bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2026–05–17
sixteen papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. Genetically targeted mTORC1 inhibitor reveals transcriptional control by nuclear mTORC1.
  2. VEGFR2 is required for VEGF-C-VEGFR3-PI3Kα-mediated sprouting lymphangiogenesis.
  3. Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.
  4. Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.
  5. Alpelisib in PIK3CA-Related Overgrowth Spectrum (PROS): A Systematic Review of Real-World Evidence in over 100 Patients.
  6. D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response.
  7. Designing genome editing experiments with EditABLE.
  8. DoTools: a cross platform framework to streamline common single cell workflows.
  9. What's your hypothesis?
  10. Cellular mechanometabolism: stimuli and implications.
  11. A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping.
  12. Automated stem cell-derived organoid platforms for disease modeling.
  13. BromoCatch: a self-labelling tag platform for protein modification and live cell imaging.
  14. RegVelo: Gene-regulatory-informed dynamics of single cells.
  15. Universal conditional knockout approach to multiple species, from fish to human induced pluripotent stem cells.
  16. Ovary single-nucleus transcriptomics reveal follicle cell maturation as a driver of reproductive hierarchy in bumblebees.
  1. Nat Chem Biol. 2026 May 14.
    Genetically targeted mTORC1 inhibitor reveals transcriptional control by nuclear mTORC1.
    Yanghao Zhong, Ayse Z Sahan, Zhengyao Shao, Qian-Yi Zhang, Xin Zhou, Jack G Haggett, Keiichi Koshizuka, Samuel A Myers, J Silvio Gutkind, Jin Zhang.
      Mechanistic target of rapamycin complex 1 (mTORC1) is a nutrient sensor that integrates diverse inputs to regulate protein translation and cell growth. While mTORC1 is activated on the lysosome in the classical model, it has become increasingly clear that this multifaceted signaling complex is active at various subcellular locations, such as the nucleus. However, what specific functions mTORC1 serves at these locations and how its signaling is compartmentalized are unclear. To interrogate subcellular pools of mTORC1, we developed TerminaTOR, a genetically encodable inhibitor of mTORC1 that can be targeted to specific subcellular locations. When TerminaTOR is directed to the lysosome, it inhibits canonical lysosomal mTORC1 and induces autophagy. Furthermore, TerminaTOR targeted to the nucleus specifically inhibits nuclear mTORC1, uncovering noncanonical roles of nuclear mTORC1 in regulating the transcription of CCAAT motif-containing genes. Thus, mTORC1 exhibits functional spatial compartmentalization and TerminaTOR serves as a powerful tool for unraveling spatially regulated functions of mTORC1 across different scales.
    DOI:  https://doi.org/10.1038/s41589-026-02188-z
  2. Nat Commun. 2026 May 15. pii: 4380. [Epub ahead of print]17(1):
    VEGFR2 is required for VEGF-C-VEGFR3-PI3Kα-mediated sprouting lymphangiogenesis.
    Hans Schoofs, Yan Zhang, Henrik Ortsäter, Mariya Lytvyn, Rui Benedito, Taija Mäkinen.
      Lymphatic vessels are essential for tissue homoeostasis and their growth is regulated by vascular endothelial growth factor C (VEGF-C) signalling through VEGFR3. However, how VEGF-C balances lymphatic endothelial cells (LECs) proliferation versus sprouting to ensure functional vessel formation has remained unclear. Using high-fidelity conditional genetics and receptor-specific ligands, we uncover a requirement for the alternative receptor VEGFR2 in VEGF-C-VEGFR3-driven lymphatic vessel sprouting. While activation of VEGFR2 alone fails to induce lymphangiogenesis, VEGFR2 loss abolishes LEC sprouting, but not proliferation, in response to VEGF-C. In contrast, deletion of the VEGFR3 downstream effector PI3Kα completely abrogates lymphangiogenesis. VEGFR2 is activated and found in proximity to VEGFR3 in LECs in vivo, with PI3Kα controlling their relative cell-surface availability and VEGF-C increasing VEGFR2 relative to VEGFR3, thereby priming LECs for sprouting. This receptor coordination balances VEGF-C-driven proliferative and sprouting responses, coupling LEC expansion to vessel growth, ensuring the formation of functional lymphatic networks.
