November 2019
Volume 60, Issue 14
Open Access
Immunology and Microbiology  |   November 2019
Composition and Diversity of Bacterial Community on the Ocular Surface of Patients With Meibomian Gland Dysfunction
Author Affiliations & Notes
  • Xiaojin Dong
    Medical College of Qingdao University, Qingdao, China
    State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, China
  • Yuqian Wang
    Medical College of Qingdao University, Qingdao, China
    State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, China
  • Weina Wang
    State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, China
  • Ping Lin
    State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, China
  • Yusen Huang
    State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, China
  • Correspondence: Yusen Huang, State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 5 Yanerdao Road, Qingdao 266071, P.R. China; huang_yusen@126.com
Investigative Ophthalmology & Visual Science November 2019, Vol.60, 4774-4783. doi:https://doi.org/10.1167/iovs.19-27719
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      Xiaojin Dong, Yuqian Wang, Weina Wang, Ping Lin, Yusen Huang; Composition and Diversity of Bacterial Community on the Ocular Surface of Patients With Meibomian Gland Dysfunction. Invest. Ophthalmol. Vis. Sci. 2019;60(14):4774-4783. doi: https://doi.org/10.1167/iovs.19-27719.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To investigate the composition and diversity of bacterial community on the ocular surface of patients with meibomian gland dysfunction (MGD) via 16S rDNA sequencing.

Methods: Forty-seven patients with MGD, who were divided into groups of mild, moderate, and severe MGD, and 42 sex- and age-matched participants without MGD (control group) were enrolled. Samples were collected from the upper and lower conjunctival sac of one randomly chosen eye of each participant. Through sequencing the hypervariable region of 16S rDNA gene obtained from samples, differences in the taxonomy and diversity between groups were compared.

Results: Principle coordinate analysis showed significantly distinct clustering of the conjunctival sac bacterial community between the severe MGD group and the other groups. At the phylum level, the relative abundances of Firmicutes (31.70% vs. 19.67%) and Proteobacteria (27.46% vs. 14.66%) were significantly higher (P < 0.05, Mann-Whitney U), and the abundance of Actinobacteria (34.17% vs. 56.98%) was lower in MGD than controls (P < 0.05, Mann-Whitney U). At the genus level, the abundances of Staphylococcus (20.71% vs. 7.88%) and Sphingomonas (5.73% vs. 0.79%) in patients with MGD were significantly higher than the controls (P < 0.05, Mann-Whitney U), while the abundance of Corynebacterium (20.22% vs. 46.43%) was significantly lower (P < 0.05, Mann-Whitney U). The abundance of Staphylococcus was positively correlated with the meiboscores in patients with MGD (r = 0.650, P < 0.001, Spearman).

Conclusions: Patients with MGD can have various degrees of bacterial microbiota imbalance in the conjunctival sac. Staphylococcus, Corynebacterium, and Sphingomonas may play roles in the pathophysiology of MGD.

