How Will the New Strains of COVID-19 Affect Our Vaccination Plans?
When the world's first Covid-19 vaccine received regulatory approval in November, it appeared that the end of the pandemic might be near. As one by one, the Pfizer/BioNTech, Moderna, AstraZeneca, and Sputnik V vaccines reported successful Phase III results, the prospect of life without lockdowns and restrictions seemed a tantalizing possibility.
But for scientists with many years' worth of experience in studying how viruses adapt over time, it remained clear that the fight against the SARS-CoV-2 virus was far from over. "The more virus circulates, the more it is likely that mutations occur," said Professor Beate Kampmann, director of the Vaccine Centre at the London School of Hygiene & Tropical Medicine. "It is inevitable that new variants will emerge."
Since the start of the pandemic, dozens of new variants of SARS-CoV-2 – containing different mutations in the viral genome sequence - have appeared as it copies itself while spreading through the human population. The majority of these mutations are inconsequential, but in recent months, some mutations have emerged in the receptor binding domain of the virus's spike protein, increasing how tightly it binds to human cells. These mutations appear to make some new strains up to 70 percent more transmissible, though estimates vary and more lab experiments are needed. Such new strains include the B.1.1.7 variant - currently the dominant strain in the UK – and the 501Y.V2 variant, which was first found in South Africa.
"I'm quite optimistic that even with these mutations, immunity is not going to suddenly fail on us."
Because so many more people are becoming infected with the SARS-CoV-2 virus as a result, vaccinologists point out that these new strains will prolong the pandemic.
"It may take longer to reach vaccine-induced herd immunity," says Deborah Fuller, professor of microbiology at the University of Washington School of Medicine. "With a more transmissible variant taking over, an even larger percentage of the population will need to get vaccinated before we can shut this pandemic down."
That is, of course, as long as the vaccinations are still highly protective. The South African variant, in particular, contains a mutation called E484K that is raising alarms among scientists. Emerging evidence indicates that this mutation allows the virus to escape from some people's immune responses, and thus could potentially weaken the effectiveness of current vaccines.
What We Know So Far
Over the past few weeks, manufacturers of the approved Covid-19 vaccines have been racing to conduct experiments, assessing whether their jabs still work well against the new variants. This process involves taking blood samples from people who have already been vaccinated and assessing whether the antibodies generated by those people can neutralize the new strains in a test tube.
Pfizer has just released results from the first of these studies, declaring that their vaccine was found to still be effective at neutralizing strains of the virus containing the N501Y mutation of the spike protein, one of the mutations present within both the UK and South African variants.
However, the study did not look at the full set of mutations contained within either of these variants. Earlier this week, academics at the Fred Hutchinson Cancer Research Center in Seattle suggested that the E484K spike protein mutation could be most problematic, publishing a study which showed that the efficacy of neutralizing antibodies against this region dropped by more than ten-fold because of the mutation.
Thankfully, this development is not expected to make vaccines useless. One of the Fred Hutch researchers, Jesse Bloom, told STAT News that he did not expect this mutation to seriously reduce vaccine efficacy, and that more harmful mutations would need to accrue over time to pose a very significant threat to vaccinations.
"I'm quite optimistic that even with these mutations, immunity is not going to suddenly fail on us," Bloom told STAT. "It might be gradually eroded, but it's not going to fail on us, at least in the short term."
While further vaccine efficacy data will emerge in the coming weeks, other vaccinologists are keen to stress this same point: At most, there will be a marginal drop in efficacy against the new variants.
"Each vaccine induces what we call polyclonal antibodies targeting multiple parts of the spike protein," said Fuller. "So if one antibody target mutates, there are other antibody targets on the spike protein that could still neutralize the virus. The vaccine platforms also induce T-cell responses that could provide a second line of defense. If some virus gets past antibodies, T-cell responses can find and eliminate infected cells before the virus does too much damage."
She estimates that if vaccine efficacy decreases, for example from 95% to 85%, against one of the new variants, the main implications will be that some individuals who might otherwise have become severely ill, may still experience mild or moderate symptoms from an infection -- but crucially, they will not end up in intensive care.
"Plug and Play" Vaccine Platforms
One of the advantages of the technologies which have been pioneered to create the Covid-19 vaccines is that they are relatively straightforward to update with a new viral sequence. The mRNA technology used in the Pfizer/BioNTech and Moderna vaccines, and the adenovirus vectors used in the Astra Zeneca and Sputnik V vaccines, are known as 'plug and play' platforms, meaning that a new form of the vaccine can be rapidly generated against any emerging variant.
"With a rapid pipeline for manufacture established, these new vaccine technologies could enable production and distribution within 1-3 months of a new variant emerging."
While the technology for the seasonal influenza vaccines is relatively inefficient, requiring scientists to grow and cultivate the new strain in the lab before vaccines can be produced - a process that takes nine months - mRNA and adenovirus-based vaccines can be updated within a matter of weeks. According to BioNTech CEO Uğur Şahin, a new version of their vaccine could be produced in six weeks.
"With a rapid pipeline for manufacture established, these new vaccine technologies could enable production and distribution within 1-3 months of a new variant emerging," says Fuller.
