bims-pideca 2026-03-29 papers

bims-pideca

Biomed News

on Class IA PI3K signalling in development and cancer

Issue of 2026–03–29
twenty-one papers selected by
Ralitsa Radostinova Madsen, MRC-PPU

Suppression of ITPK1 and IPMK activities impairs mTORC1 signaling in pancreatic β-cells and implicates IP 5 in stabilizing activated mTORC1.
Shear Stress Induces Concentration Gradient Distributions of Membrane Proteins in Live Cells.
Progress and new challenges in image-based profiling.
Tyrosine phosphoproteome profiling identifies cell-intrinsic signals limiting the efficacy of tyrosine kinase inhibitor therapies.
Human Single-cell Atlas Analysis Reveals Heterogeneous Endothelial Signaling.
TIE2 links MEKK3-KLF2/4 and PI3K signaling in cerebral cavernous malformation.
Type and Size of Fluorescent Proteins Modulate the Localization of PTEN and Its Fragments, but Have Little or No Effect on Mutant PTEN or Stressed Cells.
Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design.
Disruption of WNT/Notch signaling in pancreatic cancer reveals tumors depend on the intricate equilibrium of malignant cell states.
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics.
OBESITY-INDUCED ENDOTHELIAL FENESTRATION AND CAPILLARY LEAKAGE CONTRIBUTE TO INCREASED PAIN SENSATION.
Cells Dynamically Adapt Their Nuclear Volumes and Proliferation Rates During Single to Multicellular Transitions.
From FAIR to CURE: guidelines for computational models of biological systems.
UFMylation-dependent inhibition of AKT signaling by PHLDA3 in lung adenocarcinoma.
Polypharmacology of an Optimal Kinase Library.
The E3-ome gene-centric compendium reveals the human E3 ligase landscape.
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor.
Persistent homology-based segmentation tool for membrane images.
Distinctive DNA sequence features define epigenetic longevity of inflammatory memory.
Shinya Yamanaka.
Watching cancer begin: emerging tools to visualize the first steps of tumorigenesis.

bioRxiv. 2026 Mar 07. pii: 2026.03.04.709646. [Epub ahead of print]

Suppression of ITPK1 and IPMK activities impairs mTORC1 signaling in pancreatic β-cells and implicates IP 5 in stabilizing activated mTORC1.

Clarisse Iradukunda, Edward A Salter, Dilipkumar Uredi, Xiaodong Wang, Andrzej Wierzbicki, Lucia E Rameh.

mTORC1 integrates growth factor and nutrient signals to regulate cellular metabolism, yet there are no metabolites known to directly regulate mTORC1 activity in cells. Cryo-EM studies revealed that inositol hexakisphosphate (IP 6 ) associates with the FAT domain of mTOR, suggesting that inositol phosphates may directly modulate mTOR activity. We previously showed that higher-order inositol phosphates enhance mTORC1 kinase activity and stability in vitro. Here, we investigated whether inositol phosphate metabolism regulates mTORC1 signaling in pancreatic β-cells. Suppression or acute inhibition of inositol phosphate multikinase (IPMK), as well as knockdown of inositol trisphosphate kinase 1 (ITPK1), selectively reduced cellular IP 5 levels without altering IP 6 and resulted in impaired basal and insulin-stimulated mTORC1 signaling, particularly under physiological glucose and low growth factor conditions. Combined inhibition of IPMK and ITPK1 nearly abolished IP 5 and reduced IP 6 , demonstrating that these enzymes compensate to supply IP 5 for IP 6 synthesis. Importantly, depletion of IP 5 did not impair PI3K/Akt activation but accelerated termination of the mTORC1 signal, indicating a role for IP 5 in stabilizing the active mTORC1 complex. Reduction of inositol phosphate levels did not prevent insulin- or glucose-induced mTORC1 activation, revealing that IP 5 primarily regulates signal persistence rather than initiation. Together, these findings identify IP 5 as a metabolic regulator that prolong mTORC1 activity in β-cells, providing a mechanism by which cellular metabolic state modulates sustained mTORC1 signaling.
Significance Statement: mTORC1 is a central metabolic regulator whose chronic activation contributes to metabolic disease, yet mechanisms that sustain mTORC1 activity after its activation are poorly understood. We show that enzymes controlling inositol phosphate metabolism regulate the stability of mTORC1 signaling in pancreatic β-cells by maintaining cellular levels of inositol pentakisphosphate (IP 5 ). Reducing IP 5 impairs basal and sustained mTORC1 signaling without affecting upstream growth factor or energy-sensing pathways, revealing a mechanism that controls signal duration rather than activation. These findings identify IP 5 as a metabolic regulator of mTORC1 and suggest that targeting inositol phosphate metabolism may provide a strategy to modulate mTORC1 activity in metabolic disease.

