The Impact of Air Pollution on Intestinal Microbiome of Asthmatic Children: A Panel Study

doi:10.21203/rs.2.9514/v1

Background

Intestinal microbiome can influence human health. Previous researches showed that the intestinal microbiome played an important role in immune responses associated with allergic disease. Colonization of intestinal microorganisms in the early life could affect future susceptibility to asthma. In China the incidence of childhood asthma raised sharply in recent years and the air pollution was serious in the meantime. Based on the above theories and conditions, the main objective of this article was to explore the impact of air pollution on intestinal microbiome of asthmatic children preliminary.

Results

A total of 42 fecal samples from 21 children, among whom 11 children with asthma and 10 children without asthma, were collected twice in a clean day and a polluted day respectively. Identifying the bacteria in gut by the method of high throughput sequencing for 16SrRNA gene. The results showed that Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were the dominant phyla in all children. Proteobacteria (p=0.020) increased significantly and Bacteroidetes (p=0.072) decreased marginally significantly in asthmatic children compared with healthy children. Bacteroidetes (p=0.067) showed marginally significant increasing and Actinobacteria (p=0.001) showed significant decline in asthmatic children after air pollution. The variations of Firmicutes (p=0.106) and Proteobacteria (p=0.064) were marginally significant in healthy children after air pollution. Analyzed by multiple linear regression, the relative abundance of Firmicutes, Bacteroidetes at phylum level, Bacteroidia at class level, Clostridium_sensu_ stricto_1 and Terrisporobacter at genus level significantly correlated with environmental pollutants (p<0.05), including PM2.5, PM10, SO2, NO2 and O3.

Conclusion

Composition of intestinal microbiome in asthmatic children were different from healthy children significantly, and air pollution could impact on intestinal microbiome both in asthmatic and healthy children.

General Microbiology

childhood asthma

intestinal microbiome

air pollution

panel study

Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. It always brings heavy burden to the family and society. In recent years, prevalence of asthma was increasing in many countries, especially among children [1]. In the past 30 years, incidence of childhood asthma increased sharply in China [2], and air pollution was series in China in the meantime. Just as some researchers’ opinions [3,4], the increasing of childhood asthma might be correlated with air pollution. The mechanisms of air pollutants trigging asthma were not completely clear now. UK’s Committee on the Medical Effects of Air Pollutants proposed that air pollution might contribute to the development and exacerbation of asthma through four main mechanisms: oxidative stress and damage, airway remodeling, inflammatory pathways and immunological responses, and enhancement of respiratory sensitization to aeroallergens [5,6].

On the other hand, many studies demonstrated that intestinal microbiome were correlated with development of asthma. Human intestinal microbiome is composed of approximately 10¹⁴ microorganisms, which is 10 times the number of cells making up human body [7,8]. These microorganisms not only provide energy for metabolism directly, but also interact with host immune system to influence human health. In 1980s, David Strachan’s study showed that lack of exposure to microbes in early life could increase the susceptibility to atopic disease including asthma, which was so called hygiene hypothesis [9,10]. In recent years, Jakob et.al. studied in a cohort with 690 children, and found 1-year-old children with an immature composition of intestinal microbiome had an increased risk of asthma at age 5 years [11]. As the development of microbiology technology, more experimental evidences available suggested that there was a microbial-immune cell interface, in which microbes cross-talk with immune cells to effect on the immune system [12-15]. And the intestinal microbes influenced immune function of lung through the gut-lung axis [16-18]. All the information above illustrated that intestinal microbiome play an important role on development of childhood asthma.

The most interested finding was that air pollution was illustrated to effect on the intestinal microbiome [19]. And in an investigation process of a project, which was named “Epigenetic mechanism of black carbon on non-atopic asthma” and founded by National Natural Science Foundation of China, investigators found that more allergic children with asthma than non-allergic children with asthma came hospital to consulate in air polluted days. Viewed in connection of microbiome, asthma and air pollution, this article assumed that the polluted environmental factors might trigger the allergy mechanism of asthmatic children through impacting on intestinal microbiome.

