International plans to increase marine protected areas (MPAs) to cover 30 percent of the world’s oceans by 2030 will not be sufficient to conserve many of the world’s largest marine creatures, according to new findings[1] by hundreds of international scientists including KAUST researchers as part of the MegaMove[2] project.
Habitats and ecosystems in the world’s oceans face growing threats, from anthropogenic activities, such as fishing and industry, to the deeper pressures posed by climate change. Tied to the fate of the oceans are the fates of marine life, from the smallest creatures to the largest – marine megafauna. Over a third of marine megafauna species, including the hawksbill turtle, the North Atlantic right whale, and the shortfin mako shark, are now threatened with extinction.
Recent global commitments via the United Nations High Seas Treaty and the Kunming-Montreal Global Biodiversity Framework (GBF) seek to protect, conserve and manage at least 30 percent of the world’s oceans. But this is unlikely to be enough.
“Protecting the ocean requires a clear understanding of how marine life actually uses ocean spaces,” says Carlos Duarte, marine scientist at KAUST. “Imagine if pedestrian crossings were just randomly deployed in cities, rather than being concentrated in the busiest areas where they’re needed most? Understanding how marine megafauna use the oceans for migration, residency, feeding and breeding is critical to developing effective protection and conservation strategies.”
Duarte and hundreds of scientists across the world collaborated to collate and analyze a vast marine megafauna tracking dataset: 11 million geographical positions gathered over three decades from 15,845 tracked individual animals across 121 species of marine megafauna. Their results show that both existing and proposed MPAs will need to be coupled with enforced mitigation strategies, including strict fishing regulations and separation of wildlife and boat traffic, if they are to achieve international goals for marine megafauna conservation.
A monumental moment in movement ecology
“Marine animals are highly mobile, often engaging in large, basin-wide migrations,” says Duarte. “From the largest creatures, such as whales and albatross, to smaller fish like the endangered European eel, they all make epic journeys to breed and feed. Accurately tracking these movements is no small feat.”
Advances in Big Data analytics, which combines data from multiple animal tagging and tracking programs, have made large-scale tracking of marine animals possible. The MegaMove project was founded in 2020 based on Duarte’s vision that collaborative science and Big Data could accelerate understanding of how large marine animals use the oceans. It combines expertise and vast datasets to provide robust evidence for international marine policies and treaties.
KAUST hosted one of the foundational workshops that helped launch MegaMove. But challenges remained, not least persuading scientists to share their hard-earned datasets.
“Animal tagging is very expensive: buying sensor packages, paying for vessels and staff to catch the target animal in the wild, and then paying for satellite data transmission,” says Duarte. “It is perhaps no wonder that researchers have been reluctant to share their data.”
Over the past six years, MegaMove has helped change attitudes toward data sharing in the biologging community, Duarte notes. After demonstrating how large-scale data analytics can reveal patterns in marine animal behavior, researchers are now voluntarily contributing telemetry datasets to the project.
Unlocking the power of telemetry data
For the current study, the team developed their own data analytics tools to unify all the data acquired across species and years into a single ‘biology year’ of movement in the ‘global ocean’. They then classified each individual geo-position based on the activity of each creature, labeling each position as ‘migrating’, ‘feeding’ or ‘reproducing’.
“By aggregating data from individuals and species, we created a synoptic map of the world’s ‘hot spots’ for feeding and reproduction, and highlighted the migration corridors of marine megafauna,” says Duarte. “This revealed previously unknown features of their use of ocean space.”
The team then aligned these data insights, including their synoptic map, with existing MPAs and exclusive economic zones (EEZs). Existing MPAs encompass only 7.5 percent of the total area used by creatures in the tracking dataset, and the animals spent over 85 percent of their time outside these protected areas. The locations of future MPAs therefore need careful consideration, and the team have estimated the best possible configuration of future protected areas to offer optimal protection to marine megafauna.
“Our results should help identify critical ocean areas for protection. Also, individual nations have strong enforcement capabilities within their own EEZ jurisdictions, so they can choose to actively regulate human activities that harm marine animals, and designate specific areas for conservation and protection,” says Duarte. “Considerable challenges remain in securing and enforcing marine protected zones for international waters.”
