Open Access
Immunology and Microbiology  |   January 2017
Conjunctival Microbiome Changes Associated With Soft Contact Lens and Orthokeratology Lens Wearing
Author Affiliations & Notes
  • Haikun Zhang
    Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
    University of Chinese Academy of Sciences, Beijing, China
  • Fuxin Zhao
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Diane S. Hutchinson
    The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
  • Wenfeng Sun
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Nadim J. Ajami
    The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
  • Shujuan Lai
    Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
  • Matthew C. Wong
    The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
  • Joseph F. Petrosino
    The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
  • Jianhuo Fang
    Genomics & Synthetic Biology Core Facility of Tsinghua University, Beijing, China
  • Jun Jiang
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Wei Chen
    Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
  • Peter S. Reinach
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Jia Qu
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Changqing Zeng
    Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
  • Dake Zhang
    Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
  • Xiangtian Zhou
    School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
    State Key Laboratory Cultivation Base and Key Laboratory of Vision Science, Ministry of Health P. R. China and Zhejiang Provincial Key Laboratory of Ophthalmology and Optometry, Wenzhou, Zhejiang, China
  • Correspondence: Xiangtian Zhou, School of Optometry and Ophthalmology and Eye Hospital, Wenzhou Medical University, 270 Xueyuan Road Wenzhou 325003, Zhejiang, China; zxt-dr@wz.zj.cn
  • Dake Zhang, Beijing Institute of Genomics, Chinese Academy of Sciences, NO.1 Beichen West Road, Chaoyang District, Beijing 100101, China; zhangdk@big.ac.cn
  • Footnotes
     HZ, FZ, and DSH contributed equally to the work presented here and should therefore be regarded as equivalent authors.
Investigative Ophthalmology & Visual Science January 2017, Vol.58, 128-136. doi:10.1167/iovs.16-20231
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      Haikun Zhang, Fuxin Zhao, Diane S. Hutchinson, Wenfeng Sun, Nadim J. Ajami, Shujuan Lai, Matthew C. Wong, Joseph F. Petrosino, Jianhuo Fang, Jun Jiang, Wei Chen, Peter S. Reinach, Jia Qu, Changqing Zeng, Dake Zhang, Xiangtian Zhou; Conjunctival Microbiome Changes Associated With Soft Contact Lens and Orthokeratology Lens Wearing. Invest. Ophthalmol. Vis. Sci. 2017;58(1):128-136. doi: 10.1167/iovs.16-20231.

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Abstract

Purpose: Usage of different types of contact lenses is associated with increased risk of sight-threatening complications. Changes in the ocular microbiome caused by contact lens wear are suggested to affect infection development in those individuals. To address this question, this study compares conjunctival microbial communities in contact lens wearers with those in noncontact lens wearers.

Methods: Paired-end sequencing of the V3 region of the 16S rRNA gene was used to characterize the bacterial communities on the conjunctival surfaces of contact lens wearers and nonwearers.

Results: No differences in microbial diversity were detected between contact lens wearers and nonwearers. Nevertheless, some slight microbe variability was evident between these two different groups. Bacillus, Tatumella and Lactobacillus abundance was less in orthokeratology lens (OKL) wearers than in nonwearers. In soft contact lenses (SCL) wearers, Delftia abundance decreased whereas Elizabethkingia levels increased. The difference in the SCL and nonwearer group was smaller than that in the OKL group. Variations in the conjunctival taxonomic composition between SCL wearers were larger than those in other groups. Sex differences in the conjunctival microbiota makeup were only evident among nonwearers.

Conclusions: Even though there were slight percentage changes between contact lens wearers and nonwearers in some microbes, there were no differences in their diversity. On the other hand, contact lens usage might cause relative abundance of some taxa to change. Our results will help assess whether or not conjunctival microbiome changes caused by contact lens wear affect infection risk.

