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\begin{document}

\title{Associations between indoor microbiome exposure and allergic and
non-allergic rhinitis for junior high school students in Terengganu,
Malaysia}



\author[1]{Xi Fu}%
\author[2]{Dan Norbäck}%
\author[3]{Yanling Li}%
\author[3]{Yi Meng}%
\author[3]{Xunhua Zhu}%
\author[3]{Qianqian Yuan}%
\author[4]{Huarong Wen}%
\author[5]{Aminnuddin Ma'pol}%
\author[6]{Jamal Hashim}%
\author[7]{Zailina Hashim}%
\author[2]{Gunilla Wieslander}%
\author[3]{Yiqun Deng}%
\author[3]{Yu Sun}%
\affil[1]{Sun Yat-Sen University}%
\affil[2]{Uppsala University and University Hospital}%
\affil[3]{South China Agricultural University}%
\affil[4]{Baling Health Center}%
\affil[5]{Gombak District Health Office}%
\affil[6]{United Nations University-International Institute for Global Health}%
\affil[7]{Department of Environmental and Occupational Health}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Rhinitis is one of the most prevalent diseases in the world. Indoor
microbiome is confirmed to associate with respiratory diseases such as
asthma and infections, but no study reported the association between
indoor microbiome and the occurrence of rhinitis. In this study, 370
students were randomly selected from 8 junior schools in Terengganu,
Malaysia, and self-administered questionnaire and skin prick tests were
conducted to define the allergic and non-allergic rhinitis among
students. Vacuum dust was collected from the floor and chair/desk
surfaces in the classrooms, and culture-independent high-resolution
amplicon sequencing and quantitative PCR were conducted to characterize
the absolute concentration of bacterial and fungal species. Hierarchical
logistic regression was applied in the association analyses. We found
similar microbial associations for the students with allergic and
non-allergic rhinitis. The microbial richness in Gammaproteobacteria was
protectively associated with allergic and non-allergic rhinitis (p =
0.02 and 0.04), and total fungal richness was positively associated with
allergic and non-allergic rhinitis (p = 0.01 and 0.03). The absolute
concentration of two bacterial species, Aeromonas enteropelogenes and
Brasilonema bromeliae, were associated with both types of rhinitis, and
six bacterial and one fungal species was associated with either allergic
and non-allergic rhinitis (p \textless{} 0.005). Four species previously
reported as facultative pathogens, including A. enteropelogenes,
Escherichia fergusonii, Enterobacter xiangfangensis and Streptococcus
salivarius, were protectively (negatively) associated with rhinitis. A
higher concentration of two radiation-resistant species, including
Deinococcus gobiensis and Deinococcus grandis, were associated with an
increased odds of rhinitis.%
\end{abstract}%



\sloppy


Associations between indoor microbiome exposure and allergic and
non-allergic rhinitis for junior high school students in Terengganu,
Malaysia

Xi Fu\textsuperscript{1,2}, Dan Norb\selectlanguage{ngerman}äck\textsuperscript{3}, Yanling
Li\textsuperscript{1,4,5}, Yi Meng\textsuperscript{1,4,5}, Xunhua
Zhu\textsuperscript{1,4,5}, Qianqian Yuan\textsuperscript{1,4,5},
Huarong Wen\textsuperscript{6}, Aminnuddin Ma'pol\textsuperscript{7},
Jamal Hisham Hashim\textsuperscript{8}, Zailina
Hashim\textsuperscript{9}, Gunilla Wieslander\textsuperscript{3}, Yiqun
Deng\textsuperscript{1,4,5}, Yu Sun\textsuperscript{1,4,5 *}

\textsuperscript{1}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.

\textsuperscript{2}School of Public Health, Sun Yat-sen University,
Guangzhou, PR China.

\textsuperscript{3}Occupational and Environmental Medicine, Dept. of
Medical Science, University Hospital, Uppsala University, 75237 Uppsala,
Sweden.

\textsuperscript{4}Key Laboratory of Zoonosis of Ministry of Agriculture
and Rural Affairs, South China Agricultural University, Guangzhou,
Guangdong, 510642, PR China.

\textsuperscript{5}Guangdong Laboratory for Lingnan Modern Agriculture,
South China Agricultural University, Guangzhou, Guangdong, 510642,
China.

\textsuperscript{6}Baling Health Center, Dangyang, Hubei, 444100, PR
China

\textsuperscript{7}Gombak District Health Office, Ministry of Health,
Batu Caves, Selangor Darul Ehsan, Malaysia

\textsuperscript{8}Universiti Selangor, Shah Alam, Selangor, Malaysia.

\textsuperscript{9}Department of Environmental and Occupational Health,
Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM,
Serdang, Selangor, Malaysia.

*To whom corresponds should be addressed: Yu Sun

\emph{sunyu@scau.edu.cn,}

Abstract

Rhinitis is one of the most prevalent diseases in the world. Indoor
microbiome is confirmed to associate with respiratory diseases such as
asthma and infections, but no study reported the association between
indoor microbiome and the occurrence of rhinitis. In this study, 370
students were randomly selected from 8 junior schools in Terengganu,
Malaysia, and self-administered questionnaire and skin prick tests were
conducted to define the allergic and non-allergic rhinitis among
students. Vacuum dust was collected from the floor and chair/desk
surfaces in the classrooms, and culture-independent high-resolution
amplicon sequencing and quantitative PCR were conducted to characterize
the absolute concentration of bacterial and fungal species. Hierarchical
logistic regression was applied in the association analyses. We found
similar microbial associations for the students with allergic and
non-allergic rhinitis. The microbial richness in Gammaproteobacteria was
protectively associated with allergic and non-allergic rhinitis (p =
0.02 and 0.04), and total fungal richness was positively associated with
allergic and non-allergic rhinitis (p = 0.01 and 0.03). The absolute
concentration of two bacterial species, \emph{Aeromonas
enteropelogenes}and \emph{Brasilonema bromeliae} , were associated with
both types of rhinitis, and six bacterial and one fungal species was
associated with either allergic and non-allergic rhinitis (p \textless{}
0.005). Four species previously reported as facultative pathogens,
including\emph{A} . \emph{enteropelogenes} , \emph{Escherichia
fergusonii} ,\emph{Enterobacter xiangfangensis} and \emph{Streptococcus
salivarius} , were protectively (negatively) associated with rhinitis. A
higher concentration of two radiation-resistant species,
including\emph{Deinococcus gobiensis} and \emph{Deinococcus grandis} ,
were associated with an increased odds of rhinitis. This is the first
health association study conducted between the indoor microbiome and the
occurrence of rhinitis, revealing that microbial exposure at school can
affect both allergic and non-allergic students.

