CRISPR base editing gives measure of hope to people with muscular dystrophy
When Martin Weber climbs the steps to his apartment on the fifth floor in Munich, an attentive observer might notice that he walks a little unevenly. “That’s because my calf muscles were the first to lose strength,” Weber explains.
About three years ago, the now 19-year-old university student realized that he suddenly had trouble keeping up with his track team at school. At tennis tournaments, he seemed to lose stamina after the first hour. “But it was still within the norm,” he says. “So it took a while before I noticed something was seriously wrong.” A blood test showed highly elevated liver markers. His parents feared he had liver cancer until a week-long hospital visit and scores of tests led to a diagnosis: hereditary limb-girdle muscular dystrophy, an incurable genetic illness that causes muscles to deteriorate.
As you read this text, you will surely use several muscles without being aware of them: Your heart muscle pumps blood through your arteries, your eye muscles let you follow the words in this sentence, and your hand muscles hold the tablet or cell phone. Muscles make up 40 percent of your body weight; we usually have 656 of them. Now imagine they are slowly losing their strength. No training, no protein shake can rebuild their function.
This is the reality for most people in Simone Spuler’s outpatient clinic at the Charité Hospital in Berlin, Germany: Almost all of her 2,500 patients have muscular dystrophy, a progressive illness striking mostly young people. Muscle decline leads to a wheelchair and, eventually, an early death due to a heart attack or the inability to breathe. In Germany alone, 300,000 people live with this illness, the youngest barely a year old. The CDC estimates that its most common form, Duchenne, affects 1 in every 3,500 to 6,000 male births each year in the United States.
The devastating progression of the disease is what motivates Spuler and her team of 25 scientists to find a cure. In 2019, they made a spectacular breakthrough: For the first time, they successfully used mRNA to introduce the CRISPR-Cas9 tool into human muscle stem cells to repair the dystrophy. “It’s really just one tiny molecule that doesn’t work properly,” Spuler explains.
CRISPR-Cas9 is a technology that lets scientists select and alter parts of the genome. It’s still comparatively new but has advanced quickly since its discovery in the early 2010s. “We now have the possibility to repair certain mutations with genetic editing,” Spuler says. “It’s pure magic.”
She projects a warm, motherly air and a professional calm that inspires trust from her patients. She needs these qualities because the 60-year-old neurologist has one of the toughest jobs in the world: All day long, patients with the incurable diagnosis of muscular dystrophy come to her clinic, and she watches them decline over the years. “Apart from physiotherapy, there is nothing we can recommend right now,” she says. That motivated her early in her career, when she met her first patients at the Max Planck Institute for Neurobiology near Munich in the 1990s. “I knew I had 30, 40 years to find something.”
She learned from the luminaries of her profession with postdocs at the University of California San Diego, Harvard and Johns Hopkins, before serving as a clinical fellow at the Mayo Clinic. In 2005, the Charité offered her the opportunity to establish a specialized clinic for myasthenia, or muscular weakness. An important influence on Spuler, she says, has been the French microbiologist Emmanuelle Charpentier, who received the Nobel Prize in 2020 along with Jennifer Doudna for their CRISPR research, and has worked in Berlin since 2015.
When CRISPR was first introduced, it was mainly used to cut through DNA. However, the cut can lead to undesired side effects. For the muscle stem cells, Spuler now uses a base editor to repair the damaged molecule with super fine scissors or tweezers.
“Apart from physiotherapy, there is nothing we can recommend right now,” Spuler says about her patients with limb-girdle muscular dystrophy.
Pablo Castagnola
Last year, she proved that the method works in mice. Injecting repaired cells into the rodents led to new muscle fibers and, in 2021 and 2022, she passed the first safety meetings with the Paul-Ehrlich Institute, which is responsible for approving human gene editing trials in Germany. She raised the nearly four million Euros needed to test the new method in the first clinical trial in humans with limb-girdle muscular dystrophy, beginning with one muscle that can easily be measured, such as the biceps.
