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Genome Data Help to Track COVID-19 Superspreading Event

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Boston skyline
Credit: iStock/Chaay_Tee

When it comes to COVID-19, anyone, even without symptoms, can be a “superspreader” capable of unknowingly infecting a large number of people and causing a community outbreak. That’s why it is so important right now to wear masks when out in public and avoid large gatherings, especially those held indoors, where a superspreader can readily infect others with SARS-CoV-2, the virus responsible for COVID-19.

Driving home this point is a new NIH-funded study on the effects of just one superspreader event in the Boston area: an international biotech conference held in February, before the public health risks of COVID-19 had been fully realized [1]. Almost a hundred people were infected. But it didn’t end there.

In the study, the researchers sequenced close to 800 viral genomes, including cases from across the first wave of the epidemic in the Boston area. Using the fact that the viral genome changes in very subtle ways over time, they found that SARS-CoV-2 was actually introduced independently to the region more than 80 times, primarily from Europe and other parts of the United States. But the data also suggest that a single superspreading event at the biotech conference led to the infection of almost 20,000 people in the area, not to mention additional COVID-19 cases in other states and around the world.

The findings, posted on medRxiv as a pre-print, come from Bronwyn MacInnis and Pardis Sabeti at the Broad Institute of MIT and Harvard in Cambridge, MA, and their many close colleagues at Massachusetts General Hospital, the Massachusetts Department of Public Health, and the Boston Health Care for the Homeless Program. The initial focus of MacInnis, Sabeti, and their Broad colleagues has been on developing genome data and tools for surveillance of viruses and other infectious diseases in and viral outbreaks in West Africa, including Lassa fever and Ebola virus disease.

Closer to home, they’d expected to focus their attention on West Nile virus and tick-borne diseases. But, when the COVID-19 outbreak erupted, they were ready to pivot quickly to assist several Centers for Disease Control and Prevention (CDC) and state labs in the northeastern United States to use genomic tools to investigate local outbreaks.

It’s been clear from the beginning of the pandemic that COVID-19 cases often arise in clusters, linked to gatherings in places such as cruise ships, nursing homes, and homeless shelters. But the Broad Institute team and their colleagues realized, it’s difficult to see how extensively a virus spreads from such places into the wider community based on case counts alone.

Contact tracing certainly helps to track community spread of the virus. This surveillance strategy depends on quick, efficient identification of an infected individual. It follows up with the identification of all who’ve recently been in close contact with that person, allowing the contacts to self-quarantine and break the chain of transmission.

But contact tracing has its limitations. It’s not always possible to identify all the people that an infected person has been in recent contact with. Genome data, however, is particularly useful after the fact for connecting those dots to get a big picture view of viral transmission.

Here’s how it works: as SARS-CoV-2 spreads, the virus sometimes picks up a new mutation. Those tiny spelling changes in the viral genome usually have no effect on how the virus causes disease, but they do serve as distinct genomic fingerprints. Using those fingerprints to guide the way, researchers can trace the path the virus took through a community and beyond, identifying connections among cases that would be untrackable otherwise.

With this in mind, MacInnis and Sabeti’s team set out to help Boston’s public health officials understand just how the epidemic escalated so quickly in the Boston area, and just how much the February conference had contributed to community transmission of the virus. They also investigated other case clusters in the area, including within a skilled nursing facility, homeless shelters, and at Massachusetts General Hospital itself, to understand the spread of COVID-19 in these settings.

Based on contact tracing, officials had already connected approximately 90 cases of COVID-19 to the biotech conference, 28 of which were included in the original 772 viral genomes in this dataset. Based on the distinct genomic fingerprint carried by the 28 genomes, the researchers went on to discover that more than one-third of Boston area cases without any known link to the conference could indeed be traced back to the event.

When the researchers considered this proportion to the number of cases recorded in the region during the study, they extrapolated that the superspreader event led to nearly 20,000 cases in the Boston area. In contrast, the genome data show cases linked to another superspreader event that took place within a skilled nursing facility, while devastating to the residents, had much less of an impact on the surrounding community.

