Diagnostic biomarkers and potential treatment targets for Ankylosing Spondylitis

doi:10.21203/rs.3.rs-3336867/v1

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Diagnostic biomarkers and potential treatment targets for Ankylosing Spondylitis

https://doi.org/10.21203/rs.3.rs-3336867/v1

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Background

Ankylosing spondylitis (AS) is a chronic autoimmune disease that primarily affects the axial joints. Immune cells play a key role in the pathogenesis of AS. This study integrated bioinformatics methods with experimental validation to explore the role of natural killer (NK) cells in AS.

Methods

Two microarray datasets, GSE25101 and GSE73754, were selected, and the scRNA-seq data were obtained from GSE194315 and Liu’s research. Differentially expressed genes (DEGs) and functional enrichment analysis were performed respectively. weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules of co-expressed genes and genes involved in NK cell function. The diagnostic value of the identified key genes were evaluated using ROC curves, logistic regression analysis, and a nomogram. real-time PCR (RT-PCR) was used to quantified the expression of genes. Statistical analysis was conducted using the R software package, and a P-value of less than 0.05 was considered statistically significant.

Results

Pathways enrichment analysis revealed the involvement of NK cell-mediated immune pathways and regulation of the innate immune response, indicating the crucial role of innate immunity, especially NK cells, in AS pathogenesis. The construction of a co-expression network revealed that the MElightyellow module was most relevant to the NK cell-mediated immune pathway. IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 from the MElightyellow module were identified as key genes involved in NK cell-mediated immune response and served as potential diagnostic biomarkers for AS, with moderate to high diagnostic values based on AUC values. Further analysis using scRNA-seq profiling revealed the higher expression level of IL2RB, CD247, PLEKHF1, S1PR5, FGFBP2 in NK cells compared to that in other cell types. CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 were reduced expressed in AS patients as compare to control group verified by scRNA-seq data, CD247, EOMES, FGFBP2, IL2RB and S1PR5 were reduced expressed verified by RT-PCR, and PLEKHF1, S1PR5, and FGFBP2 was upregulated after TNF-α blocker therapy.

Conclusion

The study revealed the potential role of NK cells in the pathogenesis of AS and identified diagnostic biomarkers and potential treatment targets for AS.

Ankylosing Spondylitis

NK cell

diagnostic biomarkers

potential treatment targets

Ankylosing spondylitis (AS) is a chronic rheumatic disease that primarily affects the sacroiliac and spinal joints of young population ¹. The clinical characteristic manifestation of patient with AS is inflammatory back pain (IBP), causing osteophyte formation and spine ankylosis, which leads to irreversible structural and functional damage, and finally results in disability. It is estimated that the prevalence of AS is around 0.3–0.5% in the general population, with a higher incidence rate in males than females¹. It is assumed that a variety of factors (such as heritability, stress, disorder of immune system, etc.) involved in the pathogenesis of AS^2,3. HLA-B27 is strongly associated with AS susceptibility. However, it is positive in 73.1%-89.1% of AS patients⁴, only accounts for approximately 2 to 10% of AS risks⁵. Cytokines, such as tumor necrosis factor-alpha (TNF-α), IL-17, are central to the pathogenesis which implicate in dysregulation of immune cell and contribute to bone remodeling in AS. Targeting TNF-α and IL-17 are effective therapeutic methods to alleviate inflammation and prevent disease from worsening in AS patients, however, parts of patients still fail to respond or achieve clinical remission and stopping medication can easily lead to disease active⁶. Therefore, the pathogensis and new molecular targets of AS are need to be further unrevealed.

Natural killer (NK) cells, found in early 1970’s⁷, act as an evolutionary bridge between innate and adaptive immunity, which also play an vital role in autoimmune diseases^{8 9}. Studies have shown that both the adaptive and innate immune systems are involved in the pathogenesis of AS, which has led to an increased focus on NK cells as a therapeutic target¹⁰. Killer immunoglobulin-like receptor (KIR) genes expressed on NK cells are known to influence NK cell function and have been associated with AS¹¹. Specifically, KIR3DL1, KIR2DL1, KIR2DL5, KIR2DS5, and KIR3DS1 have all been linked to AS^12–16. Research on mice expressing human ERAP1 variants has suggested a potential role for NK cells in the pathogenesis of AS¹⁷. These findings provide valuable insights into the role of NK cells in AS, however, further research is needed to explore function of NK cells and related molecules contributing to inflammation and bone remodelling of AS .

