Investigation the role of SIRT3, SIRT7, NFATC1, and PDL-1 genes in Androgenetic Alopecia

doi:10.21203/rs.3.rs-4556639/v1

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Investigation the role of SIRT3, SIRT7, NFATC1, and PDL-1 genes in Androgenetic Alopecia

https://doi.org/10.21203/rs.3.rs-4556639/v1

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Background

Androgenetic Alopecia (AGA) stands as the most prevalent form of hair loss, affecting the hair follicles (HFs). aging emerges as a prominent contributor in this condition. In this study, our aim is to elucidate the expression patterns of candidate genes—SIRT3, SIRT7, NFATC1, and PDL-1—known to exhibit differential expression levels during HF aging, and to underscore the role of aging in AGA.

Material and Methods

mesenchymal stem cells (MSCs) were isolated from the vertex and occipital regions of six men affected by AGA. The aim was to assess the expression levels of SIRT3, SIRT7, NFATC1, and PDL-1 genes. RNA extraction was performed followed by cDNA synthesis, and gene expression levels were quantified using real-time PCR. To validate the experimental findings, two different RNA-seq datasets relevant to the study were analyzed using R software.

Results

In the present study, experimental tests revealed that the expression levels of SIRT3 and SIRT7, known to decrease during HF aging, were diminished in AGA samples as well. Conversely, the mean value of NFATC1 and PDL-1 expression level, which are known to increase during HF aging, were found to be elevated in AGA samples. Moreover, bioinformatic analyses provide additional support for the role of SIRT3, SIRT7, NFATC1, and PDL-1 in AGA pathogenesis.

Conclusion

while SIRT3 and SIRT7 may play critical roles in AGA development, further research is needed to elucidate the significance of NFATC1 and PDL-1 in this context and to explore their potential as therapeutic targets for AGA treatment.

Androgenetic Alopecia

SIRT7

SIRT3

NFATC1

PDL1

Androgenetic Alopecia (AGA), characterized by progressive hair thinning and eventual baldness, represents the most prevalent form of hair loss in humans [1]. While AGA itself does not pose an immediate medical emergency condition, its psychological implications can profoundly impact patients' self-esteem and mental well-being [2]. The common pattern of baldness observed in men, referred to as the Hamilton-Norwood classification [3], is distinguished by a sequential reduction in hair density along the forehead, followed by progressive thinning and eventual loss of hair on the vertex, leaving only the parietal and occipital areas with dense hair coverage. the prevalence of AGA escalates with advancing age, albeit the rate and extent of progression vary significantly among individuals [4].

The pathogenesis of AGA is intricately linked to dysregulation of the hair cycle [5] and the consequent miniaturization of hair follicles (HF) [6, 7]. Notably, the dysregulation primarily affects hair follicle stem cells (HFSCs), contributing significantly to the pathologic manifestations of AGA [8]. During each hair cycle, active HFSCs undergo division, giving rise to various cell types within the HF during the Anagen phase [9]. Subsequently, these stem cells enter a quiescent state during the Telogen phase [10]. HFSCs in individuals affected by AGA exhibit an extended Telogen phase compared to their healthy counterparts [5], owing to differential gene expression and altered pathway functions [8]. The dysregulation of stemness is observed in other human pathologies, as well [11, 12].

AGA arises from a complex interplay of genetic predisposition, hormonal influences, environmental factors, and advancing age [13]. In elucidating the role of aging in the context of AGA, the consequential lengthening of the Telogen phase with advancing age, culminates in a reduction in hair density. This phenomenon is attributed to the diminished capacity of HFCs to initiate new hair cycles and produce hair shafts [14]. The insights provided by previous studies offer valuable context for understanding the role of specific genes—NFATC1, SIRT7, SIRT3, and PDL-1—in AGA pathogenesis.

NFATC1 has been implicated in maintaining HFSCs in the Telogen phase of the hair cycle, inducing quiescence and inhibiting hair growth [15, 16]. NFATC1 activity is controlled by some genes such as SIRT7 [17], and SIRT7 expression decreases during aging [18]. This interplay suggests a potential link between NFATC1 dysregulation and aging-related changes in HFSC activity, contributing to AGA progression.

Mitochondrial activity may also be implicated in AGA pathogenesis. SIRT3, a mitochondrial deacetylase, exhibits altered expression levels during aging, potentially leading to mitochondrial dysfunction [19]. Considering the vital role of mitochondria in cellular function, dysregulation of SIRT3 may contribute to AGA progression.