The ocular surface, unlike other mucosal parts of human body, such as oral and gastrointestinal mucosae, was thought to be paucibacterial. However, studies have shown that there are commensal bacteria presenting on the human ocular surface, which hardly cause any infection or inflammation under normal circumstances.1,2 These commensal bacteria of the ocular surface carry out similar functions as microbiota of the skin and gut, maintaining homeostasis of the ocular surface and inhibiting the proliferation of other pathogenic bacteria through competitive growth.3 Nevertheless, under certain conditions they can result in ocular surface infections, such as blepharitis, conjunctivitis, and corneal ulceration, and even become the pathogens of endophthalmitis. Any change of the ocular surface microenvironment may make patients more vulnerable to opportunistic pathogens and increase the risk of ocular surface disorders. Therefore, understanding the ocular surface microbiota might give novel insights into the etiology of ocular surface diseases, including meibomian gland dysfunction (MGD). 
Meibomian glands, located on upper and lower tarsal plates, are a large holocrine sebaceous gland unit in human body.4,5 The International Workshop on Meibomian Gland Dysfunction defines MGD as “a chronic, diffuse abnormality of the meibomian glands, commonly characterized by terminal duct obstruction and/or qualitative/quantitative changes in the glandular secretion.”6 When meibomian glands secrete abnormal meibum and affect tear-film stability, evaporative dry eye is caused. The prevalence of MGD was reported to be 3.5% to 69.3% around the world.7 MGD can lead to ocular surface itching and/or irritation, lid margin redness, crust on lashes, and other ocular surface discomforts.8 Some cases are accompanied by blepharokeratoconjunctivitis.9 
Meibum abnormalities are considered as one of the most representative alterations of MGD. Meibum, which derives from meibomian glands and overlies the aqueous layer of the tear film, could retard evaporation and prevent bacterial infections.10 It is widely accepted that lipase and hydrolysis products secreted by bacteria may be among the factors that influence changes in meibum composition.11 Altering the meibum often causes increased lipid viscosity and decreased fluidity to obstruct ductal orifice, and meanwhile becomes good for the ductal innate microorganisms overgrowth.12,13 Therefore, changes in the composition of meibum can also be recognized as a potential predisposing factor for ocular surface bacterial proliferation. Significant alterations of meibum have been observed in MGD,11,14 but there remains a lack of definite proof on whether there are changes in the ocular surface microbiota in patients with MGD. With the traditional cultivation, the rate of positive bacterial culture of the conjunctival sac in patients with MGD was significantly higher than those without MGD.15,16 Moreover, topical administration of antibiotics has achieved therapeutic effects in the treatment of moderate to severe MGD, but the related mechanisms remain unclear.17,18 Defining the characteristics of bacterial community on the ocular surface of patients with MGD based on 16S rDNA sequencing technology may promote further investigations on the role of the ocular surface microbiota and facilitate improvement of therapies for MGD. 
Materials and Methods
Study Population and Informed Consent
A total of 89 participants were recruited from February to March 2019, including 47 patients with MGD (9 males and 38 females, aged 57.53 ± 15.10 years) and 42 controls without MGD (14 males and 28 females, aged 62.76 ± 9.73 years). Diagnosis of MGD was made based on symptoms, clinical signs, and ocular examinations (tear break-up time, slit-lamp microscopy, meibography, and tear meniscus height). According to the International Workshop on Meibomian Gland Dysfunction, all participants were divided into groups of mild MGD (stage 1 to stage 2), moderate MGD (stage 3), severe MGD (stage 4), and controls depending on eyelid margin signs, clinical symptoms, and quality of meibum.19 Meiboscores were used to measure the degree of meibomian gland loss (score 0, no loss; score 1, loss ≤ one-third; score 2, loss between one- and two-thirds; score 3, loss ≥ two-thirds),20 with the scores of both upper and lower lids as the total score. No participant used eye drops or oral antibiotics within 2 weeks, wore corneal contact lenses within 3 months, had a history of ocular surgery, or had other ocular diseases or systemic diseases which might interfere with the ocular surface indigenous microbiota. 
The demographic information of the four groups of participants is shown in the Table. There was no significant difference in age or ethnicity between any two groups (P > 0.05, Mann-Whitney U). 
Table
 
Demographic Information
Table
 
Demographic Information
This study was approved by the institutional review board of Shandong Eye Institute and registered on the International Clinical Trials Registry Platform. In accordance with the tenets of Declaration of Helsinki, informed consent was obtained from each participant. 
Sample Collection
Samples were obtained from one randomly selected eye of each participant. After topical anesthesia with 0.4% oxybuprocaine hydrochloride eye drops (Santen, Osaka, Japan), a sterile dry cotton swab was used to wipe both the upper and lower conjunctival sac and eyelid margins from the nasal to temporal side clockwise when the participant was asked to rotate the globe to different directions. The operation was repeated twice. Four blank sterile swabs were also collected. Each swab was put into a sterile tube immediately and stored in an ultra-low temperature freezer at −80°C before DNA extraction. 
DNA Extraction From Clinical Samples
DNA was extracted from all samples using the PowerMax Soil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA). Blank samples also underwent a complete extraction to exclude false-positive results of the process. DNA concentrations have been provided in Supplementary Table S1
PCR Amplification, Sequencing, and Data Analyses of Clinical Samples
Methods for PCR amplification and sequencing are described in Supplementary Methods (Supplementary Materials). Sequences with a similarity ≥97% were classified as the same operational taxonomic units (OTUs) by Vsearch (v2.3.4; https://github.com/torognes/vsearch). Through calculating and analyzing, we also obtained the alpha- and beta-diversity of all groups. Linear discriminant analysis effect size (LEfSe) was introduced to identify bacterial biomarkers of the healthy control group and the MGD group. 
Statistical Analyses
The data were statistically analyzed using SPSS 20.0 software (Chicago, IL, USA). Student's t-test and Mann-Whitney U were used to compare the difference in age, sex, ethnicity, and clinical exam results between the patients with MGD and the controls. Mann-Whitney U was performed for analyses of the α-diversity indices and the relative abundances of dominant phyla and genera between groups. Spearman correlation analysis was used to measure the correlation between meiboscores and relative abundances of Staphylococcus in patients with MGD. P < 0.05 was considered statistically significant. 
Nucleotide Sequence Accession Number
The bacterial 16S rDNA gene sequences were deposited in the National Center for Biotechnology Information (accession number PRJNA564695). 
Results
Taxonomic Assignment
A total of 1,596,000 raw tags were obtained from the 89 participants. Phylogenetic findings information has been provided in Supplementary Figure S1. There was no statistically significant difference in age, sex, or ethnicity between all patients with MGD and the control group (P > 0.05, Student's t-test). 
Bacterial Alpha-Diversity
There was no significant difference in the Chao1 index, Simpson index, Shannon index, or observed-species index (Fig. 1A) among the four groups (P > 0.05, Mann-Whitney U). Steep slopes of the rarefaction curves of individual samples (Fig. 1B) suggested the majority of the species diversities were discovered. 
Figure 1
 
