Construction of a panoramic mRNA map of adult noncystic fibrosis bronchiectasis and a preliminary study of the underlying molecular mechanisms

Abstract

Background

The pathogenesis of noncystic fibrosis bronchiectasis in adults is complex, and the relevant molecular mechanisms remain unclear. In this study, we constructed a panoramic map of bronchiectasis mRNA, explored the potential molecular mechanisms, and identified potential therapeutic targets, thus providing a new clinical perspective for the preventive management of bronchiectasis and its acute exacerbation.

Methods

The mRNA profiles of peripheral blood and bronchiectasis tissues were obtained through transcriptome sequencing and public databases, and bioinformatics methods were used to screen for differentially expressed genes (DEGs). The DEGs were then subjected to biological function and pathway analyses. Some DEGs were validated using a real-time quantitative polymerase chain reaction (RT-qPCR) in peripheral blood. Spearman’s correlation analysis was used to analyse the correlation between DEGs and clinical indicators.

Results

Based on transcriptome sequencing and public databases, the mRNA profile of bronchiectasis was determined. DEGs were obtained from the peripheral blood sequencing dataset (985 DEGs), tissue sequencing dataset (2919 DEGs), and GSE97258 dataset (1083 DEGs). Bioinformatics analysis showed that upregulated DEGs had enriched neutrophil-related pathways, and downregulated DEGs had enriched ribosome-related pathways. RT-qPCR testing confirmed the upregulated expression of VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 in bronchiectasis. These genes were related to many clinical parameters, such as neutrophils, C-reactive protein, and procalcitonin (P < 0.05).

Conclusions

Transcriptomic methods were used to construct a panoramic map of bronchiectasis mRNA expression. The findings showed that neutrophil activation, chronic inflammation, immune regulation, impaired ribosomal function, oxidative phosphorylation, and energy metabolism disorders are important factors in the development of bronchiectasis. VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 may play important roles in the pathogenesis of bronchiectasis and are potential therapeutic targets.

Background

Bronchiectasis is a chronic disease with an unknown aetiology and very complex pathogenesis. It is mainly caused by the abnormal and persistent dilation of the bronchi [1]. The symptoms include large amounts of sputum, a chronic cough, and intermittent haemoptysis, characterized by irreversibility and progressive destruction [2]. Bronchiectasis is a progressive disease, and its occurrence is closely related to inflammation in the lungs and airways and the colonization of airway bacteria [3]. The incidence of bronchiectasis is increasing globally, and the associated medical costs make bronchiectasis a major health problem [4, 5]. Due to the lack of effective treatments, the current treatment strategies for bronchiectasis mainly focus on controlling symptoms, reducing risks, reducing the number of acute exacerbations, and slowing down disease progression [5, 6]. Therefore, a comprehensive, effective approach for the treatment of bronchiectasis is still needed. Flume et al. stated that each pathophysiological step of bronchiectasis can cause other pathophysiological changes; they are related and impact each other [7, 8]. This theory indicates that antibiotic treatment alone is unlikely to prevent the myriad of pathophysiological processes that lead to airway damage, although antibiotics are key components of bronchiectasis treatment. In this study, we constructed a panoramic map of bronchiectasis mRNA, explored the potential molecular mechanisms involved, and identified potential targets for treatment, thus playing an important role in managing bronchiectasis and preventing its acute exacerbation.

Methods

Patients and clinical sample collection

We collected peripheral blood and tissue samples from patients and healthy people at the First Affiliated Hospital of Guangxi Medical University. We first collected the peripheral blood of five patients with bronchiectasis and four healthy people who underwent physical examinations between July 2021 and October 2021, and performed RNA sequencing on these samples. Between October 2021 and July 2022, tissue samples for RNA sequencing were collected from five bronchiectasis patients requiring surgery and four non-bronchiectasis patients undergoing lung space-occupying surgical resection, and the clinical information of the research subjects is shown in Supplementary Table S1. Between July 2021 and December 2022, peripheral blood samples were collected from 80 patients with bronchiectasis and 38 healthy people who underwent physical examinations, and the clinical information of the research subjects is shown in Supplementary Table S2. These samples were tested via a real-time quantitative polymerase chain reaction (RT-qPCR). RNA was extracted from the peripheral blood and tissue samples at a low temperature and stored in a refrigerator at − 80 °C. An RNA library was constructed according to NE Next^® Ultra™ RNA Library Prep Kit for Illumina^®. Illumina PE150 sequencing was then performed on the Illumina Novases 6000 platform. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University, and informed consent was obtained from all subjects.

