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, Aeromonas enteropelogenesand 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 < 0.005). Four
species previously reported as facultative pathogens, includingA . enteropelogenes , Escherichia fergusonii ,Enterobacter xiangfangensis and Streptococcus salivarius ,
were protectively (negatively) associated with rhinitis. A higher
concentration of two radiation-resistant species, includingDeinococcus gobiensis and 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 population1-3. The prevalence of
rhinitis is still increasing in most of the countries in the world,
especially in many developing countries2. 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 asthma2,
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 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 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 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 9-15.
These profiling studies showed that microbiome diversity and
compositional variation are associated with many metabolic and immune
diseases, such as asthma16. Studies identified
many potential protective and risk microorganisms for asthma in the home
and school environments17-22. 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 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 students25. 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, Phleum pretense and Artemisia
vulgaris ), animal epidermal (Equus caballus and Felis
domesticus ), house dust mite (Dermatophagoides farinae andDermatophagoides pteronyssinus ), molds (Cladosporium
cladosporioides and Alternaria alternate /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 > 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 µm, which retains
74% particles of 0.3-0.5 µm, 81% particles of 0.5-1.0 µm, 95%
particles of 1-10 µm and 100% particles larger than 10 µm 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 ˚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 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 PCR23,
28.
Bioinformatics and association analyses
The raw sequence data were deposited in the Genome Sequence Archive29 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 < 99% were removed from
further analyses. The Quantitative Insights Into Microbial Ecology
(QIIME, v1.8.0) pipeline was used to process the filtered data30. 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) 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 fungi32,
33. For each sample, an OTU table was
generated to record the abundance of all characterized OTUs. OTUs
counted for < 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) analysis34 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 qPCR35. The absolute
concentration was first transformed in the logarithmic scale
(log10 (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 > 10 classrooms with
average relative abundance > 0.1%. Only associations with
p-value < 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.Acinetobacter johnsonii (4.0%), Aliterella Antarctica(2.7%), Klebsiella pneumonia (2.0%), Methylobacterium
radiotolerans (1.9%) and 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. Wallemia sebi (10.9%), an
unidentified Pleosporales (7.1%), Sterigmatomyces halophilus(5.8%), Aspergillus penicillioides (5.7%), Eupenidiella
venezuelensis (5.3%) and 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
< 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 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 > 0.05; Table
2). The same test was also conducted for the major bacterial classes
(> 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 > 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 >
0.1% were included in the regression analyses. Brasilonema
bromeliae from the phylum of Cyanobacteria and Aeromonas
enteropelogenes and Escherichia fergusonii from the class of
Gammaproteobacteria were protectively associated with allergic rhinitis
(p < 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*105,
1.2*105 and 2.6*105 copies per gram
dust. Deinococcus grandis from the class of Deinococci andChaetomium grande from the fungal class Sordariomycetes were
positively associated with allergic rhinitis (p < 0.005, FDR =
0.09 and 0.18). D . grandis was presented in 15 classrooms
with a geometric mean concentration as 1.2*105 copies
per gram dust. C . 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 B. bromeliae and A.
enteropelogenes were protectively associated with non-allergic rhinitis
(p = 0.002 and 0.003, FDR = 0.07; Table 4). Enterobacter
xiangfangensis from the class of Gammaproteobacteria,Streptococcus salivarius from the class of Bacilli andPatulibacter minatonensis from the class of Thermoleophilia were
also protectively associated with non-allergic rhinitis (p <
0.05, FDR < 0.1). 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*105 to 6.0*105 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 CO2, NO2 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 > 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 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 comparisons36. The associations
were not significant after adjustment with the Benjamini-Hochberg (BH)
procedure (0.06 < FDR < 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 detection37. 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 countries3. The international
collaborated ISAAC and ECRHS studies reported a list of countries with
high prevalence in allergic rhinitis, including Nigeria
(>35%), Paraguay (30-35%), Sweden (31%), Hong Kong
(25-30%) Australia (15-20%), New Zealand (15-20%), the United Kingdom
(15-20%%) 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 rhinitis40. Another study
reported that the prevalence of non-allergic rhinitis was 9.6% among
high school students in Belgium41. 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 allergy42.
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 symptoms43. 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 rhinitis1. Neurogenic
dysregulation is another proposed pathway that leads to nasal
obstruction and rhinorrhea1,
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 rhinitis7,
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 B. bromeliae and A. enteropelogenes and two species
from the genus of 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 rhinitis47. 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 environment23. 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 B.
bromeliae , P. minatonensis , were protectively (negatively)
associated with rhinitis, and two of the environmental species,
including D. gobiensis , D. grandis , were positively
associated with rhinitis. Interestingly, the two potential risk species,D. gobiensis and D. grandis were both extremely
radiation-resistant bacteria48,
49. These species are physically
protected from the UV damages due to their efficient DNA repair system50. 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 E. fergusonii , A. enteropelogenes , E.
xiangfangensis and 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 neutropenia51-54. AlthoughS. 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 adults55. 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 children56. The role of
infectious pathogens and the development of asthma in children have also
been fiercely debated over the past years57,
58. Some studies suggest that rhinovirus
infections and respiratory syncytial virus (RSV) infection were
associated with increased odds of wheeze and asthma57,
59, but other population-based studies
reported that exposure to infections diseases could lead to reduced
atopic disorders 60.
The potential protective effects of childhood infections are suggested
to relate to the training and maturation of the immune system from the
exposure 58. A recent
study in university dormitories reported that exposed to facultative
pathogens, including Haemophilus , Klebsiella ,Buttiauxella , and Raoultella , increased the odds of having
respiratory infections61. 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.
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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.