bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2025–08–10
sixteen papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. Influenza A virus NS1 protein mimics oncogenic PI3K resulting in isoform specific cellular redistribution and activation.
  2. Capillary Venous Malformation With Undergrowth and Activating PIK3CA Variant: An Underrecognized Phenotype.
  3. scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.
  4. Morphological map of under- and overexpression of genes in human cells.
  5. How thoughtful experimental design can empower biologists in the omics era.
  6. Androgen deprivation-mediated activation of AKT is enhanced in prostate cancer with TMPRSS2:ERG fusion.
  7. Rag GTPases Suppress Renal Cystic Disease by Inhibiting TFEB Independently of mTORC1.
  8. Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.
  9. Vacuolar Phosphatidylinositol 3,4,5-trisphosphate controls fusion through binding Vam7, and membrane microdomain assembly.
  10. Baseline expression of c-Myc defines the tissue specificity of oncogenic K-Ras.
  11. Functional Pathway Inference Analysis (FPIA).
  12. Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.
  13. Small-molecule PI3Kα binder with selective anticancer activity.
  14. Push-button science.
  15. Longitudinal big biological data in the AI era.
  16. scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.
  1. Proc Natl Acad Sci U S A. 2025 Aug 12. 122(32): e2423066122
    Influenza A virus NS1 protein mimics oncogenic PI3K resulting in isoform specific cellular redistribution and activation.
    Sadaf Aslam, María T Sánchez-Aparicio, Braden D Siempelkamp, Gillian L Dornan, Nikos Tsolakos, John E Burke, Benjamin G Hale, Adolfo García-Sastre, Juan Ayllon.
      The nonstructural protein 1 (NS1) of influenza A virus performs a broad variety of proviral activities in the infected cell, primarily mediating evasion from the host innate immune response by being the main viral interferon antagonist. However, there are several interactions whose biological relevance remains obscure, such as the ability of NS1 to bind and activate class IA phosphoinositide 3-kinases (PI3Ks). PI3Ks are highly regulated lipid kinases that act as critical nodes in multiple cell signaling networks and are also important proto-oncogenes. This activation is mediated by NS1 binding specifically to the p85β subunit. To better understand the consequences of this interaction, we developed a bimolecular fluorescence complementation (BiFC) assay to selectively track the different PI3K heterodimers and, using this system, we found that NS1 induces an isoform-specific relocation and activation of the different PI3K heterodimers. We found that clinically relevant oncogenic mutations in both catalytic and regulatory subunits of PI3K could mimic the effect caused by NS1, and partially rescue the loss of viral fitness in a recombinant virus encoding a p85β-binding deficient NS1.
    Keywords:  PI3K; influenza; oncogenesis
    DOI:  https://doi.org/10.1073/pnas.2423066122
  2. Pediatr Dermatol. 2025 Aug 09.
    Capillary Venous Malformation With Undergrowth and Activating PIK3CA Variant: An Underrecognized Phenotype.
    Alexa G Ries, Kelly K Barry, Adam Thiessen, Matthew R Plunk, David M King, Kristen E Holland, Valerie M Carlberg.
      Congenital vascular malformations associated with segmental overgrowth and PIK3CA variants are well-documented and are classified within the PIK3CA-related overgrowth spectrum (PROS), yet PIK3CA-associated segmental undergrowth is a less understood entity. We present a case of a patient with a capillary venous malformation (CVM) and limb undergrowth associated with a pathogenic PIK3CA variant (p.Glu453Lys). An updated classification system should be considered to more broadly encompass variable phenotypic presentations of PIK3CA-related disorders, including segmental undergrowth. We propose novel terminology such as PIK3CA-related altered growth spectrum (PRAGS).
    Keywords:  PIK3CA; congenital vascular anomalies; genetic diseases; vascular malformations
    DOI:  https://doi.org/10.1111/pde.16009
  3. Genome Biol. 2025 Aug 07. 26(1): 237
    scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.
    Christophe Vanderaa, Laurent Gatto.