α-diversity indices of individual samples. (A) Scatter plots of α-diversity indices of individual samples from different groups, with Shannon and Simpson indices for the species richness of samples (the higher the value, the more diverse species of samples), Chao 1 index for estimation of OTUs of individual samples, and observed-species index for reflection of the observed OTUs of individual samples. These indices had no significant difference between any two groups. (B) By showing the variation trends in species through a simulated resampling process and estimating the richness of species in the environment, rarefaction curves could directly reflect the rationality of sequencing data size. When the curves reach the saturation platform, it indicates that the sequencing data size is reasonable. All rarefaction curves of individual samples from the patients with MGD and the controls reached the saturation platform, indicating that the sequencing data size was reasonable.
Figure 1
 
α-diversity indices of individual samples. (A) Scatter plots of α-diversity indices of individual samples from different groups, with Shannon and Simpson indices for the species richness of samples (the higher the value, the more diverse species of samples), Chao 1 index for estimation of OTUs of individual samples, and observed-species index for reflection of the observed OTUs of individual samples. These indices had no significant difference between any two groups. (B) By showing the variation trends in species through a simulated resampling process and estimating the richness of species in the environment, rarefaction curves could directly reflect the rationality of sequencing data size. When the curves reach the saturation platform, it indicates that the sequencing data size is reasonable. All rarefaction curves of individual samples from the patients with MGD and the controls reached the saturation platform, indicating that the sequencing data size was reasonable.
Bacterial Taxonomy and LEfSe Analysis of the Conjunctival Sac Bacterial Microbiota
We summarized the relative abundances of the dominant bacterial community in the patients with different degrees of MGD and the controls. At the phylum level (Fig. 2A), 22 phyla were detected from the 89 eyes. The major phyla in the patients with MGD included Actinobacteria (34.17%, 1.26%–87.11%), Firmicutes (31.70%, 3.34%–90.66%), Proteobacteria (27.46%, 2.30%–84.48%), Bacteroidetes (2.21%, 0.02%–10.54%), and Deinococcus-Thermus (1.19%, 0%–8.96%). The abundance of Actinobacteria (56.98%, 8.23%–93.82%; P < 0.05, Mann-Whitney U) in the healthy controls was significantly higher than the MGD groups, while the relative abundances of Firmicutes (19.67%, 2.58%–56.67%; P < 0.05, Mann-Whitney U), Proteobacteria (14.66%, 0.85%–65.60%; P < 0.05, Mann-Whitney U), and Deinococcus-Thermus (1.00%, 0%–10.89%; P < 0.05, Mann-Whitney U) were significantly lower. The relative abundance of Bacteroidetes in the control group (3.30%, 0%–19.78%; P > 0.05, Mann-Whitney U) had no significant difference from that in the MGD groups. 
Figure 2
 
The relative abundances of dominant phyla and genera in the patients with MGD and the controls. (A) The relative abundances of top 5 most abundant phyla. Compared with the control group, the patients with MGD had significantly higher abundances of Firmicutes, Proteobacteria, and Deinococcus-Thermus, and a significantly lower abundance of Actinobacteria. (B) The relative abundances of top 6 most abundant genera. Compared with the control group, the patients with MGD had significantly higher abundances of Staphylococcus and Sphingomonas, and a significantly lower abundance of Corynebacterium (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 2
 
The relative abundances of dominant phyla and genera in the patients with MGD and the controls. (A) The relative abundances of top 5 most abundant phyla. Compared with the control group, the patients with MGD had significantly higher abundances of Firmicutes, Proteobacteria, and Deinococcus-Thermus, and a significantly lower abundance of Actinobacteria. (B) The relative abundances of top 6 most abundant genera. Compared with the control group, the patients with MGD had significantly higher abundances of Staphylococcus and Sphingomonas, and a significantly lower abundance of Corynebacterium (*P < 0.05, **P < 0.01, ***P < 0.001).
At the genus level (Fig. 2B), the predominant genus in patients with MGD was Staphylococcus (20.71%, 0.20%–88.40%), followed by Corynebacterium (20.22%, 0.09%–85.20%), Propionibacterium (9.29%, 0.05%–59.64%), Sphingomonas (5.73%, 0.36%–22.75%), Snodgrassella (4.17%, 0%–68.12%), and Streptococcus (2.80%, 0%–22.28%). Compared with the MGD groups, the control group had a significantly higher abundance of Corynebacterium (46.43%, 0.22%–92.83%; P < 0.05, Mann-Whitney U), while the abundances of Staphylococcus (7.88%, 0.08%–45.91%; P < 0.05, Mann-Whitney U) and Sphingomonas (0.79%, 0%–2.89%; P < 0.05, Mann-Whitney U) were significantly lower. There was no significant difference in the abundance of Snodgrassella (3.60%, 0%–45.51%; P > 0.05, Mann-Whitney U), Propionibacterium (5.44%, 0.04%–41.25%; P > 0.05, Mann-Whitney U), or Streptococcus (3.89%, 0%–37.54%; P > 0.05, Mann-Whitney U). 
As a high-dimensional class comparison to determine the operational taxon that is most likely to explain differences between groups, LEfSe was employed to identify the most potentially pathogenic bacterial biomarkers by combining statistical significance with the consistency and effect correlation of coding organisms (Fig. 3). The biomarker phyla were Proteobacteria and Firmicutes in the MGD group, and Actinobacteria in the controls. Staphylococcus and Sphingomonas were the biomarker genera in the MGD group, while Corynebacterium was the biomarker in the control group. 
Figure 3
 