Furthermore, there were many studies about the relationship between intestinal microorganism colonization in early life and the asthma incidence in adult [9-11,20-22], but little researches about intestinal microbiome composition during asthma was onset. When studying the effects of air pollution on intestinal microbiome in children with asthma, this article would also focus on the community structure of intestinal microbiome in children with asthma.

Participants Characteristics

21 participants were enrolled in the panel study, including 11 asthmatic children (6 boys and 5 girls) and 10 healthy children (7 boys and 3 girls) without asthma, with a mean age of 7.8 (SD (Standard Deviation) 1.5) years old. The average value of BMI (Body Mass Index) was 17.1 (SD 2.1). All participants were living in Beijing more than 6 months. There were 8 allergic and 3 non- allergic children in asthmatic children. All healthy children were not allergic. Dietary habit for each participant didn’t change between two-time samples collecting.

Air Pollutants Level

Samples for each child were collected twice. Firstly, samples were collected in a clean day with AQI (Air Quality Index)<100, the average value of AQI for the five days before sampling was 73. Subsequently, samples were collected in a polluted day with AQI>100, the average value of AQI for the five days before sampling was 120. The concentrations of air pollutants were shown in Table 1. The effect on intestinal microorganisms of environmental factors was evaluated by 3-day moving average concentration of the following pollutants excepted O3. O3 was evaluated by the maximum daily 8-hour mean concentration.

Table 1 Concentrations of Air Pollutants in Sampling Days

Percentile	Clean Day (AQI<100)					Polluted Day (AQI>100)
Percentile	0^th	25^th	50^th	75^th	100^th	0^th	25^th	50^th	75^th	100^th
PM_2.5	16.3	26.3	29	37.3	84.7	38.3	38.3	60.3	84.7	149.3
PM₁₀	17.7	53.7	55.3	76.7	118	68.3	68.3	102	138	162
NO₂	17.7	33.3	43.3	44.7	55	25.7	25.7	44.7	48.7	69.7
SO₂	2.3	4	6.7	10	10.3	4	4	8	10	18
O₃	40	40	48	77	94	30	62	119	151	177
Temperature	6.7	6.7	11	16.7	27	9.1	15.2	20.7	27.8	29.1

The unit of PM_2.5, PM₁₀, NO₂, SO₂ and O₃ is ug/m³, Temperature is ℃.

Operational Taxonomic Units) Number Variation before and after Air Pollution

In the 42 samples, a total of sequencing reads, ranging from 59193 to 99554, were generated from the V4 hypervariable region of 16SrRNA genes. The average number of obtained tags was 71358 (SD 7897). Clustered according to 97% identity, a mean OTUs of 506 (SD 195) was identified. The average value of OTUs was 480 (asthma children:537, healthy children:416) before air pollution and 533(asthma children:511, healthy children:582) after air pollution. The total counts of OTUs and common OTUs for the two measurements varied by child (Figure 1). AS for the difference of OTUs between the two measurements among all 21 children, thirteen children showed decreasing and eight children showed increasing. OTUs of two children with non-allergic asthma and two healthy children changed more than 40 percent. The other 17 children shared more than 90 percent OTUs before and after air pollution (Figure 1).

Analyzed by Wilcoxon Test, the counts of OTUs in all children didn’t show significantly changing before and after air pollution (p=0.366). However, the OTUs number of asthmatic children changed marginally significantly after air pollution (p=0.068). The number of OTUs were significantly different between asthmatic and healthy children (before air pollution: p=0.002, after air pollution p=0.022).

Figure 1

Variation of the Relative Abundance of Bacteria

At the phylum level

22 phyla were identified in samples of clean day (athematic children: 22, healthy children:14) and 36 phyla were identified in samples of polluted day (athematic children: 20, healthy children:35) in all children. The number of phyla varied before and after air pollution. The 22 phyla obtained in clean day were all identified in air polluted day. The 14 (38.9%) new identified phyla only found in 2 asthmatic and 2 healthy children and the relative abundance of them were range from 10-4~10-5.

The relative abundance of Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were 62.1%,25.3%,8.2% and 3.9%. They accounted for more than 99.0% of all bacteria before and after smog. Constituent ratio of the four major phyla didn’t show significant difference before and after air pollution in all children (Chi square test, p>0.05) and it didn’t show significant difference between asthmatic and healthy children (Chi square test, p>0.05) either.

