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Bracken CP, Goodall GJ, Gregory PA. RNA regulatory mechanisms controlling TGF-β signaling and EMT in cancer. Semin Cancer Biol 2024; 102-103:4-16. [PMID: 38917876 DOI: 10.1016/j.semcancer.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/05/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024]
Abstract
Epithelial-mesenchymal transition (EMT) is a major contributor to metastatic progression and is prominently regulated by TGF-β signalling. Both EMT and TGF-β pathway components are tightly controlled by non-coding RNAs - including microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) - that collectively have major impacts on gene expression and resulting cellular states. While miRNAs are the best characterised regulators of EMT and TGF-β signaling and the miR-200-ZEB1/2 feedback loop plays a central role, important functions for lncRNAs and circRNAs are also now emerging. This review will summarise our current understanding of the roles of non-coding RNAs in EMT and TGF-β signaling with a focus on their functions in cancer progression.
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Affiliation(s)
- Cameron P Bracken
- Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA 5000, Australia; Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia; School of Biological Sciences, Faculty of Sciences, Engineering and Technology, The University of Adelaide, Adelaide, SA 5000, Australia.
| | - Gregory J Goodall
- Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA 5000, Australia; Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia; School of Biological Sciences, Faculty of Sciences, Engineering and Technology, The University of Adelaide, Adelaide, SA 5000, Australia.
| | - Philip A Gregory
- Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA 5000, Australia; Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia.
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Copperman J, Mclean IC, Gross SM, Singh J, Chang YH, Zuckerman DM, Heiser LM. Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576248. [PMID: 38293173 PMCID: PMC10827140 DOI: 10.1101/2024.01.18.576248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.
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Affiliation(s)
- Jeremy Copperman
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Ian C. Mclean
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
| | | | - Jalim Singh
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland OR 97239, U.S.A
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Youssef A, Paul I, Crovella M, Emili A. DESP demixes cell-state profiles from dynamic bulk molecular measurements. CELL REPORTS METHODS 2024; 4:100729. [PMID: 38490205 PMCID: PMC10985230 DOI: 10.1016/j.crmeth.2024.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024]
Abstract
Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.
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Affiliation(s)
- Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Indranil Paul
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Mark Crovella
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Computer Science Department, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
| | - Andrew Emili
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
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Wuchty S, White AK, Olthof AM, Drake K, Hume AJ, Olejnik J, Aguiar-Pulido V, Mühlberger E, Kanadia RN. Minor intron-containing genes as an ancient backbone for viral infection? PNAS NEXUS 2024; 3:pgad479. [PMID: 38274120 PMCID: PMC10810330 DOI: 10.1093/pnasnexus/pgad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024]
Abstract
Minor intron-containing genes (MIGs) account for <2% of all human protein-coding genes and are uniquely dependent on the minor spliceosome for proper excision. Despite their low numbers, we surprisingly found a significant enrichment of MIG-encoded proteins (MIG-Ps) in protein-protein interactomes and host factors of positive-sense RNA viruses, including SARS-CoV-1, SARS-CoV-2, MERS coronavirus, and Zika virus. Similarly, we observed a significant enrichment of MIG-Ps in the interactomes and sets of host factors of negative-sense RNA viruses such as Ebola virus, influenza A virus, and the retrovirus HIV-1. We also found an enrichment of MIG-Ps in double-stranded DNA viruses such as Epstein-Barr virus, human papillomavirus, and herpes simplex viruses. In general, MIG-Ps were highly connected and placed in central positions in a network of human-host protein interactions. Moreover, MIG-Ps that interact with viral proteins were enriched with essential genes. We also provide evidence that viral proteins interact with ancestral MIGs that date back to unicellular organisms and are mainly involved in basic cellular functions such as cell cycle, cell division, and signal transduction. Our results suggest that MIG-Ps form a stable, evolutionarily conserved backbone that viruses putatively tap to invade and propagate in human host cells.
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Affiliation(s)
- Stefan Wuchty
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, USA
- Department of Biology, University of Miami, Coral Gables, FL 33146, USA
- Institute of Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33134, USA
| | - Alisa K White
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Anouk M Olthof
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Kyle Drake
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Adam J Hume
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
- Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA 02118, USA
| | - Judith Olejnik
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
| | | | - Elke Mühlberger
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
| | - Rahul N Kanadia
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
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Fonseca Teixeira A, Wu S, Luwor R, Zhu HJ. A New Era of Integration between Multiomics and Spatio-Temporal Analysis for the Translation of EMT towards Clinical Applications in Cancer. Cells 2023; 12:2740. [PMID: 38067168 PMCID: PMC10706093 DOI: 10.3390/cells12232740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients.
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Affiliation(s)
- Adilson Fonseca Teixeira
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3050, Australia (S.W.); (R.L.)
- Huagene Institute, Kecheng Science and Technology Park, Pukou District, Nanjing 211800, China
| | - Siqi Wu
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3050, Australia (S.W.); (R.L.)
- Huagene Institute, Kecheng Science and Technology Park, Pukou District, Nanjing 211800, China
| | - Rodney Luwor
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3050, Australia (S.W.); (R.L.)
- Huagene Institute, Kecheng Science and Technology Park, Pukou District, Nanjing 211800, China
- Fiona Elsey Cancer Research Institute, Ballarat, VIC 3350, Australia
- Health, Innovation and Transformation Centre, Federation University, Ballarat, VIC 3350, Australia
| | - Hong-Jian Zhu
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3050, Australia (S.W.); (R.L.)
- Huagene Institute, Kecheng Science and Technology Park, Pukou District, Nanjing 211800, China
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Zhai J, Ji J, Liu J. Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm. Bioengineering (Basel) 2023; 10:909. [PMID: 37627794 PMCID: PMC10451563 DOI: 10.3390/bioengineering10080909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
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Affiliation(s)
| | | | - Jinduo Liu
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China (J.J.)
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