1
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Singh K, Showalter CA, Manring HR, Haque SJ, Chakravarti A. "Oh, Dear We Are in Tribble": An Overview of the Oncogenic Functions of Tribbles 1. Cancers (Basel) 2024; 16:1889. [PMID: 38791967 PMCID: PMC11120034 DOI: 10.3390/cancers16101889] [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: 04/18/2024] [Revised: 05/11/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Pseudokinases are catalytically inactive proteins in the human genome that lack the ability to transfer phosphate from ATP to their substrates. The Tribbles family of pseudokinases contains three members: Tribbles 1, 2, and 3. Tribbles 1 has recently gained importance because of its involvement in various diseases, including cancer. It acts as a scaffolding protein that brings about the degradation of its substrate proteins, such as C/EBPα/β, MLXIPL, and RAR/RXRα, among others, via the ubiquitin proteasome system. It also serves as an adapter protein, which sequesters different protein molecules and activates their downstream signaling, leading to processes, such as cell survival, cell proliferation, and lipid metabolism. It has been implicated in cancers such as AML, prostate cancer, breast cancer, CRC, HCC, and glioma, where it activates oncogenic signaling pathways such as PI3K-AKT and MAPK and inhibits the anti-tumor function of p53. TRIB1 also causes treatment resistance in cancers such as NSCLC, breast cancer, glioma, and promyelocytic leukemia. All these effects make TRIB1 a potential drug target. However, the lack of a catalytic domain renders TRIB1 "undruggable", but knowledge about its structure, conformational changes during substrate binding, and substrate binding sites provides an opportunity to design small-molecule inhibitors against specific TRIB1 interactions.
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Affiliation(s)
| | | | | | | | - Arnab Chakravarti
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
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2
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Singh K, Han C, Fleming JL, Becker AP, McElroy J, Cui T, Johnson B, Kumar A, Sebastian E, Showalter CA, Schrock MS, Summers MK, Becker V, Tong ZY, Meng X, Manring HR, Venere M, Bell EH, Robe PA, Grosu AL, Haque SJ, Chakravarti A. TRIB1 confers therapeutic resistance in GBM cells by activating the ERK and Akt pathways. Sci Rep 2023; 13:12424. [PMID: 37528172 PMCID: PMC10394028 DOI: 10.1038/s41598-023-32983-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/05/2023] [Indexed: 08/03/2023] Open
Abstract
GBM (Glioblastoma) is the most lethal CNS (Central nervous system) tumor in adults, which inevitably develops resistance to standard treatments leading to recurrence and mortality. TRIB1 is a serine/threonine pseudokinase which functions as a scaffold platform that initiates degradation of its substrates like C/EBPα through the ubiquitin proteasome system and also activates MEK and Akt signaling. We found that increased TRIB1 gene expression associated with worse overall survival of GBM patients across multiple cohorts. Importantly, overexpression of TRIB1 decreased RT/TMZ (radiation therapy/temozolomide)-induced apoptosis in patient derived GBM cell lines in vitro. TRIB1 directly bound to MEK and Akt and increased ERK and Akt phosphorylation/activation. We also found that TRIB1 protein expression was maximal during G2/M transition of cell cycle in GBM cells. Furthermore, TRIB1 bound directly to HDAC1 and p53. Importantly, mice bearing TRIB1 overexpressing tumors had worse overall survival. Collectively, these data suggest that TRIB1 induces resistance of GBM cells to RT/TMZ treatments by activating the cell proliferation and survival pathways thus providing an opportunity for developing new targeted therapeutics.
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Affiliation(s)
- Karnika Singh
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Chunhua Han
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Jessica L Fleming
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Aline P Becker
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Joseph McElroy
- Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University, Columbus, OH, 43210, USA
| | - Tiantian Cui
- Department of Radiation Oncology, City of Hope, Duarte, CA, 91010, USA
| | - Benjamin Johnson
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Ashok Kumar
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Ebin Sebastian
- Corewell Health William Beaumont University Hospital, Royal Oak, MI, 48073, USA
| | - Christian A Showalter
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Morgan S Schrock
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Matthew K Summers
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Valesio Becker
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Zhen-Yue Tong
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Xiaomei Meng
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Heather R Manring
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Monica Venere
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Erica H Bell
- Neroscience Research Institute/Department of Neurology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Pierre A Robe
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands
| | - A L Grosu
- Freiburg University, 79098, Freiburg, Germany
| | - S Jaharul Haque
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA
| | - Arnab Chakravarti
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, 43210, USA.