The abundance of each phylum changed to a certain degree after air pollution in every child (Figure 2). As for the relative abundance of the four major phyla respectively in the asthmatic and healthy children, Bacteroidetes and Actinobacteria varied (marginally) significantly before and after air pollution (Wilcoxon Test, Table 2) in asthmatic children, in reverse, the relative abundance of Firmicutes and Proteobacteria changed marginally significantly in healthy children before and after air pollution (Wilcoxon Test, Table 2). Taking the sequencing data of healthy children in the clean days as normal, Bacteroidetes decreased marginally significantly (Wilcoxon Test, p=0.072), and Proteobacteria increased significantly in asthmatic children (Wilcoxon Test, p=0.020).

Figure 2

Table 2 Difference of the dominant phyla in investigated children affected by air pollution.

Phylum	Clean day		Polluted day		p value
Phylum	mean	SD	Mean	SD	p value

Asthmatic Children
Firmicutes	63.777	0.175	59.942	0.107	0.365
Bacteroidetes	20.573	0.085	27.815	0.104	0.067*
Actinobacteria	9.899	0.107	7.848	0.068	0.001**
Proteobacteria	5.149	0.097	4.014	0.071	0.966
Healthy Children
Firmicutes	64.884	0.088	59.714	0.104	0.106*
Bacteroidetes	25.841	0.084	27.319	0.135	0.846
Actinobacteria	7.903	0.050	6.870	0.048	0.770
Proteobacteria	1.235	0.004	5.083	0.081	0.064*

“*” was marginally significant difference, “**” was significant difference.

At genus level

294 genera were identified in clean day, 462 genera were identified in the polluted day in all children. Ten genera disappeared and 178 genera appeared after air pollution, but they only appeared in one or two samples and the relative abundance of them ranged from 10-4 ~10-5. In clean day 282 genera were identified in asthmatic children and 188 genera were identified in healthy children; in polluted day 266 genera were obtained from asthmatic children and 421 genera were obtained from healthy children. The number of genus changed more obviously in healthy children affected by air pollution.

The genera with a relative abundance over 1% were analyzed. Among these genera, Clostridium_sensu_stricto_1 (Wilcoxon test, p=0.01) and Bacteroides (Wilcoxon test, p=0.05) changed significantly in asthmatic children, Fusicatenibacter (Wilcoxon test, p=0.03) and Terrisporobacter (Wilcoxon test, p=0.03) changed significantly in healthy team before and after air pollution. Composition of the dominant genera in each child, which with a relative abundance above 1%, changed to some extent after air pollution (Figure 3).

Figure 3

When taking the data in the clean days of healthy children as normal, Prevotella_9 (decreased, Wilcoxon Test, p=0.043), Eubacterium_hallii_group (increased, Wilcoxon Test, p=0.020), Lactobacillus (increased, Wilcoxon Test, p<0.001) varied significantly, and Bacteroides (decreased, Wilcoxon Test, p=0.099), Terrisporobacter (decreased, Wilcoxon Test, p=0.061), Eubacterium_coprostanoligenes_group (increased, Wilcoxon Test, p=0.085), Clostridium_ sensu_stricto_1 (increased, Wilcoxon Test, p=0.099) and Streptococcus (increased, Wilcoxon Test, p=0.091) showed marginally significant variation in asthmatic children.

Diversity changing

Regarding alpha diversity of the bacteria (Figure 4), the mean of chao1 parameter was 478.5±102.9 (asthmatic children:536.3±98.7, healthy children:415.0±64.4) and the mean of Shannon index was 5.8±0.5 (asthmatic children:5.9±0.6, healthy children:5.8±0.3) in the sequencing for clean day; the value of chao1 parameter was 545.1±302.9 (asthmatic children:504.4±91.4, healthy children:589.7±436.3), and the value of Shannon index was 5.9±0.6 (asthmatic children:5.9±0.9, healthy children:5.9±0.7) in sequencing for air polluted day.

