Don't Panic Over Waning Antibodies. Here's Why.
Since the Delta variant became predominant in the United States on July 7, both scientists and the media alike have been full of mixed messages ("breakthrough infections rare"; "breakthrough infections common"; "vaccines still work"; "vaccines losing their effectiveness") but – if we remember our infectious diseases history- one thing remains clear: immunity is the only way to get through a pandemic.
What Happened in the Past
The 1918 influenza pandemic was far the deadliest respiratory virus pandemic recorded in recent human history with over 50 million deaths (maybe even 100 million deaths, or 3% of the world's population) worldwide. Although they used some of the same measures we are using now (masks, distancing, event closures, as neither testing nor a vaccine existed back then), the deaths slowed only after enough of the population had either acquired immunity through natural infection or died. Indeed, the first influenza vaccine was not developed until 1942, more than 20 years later. As judged by the amount of suffering and death from 1918 influenza (and the deadly Delta surge in India in spring 2021), natural immunity is obviously a terrible way to get through a pandemic.
Similarly, measles was a highly transmissible respiratory virus that led to high levels of immunity among adults who were invariably exposed as children. However, measles led to deaths each year among the nonimmune until a vaccine was developed in 1963, largely restricting current measles outbreaks in the U.S. now to populations who decline to vaccinate. Smallpox also led to high levels of immunity through natural infection, which was often fatal. That's why unleashing smallpox on a largely nonimmune population in the New World was so deadly. Only an effective vaccine – and its administration worldwide, including among populations who declined smallpox vaccine at first via mandates – could control and then eventually eradicate smallpox from Earth.
Fully vaccinated people are already now able to generate some antibodies against all the variants we know of to date, thanks to their bank of memory B cells.
The Delta variant is extremely transmissible, making it unlikely we will ever eliminate or eradicate SARS-CoV-2. Even Australia, which had tried to maintain a COVID-zero nation with masks, distancing, lockdowns, testing and contact tracing before and during the vaccines, ended a strategy aimed at eliminating COVID-19 this week. But, luckily, since highly effective and safe vaccines were developed for COVID-19 less than a year after its advent on a nonimmune population and since vaccines are retaining their effectiveness against severe disease, we have a safe way out of the misery of this pandemic: more and more immunity. "Defanging" SARS-CoV-2 and stripping it of its ability to cause severe disease through immunity will relegate it to the fate of the other four circulating cold-causing coronaviruses, an inconvenience but not a world-stopper.
Immunity Is More Than Antibodies
When we say immunity, we have to be clear that we are talking about cellular immunity and immune memory, not only antibodies. This is a key point. Neutralizing antibodies, which prevent the virus from entering our cells, are generated by the vaccines. But those antibodies will necessarily wane over time since we cannot keep antibodies from every infection and vaccine we have ever seen in the bloodstream (or our blood would be thick as paste!). Vaccines with shorter intervals between doses (like Pfizer vaccines given 3 weeks apart) are likely to have their antibodies wane sooner than vaccines with longer intervals between doses (like Moderna), making mild symptomatic breakthroughs less likely with the Moderna vaccine than the Pfizer during our Delta surge, as a recent Mayo Clinic study showed.
Luckily, the vaccines generate B cells that get relegated to our memory banks and these memory B cells are able to produce high levels of antibodies to fight the virus if they see it again. Amazingly, these memory B cells will actually produce antibodies adapted against the COVID variants if they see a variant in the future, rather than the original antibodies directed against the ancestral strain. This is because memory B cells serve as a blueprint to make antibodies, like the blueprint of a house. If a house needs an extra column (or antibodies need to evolve to work against variants), the blueprint will oblige just as memory B cells will!
One problem with giving a 3rd dose of our current vaccines is that those antibodies won't be adapted towards the variants. Fully vaccinated people are already now able to generate some antibodies against all the variants we know of to date, thanks to their bank of memory B cells. In other words, no variant has evolved to date that completely evades our vaccines. Memory B cells, once generated by either natural infection or vaccination, should be long-lasting.
If memory B cells are formed by a vaccine, they should be as long-lasting as those from natural infection per various human studies. A 2008 Nature study found that survivors of the 1918 influenza pandemic were able to produce antibodies from memory B cells when exposed to the same influenza strain nine decades later. Of note, mild infections (such as the common cold from the cold-causing coronaviruses called 229E, NL63, OC43, and HKU1) may not reliably produce memory B cell immunity like more severe infections caused by SARS-CoV-2.
Right about now, you may be worrying about a super-variant that might yet emerge to evade all our hard-won immune responses. But most immunologists do not think this is very realistic because of T cells. How are T cells different from B Cells? While B cells are like the memory banks to produce antibodies when needed (helped by T cells), T cells will specifically amplify in response to a piece of the virus and help recruit cells to attack the pathogen directly. We likely have T cells to thank for the vaccine's incredible durability in protecting us against severe disease. Data from La Jolla Immunology Institute and UCSF show that the T cell response from the Pfizer vaccine is strong across all the variants.
Think of your spike protein as being comprised of 100 houses with a T cell there to cover each house (to protect you against severe disease). The variants have around 13 mutations along the spike protein so 13 of those T cells won't work, but there are over 80 T cells remaining to protect your "houses" or your body against severe disease.
Although these are theoretical numbers and we don't know exactly the number of T cell antigens (or "epitopes") across the spike protein, one review showed 1400 across the whole virus, with many of those in the spike protein. Another study showed that there were 87 epitopes across the spike protein to which T cells respond, and mutations in one of the variants (beta) took those down to 75. The virus cannot mutate indefinitely in its spike protein and still retain function. This is why it is unlikely we will get a variant that will evade the in-breadth, robust response of our T cells.
Where We Go From Here
So, what does this mean for getting through this pandemic? Immunity and more immunity. For those of us who are vaccinated, if we get exposed to the Delta variant, it will boost our immune response although the memory B cells might take 3-5 days to make new antibodies, which can leave us susceptible to a mild breakthrough infection. That's part of the reason the CDC put back masks for the vaccinated in late July. For those who are unvaccinated, immunity will be gained from Delta but often through terrible ways like severe disease.
The way for the U.S. to determine the need for 3rd shots among those who are not obviously immunocompromised, given the amazing immune memory generated by the vaccines among immunocompetent individuals, is to analyze the cases of the ~6000 individuals who have had severe breakthrough infections among the 171 million Americans fully vaccinated. Define their co-morbidities and age ranges, and boost those susceptible to severe infections (examples could include older people, those with co-morbidities, health care workers, and residents of long-term care facilities). This is an approach likely to be taken by the CDC's Advisory Committee on Immunization Practices.
If immunity is the only way to get through the pandemic and if variants are caused mostly by large populations being unvaccinated, there is not only a moral and ethical imperative but a practical imperative to vaccinate the world in order to keep us all safe. Immunocompetent Americans can boost their antibodies, which may enhance their ability to avoid mild breakthrough infections, but the initial shots still work well against the most important outcomes: hospitalizations and deaths.
There has been no randomized, controlled trial to assess whether three shots vs. two shots meaningfully improve those outcomes. While we ought to trust immune memory to get the immunocompetent in the United States through, we can hasten the end of this pandemic by providing surplus vaccines to poor countries to combat severe disease. Doing so would not only revitalize the role of the U.S. as a global health leader – it would save countless lives.
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.