特集 世界を救え 〜 SFC バイオの挑戦［研究論文］

Comprehensive Analysis of Microbes
and Metabolites in Human Tear Fluids

ヒト涙液中の微生物および代謝物質の網羅的解析
Shinnosuke Murakami
Doctoral Program, Graduate School of Media and Governance, Keio University
村上 慎之介慶應義塾大学大学院政策・メディア研究科後期博士課程

Fujitaka Baba
Staff, Institute for Advanced Biosciences, Keio University
馬場 藤貴
慶應義塾大学先端生命科学研究所所員

Wanping Aw
Project Researcher, Graduate School of Media and Governance, Keio University
ワンピン アウ慶應義塾大学大学院政策・メディア研究科研究員

Shinji Fukuda
Project Associate Professor, Graduate School of Media and Governance, Keio University
福田 真嗣
慶應義塾大学大学院政策・メディア研究科特任准教授

Tomoyoshi Soga
Professor, Faculty of Environment and Information Studies, Keio University
曽我 朋義
慶應義塾大学環境情報学部教授

Hiroshi Fujishima
Professor, Department of Ophthalmology, Tsurumi University Dental Hospital
藤島 浩
鶴見大学歯学部附属病院眼科教授

Masaru Tomita
Professor, Faculty of Environment and Information Studies, Keio University
冨田 勝
慶應義塾大学環境情報学部教授

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Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids

Abstract:

Lacrimal fluids are important in protecting the eyes from environmental factors
and also possess antimicrobial abilities. Although human tear fluid metabolites havebeen reported, the relationships between these metabolites and eye diseases have
yet to be elucidated. In addition, the microbial composition of human tear fluids
has not been investigated yet, even though numerous human microbiome analyses
of various body sites have been reported. Therefore, microbiome and metabolome
analyses of human tear fluids from healthy subjects and atopic keratoconjunctivitis(AKC) patients were conducted. The current study suggested that lactic acidbacteria in tear fluids might have a potential to prevent pathogenesis of AKC,
whereas o-acetylcarnitine reduction and urea increment might be involved in AKC
pathogenesis.
涙液は環境要因から眼を保護する役割や、抗菌作用を有することが知られている。ヒト涙液に含まれる代謝物組成はこれまでにも報告されているが、眼疾患との関連は不明である。近年、人体常在菌に関する研究が多数報告されているが、涙液中細菌叢に関する報告はほとんどない。本研究では、健常者とアト
ピー性角結膜炎（AKC）患者の涙液を用いて細菌叢および代謝物を網羅的に解析した。その結果、涙液中乳酸菌による AKC 発症予防の可能性や、o-アセ
チルカルニチンの減少および尿素の増加が AKC 発症に関与する可能性が示唆された。

Keywords: tear fluid, atopic keratoconjunctivitis, microbiome analysis, metabolome
analysis, lactic acid bacteria

涙液、アトピー性角結膜炎、細菌叢解析、メタボローム解析、乳酸菌

1 Introduction
Recent studies have revealed that thousands of species ofcommensal microbes are colonized in and on the human body (1-3). There
are a huge number of commensal microbes that reside in the humanbody that have a profound influence on human physiology, immunology,
and nutrition through host-microbial crosstalk (4-11). Previously,
there have been numerous reports on the microbiome analyses ofvarious human body sites such as intestine, skin and vagina. However,microbiome analysis of human tear fluids has yet to be presented.
Eyes are extremely important sensory organs for maintaining ourquality of life. Nevertheless, the eyes are constantly exposed to
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environmental factors such as microbes and possible allergens. It is
well known that tear fluids play an important role in the protection ofeyes from environmental factors by not only physically flushing themaway but also possess antimicrobial abilities derived from lysozyme,
lactoferrin, IgA and IgG (12). According to previous research, 60
metabolites including amino acids, carnitines and nucleotides weredetected from human tear fluid by liquid chromatography-tandem mass
spectrometry (LC-MS/MS) analysis (13). These molecules are expected
to be involved in the maintenance of physiological homeostasis of eyes.
However, the exact relationships between these molecules and eye
diseases are still currently unknown.
To date, it has been reported that Staphylococcus, Propionibacterium
and Corynebacterium are present in the ocular microbiota that wereobserved by a culture-based method in human conjunctival scraping
samples collected from cataract patients (14). Furthermore, microbiome
analysis of conjunctival swab samples collected from healthy
volunteers showed that the ocular microbiota was comprised of 5 phyla
and 59 genera, mainly comprising of Pseudomonas, Bradyrhizobium andPropionibacterium (15). Although partial ocular microbiota compositions
from conjunctival swabs have been observed, the exact microbiota
compositions of tear fluids that are the main players for maintaining
ocular surface homeostasis remain obscure.
Ocular allergic inflammation is a common problem amongst
individuals suffering from allergies. A previous study has reported
that 25% to 42% of atopic keratoconjunctivitis (AKC) patients haveatopic dermatitis (16). Allergic conjunctival disease is typically divided
into three types: AKC, vernal keratoconjunctivitis (VKC) and seasonalallergic conjunctivitis (SAC) (17). AKC is the most severe form and
SAC is the least severe form of allergic conjunctival diseases.
Common ocular clinical features of AKC, VKC and SAC include384

Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids

redness, itching, and tearing. Two common pathological findings of
AKC are conjunctival mast cell activation and eosinophil recruitment
to the ocular surface. Histamine and leukotriene released from mast
cells are observed to be up regulated in the tear fluid analysis ofpatients with these conditions (18, 19), suggesting that biochemical
imbalances in amino acid and fatty acid metabolism may underlie thepathogenesis of these ocular allergic diseases.
In this study, in order to investigate the characteristics of
microbial and metabolite profiles in tear fluids between normaleyes and eyes suffering from ocular allergic inflammation; and therelationships between microbiota or metabolites and eye diseases; weconducted microbiome and metabolome analysis of human tear fluids
collected from healthy subjects and AKC patients.

2 Materials and Methods
2.1 Subjects and collection of tear fluids
This study was approved by the Ethics Committee of Keio
University School of Medicine and Keio University Shonan Fujisawa
Campus. All subjects were informed of the purpose of this study, andwritten consents were obtained from all subjects. For microbiome
analysis, tear fluids were collected from 4 healthy subjects and 5
AKC patients. After tilting the face for lateral side, the suction tube
or tip was placed on the corner of the outside of eye, and tears werecollected. A minimum of 30 μl of tear fluids were collected from botheyes and stored at -20°C until further use. For metabolome analysis,tear fluids were collected from 5 healthy subjects and 10 AKC
patients. Similarly, a minimum of 30 μl of tear fluids were collectedfrom both eyes. Then, the tear fluids were immediately centrifuged
at 10,000 rpm for 5 min at 4°
C. After centrifugation, supernatants
were transferred to new tubes and stored at -20°
C until further use.
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For all cases, AKC diagnosis was based on the criteria as described
previously (20). In the case of metabolome analysis, AKC patients wereclassified into 2 groups (severe or moderate). Severe AKC patientswere defined as AKC complicated severe corneal lesions such as
ulcers, while moderate AKC patients were defined as those whohave AKC with mild or no corneal complications. Eye drops included
0.1% dexamethasone, 0.1% fluorometholone, 0.05% cyclosporine A or
cromolyn sodium were used for treatment of the AKC patients but
all patients did not receive systemic corticosteroid therapy. All AKC
patients did not have any other systemic diseases.
2.2 DNA extraction
For DNA extraction from tear fluids, QIAamp DNA Stool Mini
Kit (QIAGEN N.V., Limburg, Netherlands) was used. Initially, 1.4 ml
of Buffer ASL (included QIAamp DNA Stool Mini Kit) and 0.1 g of
0.1 mm glass beads were added to tear fluids, and then horizontallyvortexed for 5 minutes. Subsequent steps of DNA extraction wereconducted according to the manufacturer’s instructions.
2.3 PCR amplification and sequencing of 16S rRNA genes
The V3-V4 region of the 16S rRNA genes were amplified fromthe DNA isolated from the tear fluids using the bacterial universal
primer set 341F (5’-CCTACGGGAGGCAGCAG-3’) and 907R (5’-CCGTCAATTCCTTTGAGTTT-3’) (21). PCR was performed with
TaKaRa Ex Taq DNA polymerase (Takara Bio Inc., Shiga, Japan)
and amplification proceeded with one denaturation step at 95 °C for
5 min, followed by 30 cycles of 95°C for 30 s, 48°C for 30 s, and72°C for 2 min, with a final extension step at 72°C for 5 min after
which sequencing of 16S rRNA genes using Roche GS FLX Titanium
platform was outsourced to Filgen, Inc. (Aichi, Japan).