Collection of public high-throughput datasets

In addition to internal experiments, the Array Express, Sequence Read Archive, Gene Expression Omnibus Oncoming, PubMed, and Google Scholar databases were searched for literature on bronchiectasis. The search term was "Bronchiectasis AND Homo sapiens" (Supplementary Fig. S1). The inclusion criteria for the dataset were as follows: (1) noncystic fibrosis bronchiectasis; (2) the sample sources were tissue or peripheral blood; (3) the number of samples in the bronchiectasis and control groups was no less than 3; (4) the dataset contained the expression data of at least 100 genes. The exclusion criteria were as follows: (1) nonhuman research; (2) the sample source was a cell line.

Screening of differentially expressed genes

After sorting and cleaning the data, the edgeR package in R language (version 4.1.0) was used to screen out differentially expressed genes (DEGs). Difference fold Log₂ Fold change ≥ 1.2 and P < 0.05 indicated upregulated genes, and difference fold Log₂ Fold change ≤ 0.8 and P < 0.05 indicated downregulated genes.

Enrichment analysis of DEG functions and pathways

The Cluster Profiler package in R was used to perform a gene ontology (GO) analysis of the DEGs. Gene ontology includes analysing the biological process (BP), cellular components (CC), and molecular functions (MF) in three parts. An analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) was also performed.

RT-qPCR to verify DEG expression

RNA was extracted from the peripheral blood samples after low-temperature centrifugation and stored in a refrigerator at − 80 °C. cDNA was synthesized using a PrimeScriptTMRT reagent with gDNA eraser reverse transcription kit purchased from Takara Company. A TB Green Premix Ex Taq TMII kit purchased from Takara Company was used to perform the RT-qPCR. The NCBI online tool Primer-BLAST was used for primer design. The internal reference was β-actin, F: 5ʹ-CCTGGCACCCAGCACAAT-3ʹ; R: 5ʹ-GGGCCCGGACTCGTCATAC-3ʹ.

Statistical analysis

SPSS 25.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis. In the descriptive analyses, mean ± standard deviation (SD) values were used for normally distributed continuous variables, and median ± interquartile range (IQR) values were used for continuous variables with skewed distributions. Data involving homogeneity of variance with and without a normal distribution were compared using the Student’s t-test, while non-normally distributed continuous data were compared using the Mann–Whitney U test. Statistical significance was considered at P < 0.05.

Results

Acquisition of DEGs

DEGs were obtained from the peripheral blood sample sequencing, tissue sample sequencing, and gene chip datasets. Specifically, 985 DEGs were obtained from the peripheral blood sequencing dataset, including 444 upregulated DEGs and 541 downregulated DEGs, and 2919 DEGs were obtained from the tissue sequencing dataset, including 1437 DEGs with upregulated expression and 1482 DEGs with downregulated expression. The DEGs were then subjected to a cluster analysis; a heat map and volcano plot revealed good intragroup consistency between the bronchiectasis and control groups, and the differences between the groups were obvious (Figs. 1, 2). In the public database, only the GSE97298 chip data met the inclusion and exclusion criteria for this study. A total of 27 patients with a definite diagnosis of bronchiectasis and nine healthy controls in GSE97298 were selected for the DEG analysis. The final sample comprised 1083 DEGs, including 383 upregulated and 700 downregulated genes for further clarity.

