The Inside Story of Two Young Scientists Who Helped Make Moderna's Covid Vaccine Possible
In early 2020, Moderna Inc. was a barely-known biotechnology company with an unproven approach. It wanted to produce messenger RNA molecules to carry instructions into the body, teaching it to ward off disease. Experts doubted the Boston-based company would meet success.
Today, Moderna is a pharmaceutical power thanks to its success developing an effective Covid-19 vaccine. The company is worth $124 billion, more than giants including GlaxoSmithKline and Sanofi, and evidence has emerged that Moderna's shots are more protective than those produced by Pfizer-BioNTech and other vaccine makers. Pressure is building on the company to deliver more of its doses to people around the world, especially in poorer countries, and Moderna is working on vaccines against other pathogens, including Zika, influenza and cytomegalovirus.
But Moderna encountered such difficulties over the course of its eleven-year history that some executives worried it wouldn't survive. Two unlikely scientists helped save the company. Their breakthroughs paved the way for Moderna's Covid-19 shots but their work has never been publicized nor have their contributions been properly appreciated.
Derrick Rossi, a scientist at MIT, and Noubar Afeyan, a Cambridge-based investor, launched Moderna in September 2010. Their idea was to create mRNA molecules capable of delivering instructions to the body's cells, directing them to make proteins to heal ailments and cure disease. Need a statin, immunosuppressive, or other drug or vaccine? Just use mRNA to send a message to the body's cells to produce it. Rossi and Afeyan were convinced injecting mRNA into the body could turn it into its own laboratory, generating specific medications or vaccines as needed.
At the time, the notion that one might be able to teach the body to make proteins bordered on heresy. Everyone knew mRNA was unstable and set off the body's immune system on its way into cells. But in the late 2000's, two scientists at the University of Pennsylvania, Katalin Karikó and Drew Weissman, had figured out how to modify mRNA's chemical building blocks so the molecule could escape the notice of the immune system and enter the cell. Rossi and Afeyan couldn't convince the University of Pennsylvania to license Karikó and Weissman's patent, however, stymying Moderna's early ambitions. At the same time, the Penn scientists' technique seemed more applicable to an academic lab than a biotech company that needed to produce drugs or shots consistently and in bulk. Rossi and Afeyan's new company needed their own solution to help mRNA evade the body's defenses.
Some of Moderna's founders doubted Schrum could find success and they worried if their venture was doomed from the start.
The Scientist Who Modified mRNA: Jason Schrum
In 2010, Afeyan's firm subleased laboratory space in the basement of another Cambridge biotech company to begin scientific work. Afeyan chose a young scientist on his staff, Jason Schrum, to be Moderna's first employee, charging him with getting mRNA into cells without relying on Karikó and Weissman's solutions.
Schrum seemed well suited for the task. Months earlier, he had received a PhD in biological chemistry at Harvard University, where he had focused on nucleotide chemistry. Schrum even had the look of someone who might do big things. The baby-faced twenty-eight-year-old favored a relaxed, start-up look: khakis, button-downs, and Converse All-Stars.
Schrum felt immediate strain, however. He hadn't told anyone, but he was dealing with intense pain in his hands and joints, a condition that later would be diagnosed as degenerative arthritis. Soon Schrum couldn't bend two fingers on his left hand, making lab work difficult. He joined a drug trial, but the medicine proved useless. Schrum tried corticosteroid injections and anti-inflammatory drugs, but his left hand ached, restricting his experiments.
"It just wasn't useful," Schrum says, referring to his tender hand.*
He persisted, nonetheless. Each day in the fall of 2010, Schrum walked through double air-locked doors into a sterile "clean room" before entering a basement laboratory, in the bowels of an office in Cambridge's Kendall Square neighborhood, where he worked deep into the night. Schrum searched for potential modifications of mRNA nucleosides, hoping they might enable the molecule to produce proteins. Like all such rooms, there were no windows, so Schrum had to check a clock to know if it was day or night. A colleague came to visit once in a while, but most of the time, Schrum was alone.
Some of Moderna's founders doubted Schrum could find success and they worried if their venture was doomed from the start. An established MIT scientist turned down a job with the start-up to join pharmaceutical giant Novartis, dubious of Moderna's approach. Colleagues wondered if mRNA could produce proteins, at least on a consistent basis.