Keywords

Allergic rhinitis, non-allergic rhinitis, skin prick test, indoor
microbiome, high-throughput sequencing

Introduction

Rhinitis is one of the most prevalent diseases in the world, affecting
up to 30\% of the total population\textsuperscript{1-3}. The prevalence
of rhinitis is still increasing in most of the countries in the world,
especially in many developing countries\textsuperscript{2}. The disease
leads to substantial economic and medical loss as well as many indirect
costs for patients, such as absence from work and school and reduced
work productivity. The symptoms of rhinitis include rhinorrhea/runny
nose, nasal blockage/stuffy nose, itching nose and sneezing. Allergic
rhinitis is defined as rhinitis accompanied with allergen-specific IgE
production. It is frequently associated with asthma and ocular symptoms,
such as allergic rhinoconjunctivitis, and it has been estimated that
10-40\% of the patients with rhinitis also have
asthma\textsuperscript{2, 4}. Non-allergic rhinitis includes a
heterogeneous subgroup of rhinitis without any clinical signs of
allergic inflammation, such as rhinitis of the elderly, occupational
rhinitis, gustatory rhinitis, hormonal rhinitis and drug-induced
rhinitis \textsuperscript{1, 5, 6}. The mechanisms for non-allergic
rhinitis are still mostly unclear. The diagnosis of allergic and
non-allergic rhinitis is mainly based on allergen-specific IgE test. The
test can be performed either on the skin surface by skin prick tests or
in blood serum by enzyme immunoassay or radioimmunoassay
\textsuperscript{2}.

The occurrence of rhinitis is suggested to be associated with genetic
and environmental factors. Many environmental factors, such as pollen
and fungal allergen, air and traffic pollution, climate change, ozone
level, tobacco smoking, fragrance products, industrial chemicals and
social class, are also suggested to be associated with the occurrence of
rhinitis \textsuperscript{7, 8}. In recent years, the development of
culture-independent high-throughput sequencing technology enables
researchers to survey microbiome composition in various human body
sites, such as human gut, skin and respiratory tract, as well as indoor
environments, such as homes, schools, hospitals, hotels and public
transport systems \textsuperscript{9-15}. These profiling studies showed
that microbiome diversity and compositional variation are associated
with many metabolic and immune diseases, such as
asthma\textsuperscript{16}. Studies identified many potential protective
and risk microorganisms for asthma in the home and school
environments\textsuperscript{17-22}. Compared with extensive studies in
asthma, no studies reported the relationships between indoor microbiome
and occurrence of rhinitis, and thus the association pattern between the
indoor microbiome and the development of rhinitis is unclear.

In this study, we investigated associations between indoor microbiome
exposure and the occurrence of allergic and non-allergic rhinitis. The
study was conducted among randomly selected junior high school students
in Terengganu, Malaysia, and a self-administered questionnaire and skin
prick test were conducted to assess the prevalence of allergic and
non-allergic rhinitis. Species-level high-throughput amplicon sequencing
was conducted to characterize microbiome composition in the classrooms.
A regression model was applied to evaluate the associations between
indoor microbiome and disease. The study aims to screen a list of
potential protective and risk microorganisms for allergic and
non-allergic rhinitis and evaluate the health effects of indoor
microbiome exposure.

Materials and methods

Study design and ethical permission

In this study, 8 junior high schools were randomly selected from
Terengganu, Malaysia. In each school, 4 classes were randomly selected
to collect vacuum dust samples. Dust samples from 2 classrooms were used
up in previous chemical analyses, and thus in total 30 samples were
sequenced to character indoor microbiome composition. Students were
randomly selected to conduct a skin prick test and report their health
condition and personal information through the self-administered
questionnaires. Vacuum dust, self-administered questionnaires and skin
prick test were collected from November 2012 to February 2013. The study
was approved by the Medical Research and Ethics Committee at the
National University of Malaysia, Malaysia Ministry of Education,
Education Department of Terengganu State and principals and teachers of
each school. All participants gave their formal written consent, and the
records were kept in the National University of Malaysia.

Health data and skin prick test

A self-administered questionnaire in the Malay language was distributed
to students to survey the personal information, including age, gender,
current smoking and parental asthma, and the occurrence of rhinitis. The
questionnaire was also used in previous rhinitis studies in Sweden,
China and Malaysia \textsuperscript{23, 24}. One question asked whether
the students had a stuffy nose or runny nose in the last 3 months, and 4
alternatives were available to choose, including (A) ``Yes, everyday'',
(B) ``Yes, 1-4 times/week'', (C) ``Yes, 1-3 times/month'' and ``No,
never''. The students chose (A), (B) or (C) were defined as having
rhinitis. One question asked whether the students had respiratory
infections in the last 3 months, and 4 alternatives were available to
choose, including (A) ``Yes, everyday'', (B) ``Yes, 1-4 times/week'',
(C) ``Yes, 1-3 times/month'' and ``No, never''. The students chose (A),
(B) or (C) were defined as having recent respiratory infections, and
these students were excluded in the further rhinitis analyses. The
questionnaire was answered at home with the help of the students'
parents. Medical staff from our group went through the questionnaire and
had a face-to-face interview with students to clarify the uncertainties
about the questions. When they answer the questionnaire, the students
had no information regarding the data collected in the classrooms.

To define allergic and non-allergic rhinitis, we conducted skin prick
tests for the selected students\textsuperscript{25}. The tests were
performed by experienced hospital nurses, and the participants were
asked about their recent medication intake before the tests. Allergen
extract was purchased from ALK-Abello, Horsholm, Denmark, including
pollen (Birch mix, \emph{Phleum pretense} and \emph{Artemisia vulgaris}
), animal epidermal (\emph{Equus caballus} and \emph{Felis domesticus}
), house dust mite (\emph{Dermatophagoides farinae}
and\emph{Dermatophagoides pteronyssinus} ), molds (\emph{Cladosporium
cladosporioides} and \emph{Alternaria alternate} /\emph{Alternaria
tenuis} ). Each bottle contained allergen extract in 10 HEP (histamine
equivalent). A standard saline solution and histamine solution (10
mg/mL) were used as negative and positive controls. Skin prick tests
were performed on the forearm with a disposable ALK skin lancet. The
skin surface was observed after 15 minutes, and a positive reaction was
defined as wheal \textgreater{} 3mm in diameter.

Vacuum dust sampling, DNA extraction and sequencing

A vacuum cleaner (400W) was used to collect dust in classrooms. A dust
sampler (ALK Abello, Copenhagen, Denmark) with a Millipore filter was
equipped in the vacuum cleaner. The Millipore filter was made of
cellulose acetate. The pore size of the filter was 6 \selectlanguage{greek}µ\selectlanguage{english}m, which retains
74\% particles of 0.3-0.5 \selectlanguage{greek}µ\selectlanguage{english}m, 81\% particles of 0.5-1.0 \selectlanguage{greek}µ\selectlanguage{english}m, 95\%
particles of 1-10 \selectlanguage{greek}µ\selectlanguage{english}m and 100\% particles larger than 10 \selectlanguage{greek}µ\selectlanguage{english}m according to
manufacturer's instruction. Each selected classroom was divided into two
parts with approximately the same size; one near the corridor and one
near the window. Dust sample was collected from each part by 4 minutes
vacuum sampling with 2 minutes on the floor surface and 2 minutes on the
upper surfaces of desks and chairs. Two dust samples in each classroom
were combined and sieved through a 0.3-mm mesh screen to obtain the fine
dust, which was further stored in a -80 @\selectlanguage{english}C freezer until DNA extraction
and sequencing.

