In a groundbreaking study shedding light on the molecular intricacies of arousal regulation, researchers from Pompeu Fabra University (UPF) and the Centre for Genomic Regulation (CRG) have unveiled the profound influence of neuronal microexons on behavioral states in zebrafish. This study elucidates how subtle alterations in these tiny genetic fragments can trigger hyperarousal—a state marked by heightened neural excitability and pronounced insomnia-like symptoms—which echoes the pathophysiological features observed in various neurodevelopmental disorders. The implications extend far beyond aquatic models, offering a window into the conserved mechanisms of arousal that potentially inform conditions such as autism spectrum disorder and schizophrenia in humans.

Arousal represents a fundamental neurophysiological process essential for survival, enabling organisms to respond to external and internal stimuli with appropriate behavioral and neural adaptations. This highly conserved mechanism across species ensures a meticulously balanced state, modulating responsiveness between lethargy and sensory hypersensitivity. Dysregulation of this balance manifests clinically as either diminished responsiveness or excessive wakefulness and sensory overload, typical hallmarks of stress and various neurodevelopmental pathologies.

Fundamental to this regulatory system is the diversity of proteins synthesized during development and adulthood through alternative splicing—a sophisticated post-transcriptional gene editing process. Alternative splicing enables the generation of multiple functionally distinct protein isoforms from a single gene, often mediated by the inclusion or exclusion of microexons. Microexons are exceptionally short exonic sequences within neuronal genes that profoundly influence protein function and neuronal circuit dynamics despite their minuscule size.

The investigative team employed zebrafish larvae, leveraging their optical transparency and genetic tractability to scrutinize the behavioral consequences of neural microexon misregulation. Larvae exhibiting abnormal microexon patterns demonstrated conspicuous hyperarousal behaviors including disrupted swim patterns and shortened sleep duration. “These larvae not only sleep less frequently but also take considerably longer to initiate sleep,” remarks first author Tahnee Mackensen. This behavioral hyperactivity parallels neural hyperexcitability, suggesting microexon regulation as a pivotal determinant of neurobehavioral states.

At the cellular signaling level, the researchers identified dysregulated cyclic adenosine monophosphate (cAMP) cascades as a key mediator of the observed hyperactive state. cAMP is a ubiquitous second messenger involved in modulating neuronal excitability and synaptic plasticity. The altered splicing of microexons modulates cAMP synthesis and degradation pathways, leading to an aberrant excitation of forebrain neurons in hyperaroused larvae. Notably, this altered cAMP signaling manifests as heightened cAMP-dependent protein kinase A (PKA) activity and subsequent phosphorylation of the transcription factor CREB, implicating the canonical cAMP-PKA-CREB pathway in the regulation of arousal.

The study’s pharmacological interventions underscore the centrality of cAMP regulation in arousal control. Application of cAMP inhibitors normalized the elevated neural activity and behavioral hyperarousal in mutant fish, whereas artificially elevating cAMP in wild-type fish recapitulated the hyperactive phenotype. This bidirectional modulation fortifies the concept that neuronal cAMP levels function as a ‘thermostat’ for arousal states, fine-tuning neuronal excitability and behavioral responsiveness.

Beyond the immediate findings in zebrafish, this research builds upon prior observations in drosophila models demonstrating that microexon disruption similarly impairs sleep and elevates arousal. “The parallel between species indicates an evolutionarily conserved arousal mechanism,” explains Manuel Irimia, senior author. This conservation implies that microexon misregulation, while mechanistically nuanced, may contribute to the neuropsychiatric symptomatology observed in mammals, including humans.

Human neurological disorders such as autism and schizophrenia are often accompanied by sleep disruption and sensory processing anomalies attributed, in part, to aberrant microexon splicing. While microexon alterations are unlikely to be sole causative factors, they may exacerbate or modulate disease phenotypes by disturbing neural excitability homeostasis. These insights prompt a reevaluation of therapeutic strategies aimed at restoring microexon splicing fidelity or modulating cAMP signaling pathways to alleviate neurodevelopmental symptomatology.

