The preliminary investigation of potential response biomarkers to air pollution (PAHs) exposure on childhood asthma

In recent five years, air quality in China has been gradually improved; however, the prevalence of asthma in China has still increased significantly. This study aims to examine whether air pollution exposure is a significant contributor to childhood asthma and to explore the mechanisms using exposure and response biomarkers. The generalized additive model was applied to evaluate the effects of air pollutants on asthma hospitalization. Urinary 1-hydroxypyrene (1-OHPyr) was determined as PAHs exposure biomarker. Metabolic profiles and microbial diversity were screened and selected as response biomarkers. Daily average concentrations of the six common air pollutants were not significantly associated with daily childhood asthma hospitalization. Urinary 1-OHPyr level showed large variability among asthmatic children and were positively correlated with the abundance of Prevotella (genus level) and purine metabolism. The biomarker metabolic microbiological


Abstract Background
In recent five years, air quality in China has been gradually improved; however, the prevalence of asthma in China has still increased significantly. This study aims to examine whether air pollution exposure is a significant contributor to childhood asthma and to explore the mechanisms using exposure and response biomarkers.

Methods
The generalized additive model was applied to evaluate the effects of air pollutants on asthma hospitalization. Urinary 1-hydroxypyrene (1-OHPyr) was determined as PAHs exposure biomarker. Metabolic profiles and microbial diversity were screened and selected as response biomarkers.

Results
Daily average concentrations of the six common air pollutants were not significantly associated with daily childhood asthma hospitalization. Urinary 1-OHPyr level showed large variability among asthmatic children and were positively correlated with the abundance of Prevotella (genus level) and purine metabolism.

Conclusions
The biomarker of PAHs exposure is associated with metabolic and microbiological biomarkers of childhood asthma. As the potential health risk, we suggest that PAHs should be included in the national monitoring system aiming to reduce the exposure.

Introduction
Asthma is a complex disease, which is affected by both genetic and environmental factors.
Epidemiological studies have shown the links between air pollution exposure and the increased prevalence and severity of asthma [1][2][3]. However, according to China's Environmental Status Bulletin (2014-2017), PM 2.5 concentrations in the Yangtze River Delta was decreased by 34.3% from 2013 to 2017. Air quality has displayed an improving trend, while the prevalence of asthma is still on the rise [4]. At the same time, a few cohort studies have reported no significant association between air pollution and asthma prevalence [5,6]. Contradictory results bring questions regarding any changes in the role of air pollution in the prevalence of asthma [7]. Until now, there has been no update evidence regarding the association between air pollution exposure and childhood asthma in recent five years in China.
In addition, polycyclic aromatic hydrocarbons (PAHs) as the most abundant component of atmospheric particulate matters has been found related to the onsets of asthma or exacerbation of asthma symptoms [8][9][10][11][12]. Recent evidence suggests that PAHs exposure altered the abundance of commensal bacteria that were associated with health outcomes.
Atmospherica PAHs exposure could influence the composition and abundance of skin microbiota and induce endocrine signaling pathway disrupted [13]. It has been demonstrated that the development of asthma is highly associated with the microbiogical circumstance [14]. Atmospherica PAHs exposure may also induce the aberrant metabolic profiling focused on oxidative stress that leads to airway inflammation [15]. Importantly, studies in childhood asthma linking environmental PAH exposure, human commensal microbiota and metabolic pathways are largely unknown. To better understand how PAHs exerts its effects on childhood asthma, omics analysis based on microbiome and metabolome were performed. Therefore in the present study, we firstly used the public data to analyze the asscociation between the six common air pollutants (SO 2 , NO 2 , CO, O 3 , PM 2.5 , and PM 10 ) and childhood asthma hospitalization in Nanjing for 2015-2018. We aimed to clarify the effects of these air pollutants on asthma under current environment. Then by measuring urinary 1-OHPyr as internal exposure biomarker to PAHs, we expected to find response biomarkers based on the changes of the global metabolic signatures and throat microbial diversity in asthmatic children and explored the underlying mechanisms associated with the PAHs exposure. effects of air pollution on asthma with single lag days (lag 0, 1, 2, 3, 4, 5, 6) and multiple lag days (lag 0-1, 0-2, 0-3, 0-4, 0-5, and 0-6). The estimated effect of a pollutant was evaluated by the Relative Risk (RR) and its 95% confidence interval (CI) in daily asthma per unit concentration increased. All the analyses were performed in R software (version 3.5.3) with the mgcv package. All tests were two-sided, and P < 0.05 were considered statistically significant.