Our study investigated the pathogenesis of AS by analyzing the microarray and single-cell RNA sequencing (scRNA-seq) data in public databases. We confirmed that NK cells played an important role in the pathogenesis of AS. Focusing on NK cells, we identified gene modules closely related to NK cell function. Then, we selected key genes with high diagnostic value from the modules and verified them in scRNA-seq data. Finally, we further validated our findings using peripheral blood samples from AS patients.

Datasets source and preprocessing

In this study, the keyword "Ankylosing Spondylitis" was used to search the GEO database (https://www.ncbi.nlm.nih.gov/geo) for datasets¹⁸. Only those datasets that used peripheral blood mononuclear cell (PBMC) or whole blood samples and had no less than 10 samples per group were selected. Specifically, GSE25101¹⁹ (consisting of PBMC from 16 AS patients and 16 normal controls) and GSE73754²⁰ (consisting of whole blood from 52 AS patients and 20 normal controls) were chosen and their expression and phenotype data were downloaded for further analysis. Each dataset was analyzed separately due to the difference in sample sources.

Sample collection

The blood used for real-time PCR (RT-PCR) was obtained from outpatient clinics of The Seventh Affiliated Hospital Sun Yat-sen University. All patients with AS fulfilled the modified New York criteria. All participants signed the informed consent forms according to the approved protocol by the Ethics Committees of The Seventh Affiliated Hospital Sun Yat-sen University (KY-20230917-01）.

ScRNA-seq data analysis

The scRNA-seq data for PBMC from 10 AS patients and 29 healthy individuals was obtained from GSE194315²¹. Another scRNA data on tumor necrosis factor‐alpha (TNF‐α) blockers treatment of AS was derived from Jing Liu’s research²². The "Seurat" package^23,24 was used for scRNA-seq analysis. The standard Seurat pipeline, including normalization, feature selection, and dimensional reduction with principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP), was used to construct a K-nearest neighbor (KNN) graph after dimensional reduction. We filtered out low-quality cells based on two criteria: (1) genes expressed less than 200 or greater than 2500; (2) mitochondrial-associated genes expressed more than 10%. The top 3,000 variable genes were detected using the "vst" selection method. PCA was then performed, and the top 30 principal components were used for dimensional reduction using Seurat's built-in PCA, tSNE, or UMAP. Cell types were annotated using a reference-mapping approach, with the PBMC reference dataset obtained from Seuratdisk.

Differentially expressed genes (DEGs) and pathway enrichment analysis

The DEGs between AS patients and healthy controls in the two microarray data were analyzed using the limma package²⁵ in R separately. The cutoff for DEGs was set as an absolute log fold change (logFC) of ≥0.3 and an adjusted P-value of <0.05. Pathway enrichment analysis was performed using the clusterProfiler package²⁶, including Gene ontology (GO) enrichment analysis, Gene set enrichment analysis (GSEA)²⁷, and Gene set variation analysis (GSVA)²⁸.

Weighted gene co-expression network analysis (WGCNA) analysis

To identify key modules of co-expressed genes correlated with AS and genes involved in NK cell function, we performed WGCNA on GSE73754 using the WGCNA package²⁹. We used GSVA method to calculate the NK pathway scores for each sample, assesse the conservation of network modules across the cohort using the module preservation function and identify the key gene modules highly correlated with the scores.

Diagnostic analysis

After identifying key gene modules highly correlated with AS and genes related to NK cell function, we analyzed the diagnostic value of these genes. We created a Venn diagram to show the overlap between genes in the key gene modules and DEGs from two microarray data. To assess the diagnostic value, we first calculated the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values using the prediction and performance functions of the pROC package³⁰. Then, a diagnostic model was built by logistic regression analysis, visualized with nomograms, and the reliability of the model was fit and verified using the rms package.³¹.

RT-PCR analysis

After the peripheral blood sample of AS patients and healthy donors were freshly collected, red blood cell lysis buffer was added to remove the erythrocyte. Then cell total RNA was extracted by AG RNAex Pro reagent (Accurate Biology, China) and then reverse transcripted into cDNA by HiScript III All-in-one RT SuperMix Perfect for qPCR Kit (Vazyme, China). RT-PCR reaction was performed by ChamQ Universal SYBR qPCR Master Mix (Vazyme, China) in Thermo CFX96 Real-Time PCR device with the following primers (S Table 1). The data were acquired by Bio-Rad CFX Manager 3.1 and analyzed by the Comparative Ct method (ΔΔCt). The value of 2^-ΔΔCt were analyzed with the GraphPad Prism 8 software by the unpaired Student’s t-test method.

