Identification of mitochondria-related biomarkers in childhood allergic asthma

doi:10.21203/rs.3.rs-3787068/v1

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Research Article

Identification of mitochondria-related biomarkers in childhood allergic asthma

https://doi.org/10.21203/rs.3.rs-3787068/v1

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Background

The mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS.

Methods

This research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed.

Results

A total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples.

Conclusion

NDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

mitochondria-related genes

childhood allergic asthma

GEO

Asthma is a chronic airway disease characterized by eosinophilic inflammation of the airways, increased airway hyperresponsiveness (AHR), transformation of goblet cells, structural changes in the airways, elevated levels of Immunoglobulin E (IgE) levels, and reversible obstruction of airflow during exhalation [1]. Many environmental stimuli and genetic factors are known to contribute to the development of asthma [2, 3]. As a prevalent disease, asthma imposes a significant economic burden on society [4], patients, and their families. The diagnosis of childhood allergic asthma (CAS) primarily relies on patient’s medical history, clinical symptoms, and immunological examination, which has certain limitations [5] In recent decades, there has been a global increase in the incidence and mortality rate of asthma worldwide [6]. While currently available anti-asthma drugs are effective in controlling the clinical symptoms of asthma [7], they do not stop the natural progression of the disease. Therefore, it is necessary to carefully study the factors related to the progression of asthma in order to develop new therapeutic drugs for the treatment of CAS. About 50% of preschoolers experience wheezing at least once [8], but only a small number of children who wheeze go on to develop asthma. Currently, there is no "gold standard" for diagnosing CAS in children under the age of six. Therefore, the early detection of children who are at a high risk for asthma in CAS is critical. Notably, the early screening and effective prevention of high-risk CAS from a bioinformatics perspective will be of great significance for the occurrence and development of CAS. Given the extensive development of asthma research, asthma is now considered to be a complex and heterogeneous disease linked to mitochondrial genes [9, 10].

Mitochondrion (MT) is a well-known double-membrane organelle that is the power source for cells to produce energy in the form of ATP through oxidative phosphorylation (OXPHOS) in the electron transport chain [11]. Any change in the state within the mitochondria disrupts the OXPHOS mechanism, resulting in the accumulation and production of ROS [12], which in turn leads to mitochondrial dysfunction, forming a vicious cycle of cell damage and death [13]. Many studies [14, 15] have shown that MT play a crucial role in providing intermediates of oxidative metabolism through arginine/ornithine and may have an important influence on the signal transduction of airway inflammation. In addition, polymorphisms or haplotype differences in the mitochondrial genome have been found to be associated with asthma in humans [16]. The mitochondrial genome encodes the core catalytic peptides that form the main proteins in the electron transport chain [9]. Some studies [9, 17] have reported a link between allergic diseases and mitochondrial function. For example, MT provide intermediates for oxidative metabolism through arginine/ornithine Furthermore, they may have an important influence on signal transduction of airway inflammation. The pathophysiology of asthma is closely related to mitochondrial dysfunction [18], which leads to decreased ATP synthase activity, increased oxidative stress, apoptosis induction and abnormal calcium homeostasis [19–21]. Given the increasing incidence of CAS over the years, there is limited data on the role of mitochondria in mediating or regulating the effects of airway inflammation. No reports of a joint study between mitochondria-related genes (MRGs) and CAS have been found. Therefore, targeting mitochondrial dysfunction in the treatment of such diseases is currently promising and can be pursued as a research direction.

In this study, the public data of GEO database GSE40888 dataset was used to further screen the candidate characteristic genes for MRGs in CAS. We employed difference analysis, the lasso algorithm, and XGBoost algorithm. The genes obtained by the two algorithms were crossed to obtain four key genes: NDUFAF7, MTIF3, MRPS26, and NDUFAF1. We further explored four key genes, including expression correlation analysis and GSEA analysis, immune infiltration analysis, and inflammatory index analysis. In particular, the expression of MTIF3 gene affected mitochondria-related functions, which negatively affected the immune system response to asthma, mainly in the abundance of B cell naive cells. It also positively affected the inflammatory response in asthma patients, suggesting that it may be able to reduce inflammation. It has influenced the onset and progression of CAS and could potentially be a novel approach to diagnosing and treating CAS.

Data source

Two CAS-related datasets (GSE40888 and GSE40732) were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds). GSE40888 includes expression data from 41 blood samples of CAS and 86 blood samples of control samples. GSE40732 includes expression data from 97 blood samples of CAS and 97 blood samples of control samples. Additionally, 1136 MRGs were retrieved from the MitoCarta3.0 Database [22].