Sample Collection
The samples consisted of 20 asthmatic subjects and 20 healthy control subjects were collected from the Children's Hospital of Nanjing Medical University in April 2018. The Nanjing Medical University Clinical Research Ethics Committee, Nanjing, China, reviewed and approved the protocols of this study. Informed consent was obtained from the participants for the use of samples in this study. The clinic physician collected a throat swab and a morning urine sample from each asthmatic or healthy control subject. The swab and urine samples were stored at -80 °C until analysis.

Detection of 1-OHPyr in urine by UPLC-Orbitrap -MS
For each urine sample, 1 ml of urine was added with 500µL of 0.2M sodium acetate (pH = 5) and 5µL of β-glucuronidase with sulfatase (Helix pomatia-H2, G0876), and then incubated at 37℃ for 2 h. After deconjugation, the sample was centrifuged at 10,000 rpm for 10 min, and the supernatant was collected and extracted by dichloromethane. The combined organic phase was dried under nitrogen and reconstituted in 100 ml of CH 3 OH.
The final solution was analyzed for 1-OHPyr on an ultra-performance liquid chromatography -Orbitrap-mass spectrometry (UPLC-Orbitrap-MS) system (Thermo Fisher Scientific, Bremen, Germany). A volume of 20µL was loaded into the Hypersil Gold C18 column (1.9 µm, 2.1 × 100 mm) and analyzed by linear gradient elution. The mobile phase was methanol and ultrapure water. In the linear gradient elution, 5% water linearly increased to 95% in the first 8 min and restored equilibrium back to 5% water from 8.5 min. The flow rate was 3 ml/min and the temperature of the column was set at 35℃.
The mass spectrometry was adopted the heating ESI and the negative ion mode. The spray ion voltage, gas pressure of sheath and the auxiliary gas were + 3.5 kV, 35 arbitrary unit, and 10 arbitrary unit, respectively. Besides, the flow rate of N 2 was 10.0 L/min and the atomization temperature and capillary temperature was 350℃ and 263℃, respectively.
Potential biomarkers analysis were identified using Wilcoxon rank-sum test to determine the significant differences between the asthmatic and control groups. PICRUSt was used to predict a profile of putative pathways from the 16S rRNA OTU data classified accoding to the Greengenes Database. KEGG (Kyoto Encyclopaedia of Genes and Genomes) database was used to categorize pathways. All data were analyzed on the free online platform of Majorbio I-Sanger Cloud Platform (www.i-sanger.com, Shanghai Majorbio Bio-pharm Technology Co., Ltd.)

Metabolic profiles by UPLC-Orbitrap -MS
The urinary samples were pretreated by methanol (1:3, V/V). The supernatant was prepared for further analysis after centrifuged at 10000rpm/min. The analyses were also performed on the above UPLC-Orbitrap-MS system. The protocal was clearly described in our previous work [16]. In brief, A multistep gradient consisting of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) was applied. The UPLC autosampler temperature was set at 4 o C and the injection volume was 10 µl. The operating parameters of the MS were set up as follows: spray voltage of 3 kV, capillary temperature of 300 o C, and flow of the sheath gas, auxiliary gas, sweep gas, and S-Lens RF level were at 40, 10, 2, and 50 arbitrary units, respectively. In the full scan analyses (70 to 1050 amu, amu: atomic mass unit), the resolution was set at 7 × 10 5 with an automatic gain control target of 1 × 10 6 charges, and a maximum injection time of 120 ms. The mass spectrometry was calibrated every 24h to ensure mass accuracy.All the raw data files were preprocessed using the SIEVE software (Thermo Fisher Scientific, Bremen, Germany) for further multivariate statistical analyses. A supervised analyses, partial least squares discriminant analyses (PLS-DA) was then performed to optimize classification and search for variables by the SIMCA-P 13.0 software package. The variable importance in the projection (VIP) was obtained from the PLS-DA model, which indicates a relative contribution of each variable on the classification, and VIP > 1.0 were and P-value <0.05 were considered statistically significant as potential biomarkers.