Myopia is the leading cause of refractive error and can be corrected by refractive surgery, wearing eyeglasses, or using a soft contact lens (SCL) or rigid gas permeable contact lens (RGP). In 2004, it was estimated that 38 million people use contact lenses (CL) in the United States, which has increased to 40.9 million in 2015 (i.e., 16.7% of the adult population).1,2 Soft contact lenses are more comfortable to use than RGPs as they are made of conventional or silicone hydrogels. Another option is orthokeratology lenses (OKL), a type of RGP, which temporarily flattens the central corneal curvature to reduce myopic refractive error, while compressing the lens, eyelids, and tear film.3 Usage of OKL effectively controls juvenile myopia development.46 As contact lenses make direct contact with the ocular surface, they impose shear stress as well as exposure to chemical stress and increase vulnerability to bacterial infection. 
Microbial keratitis (MK) is the most severe sight-threatening complication associated with improper contact lens usage. The estimated annual incidence of MK for contact lens wearers is 1.2 to 25.4 per 10,000, depending on lens type and wearing schedule.7 Risk factors of MK in SCL wearers include dry eye, corneal hypoxia, swelling and/or thinning, erosion, tear deposits on SCL, allergic reactions, microbial contaminated lens care solutions and lens cases, and poor hygiene habits.810 Orthokeratology lenses also impose epithelial compressive forces that improve centralization and stability, but they may cause local trauma and corneal thinning. These changes increase the likelihood of pathogenic corneal infiltration.1113 Other factors promoting the likelihood of MK development in OKL wearers include less eye movement, causing declines in both microbial glycocalyx buildup and blinking that in turn impair lysozyme spreading over the corneal surface.14,15 
Much effort has been expended to characterize microorganisms involved in MK based on traditional culture techniques.1618 The major cultivable microorganisms reported in MK patients include bacteria, fungi, and acanthamoeba. For instance, Australian researchers reported the prevalence of a diverse assemblage of microorganisms in CLs wearers afflicted with infectious keratitis including Pseudomonas spp., Staphylococcus epidermidis, Serratia spp., coagulase-negative staphylococci, Burkholderia cepacia, Enterobacter spp., Staphylococcus haemolyticus, and Escherichia coli. These studies employed solely traditional culture-based methods, which were limited to detecting only a fraction of the microbes on the conjunctiva. 
Several DNA-based molecular methods have been developed to analyze microbial diversity using DNA extracted directly from primary samples. For instance, the 16S rRNA gene, found in all prokaryotes, can be amplified and sequenced to characterize microbial communities. Recent studies have applied this method to analyze ocular surface microbiota in healthy individuals, contact lens wearers, and patients with trachomatous disease.1922 Here we applied 16S rRNA gene sequencing to characterize the diversity of the microbiome on the conjunctiva of contact lens wearers and determine the effects of different contact lens types on the normal microbiota of the conjunctiva. By more fully characterizing the diversity of conjunctival microbial communities among individuals wearing different types of contact lens, we clarified the role of such variability as risk factors of ocular surface infection. 
Materials and Methods
Subjects
Institutional review board/ethics committee approval from the Eye Hospital of Wenzhou Medical University was obtained and conducted in accordance with the tenets of the Declaration of Helsinki. All of the volunteers were recruited at the Eye Hospital of Wenzhou Medical University and informed consent was obtained from each individual. This study enrolled 67 volunteers, including 20 OKL wearers, 22 SCL wearers, and 25 nonwearers (NW). 
Subject information was obtained, including sex, age, contact lens history (type of contact lenses, usage duration, and contact lens solution brands), antibiotic usage within 6 months, and ocular and general health status. Detailed participant information is shown in Supplementary Table S1. The inclusion criteria included: (1) had no ocular or systemic diseases, ocular traumas, transplantations, or laser surgery; (2) had not recently taken antibiotic and/or steroid treatment (within the previous 6 months); and (3) had no allergies to drugs, pollen, etc. 
Subjects were then divided into three groups: 
  1.  
    SCL wearers: when wearing contact lens, subjects used an ocular lubricant (Shinning Health Eyes; Hydron, Hydron Contact Lenses Co., Ltd, Danyang, Jiangsu, China), and disinfected SCL with a contact lens multipurpose care solution (Qingying; Hydron, Hydron Contact Lenses Co., Ltd).
  2.  
    OKL wearers: OKL was rewetted and disinfected with commercial drops (Boston Rewetting Drops; American Boston Co., Ltd, Rochester, NY, USA) or a multipurpose solution (MeniCare plus; Japanese MeniCare Co., Ltd, Aichi, Nagoya, Japan).
  3.  
    Noncontact lens wearers: some of these subjects wore frame eyeglasses while others did not either wear any eyeglasses or contact lens (detailed participant information is shown in Supplementary Table S1).