Fig. 1

Heat and volcano maps of the DEGs found via blood sequencing. A Heat map of the DEGs. B Volcano map of the DEGs. Green: low expression; red: high expression

Fig. 2

Heat map and volcano map of the DEGs obtained via tissue sequencing. A Heat map of the DEGs. B Volcano map of the DEGs. Blue: low expression; red: high expression

Enrichment analysis of DEG functions and pathways

GO and KEGG analyses were performed on the DEGs based on the peripheral blood sequencing data, and the analysis results were sorted according to the corrected P values (Figs. 3, 4). The top five pathways in the BP analysis of upregulated DEGs were defence response to virus, defence response to symbiont, type I interferon signalling pathway, cellular response to type I interferon, and response to virus. The top five pathways in the KEGG analysis were influenza A, hepatitis C, osteoclast differentiation, toll-like receptor signalling pathway, and cytokine–cytokine receptor interactions. The top five pathways in the BP analysis of downregulated DEGs were SRP-dependent cotranslational protein targeting the membrane, cotranslational protein targeting the membrane, nuclear-transcribed mRNA catabolic process, protein targeting the endoplasmic reticulum and establishment and protein localization to endoplasmic reticulum. The top five pathways ranked by the KEGG pathway analysis were ribosome, coronavirus disease—COVID-19, malaria, and haematopoietic cell lineage.

Fig. 3

Analysis of GO and KEGG pathways based on the upregulated DEGs obtained from blood sequencing. A BP analysis of GO. B MF analysis of GO. C CC analysis of GO. D KEGG analysis. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. BP, biological process. CC, cellular component. MF, molecular function

Fig. 4

Analysis of GO and KEGG pathways based on the downregulated DEGs obtained from blood sequencing. A BP analysis of GO. B MF analysis of GO. C CC analysis of GO. D KEGG analysis. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. BP, biological process. CC, cellular component. MF, molecular function

Based on the tissue sequencing data, GO and KEGG analyses were performed on the DEGs (Figs. 5, 6). The top five outcomes in the BP analysis of upregulated DEGs were T cell activation, regulation of leukocyte activation, immune response regulation cell surface receptor signalling pathway, immune response-activating cell surface receptor signalling pathway, and leukocyte migration. The top five outcomes in the KEGG analysis were the haematopoietic cell lineage, primary immunodeficiency, chemokine signalling, and cytokine–cytokine receptor interaction pathways. The top five outcomes in the BP analysis were protein targeting to the endoplasmic reticulum, SRP-dependent cotranslational protein targeting the membrane, cotranslational protein targeting the membrane, establishment of protein localization to the endoplasmic reticulum, and protein localization to endoplasmic reticulum. The top five ranked by KEGG analysis were ribosome, oxidative phosphorylation, chemical carcinogenesis—reactive oxygen species, Parkinson’s disease, and Huntington’s disease.

Fig. 5

Analysis of GO and KEGG pathways based on the upregulated DEGs obtained from tissue sequencing. A BP analysis of GO. B CC analysis of GO. C MF analysis of GO. D KEGG analysis. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. BP, biological process. CC, cellular component. MF, molecular function

Fig. 6

Analysis of GO and KEGG pathways based on the downregulated DEGs obtained from tissue sequencing. A BP analysis of GO. B CC analysis of GO. C MF analysis of GO. D KEGG analysis. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. BP, biological process. CC, cellular component. MF, molecular function

Based on GSE97258, GO and KEGG analyses were performed on the DEGs (Supplementary Fig. S2, Fig. S3). The top five outcomes in the BP analysis of upregulated DEGs were neutrophil activation, neutrophil degranulation, neutrophil activation involved in immune response, neutrophil-mediated immunity, and positive regulation of interleukin-8 production. The top five enriched pathways in the KEGG analysis were the tuberculosis, legionellosis, toll-like receptor signalling, and malaria and yersinia infection pathways. The top five outcomes in the BP analysis of downregulated DEGs were ribonucleoprotein complex biogenesis, ribosome synthesis, SRP-dependent cotranslational protein targeting the membrane, nuclear-transcribed mRNA catabolic process, and nuclear-transcribed mRNA catabolic process. The top five outcomes ranked by the KEGG analysis were ribosome, oxidative phosphorylation, Parkinson’s disease, coronavirus disease—COVID-19, and prion disease.

