bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2024‒08‒04
23 papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. mTORC1 activity oscillates throughout the cell cycle, promoting mitotic entry and differentially influencing autophagy induction.
  2. Limiting the impact of protein leakage in single-cell proteomics.
  3. PTEN acts as a crucial inflammatory checkpoint controlling TLR9/IL-6 axis in B cells.
  4. Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry.
  5. Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition.
  6. Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers.
  7. Prediction of protein subcellular localization in single cells.
  8. MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.
  9. Evaluating batch correction methods for image-based cell profiling.
  10. Shared and redundant proteins coordinate signal cross-talk between MAPK pathways in yeast.
  11. Data acquisition approaches for single cell proteomics.
  12. Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0.
  13. Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks.
  14. Rictor, an mTORC2 Protein, Regulates Murine Lymphatic Valve Formation Through the AKT-FOXO1 Signaling.
  15. Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.
  16. OmicScope unravels systems-level insights from quantitative proteomics data.
  17. Holimap: an accurate and efficient method for solving stochastic gene network dynamics.
  18. Structural and dynamic changes in P-Rex1 upon activation by PIP3 and inhibition by IP4.
  19. Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition.
  20. FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.
  21. Simplifying data and code sharing.
  22. Targeted dephosphorylation of TFEB promotes its nuclear translocation.
  23. Modernizing the NCI60 Cell Line Screen for Phenotypic Drug Discovery in the 21st Century.
  1. Cell Rep. 2024 Jul 26. pii: S2211-1247(24)00872-6. [Epub ahead of print]43(8): 114543
    mTORC1 activity oscillates throughout the cell cycle, promoting mitotic entry and differentially influencing autophagy induction.
    Jay N Joshi, Ariel D Lerner, Frank Scallo, Alexandra N Grumet, Paul Matteson, James H Millonig, Alexander J Valvezan.
      Mechanistic Target of Rapamycin Complex 1 (mTORC1) is a master metabolic regulator that is active in nearly all proliferating eukaryotic cells; however, it is unclear whether mTORC1 activity changes throughout the cell cycle. We find that mTORC1 activity oscillates from lowest in mitosis/G1 to highest in S/G2. The interphase oscillation is mediated through the TSC complex but is independent of major known regulatory inputs, including Akt and Mek/Erk signaling. By contrast, suppression of mTORC1 activity in mitosis does not require the TSC complex. mTORC1 has long been known to promote progression through G1. We find that mTORC1 also promotes progression through S and G2 and is important for satisfying the Chk1/Wee1-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 consequences for proliferating cells.
    Keywords:  CDK1; CP: Cell biology; G2/M checkpoint; TSC complex; TSC2; autophagy; cell cycle; mTOR; mTORC1; mitosis
    DOI:  https://doi.org/10.1016/j.celrep.2024.114543
  2. bioRxiv. 2024 Jul 27. pii: 2024.07.26.605378. [Epub ahead of print]
    Limiting the impact of protein leakage in single-cell proteomics.
    Andrew Leduc, Yanxin Xu, Gergana Shipkovenska, Zhixun Dou, Nikolai Slavov.
      Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.
    DOI:  https://doi.org/10.1101/2024.07.26.605378
  3. iScience. 2024 Jul 19. 27(7): 110388
    PTEN acts as a crucial inflammatory checkpoint controlling TLR9/IL-6 axis in B cells.
    Pei-Ju Tsai, Ming-Yu Chen, Wei-Chan Hsu, Su-Fang Lin, Po-Chiang Chan, Hsin-Hsin Chen, Cheng-Yuan Kao, Wen-Jye Lin, Tsung-Hsien Chuang, Guann-Yi Yu, Yu-Wen Su.
