A few decades later, their dreams are on the brink of coming true. The US Food and Drug Administration (FDA) has approved blood stem cell transplantation for cancer and other disorders that affect the blood and immune system. More clinical trials are underway, investigating the use of stem cells from the umbilical cord to treat knee osteoarthritis—where the cartilage slowly wears down—and nerve problems from diabetes.

But the promise of stem cells came with a dark side.

Illegal stem cell clinics popped up soon after the cells’ discovery, touting their ability to rejuvenate aged skin, joints, or even treat severe brain disorders such as Parkinson’s disease. Despite FDA regulation, as of 2021, there were nearly 2,800 unlicensed clinics across the country, each advertising stem cells therapies with little scientific evidence.

“What started as a trickle became a torrent as businesses poured into this space,” wrote an expert team in the journal Cell Stem Cell in 2021.

History is now repeating itself with an up-and-coming “cure-all:” exosomes.

Exosomes are tiny bubbles made by cells to carry proteins and genetic material to other cells. While still early, research into these mysterious bubbles suggests they may be involved in aging or be responsible for cancers spreading across the body.

Multiple clinical trials are underway, ranging from exosome therapies to slow hair loss to treatments for heart attacks, strokes, and bone and cartilage loss. They have potential.

But a growing number of clinics are also advertising exosomes as their next best seller. One forecast analyzing exosomes in the skin care industry predicts a market value of over $674 million by 2030.

The problem? We don’t really know what exosomes are, what they do to the body, or their side effects. In a way, these molecular packages are like Christmas “mystery boxes,” each containing a different mix of biological surprises that could alter cellular functions, like turning genes on or off in unexpected ways.

There have already been reports of serious complications. “There is an urgent need to develop regulations to protect patients from serious risks associated with interventions based on little or no scientific evidence,” a team recently wrote in Stem Cell Reports.

Cellular Space Shuttles

In 1996, Graça Raposo, a molecular scientist in the Netherlands, noticed something strange: The immune cells she was studying seemed to send messages to each other in tiny bubbles. Under the microscope, she saw that when treated with a “toxin” of sorts, the cells slurped up the molecules, planted them on the surfaces of tiny bubbles inside the cell, and released the bubbles into the vast wilderness of the cell’s surroundings.

She collected the bubbles and squirted them onto other immune cells. Surprisingly, they triggered a similar immune response in the cells—as if directly exposed to the toxin. In other words, the bubbles seemed to shuttle information between cells.

Dubbed exosomes, scientists previously thought they were the cell’s garbage collectors, gathering waste molecules into a bubble and spewing it outside the cell. But two years later, Raposo and colleagues found that exosomes harvested from cells that naturally fight off tumors could be used as a therapy to suppress tumors in mice.

Interest in these mysterious blobs exploded.

Scientists soon found that most cells pump out exosome “spaceships,” and they can contain both proteins and types of RNA that turn genes on or off. But despite decades of research, we’re only scratching the surface of what cargo they can carry and their biological function.

It’s still unclear what exosomes do. Some could be messengers of a dying cell, warning neighbors to shore up defenses. They could also be co-opted by tumor cells to bamboozle nearby cells into supporting cancer growth and spread. In Alzheimer’s disease, they could potentially shuttle twisted protein clumps to other cells, spreading the disease across the brain.

They’re tough to study, in part, because they’re so small and unpredictable. About one-hundredth the size of a red blood cell, exosomes are hard to capture even with modern microscopy. Each type of cell seems to have a different release schedule, with some spewing many in one shot and others taking the slow-and-steady route. Until recently, scientists didn’t even agree on how to define exosomes.

Over several years, the International Society for Extracellular Vesicles, or exosomes, has begun uniting the field with naming conventions and standardized methods for preparing exosomes.

The Wild West

While scientists are rapidly coming together to cautiously make exosome-based treatment a reality, uncertified clinics have popped up across the globe. Their first pitch to the public was tackling Covid. One analysis found 60 clinics in the US advertising exosome-based therapy as a way to prevent or treat the virus—with zero scientific support. Another trending use has been in skin care or hair growth, garnering attention in the US, UK, and Japan.

Exosomes are regulated by the FDA in the US and the European Medicines Agency (EMA) in the EU as biological medicinal products, meaning they require approval from the agencies. That did not stop clinics from marketing them, with tragic consequences. In 2019, patients in Nebraska treated with unapproved exosomes became septic—a life-threating condition caused by infection across the whole body—leading the FDA to issue a warning.

Clinics that offer unregulated exosomes “deceive patients with unsubstantiated claims about the potential for these products to prevent, treat, or cure various diseases or conditions,” the agency wrote.

Japan is struggling to catch up. Exosomes are not regulated under their laws. Nearly 670 clinics have already popped up, representing a far larger market than the US or EU. Most services have been marketed for skin care, anti-aging, hair growth, and battling fatigue, wrote the authors. More rarely, some touted their ability to battle cancers.

The rogue clinics have already led to tragedies. In one case, “a well-known private cosmetic surgery clinic administered exosomes…to at least four patients, including relatives of staff members with stage IV lung cancer, and found that the cancer rapidly worsened after administration,” wrote the authors.

Because the clinics operate on the down-low, it’s tough to gauge the extent of harm, including potential deaths.

The worry isn’t that exosomes are harmful by themselves. How they’re obtained plays a huge role in safety. In unregulated settings, there’s a large chance of the bubbles being contaminated by endotoxins—which trigger dangerous inflammatory responses—or bacteria that lingers and grows.

For now, “from a very basic point of view, we don’t really know what they’re doing, good or bad… I wouldn’t take them, let’s put it that way,” James Edgar, an exosome researcher from the University of Cambridge, told MIT Technology Review.

Unregulated clinics don’t just harm patients. They could also set a promising field back.

Scientific advances may seem to move at a snail’s pace, but it’s to ensure safety and efficacy despite the glitz and glamor of a potential new panacea. Scientists are still forging ahead using exosomes for multiple health problems—while bearing in mind there’s much we still need to understand about these cellular spaceships.

Image Credit: Steve Johnson on Unsplash

Which is why recent leaps in our understanding of protein structure and the emerging ability to design entirely new proteins from scratch, mediated by AI, is such a huge development. It’s why three computer scientists won Nobel prizes in chemistry this year for their work in the field.

Things are by no means standing still. 2024 was another winning year for AI protein design.

Earlier this year, scientists expanded AI’s ability to model how proteins bind to other biomolecules, such as DNA, RNA, and the small molecules that regulate their shape and function. The study broadened the scope of RoseTTAFold, a popular AI tool for protein design, so that it could map out complex protein-based molecular machines at the atomic level—in turn, paving the way for more sophisticated therapies.