DOI: https://doi.org/10.64898/2026.03.04.709646
Biophys J. 2026 Mar 24. pii: S0006-3495(26)00228-6. [Epub ahead of print]

Shear Stress Induces Concentration Gradient Distributions of Membrane Proteins in Live Cells.

Sawako Yamashiro, Misato Nomura, Nils Chapin, Sreeja Sasidharan, Louis Elverston, Kohjiro Nagao, Leah Knepper, Damien Thévenin, Naoki Watanabe, Aurelia R Honerkamp-Smith.

Cells sense and respond to fluid shear stress. Cell surfaces are exposed to flow, yet the influence of shear stress on the behavior of plasma membrane proteins remains unclear. Here we show that extracellular flow induces the gradient distribution of cell membrane proteins with increasing concentration toward the downstream direction of the flow. Shear stress at 10-30 dynes/cm2 caused formation of concentration gradients of both GPI-anchored proteins and transmembrane proteins, including integrinß1, E-cadherin and the insulin receptor in Xenopus XTC cells. Using single-molecule live-cell imaging, we found that GPI-anchored T-cadherin molecules are dragged along the direction of flow under shear stress. The extent of gradient formation varied among membrane proteins. VE-cadherin showed minimal gradient formation, and its response was unaffected by disruption of the actin cytoskeleton or cholesterol depletion. We also observed cell line-dependent differences in the response of GPI-anchored proteins. In Xenopus A6 kidney epithelial cells, gradient formation of GPI-anchored EGFP (EGFP-GPI) was less pronounced than in XTC cells. Actin disruption modestly enhanced EGFP-GPI gradient formation, suggesting a partial role for the cortical actin network in modulating cell line-dependent responses. In Cos-7 cells and human umbilical vein endothelial cells (HUVECs), similar gradients of GPI-anchored proteins were observed but required higher shear stress. Our findings suggest that external flow directly transports membrane proteins, establishing concentration gradients that may contribute to the cellular flow-sensing mechanism.

DOI: https://doi.org/10.1016/j.bpj.2026.03.045
Mol Syst Biol. 2026 Mar 27.

Progress and new challenges in image-based profiling.

Erik Serrano, John Peters, Jesko Wagner, Rebecca E Graham, Zhenghao Chen, Brian Y Feng, Gisele Miranda, Alexandr A Kalinin, Loan Vulliard, Jenna Tomkinson, Cameron Mattson, Michael J Lippincott, Ziqi Kang, Divya Sitani, Dave Bunten, Srijit Seal, Neil O Carragher, Anne E Carpenter, Shantanu Singh, Paula A Marin Zapata, Juan C Caicedo, Gregory P Way.

  For over two decades, image-based profiling has revolutionized cell phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into thousands of unbiased measurements that reveal phenotypic patterns powerful for drug discovery, functional genomics, and cell state classification. Here, we review the evolving computational landscape of image-based profiling, detailing the bioinformatics processes involved from feature extraction to normalization and batch correction. We discuss how deep learning has fundamentally reshaped the field. We examine key methodological advancements, such as single-cell analysis, the development of robust similarity metrics, and the expansion into new modalities like optical pooled screening, temporal imaging, and 3D organoid profiling. We also highlight the growth of public benchmarks and open-source software ecosystems as a key driver for fostering reproducibility and collaboration. Despite these advances, the field still faces substantial challenges, particularly in developing methods for emerging temporal and 3D data modalities, establishing robust quality control standards and workflows, and interpreting the processed features. By focusing on the technical evolution of image-based profiling rather than the wide-ranging biological applications, our aim with this review is to provide researchers with a roadmap for navigating the progress and new challenges in this rapidly advancing domain.

Keywords:  Cell Profiling; Deep Learning; Feature Extraction; Image-Based Profiling; Phenotypic Screening

DOI:  https://doi.org/10.1038/s44320-026-00197-7
Proc Natl Acad Sci U S A. 2026 Mar 31. 123(13): e2522090123

Tyrosine phosphoproteome profiling identifies cell-intrinsic signals limiting the efficacy of tyrosine kinase inhibitor therapies.

Cameron T Flower, Forest M White.