Moreover, the upregulation of PDL-1 expression during aging, along with its inhibitory role in hair growth observed in mice [20], suggests a potential mechanism by which PDL-1 dysregulation may contribute to AGA pathogenesis.

In this study, the goal is to validate experimental findings with bioinformatic analyses, thereby enhancing our understanding of the molecular mechanisms underlying AGA.

RNA-Seq technology serves as a pivotal tool for comparing gene expression profiles between different conditions, such as affected tissue in a disease state versus healthy tissue, thereby elucidating the differential regulation of genes in each condition. Among the various computational tools available for analyzing RNA-Seq data, DESeq2[21] from Bioconductor (http://www.bioconductor.org/) is widely recognized for its effectiveness and reliability [22]. Differential expression (DE) analysis, a cornerstone of RNA-Seq data analysis, facilitates the identification of genes that have different expression [23, 24].

Furthermore, DE analysis enables downstream systems biology analyses, such as gene ontology (GO) analysis, which offers valuable insights into the cellular processes that are altered between different biological conditions [25]. Additionally, DE analysis facilitates the enrichment analysis of biological pathways using resources like the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [26]. Such pathway enrichment analyses provide a comprehensive understanding of the molecular pathways and networks that are perturbed in the context of the studied condition.

Our primary objective was to evaluate the expression levels of four candidate genes—NFATC1, SIRT3, SIRT7, and PDL-1—which exhibit differential expression between aged and youthful cells, between HFSCs affected by AGA and healthy HFSCs. This analysis can help us elucidate the pathogenesis of AGA at transcriptome level.

Mesenchymal stem cells extraction, isolation and expansion

HFs were extracted from the vertex (balding) and occipital (high hair density) regions of six male subjects affected by AGA. The extraction process involved isolating mesenchymal stem cells (MSCs) through mechanical separation techniques. Under microscopic observation, the extracted HFs were meticulously dissected using microdissection tools to isolate the MSCs from the surrounding tissue. Following separation, MSCs were thoroughly washed with phosphate-buffered saline (PBS). Subsequently, the cells were cultured in 25 cm^2 tissue culture flasks, utilizing a medium consisting of 89.5% Dulbecco's Modified Eagle's Medium low glucose (DMEM, Biowest, France), supplemented with 10% fetal bovine serum (FBS, Gibco, USA), and 0.5% Amphotrypsin A antibiotic (Invitrogen, USA), and maintained at 37°C in an atmosphere of 95% air and 5% CO2. Upon reaching 80 to 90% confluence, the MSCs were trypsinized using 0.25% trypsin EDTA (Biowest, France), followed by media replacement. This methodology ensured the successful isolation and cultivation of HFSCs for subsequent analyses. Finally, to characterize and quantify the expression of MSCs markers based on surface molecular markers [27], flow cytometry analysis was conducted as explained before [28, 29]. Cells were incubated with monoclonal antibodies targeting specific surface markers such as CD90, CD105, CD34, and CD45, along with their respective matched-isotype controls. This rigorous methodology ensured accurate characterization and validation of MSC populations based on their surface marker expression profiles, thereby facilitating subsequent investigations into their role in AGA pathogenesis.

RNA extraction and quantitative analysis by real-time polymerase chain reaction (PCR)

First, total RNA was isolated from the cells with FavorPrep Blood/Cultured Cell Total Mini Kit or (FAFB) mini column kit (Yekta Tajhiz Azma) according to the manufacturer’s protocol. Second, 2µl pure extracted RNA was used with dNTPs, Oligo(dt)18 primer, and 5X first-strand buffer to generate cDNA with PCR device. Then, the purity of cDNA was determined by Nano Drop Technique and gel electrophoresis. Third, quantitative reverse transcriptase PCR was carried out to determine the expression of genes encoding NAD-Dependent Protein Deacetylase Sirtuin-3 (SIRT3), NAD-Dependent Protein Deacetylase Sirtuin-7 (SIRT7), Nuclear Factor Of Activated T-Cells (NFATC1), Programmed Cell Death 1 Ligand 1 (PDL-1), and Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) with Rotor-gene Q 6plex Platform device. GAPDH values were used for normalizing. Gene expression was calculated using the ΔΔCt method [30].