The researchers also examined global datasets regarding threats to marine wildlife, including fishing intensity, shipping intensity, plastic density, and water temperatures. Every year, an estimated 3,000 great whales are killed by ship strikes, and the MegaMove project has also identified ship strikes as the top source of mortality for whale sharks, the largest fish in the ocean. The team also found noise pollution from human activities to be ubiquitous across the oceans.
Their results suggest that additional forms of ocean management will be needed to curb existing threats and achieve the GBF’s goals. The team is calling for greater scrutiny of fishing and industry practices, increased enforcement, and improved direct management of marine ecosystems. Their findings should help redirect marine traffic to safer corridors and substantially reduce the risks of ship strikes on marine animals.
What’s next
The MegaMove project aims to strengthen the evidence base for marine megafauna conservation and expand participation from researchers around the world.
“Further insights are possible from long-term datasets describing movements of multiple individuals of the same species, but such datasets are not yet available. Long-term tracking data could reveal how animal movements change in response to pressures such as fishing and ocean warming, which we currently understand only anecdotally. Longitudinal studies will also allow us to verify and demonstrate the benefits of well-managed ocean protection zones,” concludes Duarte.
Ten contaminants of emerging concern (CEC) have been found in one of the Red Sea’s most abundant corals, Pocillopora favosa, in a survey of coral reefs along the central Red Sea coast[1]. This highlights how the expansion of human activities and development along the world’s coastlines is having significant effects on coastal and marine environments, not least the increase in effluent discharge into coastal waters.
“CECs are chemicals that originate from human activities, such as pharmaceuticals, steroid hormones, cosmetics and pesticides. The release of most CECs into the environment is not regulated or continuously monitored,” says Mariana Rodrigues, a Ph.D. student who worked on the project under the supervision of Susana Carvalho. “Corals are critical to reef biodiversity, so it is vital to determine whether they are exposed to or affected by these pollutants.”
Saudi Arabia is undergoing rapid coastal development through tourism and urban expansion, which may increase the release of chemical compunds into regional marine systems. The research team sought to establish baseline levels of contamination on local coral reefs now, in order to track changes in the future.
The widespread distribution of the reef-building coral Pocillopora favosa allowed the team to study CEC accumulation in coral tissues across 15 onshore and offshore reefs. Each reef had different levels of exposure and proximity to human development. The team focused on 10 pollutants from different classes, including antibiotics, anti-inflammatories, bronchodilators used in asthma inhalers, and herbicides.
The researchers examined CEC concentrations in both seawater and coral tissue samples, and detected most of their targeted CECs at nearly every site. Notably, the asthma medication salbutamol was found to accumulate in coral tissues relative to surrounding seawater at 81 percent of sites, while the herbicide atrazine was detected at 72 percent of sites. These results highlight the widespread presence of such contaminants in the region.
“While previous studies have detected pharmaceuticals in coastal waters, this research shows that corals are absorbing and accumulating these pollutants,” says Rodrigues. “We also uncovered an unexpected pattern: some contaminants were found in higher concentrations offshore rather than near the coast, suggesting that ocean currents and biological processes can transport and concentrate pollutants far from their original sources.”
Corals can act as biological indicators of long-term chemical exposure, providing scientists with a way to monitor contamination over time rather than relying on snapshots of pollution levels in seawater. Managing pollution from CECs will be challenging, notes Rodrigues, but a combination of strategies can help reduce their impact.
“For example, overprescription and improper disposal of pharmaceuticals contribute significantly to contamination, so help could come through public awareness campaigns and proper medication take-back programs,” she explains.
Data collection and monitoring are critical to determine where and how these chemical residues enter marine environments, while advanced filtration and membrane technologies can remove many CECs before they reach the sea.
“We’d like to see concerted efforts to set limits on the amount of pollutants that can be discharged by industry and wastewater facilities, together with stricter surveillance,” says Rodrigues.
“Our monitoring efforts will continue, and additional results from the city of Al Lith will be published soon,” concludes Carvalho. “Future work will examine other reef organisms, including algae and fish, to better understand how these contaminants move through the marine ecosystem.”
The human brain has an estimated 86 billion neurons, and an even greater number of support cells, called glia, long thought to provide mainly structural support and nourishment while neurons do the real cognitive work.
A KAUST-led study now reveals that this division of labor is not so clean.