RT-qPCR detection of DEG expression in the peripheral blood of bronchiectasis patients

For the clinical sample validation of bronchiectasis DEGs, we focused on DEGs with upregulated expression and selected peripheral blood samples that were easily obtained. To obtain reliable results, we selected the VCAN gene, which was upregulated in the GSE97298, peripheral blood sequencing, and tissue sequencing datasets, and the SESTD1 gene, which was upregulated in the GSE97298 and peripheral blood sequencing datasets. In addition, RT-qPCR detection was performed to verify the expression of the genes SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2, which were significantly upregulated in the peripheral blood sequencing dataset. The role of these DEGs in bronchiectasis has not been reported thus far. With β-actin as the internal reference, Primer-BLAST was used to design primers for these DEGs (Table 1). This part involved 80 patients with bronchiectasis and 38 healthy controls; the clinical information of the two groups is shown in Supplementary Table S2. Among them, there were statistically significant differences in body mass index (BMI), leukocyte count, neutrophil percentage, lymphocyte percentage, monocyte percentage, absolute neutrophil value, absolute lymphocyte value, aspartate aminotransferase (AST) level, albumin (ALB) level, and creatinine level between the bronchiectasis and control groups (P < 0.05). The overexpression of VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 in peripheral blood was verified by RT-qPCR, and significant differences in these genes were found between the bronchiectasis and control groups (P < 0.001, Figs. 7, 8, Table 2).

Table 1 Primer sequence

Fig. 7

Expression levels of different DEGs in the bronchiectasis and healthy groups. A Expression of VCAN. B Expression of SESTD1. C Expression of SLC12A1. D Expression of CD177. E Expression of IFI44L. F Expression of SIGLEC1. G Expression of RSAD2. ****P < 0.0001, ***P < 0.001

Fig. 8

ROC curve of DEG expression in the bronchiectasis and healthy groups. ROC, receiver operating characteristic curve. DEGs, differentially expressed genes. All P values < 0.05

Table 2 ROC curve of DEGs expression between bronchiectasis and health group

To analyse the correlation between VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 and the clinical indicators of bronchiectasis, we collected the clinical indicator data of patients with bronchiectasis (Tables 3, 4). The results showed that the VCAN expression level in patients with bronchiectasis was positively correlated with age, platelets, absolute neutrophil value, absolute monocyte value, and AST but negatively correlated with BMI, ALB level, and absolute lymphocyte value. The SESTD1 expression level was positively correlated with AST but negatively correlated with absolute lymphocyte and monocyte values. The SLC12A1 expression level was negatively correlated with the bronchiectasis severity index (BSI), E-FACED score, and absolute eosinophil value. CD177 expression was positively correlated with age, absolute leukocyte count, neutrophil percentage, absolute neutrophil value, BSI, E-FACED, and E-Reiff and negatively correlated with lymphocyte and creatinine percentages. Further, the IFI44L expression level was negatively correlated with ALB, SIGLEC1 expression was positively correlated with AST and negatively correlated with ALB, and RSAD2 expression was negatively correlated with ALB (all P < 0.05). We also examined whether these DEGs were related to inflammation and immune-related indicators (Table 5). The results showed that C-reactive protein (CRP) was positively correlated with VCAN and CD177 expression, and procalcitonin (PCT) was positively correlated with VCAN, CD177, IFI44L, SIGLCE1, and RSAD2 expression. Absolute CD4 count was negatively correlated with IFI44L and SIGLEC1 expression, and T lymphocyte count was negatively correlated with SIGLEC1.