As Schrum began testing the modifications in January 2011, he made an unexpected discovery. Karikó and Weissman saw that by turned one of the building blocks for mRNA, a ribonucleoside called uridine, into a slightly different form called pseudouridine, the cell's immune system ignored the mRNA and the molecule avoided an immune response. After a series of experiments in the basement lab, Schrum discovered that a variant of pseudouridine called N1- methyl-pseudouridine did an even better job reducing the cell's innate immune response. Schrum's nucleoside switch enabled even higher protein production than Karikó and Weissman had generated, and Schrum's mRNAs lasted longer than either unmodified molecules or the modified mRNA the Penn academics had used, startling the young researcher. Working alone in a dreary basement and through intense pain, he had actually improved on the Penn professors' work.
Years later, Karikó and Weissman who would win acclaim. In September 2021, the scientists were awarded the Lasker-DeBakey Clinical Medical Research Award. Some predict they eventually will win a Nobel prize. But it would be Schrum's innovation that would form the backbone of both Moderna and Pfizer-BioNTech's Covid-19 vaccine, not the chemical modifications that Karikó and Weissman developed. For Schrum, necessity had truly been the mother of invention.
The Scientist Who Solved Delivery: Kerry Benenato
For several years, Moderna would make slow progress developing drugs to treat various diseases. Eventually, the company decided that mRNA was likely better suited for vaccines. By 2017, Moderna and the National Institutes of Health were discussing working together to develop mRNA–based vaccines, a partnership that buoyed Moderna's executives. There remained a huge obstacle in Moderna's way, however. It was up to Kerry Benenato to find a solution.
Benenato received an early hint of the hurdle in front of her three years earlier, when the organic chemist was first hired. When a colleague gave her a company tour, she was introduced to Moderna's chief scientific officer, Joseph Bolen, who seemed unusually excited to meet her.
"Oh, great!" Bolen said with a smile. "She's the one who's gonna solve delivery."
Bolen gave a hearty laugh and walked away, but Benenato detected seriousness in his quip.
Solve delivery?
It was a lot to expect from a 37-year-old scientist already dealing with insecurities and self-doubt. Benenato was an accomplished researcher who most recently had worked at AstraZeneca after completing post-doctoral studies at Harvard University. Despite her impressive credentials, Benenato battled a lack of confidence that sometimes got in her way. Performance reviews from past employers had been positive, but they usually produced similar critiques: Be more vocal. Do a better job advocating for your ideas. Give us more, Kerry.
Benenato was petite and soft-spoken. She sometimes stuttered or relied on "ums" and "ahs" when she became nervous, especially in front of groups, part of why she sometimes didn't feel comfortable speaking up.
"I'm an introvert," she says. "Self-confidence is something that's always been an issue."
To Benenato, Moderna's vaccine approach seemed promising—the team was packaging mRNAs in microscopic fatty-acid compounds called lipid nanoparticles, or LNPs, that protected the molecules on their way into cells. Moderna's shots should have been producing ample and long-lasting proteins. But the company's scientists were alarmed—they were injecting shots deep into the muscle of mice, but their immune systems were mounting spirited responses to the foreign components of the LNPs, which had been developed by a Canadian company.
This toxicity was a huge issue: A vaccine or drug that caused sharp pain and awful fevers wasn't going to prove very popular. The Moderna team was in a bind: Its mRNA had to be wrapped in the fatty nanoparticles to have a chance at producing plentiful proteins, but the body wasn't tolerating the microscopic encasements, especially upon repeated dosing.
The company's scientists had done everything they could to try to make the molecule's swathing material disappear soon after entering the cells, in order to avoid the unfortunate side effects, such as chills and headaches, but they weren't making headway. Frustration mounted. Somehow, the researchers had to find a way to get the encasements—made of little balls of fat, cholesterol, and other substances—to deliver their payload mRNA and then quickly vanish, like a parent dropping a teenager off at a party, to avoid setting off the immune system in unpleasant ways, even as the RNA and the proteins the molecule created stuck around.
Benenato wasn't entirely shocked by the challenges Moderna was facing. One of the reasons she had joined the upstart company was to help develop its delivery technology. She just didn't realize how pressing the issue was, or how stymied the researchers had become. Benenato also didn't know that Moderna board members were among those most discouraged by the delivery issue. In meetings, some of them pointed out that pharmaceutical giants like Roche Holding and Novartis had worked on similar issues and hadn't managed to develop lipid nanoparticles that were both effective and well tolerated by the body. Why would Moderna have any more luck?
Stephen Hoge insisted the company could yet find a solution.
"There's no way the only innovations in LNP are going to come from some academics and a small Canadian company," insisted Hoge, who had convinced the executives that hiring Benenato might help deliver an answer.
Benenato realized that while Moderna might have been a hot Boston-area start- up, it wasn't set up to do the chemistry necessary to solve their LNP problem. Much of its equipment was old or secondhand, and it was the kind used to tinker with mRNAs, not lipids.
