bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2024–12–01
twelve papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. Nuclear mTORC1 Live-Cell Sensor nTORSEL Reports Differential Nuclear mTORC1 Activity in Cell Lines.
  2. SPACe: an open-source, single-cell analysis of Cell Painting data.
  3. Structural basis for growth factor and nutrient signal integration on the lysosomal membrane by mTORC1.
  4. High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110α with KRAS.
  5. RUCova: Removal of Unwanted Covariance in mass cytometry data.
  6. A massively parallel reporter assay library to screen short synthetic promoters in mammalian cells.
  7. Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs.
  8. Integrative Computational Framework, Dyscovr , Links Mutated Driver Genes to Expression Dysregulation Across 19 Cancer Types.
  9. Making PI3K superfamily enzymes run faster.
  10. Recovery of biological signals lost in single-cell batch integration with CellANOVA.
  11. Cytoskeletal activation of NHE1 regulates mechanosensitive cell volume adaptation and proliferation.
  12. β1-integrin controls IGF-1R internalisation and intracellular signalling.
  1. Int J Mol Sci. 2024 Nov 12. pii: 12117. [Epub ahead of print]25(22):
    Nuclear mTORC1 Live-Cell Sensor nTORSEL Reports Differential Nuclear mTORC1 Activity in Cell Lines.
    Yifan Wang, Canrong Li, Yingyi Ouyang, Xiaoduo Xie.
      The mammalian or mechanistic target of rapamycin complex 1 (mTORC1) is activated on the surface of lysosomes and phosphorylates substrates at various subcellular locations, including the lysosome, cytosol, and nucleus. However, the signaling and biological functions of nuclear mTORC1 (nmTORC1) are not well understood, primarily due to limited tools for monitoring mTORC1 activity in the nucleus. In this study, we developed a genetically encoded nmTORC1 sensor, termed nTORSEL, based on the phosphorylation of the eukaryotic initiation factor 4E (eIF4E) binding protein 1 (4EBP1) by mTORC1 within the nucleus. nTORSEL, like its predecessor TORSEL, exhibits a fluorescent punctate pattern in the nucleus through multivalent protein-protein interactions between oligomerized 4EBP1 and eIF4E when nmTORC1 activity is low. We validated nTORSEL using biochemical analyses and imaging techniques across representative cell lines with varying levels of nmTORC1 activity. Notably, nTORSEL specifically detects physiological, pharmacological, and genetic inhibition of nmTORC1 in mouse embryonic fibroblast (MEF) cells but not in HEK293T cells. Therefore, nTORSEL is an effective tool for investigating nuclear mTORC1 signaling in cell lines.
    Keywords:  PI3K-AKT-mTOR pathway; amino acid; fluorescent reporter; live-cell sensor; nuclear mTORC1
    DOI:  https://doi.org/10.3390/ijms252212117
  2. Nat Commun. 2024 Nov 23. 15(1): 10170
    SPACe: an open-source, single-cell analysis of Cell Painting data.
    Fabio Stossi, Pankaj K Singh, Michela Marini, Kazem Safari, Adam T Szafran, Alejandra Rivera Tostado, Christopher D Candler, Maureen G Mancini, Elina A Mosa, Michael J Bolt, Demetrio Labate, Michael A Mancini.
      Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. We introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting. We highlight several advantages of SPACe, including processing speed, accuracy in mechanism of action recognition, reproducibility across biological replicates, applicability to multiple models, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We illustrate SPACe in a defined screening campaign of cell metabolism small-molecule inhibitors tested in seven cell lines to highlight the importance of analyzing perturbations across models.
    DOI:  https://doi.org/10.1038/s41467-024-54264-4
  3. bioRxiv. 2024 Nov 15. pii: 2024.11.15.623810. [Epub ahead of print]
    Structural basis for growth factor and nutrient signal integration on the lysosomal membrane by mTORC1.
    Zhicheng Cui, Alessandra Esposito, Gennaro Napolitano, Andrea Ballabio, James H Hurley.
