1
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Venafra V, Sacco F, Perfetto L. SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks. NPJ Syst Biol Appl 2024; 10:95. [PMID: 39179556 PMCID: PMC11343843 DOI: 10.1038/s41540-024-00417-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/31/2024] [Indexed: 08/26/2024] Open
Abstract
Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.
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Affiliation(s)
- Veronica Venafra
- Ph.D. Program in Cellular and Molecular Biology, Department of Biology, University of Rome 'Tor Vergata', Rome, Italy
- Department of Biology, University of Rome 'Tor Vergata', Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome 'Tor Vergata', Rome, Italy.
| | - Livia Perfetto
- Department of Biology and Biotechnologies 'C.Darwin', University of Rome 'La Sapienza', Rome, Italy.
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2
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Jänes J, Müller M, Selvaraj S, Manoel D, Stephenson J, Gonçalves C, Lafita A, Polacco B, Obernier K, Alasoo K, Lemos MC, Krogan N, Martin M, Saraiva LR, Burke D, Beltrao P. Predicted mechanistic impacts of human protein missense variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596373. [PMID: 38854010 PMCID: PMC11160786 DOI: 10.1101/2024.05.29.596373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.
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Affiliation(s)
- Jürgen Jänes
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marc Müller
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Senthil Selvaraj
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Diogo Manoel
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - James Stephenson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Catarina Gonçalves
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Benjamin Polacco
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manuel C. Lemos
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal
| | - Nevan Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Maria Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Luis R. Saraiva
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - David Burke
- Faculty of Life Sciences and Medicine, King’s College, London, UK
| | - Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
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3
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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4
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Liu C, Vorderbruggen M, Muñoz-Trujillo C, Kim SH, Katzenellenbogen JA, Katzenellenbogen BS, Karpf AR. NB compounds are potent and efficacious FOXM1 inhibitors in high-grade serous ovarian cancer cells. J Ovarian Res 2024; 17:94. [PMID: 38704607 PMCID: PMC11069232 DOI: 10.1186/s13048-024-01421-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/20/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Genetic studies implicate the oncogenic transcription factor Forkhead Box M1 (FOXM1) as a potential therapeutic target in high-grade serous ovarian cancer (HGSOC). We evaluated the activity of different FOXM1 inhibitors in HGSOC cell models. RESULTS We treated HGSOC and fallopian tube epithelial (FTE) cells with a panel of previously reported FOXM1 inhibitors. Based on drug potency, efficacy, and selectivity, determined through cell viability assays, we focused on two compounds, NB-73 and NB-115 (NB compounds), for further investigation. NB compounds potently and selectively inhibited FOXM1 with lesser effects on other FOX family members. NB compounds decreased FOXM1 expression via targeting the FOXM1 protein by promoting its proteasome-mediated degradation, and effectively suppressed FOXM1 gene targets at both the protein and mRNA level. At the cellular level, NB compounds promoted apoptotic cell death. Importantly, while inhibition of apoptosis using a pan-caspase inhibitor rescued HGSOC cells from NB compound-induced cell death, it did not rescue FOXM1 protein degradation, supporting that FOXM1 protein loss from NB compound treatment is specific and not a general consequence of cytotoxicity. Drug washout studies indicated that FOXM1 reduction was retained for at least 72 h post-treatment, suggesting that NB compounds exhibit long-lasting effects in HGSOC cells. NB compounds effectively suppressed both two-dimensional and three-dimensional HGSOC cell colony formation at sub-micromolar concentrations. Finally, NB compounds exhibited synergistic activity with carboplatin in HGSOC cells. CONCLUSIONS NB compounds are potent, selective, and efficacious inhibitors of FOXM1 in HGSOC cells and are worthy of further investigation as HGSOC therapeutics.