As Pierre Magistretti and his colleagues demonstrate, one type of glial support cells in particular — astrocytes, star-shaped glial cells — do more than keep neurons fed[1]. They actively shuttle a molecule called lactate into neurons, where it triggers a chain of events that strengthens synaptic connections involved in core brain functions.
“This observation represents a paradigm shift,” says Magistretti, neuroscientist at KAUST. “It shows that glial metabolism is an integral part of information processing by neurons, with implications for learning and memory.”
At the heart of the finding is a set of proteins called NMDA receptors. These sit at synapses, the junctions where neurons communicate, and govern how strongly signals pass between cells. They are activated by neurotransmitters released by neurons.
But neurotransmitters are not the whole story. The KAUST study shows that astrocyte-derived lactate also acts on NMDA receptors, amplifying their activity and, with it, the strength of synaptic signals.
Lactate is best known for building up in fatigued muscles, but it has a second life in the brain. Astrocytes produce it continuously and ship it to neurons as fuel, a metabolic arrangement that Magistretti’s laboratory first described more than 30 years ago, naming it the ‘astrocyte-neuron lactate shuttle’.
However, lactate’s role does not end at the fuel pump. The new study shows that once inside a neuron, it also acts as a signal — one that alters the cell’s internal chemistry, amplifies NMDA receptor activity and locks in stronger synaptic connections.
This occurs through a finely tuned molecular cascade. Working with collaborators in Europe, the KAUST team found that neurons convert incoming lactate into pyruvate, a reaction that generates NADH and tips the cell’s chemical balance in a way that boosts calcium signaling. That shift tightens the grip of a key enzyme on NMDA receptors, driving a burst of synaptic activity that yields lasting changes in connection strength, cementing memories and deepening learning.
“Our study uncovers a previously unknown molecular mechanism by which lactate regulates brain function,” says Hubert Fiumelli, a research scientist in Magistretti’s lab and co-author of the study. “We show that lactate acts not only as a source of energy, but also as a signaling molecule that directly strengthens communication at synapses.”
The findings further disrupt a century of neuroscientific orthodoxy that cast glial cells as little more than “brain glue” filling gaps between neurons and lactate as a metabolic waste product. But they also point somewhere more practical.
Scientists have long suspected that NMDA receptors hold the key to treating Alzheimer’s disease, schizophrenia, and major depression — conditions in which there is a breakdown in the brain’s ability to form and maintain connections. What has been missing is a clear molecular picture of how those receptors are regulated. The lactate pathway now provides one.
“These findings open new avenues for therapeutic strategies targeting brain metabolism,” Fiumelli says.
A protein by-product from corn processing could help cut the energy cost of industrial chemical purification. Developed by KAUST researchers, the protein can be made into a biodegradable nanofiltration membrane that separates mixtures of industrial chemicals using a fraction of the energy of traditional purification methods[1].
Chemical separations account for a large share of global industrial energy consumption, typically through large-scale heat-driven processes such as evaporation, drying, or distillation. Molecular sieve membranes are a promising, energy-efficient alternative for industrial chemical purification, using selective nanofiltration rather than heat-driven separation. Adopting membrane technology can cut the carbon emissions of a chemical purification step by up to 90 percent.
Yet despite this promise to decrease industrial energy consumption, nanofiltration membranes themselves are not very green, says Claudia Oviedo, a Ph.D. student in Gyorgy Szekely’s lab, who led the research.
“Most membranes are based on synthetic polymers derived from fossil resources, which raises concerns about their sustainability and long-term environmental persistence,” Oviedo explains. “The motivation of our research is to reduce the dependence on fossil-based membrane building blocks by exploring bio-based alternatives, preferably made from abundant agricultural waste.”
An ideal candidate could be a protein called zein, a widely available byproduct of the global corn processing industry, the team showed. “Zein is particularly promising for membrane materials due to its hydrophobicity,” Oviedo says. This low water solubility was the key to turning zein into a solvent-resistant nanofiltration membrane.
Using the hydrophobic corn protein zein, a byproduct of cornstarch and corn syrup production, KAUST researchers developed a high-performance, biodegradable nanofiltration membrane that was tested for separating a toxic impurity from a pharmaceutical product. Reproduced from Oviedo et al., SusMat (2026), licensed under CC BY.
