@article {901, title = {Impact of Sample Collection Order on the Diagnostic Performance of Metagenomic Deep Sequencing for Infectious Keratitis.}, journal = {Cornea}, volume = {41}, year = {2022}, month = {2022 Jan 01}, pages = {39-44}, abstract = {

PURPOSE: The purpose of this article was to evaluate the impact of sample collection order on the diagnostic yield of metagenomic deep sequencing (MDS) for determining the causative pathogen in infectious keratitis.

METHODS: We performed a cross-sectional diagnostic test evaluation among subjects with infectious keratitis at Aravind Eye Hospital in Madurai, India. All subjects underwent corneal scrapings of the affected eye to obtain potassium hydroxide smear, Gram stain, bacterial culture, and fungal culture, in this order. The order of MDS specimen collection relative to smear and culture samples was randomized and served as the primary predictor. Outcomes included the normalized copy number of pathogenic RNA detected by MDS, the proportion of MDS samples that were diagnostic, and the agreement of MDS results with cultures.

RESULTS: MDS samples from 46 subjects with corneal ulcers were evaluated. MDS was positive in 33 subjects (76\%) and had 74\% overall agreement with culture results. There was no association between order of MDS sample collection and normalized copy number of genetic material detected (P = 0.62) or the likelihood of MDS positivity (P = 0.46). However, the likelihood of agreement between MDS and cultures decreased when MDS corneal swabs were collected after other diagnostic corneal scrapings (P = 0.05).

CONCLUSIONS: The overall yield of MDS for detecting the cause of infectious keratitis was not affected by sample collection order. However, diagnostic agreement between MDS and cultures decreased when MDS samples were collected after other specimens. Additional investigation is warranted to determine whether this represents increased sensitivity of MDS compared with cultures or higher susceptibility to contaminants.

}, keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Bacteria, Cornea, Cross-Sectional Studies, DNA, Bacterial, DNA, Fungal, Eye Infections, Bacterial, Eye Infections, Fungal, Female, Fungi, High-Throughput Nucleotide Sequencing, Humans, Keratitis, Male, Metagenome, Middle Aged, Specimen Handling, Young Adult}, issn = {1536-4798}, doi = {10.1097/ICO.0000000000002766}, author = {Redd, Travis K and Lalitha, Prajna and Prajna, N Venkatesh and Sikha, Misra and Gunasekaran, Rameshkumar and Hinterwirth, Armin and Chen, Cindi and Zhong, Lina and Liu, Zijun and Lietman, Thomas M and Keenan, Jeremy D and Doan, Thuy and Seitzman, Gerami D} } @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 {276, title = {Gut Microbial Diversity in Antibiotic-Naive Children After Systemic Antibiotic Exposure: A Randomized Controlled Trial.}, journal = {Clin Infect Dis}, volume = {64}, year = {2017}, month = {2017 05 01}, pages = {1147-1153}, abstract = {

Background: Antibiotic exposure can alter the gut microbiome. We evaluate the effects of azithromycin on the gut microbiome diversity of children from an antibiotic-naive community in Niger.

Methods: A population-based sample of 80 children aged 1-60 months in the Dosso region of Niger was randomized to receive a single dose of either oral azithromycin or placebo. Fecal samples were collected immediately before treatment and 5 days after treatment for 16S rRNA gene sequencing. The prespecified outcome was α-diversity (inverse Simpson{\textquoteright}s α-diversity index), with secondary outcomes of β and γ Simpson{\textquoteright}s and Shannon{\textquoteright}s diversities.

Results: At 5 days after treatment, 40 children aged 1-60 months were analyzed in the azithromycin-treated group and 40 children in the placebo-treated group. Diversity of the gut microbiome was significantly lower in the treated group (inverse Simpson{\textquoteright}s α-diversity, 5.03; 95\% confidence interval [CI], 4.08-6.14) than in the placebo group (6.91; 95\% CI, 5.82-8.21; P = .03). Similarly, the Shannon{\textquoteright}s α-diversity was lower in the treated group (10.60; 95\% CI, 8.82-12.36) than the placebo group (15.42; 95\% CI, 13.24-17.80; P = .004). Simpson{\textquoteright}s community-level (γ) diversity decreased with azithromycin exposure from 17.72 (95\% CI, 13.80-20.21) to 10.10 (95\% CI, 7.80-11.40; P = .00008), although β-diversity was not significantly reduced (2.56, 95\% CI, 1.88-3.12; to 2.01, 95\% CI, 1.46-2.51; P = .26).

Conclusions: Oral administration of azithromycin definitively decreases the diversity of the gut microbiome of children in an antibiotic-naive community.

Clinical Trials Registration: NCT02048007.

}, keywords = {Administration, Oral, Anti-Bacterial Agents, Azithromycin, Bacteria, Biodiversity, Child, Preschool, Cluster Analysis, DNA, Ribosomal, Female, Gastrointestinal Microbiome, Gastrointestinal Tract, Humans, Infant, Male, Niger, Phylogeny, Placebos, RNA, Ribosomal, 16S, Sequence Analysis, DNA}, issn = {1537-6591}, doi = {10.1093/cid/cix141}, author = {Doan, Thuy and Arzika, Ahmed M and Ray, Kathryn J and Cotter, Sun Y and Kim, Jessica and Maliki, Ramatou and Zhong, Lina and Zhou, Zhaoxia and Porco, Travis C and Vanderschelden, Benjamin and Keenan, Jeremy D and Lietman, Thomas M} }