"It was scary," she says.
When Benenato saw the company had a nuclear magnetic resonance spectrometer, which allows chemists to see the molecular structure of material, she let out a sigh of relief. Then Benenato inspected the machine and realized it was a jalopy. The hulking, aging instrument had been decommissioned and left behind by a previous tenant, too old and banged up to bring with them.
Benenato began experimenting with different chemical changes for Moderna's LNPs, but without a working spectrometer she and her colleagues had to have samples ready by noon each day, so they could be picked up by an outside company that would perform the necessary analysis. After a few weeks, her superiors received an enormous bill for the outsourced work and decided to pay to get the old spectrometer running again.
After months of futility, Benenato became impatient. An overachiever who could be hard on herself, she was eager to impress her new bosses. Benenato felt pressure outside the office, as well. She was married with a preschool-age daughter and an eighteen-month-old son. In her last job, Benenato's commute had been a twenty-minute trip to Astra-Zeneca's office in Waltham, outside Boston; now she was traveling an hour to Moderna's Cambridge offices. She became anxious—how was she going to devote the long hours she realized were necessary to solve their LNP quandary while providing her children proper care? Joining Moderna was beginning to feel like a possible mistake.
She turned to her husband and father for help. They reminded her of the hard work she had devoted to establishing her career and said it would be a shame if she couldn't take on the new challenge. Benenato's husband said he was happy to stay home with the kids, alleviating some of her concerns.
Back in the office, she got to work. She wanted to make lipids that were easier for the body to chop into smaller pieces, so they could be eliminated by the body's enzymes. Until then, Moderna, like most others, relied on all kinds of complicated chemicals to hold its LNP packaging together. They weren't natural, though, so the body was having a hard time breaking them down, causing the toxicity.
Benenato began experimenting with simpler chemicals. She inserted "ester bonds"—compounds referred to in chemical circles as "handles" because the body easily grabs them and breaks them apart. Ester bonds had two things going for them: They were strong enough to help ensure the LNP remained stable, acting much like a drop of oil in water, but they also gave the body's enzymes something to target and break down as soon as the LNP entered the cell, a way to quickly rid the body of the potentially toxic LNP components. Benenato thought the inclusion of these chemicals might speed the elimination of the LNP delivery material.
This idea, Benenato realized, was nothing more than traditional, medicinal chemistry. Most people didn't use ester bonds because they were pretty unsophisticated. But, hey, the tricky stuff wasn't working, so Benenato thought she'd see if the simple stuff worked.
Benenato also wanted to try to replace a group of unnatural chemicals in the LNP that was contributing to the spirited and unwelcome response from the immune system. Benenato set out to build a new and improved chemical combination. She began with ethanolamine, a colorless, natural chemical, an obvious start for any chemist hoping to build a more complex chemical combination. No one relied on ethanolamine on its own.
Benenato was curious, though. What would happen if she used just these two simple modifications to the LNP: ethanolamine with the ester bonds? Right away, Benenato noticed her new, super-simple compound helped mRNA create some protein in animals. It wasn't much, but it was a surprising and positive sign. Benenato spent over a year refining her solution, testing more than one hundred variations, all using ethanolamine and ester bonds, showing improvements with each new version of LNP. After finishing her 102nd version of the lipid molecule, which she named SM102, Benenato was confident enough in her work to show it to Hoge and others.
They immediately got excited. The team kept tweaking the composition of the lipid encasement. In 2017, they wrapped it around mRNA molecules and injected the new combination in mice and then monkeys. They saw plentiful, potent proteins were being produced and the lipids were quickly being eliminated, just as Benenato and her colleagues had hoped. Moderna had its special sauce.
That year, Benenato was asked to deliver a presentation to Stephane Bancel, Moderna's chief executive, Afeyan, and Moderna's executive committee to explain why it made sense to use the new, simpler LNP formulation for all its mRNA vaccines. She still needed approval from the executives to make the change. Ahead of the meeting, she was apprehensive, as some of her earlier anxieties returned. But an unusual calm came over her as she began speaking to the group. Benenato explained how experimenting with basic, overlooked chemicals had led to her discovery.
She said she had merely stumbled onto the company's solution, though her bosses understood the efforts that had been necessary for the breakthrough. The board complimented her work and agreed with the idea of switching to the new LNP. Benenato beamed with pride.
"As a scientist, serendipity has been my best friend," she told the executives.
Over the next few years, Benenato and her colleagues would improve on their methods and develop even more tolerable and potent LNP encasement for mRNA molecules. Their work enabled Moderna to include higher doses of vaccine in its shots. In early 2020, Moderna developed Covid-19 shots that included 100 micrograms of vaccine, compared with 30 micrograms in the Pfizer-BioNTech vaccine. That difference appears to help the Moderna vaccine generate higher titers and provide more protection.
