Indoor microbiome, environmental characteristics and asthma among junior high school students in Johor Bahru, Malaysia

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Contents lists available atScienceDirect

Environment International

journal homepage:www.elsevier.com/locate/envint

Indoor microbiome, environmental characteristics and asthma among junior high school students in Johor Bahru, Malaysia

Xi Fu

^a,b

, Dan Norbäck

^c

, Qianqian Yuan

^b,d,e

, Yanling Li

^b,d,e

, Xunhua Zhu

^b,d,e

,

Jamal Hisham Hashim

^f,g

, Zailina Hashim

^h

, Faridah Ali

ⁱ

, Yi-Wu Zheng

^j

, Xu-Xin Lai

^j

, Michael Dho Spangfort

^j

, Yiqun Deng

^b,d,e

, Yu Sun

^b,d,e,⁎

aDepartment of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, PR China

bGuangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou, Guangdong 510642, PR China

cOccupational and Environmental Medicine, Dept. of Medical Science, University Hospital, Uppsala University, 75237 Uppsala, Sweden

dKey Laboratory of Zoonosis of Ministry of Agriculture and Rural Aﬀairs, South China Agricultural University, Guangzhou, Guangdong 510642, PR China

eGuangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, Guangdong 510642, China

fUnited Nations University-International Institute for Global Health, Kuala Lumpur, Malaysia

gDepartment of Community Health, National University of Malaysia, Kuala Lumpur, Malaysia

hDepartment of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, Serdang, Selangor, Malaysia

iPrimary Care Unit, Johor State Health Department, Johor Bahru, Malaysia

jAsia Paciﬁc Research, ALK-Abello A/S, Guangzhou, China

A R T I C L E I N F O

Handling editor: Da Chen Keywords:

Bacteria Fungi

Microbial community Absolute quantity Wheezing Breathlessness Adolescence Dampness/visible mold Malaysia

Johor Bahru Tropics Junior high school

A B S T R A C T

Indoor microbial diversity and composition are suggested to affect the prevalence and severity of asthma by previous home microbiome studies, but no microbiome-health association study has been conducted in a school environment, especially in tropical countries. In this study, we collectedfloor dust and environmental characteristics from 21 classrooms, and health data related to asthma symptoms from 309 students, in junior high schools in Johor Bahru, Malaysia. The bacterial and fungal composition was characterized by sequencing 16s rRNA gene and internal transcribed spacer (ITS) region, and the absolute microbial concentration was quantified by qPCR. In total, 326 bacterial and 255 fungal genera were characterized. Five bacterial (Sphingobium, Rhodomicrobium, Shimwellia, Solirubrobacter, Pleurocapsa) and two fungal (Torulaspora and Leptosphaeriaceae) taxa were protective for asthma severity. Two bacterial taxa, Izhakiella and Robinsoniella, were positively associated with asthma severity. Several protective bacterial taxa including Rhodomicrobium, Shimwellia and Sphingobium have been reported as protective microbes in previous studies, whereas other taxa werefirst time reported. Environmental characteristics, such as age of building, size of textile curtain per room volume, oc- currence of cockroaches, concentration of house dust mite allergens transferred from homes by the occupants, were involved in shaping the overall microbial community but not asthma-associated taxa; whereas visible dampness and mold, which did not change the overall microbial community forfloor dust, was negatively associated with the concentration of protective bacteria Rhodomicrobium (β = −2.86, p = 0.021) of asthma.

The result indicates complex interactions between microbes, environmental characteristics and asthma symptoms. Overall, this is theﬁrst indoor microbiome study to characterize the asthma-associated microbes and their environmental determinant in the tropical area, promoting the understanding of microbial exposure and respiratory health in this region.

1. Introduction

Asthma prevalence has been rising globally in the past few decades (Eder et al., 2006). Many personal and environmental and

characteristics have been suggested to be associated with asthma prevalence and severity, including parental asthma, preterm delivery and low birth weight, tobacco smoking, respiratory syncytial virus infection, exposure to air/traﬃc pollution and indoor mold (de Benedictis

https://doi.org/10.1016/j.envint.2020.105664

Received 11 December 2019; Received in revised form 12 March 2020; Accepted 12 March 2020

⁎Corresponding author at: Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou, Guangdong 510642, PR China.

E-mail address:sunyu@scau.edu.cn(Y. Sun).

