Identification of Immune Microenvironment Changes, Immune-Related Pathways and Genes in Male Androgenetic Alopecia

doi:10.21203/rs.3.rs-2726436/v1

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Identification of Immune Microenvironment Changes, Immune-Related Pathways and Genes in Male Androgenetic Alopecia

https://doi.org/10.21203/rs.3.rs-2726436/v1

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Although Androgenetic Alopecia (AGA) is classified as a non-inflammatory alopecia, histological evidence of microinflammation has long been recognized. However, the changes in the immune microenvironment, the immune-related pathway and the expression of Immune-related genes (IRGs) involved in AGA remain unclear. The microarray gene expression data (GSE36169) from patients with male AGA were analyzed. Gene Set Enrichment Analysis (GSEA) among statistically changed genes was done. KEGG and GO analyses among differentially expressed genes (DEGs) were performed. DEGs were screened to identify IRGs based on the ImmPort database. The cytohubba-MCC plugin of Cytoscape was applied to screen hub immune genes. The infiltration levels of 28 immune cells were quantified adopting single-sample GSEA (ssGSEA) algorithm. The microarray gene expression data (GSE90594) of male AGA was analyzed to validate hub IRGs genes and differential infiltrated immune cells. The ssGSEA revealed γδT cell, central memory CD8+ T cell, mast cell, immature B cell, activated CD8+ T cell, effector memory CD4+ T cell, eosinophil and neutrophil were significantly increased infiltration in the bald scalp. GSEA showed statistically changed genes were most enriched in immune related pathways, including innate immune system, adaptive immune system, cytokine signaling, interferon-γ signaling, interferon signaling and interleukins signaling. The four hub IRGs, including MMP9, PTPRC, BMP2 and THBS1, were enriched in the pathways of allograft rejection, coagulation and interferon-γ response. In summary, we proposed that the increase in γδ T cells, central memory CD8+ T cells, activated CD8+ T cell as well as the infiltration of mast cells contributed to immune microenvironment changes in male AGA. The 4 hub IRGs may be involved in the development and progression of hair loss in male AGA through interferon-γ signal pathways.

alopecia

androgenetic alopecia

immune microenvironment

immune-related genes

immune-related pathways

Male Androgenetic Alopecia (AGA), also known as male pattern baldness, is the most common type of baldness in men, characterized by progressive patterned hair loss on the scalp, starting with bitemporal recession of the front hairline, followed by diffuse thinning over the vertex[1]. The level of 5α-reductase(5αR) is higher in the bald scalp of male AGA patients than in the haired scalp, which can convert testosterone to dihydrotestosterone (DHT) in the cytoplasm. Excess DHT binds more strongly to the androgen receptors, resulting in suppression of hair growth and miniaturization of hair follicle[2]. Therapeutically, there are only 2 drugs approved by the Food and Drug Administration for the treatment of AGA: oral finasteride and topical minoxidil. Finasteride, a strong inhibitor of 5αR, has been shown to modulate the progression of AGA to a certain extent by interfering with androgen metabolism. However, despite having reduced circulating DHT levels, a proportion of individuals (10-30%) still remained unresponsive to this therapy[3, 4]. In addition, only 55% of male AGA patients with microinflammation of bald scalp had hair regrowth in response to topical minoxidil treatment, which was less than the 77% of patients with no signs of inflammation[5]. These data indicated that excess synthesis of DHT may not the unique reason for some male AGA patients, and perifollicular microinflammation may play a role in the pathogeny of male AGA.