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3
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Fung H, Sgaier SK, Huang VS. Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network. Sci Rep 2023; 13:6988. [PMID: 37193707 DOI: 10.1038/s41598-023-33745-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/18/2023] [Indexed: 05/18/2023] Open
Abstract
Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.
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Affiliation(s)
- Henry Fung
- Surgo Health, Washington, DC, USA
- Surgo Ventures, Washington, DC, USA
| | - Sema K Sgaier
- Surgo Health, Washington, DC, USA.
- Surgo Ventures, Washington, DC, USA.
- Department of Global Health, University of Washington, Seattle, WA, USA.
| | - Vincent S Huang
- Surgo Health, Washington, DC, USA
- Surgo Ventures, Washington, DC, USA
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4
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Xu Y, Wang Z, Zhang L, Gao C, Li F, Li X, Ke Y, Liu HM, Hu Z, Wei L, Chen ZS. Differentiation of imatinib -resistant chronic myeloid leukemia cells with BCR-ABL-T315I mutation induced by Jiyuan Oridonin A. J Cancer 2023; 14:1182-1194. [PMID: 37215441 PMCID: PMC10197941 DOI: 10.7150/jca.83219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/19/2023] [Indexed: 05/24/2023] Open
Abstract
Chronic myeloid leukemia (CML) results from BCR-ABL oncogene, which blocks CML cells differentiation and protects these cells from apoptosis. T315I mutated BCR-ABL is the main cause of the resistance mediated by imatinib and second generation BCR-ABL inhibitor. CML with the T315I mutation has been considered to have poor prognosis. Here, we determined the effect of Jiyuan oridonin A (JOA), an ent-kaurene diterpenoid compound, on the differentiation blockade in imatinib-sensitive, particularly, imatinib-resistant CML cells with BCR-ABL-T315I mutation by cell proliferation assay, apoptosis analysis, cell differentiation analysis, cell cycle analysis and colony formation assay. We also investigated the possible molecular mechanism by mRNA sequencing, qRT-PCR and Western blotting. We found that JOA at lower concentration significantly inhibited the proliferation of CML cells expressing mutant BCR-ABL (T315I mutation included) and wild-type BCR-ABL, which was due to that JOA induced the cell differentiation and the cell cycle arrest at G0/G1 phase. Interestingly, JOA possessed stronger anti-leukemia activity than its analogues such as OGP46 and Oridonin, which has been investigated extensively. Mechanistically, the cell differentiation mediated by JOA may be originated from the inhibition of BCR-ABL/c-MYC signaling in CML cells expressing wild-type BCR-ABL and BCR-ABL-T315I. JOA displayed the activity of inhibiting the BCR-ABL and promoted differentiation of not only imatinib -sensitive but also imatinib -resistant cells with BCR-ABL mutation, which could become a potent lead compound to overcome the imatinib -resistant induced by inhibitors of BCR-ABL tyrosine kinase in CML therapy.
