bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2025–05–25
sixteen papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. Comprehensive evaluation of phosphoproteomic-based kinase activity inference.
  2. Angiopoietin-TIE2 feedforward circuit promotes PIK3CA-driven venous malformations.
  3. PDGFRα/β heterodimer activation negatively affects downstream ERK1/2 signaling and cellular proliferation.
  4. Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements.
  5. Data-driven extraction of human kinase-substrate relationships from omics datasets.
  6. Cell competition eliminates aneuploid human pluripotent stem cells.
  7. Microbiome metabolism of dietary phytochemicals controls the anticancer activity of PI3K inhibitors.
  8. Learning to estimate sample-specific transcriptional networks for 7,000 tumors.
  9. Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.
  10. Improving genetic diagnostic yield in familial and sporadic cerebral cavernous malformations: detection of copy number and deep Intronic variants.
  11. A multi-kingdom genetic barcoding system for precise clone isolation.
  12. Multigenerational cell tracking of DNA replication and heritable DNA damage.
  13. Clonal tracing with somatic epimutations reveals dynamics of blood ageing.
  14. Integrin-mediated mTOR signaling drives TGF-β overactivity and myxomatous mitral valve degeneration in hypomorphic fibrillin-1 mice.
  15. Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.
  16. Methods in quantitative biology - from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.
  1. Nat Commun. 2025 May 22. 16(1): 4771
    Comprehensive evaluation of phosphoproteomic-based kinase activity inference.
    Sophia Müller-Dott, Eric J Jaehnig, Khoi Pham Munchic, Wen Jiang, Tomer M Yaron-Barir, Sara R Savage, Martin Garrido-Rodriguez, Jared L Johnson, Alessandro Lussana, Evangelia Petsalaki, Jonathan T Lei, Aurelien Dugourd, Karsten Krug, Lewis C Cantley, D R Mani, Bing Zhang, Julio Saez-Rodriguez.
      Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.
    DOI:  https://doi.org/10.1038/s41467-025-59779-y
  2. Nat Cardiovasc Res. 2025 May 23.
    Angiopoietin-TIE2 feedforward circuit promotes PIK3CA-driven venous malformations.
    Marle Kraft, Hans Schoofs, Milena Petkova, Jorge Andrade, Ana Rita Grosso, Rui Benedito, An-Katrien De Roo, Laurence M Boon, Miikka Vikkula, Friedrich G Kapp, René Hägerling, Michael Potente, Taija Mäkinen.
      Venous malformations (VMs) are vascular anomalies lacking curative treatments, often caused by somatic PIK3CA mutations that hyperactivate the PI3Kα-AKT-mTOR signaling pathway. Here, we identify a venous-specific signaling circuit driving disease progression, where excessive PI3Kα activity amplifies upstream TIE2 receptor signaling through autocrine and paracrine mechanisms. In Pik3caH1047R-driven VM mouse models, single-cell transcriptomics and lineage tracking revealed clonal expansion of mutant endothelial cells with a post-capillary venous phenotype, characterized by suppression of the AKT-inhibited FOXO1 and its target genes, including the TIE2 antagonist ANGPT2. An imbalance in TIE2 ligands, likely exacerbated by aberrant recruitment of smooth muscle cells producing the agonist ANGPT1, increased TIE2 activity in both mouse and human VMs. While mTOR blockade had limited effects on advanced VMs in mice, inhibiting TIE2 or ANGPT effectively suppressed their growth. These findings uncover a PI3K-FOXO1-ANGPT-TIE2 circuit as a core driver of PIK3CA-related VMs and highlight TIE2 as a promising therapeutic target.
    DOI:  https://doi.org/10.1038/s44161-025-00655-9
  3. Nat Commun. 2025 May 22. 16(1): 4754
    PDGFRα/β heterodimer activation negatively affects downstream ERK1/2 signaling and cellular proliferation.
    Maria B Campaña, Madison R Perkins, Maxwell C McCabe, Andrew Neumann, Eric D Larson, Katherine A Fantauzzo.