Analyzed by Wilcoxon test , Chao1 parameter (p=0.584）and Shannon index (p=0.533）of all samples didn’t show significant differences before and after air pollution, chao1 parameter (before air pollution: p=0.004,after air pollution: p=0.020) was significantly different and Shannon index (before air pollution: p=0.121, after air pollution: p=0.349）wasn’t significantly different between asthmatic and healthy children .

Figure 4

The microbial composition was evaluated by using principal coordinates analysis (PCoA, Figure 5 ), based on the unweighted UniFrac distance. It showed difference between asthmatic and healthy children. Variation wasn’t significant before and after air pollution. Adonis test with unweighted Unifrac distance confirmed that the microbial composition of the asthmatic children was different from that of the healthy children (before air pollution: R2=0.302, p<0.05, after air pollution: R2=0.222, p<0.05).

Figure 5

Network Analysis

The network analysis showed the structure of intestinal microbiome of the four teams (Figure 6). The networks between AC1 (asthmatic children in clean day) and HC1 (healthy children in clean day) was quite different. Both AC1 and HC1 changed after air pollution. The variation was mainly about the relative abundance of taxa and correlation among each other. Accompanied by the variation, the community structure of intestinal microbiome was different before and after air pollution.

The network analysis further demonstrated that the most dominant functional bacteria were from Firmicutes. And the main variation happened in Firmicutes when air pollution happened. Firmicutes must played the most important role in maintaining intestinal microbes balance. It could be seen that the composition of intestinal bacteria showed balance relatively in healthy children in clean day. When in asthmatic children the unbalance of microbiome appeared. After experiencing the polluted day, the relative abundance of some taxa varied, the correlation among taxa became more complex than before both in asthmatic and healthy children. Specifically speaking, after the polluted days, the relative abundance of bacteria from Firmicutes varied obviously compared with that before air pollution both in the two teams, such as Rombotsia, Lachlostridium and so on. Furthermore, the correlation between main bacteria varied, such as Faecalibacterium, which was found to be correlated with less kinds of bacteria after air pollution in asthmatic children. And more negative correlation among taxa appeared when after air pollution.

There were some changes in the other phylum. Prevotella_9 from phylum of Bacteroidetes was correlated with Staphylococcus and Fusicatenibacter in team of HC1, then it influenced more bacteria after air pollution both in teams of AC2 (asthmatic children in polluted day) and HC2 (healthy children in polluted day). Mannomonas, Polycyclovorans and Vibrio, which were from phylum of Preteobacteria, were independent relatively before air pollution in team of AC1, then they correlated with more taxa after air pollution. However, there were no significant correlation between them and other taxa in AC1.

Figure 6

Linear discriminant analysis (LDA) of eﬀect size (LEfSe)

In group of healthy children, genus of Fusicatenibacter was the biomarker before air pollution happened (Figure 7). The function as biomarker of Fusicatenibacter disappeared after air pollution, which implied the impact of air pollution on it. In asthmatic children, phylum of A0839, genus of chiayiivirga and species of Actinomyces_sp._oral_clone_GU009 were the biomarkers before air pollution; class of Bacteroidia and order of Bacteroidales were the biomarkers after air pollution (Figure 7). The bacteria biomarkers between asthmatic and healthy children based on clean days were seen detailly in Figure 7 (AC1 vs HC1).

Figure 7

Analysis for impact of environmental factors

Based on the above analysis result, the following bacteria varied significantly before and after smog, including Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and A0839 at phylum level, Bacteroidia at class level, Bacteroidaies at order level, Clostridium_sensu_stricto_1, Bacteroides, Fusicatenibacter, Terrisporobacter and Chinayiivirga at genus level, Bacteroides_uniformis and Actinpmyces_sp_oral_ctone_Gu009 at species level. The relationship between relative abundance of these bacteria and environmental pollutants were analyzed by multiple level analysis and multiple linear regression analysis.

As a panel study, the result was analyzed by multiple level analysis firstly. There was no significant difference between the twice sequencing of each child for every taxon (p>0.05). Then the analysis for impact of polluted environment factors on intestinal microbiome was back to method of multiple linear regression. Backward method was used and the factors of age, male, BMI, environmental temperature and O3 were adjusted. The multiple linear regression showed that the relative abundance of Firmicutes were significantly related with SO2 and NO2, Bacteroidetes and Bacteroidia were significantly related with SO2, Clostridium_sensu_stricto_1 were significantly related with SO2 and NO2, Terrisporobacter were significantly related with PM2.5, PM10, SO2 and NO2, Fusicatenibacter_1 were significantly related with PM2.5, PM10 and NO2 (Table 3). Concentration of O3 was evaluated in every multiple linear regression model, it was correlated with every taxon above (P<0.05).