      Analyzing mass spectrometry (MS)-based single-cell proteomics (SCP) data faces important challenges inherent to MS-based technologies and single-cell experiments. We present scplainer, a principled and standardized approach for extracting meaningful insights from SCP data using minimal data processing and linear modeling. scplainer performs variance analysis, differential abundance analysis, and component analysis while streamlining result visualization. scplainer effectively corrects for technical variability, enabling the integration of data sets from different SCP experiments. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.
    Keywords:  Batch correction; Data analysis; Data interpretation; Linear modeling; Mass spectrometry; Missing values; Proteomics; Reproducible research; Single-cell
    DOI:  https://doi.org/10.1186/s13059-025-03713-4
  4. Nat Methods. 2025 Aug;22(8): 1742-1752
    Morphological map of under- and overexpression of genes in human cells.
    Srinivas Niranj Chandrasekaran, Eric Alix, John Arevalo, Adriana Borowa, Patrick J Byrne, William G Charles, Zitong S Chen, Beth A Cimini, Boxiong Deng, John G Doench, Jessica D Ewald, Briana Fritchman, Colin J Fuller, Jedidiah Gaetz, Amy Goodale, Marzieh Haghighi, Yu Han, Zahra Hanifehlou, Holger Hennig, Desiree Hernandez, Christina B Jacob, Tim James, Tomasz Jetka, Alexandr A Kalinin, Ben Komalo, Maria Kost-Alimova, Tomasz Krawiec, Brittany A Marion, Glynn Martin, Nicola Jane McCarthy, Lisa Miller, Arne Monsees, Nikita Moshkov, Alán F Muñoz, Arnaud Ogier, Magdalena Otrocka, Krzysztof Rataj, David E Root, Francesco Rubbo, Simon Scrace, Douglas W Selinger, Rebecca A Senft, Peter Sommer, Amandine Thibaudeau, Sarah Trisorus, Rahul Valiya Veettil, William J Van Trump, Sui Wang, Michał Warchoł, Erin Weisbart, Amélie Weiss, Michael Wiest, Agata Zaremba, Andrei Zinovyev, Shantanu Singh, Anne E Carpenter.
      Cell Painting images offer valuable insights into a cell's state and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features. These profiles capture the phenotypic impacts of perturbing 15,243 human genes, including overexpressing 12,609 genes (using open reading frames) and knocking out 7,975 genes (using CRISPR-Cas9). Here we mitigated technical artifacts by rigorously evaluating data processing options and validated the dataset's robustness and biological relevance. Analysis of phenotypic profiles revealed previously undiscovered gene clusters and functional relationships, including those associated with mitochondrial function, cancer and neural processes. The JUMP Cell Painting genetic dataset is a valuable resource for exploring gene relationships and uncovering previously unknown functions.
    DOI:  https://doi.org/10.1038/s41592-025-02753-9
  5. Nat Commun. 2025 Aug 06. 16(1): 7263
    How thoughtful experimental design can empower biologists in the omics era.
    Maggie R Wagner, Manuel Kleiner.
      The modern biology toolbox continues to evolve, as cutting-edge molecular techniques complement some classic approaches and replace others. However, statistical literacy and experimental design remain critical to the success of any empirical research, regardless of which methods are used to collect data. This Perspective highlights common experimental design pitfalls and explains how to avoid them. We discuss principles of experimental design that are relevant for all biology research, along with special considerations for projects using -omics approaches. Established best practices for optimizing sample size, randomizing treatments, including positive and negative controls, and reducing noise (e.g., blocking and pooling) can empower researchers to conduct experiments that become useful contributions to the scientific record, even if they generate negative results. They also reduce the risk of introducing bias, drawing incorrect conclusions, or wasting effort and resources on experiments with low chances of success. Although experimental design strategies are often covered in undergraduate- and graduate-level courses and in textbooks, here we provide a succinct overview and highlight their relevance to modern biology research. This Perspective can be used in training of early-career scientists and as a refresher for seasoned scientists.
    DOI:  https://doi.org/10.1038/s41467-025-62616-x
  6. bioRxiv. 2025 Jul 22. pii: 2024.08.26.609679. [Epub ahead of print]
    Androgen deprivation-mediated activation of AKT is enhanced in prostate cancer with TMPRSS2:ERG fusion.