Available online 19 March 2020

T

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and Bush, 2017; Castro-Rodriguez et al., 2016). However, many of these risk factors fail to explain the increasing trend of asthma epi- demics. For example, smoking is a strong risk factor for asthma, but the prevalence of smoking is significantly reduced in the past thirty years (Ng et al., 2014). Recent studies suggested that the changing of lifestyle and microbial exposure during the industrialization and urbanization process are associated with the increasing prevalence of asthma symptoms (Bello et al., 2018). Nowadays, more people live in the city than the rural area, and they spend most of the time in the indoor environment (Klepeis et al., 2001), thus it is necessary to identify the beneficial and risk exposure in various indoor environments. Progress in culture-independent microbiome studies reveals the association between indoor microbial exposure and human respiratory health in the home environment. It was reported that high bacterial richness in homes of the traditional farm area protected against childhood asthma compared with urban families (Ege et al., 2011). Similarly, a high diversity of fungal exposure is protective for childhood asthma development (Dannemiller et al., 2014). There are also studies suggest that the asthma prevalence is related to the abundance of specific taxa rather than microbial richness (Kirjavainen et al., 2019). For example, two microbiome studies used absolute quantification approaches identified only one protective or risk microbe for asthma symptoms (Pekkanen et al., 2018; Dannemiller et al., 2016), and one study in the United States using relative abundance from 16s rRNA identified a few hun- dreds of potentially associated microbes for inner-city children (O'Connor et al., 2018). Thus, although there are still some dis- crepancies among studies, the association between indoor microbial exposure and asthma development in the home environment is generally established.

In contrast to the extensive researches in the private home environment, no study has been conducted in public indoor environments, such as schools. Thus, the health eﬀect of microbial assemblage in these indoor areas is unclear. Also, these microbiome studies in the home environment are mainly focused on childhood asthma (Ege et al., 2011;

Dannemiller et al., 2014; O'Connor et al., 2018), and the health effect of indoor microbial exposure to other age groups, such as adolescents and adults, is not clear. In addition, current home microbiome health association studies are all conducted in middle and high latitude regions, mainly from developed countries in Europe and the United States. It has been indicated that the indoor microbial composition is geographically patterned, and significant compositional variations can be detected across different climates, latitudes and geographic regions (Barberan et al., 2015; Amend et al., 2010). Thus, the associated-microbes identified in middle and high latitude provides little implication regarding the microbial exposure and health in the tropical area. Overall, it is necessary to conduct more microbiome- health association studies covering different geographic regions, age groups and indoor environments.

We conducted a few previous epidemic studies in schools of Malaysia and identiﬁed a list of common risk factors for asthma, including furry pet allergens, indoor dampness and endotoxins (Norback et al., 2014, 2017; Cai et al., 2011). But no studies investigated the indoor microbiome and the association with asthma symptoms.

In this study, we conducted theﬁrst microbiome survey in a junior high school of Johor Bahru, Malaysia, to screen protective and risk microbes associated with asthma symptoms. The absolute concentration of bacterial and fungal taxa inﬂoor dust from 21 randomly selected classrooms were characterized by amplicon sequencing and quantitative PCR. Association between microbial taxa, environmental characteristics and prevalence of asthma were analyzed.

2. Materials and methods

Floor dust was collected from 8 junior high schools in Johor Bahru, Malaysia, 4 classes in each school. In total, 32 dust samples were collected, but 11 of them failed to amplify adequate DNA for amplicon

sequencing, and thus only dust samples of 21 classes could be se- quenced. The numbers of success and failed samples in each school were listed inTable S1. Health data were collected by self-reported questionnaires from 15 randomly selected students in each class. The ethical permission was approved by the Medical Research and Ethics Committee of the National University of Malaysia, and all participants gave their informed consent.

2.1. Assessment of health data

Questions about doctor-diagnosed asthma and current asthma were obtained from the European Community Respiratory Health Study (ECRHS). The questions included asthma symptoms and related information during last 12 months, including wheeze, breathlessness during wheeze, feeling of chest tightness, shortness of breath during rest, shortness of breath during exercise, woken by attack of shortness of breath, ever had asthma, attack of asthma, and current asthma medication use.

A validated asthma score, including eight items, were calculated to measure asthma severity (Pekkanen et al., 2005) were calculated, and then re-deﬁned as 0, 1, 2, > =3. Questions about current smoking and parental asthma/allergy were also included. Details about the questions were described in a previous study (Norback et al., 2014).