Table 3 Correlations between VCAN, SESTD1, SLC12A1 expression levels and clinical indicators (n = 80)

Table 4 Correlations between the expression levels of CD177, IFI44L, SIGLEC1, RSAD2 and clinical indicators (n = 80)

Table 5 The correlations between CRP, PCT, CD4, T lymphocytes and the expression of DEGs

Discussion

All bodily functions are regulated by genes, and abnormal gene expression is an important factor in the occurrence and development of many diseases. Bronchiectasis is not a disease regulated by a single gene, and no other study has used transcriptomic methods to demonstrate the pathogenesis of bronchiectasis. In this study, mRNA sequencing data and gene chip data were used to construct a panoramic gene expression map of bronchiectasis, conduct functional and pathway enrichment analyses of DEGs, and explore the potential molecular mechanisms of the disease.

Many studies have shown that inflammatory cells involved in immune regulation, such as neutrophils, macrophages, and lymphocytes, play important roles in bronchiectasis [9,10,11]. In this study, the neutrophils, lymphocytes, and monocytes in the bronchiectasis group were significantly different from those in the control group, which confirmed the existing research conclusions. In addition, the neutrophils in the sputum and bronchoalveolar lavage fluid of patients with bronchiectasis constituted the dominant cell type. Neutrophils in the peripheral blood of patients with stable bronchiectasis were higher than those of normal people, and the levels further increased with acute exacerbation [9]. The findings showed that neutrophils are the most important cells in the immune mechanism of bronchiectasis. The upregulated DEGs were mainly concentrated in biological processes such as neutrophil activation, neutrophil-mediated immunity, defence response to viruses, and leukocyte migration and in signalling pathways involving toll-like receptors, chemokines, cell adhesion molecules, and cytokine–cytokine receptor interactions. Neutrophils are the most important cells in the immune mechanism of bronchiectasis. Many of the differential genes upregulated in this study were enriched in pathways related to neutrophils, which proves that neutrophil activation is important in the pathogenesis of bronchiectasis. Toll-like receptors are pattern recognition receptors that participate in the formation of the immune system. Their role is to recognize pathogenic microorganisms and activate immune responses through the corresponding ligands. Bronchiectasis often leads to secondary infection by pathogens due to changes in the tracheal structure. Toll-like receptors recognize these pathogens and activate the downstream factors in the toll-like receptor pathway, resulting in a series of inflammatory immune responses. Thus, the activation of this pathway also plays an important role in the pathogenesis of bronchiectasis.

Multiple ribosome-related genes were seen among the downregulated DEGs, and the functional and pathway analyses showed the enrichment of many downregulated genes in ribosome-related pathways. Ribosomes are important sites for eukaryotic protein synthesis and modification. Ribosomal proteins encoded by ribosome-related genes are involved in regulating various life activities, such as cell growth, reproduction, apoptosis, and gene integrity [12]. When a viral infection occurs, the virus can replicate itself by “hijacking” the host’s ribosomal proteins [13]. Viruses can also damage host ribosomes and comprehensively interfere with host translation [14]. In addition, ribosomal proteins serve as viral receptors that specifically bind to viruses and mediate their invasion of host cells [15]. A large number of ribosome-related genes exhibited downregulated expression in the patients with bronchiectasis, and it is speculated that damage to ribosomal structure and function is involved in the pathogenesis of bronchiectasis. In addition, many genes among the downregulated DEGs are responsible for encoding proteins related to the respiratory chain. The pathway enrichment analysis focused on NADH dehydrogenase; electron transfer activity; oxidative phosphorylation pathways, such as NDUFS5, NDUFA1, COX17, COX5B, and UQCRH; ATP6V1E1, which encodes the mitochondrial membrane respiratory chain NADH dehydrogenase (complex I); ubiquitin-cytochrome C oxidoreductase (complex III); cytochrome C oxidase on the electron transport chain of the inner mitochondrial membrane (complex IV); and adenosine triphosphate (ATP) synthase. Therefore, oxidative phosphorylation and energy metabolism disorders are involved in the development of bronchiectasis.

