Research Project
9558796
ABSTRACT Next-generation sequencing (NGS) has the potential to revolutionize how infectious disease is diagnosed and treated, enabling the detection of a wide variety of pathogens in a single test. However, despite the increasing adoption of molecular diagnostics in clinical virology and microbiology labs, NGS has not become widespread due to its cost, turnaround time, and the technical and computational expertise required to produce and analyze the data. To overcome these challenges, Arc Bio is developing a suite of technologies that will enable clinical virology and microbiology labs to more easily implement NGS pipelines. The Arc Bio product includes all the experimental protocols and software necessary to go from sample to answer for pathogen detection in clinical samples, including novel tools to make pathogen sequencing less expensive, more sensitive, and more accurate. These tools include a host DNA depletion technology that helps reduce costs and increase sensitivity by removing background human DNA/RNA from sequencing libraries. In addition, Arc Bio has developed a unique bioinformatics pipeline that incorporates graph-based analytics to deliver sensitive and specific strain identification. In this project, we propose to adapt this NGS- based pipeline to detecting common transplant-related viral infections. Immunocompromised solid organ and hematopoietic stem cell transplant recipients are uniquely susceptible to severe infection due to many common and opportunistic viruses. We will determine the performance of our pipeline on 100 plasma samples containing five different known viruses (cytomegalovirus, Epstein Barr virus, BK virus, adenovirus, and human herpesvirus 6). We will also sequence 25 negative immunocompromised samples to determine baseline nucleic acid levels. We will use these datasets to optimize our host depletion and bioinformatics technologies for this application, and will examine the feasibility of simultaneously determining viral load, drug resistance, and co-infections from the same dataset. Currently, there are no existing commercial diagnostic tests that deliver all of this information in a single assay. In the clinic, this test could significantly reduce morbidity and mortality by shortening time to diagnosis, identifying common co-infections, reducing hospital stays, and limiting the number of transplant rejections. The result of this project will be a complete, validated pipeline that will include all protocols and software necessary to perform larger-scale retrospective and prospective studies with our alpha partner clinical labs.