    Fen Ma, Sen Chen, Luigi Cecchi, Betul Ersoy-Fazlioglu, Joshua Russo, Seiji Arai, Seifeldin Awad, Carla Calagua, Fang Xie, Larysa Poluben, Olga Voznesensky, Anson Ku, Fatima Karzai, Changmeng Cai, David Einstein, Huihui Ye, Xin Yuan, Alex Toker, Mary-Ellen Taplin, Adam G Sowalsky, Steven Balk.
      The TMPRSS2:ERG gene fusion (T:E fusion) in prostate adenocarcinoma (PCa) puts ERG under the androgen receptor (AR) regulated expression of TMPRSS2. The T:E fusion is frequently associated with PTEN loss, and is highly correlated with decreased expression of INPP4B, both of which may compensate for ERG-mediated suppression of PI3K/AKT signaling. We confirmed in PCa cells and a mouse PCa model that ERG suppresses AKT activation, and that one potential mechanism is through downregulation of IRS2. In contrast, ERG knockdown did not increase INPP4B, suggesting its decreased expression is indirect and reflects selective pressure to suppress INPP4B function. Notably, INPP4B expression is similarly decreased in PTEN-intact and PTEN-deficient T:E fusion tumors, suggesting selection for a function distinct from regulation of PI3K activity. As ERG expression in T:E fusion tumors is AR regulated, we further assessed the extent to which AR inhibition increased AKT activity in T:E fusion tumors. T:E fusion positive versus negative PDXs had greater increases in AKT activity after castration. Moreover, in a neoadjuvant trial of AR inhibition prior to radical prostatectomy we similarly found greater increases in AKT activation in the T:E fusion tumors. Together these findings indicate that AKT activation may mitigate the efficacy of AR targeted therapy in T:E fusion PCa, and that these patients may most benefit from combination therapy targeting AR and AKT.
    DOI:  https://doi.org/10.1101/2024.08.26.609679
  7. bioRxiv. 2025 Jul 31. pii: 2025.07.24.664930. [Epub ahead of print]
    Rag GTPases Suppress Renal Cystic Disease by Inhibiting TFEB Independently of mTORC1.
    Flaviane de Fatima Silva, Alexander R Boucher, Huawei Li, Qingbo Chen, Maria Gaughan, Ekaterina D Korobkina, Marie Sophie Isidor, Abigail O Smith, Kuang Shen, Derek B Allison, Pamela V Tran, Gregory J Pazour, David A Guertin.
      Aberrant mTORC1 activation in renal tubular epithelial cells (rTECs) is implicated as a critical driver of renal cystic diseases (RCDs), including autosomal dominant polycystic kidney disease (ADPKD) and tuberous sclerosis (TSC), yet its precise role remains unclear. Rag GTPases recruit mTORC1 to lysosomes, its intracellular activation site. Unexpectedly, we found that deleting RagA/B in rTECs, despite inhibiting mTORC1, triggers renal cystogenesis and kidney failure. We identify TFEB as the key driver of cystogenesis downstream of RagA/B loss and show that Rag GTPases, rather than mTORC1, are the primary suppressors of TFEB in vivo . We further highlight increased nuclear TFEB as a shared feature of several RCD models, whereas differences in mTORC1 activity may explain the variable efficacy of mTORC1 inhibitors. Finally, we provide evidence that nuclear TFEB, rather than mTORC1 activation, is a more consistent biomarker of cyst-lining epithelial cells in ADPKD. Overall, these findings challenge the prevailing view that mTORC1 hyperactivation is required for renal cystogenesis, which has important translational implications.
    Teaser: A serendipitous finding uncovers the Rag GTPases as strong suppressors of renal cystogenesis with important disease implications.
    DOI:  https://doi.org/10.1101/2025.07.24.664930
  8. Methods Mol Biol. 2025 ;2932 1-46
    Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.
    Marcel Ramos, Lori Shepherd, Nathan C Sheffield, Alexandru Mahmoud, Hervé Pagès, Andres Wokaty, Dario Righelli, Davide Risso, Sean Davis, Sehyun Oh, Levi Waldron, Martin Morgan, Vincent Carey.