Moreover, the link between this microexon-cAMP pathway and mood disorders such as anxiety and depression opens compelling avenues for future research. The cAMP-PKA-CREB axis has well-documented roles in synaptic plasticity and mood regulation, suggesting that microexon-associated dysregulation could contribute to broader neuropsychiatric conditions. “This discovery might just scratch the surface of a complex regulatory network influencing brain function,” notes Mackensen.

The transparency and genetic accessibility of the zebrafish model provided unparalleled opportunities to visualize and quantify internal states through behavioral readouts. Advanced imaging of larval swimming patterns and direct measurement of neurochemical parameters furnished robust correlative evidence linking genetic alterations to functional outcomes. These technical advancements highlight the integrative power of model organisms in neuroscience.

Importantly, this research received support from an array of prestigious funding bodies, including the Horizon 2020 Framework Programme, the Marie Sklodowska-Curie Actions, and the Wellcome Trust, emphasizing the global significance and collaborative nature of this work. The study’s publication in Science Advances confirms its high impact and relevance to the scientific community.

As the team pursues translational studies, the prospect of correcting arousal imbalances by manipulating cAMP pathways or restoring microexon expression presents a promising frontier. This is especially critical given that aberrant arousal and sleep disturbances profoundly impair quality of life in neurodevelopmental disorders. The findings pave the way for multidisciplinary approaches integrating molecular genetics, neurobiology, and pharmacology to develop targeted interventions.

In summation, the identification of neuronal microexons as key modulators of arousal states via the cAMP-PKA-CREB pathway in zebrafish represents a seminal advance in our understanding of the molecular substrates governing complex behavioral phenotypes. This research not only deciphers fundamental biological mechanisms but also holds translational potential to inform therapeutic avenues for neuropsychiatric conditions marked by disrupted arousal and sleep.

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Subject of Research: Animals

Article Title: Neuronal microexons modulate arousal via the cAMP-PKA-CREB pathway in zebrafish

News Publication Date: 19-Jun-2026

Web References: 10.1126/sciadv.ady8291

Image Credits: UPF – CRG

Keywords: Exons, Gene splicing, Developmental neuroscience, Anxiety, Sleep disorders, cAMP signaling, Zebrafish

In early 2024, a baffling outbreak of H5N1 avian influenza emerged among dairy cattle in the Texas Panhandle, presenting a new and unexpected clinical manifestation of the virus. Unlike the typical respiratory illness caused by H5N1 in other mammals, affected cows exhibited severe necrotizing mastitis, an inflammatory condition predominantly targeting the mammary glands. This atypical tissue tropism challenged existing paradigms and delayed the accurate diagnosis, as initial veterinary investigations focused mainly on bacterial pathogens common to mastitis. The revelation that H5N1 was the etiological agent marked a critical moment in understanding how influenza viruses can adapt to novel hosts and tissues.

The study, recently published in Science Advances by researchers at the University of Pittsburgh School of Public Health, provides pioneering mechanistic insights into this unusual manifestation. The research team, led by Dr. Suresh Kuchipudi, elucidated the molecular basis for the H5N1 virus’s propensity to infect bovine mammary tissue rather than respiratory organs. This phenomenon, now observed in over 100 avian and mammalian species globally, underscores the remarkable plasticity of the H5N1 virus and its ability to exploit unique receptor architectures in different host species.

Traditionally, H5N1 and related influenza viruses target sialic acid glycan receptors present in the respiratory tract, facilitating viral entry primarily via respiratory epithelial cells. However, in bovines, the absence of clinical respiratory disease despite the presence of these glycans suggested a more nuanced receptor interaction paradigm. Kuchipudi’s group hypothesized that not just the presence but the precise molecular subtype and distribution of influenza receptors could dictate tissue tropism. Their investigation focused on delineating the receptor landscape across bovine tissues using cutting-edge glycomic methods combined with binding assays and ultra-high-resolution microscopy.

