Abstract
The single-nucleotide polymorphisms of genes related to DNA damage repair and inflammasomes and mutated gene expression in coal workers’ pneumoconiosis (CWP) were analysed to identify the risk factors of CWP and potential biomarkers for early warning and diagnosis. Further, mutated gene pathways were analysed based on proteome and metabolome. Han Chinese male subjects were randomly selected and divided into 4 or 5 groups according to the process of CWP. MassARRAY was used to sequence single-nucleotide polymorphism genotypes. Mutated gene expression in plasma was tested using enzyme-linked immunosorbent assay (ELISA). Odds ratios (ORs) and receiver operating characteristic curves (ROC) were calculated. The serum different proteins and metabolites were identified by Ultra Performance Liquid Chromatography Quadrupole time of flight/Mass Spectrum (UPLC-Q-TOF/MS) and analysed using bioinformation software. As CWP progressed, the CC and CA genotypes of ERCC1 rs3212986 decreased and increased significantly, respectively. AA (OR = 3.016) and CA (OR = 2.130) genotypes were identified as risk factors for stage II. ERCC1 significantly decreased in processing of CWP. The cutoff value of ERCC1 was 5.265 pg/ml, with a sensitivity of 90.0% and specificity of 86.7%. ERCC1 had an indirect interaction with activator protein-1 and insulin and its pathways were mainly made with molecules related to lipid metabolism and actin dynamics. ERCC1 is a candidate biomarker for detection and precise intervention in CWP. If it reaches the threshold, workers will change other jobs in time and will not develop and diagnose as pneumoconiosis and will help the employers spend less money. Meanwhile, the signal molecules of ERCC1 pathway could be as a candidate target for drug discovery.
Introduction
Coal workers’ pneumoconiosis (CWP) is a chronic occupational lung disease caused by the long-term inhalation of coal dust and primarily characterized by pulmonary inflammation and fibrosis progression [1]. China, as the world’s largest labour market with over 775 million workers, currently experiences the largest global health losses from pneumoconiosis [2]. By 2030, significantly reduce the number of deaths and illnesses caused by hazardous chemicals and air, water and soil pollution is as one of WHO SDG project [3]. The Chinese government has carried out the National Plan for the detection and Control of Occupational Diseases (2016–2020) to solve the current key and difficult problems in the detection and control of pneumoconiosis, resolutely curb the momentum of the high incidence of pneumoconiosis, and safeguard the rights and interests of workers’ occupational health, and has formulated the present action project [4]. As one of the most severe occupational diseases in China, CWP currently lacks specific and effective treatments, resulting in significant social and economic burden [5]. The clinical diagnosis of CWP mainly relies on occupational exposure history and abnormal imaging changes. However, as the disease is progressive and incurable, early prevention is particularly crucial [6]. But there are no specific indicators for early detection of pneumoconiosis, Imaging is not suitable for accurate early detection because it is highly subjective and with some lag in early detection. Therefore, obtaining effective early detection biomarkers for pneumoconiosis is important for early intervention of pneumoconiosis.
The pathogenesis of CWP is not fully understood. Inhaled dust particles activate pulmonary macrophages, which produce reactive oxygen species (ROS) and reactive nitrogen species, damaging pulmonary epithelial cells. ROS can also damage DNA, proteins, and cell membranes [7]. Conversely, ROS activate the immune system to produce an inflammatory response, with ROS and inflammation mutually promoting each other [8]. Therefore, the main characteristic of pulmonary fibrosis is persistent lung damage caused by oxidative stress and chronic inflammation [9]. Repeated stimulation of lung tissue by coal dust particles results in persistent inflammatory responses, leading to excessive collagen accumulation and diffuse fibrosis in the lungs [10]. High levels of DNA and chromosomal damage have been observed in coal miners, and patients with CWP are also susceptible to high levels of oxidative DNA damage caused by the long-term inhalation of dust particles [11–14]. Oxidative DNA damage may play a crucial role in the development and progression of CWP, and related DNA damage repair may be essential for determining the disease outcome.
The key molecule ATM rs189037 G/A, which is involved in repairing DNA double-strand breaks, is associated with increasing mutation rates and decreasing gene expression levels during the progression of CWP. It has been identified as a risk factor (GA odds ratio [OR] = 1.22 and AA OR = 1.12) [15], indicating that the ATM protein is suggested to play an important role in the progression of CWP. DNA damage repair in human cells includes six major pathways, including base excision repair (BER), nucleotide excision repair (NER), mismatch repair, homologous recombination, non-homologous end joining, and translesion synthesis [16]. The ERCC1-XPF heterodimer is an essential component of the NER pathway, which can catalyse the 5′ incision of the damaged DNA strand [17]. XRCC1 is a DNA repair scaffold protein that mainly supports BER and is responsible for correcting many minor base damages and single-strand breaks. BER is a highly coordinated process, and XRCC1 depletion can lead to loss of the core BER mechanism. Cells with XRCC1 defects exhibit reduced DNA repair, resulting in the accumulation of persistent DNA damage and increased genomic instability [18, 19]. However, it is unclear whether other single-nucleotide polymorphism (SNP) sites of ATM affect protein function in a similar manner to rs189037 G/A, and whether key molecules in the NER and BER pathways (ERCC1 and XRCC1) play similar roles in disease development to the ATM protein.