Differential expression analysis

Differential expression analysis was performed between CAS and control samples using the limma R package [23] in the GSE40888 dataset to screen differentially expressed genes (DEGs) by setting |logFC| > 0.5 and P.adj < 0.05. To better visualize the differences in gene expression among the two groups, the R packages ggpubr and ggplot2 [24] were used to plot volcano and heat maps of DEGs respectively. Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. In addition, Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DE-MRGs were completed using the clusterProfiler package [25] to explore the functions of it.

Construction of protein protein interactions (PPI) network and Acquisition of feature Genes

To explore whether interactions existed among the DE-MRGs, a protein protein interactions (PPI) network was created using search tool for recurring instances of neighbouring genes (STRING, https://string-db.org). The CytoHubba plug-in in Cytoscape software was used to perform Maximal Clique Centrality (MCC) algorithms to obtain the degree ranking of each node in the above PPI network. The top 10 genes in the degree ranking were defined as feature genes.

Acquisition of hub genes and Construction of Alignment Diagram

To obtain valuable diagnostic genes, least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) based on feature genes were performed. The LASSO was completed by R package glmnet [26]. The XGBoost was completed by R package xgboost. The intersection of the diagnostic genes obtained by the two algorithms was defined as hub genes. Next, analysis of the expression trend of hub genes in CAS and control samples from GSE40888 and GSE40732 were finished. Moreover, to explore the potential mechanism of hub genes, Gene Set Enrichment Analysis (GSEA) for hub genes was conducted with ClusterProfiler package [25] in GSE40888.

Alignment diagram of hub genes were constructed in GSE40888 using the rms package [27] in R. The predictive power of the alignment diagram was assessed using calibration curves and decision curves. The closer the area under curve (AUC) of the receiver operating characteristic (ROC) curve was to 1, the more accurate of the diagnosis of the alignment diagram was.

Immuno-microenvironmental analysis and inflammatory microenvironmental analysis

The immune abundance of 22 immune cells in the CAS and control samples of the GSE40888 was calculated using the CIBERSORT algorithm [28]. After finding differentially expressed (DE)-immune cells between CAS and control samples, spearman correlations among the hub genes and between hub genes and DE-immune cells were calculated. A total of 200 inflammation-related genes (IRGs) were obtained from the HALLMARK_INFLAMMATORY_RESPONSE pass-through in the Molecular Signatures Database (MSigDB). The single sample gene set enrichment analysis (ssGSEA) of IRGs in all samples of the GSE127487 was conducted using the gsva [23] package in R to get the score of IRGs. Finally, the correlation between IRGs scores and hub genes was calculated.

Construction of TF-miRNA-mRNA regulatory networks

To understand the regulatory role of hub genes in the pathogenesis of CAS, potential target miRNAs of hub genes were predicted by the miRNet database (https://www.mirnet.ca/miRNet/home.xhtml), the NetworkAnalyst database (https://www.networkanalyst.ca) was used to predict the transcription factors (TFs) that could interact with hub genes. Finally, based on the above miRNAs and TFs, the TF-miRNA-mRNA network was mapped using Cytoscape software.

Quantitative real-time polymerase chain reaction (qRT‒PCR)

To further explore the role of hub genes in CAS, the expression of hub genes was validated by qRT-PCR. Totally 10 clinical blood samples in this experiment were obtained from normal and CAS patients in the Second People’s Hospital of Hefei including 5 normal and 5 CAS samples. Total RNA was prepared from blood samples using TRIZol reagent. Reverse transcription was performed using the SureScript-First-strand-cDNA-synthesis-kit to obtain cDNAs. The qRT-PCR was conducted as follows: totally 40 cycles, 95°C for 1 minutes, 95°C for 20 seconds, 55°C for 20 seconds, and 72°C for 30 seconds. GAPDH was used as the internal reference gene of hub genes. The qPCR primers were shown in Table 1. The relative expression levels of hub genes were calculated by 2^−ΔΔCt method.