The team employed glycomics—a comprehensive approach to cataloging glycan structures—collaborating with expert Dr. Lauren E. Pepi from Harvard Medical School. They identified that among various glycan receptors, only a particular subclass known as N-linked sialic acid receptors demonstrated a high affinity for the H5N1 virus. Crucially, these receptors were found to be prevalent and densely expressed in the mammary epithelial cells of the udder but conspicuously scarce in bovine airway tissues. This spatial receptor distribution offers a molecular explanation for the virus’s unusual targeting of the mammary gland, enabling productive viral replication and resulting clinical mastitis.

The discovery sheds light on the critical role of receptor architecture in species-specific influenza virus infection. Prior reports indicated the presence of flu-related glycans in bovine respiratory tissues, but those receptors did not support significant viral binding or replication. The differential receptor expression patterns in respiratory versus mammary tissues effectively reprogrammed the virus’s tissue tropism. This specialization allows H5N1 to hijack the bovine mammary environment as a niche, explaining why infected cattle shed the virus in milk rather than displaying respiratory symptoms.

Beyond animal health, this finding carries important public health implications. The intense viral shedding into milk poses a risk to farm workers exposed to raw milk and raises concerns regarding zoonotic transmission routes. Dr. Kuchipudi points out that while pasteurization effectively neutralizes the virus, the common practice of feeding raw milk to domestic pets, notably cats, can facilitate cross-species transmission. Indeed, previous studies documented feline fatalities linked to H5N1 infection. This highlights the hidden dangers of viral reservoirs in dairy cattle and underscores the need for enhanced surveillance and biosecurity protocols within agricultural settings.

Methodologically, the study stands out for its innovative multimodal approach. The integration of high-precision receptor mapping with functional binding experiments allowed researchers to move beyond conventional histochemical staining. This enabled a granular understanding of the receptor types mediating viral entry, paving the way for predictive models of viral host and tissue susceptibility. By decoding the receptor landscape at this molecular scale, the research team demonstrated an unprecedented ability to identify viral tropism determinants, which could serve as early warning indicators for emergent influenza strains.

Looking forward, these insights form the foundation for a new paradigm in influenza research and surveillance. Scientists can now preemptively screen various animal species—not just for the presence of virus-compatible receptors but for specific tissue vulnerabilities within those hosts. This capability is transformative for anticipating how H5N1 and related viruses might manifest clinically, whether through respiratory symptoms, mastitis-like conditions, or neurological involvement, as previously seen in felines. Such proactive screening could revolutionize public health responses, affording precious time to deploy targeted interventions and prevent widespread outbreaks.

The implications of receptor-based viral tropism extend beyond livestock to potential future zoonoses affecting humans. Understanding the molecular basis of host adaptation provides critical clues into viral evolution and spillover risk. This knowledge also informs vaccine development and therapeutic strategies by highlighting tissue-specific pathways of viral entry and replication. As zoonotic influenza viruses continue to adapt, harnessing receptor glycomics will be vital for staying ahead of viral evolution and protecting both animal and human health.

The study was a collaborative effort involving multiple institutions and experts, including contributors from Pennsylvania State University, Harvard University, and North Dakota State University. Their combined expertise spanned infectious diseases, glycomics, virology, and veterinary medicine, enabling a comprehensive and multidisciplinary approach. Supported by the U.S. Department of Agriculture’s National Institute of Food and Agriculture and Pitt Public Health, this research exemplifies how fundamental science can directly inform and improve agricultural biosecurity and zoonotic disease preparedness.