DeepMind soon followed with the release of AlphaFold3, an AI model that also predicts protein interactions with other molecules. Now available to researchers, the sophisticated AI tool will likely lead to a flood of innovations, therapeutics, and insights into biological processes.

Meanwhile, protein design went flexible this year. AI models generated “effector” proteins that could shape-shift in the presence of a molecular switch. This flip-flop structure altered their biological impact on cells. A subset of these morphed into a variety of arrangements, including cage-like structures that could encapsulate and deliver medicines like tiny spaceships.

They’re novel, but do any AI-designed proteins actually work? Yes, according to several studies.

One used AI to dream up a universe of potential CRISPR gene editors. Inspired by large language models—like those that gave birth to ChatGPT—the AI model in the study eventually designed a gene editing system as accurate as existing CRISPR-based tools when tested on cells. Another AI designed circle-shaped proteins that reliably turned stem cells into different blood vessel cell types. Other AI-generated proteins directed protein “junk” into the lysosome, a waste treatment blob filled with acid inside cells that keeps them neat and tidy.

Outside of medicine, AI designed mineral-forming proteins that, if integrated into aquatic microbes, could potentially soak up excess carbon and transform it into limestone. While still early, the technology could tackle climate change with a carbon sink that lasts millions of years.

It seems imagination is the only limit to AI-based protein design. But there are still a few cases that AI can’t yet fully handle. Nature has a comprehensive list, but these stand out.

Back to Basics: Binders

When proteins interact with each other, binder molecules can increase or break apart those interactions. These molecules initially caught the eyes of protein designers because they can serve as drugs that block damaging cellular responses or boost useful ones.

There have been successes. Generative AI models, such as RFdiffusion, can readily model binders, especially for free-floating proteins inside cells. These proteins coordinate much of the cell’s internal signaling, including signals that trigger senescence or cancer. Binders that break the chain of communication could potentially halt the processes. They can also be developed into diagnostic tools. In one example, scientists engineered a glow-in-the-dark tag to monitor a cell’s status, detecting the presence of a hormone when the binder grabbed onto it.

But binders remain hard to develop. They need to interact with key regions on proteins. But because proteins are dynamic 3D structures that twist and turn, it’s often tough to nail down which regions are crucial for binders to latch onto.

Then there’s the data problem. Thanks to hundreds of thousands of protein structures available in public databases, generative AI models can learn to predict protein-protein interactions. Binders, by contrast, are often kept secret by pharmaceutical companies—each organization has an in-house database cataloging how small molecules interact with proteins.

Several teams are now using AI to design simple binders for research. But experts stress these need to be tested in living organisms. AI can’t yet predict the biological consequences of a binder—it could either boost a process or shut it down. Then there’s the problem of hallucination, where an AI model dreams up binders that are completely unrealistic.

From here, the goal is to gather more and better data on how proteins grab onto molecules, and perhaps add a dose of their underlying biophysics.

Designing New Enzymes

Enzymes are proteins that catalyze life. They break down or construct new molecules, allowing us to digest food, build up our bodies, and maintain healthy brains. Synthetic enzymes can do even more, like sucking carbon dioxide from the atmosphere or breaking down plastic waste.

But designer enzymes are still tough to build. Most models are trained on natural enzymes, but biological function doesn’t always rely on the same structure to do the same thing. Enzymes that look vastly different can perform similar chemical reactions. AI evaluates structure, not function—meaning we’ll need to better understand how one leads to the other.

Like binders, enzymes also have “hotspots.” Scientists are racing to hunt these down with machine learning. There are early signs AI can design hotspots on new enzymes, but they still need to be heavily vetted. An active hotspot usually requires a good bit of scaffolding to work properly—without which it may not be able to grab its target or, if it does, let it go.

Enzymes are a tough nut to crack especially because they’re in motion. For now, AI struggles to model their transformations. This is, as it turns out, a challenge for the field at large.

Shape-Shifting Headaches

AI models are trained on static protein structures. These snapshots have been hard won with decades of work, in which scientists freeze a protein in time to image its structure. But these images only capture a protein’s most stable shape, rather than its shape in motion—like when a protein grabs onto a binder or when an enzyme twists to fit into a protein nook.

For AI to truly “understand” proteins, researchers will have to train models on the changing structures as proteins shapeshift. Biophysics can help model a protein’s twists and turns, but it’s extremely difficult. Scientists are now generating libraries of synthetic and natural proteins and gradually mutating each to see how simple changes alter their structures and flexibility.

Adding a bit of “randomness” to how an AI model generates new structures could also help. AF-Cluster, built on AlphaFold2, injected bits of uncertainty into its neural network processes when predicting a known shape-shifting protein and did well on multiple structures.

Protein prediction is a competitive race. But teams will likely need to work together too. Building a collaborative infrastructure for the rapid sharing of data could speed efforts. Adding so-called “negative data,” such as when AI-designed proteins or binders are toxic in cells, could also guide other protein designers. A harder problem is that verifying AI-designed proteins could take years—when the underlying algorithm has already been updated.

Regardless, there’s no doubt AI is speeding protein design. Let’s see what next year has to offer.

Image Credit: Baker Lab

While companies like Neuralink have recently provided some flashy demos of what could be achieved by hooking brains up to computers, the technology still has serious limitations preventing wider use.

Non-invasive approaches like electroencephalograms (EEGs) provide only coarse readings of neural signals, limiting their functionality. Directly implanting electrodes in the brain can provide a much clearer connection, but such risky medical procedures are hard to justify for all but the most serious conditions.

California-based startup Science Corporation thinks that an implant using living neurons to connect to the brain could better balance safety and precision. In recent non-peer-reviewed research posted on bioarXiv, the group showed a prototype device could connect with the brains of mice and even let them detect simple light signals.

“The principal advantages of a biohybrid implant are that it can dramatically change the scaling laws of how many neurons you can interface with versus how much damage you do to the brain,” Alan Mardinly, director of biology at Science Corporation, told New Scientist.

The company’s CEO Max Hodak is a former president of Neuralink, and his company also produces a retinal implant using more conventional electronics that can restore vision in some patients. But the company has been experimenting with so-called “biohybrid” approaches, which Hodak thinks could provide a more viable long-term solution for brain-machine interfaces.

“Placing anything into the brain inevitably destroys some amount of brain tissue,” he wrote in a recent blog post. “Destroying 10,000 cells to record from 1,000 might be perfectly justified if you have a serious injury and those thousand neurons create a lot of value—but it really hurts as a scaling characteristic.”