Table 1

The information of primer sequences in quantitative real-time polymerase chain reaction (qRT-PCR)
Primer	Sequence
NDUFAF1 F	TCTTCTATAGGATTCACCTTGGCTG
NDUFAF1 R	AGCCTTGGGTTAAGCTCTGG
MTIF3 F	CAAAAGCCTTTAGTACCGCTGA
MTIF3 R	TGTTTCCCAAATCATTGCCCT
MRPS26 F	GCTGGCTATGTGCAGAGACTT
MRPS26 R	CCTCTTCCTCCGGGTCTTTG
NDUFAF7 F	TCCAGCCAAGCTACTAGGTAT
NDUFAF7 R	CGCTCTAACGGGACCTTCTC
GAPDH F	CGAAGGTGGAGTCAACGGATTT
GAPDH R	ATGGGTGGAATCATATTGGAAC

Statistical Analysis

Limma was used to identify DEGs. Venn was used for multiple gene sets to take intersections. LASSO and XGBoost were used to screen for hub genes. The ssGSEA for calculation of proportions of immune cells. GSEA was used to analyze the biological pathways in hub genes that may be involved. Statistical analysis was carried out through R software (https://www.r-project.org/). Differences between groups were analyzed via the Wilcox test. P < 0.05 represented a significant difference.

DE-MRGs were associated with mitochondrial transcription

A total of 1505 DEGs were obtained from the GSE40888, and 877 DEGs were upregulated and 628 DEGs were downregulated in CAS (Fig. 1A-B). A total of 44 DE-MRGs were obtained by overlapping the 1505 DEGs and 1136 MRGs (Fig. 1C). Functional enrichment analysis revealed that DE-MRGs were associated with mitochondrial gene expression, mitochondrial RNA metabolic process, and mitochondrial transcription in GO entries (Fig. 1D), and enriched in propanoate metabolism, ferroptosis, and pyruvate metabolism in KEGG entries (Fig. 1E).

Four genes ( NDUFAF7, MTIF3, MRPS26 and NDUFAF1) were identified as hub genes

To explore whether interactions existed between the 44 DE-MRGs, a PPI network includes 35 nodes and 60 edges was created (Fig. 2A, Confidence = 0.4), there were strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. The top 10 genes (MTIF3, MRPS18C, MRPL44, MRPS26, MRPS31, SSBP1, NDUFAF7, NDUFA8, COX6C, and NDUFAF1) in the degree ranking of MCC algorithms were defined as feature genes (Fig. 2B). The results of the LASSO regression analysis suggested that when λ = 0.033, six model genes with regression coefficients that are not penalized to 0 are obtained after tenfold cross-validation (Fig. 2C-D). Five model genes were obtained from the XGBoost model, and histograms of gene coefficients were plotted with larger Gini coefficients indicating the greater importance of the gene (Fig. 2E). Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of the model genes of the above two models (Fig. 2F).

Hub genes were closely related to pyrimidine metabolism

The results of GSEA suggested that the MRPS26 was enriched in pyrimidine metabolism, mismatch repair, and homologous recombination in KEGG terms. MTIF3 was enriched in mismatch repair, butanoate metabolism, and lysosome in KEGG terms. NDUFAF7 was enriched in glycolysis gluconeogenesis, pyrimidine metabolism, and mismatch repair in KEGG terms. NDUFAF1 was enriched in spliceosome, mismatch repair, and pyruvate metabolism in KEGG terms (Fig. 3A). In addition, the expression validation results of the hub genes suggested that NDUFAF7 was down-regulated in CAS samples and showed consistent trends in the GSE40888 dataset and GSE40732 dataset, with other genes to be verified in further experiments. (Fig. 3B, P < 0.05).

Hub gene-based alignment diagram showed good diagnostic performance

Based on the results of the logistic regression model, an alignment diagram was constructed, and the score of each sample was calculated by the alignment diagram, the higher the total score of the patient, the higher the likelihood that the patient will develop CAS disease (Fig. 4A). The calibration curve and decision curve suggested that the ROC of the model was 0.840, indicating that the alignment diagram possesses good diagnostic efficacy (Fig. 4B).

MTIF3 was strongly negatively correlated with B cells naive

Immune cells with a cell abundance of 0 in more than 75% of the samples were filtered out, leaving 15 immune cells, and significant differences in the expression of B cells naive were observed between CAS and control groups (Fig. 5A). The results of the correlation analysis between hub genes and DE-immune cells suggested that MTIF3 is the most strongly negatively correlated with B cells naive (Fig. 5B, Cor=-0.5, P < 0.0001). The results of the correlation analysis among hub genes suggested significant correlations between NDUFAF7 and MRPS26 (Cor = 0.38, P < 0.001), NDUFAF7 and NDUFAF1 (Cor = 0.72, P < 0.001), and MRPS26 and NDUFAF1 (Cor = 0.50, P < 0.0001) (Fig. 5C). The results of the correlation analysis between Hub genes and the score of IRGs suggested that MTIF3 correlated significantly positively with IRGs score (Cor = 0.47) and MRPS26 correlated significantly negatively with IRGs score (Cor=-0.32) (Fig. 5D). A total of 25 TFs and 55 miRNAs that may interact with hub genes were obtained, and a TF-miRNA-mRNA network was constructed (Fig. 5E). There were some relationships existed such as NDUFAF1 acting on hsa-mir-499a-3p by influencing E2F1.