Ultimately, this investigation marks a significant milestone in understanding influenza virus host adaptation. By revealing the receptor basis for the bovine-specific tissue tropism of H5N1, the study not only clarifies an enigmatic veterinary health issue but also establishes a versatile scientific framework for future viral threat assessment. As influenza viruses continue to evolve in complex ecological contexts, such mechanistic insights will be indispensable tools to foresee their next unexpected moves and mitigate their impact on global health.

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Subject of Research: Animals
Article Title: Receptor Basis of Unusual Tissue Tropism of Avian Influenza H5N1 Clade 2.3.4.4b Virus in Cattle
News Publication Date: 19-Jun-2026
Web References: https://doi.org/10.1126/sciadv.aea2068
Image Credits: Department of Infectious Diseases and Microbiology, School of Public Health, University of Pittsburgh
Keywords: Avian influenza, Glycomics, Public health, Viruses

At the cutting edge of aerospace, energy, and computing, material innovation stands as a critical frontier. Traditionally, companies seeking to enhance the performance and resilience of metals encounter formidable challenges: understanding the intricate behavior of novel materials under real-world conditions often necessitates actual physical synthesis and testing. This step has remained indispensable because even the most sophisticated computational simulations struggle to capture the full complexity of atomic arrangements inherent in many solid materials, particularly metallic alloys. The result is a costly and time-consuming cycle that slows progress in material science and engineering.

A breakthrough has now emerged from a team of researchers at the Massachusetts Institute of Technology (MIT), who have devised a computational framework that promises to transform the way metals are modeled across diverse compositions. Central to this innovation are advanced machine-learning algorithms designed to predict the properties of chemically complex metal alloys with unprecedented accuracy and efficiency. By refining the training datasets used to develop these models, the team has succeeded in encapsulating the vast heterogeneity of atomic environments that characterize disordered metallic materials, thereby enhancing predictive precision.

The research, detailed in a recent publication in the journal Science Advances, offers compelling evidence that machine-learning potentials, when constructed with carefully curated training data, can reliably simulate material properties spanning a broad spectrum of metal alloys and operational conditions. This methodological advancement stands to significantly reduce reliance on expensive experimental validation, enabling faster development cycles and more cost-effective exploration of alloy design spaces.

Rodrigo Freitas, MIT’s TDK Career Development Professor of Materials Science and Engineering and senior author on the paper, emphasizes the versatility of this approach. While the study focuses primarily on metallic alloys—a domain fraught with chemical disorder—the underlying principles and techniques have the potential to be adapted for other classes of materials, including semiconductors and sustainable steels. Freitas envisions wide-ranging applications, from aerospace materials engineered for extreme environments to novel components in energy systems and beyond.

The crux of the challenge in modeling metals lies in capturing the influence of intrinsic chemical disorder on material properties. Two alloys with identical elemental compositions can exhibit starkly different mechanical behaviors depending on their atomic-scale structure; one might be brittle, while another offers impressive ductility. Computational models must therefore operate at the atomic level, simulating interactions between individual atoms to predict macroscopic properties. Over the past twenty years, machine learning has emerged as an indispensable tool for constructing these atomic interaction potentials. However, existing models conventionally assume ordered or near-ordered atomic arrangements, limiting their efficacy when faced with the irregular, heterogeneous atomic environments typical of real-world alloys.

Chemical disorder implies a staggering diversity of local atomic neighborhoods, each subtly unique in terms of bonding patterns and energetic stability. This diversity poses a formidable obstacle for machine learning, which relies heavily on representative training data to generalize effectively. Conventional data-generation approaches involve brute-force sampling, which is computationally prohibitive—often demanding upwards of 100,000 hours of supercomputer time for a single alloy—and lacking in adaptability when alloy compositions shift. Thus, the crux of the innovation lies in constructing training datasets that capture the widest possible range of relevant atomic configurations without redundancy.