"You set out in a career in drug discovery to want to make a difference," Benenato says. "Seeing it come to reality has been surreal and emotional."
Editor's Note: This essay is excerpted from A SHOT TO SAVE THE WORLD: The Inside Story of the Life-or-Death Race for a COVID-19 Vaccine by Gregory Zuckerman, now on sale from Portfolio/Penguin.
*Jason Schrum's arthritis is now in complete remission, thanks to Humira (adalimumab), a TNF-alpha blocker.
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.
DNA gathered from animal poop helps protect wildlife
On the savannah near the Botswana-Zimbabwe border, elephants grazed contentedly. Nearby, postdoctoral researcher Alida de Flamingh watched and waited. As the herd moved away, she went into action, collecting samples of elephant dung that she and other wildlife conservationists would study in the months to come. She pulled on gloves, took a swab, and ran it all over the still-warm, round blob of elephant poop.
Sequencing DNA from fecal matter is a safe, non-invasive way to track and ultimately help protect over 42,000 species currently threatened by extinction. Scientists are using this DNA to gain insights into wildlife health, genetic diversity and even the broader environment. Applied to elephants, chimpanzees, toucans and other species, it helps scientists determine the genetic diversity of groups and linkages with other groups. Such analysis can show changes in rates of inbreeding. Populations with greater genetic diversity adapt better to changes and environmental stressors than those with less diversity, thus reducing their risks of extinction, explains de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
Analyzing fecal DNA also reveals information about an animal’s diet and health, and even nearby flora that is eaten. That information gives scientists broader insights into the ecosystem, and the findings are informing conservation initiatives. Examples include restoring or maintaining genetic connections among groups, ensuring access to certain foraging areas or increasing diversity in captive breeding programs.
Approximately 27 percent of mammals and 28 percent of all assessed species are close to dying out. The IUCN Red List of threatened species, simply called the Red List, is the world’s most comprehensive record of animals’ risk of extinction status. The more information scientists gather, the better their chances of reducing those risks. In Africa, populations of vertebrates declined 69 percent between 1970 and 2022, according to the World Wildlife Fund (WWF).
“We put on sterile gloves and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says Alida de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
“When people talk about species, they often talk about ecosystems, but they often overlook genetic diversity,” says Christina Hvilsom, senior geneticist at the Copenhagen Zoo. “It’s easy to count (individuals) to assess whether the population size is increasing or decreasing, but diversity isn’t something we can see with our bare eyes. Yet, it’s actually the foundation for the species and populations.” DNA analysis can provide this critical information.
Assessing elephants’ health
“Africa’s elephant populations are facing unprecedented threats,” says de Flamingh, the postdoc, who has studied them since 2009. Challenges include ivory poaching, habitat destruction and smaller, more fragmented habitats that result in smaller mating pools with less genetic diversity. Additionally, de Flamingh studies the microbial communities living on and in elephants – their microbiomes – looking for parasites or dangerous microbes.
Approximately 415,000 elephants inhabit Africa today, but de Flamingh says the number would be four times higher without these challenges. The IUCN Red List reports African savannah elephants are endangered and African forest elephants are critically endangered. Elephants support ecosystem biodiversity by clearing paths that help other species travel. Their very footprints create small puddles that can host smaller organisms such as tadpoles. Elephants are often described as ecosystems’ engineers, so if they disappear, the rest of the ecosystem will suffer too.
There’s a process to collecting elephant feces. “We put on sterile gloves (which we change for each sample) and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says de Flamingh. They rub a sample about the size of a U.S. quarter onto a paper card embedded with DNA preservation technology. Each card is air dried and stored in a packet of desiccant to prevent mold growth. This way, samples can be stored at room temperature indefinitely without the DNA degrading.
Earlier methods required collecting dung in bags, which needed either refrigeration or the addition of preservatives, or the riskier alternative of tranquilizing the animals before approaching them to draw blood samples. The ability to collect and sequence the DNA made things much easier and safer.
“Our research provides a way to assess elephant health without having to physically interact with elephants,” de Flamingh emphasizes. “We also keep track of the GPS coordinates of each sample so that we can create a map of the sampling locations,” she adds. That helps researchers correlate elephants’ health with geographic areas and their conditions.
Although de Flamingh works with elephants in the wild, the contributions of zoos in the United States and collaborations in South Africa (notably the late Professor Rudi van Aarde and the Conservation Ecology Research Unit at the University of Pretoria) were key in studying this method to ensure it worked, she points out.