The intrinsic connection between oxidative stress response and inflammation suggests that molecules related to the inflammatory response may also have a significant impact on the development and progression of CWP. Inhibiting the inflammasome activation pathway or inflammasome-mediated cytokines can reduce fibrotic reactions in both in vitro and in vivo disease models [20]. Nucleotide-oligomerization domain-like receptor protein 1 (NLRP1), NLRP3, and NLRC4 are the most characteristic inflammasomes. Among them, the NLRP3 inflammasome plays an important role in silica-induced inflammation and fibrosis, and its inhibition results in the elimination of both inflammation and fibrosis [21]. Similar to NLRP3, NLRP1, when assembled, can activate caspase-1 and subsequently process and secrete interleukin (IL)-1β [22]. The CT genotype of NLRP3 rs34298354, Ex4-849C/T, is associated with an increased risk of silicosis (OR = 2.4). Furthermore, carrying the TT genotype of NLRP3 rs1539019 G/T is associated with an increased risk of CWP (OR = 1.39), particularly among smokers (OR = 1.67) [23, 24]. However, it is currently unclear whether NLRP3 and NLRC4 play a role in the development and progression of CWP.
In this study, we selected 14 SNP sites of ATM, ERCC1, XRCC1, NLRP1, NLRP3, and NLRC4 genes based on the minimum allele frequency (typically >0.05) to investigate their association with the development and progression of CWP in Han Chinese male subjects. Epidemiological data of the study subjects were also analysed to identify factors that may affect CWP development. Furthermore, the protein expression levels of mutated genes in plasma were observed to comprehensively evaluate the potential of proteins to act as early warning and diagnostic biomarkers for CWP.
Materials and methods
SNP selection and genotyping of study subjects
According to the method of the target value in the US FDA’s review of medical devices [25, 26] and combined with the actual receipt of samples, of the 554 randomly selected Han Chinese subjects, 131 healthy individuals (the person is not coal workers and is no coal dust exposure.) were included in the healthy group, 140 coal miners were included in the coal dust exposure group, 141 patients with stage I CWP were selected as the stage I CWP group, and 142 patients with stage II CWP were selected as the stage II CWP group. All study subjects were male. Two milliliters of EDTA anticoagulated blood were collected from each participant between January 2020 and December 2022. The Ethics Committee of Guiyang Public Health Clinical Center approved the study (approval no. 201920). All patients provided written informed consent to participate in this study. Epidemiological data, including age, work duration, and smoking, drinking, and medical history, were collected using questionnaires. Patients with other lung diseases, such as tuberculosis and lung cancer, as well as autoimmune diseases, were excluded from this study.
Fourteen SNP sites, including ATM (rs201159454, rs2234997, and rs30928443), ERCC1 (rs3212929 and rs3212986), XRCC1 (rs25489 and rs915927), NLRP1 (rs2301582 and rs12150220), NLRP3 (rs3806268 and rs10802501), and NLRP4 (rs6757121, rs455060, and rs408813), were selected based on previous literature and a minimum allele frequency >0.05. The genotype distribution of these SNP sites all complied with the Hardy–Weinberg equilibrium (P>0.05). Two millilitres of EDTA anticoagulated blood were collected from each study subject, and genomic DNA was extracted from the blood using a blood genomic DNA extraction kit. The genotyping of all study subjects was performed using MassARRAY SNP genotyping technology.
Measurement of ERCC1 in plasma
Whole blood samples (2 mL) of an additional 30 study subjects were collected randomly in EDTA anticoagulant tubes and centrifuged for 5 min at 3,500 rpm to obtain plasma supernatant. The enzyme-linked immunosorbent assay kit was selected based on genotyping information; mutated gene expression levels in the plasma were calculated according to the manufacturer’s instructions.
ERCC1 pathways analysis based on multi-omics
Five groups including healthy group (n = 32), currently receiving dust group (n = 33), previously receiving dust group (n = 32), CWP stage I (n = 34), CWP stage II (n = 26) were constructed. Whole blood samples (5 ml) were collected in heparin anticoagulant tube and centrifuged for 5 min at 3,500 rpm to obtain the plasma supernatant.
Metabolism analysis
Plasma sample containing 10 μL 0.3 mg/mL of 2-chloro-l-phenylalanine as internal standard and150 μL methanol and acetonitrile (2V/1V) was mixed, vortexed, ultrasonicated and precipitated at -20°C for 10 min, and centrifuged at 14000 rpm at 4°C for 10 min. The 150 μL supernatants were used to be analysed by UPLC–Q-TOF/MS (Waters Vion IMS Qtof)).