LEfSe analysis of the normal control group and the MGD groups. (A) A cladogram of the conjunctival sac bacterial taxa in the patients with MGD (green) and the control group (red) showed the levels from domain to species and from outside to inside. Nodes represented taxa at the corresponding level. The diameter of nodes represented the abundances of taxa in the corresponding groups. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. (B) Linear discriminant analysis scoring of biomarkers corresponding to (A), computed by the LEfSe tool. When the score of a taxon was >4.0 with P < 0.01, it was listed in the histogram, which showed all the biomarkers found from the domain to species level. “p”, “c”, “o”, “f”, “g”, and “s” referred to phylum, class, order, family, genus, and species, respectively. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. The length of the histogram represented the linear discriminate analysis values of the different taxa.
Figure 3
 
LEfSe analysis of the normal control group and the MGD groups. (A) A cladogram of the conjunctival sac bacterial taxa in the patients with MGD (green) and the control group (red) showed the levels from domain to species and from outside to inside. Nodes represented taxa at the corresponding level. The diameter of nodes represented the abundances of taxa in the corresponding groups. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. (B) Linear discriminant analysis scoring of biomarkers corresponding to (A), computed by the LEfSe tool. When the score of a taxon was >4.0 with P < 0.01, it was listed in the histogram, which showed all the biomarkers found from the domain to species level. “p”, “c”, “o”, “f”, “g”, and “s” referred to phylum, class, order, family, genus, and species, respectively. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. The length of the histogram represented the linear discriminate analysis values of the different taxa.
Bacterial Taxonomy and Beta-Diversity of the Conjunctival Sac Bacterial Microbiota
At the genus level (Fig. 4), compared with the moderate MGD group and the severe MGD group, the control group and mild MGD group both had a significantly higher abundance of Corynebacterium (all P < 0.05, Mann-Whitney U). Meanwhile, the severe MGD group had a significantly higher abundance of Staphylococcus than any other group (all P < 0.05, Mann-Whitney U). In comparison with any MGD group, the control group had a significantly lower abundance of Sphingomonas (all P < 0.05, Mann-Whitney U). 
Figure 4
 
Relative abundances of top six most abundant genera. Compared with the healthy control group and the mild MGD group, both the moderate MGD group and the severe MGD group had a significantly lower abundance of Corynebacterium; compared with the other three groups, the severe MGD group had a significantly higher abundance of Staphylococcus. And the relative abundance of Sphingomonas was significantly lower in control group. (**P < 0.01; ***P < 0.001).
Figure 4
 
Relative abundances of top six most abundant genera. Compared with the healthy control group and the mild MGD group, both the moderate MGD group and the severe MGD group had a significantly lower abundance of Corynebacterium; compared with the other three groups, the severe MGD group had a significantly higher abundance of Staphylococcus. And the relative abundance of Sphingomonas was significantly lower in control group. (**P < 0.01; ***P < 0.001).
At the species level (Fig. 5), the relative abundance of Staphylococcus epidermidis (7.76%–54.04%) in all four groups was much higher than Staphylococcus aureus (0.00%–0.39%). The relative abundance of S. epidermidis in the severe MGD group (54.04%, 2.69%–88.07%) was also significantly higher than the control group (7.76%, 0.08%–44.81%), the mild MGD group (8.06%, 0.57%–21.70%), and the moderate MGD group (8.83%, 0.20%–24.67%) (all P < 0.05, Mann-Whitney U). The relative abundance of S. aureus in the severe MGD group (0.39%, 0.00%–1.49%) was significantly higher than the control group (0.00%, 0.00%–0.04%), the mild MGD group (0.06%, 0.00%–0.60%), and the moderate MGD group (0.03%, 0.00%–0.60%) (all P < 0.05, Mann-Whitney U). 
Figure 5
 
The relative abundances of Staphylococcus aureus and S. epidermidis in the four groups. (A) The relative abundance of S. epidermidis. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. epidermidis. (B) The relative abundance of S. aureus. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. aureus (*P < 0.05; **P< 0.01; ***P < 0.001).
Figure 5
 