      The platelet-derived growth factor receptor (PDGFR) family of receptor tyrosine kinases consists of two receptors, PDGFRα and PDGFRβ, that homodimerize and heterodimerize upon ligand binding. Here, we tested the hypothesis that differential internalization and trafficking dynamics of the various PDGFR dimers underlie differences in downstream intracellular signaling and cellular behavior. Using a bimolecular fluorescence complementation approach, we demonstrated that PDGFRα/β heterodimers are rapidly internalized into early endosomes. We showed that PDGFRα/β heterodimer activation does not induce downstream phosphorylation of ERK1/2 and significantly inhibits cell proliferation. Further, we identified MYO1D as a protein that preferentially binds PDGFRα/β heterodimers and demonstrated that knockdown of MYO1D leads to retention of PDGFRα/β heterodimers at the plasma membrane, increased phosphorylation of ERK1/2 and increased cell proliferation. Collectively, our findings impart valuable insight into the molecular mechanisms by which specificity is introduced downstream of PDGFR activation to differentially propagate signaling and generate distinct cellular responses.
    DOI:  https://doi.org/10.1038/s41467-025-59938-1
  4. Nat Commun. 2025 May 21. 16(1): 4721
    Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements.
    Abhineet Ram, Michael Pargett, Yongin Choi, Devan Murphy, Carolyn Teragawa, Markhus Cabel, Nont Kosaisawe, Gerald Quon, John G Albeck.
      The RAS/ERK pathway plays a central role in diagnosis and therapy for many cancers. ERK activity is highly dynamic within individual cells and drives cell proliferation, metabolism, and other processes through effector proteins including c-Myc, c-Fos, Fra-1, and Egr-1. These proteins are sensitive to the dynamics of ERK activity, but it is not clear to what extent the pattern of ERK activity in an individual cell determines effector protein expression, or how much information about ERK dynamics is embedded in the pattern of effector expression. Here, we evaluate these relationships using live-cell biosensor measurements of ERK activity, multiplexed with immunofluorescence staining for downstream target proteins of the pathway. Combining these datasets with linear regression, machine learning, and differential equation models, we develop an interpretive framework for immunofluorescence data, wherein Fra-1 and pRb levels imply long-term activation of ERK signaling, while Egr-1 and c-Myc indicate more recent activation. Analysis of multiple cancer cell lines reveals a distorted relationship between ERK activity and cell state in malignant cells. We show that this framework can infer various classes of ERK dynamics from effector protein stains within a heterogeneous population, providing a basis for annotating ERK dynamics within fixed cells.
    DOI:  https://doi.org/10.1038/s41467-025-58348-7
  5. Mol Cell Proteomics. 2025 May 15. pii: S1535-9476(25)00092-1. [Epub ahead of print] 100994
    Data-driven extraction of human kinase-substrate relationships from omics datasets.
    Benjamin Dominik Maier, Borgthor Petursson, Alessandro Lussana, Evangelia Petsalaki.
      Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as cell division and differentiation. In human, >90% of identified phosphosites don't have annotations regarding the relevant upstream kinase. At the same time around 30% of kinases (as annotated in Uniprot) have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods and provides predictions for more kinases. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space. Our model is integrated into a web server, SELPHI2.0, to allow unbiased analysis of phosphoproteomics data, facilitating the design of downstream experiments to uncover mechanisms of signal transduction across conditions and cellular contexts.
    DOI:  https://doi.org/10.1016/j.mcpro.2025.100994
  6. Stem Cell Reports. 2025 May 08. pii: S2213-6711(25)00110-9. [Epub ahead of print] 102506
    Cell competition eliminates aneuploid human pluripotent stem cells.
    Amanda Ya, Chenhui Deng, Kristina M Godek.