  Tyrosine kinases (TKs) are frequently mutated or overexpressed in cancer, and TK inhibitors (TKIs) are an important therapeutic modality against TK-driven cancers, but many patients show an underwhelming response to TKIs prescribed on the basis of tumor genotype. To find cell-intrinsic TK signaling patterns which might be predictive of poor response to TKI therapies, we used high-sensitivity multiplexed mass spectrometry to quantify endogenous levels of 1,222 phosphotyrosine (pY) sites across the proteomes of TK-driven human cancer cell lines with variable response to genotype-matched TKIs. In direct comparisons between TKI-tolerant and TKI-sensitive lines with a common driver TK, we found that TKI treatment was equally effective at blocking driver TK signaling, and higher basal activity of the driver TK did not always predict higher sensitivity to TKI. All tolerant lines showed a dampened proteome-wide pY response to TKI exposure compared to sensitive lines, suggesting that tumor cells with more robust TK signaling are less vulnerable to driver TK blockade. We found that each tolerant line depends on a unique set of compensatory TKs and signaling axes but are unified by hyperactivity of at least one of the SRC family kinases (SFKs) or the related ABL1/2 kinases, both at rest and under TKI treatment, despite the absence of SFK or ABL genetic mutations. In time- and dose-resolved drug combination experiments, SFK/ABL inhibitors were potently synergistic with all TKIs tested, demonstrating that elevated SFK/ABL signaling is a conserved bottleneck for maximal TKI efficacy which could be exploited therapeutically.

Keywords:  drug synergy; phosphoproteomics; precision oncology; targeted therapy; tyrosine kinase signaling

DOI:  https://doi.org/10.1073/pnas.2522090123
Genomics Proteomics Bioinformatics. 2026 Mar 20. pii: qzag025. [Epub ahead of print]

Human Single-cell Atlas Analysis Reveals Heterogeneous Endothelial Signaling.

Zimo Zhu, Rongbin Zheng, Yang Yu, Lili Zhang, Kaifu Chen.

  Endothelial cells (ECs) play complex roles across tissues and vessel types. Yet, systematic investigations of EC heterogeneity in the combined context of vessel type and tissue microenvironment are still largely lacking. We integrated over three million single cells from single-cell RNA-seq datasets in 15 human tissues. We found that ECs in some tissues (e.g., heart and kidney) exhibited greater tissue specificity, whereas others showed greater vessel specificity. We developed a computational pipeline to analyze cell-cell communications (CCC) mediated by metabolites or proteins to explore microenvironmental regulation. Interestingly, our results showed that CCC events involving ECs varied vastly across tissues, highlighting tissue-specific EC interactions. Using topic modeling, we identified CCC patterns, termed CCC topics, representing metabolite- and protein-mediated interactions between ECs and other tissue-resident cells. Most CCC topics showed high tissue specificity, potentially explaining the microenvironmental regulation of EC heterogeneity. The work systematically investigates EC heterogeneity and provides insights into its regulation across diverse tissue microenvironments. The script to reproduce all analyses in this study is available at https://github.com/zhuzimoo/EC_project.

Keywords:  Cell–cell communication; Heterogeneity; Human endothelial; Single-cell; Specificity

DOI:  https://doi.org/10.1093/gpbjnl/qzag025
J Exp Med. 2026 May 04. pii: e20251374. [Epub ahead of print]223(5):

TIE2 links MEKK3-KLF2/4 and PI3K signaling in cerebral cavernous malformation.

Lun Li, Marco Castro, Hiroki Hongo, Jian Ren, Robert Shenkar, Rashad Jabarkheel, Siqi Gao, Sweta Narayan, Maxwell Frankfurter, Alan T Tang, Jisheng Yang, Mei Chen, Jenna Bockman, Patricia Mericko-Ishizuka, Roberto Alcazar, Georgio Sader, Javed Iqbal, Serena Kinkade, Rhonda Lightle, Andrew K Ressler, Xianghu Qu, H Scott Baldwin, Douglas A Marchuk, Issam A Awad, Jan-Karl Burkhardt, Michael Potente, Mark L Kahn.

Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system that can cause strokes and seizures. Aggressive CCM growth follows an endothelial cell two-hit mechanism in which enhanced MEKK3-KLF2/4 signaling stimulates PI3K signaling, but how these pathways are linked has been undefined. Here, we use human CCM specimens, two mouse models of CCM disease, and primary human endothelial cells to examine the roles of the major endothelial growth factor receptors, VEGFR2 and TIE2. We find no evidence of augmented VEGFR2 signaling in CCM lesions, and neither genetic nor pharmacologic blockade of VEGFR2 reduced CCM formation in mouse models. Instead, we observe markedly increased phospho-TIE2 levels in human and mouse CCM lesions, MEKK3-KLF2/4-driven induction of TIE2 receptor expression, and almost complete rescue of CCM formation following genetic or pharmacologic TIE2 blockade in mouse models. Our studies identify TIE2 as the molecular link between the MEKK3-KLF2/4 and PI3K signaling pathways during CCM formation and suggest that targeting TIE2 may be an effective means to treat human CCM disease.