      Mechanistic target of rapamycin complex 1 (mTORC1), which consists of mTOR, Raptor, and mLST8, receives signaling inputs from growth factor signals and nutrients. These signals are mediated by the Rheb and Rag small GTPases, respectively, which activate mTORC1 on the cytosolic face of the lysosome membrane. We biochemically reconstituted the activation of mTORC1 on membranes by physiological submicromolar concentrations of Rheb, Rags, and Ragulator. We determined the cryo-EM structure and found that Raptor and mTOR directly interact with the membrane at anchor points separated by up to 230 Å across the membrane surface. Full engagement of the membrane anchors is required for maximal activation, which is brought about by alignment of the catalytic residues in the mTOR kinase active site. The observations show at the molecular and atomic scale how converging signals from growth factors and nutrients drive mTORC1 recruitment to and activation on the lysosomal membrane in a three-step process, consisting of (1) Rag-Ragulator-driven recruitment to within ∼100 Å of the lysosomal membrane, (2) Rheb-driven recruitment to within ∼40 Å, and finally (3) direct engagement of mTOR and Raptor with the membrane. The combination of Rheb and membrane engagement leads to full catalytic activation, providing a structural explanation for growth factor and nutrient signal integration at the lysosome.
    DOI:  https://doi.org/10.1101/2024.11.15.623810
  4. SLAS Discov. 2024 Nov 27. pii: S2472-5552(24)00059-5. [Epub ahead of print] 100197
    High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110α with KRAS.
    Mohamed Ismail, Gareth Davies, Graham Sproat, Tiziana Monteverde, Jonathan Tart, Marta Acebrón-García-de-Eulate, Andrea Gohlke, David Hancock, Santosh Adhikari, Sandra Stefanovic-Barrett, David M Smith, Vikki Flemington, Emma S Gleave-Hanford, Geoffrey A Holdgate, Jason G Kettle, Julian Downward.
      The NanoBiT Biochemical Assay (NBBA) was designed as a biochemical format of the NanoBiT cellular assay, aiming to screen weak protein-protein interactions (PPIs) in mammalian cell lysates. Here we present a High Throughput Screening (HTS) application of the NBBA to screen small molecule and fragment libraries to identify compounds that block the interaction of KRAS-G12D with phosphatidylinositol 3-kinase (PI3K) p110α. This interaction promotes PI3K activity, resulting in the promotion of cell growth, proliferation and survival, and is required for tumour initiation and growth in mouse lung cancer models, whilst having little effect on the health of normal adult mice, establishing the significance of the p110α/KRAS interaction as an oncology drug target. Despite the weak binding affinity of the p110α/KRAS interaction (KD = 3 μM), the NBBA proved to be robust and displayed excellent Z'-factor statistics during the HTS primary screening of 726,000 compounds, which led to the identification of 8,000 active compounds. A concentration response screen comparing KRAS/p110α with two closely related PI3K isoforms, p110δ and p110γ, identified selective p110α-specific compounds and enabled derivation of an IC50 for these hits. We identified around 30 compounds showing greater than 20-fold selectivity towards p110α versus p110δ and p110γ with IC50 < 2 μM. By using Differential Scanning Fluorimetry (DSF) we confirmed several compounds that bind directly to purified p110α. The most potent hits will be followed up by co-crystallization with p110α to aid further elucidation of the nature of the interaction and extended optimisation of these compounds.
    Keywords:  Drug Discovery; High throughput screening; KRAS; NanoBiT assay; PI 3-kinase; assay development
    DOI:  https://doi.org/10.1016/j.slasd.2024.100197
  5. Bioinformatics. 2024 Nov 01. pii: btae669. [Epub ahead of print]40(11):
    RUCova: Removal of Unwanted Covariance in mass cytometry data.
    Rosario Astaburuaga-García, Thomas Sell, Samet Mutlu, Anja Sieber, Kirsten Lauber, Nils Blüthgen.
       MOTIVATION: High dimensional single-cell mass cytometry data are confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis, and interpretation challenging.
    RESULTS: We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance from measured markers applying multivariate linear regression based on surrogates of sources of unwanted covariance (SUCs) and principal component analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations such as apoptotic cells. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advances in our understanding of cellular biology and disease mechanisms.
    AVAILABILITY AND IMPLEMENTATION: The R package is available on https://github.com/molsysbio/RUCova. Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464.