    DOI:  https://doi.org/10.1038/s41467-026-73013-3
  3. Mol Syst Biol. 2026 May 11.
    Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.
    Mari Tolonen, Ziwei Xu, Ozgur Beker, Varun Kapoor, Bianca Dumitrascu, Jakub Sedzinski.
      Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behavior, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in the rapidly differentiating Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, and position-to create a time-resolved morphodynamic dataset spanning the full course of differentiation. While single features showed high noise and low separability of ground-truth cell types, supervised machine learning revealed that integrating time-resolved features improves the prediction of final cell fate. Gradient-boosted trees and multinomial logistic regression achieved moderate but consistent accuracy, especially for abundant epithelial lineages. Key discriminants included normalized Z position, membrane-nucleus offset, and absolute experimental time, whereas movement contributed minimally to the results. Our data show that morphodynamic signatures encode predictive information about cell identity and provide a framework linking cellular dynamics with molecular state.
    DOI:  https://doi.org/10.1038/s44320-026-00212-x
  4. Cell. 2026 May 12. pii: S0092-8674(26)00466-6. [Epub ahead of print]
    Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.
    Sriram Pendyala, Katie Partington, Nicholas Bradley, Abbye E McEwen, Gwenneth Straub, Hyeon-Jin Kim, Shawn Fayer, Daniel Lee Holmes, Katherine A Sitko, Riddhiman K Garge, Ziyu R Wang, Melinda K Wheelock, Allyssa J Vandi, Rachel L Powell, Clayton E Friedman, Evan McDermot, Nishka Kishore, Frederick P Roth, Alan F Rubin, Kai-Chun Yang, Lea M Starita, William S Noble, Douglas M Fowler.
      Genetic variants produce complex phenotypic effects that confound current assays and predictive models. We developed variant in situ sequencing (VIS-seq), a pooled, image-based method measuring variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to ∼3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles capturing changes in protein abundance, localization, activity, and cell architecture. VIS-seq identified a subset of linker-subdomain LMNA variants that increase nuclear circularity, in contrast to aggregating or low-abundance rod-subdomain variants that decrease circularity. VIS-seq also identified autism-associated PTEN variants that mislocalize and accurately distinguished autism-linked from tumor syndrome-linked and gnomAD control variants. Most variants impacted a multidimensional phenotypic continuum not recapitulated by any single functional readout. By linking variants to cell images at scale, VIS-seq illuminates how variant effects cascade from molecules to subcellular structures to cells, providing a framework for resolving the complexity of variant function.
    Keywords:  functional genomics; genetic variation; multiplexed assays of variant effect; optical pooled screening; pleiotropy
    DOI:  https://doi.org/10.1016/j.cell.2026.04.031
  5. Cells. 2026 Apr 27. pii: 788. [Epub ahead of print]15(9):
    Alpelisib in PIK3CA-Related Overgrowth Spectrum (PROS): A Systematic Review of Real-World Evidence in over 100 Patients.
    Francesco Pellegrino, Giuseppe Reynolds, Simona Cardaropoli, Maria Luca, Stefania Massuras, Diana Carli, Alessandro Mussa.
      Background: PIK3CA-related overgrowth spectrum (PROS) comprises a heterogeneous group of mosaic disorders caused by activating variants in the PIK3CA gene, resulting in dysregulation of the PI3K/AKT/mTOR signaling pathway and abnormal tissue overgrowth. Targeted inhibition of this pathway has recently emerged as a promising therapeutic strategy. Methods: We conducted a literature review to identify published reports describing patients with PROS treated with alpelisib, a selective inhibitor of the p110α catalytic subunit of PI3K. Data regarding patient characteristics, genetic variants, treatment regimens, clinical outcomes, radiological response, and adverse events were extracted and analyzed. Results: Seventeen publications met the inclusion criteria, comprising a total of 114 patients treated with alpelisib. The majority of patients were pediatric (68.4%), with a median age at treatment initiation of 12 years. Clinical manifestations were heterogeneous and included segmental overgrowth, vascular malformations, and soft-tissue hypertrophy. Clinical improvement in at least one disease manifestation was reported in 111 patients (97.3%). Radiological response, defined as reduction ≥20% in lesion volume, was documented in 26 of 60 evaluable cases (47.3%). Adverse events were reported in 64 patients (56.1%) and were generally mild and manageable, with hyperglycemia and diarrhea being the most common. Conclusions: Available real-world evidence suggests that alpelisib provides meaningful clinical benefit across multiple PROS phenotypes, with an acceptable safety profile. However, current data remain limited by small cohort sizes, heterogeneous outcome reporting, and variable follow-up duration. Prospective studies with standardized outcome measures are needed to better define long-term efficacy and safety of PI3K inhibition in PROS.