      Human pluripotent stem cells (hPSCs) maintain diploid populations for generations despite frequent mitotic errors that cause aneuploidy or chromosome imbalances. Consequently, aneuploid hPSC propagation must be prevented to sustain genome stability, but how this is achieved is unknown. Surprisingly, we find that, unlike somatic cells, uniformly aneuploid hPSC populations with heterogeneous abnormal karyotypes proliferate. Instead, in mosaic populations, cell-non-autonomous competition between neighboring diploid and aneuploid hPSCs eliminates less fit aneuploid cells, regardless of specific chromosome imbalances. Aneuploid hPSCs with lower MYC or higher p53 levels relative to diploid neighbors are outcompeted but conversely gain an advantage when MYC and p53 relative abundance switches. Thus, MYC- and p53-driven cell competition preserves hPSC genome integrity despite their low mitotic fidelity and intrinsic capacity to proliferate with an aneuploid genome. These findings have important implications for using hPSCs in regenerative medicine and for how diploid human embryos form during development despite the prevalence of aneuploidy.
    Keywords:  MYC; aneuploidy; cell competition; human pluripotent stem cells; mosaicism; p53; preimplantation embryos
    DOI:  https://doi.org/10.1016/j.stemcr.2025.102506
  7. Cell. 2025 May 15. pii: S0092-8674(25)00511-2. [Epub ahead of print]
    Microbiome metabolism of dietary phytochemicals controls the anticancer activity of PI3K inhibitors.
    Asael Roichman, Qianying Zuo, Sunghoon Hwang, Wenyun Lu, Ricardo A Cordova, Michael R MacArthur, Jacob A Boyer, Sarah J Mitchell, Jesse Powers, Sophia A Koval, Craig J Hunter, Jamie Rijmers, Rolf-Peter Ryseck, Jenna E AbuSalim, Seema Chatterjee, Won Dong Lee, Xincheng Xu, Xi Xing, Zihong Chen, Xianfeng Zeng, Siddharth Marwaha, Matthew J McBride, Jessie Y Guo, Yibin Kang, Mohamed S Donia, Joshua D Rabinowitz.
      Phosphatidylinositol 3-kinase (PI3K) signaling is both the effector pathway of insulin and among the most frequently activated pathways in human cancer. In murine cancer models, the efficacy of PI3K inhibitors is dramatically enhanced by a ketogenic diet, with a proposed mechanism involving dietary suppression of insulin. Here, we confirm profound diet-PI3K anticancer synergy but show that it is, surprisingly, unrelated to diet macronutrient composition. Instead, the diet-PI3K interaction involves microbiome metabolism of ingested phytochemicals. Specifically, murine ketogenic diet lacks the complex spectrum of phytochemicals found in standard chow, including the soy phytochemicals soyasaponins. We find that soyasaponins are converted by the microbiome into inducers of hepatic cytochrome P450 enzymes, and thereby lower PI3K inhibitor blood levels and anticancer activity. A high-carbohydrate, low-phytochemical diet synergizes with PI3K inhibition to treat cancer in mice, as do antibiotics that curtail the gut microbiome. Thus, diet impacts anticancer drug activity through phytochemical-microbiome-liver interactions.
    Keywords:  PI3Ki; breast cancer; cytochrome P450; diet and cancer treatment; gut-liver axis; microbiome metabolites; pancreatic cancer; pharmacokinetics; phytochemicals; soyasaponins
    DOI:  https://doi.org/10.1016/j.cell.2025.04.041
  8. Proc Natl Acad Sci U S A. 2025 May 27. 122(21): e2411930122
    Learning to estimate sample-specific transcriptional networks for 7,000 tumors.
    Caleb N Ellington, Benjamin J Lengerich, Thomas B K Watkins, Jiekun Yang, Abhinav K Adduri, Sazan Mahbub, Hanxi Xiao, Manolis Kellis, Eric P Xing.
      Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intracluster heterogeneity. We propose to move beyond coarse analyses of predefined subgroups by using contextualized learning, a multitask learning paradigm that uses multiview contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, and Neighborhood Selection) and estimate context-specific GRNs for 7,997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized.
    Keywords:  cancer; heterogeneity; multitask learning; networks; personalized models
    DOI:  https://doi.org/10.1073/pnas.2411930122
  9. Nat Commun. 2025 May 17. 16(1): 4606
    Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.
    Ihuan Gunawan, Felix V Kohane, Moumitha Dey, Kathy Nguyen, Ye Zheng, Daniel P Neumann, Fatemeh Vafaee, Erik Meijering, John G Lock.