      Phosphatase and tensin homolog (PTEN) is vital for B cell development, acting as a key negative regulator in the PI3K signaling pathway. We used CD23-cre to generate PTEN-conditional knockout mice (CD23-cKO) to examine the impact of PTEN mutation on peripheral B cells. Unlike mb1-cre-mediated PTEN deletion in early B cells, CD23-cKO mutants exhibited systemic inflammation with increased IL-6 production in mature B cells upon CpG stimulation. Inflammatory B cells in CD23-cKO mice showed elevated phosphatidylinositol 3-phosphate [PI(3)P] levels and increased TLR9 endosomal localization. Pharmacological inhibition of PI(3)P synthesis markedly reduced TLR9-mediated IL-6. Single-cell RNA-sequencing (RNA-seq) revealed altered endocytosis, BANK1, and NF-κB1 expression in PTEN-deficient B cells. Ectopic B cell receptor (BCR) expression on non-inflammatory mb1-cKO B cells restored BANK1 and NF-κB1 expression, enhancing TLR9-mediated IL-6 production. Our study highlights PTEN as a crucial inflammatory checkpoint, regulating TLR9/IL-6 axis by fine-tuning PI(3)P homeostasis. Additionally, BCR downregulation prevents the differentiation of inflammatory B cells in PTEN deficiency.
    Keywords:  Cell biology; Genetics; Immunology
    DOI:  https://doi.org/10.1016/j.isci.2024.110388
  4. Nat Biotechnol. 2024 Jul 29.
    Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry.
    Xiao-Kang Lun, Kuanwei Sheng, Xueyang Yu, Ching Yeung Lam, Gokul Gowri, Matthew Serrata, Yunhao Zhai, Hanquan Su, Jingyi Luan, Youngeun Kim, Donald E Ingber, Hartland W Jackson, Michael B Yaffe, Peng Yin.
      Mass cytometry uses metal-isotope-tagged antibodies to label targets of interest, which enables simultaneous measurements of ~50 proteins or protein modifications in millions of single cells, but its sensitivity is limited. Here, we present a signal amplification technology, termed Amplification by Cyclic Extension (ACE), implementing thermal-cycling-based DNA in situ concatenation in combination with 3-cyanovinylcarbazole phosphoramidite-based DNA crosslinking to enable signal amplification simultaneously on >30 protein epitopes. We demonstrate the utility of ACE in low-abundance protein quantification with suspension mass cytometry to characterize molecular reprogramming during the epithelial-to-mesenchymal transition as well as the mesenchymal-to-epithelial transition. We show the capability of ACE to quantify the dynamics of signaling network responses in human T lymphocytes. We further present the application of ACE in imaging mass cytometry-based multiparametric tissue imaging to identify tissue compartments and profile spatial aspects related to pathological states in polycystic kidney tissues.
    DOI:  https://doi.org/10.1038/s41587-024-02316-x
  5. bioRxiv. 2024 Feb 21. pii: 2024.02.19.581004. [Epub ahead of print]
    Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition.
    Cameron T Flower, Chunmei Liu, Hui-Yu Chuang, Xiaoyang Ye, Hanjun Cheng, James R Heath, Wei Wei, Forest M White.
      A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF -mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.
    DOI:  https://doi.org/10.1101/2024.02.19.581004
  6. Cancer Res. 2024 Aug 01.
    Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers.
    Sungyoung Shin, Nicole J Chew, Milad Ghomlaghi, Anderly C Chüeh, Yunhui Jeong, Lan K Nguyen, Roger J Daly.
      Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in TNBC cells. Analyzing the effects of co-targeting specific network nodes by systematically simulating the model predicted synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, co-targeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that while AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven HCC cell line, where there is a synergistic effect of co-targeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-23-3409
  7. bioRxiv. 2024 Jul 25. pii: 2024.07.25.605178. [Epub ahead of print]
    Prediction of protein subcellular localization in single cells.
    Xinyi Zhang, Yitong Tseo, Yunhao Bai, Fei Chen, Caroline Uhler.
      The subcellular localization of a protein is important for its function and interaction with other molecules, and its mislocalization is linked to numerous diseases. While atlas-scale efforts have been made to profile protein localization across various cell lines, existing datasets only contain limited pairs of proteins and cell lines which do not cover all human proteins. We present a method that uses both protein sequences and cellular landmark images to perform P redictions of U nseen P roteins' S ubcellular localization ( PUPS ), which can generalize to both proteins and cell lines not used for model training. PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images for protein localization prediction. The protein sequence input enables generalization to unseen proteins and the cellular image input enables cell type specific prediction that captures single-cell variability. PUPS' ability to generalize to unseen proteins and cell lines enables us to assess the variability in protein localization across cell lines as well as across single cells within a cell line and to identify the biological processes associated with the proteins that have variable localization. Experimental validation shows that PUPS can be used to predict protein localization in newly performed experiments outside of the Human Protein Atlas used for training. Collectively, PUPS utilizes both protein sequences and cellular images to predict protein localization in unseen proteins and cell lines with the ability to capture single-cell variability.