DNA extraction and multiplex amplicon sequencing were performed by
Personal Biotechnology Co., Ltd (Shanghai, China). First, the total
bacterial and fungal DNA was extracted from vacuum dust by DNA kit
D5625-01 (Omega Bio-Tek, Inc., Norcross, GA, USA) and Fast DNA SPIN kit
(MP Biomedicals, Santa Ana, CA, USA). The quality and quantity of the
extracted DNA were assessed by agarose gel electrophoresis and NanoDrop
ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
The full-length of bacterial 16S rRNA gene was amplified by the forward
primer 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and the reverse primer 1492R
(5'-ACCTTGTTACGACTT-3'), and the fungal ITS region was amplified by the
forward primer ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and the reverse
primer LR3 (5'-CCGTGTTTCAAGACGGG-3'). Then sample-specific tag sequences
(16 bp) were incorporated into the PCR amplicons by the Barcoded Univ
F/R Primers Plate-96v2 kit (Pacific Biosciences, Menlo Park, CA, USA).
The PCR amplicons were purified by Agencourt AMPure Beads (Beckman
Coulter, Indianapolis, IN) and further quantified by PicoGreen dsDNA
Assay Kit (Invitrogen, Carlsbad, CA, USA). The amplicons were sequenced
by the Single-Molecule Real-Time (SMRT) technology combined with
circular consensus sequencing (CCS) technique at the PacBio Sequel
platforms \textsuperscript{26, 27}.

The total bacterial and fungal concentration in dust was quantified by
quantitative PCR. First, the bacterial and fungal DNA was extracted from
10 mg dust, respectively. Universal primers targeted bacterial 16S rRNA
gene (forward: 5'-GCAGGCCTAACACATGCAAGTC-3' and reverse:
5'-CTGCTGCCTCCCGTAGGAGT-3') and fungal ITS 1 regions (forward primer
5.8F1: 5'-AACTTTCAACAACGGATCTCTTGG-3', the reverse primer is 5.8R1:
5'-GCGTTCAAAGACTCGATGATTCAC-3') were used for the quantitative
PCR\textsuperscript{23, 28}.

Bioinformatics and association analyses

The raw sequence data were deposited in the Genome Sequence
Archive\textsuperscript{29} with accession numbers CRA002825 and
CRA002876. The sequenced circular consensus sequences were first
processed by the PacBio SMRT Link portal (version 5.0.1.9585) with
minfullpass as 3 and minPredictedAccuracy as 99. Sequences with
predicted accurate \textless{} 99\% were removed from further analyses.
The Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0)
pipeline was used to process the filtered data\textsuperscript{30}.
Sequences were first assigned to the corresponding samples based on the
sample-specific tag information. In each sample, sequences were
clustered into operational taxonomic units (OTUs) with 97\% of sequence
similarity by UCLUST (v5.2.236) \textsuperscript{31}, and a
representative sequence from each OUT was picked and annotated by
searching against the Silva (release 115) and UNITE database (release 5)
for bacteria and fungi\textsuperscript{32, 33}. For each sample, an OTU
table was generated to record the abundance of all characterized OTUs.
OTUs counted for \textless{} 0.001\% of total sequences were removed
from further analyses. All samples were rarefied at even depth for the
comparative analyses. Microbial compositional variation was visualized
by NMDS (non-metric multidimensional scaling)
analysis\textsuperscript{34} based on Bray-Curtis distance metrics.

We performed three-level (class and school as second and third levels)
hierarchical logistic regression between microbial richness/absolute
concentration and occurrence of allergic/non-allergic rhinitis by
StataSE 15.0 (StataCorp LLC). Gender, current smoking and parental
asthma were included as adjustments in the regression analyses. The
microbial richness was represented as the number of OTUs in bacteria and
fungi. The absolute concentration of microbial species was calculated as
multiplying the relative abundance of species with the total
bacterial/fungal concentration from the qPCR\textsuperscript{35}. The
absolute concentration was first transformed in the logarithmic scale
(log\textsubscript{10} (absolute concentration + 1)) and then analyzed
in the logistic regression model. The association between the absolute
microbial concentration and allergic/non-allergic rhinitis were
conducted for species presented in \textgreater{} 10 classrooms with
average relative abundance \textgreater{} 0.1\%. Only associations with
p-value \textless{} 0.005 were considered as significant throughout the
study. The false discovery rate (FDR) was calculated by the p.adjust
function with the Benjamini-Hochberg procedure in R.

Results

Demographics and prevalence of rhinitis

In this study, we collected vacuum dust from 30 classrooms of 8 junior
high schools in Terengganu, Malaysia. Four schools were from the urban
area and four schools from the rural area. Self-administered
questionnaires and skin prick test results were obtained from 370
students in the selected classes. Among them, 23 students reported
recent respiratory infections in the last 3 months and thus were removed
from the analyses. In total, 347 students were included in the final
dataset, included 129 males and 218 females. Based on health information
from questionnaire and skin prick tests, we defined 107 students as
healthy subjects, 110 students with allergic rhinitis and 130 students
with non-allergic rhinitis. The prevalence of house dust mite and cat
allergy was high among students (38.3\% and 11.0\%; Table S1). There was
no difference between male and female students in the prevalence of
allergic and non-allergic rhinitis (p = 0.98; Table 1). Ten students
reported current smoking, and there was no difference between smoking
and non-smoking students in the prevalence of allergic and non-allergic
rhinitis (p = 0.38).

Species-level microbiome composition and variation

The abundance of microbial taxa at the taxonomical class and species
level were analyzed first. Alphaproteobacteria (17.2\%),
Gammaproteobacteria (15.9\%), Cyanobacteria (14.3\%), Actinobacteria
(12.9\%), Bacilli (10.5\%), Deinococci (7.2\%) and Betaproteobacteria
(7.2\%) were the high abundant bacterial classes in the
classrooms.\emph{Acinetobacter johnsonii} (4.0\%), \emph{Aliterella
Antarctica}(2.7\%), \emph{Klebsiella pneumonia} (2.0\%),
\emph{Methylobacterium radiotolerans} (1.9\%) and
\emph{Saccharopolyspora halotolerans} (1.9\%) were the high abundant
bacterial species (Figure 1A; Table S2). For fungi, Dothideomycetes
(30.7\%), Eurotiomycetes (20.6\%), Wallemiomycetes (12.2\%),
Agaricomycetes (11.9\%), Sordariomycetes (7.8\%) and Agaricostibomycetes
(5.8\%) were the high abundant fungal classes in the classrooms.
\emph{Wallemia sebi} (10.9\%), an unidentified Pleosporales (7.1\%),
\emph{Sterigmatomyces halophilus}(5.8\%), \emph{Aspergillus
penicillioides} (5.7\%), \emph{Eupenidiella venezuelensis} (5.3\%) and
\emph{Bambusaria bambusae} (5.2\%) were the high abundant fungal species
in the junior high school of Terengganu (Figure 1B; Table S3).

We further characterize and visualized the overall microbial
compositional variation among classrooms by non-metric multidimensional
scaling (NMDS) analysis. The bacterial and fungal composition in urban
schools did not differ from rural schools (Figure 2A and 2B). Samples
collected in the same school were compositionally more similarity
compared with samples collected in different schools (Mann-Whitney U
test on Bray-Curtis distance matrix, bacteria p = 0.003, fungi p
\textless{} 0.001). However, there were also exceptions that samples
from the same school had a low compositional similarity. For example,
the bacterial composition in T7.3 (school 7 class 3) differed
drastically from the bacterial composition in T7.1, T7.2, and T7.4, due
to high abundance of \emph{K. pneumoniae} in T7.3 (46.2\%; Figure 1A and
2A).