The researchers made their bio-based membranes by dissolving the zein in a green solvent, casting this solution into a thin film, then adding water as a nonsolvent. As water displaced the solvent and the hydrophobic protein chains were squeezed together, they experienced a phenomenon called macromolecular crowding.
“Macromolecular crowding occurs when large molecules are present in high concentrations,” Oviedo says. Repelled by the water, the zein protein chains are forced to interact and organize, packing closely together to form a well-defined structured membrane.
Collaborating with Satoshi Habuchi and his KAUST team, the researchers tracked the process in microscopic detail by adding nanoscale luminous tracers called quantum dots to the starting protein solution.
Once water was added, the quantum dots’ motion gradually separated into two bands, with fast-moving dots in the upper layer and slow-moving dots in the lower layer where the membrane was forming. “This motion indicated the formation of zein-rich and zein-poor domains as the membrane structure developed,” Oviedo says.
Computational molecular dynamics simulations showed that hydrophobic protein-protein interactions drove the zein chains to pack closely and entangle, creating a crowded network where nanoscale pores are surrounded by densely arranged protein chains.
The performance of the new zein membrane’s nanofiltration was competitive with commercial synthetic nanofiltration membranes, the team found, and they used their sustainable membrane material to separate a toxic impurity from a pharmaceutical product.
“Our trials also showed that zein membranes biodegraded rapidly, in contrast to conventional fossil-based membranes that persist in the environment post-disposal,” Szekely says.
The team’s next goal is to evaluate whether bio-based nanofiltration membranes provide lower environmental impacts from cradle to grave. “Our ongoing research focuses on life cycle assessment comparing zein extraction and membrane fabrication with fossil-based membranes,” Szekely says.
A thin-film, flexible optical device that mimics the way the human brain senses and interprets visual information has been developed by KAUST researchers[1]. This “optical synapse” may help address the growing need for more efficient artificial vision systems.
“Today’s cameras and computers usually separate sensing, memory, and processing into different parts, which requires data to move back and forth constantly, wasting both time and energy,” explains Manoj Kumar Rajbhar, who worked on the project supervised by Nazek El-Atab. “Our goal was to move closer to the human visual system, where sensing and processing are tightly linked.”
Previous designs for light-sensitive synaptic devices required both electrical and optical signals to operate. They were composed of complex ‘stacks’ of materials, or utilized unstable compounds such as perovskites or black phosphorus. Rajbhar and colleagues designed a synaptic device controlled entirely by light, improving energy efficiency and eliminating the need for electrical signals. Exposure to different colors of light can strengthen or weaken their device’s response, similar to how synapses in the human brain behave during learning.
“In the brain, synapses don’t only become stronger, they can also become weaker,” says Rajbhar. “That balance is essential for learning new information, filtering noise, forgetting unimportant signals, and adapting to changing environments.”
For example, the researchers imitated the classic Pavlov’s dog experiment, in which a dog learns to associate the sound of a bell with food and begins salivating in response. By using one wavelength of light to represent a bell and another to represent food, they trained the device to associate the ‘bell’ signal with ‘food’ and initiate a ‘salivation’ response.
“This type of training is especially valuable for future machine vision and neuromorphic hardware, because it allows the device to process visual information in a more brain-like way,” says Rajbhar. “Practically, this helps systems become more adaptive and better at tasks such as recognition, memory formation, and decision-making without relying on separate sensing and processing units.”
The team also simplified their device’s structure by using an ultrathin layer of manganese oxide on a flexible silicon substrate. The device works even when the silicon substrate is bent. Manganese oxide is relatively abundant and affordable, supporting future scalability. It is also more environmentally friendly than some other potential materials and hosts multiple oxidation states.
“These mixed oxidation states help create controllable defect states and oxygen vacancies, which are important for tuning how the device stores and modulates information,” says Rajbhar. “We consider these features to play an important role in the light response and memory behavior of the device.”
Their device is capable of real-time image detection and processing, and preforms logical operations compatible with existing semiconductor computing.
“This device could be useful where there is a need for lightweight, low-power systems that can sense visual information and process it locally,” says El-Atab. “Our results support advances in AI hardware, robotics, wearable electronics, and artificial vision. The study also aligns with Saudi Arabia’s Vision 2030 goals in advanced technology and semiconductor research.”