    DOI:  https://doi.org/10.1101/2024.07.25.605178
  8. Genome Biol. 2024 Aug 01. 25(1): 205
    MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.
    Irzam Sarfraz, Yichen Wang, Amulya Shastry, Wei Kheng Teh, Artem Sokolov, Brian R Herb, Heather H Creasy, Isaac Virshup, Ruben Dries, Kylee Degatano, Anup Mahurkar, Daniel J Schnell, Pedro Madrigal, Jason Hilton, Nils Gehlenborg, Timothy Tickle, Joshua D Campbell.
      Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.
    DOI:  https://doi.org/10.1186/s13059-024-03349-w
  9. Nat Commun. 2024 Aug 02. 15(1): 6516
    Evaluating batch correction methods for image-based cell profiling.
    John Arevalo, Ellen Su, Jessica D Ewald, Robert van Dijk, Anne E Carpenter, Shantanu Singh.
      High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.
    DOI:  https://doi.org/10.1038/s41467-024-50613-5
  10. Mol Biol Cell. 2024 Jul 31. mbcE24060270
    Shared and redundant proteins coordinate signal cross-talk between MAPK pathways in yeast.
    Shu Zhang, Hao Wang, Emily L Sipko, Shuang Li, Timothy A Daugird, Wesley R Legant, Henrik G Dohlman.
      All cells must detect, interpret and adapt to multiple and concurrent stimuli. While signaling pathways are highly specialized, different pathways often share components or have components with overlapping functions. In the yeast Saccharomyces cerevisiae, the high osmolarity glycerol (HOG) pathway has two seemingly redundant branches, mediated by Sln1 and Sho1. Both branches are activated by osmotic pressure, leading to phosphorylation of the MAPKs Hog1 and Kss1. The mating pathway is activated by pheromone, leading to phosphorylation of the MAPKs Fus3 and Kss1. Given that Kss1 is shared by the two pathways, we investigated its role in signal coordination. We activated both pathways with a combination of salt and pheromone, in cells lacking the shared MAPK and in cells lacking either of the redundant branches of the HOG pathway. By systematically evaluating MAPK activation, translocation, and transcription programs, we determined that Sho1 mediates cross talk between the HOG and mating pathways and does so through Kss1. Further, we show that Kss1 initiates a transcriptional program that is distinct from that induced by Hog1 and Fus3. Our findings reveal how redundant and shared components coordinate concurrent signals and thereby adapt to sudden environmental changes.
    DOI:  https://doi.org/10.1091/mbc.E24-06-0270
  11. Proteomics. 2024 Aug 01. e2400022
    Data acquisition approaches for single cell proteomics.
    Gautam Ghosh, Ariana E Shannon, Brian C Searle.
      Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.
    Keywords:  data dependent acquisition; data independent acquisition; mass spectrometry; multiplex; proteomics; single cell
    DOI:  https://doi.org/10.1002/pmic.202400022
  12. Nucleic Acids Res. 2024 Jul 30. pii: gkae651. [Epub ahead of print]
    Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0.
    Guy Assa, Nechama Kalter, Michael Rosenberg, Avigail Beck, Oshry Markovich, Tanya Gontmakher, Ayal Hendel, Zohar Yakhini.
