Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning.

Smadar Hada-Neeman, Yael Weiss-Ottolenghi, Naama Wagner, Oren Avram, Haim Ashkenazy, Yaakov Maor, Ella H Sklan, Dmitry Shcherbakov, Tal Pupko, Jonathan M Gershoni

Journal: Frontiers in immunology 2021;11():619896

PMID: 33643301

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Abstract

The presence of pathogen-specific antibodies in an individual's blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term "Domain-Scan". We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant ("domain") is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.

Address: The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.; Max Planck Institute for Developmental Biology, Max Planck Society (MPG), Tübingen, Germany.; Institute of Gastroenterology and Hepatology, Kaplan Medical Center, Rehovot, Israel.; Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.; Russian-American Anti-Cancer Center, Altai State University, Barnaul, Russia.

DOI: 10.3389/fimmu.2020.619896

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MeSH Terms: AIDS Serodiagnosis, Amino Acid Sequence, Antigen-Antibody Reactions, Base Sequence, Communicable Diseases, DNA Barcoding, Taxonomic, DNA, Recombinant, Epitopes, Genetic Vectors, HIV Antibodies, HIV Core Protein p24, HIV Envelope Protein gp160, HIV Infections, Hepatitis C, Hepatitis C Antibodies, Hepatitis C Antigens, High-Throughput Nucleotide Sequencing, Humans, Machine Learning, Oligonucleotides, Peptide Fragments, Peptide Library, Polymerase Chain Reaction, Serologic Tests