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Ferrena A, Zhang R, Wang J, Zheng XY, Göker B, Borjihan H, Chae SS, Lo Y, Zhao H, Schwartz E, Loeb D, Yang R, Geller D, Zheng D, Hoang B. Comprehensive single cell transcriptomics analysis of murine osteosarcoma uncovers Skp2 function in metastasis, genomic instability and immune activation and reveals additional target pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597347. [PMID: 38895216 PMCID: PMC11185585 DOI: 10.1101/2024.06.04.597347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Osteosarcoma (OS) is the most common primary pediatric bone malignancy. One promising new therapeutic target is SKP2, encoding a substrate recognition factor of the SCF E3 ubiquitin ligase responsible for ubiquitination and proteasome degradation of substrate p27, thus driving cellular proliferation. We have shown previously that knockout of Skp2 in an immunocompetent transgenic mouse model of OS improved survival, drove apoptosis, and induced tumor inflammation. Here, we applied single-cell RNA-sequencing (scRNA-seq) to study primary OS tumors derived from Osx-Cre driven conditional knockout of Rb1 and Trp53. We showed that murine OS models recapitulate the tumor heterogeneity and microenvironment complexity observed in patient tumors. We further compared this model with OS models with functional disruption of Skp2: one with Skp2 knockout and the other with the Skp2-p27 interaction disrupted (resulting in p27 overexpression). We found reduction of T cell exhaustion and upregulation of interferon activation, along with evidence of replicative and endoplasmic reticulum-related stress in the Skp2 disruption models, and showed that interferon induction was correlated with improved survival in OS patients. Additionally, our scRNA-seq analysis uncovered decreased activities of metastasis-related gene signatures in the Skp2-disrupted OS, which we validated by observation of a strong reduction in lung metastasis in the Skp2 knockout mice. Finally, we report several potential mechanisms of escape from targeting Skp2 in OS, including upregulation of Myc targets, DNA copy number amplification and overexpression of alternative E3 ligase genes, and potential alternative lineage activation. These mechanistic insights into OS tumor biology and Skp2 function suggest novel targets for new, synergistic therapies, while the data and our comprehensive analysis may serve as a public resource for further big data-driven OS research.
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Affiliation(s)
- Alexander Ferrena
- Institute for Clinical and Translational Research, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ranxin Zhang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Musculoskeletal Tumor Center, Beijing Key Laboratory for Musculoskeletal Tumors, Peking University People’s Hospital, Beijing, China
| | - Jichuan Wang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Musculoskeletal Tumor Center, Beijing Key Laboratory for Musculoskeletal Tumors, Peking University People’s Hospital, Beijing, China
| | - Xiang Yu Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Barlas Göker
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Hasibagan Borjihan
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sung-Suk Chae
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Yungtai Lo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Hongling Zhao
- Department of Developmental & Molecular Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Edward Schwartz
- Department of Oncology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - David Loeb
- Department of Developmental & Molecular Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rui Yang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - David Geller
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bang Hoang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
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Chen YH, Chao KH, Wong JY, Liu CF, Leu JY, Tsai HK. A feature extraction free approach for protein interactome inference from co-elution data. Brief Bioinform 2023; 24:bbad229. [PMID: 37328692 DOI: 10.1093/bib/bbad229] [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: 12/16/2022] [Revised: 05/01/2023] [Accepted: 05/29/2023] [Indexed: 06/18/2023] Open
Abstract
Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.
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Affiliation(s)
- Yu-Hsin Chen
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei 106, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Academic Sinica, Taipei 11529, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Kuan-Hao Chao
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Jin Yung Wong
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Chien-Fu Liu
- Institute of Molecular Biology, Academia Sinica, Taipei, 11529, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, 11529, Taiwan
| | - Huai-Kuang Tsai
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei 106, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Academic Sinica, Taipei 11529, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
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Chou JY, Hsu PC, Leu JY. Enforcement of Postzygotic Species Boundaries in the Fungal Kingdom. Microbiol Mol Biol Rev 2022; 86:e0009822. [PMID: 36098649 PMCID: PMC9769731 DOI: 10.1128/mmbr.00098-22] [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] [Indexed: 01/01/2023] Open
Abstract
Understanding the molecular basis of speciation is a primary goal in evolutionary biology. The formation of the postzygotic reproductive isolation that causes hybrid dysfunction, thereby reducing gene flow between diverging populations, is crucial for speciation. Using various advanced approaches, including chromosome replacement, hybrid introgression and transcriptomics, population genomics, and experimental evolution, scientists have revealed multiple mechanisms involved in postzygotic barriers in the fungal kingdom. These results illuminate both unique and general features of fungal speciation. Our review summarizes experiments on fungi exploring how Dobzhansky-Muller incompatibility, killer meiotic drive, chromosome rearrangements, and antirecombination contribute to postzygotic reproductive isolation. We also discuss possible evolutionary forces underlying different reproductive isolation mechanisms and the potential roles of the evolutionary arms race under the Red Queen hypothesis and epigenetic divergence in speciation.
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Affiliation(s)
- Jui-Yu Chou
- Department of Biology, National Changhua University of Education, Changhua, Taiwan
| | - Po-Chen Hsu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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