Mitochondrial Dysfunction and Programmed Cell Death in Allergic Rhinitis: Potential Biomarkers and Therapeutic Targets

doi:10.21203/rs.3.rs-5308027/v1

Download PDF

Research Article

Mitochondrial Dysfunction and Programmed Cell Death in Allergic Rhinitis: Potential Biomarkers and Therapeutic Targets

https://doi.org/10.21203/rs.3.rs-5308027/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Allergic rhinitis (AR) is a prevalent chronic inflammatory disease, and its pathological mechanisms have not been fully elucidated. This study aims to identify potential biomarkers of AR and explore its role in disease development through integrated bioinformatics analysis.

Methods

We downloaded GSE75011 and GSE46171 datasets from public database to screen out differentially expressed genes (DEGs) between AR patients and control samples. Using MitoCarta 3.0 and literature appendices, we identified mitochondria-associated genes (MitoRGs) and programmed cell death-related genes (PCDRGs). Using Spearman correlation analysis, we screened out DE MitoRGs-PCDRGs with significant correlations. Further, we constructed a protein-protein interaction (PPI) network using the search tool for the retrieval of interacting genes/proteins (STRING) database and visualized it by Cytoscape software. Using machine learning algorithms, we identified biomarkers of AR from candidate genes. In addition, we analyzed the biological functions and signaling pathways of these biomarkers by Gene Set Enrichment Analysis (GSEA), and assessed the infiltration of immune cells by immunoinfiltration analysis.

Results

We identified a total of 50 AR-associated DE MitoRGs-PCDRGs that were strongly associated with apoptosis. Through machine learning algorithms, we identified HPDL, PLEKHF1, PFKFB3, and TNFAIP3 as potential biomarkers for AR. The distribution of these biomarkers on chromosomes and the strong correlation between them suggested that they might play a synergistic role in the pathogenesis of AR. GSEA analysis reveals the potential role of these biomarkers in immune response and cell signaling. Immunoinfiltration analysis revealed significant differences in immune cells between AR and normal control (NC) samples, which were closely related to the expression levels of biomarkers.

Conclusion

This study reveals potential biomarkers of AR through comprehensive analysis and explores their possible mechanisms in disease development. These findings provide new perspectives for the diagnosis and treatment of AR and lay the foundation for future research and clinical applications.

Allergic rhinitis

Mitochondrial dysfunction

Programmed cell death

Biomarkers

Allergic rhinitis (AR) is a chronic inflammatory disease of the nasal mucosa, characterized by increased activation and infiltration of inflammatory cells and various cytokines following exposure to allergens in atopic individuals (1). The prevalence of AR ranges from 10–40%, with an increasing trend (2–3). Some patients also suffer from respiratory diseases such as asthma, which significantly impact their quality of life (4). Currently, the primary treatments for AR include allergen avoidance, drug therapy, immunotherapy, and surgery. However, the treatment outcomes for some patients are suboptimal. Therefore, there is an urgent need to further investigate the pathogenesis of AR, identify effective therapeutic targets, and make significant advancements in the diagnosis and treatment of this condition.

Programmed cell death (PCD) plays a pivotal role in human growth and development, encompassing 18 distinct mechanisms such as apoptosis, pyroptosis, programmed necrosis, and ferroptosis, among others (5). Pyroptosis, characterized by cell enlargement, membrane rupture, and the production of pro-inflammatory cytokines, is a form of PCD that transpires following the activation of the inflammasome and caspase-1 cleavage. This process may serve as a critical risk factor for both initiating and exacerbating Th2 inflammation. The NOD-like receptor family pyrin domain containing 3 (NLRP3) is a multi-protein complex that belongs to the NOD-like receptor protein family. It plays a crucial role in both the inflammatory response and programmed cell death. Research has identified various inhaled allergens, including house dust mites, silica, and toluene diisocyanate, as potential triggers for NLRP3 activation. This activation can lead to airway inflammation and remodeling, resulting in programmed cell death such as apoptosis and pyroptosis, and ultimately progressing to allergic rhinitis. NLRP3 is posited to be a key signaling molecule that bridges the gap between innate and acquired immune responses (6). However, the inhibition of the NLRP3-mediated inflammatory response may play a protective role in respiratory mucosal tissue during the pathological process of asthma and AR. NLRP3 can promote AR development by enhancing inflammation and pyroptosis. Conversely, NLRP3 inhibits inflammation and pyroptosis in AR mice in a model of NLRP3 knockout mice (7). These findings underscore the crucial role of programmed cell death in AR, and its specific mechanism warrants further investigation.

