Computational method detects epidemic viruses

To this day, many people still recall the widespread Zika outbreak in the wake of 2016 that caught the majority of South and North America off guard. In November 2016, the World Health Organization (WHO) declared the end of the epidemic after many months of struggle. 

Throughout history, Zika is certainly not the only example where a seemingly obscure disease suddenly escalated into a world health emergency. Some epidemics are very difficult to manage and contain because many of them have nearly undetectable levels in human blood. As a result, the virus is not picked up in its early stages and does not receive sufficient attention until long after the pathogen has spread into the population.

Recently, scientists at the Broad Institute of the Massachusetts Institute of Technology (MIT) and Harvard came up with a new computational method that could potentially help scientists identify any particular strain of virus that is known to infect humans. The method, commonly known as Compact Aggregation of Targets for Comprehensive Hybridization (CATCH), was first developed in the lab of Pardis Sabeti, a computational biologist, medical geneticist and evolutionary geneticist at Harvard.

The premise of CATCH revolves around the idea of a “molecular bait,” where users design specific probes that can anneal to and detect the genetic composition of any human-related virus strain. One of the greatest advantages of CATCH is its ability to pick up specific strains of viruses in clinical samples even when they are in very low abundance. Additionally, this approach is cost-effective and efficient, which shows promise in controlling any future epidemic outbreaks.

The study, published online in Nature Biotechnology, was pioneered by MIT graduate student Hayden Metsky and postdoctoral researcher Katie Siddle. Furthermore, the CATCH software is now made publicly accessible on GitHub.

Christian Matranga, a co-senior author of the new study, expanded on the impact that CATCH could potentially make in disease prevention and control.

“As genomic sequencing becomes a critical part of disease surveillance, tools like CATCH will help us and others detect outbreaks earlier and generate more data on pathogens that can be shared with the wider scientific and medical research communities,” Matranga said, according to ScienceDaily.

Prior to the development of CATCH, scientists used a variety of other methods to detect viruses in clinical samples. One of the popular methods, known as metagenomic sequencing, involved an examination of the genetic material in a sample. However, this method was not very effective because it often missed the viral DNA amidst other microbes.

Scientists also attempted to enrich molecules in clinical samples in order to detect viruses that were initially in low abundance. In fact, researchers in the Sabeti lab had previously utilized this approach to analyze Ebola and Lassa DNA. Unfortunately, this method was flawed because it could only be used under the circumstance where scientists have prior knowledge of the specific strain of virus they are looking for. 

In a sense, CATCH embodies the strengths of all of its predecessors and mitigates their weaknesses. Not only can it be targeted to pick up any non-abundant virus genome that affects humans, it also allows for holistic screening without compromising efficiency.

“We realized that we could capture viruses, including their known diversity, with fewer probes than we’d used before. To make this an effective tool for surveillance, we then decided to try targeting about 20 viruses at a time, and we eventually scaled up to the 356 viral species known to infect humans,” Metsky said, according to ScienceDaily.

Currently, Sabeti’s team hopes to dive deeper into understanding microbial agents through the computational convenience provided by CATCH. In the long term, the team is also working on using CATCH to improve disease surveillance and prevent another global outbreak like Zika.