For all volunteers, the wearing of contact lenses was under the guidance of an ophthalmologist at the Wenzhou Medical University Eye Hospital. 
Conjunctival Swab Collection
All samples were collected from June to August 2014. For each subject, the lower bulbar conjunctiva (including fornices) of the right eyes were gently wiped two to three times using a commercial swab (Specimen Collection Flocked Swabs; Huachenyang [Shenzhen] Technology Co., Ltd, Shenzhen, Guangdong, China). One swab was exposed to air for 30 seconds (time interval for sampling) as a negative control. The swabs were placed into 1.5-mL tubes (Axygen Biotechnology [Hangzhou] Co., Ltd, Hangzhou, Zhejiang, China) containing 300 μL DNase-Free double-distilled water (ddH2O; Ambion, Thermo Fisher Scientific Inc., Cleveland, OH, USA). The samples were then quickly stored at −80°C prior to use. 
DNA Extraction and Whole Genome Amplification (WGA)
All samples were vortexed for 5 minutes before removing 200 μL from the tubes for DNA extraction. The samples were frozen in liquid nitrogen for 3 minutes and then thawed at 80°C for 3 minutes. This process was then repeated twice. Then, 1.5 μL of an acryl polymer solution (Acryl Carrier; BioTeke Corp., Beijing, China) was added to improve the yield of nucleic acid precipitation by following the manufacturer's instructions. Next, 200 μL ethanol was added into the mixture for precipitation, and the final precipitate was dissolved in 15 μL DNase-Free ddH2O. Subsequently, 2 μL of the enriched sample was used for WGA using multiple annealing and looping-based amplification cycles (MALBAC) technology (Yikon Genomics, Inc., Suzhou, Jiangsu, China). The amplified products were purified using commercial beads (Agencourt AMPure XP; Beckman Coulter, Inc., Indianapolis, IN, USA). The concentration of these products was determined using quantum computing (Qubit; Thermo Fisher Scientific Inc.). All samples were stored at –20°C before library construction. 
Sequencing of 16S rRNA Gene
For each sample, 10 ng of precipitate was used to amplify the V3 region of the 16S rRNA gene following the procedure developed by the collaborators in the Center of Biomedical Analysis, Tsinghua University, and this method also uses molecular barcodes to enable multiplex sequencing as previously described.23 Paired-end sequencing (2 × 150 base pair [bp]) of these amplicons was performed on a desktop sequencer (MiSeq; Illumina, Inc., San Diego, CA, USA). 
Sequencing Data Processing and Analysis
The 16S rRNA gene pipeline data acquisition incorporates phylogenetic and alignment-based approaches to maximize data resolution. Read pairs were demultiplexed based on the unique molecular barcodes, and reads were merged using USEARCH v7.0.109024 with at least a 50-bp overlap and no more than 1-bp mismatch. Merged sequences were clustered into operational taxonomic units (OTUs) at a similarity cutoff value of 97% using the UPARSE algorithm. An expected error rate of 0.5 was applied for quality filtering. We mapped OTUs to the SILVA database (version 123) to determine taxonomies.25 Abundances were recovered by mapping the demultiplexed reads to the UPARSE OTUs. A custom script constructed an OTU table from the output files generated in the previous two steps for downstream analyses of alpha-diversity (observed OTUs, Chao 1 estimator, and Shannon diversity index); beta diversity (unweighted and weighted unifrac); and taxonomic trends (at the phylum and genus level).26 We excluded OTUs present in the blank in QIIME, to eliminate possible bias introduced during sample collection and processing. 
Results
After whole genome amplification, the V3 region of 16S rRNA gene from 40 samples was sequenced on the sequencer platform (Illumina, Inc.), including 12 OKL wearers, 13 SCL wearers, 14 nonwearers (NWs), and 1 blank control sample. After quality control, sequencing data from 36 samples were included in the subsequent analysis, including 11 OKL wearers (7 males, 4 females); 12 SCL wearers (3 males, 9 females); and 12 NWs (6 males, 6 females); and 1 blank control sample. 
Approximately 28,491,858 paired-end reads for 36 samples were generated, ranging from 396,864 to 1,526,965. After quality filtering and merging paired-end reads, 19,371,456 high quality merged reads (average 553,470 reads per sample) were used in further analysis of the bacterial composition of the conjunctiva (Supplementary Table S2). Next, these high quality merged reads were clustered into OTUs. We then removed the OTUs observed in the blank control. To compare the OTU composition in samples with diverse sequencing depth, each of them were rarefied to 2708 sequences, which generated 598 total OTUs and 17 to 105 OTUs per sample, covering 24 phyla and 289 genera (Supplementary Table S2). 
To characterize the diversity of the bacterial community, we quantified the bacterial diversity within each sample. The observed OTUs of OKL, SCL, and NWs were not significantly different (P = 0.84, Kruskall-Wallis test; Fig. 1A). In addition, the observed OTUs in each sample were fewer than the estimated OTUs (Chao 1 in Fig. 1A), indicating many remaining unexploited OTUs. We found no statistically significant differences in microbiome compositions among the three groups (P = 0.478, PERMANOVA test; Fig. 1B). 
Figure 1
 
