This article is part of Harvard Medical School continuous coverage of COVID-19.
Omicron was the first reported in South Africa on November 24, 2021, and within days it was making the rounds in the United States, peaking in SARS-CoV-2 case numbers as it infiltrated every school, restaurant, and family gathering. But when exactly did omicron deprive the delta variant from becoming dominant? And how quickly did it take over?
These are the questions that a team led by Harvard Medical School researchers wanted to study in real time using a new, faster, variant-determining technique to analyze SARS-CoV-2 samples from screening programs at universities in the region.
their analysis, published on May 25 in Clinical infectious diseasesshows that omicron arrived in Massachusetts earlier than experts predicted, taking over within days — information the study authors immediately presented to local hospitals and public health departments to prepare for a wave of COVID-19 cases.
“Omicron’s rise to global dominance was extremely rapid, and so was his rise here in Boston. It happened so fast that we would have missed many cases without these college screening programs, but they allowed us to document the acquisition,” said Bill Hanagean associate professor of epidemiology at Harvard TH Chan School of Public Health and an author on the paper
Researchers from Boston University, Harvard University and Northeastern University teamed up to analyze SARS-CoV-2 samples from their asymptomatic screening programs. They found that just nine days after arriving in a community, omicron was responsible for more than 90 percent of SARS-CoV-2 infections. In addition, 10 percent of cases in university communities were from omicron up to 10 days before omicron reached the 10 percent mark in Massachusetts.
Omicron beat the delta variant on universities one to two weeks earlier than in the state as a whole. In addition, patients infected with omicron had a lower viral load than those infected with delta, indicating that the increased transmission of omicron was due to features of the variant itself, rather than the presence of more virus.
The research has not only helped raise the alarm about omicron, but suggests that university campuses could provide valuable monitoring centers to establish surveillance programs for the early detection of incipient infectious disease outbreaks.
“Universities are a bit of a melting pot that reflects the surrounding community, so they can be a good place to pick things up as they arrive,” says senior author Michael Springerassociate professor of systems biology at the Blavatnik Institute at HMS.
A quick takeover
In early December, the researchers began to see an increase in COVID-19 cases in screening programs at universities in the Boston area, coinciding with a rise in the number of cases in Massachusetts as a whole — and by mid-December, universities were flooded with positive cases.
“We had all seen omicron spread around the world and come to Massachusetts,” Springer recalls, adding that “the number of positive cases we had in the testing lab was pretty shocking,” as it jumped exponentially from what it was just a few weeks before.
The standard technique for determining whether a SARS-CoV-2 sample is one variant or the other involves sequencing the entire viral genome — a process that often takes seven to 10 days. When omicron arrived in Massachusetts, many labs performing genetic sequencing for SARS-CoV-2 were behind on samples, leaving them a week or two behind in understanding the true prevalence of omicron.
With the clock ticking and the number of COVID-19 cases rising, the researchers knew they needed a more efficient way to distinguish omicron from delta, which had accounted for more than 99 percent of cases until then. They used a variant-determining technique recently developed by Nicole Welch, a PhD candidate at HMS and the Broad Institute of MIT and Harvard and an author on the paper. The technique combined PCR gene amplification and CRISPR gene editing technologies to gain insight into the specific genetic mutations that distinguish delta from omicron.
“Instead of sequencing the whole virus, we asked if there are defining mutations at certain sites that together served as markers of the viral variants,” said first author Brittany Petros, an MD-PhD candidate at HMS and the Broad Institute.
The team found that omicron can be distinguished from delta within hours based on just three nucleic acid differences between the variants. In addition, the researchers used GISAID, a database of SARS-CoV-2 sequences from around the world, to confirm that these three nucleotide changes differentiated omicn from delta in 99 percent of the cases.
“That really allowed us to say yes. The shortcut method is sensitive and specific to the variants we want to distinguish,” Petros said.
Using this technique, the researchers determined that omicron delta completely caught up within a period of nine to 12 days in university communities. They also found that omicron was present and became dominant on local college campuses about one to two weeks earlier than in Massachusetts as a whole — and it spread rapidly despite patients with omicron having a lower viral load than those with delta.
“Studying these things is very important to understand how transmissible new variants are, and how much of that is due to the ability to evade immunity, which could mean we need to update vaccines,” Hanage said.
Spread the word
The researchers shared their data with hospitals and public health departments in real time, prompting some hospitals to suspend elective surgeries in anticipation of more people being hospitalized with COVID-19.
“We realized that Omicron wasn’t coming, Omicron was already there, and we had to let everyone know,” Springer said.