Mitochondria, a double-membrane closed organelle, are among the most sensitive to various damages and serve as the site of biological oxidation and energy conversion in eukaryotic animal cells. Recent findings indicate that mitochondria and endoplasmic reticulum collaboratively regulate Ca²⁺ function within cells, playing a pivotal role in apoptosis and signaling pathways (8). The normal morphological function of mitochondria is crucial for the proper functioning of cell tissues. Dysfunctions such as loss of membrane potential and production of reactive oxygen species (ROS) can result in cell death. In AR, mitochondrial damage may precipitate cell pyroptosis, a form of programmed cell death, which exacerbates the inflammatory response. Mitochondrial function is also intimately linked to the process of PCD. Dysfunctional mitochondrial oxidative phosphorylation and impaired ATP production can induce cellular stress, thereby initiating the PCD pathway (9). Despite the importance of mitochondrial function and PCD in AR, there is a paucity of research exploring their detailed functions, mechanisms, and associated genes. These factors are crucial for understanding the prognosis and treatment options for AR patients. To establish a clearer link between mitochondrial function and PCD, this study conducted a comprehensive screening of relevant biomarkers. We performed a series of bioinformatics analyses on Mitochondrial-Related Genes (MitoRGs) and Programmed Cell Death-Related Genes (PCDRGs). The aim was to investigate the potential of MitoRGs - PCDRGs as biomarkers for AR, as well as to uncover their possible molecular mechanisms. This research offers a novel reference point for the clinical diagnosis and treatment of AR.

Data source

In this study, GSE75011 (platform: GPL16791) and GSE46171 (platform: GPL6480) datasets were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The GSE75011 dataset contained blood samples from 25 allergic rhinitis (AR) and 15 normal control (NC) and the GSE46171 dataset contained the nasal mucosa tissue from 6 AR and 3 NC samples. In addition, 1,136 mitochondrial-related genes (MitoRGs) were retrieved from MitoCarta3.0 database (https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways) (Additional file 1). A total of 1,548 programmed cell death-related genes (PCDRGs) were mined from the annexes of the published literature (Additional file 2) (10).

Differential expression and functional enrichment analyses

The differentially expressed genes (DEGs) between AR and NC samples in the GSE75011 dataset were selected by limma package (v3.52.4) (11). The screening criteria for DEGs were p.value < 0.05 and |log₂Fold Change (FC)| > 0.5. Then, DEGs were intersected with MitoRGs and PCDRGs to obtained differentially expressed MitoRGs and PCDRGs, respectively. After that, Spearman correlation analysis was performed on differentially expressed MitoRGs and PCDRGs, and genes with p < 0.05 and |cor| > 0.4 were selected as differentially expressed MitoRGs-PCDRGs. To research the biological pathways involved in differentially expressed MitoRGs-PCDRGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were finished via clusterProfiler package (v4.4.4, adj.p < 0.05) (12).

Establishment of the protein-protein interaction (PPI) network

The PPI network was established by STRING database (http://www/string-db.org/) to investigate the interactions among differentially expressed MitoRGs-PCDRGs. Cytoscape software (v3.8.2) (13) was used to visualize the PPI network.

Identification of biomarkers for AR

DEGs between AR and NC samples in the GSE46171 dataset were acquired by limma package (|log₂FC| > 0.5 and p.value < 0.05). The DEGs shared by GSE75011 and GSE46171 datasets with the same expression trends were considered as candidate genes. Depending on the candidate genes, least absolute shrinkage and selection operator (LASSO), Boruta, and support vector machine recursive feature elimination (SVM-RFE) were employed to recognize biomarkers for AR. LASSO algorithm was implemented through glmnet package (v4.1-4) (14). Boruta package (v8.0.0) (15) was utilized to carry out Boruta algorithm, in which Z-score served as a measure of significance. Meanwhile, the e1071 package (v1.7-13) (16) was utilized to compute for all genes, and the genes contained in the feature set with the smallest error were screened out. Afterthat, biomarkers were obtained by overlapping the genes from three machine learning algorithms. Finally, pROC package (v1.18.0) (17) was utilized to draw receiver operating characteristic (ROC) curves to assess the diagnostic value of biomarkers in GSE75011 and GSE46171 datasets.

Chromosomal localization and correlation analysis for biomarkers

In order to observe whether the biomarkers were clustered on the chromosomes, we downloaded their locations from the ENSEMBL database and mapped in the chromosomes using the RCircos package (v1.2.2) (18). Then, Spearman correlation analysis was implemented by cowplot package (v1.1.1) (19) to explore the relationship among biomarkers. The results were displayed by heatmap, scatterplot and correlation curves.

Gene Set Enrichment Analysis (GSEA) analysis

Firstly, we performed correlation analysis of the biomarkers with the remaining genes in the GSE75011 dataset to obtain the correlation coefficients and ranked them. Then, GSEA enrichment analysis was performed on biomarkers through clusterProfiler package based on the ranked results of the correlation coefficients (adj.p < 0.05 & |NES| > 1). In addition, GeneMANIA was utilized to mine genes that interact with biomarkers and to demonstrate their biological functions by constructing the networks.

Immune infiltration analysis

To make a comparison of the discrepancy in immune cell infiltration between AR and NC samples, we assessed the relative abundance of 28 immune cells in all samples of the GSE75011 dataset using ssGSEA algorithm. Wilcoxon.test was utilized to recognize differential immune cells between AR and NC groups (p < 0.05). Subsequently, Spearman correlation analysis was utilized to investigate the relevance between differential immune cells and their correlation with biomarkers.

Prediction of the transcription factors (TFs) and drugs

TFs targeting biomarkers were predicted through ENCODE ChIP-seq database, and interaction was analyzed by NetworkAnalyst3.0 tool (https://www.networkanalyst.ca/). Additionally, DSigDB database (http://tanlab.ucdenver.edu/DSigDB) and cytoscape were used to predict potential drugs targeting biomarkers and construct interaction network, respectively.

