Tuberculosis (TB), not otherwise known as the world’s deadliest infectious disease, is a longstanding and formidable challenge to public health. A new, groundbreaking study has provided key clues to the role dormancy plays in shaping evolution. To do so, researchers combed through bacterial genome sequences using a specialized computational tool. This research, which analyzed DNA from a tuberculosis outbreak in New Zealand spanning from 1992 to 2011, underscores the complexity of TB’s dormant state and its implications for disease control.
Conducted by a team of experts from Cornell University, the study, titled “Bayesian Phylodynamic Inference of Population Dynamics with Dormancy,” appears in the Proceedings of the National Academy of Sciences. The researchers argue that neglecting to account for the dormant stage of TB would mislead strategies designed to curb its spread.
Understanding Tuberculosis and Its Dormant State
Tuberculosis can lie dormant in the lungs for decades before emerging as a disease. Unfortunately, this dormant state leaves researchers with a complex hurdle in finding ways to treat the disease. Martin Wells, one of the study’s lead authors and a professor of statistical sciences at Cornell, emphasized the importance of accurately understanding TB’s dynamics.
“To effectively inform both scientific inquiry and policy decisions, a probability-based risk assessment is essential.” – Martin Wells
The study’s authors highlight that ignoring dormancy could lead to serious errors in understanding TB’s historical evolution and its potential future adaptations. Undoubtedly, this insight is important, as public health officials are still reeling from TB outbreaks around the globe.
The Role of SeedbankTree
To tackle the intricacies of dormancy in TB, the research team created an open-source software program named SeedbankTree. This crafty new device measures the genome sequences from cultures rich in dormant microbes. Drew Harwell/Washington Post via Getty Images Jaehee Kim, an assistant professor of computational biology at Cornell, was instrumental to the project. She testified about how important this change is.
“If we want to accurately reconstruct parts of evolutionary history that we can’t directly observe, it’s essential to account for dormancy.” – Jaehee Kim
SeedbankTree takes a different, original approach. It incorporates recent findings providing that dormant Mycobacterium tuberculosis (Mtb)67,68 mutates at a rate about one-eighth that of active bacteria. This finding is especially important for understanding pathogenic evolution and adaptation in TB. This kind of data sharing strengthens researchers’ abilities to focus their attacks on the disease.
Implications for Disease Control
The significance of this research goes beyond academic interest. It carries tremendous implications on public health strategies. It shouldn’t be a surprise—after all, Andrew Clark, a co-author of the study and a Cornell professor of population genetics, says recognizing dormancy is critical. He thinks it’s important in knowing about new strains of TB that might spread faster.
“Our analysis is especially important for improving the identification of novel strains of pathogens that have an increased rate of transmission. Failing to account for dormancy can seriously mislead those efforts.” – Andrew Clark
Researchers are trying to get dormancy into the TB transmission models. This strategy prepares federal, state, and local public health officials to more effectively fight this widespread disease. Researchers estimated that dormant Mtb reactivates, on average, after 1.27 years. This important finding can have a tremendous impact on the development of epidemiological modeling and intervention strategies.
Collaborative Efforts and Future Directions
The large study included researchers from multiple perspectives. Their co-authors on the new resource include Wai Tung “Jack” Lo, Joy Zhang, Peiyu Xu, Daniel Barrow, Ishani Chopra and Lorenzo Cappello from Universitat Pompeu Fabra in Spain. Their broad professional perspective made for the rich, layered discoveries found throughout this study.
The team began by trying out SeedbankTree on synthetic genetic data. Then, they tested it out on actual outbreak sequences from New Zealand. This extensive testing affirms the capabilities of the software. It provides an important basis for future investigation of other organisms that enter comparable dormant states.