Instead, the company has developed a honeycomb-like structure made of silicon featuring more than 100,000 “microwells”—cylindrical holes roughly 15 micrometers deep. Individual neurons are inserted into each of these microwells, and the array can then be surgically implanted onto the surface of the brain.

The idea is that while the neurons remain housed in the implant, their axons—long strands that carry nerve signals away from the cell body—and their dendrites—the branched structures that form synapses with other cells—will be free to integrate with the host’s brain cells.

To see if the idea works in practice they installed the device in mice, using neurons genetically modified to react to light. Three weeks after implantation, they carried out a series of experiments where they trained the mice to respond whenever a light was shone on the device. The mice were able to detect when this happened, suggesting the light-sensitive neurons had merged with their native brain cells.

While it’s early days, the approach has significant benefits. You can squeeze a lot more neurons into a millimeter-scale chip than electrodes and each of those neurons can form many connections. That means the potential bandwidth of a biohybrid device could be much more than a conventional neural implant. The approach is also much less damaging to the patient’s brain.

However, the lifetime of these kinds of devices could be a concern—after 21 days, only 50 percent of the neurons had survived. And the company needs to find a way to ensure the neurons don’t illicit a negative immune response in the patient.

If the approach works though, it could be an elegant and potentially safer way to merge man and machine.

Image Credit: Science Corporation

Teasing apart the intricacies of human longevity is complicated. Diet, exercise, and other habits can change the trajectory of a person’s health as they grow older. Genetics also plays a role—especially during the twilight years. But experiments to test these ideas are difficult, in part because of our relatively long lifespan. Following a large population of people as they age is prohibitively expensive, and results could take decades. So, most studies have turned to animal aging models—including flies, rodents, and dogs—with far shorter lives.

But what if we could model human “aging in a dish” using cells derived from people with exceptionally long lives?

A new study, published in Aging Cell, did just that. Leveraging blood draws from the New England Centenarian Study—the largest and most comprehensive database of centenarians—they transformed blood cells into induced-pluripotent stem cells (iPSCs).

These cells contain their donor’s genetic blueprint. In essence, the team created a biobank of cells that could aid researchers in their search for longevity-related genes.

“Models of human aging, longevity, and resistance to and/or resilience against disease that allow for the functional testing of potential interventions are virtually non-existent,” wrote the team.

They’ve already shared these “super-aging” stem cells with the rest of the longevity community to advance understanding of the genes and other factors contributing to a healthier, longer life.

“This bank is really exciting,” Chiara Herzog, a longevity researcher at Kings College London, who was not involved in the study, told Nature.

Precious Resource

Centenarians are rare. According to the Pew Research Center, based on data from the US Census Bureau, they make up only 0.03 percent of the country’s population. Across the globe, roughly 722,000 people have celebrated their 100th birthday—a tiny fraction of the over eight billion people currently on Earth.

Centenarians don’t just live longer. They’re also healthier, even in extreme old age, and less likely to suffer age-related diseases, such as dementia, Type 2 diabetes, cancer, or stroke. Some evade these dangerous health problems altogether until the very end.

What makes them special? In the last decade, several studies have begun digging into their genes to see which are active (or not) and how this relates to healthy aging. Others have developed aging clocks, which use myriad biomarkers to determine a person’s biological age—that is, how well their bodies are working. Centenarians frequently stood out, with a genetic landscape and bodily functions resembling people far younger than expected for their chronological age.

Realizing the potential for studying human aging, the New England Centenarian Study launched in 1995. Now based at Boston University and led by Tom Perls and Stacy Andersen, both authors of the new study, the project has recruited centenarians through a variety of methods—voter registries, news articles, or mail to elderly care facilities.

Because longevity may have a genetic basis, their children were also invited to join, with spouses serving as controls. All participants reported on their socioeconomic status and medical history. Researchers assessed their cognition on video calls and screened for potential mental health problems. Finally, some participants had blood samples taken. Despite their age, many centenarians remained sharp and could take care of themselves.

Super-Ager Stem Cells

The team first tested participants with a variety of aging clocks. These measured methylation, which shuts genes down without changing their DNA sequences. Matching previous results, centenarians were, on average, six and a half years younger than their chronological age.

The anti-aging boost wasn’t as prominent in their children. Some had higher biological ages and others lower. This could be because of variation in who inherited a genetic “signature” associated with longevity, wrote the team.

They then transformed blood cells from 45 centenarians into iPSCs. The people they chose were “at the extremes of health and functionality,” the team wrote. Because of their age, they initially expected that turning back the clock might not work on old blood cells.

Luckily, they were wrong. Several proteins showed the iPSCs were healthy and capable of making other cells. They also mostly maintained their genomic integrity—although surprisingly, cells from three male centenarians showed a slight loss of the Y chromosome.

Previous studies have found a similar deletion pattern in blood cells from males over 70 years of age. It could be a marker for aging and a potential risk factor for age-related conditions such as cancer and heart disease. Women, on average, live longer than men. The findings “allow for interesting research opportunities” to better understand why Y chromosome loss happens.

Unraveling Aging

Turning blood cells into stem cells erases signs of aging, especially those related to the cells’ epigenetic state. This controls whether genes are turned on or off, and it changes with age. But the underlying genetic code remains the same.

If the secrets to longevity are, even only partially, hidden in the genes, these super-aging stem cells could help researchers figure out what’s protective or damaging, in turn prompting new ideas that slow the ticking of the clock.

In one example, the team nudged the stem cells to become cortical neurons. These neurons form the outermost part of the brain responsible for sensing and reasoning. They’re also the first to decay in dementia or Alzheimer’s disease. Those derived from centenarians better fought off damage, such as rapidly limiting the spread of toxic proteins that accumulate with age.

Researchers are also using the cells to test for resilience against Alzheimer’s. Another experiment observed cell cultures made of healthy neurons, immune cells, and astrocytes. The latter, supporting cells that help keep brains healthy, were created using centenarian stem cells. Astrocytes have increasingly been implicated in Alzheimer’s, but their role has been hard to study in humans. Those derived from centenarian stem cells offer a way forward.

Each line of centenarian stem cells is linked to its donor—their demographics, cognitive, and physical state. This additional information could guide researchers in choosing the best centenarian cell line for their investigations into different aspects of aging. And because the cells can be transformed into a wide variety of tissues that decline with age—muscles, heart, or immune cells—they offer a new way to explore how aging affects different organs, and at what pace.

“The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease,” wrote the authors.

