Understanding Biological Processes

Our new study shows that SARS-CoV-2 spike protein accumulates & persists in the body for years after infection, especially in the skull-meninges-brain axis, potentially driving long COVID. mRNA vaccines help but cannot stop it🔬🧠🦠🧵👇 🔬 Using our cutting-edge DISCO tissue clearing technology, we mapped spike protein presence in mouse models and human post-mortem tissues. Key findings: 1️⃣ Spike protein accumulates in the skull marrow niches and skull-meninges connections (SMCs), a newly discovered route into the brain. 2️⃣ Spike protein persists in brain tissue even when PCR tests show no viral presence, indicating a longer half-life than viral particles. 3️⃣ The spike protein is associated with vascular, inflammatory changes and neuronal injury, leading to proteomic changes linked to neurodegeneration. 4️⃣ Long COVID patients exhibited elevated neurodegeneration markers in cerebrospinal fluid, such as Tau protein and NfL. 5️⃣ mRNA vaccines (e.g., BioNTech/Pfizer) significantly reduced spike protein accumulation but did not eliminate it completely. These findings open pathways for new diagnostic and therapeutic strategies to tackle long COVID and its neurological sequelae. For instance: • Neural injury markers in cerebrospinal fluid could help evaluate long COVID. • Spike protein removal or inhibition could become a therapeutic focus. Read the full study in Cell Host & Microbe: 🔗 https://lnkd.in/dPYc5uVW Let’s work together to uncover more about the lasting impact of COVID-19 and drive solutions for those suffering from its long-term effects. #COVID19 #LongCOVID #Neuroscience #DISCO #Clearing #Immunology #AIinScience #Proteomics

Your immune system is not mine. And that changes everything. Exposed to the same virus, each of us produces a distinct antibody profile. Not just in quantity, but in precision: the regions of the virus we target, the proteins we recognize, the memory we build. Age, biological sex and genetics reach into the very architecture of how we respond to infections. A study just published in Nature Immunology, from teams at Institut Pasteur, CNRS and the Collège de France, makes this strikingly clear — and speaks directly to one of Pasteur 2030's growing research priorities: understanding how individual biological factors shape immunity and what that means for human health. The study analyzed antibodies from 1,000 healthy individuals against more than 90,000 viral protein fragments. Age alone accounts for over half of the variation in our antibody repertoire. Against influenza H1N1 and H3N2, younger adults target the variable surface of the virus while older individuals shift toward its stable core. Women and men mount different responses to the same flu strains, despite comparable vaccination rates. And against a shared pathogen, European and African cohorts produce antibodies targeting entirely different proteins — shaped by geography and exposure history. Each body writes its own immunological story. For decades, we have designed vaccines and treatments as if immune responses were universal. Integrating this variability — across individuals, populations, and geographies underrepresented in global research — is a rethinking of medicine's foundations. Congratulations to Lluis Quintana-Murci and all the teams behind this landmark work. #Immunology #Vaccines #PersonalizedMedicine 

🔬 Key Insight: GLP-1 therapies aren’t just about weight loss anymore. A compelling new review by Dr. Dan Drucker and colleagues https://lnkd.in/eSup6R3u r. shows that the blockbuster class of GLP-1-based medicines are emerging as powerful anti-inflammatory agents — and that may help explain why they’re impacting so many conditions beyond obesity and diabetes. Here are the top take-aways you’ll want to know: 1. Broad disease impact beyond weight control with meaningful benefits in diseases such as cardiovascular disease, chronic kidney disease, liver disease, osteoarthritis, sleep apnea, neurodegeneration, and more. 2. Inflammation reduction as a key mechanism. While improved glucose metabolism and weight loss matter, a large portion of the benefit appears to stem from direct or indirect anti-inflammatory effects that are independent of those metabolic changes. 3. Multi‐organ, multi‐pathway effects. The review maps how GLP-1 receptor agonists act across organs (liver, kidney, cardiovascular, CNS, pulmonary, intestine, joints) via immune, vascular, neural and inter-organ communication pathways. 4. Clinical and preclinical evidence aligning. Examples include: reductions in CRP and pro-inflammatory cytokines; mouse and human studies showing immune modulation; and trials in osteoarthritis where pain/inflammation measures improved, even before major weight loss. 5. Implications for strategy, not just therapy. For healthcare leaders, biotech innovators, and strategy folks: this signals that the value proposition for GLP-1 therapies isn’t solely weight loss or glycaemic control — its anti-inflammation opens new therapeutic categories, pricing/benefit models, and organisational partnerships (endocrinology + immunology, metabolism + organ disease). ⸻ 💡 Why this matters for leaders and innovators:    •   It’s a moment where the mechanism of action is expanding: weight loss is important, but perhaps no longer sufficient to explain the full benefit.    •   This opens a “platform” view: GLP-1 therapies as organ-protective/inflammation-modulating agents, not just anti-obesity drugs.    •   It requires rethinking product positioning, outcome metrics, reimbursement models, and cross‐discipline collaborations (e.g., metabolic, renal, neuro, immunology).    •   It is a call to education: there are strong diagrams and mechanistic maps in the article that can be used for teaching, internal innovation workshops, or framing strategy discussions. ⸻ 📖 If you’re working in biopharma, healthcare strategy, or wellness innovation, this is one review worth reading. It helps contextualise how the “hottest thing in GLP-1” is not just more % weight loss — it’s why that weight loss connects to so many diseases, and what lies beyond. #GLP1 #Inflammation #Metabolism #Biotech #HealthcareInnovation #DrugMechanism #ChronicDisease