Bacterial diversity in the conjunctiva of NW, OKL, and SCL wearers. (A) Alpha diversity measured by observed OTUs, Chao1 estimator Statistical (P) value generated by Kruskall-Wallis test. (B) Principle coordinate analysis (PCoA) plot of conjunctival microbial communities from non-, OKL, and SCL wearers, based on weighted unifrac distance. Statistical (P) value generated by PERMANOVA test.
Figure 1
 
Bacterial diversity in the conjunctiva of NW, OKL, and SCL wearers. (A) Alpha diversity measured by observed OTUs, Chao1 estimator Statistical (P) value generated by Kruskall-Wallis test. (B) Principle coordinate analysis (PCoA) plot of conjunctival microbial communities from non-, OKL, and SCL wearers, based on weighted unifrac distance. Statistical (P) value generated by PERMANOVA test.
Generally, the major bacterial taxa were similar across groups. At the phylum level, five phyla, Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Cyanobacteria, were the most prevalent in all samples (Fig. 2A), consistent with previous studies.19,20,27 At the genus level, Bacillus, Rothia, Massilia, Betaproteobacteria sp., Actinomyces, Arcobacter, Shewanella, Acinetobacter, Rhodocyclaceae sp., Comamonadaceae sp., and Propionibacterium were identified as common conjunctival bacteria in most subjects, regardless of the contact lens usage (Fig. 2B). The relative abundance of the genera varied among individuals more than it did between contact lens groups. 
Figure 2
 
Relative bacterial compositions of major taxa in the conjunctiva of NW, OKL wearers, SCL wearers at phylum (A) and genus (B) levels.
Figure 2
 
Relative bacterial compositions of major taxa in the conjunctiva of NW, OKL wearers, SCL wearers at phylum (A) and genus (B) levels.
The abundance of each taxon varied within each group and across the three groups. Compared with NWs, the genera that decreased in abundance in OKL wearers included Bacillus, Delftia, and Lactobacillus (P < 0.03, Mann-Whitney U test; Fig. 2B; Supplementary Table S3). Decreased abundance of Delftia and increased abundance of Elizabethkingia were observed in SCL wearers compared with NWs (P < 0.03, Mann-Whitney U test; Fig. 2B; Supplementary Table S3). 
Overall, the conjunctival microbiome of NWs displayed gender differences, and the contact lens wearers showed no distinct clustering caused by either gender or wearing history variables. Although the observed OTUs in males and females in NW groups were similar (Fig. 3, Mann-Whitney U test, P = 0.09), PERMANOVA tests demonstrated differences in community composition among different sexes (Fig. 4C, P = 0.003). In the other two groups, observed OTUs were nearly the same in males and females (Figs. 3, 4A, 4B; Supplementary Figs. S1, S2). Although sample size was limited for this comparison, Shin et al.22 also showed this kind of sex difference in NWs in their recent study. 
Figure 3
 
Differences in alpha diversity between males and females. Alpha diversity measured by observed OTUs and Shannon diversity index.
Figure 3
 
