogonia and somatic cells [42]. Moreover, one of the primary signal transduction mechanisms involved in Sertoli cell proliferation is the PI3 K-Akt pathway, further affecting the production of sperm [43]. We hypothesized that MDRGs are likely to interfere with the metabolism of this pathway in MDD-associated abnormal sperm parameters, which needs further validation.

Based on the Drug Gene Interaction database, ten drug candidates with a high score were identified. Alsterpaullone, a GSK3 inhibitor, was found to have a beneficial effect when added after sperm thawing, which had a favorable effect on plasma membrane lipid disorganization, especially enhancing sperm velocity metrics and coefficients, and the proportion of rapid and medium-speed spermatozoa [44, 45]. Interestingly, another known antagonist of GSK3 activity, kenpaullone, has the capability of inducing a phase delay in mPer2 transcription and ameliorating the iNOS-dependent macrophage migration, thus having potential benefits for depression status in bipolar disorder [46, 47]. Supported by the available evidence, our results suggested that alsterpaullone, kenpaullone, and other GSK3 inhibitors may show therapeutic effects on both depression phenotypes and testicular dysfunction, and its medicinal value is expected.

To obtain the critical regulator in abnormal sperm parameters-associated MDD, we screened 23 MDRGs with high connectivity. In the next step, to enhance the authority and stringency of our data, we downloaded two independent transcriptome data involving MDD and abnormal sperm parameters for external validation. ROC results showed that CNDP2 and LGALS3BP had good diagnostic performance (AUC > 0.65), and they could be confirmed as hub MDRGs. In our study, LGALS3BP and CNDP2 are evidently elevated in MDD and abnormal sperm parameters samples. LGALS3BP (Galectin 3 Binding Protein) is one of the family of beta-galactoside-binding proteins. It was first noticed for its role in the innate immune response to viral infection and its relationship with cancer and metastasis [48]. Its role in psychological disorders has also attracted attention. A high level of LGALS3BP was shown to closely correlate with depression scores in type 1 diabetes (T1D) patients with alexithymia [49]. Moreover, a community-based Dallas Heart Study on 2554 individuals reported that its ligand, Galectin- 3, was statistically significantly related to depressive symptom severity [50]. In addition, an in vivo research found higher levels of LGALS3BP existed in the seminal fluid of Bos Taurus with low fertility [51]. Similarly, LGALS3BP has been widely reported in male patients with abnormal sperm parameters. For example, LGALS3BP was only detected in semen samples with low levels of reactive oxygen species [52], significantly decreased in oligoasthenozoospermic seminal plasmas (compared with normozoospermia) [53], and significantly increased in azoospermic seminal plasmas (compared with normozoospermia, oligozoospermia, and asthenozoospermia) [54]. Moreover, LGALS3BP was regarded as to have the potential to be used as predictors of TESE outcome [55].

As for CNDP2 (Carnosine Dipeptidase 2), it is a nonspecific dipeptidase and is regarded as a tumor activator in a variety of tumorigenesis. Buñay et al. found that a remarkable increment of CNDP2 was present in testicular tissue damaged by toxic chemicals [56]. The above findings symmetrically verified the increased expression of hub MDRGs in tissues or body fluids of the model with depression or fertility disorder, exactly as our results. Consistently, immunohistochemical data also showed the low expression of CNDP2 and LGALS3BP in human neuronal and neuropil cells and low/moderate expression of those proteins in a series of spermatogenic cells. Thus, these two MDRGs deserve in-depth exploration in the future.