Proteomic analysis
The samples in each group were selected at random with 10 cases and mixed, and each group was represented by three biological replicates. The total serum samples (n = 15) were analysed.
EasyDeeP Sample Preparation kit (OSFP0002) was used to analysis plasma according to protocols provided. Mainly,100 μL plasma and 1mg beads mixed and incubated at 37°C for 1 h, and beads were washed to obtain the precipitate with wash solution buffer. The precipitate was digested, collected and desalted to obtain the peptides. The peptides were re-dissolved in solvent A (A: 0.1% formic acid in water) and analyzed by by UPLC–Q-TOF/MS (Orbitrap Fusion™ Tribrid™ coupled to an EASY-nanoLC 1200 system (Thermo Fisher Scientific, MA, USA)). The mass spectrometer was run under data independent acquisition (DIA) mode with hybrid data strategy.
Bioinformation analysis
The UPLC–Q-TOF/MS raw data were analyzed by progenesis QI (Waters Corporation, Milford, USA) software. Including m/z, peak RT and peak intensities, and RT–m/z pairs were used as the identifier for each ion. The different metabolites standard was variable important in projection (VIP)>1. KEGG enrichment was analyzed with different metabolites. The pathways overview diagram was drawed using MetaboAnalyst 4.0.
Data of DIA were processed and analyzed by Spectronaut 15.0 (Biognosys AG, Switzerland) with default settings. Spectronaut was set up to search the database of uniprot-homo sapiens (version201907, 20428 entries). The different expressed proteins were selected if their p adj value <0.05 and absolute fold change >1.5. Blast2GO version 5 was used for functional annotation and GOATOOLS was used to perform GO enrichment analysis. Pathway analysis was processed by KOBAS (http://kobas.cbi.pku.edu.cn/). Protein-protein interaction network was constructed by using STRING v10 (www.string-db.org).
Statistical analysis
Statistical analysis was performed using SPSS 23.0 software. Quantitative data that followed a normal distribution are expressed as ±S, and one-way ANOVA was used for comparison. Count data were compared using the χ2 test of the R×C contingency table. Multinomial logistic regression analysis was used to adjust the OR and 95% confidence interval (CI). The optimal cutoff value of the protein marker was determined by receiver operating characteristic (ROC) curve analysis, and the accuracy of the biomarker was evaluated based on the corresponding area under the curve (i.e., >0.700, 0.700–0.900 and >0.900), sensitivity, specificity, and Youden index. P<0.05 was considered significant in all analyses.
Results
Basic status of subjects
A total of 554 subjects were included in this study. The basic clinical information of the study subjects is presented in Table 1.
Table 1. Analysis of basic clinical data of the study population (± s).
| Variable | Group | |||
|---|---|---|---|---|
| Healthy group (n = 131) |
Coal dust exposure group (n = 140) |
Stage I CWP group (n = 141) |
Stage II CWP group (n = 142) |
|
| Age (y) | 31.01±8.45 | 48.96±5.28 | 50.97±9.28 | 54.69±9.57 |
| Work duration (y) (year) | 0 | 19.55±8.11 | 19.30±7.31 | 17.72±7.44 |
| Smoking history: | ||||
| never smoked | 54 (41.2%) | 49 (35.0%) | 69 (48.9%) | 55 (38.7%) |
| occasional smoker | 19 (14.5%) | 13 (9.3%) | 10 (7.1%) | 20 (14.1%) |
| regular smoker | 58 (44.3%) | 78 (55.7%) | 62 (44.0%) | 67 (47.8%) |
| Drinking history | ||||
| never drank | 71 (54.2%) | 69 (49.3%) | 98 (69.5%) | 76 (53.5%) |
| occasional drinker | 59 (45.0%) | 62 (44.3%) | 37 (26.2%) | 54 (38.0%) |
| regular drinker | 1 (0.8%) | 9 (6.4%) | 6 (4.3%) | 12 (8.5%) |
Note: numbers in parentheses indicate the composition percentage (%).
Change in genotypes with CWP progression
The SNP genotypes of ATM (rs201159454, rs2234997, and rs30928443), ERCC1 (rs3212929 and rs3212986), and XRCC1 (rs25489 and rs915927) in the study subjects are listed in Table 2. As CWP developed, we observed a decrease in the CC genotype and an increase in the CA genotype of ERCC1 rs3212986 (P<0.05). However, the genotypes of ATM (rs201159454, rs2234997, and rs30928443), ERCC1 rs3212929, and XRCC1 (rs25489 and rs915927) showed no change with CWP progression (P > 0.05). The SNP genotypes of NLRP1 (rs2301582 and rs12150220), NLRP3 (rs3806268 and rs10802501), and NLRP4 (rs6757121, rs455060, and rs408813) in the study subjects are listed in Table 3. None of the genotypes exhibited any change with CWP progression (P > 0.05).