DOI: https://doi.org/10.1084/jem.20251374
J Histochem Cytochem. 2026 Mar 27. 221554261434309

Type and Size of Fluorescent Proteins Modulate the Localization of PTEN and Its Fragments, but Have Little or No Effect on Mutant PTEN or Stressed Cells.

Takashi Kato, Yuuri Kawakami.

  The functions of phosphatase and tensin homolog deleted on chromosome 10 (PTEN), a tumor suppressor, depend on its subcellular localization. At the plasma membrane, PTEN dephosphorylates phosphatidylinositol-3,4,5-triphosphate to inhibit AKT signaling, whereas nuclear PTEN contributes to the maintenance of genomic stability. Fluorescent proteins (FPs) are widely used to assess PTEN's subcellular localization; however, both the intrinsic properties of FPs (e.g., molecular size) and the choice of FP can influence subcellular localization. This study aimed to determine whether FP fusion affects the subcellular localization of PTEN and its mutant forms under conditions involving DNA damage. mCherry typically promotes cytosolic localization of FP-fused PTEN, indicating that FP selection may affect the interpretation of localization data. Furthermore, FP fusion increases the molecular size of the truncated PTEN fragment, which may impede its nuclear import. In comparison, PTEN mutants such as PTENK13R or PTENA4, which predominantly localize to the cytoplasm or nucleus, respectively, show a minimal dependence on the type of FP. Similarly, DNA damage-induced nuclear accumulation of PTEN appears to be independent of the FP type. These findings underscore the importance of carefully considering the effects of FP fusion when investigating the mechanisms regulating the nuclear translocation of PTEN.

Keywords:  PTEN; fluorescent proteins; subcellular localization

DOI:  https://doi.org/10.1369/00221554261434309
Cell Genom. 2026 Mar 25. pii: S2666-979X(26)00052-2. [Epub ahead of print] 101190

Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design.

Laura M Drepanos, Smriti Srikanth, Eleanor G Kaplan, Spencer T Shah, Berta Escude Velasco, Sarra Merzouk, John G Doench.

  The continued development of high-dimensional CRISPR screen readouts, such as single-cell RNA sequencing and high-content imaging, necessitates compact libraries to enable functional interrogation at genome scale. Improved genome annotations cause library deprecation over time, further motivating an updated genome-wide design effort. Additionally, while on-target efficacy and off-target avoidance are often optimized in isolation, we lack a robust framework for simultaneously weighing and balancing these competing priorities. Here, we present a selection strategy that identifies guides with sufficient off-target activity to justify omission from the library, thus avoiding the unnecessary exclusion of active guides, allowing the inclusion of those with maximal on-target activity. We create, validate, and make available to the community the Jacquere library for knockout screens of the human genome, as well as its mouse counterpart, Julianna, to facilitate gene function discovery at scale.

Keywords:  CRISPR; Cas9; functional genomics; genetic screens

DOI:  https://doi.org/10.1016/j.xgen.2026.101190
Dev Cell. 2026 Mar 23. pii: S1534-5807(26)00082-1. [Epub ahead of print]

Disruption of WNT/Notch signaling in pancreatic cancer reveals tumors depend on the intricate equilibrium of malignant cell states.

Stefan R Torborg, Jung Yun Kim, Anupriya Singhal, Olivera Grbovic-Huezo, Matilda Holm, Katherine Wu, Xuexiang Han, Yu-Jui Ho, Caj Haglund, Michael J Mitchell, Scott W Lowe, Lukas E Dow, Kenneth L Pitter, Francisco J Sanchez-Rivera, Andre Levchenko, Tuomas Tammela.

  Control of cell identity and number is central to tissue function, yet principles governing the organization of malignant cells remain poorly understood. Using genetically engineered mouse models and orthotopic allografts with dual WNT reporter systems, we discover that pancreatic ductal adenocarcinoma (PDAC) organizes in a stereotypical pattern, whereby PDAC cells responding to WNT signals (WNT-R) neighbor WNT-secreting cancer cells (WNT-S). Lineage tracing reveals that the WNT-R state is transient and gives rise to a stable WNT-S state. A subset of WNT-S cells expressing DLL1 forms a functional niche for WNT-R cells. The genetic inactivation of WNT secretion or Notch pathway components, or the cytoablation of WNT-S cells, disrupts PDAC tissue organization, suppressing tumor growth and metastasis. Analysis of human PDAC tissues confirms conservation of these populations. PDAC growth depends on an intricately controlled equilibrium of functionally distinct cancer cell states, revealing the fundamental principles governing solid tumor organization and therapeutic opportunities.

