bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2024‒02‒25
seventeen papers selected by
Ralitsa Radostinova Madsen, MRC-PPU

  1. bioRxiv. 2024 Feb 07. pii: 2024.02.06.579216. [Epub ahead of print]
      Mechanistic Target of Rapamycin Complex 1 (mTORC1) is a master metabolic regulator that stimulates anabolic cell growth while suppressing catabolic processes such as autophagy. mTORC1 is active in most, if not all, proliferating eukaryotic cells. However, it remains unclear whether and how mTORC1 activity changes from one cell cycle phase to another. Here we tracked mTORC1 activity through the complete cell cycle and uncover oscillations in its activity. We find that mTORC1 activity peaks in S and G2, and is lowest in mitosis and G1. We further demonstrate that multiple mechanisms are involved in controlling this oscillation. The interphase oscillation is mediated through the TSC complex, an upstream negative regulator of mTORC1, but is independent of major known regulatory inputs to the TSC complex, including Akt, Mek/Erk, and CDK4/6 signaling. By contrast, suppression of mTORC1 activity in mitosis does not require the TSC complex, and instead involves CDK1-dependent control of the subcellular localization of mTORC1 itself. Functionally, we find that in addition to its well-established role in promoting progression through G1, mTORC1 also promotes progression through S and G2, and is important for satisfying the Wee1- and Chk1-dependent G2/M checkpoint to allow entry into mitosis. We also find that low mTORC1 activity in G1 sensitizes cells to autophagy induction in response to partial mTORC1 inhibition or reduced nutrient levels. Together these findings demonstrate that mTORC1 is differentially regulated throughout the cell cycle, with important phase-specific functional consequences in proliferating cells.
  2. Elife. 2024 Feb 20. pii: RP89262. [Epub ahead of print]12
      Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell-cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip-Stalk cell patterning can undergo an order-disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.
    Keywords:  NOTCH signaling; Tip–Stalk fate; Turing pattern; angiogenesis; computational biology; human; order–disorder transition; systems biology
  3. J Biol Chem. 2024 Feb 15. pii: S0021-9258(24)00139-X. [Epub ahead of print] 105763
      The EGF receptor is mutated in a number of cancers. In most cases, the mutations occur in the intracellular tyrosine kinase domain. However, in glioblastomas, many of the mutations are in the extracellular ligand binding domain. To determine what changes in receptor function are induced by such extracellular domain mutations, we analyzed the binding and biological response to the seven different EGF receptor ligands in three common glioblastoma mutants--R84K, A265V, and G574V. Our data indicate that all three mutations significantly increase the binding affinity of all seven ligands. In addition, the mutations increase the potency of all ligands for stimulating receptor autophosphorylation, phospholipase Cγ, Akt, and MAP kinase activity. In all mutants, the rank order of ligand potency seen at the wild-type receptor was retained, suggesting that the receptors still discriminate among the different ligands. However, the low affinity ligands, EPR and EPG, did show larger than average enhancements of potency for stimulating Akt and MAPK, but not receptor autophosphorylation and phospholipase Cγ activation. Relative to the wild-type receptor, these changes lead to an increase in the responsiveness of these mutants to physiological concentrations of ligands and an alteration in the ratio of activation of the different pathways. This may contribute to their oncogenic potential. In the context of recent findings, our data also suggest that so-called 'high' affinity biological responses arise from activation by isolated receptor dimers, whereas 'low' affinity biological responses require clustering of receptors which occurs at higher concentrations of ligand.
    Keywords:  Akt PKB; EGF receptor; Receptor tyrosine kinase; autophosphorylation; mitogen-activated protein kinase (MAPK); mutant; phospholipase C; signal transduction
  4. bioRxiv. 2024 Feb 11. pii: 2024.02.10.579766. [Epub ahead of print]
      Fluorescent biosensors revolutionized biomedical science by enabling the direct measurement of signaling activities in living cells, yet the current technology is limited in resolution and dimensionality. Here, we introduce highly sensitive chemigenetic kinase activity biosensors that combine the genetically encodable self-labeling protein tag HaloTag7 with bright far-red-emitting synthetic fluorophores. This technology enables five-color biosensor multiplexing, 4D activity imaging, and functional super-resolution imaging via stimulated emission depletion (STED) microscopy.