This spring, Weber and his parents drove the 400 miles from Munich to Berlin. At Spuler’s lab, her team took a biopsy from muscles in his left arm. The first two steps – extraction and repair in a culture dish – went according to plan; Spuler was able to repair the mutation in Weber’s cells outside his body.
Next year, Weber will be the youngest participant when Spuler starts to test the method in a trial of five people “in vivo,” inside their bodies. This will be the real moment of truth: Will the participants’ muscles accept the corrected cells? Will the cells multiply and take over the function of damaged cells, just like Spuler was able to do in her lab with the rodents?
The effort is costly and complex. “The biggest challenge is to make absolutely sure that we don’t harm the patient,” Spuler says. This means scanning their entire genomes, “so we don’t accidentally damage or knock out an important gene.”
Weber, who asked not to be identified by his real name, is looking forward to the trial and he feels confident that “the risks are comparatively small because the method will only be applied to one muscle. The worst that can happen is that it doesn’t work. But in the best case, the muscle function will improve.”
He was so impressed with the Charité scientists that he decided to study biology at his university. He’s read extensively about CRISPR, so he understands why he has three healthy siblings. “That’s the statistics,” the biologist in training explains. “You get two sets of genes from each parent, and you have to get two faulty mutations to have muscular dystrophy. So we fit the statistics exactly: One of us four kids inherited the mutation.”
It was his mother, a college teacher, and father, a physicist by training, who heard about Spuler’s research. Even though Weber does not live at home anymore, having a chronically ill son is nearly a full-time job for his mother, Annette. The Berlin visit and the trial are financed separately through private sponsors, but the fights with Weber’s health insurance are frustrating and time-consuming. “Physiotherapy is the only thing that helps a bit,” Weber says, “and yet, they fought us on approving it every step of the way.”
Spuler does not want to evoke unrealistic expectations. “Patients who are wheelchair-bound won’t suddenly get up and walk."
Her son continues to exercise as much as possible. Riding his bicycle to the university has become too difficult, so he got an e-scooter. He had to give up competitive tennis because he does not have the stamina for a two-hour match, but he can still play with his dad or his buddies for an hour. His closest friends know about the diagnosis. “They help me, for instance, to lift something heavy because I can’t do that anymore,” Weber says.
The family was elated to find medical support at the Munich Muscle Center by the German Alliance for Muscular Patients and then at Spuler’s clinic in Berlin. “When you hear that this is a progressive illness with no chance of improvement, your world collapses as a parent,” Annette Weber says. “And then all of a sudden, there is this woman who sees scientific progress as an opportunity. Even just to be able to participate in the study is fantastic.”
Spuler does not want to evoke unrealistic expectations. “Patients who are wheelchair-bound won’t suddenly get up and walk,” she says. After all, she will start by applying the gene editor to only one muscle, “but it would be a big step if even a small muscle that is essential to grip something, or to swallow, regains function.”
Weber agrees. “I understand that I won’t regain 100 percent of my muscle function but even a small improvement or at least halting the deterioration is the goal.”
And yet, Spuler and others are ultimately searching for a true solution. In a separate effort, Massachusetts-based biotech company Sarepta announced this month it will seek expedited regulators’ approval to treat Duchenne patients with its investigational gene therapy. Unlike Spuler’s methods, Sarepta focuses specifically on the Duchenne form of muscular dystrophy, and it uses an adeno-assisted virus to deliver the therapy.
Spuler’s vision is to eventually apply gene editing to the entire body of her patients. To speed up the research, she and a colleague founded a private research company, Myopax. If she is able to prove that the body accepts the edited cells, the technique could be used for other monogenetic illnesses as well. “When we speak of genetic editing, many are scared and say, oh no, this is God’s work,” says Spuler. But she sees herself as a mechanic, not a divine being. “We really just exchange a molecule, that’s it.”
If everything goes well, Weber hopes that ten years from now, he will be the one taking biopsies from the next generation of patients and repairing their genes.
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.”