An intelligent optoelectronic transistor can not only sense light but also remember and learn from what it has seen. Developed by researchers at KAUST and Peking University, the “optoelectronic synapse” uses an organic material called gDPP-MeOT2 — a light-absorbing polymer that can also store and transport ions[1].
“A conventional photodetector simply turns light into an electrical signal,” says Yazhou Wang, a postdoc in Sahika Inal’s lab who led the research. “Our device goes a step further – it can process and remember what it sees. Light triggers not only electronic signals but also ionic motion in the material, allowing the device to dynamically adjust its response over time, similar to how synapses in the brain strengthen or weaken with experience,” explains Wang.
By integrating the gDPP-MeOT2 material into an electrically controllable transistor architecture, Inal’s team, in collaboration with Nazek El-Atab’s group at KAUST, showed that the device can mimic key functions of a neural synapse in the human brain. The researchers demonstrated behaviors linked to learning and memory, including the ability to strengthen responses to repeated light signals and transition from short-term to long-term memory. These functions operate at an very low programming voltage of just 0.4 volts and on the timescale of seconds.
Using these capabilities, the team carried out optical logic operations, such as switching between binary states using light, as well as image processing tasks including image denoising through adaptive filtering.
“We were inspired by how the human retina seamlessly combines sensing, processing, and memory in a single energy-efficient system. In contrast, today’s electronics typically separate these functions, which increases complexity and power consumption,” explained Wang. “Our goal is to bring these capabilities within a single material platform, enabling devices that can not only detect light but also learn from it and adapt, much like biological vision systems.”
The device responds to both visible and near-infrared light, covering wavelengths from around 455 nm to 1100 nm. This makes it suitable for vision-related technologies that require fast, built-in image processing. Potential applications include artificial retinas, wearable sensors and autonomous systems that must interpret visual information in real time. The aim is to enable efficient, brainlike sensing and computing without relying on cloud-based processing.
According to Wang, the key challenges are to further speed up the technology and scale it into large, high-density arrays for practical implementation.
“Looking ahead, we aim to make these devices even more ‘brain-like’ by introducing functionalities such as selective responses to different chemical signals,” says Inal. “We are also working to integrate many of these synapses into large arrays, to enable real-time, on-chip visual processing. Ultimately, we hope to build systems that can sense, learn, and make decisions directly at the hardware level.”
A gas sensor that can provide an early warning of battery failure could improve the safety of lithium-ion batteries, used in everything from electric vehicles to grid-scale storage systems.
Lithium-ion batteries are transforming the global energy landscape, but they also come with risks. If a battery is damaged or overheated, it can trigger an unstoppable “thermal runaway” reaction that releases flammable and toxic gases and leads to catastrophic battery failure.
KAUST researchers have developed a sensor designed to detect those gases at a very early stage before conditions escalate[1]. “Our ultimate goal is to demonstrate a closed-loop early warning and response system that could serve as the basis for a practical safety architecture in battery packs,” says Aamir Farooq, who led the team.
When a battery gets too hot, the chemicals inside start to break down and release gases. As pressure builds, a safety valve ruptures to vent them.
This breakdown also generates heat, potentially causing a chain reaction that accelerates gas formation and can ultimately cause the battery to catch fire or explode.
Among the most hazardous gases released during battery failure is hydrogen fluoride (HF), a highly toxic and corrosive compound. Previous studies have shown that HF can be detected during safety venting, before the thermal runaway process takes hold. However, conventional methods for measuring HF rely on bulky equipment and tend to underestimate emissions.
The KAUST team developed a more compact gas-sensing technique called tunable diode laser absorption spectroscopy (TDLAS). Positioned directly above the safety vent, the system provides a much more accurate HF reading.
The researchers tested the TDLAS sensor on two types of lithium-ion batteries, commonly found in laptops and electric vehicles. One type uses a cathode containing nickel, manganese and cobalt (NMC), while the other has a lithium iron phosphate (LFP) cathode. These batteries, charged to either 50 percent or 100 percent of their capacity, were heated to trigger thermal runaway.