Image Credit: Danie Franco on Unsplash

Pre-eclampsia causes 75,000 maternal deaths and 500,000 fetal and newborn deaths every year around the globe. Trademark signs of the condition are extreme high blood pressure, reduced blood flow to the placenta, and sometimes seizures. Existing drugs, such as those that lower blood pressure, manage the symptoms but not the underlying causes.

“There aren’t any therapeutics that address the underlying problem, which is in the placenta,” study author Kelsey Swingle at the University of Pennsylvania told Nature.

Thanks to previous studies in mice, scientists already have an idea of what triggers pre-eclampsia: The placenta struggles to produce a protein crucial to the maintenance of structure and growth. Called vascular endothelial growth factor (VEGF), the condition inhibits the protein’s activity, interfering with the maternal blood vessels supporting placental health.

Restoring the protein could treat the condition at its core. The challenge is delivering it.

The team developed a lipid-nanoparticle system that directly targets the placenta. Like in Covid vaccines, these fatty “bubbles” are loaded with mRNA molecules that instruct cells to make the missing protein. But compared to standard lipid nanoparticles used in mRNA vaccines, the new bubbles—dubbed LNP 55—were 150 times more likely to home in on their target.

In two mouse models of pre-eclampsia, a single shot of the treatment boosted VEGF levels in the placenta, spurred growth of healthy blood vessels, and prevented symptoms. The treatment didn’t harm the fetuses. Rather, it helped them grow, and the newborn mouse pups were closer to a healthy weight.

The new approach is “an innovative method,” wrote Ravi Thadhani at Emory University and Ananth Karumanchi at the Cedars-Sinai Medical Center, who were not involved in the study.

A Surprising Start

The team didn’t originally focus on treating pre-eclampsia.

“We’re a drug delivery lab,” study author Michael Mitchell told Nature. But his interest was piqued when he started receiving emails from pregnant mothers, asking whether Covid-19 mRNA vaccines were safe for fetuses.

A quick recap: Covid vaccines contain two parts.

One is a strand of mRNA encoding the spike protein attached to the surface of the virus. Once in the body, the cell’s machinery processes the mRNA, makes the protein, and this triggers an immune response—so the body recognizes the actual virus after infection.

The other part is a lipid nanoparticle to deliver the mRNA cargo. These fatty bubbles are bioengineering wonders with multiple components. Some of these grab onto the mRNA; others stabilize the overall structure. A bit of cholesterol and other modified lipids lower the chance of immune attack.

Previously, scientists found that most lipid nanoparticles zoom towards the liver and release their cargo. But “being able to deliver lipid nanoparticles to parts of the body other than the liver is desirable, because it would allow designer therapeutics to be targeted specifically to the organ or tissue of interest,” wrote Thadhani and Karumanchi.

Inspired by the emails, the team first engineered a custom lipid nanoparticle that targets the placenta. They designed nearly 100 delivery bubbles—each with a slightly different lipid recipe—injected them into the bloodstream of pregnant mice, and tracked where they went.

One candidate, called LNP 55, especially stood out. The particles collected in the placenta, without going into the fetus. This is “ideal because the fetus is an ‘innocent bystander’ in pre-eclampsia” and likely not involved in triggering the complication, wrote Thadhani and Karumanchi. It could also lower any potential side effects to the fetus.

Compared to standard lipid nanoparticles, LNP 55 was 150 times more likely to move into multiple placental cell types, rather than the liver. The results got the team wondering: Can we use LNP 55 to treat pregnancy conditions?

Load It Up

The next step was finding the right cargo to tackle pre-eclampsia. The team decided on VEGF mRNA, which can fortify blood vessels in the placenta.

In two mouse models of pre-eclampsia in the middle of their pregnancy, a single injection reduced their high blood pressure almost immediately, and their blood pressure was stable until delivery of their pups. The treatment also lowered “toxins” secreted by the damaged placenta.

“This is really exciting outcome, and it suggests that perhaps we’re remolding the vasculature [blood vessel structure] to kind of see a really sustained therapeutic effect,” said Swingle.

The treatment also benefited the developing pups. Moms with pre-eclampsia often give birth to babies that weigh less. This is partly because doctors induce early delivery as a mother’s health declines. But an unhealthy placenta also contributes. Standard care for the condition can manage the mother’s symptoms, but it doesn’t change birth weight. The fetuses look almost “shriveled up” because of poor nutrient and lack of oxygen, said Mitchell.

Pups from moms treated with VEGF mRNA were far larger and healthier, looking almost exactly the same as normal mice born without pre-eclampsia.

A Long Road Ahead

Though promising, there are a few roadblocks before the treatment can help pregnant humans.

Our placentas are vastly different compared to those of mice, especially in their cellular makeup. The team is considering guinea pigs, which surprisingly have placentas more like humans, for future testing. Higher doses of VEGF may also trigger side effects, such as making blood vessels leakier—although the problem wasn’t seen in this study.

Dosing schedule is another problem. Mice are pregnant for roughly 20 days, a sliver of time compared to a human’s 40 weeks. While a single dose worked in mice, the effects may not last for longer pregnancies.

Then there’s timing. In humans, pre-eclampsia begins early when the placenta is just taking shape. Starting the treatment earlier, rather than in the middle of a pregnancy, could have different results.

Regardless, the study is welcome. Research into pregnancy complications has lagged cancer, heart conditions, metabolic disorders, and even some rare diseases. Limited funding aside, developing drugs for pregnancy is far more difficult because of stringent regulations in place to protect mother and fetus from unexpected and potentially catastrophic side effects.

The new work “offers a promising opportunity to tackle pre-eclampsia, one of the most common and devastating medical complications in pregnancy, and one that is in dire need of intervention,” wrote Thadhani and Karumanchi.

Image Credit: Isaac Quesada on Unsplash

Previously thought to be noise left over from evolution, a new study found that some of these tiny DNA snippets can make miniproteins—potentially opening a new universe of treatments, from vaccines to immunotherapies for deadly brain cancers.

The preprint, not yet peer-reviewed, is the latest from a global consortium that hunts down potential new genes. Ever since the Human Genome Project completed its first draft at the turn of the century, scientists have tried to decipher the genetic book of life. Buried within the four genetic letters—A, T, C, and G—and the proteins they encode is a wealth of information that could help tackle our most frustrating medical foes, such as cancer.

The Human Genome Project’s initial findings came as a surprise. Scientists found less than 30,000 genes that build our bodies and keep them running—roughly a third of that previously predicted. Now, roughly 20 years later, as the technologies that sequence our DNA or map proteins have become increasingly sophisticated, scientists are asking: “What have we missed?”