Statistical analysis

This study utilized open databases and R software packages for analysis and visualization. The venn diagram was painted by VennDiagram package (v1.7.3) (20). Comparison between the two groups was accomplished by the Wilcoxon.test and t-test. If not stated above, p-values below 0.05 were taken to be statistically meaningful.

A total of 50 differentially expressed MitoRGs-PCDRGs were identified for AR

After differential expression analysis in the GSE75011 dataset, 498 DEGs were obtained between AR and NC samples, containing 332 down-regulated and 166 up-regulated genes in AR samples. The top5 and top20 up- and down-regulated DEGs were displayed in the volcano map and heatmap, respectively (Fig. 1a, b). A total of 12 differentially expressed MitoRGs and 49 differentially expressed PCDRGs were acquired by overlapping DEGs and MitoRGs and PCDRGs, respectively (Fig. 1c). Next, 50 differentially expressed MitoRGs-PCDRGs were recognized by correlation analysis of 12 differentially expressed MitoRGs and 49 differentially expressed PCDRGs (Fig. 1d).

Differentially expressed MitoRGs-PCDRGs were associated with apoptosis

Currently, PPI networks are used to view the connections between genes of interest (21). The PPI network (interaction score > 0.4) including 35 nodes and 115 edges was established for differentially expressed MitoRGs-PCDRGs, in which complex interactions existed among all genes except CDK5R1, CTSZ, ACVR1, SIK1 and NR4A3 (Fig. 2a). Afterthat, GO and KEGG enrichment analyses were implemented to understand the biological significance of differentially expressed MitoRGs-PCDRGs. In GO terms, differentially expressed MitoRGs-PCDRGs were significantly associated with DNA binding, smooth muscle cell, and apoptosis-related pathways, such as positive regulation of DNA binding, intrinsic apoptotic signaling pathway, and smooth muscle cell proliferation (Fig. 2b, Additional file 3). As for KEGG pathways, IL-17 signaling pathways and TNF signaling pathways were enriched by some genes in differentially expressed MitoRGs-PCDRGs (Fig. 2c).

Four biomarkers with diagnostic value for AR were identified

After differential expression analysis in the GSE46171 dataset, the expression trends of MMAB, HPDL, PLEKHF1, PFKFB3, NLRP3, TNFAIP3, TGFB1, and CD38 remained consistent with those in GSE75011, and were regarded as candidate genes (Fig. 2d). Subsequently, three types of machine learning algorithms were used to mine biomarkers from candidate genes. In the LASSO regression analysis, the residual sum of squares was minimized when lambda was 0.0969, and then four candidate biomarkers (HPDL, PLEKHF1, PFKFB3, and TNFAIP3) were obtained (Fig. 3a). Thereafter, when cutoff value was 2.44, MMAB, HPDL, PLEKHF1, PFKFB3, NLRP3, TNFAIP3, and TGFB1 were selected by Boruta algorithm (Fig. 3b). Meanwhile, the model error was minimized when PLEKHF1, TNFAIP3, CD38, MMAB, NLRP3, HPDL, PFKFB3, and TGFB1 were taken as candidate biomarkers in SVM-RFE (Fig. 3c). An intersection of the above candidate biomarkers was taken and four biomarkers (HPDL, PLEKHF1, PFKFB3, and TNFAIP3) were identified for AR (Fig. 3d). The AUC values of biomarkers in both GSE75011 and GSE46171 datasets were greater than 0.7, demonstrating the decent abilities to discriminate between AR and NC samples (Fig. 3e, f).

Strong correlations exist among biomarkers

To figure out how the four biomarkers work, we performed a correlation and chromosome localization analyses. As shown in Fig. 4a, the positive correlation between PLEKHF1, PFKFB3, and TNFAIP3 was strong, while HPDL was significantly negatively correlated with PLEKHF1. According to chromosome localization analysis, HPDL, PLEKHF1, PFKFB3, and TNFAIP3 were located on chromosomes 1, 19, 10, and 6, respectively (Fig. 4b). In addition, genes interacting with biomarkers and their potential biological functions were predicted in GeneMANIA. In the network, TAX1BP1, TNIP1, and ZFAND5 might have strong interaction with biomarkers, and TNIP1 and TNFAIP3 were linked with toll-like receptor, pattern recognition receptor, and regulation of I-kappaB kinase/NF-kappaB signaling pathways (Fig. 4c). In conclusion, we hypothesized that four biomarkers might be involved in the onset and development of AR through interactions.

Exploration of potential biological functions of biomarkers

Although the functions of the biomarkers were predicted in the above analysis, single-gene GSEA was used to further explore the biological functions and pathways involved. The results revealed that HPDL was associated with ribosome, PLEKHF1 with leukocyte and lymphocyte activation, PFKFB3 with transcription and DNA, and TNFAIP3 with small molecule catabolic process in GO enrichment analysis (Fig. 5a-d). In KEGG enrichment analysis, MAPK and FOXO signaling pathways were enriched by PFKFB3 and TNFAIP3, and TNF and C-type lectin receptor signaling pathways were enriched by PFKFB3 and PLEKHF1 (Fig. 5e-h). Thus, biomarkers might be involved in the developmental process of AR through the above pathways.