    Keywords:  PI3K pathway; PIK3CA-related overgrowth spectrum; PROS; alpelisib; targeted therapy
    DOI:  https://doi.org/10.3390/cells15090788
  6. Cell. 2026 May 12. pii: S0092-8674(26)00463-0. [Epub ahead of print]
    D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response.
    Jialong Jiang, Sisi Chen, Tiffany Tsou, Christopher S McGinnis, Tahmineh Khazaei, Qin Zhu, Jong H Park, Inna-Marie Strazhnik, Jost Vielmetter, Yingying Gong, John Hanna, Eric D Chow, David A Sivak, Zev J Gartner, Matt Thomson.
      Gene regulatory networks modulate the expression of the genome in response to signals and environmental conditions. Reconstructions of such networks can reveal the control principles cells use to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, dimension-scalable single-cell perturbation integration network (D-SPIN), that infers mechanistically interpretable and generative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of perturbation conditions. The models explain how perturbations modulate cell-state proportions by reconfiguring underlying regulatory interactions. Using large Perturb-seq and drug response datasets, D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdowns and drug treatments, elucidate how combinations of immunomodulatory drugs induce combinatorial cell states through additive recruitment of gene expression programs, and simulate shifts in immune cell population structures across unobserved drug dosage combinations. D-SPIN provides a computational framework for revealing principles of cellular information processing and physiological control.
    Keywords:  D-SPIN; Perturb-seq; cell-state transition; drug combination; gene regulatory network; immunomodulatory drug; perturbation response; probabilistic graphical model; regulatory network inference; single-cell RNA sequencing
    DOI:  https://doi.org/10.1016/j.cell.2026.04.028
  7. Genome Biol. 2026 May 11.
    Designing genome editing experiments with EditABLE.
    Demetrios S Maxim, Juliet Sostena, Najani Shanee Johnson, David Wei Wu, Vivek Charu, Jennefer N Carter, Shuchi Anand, George M Church, Vivek Bhalla.
      While many computational tools exist for designing CRISPR-Cas experiments, there is a need for a centralized resource that combines individual tools to predict the most efficient genome editing strategy for a given application. To fill this gap, we develop EditABLE (EditABLE-app.stanford.edu), an online resource that provides optimal CRISPR editors and guide RNAs based on user provided sequence data with functionalities for base editing, prime editing, and integrase-mediated editing. We demonstrate the utility of EditABLE by applying it to one of the most common monogenic disorders, autosomal dominant polycystic kidney disease (ADPKD), identifying specific editing tools across the ADPKD mutation landscape.
    Keywords:  CRISPR–Cas; Gene therapy; Genome editing; Kidney disease
    DOI:  https://doi.org/10.1186/s13059-026-04095-x
  8. Bioinform Adv. 2026 ;6(1): vbag098
    DoTools: a cross platform framework to streamline common single cell workflows.
    Mariano Ruz Jurado, David Rodriguez Morales, Lukas Zanders, Elijah Genetzakis, Stefanie Dimmeler, David John.
       Motivation: Throughout the years, single-cell RNA sequencing (scRNA-seq) has become a standard approach for characterising transcriptomic changes associated with diseases and other biological conditions. However, the rapid expansion of tools and algorithms developed in various programming languages has made single-cell data analysis increasingly complex. In particular, integrating multiple tools into a single workflow often demands substantial learning time and coding expertise.