The new study filled the gap by digging into relatively unexplored portions of the genome. Called “non-coding,” these parts haven’t yet been linked to any proteins. Combining several existing datasets, the team zeroed in on thousands of potential new genes that make roughly 3,000 miniproteins.

Whether these proteins are functional remains to be tested, but initial studies suggest some are involved in a deadly childhood brain cancer. The team is releasing their tools and results to the wider scientific community for further exploration. The platform isn’t just limited to deciphering the human genome; it can delve into the genetic blueprint of other animals and plants as well.

Even though mysteries remain, the results “help provide a more complete picture of the coding portion of the genome,” Ami Bhatt at Stanford University told Science.

What’s in a Gene?

A genome is like a book without punctuation. Sequencing one is relatively easy today, thanks to cheaper costs and higher efficiency. Making sense of it is another matter.

Ever since the Human Genome Project, scientists have searched our genetic blueprint to find the “words,” or genes, that make proteins. These DNA words are further broken down into three-letter codons, each one encoding a specific amino acid—the building block of a protein.

A gene, when turned on, is transcribed into messenger RNA. These molecules shuttle genetic information from DNA to the cell’s protein-making factory, called the ribosome. Picture it as a sliced bun, with an RNA molecule running through it like a piece of bacon.

When first defining a gene, scientists focus on open reading frames. These are made of specific DNA sequences that dictate where a gene starts and stops. Like a search function, the framework scans the genome for potential genes, which are then validated with lab experiments based on myriad criteria. These include whether they can make proteins of a certain size—more than 100 amino acids. Sequences that meet the mark are compiled into GENCODE, an international database of officially recognized genes.

Genes that encode proteins have attracted the most attention because they aid our understanding of disease and inspire ways to treat it. But much of our genome is “non-coding,” in that large sections of it don’t make any known proteins.

For years, these chunks of DNA were considered junk—the defunct remains of our evolutionary past. Recent studies, however, have begun revealing hidden value. Some bits regulate when genes turn on or off. Others, such as telomeres, protect against the degradation of DNA as it replicates during cell division and ward off aging.

Still, the dogma was that these sequences don’t make proteins.

A New Lens

Recent evidence is piling up that non-coding areas do have protein-making segments that affect health.

One study found that a small missing section in supposedly non-coding areas caused inherited bowel troubles in infants. In mice genetically engineered to mimic the same problem, restoring the DNA snippet—not yet defined as a gene—reduced their symptoms. The results highlight the need to go beyond known protein-coding genes to explain clinical findings, the authors wrote.

Dubbed non-canonical open reading frames (ncORFs), or “maybe-genes,” these snippets have popped up across human cell types and diseases, suggesting they have physiological roles.

In 2022, the consortium behind the new study began peeking into potential functions, hoping to broaden our genetic vocabulary. Rather than sequencing the genome, they looked at datasets that sequenced RNA as it was being turned into proteins in the ribosome.

The method captures the actual output of the genome—even extremely short amino acid chains normally thought too small to make proteins. Their search produced a catalog of over 7,000 human “maybe-genes,” some of which made microproteins that were eventually detected inside cancer and heart cells.

But overall, at that time “we did not focus on the questions of protein expression or functionality,” wrote the team. So, they broadened their collaboration in the new study, welcoming specialists in protein science from over 20 institutions across the globe to make sense of the “maybe-genes.”

They also included several resources that provide protein databases from various experiments—such as the Human Proteome Organization and the PeptideAtlas—and added data from published experiments that use the human immune system to detect protein fragments.

In all, the team analyzed over 7,000 “maybe-genes” from a variety of cells: Healthy, cancerous, and also immortal cell lines grown in the lab. At least a quarter of these “maybe-genes” translated into over 3,000 miniproteins. These are far smaller than normal proteins and have a unique amino acid makeup. They also seem to be more attuned to parts of the immune system—meaning they could potentially help scientists develop vaccines, autoimmune treatments, or immunotherapies.

Some of these newly found miniproteins may not have a biological role at all. But the study gives scientists a new way to interpret potential functions. For quality control, the team organized each miniprotein into a different tier, based on the amount of evidence from experiments, and integrated them into an existing database for others to explore.

We’re just beginning to probe our genome’s dark matter. Many questions remain.

“A unique capacity of our multi-consortium collaboration is the ability to develop consensus on the key challenges” that we feel need answers, wrote the team.

For example, some experiments used cancer cells, meaning that certain “maybe-genes” might only be active in those cells—but not in normal ones. Should they be called genes?

From here, deep learning and other AI methods may help speed up analysis. Although annotating genes is “historically rooted in manual inspection” of the data, wrote the authors, AI can churn through multiple datasets far faster, if only as a first pass to find new genes.

How many might scientists discover? “50,000 is in the realm of possibility,” study author Thomas Martinez told Science.

Image Credit: Miroslaw Miras from Pixabay

A jab is a standard way to deliver insulin, antibodies, RNA vaccines, GLP-1 drugs such as Ozempic, and other large molecules. Compared to small chemicals—say, aspirin—these drugs often contain molecules that are easily destroyed if taken as pills, making injection the best option.

But no one likes needles. Discomfort aside, they can also cause infection, skin irritation, and other side effects. Scientists have long tried to avoid injections with other drug delivery options—most commonly, pills—if they can overcome the downsides.

This month, researchers from MIT and the pharmaceutical company Novo Nordisk took inspiration from squids to engineer ingestible capsules that burst inside the stomach and other parts of the digestive system.

The pills mimic a squid-like jet to “spray” their cargo into tissue. They make use of two spraying mechanisms. One works best in larger organs, such as the stomach and colon. Another delivers treatments in narrower organs, like the esophagus.

“These innovative devices deliver drugs directly” into the gut with minimal pain and no needles, the researchers wrote. When tested on dogs and pigs, the system delivered insulin, GLP-1-like hormones, and RNA-based molecules to target tissue in amounts similar to injections.

Delivery Headaches

Getting shots, whether for vaccines, antibodies, or cancer treatments, can be stressful. But there’s a reason these medicines require an injection rather than a pill: They’re usually made of larger biological molecules. These include antibodies or RNA-based vaccines that rely on proteins and other complex molecules. Delivering them as a pill is extremely difficult.

Once swallowed, large molecules are often quickly destroyed by digestive enzymes or the liver, limiting their efficacy and increasing the likelihood of potential side effects. But of course, a pill is easier to take compared to getting a shot. So, despite the challenges, scientists have long sought to make pills that can replace injections for vaccines and other medicines.