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Affiliation(s)
- Yun Xu
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Ziting Wang
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Lei Zhang
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Congying Gao
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Fahui Li
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Xueming Li
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Yu Ke
- School of Pharmacy, Zhengzhou University, Zhengzhou, 450052, China
| | - Hong-Min Liu
- School of Pharmacy, Zhengzhou University, Zhengzhou, 450052, China
| | - Zhenbo Hu
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang 261042, China
| | - Liuya Wei
- School of Pharmacy, Weifang Medical University, Weifang, 261053, China
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY, 11439, USA
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5
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Voros S, Bansal AT, Barnes MR, Narula J, Maurovich-Horvat P, Vazquez G, Marvasty IB, Brown BO, Voros ID, Harris W, Voros V, Dayspring T, Neff D, Greenfield A, Furchtgott L, Church B, Runge K, Khalil I, Hayete B, Lucero D, Remaley AT, Newton RS. Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development. Front Cardiovasc Med 2023; 9:960419. [PMID: 36684605 PMCID: PMC9845579 DOI: 10.3389/fcvm.2022.960419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/23/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction We sought to explore biomarkers of coronary atherosclerosis in an unbiased fashion. Methods We analyzed 665 patients (mean ± SD age, 56 ± 11 years; 47% male) from the GLOBAL clinical study (NCT01738828). Cases were defined by the presence of any discernable atherosclerotic plaque based on comprehensive cardiac computed tomography (CT). De novo Bayesian networks built out of 37,000 molecular measurements and 99 conventional biomarkers per patient examined the potential causality of specific biomarkers. Results Most highly ranked biomarkers by gradient boosting were interleukin-6, symmetric dimethylarginine, LDL-triglycerides [LDL-TG], apolipoprotein B48, palmitoleic acid, small dense LDL, alkaline phosphatase, and asymmetric dimethylarginine. In Bayesian analysis, LDL-TG was directly linked to atherosclerosis in over 95% of the ensembles. Genetic variants in the genomic region encoding hepatic lipase (LIPC) were associated with LIPC gene expression, LDL-TG levels and with atherosclerosis. Discussion Triglyceride-rich LDL particles, which can now be routinely measured with a direct homogenous assay, may play an important role in atherosclerosis development. Clinical trial registration GLOBAL clinical study (Genetic Loci and the Burden of Atherosclerotic Lesions); [https://clinicaltrials.gov/ct2/show/NCT01738828?term=NCT01738828&rank=1], identifier [NCT01738828].
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Affiliation(s)
- Szilard Voros
- Global Genomics Group, Atlanta, GA, United States,*Correspondence: Szilard Voros,
| | | | | | - Jagat Narula
- Mount Sinai School of Medicine, New York, NY, United States
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Gustavo Vazquez
- Global Institute for Research, LLC, Richmond, VA, United States
| | | | | | | | | | - Viktor Voros
- Global Genomics Group, Atlanta, GA, United States,Department of Psychiatry, Medical School, University of Pécs, Pécs, Hungary
| | | | - David Neff
- Global Genomics Group, Atlanta, GA, United States
| | | | | | | | - Karl Runge
- GNS Healthcare, Somerville, MA, United States
| | - Iya Khalil
- GNS Healthcare, Somerville, MA, United States
| | | | - Diego Lucero
- Lipoprotein Metabolism Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Alan T. Remaley
- Lipoprotein Metabolism Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
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6
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Qu HQ, Kao C, Garifallou J, Wang F, Snyder J, Slater DJ, Hou C, March M, Connolly JJ, Glessner JT, Hakonarson H. Single Cell Transcriptome Analysis of Peripheral Blood Mononuclear Cells in Freshly Isolated versus Stored Blood Samples. Genes (Basel) 2023; 14:142. [PMID: 36672883 PMCID: PMC9859202 DOI: 10.3390/genes14010142] [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: 12/12/2022] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Peripheral blood mononuclear cells (PBMCs) are widely used as a model in the study of different human diseases. There is often a time delay from blood collection to PBMC isolation during the sampling process, which can result in an experimental bias, particularly when performing single cell RNA-seq (scRNAseq) studies. METHODS This study examined the impact of different time periods from blood draw to PBMC isolation on the subsequent transcriptome profiling of different cell types in PBMCs by scRNAseq using the 10X Chromium Single Cell Gene Expression assay. RESULTS Examining the five major cell types constituting the PBMC cell population, i.e., CD4+ T cells, CD8+ T cells, NK cells, monocytes, and B cells, both common changes and cell-type-specific changes were observed in the single cell transcriptome profiling over time. In particular, the upregulation of genes regulated by NF-kB in response to TNF was observed in all five cell types. Significant changes in key genes involved in AP-1 signaling were also observed. RBC contamination was a major issue in stored blood, whereas RBC adherence had no direct impact on the cell transcriptome. CONCLUSIONS Significant transcriptome changes were observed across different PBMC cell types as a factor of time from blood draw to PBMC isolation and as a consequence of blood storage. This should be kept in mind when interpreting experimental results.
