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On December 18, 2019, Wuhan Central Hospital admitted a patient with symptoms common to the winter flu season: a 65-year-old man with fever and pneumonia. Ai Fen, director of the emergency department, oversaw a routine treatment plan, including antibiotics and anti-flu drugs.
Six days later, the patient was still sick, and Ai puzzled, according to news reports and a detailed reconstruction of this period by evolutionary biologist Michael Worobey. The respiratory department decided to try to identify the culprit pathogen by reading their genetic code, a process known as 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 received the results: The man had a new coronavirus that was closely related to the one that caused the SARS outbreak that started 17 years earlier.
The original SARS virus was sequenced five months after the first cases were recorded. This type of traditional sequencing reads the entire genetic code, or genome, of one organism at a time, which must first be carefully isolated from a sample. The researchers employed by Wuhan Central Hospital were able to map the new virus so quickly using a more sophisticated technique called metagenomic sequencing, which reads the genome of each organism in a sample at once – without such time-intensive preparation . If the traditional approach is like finding one book on a shelf and copying it, a metanomic sequence is like grabbing all the books from the shelf and scanning them all at once.
This ability to rapidly read an array of genomes is useful in fields from ecology to cancer treatment. And the COVID-19 Pandemic has pushed some researchers to use metagenomics to try to find and respond to new diseases earlier — before they become epidemics, and perhaps before they even infect people. Some of these experts say that the early spread of COVID-19 in the United States could have been prevented more quickly if the medical community had implemented this technology.
“If metagenomic sequencing was done more regularly, we might know what it was when there were only 20 infections,” said Joe DeRisi, professor of biochemistry and biophysics at the University of California, San Francisco and president of the University. the Chan Zuckerberg Biohub, a non-profit research center.
But while the raw power of metagenomics is clear, there are challenges in using it to squelch potential pandemics. The technique requires intensive computer processing, which makes it more expensive than some others, and requires more expertise to interpret the results. Using the multitude of metagenomics data produced to guide treatment raises quandaries about medical decision-making when, for example, it is unclear whether a particular pathogen is causing a particular illness.
Still, advocates say it’s worth the costs. “Metagenomics plays a critical role in pandemic preparedness, by looking for what we don’t know,” said Jessica Manning, an infectious disease researcher at the National Institute of Allergy and Infectious Diseases.
The rise of metagenomics over the past few 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, on long chains of nucleotides, the letters of the genetic library. They then cut the long molecules into shorter chunks and read the order of the letters in each chunk. Finally, they combine the shortest “reads” to reconstruct the entire genome.
Over the past 40 years, innovation, especially automation, has greatly improved every part of this process. The Human Genome Project, launched in 1990, took more than ten years of coordinated work between 20 research groups and cost about a billion dollars. Today the human genome can be sequenced more accurately, for less than one millionth of the cost, by one scientist in one day.
As technology improved, researchers began trying to sequence many organisms at once, a complex task that requires figuring out how millions of short reads fit together to make any number of genomes. Researchers eventually wrote sophisticated software that can solve the sequences using networks of powerful computers.