“Showing our data to people in hospitals and public health departments as we were generating it allowed us to respond quickly to public health,” Petros added.
Massachusetts public health departments also began to implement this variant-determining technique to more quickly analyze SARS-CoV-2 samples.
“The state took over the sample processing pipeline and worked at astonishing speed to bring it out into the open,” Springer said.
Petros noted that the same platform can be easily adapted to differentiate between new SARS-CoV-2 variants, which will be important as the COVID-19 pandemic continues and the virus continues to evolve.
Springer and Petros say that several factors made universities the ideal place to profile the dynamics of ommicron. The schools had extensive screening programs where everyone was tested once or twice a week, rather than just when they had symptoms and sought clinical help. In addition, university communities usually contain many people from the surrounding area. So all those tests of all those different people resulted in a large, varied dataset that could be easily studied.
People are often not hospitalized for COVID-19 until days or even weeks after being infected with SARS-CoV-2, but university samples, based on regular testing of everyone regardless of symptoms, recorded ommicron as soon as it was diagnosed. arrived.
“We’re talking about omicron that completely outcompeted delta in nine days — turning the full cycle from less than one person to get infected and be hospitalized for COVID-19,” Petros said.
Springer added, “There’s actually a pretty big delay between when something hits and spreads and becomes problematic, and when it gets to the hospitals.”
On the logistics side, universities had a lot of SARS-CoV-2 samples and a lot of researchers and technology. “Universities are centers of innovation. We have new and useful technologies and everyone is open to collaboration, so it came down to how we could help figure out what was going on,” Springer said.
Many universities are now discontinuing their SARS-CoV-2 screening programs, but Springer and Petros agree that similar programs could be a valuable resource in the future.
“In the future, we need to think about how we can stop future pandemics and how we can better fight common, endemic, communicable diseases. Monitoring certain communities can be helpful for this because they give us an early response,” Springer said.
“This points to universities as the place to monitor emerging infectious diseases and future outbreaks,” Petros added. such supervisionshe said, could shed light on how an emerging disease spreads and how different pathogenic lineages might compete with each other.
Now Springer’s lab is developing large-scale diagnostic panels that will make it cheaper and easier to analyze SARS-CoV-2 and other pathogens. Petros is investigating whether it would be possible to alter technologies like those used in the study to sequence SARS-CoV-2 samples taken with rapid at-home antigen testing. Such tests are likely to become even more important in understanding circulating SARS-CoV-2 strains or lines, she noted, as asymptomatic screening programs were discontinued.
Springer and Petros both stressed that the research could not have been accomplished without substantial collaboration and rapid data sharing between researchers and institutions — something they hope will continue in the future.
“Any of the studies from these schools alone wouldn’t have been as strong as having data from multiple different schools put together where you can see the same responses and the same trajectories,” Springer said. “We’re trying to solve a real problem, so we’ll have to work together.”
The study was supported by the National Institutes of Health (T32GM007753; R35GM141821; K23 grant AI152930-01A1; 5R01GM120122), Boston University, the Massachusetts Consortium on Pathogen Readiness, the China Evergrande Group, and the US Centers for Disease Control and Prevention (75D30121C10501 ; 75D30120C09605).
Co-authors of the paper include Eric Kolaczyk, Judy Platt, Lena Landaverde, Lynn Doucette-Stamm, and Catherine Klapperich of Boston University; Sabrina Dobbins, Tien Nguyen and Bronwyn MacInnis of the Broad Institute; Michael Cleary and Michele Hope of Harvard University; Jacquelyn Turcinovic of Boston University and the National Emerging Infectious Diseases Laboratories; Matthew Bauer of HMS and the Broad Institute; Seyho Yune and Jared Auclair of Northeastern University; Mitch Gore, Daniel Tsang, Erik Wendlandt, and Scott Rose of Integrated DNA Technologies, Inc.; Karen Jacobson and Tara Bouton of Boston University School of Medicine and Boston Medical Center; Parvathy Nair of the Howard Hughes Medical Institute; Laura White of Boston University School of Public Health; Bradford Taylor of the Harvard T.H. Chan School of Public Health; Davidson Hamer of the National Emerging Infectious Diseases Laboratories, Boston University, Boston University School of Public Health and Boston Medical Center; Pardis Sabeti of the Broad Institute, Howard Hughes Medical Institute, Harvard University, Harvard TH Chan School of Public Health, Massachusetts General Hospital and MassCPR; and John Connor of Boston University, Boston University School of Medicine, and the National Emerging Infectious Diseases Laboratories.