Table 2. Change in the genotypes of ATM, ERCC1, and XRCC1.
| Gene | SNP | Healthy group | Coal dust exposure group | Stage I CWP group | Stage II CWP group | P | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | |||
| ATM | rs201159454 | * | ||||||||
| TT | 131 | 100 | 140 | 100 | 141 | 100 | 142 | 100 | ||
| rs2234997 | * | |||||||||
| TT | 131 | 100 | 140 | 100 | 141 | 100 | 142 | 100 | ||
| rs3092844 | * | |||||||||
| CC | 131 | 100 | 140 | 100 | 141 | 100 | 142 | 100 | ||
| ERCC1 | rs3212929 | * | ||||||||
| CC | 131 | 100 | 140 | 100 | 141 | 100 | 142 | 100 | ||
| rs3212986 | ||||||||||
| CC | 55 | 42.0 | 60 | 42.9 | 49 | 34.8 | 39 | 27.5 | 0.031 | |
| AA | 22 | 16.8 | 20 | 14.3 | 16 | 11.3 | 30 | 21.1 | ||
| CA | 54 | 41.2 | 60 | 42.9 | 76 | 53.9 | 73 | 51.4 | ||
| XRCC1 | rs25489 | |||||||||
| TT | 2 | 1.5 | 1 | 0.7 | 2 | 1.4 | 2 | 1.4 | 0.993 | |
| CC | 105 | 80.2 | 116 | 82.9 | 116 | 82.3 | 117 | 82.4 | ||
| TC | 24 | 18.3 | 117 | 16.4 | 23 | 16.3 | 23 | 16.2 | ||
| rs915927 | ||||||||||
| CC | 1 | 0.8 | 0 | 0 | 3 | 2.1 | 3 | 2.1 | 0.121 | |
| TT | 106 | 81.5 | 109 | 77.9 | 109 | 77.3 | 97 | 68.3 | ||
| CT | 23 | 17.7 | 31 | 22.1 | 29 | 20.6 | 42 | 29.6 | ||
Note: * indicates that the statistical measure cannot be calculated.
Table 3. Changes in the genotypes of NLRP1, NLRP3, and NLRP4.
| Gene | SNP | Healthy group | Coal dust exposure group | Stage I CWP group | Stage II CWP group | P | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | |||
| NLRP1 | rs2301582 | 0.690 | ||||||||
| TT | 0 | 0 | 1 | 0.7 | 0 | 0 | 0 | 0 | ||
| CC | 125 | 95.4 | 133 | 95.0 | 132 | 93.6 | 136 | 95.8 | ||
| TC | 5 | 4.6 | 6 | 4.3 | 9 | 6.4 | 6 | 4.2 | ||
| rs12150220 | 0.126 | |||||||||
| AA | 125 | 95.4 | 136 | 97.1 | 129 | 91.5 | 137 | 96.5 | ||
| TT | 0 | 0 | 1 | 0.7 | 0 | 0 | 0 | 0 | ||
| AT | 6 | 4.6 | 3 | 2.1 | 12 | 8.5 | 5 | 3.5 | ||
| NLRP3 | rs3806268 | 0.589 | ||||||||
| GG | 31 | 23.7 | 33 | 23.6 | 45 | 31.9 | 40 | 28.2 | ||
| AA | 40 | 30.5 | 37 | 26.4 | 38 | 27.0 | 35 | 24.6 | ||
| GA | 60 | 45.8 | 70 | 50.0 | 58 | 41.1 | 67 | 47.2 | ||
| rs10802501 | ||||||||||
| AA | 0 | 0 | 0 | 0 | 1 | 0.7 | 0 | 0 | 0.270 | |
| TT | 123 | 93.9 | 131 | 93.6 | 134 | 95.0 | 127 | 89.4 | ||
| AT | 8 | 6.1 | 9 | 6.4 | 6 | 4.3 | 15 | 10.6 | ||
| NLRP4 | rs6757121 | |||||||||
| TT | 3 | 2.3 | 2 | 1.4 | 3 | 2.1 | 1 | 0.7 | 0.454 | |
| CC | 105 | 80.2 | 111 | 79.3 | 122 | 86.5 | 122 | 85.9 | ||
| TC | 23 | 17.6 | 27 | 19.3 | 16 | 11.3 | 19 | 13.4 | ||
| rs455060 | ||||||||||
| GG | 52 | 39.7 | 50 | 35.7 | 43 | 30.5 | 50 | 35.2 | 0.758 | |
| AA | 26 | 19.8 | 25 | 17.9 | 27 | 19.1 | 25 | 17.6 | ||
| GA | 53 | 40.5 | 65 | 46.4 | 71 | 50.4 | 67 | 47.2 | ||
| rs408813 | * | |||||||||
| GG | 131 | 100 | 140 | 100 | 141 | 100 | 142 | 100 | ||
Note: * indicates that the statistical measure cannot be calculated.