Disturbances in the immune microenvironment were associated with AR

AR is known to be caused by an immunoglobulin E-mediated reaction to inhaled allergens (22). Therefore, we believed the immune microenvironment must play an essential part in the development of AR. The relative abundance of 28 immune cells in AR and NC samples was shown in Fig. 6a. Moreover, 6 types of differential immune cell (effector memeory CD8 T cell, activated CD4 T cell, T follicular helper cell, natural killer T cell, and type 1 and type 2 T helper cell) were identified between AR and NC groups (Fig. 6b). In addition, we investigated the relevance of biomarkers and differential immune cells. The results demonstrated that T follicular helper cell, type 1 T helper cell, and effector memory CD8 T cell significantly correlated with PFKFB3 and PLEKHF1 (Fig. 6c). At the same time, we noted varying degrees of correlation among differential immune cells (Fig. 6c). In summary, we inferred that dysregulation of the immune microenvironment played an immeasurable contribution to the pathogenesis of AR, especially the decline in the proportion of multiple T cells.

Regulatory network and drugs might contribute to the treatment of AR

A total of 14, 20, 48, and 104 TFs might be involved in regulating the expression of HPDL, PFKFB3, PLEKHF1, and TNFAIP3, respectively, in which KDM5B, PHF8, and SAP30 could act simultaneously on HPDL, PFKFB3, and PLEKHF1 (Fig. 7a). To find potential therapeutic drugs for AR, we implemented the drug prediction. In total, 26 drugs targeting PFKFB3, 3 drugs targeting HPDL, 7 drugs targeting PLEKHF1, and 92 drugs targeting TNFAIP3 were predicted. The network including 112 nodes and 128 edges was established for biomarkers, of which Pirinixic acid CTD 00000280, ETHYL METHANESULFONATE CTD 00005938, and Tetradioxin CTD 00006848 could target HPDL, PLEKHF1, and TNFAIP3 simultaneously (Fig. 7b).

The consistent expression trends of biomarkers were existed in GSE75011 and GSE46171 datasets

Firstly, we delved the differences in the expression of biomarkers between AR and NC samples in the GSE75011 and GSE46171 datasets. The expression of HPDL was higher in the AR group compared to the NC group, while the expression of PLEKHF1, PFKFB3 and TNFAIP3 was lower (Fig. 8a,b). To be sure, four biomarkers showed consistent expression trends in datasets GSE75011 and GSE46171.

Allergic rhinitis, a chronic IgE-mediated inflammation of the nasal mucosa, continues to pose challenges for current treatment methods, thereby negatively impacting the quality of life for patients. Consequently, identifying suitable targets and treatment strategies has become a focal point in allergic rhinitis research. Mitochondrial dysfunction is increasingly recognized as a potential mechanism underlying various inflammatory and autoimmune diseases, including cancer, atherosclerosis, neurodegenerative diseases, diabetes, and both autoimmune and allergic conditions (23–26). The progression of these diseases is often exacerbated when systemic inflammation and oxidative stress are present, due in part to the impairment of normal mitochondrial function and subsequent programmed cell death (27). Mutations in mitochondrial genes have been linked to the pathogenesis and local inflammation associated with allergic rhinitis (28). However, the role of mitochondrial function and the mechanism of PCD in AR remain unclear, with limited related studies available. Therefore, this study employed a series of bioinformatics analyses, integrating MitoRGs and PCDRGs, to investigate key biomarkers of MitoRGs - PCDRGs in AR. This research aims to provide new directions for the clinical diagnosis and treatment of AR.

This study employs bioinformatics methodologies to investigate the mtPCD gene in AR and identify potential biomarkers, including HPDL, PLEKHF1, PFKFB3, and TNFAIP3. The aim is to understand the influence of these biomarkers on AR development, pinpoint effective ones, and elucidate their significant roles in this process. Subsequently, the transcription factors (TF) associated with these biomarkers were predicted using NetworkAnalyst to establish an mRNA-TF regulatory network. Furthermore, predictions of drugs corresponding to these biomarkers, based on the DSigDB database, offer a foundational framework for future clinical research endeavors.

Evidence suggests that the activation of NF-κB signaling plays a significant role in the pathogenesis of Ankylosing Spondylitis (AR), with inappropriate NF-κB activity implicated in numerous autoimmune and inflammatory diseases (29). Tumor Necrosis Factor α Inducing Protein 3 (TNFAIP3), a variant of Tumor Necrosis Factor α (TNF-α), is crucial for modulating immune responses and cell apoptosis (30). TNFAIP3 encodes ubiquitin-modifying enzyme (A20) as a negative regulator of TNF-induced NF-κB activity, thereby playing a key role in managing inflammatory responses across various biological environments (31–33). Li et al. identified the rs9494885SNPTNFAIP3 gene as a significant risk factor for Behcet's disease in Han Chinese population (34). Recent research has demonstrated that PFK15, a small molecule inhibitor of PFKFB3, can impede RAFLS migration, MAPK and NF-κB signaling, lactate secretion, and alleviate collagen-induced arthritis inflammation (35). Consequently, targeting TNFAIP3 to inhibit the inappropriate activation of the NF-κB signaling pathway may offer a potential treatment for AR.