Differences in alpha diversity between males and females. Alpha diversity measured by observed OTUs and Shannon diversity index.
Figure 4
 
Plots of PCoA based on unweighted unifrac distance matrices of microbial communities in conjunctival samples of SCL (A), OKL (B), and NW (C) wearers between males (blue dots) and females (red dots).
Figure 4
 
Plots of PCoA based on unweighted unifrac distance matrices of microbial communities in conjunctival samples of SCL (A), OKL (B), and NW (C) wearers between males (blue dots) and females (red dots).
According to duration of OKL wear, we separated these individuals into two groups: short wearing (<12 months) and long wearing (≥12 months). In summary, neither abundance nor diversity was different between the short- and long-wearing groups (observed OTUs: P = 0.65; Shannon diversity index: P = 0.93; Fig. 5A); and no significant difference in unweighted unifrac distance was found when comparing short and long wearing groups (P = 0.133, PERMANOVA test; Fig. 5B). This finding is consistent with those in previous studies, in which ocular microbial community became stable after transient changes occurred initially at the beginning of contact lens wearing.9,28,29 
Figure 5
 
Differences in microbial community diversity between short- and long-wearing history individuals of OKL wearers. (A) Alpha-diversity measured by observed OTUs and Shannon diversity index. (B) Plot of PCoA based on unweighted unifrac distance.
Figure 5
 
Differences in microbial community diversity between short- and long-wearing history individuals of OKL wearers. (A) Alpha-diversity measured by observed OTUs and Shannon diversity index. (B) Plot of PCoA based on unweighted unifrac distance.
Discussion
The maintenance of conjunctival epithelial integrity plays a crucial role in protecting the eye from infection.30 Although tears contain antimicrobial compounds,31 limiting amounts of microbes still colonize the conjunctiva.27,32 The low biomass of bacteria makes it difficult to characterize microbial community diversity,33,34 which accounts for why we were unable to obtain adequate amplicon products by direct V3 region amplification with 25 PCR cycles. Previous studies showed that more than 30 amplifying cycles can lead to greater than 30% PCR chimera in the final product.35 To account for this limitation, previous metagenomic studies performed WGA before 16S rDNA amplification,19,33,36 and we adopted the same strategy. For all WGA methods, bacterial species with lower GC content might be overrepresented. However, using new common sequences and temperature cycling that can promote looping of the isothermal amplicons to inhibit further amplification before the PCR step, the MALBAC technique would cover a greater proportion of the genome, yielding a stable and homogeneous amplification.3739 Another challenge for gram-positive bacteria is breaking their cell wall to extract DNA. To overcome this difficulty, the samples were frozen in liquid nitrogen for 3 minutes and then thawed at 80°C for 3 minutes. This process was then repeated twice to ensure cell wall lysis. In addition, the amount of OTUs did not correlate with the template concentration after WGA (Supplementary Fig. S3). 
To exclude possible contamination from the environment during sampling and genome amplification (both WGA and 16S rRNA), one blank swab was exposed to air for 30 seconds as a negative control. The number of conjunctival observed OTUs without blank control removed (Supplementary Figs. S4, S5; Supplementary Table S3), was actually similar to previous studies on the eye,1922 which is lower than at other body sites.4042 With and without blank control removal, the taxa composition pattern changed to some extent. Notably, we observed significant increases in some contaminative taxa (Burkholderia) before the blank control removal. Negative control samples were included during sampling and amplification to evaluate microbial composition. We observed some OTUs slightly enriched in OKL and SCL groups before blank removal, but these differences were excluded only due to their occurrence in the blank sample (Fig. 6, Acinetobacter and Cupriavidus in red). Therefore, it is crucial to include a blank control with low biomass samples. Although blank control inclusion is informative for identifying potential sample contaminants, it may also be too strict removing all OTUs observed in a blank sample without considering their abundance. In our study, only one negative control sample was used. We realize that obtaining more negative control samples will make it possible to exclude with greater assurance contaminants and reduce sample variability. Moreover, obtaining a positive control will provide more insight regarding the origin of some microbes in future experiments. This can be done as described by Shin et al.22 by comparing the microbe composition in tear samples with those collected from the skin beneath the eyes. 
Figure 6
 