    DOI:  https://doi.org/10.1093/bioinformatics/btae669
  6. Nat Commun. 2024 Nov 28. 15(1): 10353
    A massively parallel reporter assay library to screen short synthetic promoters in mammalian cells.
    Adam M Zahm, William S Owens, Samuel R Himes, Braden S Fallon, Kathleen E Rondem, Alexa N Gormick, Joshua S Bloom, Sriram Kosuri, Henry Chan, Justin G English.
      Cellular responses to stimuli underpin discoveries in drug development, synthetic biology, and general life sciences. We introduce a library comprising 6144 synthetic promoters, each shorter than 250 bp, designed as transcriptional readouts of cellular stimulus responses in massively parallel reporter assay format. This library facilitates precise detection and amplification of transcriptional activity from our promoters, enabling the systematic development of tunable reporters with dynamic ranges of 50-100 fold. Our library proved functional in numerous cell lines and responsive to a variety of stimuli, including metabolites, mitogens, toxins, and pharmaceutical agents, generating robust and scalable reporters effective in screening assays, biomarkers, and synthetic circuits attuned to endogenous cellular activities. Particularly valuable in therapeutic development, our library excels in capturing candidate reporters to signals mediated by drug targets, a feature we illustrate across nine diverse G-protein coupled receptors (GPCRs), critical targets in drug development. We detail how this tool isolates and defines discrete signaling pathways associated with specific GPCRs, elucidating their transcriptional signatures. With its ease of implementation, broad utility, publicly available data, and comprehensive documentation, our library will be beneficial in synthetic biology, cellular engineering, ligand exploration, and drug development.
    DOI:  https://doi.org/10.1038/s41467-024-54502-9
  7. Sci Rep. 2024 Nov 28. 14(1): 29585
    Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs.
    Xiao Li.
      Understanding spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical conditions. Despite significant progress in analyzing digital pathology images, current methods for capturing spatial relationships are limited. They often rely on specific spatial features that only partially describe the complex spatial distributions of cells and are frequently tied to particular outcomes within predefined model frameworks. Furthermore, these methods are typically limited to field of view analysis, which restricts their capacity to capture spatial patterns across whole-slide images, thereby limiting their ability to fully address the complexities of tissue architecture. To address these limitations, we present SpatialQPFs (Spatial Quantitative Pathology Features), an R package designed to extract interpretable spatial features from cell imaging data using spatial statistical methodologies. Leveraging segmented cell information, our package offers a comprehensive toolkit for applying a range of spatial statistical methods within a stochastic process framework, including analyses of point process data, areal data, and geostatistical data. By decoupling feature extraction from specific outcome models, SpatialQPFs enables thorough large-scale spatial analyses applicable across diverse clinical and biological contexts. This approach enhances the depth and accuracy of spatial insights derived from tissue data, empowering researchers to conduct comprehensive spatial analyses efficiently and reproducibly. By providing a flexible and robust framework for spatial feature extraction, SpatialQPFs facilitates advanced spatial analyses, paving the way for new discoveries in tissue biology and pathology. SpatialQPFs code and documentation are publicly available at https://github.com/Genentech/SpatialQPFs .
    DOI:  https://doi.org/10.1038/s41598-024-81383-1
  8. bioRxiv. 2024 Nov 21. pii: 2024.11.20.624509. [Epub ahead of print]
    Integrative Computational Framework, Dyscovr , Links Mutated Driver Genes to Expression Dysregulation Across 19 Cancer Types.
    Sara Geraghty, Jacob A Boyer, Mahya Fazel-Zarandi, Nibal Arzouni, Rolf-Peter Ryseck, Matthew J McBride, Lance R Parsons, Joshua D Rabinowitz, Mona Singh.
      Though somatic mutations play a critical role in driving cancer initiation and progression, the systems-level functional impacts of these mutations-particularly, how they alter expression across the genome and give rise to cancer hallmarks-are not yet well-understood, even for well-studied cancer driver genes. To address this, we designed an integrative machine learning model, Dyscovr, that leverages mutation, gene expression, copy number alteration (CNA), methylation, and clinical data to uncover putative relationships between nonsynonymous mutations in key cancer driver genes and transcriptional changes across the genome. We applied Dyscovr pan-cancer and within 19 individual cancer types, finding both broadly relevant and cancer type-specific links between driver genes and putative targets, including a subset we further identify as exhibiting negative genetic relationships. Our work newly implicates-and validates in cell lines- KBTBD2 and mutant PIK3CA as putative synthetic lethals in breast cancer, suggesting a novel combinatorial treatment approach.