The relative abundances of Staphylococcus aureus and S. epidermidis in the four groups. (A) The relative abundance of S. epidermidis. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. epidermidis. (B) The relative abundance of S. aureus. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. aureus (*P < 0.05; **P< 0.01; ***P < 0.001).
Through the phylogenetic and distance matrix, principal coordinates analysis (PCoA) rearranged samples in low-dimensional space according to the resemblance indices of samples. Usually 2 to 3 eigenvalues, which accounted for more than 50% differentiations of data were selected to establish the coordinates for visualization of the similarities among samples. The analysis gathered samples with high community structure similarity, while samples with large community structure difference were far apart. Most of the samples from the mild and moderate MGD groups and the normal controls were found to be mixed together, whereas the samples from the severe MGD group were clustered themselves (Fig. 6). This indicated significant difference in the bacterial community composition between the severe MGD group and the other groups. 
Figure 6
 
Three-dimensional PCoA plots of samples from the MGD groups and the control group. PCoA plots based on weighted UniFrac distances maximally showed the relations of bacterial microbiota among individual samples. When the similarities of the microbiota composition structure were high, the samples were highly clustered. Separation of the severe MGD group (blue) from the other groups (red) illustrated a certain differentiation of the bacterial microbiota composition in the conjunctival sac.
Figure 6
 
Three-dimensional PCoA plots of samples from the MGD groups and the control group. PCoA plots based on weighted UniFrac distances maximally showed the relations of bacterial microbiota among individual samples. When the similarities of the microbiota composition structure were high, the samples were highly clustered. Separation of the severe MGD group (blue) from the other groups (red) illustrated a certain differentiation of the bacterial microbiota composition in the conjunctival sac.
Association with Clinical Parameters
We combined the relative abundances of the microbiota in the patients with MGD with clinical parameters, finding meiboscores were positively correlated with the abundance of Staphylococcus (P < 0.001, Spearman) (Fig. 7). Moreover, there was significant difference in the abundances of Staphylococcus, Corynebacterium, and Sphingomonas between the female patients with and without MGD (P < 0.05, Mann-Whitney U) (Fig. 8). However, there was no such difference in males. Given the small size of male participants involved, it is premature to conclude any association between the altered microbiota abundances and sex. 
Figure 7
 
The curve for measure of the correlation between the abundance of Staphylococcus and meiboscores.
Figure 7
 
The curve for measure of the correlation between the abundance of Staphylococcus and meiboscores.
Figure 8
 
The top six most abundant genera in different genders. (A) In females, Corynebacterium, Staphylococcus, and Sphingomonas had significant differences between the patients with MGD and the controls. (B) In males, there was no significant difference (**P < 0.01; ***P < 0.001).
Figure 8
 