The NMC batteries reached thermal runaway at 174–215 °C, while LFP cells were more stable, doing so at 242–249 degrees Celsius. Both types released a small burst of HF during initial safety venting, followed by a much larger burst once thermal runaway had started. “These concentrations of HF are orders of magnitude above the level that is immediately dangerous to life and health,” says Ahmad Alsewailem, a Ph.D. student in the team who led the experimental work.
Crucially, the time between the early and later bursts was about one minute for NMC cells and five minutes for LFP cells. That would be long enough for a miniaturized TDLAS sensor to act as an early warning system in electric vehicle battery packs or grid-scale energy storage systems.
“A single sensor positioned within a battery pack enclosure could monitor the gas environment continuously and trigger an alarm or automatic shutdown the moment HF is detected above a threshold level,” says Janardhanraj Subburaj, a research scientist in the team. “This is faster than other thermal runaway sensors, giving a warning window before the more violent thermal runaway phase.”
The researchers now plan to study how overcharging and mechanical damage can cause similar gas releases, and hope to couple their HF sensor with an automated battery safety system.
Advances in DNA sequencing have expanded our view of the microbial world, but the inability to cultivate most microbes has been a major constraint. Now, a systematic, predictive framework that combines existing genomic and computational modeling approaches to accelerate the discovery and cultivation of novel prokaryotic taxa has been proposed by KAUST researchers, in collaboration with an international team of scientists[1].
“Prokaryotic microbes — bacteria and archaea — are extraordinary microorganisms with unique capabilities that are already proving useful in industrial settings and biotechnological applications,” says Diego Javier Jiménez Avella, who led the perspective study within the research group of Alexandre Rosado at KAUST. “For example, cultivated microorganisms can be used in bioremediation processes, generating bioactive compounds, and enhancing plant growth in agricultural systems.”
Current DNA sequencing technologies have unveiled an enormous diversity of microbes on a global scale, but many potentially useful prokaryotic species remain undiscovered and uncultivated. Isolating and cultivating novel microbial species remains a major bottleneck in microbiology, notes Jiménez.
“We are at a very interesting time in microbiology, where we are revealing a vast microbial world through DNA, but we still cannot access much of it experimentally,” says Rosado. “Without cultivation, we cannot properly understand how these organisms function, or translate that knowledge into applications. There is also an urgency here: environments across the globe are changing rapidly, and we risk losing microbial diversity before we have had a chance to characterize it.”
Cultivation challenges
Scientists still do not fully understand the environmental and nutritional requirements needed to sustain microbial species in laboratory settings. Each microbe requires a unique combination of factors to grow, and this challenge is compounded by competition and interactions between microbial species.
“A wide range of techniques have been developed to isolate and cultivate microbial species from natural environments, but most of these rely on set nutrient and environmental conditions that tend to favor fast-growing, heterotrophic microorganisms,” says Jiménez. “Moreover, conventional isolation methods often disrupt obligate microbial interactions,” adds Jiménez, referring to the close interdependent relationships on which some species depend.
Many microbes live in diverse symbiotic communities and rely on interactions between species to survive. These communities are often disrupted by isolation techniques, causing scientists to lose the more sensitive, rare species — or miss them entirely.
“In many cases, we are simply not recreating the right ecological context,” says Rosado. “The limitation is not the methods themselves, but the fact that they were never designed to capture the full diversity we now know exists.”
Genome-based cultivation strategies have improved knowledge of the nutritional requirements needed to cultivate specific prokaryotic taxa. However, many prokaryotic genomes contain genes with unknown or poorly annotated functions, making it more difficult to understand the microbes’ physiology and predict their metabolic needs.
“Our new approach tries to reduce these challenges by using genomic information to guide cultivation, moving away from trial and error towards more informed hypotheses,” says Jiménez. “What we propose is not a new tool, but a way to connect existing approaches into a more systematic and predictive framework.
Harnessing technological advances
Analyzing and understanding thousands of microbial species in their natural environments requires a combination of multidisciplinary techniques. In their latest paper, the research team proposes combining direct environmental DNA analyses with genome-based metabolic modeling, physiological inference, and the design of tailored growth media to improve the targeted cultivation and isolation of novel taxa.
The team first proposes targeted perturbations in natural ecosystems to increase the abundance of rare or hidden microorganisms that may otherwise remain undetected. Once these microbes have been identified and collected, their genomes can be sequenced, reconstructed, and annotated. Scientists can then infer each microbe’s metabolism and potential growth requirements. Finally, this information can be used, together with AI and machine learning, to design better cultivation strategies.