2.2. Dust sampling and building inspection

The detailed dust sampling procedure was reported in a previous publication (Norback et al., 2014). Floor dust in the classroom was collected by a 400 W vacuum cleaner with a dust sampler (ALK Abello, Copenhagen, Denmark) through a Milliporefilter (pore size 6 µm). The filter is made of cellulose acetate, which retains 74% of particles of 0.3–0.5 mm, 81% of particles of 0.5–1.0 mm, 95% of particles of 1–10 mm and 100% of larger particles (> 10 mm). Three dust samples were collected at the same time for each classroom. The total vacuum sampling procedure for each sample lasted 4 min, 2 min on thefloor and 2 min on other surfaces above thefloor like chairs and desks. The floor area range from 39 to 82 m²with a mean of 69 m². Each classroom was divided into the corridor part and window part, which were sampled separately as two samples for allergen analysis in the previous publication. The remaining dust was then sieved in the lab, through a 0.3-mm mesh screen to fine dust, and was stored in the freezer at

−80˚C. In this study, dust from the two parts of the classroom was combined together for the amplicon sequencing and quantitative PCR.

A third sample was collected by repeating the same procedure for the whole classroom, and the dust was used to quantify the house dust mite and cockroach allergens at the ALK laboratory in Guangzhou. En- vironmental characteristics, including relative humidity, indoor CO2

and outdoor NO2concentration, textile curtain factor, the concentration of house dust mite and cockroach allergen, were measured, and information about the construction year, visible dampness and mold were noted (Norback et al., 2014). The textile curtain factor was de- fined as the area of textile curtain per room volume. The environmental characteristics showed no difference between the successfully amplified samples and those failed in amplification.

2.3. DNA extraction and sequencing

DNA extraction and multiplex sequencing services were provided by GENEWIZ, Suzhou lab. Total genome DNA was extracted for amplicon sequencing by Soil DNA Kit for all dust samples with bead beating and spin ﬁlter technology. Negative controls were added to avoid mass contamination in the ampliﬁcation process. DNA quality and concentration were evaluated with a NanoDrop One spectrophotometer.

Amplicons were generated by primers targeting the v3 and v4 regions on the 16s ribosome RNA (16s rRNA) gene for bacteria, and internal transcript space 2 (ITS2) region for fungi. The FASTQ formatted raw

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sequence data were uploaded in QIITA with accession number 12875 (https://qiita.ucsd.edu/study/description/12875). Two separate ex- tractions of 10 mg dust were conducted for real-time PCR to quantify the absolute concentration of total bacteria and fungi in the dust. The SYBR Green method was used for universal bacterial detection. A 20 µl reaction mixture containing 10 µl of Master Mix (Hieﬀ™ qPCR SYBR® Green Master Mix), 2 µl of template DNA, 0.5 µl of each primer (forward: 5′-GCAGGCCTAACACATGCAAGTC-3′ and reverse: 5′-CTGC TGCCTCCCGTAGGAGT-3′) (Nadkarni et al., 2002). Quantitative PCR for fungal DNA was described in a previous publication (Norback et al., 2016).

2.4. Bioinformatics analysis and statistics

The forward and reverse reads were joined and assigned to samples by barcoding information, and the quality ﬁlter was set as sequence length > =200 bp. The sequences were then assigned to operational taxonomic units (OTUs) with a sequence similarity of 97% and anno- tated against Silva and Unite database, respectively. Principle compo- nent analysis (PCoA) and Adonis analysis were performed to assess the inﬂuence of environmental characteristics to microbial richness and composition based on Bray-Curtis distance matrix. Analyses for microbiome dataset were mainly conducted with the Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) platform (Caporaso et al., 2010;

Lawley and Tannock, 2017). Two-level hierarchical ordinal regression models were performed to analyze associations between microbial richness (number of observed taxonomic units, OTUs) and asthma score, and between the quantity of single bacterial or fungal phylum, class, and genus (in log10 format) and asthma score. The latter analysis only included microbial taxa presented in at least four classrooms. In all analyses, gender, race, smoking, and parental asthma/allergy were included as adjustment. Parallel line assumption test was performed for the ordinal regression models, and those violated the parallel line assumption were calculated in a multi-nominal regression model. Asso- ciation of single environmental characteristics with asthma score were assessed by a hierarchical ordinal regression model. The environmental characteristics potentially related to asthma (p < 0.1) were further analyzed with asthma-associated microbes in multivariate linear regression models with a forward stepwise method. All hierarchical models and parallel line test was conducted by StataSE 15.0 (StataCorp LLC), and other statistics were conducted with IBM SPSS software 21.0 (IBM). Association analysis between environmental characteristics and microbial richness and community variation were conducted by Adonis in R v3.3.

3. Results

The response rate of the questionnaire was 96% (n = 309). The students were aged from 14 to 16 years, and 52% were girls. The ethnic group of the participants were Malay (43%), Chinese (42%), and Indian (15%). The detailed demographic data were described in previous studies (Norback et al., 2014; Cai et al., 2011). The prevalence of asthma symptoms and asthma score is presented inTable 1.