Statistical analysis

All statistical analyses and visualization were performed using R software v4.0.4 (https://www.r-project.org/). Wilcoxon test was applied for the comparisons between two groups. Correlation was examined by Pearson’s correlation analysis. P-value less than 0.05 was regarded as statistical significance.

DEG screening and pathway enrichment analysis

A total of 88 up-regulated and 43 down-regulated genes were identified between AS and healthy samples in GSE73754 (Sup.Figure 1A), and 94 up-regulated and 107 down-regulated genes were identified in GSE25101 (Sup.Figure 1B). GO enrichment analysis of DEGs suggested the involvement of NK cell-mediated cytotoxicity (Figure 1A) in GSE73754 and positive regulation of cytokine production (Figure 1C) in GSE25101. GSEA analysis further confirmed the enrichment of NK cell-mediated immunity in both GSE73754 and GSE25101 datasets, with statistical significance (Figure 1B, 1D). These results suggested that innate immunity, especially NK cells, played a crucial role in the pathogenesis of AS.

Construction of co-expression network

We selected the dataset GSE73754 to further explore the role of the NK cell-mediated immune pathway in AS. Firstly, we calculated the enrichment scores of this pathway for each sample, and then identified 27 co-expressed gene modules through WGCNA analysis. After removing four poorly conserved gene modules, we obtained 23 co-expressed gene modules. We identified the gene module correlated with the enrichment scores by correlation analysis (Figrue 2A-2C), and found that the MElightyellow module was the most relevant to the NK cell-mediated immune pathway according to the heatmap (Figure 2D) (r=0.52, p=3e-6).

Diagnostic predictive value of selected genes

Using a Venn diagram, we identified 6 key genes (IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2) in the MElightyellow module that showed differential expression in both datasets (Figure 3A). We evaluated the diagnostic value of these genes using ROC curves and AUC values, and found AUC values ranging from 0.65 to 0.87, indicating moderate to high diagnostic value (Figures 3B, 3C). We then constructed a diagnostic prediction model using logistic regression and visualized the results using a nomogram (Figures 3D, 3F). Through calibration testing, the p-values of 0.935 (GSE73754) and 0.812 (GSE25101) indicated good model fit. The AUC values of 0.878 in GSE7375, 0.805 in GSE25101 demonstrated excellent discriminatory ability (Figures 3E, 3G). Overall, the predictive capability of the model was considered to be excellent.

We further analyzed the expression of these 6 genes in both datasets and their correlation with enrichment scores. Among them, IL2RB, CD247, S1PR5 and FGFBP2 showed differential expression between the AS and healthy groups in both datasets (Figures 4A-4B). Additionally, the expression of IL2RB, PLEKHF1, EOMES, S1PR5, and FGFBP2 showed significant correlation with the enrichment score of the NK-mediated immune pathway (Figures 4C-4D). These results indicated that IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 might involve in dysfunction of NK cells leading to the pathogenesis of AS.

ScRNA-seq profiling, clustering and gene expression

We explored the role of the 6 genes mentioned above in NK cells using scRNA-seq data from GSE194315. After preprocessing scRNA-seq data based on the stringent quality control metrics as noted above, 15926 AS cells and 66433 control cells were included in the analysis (sup Figure 2). All cells were divided into eight clusters using PCA, tSNE, and UMAP classification methods (sup Figure 3). It was found that except for EOMES, the average expression level of the 6 genes in NK cells was higher than in other cells (Figure 5A). Further analysis of gene expression levels in NK cells revealed statistically significant differences in CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 between AS and control groups (Figure 5B).

Experimental validation of hub gene expression

We quantified the expression levels of these 6 genes in the peripheral blood of AS patients and healthy controls. And the results showed that the most highly expressed gene was FGFBP2, while the least expressed gene was PLEKHF1 (Figure 6). Notably, the expression of 5 of the 6 genes was significantly reduced in AS patients compared to healthy donors, which were CD247, EOMES, FGFBP2, IL2RB and S1PR5.

Gene upregulation after TNF‐α blocker therapy

In order to clarify the effect of TNF-α blocker on NK cells, we analyzed Jing Liu’s scRNA data from NK cells of AS patients treated with TNF-α blocker²². After a standard Seurat treatment pipeline, 7426 pre-treatment and 7648 post-treatment NK cells were included in further analysis (sup. Figure 4). Expression of PLEKHF1, S1PR5, and FGFBP2 was upregulated in post-treatment AS patients compared to pre-treatment AS patients, indicating that TNF-α blocker therapy reversed NK cell dysregulation in AS (Figure 7).