The coding gene PFKFB3, also known as 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3, plays a significant role in the regulation of the glycolysis process. While its involvement in the pathological progression of allergic rhinitis remains unconfirmed, PFKFB3 is implicated in the advancement of inflammation and glucose metabolism via Toll-like receptor 4 (TLR4). Furthermore, PFKFB3 can modulate LPS (lipopolysaccharide)-induced inflammation through the NF-κB signaling pathway. This influence can alter the functions of trophoblast cells, including adhesion, oxidative stress, apoptosis, and migration. Such changes may lead to mitochondrial dysfunction, thereby potentially inducing allergic rhinitis (36).

PLEKHF1, an endosomal protein comprising PH and FYVE membrane-bound domains, plays a crucial role in endosomal trafficking, lysosomal targeting, and autophagosome formation (37). Its expression is notably upregulated during the progression of various pathological conditions. An increase in PLEKHF1 expression may trigger AR by modulating macrophages. In AR patients, macrophages in the nasal cavity and peripheral blood are predominantly of the M2 type. These M2 type macrophages can enhance the Th2 immune response by releasing specific molecules such as YKL-40 and a series of chemokines, including CCL17, CCL18, and CCL26. This suggests that PLEKHF1 plays a significant role in the pathogenesis of AR (38). Mendelian randomization (MR) analysis and in vivo experiments have shown a causal relationship between AR and idiopathic pulmonary fibrosis (IPF). Inhibiting the expression of PLEKHF1 in the lungs could increase the inhibition of PI3K/Akt signaling and reduce pulmonary fibrosis in mice (39–40). The present study demonstrated that PLEKHF1 was differentially expressed in AR. We hypothesize that it is possible to attenuate AR and reduce the level of pulmonary fibrosis by regulating the expression of PLEKHF1. However, the specific mechanism requires further investigation.

HPDL, a single-exon intronless gene situated in the nuclear genome of N2a cells, is located within mitochondria. Its deletion influences the mitochondrial oxygen consumption rate and encodes a 4-hydroxyphenylpyruvate dioxygenase-like protein integral to the tyrosine metabolic pathway (41). In WTHeLa cells, HPDL is isotopically present in the cytoplasm alongside the mitochondrial protein HSP60 and exhibits slight dispersion in the nucleus. Western blotting of HeLa cell subcellular fractions reveals that HPDL can be identified in mitochondria, mirroring the mitochondrial marker TOM20. In conclusion, variants of HPDL induce mitochondrial neuropathy with diverse features such as DD/ID, spasticity, and hypertonia. A deficiency in HPDL impairs mitochondrial respiration processes, potentially leading to a spectrum of inflammatory diseases, including AR (42). Furthermore, our study discovered that both HPDL and PFKB3 upregulate AR, while PLEKHF1 downregulates it. Notably, HPDL was initially identified as a potential biomarker for AR and is involved in classical AR pathways such as the Toll receptor signaling pathway and pattern recognition receptor signaling pathway. These biomarkers are likely implicated in the inflammatory process of AR, suggesting that developing clinical tests targeting these biomarkers could enhance the diagnosis and treatment of AR.

Immune cells, a type of white blood cell, primarily function to safeguard the human body from foreign pathogens such as bacteria and viruses, and also to eliminate abnormal or dead cells. Innate immune cells, including neutrophils, dendritic cells, and natural killer (NK) cells, are born within humans and offer rapid non-specific immune responses to pathogens (43). A growing body of research indicates that immune cell infiltration is closely linked to the efficacy of immunotherapy and patient prognosis (44). Our study suggests that the aforementioned biomarkers are strongly correlated with activated CD4 + T cells, NK cells, and T follicular helper cells. CD4 + T cells, innate immune cells involved in allergic reactions, differentiate into two subsets, Th1 and Th2 cells, upon activation (45). An imbalance between Th1 and Th2 cells has been identified in AR, where Th2 promotes allergic reactions while Th1 counteracts them (46). This imbalance can be mitigated by ameliorating the disproportion between the two cell types, thereby alleviating AR. Furthermore, allergic respiratory diseases are associated with a decrease in natural killer cells and impaired function, which exacerbates the inflammatory response (47). T follicular helper (Tfh) cells are derived from T cells (48). In AR, alterations in the number and activity of Tfh cells have been observed. Some research posits that the quantity of Tfh cells in AR patients may be diminished (49). Tfh cells significantly contribute to AR pathogenesis by secreting cytokines such as IL-4, which encourage the differentiation of B cells into plasma cells, thereby escalating IgE production. Allergen-specific immunotherapy (AIT) could potentially influence the number and function of Tfh cells, thereby mitigating the inflammatory response in AR patients (50). In conclusion, Tfh cells may enhance the inflammatory response and participate in immunoglobulin production in AR. Conversely, immunotherapy might reduce this inflammatory response by modulating Tfh cells. Given our findings that four biomarkers were strongly associated with three immune cells, and considering these biomarkers' involvement in previous studies on inflammatory responses, we hypothesize that HPDL, PLEKHF1, PFKFB3, and TNFAIP3 may directly or indirectly impact the function of CD4 + T cells, NK cells, and trifollicular helper cells. Their activity is crucial for regulating B cell survival, T cell function, and the inflammatory response. Targeting these biomarkers can regulate the function and infiltration degree of immune cells, thereby effectively ameliorating AR.