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2.4 Analysis of 16S rRNA gene sequences
The 16S rRNA gene sequences were analyzed using the
RDP-Classifier and RDP-Seqmatch provided by the Ribosomal
Database Project (RDP) (22) In advance, sequences whose lengths
were less than 250 bp were removed as RDP-Classifier only
accepts sequences that are over 250 bp long. All sequences wereassigned genus level taxonomy using the RDP-Classifier. Species
level taxonomies were also assigned using RDP-Seqmatch using
the following parameters; Strain: type, Source: isolates, Size:≥1200. Sequences whose homologies against known 16S rRNA
gene sequences were less than 95% were filtered out for further
analysis. The microbial profiles of tear fluids were compared withthat of skin microbiota. The data of skin microbiota were obtained
from a previous study conducted (23). Coefficient of determinations(square of the Pearson correlation) between microbial compositionsof tear (average of the microbial components collected from 5
healthy subjects) and that of each skin category were calculated.
Orthogonal partial least squares discriminate analysis (OPLSDA) on the tear microbial data was conducted with the SIMCA-P+
software v12.0 (Umetrics Inc., Umetrics AB, Umeå, Sweden).
For the drawing of the autocorrelation map, Spearman’s rankcorrelations between each microbial pairs were calculated
by JMP (SAS Institute Inc., North Carolina, USA). Bacterial
populations that dominated at least 1% in any samples were used
for autocorrelation analysis. Network analysis of tear microbiota
was conducted using Cytoscape v2.8.1 (24). A force-directed layout
algorithm was used to draw the network. Bacterial species that
dominated at least 0.01% in any samples were used for network
analysis.

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2.5 Metabolome analysis
In order to extract metabolites from tear fluids,
900 μl of methanol including the internal standards (20 μM
of methionine sulfone (Alfa Aesar, Massachusetts, USA) and25 μM of 2-Morpholinoethanesulfonic acid (MES) (DOJINDOLABORATORIES, Kumamoto, Japan)) were added to the samples andthen mixed with 400 μl Milli-Q water. Each 600 μl of the solutions
were transferred to a centrifugal filter tube to remove protein andlipid molecules. The filtrate was centrifugally concentrated anddissolved in 120 μl of Milli-Q water containing reference compounds(200 μM of both 3-aminopyrrolidine (Sigma-Aldrich, Missouri, USA)and trimesic acid (Wako Pure Chemical Industries, Ltd., Osaka,
Japan)) immediately before capillary electrophoresis with electrosprayionization time-of-flight mass spectrometry (CE-TOFMS) analysis.
The measurement of extracted metabolites in both positive andnegative modes was performed by CE-TOFMS (Agilent Technologies,
California, USA). The alignment of detected peaks was performed
according to the m/z value and normalized migration time. Then,peak areas were normalized against those of the internal standards
methionine sulfone and MES for cationic and anionic metabolites,
respectively. Annotation tables were produced from measurement ofstandard compounds and were aligned with the datasets according
to similar m/z value and normalized migration time. Relative
concentrations of each metabolite were transformed to Z-score by
subjects and demonstrated as heatmap using MeV (v4.8) (25). Principal
component analysis (PCA) and OPLS-DA on the tear metabolome
data were conducted with the SIMCA-P+ software v12.0.
2.6 Statistical analysis
Statistical evaluation between two groups and normality of388

Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids

the distribution of each data were investigated by KolmogorovSmirnov’s test. When the data was determined to model normal
distribution, P values were calculated by Student’s t-test or Welch’s
t-test. Statistical analyses were performed by Microsoft Excel. All
statements indicating significant differences show at least a 5% levelof probability.
2.7 Nucleotide sequence accession number
The microbiome analysis data have been deposited at the DDBJ
Sequence Read Archive (http://trace.ddbj.nig.ac.jp/dra/) under accession
number DRA003590.

