Health breakthroughs of 2022 that should have made bigger news
As the world has attempted to move on from COVID-19 in 2022, attention has returned to other areas of health and biotech with major regulatory approvals such as the Alzheimer's drug lecanemab – which can slow the destruction of brain cells in the early stages of the disease – being hailed by some as momentous breakthroughs.
This has been a year where psychedelic medicines have gained the attention of mainstream researchers with a groundbreaking clinical trial showing that psilocybin treatment can help relieve some of the symptoms of major depressive disorder. And with messenger RNA (mRNA) technology still very much capturing the imagination, the readouts of cancer vaccine trials have made headlines around the world.
But at the same time there have been vital advances which will likely go on to change medicine, and yet have slipped beneath the radar. I asked nine forward-thinking experts on health and biotech about the most important, but underappreciated, breakthrough of 2022.
Their descriptions, below, were lightly edited by Leaps.org for style and format.
New drug targets for Alzheimer’s disease
Professor Julie Williams, Director, Dementia Research Institute, Cardiff University
Genetics has changed our view of Alzheimer’s disease in the last five to six years. The beta amyloid hypothesis has dominated Alzheimer’s research for a long time, but there are multiple components to this complex disease, of which getting rid of amyloid plaques is one, but it is not the whole story. In April 2022, Nature published a paper which is the culmination of a decade’s worth of work - groups all over the world working together to identify 75 genes associated with risk of developing Alzheimer’s. This provides us with a roadmap for understanding the disease mechanisms.
For example, it is showing that there is something different about the immune systems of people who develop Alzheimer’s disease. There is something different about the way they process lipids in the brain, and very specific processes of how things travel through cells called endocytosis. When it comes to immunity, it indicates that the complement system is affecting whether synapses, which are the connections between neurons, get eliminated or not. In Alzheimer’s this process is more severe, so patients are losing more synapses, and this is correlated with cognition.
The genetics also implicates very specific tissues like microglia, which are the housekeepers in the brain. One of their functions is to clear away beta amyloid, but they also prune and nibble away at parts of the brain that are indicated to be diseased. If you have these risk genes, it seems that you are likely to prune more tissue, which may be part of the cell death and neurodegeneration that we observe in Alzheimer’s patients.
Genetics is telling us that we need to be looking at multiple causes of this complex disease, and we are doing that now. It is showing us that there are a number of different processes which combine to push patients into a disease state which results in the death of connections between nerve cells. These findings around the complement system and other immune-related mechanisms are very interesting as there are already drugs which are available for other diseases which could be repurposed in clinical trials. So it is really a turning point for us in the Alzheimer’s disease field.
Preventing Pandemics with Organ-Tissue Equivalents
Anthony Atala, Director of the Wake Forest Institute for Regenerative Medicine
COVID-19 has shown us that we need to be better prepared ahead of future pandemics and have systems in place where we can quickly catalogue a new virus and have an idea of which treatment agents would work best against it.
At Wake Forest Institute, our scientists have developed what we call organ-tissue equivalents. These are miniature tissues and organs, created using the same regenerative medicine technologies which we have been using to create tissues for patients. For example, if we are making a miniature liver, we will recreate this structure using the six different cell types you find in the liver, in the right proportions, and then the right extracellular matrix which holds the structure together. You're trying to replicate all the characteristics of the liver, but just in a miniature format.
We can now put these organ-tissue equivalents in a chip-like device, where we can expose them to different types of viral infections, and start to get a realistic idea of how the human body reacts to these viruses. We can use artificial intelligence and machine learning to map the pathways of the body’s response. This will allow us to catalogue known viruses far more effectively, and begin storing information on them.
Powering Deep Brain Stimulators with Breath
Islam Mosa, Co-Founder and CTO of VoltXon
Deep brain stimulation (DBS) devices are becoming increasingly common with 150,000 new devices being implanted every year for people with Parkinson’s disease, but also psychiatric conditions such as treatment-resistant depression and obsessive-compulsive disorders. But one of the biggest limitations is the power source – I call DBS devices energy monsters. While cardiac pacemakers use similar technology, their batteries last seven to ten years, but DBS batteries need changing every two to three years. This is because they are generating between 60-180 pulses per second.
Replacing the batteries requires surgery which costs a lot of money, and with every repeat operation comes a risk of infection, plus there is a lot of anxiety on behalf of the patient that the battery is running out.