Associations between indoor microbial richness/concentration and
allergic/non-allergic rhinitis

In total, 4,977 bacterial OTUs (operational taxonomic units) were
identified in this study. The overall bacterial richness (represented as
the number of OTUs) in each classroom was not associated with the
occurrence of rhinitis in the classroom (p \textgreater{} 0.05; Table
2). The same test was also conducted for the major bacterial classes
(\textgreater{} 200 OTUs). The richness in Gammaproteobacteria was
protectively/negatively associated with allergic and non-allergic
rhinitis (p = 0.02 and 0.04). Similarly, 2,632 fungal OTUs were
identified in this study, and the overall fungal richness was positively
associated with allergic and non-allergic rhinitis (p = 0.01 and 0.03).
We did not find significant associations between specific fungal class
and the occurrence of rhinitis (p \textgreater{} 0.05). The richness in
Agaricomycetes had a small trend to be associated with allergic rhinitis
(p = 0.06) and non-allergic rhinitis (p = 0.10).

We further analyzed the associations between the absolute concentration
of indoor bacterial and fungal species and the occurrence of allergic
and non-allergic rhinitis. To control the number of tests, only species
presented in at least 10 classrooms and mean abundance \textgreater{}
0.1\% were included in the regression analyses. \emph{Brasilonema
bromeliae} from the phylum of Cyanobacteria and \emph{Aeromonas
enteropelogenes} and \emph{Escherichia fergusonii} from the class of
Gammaproteobacteria were protectively associated with allergic rhinitis
(p \textless{} 0.005, FDR = 0.17; Table 3). These species were presented
in 21, 10 and 23 classrooms out of the total 30 classrooms, and the
geometric mean absolute concentration was 4.1*10\textsuperscript{5},
1.2*10\textsuperscript{5} and 2.6*10\textsuperscript{5} copies per gram
dust. \emph{Deinococcus grandis} from the class of Deinococci
and\emph{Chaetomium grande} from the fungal class Sordariomycetes were
positively associated with allergic rhinitis (p \textless{} 0.005, FDR =
0.09 and 0.18). \emph{D} . \emph{grandis} was presented in 15 classrooms
with a geometric mean concentration as 1.2*10\textsuperscript{5} copies
per gram dust. \emph{C} . \emph{grande} was presented in 11 classrooms
with a geometric mean of concentration as 399 copy per gram dust. The
results indicate that the concentration of health-associated bacterial
species is three orders of magnitude higher than the fungal species in
the classrooms of Terengganu.

Interestingly, we also found \emph{B. bromeliae} and \emph{A.
enteropelogenes} were protectively associated with non-allergic rhinitis
(p = 0.002 and 0.003, FDR = 0.07; Table 4). \emph{Enterobacter
xiangfangensis} from the class of
Gammaproteobacteria,\emph{Streptococcus salivarius} from the class of
Bacilli and\emph{Patulibacter minatonensis} from the class of
Thermoleophilia were also protectively associated with non-allergic
rhinitis (p \textless{} 0.05, FDR \textless{} 0.1). \emph{Deinococcus
gobiensis} was the only bacterial species that were positively
associated with non-allergic rhinitis. These species were presented in
10 to 23 classrooms in Terengganu with a geometric mean concentration
from 1.2*10\textsuperscript{5} to 6.0*10\textsuperscript{5} copy per
gram dust. No fungal species was associated with the occurrence of
non-allergic rhinitis.

No association between environmental characteristics and
rhinitis-related species

We further tested the associations between environmental characteristics
and the identified rhinitis-related species. Environmental
characteristics were tested separately with the rhinitis-related
species, including location of schools (urban/rural), the concentration
of house dust mite (Der p 1 and Der f 1) and cockroach allergen (Bla g 1
and Per a 1), indoor CO\textsubscript{2}, NO\textsubscript{2} levels and
relative humidity and indoor visible dampness and mold. We did not find
any significant associations between the environmental characteristics
and the rhinitis-related species (p \textgreater{} 0.05).

Discussion

Strengths and limitations of the study

Here, we report the first association study between indoor microbiome
exposure and the occurrence of rhinitis. With this approach, we
identified a list of potential health-related species and found similar
association patterns between allergic rhinitis and non-allergic
rhinitis, which can be further tested in future studies. Another
strength is that we applied high-resolution amplicon sequencing (PacBio)
to characterize the bacterial and fungal composition and diversity. The
species-level taxonomic resolution facilitates accurate microbial
diversity estimation compared to standard second-generation sequencing,
which can only resolve taxa at the genus level. Also, we applied
absolute concentration rather than the relative abundance of microbial
species in the association analysis. Previous studies confirmed that
absolute concentration is a superior approach compared with the relative
abundance in terms of finding the correct microorganism-phenotype
associations \textsuperscript{35}.

There are also limitations in this study. The cross-sectional study
design limits the causal inference in the relationship between indoor
microbial exposure and rhinitis. The cross-sectional study design is
widely used in epidemiological studies, as it is an economical approach
to obtain health associations among populations. However, this study
design cannot draw the causal relationships, and thus future
longitudinal studies are needed to verify the patterns we observed in
this study. Also, we applied amplicon sequencing to characterize
microbiome composition in this study. Since only one marker gene was
sequenced, this approach can provide microbial taxonomic information
rather than accurate functional inference, which can be achieved by the
shotgun metagenomics sequencing. We conducted the false discovery rate
correction in this study, which is one of the widely used approaches to
adjust the type I error in null hypothesis testing when conducting
multiple comparisons\textsuperscript{36}. The associations were not
significant after adjustment with the Benjamini-Hochberg (BH) procedure
(0.06 \textless{} FDR \textless{} 0.19). However, the BH procedure was
confirmed to be over-conservative in microbiome analysis with
much-reduced power in detecting the accurate significant findings. To
solve the problem, a new approach DS-FDR has been proposed to improve
the sensitivity of detection\textsuperscript{37}. However, the new
approach can only be applied in the microbiome dataset with relative
abundance, and thus it is not applicable in this study. Future
approaches dealing with microbiome dataset with transformed absolute
concentration are needed to solve the issue.

Prevalence of allergic/non-allergic rhinitis and environmental stimuli

Rhinitis is one of the most prevalent diseases in the world. It is
estimated that allergic rhinitis affects approximately 10\%-40\% of the
population in different countries\textsuperscript{3}. The international
collaborated ISAAC and ECRHS studies reported a list of countries with
high prevalence in allergic rhinitis, including Nigeria
(\textgreater{}35\%), Paraguay (30-35\%), Sweden (31\%), Hong Kong
(25-30\%) Australia (15-20\%), New Zealand (15-20\%), the United Kingdom
(15-20\%\%) \textsuperscript{2, 38, 39}. In this study, we reported that
the prevalence of allergic rhinitis was 31.7\% among junior high schools
students in Terengganu, Malaysia, which is comparable with the countries
with high prevalence. The prevalence of non-allergic rhinitis is less
well defined, and no international collaboration has been conducted to
survey the prevalence among different countries systematically. One
study estimated that in the United States, 17 million people had
non-allergic rhinitis and 22 million people had mixed rhinitis, a
combination of allergic and non-allergic rhinitis\textsuperscript{40}.
Another study reported that the prevalence of non-allergic rhinitis was
9.6\% among high school students in Belgium\textsuperscript{41}. The
prevalence of non-allergic rhinitis is higher in our study (37.5\%)
compared with previous studies. In this study, we applied skin prick
tests to define allergic and non-allergic rhinitis, by using nine
allergen extracts from plants, animals, mites and molds. The allergic
reaction for students in Terengganu was mainly from the sensitization of
house dust mites and cat allergen, which is consistent with a previous
study in Malaysia that approximately 50\% and 25\% office workers had
mites and cat allergy\textsuperscript{42}.