Table 3 Impact of Environmental Pollutants on Intestinal Microbiome

taxa	Level	air pollutant	β value	p value	Model
taxa	Level	air pollutant	β value	p value	F value	p value	Adjusted R²
Firmicutes	phylum	SO₂	-0.0316	0.019	3.85	0.0103	0.218
		NO₂	-0.0095	0.029	3.67	0.0129	0.207
Bacteroidetes	phylum	SO₂	0.0259	0.033	2.32	0.0909	0.088
Bacteroidia	class	SO₂	0.0287	0.015	2.97	0.0439	0.126
Clostridium_sensu_stricto_1	genus	SO₂	-0.0027	0.004	3.84	0.0104	0.217
		NO₂	-0.0006	0.075	2.13	0.0965	0.099
Fusicatenibacter	genus	PM_2.5	-0.0009	0.019	4.61	0.0024	0.305
		PM₁₀	-0.0006	0.009	4.58	0.0025	0.304
		NO₂	-0.0021	0.015	4.30	0.0036	0.287
Terrisporobacter	genus	PM_2.5	-0.0006	0.003	5.67	0.0003	0.406
		PM₁₀	-0.0004	0.001	7.18	0.0001	0.430
		SO₂	-0.0026	0.031	4.28	0.0037	0.286
		NO₂	-0.0013	0.001	6.22	0.0002	0.433

Adjusted the factors of age, sex, BMI, environmental temperature and O₃.

This study confirmed that composition of intestinal microbiome in asthmatic children were different from healthy children significantly, and air pollution had effects on intestinal microbiome both in asthmatic and healthy children.

As same as previous studies, Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria were the dominant phyla in human gut [23], they accounting for >99% of the total phyla both in asthmatic and healthy children in this study. Composition of intestinal microbiome was significantly different between asthmatic and healthy children, which was confirmed by analysis of PCoA and Adonis test. Network analysis showed the difference of community structure of intestinal microbiome between asthmatic and healthy children too. Most previous studies indicated an association between the intestinal microbiome composition and atopic sensitization or symptoms in time of early life [9,11,20-22]. The discovery of this article showed that intestinal microbiome was significantly different between asthmatic and healthy children. It suggested that action on mucosal of gut bacteria was a sustained process, which may influence the immune response all the time, intestinal microbiome may play an important role during the development of asthma.

As for the relative abundance of some main bacteria (Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria), constituent ratio of them didn’t change significantly after the smog, but the relative abundance of them varied. Bacteroidetes and Actinobacteria changed significantly in asthmatic children, in verse Firmicutes and Proteobacteria varied significantly in healthy children (Table 2). It illustrated that air pollution could influence intestinal microbiome and the impact of air pollution on intestinal bacteria was different between asthmatic and healthy children.

Firmicutes with the biggest relative abundance (>60%), occupied a dominant position in human’s gut bacteria. Fujimura [24] had reported that Firmicutes was a protected factor for asthma. And Faecalibacterium, which came from Firmicutes, was enriched in the gut of healthy people [25]. The result of this article indicated that the variation of intestinal bacteria induced by asthma or air pollution all mainly happened on Firmicutes (Figure 6), the relative abundance of Firmicutes in asthmatic children was lower than it in healthy children, and it declined marginally significantly after smog (p=0.106, Table 2). The statistical analysis showed that the relative abundance of Firmicutes was significantly negatively correlated with concentration of SO₂and NO₂in all participants. These findings suggested that reduction of Firmicutes would do bad on health, which agreed with previous studies, and air pollution could act on bacteria of Firmicutes to do harm to health.