Fuller predicts that more new variants of the virus are almost certain to emerge within the coming months and years, potentially requiring the public to receive booster shots. This means there is one key advantage the mRNA-based vaccines have over the adenovirus technologies. mRNA vaccines only express the spike protein, while the AstraZeneca and Sputnik V vaccines use adenoviruses - common viruses most of us are exposed to - as a delivery mechanism for genes from the SARS-CoV-2 virus.
"For the adenovirus vaccines, our bodies make immune responses against both SARS-CoV-2 and the adenovirus backbone of the vaccine," says Fuller. "That means if you update the adenovirus-based vaccine with the new variant and then try to boost people, they may respond less well to the new vaccine, because they already have antibodies against the adenovirus that could block the vaccine from working. This makes mRNA vaccines more amenable to repeated use."
Regulatory Unknowns
One of the key questions remains whether regulators would require new versions of the vaccine to go through clinical trials, a hurdle which would slow down the response to emerging strains, or whether the seasonal influenza paradigm will be followed, whereby a new form of the vaccine can be released without further clinical testing.
Regulators are currently remaining tight-lipped on which process they will choose to follow, until there is more information on how vaccines respond against the new variants. "Only when such information becomes available can we start the scientific evaluation of what data would be needed to support such a change and assess what regulatory procedure would be required for that," said Rebecca Harding, communications officer for the European Medicines Agency.
The Food and Drug Administration (FDA) did not respond to requests for comment before press time.
While vaccinologists feel it is unlikely that a new complete Phase III trial would be required, some believe that because these are new technologies, regulators may well demand further safety data before approving an updated version of the vaccine.
"I would hope if we ever have to update the current vaccines, regulatory authorities will treat it like influenza," said Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the Pfizer/BioNTech and Moderna vaccines. "I would guess, at worst, they may want a new Phase 1 or 1 and 2 clinical trials."
Others suggest that rather than new trials, some bridging experiments may suffice to demonstrate that the levels of neutralizing antibodies induced by the new form of the vaccine are comparable to the previous one. "Vaccines have previously been licensed by this kind of immunogenicity data only, for example meningitis vaccines," said Kampmann.
While further mutations and strains of SARS-CoV-2 are inevitable, some scientists are concerned that the vaccine rollout strategy being employed in some countries -- of distributing a first shot to as many people as possible, and potentially delaying second shots as a result -- could encourage more new variants to emerge. Just today, the Biden administration announced its intention to release nearly all vaccine doses on hand right away, without keeping a reserve for second shots. This plan risks relying on vaccine manufacturing to ramp up quickly to keep pace if people are to receive their second shots at the right intervals.
"I am not very happy about this change as it could lead to a large number of people out there with partial immunity and this could select new mutations, and escalate the potential problem of vaccine escape."
The Biden administration's shift appears to conflict with the FDA's recent position that second doses should be given on a strict schedule, without any departure from the three- and four-week intervals established in clinical trials. Two top FDA officials said in a statement that changing the dosing schedule "is premature and not rooted solidly in the available evidence. Without appropriate data supporting such changes in vaccine administration, we run a significant risk of placing public health at risk, undermining the historic vaccination efforts to protect the population from COVID-19."
"I understand the argument of trying to get at least partial protection to as many people as possible, but I am concerned about the increased interval between the doses that is now being proposed," said Kampmann. "I am not very happy about this change as it could lead to a large number of people out there with partial immunity and this could select new mutations, and escalate the potential problem of vaccine escape."
But it's worth emphasizing that the virus is unlikely for now to accumulate enough harmful mutations to render the current vaccines completely ineffective.
"It will be very hard for the virus to evolve to completely evade the antibody responses the vaccines induce," said Fuller. "The parts of the virus that are targeted by vaccine-induced antibodies are essential for the virus to infect our cells. If the virus tries to mutate these parts to evade antibodies, then it could compromise its own fitness or even abort its ability to infect. To be sure, the virus is developing these mutations, but we just don't see these variants emerge because they die out."
Autonomous, indoor farming gives a boost to crops
The glass-encased cabinet looks like a display meant to hold reasonably priced watches, or drugstore beauty creams shipped from France. But instead of this stagnant merchandise, each of its five shelves is overgrown with leaves — moss-soft pea sprouts, spikes of Lolla rosa lettuces, pale bok choy, dark kale, purple basil or red-veined sorrel or green wisps of dill. The glass structure isn’t a cabinet, but rather a “micro farm.”
The gadget is on display at the Richmond, Virginia headquarters of Babylon Micro-Farms, a company that aims to make indoor farming in the U.S. more accessible and sustainable. Babylon’s soilless hydroponic growing system, which feeds plants via nutrient-enriched water, allows chefs on cruise ships, cafeterias and elsewhere to provide home-grown produce to patrons, just seconds after it’s harvested. Currently, there are over 200 functioning systems, either sold or leased to customers, and more of them are on the way.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Scientists make progress with growing organs for transplants
Story by Big Think
For over a century, scientists have dreamed of growing human organs sans humans. This technology could put an end to the scarcity of organs for transplants. But that’s just the tip of the iceberg. The capability to grow fully functional organs would revolutionize research. For example, scientists could observe mysterious biological processes, such as how human cells and organs develop a disease and respond (or fail to respond) to medication without involving human subjects.
Recently, a team of researchers from the University of Cambridge has laid the foundations not just for growing functional organs but functional synthetic embryos capable of developing a beating heart, gut, and brain. Their report was published in Nature.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.