Ankylosing spondylitis (AS) is considered as an immune-mediated disease. Although the etiology is unknown, it is shown that dysregulation not only innate immunity cells but also

adaptive immunity cells are engaged in the pathogenesis of AS^2,3. Our study confirms that immune function, particularly the immune function mediated by NK cells, plays a dominant role in the pathogenesis of AS according to different sources.

Due to the strong association between AS and HLA-B27, and the specific recognition of HLA-B27 by the killer immunoglobulin-like receptor (KIR)s expressed mainly on NK cells, early studies have evaluated the potential involvement of NK cells in AS^{13–16,32,33}. Then, it has been found that there was higher expression of the inhibitory receptor NKG2A compared to the activating receptor NKG2C in NK cells of AS patients³². This imbalance in receptor expression could lead to alterations in AS-related signaling pathways. The proportion of CD56⁺ NK cells in peripheral blood was significantly elevated in AS, affecting the balance of intracellular cytokines³⁴. Moreover, patients with AS expressed higher levels of T-bet in NK and CD8⁺ T cells, also leading to increased secretion of pro-inflammatory cytokines such as IFN-γ and IL-17³⁵. Recent years, with the development of single-cell sequencing (scRNA-seq), there is increasing evidence supporting the involvement of immune cells, especially NK cells, in the pathogenesis of AS. One study using scRNA-seq revealed that total NK cells and the CD56^dim NK subset were decreased in AS patients, while the CD56^bright NK cells were increased. Additionally, the expression of cytotoxic genes in NK cells of AS patients was impaired compared to healthy controls³⁶. Another scRNA-seq research demonstrated that CD16^medium/low NK cells were pathogenically activated and involved in the pathogenesis of AS²².

As AS is a disabling disease and still incurable, it is vital to identify novel targeting molecule participating in the process of inflammation and new bone formation. Therefore, we further using bioinformatics mentods selected 6 highly expressed genes (IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2) from the module which was most relevant to the NK cell-mediated immune pathway. The 6 hub genes showed potential diagnostic values for AS with AUC 0.65–0.87. Among them, FGFBP2, known as fibroblast growth factor binding protein 2, also called killer-specific secretory protein of 37 kDa (Ksp37), is specific expressed by Th1-type CD4⁺ T cells, effector CD8⁺ T cells, γ/δ T cells, and CD16⁺ NK cells that have cytotoxic potential³⁷. FGFBP2⁺ NK cells expressed high levels of cytotoxic effectors (GZMA, GZMB, GNLY, and PRF1) and low levels of inhibitory markers (PDCD1, TIGIT, and CTLA4)³⁸. Early studies showed FGFBP2 is associated with asthma and more recently study in cancer found that a high frequency of tumor-infiltrating FGFBP2⁺ NK cells was closely associated with improved patient survival^39,40. We found that FGFBP2 was the highly expressed in NK cells, was extremely lower in peripheral blood of AS patients as compared to controls, and TNF-α blocker increased expression of FGFBP2 in AS patients, therefore, we assume that reduced expression of FGFBP2 leading to NK cell dysfunction results in the development of AS

CD247, also known as CD3ζ, is first described as part of T-cell receptor-CD3 multi-subunit complex in T lymphocytes^41,42. Then, it is also identified in NK cell as an essential part of two activating receptors, NK-cell protein 46 (NKp46) and NKp30, and the low affinity receptor for IgG Fc (FcγRIII; also called CD16)^43–45. CD247 contains immunoreceptor tyrosine-based activation motifs (ITAMs) in its cytoplasmic domain which undergo tyrosine phosphorylation after reorganization of antigen, therefore, involves in NK cell activation. In autoimmune diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), decreased expression of CD247 was found to be associated with impaired T cell and NK cell function^46–49. EOMES, known as Eomesodermin, is a transcription factor belong to the T-box family characterized by strong homology in DNA-binding T-box domain⁵⁰. EOMES involved in development and function of NK cells⁵¹. IL2RB encodes the beta subunit which comprises intermediate and high affinity forms of interleukin two receptor expressed on T cells and NK cells and mediates endocytosis and transduction the mitogenic signals of IL-2⁵². Recent study showed human defect in IL-2Rβ demonstrated an expansion of NK cells, particularly the CD56^bright subset, and a lack of terminally differentiated NK cells leading to functional deficits of NK cells which resulted in autoimmune diseases and immunodeficiency diseases⁵³. In AS, SNPs of IL2RB gene is associated with the presence of peripheral arthritis at disease onset⁵⁴. Recent study showed that IL2RB were significantly decreased expression in peripheral blood samples of AS patients⁵⁵, which was consistent with our result. S1PR5 is receptor for the lysosphingolipid sphingosine 1-phosphate (S1P), mainly expressed on NK cells and oligodendrocytes⁵⁶. S1PR5 and its function are not well understood. It is shown that during NK cell maturation S1PR5 is strongly induced and essential for human NK-cell response to S1P and egression from the bone marrow and secondary lymphoid organs⁵⁷. The expression of S1PR5 might participate in the pathogenesis of AS thought affecting NK cell egression to inflammatory site. PLEKHF1 encodes pleckstrin homology (PH) and Fab1-YotB-Vac1p-EEA1 (FYVE) domain containing 1, which is membrane lipid-binding protein that participates in receptor-mediated endocytosis, autophagy, and caspase-independent apoptosis through changing mitochondrial membrane permeabilization (MMP)^40,58,59. The expression of PLEKHF1 gene was low and had no statistical significance between AS and control groups, but TNF-α blocker could upregulate PLEKHF1. Therefore, further study need to be explored on the role of PLEKHF1 in the pathogensis of AS.