Though the relative abundance of Firmicutes in asthmatic children also declined in our study on the whole (Table 2), the relative abundance of some bacteria belonging to Firmicutes increased in genus level in asthmatic children, including Streptococcus (Wilcoxon test, p=0.091), Lactobacillus（Wilcoxon test, p<0.001）, Clostridium_sensu_stricto_1 (Wilcoxon test, p= 0.099), Eubacterium_ hallii_group (Wilcoxon test, p=0.020) and Eubacterium_coprostanoligenes_ group（Wilcoxon test, p =0.085）. It revealed that balance of Firmicutes in intestinal bacteria may be more important for human health. And there was a study reported that Lactobacilli and Eubacteria were more prevalent in a low prevalence of childhood allergy [26], which was different with this article. The reason may be that the research objects of these articles were different, the previous study focused on the allergic disease, and this article only aimed to study the asthma, and some asthmatics were not allergic in this study.

Bacteroidetes was another important phylum in this study, the relative abundance of it increased marginally significantly after air pollution in asthmatic children. The relative abundance of Bacteroidetes in phylum level and Bacteroidia in class level positive correlated with concentration of SO₂. Bacteroidates in Order level, Bacteroidia in class level and Bacteroides_uniformis were the biomarkers influenced by air pollution in asthmatic children. All of these results illustrated that Bacteroidetes could be influenced by air pollution. In Jakob’s article [11], children at 1-year -old, who had high risk for asthma, had more relative abundance of Bacteroides. Whether the air pollution could trigger asthma through making Bacteroidates increase was worthy of further study. In this study, the relative abundance of Bacteroides was lower in asthma children compared with healthy children (Wilcoxon test, p=0.072). The reason may be related with a limitation of this study. The size of samples in this research was small. As mentioned before, there were 27% non-allergic children in asthmatic team, so the reason needed to be further researched. Prevotella_9 with a larger relative abundance in each team was another important bacterium from phylum of Bacteroidetes. The relative abundance of Prevotella_9 declined in asthmatic children (Wilcoxon test, p=0.043), though multiple linear regression analysis didn’t show it correlated with environment pollutants, the correlation with other bacteria changed in all teams (Figure 6). The function of it was worthy of further research.

Actinobacteria and Proteobacteria were all varied before and after air pollution. Actinobacteria declined significantly after air pollution in asthmatic children. Proteobacteria was the biomarker of asthmatic children (Figure 7), the abundance of it increased after air pollution, and correlated with more other bacteria in asthmatic children (Figure 6). And Proteobacteria increased marginally significantly after air pollution in healthy children (Table 1). Air pollution may increase risk of asthma developing through Proteobacteria.

Previous studies have found that the diversity of intestinal microbiome decreased in some allergic disease, such as atopic dermatitis, and low species diversity may relate with disease development. Result of this study didn’t show a significant changing about species diversity in healthy and asthmatic team. The reason may be that some non-allergic asthma children (p9, p10 and p11) were in the asthmatic team, they have more OTUs than other asthmatic children, and they have higher Chao1 index than other asthmatic children. But size of them was small. We didn’t analyze them separately. Microbiome of non-allergic asthma deserves to be further studied. Species diversity of intestinal microbiome didn’t show significantly variation before and after air pollution. The impacts of polluted environmental factors were analyzed based on number and relative abundance of the identified species. There were some changings before and after air pollution. The number of OTUs identified from each child has changed ranging from 4.7%-79% after air pollution. More phyla and genera were identified after air pollution, they all account for less than 10^-4. However, this phenomenon may be the first step for the changing of composition of intestinal microbiome. This changing may disturb the balance of intestinal microflora. Just as it was shown in the network analysis, the taxa with significant correlations changed and the correlation among each taxon varied before and after air pollution both in healthy and asthmatic teams. The findings about no significant changing before and after air pollution might related with that the interval between sampling was short. Based on theories of previous researches, the difference between the asthmatic and healthy children was shaped from birth, even in uterus. Composition of intestinal microbiome maintain a dynamic balance. There were many factors can influence intestinal microbiome, such as age, gender, diet and environment and so on. But the influence of them didn’t work in a short time [27,28], maybe so did the environment. From the result of this article, changings induced by air pollutants had emerged. When put all samples together to analyzed using multiple linear regression, the relative abundance of some bacteria was significantly influenced by environment pollutants, which illustrated the environmental action on intestinal bacteria.