Keywords:  Notch; WNT; gene perturbation; intratumoral heterogeneity; lineage ablation; lineage tracing; pancreas cancer; tissue organization

DOI:  https://doi.org/10.1016/j.devcel.2026.02.017
bioRxiv. 2026 Mar 09. pii: 2026.02.26.708361. [Epub ahead of print]

ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics.

Chen Yang, Xianyang Zhang, Jun Chen.

Spatial transcriptomics has transformed our ability to study tissue architecture at molecular resolution, yet analyzing these data demands navigating dozens of computational methods across incompatible Python and R ecosystems- forcing researchers to devote more effort to making tools function than to pursuing biological questions. We present ChatSpatial, a platform in which the LLM selects from pre-validated tool schemas rather than generating free-form code, with domain expertise embedded in schema descriptions for context-aware parameter inference. Built on the Model Context Protocol (MCP), ChatSpatial unifies 60+ methods across 15 analytical categories into a single conversational workflow spanning Python and R ecosystems. Replication of two published studies-recovering subclonal heterogeneity in ovarian cancer and tumor microenvironment organization in oral squamous cell carcinoma-and validation across seven LLM platforms demonstrate that schema-enforced orchestration yields near-deterministic reproducibility at the workflow level for multi-step spatial analyses. Beyond replication, exploratory cross-method analyses illustrate practical triangulation across independent analytical frameworks.

DOI: https://doi.org/10.64898/2026.02.26.708361
bioRxiv. 2026 Mar 16. pii: 2026.03.13.711502. [Epub ahead of print]

OBESITY-INDUCED ENDOTHELIAL FENESTRATION AND CAPILLARY LEAKAGE CONTRIBUTE TO INCREASED PAIN SENSATION.

Yuta Koui, Jonathan Richard Shin, Shuxuan Song, Christian A Combs, Yoh-Suke Mukouyama.

Peripheral pain sensation is regulated by interactions between sensory nerves and various tissue cells. In obese patients with painful small fiber neuropathy, skin sensory nerves are often hypersensitive. While obesity is known to cause circulation-related vascular abnormalities, how these changes affect sensory dysfunction is not fully understood. In this study, we found that in a diet-induced obesity mouse model, skin capillaries become fenestrated, allowing insulin to diffuse into the avascular epidermis. This exposure triggers the production and secretion of nerve growth factor (NGF) from epidermal keratinocytes via insulin signaling with the forkhead box O1 (FOXO1) transcription factor. Elevated NGF leads to heightened sensory hypersensitivity by enhancing transient receptor potential vanilloid subtype 1 (TRPV1) in sensory nerves. Controlling capillary permeability reduces abnormal NGF expression and attenuates pain hypersensitivity. These findings nominate peripheral nerve-associated capillary permeability as a novel therapeutic target in obesity-associated sensory dysfunction.

DOI: https://doi.org/10.64898/2026.03.13.711502
Adv Sci (Weinh). 2026 Mar 23. e24325

Cells Dynamically Adapt Their Nuclear Volumes and Proliferation Rates During Single to Multicellular Transitions.

Vaibhav Mahajan, Keshav Gajendra Babu, Markus Mukenhirn, Antje Garside, Vinita Ajit Kini, Trishla Adhikari, Timon Beck, Byung Ho Lee, Kyoohyun Kim, Carsten Werner, Raimund Schlüßler, Alf Honigmann, Sebastian Aland, Anna Verena Taubenberger.

  Tumor development and progression involve biophysical changes across spatial scales, from the subcellular to the multicellular tissue scale. While cells are known to dynamically regulate their volumes and mechanics in dependence of cell state and function, it is unclear how these properties are controlled in dense multicellular environments like developing tumors. Here, we quantified cell and nuclear volumes of cancer cells forming multicellular spheroids within mechanically tunable biohybrid polymer hydrogels. We quantitatively showed that formation of multicellular structures is associated with marked reductions of cellular and nuclear volumes, cell cycle delays as well as cell mechanical alterations, and that these changes are coupled. Single-to-multicellular transitions led to up to 60% decreases in median nuclear volumes, which was not explained by growth-induced compressive stress. Instead, nuclear volume reductions in emerging clusters arose from cell cycle adaptations, with accumulation of smaller G1-phase cells-reversed by CDK1 inhibition. Additional nuclear downsizing in forming clusters was associated with cell mass density and stiffness increases and reverted upon cell release. Conversely, multicellular-to-single cell transitions during invasion were accompanied by nuclear volume expansion and cell softening. Together, these findings reveal dynamic regulation of cellular and nuclear volumes, mechanics, and cell cycle progression in response to multicellular state.