  5. Nat Methods. 2024 Feb 19.
      Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
  6. Methods Mol Biol. 2024 ;2764 77-105
      Over the past 50 years, researchers from the mammary gland field have launched a collection of distinctive 3D cell culture systems to study multiple aspects of mammary gland physiology and disease. As our knowledge about the mammary gland evolves, more sophisticated 3D cell culture systems are required to answer more and more complex questions. Nowadays, morphologically complex mammary organoids can be generated in distinct 3D settings, along with reproduction of multiple aspects of the gland microenvironment. Yet, each 3D culture protocol comes with its advantages and limitations, where some culture systems are best suited to study stemness potential, whereas others are tailored towards the study of mammary gland morphogenesis. Therefore, prior to starting a 3D mammary culture experiment, it is important to consider and select the ideal culture model to address the biological question of interest. The number and technical requirements of novel 3D cell culture methods vastly increased over the past decades, making it currently challenging and time consuming to identify the best experimental testing. In this chapter, we provide a summary of the most promising murine and human 3D organoid models that are currently used in mammary gland biology research. For each model, we will provide a brief description of the protocol and an overview of the expected morphological outcome, the advantages of the model, and the potential pitfalls, to guide the reader to the best model of choice for specific applications.
    Keywords:  Branching morphogenesis; Breast organoid; Extracellular matrix; Mammary gland organoid; Primary 3D cultures
  7. Cell Metab. 2024 Feb 12. pii: S1550-4131(24)00015-9. [Epub ahead of print]
      Aging is underpinned by pronounced metabolic decline; however, the drivers remain obscure. Here, we report that IgG accumulates during aging, particularly in white adipose tissue (WAT), to impair adipose tissue function and metabolic health. Caloric restriction (CR) decreases IgG accumulation in WAT, whereas replenishing IgG counteracts CR's metabolic benefits. IgG activates macrophages via Ras signaling and consequently induces fibrosis in WAT through the TGF-β/SMAD pathway. Consistently, B cell null mice are protected from aging-associated WAT fibrosis, inflammation, and insulin resistance, unless exposed to IgG. Conditional ablation of the IgG recycling receptor, neonatal Fc receptor (FcRn), in macrophages prevents IgG accumulation in aging, resulting in prolonged healthspan and lifespan. Further, targeting FcRn by antisense oligonucleotide restores WAT integrity and metabolic health in aged mice. These findings pinpoint IgG as a hidden culprit in aging and enlighten a novel strategy to rejuvenate metabolic health.
    Keywords:  IgG; adipose tissue; aging; fibrosis; metabolic dysfunction
  8. PLoS One. 2024 ;19(2): e0298446
      To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.
  9. Genome Biol. 2024 Feb 23. 25(1): 55
      Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.
    Keywords:  Bayesian modeling; Gene regulation; Gene regulator in cancer; Interpretability; Lymphoma; Single-cell multi-omics
  10. Hum Pathol. 2024 Feb 15. pii: S0046-8177(24)00018-2. [Epub ahead of print]
      Venous malformations (VMs) are the most common vascular malformations. TEK and PIK3CA are the causal genes of VMs, and may be involved in the PI3K/AKT pathway. However, the downstream mechanisms underlying the TEK or PIK3CA mutations in VMs are not completely understood. This study aimed to identify a possible association between genetic mutations and clinicopathological features. A retrospective clinical, pathological, and genetic study of 114 patients with VMs was performed. TEK, PIK3CA, and combined TEK/PIK3CA mutations were identified in 49 (43%), 13 (11.4%), and 2 (1.75%) patients, respectively. TEK-mutant VMs more commonly occurred in younger patients than TEK and PIK3CA mutation-negative VMs (other-mutant VMs), and showed more frequent skin involvement and no lymphocytic aggregates. No significant differences were observed in sex, location of occurrence, malformed vessel size, vessel density, or thickness of the vascular smooth muscle among the VM genotypes. Immunohistochemical analysis revealed that the expression levels of phosphorylated AKT (p-AKT) were higher in the TEK-mutant VMs than those in PIK3CA-mutant and other-mutant VMs. The expression levels of p-mTOR and its downstream effectors were higher in all the VM genotypes than those in normal vessels. Spatial transcriptomics revealed that the genes involved in "blood vessel development", "positive regulation of cell migration", and "extracellular matrix organization" were up-regulated in a TEK-mutant VM. Significant genotype-phenotype correlations in clinical and pathological features were observed among the VM genotypes, indicating gene-specific effects. Detailed analysis of gene-specific effects in VMs may offer insights into the underlying molecular pathways and implications for targeted therapies.