The top six most abundant genera in different genders. (A) In females, Corynebacterium, Staphylococcus, and Sphingomonas had significant differences between the patients with MGD and the controls. (B) In males, there was no significant difference (**P < 0.01; ***P < 0.001).
Discussion
MGD has received increasing attention for it can lead to visual fluctuation and blurred vision, and even corneal conjunctival damage and lesions.9,21,22 The ocular surface bacterial products were proposed to be associated with ocular surface irritation symptoms accompanied by MGD.21 Meanwhile, patients who suffered MGD had a higher prevalence of infection after intraocular or refractive surgery than healthy people, and 67.7% of the microbiota isolated from endophthalmitis coincided with the eyelid microbiota.23,24 Adequate knowledge of the characteristics of bacterial microbiota in the conjunctival sac in patients with MGD is critical to raise our awareness of the pathogenesis of MGD. Using traditional bacterial culture techniques, Zhang et al.15 showed that the conjunctival sac in patients with MGD had more complex bacterial species than healthy subjects. Jiang et al.13 also found that the positivity rate of bacterial culture in patients with MGD was positively correlated with the severity of the disease. According to Watters et al.,16 however, participants with or without MGD showed a similar microbiome in the conjunctival sac. Because bacterial culture methods have limitations in bacterial identification, we used 16S rDNA sequencing to compare the conjunctival sac bacterial microbiota between participants with and without MGD in this study. The most prevalent microbes on the ocular surface were similar between the patients with MGD and the controls. The relative abundance change could be determined in MGD, with Staphylococcus and Sphingomonas being higher and Corynebacterium being lower than the healthy control group. The relative abundance of Staphylococcus was correlated with the degree of meibomian gland loss. 
Bacterial microbiota assessments of the ocular surface have disclosed that Coagulase-negative Staphylococci, Propionibacterium acnes, Corynebacterium macginleyi, and S. aureus are most commonly isolated from the conjunctival sac using the conventional cultivation in participants with and without MGD.16,25 As the gold standard of microorganism detection in clinical laboratories, culture-based methods can be used to identify samples at the species level and measure the density of strains, but the results are generally affected by factors like culture conditions and culture duration.26,27 16S rDNA gene sequencing is able to identify the unculturable microorganisms and provide an efficient, comprehensive, and accurate investigation on the composition of microbial communities. Graham et al.28 detected the conjunctival sac microbiome of 12 healthy subjects, disclosing that the major ocular surface bacteria were S. epidermidis, Coagulase-negative Staphylococcus, Corynebacterium sp., and P. acnes. Dong et al.29 identified five leading abundant genera of the conjunctival microbiome, which were Pseudomonas, Bradyrhizobium, Propionibacterium, Corynebacterium, and Acinetobacter. Zhou et al.30 reported Corynebacterium, Streptococcus, Propionibacterium, Bacillus, and Staphylococcus as the five most abundant genera in healthy conjunctival sac. In our previous study, the most abundant genera in the normal conjunctival sac were found to be Corynebacterium, Pseudomonas, Staphylococcus, Acinetobacter, and Streptococcus.31 Although Pseudomonas was most abundant in the study of Dong et al.29 and the second most abundant in our previous study, its relative abundance was below 1% in the current study and the report of Zhou et al.30 In an investigation on the biogeography microbiota of the ocular surface, Pseudomonas was discovered to be the predominant microbiota in the eyelids and conjunctival tissue,1 so the location and depth of swabbed samples may influence the results. Moreover, as a genus belonging to exogenous microbes, Snodgrassella, which has only been reported once,32 ranked the fourth among the abundant genera in this study. Whether it was randomly generated from the external environment as transient microorganisms in the conjunctival sac or was stably present in the conjunctival sac of some special populations needs to be further investigated. Furthermore, it was confirmed that various pressures of swabbing, methods of filtering contamination, and different sterile swab materials could generate variable detection results.33,34 
Bacteria of the Corynebacterium genus are commonly associated with indigenous microbiota in the conjunctival sac of healthy individuals. Consistent with one of our prior studies,31 Corynebacterium was the most abundant genus in the conjunctival sac of healthy controls in the current study. The reduction of relative abundance of Corynebacterium has also been observed in some infectious diseases of ocular surface. We previously demonstrated that the decreased abundance of Corynebacterium was associated with fungal keratitis.35 As the “resident microbiota” on the ocular surface, Corynebacterium was indicated to protect hosts from microbial infections through enhancing the immunological response by eliciting γδT cells to secrete IL-17.36 In combination with our results, it may be deduced that the decrease of relative abundance of Corynebacterium can lead to the impaired ability to resistance to pathogenic organisms for hosts and may be a factor of the pathogenic bacterial proliferation in the conjunctival sac. In clinical practice, the alleviation of MGD is often observed with antibiotic therapies. Compared with lid hygiene alone, patients who receive lid hygiene combined with topical metronidazole usually achieve significant improvement in ocular surface scores.37 After treatment with azithromycin in 26 patients, the positivity rate of Coagulase-negative Staphylococci and Corynebacterium xerosis isolated from eyelid margins was reported to be significantly decreased.38 Azithromycin could help improve clinical signs and parameters, such as tear film break-up time, Schirmer test, and meibomian gland plugging, of patients with MGD.3941 In this study, we found conjunctival sac bacterial imbalances in patients with MGD. Overgrowth of Staphylococcus and Sphingomonas provided microbiological evidence for investigating the mechanisms of the efficacy of antibiotics in MGD. Staphylococcus is the most commonly encountered parasitic bacterium on human skin. As an opportunistic pathogen, it has been recognized to be closely associated with the incidence of bacterial keratitis, conjunctivitis, and endophthalmitis after cataract surgery.4244 Sphingomonas is an aerobic with low pathogenicity. Consistent with a previous study of dry eye disease,45 the abundance of Sphingomonas was higher in the MGD groups than the controls in our series. An increased relative abundance of Sphingomonas was also observed in the conjunctival sac of diabetic patients.46 Moreover, several clinical cases of endophthalmitis caused by Sphingomonas were reported.4750 Although the specific role that these bacteria play in MGD remains unclear, their proliferation in the conjunctival sac of patients with MGD requires more attention. Clinicians should attach importance to preoperative management to assure the reduction of pathogens on the ocular surface before any ocular surgical procedure for MGD.51 
Another important observation in our research was the correlation of the increased Staphylococcus abundance with the meibomian gland loss in patients with MGD. When the population of Staphylococcus reaches a certain density, it could secrete ample exotoxins and enzymes or form biofilms to cause infection in specific body parts.52 The lipase production of Staphylococcus isolated from the ocular surface is significantly higher than that of Corynebacterium and Propionibacterium.53 Bacterial lipases can affect the composition of the meibum secreted from meibomian glands. Altered meibum and increased bacterial toxin not only contribute to the changes in composition of the lipid layer of patients with MGD, but also result in tear film instability, meibomiantis, ocular surface irritation, and inflammation.11 The blockage of meibomian gland ducts further induces the bacterial proliferation, which facilitates to produce more lipases and induce a vicious cycle. These inflammatory processes caused by Staphylococcus may be correlated with hyperkeratinization and atrophy of meibomian gland ducts. 
S. aureus was much pathogenic with a high level of lipase production,53 but in our study its relative abundance was markedly lower than S. epidermidis in all groups. Using culture-based methods, the positively isolated S. epidermidis from the ocular surface of patients with MGD was found to be at a higher rate than S. aureus,13,15,16 which was consistent with our results. However, based on these findings available, it is clearly difficult to confirm which of these two Staphylococcus species plays a more important role in the process of MGD. Whether these results are influenced by the ocular surface indigenous microbiota and other factors implicated in bacterial pathogenic quantity, such as bacterial density and bacterial metabolism, also needs to be taken into account in the future study. 
In our study, all patients with MGD had different degrees of bacterial imbalance in the conjunctival sac, and dysbacteriosis occurred in severe cases. Because antibiotic treatment can alleviate clinical symptoms of patients with severe MGD,18,3740 it can be speculated that dysbacteriosis linked closely with the disease process, despite the unclear mechanisms. However, the conditions of patients with MGD are usually complicated, and some cases overlap with other diseases, such as blepharitis and dry eye diseases.7 Thus far, it is tricky to predict the altered ocular surface microbiota is a cause at the onset of the alteration of meibum or a consequence of the damage to meibomian glands. 
There were limitations in this study. First, we just confirmed bacterial microbiota disorders in patients with MGD, but the mechanisms remain unknown. Whether these bacteria are involved in MGD and their roles in the pathophysiologic process of MGD are also elusive. Second, more participants from a larger area and samples obtained in different seasons are needed for further investigations in terms of the influence of sex on the ocular surface microbiota in MGD. Third, gene sequencing can only detect the relative abundances of microorganisms, which cannot represent the density of microorganisms in the environment. Fourth, since a large number of sequences are still unknown, it remains difficult to identify strains at the species level via 16S rDNA sequencing. 
In conclusion, with the use of 16S rDNA gene sequencing, we confirmed that both the bacterial imbalance in the conjunctival sac of patients with MGD and the composition of bacterial community on the ocular surface of patients with severe MGD changed significantly. There was a positive correlation between the abundance of Staphylococcus and the degree of meibomian gland loss in these patients. The findings facilitated understanding of the role of ocular surface bacterial microbiota in MGD. 
Acknowledgments
Supported by grants from the National Natural Science Foundation of China (81670839; 81970788; Beijing, China), the Shandong Provincial Key Research and Development Program (2018CXGC1205; Jinan, Shandong, China), and the Innovation Project of Shandong Academy of Medical Sciences (Jinan, Shandong, China). 
Disclosure: X. Dong, None; Y. Wang, None; W. Wang, None; P. Lin, None; Y. Huang, None 
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Figure 1
 