No correlation between asthma and six criteria air pollutants
In this study, children's asthma hospitalization peaked in late spring (from March to May) and fall (from September to November), as shown in Fig. 1. Bronchial asthma attack often occurs in the spring and fall [17]. The arrival of spring brings pollen allergy from grasses, weeds, and trees, and the fall means the transition to the cold days, which are common triggers of asthma attacks. There was a gender difference in childhood asthma (STable 1).
Male children were significantly more likely to be sensitized to allergens [17]. Figure 2 showed that there was no significant association between the six common air pollutants, except for lag0 of SO 2 , lag3 of NO 2 , lag3 of O 3 and lag0 of PM 10 . Subgroup analysis by gender and age showed no significant effects. The comparison of the trend pattern between the month average concentrations of six-air pollutant and asthma hospitalization was shown in Sfig.1. Except for O 3 , the peaks of SO 2 , NO 2 , CO, PM 2.5 , and PM 10 concentrations were ahead of that of asthma hospitalization. Although it is not statistically significant, it still indicated that after the peak of O 3 concentration, there was an increase in asthma-related hospital admissions. The monthly average relative humidity, temperature and average wind speed also matched with the hospital admissions, as shown in Sfig.2; however there was still no significant correlation .

1-OHPyr Levels In Urine Samples
In this study, the characteristics of the subjects being sampled were described in Table 1.
All the asthmatic children adminstrated control medicine, such as inhaled corticosteroid (ICS), long-acting beta agonists (LABAs), short-acting beta agonists (SABAs), oral antiallergic medicine, and immunomodulator. asthmatic and control subjects had healthy diets with fried or grilled food intake at 0-1 time/week, were living in the city, lived within 2 kilometers from schools, walked or were sent by electric bike to school, lived in homes with vent-out kitchens, and had non-smoking parents. The urinary 1-OHPyr level ranged from non-detected (ND) to 2.477 ng/ml in the asthma group and from ND to 0.156 ng/ml in the control group. The creatinine correction average value was 0.219 ± 0. 33  year old children was 119(in ng/g of creatinine), that was 0.05 µmol/mol Cr [18].
Compared with American children 1-OHPyr exposure level, the exposure level of 1-OHPyr of children in Nanjing was much higher.  3 showed that the microbial diversity and richness of the control group was more abundant than that of the asthmatic group. Rarefaction curve almost tended to approach an asymptote that reflected a perfect estimate of sampling.
The compositions of the two groups had remarkable similarity. The taxonomic composition of the microbial communities at the phylum level in asthmatic group and the control was shown in Fig. 3A and B, which mainly consisted of Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria and so on, and at genus level, mainly consisted of Streptococcus, Veillonella, Prevotella, Neisseria, Haemophilus, Actinomyce, Leptotrichia and so on ( Fig. 3C and D). The significant differences in microbial communities were performed using the Wilcoxon rank-sum test. At the phylum level, the abundance of Proteobacteria was decreased (P = 0.0461) in the asthmatic group, and that of Firmicutes ( P > 0.05) and Bacteroidetes ( P = 0.0238) was increased. At the genus level, the abundance of Veillonella ( P = 0.0159) and Prevotella(P = 0.0066) was increased ( Fig. 4), and that of Neisseria and Haemophilus was decreased(P > 0.05) in asthma group.
Multiple biological pathway, such as cancer, environmental adaptation, nucleotide metabolism and lipid metabolism were predicted from the throat metagenome by PICRUSt( Figure 5), and the detailed pathway was illustrated in Sig.4. Pearson correlation coefficient was applied to investigate the association between 1-OHPyr and microbiota diversity. There was some positive correlation between microorganism and 1-OHPyr level.
Stomatobaculum r = 0.80, P < 0.0001 , Selenomonas r = 0.73, P < 0.0001 ,and Prevotella r = 0.65, P < 0.0001 was associated with higher PAHs concentration. Then a redundancy analysis (RDA) was further performed to determine the multivariate relationship between microbiota in samples and the environmental variable-PAHs by R.
Although Stomatobaculum and Selenomonas was associated with PAHs level, their abundance were very low. The RDA confirmed Prevotella was positively correlated with 1-OHPyr level (Fig. 6).
3.4 1-OHPyr are associated with metabolic profile in asthmatic children We also detected the metabolic profiles in asthmatic and the control group by UPLC-Orbitrap-MS. The variables with both VIP score > 1.0 and P-value < 0.05 were identified as candidate biomarkers. The metabolites that were confidently and putatively identified in the samples were listed in STable 2. After mapping to KEGG, the results showed that most of differential expressed metabolites (DEMs) were involved in purine metabolism, amino acid metabolism and lipid and fatty acid metabolism. Purine metabolic pathway was significantly aberrant (P < 0.0001, Fig. 7), which was in consistent with the results from studies [19,20]. occurred and involved in the development of pneumonia and asthma [22]. TG, DG, MG, free fatty acids was increased, but Linoleic acid, polyunsaturated fatty acid, PUFA was decreased. It has been shown that PUFAs have significant roles in inflammatory process on different diseases. Our data was consistent with [23], suggesting a weakened antiinflammatory effect. In addition, valproylglycine, prolylglycine, valyl-hydroxyproline