Oxidative phosphorylation not only provides most of the ATP required for life activities, but also maintains homeostasis of energy metabolism in organisms. Inflammation can cause damage to mitochondrial function and affect oxidative phosphorylation. Further, mitochondria produce large amounts of reactive oxygen species (ROS), and neutrophils also produce ROS when they engulf and kill pathogens [16]. ROS interact with excessive nitric oxide to produce peroxynitrite anions, which cause irreversible degradation of the respiratory electron transport chain, further affecting the process of oxidative phosphorylation [17]. ROS can also increase the expression of inflammatory factors and activate pathways that promote the inflammatory response. Studies have shown that stimulating the airway epithelium with inflammatory substances can weaken mitochondrial respiration and inhibit cell function [18]. ROS production, mitochondrial damage, and oxidative phosphorylation disorders are all related to pathophysiological processes such as apoptosis and autophagy. Therefore, we speculate that oxidative phosphorylation and energy metabolism disorders are important factors that cause bronchiectasis.

For clinical sample verification, we focused on bronchiectasis DEGs with significantly upregulated expression levels, including VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2. VCAN is a member of the proteoglycan family, and the protein it encodes is a major component of the extracellular matrix. The VCAN protein is involved in cell adhesion, proliferation, migration, and angiogenesis, and it plays a central role in tissue morphogenesis and maintenance. An increase in the VCAN protein is usually associated with leukocyte infiltration in the early stages of inflammation. Studies have shown that the VCAN protein directly or indirectly through hyaluronic acid interacts with inflammatory cells, such as CD44, P-selectin glycoprotein ligand-1 (PSGL-1), and toll-like receptors (TLRs) present on the surface of immune and nonimmune cells. VCAN protein also affects inflammation by interacting with various growth factors and cytokines that regulate inflammation, which, in turn, affects its bioavailability and bioactivity [19]. In this study, the expression level of VCAN increased significantly in the bronchiectasis group; it was negatively correlated with nutritional status (BMI and ALB) and lymphocytes, positively correlated with platelets, and positively correlated with neutrophils and monocytes. Considering this, we speculate that high VCAN expression may be related to the infiltration of inflammatory cells, as it can increase VCAN protein levels and recruit more neutrophils and monocytes to participate in the inflammatory response. These are also the main inflammation mechanisms involved in the pathogenesis of bronchiectasis. An increase in VCAN during branch expansion can expand the inflammatory response and cause damage to local tissues, and the high expression of VCAN may be inhibited by an increase in the nutritional index. Therefore, VCAN is a potential biomarker for bronchiectasis; this requires further validation through cohort studies.

SESTD1 encodes a docking protein that binds phospholipids. A study has reported that SESTD1 may act as a negative regulator of the Rac1-Trio8 signalling pathway, reducing dendritic spine density and excitatory synaptic transmission in hippocampal neurons [20]. In addition, SESTD1 may bind to several phospholipids and participate in the virus replication process by regulating calcium channels [21]. Whether SESTD1 is involved in the pathogenesis of bronchiectasis via calcium channel regulation needs to be studied in the future. SESTD1 may be revealed as a biomarker for bronchiectasis, but more cohort experimental studies are needed.

SLC12A1 encodes a kidney-specific sodium–potassium–chloride cotransporter that serves as a membrane-bound channel and plays a major role in various epithelial absorption and secretion processes. A study has found that Gram-negative bacterial infections upregulate the sodium–potassium–chloride cotransporters in lung endothelial and epithelial cells, producing endotoxin and stimulating proinflammatory cytokine production, thereby leading to cell swelling [22]. In our clinical sample verification experiments, the expression level of SLC12A1 was significantly higher in the bronchiectasis group than in the control group, which indicates that SLC12A1 may be involved in chronic inflammation and structural changes in bronchiectasis by regulating the sodium–potassium–chloride cotransporter. Further, SLC12A1 expression was negatively correlated with bronchiectasis severity (BSI, E-FACED score), suggesting that SLC12A1 can predict bronchiectasis severity and provide a reference for clinical prognosis.