    Results: To address these challenges, we developed DoTools, a unified framework for R/Bioconductor and Python/PyPI that simplifies the integration of third-party tools such as scVI, CellTypist, and CellBender into standard pipelines like Seurat, SingleCellExperiment and Scanpy. DoTools provides advanced cross-language wrappers and visualisation utilities to streamline data preprocessing, quality control, cell type annotation, and downstream analysis, while implementing best practices in scRNA-seq analysis regardless of the computational language. Its modular design and compatibility with widely used bioinformatics environments makes it accessible and valuable to both novice and experienced data scientists.
    Availability and implementation: DoTools is freely available for R and Python at Bioconductor and PyPI (https://bioconductor.org/packages/release/bioc/html/DOtools.html and https://pypi.org/project/DoTools-py/), the developmental versions of DoTools are maintained on GitHub (https://github.com/MarianoRuzJurado/DoTools and https://github.com/davidrm-bio/DoTools_py).
    DOI:  https://doi.org/10.1093/bioadv/vbag098
  9. Nat Methods. 2026 May;23(5): 865-866
    What's your hypothesis?
      
    DOI:  https://doi.org/10.1038/s41592-026-03104-y
  10. EMBO Rep. 2026 May 09.
    Cellular mechanometabolism: stimuli and implications.
    Ismael Ortiz, Santiago Lopez, Raghu Vamsi Kondapaneni, Jose V Hernandez, Keefer Boone, Cynthia A Reinhart-King.
      While much is known about the effects of the chemical microenvironment on cellular metabolism, mechanical cues have emerged as critical stimuli of intracellular metabolic pathways. Mechanical signals from the extracellular matrix (ECM), neighboring cells, and the microenvironment intersect with key regulators of cellular metabolism, often leading to changes in fundamental cell behaviors, including cell proliferation and migration. Here, we review recent work that has uncovered a role for mechanical cues from microenvironmental factors on cellular metabolism. We discuss how cell-ECM interactions and forces such as shear, tension, and compression affect cellular metabolic requirements and energy production. Importantly, mechanometabolism shapes both physiological homeostasis and pathological states, and further investigation has implications for understanding tissue function and disease progression and uncovering potential therapeutic strategies.
    DOI:  https://doi.org/10.1038/s44319-026-00795-4
  11. bioRxiv. 2026 Feb 24. pii: 2026.02.14.705396. [Epub ahead of print]
    A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping.
    Rosyli F Reveron-Thornton, James P Agolia, Chuner Guo, Maria Korah, Chia-Hsin Hsu, Peter Y Xie, Renceh Ab Flojo, Andrea E Delitto, Amanda Gonçalves, Angela D Tabora, Michael Januszyk, Victoria E Sanchez, Kevin Nee, Biren Reddy, Wesley Bobst, Byrne Lee, George A Poultsides, Amanda R Kirane, Derrick C Wan, Jeffrey A Norton, Edgar G Engleman, Aaron M Newman, Michael T Longaker, Deshka S Foster, Daniel Delitto.
      Bulk RNA sequencing enables pan-cancer transcriptional analyses, but obscures cancer cell-specific programs due to admixture with nonmalignant cells, thereby limiting direct comparison between experimental models and primary tumors. Single-cell RNA sequencing (scRNA-seq) overcomes these limitations; however, the biological interpretability of public datasets is often compromised by variable data quality, inconsistent annotation, and atlas-scale aggregation strategies that prioritize data volume over biological coherence. We therefore developed a stringent integration framework that prioritizes representative malignant transcriptional states. Using Mahalanobis distance-based selection within batch-corrected latent space, we constructed a pan-cancer atlas comprising 135,424 high-quality malignant cells from 499 samples across 36 adult and pediatric cancers. Atlas-derived cancer signatures were used to determine tumor-cell line concordance and project ElasticNet models trained on DepMap CRISPR screens to infer cancer-specific gene dependencies. The scTumor Atlas establishes a scalable framework for tumor identity inference, cancer cell line benchmarking, and systematic identification of genetic vulnerabilities.