Effects of ERCC1 rs3212986 on the risk of CWP
Multiple-factor multinomial logistic regression analysis was conducted on the groups based on the development and progression of CWP as the dependent variable, and age, work duration, smoking, alcohol drinking, and the ERCC1 rs3212986 genotype as independent variables (see Table 4). The goodness-of-fit was P < 0.05. Compared to the CC genotype in the rs3212986 locus, significant differences (P<0.05) were observed in the AA (OR = 3.016, 95% CI = 1.409–6.456, P<0.05) and CA (OR = 2.130, 95% CI = 1.202–3.776, P<0.05) genotypes in patients with stage II CWP. Compared to workers in the coal dust exposure group, significant differences (P<0.001) in age were observed in patients with stage I CWP (OR = 1.047, 95% CI = 1.008–1.088, P<0.05) and stage II CWP (OR = 1.1267, 95% CI = 1.084–1.171, P<0.05). Compared to workers in the coal dust exposure group, significant differences (P<0.001) in work duration were observed in patients with stage II CWP (OR = 0.924, 95% CI = 0.890–0.959, P<0.05).
Table 4. Regression model analysis of the influence of ERCC1 rs3212986 on CWP.
| Variable | Stage I CWP group | Stage II CWP group | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | p | OR | 95%CI | p | |
| Age | 1.047 | 1.008–1.088 | P<0.05 | 1.1267 | 1.084–1.171 | P<0.001 |
| Work duration (year) | 0.983 | 0.950–1.017 | 0.316 | 0.924 | 0.890–0.959 | P<0.001 |
| Never smoked* | ||||||
| Occasional smoker | 0.828 | 0.322–2.133 | 0.696 | 2.312 | 0.943–5.666 | 0.067 |
| Regular smoker | 0.800 | 0.465–1.378 | 0.421 | 1.054 | 0.580–1.917 | 0.863 |
| Never drank* | ||||||
| Occasional drinker | 0.466 | 0.269–0.808 | 0.007 | 0.720 | 0.404–1.282 | 0.264 |
| Regular drinker | 0.475 | 0.154–1.462 | 0.194 | 0.940 | 0.326–2.716 | 0.910 |
| CC* | ||||||
| AA | 1.164 | 0.533–2.542 | 0.704 | 3.016 | 1.409–6.456 | P<0.05 |
| CA | 1.593 | 0.947–2.681 | 0.079 | 2.130 | 1.202–3.776 | P<0.05 |
Note: * indicates the control.
To explore the interaction effect of these factors, further interactive analysis was conducted using binary logistic regression analysis, with dust-exposed workers with or without CWP as the dependent variable and rs3212986, smoking, and drinking, as well as their interactions, as independent variables. The interaction of smoking and drinking (OR = 1.787, 95% CI = 1.251–2.552, P < 0.05) increased the risk of CWP with a synergistic effect when rs3212986 was not included. When rs3212986 was included, smoking, drinking, and ERCC1 rs3212986 all increased the risk of CWP independently (OR>1). However, the interaction of rs3212986 and smoking (OR = 0.527, 95% CI = 0.324–0.858, P < 0.05) and the interaction of rs3212986 and drinking (OR = 0.769, 95% CI = 0.559–1.058, P < 0.05) reduced the risk of CWP with an antagonistic effect (see Table 5).
Table 5. Effect of the interaction of drinking, smoking and ERCC1 rs3212986 on workers exposed to coal dust.
| Variable | B | β | Wald | P | OR (95% CI) |
|---|---|---|---|---|---|
| Smoking | −1.814 | 0.614 | 8.745 | 0.003 | 0.163 (0.049–0.542) |
| Drinking | −1.607 | 0.595 | 7.302 | 0.007 | 0.200 (0.062–0.643) |
| Smoking × Alcohol Drinking | 0.580 | 0.182 | 10.195 | 0.001 | 1.787 (1.251–2.552) |
| rs3212986 | 1.768 | 0.811 | 4.747 | 0.029 | 5.858 (1.194–28.730) |
| Smoking | 2.274 | 0.831 | 7.485 | 0.006 | 9.713 (1.906–49.513) |
| rs3212986 × Smoking | −0.640 | 0.248 | 6.650 | 0.010 | 0.527 (0.324–0.858) |
| Drinking | 1.146 | 0.540 | 4.496 | 0.034 | 3.144 (1.091–9.066) |
| rs3212986 | 0.450 | 0.509 | 0.783 | 0.376 | 1.569 (0.578–4.255) |
| rs3212986 × Drinking | −0.263 | 0.163 | 2.605 | 0.107 | 0.769 (0.559–1.058) |
Plasma concentrations and ROC curve analysis of the ERCC1 protein in CWP
Plasma ERCC1 protein levels differed among the five groups (P < 0.05). Compared to the healthy group, the coal dust exposure group showed elevated ERCC1 levels (P < 0.05). Compared to the coal dust exposure group, stage I and stage II CWP groups showed decreased ERCC1 levels (P < 0.05) (Fig 1). ROC curve analysis was conducted using the ERCC1 level in the plasma of study subjects as the test variable, and the results showed that the area under the curve was 0.935. The sensitivity was 90.0% and the specificity was 86.7%, indicating a higher value for early warning and diagnosis. Thus, ERCC1 can be used as an auxiliary indicator for the early warning and diagnosis of CWP, and the cut-off value for early warning and diagnosis of the disease is 5.265 pg/ml (Fig 2).