      Standard immunofluorescence imaging captures just ~4 molecular markers (4-plex) per cell, limiting dissection of complex biology. Inspired by multimodal omics-based data integration approaches, we propose an Extensible Immunofluorescence (ExIF) framework that transforms carefully designed but easily produced panels of 4-plex immunofluorescence into a unified dataset with theoretically unlimited marker plexity, using generative deep learning-based virtual labelling. ExIF enables integrated analyses of complex cell biology, exemplified here through interrogation of the epithelial-mesenchymal transition (EMT), driving significant improvements in downstream quantitative analyses usually reserved for omics data, including: classification of cell phenotypes; manifold learning of cell phenotype heterogeneity; and pseudotemporal inference of molecular marker dynamics. Introducing data integration concepts from omics to microscopy, ExIF empowers life scientists to use routine 4-plex fluorescence microscopy to quantitatively interrogate complex, multimolecular single-cell processes in a manner that approaches the performance of multiplexed labelling methods whose uptake remains limited.
    DOI:  https://doi.org/10.1038/s41467-025-59592-7
  10. Hum Mol Genet. 2025 May 22. pii: ddaf077. [Epub ahead of print]
    Improving genetic diagnostic yield in familial and sporadic cerebral cavernous malformations: detection of copy number and deep Intronic variants.
    Neblina Sikta, Samuel Gooley, Timothy E Green, Olivia Hoeper, Tom Witkowski, Caitlin Bennett, David Francis, Joshua Reid, Kevin Mao, Mohammed Awad, Samuel Roberts-Thomson, Kristian Bulluss, Jonathan Clark, Ingrid E Scheffer, Piero Perucca, Mark F Bennett, Melanie Bahlo, Samuel F Berkovic, Michael S Hildebrand.
      Cerebral cavernous malformations (CCMs) are intracranial vascular lesions associated with risk of haemorrhages and seizures. While the majority are sporadic and often associated with somatic variants in PIK3CA and MAP3K3, around 20% are familial with germline variants in one of three CCM genes-KRIT1/CCM1, CCM2 and PDCD10/CCM3. We performed comprehensive phenotyping and genetic analysis of nine multiplex families and ten sporadic individuals with CCM. In the familial cases, initial standard analyses had a low yield, we therefore searched for small copy number changes and deep intronic variants. Subsequently, pathogenic germline variants in KRIT1/CCM1 or CCM2 were identified in all 9 multiplex families. Single or multiple exon deletions or splice site variants in KRIT1/CCM1 were found in 3/9 families. Where cavernous malformation tissue was available, second hit somatic PIK3CA variants were identified in 4/7 individuals. These 4 individuals were from separate families with germline KRIT1/CCM1 variants. In 8/10 sporadic cases, we detected recurrent pathogenic somatic PIK3CA, MAP3K3 or CCM2 variants. All familial cases had multiple CCMs, whereas the sporadic cases had a single lesion only, which was in the temporal lobe in 9/10 individuals. Our comprehensive approach interrogating deep intronic variants combined with detection of small copy number variants warrants implementation in standard clinical genetic testing pipelines to increase diagnostic yield. We also build on the established second hit germline and somatic variant mechanism in some CCM lesions. Genetic diagnosis has clinical implications such as reproductive counselling and provides potential eligibility for precision medicine therapies to treat rapidly growing CCMs.
    Keywords:  Cerebral cavernous malformations; Copy number variant; Deep intronic variant; Germline variant; Somatic mosaicism
    DOI:  https://doi.org/10.1093/hmg/ddaf077
  11. Nat Biotechnol. 2025 May 21.
    A multi-kingdom genetic barcoding system for precise clone isolation.