The analysis also uncovered some unexpected connections. The dataset showed that SARS-CoV-2 was brought to clients and staff at the Boston Health Care for the Homeless Program at least seven times. Remarkably, two of those introductions also traced back to the biotech conference. Researchers also found infections in Chelsea, Revere, and Everett, which were some of the hardest hit communities in the Boston area, that were connected to the original superspreading event.

There was some reassuring news about how precautions in hospitals are working. The researchers examined cases that were diagnosed among patients at Massachusetts General Hospital, raising concerns that the virus might have spread from one patient to another within the hospital. But the genome data show that those cases, in fact, weren’t part of the same transmission chain. They may have contracted the virus before they were hospitalized. Or it’s possible that staff may have inadvertently brought the virus into the hospital. But there was no patient-to-patient transmission.

Massachusetts is one of the states in which the COVID-19 pandemic had a particularly severe early impact. As such, these results present broadly applicable lessons for other states and urban areas about how the virus spreads. The findings highlight the value of genomic surveillance, along with standard contact tracing, for better understanding of viral transmission in our communities and improved prevention of future outbreaks.

Reference:

[1] Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. Lemieux J. et al. medRxiv. August 25, 2020.

Links:

Coronavirus (COVID-19) (NIH)

Bronwyn MacInnis (Broad Institute of Harvard and MIT, Cambridge, MA)

Sabeti Lab (Broad Institute of Harvard and MIT)

NIH Support: National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of General Medical Sciences


Has an Alternative to Table Sugar Contributed to the C. Diff. Epidemic?

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Ice cream sundae

Thinkstock/piyaphat50

Most of us know how hard it is to resist the creamy sweetness of ice cream. But it might surprise you to learn that, over the past 15 years or so, some makers of ice cream and many other processed foods—from pasta to ground beef products—have changed their recipes to swap out some of the table sugar (sucrose) with a sweetening/texturizing ingredient called trehalose that depresses the freezing point of food. Both sucrose and trehalose are “disaccharides.” Though they have different chemical linkages, both get broken down into glucose in the body. Now, comes word that this switch may be an important piece of a major medical puzzle: why Clostridium difficile (C. diff) has emerged as a leading cause of hospital-acquired infections.

A new study in the journal Nature indicates that trehalose-laden food may have helped fuel the recent epidemic spread of C. diff., which is a microbe that can cause life-threatening gastrointestinal distress, especially in older patients getting antibiotics and antacid medicines [1, 2]. In laboratory experiments, an NIH-funded team found that the two strains of C. diff. most likely to make people sick possess an unusual ability to thrive on trehalose, even at very low levels. And that’s not all: a diet containing trehalose significantly increased the severity of symptoms in a mouse model of C. diff. infection.


Portable System Uses Light to Diagnose Bacterial Infections Faster

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PAD system

Caption: PAD system. Left, four optical testing cubes (blue and white) stacked on the electronic base station (white with initials); right, a smartphone with a special app to receive test results transmitted by the electronic base station.
Credit: Park et al. Sci. Adv. 2016

Every year, hundreds of thousands of Americans acquire potentially life-threatening bacterial infections while in the hospital, nursing home, or other health-care settings [1]. Such infections can be caused by a variety of bacteria, which may respond quite differently to different antibiotics. To match a patient with the most appropriate antibiotic therapy, it’s crucial to determine as quickly as possible what type of bacteria is causing his or her infection. In an effort to improve that process, an NIH-funded team is working to develop a point-of-care system and smartphone app aimed at diagnosing bacterial infections in a faster, more cost-effective manner.

The portable new system, described recently in the journal Science Advances, uses a novel light-based method for detecting telltale genetic sequences from bacteria in bodily fluids, such as blood, urine, or drainage from a skin abscess. Testing takes place within small, optical cubes that, when placed on an electronic base station, deliver test results within a couple of hours via a simple readout sent directly to a smartphone [2]. When the system was tested on clinical samples from a small number of hospitalized patients, researchers found that not only did it diagnose bacterial infections about as accurately and more swiftly than current methods, but it was also cheaper. This new system can potentially also be used to test for the presence of antibiotic-resistant bacteria and contamination of medical devices.