Biological Systems Modeling

We just found a rare gene mutation that lets people thrive on just 4 hours of sleep. New research has identified a rare genetic mutation in the SIK3 gene that allows “natural short sleepers,” people who can thrive on dramatically less rest than the average person. This mutation joins five others already discovered by neuroscientist Ying-Hui Fu and her team, who have spent years studying how certain genes affect sleep cycles, energy metabolism, and circadian rhythms. The teams latest findings, published in Proceedings of the National Academy of Sciences, suggest that the brain of a short sleeper can detox and repair itself far more efficiently than usual during non-REM sleep. By analyzing human cases and genetically modifying mice, Fu’s research reveals how the SIK3 mutation might reduce the body’s demand for sleep. While the average person needs 8–9 hours, those with the mutation might function optimally on just 3–6. The team also revisited the DEC2 gene, which boosts the brain chemical orexin to help some short sleepers stay alert all day. The hope is that unlocking these genetic secrets could eventually lead to new treatments for sleep disorders like insomnia or narcolepsy—and maybe even revolutionize how much sleep we all need to feel human again. learn more https://lnkd.in/geDxUG9a

Recently, a study published in Nature Immunology caught my eye. In it, the authors undertook an extensive study that charts generic variations influencing the tumour microenvironment (TME). The TME plays a crucial role in tumour progression and response to treatment. Understanding the genetic underpinnings of the TME could help pave the way for novel therapeutic approaches and enhanced treatment targeting. One of the study's most interesting aspects is its use of machine learning methods and advanced bioinformatic approaches to analyze and integrate large-scale datasets. The advanced computational methods used enabled identification of genetic variations that may have otherwise been overlooked, highlighting the power of computational biology in advancing our understanding of cancer. Leveraging these techniques, the researchers created a detailed atlas of genetic factors impacting the TME, which they refer to as immunity quantitative trait loci (immunQTLs), and showed that many of these genetic factors were likely co-localized with previously known expression quantitative trait loci. This observation suggests that the immunQTLs may contribute to the cellular heterogeneity observed within the TME by influencing the expression of genes modulating immune infiltration. Going beyond their initial discovery-driven computational work to further validate their findings, they mapped immunQTLs across >1,600 genes and 23 cancers that are associated with cancer pathogenesis and immune regulation. Diving even deeper, they went on to experimentally validate that one of the identified genes, CCL2, which is implicated in promoting colorectal carcinoma (CRC) progression by allowing tumour cells to evade immunity, may be a promising therapeutic target. This finding demonstrates the potential of the depth of the data set and how it might be used to identify and validate targets. This publication presents a significant amount of work that I have only scratched the surface of here. It offers new insights into the complexity of genetic factors influencing the TME, providing a comprehensive genetic map of the TME and its implications for cancer therapy. The authors have made their data available through a publicly accessible database to help propel further work by the research community. To me, an exciting aspect of this work is that it may help open the door to future combination therapeutic approaches that target both the tumour cells and their microenvironment. https://lnkd.in/ezRckvFh

Solid Tumor Cell Therapy Wars: Where are the bets going? The CT landscape is populating rapidly. It all started with hematological diseases, and now the fields of solid tumors and autoimmunity have become the new competitive arenas. But first, which assets are being developed for solid tumors? Currently, CAR-T, CAR-M, and CAR-NK are the most used; let´s break it down. CAR-T cells are engineered T-cells designed to attack cancer by targeting specific antigens. While highly effective in blood cancers (e.g., 80-90% response rates in lymphoma), they struggle in solid tumors due to the immunosuppressive tumor microenvironment (TME), antigen variability, and difficulty penetrating dense tumors. Clinical trials in solid tumors show modest results (10-20% response rates) and significant safety risks, including cytokine release syndrome (CRS) in 30-50% of patients. To increase efficacy and safety, modifications such as dual-target CAR-T, TanCARTs, iCARs, or TRUCKs (which produce and secrete proinflammatory cytokines and other transgenic products) are being developed. CAR-M therapies use engineered macrophages to engulf cancer cells and reprogram the TME by releasing immune-stimulating cytokines. Early-phase trials, like HER2-targeted CAR-M, showed promise with partial responses in 2/7 patients and no severe CRS. However, scalability and durability are challenges. Investors see potential in partnerships to pair CAR-M with mRNA tech. Preclinical data in ovarian cancer models show tumor shrinkage, but clinical validation is still limited. CAR-NK therapies use “off-the-shelf” natural killer cells, which attack tumors without requiring HLA matching. They have a safer profile than CAR-T (minimal CRS) and faster production. Early trials report partial success from 16% up to 40%. However, NK cells’ short persistence limits long-term efficacy. Solutions like engineering NK cells with cytokines like IL-15 to enhance durability are moving forward. Investors favor CAR-NK for scalability and lower toxicity, but durability remains a key focus. With over USD 10 B invested in these technologies and only 1 treatment approved, we might expect more news in the coming years. What are your thoughts? I read you in the comments ____________________________________________________________________________ 🔔 Follow for insights ♻️ Share if you find it interesting #celltherapy #biotech #investment #investor

