Scientists Are Growing an Edible Cholera Vaccine in Rice
The world's attention has been focused on the coronavirus crisis but Yemen, Bangladesh and many others countries in Asia and Africa are also in the grips of another pandemic: cholera. The current cholera pandemic first emerged in the 1970s and has devastated many communities in low-income countries. Each year, cholera is responsible for an estimated 1.3 million to 4 million cases and 21,000 to 143,000 deaths worldwide.
Immunologist Hiroshi Kiyono and his team at the University of Tokyo hope they can be part of the solution: They're making a cholera vaccine out of rice.
"It is much less expensive than a traditional vaccine, by a long shot."
Cholera is caused by eating food or drinking water that's contaminated by the feces of a person infected with the cholera bacteria, Vibrio cholerae. The bacteria produces the cholera toxin in the intestines, leading to vomiting, diarrhea and severe dehydration. Cholera can kill within hours of infection if it if's not treated quickly.
Current cholera vaccines are mainly oral. The most common oral are given in two doses and are made out of animal or insect cells that are infected with killed or weakened cholera bacteria. Dukoral also includes cells infected with CTB, a non-harmful part of the cholera toxin. Scientists grow cells containing the cholera bacteria and the CTB in bioreactors, large tanks in which conditions can be carefully controlled.
These cholera vaccines offer moderate protection but it wears off relatively quickly. Cold storage can also be an issue. The most common oral vaccines can be stored at room temperature but only for 14 days.
"Current vaccines confer around 60% efficacy over five years post-vaccination," says Lucy Breakwell, who leads the U.S. Centers for Disease Control and Prevention's cholera work within Global Immunization Division. Given the limited protection, refrigeration issue, and the fact that current oral vaccines require two disease, delivery of cholera vaccines in a campaign or emergency setting can be challenging. "There is a need to develop and test new vaccines to improve public health response to cholera outbreaks."
A New Kind of Vaccine
Kiyono and scientists at Tokyo University are creating a new, plant-based cholera vaccine dubbed MucoRice-CTB. The researchers genetically modify rice so that it contains CTB, a non-harmful part of the cholera toxin. The rice is crushed into a powder, mixed with saline solution and then drunk. The digestive tract is lined with mucosal membranes which contain the mucosal immune system. The mucosal immune system gets trained to recognize the cholera toxin as the rice passes through the intestines.
The cholera toxin has two main parts: the A subunit, which is harmful, and the B subunit, also known as CTB, which is nontoxic but allows the cholera bacteria to attach to gut cells. By inducing CTB-specific antibodies, "we might be able to block the binding of the vaccine toxin to gut cells, leading to the prevention of the toxin causing diarrhea," Kiyono says.
Kiyono studies the immune responses that occur at mucosal membranes across the body. He chose to focus on cholera because he wanted to replicate the way traditional vaccines work to get mucosal membranes in the digestive tract to produce an immune response. The difference is that his team is creating a food-based vaccine to induce this immune response. They are also solely focusing on getting the vaccine to induce antibodies for the cholera toxin. Since the cholera toxin is responsible for bacteria sticking to gut cells, the hope is that they can stop this process by producing antibodies for the cholera toxin. Current cholera vaccines target the cholera bacteria or both the bacteria and the toxin.
David Pascual, an expert in infectious diseases and immunology at the University of Florida, thinks that the MucoRice vaccine has huge promise. "I truly believe that the development of a food-based vaccine can be effective. CTB has a natural affinity for sampling cells in the gut to adhere, be processed, and then stimulate our immune system, he says. "In addition to vaccinating the gut, MucoRice has the potential to touch other mucosal surfaces in the mouth, which can help generate an immune response locally in the mouth and distally in the gut."
Cost Effectiveness
Kiyono says the MucoRice vaccine is much cheaper to produce than a traditional vaccine. Current vaccines need expensive bioreactors to grow cell cultures under very controlled, sterile conditions. This makes them expensive to manufacture, as different types of cell cultures need to be grown in separate buildings to avoid any chance of contamination. MucoRice doesn't require such an expensive manufacturing process because the rice plants themselves act as bioreactors.
The MucoRice vaccine also doesn't require the high cost of cold storage. It can be stored at room temperature for up to three years unlike traditional vaccines. "Plant-based vaccine development platforms present an exciting tool to reduce vaccine manufacturing costs, expand vaccine shelf life, and remove refrigeration requirements, all of which are factors that can limit vaccine supply and accessibility," Breakwell says.
Kathleen Hefferon, a microbiologist at Cornell University agrees. "It is much less expensive than a traditional vaccine, by a long shot," she says. "The fact that it is made in rice means the vaccine can be stored for long periods on the shelf, without losing its activity."
A plant-based vaccine may even be able to address vaccine hesitancy, which has become a growing problem in recent years. Hefferon suggests that "using well-known food plants may serve to reduce the anxiety of some vaccine hesitant people."
Challenges of Plant Vaccines
Despite their advantages, no plant-based vaccines have been commercialized for human use. There are a number of reasons for this, ranging from the potential for too much variation in plants to the lack of facilities large enough to grow crops that comply with good manufacturing practices. Several plant vaccines for diseases like HIV and COVID-19 are in development, but they're still in early stages.
In developing the MucoRice vaccine, scientists at the University of Tokyo have tried to overcome some of the problems with plant vaccines. They've created a closed facility where they can grow rice plants directly in nutrient-rich water rather than soil. This ensures they can grow crops all year round in a space that satisfies regulations. There's also less chance for variation since the environment is tightly controlled.
Clinical Trials and Beyond
After successfully growing rice plants containing the vaccine, the team carried out their first clinical trial. It was completed early this year. Thirty participants received a placebo and 30 received the vaccine. They were all Japanese men between the ages of 20 and 40 years old. 60 percent produced antibodies against the cholera toxin with no side effects. It was a promising result. However, there are still some issues Kiyono's team need to address.
The vaccine may not provide enough protection on its own. The antigen in any vaccine is the substance it contains to induce an immune response. For the MucoRice vaccine, the antigen is not the cholera bacteria itself but the cholera toxin the bacteria produces.
"The development of the antigen in rice is innovative," says David Sack, a professor at John Hopkins University and expert in cholera vaccine development. "But antibodies against only the toxin have not been very protective. The major protective antigen is thought to be the LPS." LPS, or lipopolysaccharide, is a component of the outer wall of the cholera bacteria that plays an important role in eliciting an immune response.
The Japanese team is considering getting the rice to also express the O antigen, a core part of the LPS. Further investigation and clinical trials will look into improving the vaccine's efficacy.
Beyond cholera, Kiyono hopes that the vaccine platform could one day be used to make cost-effective vaccines for other pathogens, such as norovirus or coronavirus.
"We believe the MucoRice system may become a new generation of vaccine production, storage, and delivery system."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.