3 Results
3.1 Microbiome analysis of human tear fluids collected from healthy
subjects and AKC patients
In this study, microbial profiles of human tear fluids collectedfrom 4 healthy subjects and 5 AKC patients were analyzed. 16SrRNA gene sequences collected from tear fluids were assigned to 462
strains. Propionibacterium acnes and Leuconostoc citreum occupied 3050% of tear microbiota in both healthy subjects and AKC patients(Fig. 1A). Microbial profiles of tear fluids were comparable withthat of skin (Fig. 1B-C). Microbial profile of tear fluid that excluded
Lactobacillales was similar to the microbial profiles of sebaceous sites
such as glabella and alar crease that are located around eyes.
To investigate the differences of microbial components between
healthy subjects and AKC patients, PLS-DA was conducted (Fig.
2A-B). According to the S-plot, proportions of lactic acid bacteriasuch as Lactococcus plantarum and L. citreum were higher in healthy
subjects. Subsequently, correlation analysis using proportions of 21
dominant microbes was performed. As a result, several lactic acid
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D
ry

st
oi
M
s
ou
ce
ba
Se

ar

ry

Te

D

ar

st
oi
M
s
ou
ce
ba
Se

Te

Figure 1

Comparisons of tear microbiota compositions between healthy subjects
and AKC patients.
(A) Species level composition of tear microbiota of healthy subjects and AKC
patients. (B-C) Microbial compositions of both human tear fluid and skin that were
included (B) and excluded (C) Lactobacillales. The data of skin microbiota (sebaceous,moist and dry) were obtained from a previous study conducted (23). Coefficient ofdeterminations (square of the Pearson correlation) between microbial compositions oftear and that of each skin category were demonstrated.

390Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids

A

B

Figure 2

Determination of effective microbes to separate healthy subjects and AKC
patients.
(A) Cross-validated score plots from OPLS-DA of tear microbiota collected fromhealthy subjects and AKC patients. (B) S-plot derived from the predictive componentof OPLS-DA of tear microbiota collected from healthy subjects and AKC patients.
The white or gray plots (∣covariance∣> 0.2) in the S-plot have high contribution andreliability for class separation between healthy subjects and AKC patients.

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bacteria (E.g. Lactococcus lactis and Lactococcus raffinolactis) werepositively correlated among each other and potential pathogens ofvarious diseases such as P. acnes, Corynebacterium tuberculostearicum,
Staphylococcus aureus and Shigella flexneri (26-29) were also positively
correlated to each other. However lactic acid bacteria and potential
pathogens were strongly, negatively correlated (Fig. 3A). Network
analysis showed that lactic acid bacteria were well clustered andthe cluster including potential pathogens such as P. acnes was also
constructed; however S. aureus was not clustered together (Fig. 3B).
Network analysis also revealed that lactic acid bacteria cluster andpotential pathogens cluster were negatively correlated with eachother, and this was consistent with correlation analysis.
3.2

Metabolome analysis of human tear fluids collected from healthy
subjects and AKC patients
In the present study, metabolome profiles of human tear fluids

were collected from 5 healthy subjects and 10 AKC patients. To
compare the metabolome profiles derived from distinct severityof AKC, AKC patients were classified into 2 groups (severe or
moderate). Representative pictures of eyes in healthy subject,moderate and severe patients are as depicted in Fig. 4A. A total
number of 47 metabolites were detected from human tear fluid byusing CE-TOFMS. Relative concentrations of each metabolite weretransformed to Z-score by subjects and demonstrated as heatmap (Fig.
4A-B).
To investigate the differences of metabolites between healthy
subjects and AKC patients, PLS-DA was conducted (Fig. 5AB). According to the S-plot, the proportion of o-acetylcarnitine
was higher in healthy subjects whereas that of urea was higher in
AKC patients. In the tear fluids of AKC patients, concentration of392Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids

Figure 3 Correlations between each microbe observed in the current study.
(A) Autocorrelation map using bacterial populations that dominated at least 1% in
any samples was demonstrated based on the Spearman’s rank correlation. Bacterial
species that highlighted as white or gray in Figure 2B were colored as blue (proportions
were higher in healthy subjects) or red (proportions were higher in AKC patients),
respectively. (B) Network of tear microbiota collected from healthy subjects and
AKC patients was constructed based on the Spearman’s rank correlation. Bacterial
species that dominated at least 0.01% in any samples were used. Blue, red and whitecircles indicate lactic acid bacteria, potential pathogens of various diseases and others,
respectively. Size of each circle corresponds to numbers of positively correlated pairs.
Positive and negative correlations are shown as purple and white lines, respectively.