Fig 1. Change in the ERCC1 secretion level in plasma(pg/ml).
Note: a indicates a significant difference compared to the healthy group (P<0.05); b indicates a significant difference compared to the coal dust exposure group.
Fig 2. ROC curve analysis for ERCC1 with CWP progression.
Pathways of ERCC1
Briefly, ERCC1, osteopontin (SPP1), annexin A1(ANXA1), low-density lipoprotein (LDL), high-density lipoprotein (HDL), apolipoprotein M(APOM), vascular endothelial growth factor (Vegf), coactosin-like protein (COTL1) (that is binding proteins of actin.), activator protein-1(Ap1), and polymeric filamentous actin (F-actin) had strongly activated and non-esterified fatty acid was less active. But insulin and Mpo (Myeloperoxidase) were inhibited. ERCC1 had indirect interaction with insulin and Ap-1 and the change between ERCC1 and insulin was opposite. Inhibited insulin as an import core had an indirect interaction with LDL, HDL, APOM, non-esterified fatty acid, ANXA1 and Vegf. Between insulin and LDL was an inhibitive interaction and between Insulin and Vegf had an interaction of inconsistment with state of downstream molecule. The rest interactions from insulin were activated. Active Ap1 as a core had an indirect and active interaction with SPP1, ANXA1 and Vegf indirectly and it had two types of interaction with insulin including active effect and inconsisitment effect with state of downstream molecule. The remaining indirect interactions of Vegf were with SPP1, F-actin, ANXA, APOM and HDL. There was a direct interaction between F-Actin and COTL1. APOM had direct and active interactions with LDL and HDL. LDL had a direct interaction with non-esterified fatty acid but it had an indirect and inconsisitment with state of downstream molecule interaction with HDL. SPP1 had indirect interaction with F-actin and ANXA1 had indirect interaction F-actin. Mpo had Inhibited interactions with ANXA1 and HDL (Fig 3).
Fig 3. Pathways analysis of ERCC1 based on CWP metabolome and proteome.
The arrows in the figure indicate that the proteins interact, and there is no upstream-downstream relationship. Dashed lines mean indirect relationship and solid lines mean direct relationship.
Discussion
Dust entering the body through the respiratory tract triggers oxidative stress reactions, which first damage mitochondrial DNA in cells [27]. The body then activates the DNA damage repair system. Meanwhile, mitochondrial DNA mediates the NLRP3 pathway, which in turn activates inflammasomes [28]. Based on DNA damage repair pathways, previous research identified a significant mutation in ATM rs189037, which is a key gene involved in double-stranded DNA repair [15], and identified ATM as an early warning and diagnostic biomarker for CWP [29]. In the present study, based on the NER pathway of DNA damage repair, we identified for the first time that the ERCC1 rs3212986 CA and AA genotypes are risk factors for the development and progression of CWP (OR>1). Previous studies have found that ERCC1 rs3212986 is a genetic susceptibility factor for lung cancer and is associated with a reduced overall survival rate in patients with late-stage non-small cell lung cancer and decreased activity of the chemotherapy drug cisplatin [30–35]. In addition, rs3212986 alters the DNA damage repair capacity of the body by regulating ERCC1 expression. rs3212986 is a risk factor for smoking-related lung cancer and may also be an indicative biomarker [36]. In addition to smoking, alcohol consumption in men living in highly polluted areas is a risk factor for lung cancer [37]. The results of the present study demonstrated that, after exposure to coal dust, ERCC1 rs3212986 is prone to mutation, which might reduce its expression and the DNA damage repair capacity, ultimately increasing susceptibility to CWP. ROC curve analysis revealed a decrease in plasma ERCC1 protein levels with disease progression, indicating its potential value as an early warning and diagnostic biomarker. This may be associated with the positive DNA damage repair function of ERCC1 in the development and progression of CWP. Subjects in the coal dust exposure group exhibited the highest levels of ERCC1, indicating an increased DNA repair capacity at this stage after dust exposure, which may be advantageous in inhibiting the occurrence and development of CWP. However, as the disease progressed, plasma ERCC1 levels significantly decreased in patients with stage I and stage II CWP, to a level comparable to that of the control group, suggesting that the body lost its DNA damage repair capacity, which led to the final stage of the disease. Therefore, effective biomarkers should be used for real-time monitoring of the health status of workers exposed to dust in the early stages, so that early detection and control measures can be taken to alleviate disease progression and change the outcome of the disease. According to this study, when the plasma concentration of ERCC1 in individuals exposed to dust exceeds the cut-off value of 5.265 pg/mL, they are at risk of developing CWP and intervention should be performed. When the cut-off value is reached, individuals exposed to dust should promptly discontinue contact with the dust and receive regular follow-up observations to determine subsequent disease detection and control strategies.