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Affiliation(s)
- Cassie Liu
- Eppley Institute, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
| | - Makenzie Vorderbruggen
- Eppley Institute, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
| | - Catalina Muñoz-Trujillo
- Eppley Institute, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA
| | - Sung Hoon Kim
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - John A Katzenellenbogen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Benita S Katzenellenbogen
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Adam R Karpf
- Eppley Institute, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA.
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68918-6805, USA.
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5
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Homma MK, Nakato R, Niida A, Bando M, Fujiki K, Yokota N, Yamamoto S, Shibata T, Takagi M, Yamaki J, Kozuka-Hata H, Oyama M, Shirahige K, Homma Y. Cell cycle-dependent gene networks for cell proliferation activated by nuclear CK2α complexes. Life Sci Alliance 2024; 7:e202302077. [PMID: 37907238 PMCID: PMC10618106 DOI: 10.26508/lsa.202302077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/02/2023] Open
Abstract
Nuclear expression of protein kinase CK2α is reportedly elevated in human carcinomas, but mechanisms underlying its variable localization in cells are poorly understood. This study demonstrates a functional connection between nuclear CK2 and gene expression in relation to cell proliferation. Growth stimulation of quiescent human normal fibroblasts and phospho-proteomic analysis identified a pool of CK2α that is highly phosphorylated at serine 7. Phosphorylated CK2α translocates into the nucleus, and this phosphorylation appears essential for nuclear localization and catalytic activity. Protein signatures associated with nuclear CK2 complexes reveal enrichment of apparently unique transcription factors and chromatin remodelers during progression through the G1 phase of the cell cycle. Chromatin immunoprecipitation-sequencing profiling demonstrated recruitment of CK2α to active gene loci, more abundantly in late G1 phase than in early G1, notably at transcriptional start sites of core histone genes, growth stimulus-associated genes, and ribosomal RNAs. Our findings reveal that nuclear CK2α complexes may be essential to facilitate progression of the cell cycle, by activating histone genes and triggering ribosomal biogenesis, specified in association with nuclear and nucleolar transcriptional regulators.
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Affiliation(s)
- Miwako Kato Homma
- Department of Biomolecular Sciences, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Ryuichiro Nakato
- Laboratory of Computational Genomics, Institute for Quantitative Biosciences, University of Tokyo, Bunkyo, Japan
| | - Atsushi Niida
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato, Japan
| | - Masashige Bando
- Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo, Japan
| | - Katsunori Fujiki
- Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo, Japan
| | - Naoko Yokota
- Laboratory of Computational Genomics, Institute for Quantitative Biosciences, University of Tokyo, Bunkyo, Japan
| | - So Yamamoto
- Department of Biomolecular Sciences, Fukushima Medical University School of Medicine, Fukushima, Japan
| | | | - Motoki Takagi
- Translational Research Center, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Junko Yamaki
- Department of Biomolecular Sciences, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hiroko Kozuka-Hata
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato, Japan
| | - Masaaki Oyama
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato, Japan
| | - Katsuhiko Shirahige
- Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo, Japan
- Department of Biosciences and Nutrition, Karolinska Institutet, Biomedicum, Stockholm, Sweden
- Department of Cell and Molecular Biology, Karolinska Institutet, Biomedicum, Stockholm, Sweden
| | - Yoshimi Homma
- Department of Biomolecular Sciences, Fukushima Medical University School of Medicine, Fukushima, Japan
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6
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Ramos Zapatero M, Tong A, Opzoomer JW, O'Sullivan R, Cardoso Rodriguez F, Sufi J, Vlckova P, Nattress C, Qin X, Claus J, Hochhauser D, Krishnaswamy S, Tape CJ. Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses. Cell 2023; 186:5606-5619.e24. [PMID: 38065081 DOI: 10.1016/j.cell.2023.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/18/2023]
Abstract
Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.