The mechanisms for allergic and non-allergic rhinitis are suggested to
be different. Allergic rhinitis is related to the nasal inflammation
caused by IgE-mediated allergy. Exposure to the indoor allergen and
environmental stimuli lead to overproduction of IgE (immunoglobulin E),
which can further activate the receptors on the surface of cells and
lead to the allergic and inflammatory symptoms\textsuperscript{43}.
Compared with allergic rhinitis, the mechanisms for non-allergic
rhinitis are less clear. Classic T-cell and cytokine inflammation are
suggested to be the major mechanisms for non-allergic
rhinitis\textsuperscript{1}. Neurogenic dysregulation is another
proposed pathway that leads to nasal obstruction and
rhinorrhea\textsuperscript{1, 44}. Rhinitis of the elderly is suggested
to be mainly associated with the neural dysregulation.

Although the mechanisms for allergic and non-allergic rhinitis are quite
different, the environmental factors associate with the development of
rhinitis could be similar. For example, environmental and traffic
pollution, including NOx, SOx and ozone, environmental tobacco smoking,
mold growth and chemical exposures, including volatile organic compounds
(VOCs) are related to both allergic and non-allergic
rhinitis\textsuperscript{7, 8, 45, 46}. In this study, we found that the
association pattern for indoor microbiome was similar between the two
types of rhinitis. The phenomenon was supported by analysis in microbial
richness and absolute concentration. For example, the richness (number
of observed OTUs) in Gammaproteobacteria and in fungi was associated
with both allergic and non-allergic rhinitis. The absolute concentration
of \emph{B. bromeliae} and \emph{A. enteropelogenes} and two species
from the genus of \emph{Deinococcus} were also associated with both
allergic and non-allergic rhinitis. The detailed mechanisms for how
microorganisms affect nasal inflammation still need to be explored. It
is possible that certain microorganisms can stimulate the inflammatory
symptoms by multiple pathways, either with allergen sensitization or
disturb the epithelium barrier. Future studies can explore the
mechanisms in depth by using the microorganisms identified in this
study. Epidemiological surveys should also consider to routinely survey
the indoor microbiome exposure, together with other environmental
factors, to add a new dimension to the exposure science.

Health effects of the microbial species

As this is the first association study between indoor microbiome
exposure and rhinitis, we cannot directly compare our results with
studies with the same experimental design, but there are some studies
available with similar framework. One study in junior high school of
Johor Bahru, Malaysia reported that the levels of total endotoxin
(lipopolysaccharide, LPS) was negatively associated with
rhinitis\textsuperscript{47}. LPS is a bacterial cell-wall component and
is widely used as a chemical marker for Gram-negative bacteria. In this
study, we found that the diversity of Gammaproteobacteria, one of the
most diverse classes of Gram-negative bacteria, was protectively
associated with rhinitis. Another study reported that the total fungal
DNA, assessed by quantitative PCR, was a potential risk factor for
rhinitis in the indoor environment\textsuperscript{23}. This is also
consistent with our finding in this study that total fungal diversity
was positively associated with the occurrence of rhinitis.

As this is the first association study for indoor microbiome and
rhinitis, all these species were first time reported to be associated
rhinitis. Several of the identified species were from the outdoor
environment. Two of the environmental species, including \emph{B.
bromeliae} , \emph{P. minatonensis} , were protectively (negatively)
associated with rhinitis, and two of the environmental species,
including \emph{D. gobiensis} , \emph{D. grandis} , were positively
associated with rhinitis. Interestingly, the two potential risk
species,\emph{D. gobiensis} and \emph{D. grandis} were both extremely
radiation-resistant bacteria\textsuperscript{48, 49}. These species are
physically protected from the UV damages due to their efficient DNA
repair system\textsuperscript{50}. No study reported their health
effects for humans, and it is unclear whether the radiation-resistant
feature of these bacteria is related to the potential adverse health
effect. Four rhinitis-related bacteria species, including \emph{E.
fergusonii} , \emph{A. enteropelogenes} , \emph{E. xiangfangensis} and
\emph{S. salivarius} , were protectively associated the rhinitis, and
interestingly, these bacteria were all reported to be facultative
pathogens that can cause various diseases in human and animals, such as
respiratory infections, diarrhea and neutropenia\textsuperscript{51-54}.
Although\emph{S. salivarius} is a facultative pathogen that can infect
immunocompromised patients, the species can also be used as probiotic
that could reduced 80\% of nasal streptococcus infections in
adults\textsuperscript{55}. The results suggest complex health effects
of the species. The ``hygiene hypothesis'' proposes that having
respiratory infections could reduce the occurrence of allergic diseases
for children\textsuperscript{56}. The role of infectious pathogens and
the development of asthma in children have also been fiercely debated
over the past years\textsuperscript{57, 58}. Some studies suggest that
rhinovirus infections and respiratory syncytial virus (RSV) infection
were associated with increased odds of wheeze and
asthma\textsuperscript{57, 59}, but other population-based studies
reported that exposure to infections diseases could lead to reduced
atopic disorders \textsuperscript{60}. The potential protective effects
of childhood infections are suggested to relate to the training and
maturation of the immune system from the exposure \textsuperscript{58}.
A recent study in university dormitories reported that exposed to
facultative pathogens, including \emph{Haemophilus} , \emph{Klebsiella}
,\emph{Buttiauxella} , and \emph{Raoultella} , increased the odds of
having respiratory infections\textsuperscript{61}. Overall, more studies
are needed to verify the pattern we observed in this study and further
disentangle the health effects of the indoor microbiome on different
respiratory diseases.

Acknowledgements

We thank the Natural Science Foundation of Guangdong Province
(2020A1515010845) and the Department of Education of Guangdong Province
(2018KTSCX021) for financial support. We thank Personalbio
(www.personalbio.cn) for help in sequencing analysis.

References

1. Hellings PW, Klimek L, Cingi C, Agache I, Akdis C, Bachert C, et al.
Non-allergic rhinitis: Position paper of the European Academy of Allergy
and Clinical Immunology. Allergy 2017; 72:1657-65.

2. Bousquet J, Khaltaev N, Cruz AA, Denburg J, Fokkens WJ, Togias A, et
al. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008*. Allergy
2008; 63:8-160.

3. Bro\selectlanguage{polish}ż\selectlanguage{english}ek JL, Bousquet J, Agache I, Agarwal A, Bachert C,
Bosnic-Anticevich S, et al. Allergic Rhinitis and its Impact on Asthma
(ARIA) guidelines---2016 revision. Journal of Allergy and Clinical
Immunology 2017; 140:950-8.

4. Togias A, Gergen PJ, Hu JW, Babineau DC, Wood RA, Cohen RT, et al.
Rhinitis in children and adolescents with asthma: Ubiquitous, difficult
to control, and associated with asthma outcomes. Journal of Allergy and
Clinical Immunology 2019; 143:1003-11.e10.