      Off-target effects present a significant impediment to the safe and efficient use of CRISPR-Cas genome editing. Since off-target activity is influenced by the genomic sequence, the presence of sequence variants leads to varying on- and off-target profiles among different alleles or individuals. However, a reliable tool that quantifies genome editing activity in an allelic context is not available. Here, we introduce CRISPECTOR2.0, an extended version of our previously published software tool CRISPECTOR, with an allele-specific editing activity quantification option. CRISPECTOR2.0 enables reference-free, allele-aware, precise quantification of on- and off-target activity, by using de novo sample-specific single nucleotide variant (SNV) detection and statistical-based allele-calling algorithms. We demonstrate CRISPECTOR2.0 efficacy in analyzing samples containing multiple alleles and quantifying allele-specific editing activity, using data from diverse cell types, including primary human cells, plants, and an original extensive human cell line database. We identified instances where an SNV induced changes in the protospacer adjacent motif sequence, resulting in allele-specific editing. Intriguingly, differential allelic editing was also observed in regions carrying distal SNVs, hinting at the involvement of additional epigenetic factors. Our findings highlight the importance of allele-specific editing measurement as a milestone in the adaptation of efficient, accurate, and safe personalized genome editing.
    DOI:  https://doi.org/10.1093/nar/gkae651
  13. BMC Bioinformatics. 2024 Jul 30. 25(1): 249
    Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks.
    Helen Xie, Lorin Crawford, Ashley Mae Conard.
      In this paper, we aim to build a platform that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real dataset from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the Multioviz R package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at https://multioviz.ccv.brown.edu/ .
    DOI:  https://doi.org/10.1186/s12859-024-05819-1
  14. Arterioscler Thromb Vasc Biol. 2024 Aug 01.
    Rictor, an mTORC2 Protein, Regulates Murine Lymphatic Valve Formation Through the AKT-FOXO1 Signaling.
    Richa Banerjee, Luz A Knauer, Drishya Iyer, Sara E Barlow, Hanan Shalaby, Razieh Dehghan, Joshua P Scallan, Ying Yang.
      BACKGROUND: Lymphatic valves are specialized structures in collecting lymphatic vessels and are crucial for preventing retrograde lymph flow. Mutations in valve-forming genes have been clinically implicated in the pathology of congenital lymphedema. Lymphatic valves form when oscillatory shear stress from lymph flow signals through the PI3K/AKT pathway to promote the transcription of valve-forming genes that trigger the growth and maintenance of lymphatic valves. Conventionally, in many cell types, AKT is phosphorylated at Ser473 by the mTORC2 (mammalian target of rapamycin complex 2). However, mTORC2 has not yet been implicated in lymphatic valve formation.METHODS: In vivo and in vitro techniques were used to investigate the role of Rictor, a critical component of mTORC2, in lymphatic endothelium.
    RESULTS: Here, we showed that embryonic and postnatal lymphatic deletion of Rictor, a critical component of mTORC2, led to a significant decrease in lymphatic valves and prevented the maturation of collecting lymphatic vessels. RICTOR knockdown in human dermal lymphatic endothelial cells not only reduced the level of activated AKT and the expression of valve-forming genes under no-flow conditions but also abolished the upregulation of AKT activity and valve-forming genes in response to oscillatory shear stress. We further showed that the AKT target, FOXO1 (forkhead box protein O1), a repressor of lymphatic valve formation, had increased nuclear activity in Rictor knockout mesenteric lymphatic endothelial cells in vivo. Deletion of Foxo1 in Rictor knockout mice restored the number of valves to control levels in lymphatic vessels of the ear and mesentery.
    CONCLUSIONS: Our work identifies a novel role for RICTOR in the mechanotransduction signaling pathway, wherein it activates AKT and prevents the nuclear accumulation of the valve repressor, FOXO1, which ultimately enables the formation and maintenance of lymphatic valves.
    Keywords:  extracellular fluid; mice; mitral valve; phosphorylation; transcription factors
    DOI:  https://doi.org/10.1161/ATVBAHA.124.321164
  15. bioRxiv. 2024 Jul 27. pii: 2024.07.26.605307. [Epub ahead of print]
    Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.
    Eli Metzner, Kaden M Southard, Thomas M Norman.
      Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.
    DOI:  https://doi.org/10.1101/2024.07.26.605307
  16. Nat Commun. 2024 Aug 02. 15(1): 6510
    OmicScope unravels systems-level insights from quantitative proteomics data.
    Guilherme Reis-de-Oliveira, Victor Corasolla Carregari, Gabriel Rodrigues Dos Reis de Sousa, Daniel Martins-de-Souza.