Ink-Jet Squids

The new study looked to cuttlefish, squid, and octopi for inspiration.

These critters are versatile in their ability to adjust the pressure and direction of their ink jets. The team tapped into the same idea to distribute drugs in the gastrointestinal (GI) tract. By jetting medication directly into tissue, more can be absorbed before the body breaks it down.

“One aspect that I think is important here to appreciate is that the GI tract is composed” of many segments, and each has its own unique challenges, study author Giovanni Traverso told Nature. The stomach is like a balloon, for example, whereas the intestines are more sinewy. These differences require slightly different pressures for the therapy to work. In general, the pressure can’t be too high or it risks damaging the tissue. Pressure too low is also detrimental, in that it can’t deliver enough medication. The direction of the spray also matters.

“Part of the work we did was to define how much force needs to be applied so that the jet can go through the tissue,” said Traverso. They teased out how each part of the gastrointestinal tract absorbs drugs so they could dial in levels absorption without damage. Next, they engineered ingestible capsules that mimic the way squids and octopi project their ink.

The design has two jetting systems—one powered by coiled springs and the other compressed carbon dioxide—that are unleashed by humidity or acid and can target different tissues. The medication is encapsulated in normal-sized pills. One jet shoots the drugs into large organs, such as the stomach. The other jet targets smaller GI pathways, including the small intestines.

Prime Delivery

As proof of concept, the team used their system to deliver insulin in dogs and pigs suffering from diabetes-like conditions.

In one test, the system dramatically increased levels of the test medication—with effects similar to daily insulin injections. Other medications, such as GLP-1 drugs, RNA-type therapies, and antibodies—proteins that fight off infections and cancers—also accumulated at levels similar to injections. After releasing drugs, the biocompatible capsules passed through the digestive tract.

It’s still too early to know if the method would work in people. But the work suggests it just might be possible to one day swap out needles for pills.

“In contrast to a small needle, which needs to have intimate contact with the tissue, our experiments indicated that a jet may be able to deliver most of the dose from a distance or at a slight angle,” study author Graham Arrick said in a press release.

These pills could be used at home for people who need to take insulin or other injected drugs every day, making it easier to manage chronic diseases.

“This is an exciting approach which could be impactful for many biologics” that need to be injected, said Omid Veiseh at Rice University, who was not involved in the research, in the press release. It “is a significant leap forward in oral drug delivery.”

Image Credit: Meressa Chartrand on Unsplash

Called Evo, the AI was inspired by the large language models, or LLMs, underlying popular chatbots such as OpenAI’s ChatGPT and Anthropic’s Claude. These models have taken the world by storm for their prowess at generating human-like responses. From simple tasks, such as defining an obtuse word, to summarizing scientific papers or spewing verses fit for a rap battle, LLMs have entered our everyday lives.

If LLMs can master written languages—could they do the same for the language of life?

This month, a team from Stanford University and the Arc Institute put the theory to the test. Rather than training Evo on content scraped from the internet, they trained the AI on nearly three million genomes—amounting to billions of lines of genetic code—from various microbes and bacteria-infecting viruses.

Evo was better than previous AI models at predicting how mutations to genetic material—DNA and RNA—could alter function. The AI also got creative, dreaming up several new components for the gene editing tool, CRISPR. Even more impressively, the AI generated a genome more than a megabase long—roughly the size of some bacterial genomes.

“Overall, Evo represents a genomic foundation model,” wrote Christina Theodoris at the Gladstone Institute in San Francisco, who was not involved in the work.

Having learned the genomic vocabulary, algorithms like Evo could help scientists probe evolution, decipher our cells’ inner workings, tackle biological mysteries, and fast-track synthetic biology by designing complex new biomolecules.

The DNA Multiverse

Compared to the English alphabet’s 26 letters, DNA only has A, T, C, and G. These ‘letters’ are shorthand for the four molecules—adenine (A), thymine (T), cytosine (C), and guanine (G)— that, combined, spell out our genes. If LLMs can conquer languages and generate new prose, rewriting the genetic handbook with only four letters should be a piece of cake.

Not quite. Human language is organized into words, phrases, and punctuated into sentences to convey information. DNA, in contrast, is more continuous, and genetic components are complex. The same DNA letters carry “parallel threads of information,” wrote Theodoris.

The most familiar is DNA’s role as genetic carrier. A specific combination of three DNA letters, called a codon, encodes a protein building block. These are strung together into the proteins that make up our tissues, organs, and direct the inner workings of our cells.

But the same genetic sequence, depending on its structure, can also recruit the molecules needed to turn codons into proteins. And sometimes, the same DNA letters can turn one gene into different proteins depending on a cell’s health and environment or even turn the gene off.

In other words, DNA letters contain a wealth of information about the genome’s complexity. And any changes can jeopardize a protein’s function, resulting in genetic disease and other health problems. This makes it critical for AI to work at the resolution of single DNA letters.

But it’s hard for AI to capture multiple threads of information on a large scale by analyzing genetic letters alone, partially due to high computational costs. Like ancient Roman scripts, DNA is a continuum of letters without clear punctuation. So, it could be necessary to “read” whole strands to gain an overall picture of their structure and function—that is, to decipher meaning.

Previous attempts have “bundled” DNA letters into blocks—a bit like making artificial words. While easier to process, these methods disrupt the continuity of DNA, resulting in the retention of “ some threads of information at the expense of others,” wrote Theodoris.

Building Foundations

Evo addressed these problems head on. Its designers aimed to preserve all threads of information, while operating at single-DNA-letter resolution with lower computational costs.

The trick was to give Evo a broader context for any given chunk of the genome by leveraging a specific type of AI setup used in a family of algorithms called StripedHyena. Compared to GPT-4 and other AI models, StripedHyena is designed to be faster and more capable of processing large inputs—for example, long lengths of DNA. This broadened Evo’s so-called “search window,” allowing it to better find patterns across a larger genetic landscape.

The researchers then trained the AI on a database of nearly three million genomes from bacteria and viruses that infect bacteria, known as phages. It also learned from plasmids, circular bits of DNA often found in bacteria that transmit genetic information between microbes, spurring evolution and perpetuating antibiotic resistance.

Once trained, the team pitted Evo against other AI models to predict how mutations in a given genetic sequence might impact the sequence’s function, such as coding for proteins. Even though it was never told which genetic letters form codons, Evo outperformed an AI model explicitly trained to recognize protein-coding DNA letters on the task.