      The Bioconductor project enters its third decade with over two thousand packages for genomic data science, over 100,000 annotation and experiment resources, and a global system for convenient distribution to researchers. Over 60,000 PubMed Central citations and terabytes of content shipped per month attest to the impact of the project on cancer genomic data science. This report provides an overview of cancer genomics resources in Bioconductor. After an overview of Bioconductor project principles, we address exploration of institutionally curated cancer genomics data such as TCGA. We then review genomic annotation and ontology resources relevant to cancer and then briefly survey analytical workflows addressing specific topics in cancer genomics. Concluding sections cover how new software and data resources are brought into the ecosystem and how the project is tackling needs for training of the research workforce. Bioconductor's strategies for supporting methods developers and researchers in cancer genomics are evolving along with experimental and computational technologies. All the tools described in this report are backed by regularly maintained learning resources that can be used locally or in cloud computing environments.
    Keywords:  Cancer genomics; data structures; epigenomics; mutations; ontology; open source software; spatial transcriptomics; transcriptomics
    DOI:  https://doi.org/10.1007/978-1-0716-4566-6_1
  9. bioRxiv. 2025 Aug 02. pii: 2025.08.01.668199. [Epub ahead of print]
    Vacuolar Phosphatidylinositol 3,4,5-trisphosphate controls fusion through binding Vam7, and membrane microdomain assembly.
    Chi Zhang, Jorge D Calderin, Aliasgar Topiwalla, Ved Shah, Jahnavi M Karat, Charlie T Knapp, Razeen Ahmed, Daniel Grudzien, Elizabeth F Williamson, Rutilio A Fratti.
      Membrane trafficking is regulated by phosphoinositides (PI) and their modification by kinases, phosphatases, and phospholipases. The endolysosomal pathway is primarily controlled by PI3P, PI(4,5)P2 and PI(3,5)P2, whereas a role for PI(3,4,5)P3 is less clear. We report that yeast vacuoles produce PI(3,4,5)P3 from PI(4,5)P2 through class III PI 3-kinase activity. In vitro assays showed that adding dioctanoyl (C8) PI(3,4,5)P3 or the PI(3,4,5)P3-binding domain Grp1-PH blocked fusion. Furthermore, modifying endogenous PI(3,4,5)P3 with the phosphatase PTEN also blocked fusion. Fluorescence microscopy showed that PI(3,4,5)P3 was enriched at membrane vertex microdomains, which was blocked by PTEN, C8-PI(3,4,5)P3, and the class III PI 3-kinase inhibitor SAR405. Importantly, blocking or eliminating PI(3,4,5)P3 prevented the vertex enrichment of Ypt7 and the HOPS subunit Vps33. Finally, we show that the soluble SNARE Vam7 binds PI(3,4,5)P3 and that PTEN abolished trans-SNARE pairing between partner vesicles. Together these data indicate that vacuolar PI(3,4,5)P3 coordinates the assembly of microdomains and SNARE function.
    DOI:  https://doi.org/10.1101/2025.08.01.668199
  10. bioRxiv. 2025 Jul 25. pii: 2025.07.21.665965. [Epub ahead of print]
    Baseline expression of c-Myc defines the tissue specificity of oncogenic K-Ras.
    Olesja Popow, Qing Yu, Clarance Yapp, Shannon Hull, João A Paulo, Benjamin Hanna, Shidong Xu, Yanan Kuang, Carlie Sigel, Cloud P Paweletz, Steven P Gygi, Kevin M Haigis.
      KRAS is among the most frequently mutated oncogenes in cancer. Yet, mutations in KRAS are common only in tumors originating from a subset of tissues. It is critical to understand the molecular mechanisms underlying this oncogene tissue specificity. Utilizing genetically engineered mouse models carrying a conditional oncogenic allele of Kras , we expressed activated K-Ras in adult tissues to investigate its specificity. We discovered that the ability of K-Ras G12D to influence the fitness of cells in a given tissue is not determined by its canonical signaling through MAPK. Instead, low baseline expression of c-Myc renders tissues non-permissive to oncogenic K-Ras, a context that can be reversed in the liver by ectopically expressing c-Myc. This functions independently of the proliferative index of the tissue or the induction of cell cycle arrest or apoptosis. Our findings reveal the importance of the basal state of the tissue-inherent signaling network for determining oncogene specificity.