A groundbreaking development in biohybrid robots has brought us closer to creating machines powered by real human muscle cells. Researchers, led by Shoji Takeuchi from Tokyo University, have built a full-size robotic hand, complete with five fingers, using lab-grown human muscle tissue. This innovative biohybrid hand is powered by muscle fibers that are cultured, rolled into tubes, and then electrically stimulated to create movement. Known as MuMuTAs, these muscle tubes are designed to mimic the contraction and movement of natural muscles, overcoming past challenges with maintaining muscle health in robotic systems. The process behind making these muscle tubes involves growing thin muscle sheets and rolling them into cylindrical shapes, much like sushi rolls. This technique ensures that the cells get the oxygen and nutrients they need, which is crucial to avoid cell death (necrosis) in thicker muscle structures. The MuMuTAs are activated with electrical signals, making them capable of bending, rotating, and generating enough force to perform tasks like playing rock-paper-scissors or even manipulating objects like pipettes. Each MuMuTA can generate 8 mN of force, enough to lift light objects. However, the team faced challenges. For example, the hand's fingers could only move in one direction, and the hand relied on a liquid suspension to keep the muscles functioning. Plus, the muscles fatigued after just 10 minutes of use, highlighting a need for further development. The next steps will involve improving muscle endurance and creating systems to keep the muscles alive outside the liquid medium. Research Paper 📄 https://lnkd.in/e8hweZZR

Collaborative innovation combining AI with neuropsychology is proving to be transformative. Six research clusters show specific value and potential: 🌱 Neuroscience and Mental Health: Understanding mental health through neuroimaging and machine learning enables earlier, more precise interventions for conditions like ADHD and depression. By examining correlations in brain function, this research helps identify key markers for cognitive impairments, aiding in early diagnosis and personalized treatment plans. 🔍 Computational Modeling: Computational models simulate decision-making and cognitive markers, which are crucial for neurological conditions like epilepsy. Machine learning applied to seizure detection, for instance, offers a potential breakthrough in predicting and managing epilepsy, helping patients gain better control and care. 🧠 Cognitive Neuroscience: Studies of cognitive decline and neurodegenerative diseases, such as Alzheimer’s, benefit from reinforcement learning models that reveal patterns in brain degeneration. These insights are essential for developing strategies to slow disease progression, offering hope for more effective interventions. 💡 Cognitive Neurology and Neuropsychology: Examining cognitive functions through neuroimaging and machine learning provides deeper insights into disorders like aphasia and neurocognitive deficits. By mapping brain functions and assessing structural changes, these studies advance our understanding of how specific neurological impairments affect behavior and cognition. 💗 Neuropsychological Features: Machine learning models predict mental health outcomes and cognitive declines by analyzing attention and processing speed. This focus on prediction and prevention, especially for conditions like cardiovascular disease impacting cognition, enables proactive care and lifestyle adjustments to mitigate risks. ⚙️ Neurodegenerative Conditions: AI-based predictive models for neurodegenerative diseases like Parkinson’s allow for early, more accurate diagnoses. By analyzing markers in social cognition and emotional processing, this cluster supports personalized interventions, helping to maintain patient quality of life and reduce care burdens. This is only the beginning. This field is absolutely ripe for rapid advance and massive real-world value.