However, even with a robust, reproducible framework in place, significant challenges remain, say Jiménez and Rosado.
“A large fraction of genes in microbial genomes have unknown functions, which limits how accurately we can predict physiology. While AI and machine learning are helpful, especially for gene annotation and metabolic prediction, they are not shortcuts. They depend on good data and still require experimental validation.”
Wide-reaching potential applications
This study brings together microbiology, bioinformatics, and expertise in AI and machine learning at KAUST, and represents an important step toward improving microbial cultivation.
“Once you bring new microbes into culture, you can understand their biology and begin to use it,” says Rosado. “This can translate into environmental biotechnology, agriculture, and medical applications. For example, microbes that degrade pollutants or plastics, or that support plant growth under extreme conditions, are particularly relevant in regions like Saudi Arabia.”
“We have already identified previously uncharacterized microbial taxa, discovered novel enzymes, and generated new insights into environmental microbiomes from Saudi Arabia and beyond,” says Jiménez.
“Building on these advances, the opportunity ahead is to translate this knowledge into scalable solutions,” concludes Rosado. “From human health and desert agriculture to mangrove restoration, Red Sea ecosystems, and space biology, microbiome-based approaches could support sustainable bio-based approaches aligned with KAUST priorities in energy, water, food, and health, as well as Saudi Arabia’s Vision 2030.”
A simple yet efficient method of generating models of early human embryo development offers a robust platform for investigating infertility and developmental disorders. Developed by an international team led by KAUST, the model is already providing biological insights into the earliest stages of blastocyst formation[1].
“We were motivated by a fundamental question in biology: how does a single cell organize into a complex embryo during the earliest stages of development?” says Mo Li, who supervised the study. “These stages, particularly blastocyst formation, are critical for implantation and successful pregnancy, yet they remain poorly understood.”
Amid global fertility decline and increasing reliance on treatments such as in vitro fertilization (IVF), there is a clear need to develop reproducible, ethical models of blastoids – early embryo-like structures – from human naïve pluripotent stem cells.
“Many IVF failures occur at or before the blastocyst stage, highlighting the need to better understand early embryonic mechanisms and improve clinical outcomes,” says Arun Pandian Chandrasekaran, a postdoc who worked on the project.
A fortuitous discovery
The team made an unexpected discovery in the lab while they were exploring potential new models for trophectoderm, one of the key lineages of the blastocyst. They observed an unusual developmental phenotype during experiments, and traced the trigger for this phenotype to a specific small molecule.
“The reagent dimethyl sulfoxide (DMSO) is widely used as a solvent to dissolve a broad range of chemicals and as a cryoprotectant cell preservation. We noticed that it appeared to be doing something far more profound in our setup, so we followed the science,” says Li.
“Existing trophectoderm and blastoid models typically rely on combinations of multiple signaling molecules,” says Samhan Alsolami, a former KAUST team member, now at the Salk Institute for Biological Studies in California, United States. Alsolami remains affiliated with KAUST via the Ibn Rushd fellowship program. “We found that DMSO alone could induce trophectoderm-like differentiation. Building on this, we showed that DMSO is sufficient to drive robust blastoid formation from human pluripotent stem cells without the need for any additional factors,” he explains.
The simplicity of the model surprised the team, who were expecting to need a complex cocktail of molecules and signals to generate functioning blastoids.
Using this model, the researchers uncovered a crucial biological mechanism that helps the embryo correctly form its first cavity. They found that tiny structures called lysosomes, located inside cells, must function properly for this cavity to form. The team proved that the molecular pump V-ATPase plays a central role in driving and regulating blastoid cavitation. Furthermore, DMSO treatment upregulates V-ATPase activity to ensure the cavity forms correctly.
“This discovery opens the door to numerous further investigations, including examining why early developmental failure happens in some embryos but not others,” says Yiqing Jin from the KAUST team. “Our model may help researchers understand early implantation-related defects, pregnancy loss, and developmental abnormalities that begin before pregnancy is even clinically recognized.”