Remarkably, Evo also predicted the effect of mutations on a wide variety of RNA molecules—for example, those regulating gene expression, shuttling protein building blocks to the cell’s protein-making factory, and acting as enzymes to fine-tune protein function.

Evo seemed to have gained a “fundamental understanding of DNA grammar,” wrote Theodoris, making it a perfect tool to create “meaningful” new genetic code.

To test this, the team used the AI to design new versions of the gene editing tool CRISPR. The task is especially difficult as the system contains two elements that work together—a guide RNA molecule and a pair of protein “scissors” called Cas. Evo generated millions of potential Cas proteins and their accompanying guide RNA. The team picked 11 of the most promising combinations, synthesized them in the lab, and tested their activity in test tubes.

One stood out. A variant of Cas9, the AI-designed protein cleaved its DNA target when paired with its guide RNA partner.  These designer biomolecules represent the “first examples” of codesign between proteins and DNA or RNA with a language model, wrote the team.

The team also asked Evo to generate a DNA sequence similar in length to some bacterial genomes and compared the results to natural genomes. The designer genome contained some essential genes for cell survival, but with myriad unnatural characteristics preventing it from being functional. This suggests the AI can only make a “blurry image” of a genome, one that contains key elements, but lacks finer-grained details, wrote the team.

Like other LLMs, Evo sometimes “hallucinates,” spewing CRISPR systems with no chance of working. Despite the problems, the AI suggests future LLMs could predict and generate genomes on a broader scale. The tool could also help scientists examine long-range genetic interactions in microbes and phages, potentially sparking insights into how we might rewire their genomes to produce biofuels, plastic-eating bugs, or medicines.

It’s yet unclear whether Evo could decipher or generate far longer genomes, like those in plants, animals, or humans. If the model can scale, however, it “would have tremendous diagnostic and therapeutic implications for disease,” wrote Theodoris.

Image Credit: Warren Umoh on Unsplash

The tomatoes were hideous to look at—small, gnarled, miscolored, and nothing like the perfectly plump and bright beefsteak or Roma tomatoes that eventually flooded supermarkets. But they were oh-so-tasty, with a perfect ratio of tart and sweet flavors that burst in my mouth.

These days, when I ask for the same dish, my mom will always say, “Tomatoes just don’t taste the same anymore.”

She’s not alone. Many people have noticed that today’s produce is watery, waxy, and lacking in flavor—despite looking ripe and inviting. One reason is it was bred that way. Today’s crops are often genetically selected to prioritize appearance, size, shelf life, and transportability. But these perks can sacrifice taste—most often, in the form of sugar. Even broccoli, known for its bitterness, has variants that accumulate sugar inside their stems for a slightly sweeter taste.

The problem is that larger fruit sizes are often less sweet, explains Sanwen Huang and colleagues in Shenzhen, China. The key is to break that correlation. His team may have found a way using a globally popular crop—the tomato—as an example.

By comparing wild and domesticated tomatoes, the team hunted down a set of genes that put the brakes on sugar production. Inhibiting those genes using CRISPR-Cas9, the popular gene-editing tool, bumped up the fruit’s sugar content by 30 percent—enough for a consumer panel to find a noticeable increase in sweetness—without sacrificing size or yields.

Seeds from the edited plants germinated as usual, allowing the edits to pass on to the next generations.

The study isn’t just about satisfying our sweet tooth. Crops, not just tomatoes, with higher sugar content also contain more calories, which are necessary if we’re to meet the needs of a growing global population. The analysis pipeline established in the study is set to identify other genetic trade-offs between size and nutrition, with the goal of rapidly engineering better crops.

The work “represents an exciting step forward…for crop improvement worldwide,” wrote Amy Lanctot and Patrick Shih at the University of California, Berkeley, who were not involved in the study.

Hot Links

For eons, humanity has cultivated crops to enhance desirable aspects—for example, better yields, higher nutrition, or looks.

Tomatoes are a perfect example. The fruit “is the most valuable vegetable crop, worldwide, and makes substantial overall health and nutritional contributions to the human diet,” wrote the team. Its wild versions range in size from cherries to peas—far smaller than most current variants found in grocery stores. Flavor comes from two types of sugars packed in their solid bits.

After thousands of years of domestication, sugars remain the key ingredient to better-tasting tomatoes. But in recent decades, breeders mostly prioritized increasing fruit size. The result are tomatoes that are easily sliced for sandwiches, crushed for canning, or further processed into sauces or pastes. Compared to their wild ancestors, today’s cultivated tomatoes are roughly between 10 to 100 times larger in size, making them far more economical.

But these improvements come a cost. Multiple studies have found that as size goes up, sugar levels and flavor tank. A similar trend has also been found in other large farming fruits.

Ever since, scientists have tried teasing out the tomato’s inner workings—especially genes that produces sugar—to restore its taste and nutritious value. One study in 2017 combined genomic analysis of nearly 400 varieties of tomatoes with results from a human taste panel to home in on a slew of metabolic chemicals that made the fruit taste better. A year later, Huang’s team, who led the new study, analyzed the genetic makeup and cell function of hundreds of tomato types. Domestication was associated with several large changes in the plant’s genome—but the team didn’t know how each genetic mutation altered the fruit’s metabolism.

It’s tough to link a gene to a trait. Our genes, as DNA strands, are tightly wound into mostly X-shaped chromosomes. Like braided balls of yarn, these 3D structures bring genes normally separated on a linear strand into close proximity. This means nearby, or “linked,” genes often turn on or off together.

“Genetic linkage makes it difficult to alter one gene without affecting the other,” wrote Lanctot and Shih.

Fast Track Evolution

The new study used two technologies to overcome the problem.

The first was cheaper genetic sequencing. By scanning through genetic variations between domesticated and wild tomatoes, the team pinpointed six tomato genes likely responsible for the fruit’s sweetness.

One gene especially caught their eye. It was turned off in sweeter tomato species, putting the brakes on the plants’ ability to accumulate sugar. Using the gene-editing tool CRISPR-Cas9, the team mutated the gene so it could no longer function and grew the edited species—along with normal ones—under the same conditions in a garden.

The Sweet Spot

Roughly 100 volunteers tried the edited and normal tomatoes in a blind trial. The CRISPRed tomatoes won in a landslide for their perceived sweetness.

The study isn’t just about a better tomato. “This research demonstrates the value hidden in the genomes of crop species varieties and their wild relatives,” wrote Lanctot and Shih.

Domestication, while boosting yield or size of a fruit, often decreases genetic diversity for a species because selected crops eventually contain mostly the same genetic blueprint. Some crops, such as bananas, can’t reproduce on their own and are extremely vulnerable to fungi. Analyzing genes related to these traits could help form a defense strategy.