α-diversity indices of individual samples. (A) Scatter plots of α-diversity indices of individual samples from different groups, with Shannon and Simpson indices for the species richness of samples (the higher the value, the more diverse species of samples), Chao 1 index for estimation of OTUs of individual samples, and observed-species index for reflection of the observed OTUs of individual samples. These indices had no significant difference between any two groups. (B) By showing the variation trends in species through a simulated resampling process and estimating the richness of species in the environment, rarefaction curves could directly reflect the rationality of sequencing data size. When the curves reach the saturation platform, it indicates that the sequencing data size is reasonable. All rarefaction curves of individual samples from the patients with MGD and the controls reached the saturation platform, indicating that the sequencing data size was reasonable.
Figure 1
 
α-diversity indices of individual samples. (A) Scatter plots of α-diversity indices of individual samples from different groups, with Shannon and Simpson indices for the species richness of samples (the higher the value, the more diverse species of samples), Chao 1 index for estimation of OTUs of individual samples, and observed-species index for reflection of the observed OTUs of individual samples. These indices had no significant difference between any two groups. (B) By showing the variation trends in species through a simulated resampling process and estimating the richness of species in the environment, rarefaction curves could directly reflect the rationality of sequencing data size. When the curves reach the saturation platform, it indicates that the sequencing data size is reasonable. All rarefaction curves of individual samples from the patients with MGD and the controls reached the saturation platform, indicating that the sequencing data size was reasonable.
Figure 2
 
The relative abundances of dominant phyla and genera in the patients with MGD and the controls. (A) The relative abundances of top 5 most abundant phyla. Compared with the control group, the patients with MGD had significantly higher abundances of Firmicutes, Proteobacteria, and Deinococcus-Thermus, and a significantly lower abundance of Actinobacteria. (B) The relative abundances of top 6 most abundant genera. Compared with the control group, the patients with MGD had significantly higher abundances of Staphylococcus and Sphingomonas, and a significantly lower abundance of Corynebacterium (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 2
 
The relative abundances of dominant phyla and genera in the patients with MGD and the controls. (A) The relative abundances of top 5 most abundant phyla. Compared with the control group, the patients with MGD had significantly higher abundances of Firmicutes, Proteobacteria, and Deinococcus-Thermus, and a significantly lower abundance of Actinobacteria. (B) The relative abundances of top 6 most abundant genera. Compared with the control group, the patients with MGD had significantly higher abundances of Staphylococcus and Sphingomonas, and a significantly lower abundance of Corynebacterium (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 3
 