Protecting chimpanzees
Genetic work with chimpanzees began about a decade ago. Hvilsom and her group at the Copenhagen Zoo analyzed DNA from nearly 1,000 fecal samples collected between 2003 and 2018 by a team of international researchers. The goal was to assess the status of the West African subspecies, which is critically endangered after rapid population declines. Of the four subspecies of chimpanzees, the West African subspecies is considered the most at-risk.
In total, the WWF estimates the numbers of chimpanzees inhabiting Africa’s forests and savannah woodlands at between 173,000 and 300,000. Poaching, disease and human-caused changes to their lands are their major risks.
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes.
“One of the threats is mining near the Nimba Mountains in Guinea,” a stronghold for the West African subspecies, Hvilsom says. The Nimba Mountains are a UNESCO World Heritage Site, but they are rich in iron ore, which is used to make the steel that is vital to the Asian construction boom. As she and colleagues wrote in a recent paper, “Many extractive industries are currently developing projects in chimpanzee habitat.”
Analyzing DNA allows researchers to identify individual chimpanzees more accurately than simply observing them, she says. Normally, field researchers would install cameras and manually inspect each picture to determine how many chimpanzees were in an area. But, Hvilsom says, “That’s very tricky. Chimpanzees move a lot and are fast, so it’s difficult to get clear pictures. Often, they find and destroy the cameras. Also, they live in large areas, so you need a lot of cameras.”
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes based upon DNA comparisons with other chimpanzee groups. The mining companies and builders are using this information to locate future roads where they won’t disrupt migration – a more effective solution than trying to build artificial corridors for wildlife.
“The current route cuts off communities of chimpanzees,” Hvilsom elaborates. That effectively prevents young adult chimps from joining other groups when the time comes, eventually reducing the currently-high levels of genetic diversity.
“The mining company helped pay for the genetics work,” Hvilsom says, “as part of its obligation to assess and monitor biodiversity and the effect of the mining in the area.”
Of 50 toucan subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching.
Identifying toucan families
Feces aren't the only substance researchers draw DNA samples from. Jeffrey Coleman, a Ph.D. candidate at the University of Texas at Austin relies on blood tests for studying the genetic diversity of toucans---birds species native to Central America and nearby regions. They live in the jungles, where they hop among branches, snip fruit from trees, toss it in the air and catch it with their large beaks. “Toucans are beautiful, charismatic birds that are really important to the ecosystem,” says Coleman.
Of their 50 subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching. “When people see these aesthetically pleasing birds, they’re motivated to care about conservation practices,” he points out.
Coleman works with the Dallas World Aquarium and its partner zoos to analyze DNA from blood draws, using it to identify which toucans are related and how closely. His goal is to use science to improve the genetic diversity among toucan offspring.
Specifically, he’s looking at sections of the genome of captive birds in which the nucleotides repeat multiple times, such as AGATAGATAGAT. Called microsatellites, these consecutively-repeating sections can be passed from parents to children, helping scientists identify parent-child and sibling-sibling relationships. “That allows you to make strategic decisions about how to pair (captive) individuals for mating...to avoid inbreeding,” Coleman says.
Jeffrey Coleman is studying the microsatellites inside the toucan genomes.
Courtesy Jeffrey Coleman
The alternative is to use a type of analysis that looks for a single DNA building block – a nucleotide – that differs in a given sequence. Called single nucleotide polymorphisms (SNPs, pronounced “snips”), they are very common and very accurate. Coleman says they are better than microsatellites for some uses. But scientists have already developed a large body of microsatellite data from multiple species, so microsatellites can shed more insights on relations.
Regardless of whether conservation programs use SNPs or microsatellites to guide captive breeding efforts, the goal is to help them build genetically diverse populations that eventually may supplement endangered populations in the wild. “The hope is that the ecosystem will be stable enough and that the populations (once reintroduced into the wild) will be able to survive and thrive,” says Coleman. History knows some good examples of captive breeding success.
The California condor, which had a total population of 27 in 1987, when the last wild birds were captured, is one of them. A captive breeding program boosted their numbers to 561 by the end of 2022. Of those, 347 of those are in the wild, according to the National Park Service.
Conservationists hope that their work on animals’ genetic diversity will help preserve and restore endangered species in captivity and the wild. DNA analysis is crucial to both types of efforts. The ability to apply genome sequencing to wildlife conservation brings a new level of accuracy that helps protect species and gives fresh insights that observation alone can’t provide.
“A lot of species are threatened,” Coleman says. “I hope this research will be a resource people can use to get more information on longer-term genealogies and different populations.”