In new research we show how matter can be both process and archive - a living record of forces, environments, and functions. This blurs boundaries between hardware and cognition where infrastructure, implants, and devices "think" and evolve through their own changing manifestation across all scales, from atoms to ecosystems and beyond, a form of "scalogenesis". Check out this new paper in MRS Bulletin "Frontiers of Biological Material Intelligence" (link below), led by my student Lee Marom. A key thesis behind this work is that for too long we treated "matter" and "mind", hardware and theory, or science and art as separate, when what we really needed was a set of constructional principles, shared rules of structure, interaction, and evolution that connect them into one continuous fabric! We explore how the convergence of deep biological insight, computational modeling & advanced fabrication is driving a shift from static synthetic materials to systems capable of sensing, adapting & self-optimizing. Key insights: 1️⃣ Definition of material intelligence: We argue that intelligence is not limited to cognitive systems but can be embedded within a material's physical structure, across all scales (from electrons to the world). Unlike traditional "smart" materials that rely on external sensors or control, intelligent materials possess "agency" - the capacity to initiate context-sensitive action through intrinsic chemical and structural properties. 2️⃣ Three Core Biological Principles: We identify three mechanisms nature uses to achieve this intelligence: 1: Sensing and Responding: Illustrated by sea cucumbers that reversibly alter their stiffness for defense. 2: Self-optimization: Seen across scales (for example in bone, trees or cellular remodeling), where structure is continuously refined based on mechanical stress. 3: Memory encoding: Demonstrated by tree rings and mollusk shells that physically archive environmental history, but extending to evolution of DNA and proteins as populations and ecosystems adapt and realize never-before-seen functions. 3️⃣ Formalizing Nature: To translate these biological behaviors into engineering, we highlight the need for computational tools like Category Theory & graph-based reasoning systems (neural networks extract features; and symbolic logic reason over them for abstraction and explanation). These frameworks allow us to abstract the complex, hierarchical logic of biological systems and predict emergent behaviors. We also explore the future of fabrication to incorporate 4D printing and biofabrication are essential for physically realizing these designs. Altogether we envision a future where materials function as "semi-autonomous experimenters" capable of learning from their environment and evolving their properties in a continuous loop (independent of human intervention). Congrats to Lee on an amazing paper and excited to hear the feedback from the community! Materials Research Society #MRSFall2025

Drug development upside down: Drug development has undergone a significant paradigm shift, turning the traditional reductionist approach on its head. Instead of starting with the molecule and searching for the right application, the new trend is the holistic "bedside to bench to bedside" approach. Let's explore the differences between these two approaches. When developing a drug, several critical factors come into play: #Ligandability: The molecule must effectively bind to the drug target and modulate its function without causing toxicity. #Druggability: Modulating the drug target should lead to a desirable effect in the organism, ideally treating or even curing the disease. #Causality: Even if ligandability and druggability are achieved, proving the drug's efficacy in a clinical trial is not guaranteed. Success depends on choosing the right patients and, most importantly, achieving positive clinical outcomes. The classical or reductionist method traditionally starts with identifying a ligand and then searching for the appropriate indication. This approach is driven by the fact that, in the prevailing monetization model, the primary value lies in the ligand itself. This is because only the ligand can be protected legally, while knowledge about the disease mechanism or patient information cannot. Consequently, investors eagerly invest in new chemical entities based on their ligand potential. However, this approach significantly diminishes the chances of success from the outset. As a response to the limitations of the reductionist approach, drug development has now embraced the opposite strategy. Instead of beginning with the molecule, researchers focus on studying large patient groups. Population genetics is utilized to identify potential drug targets and understand the clinical effects early in the process. Simultaneously, insights into druggability are quickly gained. The actual ligand development becomes a late-stage endeavor. This shift in approach allows drug development to be more patient-centric and increases the chances of success by aligning research efforts with the real clinical needs of the population. By understanding the disease mechanisms and selecting appropriate drug targets from the beginning, the new strategy paves the way for more effective and efficient drug development. PS: There is a common misconception in the biotech world where #ligandability is often equated with #druggability, with the aim of "making undruggable targets druggable." This notion is frequently observed among new chemical entity biotech companies. However, I approach this concept with a critical perspective. Just because a ligand can now be developed does not automatically imply that we have demonstrated a significant effect by modulating the drug target. There is still a need to carefully validate the actual impact of the ligand on the target's function and its overall efficacy in treating the intended disease.