3.1. Sequencing statistics and microbial taxa

The bacterial 16s rRNA dataset was rarefied to the depth of 27,000 reads for each sample, and fungal ITS was rarefied to 35,000 reads. The rarefaction curves indicate that the sequencing depth is deep enough to capture the majority of operational taxonomic units (OTUs) in thefloor dust (Fig. S1). In total, 895 bacterial and 1512 fungal OTUs were obtained, and distinct distribution patterns were observed. For bacteria, 36.8% of bacterial OTUs were presented in all samples, whereas only 10.3% of fungal OTUs were presented in all samples and approximately half of the OTUs were presented in ten or fewer samples (Fig. 1A and B).

The result suggests that, inﬂoor dust, many fungal taxa are presented in

a few classrooms and have more restricted distributions compared to bacterial taxa.

The major phylum included Proteobacteria (35.0 ± 7.3%, mean and standard deviation), Actinobacteria (21.2 ± 6.3%), Cyanobacteria (17.6 ± 7.3%) and Firmicutes (17.3 ± 8.6%;Fig. S2andTable S3).

The top genus mainly included environmental taxa such as Bacillus (4.2 ± 5.8%), Paracoccus (3.2 ± 1.2%), Sphingomonas (2.8 ± 0.9%) and Saccharopolyspora (2.5 ± 2.0%), and human skin taxa Staphylo- coccus (3.4 ± 2.5%;Fig. 1C andTable S4). Distinct bacterial compositional variation has been observed even for samples collected from the same school. For example, Bacillus accounted for 24.3% of the total bacterial load in classroom No. 1 of school No. 3 (S3C1), whereas accounted for only 2.4%, 1.4% and 1.5% of total loads in the other three classrooms in school No. 3 (Fig. 1C). The fungal phylum was dominated by Ascomycota (72.5 ± 11.6%), followed by Basidiomycota (17.8 ± 8.9%; Fig. S3, Table S5). The top fungal genus included common mold taxa such as Aspergillus (16.6 ± 7.9%), Penicillium (10.2 ± 15.7%) and Cladosporium (7.8 ± 8.2%), as well as outdoor environmental fungi such as Hortaea (8.0 ± 7.5%), Wallernia (6.6 ± 9.2%) and Emericella (4.1 ± 9.6%) (Fig. 1D,Table S6). As- pergillus presented in high abundance (> 9%) in all samples. But large- scale variations were detected for some other genera. For example, Penicillium accounted for 48.1% in S5C2, but less than 5% in S5C1 and S5C3 (Fig. 1D). The compositional variation of bacterial and fungal community in all samples are illustrated by the principal coordinate analysis (PCoA) (Fig. S4A and S4B).

3.2. Environmental characteristics associated with overall microbial richness/composition

In this study, we collected eight environmental characteristics and tested their association with overall microbial richness (Table S7). A high concentration of house dust mite and cockroach allergens and high textile curtain factor were negatively associated with the number of fungal observed OTUs (Adonis, p < 0.05;Table S7). No environmental characteristic had a signiﬁcant association with bacterial richness.

The overall bacterial community composition was affected by the concentration of house dust mite allergens and textile curtain factor in the classroom; the fungal community was affected by the concentration of house dust mite allergens and cockroach allergens, and age of the building (Adonis, p < 0.05; Table 2). We visualize the abundance variation of the top bacterial and fungal genera affected by these environmental characteristics. Classrooms with a high textile curtain factor had a higher abundance of bacterial genera Enterococcus, Chroococcidiopisis and an unidentified genus from Nostocaceae, and Table 1

Prevalence of asthma symptoms and asthma score.

Symptoms Number (n = 309) Prevalence (%)

Wheeze and breathlessness during wheeze 20 6.56

Feeling of chest tight 16 5.18

Attack of shortness of breath during rest 28 9.1 Attack of shortness of breath during

exercise

114 36.9

Woken by attack of shortness of breath 23 7.4

Ever asthma 39 12.6

Attack of asthma 7 2.3

Current medication for asthma 11 3.6

Asthma score*

0 148 47.9

1 101 32.7

2 28 9.1

> =3 32 10.3

* Firstly, an“asthma score 8” was calculated by summarizing the eight items listed above. The displayed asthma score is re-deﬁned from “asthma score 8” as 0, 1, 2, and > =3.

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Fig. 1. (A) Frequency spectrum of bacterial OTU presence in samples; (B) Frequency spectrum of fungal OTU presence in samples. Taxonomic composition of (C) bacteria and (D) fungi at the genus level for all samples. Each sample is labelled with school and class numbers.