In the logistic regression model, smoking and drinking interaction increased the risk of CWP in individuals exposed to dust when ERCC1 rs3212986 was not included; however, smoking and drinking alone had an antagonistic effect. When rs3212986 was included, rs3212986, smoking, and drinking all increased the risk of disease individually. However, when the interactions with smoking or drinking were considered, the OR value of rs3212986 decreased significantly, indicating an antagonistic effect. The possible reasons for this phenomenon may be related to whether different types of dust cause lung cancer; unfortunately, the mechanism of dust-induced carcinogenesis is currently not well understood. It is generally believed that asbestos is a carcinogen that causes lung cancer, exhibiting a synergistic interaction with tobacco in lung cancer [38]. Silica is usually considered a carcinogen and has a synergistic interaction with tobacco [39], although this is not always the case. Compared with subjects exposed to dust but not exhibiting silicosis, patients with silicosis have a higher risk of developing lung cancer. However, the carcinogenic effect of silica itself is controversial for exposed populations without silicosis [40]. Smoking increases the incidence of lung cancer compared to that in subjects without CWP, but CWP status increases the incidence of cancer in subjects who do not smoke. In addition, CWP is associated with a decrease in the incidence of non-lung cancer [41]. The incidence of small cell carcinoma is three times higher in the non-CWP population than in the CWP population and is associated with smoking in the non-CWP population [42]. These findings indicate that coal dust exposure may not be related to the risk of lung cancer and may show no synergistic interaction with smoking. Therefore, the role of rs3212986 in CWP may be different from its role in lung cancer, and the environmental risk factors of smoking and drinking may also play different roles in CWP and in lung cancer. Age and work duration may indirectly affect the level of DNA damage repair in the body following exposure to coal dust. With increasing age, the DNA damage repair capacity of the body gradually decreases [43], and the risk of CWP correspondingly gradually increases, making age a risk factor for the disease. The longer the duration of work, the longer the body is exposed to dust, and the greater the DNA damage caused by dust, which reduces the DNA damage repair capacity of the body and leads to CWP. Conversely, the shorter the duration of work, the shorter the exposure time to dust, and the lesser the DNA damage caused by dust. The body has a relatively high capacity for DNA damage repair, and the risk of developing the disease decreases. Therefore, the duration of work is a protective factor for CWP.
Based on multi-omics analysis of serum proteome and metabolome of IPA database, we found that ERRC1 played a much more important role in lipid metabolism and actin dynamics. ERCC1 pathways are mainly composed of non-esterified fatty acid, proteins involved in metabolizing lipids including LDL, HDL, APOM and other proteins that affect actin dynamics. ERCC1 may affect lipid metabolism by the pathways of Ap1 and insulin. Lipid peroxidation (LPO) is a product of oxidative damage and can react with DNA. High concentration LPO inhibits NER and BER and down-regulates DNA repair and up-regulates DNA damage. Mouse embryonic fibroblasts and mice with ERCC1-XPF deficient were hypersensitive to LPO and levels of DNA damage were increased [44–47]. Therefore, ERCC1 level decreased and LPO concentration increased with CWP development. LPO-induced DNA lesion level was higher and DNA repair level was lower. The above results had already confirmed ERCC1 rs3212986 was a risk factor and ERCC1 alteration had diagnosis value. The role of ERCC1 in regulating lipids metabolism further provided further evidence that DNA repair inhibits CWP development. Ap1 regulates various biology responses and is associated with serve diseases including fibrosis, organ injury, and various inflammatory disorders. The targets of Ap1 singaling comprise DNA, histone and protein methyltransferases [48]. The relationship between ERCC1and Ap1 suggests Ap1 may take part in CWP development and play multiple roles such as fibrosis, inflammatory disorders and so on. ERCC1 has a negative impact on pancreatic beta-cell and causes the increasement of insulin sensitivity [49]. Lipid intermediates made with free fatty acids and lipid droplet leads to down -regulates insulin signaling pathways and glucose metabolic disorders [50]. Hence, ERCC1 may affect the insulin metabolism in patients with CWP mediated via its impact on lipids metabolism. SPP1 and ANXA1 is associated with pneumoconiosis and they may be the disease biomarkers [51, 52]. Mpo is a peroxidase which takes part in oxidative stress response [53] and thus it may involve in the CWP development. The actin cytoskeleton reorganization is a response to TGF-β1 singaling early. Rho-like GTPases (RhoB) and α-SMA in fibroblasts are active because long-term actin cytoskeleton reorganization activates the transforming growth factor β-SMAD (TGFβ-SMAD) pathway so that actin dynamics affects the pathological process such as wound repair, inflammation and so on [54]. The correction both actin and TGF-β1 suggests that actin dynamics change may be an indicative marker of fibrosis. On the other hand, when oxidative stress and inflammatory injures endothelial