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o-acetylcarnitine was significantly low and that of urea tended to beelevated (Fig. 5C). Subsequently, concentrations of 20 amino acids
were used in PCA to investigate the differences in amino acids
profiles of healthy subjects and AKC patients. Based on the PCA,
it was found that healthy subjects and AKC patients were separated
into 2 different clusters, whereas moderate and severe patients were
not separated (Fig. 6A). Loading plot of the PCA demonstrated
that aspartic acid was higher in healthy subjects, whereas leucine,phenylalanine, lysine, methionine, valine, tyrosine and alanine werehigher in AKC patients, especially in severe patients (Fig. 6B).

Figure 4

Comparisons of tearmetabolites collected fromhealthy subjects and AKC
patients.
Patients were classified into 2
groups based on with (severe) orwithout (moderate) complication. (A)Representative pictures of eyes inhealthy subject, moderate and severe
patients. (B) Heatmap profiles ofindividual metabolites in tear. 47
metabolites that detected at least two
thirds of the samples were shown.
R ela t i v e c o n c e n t r a t io n s o f e a c hmetabolite were transformed to Z-scoreby subjects.

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Figure 5

Determination of effective metabolites to separate healthy subjects and
AKC patients.
(A) Cross-validated score plots from OPLS-DA of tear metabolites collected fromhealthy subjects and AKC patients. (B) S-plot derived from the predictive componentof OPLS-DA of tear metabolites collected from healthy subjects and AKC patients.
(C) Comparisons of o-Acetylcarnitine and Urea concentrations between healthy subjects
and AKC patients. Data were expressed as mean ± standard deviation. P values were
calculated using t-test.

Figure 6

PCA on amino acids concentrations derived from metabolome analysisof tear fluids.
Amino acid concentrations were normalized by dividing by total concentrationsof 20 amino acids included in same sample as previously reported (30). PCA score plotis color-coded according to healthy subject, moderate and severe patients (A) and (B)demonstrates the loading plot of amino acid concentrations.
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4 Discussion
In the present study, microbiome and metabolome analyses ofhuman tear fluids collected from healthy subjects and AKC patientswere conducted. Interestingly, the microbial profile of tear fluidsthat excluded Lactobacillales was similar to the microbial profile
of sebaceous sites. Since sebaceous sites such as glabella and alarcreases are located around eyes, tear microbiota might be constructedfrom skin microbiota located around the eyes. Additionally, the results
also indicate that Lactobacillales species were unique in tear fluid ascompared to skin microbiota. However, the origin of the Lactobacillales
species in the current study is unknown. The Lactobacillales species
were abundant in healthy subjects when compared with AKC patients
according to the PLS-DA, and Lactobacillales species has been known
to have preventive effects for various diseases in intestine and vagina(10, 31, 32). In addition, autocorrelation analysis and network analysis

have demonstrated a negative correlation between lactic acid bacteria
and possible pathogens. Taken together, Lactobacillales species in tearfluids may have a potential to prevent pathogenesis of eye diseases.
Since the abundance of P. acnes was higher in AKC patientsand P. acnes has been reported to be involved in postoperative
endophthalmitis (26), this bacteria might be one of the possible
pathogens of AKC. As Propionibacterium is presented as the highest
population in sebaceous sites of skin, influx of the bacteria from thesurrounding environment may be an initiation factor for eye diseases.
On the other hand, S. aureus that has been reported to be a possible
pathogen of several diseases such as atopic dermatitis (28), was notstrongly correlated with other bacteria and this bacteria was also nothigher in AKC patients. Although it has been reported that atopic
dermatitis is a significant cause of ocular morbidity (17), S. aureus may
not influence AKC pathogenesis.