My colleagues at the University of Connecticut and I, have developed a new way of charging these devices using the person’s own breathing movements, which would mean that the batteries never need to be changed. As the patient breathes in and out, their chest wall presses on a thin electric generator, which converts that movement into static electricity, charging a supercapacitor. This discharges the electricity required to power the DBS device and send the necessary pulses to the brain.
So far it has only been tested in a simulated pig, using a pig lung connected to a pump, but there are plans now to test it in a real animal, and then progress to clinical trials.
Smartwatches for Disease Detection
Jessilyn Dunn, Assistant Professor in Duke Biomedical Engineering
A group of researchers recently showed that digital biomarkers of infection can reveal when someone is sick, often before they feel sick. The team, which included Duke biomedical engineers, used information from smartwatches to detect Covid-19 cases five to 10 days earlier than diagnostic tests. Smartwatch data included aspects of heart rate, sleep quality and physical activity. Based on this data, we developed an algorithm to decide which people have the most need to take the diagnostic tests. With this approach, the percent of tests that come back positive are about four- to six-times higher, depending on which factors we monitor through the watches.
Our study was one of several showing the value of digital biomarkers, rather than a single blockbuster paper. With so many new ideas and technologies coming out around Covid, it’s hard to be that signal through the noise. More studies are needed, but this line of research is important because, rather than treat everyone as equally likely to have an infectious disease, we can use prior knowledge from smartwatches. With monkeypox, for example, you've got many more people who need to be tested than you have tests available. Information from the smartwatches enables you to improve how you allocate those tests.
Smartwatch data could also be applied to chronic diseases. For viruses, we’re looking for information about anomalies – a big change point in people’s health. For chronic diseases, it’s more like a slow, steady change. Our research lays the groundwork for the signals coming from smartwatches to be useful in a health setting, and now it’s up to us to detect more of these chronic cases. We want to go from the idea that we have this single change point, like a heart attack or stroke, and focus on the part before that, to see if we can detect it.
A Vaccine For RSV
Norbert Pardi, Vaccines Group Lead, Penn Institute for RNA Innovation, University of Pennsylvania
Scientists have long been trying to develop a vaccine for respiratory syncytial virus (RSV), and it looks like Pfizer are closing in on this goal, based on the latest clinical trial data in newborns which they released in November. Pfizer have developed a protein-based vaccine against the F protein of RSV, which they are giving to pregnant women. It turns out that it induces a robust immune response after the administration of a single shot and it seems to be highly protective in newborns. The efficacy was over 80% after 90 days, so it protected very well against severe disease, and even though this dropped a little after six month, it was still pretty high.
I think this has been a very important breakthrough, and very timely at the moment with both COVID-19, influenza and RSV circulating, which just shows the importance of having a vaccine which works well in both the very young and the very old.
The road to an RSV vaccine has also illustrated the importance of teamwork in 21st century vaccine development. You need people with different backgrounds to solve these challenges – microbiologists, immunologists and structural biologists working together to understand how viruses work, and how our immune system induces protective responses against certain viruses. It has been this kind of teamwork which has yielded the findings that targeting the prefusion stabilized form of the F protein in RSV induces much stronger and highly protective immune responses.
Gene therapy shows its potential
Nicole Paulk, Assistant Professor of Gene Therapy at the University of California, San Francisco
The recent US Food and Drug Administration (FDA) approval of Hemgenix, a gene therapy for hemophilia B, is big for a lot of reasons. While hemophilia is absolutely a rare disease, it is astronomically more common than the first two approvals – Luxturna for RPE65-meidated inherited retinal dystrophy and Zolgensma for spinal muscular atrophy - so many more patients will be treated with this. In terms of numbers of patients, we are now starting to creep up into things that are much more common, which is a huge step in terms of our ability to scale the production of an adeno-associated virus (AAV) vector for gene therapy.
Hemophilia is also a really special patient population because this has been the darling indication for AAV gene therapy for the last 20 to 30 years. AAV trafficks to the liver so well, it’s really easy for us to target the tissues that we want. If you look at the numbers, there have been more gene therapy scientists working on hemophilia than any other condition. There have just been thousands and thousands of us working on gene therapy indications for the last 20 or 30 years, so to see the first of these approvals make it, feels really special.
I am sure it is even more special for the patients because now they have a choice – do I want to stay on my recombinant factor drug that I need to take every day for the rest of my life, or right now I could get a one-time infusion of this virus and possibly experience curative levels of expression for the rest of my life. And this is just the first one for hemophilia, there’s going to end up being a dozen gene therapies within the next five years, targeted towards different hemophilias.