Data processing method and software sources

Two sets of RNA-Seq raw data, (GSE212301) by Wu W, Tang Y, et al. [31] and (GSE101451) by Hu Z, Qu Q et al.[32], were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) in HTTP format. The first dataset encompassed the whole transcriptome of HFs obtained from the vertex and occipital regions of several AGA patients. Conversely, the second dataset comprised the whole transcriptome of bulge regions, which contain HFSCs, from the vertex and occipital regions of a subset of AGA patients.

Statistical analyses, including the calculation and interpretation of Differentially Expressed Genes (DEGs), were conducted utilizing the R statistical software (version 4.4.0, https://www.r-project.org/). Subsequently, GO Enrichment analysis of the DEGs was performed using the WEB-based GEne SeT AnaLysis Toolkit (https://www.webgestalt.org/). This analysis encompassed biological process, cellular component, and molecular function categories based on Gene Set Enrichment Analysis (GSEA) as the enrichment method, along with the exploration of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Determining the efficiency of primers and the purity of the PCR product by using gel electrophoresis

Following real-time PCR, gel electrophoresis was employed to assess the efficiency of primers and the purity of the PCR products. Notably, for each gene, bands of similar length to those anticipated based on BLAST analysis were observed (see Fig. 1). This consistency confirms the specificity of the amplification and the absence of non-specific products.

SIRT7 and SIRT3 have lower gene expression in AGA patients

To investigate differential gene expression between patient samples and control samples, comparative real-time PCR was employed. The results, depicted in Fig. 2 and Fig. 3, reveal noteworthy findings. Given the constraints imposed by the limited sample size, the results were presented graphically for each pair, with one control sample and one AGA sample from each patient. This approach aimed to provide a more detailed understanding of the findings within the context of the study's limitations. Subsequently, the mean values of gene expression results were analyzed using an independent sample t-test.

The mean value of both sets of results demonstrate lower gene expression levels in AGA patient samples. Statistical analysis revealed a significant difference between these groups, underscoring the relevance of these findings in elucidating the molecular underpinnings of Androgenetic Alopecia.

NFATC1 and PDL-1 have higher gene expression in AGA patients

The comparative real-time PCR results, as illustrated in Fig. 4 and Fig. 5, unveil significant insights. While statistical analysis demonstrated a significant difference between the patient and control groups for PDL-1 gene expression, this difference did not reach statistical significance for NFATC1 gene expression.

Hierarchical clustering of differentially expressed genes (DEGs)

Hierarchical clustering was performed using the heatmap function from the ggplot2 library in R software to discern gene expression patterns between control samples and AGA patients' first dataset (GSE212301). In the heatmap presented in Fig. 6, rows represent genes, while columns represent samples. Fifty differential genes, identified based on DESeq2 normalized gene expression with the lowest padj < 0.05, were included in the analysis.

The heatmap illustrates those genes with similar expression patterns are clustered together, with up-regulated genes depicted in red and down-regulated genes in blue. Notably, despite the inclusion of our gene of interest in the DEG Excel file, none of them were among the 50 genes exhibiting significant P values.

MA-plot and Volcano plot

In DESeq2, the plotMA function is employed to visualize the log2 fold change attributed to a given variable over the mean of normalized counts in an experiment with a two-group comparison within the DESeqDataSet. For our analysis, we utilized the first dataset (GSE212301) to generate Fig. 7 and the second dataset (GSE101451) to produce Fig. 8. Each plot represents individual genes as dots, with the x-axis denoting the average expression over all samples and the y-axis representing the log2 fold change between the normal and patient groups. These plots serve to illustrate that only genes with a large average normalized count contain sufficient information to yield a significant call. Genes exhibiting greater expression in AGA patients are depicted in blue, whereas those with lower expression in AGA patients are represented in red. The analysis depicted in Fig. 7 reveals a significant downregulation of SIRT3 and SIRT7 expression in AGA patients compared to controls. Conversely, Fig. 8 illustrates a significant upregulation of NFATC1 expression in AGA patients.

Figure 9 presents a volcano plot depicting differential gene expression based on the first dataset (GSE212301). The y-axis corresponds to the mean expression value of the negative logarithm (base 10) of the adjusted P-value, while the x-axis displays the log2 fold change (FC) value. In this plot, blue dots represent genes that are upregulated in expression (Padj < 0.05, log2 FC > 1) between control and AGA patient groups, while red dots represent genes that are downregulated in expression (Padj < 0.05, log2 FC<-1) between these groups. As indicated by the plot, SIRT3 and SIRT7 exhibit lower expression levels in AGA patients compared to controls.