CD177 encodes a glycosylphosphatidylinositol-linked surface glycoprotein that plays an important role in neutrophil activation [23] and transport by binding to platelet endothelial cell adhesion molecule-1[24]. The results of this study showed that the expression level of CD177 was significantly higher in the bronchiectasis group than in the control group. The correlation analysis showed that CD177 was positively correlated with BSI, E-FACED, and E-Reiff scores as well as with absolute leukocyte and neutrophil counts. This indicates that CD177 may be involved in the pathogenesis of bronchiectasis by activating and transporting neutrophils and regulating immunity. CD177 can also predict disease severity: the greater the expression of CD177, the more serious the disease and the worse the prognosis. Therefore, CD177 has value and significance in assessing the severity of bronchiectasis.

IFI44L encodes an interferon-induced protein that promotes macrophage differentiation and inflammatory cytokine secretion during bacterial infection [25]. IFI44L can reduce the replication ability of respiratory syncytial virus [26] and plays an important role in antiviral and antibacterial activity [25]. In this study, the expression level of IFI44L was higher in the bronchiectasis group than in the control group. The correlation analysis showed that IFI44L expression was negatively correlated with ALB and CD4 cells and positively correlated with PCT. Thus, we speculate that although an increase in IFI44L can inhibit virus replication and promote macrophage differentiation for antibacterial effects, it also promotes the secretion of inflammatory cytokines. As the expression of IFI44L increases during branch expansion, the inflammatory index PCT also increases; in turn, improvements in nutrition and immune function inhibit IFI44L expression, thereby inhibiting the inflammatory response.

SIGLEC1 encodes a member of the immunoglobulin superfamily. Studies have confirmed that SIGLEC1 expression is upregulated in patients with early-stages or mild viral pneumonia but not significantly upregulated in severe cases, proving that SIGLEC1 plays a protective role in the progression of severe pneumonia [27]. SIGLEC1 knockout mice infected with Mycobacterium tuberculosis have been found to develop a wider range of lesions than wild-type mice, proving that SIGLEC1 plays an immune protective role during infection [28]. In the present study, the expression level of SIGLEC1 was higher in the bronchiectasis group than in the control group. The correlation analysis showed a negative correlation with ALB, CD4, and T lymphocytes and a positive correlation with PCT. Therefore, we speculate that SIGLEC1 is involved in the pathogenesis of bronchiectasis via immunity regulation, but the specific pathway needs further study.

The protein encoded by RSAD2 is an interferon-induced antiviral protein. This protein plays a role in cellular antiviral responses and innate immune signalling. The antiviral effect involves inhibiting viral RNA replication, interfering with the secretory pathway, and binding to viral proteins to cause cellular lipid metabolism disorders. The protein has also been found to inhibit the replication of DNA and RNA viruses, including the influenza virus, human immunodeficiency virus (HIV-1), and Zika virus [29,30,31,32]. In this study, the expression level of RSAD2 was higher in the bronchiectasis group than in the control group, and the correlation analysis revealed a negative correlation with ALB and a positive correlation with PCT. RSAD2 may indicate the severity of bronchiectasis infection; as nutritional status improves, RSAD2 expression decreases, and the inflammatory response is alleviated.

This study has certain limitations. It included a small number of samples and data from a single centre. Follow-up studies need to include larger sample sizes, collect multicentre sample data, and design sophisticated experiments to verify the results.

Conclusion

We used a combination of gene chip and mRNA sequencing data to analyse the DEGs of bronchiectasis. We also performed functional and pathway enrichment analyses and used transcriptomics to construct a panoramic view of mRNA expression in bronchiectasis. The findings of this study revealed that neutrophil activation, chronic inflammation, immune regulation, impaired ribosome function, oxidative phosphorylation, and energy metabolism disorders are important factors in bronchiectasis development. Further, VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 may play important roles in the pathogenesis of bronchiectasis. This study provides new ideas and directions for elucidating the pathogenesis of bronchiectasis.