Every single approval is momentous for the entire field because it gets investors excited, it gets companies and physicians excited, and that helps speed things up. Right now, it's still a challenge to produce enough for double digit patients. But with more interest comes the experiments and trials that allow us to pick up the knowledge to scale things up, so that we can go after bigger diseases like diabetes, congestive heart failure, cancer, all of these much bigger afflictions.
Treating Thickened Hearts
John Spertus, Professor in Metabolic and Vascular Disease Research, UMKC School of Medicine
Hypertrophic cardiomyopathy (HCM) is a disease that causes your heart muscle to enlarge, and the walls of your heart chambers thicken and reduce in size. Because of this, they cannot hold as much blood and may stiffen, causing some sufferers to experience progressive shortness of breath, fatigue and ultimately heart failure.
So far we have only had very crude ways of treating it, using beta blockers, calcium channel blockers or other medications which cause the heart to beat less strongly. This works for some patients but a lot of time it does not, which means you have to consider removing part of the wall of the heart with surgery.
Earlier this year, a trial of a drug called mavacamten, became the first study to show positive results in treating HCM. What is remarkable about mavacamten is that it is directed at trying to block the overly vigorous contractile proteins in the heart, so it is a highly targeted, focused way of addressing the key problem in these patients. The study demonstrated a really large improvement in patient quality of life where they were on the drug, and when they went off the drug, the quality of life went away.
Some specialists are now hypothesizing that it may work for other cardiovascular diseases where the heart either beats too strongly or it does not relax well enough, but just having a treatment for HCM is a really big deal. For years we have not been very aggressive in identifying and treating these patients because there have not been great treatments available, so this could lead to a new era.
Regenerating Organs
David Andrijevic, Associate Research Scientist in neuroscience at Yale School of Medicine
As soon as the heartbeat stops, a whole chain of biochemical processes resulting from ischemia – the lack of blood flow, oxygen and nutrients – begins to destroy the body’s cells and organs. My colleagues and I at Yale School of Medicine have been investigating whether we can recover organs after prolonged ischemia, with the main goal of expanding the organ donor pool.
Earlier this year we published a paper in which we showed that we could use technology to restore blood circulation, other cellular functions and even heart activity in pigs, one hour after their deaths. This was done using a perfusion technology to substitute heart, lung and kidney function, and deliver an experimental cell protective fluid to these organs which aimed to stop cell death and aid in the recovery.
One of the aims of this technology is that it can be used in future to lengthen the time window for recovering organs for donation after a person has been declared dead, a logistical hurdle which would allow us to substantially increase the donor pool. We might also be able to use this cell protective fluid in studies to see if it can help people who have suffered from strokes and myocardial infarction. In future, if we managed to achieve an adequate brain recovery – and the brain, out of all the organs, is the most susceptible to ischemia – this might also change some paradigms in resuscitation medicine.
Antibody-Drug Conjugates for Cancer
Yosi Shamay, Cancer Nanomedicine and Nanoinformatics researcher at the Technion Israel Institute of Technology
For the past four or five years, antibody-drug conjugates (ADCs) - a cancer drug where you have an antibody conjugated to a toxin - have been used only in patients with specific cancers that display high expression of a target protein, for example HER2-positive breast cancer. But in 2022, there have been clinical trials where ADCs have shown remarkable results in patients with low expression of HER2, which is something we never expected to see.
In July 2022, AstraZeneca published the results of a clinical trial, which showed that an ADC called trastuzumab deruxtecan can offer a very big survival benefit to breast cancer patients with very little expression of HER2, levels so low that they would be borderline undetectable for a pathologist. They got a strong survival signal for patients with very aggressive, metastatic disease.
I think this is very interesting and important because it means that it might pave the way to include more patients in clinical trials looking at ADCs for other cancers, for example lymphoma, colon cancer, lung cancers, even if they have low expression of the protein target. It also holds implications for CAR-T cells - where you genetically engineer a T cell to attack the cancer - because the concept is very similar. If we now know that an ADC can have a survival benefit, even in patients with very low target expression, the same might be true for T cells.
Look back further: Breakthroughs of 2021
https://leaps.org/6-biotech-breakthroughs-of-2021-that-missed-the-attention-they-deserved/
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.”