    HIGHLIGHTS: Integrative framework Dyscovr links mutations within cancer drivers to downstream expression changesDyscovr uncovers known and novel targets of cancer-driver genesDyscovr reveals clinically important negative genetic interaction pairingsWeb platform to explore uncovered driver gene-target relationships.
    eTOC BLURB: An integrative computational framework, Dyscovr, links mutated cancer driver genes to expression changes in putative target genes within and across 19 TCGA cancer types. Dyscovr's results include experimentally verifiable synthetic lethal driver-target pairings.
    Graphical abstract:
    DOI:  https://doi.org/10.1101/2024.11.20.624509
  9. Adv Biol Regul. 2024 Nov 19. pii: S2212-4926(24)00048-4. [Epub ahead of print] 101060
    Making PI3K superfamily enzymes run faster.
    Grace Q Gong, Madhangopal Anandapadamanaban, Md Saiful Islam, Iain M Hay, Maxime Bourguet, Saulė Špokaitė, Antoine N Dessus, Yohei Ohashi, Olga Perisic, Roger L Williams.
      The phosphoinositide 3-kinase (PI3K) superfamily includes lipid kinases (PI3Ks and type III PI4Ks) and a group of PI3K-like Ser/Thr protein kinases (PIKKs: mTOR, ATM, ATR, DNA-PKcs, SMG1 and TRRAP) that have a conserved C-terminal kinase domain. A common feature of the superfamily is that they have very low basal activity that can be greatly increased by a range of regulatory factors. Activators reconfigure the active site, causing a subtle realignment of the N-lobe of the kinase domain relative to the C-lobe. This realignment brings the ATP-binding loop in the N-lobe closer to the catalytic residues in the C-lobe. In addition, a conserved C-lobe feature known as the PIKK regulatory domain (PRD) also can change conformation, and PI3K activators can alter an analogous PRD-like region. Recent structures have shown that diverse activating influences can trigger these conformational changes, and a helical region clamping onto the kinase domain transmits regulatory interactions to bring about the active site realignment for more efficient catalysis. A recent report of a small-molecule activator of PI3Kα for application in nerve regeneration suggests that flexibility of these regulatory elements might be exploited to develop specific activators of all PI3K superfamily members. These activators could have roles in wound healing, anti-stroke therapy and treating neurodegeneration. We review common structural features of the PI3K superfamily that may make them amenable to activation.
    DOI:  https://doi.org/10.1016/j.jbior.2024.101060
  10. Nat Biotechnol. 2024 Nov 26.
    Recovery of biological signals lost in single-cell batch integration with CellANOVA.
    Zhaojun Zhang, Divij Mathew, Tristan L Lim, Kaishu Mason, Clara Morral Martinez, Sijia Huang, E John Wherry, Katalin Susztak, Andy J Minn, Zongming Ma, Nancy R Zhang.
      Data integration to align cells across batches has become a cornerstone of single-cell data analysis, critically affecting downstream results. Currently, there are no guidelines for when the biological differences between samples are separable from batch effects. Here we show that current paradigms for single-cell data integration remove biologically meaningful variation and introduce distortion. We present a statistical model and computationally scalable algorithm, CellANOVA (cell state space analysis of variance), that harnesses experimental design to explicitly recover biological signals that are erased during single-cell data integration. CellANOVA uses a 'pool-of-controls' design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest and allow the recovery of subtle biological signals. We apply CellANOVA to diverse contexts and validate the recovered biological signals by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nucleus data integration, where it recovers subtle biological signals that can be validated and replicated by external data.
    DOI:  https://doi.org/10.1038/s41587-024-02463-1
  11. Cell Rep. 2024 Nov 22. pii: S2211-1247(24)01343-3. [Epub ahead of print]43(12): 114992
    Cytoskeletal activation of NHE1 regulates mechanosensitive cell volume adaptation and proliferation.