In a recent study (May 2025) published in Nature Genetics, Masashi Nomura et al.(Roel Verhaak, Lavarone, Mario Suva, Itay Tirosh lab) leveraged single-nucleus RNA sequencing and bulk tumor DNA sequencing to unveil new dimensions of GBM complexity. The team discovered three previously unknown malignant cellular states: differentiated neuronal (NEU-like), glial progenitor (GPC-like), and cilia-related. By integrating cellular composition, diverse cell states, and baseline gene expression programs, they defined three distinct GBM ecosystems—glial, ECM, and NEU—each characterized by unique genetic profiles, microenvironmental contexts, and transcriptional signatures. This innovative framework highlights the interplay between tumor genetics and anatomical location in shaping GBM biology, providing a powerful roadmap for developing targeted and personalized therapeutic strategies. Avishay Spitzer, MD, PhD, Kevin Johnson, Luciano Garofano, Djamel Nehar-Belaid, Alissa Greenwald, Lillian Bussema, Fulvio D'Angelo, Simon Gritsch, Simona Migliozzi, L. Nicolas Gonzalez Castro, Tamrin Chowdhury, Nicolas Robine, Catherine Reeves, JONG BAE PARK, Anuja Lipsa PhD, Anna Golebiewska, Simone P. Niclou, Labeeba Nusrat, Sunit Das, Hyo Eun Moon, Sun Ha Paek, Franck Bielle, Alice Laurenge-Leprince, Anna Luisa Di Stefano, Keith Ligon, Alfred Yung, Anna Lasorella https://lnkd.in/egDc26zY

Scientists at the University of California, San Francisco, have identified a rare gene mutation, SIK3-N783Y, in humans that enables natural short sleep. This mutation allows individuals to function optimally on just four to six hours of rest. Experiments on mice engineered with the same mutation confirmed its impact, showing significantly reduced sleep duration without adverse effects, even after sleep deprivation. Researchers say the mutation enhances sleep efficiency, potentially allowing new treatments for sleep disorders and expanding our understanding of how genetics influence rest and recovery.

🌐 Tumor-associated macrophages (TAMs) are a key immune component in the tumor microenvironment (TME) and are linked to poor prognosis in various cancers. 🔄 Besides their known role in promoting tumor growth and metastasis, recent studies highlight TAMs' involvement in immune suppression. 🎯 PD-L1 expression in host cells, including TAMs, is crucial for melanoma patients' response to PD-1 blockade immunotherapy. 🐭 Macrophage depletion in mice enhances the efficacy of PD-1/PD-L1 blockade, leading to increased recruitment and improved function of cytotoxic CD8+ T cells in tumors. ⚙️ Therapeutic strategies targeting macrophages show promising combinatorial effects with PD-1/PD-L1 blockade. 🔬 Researchers find that TAMs release PD-L1+ extracellular vesicles, with Akt promoting exosome secretion through MADD phosphorylation. 🚫 TAM-derived exosomes inhibit CD8 T cell proliferation and function. 🎯 Targeting macrophage RAB27A with LNPs sensitizes tumors to anti-PD-1 antibody. 🔍 Further investigations are essential: The study suggests that PD-L1 on TAM-derived exosomes plays a role in T cell suppression, but other molecules like TGF-β may also contribute. 📊 While LNPs were designed for preferential macrophage uptake, the study acknowledges the possibility of uptake by other cells, emphasizing the need for additional research. #translationalresearch #macrophages #exosomes #immunotherapy https://lnkd.in/eN4EXCG5

