Date of Award

January 2025

Document Type

Open Access Thesis

Degree Name

Medical Doctor (MD)

Department

Medicine

First Advisor

Seth N. Redmond

Second Advisor

Nathan D. Grubaugh

Abstract

Tuberculosis (TB) is a leading cause of infection-related mortality worldwide, causing an estimated 1.13 million deaths in 2022. The majority of infections can be successfully treated with antibiotics, but the prevalence of drug-resistant TB and a lack of rapid and cost-effective drug susceptibility tests for many key antibiotics is a significant barrier to treatment. Whole-genome sequencing of Mycobacterium tuberculosis can be used for drug susceptibility testing and can provide valuable insight into transmission patterns. However, M. tuberculosis is slow to grow, meaning traditional sequencing methods which require culture can take many weeks to return results, greatly limiting the potential clinical benefits. Tiled amplicon sequencing is a low-cost method of amplifying target nucleic acid which has been used widely to sequence viruses such as SARS-CoV-2 and MPox directly from clinical samples. We hypothesized that extending this approach to M. tuberculosis would significantly reduce the cost, labor, and turnaround time for whole-genome sequencing, enabling more rapid determination of drug susceptibility and insight into transmission. We designed a tiled amplicon panel consisting of 5128 primers, the largest tiled amplicon sequencing panel we are aware of to date, and employed the widely-used Illumina COVIDSeq protocol to enable sequencing of the full M. tuberculosis genome from minimal input samples. Compared to the same sample without amplification, we achieved >80% genome coverage with 500-1000x lower input DNA with our primer scheme. Further, we were able to perform whole-genome sequencing on DNA extracted directly from clinical specimens, without culture, and use this data to detect drug resistance and perform lineage assignment. This approach to sequencing M. tuberculosis could revolutionize TB control programs, enabling genomic epidemiology to be performed in resource-limited settings and reducing the time needed for comprehensive drug susceptibility testing from weeks to days.

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This is an Open Access Thesis.

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