Evaluation of commercial RNA extraction protocols for Avian Influenza virus using Nanopore metagenomic sequencing

Authors: CHAVES, MARIA - Iowa State University; HASHISH, AMRO - Iowa State University; OSEMEKE, ONYEKACHUKWU - Iowa State University; SATO, YUKO - Iowa State University; Suarez, David; EL-GAZZAR, MOHAMED - Iowa State University

Submitted to: Pathogens
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/4/2024
Publication Date: 9/7/2024
Citation: Chaves, M., Hashish, A., Osemeke, O., Sato, Y., Suarez, D.L., El-Gazzar, M. 2024. Evaluation of commercial RNA extraction protocols for Avian Influenza virus using Nanopore metagenomic sequencing. Pathogens. Viruses 2024, 16, 1429. https://doi.org/10.3390/v16091429.
DOI: https://doi.org/10.3390/v16091429

Interpretive Summary: It has become standard practice to sequence viruses to better understand how they spread and to predict how virulent an isolate can be. Sequencing technology continues to improve and has the potential to sequence the multiple pathogens directly from clinical samples. This new technology is often referred to as Next Generation Sequencing or NGS. The Oxford Nanopore sequencing platform in particular is useful because of its rather low equipment cost and ability to operate outside the laboratory. For NGS to work you must have purified RNA to sequence. This study compared different RNA extraction methods to purify the RNA and the RNA was sequenced on the Oxford Nanopore sequencer. Several RNA techniques were compared, but no method was clearly superior in every measure of the sequence results.

Technical Abstract: Next-generation sequencing (NGS) platforms have revolutionized the field of infectious disease diagnostics in human and animal medicine, providing a comprehensive characterization of pathogens. Avian influenza virus (AIV) is a major threat to the poultry industry, necessitating rapid and accurate disease identification and characterization. The current diagnostic process for AIV begins by identifying the virus through a quantitative real-time reverse transcription–polymerase chain reaction (q RT-PCR). Subsequently, the virus is characterized using either conventional Sanger sequencing or Illumina sequencing techniques. Among NGS platforms, Oxford Nanopore Technologies (ONT) stands out as the only platform providing real-time analysis of results, flexible run time, and library preparation workflows. However, the current use of NGS in AIV diagnostics is limited to characterization by Whole Genome Sequencing (WGS) of already identified positive samples. Therefore, our overall goal is to optimize and validate nucleic acid extraction, library preparation, sequencing generation, and data analysis tools necessary to establish ONT as a diagnostic tool for both the identification and characterization of AIV from clinical samples. Thus, this study focused on evaluating the performance of four commercially available RNA extraction protocols using AIV-positive clinical samples. The study assessed the protocols’ extract using total RNA concentration, viral copies, and purity in addition to each extraction procedure's sequencing performance and applicability. Standard extraction protocols in diagnostic laboratories were utilized, which included the magnetic particle-based (MagMAX ™ Pathogen RNA/DNA Kit), silica column (QIAamp® Viral RNA), liquid-phase separation (TRIzol™ LS Reagent), and enzymatic (SwiftX™ Swabs) extraction methods. The results show that no single protocol outperforms all parameters; for instance, TRIzol™ presented the highest concentration results, while the QIAamp® kit achieved the best purity. Across the different aspects evaluated, MagMax was the most consistent and less error-prone method. Finally, TRIzol and MagMAX ™ extraction protocols generated the highest number of total and AIV-specific reads after nanopore sequencing. This study provides evidence that selecting the appropriate extraction protocol may significantly impact ONT sequencing performance. Additional research is required to investigate strategies for AIV enrichment, library preparation procedures, and sequencing analysis to optimize and validate the complete ONT workflow, thus making it suitable for routine diagnostics.