Currently, the only approved AR-targeted therapies are omalizumab and dupilumab. These drugs target proliferating adaptive immune cells, the interleukin-6 receptor (IL-6R), and CD20 + B cells, respectively. Our research has identified that PFKFB1 and HPDL both utilize the transcription factor KDM5B. KDM5B is a histone lysine demethylase that suppresses gene transcription and functions as an oncogenic factor in cancer initiation and progression (51). Currently, researchers have developed an inhibitor for KDM5B (52). Furthermore, KDM5B may inhibit the expression of NF-κB pathway mRNA in immune diseases. Its congener, KDM5A, is necessary for NK cell activation by suppressing the expression of cytokine signaling inhibitor 1 (SOCS1) (53). However, the differences between KDM5B and other members of the KDM5 family remain largely undefined. Emerging therapies and targeted agents have shown positive effects in treating immune diseases such as AR (54). Given the complexity of pharmacological effects, these epigenetic targets and inhibitors are being evaluated for the treatment of autoimmune diseases (55). These studies suggest that these epigenetic factors are under investigation for the treatment of immune-related diseases, and targeting KDM5B holds significant potential for the treatment of AR.

This study utilized bioinformatics technology to investigate the role of mtPCD-related genes in AR activity, identifying four potential biomarkers. These findings offer novel insights into the pathogenesis of AR, thereby providing fresh evidence for the involvement of mtPCD-related genes in AR development. Furthermore, these results could potentially enhance our understanding of AR diagnosis, prognosis, and treatment. However, it is important to note that this study has certain limitations. More comprehensive clinical data and results are required to further elucidate the function of these genes and their association with AR. Using public databases, we employed bioinformatics methods to identify biomarkers linked to mitochondrial and programmed cell death. The role of these genes in disease progression requires further analysis, supported by additional clinical data and results. Moreover, there is currently a lack of strong mechanistic evidence to substantiate the pathogenesis of biomarkers such as HPDL, PLEKHF1, PFKFB3, and TNFAIP3 in AR. The genetic polymorphisms associated with their susceptibility to AR also warrant further exploration.

Abbreviations	Full name
AR	Allergic rhinitis
DEGs	Differentially expressed genes
MitoRGs	Mitochondria-associated genes
PCDRGs	Programmed cell death-related genes
STRING	Search tool for the retrieval of interacting genes/proteins
GSEA	Gene set enrichment analysis
PCD	Programmed cell death
NLRP3	NOD-like receptor family pyrin domain containing 3
ROS	Reactive oxygen species
GEO	Gene expression omnibus
NC	Normal control
FC	Fold change
KEGG	Kyoto encyclopedia of genes and genomes
LASSO	Least absolute shrinkage and selection operator
SVM-RFE	Support vector machine recursive feature elimination
ROC	Receiver operating characteristic
TFs	Transcription factors
TNFAIP3	Tumor necrosis factor α inducing protein 3
TNF-α	Tumor necrosis factor α
TLR4	Toll-like receptor 4
MR	Mendelian randomization
IPF	Idiopathic pulmonary fibrosis
NK	Natural killer
Tfh	T follicular helper
AIT	Allergen-specific immunotherapy
IL-6R	Interleukin-6 receptor

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analysed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) and the mitochondrial-related genes (MitoRGs) database (https://www.broARinstitute.org/mitocarta).

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The research reported in this project was generously supported by “2024 Guangxi Traditional Chinese Medicine Appropriate Technology Development and Promotion Project” under grant agreement number “GZSY2024039”.

Authors' contributions

H K analyzes and interprets data from patients with allergic rhinitis. Y X performed blood tests for patients with allergic rhinitis and was a major contributor to the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We sincerely acknowledge the support from the Department of Otorhinolaryngol, the Second Affiliated Hospital of Dalian Medical University.