Abundance of core genera shared by all samples before blank control removed. The words on the right in green represent the decreased abundance in the contact lens wearers. The words in red represent the increased genus abundance in the contact lens wearers. P < 0.03; P value generated by Mann-Whitney U test.
Figure 6
 
Abundance of core genera shared by all samples before blank control removed. The words on the right in green represent the decreased abundance in the contact lens wearers. The words in red represent the increased genus abundance in the contact lens wearers. P < 0.03; P value generated by Mann-Whitney U test.
Our study evaluated whether the microbiota of contact lens wearers differed from nonlens wearers. We found that the conjunctival microbiome of contact lens wearers was not significantly different from NWs and did not detect a significant increase in pathogenic microorganisms (Pseudomonas, Staphylococcus). One possible explanation for this lack of any difference is that each individual has unique conjunctival bacterial patterns. Such a possibility agrees with a proposal by Franzosa et al.43 that each individual in the human population possesses unique live microbial communities on and in their bodies that are distinguishing microbial “fingerprints.” Furthermore, the 16S rRNA gene sequencing method that we used prevents resolution at the species level, which is important in identifying origin of infections. More studies at the species or strain levels will be required to test the validity of our results. 
Despite this limitation, our sensitivity was sufficient to detect that the bacterial community of the OKL group was more different than the SCL wearers from the NW population. We found OKL wearers had decreased abundance of the genera Bacillus, Tatumella, and Lactobacillus, belonging to the phylum Firmicutes. Since OKL wearers experience increased pressure and hypoxia, these factors may impose changes on the microbiota community, favoring increased incidence of eye infection in these individuals. Other detectable differences in SCL wearers included significant abundance changes of two genera, decreased abundance of Delftia, and increased abundance of Elizabethkingia
The conjunctival phyla profile of NWs was similar to that described,1922 while the genera differed slightly across studies. One of the reasons for this difference may be the sample size. For instance, Dong et al.19 used four healthy conjunctival samples to characterize the conjunctival bacterial community. Moreover, previous studies amplified different regions of the 16S rRNA gene even without performing WGA. Based on the 16S rDNA V1-V3 region, a trachomatous disease study21 found that the five most frequent genera in the normal healthy conjunctiva are Corynebacterium, Simonsiella, Streptococcus, Propionibacterium, and Staphylococcus, which is not entirely consistent with our results. Recently, Shin et al.22 used the V4 region sequence of the 16S rRNA gene to compare the microbiota of the ocular surface of SCL wearers with that of nonlens wearers. No significant differences in bacterial alpha diversity were observed between the conjunctival samples obtained from these two groups, which is consistent with our results (Supplementary Fig. S4). Some of the main genera, Acinetobacter, Streptococcus, Comamonadaceae, Methylobacterium, Pseudomonas, and Staphylococcus (Supplementary Fig. S5; Supplementary Table S3), were also found in our samples, but the relative abundance varied much across the studies. Furthermore, another crucial factor affecting microbial community composition is the sample population difference, which may be influenced by environment, lifestyle, and physiologic variability. Even the remaining microbes in the eye drops and those on the contact lens cases may contribute to the results, which should be further examined. The differences across the different studies show that variations in the population, sampling methods, WGA methods, sequencing region, and analysis methods could greatly influence characterization of the results. Accordingly, further studies employing different and larger populations are required to confirm our results and clarify if differences in methodology can account for variability in results and interpretations. 
Conclusions
This is the first study comparing conjunctival microbial communities in SCL and OKL wearers with those in non–contact lens wearers. Our study shows amplicon sequencing is a useful tool for evaluating infection risk caused by wearing SCL and OKL. We found that the microbiome of contact lens wearers and nonwearers is relatively similar. The difference between OKL wearers and NWs was greater than those in the SCL group. Host environmental changes can lead to changes in relative abundance of bacterial taxa. Compared with NWs, decreased abundance of Bacillus, Tatumella, and Lactobacillus were observed in OKL wearers while Delftia was less abundant; but Elizabethkingia abundance increased in SCL wearers. Therefore, monitoring the variations in bacterial community composition caused by using different types of contact lenses will help ophthalmologists choose a course of therapy that is most effective in combating infections caused by a specific type of bacteria. 
Note
The datasets supporting the conclusions of this article are available in the GSA repository, PRJCA000237 (http://gsa.big.ac.cn). 
Acknowledgments
The authors thank Cecilia Chao from State University of New York (SUNY) College of Optometry for critical reading and comments regarding the manuscript. 
Supported by National Natural Science Foundation of China (81570881, 81422007, 81470659, 81371047 and 81170880); National Natural Science Foundation of Zhejiang (LZ14H120001); National Basic Research Program of China (973 project: 2011CB504602); National Science and Technology Support Program (2012BAI11B05); Wenzhou Municipal Science and Technology Bureau Foundation (Y20160014); Innovation Promotion Association CAS (2016098); and Zhejiang Provincial Program for Leading of High-level Innovative Health Talents. The authors alone are responsible for the content and writing of the paper. 
Disclosure: H. Zhang, None; F. Zhao, None; D.S. Hutchinson, None; W. Sun, None; N.J. Ajami, None; S. Lai, None; M.C. Wong, None; J.F. Petrosino, None; J. Fang, None; J. Jiang, None; W. Chen, None; P.S. Reinach, None; J. Qu, None; C. Zeng, None; D. Zhang, None; X. Zhou, None 
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Figure 1
 