I graduated from the University of Cambridge with a First-class Genetics Undergraduate Diploma, and I worked on something that fascinated me: HER2-positive breast cancer. Specifically, how a single protein can control so much about cancer behaviour. Let me explain why this matters: → HER2 isn't just a surface marker Human Epidermal growth factor Receptor 2. For years, we knew HER2-positive breast cancers were aggressive. About 20% of breast cancers overexpress this protein. But here's what's fascinating: HER2 doesn't just sit on the cell surface receiving signals. It actively regulates gene expression INSIDE the nucleus. It acts as a transcription factor. That changes everything about how we think about targeting it. → Three mechanisms, one target My project explored how HER2 controls gene expression through three pathways: 1. Direct transcriptional control → HER2 enters the nucleus and regulates genes directly 2. Downstream signalling cascades → HER2 activates pathways that affect hundreds of genes 3. Targeted intervention → Using shRNA to knock down HER2 and observe which genes change Understanding ALL three mechanisms gives us multiple ways to intervene. → Why this matters for treatment Current HER2-targeted therapies (like Herceptin) work by blocking the receptor from outside the cell. They're good. They've saved lives. But they're not perfect. Resistance develops. If we understand how HER2 regulates genes INSIDE the cell? We can design therapies that work differently. Combination approaches. Multiple targets. Better outcomes. → The future is precision medicine This research points toward something bigger: Understanding cancer at the molecular level. Not just "breast cancer." But THIS patient's cancer. With THIS specific genetic profile. With THESE gene expression patterns. That's where Clinical Science is heading. And why programmes like the NHS STP train us in BOTH genetics AND clinical application. For anyone interested in cancer research: The most exciting breakthroughs aren't always the loudest ones. Sometimes it's understanding ONE protein's role in gene regulation. That small piece of knowledge? It opens doors to entirely new treatment strategies. That's the power of biomedical research. #CancerResearch #GeneticsResearch #BreastCancer #PrecisionMedicine #BiomedicalResearch #Cambridge #Genetics #MolecularBiology

COVID’s immune system lessons As COVID-19 began to surge five years ago, no one knew what to expect. Neither did the immune system. Confronted with a new #virus, humanity was immunologically naïve. The emergence of #SARS-CoV-2 provided a rare opportunity to study the immune system in action. Scientists are still taking stock of the data they gathered on how the #immune system reacted to SARS-CoV-2, but four lessons have already emerged. Lesson No. 1: Antibodies aren’t everything. Immune response against the virus relied heavily on T cells, not just antibodies. #Antibodies against SARS-CoV-2 waned in the months after vaccination, but #vaccinated people continued to make T cells that recognized the virus. That T-cell protection remained strong, even against viral #variants that dodged the antibody defenses raised by the first generation of COVID #vaccines. Lesson No. 2: Early-warning immune alarm reaches the whole body. The body’s early-warning alarm, the innate #immuneresponse, rings out across the whole body, not just around the site of #infection. Signs of #interferon responses were found throughout the body, even in organs far away from infected cells with people #infected with SARS-CoV-2. The same also found later in #flu virus. Lesson No. 3: The nose knows. It is important to understand immune responses in specific #tissues, and especially in the place where infection often starts: the nose. Antibodies and #Tcells differ sharply from tissue to tissue. #Injected vaccines, which generate antibodies in the bloodstream, might not be ideal for blocking infection in the nose. Lesson No. 4: Post-viral illness need attention. #Researchers have learnt that a variety of factors, such as SARS-CoV-2 hiding dormant in the body, might contribute to #longCOVID. Viral infection can trigger the immune system to produce antibodies against the body’s own #proteins and how post-viral #illnesses can cause the reactivation of other dormant #viruses in the body, such as Epstein–Barr virus. Pic Credit: KTSDesign/Science Photo Library References: [1] https://lnkd.in/gs7PD-tj [2] https://lnkd.in/gmAvtWzf; [3] https://lnkd.in/gYB2EhcW [4] https://lnkd.in/gip6HamY; [5] https://lnkd.in/gAX6nkqz

🔥When Cancer Outsmarts Treatment, Science Must Outthink Cancer🔥 A recent Science Advances paper caught my attention, not just as a physician, but as someone who has spent decades watching cancer evolve faster than our therapies. The study reveals a critical mechanism in EGFR-mutant non-small cell lung cancer (NSCLC): 🧬 cancer cells don’t just mutate to resist drugs , they actively protect those mutations. 🔬 The key insight: Mutant EGFR proteins are stabilised by a newly identified P2Y2–integrin axis, driven by high extracellular ATP. This “protective shield” prevents EGFR degradation, allowing cancer cells to survive and thrive despite EGFR-TKI therapy. In simple terms: 👉 The cancer cell builds a biochemical bunker around its most dangerous mutation. ⚛️ What’s compelling is that when researchers disrupted this axis via P2Y2, FAK, or ATP-related pathways, especially in combination with TKIs , drug resistance weakened and tumour growth slowed. 👨⚕️ My perspective as a doctor: For years, we’ve focused almost exclusively on blocking the signal. This research reminds us that stability, trafficking, and cellular context matter just as much. 💎 Cancer is not static. It adapts, shields itself, and rewires survival pathways. ⚛️ Future oncology will not be: • One drug • One target • One pathway 👉 It will be systems-based, combination-driven, and biology-respecting. This is how we move from: ❌ chasing resistance ➡️ anticipating it And ultimately, this is how precision medicine becomes truly precise. 👉 Science like this gives hope not hype for patients facing resistant disease. Aspire. Inspire. Achieve.