Building on prior work where they quantified chemical complexity by analyzing the frequency and distribution of small atomic clusters, Freitas’ group has now developed an information-theoretic approach to optimize training data generation. Through a process of atomic substitutions and iterative refinements, they curate datasets that maximize the diversity of local chemical environments presented to the machine-learning models. This ensures that each training example uniquely contributes to the model’s learning, avoiding the pitfalls of repetitive and uninformative data.

Implemented in this way, the newly trained potentials exhibit marked improvements in predicting metallurgical properties compared to models trained on randomly sampled data or even other sophisticated sampling strategies. The fidelity of these simulations to true chemical bonding dynamics is critical; without it, models risk providing generic insights into material behavior rather than precise predictions applicable to specific alloys and practical conditions. This heightened level of chemical realism opens the door to highly reliable simulations that can stand in place of expensive, time-consuming lab tests.

The team put their methodology through rigorous validation against a variety of metal alloys, assessing the performance of their machine-learning models against industry-standard counterparts developed by tech giants like Google and Microsoft. Remarkably, the MIT-trained models consistently outperformed these much larger, computationally intensive models, demonstrating that thoughtful data curation can eclipse brute-force data volume in enhancing machine learning potentials.

Killian Sheriff, a lead author of the paper and a PhD candidate at MIT, spearheaded extensive testing across alloy systems and a wide array of material properties, supported by complementary efforts from colleagues Daniel Xiao, Yifan Cao, and University of Sheffield’s Lewis R. Owen, who contributed experimental data for benchmarking. This collaborative effort provided comprehensive evidence that the models could predict phase diagrams—a cornerstone of materials science that chart alloy phase stability across temperatures and compositions—with accuracy rivaling direct experimental observations.

Phase diagrams are particularly important because they inform practical metallurgical processes like welding, casting, and heat treatment. Accurately capturing the subtle energetic preferences for different atomic arrangements within alloys is essential to forecasting phase transformations and resultant material properties under various conditions. The models’ ability to reveal these “subtle energetic biases” is a testament to the deep chemical insight that machine-learning potentials can now attain.

Beyond phase stability, the researchers are actively deploying their technique to predict mechanical resilience and radiation damage tolerance in alloys—a critical consideration for materials operating in extreme environments such as nuclear reactors and aerospace applications. Their goal is to design alloys that maintain strength and resist degradation under stressors that include high temperatures, intense radiation, and mechanical loads.

A key element of this initiative is harmonizing these advanced computational tools with existing industrial workflows and engineering software. Freitas underscores the necessity of integrating these innovations seamlessly into current decision-making frameworks if they are to foster widespread adoption in materials development pipelines. This pragmatic orientation aims to ensure that the profound scientific advances translate swiftly and effectively into tangible industrial benefits.

The work has garnered support from the U.S. Air Force Office of Scientific Research, reflecting its strategic significance for advanced materials in defense and aerospace contexts where performance margins are tight and failure costs are high. As industries seek materials that combine unmatched performance, sustainability, and economic viability, the MIT team’s contribution could represent a paradigm shift—linking detailed atomic-level understanding with scalable, robust material design accelerated by intelligent data-driven simulations.

By dismantling the bottleneck of excessive computational costs and enabling high-fidelity modeling of chemically complex alloys, this research charts a path toward rapid, informed innovation in materials science. With this toolkit in hand, scientists and engineers can explore uncharted compositional spaces more confidently, accelerating progress toward next-generation metals engineered for the demands of the future.

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Subject of Research: Machine-learning models for atomic-level simulations of chemically disordered metal alloys

Article Title: Machine learning potentials for modeling alloys across compositions

News Publication Date: 19-Jun-2026

Web References: DOI: 10.1126/sciadv.aea9951

References: Science Advances, 2026

Image Credits: Not provided

Keywords

Materials science, Material properties, Metals, Alloys, Chemical disorder, Machine learning, Atomic simulations, Phase diagrams, Materials engineering, Computational materials science, Artificial intelligence, Chemistry

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