Bacterial diversity in the conjunctiva of NW, OKL, and SCL wearers. (A) Alpha diversity measured by observed OTUs, Chao1 estimator Statistical (P) value generated by Kruskall-Wallis test. (B) Principle coordinate analysis (PCoA) plot of conjunctival microbial communities from non-, OKL, and SCL wearers, based on weighted unifrac distance. Statistical (P) value generated by PERMANOVA test.
Figure 1
 
Bacterial diversity in the conjunctiva of NW, OKL, and SCL wearers. (A) Alpha diversity measured by observed OTUs, Chao1 estimator Statistical (P) value generated by Kruskall-Wallis test. (B) Principle coordinate analysis (PCoA) plot of conjunctival microbial communities from non-, OKL, and SCL wearers, based on weighted unifrac distance. Statistical (P) value generated by PERMANOVA test.
Figure 2
 
Relative bacterial compositions of major taxa in the conjunctiva of NW, OKL wearers, SCL wearers at phylum (A) and genus (B) levels.
Figure 2
 
Relative bacterial compositions of major taxa in the conjunctiva of NW, OKL wearers, SCL wearers at phylum (A) and genus (B) levels.
Figure 3
 
Differences in alpha diversity between males and females. Alpha diversity measured by observed OTUs and Shannon diversity index.
Figure 3
 
Differences in alpha diversity between males and females. Alpha diversity measured by observed OTUs and Shannon diversity index.
Figure 4
 
Plots of PCoA based on unweighted unifrac distance matrices of microbial communities in conjunctival samples of SCL (A), OKL (B), and NW (C) wearers between males (blue dots) and females (red dots).
Figure 4
 
Plots of PCoA based on unweighted unifrac distance matrices of microbial communities in conjunctival samples of SCL (A), OKL (B), and NW (C) wearers between males (blue dots) and females (red dots).
Figure 5
 
Differences in microbial community diversity between short- and long-wearing history individuals of OKL wearers. (A) Alpha-diversity measured by observed OTUs and Shannon diversity index. (B) Plot of PCoA based on unweighted unifrac distance.
Figure 5
 
Differences in microbial community diversity between short- and long-wearing history individuals of OKL wearers. (A) Alpha-diversity measured by observed OTUs and Shannon diversity index. (B) Plot of PCoA based on unweighted unifrac distance.
Figure 6
 
Abundance of core genera shared by all samples before blank control removed. The words on the right in green represent the decreased abundance in the contact lens wearers. The words in red represent the increased genus abundance in the contact lens wearers. P < 0.03; P value generated by Mann-Whitney U test.
Figure 6
 
Abundance of core genera shared by all samples before blank control removed. The words on the right in green represent the decreased abundance in the contact lens wearers. The words in red represent the increased genus abundance in the contact lens wearers. P < 0.03; P value generated by Mann-Whitney U test.
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