Ph.D.
Hien T.T. Ngo

VinUni-Illinois Smart Health Center, VinUniversity

Title: A platform for in silico bacteriophage screening for antimicrobial resistance

Hien T.T. Ngo¹, Duy-Minh Nguyen¹, Tan-Sang Nguyen Dinh¹, Yuqing Mao^2,3, Andrew W.

Taylor-Robinson^1.4, Khoa Doan^1,5, Thanh H.(Helen) Nguyen^1,2,3

¹VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam

²Department of Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, IL, USA

³Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-

Champaign, IL, USA

⁴College of Health Sciences, VinUniversity, Hanoi, Vietnam

⁵College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam

The rise of antimicrobial resistance (AMR), notably rendering frontline antibiotics increasingly ineffective against pathogenic bacteria, poses a major global health threat. This highlights the pressing need for novel alternatives to, or cotherapies for, existing antibiotics. Bacteriophages (“phages”), viruses that infect bacteria, have emerged as a promising candidate. Unlike antibiotics, phages co‑evolve highly specific interactions with unique bacterial surface receptors, leading to narrow host ranges, and so typically infect only a restricted set of bacterial strains or lineages. While this limits side effects of phage resistance like antibiotic resistance, it also presents a bottleneck, relying on labor-intensive and low-throughput in-vitro experiments to identify effective phage-host matches. Furthermore, the ability of phages to kill a host is not the sole determinant of clinical viability; other characteristics must also be considered, such as the absence of virulence factors or antibiotic resistance genes (ARGs) and optimizing selection of lytic dsDNA phages.

In order to address these mechanistic challenges, we have introduced an integrated web-based platform for end-to-end phage screening, which provides three core functionalities. The first module handles genomic data processing and quality checking, including read trimming, assembly, and genome quality checking. The second module performs annotation and phage quality assessment, specifically screening for lifestyle, nucleic acid type, and genetic elements such as lysogeny-related genes, toxins, and ARG biomarkers. The last module leverages machine learning techniques to offer both broad host range estimation against bacterial databases and high resolution, strain-level lytic interaction predictions for specific phage-host pairs. By consolidating these separate workflows, our platform aims to streamline the discovery of candidates for bacteriophage therapy.

Biography of the presenting author:

Hien T.T. Ngo, PhD, serves as a Co-Principal Investigator for the Phage-Host Interaction Research Project at the VinUni-Illinois Smart Health Center, VinUniversity. She earned her doctorate from Kyung Hee University in South Korea and completed her postdoctoral training at Aarhus University Hospital in Denmark. An accomplished researcher with over 45 peerreviewed publications, Dr. Ngo also serves as the Managing Editor for the journal Environmental Pollution.