glycylprolylhydroxyproline was increased in the asthmatic group, which were the major components of collagen-derived dipeptides and may be risk markers of osteoporosis and bone injury associated with corticosteroid treatment. Reinke et al found that prolylhydroxyproline was correlated with inhaled corticosteroid [19]. It is noted that me melatonin metabolite was very lower in the asthmatic group than that in the control group. The activation of the immune system may lead to free radical production, which may be exacerbated by PAHs exposure, associated with decreased melatonin levels in inflammatory diseases, such as allergic disease, asthma [24]. We conducted RDA analysis between 1-OHPyr and metabolites (Fig. 8). Pearson correlation showed that 1-OHPyr was positively correlated with deoxyadenosine monophosphate (r = 0.86, P < 0.0001), which is and SO 2 generally decreased the most in Yangtze River Delta Region, but NO 2 , CO, and O 3 are still at a high level. In our study, we did not find out the significant association between these six common air pollutants and asthma hospitalization. However, the pattern that O 3 affected the prevalence of asthma was other than that of SO 2 , NO 2 , CO, PM 2.5 , and PM 10 , which may increase the risk for asthma [25]. The numerous inconsistencies in epidemiological data may be indirect evidence of no association between air pollutants and the prevalence of childhood asthma [26]. The role of air pollution on the prevalence of asthma is weakened? The effects of air pollution on asthma may be masked by other, more influential seasonal triggers, such as infections or allergies [7]. Pollen counts usually peaks in April-May and September-October in NANJING [27]. The peak of asthma hospitalization shown in our study was consistent with these trend. What's more, even if air quality has been significantly improved recently, the accumulated effects of environmental pollution still exsit, leading to the prevalence of asthma not to decline.
The three times national survey on asthma prevalence among children aged 0-14 years in China administered in 1990, 2000, and 2010, respectively. Three previous surveys have found that the prevalence of asthma was increasing. A new round of national survey on asthma prevalence will be coming soon. Due to the increasing of school absence rate and home burden, we should still make effort to find out the factors that exacerbates asthma.
Inheritance of asthma is something that we can't stop, but we can lower risk factors to prevent from asthma exacerbation. Except for particulate matter, PAHs is also an  [28]. A recent systematic review of prenatal exposure to air pollutants on childhood wheezing and asthma reported an overall randomeffects risk estimate (95% CI) was 1.04(0.94-1.15) for PAHs, which were focused on the 0-6 years of age [29]. These contradictory results may be due to the exposure window of PAHs, or the different measurement of PAHs levels, such as cord blood and urine sample test, land-use regression and inverse distance weight models. Moreover, in some studies there was no clear distinction between the effects of prenatal exposure from post exposure [30]. For now, China has not nation-wide monitored personal PAHs exposure level. We still lack official data to systematic evaluate the effects of the PAHs exposure on childhood asthma. Although our study did not find the difference of 1-OHPyr level between the asthmatic group and the control by targeted UPLC-MS method, the average 1-OHPyr level in Nanjing children was higher than that in USA children. What's more, the 2-OHFlu level was increased in asthmatic group by untargeted UPLC-MS method. There was a trend towards elevated PAHs, but the data were not sufficiently robust to conclude on Recently the use of omics methods has been recommended in exposomics studies to identify the link between exposures and health outcomes [31]. Therefore, in this study, we used omics methods to identify potential microbiota and metabolites as putative intermediate biomarkers linking PAHs exposures to asthma outcome with plausible exposures-related pathways. In this study, Firmicutes, Bacteroidetes, and Proteobacteria (in phylum level), and Streptococcus, Neisseria, Veillonella, Haemophilus, and Prevotella (in genus level)were mainly detected in the collected throat swab, which were consistent with previous studies [32,33]. The composition of Veillonella and Prevotella was increased, and potential pathogen, Neisseria and Haemophilus was decreased in the asthmatic group. However, it was reported that compared with the control, Proteobacteria (Haemophilus) was increased and Bacteroidetes ( Prevotella) was decreased in asthmatic subject with adults (37 ~ yrs) and children (11 ~ yrs) [34]. In our study, Prevotella and Veillonella spp. were more common in asthmatic subjects than that in the control individuals. Pearson correlation and RDA analysis showed that 1-OHP level was significantly positively correlated with the abundance of Prevotella (in genus level).
Prevotella are Gram-negative anaerobes belonged to colonized bacteria in the normal oral, which have been found in high abundance in the lungs of children (< 2yrs) with cystic fibrosis [35]. However, in present study, Proteobacteria was significantly decreased. We were predicted from the throat metagenome by PICRUSt, the effects of the microbiome on asthma and the potential functional mechanism should be further elucidated. Thus, our results suggested that the risk assessment of PAHs shaping commensal microbiota and metabolome profiles may need to take account into the development of microbialmetabolic interaction, which will potentially impair the human host homeostasis.