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classrooms with a low textile curtain factor had a higher abundance of Deinococcus, Kocuria, Rubellimicrobium, and Paracoccus (Kruskal-Wallis test, p < 0.05;Fig. 2A). Classrooms with a low concentration of house dust mite allergens (which could be transferred from homes) had a significantly higher abundance of bacterial genera Deinococcus, Kocuria, Acinetobacter, and a fungal genus Candida and an unidentified fungal genus from Pleosporales (p < 0.05;Fig. 2B and 2C). The classrooms with no cockroach allergen detected had a higher abundance of an unidentified fungal genus from Pleosporaceae (Fig. 2D). The abundance of common mold genera, like Aspergillus, Cladosporium, Penicillium, Stschybotrys, were not changed by these factors (Fig. 2).

3.3. Identifying protective and risk microbes for asthma

The prevalence of asthma symptoms is presented inTable 1. Asthma score was calculated based on all eight items to represent the severity of asthma. There were no associations between the number of OTUs within the major phylum and class and asthma severity, suggesting microbial richness in the indoor environment was not signiﬁcantly af- fecting asthma symptoms (Table S8). Although Proteobacteria (95% CI 0.92–1.01, p = 0.09) and Cyanobacteria (0.95–1.0, p = 0.07) showed marginally protective associations with the asthma score.

We screened associations between the absolute quantity of bacterial and fungal genus and asthma score with a hierarchical ordinal regression model. Microbes presented in less thanfive classrooms were not included in the analyses, and in total 284 bacteria and 202 fungi genera were examined in the association analyses. P-value < 0.01 was set as a cutoff to screen associated microbes. Five bacterial genera were negatively associated with asthma score (p < 0.01), including Sphingobium, Rhodomicrobium and Shimwellia in Proteobacteria, Solirubrobacter in Actinobacteria, Pleurocapsa in Cyanobacteria. Two genera, including Izhakiella in Proteobacteria and Robinsoniella in Firmicutes, were positively associated with asthma score (Table 3). Two fungal genera in Ascomycota phylum were negatively associated with asthma score (p < 0.01) (Table 3), including Torulaspora, and an unidentified genus in Leptosphaeriaceae family. The model for Robinsoniella and Lepto- sphaeriaceae violated the parallel line test (p < 0.01), and the associations were then assessed by multi-nominal regression. Associations were observed between Robinsoniella and asthma score 0 to 1 (RRR = 1.34, p < 0.0001) and 0 to 2 (RRR = 1.39, p = 0.006), and between Leptosphaeriaceae and asthma score 0 to 1 (RRR = 0.51, p = 0.0001).

Recent studies claimed that absolute abundance approaches for microbial quantiﬁcation should be used to link correct microbes to phenotypes and quantitative features, and the relative abundance approach can produce some erroneous identiﬁcation and false-positive

results (Dannemiller et al., 2014; Vandeputte et al., 2017). However, very few studies compare the absolute abundance with relative abundance in indoor microbiome survey studies. We conducted the association analysis between relative abundance and asthma score with the same regression model and found drastic variations between the two approaches. Among the seven associated microbes identified by absolute quantification, only three were identified by the relative abundance approach. Two additional genera, including Wolbachia and No- cardiopsis, were identified by the relative approach (Table S9).

Similarly, only one fungal genus was identiﬁed by the relative abundance (Table S10).

3.4. Environmental characteristics associated with protective microbes

We investigated the associations between the environmental characteristics and the protective or risk microbes of asthma and found that although the indoor dampness/visible mold was not a significant characteristic changing the overall indoor microbial composition for settled dust, it was negatively associated with the concentration of protective microbes, including Rhodomicrobium (β = −2.86, p = 0.021) and Solirubrobacter (β = −1.62, p = 0.07). Thus, high indoor dampness can not only increase the prevalence of asthma by releasing submicron-sized cellular fragments and Microbial Volatile Organic Compounds (MVOCs) (Nevalainen et al., 2015), it could also affect the respiratory health of occupants by suppressing the abundance of protective bacteria of asthma. To our knowledge, this is a new finding.

4. Discussion

We identiﬁed 326 bacterial and 255 fungal genera from the indoor ﬂoor dust in seven junior high schools of Johor Bahru, Malaysia. Seven microbial taxa were quantitatively negatively associated with asthma severity, and two microbial taxa were positively associated. Visible indoor dampness and mold were not involved in shaping the overall microbial composition but were negatively associated with the concentration of protective bacteria.

