1. Machine Learning Prediction of Adenovirus D8 Conjunctivitis Complications from Viral Whole-Genome Sequence
- Author
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Kenji Nakamichi, Lakshmi Akileswaran, PhD, Thomas Meirick, MD, Michele D. Lee, MD, James Chodosh, MD, Jaya Rajaiya, PhD, David Stroman, PhD, Alejandro Wolf-Yadlin, PhD, Quinn Jackson, W. Bradley Holtz, Aaron Y. Lee, MD, Cecilia S. Lee, MD, Russell N. Van Gelder, MD, PhD, Gregg J. Berdy, James D. Branch, El-Roy Dixon, Sherif M. El-Harazi, Jack V. Greiner, Joshua Herz, Larry L. Lothringer, Damien Macaluso, Andrew L. Moyes, George Nardin, Bernard R. Perez, Lawerence E. Roel, Syamala H.K. Reddy, Stephanie Becker, Neil Shmunes, Stephen Smith, Michael Tepedino, Jonathan Macy, Prashant Garg, Nivedita Patil, Yasmin Bhagat, Malavika Krishnaswamy, Nagappa Somshekhar, Manisha Acharya, Shree Kumar Reddy, Mary Abraham, Shobha Kini, Nita Shanbag, P.N. Biswas, Virendra Agarwal, Anshu Sahai, P.S. Girija Devi, Vupputuri Venkata Lakshmi, Narasimha Rao, Radhika Tandon, Priti Kapadia, Deepak Mehta, Anju Kochar, Adriana dos Santos Forseto, Rubens Belfort, Jr., Jacob Moyses Cohen, Ramon Coral Ghanem, Roberta De Ventura, Sergio Luis Gianotti Pimentel, Sergio Kwitko, Maria Cristina Nishiwaki Dantas, Anna Maria Hofling-Lima, Walton Nose, D. Wariyapola, M. Wijetunge, Charith Fonseka, Champa Banagala, K.A. Salvin, and D.R. Kodikara
- Subjects
Adenovirus D8 ,Conjunctivitis ,Machine learning ,Subepithelial infiltrates ,Ophthalmology ,RE1-994 - Abstract
Objective: To obtain complete DNA sequences of adenoviral (AdV) D8 genome from patients with conjunctivitis and determine the relation of sequence variation to clinical outcomes. Design: This study is a post hoc analysis of banked conjunctival swab samples from the BAYnovation Study, a previously conducted, randomized controlled clinical trial for AdV conjunctivitis. Participants: Ninety-six patients with AdV D8-positive conjunctivitis who received placebo treatment in the BAYnovation Study were included in the study. Methods: DNA from conjunctival swabs was purified and subjected to whole-genome viral DNA sequencing. Adenovirus D8 variants were identified and correlated with clinical outcomes, including 2 machine learning methods. Main Outcome Measures: Viral DNA sequence and development of subepithelial infiltrates (SEIs) were the main outcome measures. Results: From initial sequencing of 80 AdV D8-positive samples, full adenoviral genome reconstructions were obtained for 71. A total of 630 single-nucleotide variants were identified, including 156 missense mutations. Sequence clustering revealed 3 previously unappreciated viral clades within the AdV D8 type. The likelihood of SEI development differed significantly between clades, ranging from 83% for Clade 1 to 46% for Clade 3. Genome-wide analysis of viral single-nucleotide polymorphisms failed to identify single-gene determinants of outcome. Two machine learning models were independently trained to predict clinical outcome using polymorphic sequences. Both machine learning models correctly predicted development of SEI outcomes in a newly sequenced validation set of 16 cases (P = 1.5 × 10−5). Prediction was dependent on ensemble groups of polymorphisms across multiple genes. Conclusions: Adenovirus D8 has ≥ 3 prevalent molecular substrains, which differ in propensity to result in SEIs. Development of SEIs can be accurately predicted from knowledge of full viral sequence. These results suggest that development of SEIs in AdV D8 conjunctivitis is largely attributable to pathologic viral sequence variants within the D8 type and establishes machine learning paradigms as a powerful technique for understanding viral pathogenicity.
- Published
- 2022
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