@article {86, title = {Post-antibiotic Ocular Surface Microbiome in Children: A Cluster-Randomized Trial.}, journal = {Ophthalmology}, volume = {127}, year = {2020}, month = {2020 08}, pages = {1127-1130}, keywords = {Anti-Bacterial Agents, Child, Cornea, Humans, Microbiota, Randomized Controlled Trials as Topic}, issn = {1549-4713}, doi = {10.1016/j.ophtha.2020.02.014}, author = {Doan, Thuy and Hinterwirth, Armin and Worden, Lee and Arzika, Ahmed M and Maliki, Ramatou and Chen, Cindi and Zhong, Lina and Zhou, Zhaoxia and Acharya, Nisha R and Porco, Travis C and Keenan, Jeremy D and Lietman, Thomas M} } @article {151, title = {High-throughput sequencing of pooled samples to determine community-level microbiome diversity.}, journal = {Ann Epidemiol}, volume = {39}, year = {2019}, month = {2019 11}, pages = {63-68}, abstract = {

PURPOSE: Community-level interventions in cluster randomized controlled trials may alter the gut microbiome of individuals. The current method of estimating community diversities uses microbiome data obtained from multiple individual{\textquoteright}s specimens. Here we propose randomly pooling a number of microbiome samples from the same community into one sample before sequencing to estimate community-level microbiome diversity.

METHODS: We design and analyze an experiment to compare community microbiome diversity (gamma-diversity) estimates derived from 16S rRNA gene sequencing of 1) individually sequenced specimens vs. 2) pooled specimens collected from a community. Pool sizes of 10, 20, and 40 are considered. We then compare the gamma-estimates using Pearson{\textquoteright}s correlation as well as using Bland and Altman agreement analysis for three established diversity indices including richness, Simpson{\textquoteright}s and Shannon{\textquoteright}s.

RESULTS: The gamma-diversity estimates are highly correlated, with most being statistically significant. All correlations between all three diversity estimates are significant in the 10-pooled data. Pools comprising 40 specimens are closest to the line of agreement, but all pooled samples and individual samples fall within the 95\% limits of agreement.

CONCLUSIONS: Pooling microbiome samples before DNA amplification and metagenomics sequencing to estimate community-level diversity is a viable measure to consider in population-level association research studies.

}, keywords = {Administration, Oral, Anti-Bacterial Agents, Azithromycin, Bacteria, Biodiversity, Child, Preschool, Female, Gastrointestinal Microbiome, Gastrointestinal Tract, High-Throughput Nucleotide Sequencing, Humans, Infant, Infant, Newborn, Male, Metagenomics, Microbiota, Niger, Placebos, RNA, Ribosomal, 16S, Sequence Analysis, DNA}, issn = {1873-2585}, doi = {10.1016/j.annepidem.2019.09.002}, author = {Ray, Kathryn J and Cotter, Sun Y and Arzika, Ahmed M and Kim, Jessica and Boubacar, Nameywa and Zhou, Zhaoxia and Zhong, Lina and Porco, Travis C and Keenan, Jeremy D and Lietman, Thomas M and Doan, Thuy} } @article {256, title = {Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva.}, journal = {Invest Ophthalmol Vis Sci}, volume = {57}, year = {2016}, month = {2016 Oct 01}, pages = {5116-5126}, abstract = {

Purpose: To characterize the ocular surface microbiome of healthy volunteers using a combination of microbial culture and high-throughput DNA sequencing techniques.

Methods: Conjunctival swab samples from 107 healthy volunteers were analyzed by bacterial culture, 16S rDNA gene deep sequencing (n = 89), and biome representational in silico karyotyping (BRiSK; n = 80). Swab samples of the facial skin (n = 42), buccal mucosa (n = 50), and environmental controls (n = 27) were processed in parallel. 16S rDNA gene quantitative PCR was used to calculate the bacterial load in each site. Bacteria were characterized by site using principal coordinate analysis of metagenomics data. BRiSK data were analyzed for presence of fungi and viruses.

Results: Corynebacteria, Propionibacteria, and coagulase-negative Staphylococci were the predominant organisms identified by all three techniques. Quantitative 16S PCR demonstrated approximately 0.1 bacterial 16S rDNA/human actin copy on the ocular surface compared with greater than 10 16S rDNA/human actin copy for facial skin or the buccal mucosa. The conjunctival bacterial community structure is distinct compared with the facial skin (R = 0.474, analysis of similarities P = 0.0001), the buccal mucosa (R = 0.893, P = 0.0001), and environmental control samples (R = 0.536, P = 0.0001). 16S metagenomics revealed substantially more bacterial diversity on the ocular surface than other techniques, which appears to be artifactual. BRiSK revealed presence of torque teno virus (TTV) on the healthy ocular surface, which was confirmed by direct PCR to be present in 65\% of all conjunctiva samples tested.

Conclusions: Relative to adjacent skin or other mucosa, healthy ocular surface microbiome is paucibacterial. Its flora are distinct from adjacent skin. Torque teno virus is a frequent constituent of the ocular surface microbiome. (ClinicalTrials.gov number, NCT02298881.).

}, keywords = {Adult, Bacteria, Conjunctiva, DNA, Bacterial, Eye Infections, Bacterial, Female, Healthy Volunteers, High-Throughput Nucleotide Sequencing, Humans, Male, Microbiota, Middle Aged, Prospective Studies, Real-Time Polymerase Chain Reaction, Sequence Analysis, DNA}, issn = {1552-5783}, doi = {10.1167/iovs.16-19803}, author = {Doan, Thuy and Akileswaran, Lakshmi and Andersen, Dallin and Johnson, Benjamin and Ko, Narae and Shrestha, Angira and Shestopalov, Valery and Lee, Cecilia S and Lee, Aaron Y and Van Gelder, Russell N} }