Keywords:  3D model; cell mechanics; cell volume; multicellularity; spheroid; tumour microenvironment

DOI:  https://doi.org/10.1002/advs.202524325
NPJ Syst Biol Appl. 2026 Mar 27.

From FAIR to CURE: guidelines for computational models of biological systems.

Herbert M Sauro, Eran Agmon, Michael L Blinov, John H Gennari, Joseph L Hellerstein, Adel Heydarabadipour, Bartholomew E Jardine, Elebeoba May, David P Nickerson, Lucian P Smith, Gary D Bader, Frank T Bergmann, Patrick M Boyle, Andreas Dräger, James R Faeder, Song Feng, Juliana Freire, Fabian Fröhlich, James A Glazier, Thomas E Gorochowski, Tomas Helikar, Henning Hermjakob, Stefan Hoops, Peter Hunter, Princess I Imoukhuede, Sarah M Keating, Matthias König, Reinhard Laubenbacher, Leslie M Loew, Carlos F Lopez, William W Lytton, Rahuman S Malik-Sheriff, Andrew McCulloch, Pedro Mendes, Lealem Mulugeta, Chris J Myers, Jerry G Myers, Anna Niarakis, David D van Niekerk, Brett G Olivier, Alexander A Patrie, Ellen M Quardokus, Nicole Radde, Johann M Rohwer, Sven Sahle, James C Schaff, Falk Schreiber, T J Sego, Janis Shin, Jacky L Snoep, Rajanikanth Vadigepalli, H Steven Wiley, Dagmar Waltemath, Ion I Moraru.

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community should strive to automate as many of the guidelines as possible.

DOI: https://doi.org/10.1038/s41540-026-00651-0
Cell Rep. 2026 Mar 25. pii: S2211-1247(26)00232-9. [Epub ahead of print]45(4): 117154

UFMylation-dependent inhibition of AKT signaling by PHLDA3 in lung adenocarcinoma.

Xiuqing Ma, Rui Wan, Xiao Yang, Jiaxin Tang, Yalei Wen, Lei Huang, Xinying Li, Yan Song, Yang Zhou, Shao-Hua Wang, Tongzheng Liu.

  UFMylation, a recently identified ubiquitin-like modification mediated by the E3 ligase UFL1, plays context-specific roles in cancers, but its substrates and functions in lung adenocarcinoma (LUAD) remain poorly defined. Here, we identify the AKT signaling repressor PHLDA3 as a substrate of UFL1 in LUAD. UFMylation of PHLDA3 at Lys51 and Lys106 promotes its membrane localization, thereby blocking AKT membrane recruitment and suppressing downstream signaling. Tumor-associated PHLDA3 mutations F41L, E82G, and K106N impair its UFMylation and membrane translocation, resulting in AKT hyperactivation and enhanced tumor growth. In samples from patients with LUAD, UFL1 expression inversely correlates with phospho-AKT levels. Functionally, the UFL1-PHLDA3 axis inhibits LUAD progression in both cell line-based and patient-derived xenograft models. These findings define a tumor-suppressive UFMylation pathway that modulates AKT activity and provides a mechanistic rationale for targeting UFL1-PHLDA3 signaling in LUAD.

Keywords:  CP: cancer; PHLDA3; UFL1; UFMylation; lung adenocarcinoma

DOI:  https://doi.org/10.1016/j.celrep.2026.117154
bioRxiv. 2026 Mar 19. pii: 2026.03.17.711623. [Epub ahead of print]

Polypharmacology of an Optimal Kinase Library.

Caitlin E Mills, Clemens Hug, Karuna Anna Sajeevan, Nicholas Clark, Chiara Victor, Mirra Chung, Sameer Rawat, Bree Aldridge, Mark W Albers, Ratul Chowdhury, Benjamin M Gyori, Peter K Sorger.