It well established that AGA is classified as a non-inflammatory and non-scarring alopecia, however, microinflammation has observed in some studies. It has been demonstrated that mononuclear cells and lymphocytes infiltrate in approximately 50% of the male AGA scalp samples[6], and mast cell degranulation and fibroblast activation within the fibrous sheaths were observed. Moreover, transitional regions of bald scalp showed activated T cell infiltrates about the lower portions of follicular infundibula, and inflammatory cells infiltrate the region of follicular bulge[7], which is a putative source of hair follicle stem cells (HFSCs). A variety of HFSCs play an important role in the repeated and orchestrated regeneration of hair follicles (HFs). HFs can regulate the quiescence, proliferation and differentiation of epithelial stem cells of the bulge by chemotactically attracting T cells during physiological and damaged. Disruption of this dynamic relationship results in clinically significant hair loss[8]. The imbalance of immune cell infiltration may result in hair loss through affecting the HFSCs and HFs cycle. But there is a little research on the immune infiltration of male AGA, and the subtypes of infiltrated immune cell was detected only under limited experimental method by immunohistochemically. In addition, immunity-related genes (IRGs) play essential roles in immune infiltration, however, the expression characteristics of IRGs in male AGA remain unclear.

The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm have made it possible to detect 28 types of immune cell in the tissue. Therefore, to completely detect the immune infiltration in the bald scalp of male AGA, we performed a systematic bioinformatics analysis to outline the landscape of immune cells infiltration, and identify the involved immune-related pathway and the expression characteristic of IRGs. In addition, the underlying function of the hub IRGs was investigated by Gene Set Enrichment Analysis (GSEA) to gain a better understanding of the potential molecular process during the development of hair loss.

Dataset information

Two microarray expression datasets of male AGA were downloaded from the GEO (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/), namely GSE36169 and GSE90594. The GSE36169 dataset contains 5 pair of scalp samples, which were obtained from both the occipital haired and the frontal bald scalp of 5 male AGA patients, respectively. Haired and bald samples were taken randomly and included both interfollicular epidermis and hair follicles[9]. A total of 28 scalp samples were included from the GSE90594 dataset, with 14 bald scalps of male AGA and 14 haired scalps of healthy men. These samples were taken from the vertex, either at the edges of the alopecia area in male AGA patients, or at a similar area for the healthy controls[10]. We downloaded series matrix files and corresponding annotation documents from the GEO. GSE36169 was used as the training set for the following analysis, with GSE90594 as the verification data.

Assessment of Immune Cell Infiltration

The relative infiltration levels of 28 immune cells in the GSE36169 dataset were quantified using the ssGSEA algorithm[11]. The differential expression levels of the 28 immune infiltrating cells were shown in box plots.

Biological Function and Pathway Enrichment Analyses

GSEA was performed using the “ClusterProfiler” and “enrichplot” packages of R software (version 4.2.1) [12]. The reference gene lists, c2.cp.reactome.v2022.1.Hs.symbols.gmt and h.all.v2022.1.Hs.entrez.gmt, were downloaded from Molecular Signature Database (MsigDB)[13]. Normalized enrichment score > 1, adjusted p-value < 0.05 and q-value < 0.25 were considered significantly enriched. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the “tinyarray” R package. p-value < 0.05 was considered significantly enriched.

Identification of DEGs and differentially expressed IRGs

We used the “limma” R package to obtain statistically changed genes (adjusted p-value < 0.05) and differentially expressed genes (DEGs; adjusted p-value < 0.05 and |log ₂(fold-change) | > 1). The 1793 IRGs were obtained based on the ImmPort database (https://www.immport.org/shared/home). Intersection of IRGs and DEGs was exhibited using the “VennDiagram” R package.

PPI Network Construction and Screening the hub IRGs

Protein-protein interaction (PPI) network analysis was performed using STRING (https://string-db.org/). A functional network was constructed through Cytoscape software (Version 3.9.1)[14]. The Cytohubba-MCC plugin with default parameters was used to pick out the hub genes[15]. The hub IRGs were screened by intersecting the hub genes with the differentially expressed IRGs.

Correlation Analysis

Spearman correlation was calculated using the “psych” R package and then visualized using the “ggcorrplot” R package. p-values < 0.05 was considered statistically significant.