“The model could also be used to investigate congenital disease mechanisms in a controlled setting,” adds Alsolami. “The system’s simplicity and reproducibility make it well suited for mechanistic studies and screening approaches.”
Future goals
The KAUST team is extending this work to build models that capture all stages of embryogenesis with greater fidelity. In particular, Chandrasekaran is developing a model that captures the features of day 14 of human embryogenesis.
“Our vision is not only to generate embryo models but to use them as discovery platforms,” concludes Li. “We hope to combine stem cell biology with imaging, genomics, and functional perturbation approaches to answer deeper questions about how mammalian embryos self-organize, why development sometimes fails, and how these earliest events influence health later in life.”
Modern biology is awash in data. Scientists can sequence DNA, track gene activity cell-by-cell, map proteins in space, and image tissues at microscopic resolution. However, it is a struggle to put all that information together to form a cohesive view.
A KAUST-led vision for artificial intelligence (AI) could help bridge that gap. An AI system that combines multiple biological data modalities into a single model has been described by members of the AI4BioMedicine lab in the Biomedical Division. Called a “super transformer”, the new AI architecture aims to turn today’s fragmented measurements by different technologies into a more coherent picture of life inside cells and tissues[1].
“This bridges the gaps between siloed computational approaches,” says Jesper Tegnér, professor of bioscience and computer science at KAUST who led the work.
“Such integration will be necessary if AI is to move beyond narrow, single-purpose biological analyses,” says research scientist Sumeer Khan, who co-authored the paper with Xabier Martínez de Morentin, a postdoctoral researcher. The proposed architecture, he explains, is meant to “facilitate scalable integration across data types” and thus provide a framework that can be used across genomic and biomedical research.
The idea fits into a broader effort by Tegnér and his KAUST colleagues to build AI systems that can both integrate biological measurements and explain what they have inferred from them. A single model that can learn from DNA sequences, gene activity, tissue images, and other data simultaneously could begin to link cause and effect across levels of biology, connecting genetic changes to altered cells, tissues, and, eventually, disease.
The appeal of such a system becomes clearer when considering how biological data are analyzed today. Most computational tools are built for a single task: one algorithm for DNA sequences, another for single-cell gene expression, another for tissue images. Integrating their outputs often requires bespoke pipelines, expert judgment, and guesswork. And as datasets grow in scale and complexity, this patchwork approach begins to fray.
Transformers offer one possible way forward. Originally developed for language processing, they were designed to understand how words relate to one another across sentences, paragraphs or extended treatises.
Their key innovation lies in a machine-learning technique called “attention,” which enables models to weigh relationships across a dataset. Rather than processing information strictly in order, a transformer learns which elements matter most to one another, even when they are far apart. That ability proved essential for systems that translate languages or summarize documents, and the same logic applies to biology.
Biological systems are full of distant and indirect interactions. Genes influence one another across long stretches of DNA. Cells respond to signals from their neighbors and from faraway tissues. Molecular events, including disease-related deficiencies, can ripple upward to shape organs and whole organisms. In that sense, life — and disease — have a grammar of their own, and transformers trained on enough data may be able to learn it.
The “super” in “super transformer” reflects an ambition to extend this approach. Rather than applying transformers to a single data type at a time, the KAUST team envisions an architecture that can handle multiple modalities simultaneously. In their proposal, DNA sequences, gene-expression profiles, spatial maps and images would all be translated into a shared internal representation, then linked through the same attention-based machinery.
That vision comes with important caveats. As Tegnér and his collaborators reported late last year, architectural choices and the fine structure of large neural networks can strongly influence robustness, bias, and interpretability, particularly when biological data are noisy or incomplete[2]. Scaling models without careful design can amplify spurious correlations rather than reveal meaningful structure.
Those concerns motivate the design of the next generation of AI systems tailored for biomedicine — with innovations emerging from across KAUST laboratories. Taken together, these efforts point toward a future in which AI acts less like a collection of specialized tools and more like a unifying layer for biology. It could integrate diverse data, reason across them, and return answers that make sense to human researchers.
In that light, the “super transformer” is best understood not as a finished product, but as a blueprint for how biological AI might finally be built to connect scales rather than fragment them.
Many practical challenges remain, from data standardization to computational cost. Still, Tegnér argues that the direction is clear. Biology no longer lacks data; it lacks a way to see the whole.