Multiple disturbances of immune suppression and immune activation were reported in the disease status of MDD and abnormal sperm parameters. Immune infiltration results suggested that there were elevated levels of CD8 + T and NK cells in MDD samples and increased levels of neutrophils, macrophages, and Treg cells in abnormal sperm parameters samples. Zhou et al. [57] identified the ratio of CD4 +/CD8 + T lymphocytes as a significantly useful indicator to predict the severity of MDD. Furthermore, dysfunction of CD8 + T lymphocytes and natural killer cells serve as the key mechanism in depression-mediated mortality increase of immunodeficient patients [58]. The testicular immune microenvironment consists of multiple immune cells and plays a vital role in the maintenance of spermatogenesis homeostasis. Excess neutrophils in semen are harmful to fertilization capacity, and the production of reactive oxygen species by activated poly-morphonuclear neutrophils deteriorates sperm motility, even at 0.6 × 106 cells/mL [59]. A notably higher number of M1 and M2 macrophages was discovered in males with NOA, together with activated inflammatory factors [30]. Consistent with our data, we found that the level of neutrophils and macrophages positively correlates with the expression of hub MDRGs in abnormal sperm parameters samples. However, the correlation between CD8 + T cells and MDD is negative in immune infiltration analysis.

This is the first study on the genetic profile of MDD and abnormal sperm parameters. However, there are several limitations in the present study. First, our sequencing data come from diverse continents and regions, and differences between ethnic groups have an impact on the stability of outcomes. Second, we used external independent validation data and immunohistochemistry data to validate the accuracy of hub MDRGs, but there remain some unavoidable biases in different algorithmic platforms. Third, there are still aspects of the clinical profiles of the included patients that could benefit from further elaboration. Last but not least, the correlation analysis regarding immunological/transcriptional regulation and hub MDRGs was not sufficient. In our future investigation, detailed and in-depth exploration is needed to clarify the molecular mechanisms of MDD-associated abnormal sperm parameters.

Conclusions

In summary, we comprehensively analyzed the genetic landscape and biological pathways associated with MDD in the disease state of abnormal sperm parameters. A hub gene signature of LGALS3BP and CNDP2 containing immunological and transcriptional regulation was mined. These findings broaden our understanding of potential biomarkers and pathological mechanisms involved in the co-occurrence of MDD and abnormal sperm parameters.

Supplementary Information

Below is the link to the electronic supplementary material.

ESM 1 (6.9MB, jpg)

DEGs in the two independent validation sets. (A) Heatmap displays DEGs in the MDD validation set (GSE54562). (B) Heatmap displays DEGs in the abnormal sperm parameters validation set (GSE26881). DEGs: differentially expressed genes; GSE: GEO Series; MDD: major depression disorder; abnormal sperm parameters: abnormal sperm parameters. (JPG 6.88 MB)

ESM 2 (16.6KB, docx)

(DOCX 16.5 KB)

Acknowledgements

We would like to thank the database and data providers who generously supported us in our research during the difficult pandemic years.

Author contribution

Y.C. and P.Y. contributed to the data analysis and writing. C.L. contributed to the study design, supervision, and revising of the manuscript. T.S. contributed to data analyzing and presenting. All authors contributed to the article and approved the final manuscript.

Funding

This work was supported by a grant from the National Natural Science Foundation of China (no. 82201758, no. 82201811, and no. 82201775), the Joint Construction Project between Medical Science and Technology Research Project of Henan Province (no. LHGJ20220343), and Jiangsu Natural Science Foundation (BK20220173).

Data availability

The transcriptome matrixes in the study can be downloaded from the public GEO repository.

Declarations

Ethical approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Conflict of interest

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Penghui Yuan, Email: yuanph2018@126.com.

Chang Liu, Email: lichi608@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ESM 1 (6.9MB, jpg)

DEGs in the two independent validation sets. (A) Heatmap displays DEGs in the MDD validation set (GSE54562). (B) Heatmap displays DEGs in the abnormal sperm parameters validation set (GSE26881). DEGs: differentially expressed genes; GSE: GEO Series; MDD: major depression disorder; abnormal sperm parameters: abnormal sperm parameters. (JPG 6.88 MB)

ESM 2 (16.6KB, docx)

(DOCX 16.5 KB)

Data Availability Statement

The transcriptome matrixes in the study can be downloaded from the public GEO repository.

Articles from Journal of Assisted Reproduction and Genetics are provided here courtesy of Springer

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