Functional analysis of DEGs

To identify functional categories of differentially expressed genes, Gene Ontology (GO) enrichment analysis was performed using the WEB-based GEne SeT AnaLysis Toolkit (https://www.webgestalt.org/). The groups with an Padj < 0.05 were examined. Gene ontology (GO) was applied to identify characteristic biological attributes of the first RNA-seq dataset (GSE212301). Results were classified into different functional categories for biological process, molecular process, and cellular component (Fig. 10).

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

To explore the activation and suppression of Differentially Expressed Genes (DEGs) within different classes of pathways, gene expression data was mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using the WEB-based GEne SeT AnaLysis Toolkit (https://www.webgestalt.org/). This analysis was based on the first RNA-seq dataset (GSE212301), and illustrates the top ten enriched pathways identified through this analysis (Fig. 11).

The aging process exerts its effects on HFs through two distinct mechanisms: external aging mechanisms and internal aging mechanisms. External aging mechanisms encompass alterations within the niche environment, characterized by physical atrophy and perturbations in the secretion of crucial factors [33]. Conversely, internal aging mechanisms involve alterations in functional pathways intrinsic to hair HF physiology, notably including the Wnt/β-catenin and BMP pathways[34].

The Wnt pathway plays a pivotal role in instigating the onset of a new hair cycle within HFSCs[35]. However, during the Telogen phase or as a consequence of aging, this pathway has some interaction [36] with the BMP pathway. The BMP pathway is responsible for maintaining cells in a state of rest or quiescence, primarily through the upregulation of the NFATC1 gene expression [15].

In this study, we indicate NFATC1 gene expression is more in AGA samples, but statistical analysis showed that this difference was not meaningful. Although bioinformatic analysis proved our results by a meaningful Pvalue. In addition, the graph in Fig. 11 depicting the higher function of the Wnt signaling pathway in AGA samples aligns with our experimental and bioinformatic findings, which revealed higher expression levels of NFATC1 in AGA patients. Indeed, the Wnt pathway is pivotal in regulating hair follicle cycling and is implicated in various aspects of hair biology, including hair loss and graying during aging.

sirt7 gene in HFSCs of aged mice has lower expression compared to their youthful counterparts[17]. Notably, SIRT7 encodes a nuclear deacetylase, functioning as an upregulator for NFATC1 gene expression [18]. Thus, one possible explanation for the observed increase in NFATC1 gene expression in aged HFSCs may be attributed to the decreased expression of the SIRT7 gene [17]. This elucidates a potential molecular mechanism underlying the dysregulation of NFATC1 gene expression in the context of aging-related changes in HFSCs, and our finding has showed the same results.

According to our functional bioinformatic analysis, The observed decrease in metabolic pathway activation, coupled with the downregulation of SIRT3 and SIRT7, which are members of the metabolic pathway, underscores the intricate interplay between metabolic dysregulation and HF biology in the pathogenesis of AGA.

Mitochondrial damages in dermal papilla cells of HFs who were affected by AGA have higher level, compared to those of healthy HFs [37]. This pronounced mitochondrial damage was also observed in aged HFs when juxtaposed with their youthful counterparts [38]. Considering that one of the pivotal regulators of mitochondrial function is SIRT3, a mitochondrial deacetylase [20], we proposed the hypothesis that dysregulation of SIRT3 gene expression may play a role in the pathogenesis of AGA.

By blocking the PD-1/PDL-1 pathway, hair growth has observed to improve in cancer patients[39]. In addition, the pd-1/pdl-1 signaling pathway may act as an inhibitor of hair growth in mice[40].Given that the activity of this signaling pathway intensifies with advancing age, coupled with evidence suggesting a correlation between increased expression of NFATC1 and PDL-1 genes during aging [41, 42], we hypothesize that the heightened expression of the PDL-1 gene due to aging may exert effects on AGA. Our finding has approved the previous researches, However, despite the observed differences, our subsequent bioinformatic analysis revealed that the associated P-value did not meet the threshold for significance among the group of genes. This discrepancy highlights the nuanced nature of gene expression analysis and underscores the importance of considering various factors when interpreting research findings. Based on the results that are showed in Fig. 10, it appears that multicellular organismal processes and biological regulation are the major biological processes affected in AGA. Dysregulation of these processes may contribute to the disruption of HF function and ultimately lead to hair loss characteristic of AGA.