Immune Cells Landscape of the Training Dataset

GSE36169 expression data were processed and normalized. Boxplots showed the normalized gene expression profiles. Hierarchical cluster analysis was performed using the "hclust" function in R. In GSE36169 dataset, the outlier data, GSM882149 and GSM882150, were excluded from further analysis. Principal component analysis (PCA) scatter plots showed significant differences between the haired group and the bald group (Supplementary Fig. 1a–h). We used the ssGSEA algorithm to investigate the differences in immune cell infiltration between the haired group and the bald group (Fig. 1a). The increased infiltration of γδT cell, central memory CD8⁺ T cell, mast cell and immature B cell were significantly higher in the bald group than in the haired group (Fig. 1b). At the same time, the decreased infiltration of activated CD8⁺ T cell, effector memory CD4⁺ T cell, eosinophils and neutrophils were significantly higher in the bald group than in the haired group (Fig. 1c). The results indicating that these immune cells may are essential to the changes in the immune microenvironment.

GSEA Revealed the Involvement of the Related Immune System

We used the "limma" R package to identify 239 statistically changed genes (adjusted p-value < 0.05) from the GSE36169 dataset and then subjected them to GSEA. The statistically changed genes were enriched in a total of 13 pathways, of which 6 were immunity-related pathways, including innate immune system, adaptive immune system, cytokine signaling, interferon-γ (IFN-γ) signaling, interferon signaling and interleukins signaling (Fig. 2a, c). At the same time, these pathways were activated (Fig. 2b), suggesting that immune responses may be involved in the progression of hair loss.

Identification of DEGs and Functional Enrichment Analysis

A total of 112 DEGs, including 22 up-regulated and 90 down-regulated genes, were obtained based on adjusted p-value < 0.05 and |log₂FC| > 1 between the bald group and the haired group in the GSE36169 dataset (Fig. 3a-b). Furthermore, KEGG and GO functional enrichment analyses were performed to determine the biological features of these 112 robust DEGs. KEGG pathway analysis revealed that DEGs were markedly enriched in hair growth related pathways (e.g., Wnt signaling pathway, TGF-β signaling pathway, Hippo signaling pathway) and IL − 17 signaling pathway (Fig. 3c). GO functional enrichment analysis revealed hair cycle, keratinocyte differentiation and keratinocyte differentiation were downregulating (Fig. 3d). These results indicated that hair growth and pro-inflammatory factors were involved in the progression of hair loss.

Identification of Hub Genes and hub IRGs

An interaction network between proteins encoded by the 112 DEGs was constructed using the STRING database. The interaction network comprised 84 nodes and 158 edges, visualized using the Cytoscape software (Fig. 4a). We then used the cytoHubba-MCC plugin to identify 10 hub genes, including COL1A1, MMP2, MMP9, TIMP3, THBS1, LOX, BMP2, COL11A1, CTSK and PTPRC (Fig. 4b). To investigate the IRGs expression characteristics in the bald group, DEGs were screened to generate IRGs based on the ImmPort database. The number of overlapping IRGs between the ImmPort database and DEGs were 21 (Fig. 4e) (Table 1). The PPI network was constructed to show the relationship between IRGs (Fig. 4f). The 4 hub IRGs, namely MMP9, PTPRC, BMP2 and THBS1, were obtained by the intersection with hub genes and IRGs (Fig. 4g). Boxplot showed MMP9 and PTPRC were significantly upregulated in the bald group compared to those in the haired group. In contrast, BMP2 and THBS1 were significantly downregulated in the bald group compared to those in the haired group (Fig. 4h).

Correlation analysis was performed to explore the expression patterns of hub genes and IRGs. First of all, various correlation was observed between the hub genes. In terms of the hub IRGs, MMP9 was positively correlated with PTPRC, and BMP2 was positively correlated with THBS1 (Fig. 4c). Secondly, we further investigated the correlation between the hub gene and the differentially infiltrated immune cells. COL11A1, CTSK, LOX, MMP2, MMP9 and PTPRC were positively correlated with the differentially infiltrated immune cells. In contrast, a negative correlation was observed between the differentially infiltrated immune cells and BMP2, COL11A1, THBS1 and TIMP3. (Fig. 4d).