Despite decades of research, current understanding of the spectrum of targets bound by kinase inhibitors remains incomplete. This complicates mechanism of action studies, drug repurposing, and understanding of adverse responses. Here, we describe kinome-wide profiling of an optimal kinase library (OKL) comprising 192 small molecules selected based on stage of clinical development, chemical diversity, and target coverage. Our results show that polypharmacology is widespread among kinase inhibitors independent of regulatory approval. The generally understood ("assigned") targets of approved molecules are not necessarily the most potently inhibited and off targets include multiple understudied kinases. Moreover, median selectivity has not increased over time. We illustrate the use of synoptic OKL-kinome profiles in identifying potential toxicity targets, repurposing anti-inflammatory drugs for neurodegenerative and infectious diseases, and performing chemical genetic studies. Our studies illustrate how much remains to be discovered about the chemistry and biology of one of the largest classes of human therapeutics.

DOI: https://doi.org/10.64898/2026.03.17.711623
Cell. 2026 Mar 20. pii: S0092-8674(26)00116-9. [Epub ahead of print]

The E3-ome gene-centric compendium reveals the human E3 ligase landscape.

Ngee Kiat Chua, Tania J González-Robles, Cameron J Reddington, Jane Dudley-Fraser, Richard W Birkinshaw, Jiru Han, Ashleigh Solano, Soon Wei Wong, Tomasz Kochańczyk, Joshua J Peter, Mark A Nakasone, Florian Aust, Jacob Munro, Yeh Huei Tong, Julie Iskander, Waruni Abeysekera, Alex Garnham, Hannah Huckstep, Matthew E Ritchie, Ingrid Wertz, Sarah Hymowitz, Sharad Kumar, Ron C Conaway, Gilbert G Privé, Alex N Bullock, Jeffrey J Babon, Rachel E Klevit, Sonja Lorenz, Alessio Ciulli, Eric S Fischer, Nicolas H Thomä, Radosław P Nowak, Brenda A Schulman, Michael Rapé, Katrin Rittinger, Julia K Pagan, Melanie Bahlo, Joel P Mackay, Peter D Mace, Christopher D Lima, Ronald T Hay, David Komander, Bernhard C Lechtenberg, Claudio A P Joazeiro, Michele Pagano, Kay Hofmann, Rebecca Feltham.

To define and systematically characterize the human E3 ubiquitin ligase (E3) landscape, we generated the E3-ome, a compendium of E3s encoded by the human genome. The E3-ome integrates experimental data, bioinformatics, and published research, revealing 672 high-confidence E3s. We standardized E3 classifications to create a unified framework for annotation and comparative analysis. The E3-ome identified several previously unrecognized domains, motifs, E3 candidates, and relationships, expanding the diversity of E3s. Furthermore, the E3-ome mapped the spatial and physiological organization of E3s across human tissues and cell types, revealing context-dependent E3s. Genetic analyses identified disease-associated variants across the E3-ome, linking E3s to diverse human pathologies. Together, these analyses define the human E3 landscape at high resolution and deliver a foundational resource to drive mechanistic and therapeutic discovery.

DOI: https://doi.org/10.1016/j.cell.2026.01.029
Bioinformatics. 2026 Mar 27. pii: btag157. [Epub ahead of print]

DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor.

Najla Abassi, Lea Schwarz, Edoardo Filippi, Federico Marini.

   SUMMARY: Modern omics experiments now involve multiple conditions and complex designs, producing an increasingly large set of differential expression and functional enrichment analysis results. However, no standardized data structure exists to store and contextualize these results together with their metadata, leaving researchers with an unmanageable and potentially non-reproducible collection of results that are difficult to navigate and/or share. Here we introduce DeeDeeExperiment, a new S4 class for managing and storing omics data analysis results, implemented within the Bioconductor ecosystem, which promotes interoperability, reproducibility and good documentation. This class extends the widely used SingleCellExperiment object by introducing dedicated slots for Differential Expression (DEA) and Functional Enrichment Analysis (FEA) results, allowing users to organize, store, and retrieve information on multiple contrasts and associated metadata within a single data object, ultimately streamlining the management and interpretation of many omics datasets.
AVAILABILITY AND IMPLEMENTATION: DeeDeeExperiment is available on Bioconductor under the MIT license (https://bioconductor.org/packages/DeeDeeExperiment), with its development version also available on Github (https://github.com/imbeimainz/DeeDeeExperiment).
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Keywords:  Differential Expression; Functional Enrichment; Gene Expression; Omics; Pathways; Transcriptomics

DOI:  https://doi.org/10.1093/bioinformatics/btag157
Cell Rep Methods. 2026 Mar 26. pii: S2667-2375(26)00066-4. [Epub ahead of print] 101366

Persistent homology-based segmentation tool for membrane images.

Haruhisa Oda, Yusuke Imoto.