Steelant B, Seys SF, Van Gerven L, Van Woensel M, Farré R, Wawrzyniak P et al (2018) Histamine and T helper cytokine-driven epithelial barrier dysfunction in allergic rhinitis. J Allergy Clin Immunol 141:951–963
Bernstein DI, Schwartz G, Bernstein JA (2016) Allergic rhinitis: Mechanisms and treatment. Immunol Allergy Clin North Am 36:261–278
Yang L, Fu J, Zhou Y (2020) Research progress in atopic march. Front Immunol 11:1907
Pakkasela J, Ilmarinen P, Honkamäki J, Tuomisto LE, Andersén H, Piirilä P et al (2020) Age-specific incidence of allergic and non-allergic asthma. BMC Pulm Med 20:9
Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, Agostinis P (2018) Molecular mechanisms of cell death:recommendations of the nomenclature committee on cell death 2018. Cell Death Differ 25:486–541
Zhang W, Ba G, Tang R, Li M, Lin H (2020) Ameliorative effect of selective NLRP3 inflammasome inhibitor MCC950 in an ovalbumin-induced allergic rhinitis murine model. Int Immunopharm 83:106394
Yang Z, Liang C, Wang T, Zou Q, Zhou M, Cheng Y et al (2020) NLRP3 inflammasome activation promotes the development of allergic rhinitis via epithelium pyroptosis. Biochem Biophys Res Commun 522:61–67
Breckenridge DG, Germain M, Mathai JP, Nguyen M, Shore GC (2003) Regulation of apoptosis by endoplasmic reticulum pathways. Oncogene 22:8608–8618
Dadsena S, Zollo C, García-Sáez AJ (2021) Mechanisms of mitochondrial cell death. Biochem Soc Trans 49:663–674
Qin H, Abulaiti A, Maimaiti A, Abulaiti Z, Fan G, Aili Y et al (2023) Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma. J Transl Med 21:588
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47
Yu G, Wang LG, Han Y, He QY (2012) ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Engebretsen S, Bohlin J (2019) Statistical predictions with glmnet. Clin Epigenetics 11:123
Twait EL, Andaur Navarro CL, Gudnason V, Hu YH, Launer LJ, Geerlings MI (2023) Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study. BMC Med Inf Decis Mak 23:168
Chen H, Zhang J, Sun X, Wang Y, Qian Y (2022) Mitophagy-mediated molecular subtypes depict the hallmarks of the tumour metabolism and guide precision chemotherapy in pancreatic adenocarcinoma. Front Cell Dev Biol 10:901207
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC et al (2011) pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics 12:77
Zhang H, Meltzer P, Davis S (2013) RCircos: an R package for Circos 2D track plots. BMC Bioinformatics 14:244
Qin X, Zhou J, Wang Z, Feng C, Fan J, Huang J et al (2022) Metagenomic analysis of the microbiome of the upper reproductive tract: combating ovarian cancer through predictive, preventive, and personalized medicine. EPMA J 13:487–498
Chen H, Boutros PC (2011) VennDiagram: a package for the generation of highly-customizable venn and euler diagrams in R. BMC Bioinformatics 12:35
Wimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC (2023) Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa). J Genet Eng Biotechnol 21:69
Czech EJ, Overholser A, Schultz P (2023) Allergic rhinitis. Prim Care 50:159–178
Medina-Gomez G (2012) Mitochondria and endocrine function of adipose tissue. Best Pract Res Clin Endocrinol Metab 26:791–804
Pagano G, Castello G, Pallardo FV (2013) Sjøgren’s syndrome-associated oxidative stress and mitochondrial dysfunction: prospects for chemoprevention trials. Free Radic Res 47:71–73
Ouyang J, Wu M, Huang C, Cao L, Li G (2013) Overexpression of oxidored-nitro domain containing protein 1 inhibits human nasopharyngeal carcinoma and cervical cancer cell proliferation and induces apoptosis: Involvement of mitochondrial apoptotic pathways. Oncol Rep 29:79–86
Osellame LD, Blacker TS, Duchen MR (2012) Cellular and molecular mechanisms of mitochondrial function. Best Pract Res Clin Endocrinol Metab 26:711–723
Hernández-Aguilera A, Rull A, Rodríguez-Gallego E, Riera-Borrull M, Luciano-Mateo F, Camps J et al (2013) Mitochondrial dysfunction: a basic mechanism in inflammation-related noncommunicable diseases and therapeutic opportunities. Mediators Inflamm 2013:135698
Kim EK, Kwon JE, Lee SY, Lee EJ, Kim DS, Moon SJ et al (2017) IL-17-mediated mitochondrial dysfunction impairs apoptosis in rheumatoid arthritis synovial fibroblasts through activation of autophagy. Cell Death Dis 8:e2565
Wang SZ, Ma FM, Zhao JD (2013) Expressions of nuclear factor-kappa B p50 and p65 and their significance in the up-regulation of intercellular cell adhesion molecule-1 mRNA in the nasal mucosa of allergic rhinitis patients. Eur Arch Otorhinolaryngol 270:1329–1334
Ke X, Yang Y, Shen Y, Wang X, Hong S (2016) Association between TNFAIP3 gene polymorphisms and risk of allergic rhinitis in a chinese han population. Iran J Allergy Asthma Immunol 15:46–52
Lee EG, Boone DL, Chai S, Libby SL, Chien M, Lodolce JP et al (2000) Failure to regulateTNF-induced NF-kappaB and cell death responses in A20-deficient mice. Science 289:2350–2354
Matmati M, Jacques P, Maelfait J, Verheugen E, Kool M, Sze M et al (2011) A20 (TNFAIP3) deficiency in myeloid cells triggers erosive polyarthritis resembling rheumatoid arthritis. Nat Genet 43:908–128
Catrysse L, Vereecke L, Beyaert R, van Loo G (2014) A20 in inflammation and autoimmunity. Trends Immunol 35:22–31
Li H, Liu Q, Hou S, Du L, Zhou Q, Zhou Y et al (2013) TNFAIP3 gene polymorphisms confer risk for Behcet's disease in a Chinese Han population. Hum Genet 132:293–300