    Qin Ni, Zhuoxu Ge, Yizeng Li, Gabriel Shatkin, Jinyu Fu, Anindya Sen, Kaustav Bera, Yuhan Yang, Yichen Wang, Yufei Wu, Ana Carina Nogueira Vasconcelos, Yuqing Yan, Dingchang Lin, Andrew P Feinberg, Konstantinos Konstantopoulos, Sean X Sun.
      Mammalian cells rapidly respond to environmental changes by altering transmembrane water and ion fluxes, changing cell volume. Contractile forces generated by actomyosin have been proposed to mechanically regulate cell volume. However, our findings reveal a different mechanism in adherent cells, where elevated actomyosin activity increases cell volume in normal-like cells (NIH 3T3 and others) through interaction with the sodium-hydrogen exchanger isoform 1 (NHE1). This leads to a slow secondary volume increase (SVI) following the initial regulatory volume decrease during hypotonic shock. The active cell response is further confirmed by intracellular alkalinization during mechanical stretch. Moreover, cytoskeletal activation of NHE1 during SVI deforms the nucleus, causing immediate transcriptomic changes and ERK-dependent growth inhibition. Notably, SVI and its associated changes are absent in many cancer cell lines or cells on compliant substrates with reduced actomyosin activity. Thus, actomyosin acts as a sensory element rather than a force generator during adaptation to environmental challenges.
    Keywords:  CP: Cell biology; ERK/MAPK; NHE1; PI3K; RNA-seq; cell volume; cytoskeleton; epigenome; math model; mechanosensation; nucleus volume
    DOI:  https://doi.org/10.1016/j.celrep.2024.114992
  12. J Biol Chem. 2024 Nov 26. pii: S0021-9258(24)02523-7. [Epub ahead of print] 108021
    β1-integrin controls IGF-1R internalisation and intracellular signalling.
    Niamh McDermott, Stephen O'Shea, Leonie Rieger, Orla T Cox, Rosemary O'Connor.
      Cell adhesion-dependent phosphorylation of Insulin-like Growth Factor 1 Receptor (IGF-1R) on its C-terminal tail (CT) at Tyr1250/1251 promotes receptor internalisation and Golgi accumulation. We previously proposed that this phosphorylation is associated with cell migration and cancer aggressiveness, distinguishing IGF-1R activity from that of Insulin Receptor, which lacks these tyrosines. Here, we further investigated how adhesion signalling influences IGF-1R location and activity in migratory cancer cells and R- fibroblasts. We observed that IGF-1R, in triple-negative breast cancer (TNBC) tissues, is predominantly intracellular and dispersed from the plasma membrane compared with non-tumour tissue. Datasets from basal-like breast cancer patients indicated a strong, positive correlation between IGF-1R protein expression and that of β1-integrin (ITGB1). In TNBC cells with high ITGB1 expression, suppressing ITGB1 enhanced IGF-1R stability and its retention at the plasma membrane, and reduced IGF-1R internalisation during cell adhesion. In R- fibroblasts, we observed reduced IGF-1R autophosphorylation and Golgi accumulation when ITGB1 was suppressed. The stability of a Tyr1250/1251Phe (FF) IGF-1R mutant was less affected by ITGB1 suppression, indicating that Tyr1250/1251 phosphorylation is required for ITGB1-enhanced receptor internalisation. Furthermore, a Tyr1250/1251Glu (EE) IGF-1R mutant exhibited a gain of cell migration and colony formation potential compared to wild-type IGF-1R or FF mutant. Tyr1250/1251 resides within the CT 1248SFYYS1252 motif, which engages the IGF-1R kinase domain. In silico, we investigated how mutation of these tyrosines may alter 1248SFYYS1252 conformation, dictating trajectory of the distal CT. We conclude that Tyr1250/1251 phosphorylation confers IGF-1R with unique pro-tumourigenic signalling in a manner that is enhanced by ITGB1.
    Keywords:  breast cancer; cancer; cell adhesion; insulin-like growth factor 1 receptor (IGF-1R); integrin; phosphorylation; receptor internalisation; receptor structure-function; subcellular location; transformation
    DOI:  https://doi.org/10.1016/j.jbc.2024.108021
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