Functional enrichment analysis of the hub IRGs

Functional enrichment analysis of the hub IRGs were explored using GSEA, and indicated that the pathways of allograft rejection, coagulation and IFN-γ response are highly enriched (Fig. 5a-d). These analyses verified that the hub IRGs are closely involved in the adaptive immune response and chronic inflammatory.

Validation of the Differentially Infiltrated Immune Cells and Hub IRGs in the Verification Dataset

We validate the differentially infiltrated immune cells and the expression levels of the hub IRGs in the GSE90594 dataset. GSE90594 expression data were processed and normalized. Boxplots showed the normalized gene expression profiles. Hierarchical cluster analysis was performed using the "hclust" function in R. In GSE90594 dataset, the outlier data, GSM2407377, GSM2407382, GSM2407383, GSM2407384, GSM2407388, GSM2407390, GSM2407391, GSM2407392, GSM2407397, GSM2407398 and GSM2407399, were excluded from further analysis. Principal component analysis (PCA) scatter plots showed significant differences between the bald scalp of male AGA patients (AGA group) and the haired scalp of healthy men (control group) (Supplementary Fig. 2a–h). First, the immune cells landscape of the verification dataset was largely consistent with these of the training dataset (Fig. 6a). Second, the increased infiltration of γδT cell, central memory CD8⁺ T cell, mast cell and immature B cell were significantly higher in the AGA group than in the control group (Fig. 6b). Third, the decreased infiltration of activated CD8⁺ T cell, effector memory CD4⁺ T cell, and eosinophils was significantly higher in the AGA group than in the control group, whereas, there was no significant difference in the decreased infiltration of neutrophils between the two groups (Fig. 6c). At last, the expression difference of the hub IRGs genes between the AGA group and the control group in the verification dataset was consistent with these of the training dataset (Fig. 6d). These validated analyses demonstrated that the significant difference of the immune cell infiltration and the hub IRGs expression was not only between the bald scalp and the haired scalp of the male AGA patients, but also between the bald scalp of male AGA patients and the haired scalp of healthy men.

Previous studies have discovered that perifollicular inflammation is exist in the bald scalp of male AGA patients, however, the subtypes of infiltrated immune cell and the IRGs are not quite clear. Thus, in our study, we performed a systematic bioinformatics analysis to completely detect the differences in perifollicular inflammation between the bald scalp and the haired of male AGA patients. Firstly, we outlined the landscape of immune cells infiltration, and identified the involved immune-related pathway and the expression characteristic of IRGs. Secondly, we screened out the hub IRGs and detect the underlying function of it. At last, we validated the differences between the bald scalp of male AGA patients and the haired scalp of healthy individuals.

There are three interesting findings in our studies. First, alterations in the immune microenvironment may be involved in the development of hair loss by damaging HF cycle and promoting hair follicle fibrosis. During the normal the hair follicle cycle, the perifollicular numbers of CD8⁺T cell and γδT cell reach their nadir in telogen before peaking during mature stages of anagen. On the other hand, mast cell decrease dramatically during anagen, while γδT cell numbers remain unchanged during the hair cycle[16, 17]. Mast cell play an important role in fibrotic[18], and research have proved fibrosis occurs in the bulge region of AGA-affected HFs and correlates with their miniaturization stage[19]. In our study, γδ T cell, central memory CD8⁺ cell and mast cell were significantly more infiltrated in the bald group than in the haired group. This imbalance in immune cell infiltration may correlate with suppression of hair growth and miniaturization of hair follicle due to excessive DHT binds to the androgen receptors[2].