      Shotgun proteomics analysis presents multifaceted challenges, demanding diverse tool integration for insights. Addressing this complexity, OmicScope emerges as an innovative solution for quantitative proteomics data analysis. Engineered to handle various data formats, it performs data pre-processing - including joining replicates, normalization, data imputation - and conducts differential proteomics analysis for both static and longitudinal experimental designs. Empowered by Enrichr with over 224 databases, OmicScope performs Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Additionally, its Nebula module facilitates meta-analysis from independent datasets, providing a systems biology approach for enriched insights. Complete with a data visualization toolkit and accessible as Python package and a web application, OmicScope democratizes proteomics analysis, offering an efficient and high-quality pipeline for researchers.
    DOI:  https://doi.org/10.1038/s41467-024-50875-z
  17. Nat Commun. 2024 Aug 02. 15(1): 6557
    Holimap: an accurate and efficient method for solving stochastic gene network dynamics.
    Chen Jia, Ramon Grima.
      Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of gene product numbers vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein or mRNA number distributions of a complex gene regulatory network by the distributions of a much simpler reaction system. We demonstrate Holimap's computational advantages over conventional methods by applying it to predict the stochastic time-dependent dynamics of various gene networks, including transcriptional networks ranging from simple autoregulatory loops to complex randomly connected networks, post-transcriptional networks, and post-translational networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.
    DOI:  https://doi.org/10.1038/s41467-024-50716-z
  18. Elife. 2024 Jul 31. pii: RP92822. [Epub ahead of print]12
    Structural and dynamic changes in P-Rex1 upon activation by PIP3 and inhibition by IP4.
    Sandeep K Ravala, Sendi Rafael Adame-Garcia, Sheng Li, Chun-Liang Chen, Michael A Cianfrocco, J Silvio Gutkind, Jennifer N Cash, John J G Tesmer.
      PIP3-dependent Rac exchanger 1 (P-Rex1) is abundantly expressed in neutrophils and plays central roles in chemotaxis and cancer metastasis by serving as a guanine-nucleotide exchange factor (GEF) for Rac. The enzyme is synergistically activated by PIP3 and heterotrimeric Gβγ subunits, but mechanistic details remain poorly understood. While investigating the regulation of P-Rex1 by PIP3, we discovered that Ins(1,3,4,5)P4 (IP4) inhibits P-Rex1 activity and induces large decreases in backbone dynamics in diverse regions of the protein. Cryo-electron microscopy analysis of the P-Rex1·IP4 complex revealed a conformation wherein the pleckstrin homology (PH) domain occludes the active site of the Dbl homology (DH) domain. This configuration is stabilized by interactions between the first DEP domain (DEP1) and the DH domain and between the PH domain and a 4-helix bundle (4HB) subdomain that extends from the C-terminal domain of P-Rex1. Disruption of the DH-DEP1 interface in a DH/PH-DEP1 fragment enhanced activity and led to a more extended conformation in solution, whereas mutations that constrain the occluded conformation led to decreased GEF activity. Variants of full-length P-Rex1 in which the DH-DEP1 and PH-4HB interfaces were disturbed exhibited enhanced activity during chemokine-induced cell migration, confirming that the observed structure represents the autoinhibited state in living cells. Interactions with PIP3-containing liposomes led to disruption of these interfaces and increased dynamics protein-wide. Our results further suggest that inositol phosphates such as IP4 help to inhibit basal P-Rex1 activity in neutrophils, similar to their inhibitory effects on phosphatidylinositol-3-kinase.
    Keywords:  E. coli; PIP3; RhoGEF; biochemistry; chemical biology; cryo-EM; molecular biophysics; signaling; structural biology
    DOI:  https://doi.org/10.7554/eLife.92822
  19. Proc Natl Acad Sci U S A. 2024 Aug 06. 121(32): e2406842121
    Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition.
    Yu-Chen Cheng, Yun Zhang, Shubham Tripathi, B V Harshavardhan, Mohit Kumar Jolly, Geoffrey Schiebinger, Herbert Levine, Thomas O McDonald, Franziska Michor.
      Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.