    Soh Ishiguro, Kana Ishida, Rina C Sakata, Minori Ichiraku, Ren Takimoto, Rina Yogo, Yusuke Kijima, Hideto Mori, Mamoru Tanaka, Samuel King, Shoko Tarumoto, Taro Tsujimura, Omar Bashth, Nanami Masuyama, Arman Adel, Hiromi Toyoshima, Motoaki Seki, Ju Hee Oh, Anne-Sophie Archambault, Keiji Nishida, Akihiko Kondo, Satoru Kuhara, Hiroyuki Aburatani, Ramon I Klein Geltink, Takuya Yamamoto, Nika Shakiba, Yasuhiro Takashima, Nozomu Yachie.
      Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.
    DOI:  https://doi.org/10.1038/s41587-025-02649-1
  12. Nature. 2025 May 21.
    Multigenerational cell tracking of DNA replication and heritable DNA damage.
    Andreas Panagopoulos, Merula Stout, Sinan Kilic, Peter Leary, Julia Vornberger, Virginia Pasti, Antonio Galarreta, Aleksandra Lezaja, Kyra Kirschenbühler, Ralph Imhof, Hubert Rehrauer, Urs Ziegler, Matthias Altmeyer.
      Cell heterogeneity is a universal feature of life. Although biological processes affected by cell-to-cell variation are manifold, from developmental plasticity to tumour heterogeneity and differential drug responses, the sources of cell heterogeneity remain largely unclear1,2. Mutational and epigenetic signatures from cancer (epi)genomics are powerful for deducing processes that shaped cancer genome evolution3-5. However, retrospective analyses face difficulties in resolving how cellular heterogeneity emerges and is propagated to subsequent cell generations. Here, we used multigenerational single-cell tracking based on endogenously labelled proteins and custom-designed computational tools to elucidate how oncogenic perturbations induce sister cell asymmetry and phenotypic heterogeneity. Dual CRISPR-based genome editing enabled simultaneous tracking of DNA replication patterns and heritable endogenous DNA lesions. Cell lineage trees of up to four generations were tracked in asynchronously growing cells, and time-resolved lineage analyses were combined with end-point measurements of cell cycle and DNA damage markers through iterative staining. Besides revealing replication and repair dynamics, damage inheritance and emergence of sister cell heterogeneity across multiple cell generations, through combination with single-cell transcriptomics, we delineate how common oncogenic events trigger multiple routes towards polyploidization with distinct outcomes for genome integrity. Our study provides a framework to dissect phenotypic plasticity at the single-cell level and sheds light onto cellular processes that may resemble early events during cancer development.
    DOI:  https://doi.org/10.1038/s41586-025-08986-0
  13. Nature. 2025 May 21.
    Clonal tracing with somatic epimutations reveals dynamics of blood ageing.
    Michael Scherer, Indranil Singh, Martina Maria Braun, Chelsea Szu-Tu, Pedro Sanchez Sanchez, Dominik Lindenhofer, Niels Asger Jakobsen, Verena Körber, Michael Kardorff, Lena Nitsch, Pauline Kautz, Julia Rühle, Agostina Bianchi, Luca Cozzuto, Robert Frömel, Sergi Beneyto-Calabuig, Caleb Lareau, Ansuman T Satpathy, Renée Beekman, Lars M Steinmetz, Simon Raffel, Leif S Ludwig, Paresh Vyas, Alejo Rodriguez-Fraticelli, Lars Velten.
      Current approaches used to track stem cell clones through differentiation require genetic engineering1,2 or rely on sparse somatic DNA variants3,4, which limits their wide application. Here we discover that DNA methylation of a subset of CpG sites reflects cellular differentiation, whereas another subset undergoes stochastic epimutations and can serve as digital barcodes of clonal identity. We demonstrate that targeted single-cell profiling of DNA methylation5 at single-CpG resolution can accurately extract both layers of information. To that end, we develop EPI-Clone, a method for transgene-free lineage tracing at scale. Applied to mouse and human haematopoiesis, we capture hundreds of clonal differentiation trajectories across tens of individuals and 230,358 single cells. In mouse ageing, we demonstrate that myeloid bias and low output of old haematopoietic stem cells6 are restricted to a small number of expanded clones, whereas many functionally young-like clones persist in old age. In human ageing, clones with and without known driver mutations of clonal haematopoieis7 are part of a spectrum of age-related clonal expansions that display similar lineage biases. EPI-Clone enables accurate and transgene-free single-cell lineage tracing on hematopoietic cell state landscapes at scale.