    DOI:  https://doi.org/10.1101/2025.07.21.665965
  11. Methods Mol Biol. 2025 ;2932 203-229
    Functional Pathway Inference Analysis (FPIA).
    Irbaz I Badshah, Pedro R Cutillas.
      Pathway inference methods allow the mapping of biochemical networks, the discovery of signaling components, and the assignment of functions to understudied proteins and genes. Literature and automated text mining have been successfully used to reconstruct metabolic and signaling circuits, while gene regulatory networks may be inferred from gene expression data. As an alternative approach to map members of proliferative pathways, functional pathway inference analysis (FPIA) is based on the premise that genes producing similar phenotypes following perturbation across multiple cell lines belong to a common pathway. We have demonstrated this concept with the use of gene dependency datasets that allow the provision of probabilistic values of pathway membership for thousands of genes. Here, we provide a detailed protocol for the implementation of FPIA in the `cordial` R package. As an illustration of how FPIA may be used to identify new pathway members, we present a step-by-step description of its use for the investigation of genes functionally associated to PI3K and TP53.
    Keywords:  CRISPR-Cas9; Cancer; Cell signaling; Cordial; FPIA; Functional pathway inference analysis; Gene dependency; Network; Pathway; R; RNAi
    DOI:  https://doi.org/10.1007/978-1-0716-4566-6_11
  12. Nat Methods. 2025 Aug;22(8): 1657-1661
    Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.
    Constantin Ahlmann-Eltze, Wolfgang Huber, Simon Anders.
      Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development.
    DOI:  https://doi.org/10.1038/s41592-025-02772-6
  13. Nat Rev Drug Discov. 2025 Aug 07.
    Small-molecule PI3Kα binder with selective anticancer activity.
    Katie Kingwell.
      
    DOI:  https://doi.org/10.1038/d41573-025-00136-5
  14. Nat Methods. 2025 Aug;22(8): 1605
    Push-button science.
      
    DOI:  https://doi.org/10.1038/s41592-025-02786-0
  15. Mol Syst Biol. 2025 Aug 05.
    Longitudinal big biological data in the AI era.
    Adil Mardinoglu, Hasan Turkez, Minho Shong, Vishnuvardhan Pogunulu Srinivasulu, Jens Nielsen, Bernhard O Palsson, Leroy Hood, Mathias Uhlen.
      Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.
    Keywords:  Artificial Intelligence; Digital Twins; Longitudinal Multi-omics Data; Precision Medicine; Systems Biology
    DOI:  https://doi.org/10.1038/s44320-025-00134-0
  16. Stat Med. 2025 Aug;44(18-19): e70213
    scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.
    Anderson Bussing, Giampiero Marra, Daping Fan, Russell Shinohara, Danni Tu, Yen-Yi Ho.
      Advancements in single-cell RNA-sequencing (scRNA-seq) technologies generate a wealth of gene expression data that provide exciting opportunities for studying gene-gene interactions systematically at individual cell resolution. Genetic interactions within a cell are tightly regulated and often highly dynamic in response to internal cellular signals and external stimuli. Evidence of these dynamic interactions can often be observed in scRNA-seq data by examining conditional co-expression changes. Existing approaches for studying these dynamic interaction changes in scRNA-seq data do not address the multi-subject hierarchical design commonly considered in single-cell experiments. In this paper, we propose a Mixed-effects framework for differential Coexpression and transcriptional interaction modeling in Single-Cell RNA-seq (scCOSMiX) to account for the cell-cell correlation from the same individual. The proposed copula-based approach allows the zero-inflation, marginal, and association parameters to be modeled as functions of covariates with subject-level random effects, to enable analyses to be tailored to the data under consideration. A series of simulation analyses were conducted to evaluate and compare the performance of scCOSMiX to other existing approaches. We applied the proposed method to both droplet and plate-based scRNA-seq data sets GSE266919 and GSE108989 to illustrate its applicability across distinct scRNA-seq experimental protocols.
    Keywords:  Differential co‐expression; hierarchical study design; mixed effects; single‐cell RNA‐seq; zero‐inflated copula model
    DOI:  https://doi.org/10.1002/sim.70213
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