Hub genes were confirmed by qRT-PCR

The qRT-PCR results showed that the expression levels of four genes (NDUFAF1, MTIF3, MRPS26 and NDUFAF7) in the CAS group were significantly lower compared with the normal group (Fig. 6).

At present, there have been few studies on asthma and mitochondria-related genes. Therefore, the aim of our research is of great significance to as we explore mitochondrial related biomarkers in CAS. In this study, we investigated the hub genes (NDUFAF7, MTIF3, MRPS26, NDUFAF1) that can be used for accurate diagnosis of CAS. Significantly, we detected the expression of each hub gene and obtained the corresponding score. The incidence of CAS could be determined based on the total score.

Over-expression of the target protein amyloid precursor protein (APP) gene caused by exon variants of NDUFAF7 is associated with the risk of Alzheimer's disease [29]. Mammalian mitochondrial ribosomal protein MRPS26 expression levels are correlated with tumor purity in non-small cell lung cancer (NSCLC). MRPS26 has been observed to participate in mitochondrial activity of muscle stem cells [30]. MTIF3 encodes 29 kDa proteins that promote the formation of initiation complexes on mitochondrial 55S ribosomes and play an active role in translation initiation. The MTIF3 gene may influence Parkinson's disease by causing mitochondrial dysfunction [31]. In our study, in the GSE40888 dataset, the expression of MTIF3 in the CAS sample was lower than that in the normal sample. This suggested that, similar to Parkinson's disease, MTIF3 may trigger CAS by causing mitochondrial dysfunction. A Study [32] has demonstrated that NDUFAF1 is an crucial mitochondrial protein involved in complex I assembly and stabilization, with important functions in electron transport and proton pumping. Expression levels of NDUFAF1 in two central nodes involved in epidermal growth factor (EGF) and Olfactomedin 4 (OLFM4) have been associated with acute kidney injury and septic shock [33]. It is suggested that NDUFAF1 is involved in the cellular inflammatory response pathway.

Some scholars have found that [14] MT function also has a positive effect on the inflammatory response of asthma patients, suggesting that inflammatory response may affect the occurrence and progression of asthma. Wu et al. [34] found that an iron chelator could relieve allergic airway inflammation. CAMKK2 and CISD1 are key suppressors of iron-induced cell death in asthma. CAMKK2 is down-regulated in patients with asthma. Interestingly, the negative correlation between CISD1 gene expression and Tregs suggests a potential role of CISD1 in regulating immune cell infiltration [35]. Tregs are viable targets of airway allergic inflammatory responses and play an integral role in maintaining immune tolerance in asthma [36]. By up regulating immunosuppressive molecules and suppressor genes, the Tregs portion of CD4 T cells prevents the development of pro-inflammatory activity and reduces inflammation [37, 38]. In our study, an analysis to examine the correlation between the hub gene and immune cells. The results revealed that the immune system's response to asthma is negatively affected, mainly due to the high the abundance of B-cell naive cells. MTIF3 had the strongest negative correlation with B cell naive. The activation of B cells in the airway during the inflammatory response is quite different from the classical B cell activation observed in secondary lymphoid organs, and has been demonstrated [39]. In addition, the expression of the MTIF3 and MRPS26 genes negatively affects the immune system's response to asthma, impacting mitochondrial-related functions.