    DOI:  https://doi.org/10.1038/s41586-025-09041-8
  14. J Clin Invest. 2025 May 20. pii: e183558. [Epub ahead of print]
    Integrin-mediated mTOR signaling drives TGF-β overactivity and myxomatous mitral valve degeneration in hypomorphic fibrillin-1 mice.
    Fu Gao, Qixin Chen, Makoto Mori, Sufang Li, Giovanni Ferrari, Markus Krane, Rong Fan, George Tellides, Yang Liu, Arnar Geirsson.
      Mitral valve prolapse is often benign but progression to mitral regurgitation may require invasive intervention and there is no specific medical therapy. An association of mitral valve prolapse with Marfan syndrome resulting from pathogenic FBN1 variants supports the use of hypomorphic fibrillin-1 mgR mice to investigate mechanisms and therapy for mitral valve disease. mgR mice developed severe myxomatous mitral valve degeneration with mitral regurgitation by 12 weeks of age. Persistent activation of TGF-β and mTOR signaling along with macrophage recruitment preceded histological changes at 4 weeks of age. Short-term mTOR inhibition with rapamycin from 4 to 5 weeks of age prevented TGF-β overactivity and leukocytic infiltrates, while long-term inhibition of mTOR or TGF-β signaling from 4 to 12 weeks of age rescued mitral valve leaflet degeneration. Transcriptomic analysis identified integrins as key receptors in signaling interactions and serologic neutralization of integrin signaling or a chimeric integrin receptor altering signaling prevented mTOR activation. We confirmed increased mTOR signaling and a conserved transcriptome signature in human specimens of sporadic mitral valve prolapse. Thus, mTOR activation from abnormal integrin-dependent cell-matrix interactions drives TGF-β overactivity and myxomatous mitral valve degeneration, and mTOR inhibition may prevent disease progression of mitral valve prolapse.
    Keywords:  Cardiology; Cardiovascular disease; Cell biology; Extracellular matrix; Signal transduction
    DOI:  https://doi.org/10.1172/JCI183558
  15. Nat Commun. 2025 May 23. 16(1): 4785
    Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.
    Hefei Zhang, Xuhang Li, Dongyuan Song, Onur Yukselen, Shivani Nanda, Alper Kucukural, Jingyi Jessica Li, Manuel Garber, Albertha J M Walhout.
      Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.
    DOI:  https://doi.org/10.1038/s41467-025-60154-0
  16. Phys Biol. 2025 May 19.
    Methods in quantitative biology - from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.
    Luis Aguilera, Lisa M Weber, Eric Ron, Connor R King, Kaan Öcal, Alex Popinga, Joshua Cook, Michael P May, William S Raymond, Zachary Fox, Linda S Forero-Quintero, Jack R Forman, Alexandre David, Brian Munsky.
      The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying mathematical and computational methods. The central goals of q-bio are to (1) systematically propose quantitative hypotheses in the form of mathematical models, (2) demonstrate that these models faithfully capture a specific essence of a biological process, and (3) correctly forecast the dynamics of the process in new, and previously untested circumstances. Achieving these goals depends on accurate analysis and incorporating informative experimental data to constrain the set of potential mathematical representations. In this introductory tutorial, we provide an overview of the state of the field and introduce some of the computational methods most commonly used in q-bio. In particular, we examine experimental techniques in single-cell imaging, computational tools to process images and extract quantitative data, various mechanistic modeling approaches used to reproduce these quantitative data, and techniques for data-driven model inference and model-driven experiment design. All topics are presented in the context of additional online resources, including open-source Python notebooks and open-ended practice problems that comprise the technical content of the annual Undergraduate Quantitative Biology Summer School (UQ-Bio).
    Keywords:  Fluorescence microscopy; Mechanistic Models; Model inference; Quantitative Biology; Single-cell Imaging; Stochastic gene expression
    DOI:  https://doi.org/10.1088/1478-3975/adda85
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