    Keywords:  EMT; cell fate; scRNA-seq
    DOI:  https://doi.org/10.1073/pnas.2406842121
  20. Front Immunol. 2024 ;15 1425488
    FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.
    Valerie Coppard, Grisha Szep, Zoya Georgieva, Sarah K Howlett, Lorna B Jarvis, Daniel B Rainbow, Ondrej Suchanek, Edward J Needham, Hani S Mousa, David K Menon, Felix Feyertag, Krishnaa T Mahbubani, Kourosh Saeb-Parsy, Joanne L Jones.
      As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.
    Keywords:  Julia programming language; dimensionality reduction; flow cytometry analysis; high-dimensional cytometry; immunophenotyping; spectral flow cytometry
    DOI:  https://doi.org/10.3389/fimmu.2024.1425488
  21. Nat Commun. 2024 Jul 29. 15(1): 6380
    Simplifying data and code sharing.
      
    DOI:  https://doi.org/10.1038/s41467-024-50517-4
  22. iScience. 2024 Aug 16. 27(8): 110432
    Targeted dephosphorylation of TFEB promotes its nuclear translocation.
    Jin-Feng Zhao, Natalia Shpiro, Gajanan Sathe, Abigail Brewer, Thomas J Macartney, Nicola T Wood, Florentina Negoita, Kei Sakamoto, Gopal P Sapkota.
      Reversible phosphorylation of the transcription factor EB (TFEB) coordinates cellular responses to metabolic and other stresses. During nutrient replete and stressor-free conditions, phosphorylated TFEB is primarily localized to the cytoplasm. Stressor-mediated reduction of TFEB phosphorylation promotes its nuclear translocation and context-dependent transcriptional activity. In this study, we explored targeted dephosphorylation of TFEB as an approach to activate TFEB in the absence of nutrient deprivation or other cellular stress. Through an induction of proximity between TFEB and several phosphatases using the AdPhosphatase system, we demonstrate targeted dephosphorylation of TFEB in cells. Furthermore, by developing a heterobifunctional molecule BDPIC (bromoTAG-dTAG proximity-inducing chimera), we demonstrate targeted dephosphorylation of TFEB-dTAG through induced proximity to bromoTAG-PPP2CA. Targeted dephosphorylation of TFEB-dTAG by bromoTAG-PPP2CA with BDPIC at the endogenous levels is sufficient to induce nuclear translocation and some transcriptional activity of TFEB.
    Keywords:  Health sciences; Medical specialty; Medicine; Precision medicine
    DOI:  https://doi.org/10.1016/j.isci.2024.110432
  23. Cancer Res. 2024 Aug 01. 84(15): 2397-2399
    Modernizing the NCI60 Cell Line Screen for Phenotypic Drug Discovery in the 21st Century.
    Gianna M Colombo, Steven M Corsello.
      Over the past three decades, high-throughput phenotypic cancer cell line screens have revealed unanticipated small-molecule activities and illuminated connections between tumor genotypes and anticancer efficacy. Founded in 1984, the National Cancer Institute's "NCI60" screen laid the conceptual groundwork for the contemporary landscape of phenotypic drug discovery. NCI60 first operated as a primary bioactivity screen, but molecular characterization of the NCI60 cell line panel and development of a small-molecule sensitivity pattern recognition algorithm (called "COMPARE") have enabled subsequent studies into drug mechanisms of action and biomarker identification. In this issue of Cancer Research, Kunkel and colleagues report an updated version of the NCI60 screen, dubbed "HTS384" NCI60, that better aligns with current cell proliferation assay standards and has higher throughput. Changes include the use of a 384-well plate format, automated laboratory equipment, 3 days of compound exposure, and a CellTiter-Glo luminescent endpoint. To confirm that data from the HTS384 and classic NCI60 screen are comparable, the authors tested a library of 1,003 anticancer agents using both protocols and applied COMPARE to analyze patterns of cell line sensitivities. More than three dozen groups of targeted therapies showed high comparability between screens. Modernization of NCI60, and closer integration with other large-scale pharmacogenomic screens and molecular feature sets, will help this public screening service remain pertinent for cancer drug discovery efforts for years to come. See related article by Kunkel et al., p. 2403.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-24-1506
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