Lin et al. [40] showed that CAMKK2 participated in mitochondrial autophagy and promoted iron death. Autophagy may play a key role in chronic airway inflammation [41]. Increased autophagy plays an important role in airway remodeling, extracellular matrix deposition and fibrosis in asthma [42]. In addition, genetic mutations in autophagy genes were associated with asthma [43]. Particulate matter 2.5 (PM2.5) has been reported [44] to drive mitochondrial autophagy and induce cell cycle arrest and senescence. Our KEGG enrichment results indicate that differentially expressed mitochondria-related genes are associated with thermogenesis, mitochondrial phagocytosis - animal, diabetic cardiomyopathy, citric acid cycle (TCA cycle), propionic acid metabolism, Parkinson's disease, oxidative phosphorylation, nonalcoholic fatty liver, valine, leucine, and isoleucine degradation. Additionally, these genes are correlated with chemo-9 - Carcinogenic effects - reactive oxygen species and other pathways. GSEA enrichment analysis based on KEGG gene set showed that the enrichment results of four hub genes were enriched in a total of one cell cycle pathway. Four hub genes regulate the occurrence of autophagy through some mitochondrial signaling pathways, resulting in cell cycle arrest and cell senescence, which contribute to the development of asthma phenotype. This is in line with some studies suggesting that cell senescence in the lung may be an important risk factor for asthma [45, 46]. And mitochondrial dysfunction has been shown to drive premature cellular aging, consistent with its impact on airway diseases [47, 48].

This study is similar to previous research protocols, but is different in that it is a biomarker related to mitochondrial function in CAS, simple and straightforward. The results of this study were obtained from a dataset of blood samples. The chronic inflammation, vasoconstriction, and hyperresponsiveness of CAS often lead to systemic inflammatory changes. Therefore, it is possible to collect blood samples of CAS. Screening out dysregulated mitochondria could be a promising way to prevent or stop the development of these chronic lung diseases. The clinical application of the results of bioinformatics analysis requires more sample data support and further clinical experiments. It also shows that we will continue to pay attention to the role of these genes.

In this work, the study identified four biomarkers (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) related to mitochondrial function in CAS using bioinformatics analysis and conducted correlation analysis to explore the mechanism of action, which provides some reference for further research on CAS.

MRGs

mitochondria-related genes

CAS

childhood allergic asthma

DEGs

differentially expressed genes

DE-MRGs

differentially expressed MRGs

PPI

protein-protein interactions

MCC

Maximal Clique Centrality

LASSO

least absolute shrinkage and selection operator

XGBoost

EXtreme Gradient Boosting

TFs

transcription factors

GSEA

Gene Set Enrichment Analysis

AHR

airway hyperresponsiveness

IgE

Immunoglobulin E

Mitochondrion

OXPHOS

oxidative phosphorylation

ROS

reactive oxygen species

GEO

Gene Expression Omnibus

Gene ontology

KEGG

Kyoto encyclopedia of genes and genomes

AUC

area under curve

ROC

receiver operating characteristic

MSigDB

Molecular Signatures Database

IRGs

inflammation-related genes

ssGSEA

single sample gene set enrichment analysis

APP

amyloid precursor protein

NSCLC

non-small cell lung cancer

EGF

epidermal growth factor

OLFM4

Olfactomedin 4

PM2.5

particulate matter 2.5

TCA cycle

tricarboxylic acid cycle.

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of the Second People's Hospital of Hefei (Ethics approval number: 2023-Research-126). All experiments were conducted in accordance with the relevant guidelines and regulations of the Ethics Committee of the Second People's Hospital of Hefei, and the participants have provided informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed in the current study are available from the GEO database (https://www.ncbi.nlm.nih.gov/gds) [GSE40888 and GSE40732], the miRNet database (https://www.mirnet.ca/miRNet/home.xhtml), the NetworkAnalyst database (https://www.networkanalyst.ca).

Competing interests

The authors declare that they have no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions

ZW was mainly responsible for case screening, specimen collection and article writing, FHJ was responsible for data analysis, and WT was responsible for determining specific gene expression levels. All authors have read and approved the final manuscript.

Acknowledgements

The authors state that the study was conducted in the absence of any business or financial relationships with potential conflicts of interest. All statements expressed in this article are those of the authors only and do not necessarily represent the statements of their affiliated organizations or publishers, editors and reviewers. The experimental data in this paper may have errors due to the small sample size, and the later research can obtain more informative data under the research of multi-center big data.

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No competing interests reported.

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Identification of mitochondria-related biomarkers in childhood allergic asthma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Data source

Differential expression analysis

Construction of protein protein interactions (PPI) network and Acquisition of feature Genes

Acquisition of hub genes and Construction of Alignment Diagram

Immuno-microenvironmental analysis and inflammatory microenvironmental analysis

Construction of TF-miRNA-mRNA regulatory networks

Quantitative real-time polymerase chain reaction (qRT‒PCR)

Statistical Analysis

Results

DE-MRGs were associated with mitochondrial transcription

Hub genes were closely related to pyrimidine metabolism

Hub gene-based alignment diagram showed good diagnostic performance

Hub genes were confirmed by qRT-PCR

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1