  While advances in machine learning have enabled automated cell segmentation, users often face challenges in parameter tuning until reaching their desired results. To address this issue, we developed PomSeg, a membrane segmentation method based on persistent homology. Since persistent homology captures topological features of input data, PomSeg parameters reflect cell shape information, enabling intuitive and efficient parameter tuning. This adaptivity, together with stability for noise of persistent homology, enables robust application of PomSeg to various image types, including coarse-resolution data. By applying PomSeg to early mouse embryo membrane images and other publicly available datasets, we demonstrated its flexibility, versatility, and robustness, along with agreement with ground truth. Additionally, we showed the potential of PomSeg extension by incorporating a machine learning tool in its process. These features make PomSeg a valuable tool for researchers pursuing control and interpretability in segmentation, as well as indicating wider applications beyond a machine learning alternative.

Keywords:  CP: computational biology; CP: imaging; embryo membrane image; persistent homology; segmentation; topological data analysis; tracking

DOI:  https://doi.org/10.1016/j.crmeth.2026.101366
Science. 2026 Mar 26. 391(6792): eadz6830

Distinctive DNA sequence features define epigenetic longevity of inflammatory memory.

Christopher J Cowley, Sairaj M Sajjath, Luis F Soto-Ugaldi, Mara Steiger, Samantha B Larsen, Thomas Carroll, Douglas Barrows, Alexandra Mattei, Kevin A U Gonzales, Wei Wang, Kevin Li, Alexander Meissner, Helene Kretzmer, Dana Pe'er, Elaine Fuchs.

Tissues harbor memories of inflammation, which heighten sensitivity to diverse future assaults. Whether and how these adaptations are sustained through time and cell division remain poorly understood. We show that in mice, epidermal stem cells store lifelong, functional epigenetic records of psoriasis-like skin flares. Applying deep learning to investigate these chromatin dynamics, we unearth CpG dinucleotide density as a major driver of memory persistence. Although unnecessary for inflammation-induced transcription factors to open and establish memories, CpG-enriched sequences thereafter become essential, reinforcing accessibility across cellular generations by integrating DNA demethylation, methylation-sensitive transcription factors, sequence-intrinsic nucleosome disaffinity, and the nucleosome-destabilizing histone variant H2A.Z. Thus, once activated by inflammation-induced transcription factors, DNA sequences orchestrate persistent poise, imparting long-lasting memory to stress-sensitive genes and profoundly affecting tissue fitness upon recall.

DOI: https://doi.org/10.1126/science.adz6830
Cell Rep Med. 2026 Mar 23. pii: S2666-3791(26)00136-9. [Epub ahead of print] 102719

Shinya Yamanaka.

Shinya Yamanaka.

Dr. Shinya Yamanaka is recognized for the generation of induced pluripotent stem cells (iPSCs) from fibroblasts by a combination of multiple transcription factors, and he won the Nobel Prize in Physiology or Medicine in 2012 jointly with Sir John B. Gurdon for this discovery. Twenty years after the discovery, the Cell Reports Medicine editorial team discusses with Dr. Yamanaka the scientific, technical, and translational milestones that have shaped the field of regenerative medicine. We also discuss the role of iPSCs in disease modeling and drug discovery, the interplay with genome editing, and ongoing issues that still prevent the widespread clinical application of iPSC-derived therapies. Finally, Dr. Yamanaka reflects on promising, yet underexplored, applications of iPSCs.

DOI: https://doi.org/10.1016/j.xcrm.2026.102719
Trends Cancer. 2026 Mar 24. pii: S2405-8033(26)00038-5. [Epub ahead of print]

Watching cancer begin: emerging tools to visualize the first steps of tumorigenesis.

Hendrik A Messal, Larissa Mourao, Lea Dörner, Colinda L G J Scheele, Jacco van Rheenen.

  Understanding tumor initiation is crucial for early interception and prevention. Tumors arise from genetic alterations and microenvironmental changes that together create a niche for malignant growth. Previously, the spatiotemporal dynamics of tumorigenesis were difficult to study. Recent advances in high-resolution intravital microscopy, tissue clearing, and spatial molecular profiling enable direct visualization of mutated cells and clones within their microenvironment in situ. These tools transform tumor initiation from a theoretical construct into a mechanistically dissectible process. Here, we synthesize recent insights into how mutated clones expand or regress, how clonal dynamics drive transformation, and how niche signals shape tumor-initiating cell fate. We highlight key imaging innovations and outline limitations and opportunities for capturing tumor initiation in vivo.

Keywords:  clonal dynamics; intravital microscopy; spatial transcriptomics; tissue clearing; tumor initiation; tumor microenvironment

DOI:  https://doi.org/10.1016/j.trecan.2026.02.007