    DOI:  https://doi.org/10.64898/2026.02.14.705396
  12. Trends Biotechnol. 2026 May 14. pii: S0167-7799(26)00148-4. [Epub ahead of print]
    Automated stem cell-derived organoid platforms for disease modeling.
    Xingrui Mou, Nathan Dale, Kiran Ramnarine, Filippo Cipriani.
      Complex diseases arise from genetic, environmental, and lifestyle factors, the combination of which is difficult to model. Conventional animal and 2D cell culture models have limitations in scalability, reproducibility, or human relevance. Human-induced pluripotent stem cells (iPSCs) can be differentiated into 3D organoids that better mimic human biology. However, organoid protocols can be lengthy, variable, and labor-intensive, limiting high-throughput applications. Suspension bioreactors and multilineage differentiation have improved yield and function, but challenges remain in tissue maturity, vascularization, and consistency. Automated high-throughput liquid handling systems are emerging as a solution, enabling large-scale, reproducible production. Here, we discuss how combining iPSC-derived organoids with automation is poised to transform disease modeling and drug development.
    Keywords:  automation and high-throughput systems; complex disease modeling; iPSC-derived organoids
    DOI:  https://doi.org/10.1016/j.tibtech.2026.04.013
  13. Nat Commun. 2026 May 13.
    BromoCatch: a self-labelling tag platform for protein modification and live cell imaging.
    Maria Rodriguez-Rios, Conner Craigon, Mark A Nakasone, Gajanan Sathe, Adam G Bond, Mark Dorward, Anthony K Edmonds, Mark C Norley, Robert E Arnold, Paul M Wood, Stephen J Reynolds, Joel O Cresser-Brown, Graham P Marsh, Hannah J Maple, Alessio Ciulli.
      Visualizing and manipulating proteins in live cells is crucial for studying complex biological processes. Self-labelling protein (SLP) tags such as HaloTag and SNAP-tag are widely used for protein labelling, and new systems are needed to expand multiplexing capabilities and broaden the scope of applications. Here we present BromoCatch, a small ~13 kDa bromodomain (BD)-based SLP platform, engineered with a nucleophilic cysteine for covalent ligand engagement. A structure-based designed library of electrophilic ligands was screened against two cysteine-containing mutants using differential scanning fluorimetry and intact protein mass spectrometry to assess covalent complex formation. We identified a para-acrylamide bumped derivative MR116 and the Brd4-BD2 double mutant L387A,E438C as the optimal protein-ligand pair, and reveal the binding mode through an X-ray co-crystal structure solved to 1.3 Å resolution. BromoCatch demonstrated potent and irreversible cellular target engagement in NanoBRET and residence-time assays. Its versatility was demonstrated through the design of a biotinylated conjugate, PROTAC-based degraders, and fluorescent full-on and "switch-on" probes for ex-cellulo and live-cell imaging, including side-by-side comparison and orthogonality with HaloTag. Together, these results establish BromoCatch as a robust, modular, and orthogonal SLP tool with broad potential for multiplexed labelling and targeted protein manipulation.
    DOI:  https://doi.org/10.1038/s41467-026-72539-w
  14. Cell. 2026 May 11. pii: S0092-8674(26)00457-5. [Epub ahead of print]
    RegVelo: Gene-regulatory-informed dynamics of single cells.
    Weixu Wang, Zhiyuan Hu, Philipp Weiler, Sarah Mayes, Marius Lange, Daniel M Fountain, Julianna O Haug, Jingye Wang, Zhengyuan Xue, Tatjana Sauka-Spengler, Fabian J Theis.
      Cell fate transitions are driven by regulatory circuitry, yet RNA velocity models cellular dynamics without explicitly accounting for gene regulatory interactions, limiting mechanistic insight. Conversely, gene regulatory network (GRN) inference methods largely neglect the dynamic nature of biological systems. To overcome this conceptual disconnect, we present RegVelo, a bottom-up, actionable, and interpretable deep learning framework that jointly models splicing kinetics and gene regulatory interactions. Across diverse biological systems, RegVelo provides reliable predictive power for terminal states, gene interactions, and perturbation simulations. By applying RegVelo to zebrafish neural crest development using full-length Smart-seq3 and shared gene expression and chromatin accessibility measurements, we delineate regulatory programs underlying fate specification. Guided by in silico perturbations and validated by CRISPR-Cas9 knockout and single-cell Perturb-seq, we establish tfec as an early driver and elf1 as a regulator of pigment cell fate. RegVelo establishes a quantitative framework for bridging gene regulation and cell fate decisions.