Conclusion
Six national supervised air pollutant indices has no association with the prevalence of children's asthma in the studied population from 2015-2018. The internal exposure 1-OHPyr was no significant change, but 2-OHFlu level in the asthmatic group was higher than that in the control. PAHs related biomarker showed changes in throat microbial profiles that Prevotella was positively correlated with 1-OHPyr level, and in urine metabolic profiles that the DEMs were involved in purine metabolism, phenylalanine metabolism and one-carbon metabolism, which is linked to oxidative DNA damage. We shed light on preliminary indication of changes in microbiome and metabolome upon PAHs exposure (Fig. 9). however, further investigations are required to confirm our results. At the same time, we recommend to national-wide supervise the pollution level of polycyclic aromatic hydrocarbons.     The results were presented by STAMP. Differences between groups were determined by

Abbreviations
Wilcoxon rank-sum test. Figure 1 Daily hopitalized children due to asthma onset from 2015 to 2018.    Redundancy analysis (RDA) plot summarizing variation in microbiota across sample properties and 1-OHPyr level(p). Circles and triangles represent different group samples; red and green arrows illustrate PAHs and microbiota, respectively.

Figures
Eigenvalues for the first two were presented.

Figure 7
The differential metabolites involved in purine metabolic pathways compared with the control group. The changed metabolites are labelled for up-and downregulation.  PAHs induced asthma by intermediating the change of microbiome and metabolome.