4.1. Advantages and limitations of the study

This is thefirst study to investigate the association between bacterial and fungal taxa and asthma symptoms in a tropical region. The study applied culture-free high-throughput sequencing and quantitative PCR to characterize the absolute concentration of microbial exposure in the classroom environment for adolescents in junior high schools. We systematically investigated the associations between microbial exposure and asthma severity, and environmental characteristics, re- vealing the complex relationship between these factors. There are also some limitations in our study. Onlyfloor dust was collected and evaluated in this study. The active sampling of airborne dust is expected to be the most direct way to evaluate the inhalable microbial exposure for occupants. However, the approach is relatively expensive, and also, the microbial composition can vary temporally. Thus, the air sampling generally represents a short-term microbial exposure compared with settled dust sampling strategies. We used amplicon sequencing to characterize microbial composition in settle dust. Due to the technical limitation of amplicon sequencing, we can only identify the microbes down to the genus level, rather than more taxonomically resolved species and strain level. It is common that species within a genus or even strains from the same species could have different virulent factors, thus posing different health effect for human. Thus, more taxonomically resolved technique, such as shotgun metagenomics, will improve the identification accuracy for future indoor microbiome survey. We identified several genera quantitatively associated with an asthma score, but due to the limitation of the cross-sectional study design, we can only report the association instead of a conclusion of a causal effect.

Table 2

Association between outdoor/indoor characteristics and microbial community variation.^a

Bacteria Fungi

Environmental Characteristics R² p R² p

Relative humidity 0.05 0.39 0.06 0.25

Indoor CO2 0.02 0.91 0.02 0.99

Outdoor NO2 0.09 0.06 0.04 0.6

Building age 0.06 0.29 0.1 0.03

Visible indoor dampness/mold 0.03 0.84 0.03 0.81

Textile curtain factor^b 0.16 0.005 0.09 0.06

Concentration of house dust mite allergen in dust 0.2 0.004 0.15 0.009 Concentration of cockroach allergen in dust 0.05 0.4 0.17 0.04

a The calculation is based on Bray-Curtis distance (beta diversity). P-value was calculated based on 10,000 permutation bivariate Adonis analysis.

Associations with p < 0.05 are formatted with bold font.

b The textile curtain factor was deﬁned as the area of textile curtain per room volume.

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Further longitudinal studies are needed to disentangle the causal eﬀect and temporal dynamic of the indoor microbiome.

4.2. Bacterial and fungal taxa associated with asthma score

In our study, we observedﬁve bacterial genera protectively associated with asthma score (p < 0.01) among students, including Sphingobium, Rhodomicrobium, Shimwellia in Proteobacteria, Solirubrobacter in Actinobacteria, Pleurocapsa in Cyanobacteria. The protective eﬀect of these taxa has been previously reported in a microbiome study from the farm and non-farm rural homes in Finland and Germany. In this study, the relative abundance of family Hyphomicrobiaceae (including Rhodomicrobium), Enterobacteriaceae (including Shimwellia), Sphigomonadaceae (including Sphingobium), the class Thermoleophilia (including Solirubrobacter), and the phylum Cyanobacteria (including Pleurocapsa) were higher in the farm home

environment, which had protective eﬀect for asthma symptoms (Kirjavainen et al., 2019). The consistent result in tropical and Eur- opean countries indicates a possible universal protective eﬀect of these taxa across large geographic regions and in various climate conditions.

The positively associated microbes identified in this study were not reported to be associated with respiratory health in previous studies. It is possible that the presence of these microbes is geographically restricted in tropical areas. Izhakiella is a newly identified genus, and recently isolated form mired bug and Australian desert soil (Ji et al., 2017). The genus of Robinsoniella belongs to the class of Clostridia. We found no research articles about this genus, but many other taxa in Clostridia class associated with asthma and human health. For example, Clostridium cluster XI in the home environment was shown to be protectively associated with asthma prevalence among adults (Pekkanen et al., 2018). Several families in Clostridia, including Phascolarcto- bacterium, Mogibacterium and Proteiniclasticum, were more abundant Fig. 2. Relative abundance of bacterial and fungal genera in different environmental factors. The abundance of bacterial genera in different (A) textile curtain factor and B) concentration of house dust mite allergens. The abundance of fungal genera in different (C) concentration of house dust mite allergens and (D) the presence of cockroach allergens. Only genera with relative abundance differences > 0.5% are plotted. Error bars represent the standard error, and a Kruskal-Wallis test was conducted to calculate p values (*** p < 0.001, ** p < 0.01, * p < 0.05).