Furthermore, this functional study revealed that genes exhibiting the most significant differences between control and AGA samples predominantly belong to the membrane category within the cellular component classification. The membrane category encompasses a diverse array of proteins involved in various cellular functions, including cell signaling, transport, and adhesion. As PDL-1 protein is one of the cell adhesion molecules (according to KEGG analysis), different expression level of this gene in AGA group was predicted.

Overall, The multifaceted role of PDL-1 in diverse signaling pathways suggests that its expression levels may vary widely across different health conditions and among individuals. These variations may contribute to the complex pathophysiology of AGA and underscore the need for comprehensive analysis approaches that account for such intricacies.

Our findings underscore the pivotal role of aging in the pathogenesis of AGA. Our results reveal that SIRT3 and SIRT7 exhibit significant differences in gene expression levels between control and AGA sample groups, both from experimental and analytic perspectives. This observation highlights the importance of these genes in AGA pathology and suggests their potential as therapeutic targets.

However, while NFATC1 and PDL-1 also demonstrate differential expression levels between control and patient groups, our experimental analysis indicates that the difference in NFATC1 expression may not be statistically significant. Similarly, our bioinformatic analysis suggests that the differential expression of PDL-1 may not be significant. These discrepancies underscore the complexity of AGA pathogenesis and emphasize the importance of integrating experimental and computational approaches to gain a comprehensive understanding of the molecular mechanisms underlying this condition.

Overall, our findings suggest that while SIRT3 and SIRT7 may play critical roles in AGA development, further research is needed to elucidate the significance of NFATC1 and PDL-1 in this context and to explore their potential as therapeutic targets for AGA treatment.

AGA

Androgenetic Alopecia

Hair Follicle

HFSC

Hair Follicle Stem Cell

MSC

Mesenchymal Stem Cell

Acknowledgments

This study was technically supported by Dena and Sava Genome genetic laboratories.

Consent for publication

All authors reviewed the results and approved the final version of the manuscript

Competing interests

The authors declare that they have no competing interests.

Funding

No financial support

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethical approval

This research was approved by the Ethics Committee of Tarbiat Modares University (IR.MODARES.REC.1402.244), Tehran, Iran. All the participants have accepted and signed the informed consent during the standard genetic counselling sessions. Written informed consent was obtained from the patients for publication of this study. A copy of the written consent is available for review by the Editor of this journal.

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Table.1: candidate gene primers with their product size and temperature melt (TM)

Product size	TM	Sequence	Primer ID	Gene
132bp	61.69	CACCAAAGTCCTGGAGATCCCA	NFATC1-F	NFATC1
132bp	62.87	TTCTTCCTCCCGATGTCCGTCT	NFATC1-R	NFATC1
144bp	62.44	TGGAGTGTGGACACTGCTTCAG	SIRT7-F	SIRT7
144bp	62.16	CCGTCACAGTTCTGAGACACCA	SIRT7-R	SIRT7
162bp	61.40	CCCTGGAAACTACAAGCCCAAC	SIRT3-F	SIRT3
162bp	61.78	GCAGAGGCAAAGGTTCCATGAG	SIRT3-R	SIRT3
150bp	61.22	TGCCGACTACAAGCGAATTACTG	PDL-1-F	PDL-1
150bp	61.25	CTGCTTGTCCAGATGACTTCGG	PDL-1-R	PDL-1
131bp	60.92	GTCTCCTCTGACTTCAACAGCG	GAPDH-R	GAPDH
131bp	64.41	ACCACCCTGTTGCTGTAGCCAA	GAPDH-F	GAPDH

No competing interests reported.

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Investigation the role of SIRT3, SIRT7, NFATC1, and PDL-1 genes in Androgenetic Alopecia

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Abstract

Figures

Introduction

Material and Methods

Mesenchymal stem cells extraction, isolation and expansion

RNA extraction and quantitative analysis by real-time polymerase chain reaction (PCR)

Data processing method and software sources

Results

Hierarchical clustering of differentially expressed genes (DEGs)

MA-plot and Volcano plot

Functional analysis of DEGs

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

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