5. Hox V, Steelant B, Fokkens W, Nemery B, Hellings PW. Occupational
upper airway disease: how work affects the nose. Allergy 2014;
69:282-91.

6. Kowalski ML, Asero R, Bavbek S, Blanca M, Blanca-Lopez N, Bochenek G,
et al. Classification and practical approach to the diagnosis and
management of hypersensitivity to nonsteroidal anti-inflammatory drugs.
Allergy 2013; 68:1219-32.

7. Shusterman D. Nonallergic Rhinitis: Environmental Determinants.
Immunology and Allergy Clinics of North America 2016; 36:379-99.

8. Dunlop J, Matsui E, Sharma HP. Allergic Rhinitis: Environmental
Determinants. Immunology and Allergy Clinics of North America 2016;
36:367-77.

9. Fu X, Li Y, Yuan Q, Cai G-h, Deng Y, Zhang X, et al.
Continental-Scale Microbiome Study Reveals Different Environmental
Characteristics Determining Microbial Richness, Composition, and
Quantity in Hotel Rooms. mSystems 2020; 5:e00119-20.

10. Sun Y, Fu X, Li Y, Yuan Q, Ou Z, Lindgren T, et al. Shotgun
Metagenomics Of Dust Microbiome From Flight Deck And Cabin In Civil
Aviation Aircraft. Indoor Air 2020; 00:1-14.

11. Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R.
Current understanding of the human microbiome. Nat Med 2018; 24:392-400.

12. Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall
AB, et al. Strains, functions and dynamics in the expanded Human
Microbiome Project. Nature 2017; 550:61-6.

13. Lax S, Sangwan N, Smith D, Larsen P, Handley KM, Richardson M, et
al. Bacterial colonization and succession in a newly opened hospital.
Sci Transl Med 2017; 9.

14. Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM,
et al. Longitudinal analysis of microbial interaction between humans and
the indoor environment. Science 2014; 345:1048-52.

15. Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla
AT, et al. Structure, function and diversity of the healthy human
microbiome. Nature 2012; 486:207-14.

16. Bello MGD, Knight R, Gilbert JA, Blaser MJ. Preserving microbial
diversity. Science 2018; 362:33-4.

17. Fu X, Norb\selectlanguage{ngerman}äck D, Yuan Q, Li Y, Zhu X, Hashim JH, et al. Indoor
microbiome, environmental characteristics and asthma among junior high
school students in Johor Bahru, Malaysia. Environment International
2020; 138:105664.

18. Kirjavainen PV, Karvonen AM, Adams RI, Täubel M, Roponen M,
Tuoresmäki P, et al. Farm-like indoor microbiota in non-farm homes
protects children from asthma development. Nature Medicine 2019;
25:1089-95.

19. Pekkanen J, Valkonen M, Täubel M, Tischer C, Leppänen H, M.
Kärkkäinen P, et al. Indoor bacteria and asthma in adults: a multicentre
case--control study within ECRHS II; 2018.

20. Dannemiller KC, Gent JF, Leaderer BP, Peccia J. Indoor microbial
communities: Influence on asthma severity in atopic and nonatopic
children. Journal of Allergy and Clinical Immunology 2016; 138:76-83.e1.

21. Ege MJ, Mayer M, Normand AC, Genuneit J, Cookson WO,
Braun-Fahrlander C, et al. Exposure to environmental microorganisms and
childhood asthma. N Engl J Med 2011; 364:701-9.

22. O'Connor GT, Lynch SV, Bloomberg GR, Kattan M, Wood RA, Gergen PJ,
et al. Early-life home environment and risk of asthma among inner-city
children. Journal of Allergy and Clinical Immunology 2018; 141:1468-75.

23. Norback D, Hashim JH, Cai GH, Hashim Z, Ali F, Bloom E, et al.
Rhinitis, Ocular, Throat and Dermal Symptoms, Headache and Tiredness
among Students in Schools from Johor Bahru, Malaysia: Associations with
Fungal DNA and Mycotoxins in Classroom Dust. PLoS One 2016; 11:e0147996.

24. Wang J, Engvall K, Smedje G, Norbäck D. Rhinitis, asthma and
respiratory infections among adults in relation to the home environment
in multi-family buildings in Sweden. PloS one 2014; 9:e105125-e.

25. Ma'pol A, Hashim JH, Norback D, Weislander G, Hashim Z, Isa ZM. FeNO
level and allergy status among school children in Terengganu, Malaysia.
J Asthma 2019:1-8.

26. Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, et al. Real-time DNA
sequencing from single polymerase molecules. Science 2009; 323:133-8.

27. Travers KJ, Chin C-S, Rank DR, Eid JS, Turner SW. A flexible and
efficient template format for circular consensus sequencing and SNP
detection. Nucleic acids research 2010; 38:e159-e.

28. Nadkarni MA, Martin FE, Jacques NA, Hunter N. Determination of
bacterial load by real-time PCR using a broad-range (universal) probe
and primers set. Microbiology 2002; 148:257-66.

29. Wang Y, Song F, Zhu J, Zhang S, Yang Y, Chen T, et al. GSA: Genome
Sequence Archive. Genomics Proteomics Bioinformatics 2017; 15:14-8.

30. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD,
Costello EK, et al. QIIME allows analysis of high-throughput community
sequencing data. Nat Methods 2010; 7:335-6.

31. Edgar RC. Search and clustering orders of magnitude faster than
BLAST. Bioinformatics 2010; 26:2460-1.

32. Quast C, Pruesse E, Gerken J, Peplies J, Yarza P, Yilmaz P, et al.
The SILVA ribosomal RNA gene database project: improved data processing
and web-based tools. Nucleic Acids Research 2012; 41:D590-D6.

33. Koljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AF, Bahram M,
et al. Towards a unified paradigm for sequence-based identification of
fungi. Mol Ecol 2013; 22:5271-7.

34. Kruskal JB. Multidimensional scaling by optimizing goodness of fit
to a nonmetric hypothesis. Psychometrika 1964; 29:1-27.

35. Dannemiller KC, Lang-Yona N, Yamamoto N, Rudich Y, Peccia J.
Combining real-time PCR and next-generation DNA sequencing to provide
quantitative comparisons of fungal aerosol populations. Atmospheric
Environment 2014; 84:113-21.

36. Noble WS. How does multiple testing correction work? Nature
Biotechnology 2009; 27:1135-7.

37. Jiang L, Amir A, Morton JT, Heller R, Arias-Castro E, Knight R.
Discrete False-Discovery Rate Improves Identification of Differentially
Abundant Microbes. mSystems 2017; 2:e00092-17.

38. Asher MI, Montefort S, Bjorksten B, Lai CK, Strachan DP, Weiland SK,
et al. Worldwide time trends in the prevalence of symptoms of asthma,
allergic rhinoconjunctivitis, and eczema in childhood: ISAAC Phases One
and Three repeat multicountry cross-sectional surveys. Lancet 2006;
368:733-43.

39. Bjerg A, Ekerljung L, Middelveld R, Dahlén S-E, Forsberg B, Franklin
K, et al. Increased prevalence of symptoms of rhinitis but not of asthma
between 1990 and 2008 in Swedish adults: comparisons of the ECRHS and
GA²LEN surveys. PloS one 2011; 6:e16082-e.