LEfSe analysis of the normal control group and the MGD groups. (A) A cladogram of the conjunctival sac bacterial taxa in the patients with MGD (green) and the control group (red) showed the levels from domain to species and from outside to inside. Nodes represented taxa at the corresponding level. The diameter of nodes represented the abundances of taxa in the corresponding groups. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. (B) Linear discriminant analysis scoring of biomarkers corresponding to (A), computed by the LEfSe tool. When the score of a taxon was >4.0 with P < 0.01, it was listed in the histogram, which showed all the biomarkers found from the domain to species level. “p”, “c”, “o”, “f”, “g”, and “s” referred to phylum, class, order, family, genus, and species, respectively. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. The length of the histogram represented the linear discriminate analysis values of the different taxa.
Figure 3
 
LEfSe analysis of the normal control group and the MGD groups. (A) A cladogram of the conjunctival sac bacterial taxa in the patients with MGD (green) and the control group (red) showed the levels from domain to species and from outside to inside. Nodes represented taxa at the corresponding level. The diameter of nodes represented the abundances of taxa in the corresponding groups. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. (B) Linear discriminant analysis scoring of biomarkers corresponding to (A), computed by the LEfSe tool. When the score of a taxon was >4.0 with P < 0.01, it was listed in the histogram, which showed all the biomarkers found from the domain to species level. “p”, “c”, “o”, “f”, “g”, and “s” referred to phylum, class, order, family, genus, and species, respectively. At the genus level, the biomarkers were Staphylococcus and Sphingomonas in the patients with MGD, and Corynebacterium in the controls. The length of the histogram represented the linear discriminate analysis values of the different taxa.
Figure 4
 
Relative abundances of top six most abundant genera. Compared with the healthy control group and the mild MGD group, both the moderate MGD group and the severe MGD group had a significantly lower abundance of Corynebacterium; compared with the other three groups, the severe MGD group had a significantly higher abundance of Staphylococcus. And the relative abundance of Sphingomonas was significantly lower in control group. (**P < 0.01; ***P < 0.001).
Figure 4
 
Relative abundances of top six most abundant genera. Compared with the healthy control group and the mild MGD group, both the moderate MGD group and the severe MGD group had a significantly lower abundance of Corynebacterium; compared with the other three groups, the severe MGD group had a significantly higher abundance of Staphylococcus. And the relative abundance of Sphingomonas was significantly lower in control group. (**P < 0.01; ***P < 0.001).
Figure 5
 
The relative abundances of Staphylococcus aureus and S. epidermidis in the four groups. (A) The relative abundance of S. epidermidis. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. epidermidis. (B) The relative abundance of S. aureus. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. aureus (*P < 0.05; **P< 0.01; ***P < 0.001).
Figure 5
 
The relative abundances of Staphylococcus aureus and S. epidermidis in the four groups. (A) The relative abundance of S. epidermidis. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. epidermidis. (B) The relative abundance of S. aureus. Compared with the other groups, the severe MGD group had a significantly higher abundance of S. aureus (*P < 0.05; **P< 0.01; ***P < 0.001).
Figure 6
 
Three-dimensional PCoA plots of samples from the MGD groups and the control group. PCoA plots based on weighted UniFrac distances maximally showed the relations of bacterial microbiota among individual samples. When the similarities of the microbiota composition structure were high, the samples were highly clustered. Separation of the severe MGD group (blue) from the other groups (red) illustrated a certain differentiation of the bacterial microbiota composition in the conjunctival sac.
Figure 6
 
Three-dimensional PCoA plots of samples from the MGD groups and the control group. PCoA plots based on weighted UniFrac distances maximally showed the relations of bacterial microbiota among individual samples. When the similarities of the microbiota composition structure were high, the samples were highly clustered. Separation of the severe MGD group (blue) from the other groups (red) illustrated a certain differentiation of the bacterial microbiota composition in the conjunctival sac.
Figure 7
 
The curve for measure of the correlation between the abundance of Staphylococcus and meiboscores.
Figure 7
 
The curve for measure of the correlation between the abundance of Staphylococcus and meiboscores.
Figure 8
 
The top six most abundant genera in different genders. (A) In females, Corynebacterium, Staphylococcus, and Sphingomonas had significant differences between the patients with MGD and the controls. (B) In males, there was no significant difference (**P < 0.01; ***P < 0.001).
Figure 8
 
The top six most abundant genera in different genders. (A) In females, Corynebacterium, Staphylococcus, and Sphingomonas had significant differences between the patients with MGD and the controls. (B) In males, there was no significant difference (**P < 0.01; ***P < 0.001).
Table
 
Demographic Information
Table
 
Demographic Information
Supplement 1
Supplement 2
Supplement 3
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