AI can now create virtual tumor models in minutes For decades, turning published papers into realistic computational models has been nearly impossible—particularly for complex biological processes such as tumor evolution. In my previous post exploring what AGI for biomedical research might look like, I highlighted the critical role of modeling complex systems (https://lnkd.in/gkSJKAcE). I had wondered if recent AI advances could dramatically change our ability to build these sophisticated models. Realistic computational models are essential, as they enable rapid hypothesis testing and deeper exploration of complex biological mechanisms. In two recent studies from our group and colleagues (paper 1: https://lnkd.in/gUj4_d57, paper 2: https://lnkd.in/ekh_HkH4), we characterized early lung cancer evolution at single-cell resolution, uncovering immune cell dynamics and tumor microenvironment interactions. Curious if these findings could rapidly become detailed virtual models, I gave Sonnet 3.7 a simple request: "Based on the content of the paper, create a comprehensive hybrid, multi-scale agent-based model in Python (using Mesa or similar) to recapitulate our results." Remarkably, Sonnet 3.7 immediately generated ~600 lines of robust Python code, requiring only modest refinement with Sonnet-assisted Cursor AI. The resulting hybrid agent-based model, built using Mesa (a Python framework for modeling complex adaptive systems), includes tumor cells, immune cells (cytotoxic T cells, regulatory T cells, polarized macrophages), endothelial cells, and environmental signaling molecules (VEGFA, TREM2, CXCL13). Agents follow biologically informed rules directly derived from experimental observations. Remarkably and despite many parameter assumptions, the virtual tumor faithfully reproduced key experimental observations: 🔸 Stepwise progression from preinvasive to invasive adenocarcinoma 🔸 Immune shifts: fewer cytotoxic cells, more suppressive populations 🔸 Realistic spatial signaling patterns (angiogenesis, immune polarization) As statistician George Box famously said, "All models are wrong, but some are useful." While no model is perfect, this AI-enabled approach rapidly bridges scientific papers to highly useful virtual experiments. The ability to create virtual tumor models in minutes could profoundly accelerate discovery—enabling entirely new ways of exploring and answering some of cancer’s most complex and pressing questions.

More Highlights of the Crucial Role of Inflammatory Monocytes in Anti-Tumor Immunity from Elewaut et al. Cancer is an evolution of cell growth under multitude of pressures, therefore finding what cancer cells successfully suppress is essential for designing affective treatments. Inflammatory monocytes are again shown play a critical role in stimulating anti-tumor T cell responses within the tumor microenvironment (in addition to what happens in lymph nodes). These monocytes contribute significantly to T cell activation by: Expressing key stimulatory molecules like CXCR3 ligands (CXCL9, CXCL10) and IL-15, to recruit and activate T (and NK?) cells. Uniquely acquiring and presenting tumor-derived antigens through a process called 'cross-dressing.' However, hyperactive MAPK signaling in cancer cells can disrupt this crucial process by: Suppressing the production of essential type I interferon (IFN-I) cytokines. Inducing the secretion of prostaglandin E2 (PGE2), which impairs monocyte function. This research has significant implications for cancer immunotherapy. By understanding how oncogenic signaling disrupts T cell responses via suppressing macrophages, rationally designed combination therapies will be developed. #CancerResearch #Immunotherapy #TumorMicroenvironment #Tcells #Oncology #monocytes #macrophages https://lnkd.in/ei6mJEDp

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor—a representative cold tumor—we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample—a hot tumor—we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures. Interesting spatial proteomics study by Matthias Mann and larger team. https://lnkd.in/edAHnaWz 

Crosstalk between #CAF and #TAM in the #tumorimmunemicroenvironment Tumor cells interact with stromal cells by secreting an array of cytokines, chemokines, and other tumor-promoting factors in the TME. The tumor-stromal cell interaction induces non-cancerous cells to acquire new tumor-promoting phenotypes, increasing tumor progression, multidrug resistance, distant metastasis, and immune suppression. Studies using patient specimens have also shown a positive and reciprocal feedback responses among stromal cells. As discussed in the earlier section, CAFs are one of the most critical stromal cells in the TME, which is known to participate in various stages of tumor development through multiple mechanisms. Among all immune cells, macrophages play a vital role in the TIME and are known to enhance several hallmarks of cancer by infiltrating into tumors. Macrophages display a wide range of plasticity and various functional activities in TIME. TAMs are the most prominent immune cells near CAF-populated areas, suggesting strong interactions between these two cell types. Several studies in spheroid/ in vivo models have reported that macrophage recruitment and differentiation are triggered by CAFs via several secretory factors and regulatory networks, thereby imparting pro-tumorigenic capabilities in TAMs. For instance, in melanoma, CAF-secreted cytokines such as IL-10, IL-8, CCL2, and TGFβ stimulate macrophage recruitment and polarisation into the M2 phenotype with tumor-promoting functions. Similarly, CAFs trigger monocyte recruitment and provoke differentiation of monocytes to M2 macrophages by secreting SDF-1 (CXCL12), monocyte chemotactic protein 1 (MCP1), and CHI3L1 in breast cancer Image: Crosstalk between CAF and TAM in the tumor immune microenvironment. CAFs trigger macrophage recruitment and differentiation through various secretory factors and regulatory networks, inducing the pro-tumorigenic capabilities of TAMs. TAMs can also induce CAF generation and activation. The interaction of CAF and TAM causes immunosuppression to promote cancer growth via a feedback loop Source: https://lnkd.in/eRGRwbpE