40. Settipane RA, Lieberman P. Update on nonallergic rhinitis. Ann
Allergy Asthma Immunol 2001; 86:494-507; quiz -8.

41. Bachert C, Van Cauwenberge P, Olbrecht J, Van Schoor J. Prevalence,
classification and perception of allergic and nonallergic rhinitis in
Belgium. Allergy 2006; 61:693-8.

42. Lim FL, Hashim Z, Than LTL, Md Said S, Hisham Hashim J, Norbäck D.
Asthma, Airway Symptoms and Rhinitis in Office Workers in Malaysia:
Associations with House Dust Mite (HDM) Allergy, Cat Allergy and Levels
of House Dust Mite Allergens in Office Dust. PloS one 2015;
10:e0124905-e.

43. Poole JA, Rosenwasser LJ. The role of Immunoglobulin E and immune
inflammation: Implications in allergic rhinitis. Current Allergy and
Asthma Reports 2005; 5:252-8.

44. Baroody FM. Nonallergic Rhinitis: Mechanism of Action. Immunol
Allergy Clin North Am 2016; 36:279-87.

45. Junker MH, Danuser B, Monn C, Koller T. Acute sensory responses of
nonsmokers at very low environmental tobacco smoke concentrations in
controlled laboratory settings. Environmental health perspectives 2001;
109:1045-52.

46. Andersen I, Lundqvist GR, Mølhave L, Pedersen OF, Proctor DF, Vaeth
M, et al. Human response to controlled levels of toluene in six-hour
exposures. Scand J Work Environ Health 1983; 9:405-18.

47. Norbäck D, Hashim JH, Markowicz P, Cai G-H, Hashim Z, Ali F, et al.
Endotoxin, ergosterol, muramic acid and fungal DNA in dust from schools
in Johor Bahru, Malaysia --- Associations with rhinitis and sick
building syndrome (SBS) in junior high school students. Science of The
Total Environment 2016; 545-546:95-103.

48. Satoh K, Onodera T, Omoso K, Takeda-Yano K, Katayama T, Oono Y, et
al. Draft Genome Sequence of the Radioresistant Bacterium \&lt;span
class=\&quot;named-content genus-species\&quot;
id=\&quot;named-content-1\&quot;\&gt;Deinococcus grandis\&lt;/span\&gt;,
Isolated from Freshwater Fish in Japan. Genome Announcements 2016;
4:e01631-15.

49. Yuan M, Zhang W, Dai S, Wu J, Wang Y, Tao T, et al. Deinococcus
gobiensis sp. nov., an extremely radiation-resistant bacterium.
International Journal of Systematic and Evolutionary Microbiology 2009;
59:1513-7.

50. Kawaguchi Y, Yang Y, Kawashiri N, Shiraishi K, Takasu M, Narumi I,
et al. The possible interplanetary transfer of microbes: assessing the
viability of Deinococcus spp. under the ISS Environmental conditions for
performing exposure experiments of microbes in the Tanpopo mission. Orig
Life Evol Biosph 2013; 43:411-28.

51. Sanders WE, Jr., Sanders CC. Enterobacter spp.: pathogens poised to
flourish at the turn of the century. Clinical microbiology reviews 1997;
10:220-41.

52. Janda JM, Abbott SL. The Genus Aeromonas: Taxonomy, Pathogenicity,
and Infection. Clinical Microbiology Reviews 2010; 23:35.

53. Tunkel AR, Sepkowitz KA. Infections caused by viridans streptococci
in patients with neutropenia. Clin Infect Dis 2002; 34:1524-9.

54. Gaastra W, Kusters JG, van Duijkeren E, Lipman LJA. Escherichia
fergusonii. Veterinary Microbiology 2014; 172:7-12.

55. Di Pierro F, Adami T, Rapacioli G, Giardini N, Streitberger C.
Clinical evaluation of the oral probiotic Streptococcus salivarius K12
in the prevention of recurrent pharyngitis and/or tonsillitis caused by
Streptococcus pyogenes in adults. Expert Opin Biol Ther 2013; 13:339-43.

56. Strachan DP. Hay fever, hygiene, and household size. BMJ (Clinical
research ed.) 1989; 299:1259-60.

57. Eder W, Ege MJ, von Mutius E. The asthma epidemic. N Engl J Med
2006; 355:2226-35.

58. von Mutius E. Infection: friend or foe in the development of atopy
and asthma? The epidemiological evidence. European Respiratory Journal
2001; 18:872.

59. Régnier SA, Huels J. Association between respiratory syncytial virus
hospitalizations in infants and respiratory sequelae: systematic review
and meta-analysis. Pediatr Infect Dis J 2013; 32:820-6.

60. Smits HH, Hartgers FC, Yazdanbakhsh M. Helminth infections:
Protection from atopic disorders. Current Allergy and Asthma Reports
2005; 5:42-50.

61. Fu X, Li Y, Meng Y, Yuan Q, Zhang Z, Norbäck D, et al. Associations
between respiratory infections and bacterial microbiome in student
dormitories in Northern China. Indoor Air 2020; 00:1-11.

Figure 1. Relative abundance of top (A) bacteria and (B) fungal species
level in the classrooms.

Table 1. Prevalence of allergic and non-allergic rhinitis among junior
high school students (N=347) in Terengganu, Malaysia. P-values were
calculated by Chi-square test.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& Number & Prevalence (\%) & Male (\%) & Female (\%) & p-value & Smoking
(\%) & Non-smoking & p-value\tabularnewline
\midrule
\endhead
Healthy & 107 & 30.8 & 31.0 & 30.7 & 0.98 & 30.0 & 30.9 &
0.38\tabularnewline
Allergic rhinitis & 110 & 31.7 & 31.0 & 32.1 & & 40.0 & 31.5
&\tabularnewline
Non-allergic rhinitis & 130 & 37.5 & 38.0 & 37.2 & & 30.0 & 37.6
&\tabularnewline
\bottomrule
\end{longtable}