There were two limitations about this study. The first one was that the sample size was small. Because this study was part of a big project, which main aimed to study on non-allergic asthma. As well know, morbidity of non-allergic asthma accounts for a relatively low proportion among all asthma. This study was implemented at the beginning of the main project, when fewer patients were enrolled to the group. Compared with other article about study of composition of intestinal microbiome with 3-4 individuals, this research still had a reference value [29,30]. The second limitation was that the interval of sampling was short. This article aimed to explore the air-pollution’s effects on asthmatic children preliminary, few studies could be referenced. So that this article designed sampling interval depending on the growth cycle of microorganisms. As shown in the result, too short time might not reflect the effect of air pollution significantly. Based on the result preliminary, project team will overcome the limitation to further study on impact of air pollution on children asthma.

Conclusion

Just as microbiome in asthma play an important role in pathogenesis, phenotype and response to treatment [31,32], gut microbiota had been as a target for preventing and treating allergic disease [33].This study found the main variation in composition of intestinal microbiome in asthmatic children, it always found some microorganism biomarkers for asthma or air pollution, which may help to reduce and prevent children asthma through adjusted the composition of intestinal microbiome. And air pollution could impact on intestinal microbiome both in asthmatic and healthy children, personal protective measures for children should be strengthened in polluted day, which can help to research the mechanism of asthma influenced by air pollution and help to prevent asthma development. In addition, the effect of air pollution on allergic and non-allergic children may be different, we will further study on the character of intestinal microbiome of allergic and non-allergic asthma. Study of this article would have a quite significance for public health and clinic.

Study populations

This study was part of a panel study, founded by National Natural Science Foundation of China. 11 children with asthma and 10 healthy children without asthma among 176 child participants of the study were selected and agreed to join the air pollution study. All the 21 participants aged from 5 to 12 years old were asked to offer information about the living environment, study environment, history of disease, family history of asthma or allergy, and diet with a questionnaire which was revised on the basis of ISAAC ( The International Study of Asthma and Allergies in Childhood) Phase Three Environmental Questionnaire, ISAAC core questionnaires on asthma, and NHANES ( National Health and Nutrition Examination Survey ) Food Questionnaire. Asthmatic children were diagnosed according to the Global Initiative for Asthma (GINA, http://ginasthma.org) by physicians from Children Hospital of Capital Institute of Pediatrics in Beijing. This project was approved by ethical review committee of Institute for Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention. All participants signed the written informed consents, which described the purposes, methods and expected results of the study detailly.

Samples collection

Feces collection were required for all participants. All participants were asked to meet the followed criteria, including no antibiotic using history and vaccination history in the past one month, no injury and diarrhea in the past two weeks. In addition, girls were not in the period of menstrual cycle when offering feces.

Feces samples were collected twice. The collections were performed at the time before and after the smog developed respectively. AQI of the five days before the day sampling for clean day were all less than 100. The samples collection for polluted day were at the 3rd to 5th day in the polluted days, AQI of these days were all more than 100. Feces samples were collected with a sterile 30 mL plastic tubes, then the tubes were transported to the laboratory in an anaerobic tank at 4℃ within four hours. Before detecting, the samples were stored at temperature of -80℃.

Samples were divided into four teams. They were asthmatic children in clean days (AC1), asthmatic children in polluted days (AC2), healthy children in clean days (HC1) and healthy children in polluted days (HC2).

DNA extracting and Sequencing

Total genome DNA was extracted from 0.5g feces sample using a modified CTAB (Cetyltrimethylammonium Ammonium Bromide) method [34,35]. DNA was assessed by 1% agarose gel electrophoresis, then it was diluted into 1ng/μL using sterile water for sequencing. 16SrRNA genes were sequenced on Illumina platform at Novogene Bioinformatics Technology Co., Ltd. The V4 region of 16SrRNA genes were amplified by using the specific primers of 515F（GTGCCAGCMGCCGCGGTAA）and 806R（GGACTACHVGGGTWTCTAAT）with the barcodes. PCR reactions were carried out in a 30 μL system with 15 μL of Phusion High-Fidelity PCR Master Mix (New England Biolabs, USA), 0.2 μM of forward and reverse primers, and 10 ng template DNA. Thermal cycling consisted of initial denaturation at 98 ℃ for 1 min, 30 cycles of denaturation at 98℃ for 10 s, annealing at 50 ℃ for 30 s, elongation at 72 ℃ for 30 s, and finally keeping at 72℃ for 5min (Bio-rad T100 Thermal Cycler, USA). Then mixed PCR products with same volume of loading buffer and operated electrophoresis on 2% agarose gel for detection. Samples with 400-450bp were chosen and purified (GeneJET Gel Extraction Kit, Thermo Scientific, USA) for further experiments. Then the sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) and sequenced on an Illumina HiSeq 2500 (Illumina, USA).