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in rural farm homes, where there were lower asthma prevalence and healthier indoor microbiomes as compared to non-farm rural homes (Kirjavainen et al., 2019). In addition, Clostridium butyricum was suggested to be a potential therapeutic microbe combined with specific immunotherapy for asthma treatment. However, the harmful effect of Clostridia taxa has also been reported, such as Clostridium difficile, which can cause severe diarrhea to life-threatening colitis (Smits et al., 2016; Borali and De Giacomo, 2016). Moreover, we tested the health association for some previously reported pathogen genera. A pathogen genus Bacillus was detected in the classroom microbiome with a high abundance, but there was no association with asthma severity. A common nosocomial pathogen genus Staphylococcus was also common in the classroom, but with no health association observed.

Previous culture-dependent studies have reported Aspergillus, Penicillium, Alternaria and Cladosporium as risk fungal taxa for asthma symptoms (Sharpe et al., 2015). However, very few studies apply culture-independent approaches to systematically screen fungal microbes for asthma symptoms. We examined the health associations for the mold genera in classrooms, and there were no associations for asthma severity. Dannemiller et al. identified that Volutella was positively and Kondoa was protectively associated with asthma severity among atopic and nonatopic children in the Northeast of the United States (Dannemiller et al., 2016). In this study, we identified two protective fungi taxa, Torulaspora and an unidentified genus from the family of Leptosphaeriaceae. In a previous study, Torulaspora delbrueckii has been shown to have probiotic potential that can be used as a supplement in food production to regulate intestine response and promote human health (Zivkovic et al., 2015). The family of Leptosphaeriaceae has not been previously reported to be associated with human health.

4.3. Indoor dampness/mold aﬀected protective bacteria for asthma

Among the eight environmental characteristics examined in this study, only indoor dampness/visible mold was associated with asthma- related genus. Indoor dampness/visible mold was negatively associated with bacterial genera protective to asthma severity, Solirubrobacter and Rhodomicrobium, which is new to our knowledge. Dampness and mold have been proved as risk factors for respiratory health, including asthma (Castro-Rodriguez et al., 2016; Quansah et al., 2012). A recent study has reported that indoor dampness and mold increase the onset of asthma symptoms and reduce remission from asthma among adults (Wang et al., 2019). Previous studies on dampness and mold in buildings established the direct association between mold species, fungal cellular fragment and MVOCs and related airway inﬂammation (Nevalainen et al., 2015; An and Yamamoto, 2016; Zhang et al., 2016).

Our results suggest that, despite the direct harmful effect from fungi, mold growth may suppress the concentration of beneficial bacteria that are protective for asthma symptoms. It has been reported that infloor dust, the absolute concentration of most mold taxa, including Asper- gillus, Penicillium and Alternaria, keeps increasing with elevated humidity, whereas the concentration of specific bacterial taxa, including Pasteurellaceae, Prevotella and Cytophaga, decreases with elevated humidity (Dannemiller et al., 2017). However, as the dynamics of fungal and bacterial growth is a complex issue, the detailed interaction among microbes are still unclear. As the majority of fungal and bacterial species are non-culturable, new study designs such as shotgun metagenomic sequencing strategy combined with in silico growth rate analysis, such as tools like GRiD (Emiola and Oh, 2018), holds a promising so- lution for the issue.

4.4. Absolute and relative quantiﬁcations in microbiome phenotype association analysis

In our study, we used absolute taxonomic quantities to assess the associations with asthma score, while some previous studies used relative abundance (Kirjavainen et al., 2019; O'Connor et al., 2018). An Table3 Microbialtaxaassociatedwithasthmaseveritystatusinjuniorhighschool. KingdomPhylumClassGenusAbsolutionabundanceGM±GSD(copies/g dust)Rangeofrelativeabundance (%)Numberofclassroomswithpresence (N=21)OR(95%CI)*pvalue BacteriaProteobacteriaAlphaproteobacteriaSphingobium156±1.40.02–0.15210.368(0.187–0.724)0.004 Rhodomicrobium53±7.50–0.13170.837(0.749–0.939)0.002 GammaproteobacteriaShimwellia167±2.00.01–1.65210.560(0.385–0.813)0.002 Izhakiella3±8.00–0.1351.187(1.051–1.342)0.006 ActinobacteriaThermoleophiliaSolirubrobacter66±4.10–0.04190.787(0.672–0.923)0.003 CyanobacteriaOxyphotobacteriaPleurocapsa140±1.60.01–0.26210.465(0.275–0.788)0.005 FirmicutesClostridiaRobinsoniella17±9.40–0.11131.182(1.054–1.326)0.004 FungiAscomycotaSaccharomycetesTorulaspora2±2.20–0.0170.643(0.476–0.870)0.004 Dothideomycetesf_Leptosphaeriaceae g_unidentified2±2.50–0.1270.672(0.505–0.893)0.006 *Theassociationswerecalculatedinordinalregressionmodels.Sex,race,smoking,andparentalasthmaorallergywereadjustedforintheassociationanalyses.ThemicrobesRobinsoniellaandLeptosphaeriaceae, whichfailedforparallellinetest,weretestedbynominalregressionmodel,andtheassociationsweresignificant.TheORsdisplayedhereareallfromtheordinalregressionmodel.Taxonomicinformationofthe associatedmicrobesarelisted.Significancelevel:p<0.01.

