On December 18, 2019, Wuhan Central Hospital admitted a patient with common winter flu season symptoms: a 65-year-old man with fever and pneumonia. Ai Fen, director of the emergency department, oversaw a typical treatment plan, including antibiotics and flu medication.
Six days later, the patient was still ill, and Ai was puzzled, according to news reports and a detailed reconstruction of that time period by evolutionary biologist Michael Worobey. The respiratory service decided to try to identify the culprit pathogen by reading its genetic code, a process called sequencing. They flushed part of the patient’s lungs with saline, collected the fluid, and sent the sample to a biotech company. On December 27, the hospital got the results: the man had contracted a novel coronavirus closely related to the one that caused the SARS outbreak that began 17 years earlier.
The original SARS virus was sequenced five months after the first cases were recorded. This type of traditional sequencing reads the complete genetic code, or genome, of a single organism at a time, which must first be carefully isolated from a sample. Researchers hired by Wuhan Central Hospital were able to map the new virus so quickly using a more demanding technique called metagenomic sequencing, which reads the genomes of each organism in one sample at a time – without such lengthy preparation. While the traditional approach is to locate a single book on a shelf and copy it, metagenomic sequencing is to grab all the books on the shelf and scan them all at once.
This ability to quickly read a range of genomes has proven useful in fields ranging from ecology to cancer treatment. And the COVID-19 pandemic has prompted some researchers to use metagenomics to try to detect and respond to new diseases earlier — before they become epidemics, and potentially before they even infect people. folks. Some of these experts say the early spread of COVID-19 in the United States could have been stopped more quickly if the medical community had applied this technology.
“If metagenomic sequencing had been done more systematically, we might have known what it was when there were only 20 infections,” in the US, said biochemistry professor Joe DeRisi and biophysics at the University of California, San Francisco and president of the Chan Zuckerberg Biohub, a nonprofit research center.
But while the raw power of metagenomics is clear, using it to quell potential pandemics presents challenges. The technique requires intensive computer processing, which makes it more expensive than some others, and requires greater expertise to interpret the results. Using the wealth of data produced by metagenomics to guide treatment also raises dilemmas for medical decision-making when, for example, it is unclear whether a certain pathogen causes a certain disease.
Still, advocates say the costs are worth it. “Metagenomics plays a critical role in pandemic preparedness, looking for the things we don’t know how to look for,” said Jessica Manning, infectious disease researcher at the National Institute of Allergy and Infectious Diseases.
The rise of metagenomics over the past two decades is due in part to advances in genome sequencing. To read the contents of the genome, researchers first isolate the molecules that store genetic information, DNA and RNA, which are long strings of nucleotides, the letters of the genetic library. Then they cut the long molecules into shorter pieces and read the order of the letters in each piece. Finally, they combine the shorter “reads” to reconstruct the complete genome.
Over the past 40 years, innovation, especially automation, has dramatically improved every part of this process. The Human Genome Project, launched in 1990, took more than a decade of coordinated work between 20 research groups and cost around $1 billion. Today, a human genome can be sequenced more accurately, for less than one millionth the cost, by a scientist in a day.
As technology improved, researchers began trying to sequence many organisms at once, a complex task that requires understanding how millions of short reads fit together to create any number of genomes. . Eventually, the researchers wrote sophisticated software that could sort the sequences using powerful computer networks.