Sequence analysis

Paired-end reads from the original DNA fragments were merged using FLASH [36], which merged paired-end reads when there were overlaps between reads. Paired-end reads was assigned to each sample according to the unique barcode. Sequences were analyzed using the Quantitative Insights into Microbial Ecology (QIIME). Reads were filtered by QIIME quality filters to get Operational Taxonomic Units (OTUs). Sequences with ≥97% similarity were assigned to the same OTUs. A representative sequence was picked for each OTU and the RDP Classifier used to annotate taxonomic information for each representative sequence. Species were annotated according to the SSU rRNA data base in SILA(http://www.arb-silva.de) using the MUSCLE software (Version 3.9.31).

Statistical analysis

Statistical analyses were performed by the R software (V 3.4.). The signiﬁcance of OTUs, α-diversity index (including chao1 and Shannon Index) and the relative abundance of bacterial at level of phylum and genus among the four teams were tested using Wilcoxon test. The changing of microbial composition was showed using principal coordinates analysis (PCoA) based on the unweighted UniFrac distance [37]. Correlation of co-currency bacteria was analyzed by network analysis, in which a Spearman’s correlation between two genera was calculated [38,39]. Signiﬁcant biomarkers were evaluated by the Linear Discriminant Analysis (LDA) of eﬀect size (LEfSe) analysis [40]. Multiple level analysis and multiple linear regression analysis was used to analyzed the impact of air pollution on intestinal microbiome. All reported p values were two-tailed, and a p value of<0.05 was considered signiﬁcant.

Abbreviations	Full Name
AQI	Air Quality Index
AC1	asthmatic children in clean day
AC2	asthmatic children in polluted day
BMI	Body Mass Index
GINA	Global Initiative for Asthma
HC1	healthy children in the clean day
HC2	healthy children in polluted day
ISAAC	International Study of Asthma and Allergies in Childhood
LDA	Linear discriminant analysis
LEfSe	Linear discriminant analysis of eﬀect size
OTUs	Operational Taxonomic Units
SD	Standard Deviation

Ethics approval and consent to participate

This project was approved by ethical review committee of Institute for Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention at Feb 15, 2015.

Consent for publication

The consents to participate in this study were obtained from all participating children and their parents or legal guardians. All participants and their parents or legal guardians signed the written informed consents, which described the purposes, methods and expected results of the study detailly.

Availability of data and materials

The datasets generated or analyzed during the current study will be available in National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was founded by National Natural Science Foundation of China.

Authors' contributions

PZ, QW and XB were in charge of design the study. CL and LS performed the diagnosis of asthmatic children. PZ, KZ, XL, XN and NX were responsible for collecting feces samples. PZ analyzed and interpreted the data and wrote the paper. All authors read and approved the final manuscript.

Acknowledgements

Not applicable

Authors' information

¹ National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 29 Nanwei Road, Xicheng District, Beijing 100050, China. ² Xinwu District Center for Disease Control and Prevention, No.111 Xingchuang Fourth Road, Xinwu District, Wuxi 214000, China. ³ Children’s Hospital, Capital Institute of Pediatrics, No. 2 Yabao Road, Chaoyang District, Beijing 100020, China.

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The Impact of Air Pollution on Intestinal Microbiome of Asthmatic Children: A Panel Study

Status:

Version 1

Abstract

Figures

Background

Results

Discussion

Conclusion

Methods

Study populations

Samples collection

Sequence analysis

Statistical analysis

List of abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

Authors' information

References

Status:

Version 1