Table 2. Associations between bacterial and fungal richness (represented
as number of observed OTUs) and allergic and non-allergic rhinitis among
students (N = 347) in junior high school, Terengganu. Taxonomical
classes with the number of OTUs \textgreater{} 200 were included in the
analysis. Cyanobacteria were not resolved at the class level, and thus
the calculation was conducted at the phylum level. Odds ratio (OR) and
95\% confidence interval (CI) were calculated by 3-level hierarchic
linear regression models adjusted for gender, smoking and parental
asthma. Odds ratio was calculated for 100 OTU increase. P values
\textless{} 0.05 were formatted with bold font.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Kingdom & Phylum & Class & Number of OTUs & Allergic rhinitis & Allergic
rhinitis & Non-allergic rhinitis & Non-allergic rhinitis\tabularnewline
\midrule
\endhead
& & & & OR (95\% CI) & p-value & OR (95\% CI) & p-value\tabularnewline
Bacteria & Proteobacteria & Alphaproteobacteria & 999 & 1.00 (1.00,1.01)
& 0.39 & 1.00 (0.99,1.01) & 0.82\tabularnewline
& & Gammaproteobacteria & 307 & \textbf{0.96 (0.93,0.99)} &
\textbf{0.02} & \textbf{0.97 (0.94,1.00)} & \textbf{0.04}\tabularnewline
& & Betaproteobacteria & 226 & 1.02 (0.99,1.06) & 0.19 & 1.00
(0.96,1.03) & 0.92\tabularnewline
& Actinobacteria & Actinobacteria & 593 & 1.01 (0.98,1.04) & 0.49 & 1.00
(0.98,1.03) & 0.86\tabularnewline
& Cyanobacteria & & 937 & 0.99 (0.99,1.00) & 0.28 & 0.99 (0.98,1.00) &
0.10\tabularnewline
& Firmicutes & Bacilli & 287 & 0.99 (0.97,1.03) & 0.93 & 1.00
(0.97,1.03) & 0.86\tabularnewline
& Deinococcus-Thermus & Deinococci & 230 & 1.01 (0.98,1.03) & 0.61 &
1.00 (0.98,1.03) & 0.97\tabularnewline
Total bacterial OTUs & & & 4977 & 1.00 (1.00,1.01) & 0.37 & 1.00
(1.00,1.00) & 0.80\tabularnewline
Fungi & Ascomycota & Dothideomycetes & 803 & 1.01 (0.99,1.02) & 0.47 &
1.01 (1.00,1.02) & 0.16\tabularnewline
& & Eurotiomycete & 420 & 1.01 (0.99,1.03) & 0.28 & 1.01 (0.99,1.03) &
0.22\tabularnewline
& & Sordariomycetes & 265 & 1.00 (0.97,1.03) & 0.84 & 1.01 (0.98,1.03) &
0.65\tabularnewline
& Basidiomycota & Agaricomycetes & 490 & 1.01 (1.00,1.02) & 0.06 & 1.01
(1.00,1.02) & 0.10\tabularnewline
& & Wallemiomycetes & 274 & 1.00 (0.98,1.02) & 0.82 & 1.00 (0.98,1.02) &
0.96\tabularnewline
Total fungal OTUs & & & 2632 & \textbf{1.01 (1.00,1.01)} & \textbf{0.01}
& \textbf{1.01 (1.00,1.01)} & \textbf{0.03}\tabularnewline
\bottomrule
\end{longtable}

Table 3. Associations between the absolute concentration of indoor
microbial species and allergic rhinitis in junior high school,
Terengganu. Odds ratio and 95\% confidence interval (CI) were calculated
by 3-level hierarchic linear regression models adjusted for gender,
smoking and parental asthma. The regression was conducted for species
presented in at least 10 classrooms with mean relative abundance
\textgreater{} 0.1\%. In total, 170 bacterial and 89 fungal species were
analyzed. Only associations with p \textless{} 0.005 are presented in
the table. GM stands for geometric mean. The false discovery rate (FDR)
was calculated by Benjamini-Hochberg (BH) procedure.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Kingdom & Phylum & Class & Species & Absolute abundance GM (95\%CI) &
Range of relative & Number of classroom with presence & Odds Ratio
(95\%CI) & p-value & FDR\tabularnewline
\midrule
\endhead
& & & & (copy / g dust) & abundance (\%) & & & &\tabularnewline
Bacteria & Cyanobacteria & & \emph{Brasilonema bromeliae} &
4.1*10\textsuperscript{5} (1.1-14.7) & 0-0.79 & 21 & 0.83 (0.73,0.94) &
0.004 & 0.17\tabularnewline
& Deinococcus-Thermus & Deinococci & \emph{Deinococcus grandis} &
1.3*10\textsuperscript{5} (0.3-5.2) & 0-0.39 & 15 & 1.26 (1.10,1.45) &
0.001 & 0.09\tabularnewline
& Proteobacteria & Gammaproteobacteria & \emph{Aeromonas
enteropelogenes} & 1.2*10\textsuperscript{5} (0.1-10.8) & 0-0.81 & 10 &
0.82 (0.71,0.95) & 0.005 & 0.17\tabularnewline
& & & \emph{Escherichia fergusonii} & 2.6*10\textsuperscript{5}
(0.8-8.1) & 0-3.09 & 23 & 0.80 (0.69,0.93) & 0.003 & 0.17\tabularnewline
fungi & Ascomycota & Sordariomycetes & \emph{Chaetomium grande} & 399
(183-871) & 0-1.15 & 11 & 1.47 (1.13,1.90) & 0.004 &
0.18\tabularnewline
\bottomrule
\end{longtable}

Table 4. Associations between the absolute concentration of indoor
microbial species and non-allergic rhinitis in junior high school,
Terengganu. Odds ratio and 95\% confidence interval (CI) were calculated
by 3-level hierarchic linear regression models adjusted for gender,
smoking and parental asthma. The regression was conducted for species
presented in at least 10 classrooms with mean relative abundance
\textgreater{} 0.1\%. In total, 170 bacterial and 89 fungal species were
analyzed. Taxonomic information of the associated microbes is presented.
Only significantly associated species (p \textless{} 0.005) are
presented in the table. GM stands for geometric mean. The false
discovery rate (FDR) was calculated by Benjamini-Hochberg (BH)
procedure.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Kingdom & Phylum & Class & Species & Absolute abundance GM (95\%CI) &
Range of relative & Number of classroom with presence & Odds Ratio
(95\%CI) & p-value & FDR\tabularnewline
\midrule
\endhead
& & & & (copy / g dust) & abundance (\%) & & & &\tabularnewline
Bacteria & Actinobacteria & Thermoleophilia & \emph{Patulibacter
minatonensis} & 5.4*10\textsuperscript{5} (1.0-30.6) & 0-0.58 & 10 &
0.81 (0.71,0.91) & 0.001 & 0.06\tabularnewline
& Cyanobacteria & & \emph{Brasilonema bromeliae} &
4.1*10\textsuperscript{5} (1.1-14.7) & 0-0.79 & 21 & 0.82 (0.73,0.93) &
0.002 & 0.07\tabularnewline
& Deinococcus-Thermus & Deinococci & \emph{Deinococcus gobiensis} &
3.9*10\textsuperscript{5} (1.5-9.8) & 0-1.24 & 23 & 1.23 (1.07,1.41) &
0.003 & 0.07\tabularnewline
& Firmicutes & Bacilli & \emph{Streptococcus salivarius} &
4.0*10\textsuperscript{5} (1.0-15.7) & 0-1.18 & 16 & 0.84 (0.75,0.93) &
0.001 & 0.06\tabularnewline
& Proteobacteria & Gammaproteobacteria & \emph{Aeromonas
enteropelogenes} & 1.2*10\textsuperscript{5} (0.1-10.8) & 0-0.81 & 10 &
0.82 (0.72,0.94) & 0.003 & 0.07\tabularnewline
& & & \emph{Enterobacter xiangfangensis} & 6.0*10\textsuperscript{5}
(1.5-24.0) & 0-1.23 & 17 & 0.85 (0.76,0.95) & 0.003 &
0.07\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1-species-abundance/Figure1-species-abundance}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2-NMDS/Figure2-NMDS}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`SupplementaryTables(1).xlsx`  available at \url{https://authorea.com/users/367537/articles/486915-associations-between-indoor-microbiome-exposure-and-allergic-and-non-allergic-rhinitis-for-junior-high-school-students-in-terengganu-malaysia}

\selectlanguage{english}
\FloatBarrier
\end{document}