cells, the endothelial-mesenchymal transition (E(nd)MT) will happen. E(nd)MT leads to fibrosis and then Vegf is increased [55]. Similarly, the dust stimulation induces the same injury of endothelial cells, and causes fibrosis and high level Vegf. The interaction among Vegf, insulin and Ap1 confirms that ERCC1 may alleviate abnormal changes in endothelial cells by affecting the pathways of insulin and Ap1. Above all, ERCC1 may play a critical role in inhibiting CWP fibrosis though changing lipids metabolism and actin dynamics. ERCC1 has been studied mainly in the field of cancer, focusing on the relationship between its polymorphisms and the development of cancers, such as lung cancer. The reason is relative with its function of DNA repair. However, its association with human pulmonary fibrosis disease has not been studied so far. In this paper, we propose for the first time that ERCC1 polymorphisms are risk factors for CWP and find it has much better sensitivity, specificity and provide the cut-off value for early detection and intervention.
Given the relatively small sample size of this study, it is necessary to increase the sample size in future studies to verify the changing patterns of rs3212986 and ERCC1 pathways based on proteome and metabolome. Meanwhile, there was a difference between SNP analysis and multi-omics analysis in the divided groups. Because the sample size of currently receiving dust group and previously receiving dust group alone was not enough to be analysed with SNP, the two groups were combined the coal dust exposure group. Therefore, it should collect much more samples of currently receiving dust and previously receiving dust group for better SNP analysis of CWP, respectively. Additionally, further studies are required to investigate whether other SNP sites of the six genes affect the development of CWP. In this study, we did not find any interaction between the six genes involved in DNA damage repair and inflammatory response in CWP. Therefore, other important genes that affect DNA damage repair and the inflammatory response should be studied. Furthermore, environmental factors such as smoking and drinking have a certain impact on the disease outcome in combination with susceptible genes and different types of dust, and many confounding factors exist, leading to heterogeneity in research findings. However, the current lack of research in this area highlights the need to further clarify this issue. Finally, future research is required to investigate the key molecules involved in the pathway to ascertain mechanisms of ERCC1 on CWP development and progression and its interaction with environmental factors.
Conclusions
In this study, we investigated the polymorphisms of SNP sites in genes closely related to DNA damage repair and the inflammatory response involved in the development of CWP. The results showed that ERCC1 rs3212986 is a risk factor for CWP. Moreover, the plasma level of ERCC1 decreased with CWP development, indicating its potential as a biomarker for early warning and diagnosis. Age and work duration indirectly affected the DNA damage repair capacity of the body. Finally, we observed an antagonistic interaction between rs3212986 and environmental factors including smoking and drinking. This study provides an effective biomarker for the real-time monitoring of the health status of workers exposed to dust and will help develop a more precise intervention for CWP progression. When ERCC1 concentration reaches cut-off value, workers should leave the workplace and change other jobs and will failure to develop and diagnose pneumoconiosis. ERCC1 could be used for regular screening and at the sametime reduction of working hours, no smoking, improvement in air ventilation, better personal protection equipment (PPE) for the workers are workable and excellent measures. For healthy coal workers, the cohort studies should be carried on using ERCC1, working hours and jobs to build the connection of them and to find a cut-off value of working hours. Early detection and interventions should be carried in factory using the threshold to decrease morbidity of CWP and the burden of disease. The alteration and mechanism of ERCC1 in CWP development should be studied in the animal models and cell models exposed to different coal dust dosages to build the link between the two to provide reference for clinical. The study provides a useful judgement criteria for early detection and invention. Applying it wisely could raise the attention of employers to the hazards of occupational diseases, enhance workers’ awareness of self-protection, and promote the harmonious development of society as a whole. The application of ERCC1 will facilitate the strengthening administrative supervision and management, the promotion of industry self-regulation, and the improvement of the diagnostic and treatment level of occupational disease examination and diagnosis institutions.
Supporting information
(XLSX)
Data Availability
All relevant data are within the manuscript and its Supporting Information files.
Funding Statement
This work was supported by Guizhou Provincial Science and Technology Projects (contract No. Qiankehe Support [2020] 4Y152 and No. Qiankehe Support [2021] General 433). There was no additional external funding received for this study.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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All relevant data are within the manuscript and its Supporting Information files.