Second, hub IRGs may play an important role in male AGA. A total of 4 hub IRGs were screened out in our results, and matrix metallopeptidase 9 (MMP9) and protein tyrosine phosphatase receptor type C (PTPRC) were significantly upregulated in the bald group of male AGA, whereas, bone morphogenetic protein 2 (BMP2) and thrombospondin 1 (THBS1) were significantly downregulated. MMPs are a large family of zinc-dependent proteinases that degrade the extracellular matrix by cleavage of protein substrates, such as interleukin-1beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), thereby controlling various aspects of inflammation and immunity[20]. In human anagen hair follicles, MMP-9 is localized in the lower part of the inner root sheath (Henle’s layer). When human hair follicles in culture in vitro, MMP-9 gelatinolytic activities was produced, and MMP-9 production was strongly increased by the stimulation with epidermal growth factor (EGF), TNF-α, or interleukin-1 alpha (IL-1α), which were strong inhibitors of human hair growth in culture ex vivo[21]. And immoderate MMP-9 could suppress hair growth by interfering with the hair cycle[22]. PTPRC, also known as CD45, is a transmembrane glycoprotein, expressed on almost all haematopoietic cells except for mature erythrocytes. The CD45 is a key molecule for signal transduction on cell membranes and is the most abundant protein tyrosine phosphatase in T cells, playing a key role in TCR signaling[23]. The higher level of CD45 is consistent with the significantly different infiltration of perifollicular CD8⁺T cell, γδT cell, activated CD8⁺T cell and effector memory CD4⁺ T cell in the bald group of male AGA. Bone morphogenetic proteins (BMPs) are members of the tumor necrosis factor-beta (TGF-β) family of signaling proteins and play a vital role during development and tissue formation. BMP2 as an androgen-downregulated paracrine factor that contributes to dermal papilla cell (DPC) inductivity and favors DPC-induced HFSCs differentiation to hair lineage[24]. THBS1 is an extracellular matrix glycoprotein with multiple functions, and is restricted to the mesenchymal cells of hair follicle papilla in adult mouse skin[25]. In additional, THBS1 is a specific protein of DPC with a potential role in hair development[26]. The downregulate of BMP2 and THBS1 indicated that immoderate androgen could suppress hair growth by interfering with the functions of DPC.

Third, the hair follicle immune privilege (HFIP) of bald scalp may collapse. The hair bulge of human anagen HFs represents a site of relative immune privilege (IP) in human skin. Collapse of HFIP can be characterized by upregulation of MHC-I and -II expression, rendering the HFs susceptible to inflammatory attack. The pro-inflammatory cytokine INF-γ was a robust up-regulator of MHC-I in vivo in anagen hair bulbs from murine back skin[27], and was involved in immune-mediated damage to epithelial hair follicle stem cells[28]. In addition, the higher level of MHC-I expression may facilitate the autoimmune attack on HFs by CD8⁺ T cells[29]. In our study, GSEA of 4 hub IRGs all reveal that the INF-γ response pathway was the key pathway, and central memory CD8⁺ T cell and activated CD8⁺T cell were significant increase in the bald group of male AGA. These results indicated that the HFIP of the bald scalp may be destroyed, and the collapse of HFIP may correlate with hair loss.

Our study provides new insights into the immune mechanism of male AGA, but further research is needed to determine whether changes in the immune microenvironment, immune-related pathways and genes are associated with the onset and development of male AGA. However, the study had several limitations. First, the sample size was not large enough to draw accurate conclusions. Second, further experiment should be done to validate our findings.

In summary, we proposed that the increase in γδ T cells central memory CD8⁺ T cell, activated CD8⁺T cell as well as the infiltration of mast cell contributed to immune microenvironment changes in male AGA. The identified hub IRGs and INF-γ response pathways may play critical roles in the development and progression of hair loss.

Author contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hongdi Xiong, Lulu Tang and Haiju Chen. The first draft of the manuscript was written by Hongdi Xiong, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Sijian Wen and Youkun Lin contributed equally to this work, and they are all corresponding author.

Acknowledgements: This study was funded by the Guangxi Natural Science Foundation, China (grant no. 2020GXNSFAA297150).

Compliance with ethical standards

Conflict of Interest: The authors declare that they have no conflict of interest.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent: No original data are collected for this analysis, hence this study did not meet the criteria for ethical approval and formal consent is not required.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

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Table 1 The immune-related genes of differentially expressed genes in the training dataset

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Identification of Immune Microenvironment Changes, Immune-Related Pathways and Genes in Male Androgenetic Alopecia

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