Umar S, Palasiewicz K, Volin MV, Romay B, Rahat R, Tetali C et al (2021) Metabolic regulation of RA macrophages is distinct from RA fibroblasts and blockade of glycolysis alleviates inflammatory phenotype in both cell types. Cell Mol Life Sci 78:7693–7707
Zhang Y, Liu W, Wu M, Li Q, Liu Y, Yang L et al (2021) PFKFB3 regulates lipopolysaccharide-induced excessive inflammation and cellular dysfunction in HTR-8/Svneo cells: Implications for the role of PFKFB3 in preeclampsia. Placenta 106:67–78
Lin WJ, Yang CY, Li LL, Yi YH, Chen KW, Lin YC et al (2012) Lysosomal targeting of phafin1 mediated by Rab7 induces autophagosome formation. Biochem Biophys Res Commun 417:35–42
Prasse A, Pechkovsky DV, Toews GB, Jungraithmayr W, Kollert F, Goldmann T et al (2006) A vicious circle of alveolar macrophages and fibroblasts perpetuates pulmonary fibrosis via CCL18. Am J Respir Crit Care Med 173:781–792
Wang J, Hu K, Cai X, Yang B, He Q, Wang J et al (2022) Targeting PI3K/AKT signaling for treatment of idiopathic pulmonary fibrosis. Acta Pharm Sin B 12:18–32
Zhang Z, Li G, Yu L, Jiang J, Zhou S, Jiang Y (2023) Using a two-sample mendelian randomization study based on genome-wide association studies to assess and demonstrate the causal effects of allergic rhinitis on chronic lower respiratory diseases and lung function. Int Arch Allergy Immunol 184:311–319
Micule I, Lace B, Wright NT, Chrestian N, Strautmanis J, Diriks M et al (2022) Case Report: Two families with HPDL related neurodegeneration. Front Genet 13:780764
Husain RA, Grimmel M, Wagner M, Hennings JC, Marx C, Feichtinger RG et al (2020) Bi-allelic HPDL variants cause a neurodegenerative disease ranging from neonatal encephalopathy to adolescent-onset spastic paraplegia. Am J Hum Genet 107:364e373
Wang Y, Zheng X, Feng C, Fan X, Liu L, Guo P et al (2022) HPDL mutations identified by exome sequencing are associated with infant neurodevelopmental disorders. Mol Genet Genomic Med 10:e2025
Zhou S, Zhou C, Wang X, Luo P, Lin A, Cui Y et al (2023) Profiles of immune infiltration in seasonal allergic rhinitis and related genes and pathways. Int Immunopharmacol 120:110174
Tan L, Fu L, Zheng L, Fan W, Tan H, Tao Z et al (2022) TET2 regulates 5-Hydroxymethylcytosine signature and CD4 + T-Cell balance in allergic rhinitis. Allergy Asthma Immunol Res 14:254–272
Ke X, Chen Z, Wang X, Kang H, Hong S (2023) Quercetin improves the imbalance of Th1/Th2 cells and Treg/Th17 cells to attenuate allergic rhinitis. Autoimmunity 56:2189133
Kim JH, Gong CH, Choi GE, Kim SA, Kim HS, Jang YJ (2016) Natural killer cell deficits aggravate allergic rhinosinusitis in a murine model. ORL J Otorhinolaryngol Relat Spec 78:199–207
Meng C, Gu L, Li Y, Li R, Cao Y, Li Z et al (2021) Ten-eleven translocation 2 modulates allergic inflammation by 5-hydroxymethylcytosine remodeling of immunologic pathways. Hum Mol Genet 30:1985–1995
Teng ZX, Zhou XC, Xu RT, Zhu FY, Bing X, Guo N et al (2022) Tfh exosomes derived from allergic rhinitis promote DC maturation through miR-142-5p/CDK5/STAT3 pathway. J Inflamm Res 15:3187–3205
Drazdauskaitė G, Layhadi JA, Shamji MH (2020) Mechanisms of allergen immunotherapy in allergic rhinitis. Curr Allergy Asthma Rep 21:2
Fu YD, Huang MJ, Guo JW, You YZ, Liu HM, Huang LH et al (2020) Targeting histone demethylase KDM5B for cancer treatment. Eur J Med Chem 208:112760
Metzler VM, de Brot S, Haigh DB, Woodcock CL, Lothion-Roy J, Harris AE et al (2023) The KDM5B and KDM1A lysine demethylases cooperate in regulating androgen receptor expression and signalling in prostate cancer. Front Cell Dev Biol 11:1116424
Zhang SM, Cai WL, Liu X, Thakral D, Luo J, Chan LH et al KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements. Nature 202;598:682–687
Ferro F, Elefante E, Luciano N, Talarico R, Todoerti M (2017) One year in review 2017:novelties in the treatment of rheumatoid arthritis. Clin Exp Rheumatol 35:721–734
Nguyen HCB, Adlanmerini M, Hauck AK, Lazar MA (2020) Dichotomous engagement of HDAC3 activity governs inflammatory responses. Nature 584:286–290

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Mitochondrial Dysfunction and Programmed Cell Death in Allergic Rhinitis: Potential Biomarkers and Therapeutic Targets

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Data source

Differential expression and functional enrichment analyses

Establishment of the protein-protein interaction (PPI) network

Identification of biomarkers for AR

Chromosomal localization and correlation analysis for biomarkers

Gene Set Enrichment Analysis (GSEA) analysis

Immune infiltration analysis

Prediction of the transcription factors (TFs) and drugs

Statistical analysis

Results

A total of 50 differentially expressed MitoRGs-PCDRGs were identified for AR

Differentially expressed MitoRGs-PCDRGs were associated with apoptosis

Four biomarkers with diagnostic value for AR were identified

Strong correlations exist among biomarkers

Exploration of potential biological functions of biomarkers

Disturbances in the immune microenvironment were associated with AR

Regulatory network and drugs might contribute to the treatment of AR

The consistent expression trends of biomarkers were existed in GSE75011 and GSE46171 datasets

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1