@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 {126, title = {Unbiased Pathogen Detection and Host Gene Profiling for Conjunctivitis.}, journal = {Ophthalmology}, volume = {126}, year = {2019}, month = {2019 08}, pages = {1090-1094}, abstract = {

PURPOSE: The etiology of conjunctivitis is often misdiagnosed. An ideal diagnostic test would identify all possible infectious causes. In this study, we apply unbiased metagenomic RNA deep sequencing (MDS) to identify pathogens causing conjunctivitis.

DESIGN: Molecular study of prospectively collected conjunctival swabs from patients with presumed infectious conjunctivitis.

PARTICIPANTS: Patients with presumed acute infectious conjunctivitis.

METHODS: Conjunctival swabs were collected from patients presenting with acute conjunctivitis. Swabs were processed for MDS. Pathogens were identified using a rapid computational pipeline to analyze the nonhost sequences obtained from MDS. Differential gene expression analysis was performed to evaluate for host transcriptome signatures for infectious types. Clinical samples were deidentified, and laboratory personnel handling the samples and interpreting the data were masked.

MAIN OUTCOME MEASURES: Pathogens and differential transcripts identified by MDS.

RESULTS: Metagenomic RNA deep sequencing detected pathogens in 86\% (12/14) of the patients tested. Swabs from 10 of 14 patients were positive for human adenovirus (HAdV) while swabs from 2 of 14 patients were positive for Vittaforma corneae (a parasitic fungal species of the microsporidia group). Samples positive for HAdV by RNA-seq were independently verified in a CLIA-certified laboratory. Pathogen-directed polymerase chain reaction confirmed the presence of V.\ corneae genome in the samples positive by RNA-seq. Local host transcriptome analysis identified 12 differentially expressed genes that provided distinct expression signatures for patients infected with HAdV compared with V.\ corneae.

CONCLUSIONS: Metagenomic RNA deep sequencing can reliably detect and quantify common and rare pathogens causing conjunctivitis, and identify strains. The unbiased nature of metagenomic RNA deep sequencing allowed an expanded scope of pathogen detection, including fungal species not commonly associated with acute conjunctivitis. In addition, the identification of infection type-specific local host transcriptome signatures may allow for pathogen detection even when the pathogen load is too low for direct identification.

}, keywords = {Adult, Aged, Conjunctivitis, DNA, Bacterial, DNA, Fungal, DNA, Viral, Female, High-Throughput Nucleotide Sequencing, Humans, Male, Metagenomics, Middle Aged, Young Adult}, issn = {1549-4713}, doi = {10.1016/j.ophtha.2019.03.039}, author = {Lalitha, Prajna and Seitzman, Gerami D and Kotecha, Ritesh and Hinterwirth, Armin and Chen, Cindi and Zhong, Lina and Cummings, Susie and Lebas, Elodie and Sahoo, Malaya K and Pinsky, Benjamin A 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} }