July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Genomic and gene expression analyses of pathogens in post-surgical endophthalmitis
Author Affiliations & Notes
  • Cecilia S Lee
    University of Washington, Seattle, Washington, United States
  • Sundeep Kasi
    Wills Eye Hospital, Pennsylvania, United States
    The Retina Group of Washington, Virginia, United States
  • Bryan Hong
    Wills Eye Hospital, Pennsylvania, United States
    Vitreo Retinal Associates, Massachusetts, United States
  • Ferhina Ali
    Wills Eye Hospital, Pennsylvania, United States
  • Lakshmi Akleswaran
    University of Washington, Seattle, Washington, United States
  • Aaron Lee
    University of Washington, Seattle, Washington, United States
  • Sunir Garg
    Wills Eye Hospital, Pennsylvania, United States
    Mid Atlantic Retina, Pennsylvania, United States
  • Russell N Van Gelder
    University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Cecilia Lee, None; Sundeep Kasi, None; Bryan Hong, None; Ferhina Ali, None; Lakshmi Akleswaran, None; Aaron Lee, Novartis (F), Topcon (R), Zeiss (F); Sunir Garg, Aerpio (F), Bausch & Lomb (C), Deciphera (C), Eyegate (F), Topivert (C), Wills Eye/Retina Implant AG (F); Russell Van Gelder, None
  • Footnotes
    Support  NIH Grant K23EY024921
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 3255. doi:
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      Cecilia S Lee, Sundeep Kasi, Bryan Hong, Ferhina Ali, Lakshmi Akleswaran, Aaron Lee, Sunir Garg, Russell N Van Gelder; Genomic and gene expression analyses of pathogens in post-surgical endophthalmitis. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3255.

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

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Abstract

Purpose : To characterize the pathogens involved in post-surgical endophthalmitis using whole genome DNA sequencing (WGS) and RNA-seq.

Methods : Intraocular samples from patients diagnosed with post-surgical endophthalmitis were collected from Wills Eye Hospital and University of Washington between 9/1/14 and 5/31/16. The specimens were sent for microbial culture, WGS and RNA-seq. Sequencing data were processed with the Scalable Metagenomics Alignment Research Tool (SMART) and the pathogen reads were aligned against the reference genomes of corresponding pathogens. We used a BWA software package to align the reads to the antibiotic resistance genes, then created a pipeline to quantify RNA transcript counts for each antibiotic resistance genes.

Results : Samples from 52 patients (mean age 70, 52% male) were included. 24 were culture positive and 28 were culture negative. WGS detected the cultured pathogen as the predominant organism in 20/24 (83%) culture-positive samples. Using WGS and RNA-seq, we identified both the genomic and transcriptomic profiles of the pathogens. (Fig A,B) Additionally, we detected multiple antibiotic resistance genes and gene expressions of cultured pathogens de novo (Fig D). In culture-negative samples, WGS detected a potential pathogen in 11/28 (39%) cases. We suspected 7/11 (64%) cases to be infected with S. epidermidis and were able to align the matches against the S. epidermidis reference genome in all cases (Fig C).

Conclusions : WGS and RNA-seq allow unprecedented characterization of the pathogens involved in endophthalmitis. The pathogens’ genomic and gene expression profiles may provide important biomarkers of endophthalmitis prognosis in future.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Circo plots of DNA (inner blue circle) and RNA sequences (outer blue circle) detected from an Enterococcus faecalis (A) and a S. epidermidis (B) case. Colors in innermost circle: red, ribosomal RNA; black, antibiotic resistance coding region; green, stress or shock protein coding region. Yellow circle, genomic variants; Outermost circle, map of the pathogen reference genome. (C) Coverage of S. epidermidis reference genome in 7 different culture-negative cases. (D) Antibiotic resistance detection in a S. epidermidis case using WGS (DNA) and RNA-seq (RNA). Each column represents an antibiotic resistance gene. X-axis, position in the S. epidermidis genome; Y-axis, coverage depth in log scale.

Circo plots of DNA (inner blue circle) and RNA sequences (outer blue circle) detected from an Enterococcus faecalis (A) and a S. epidermidis (B) case. Colors in innermost circle: red, ribosomal RNA; black, antibiotic resistance coding region; green, stress or shock protein coding region. Yellow circle, genomic variants; Outermost circle, map of the pathogen reference genome. (C) Coverage of S. epidermidis reference genome in 7 different culture-negative cases. (D) Antibiotic resistance detection in a S. epidermidis case using WGS (DNA) and RNA-seq (RNA). Each column represents an antibiotic resistance gene. X-axis, position in the S. epidermidis genome; Y-axis, coverage depth in log scale.

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