    Keywords:  PIK3CA; Sirolimus; Spatial transcriptomics; TEK; Venous malformation; mTOR
  11. Cell. 2024 Feb 09. pii: S0092-8674(24)00068-0. [Epub ahead of print]
      Oocytes are among the longest-lived cells in the body and need to preserve their cytoplasm to support proper embryonic development. Protein aggregation is a major threat for intracellular homeostasis in long-lived cells. How oocytes cope with protein aggregation during their extended life is unknown. Here, we find that mouse oocytes accumulate protein aggregates in specialized compartments that we named endolysosomal vesicular assemblies (ELVAs). Combining live-cell imaging, electron microscopy, and proteomics, we found that ELVAs are non-membrane-bound compartments composed of endolysosomes, autophagosomes, and proteasomes held together by a protein matrix formed by RUFY1. Functional assays revealed that in immature oocytes, ELVAs sequester aggregated proteins, including TDP-43, and degrade them upon oocyte maturation. Inhibiting degradative activity in ELVAs leads to the accumulation of protein aggregates in the embryo and is detrimental for embryo survival. Thus, ELVAs represent a strategy to safeguard protein homeostasis in long-lived cells.
    Keywords:  RUFY1; embryo; female fertility; lysosomal acidification; membraneless organelles; oocyte; oocyte quality; protein aggregation; proteostasis; super-organelles
  12. Biochem J. 2024 Feb 21. pii: BCJ20240015. [Epub ahead of print]
      The RAS-regulated RAF-MEK1/2-ERK1/2 signalling pathway is activated in cancer due to mutations in RAS proteins (especially KRAS), BRAF, CRAF, MEK1 and MEK2. Whilst inhibitors of KRASG12C (lung adenocarcinoma) and BRAF and MEK1/2 (melanoma and colorectal cancer) are clinically approved, acquired resistance remains a problem. Consequently, the search for new inhibitors (especially of RAS proteins), new inhibitor modalities and regulators of this pathway, which may be new drug targets, continues and increasingly involves cell-based screens with small molecules or genetic screens such as RNAi, CRISPR or Protein Interference. Here we describe cell lines that exhibit doxycycline-dependent expression KRASG12V or BRAFV600E and harbour a stably integrated EGR1:EmGFP reporter gene that can be detected by flow cytometry, high-content microscopy or immunoblotting. KRASG12V or BRAFV600E-driven EmGFP expression is inhibited by MEK1/2 or ERK1/2 inhibitors (MEKi and ERKi). BRAFi inhibit BRAFV600E-driven EmGFP expression but enhance the response to KRASG12V, recapitulating paradoxical activation of wild type RAF proteins. In addition to small molecules, expression of iDab6, encoding a RAS-specific antibody fragment inhibited KRASG12V- but not BRAFV600E-driven EmGFP expression. Finally, substitution of EmGFP for a bacterial nitroreductase gene allowed KRASG12V or BRAFV600E to drive cell death in the presence of a pro-drug, which may allow selection of pathway inhibitors that promote survival. These cell lines should prove useful for cell-based screens to identify new regulators of KRAS- or BRAF-dependent ERK1/2 signalling (drug target discovery) as well as screening or triaging 'hits' from drug discovery screens.
    Keywords:  BRAF; ERK1/2; Gene reporters; KRAS; MEK1/2; small molecules
  13. Bioinformatics. 2024 Feb 21. pii: btae102. [Epub ahead of print]
      MOTIVATION: The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks such as identifying cellular barcodes and UMIs in single cell data, and specific genetic variants for genotyping. However, existing tools which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data.RESULTS: To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell line specific sequences in Illumina short-read single cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools.
    AVAILABILITY: Flexiplex is available at
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
  14. Nat Rev Mol Cell Biol. 2024 Feb 20.
      The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
  15. Nat Commun. 2024 Feb 23. 15(1): 1664
    iPSCORE Consortium
      Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.
  16. Patterns (N Y). 2024 Feb 09. 5(2): 100894
      Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.
    Keywords:  R Shiny app; cancer; deep learning; drug response; prediction; web tool