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Affiliation(s)
- María Ramos Zapatero
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada; Mila - Quebec AI Institute, Montréal, QC, Canada
| | - James W Opzoomer
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Rhianna O'Sullivan
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ferran Cardoso Rodriguez
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jahangir Sufi
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Petra Vlckova
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Callum Nattress
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Xiao Qin
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jeroen Claus
- Phospho Biomedical Animation, The Greenhouse Studio 6, London N17 9QU, UK
| | - Daniel Hochhauser
- Drug-DNA Interactions Group, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Genetics, Yale University, New Haven, CT, USA; Program for Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Program for Applied Math, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK.
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7
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Tudose C, Bond J, Ryan CJ. Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity. NAR Cancer 2023; 5:zcad056. [PMID: 38035131 PMCID: PMC10683780 DOI: 10.1093/narcan/zcad056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.
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Affiliation(s)
- Cosmin Tudose
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
- The SFI Centre for Research Training in Genomics Data Science, Ireland
| | - Jonathan Bond
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
- Children's Health Ireland at Crumlin, Dublin, Ireland
| | - Colm J Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Computer Science, University College Dublin, Dublin, Ireland
- Conway Institute, University College Dublin, Dublin, Ireland
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8
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Hassan AHE, Wang CY, Lee CJ, Jeon HR, Choi Y, Moon S, Lee CH, Kim YJ, Cho SB, Mahmoud K, El-Sayed SM, Lee SK, Lee YS. Repurposing Synthetic Congeners of a Natural Product Aurone Unveils a Lead Antitumor Agent Inhibiting Folded P-Loop Conformation of MET Receptor Tyrosine Kinase. Pharmaceuticals (Basel) 2023; 16:1597. [PMID: 38004462 PMCID: PMC10675456 DOI: 10.3390/ph16111597] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
A library of 24 congeners of the natural product sulfuretin were evaluated against nine panels representing nine cancer diseases. While sulfuretin elicited very weak activities at 10 µM concentration, congener 1t was identified as a potential compound triggering growth inhibition of diverse cell lines. Mechanistic studies in HCT116 colon cancer cells revealed that congener 1t dose-dependently increased levels of cleaved-caspases 8 and 9 and cleaved-PARP, while it concentration-dependently decreased levels of CDK4, CDK6, Cdc25A, and Cyclin D and E resulting in induction of cell cycle arrest and apoptosis in colon cancer HCT116 cells. Mechanistic study also presented MET receptor tyrosine kinase as the molecular target mediating the anticancer activity of compound 1t in HCT116 cells. In silico study predicted folded p-loop conformation as the form of MET receptor tyrosine kinase responsible for binding of compound 1t. Together, the current study presents compound 1t as an interesting anticancer lead for further development.
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Affiliation(s)
- Ahmed H. E. Hassan
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
- Medicinal Chemistry Laboratory, Department of Pharmacy, College of Pharmacy, Kyung Hee University, 26 Kyungheedae-ro, Seoul 02447, Republic of Korea
| | - Cai Yi Wang
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Cheol Jung Lee
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Hye Rim Jeon
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeonwoo Choi
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Suyeon Moon
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Chae Hyeon Lee
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeon Ju Kim
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Soo Bin Cho
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Kazem Mahmoud
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Badr City 11829, Egypt
| | - Selwan M. El-Sayed
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Sang Kook Lee
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Yong Sup Lee
- Medicinal Chemistry Laboratory, Department of Pharmacy, College of Pharmacy, Kyung Hee University, 26 Kyungheedae-ro, Seoul 02447, Republic of Korea
- Department of Fundamental Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea
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9
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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Higgins L, Gerdes H, Cutillas PR. Principles of phosphoproteomics and applications in cancer research. Biochem J 2023; 480:403-420. [PMID: 36961757 PMCID: PMC10212522 DOI: 10.1042/bcj20220220] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/25/2023]
Abstract
Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.
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Affiliation(s)
- Luke Higgins
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
| | - Henry Gerdes
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
| | - Pedro R. Cutillas
- Cell Signaling and Proteomics Group, Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, U.K
- Alan Turing Institute, The British Library, London, U.K
- Digital Environment Research Institute, Queen Mary University of London, London, U.K
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