Conservation and taste aside, scientists have also tried to endow crops with more exotic traits. In 2021, Sanatech Seed, a company based in Japan, engineered tomatoes using CRISPR-Cas9 to increase the amount of a chemical that dampens neural transmission. According to the company, the tomatoes can lower blood pressure and help people relax. The fruit is already on the market following regulatory approval in Japan.

Studies that directly link a gene to a trait in plants are still extremely rare. Thanks to cheaper and faster DNA sequencing technologies, and increasingly precise CRISPR tools, it’s becoming easier to test these connections.

“The more researchers understand about the genetic pathways underlying these trade-offs, the more they can take advantage of modern genome-editing tools to attempt to disentangle them to boost crucial agricultural traits,” wrote Lanctot and Shih.

Image Credit: Thomas Martinsen on Unsplash

Many living species remain mysterious to science. A database resulting from the project would be a precious resource for monitoring biodiversity. It could also shed light on the genetic “dark matter” of complex life to inspire new biomaterials, medicines, or spark ideas for synthetic biology. Other insights could tailor agricultural practices to ramp up food production and feed a growing global population.

In other words, digging into living creatures’ genetic data is set to unveil “unimaginable biological secrets,” wrote the team.

The problem? A hefty price tag. With an estimated cost of $4.7 billion, even the founders of the project called it a moonshot. However, against all odds, the project has made progress, with 3,000 genomes already sequenced and 10,000 more species expected by 2026.

While lagging its original goal of sequencing roughly 1.7 million genomes in a decade, the project still hopes to hit this goal by 2032—later than the original goalpost, but with a much lower price tag thanks to more efficient DNA sequencing technologies.

Meanwhile, the international team has also built infrastructure to share gene sequencing data, and machine learning methods are further helping the consortium analyze thousands of datasets—helping characterize new species and monitor DNA data for endangered ones.

Expanding the Scope

Genetic material is everywhere. It’s an abundant resource to make sense of life of Earth. As genetic sequencing becomes faster, cheaper, and more reliable, recent studies have begun digging into information represented by DNA from species across the globe.

One method, dubbed metagenomics, captures and analyzes microbial DNA gathered in a variety of environments, from city sewers to boiling hot springs. The method captures and analyzes all DNA from a particular source to paint a broad genetic picture of bacteria from a given environment. Rather than bacteria, the Earth BioGenome Project, or EBP, is aiming to sequence the genomes of individual eukaryotic creatures—basically, those that keep most of their DNA in a nut-like structure, or nucleus, inside each cell.

Humans, plants, fungi, and other animals all fall into this group. In one estimate, there are roughly 10 to 15 million eukaryotic species on our planet. But just a little over two million have been documented.

Sequencing DNA from eukaryotic cells could vastly expand our knowledge of Earth’s genetic diversity. Such a database could also be a treasure trove for synthetic biology. Scientists have already tinkered with the genetic blueprints of life in bacteria and yeast cells. Deciphering—and then reprogramming—their genes has led to advances such as coaxing bacteria cells to pump out biofuels, degradable materials, and medicines such as insulin.

Charting eukaryotes’ genomes could further inspire new materials or medicines. For example, cytarabine, a chemotherapy drug, was initially isolated from a sponge-like sea creature and approved by the FDA to treat blood cancers that spread to the brain. Other plant-derived medications are already being used to tackle viral infections or to control pain. From nearly 400,000 different plant species, hundreds of medicines have already been approved and are on the market. Similarly, deciphering plant genetics have galvanized ideas for new biodegradable materials and biofuels.

Genetic sequences from complex organisms can “provide the raw materials for genome engineering and synthetic biology to produce valuable bioproducts at industrial scale,” wrote the team.

Medical and industrial uses aside, the effort also documents biodiversity. Creating a DNA digital library of all known eukaryotic life can pinpoint which species are most at risk—including species not yet fully characterized—providing data for earlier intervention.

“For the first time in history, it is possible to efficiently sequence the genomes of all known species and to use genomics to help discover the remaining 80 to 90 percent of species that are currently hidden from science,” wrote the team.

Soldiering On

The project has three phases.

Phase one lays the groundwork. It establishes the species to be sequenced, builds digital infrastructure for data sharing, develops an analysis toolkit. The most important goal is to build a reference DNA sequence for species similar in genetic makeup—that is, those in a “family.”

Reference genomes are incredibly important for genetic studies. True to their name, scientists rely on them as a baseline when comparing genetic variants—for example, to track down genes related to inherited diseases in humans or sugar content in different variants of crops.

Phase two of the project will begin analyzing the sequencing data and form strategies to maintain biodiversity. The last phase integrates all previous work to potentially revise how different species fit into our evolutionary tree. Scientists will also integrate climate data into this phase and tease out the impacts of climate change on biodiversity.

The international project began in 2018 and included the US, UK, Denmark, and China, with most DNA specimens sequenced at facilities in China and the UK. Today, 28 countries spanning six continents have signed on. Most DNA material isolated from individual species is directly sequenced on site, reducing the cost of transportation while increasing fidelity.

Not all participants have easy access to DNA sequencing facilities. One institution, Wellcome Sanger, developed a portable DNA sequencing lab that could help scientists working in rural areas to capture the genetic blueprints of exotic plants and animals. The device sequenced the DNA of a type of sunflower with potential medicinal properties in Africa, among other specimens from exotic locations.

EBP follows in the footsteps of other global projects aiming to sequence the Earth’s microbes, such as the National Microbiome Initiative or the Earth Microbiome Project. Once also considered moonshots, these have secured funding from government agencies and private investments.

Despite the enthusiasm of its participants, EBP is still short billions of dollars to guide it to full completion. But the project’s price tag—originally estimated in the billions of dollars—may be far less.

Thanks to more efficient and cheaper genetic sequencing methods, the current cost of phase one is expected to be half the original estimate—around $265 million.

It’s still a hefty sum, but for participants, the resulting database and methods are worth it. “We now have a common forum to learn together about how to produce genomes with the highest possible quality,” Alexandre Aleixo at the Vale Institute of Technology, who participated in the project, told Science.

Given the influence bacterial genetics has already had on biomedicine and biofuels, it’s likely that deciphering eukaryote DNA can spur further inspiration. In the end, the project relies on a global collaboration to benefit humanity.

“The far-reaching potential benefits of creating an open digital repository of genomic information for life on Earth can be realized only by a coordinated international effort,” wrote the team.

Image Credit: M. Richter on Pixabay