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Metabolome analysis of human tear fluids revealed thatconcentration of o-acetylcarnitine was significantly decreased and thatof urea was tended to increase in AKC patients. O-acetylcarnitine
has been known to be catalyzed from acyl-CoA and carnitine bycarnitine o-acetyltransferase (CRAT) in humans (33). Thus, CRAT
activity and/or expression may be downregulated in AKC patients.
Despite acetylcarnitine being beneficial in depression and Alzheimer’s
disease (34, 35), its role in other positive effects for human health isunclear. It was reported that urea concentrations in tear fluids andserum were not correlated although other components of tear fluids
and serum has been known to be similar (36). In addition, arginase,which converts L-arginine into urea and L-ornithine, has beenreported to express in lacrimal gland, conjunctiva and cornea, thus
urea is expected to be locally supplied from these tissues to the tearfluid (36). According to the PCA of amino acid concentrations, arginine
concentration was lower in AKC patients, it may be the result that
arginine was converted to urea in AKC patients. Consequently,
increment of urea concentration in AKC patients may be derived fromincreasing of activity and/or expression of arginase. Additionally,
profiles of amino acid concentrations were different between healthy
subjects and AKC patients. Acetylcarnitine, urea and amino acids
were previously detected from tear fluid (13), and the current researchalso demonstrated that alterations of those metabolites could possibly
be involved with AKC.
Finally, this present study showed novel features of microbial andmetabolite compositions of human tear fluids and their variations in
AKC patients when compared with healthy subjects. Since the current
results of this study are not sufficient to completely understand thepathogenesis of AKC, further analyses are required to reveal the
relationships between tear microbiota, metabolites and eye diseases.
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Acknowledgements
We would like to thank Ms. Ayako Igarashi for technical assistance. We also
wish to thank Dr. Masahiro Sugimoto, Dr. Akiyoshi Hirayama, Ms. Emmy Umeda
and technical staff of Institute for Advanced Biosciences for their experimental
and/or data analysis support; and Ms. Kotone Itaya for the critical reading andediting of the manuscript. This work was supported in part by research funds fromthe Yamagata Prefectural Government and Tsuruoka City, Japan.

References
1

Spor, A. et al.,“Unravelling the effects of the environment and host genotype on
the gut microbiome.”Nat. Rev. Microbiol., 9, 2011, pp.279-290.
2 Consortium., H.M.P.,“Structure, function and diversity of the healthy humanmicrobiome. ”Nature, 486, 2012, pp.207-214.
3 Consortium., H.M.P.,“A framework for human microbiome research.”Nature,
486, 2012, pp.215-221.
4 Jia, W. et al.,“Gut microbiota: a potential new territory for drug targeting.”
Nat. Rev. Drug Discov., 7, 2008, pp.123-129.
5 Mazmanian, S.K. et al., “A microbial symbiosis factor prevents intestinal
inflammatory disease.”Nature, 453, pp.620-625.
6 Ventura, M. et al., “Genome-scale analyses of health-promoting bacteria:
probiogenomics.”Nat. Rev. Microbiol., 7, 2009, pp.61-71.
7 Ivanov, I.I. et al.,“Induction of intestinal Th17 cells by segmented filamentous
bacteria.”Cell, 139, 2009, pp.485-498.
8 Lebeer, S. et al.,“Host interactions of probiotic bacterial surface molecules:
comparison with commensals and pathogens.”Nat. Rev. Microbiol, 8, 2010,
pp.171-184.
9 Atarashi, K. et al.,“Induction of colonic regulatory T cells by indigenous
Clostridium species.”Science, 331, 2011, pp.337-341.
10 Fukuda, S. et al.,“Bifidobacteria can protect from enteropathogenic infection
through production of acetate.”Nature, 469, 2011, pp.543-547.
11 Furusawa, Y. et al., “Commensal microbe-derived butyrate induces thedifferentiation of colonic regulatory T cells.”Nature, 504, 2013, pp.446-450.
12 Albietz, J.M. and Lenton, L.M.,“Effect of antibacterial honey on the ocularflora in tear deficiency and meibomian gland disease.”Cornea, 25, 2006,
pp.1012-1019.
13 Chen, L. et al.,“Characterization of the human tear metabolome by LC-MS/
MS.”J. Proteome Res., 10, 2011, pp.4876-4882.
14 Inoue, Y. et al., “Preoperative disinfection of the conjunctival sac with
antibiotics and iodine compounds: a prospective randomized multicenter study .”Jpn. J. Ophthalmol., 52, 2008, pp.151-161.
15 Dong, Q. et al.,“Diversity of bacteria at healthy human conjunctiva.”Invest.
Ophthalmol. Vis. Sci., 52, 2011, pp.5408-5413.
16 Bielory, L.,“Allergic and immunologic disorders of the eye. Part II: ocularallergy.”J. Allergy Clin.Immunol., 106, 2000, pp.1019-1032.