    Keywords:  cell fate decision; deep generative modeling; early drivers; gene regulatory network; in silico perturbation; in vivo Perturb-seq; mechanistic modeling; regulatory dynamics; transcriptional dynamics; zebrafish neural crest
    DOI:  https://doi.org/10.1016/j.cell.2026.04.022
  15. Nucleic Acids Res. 2026 May 05. pii: gkag480. [Epub ahead of print]54(9):
    Universal conditional knockout approach to multiple species, from fish to human induced pluripotent stem cells.
    Jung-Hwa Choi, Jihoon Moon, Youngchul Oh, Thomas M Klompstra, Yujin Kim, Gayoung Baek, Yejin Kwak, Yeongjun Kim, Sangmin Lee, Sujin Park, Jeongmin Ha, Ohbin Kwon, Young Ki Choi, Jeong Hun Kim, Ji-Hyun Lee, Jeongbin Park, Jong Kyoung Kim, Dong Ho Woo, Ki-Jun Yoon, Bon-Kyoung Koo, Heetak Lee.
      Investigating essential gene function in vertebrate development and disease is challenging due to associated lethality, necessitating precise conditional inactivation. While existing conditional knockout methods can be inefficient and demanding, especially in models like zebrafish and human induced pluripotent stem cells (iPSCs), a widely applicable system has remained elusive. Here, we demonstrate the expanded utility and versatility of a Short Conditional intrON (SCON) knockout cassette, now validated for efficient conditional loss-of-function mutation generation in diverse vertebrate models, including zebrafish, human iPSCs, and intestinal organoids. Building on this validated broad applicability, we establish a comprehensive, user-friendly web-based GenPos-SCON database (https://genpos.org/) to streamline conditional knockout design for over 300 vertebrate species. This powerful resource and proven methodology significantly accelerate systematic gene functional studies across the vertebrate tree, providing an unprecedented tool for dissecting fundamental biological processes and disease mechanisms in relevant contexts.
    DOI:  https://doi.org/10.1093/nar/gkag480
  16. Insect Sci. 2026 May 11.
    Ovary single-nucleus transcriptomics reveal follicle cell maturation as a driver of reproductive hierarchy in bumblebees.
    Hao Wang, Yuwen Liu, Xiaohuan Mu, Jie Ma, Wenjing Xu, Huiling Liu, Qiyao Yong, Xiaofei Wang, Yifan Zhai, Shanshan Liu, Hao Zheng.
      The regulation of reproductive division of labor in eusocial insects is pivotal for the evolution of social behavior and the maintenance of eusociality. Primitively eusocial bumblebee workers retain reproductive totipotency, with dominant workers capable of ovarian activation and egg-laying. Here, we investigated the cellular and molecular basis of reproductive hierarchy in Bombus terrestris by constructing a single-nucleus transcriptomic atlas of the ovary in queenless bumblebee groups. Using single-nucleus RNA sequencing, we profiled ovarian cell types and revealed that α-worker bees possess more mature follicle cells, which are essential for ovarian development. Differential maturation of follicle cells, particularly at the vitellogenic stage, emerges as a key regulatory node in this process. More mature follicle cells promote the production of growth factors that activate PDK1. This activation subsequently induces AKT phosphorylation and downstream signaling. As a result, the levels of 20-hydroxyecdysone are elevated in dominant α-workers. By demonstrating how follicle cell maturation and signaling drive reproductive activation, our findings link cellular physiology to social organization and provide new insight into the molecular mechanisms underlying the evolution of eusociality.
    Keywords:  20‐hydroxyecdysone; Bombus terrestris; dominant worker; follicle cell; reproductive hierarchy
    DOI:  https://doi.org/10.1111/1744-7917.70293
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