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issue of relative abundance is that the abundance change of one of the taxa will lead to abundance changes of all other taxa, which may lead to over-identification or misidentification. O’Connor et al. have identified 201 risk and 171 protective microbes from household dust associated with prevalent asthma among children (O'Connor et al., 2018). Among these microbes, some common human skin bacteria have been identified as risky microbes for childhood asthma, including Staphylococcus, Corynebacterium, Haemophilus and Sphingomonas, but they are unlikely to be risk agents since these taxa are universally present around human occupants. One study on quantification profiling of gut microbiome has reported that absolute abundance of microbes significantly differs from the rank by relative abundance, which affects the result of association analysis for phenotypes (Vandeputte et al., 2017). In this study, only 3 bacteria identified by the relative approach were consistent with the absolute quantification approach, indicating the discrepancy between the two approaches. From our results, we observed that most the identified taxa related to health were low-frequency taxa with a relative abundance < 0.2% in all samples (Tables 3). The relative abundance of these low-frequency microbes can be impacted by the variation of dominant microbes, and their concentration is more appropriately presented by the absolute approach.

4.5. Abundance and distribution of fungal taxa

In this study, we found that Ascomycota is the most abundant fungal phylum inﬂoor dust. The results are consistent with several previous fungal microbiome composition surveys. For example, a global survey across multiple continents revealed that Ascomycota is the most abundant fungal phylum in various indoor environments (Amend et al., 2010). Another intensive sampling of 1200 households in the United States revealed that Ascomycota, including Cladosporium, Tox- icocladosporium and Alternaria, dominated the indoor environment.

However, all of these results were based on dust analysis from floor, door-frame or passive air dust collection with petri-dish. A recent airborne microbiome study in Singapore revealed that Basidiomycota is the most dominant phylum in the near-surface atmosphere, and its community structure was temporally stable (Gusareva et al., 2019). The claim was supported by another study with active sampling for the Amazon rainforest air (Womack et al., 2015). However, as the active air sampling is much more expensive than thefloor dust sampling, these two studies collected samples in only one site, and thus the geographic distribution of Basidiomycota taxa is unclear. In this study, we found that, infloor dust, the fungal taxa had more restricted geographic distribution compared with bacterial taxa. Two studies that sampled settle dust also reported the same pattern y (Adams et al., 2013; Barberan et al., 2015). Future studies incorporating active air sampling with geographic distribution are needed to address whether the pattern still holds for airborne Basidiomycota.

5. Conclusion

In this study, we reported a list of low frequency bacterial and fungal taxa that were associated with asthma severity in classrooms in junior high school students, in Johor Bahru, Malaysia. Environmental characteristics associated with the overall microbial community were not associated with the protective or risk microbes, but indoor dampness/ visible mold, which did not associate with microbial community variation, was associated with the asthma-associated bacteria. This is theﬁrst study to reveal the complex interaction between the microbiome, environmental characteristics and asthma symptoms in a tropical area. The study contributes to new knowledge on how to promote the establishment of a healthy building microbiome in this region.

CRediT authorship contribution statement

Xi Fu: Conceptualization, Methodology, Formal analysis, Writing -

original draft, Writing - review & editing. Dan Norbäck:

Conceptualization, Methodology, Writing - review & editing.Qianqian Yuan: Data curation, Formal analysis, Visualization. Yanling Li: Data curation, Formal analysis, Visualization. Xunhua Zhu: Investigation.

Jamal Hisham Hashim: Resources. Zailina Hashim: Resources.

Faridah Ali: Data curation. Yi-Wu Zheng: Investigation. Xu-Xin Lai:

Investigation. Michael Dho Spangfort: Investigation. Yiqun Deng:

Funding acquisition, Project administration. Yu Sun:

Conceptualization, Writing - review & editing, Visualization, Supervision, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competingﬁnancial interests or personal relationships that could have appeared to inﬂu- ence the work reported in this paper.

Acknowledgements

We thank South China Agricultural University and Department of Education of Guangdong Province (2018KTSCX021) forﬁnancial sup- port.

Appendix A. Supplementary material

Supplementary data to this article can be found online athttps://

doi.org/10.1016/j.envint.2020.105664.

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