398

Comprehensive Analysis of Microbes and Metabolites in Human Tear Fluids
17

Chen, J.J. et al., “Atopic keratoconjunctivitis: A review.”J. Am. Acad.
Dermatol., 70, 2014, pp.569-575.
18 Abelson, M.B. et al.,“Tear histamine levels in vernal conjunctivitis and otherocular inflammations.”Ophthalmology, 87, 1980, pp.812-814.
19 Fukagawa, K. et al.,“Histamine and tryptase levels in allergic conjunctivitis
and vernal keratoconjunctivitis.”Cornea, 13, 1994, pp.345-348.
20 Hanifin, J.M.,“Diagnostic features of atopic dermatitis.”Acta Derm. Venereol.,
92, 1980, pp.44-47.
21 Wilms, R. et al.,“Specific bacterial, archaeal, and eukaryotic communities intidal-flat sediments along a vertical profile of several meters.”Appl. Environ.
Microbiol., 72, 2006, pp.2756-2764.
22 Wang, Q. et al.,“Naive Bayesian classifier for rapid assignment of rRNA
sequences into the new bacterial taxonomy.”Appl. Environ. Microbiol., 73, 2007,
pp.5261-5267.
23 Grice, E.A. et al.,“Topographical and temporal diversity of the human skinmicrobiome.”Science, 324, 2009, pp.1190-1192.
24 Smoot, M.E.,“Cytoscape 2.8: new features for data integration and network
visualization.”Bioinformatics, 27, 2011, pp.431-432.
25 Saeed, A.I. et al.,“TM4: a free, open-source system for microarray data
management and analysis.”Biotechniques, 34, 2003, pp.374-378.
26 Roussel, T.J. et al.,“Chronic postoperative endophthalmitis associated with
Propionibacterium acnes.”Arch. Ophthalmol., 105, 1987, pp.1199-1201.
27 Jennison, A.V. and Verma, N.K.,“Shigella flexneri infection: pathogenesis andvaccine development.”FEMS Microbiol. Rev., 28, 2004, pp.43-58.
28 Kong, H.H. et al.,“Temporal shifts in the skin microbiome associated withdisease flares and treatment in children with atopic dermatitis.”Genome Res.,22, 2012, pp.850-859.
29 Abreu, N.A. et al.,“Sinus microbiome diversity depletion and Corynebacterium
tuberculostearicum enrichment mediates rhinosinusitis.”Sci. Transl. Med,, 4,2012, 151ra124.
30 Nakatsukasa, M. et al.,“Amino Acid profiles in human tear fluids analyzed byhigh-performance liquid chromatography and electrospray ionization tandem
mass spectrometry.”Am. J. Ophthalmol., 151, 2011, pp.799-808 e791.
31 Borges, S. et al.,“The role of lactobacilli and probiotics in maintaining vaginal
health.”Arch.Gynecol. Obstet., 289, 2014, pp.479-489.
32 Enomoto, T. et al.,“Effects of bifidobacterial supplementation to pregnant
women and infants in the prevention of allergy development in infants and on
fecal microbiota.”Allergol.Int., 63, 2014, pp.575-585.
33 Ramsay, R.R. and Arduini, A.,“The carnitine acyltransferases and their rolein modulating acyl-CoA pools.”Arch. Biochem. Biophys., 302, 1993, pp.307-314.
34 Shenk, J.C. et al.,“The effect of acetyl-L-carnitine and R-alpha-lipoic acidtreatment in ApoE4 mouse as a model of human Alzheimer's disease.”J. Neurol.
Sci., 283, 2009, pp.199-206.
35 Nasca, C. et al.,“L-acetylcarnitine causes rapid antidepressant effects throughthe epigenetic induction of mGlu2 receptors.”Proc. Natl. Acad. Sci. U S A., 110,2013, pp.4804-4809.
36 Jager, K. et al.,“Enzymes of urea synthesis are expressed at the ocular

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surface, and decreased urea in the tear fluid is associated with dry-eye
syndrome.”Graefes Arch. Clin. Exp. Ophthalmol., 251, 2013, pp.1995-2002.

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