In conclusion, we used a comprehensive bioinformatics method to analyze transcriptome profiles of GSE25101 and GSE73754, and scRNA-seq of GSE194315 from GEO database to identify that NK cells acted as an essential component contributing to the pathogenesis of AS and revealed that IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 associated with NK cells were the hub genes which might serve as diagnostic biomarkers and offer new promising treatment targets for AS. These findings provide numerous leads for future AS studies and a theoretical basis for the treatment of patients with AS. Although we validated decreased gene expression in the peripheral blood of AS patients which was reversed by TNF-α blocker therapy, additional studies are still required to address the potential role of NK cells and these hub genes in the pathogenesis of AS.

AS: Ankylosing spondylitis

AUC: Area under the ROC curve

DEGs: Differentially expressed genes

GO: Gene ontology

GSEA: Gene set enrichment analysis

GSVA: Gene set variation analysis

IBP: Inflammatory back pain

KIR: Killer immunoglobulin-like receptor

KNN: K-Nearest Neighbor

Ksp37: Killer-specific secretory protein of 37 kDa

NK: Natural killer

MMP: Mitochondrial membrane permeabilization

NKp46: NK-cell protein 46

PBMC: Peripheral blood mononuclear cell

PCA: Principal component analysis

PH: Pleckstrin homology

RA: Rheumatoid arthritis

ROC: Receiver operating characteristic

RT-PCR: Real-time PCR

ScRNA-seq: Single-cell RNA sequencing

SLE: Systemic lupus erythematosus

S1P: Sphingosine 1-phosphate

TNF-α: Tumor necrosis factor-alpha

tSNE: t-Distributed Stochastic Neighbor Embedding

UMAP: Uniform manifold approximation and projection

WGCNA: Weighted gene co-expression network analysis

Ethics approval and consent to participate

The blood used for real time PCR was obtained from outpatient clinics of The Seventh Affiliated Hospital Sun Yat-sen University. All patients with AS fulfilled the modified New York criteria. All participants signed the informed consent forms according to the approved protocol by the Ethics Committees of The Seventh Affiliated Hospital Sun Yat-sen University (KY-20230917-01).

Availability of data and materials

The datasets used in the current study are publicly available data from GEO (https://www.ncbi.nlm.nih.gov/geo) databases and from Liu’s group. The original independent cohorts in the current study are included in the article/supplementary files. Further inquiries can be obtained directly from the corresponding author in a reasonable request.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

The project was supported by Open Fund of Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province.

Author contributions

Jieruo Gu and Yuling Chen conceived and supervised the research, Yuanchun Ye design the research, performed the analysis, and prepared the figures and tables. Yuling Chen wrote the manuscript. Yan li performed RT-PCR, Yuan Xu and Qing Lv collected samples. All authors contributed to interpreting the experimental, reviewing the manuscript and final approval.

Acknowledgements

The authors thank Liu’s group for providing scRNA data and thank those authors who shared their datasets on the GEO databases

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No competing interests reported.

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Diagnostic biomarkers and potential treatment targets for Ankylosing Spondylitis

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Datasets source and preprocessing

Sample collection

ScRNA-seq data analysis

Differentially expressed genes (DEGs) and pathway enrichment analysis

Weighted gene co-expression network analysis (WGCNA) analysis

Diagnostic analysis

RT-PCR analysis

Statistical analysis

Results

DEG screening and pathway enrichment analysis

Construction of co-expression network

Diagnostic predictive value of selected genes

ScRNA-seq profiling, clustering and gene expression

Experimental validation of hub gene expression

Gene upregulation after TNF‐α blocker therapy

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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