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Affiliation(s)
- Hui-Qi Qu
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Charlly Kao
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - James Garifallou
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Fengxiang Wang
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - James Snyder
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Diana J. Slater
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Cuiping Hou
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michael March
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - John J. Connolly
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Joseph T. Glessner
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Pulmonary Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
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7
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Ye Q, Guo NL. Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets. Cells 2022; 12:cells12010101. [PMID: 36611894 PMCID: PMC9818242 DOI: 10.3390/cells12010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 12/28/2022] Open
Abstract
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Affiliation(s)
- Qing Ye
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Nancy Lan Guo
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Department of Occupational and Environmental Health Sciences, School of Public Health, West Virginia University, Morgantown, WV 26506, USA
- Correspondence: ; Tel.: +1-304-293-6455
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Exosomal circRNA Scm-like with four malignant brain tumor domains 2 (circ-SFMBT2) enhances the docetaxel resistance of prostate cancer via the microRNA-136-5p/tribbles homolog 1 pathway. Anticancer Drugs 2022; 33:871-882. [PMID: 36136987 DOI: 10.1097/cad.0000000000001365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Exosomal circular RNA was found to mediate cancer chemoresistance. However, whether exosomal circRNA Scm-like with four malignant brain tumor domains 2 (circ-SFMBT2) was involved in the chemoresistance of prostate cancer (PCa) remains unclear. The docetaxel (DTX) resistance of PCa cells was analyzed by Cell Counting Kit 8 assay. Quantitative real-time PCR was used to measure circSFMBT2, microRNA (miR)-136-5p and tribbles homolog 1 (TRIB1) expression. Cell proliferation, apoptosis, migration and invasion were analyzed by 5-ethynyl-2'-deoxyuridine (EdU) assay, flow cytometry, wound-healing assay and transwell assay. RNA interaction was verified by dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Protein expression was measured by western blot analysis. Exosomes-extracted from cells were identified by transmission electron microscope, nanoparticles tracking analysis and western blot. Xenograft mice models were constructed to analyze the effect of exosomal circSFMBT2 on the DTX sensitivity of PCa tumors in vivo. CircSFMBT2 was upregulated in DTX-resistant PCa cells, and its knockdown enhanced the DTX sensitivity of DTX-resistant PCa cells by suppressing cell proliferation, migration, invasion and enhancing apoptosis. CircSFMBT2 severed as miR-136-5p sponge to positively regulate TRIB1. The regulation of circSFMBT2 knockdown on the DTX sensitivity of DTX-resistant PCa cells could be reversed by miR-136-5p inhibitor or TRIB1 overexpression. Exosomal circSFMBT2 from DTX-resistant PCa could increase the DTX resistance of normal PCa cells. In addition, exosomal circSFMBT2 also enhanced the DTX resistance of PCa tumors in vivo, and it was highly expressed in the serum of DTX-resistance PCa patients. Exosomal circSFMBT2 enhanced the DTX resistance of PCa by miR-136-5p/TRIB1 axis, indicating that circSFMBT2 might be a potential target for the treatment of PCa chemoresistance.
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9
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Li F, Gao C, Li X, Wang J, Zhao Y, Ke Y, Liu Y, Liu HM, Hu Z, Wei L, Chen ZS. Jiyuan oridonin A induces differentiation of acute myeloid leukemia cells including leukemic stem-like cells. Front Pharmacol 2022; 13:1001552. [PMID: 36133825 PMCID: PMC9484275 DOI: 10.3389/fphar.2022.1001552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Acute myeloid leukemia (AML) is an aggressive form of hematological neoplasia characterized by failure of myeloid differentiation. AML is a leading cause of death from leukemia. Cytarabine chemotherapy resistance is a major source of refractory/relapsed AML. A major obstacle to the successful treatment of AML results from residual disease maintained by leukemic stem cells (LSCs), which are mostly resistant to conventional chemotherapy. Here, we determined the effect of a natural compound, Jiyuan oridonin A (JOA), on the differentiation blockade in the M2 subtype [particularly t (8;21)] of AML cells, M3 subtype of AML cells (APL cells), and leukemic stem-like cells both in vitro and in vivo. We found that JOA induced cell differentiation and suppressed the colony formation capacity in various AML cell lines (Kasumi-1, KG-1, MUTZ-8, NB4, and HL-60) without eliciting apoptosis. The mechanism of JOA-induced cell differentiation depends on the specificity of cell type. JOA mediated the differentiation of Kasumi-1 cells by activating the hematopoietic cell lineage signaling pathway, while inhibition of c-MYC was involved in the JOA-induced differentiation of NB4 cells. Moreover, JOA was identified to target leukemic stem-like cells by induced cell differentiation in vivo. These findings demonstrated that JOA could inhibit the proliferation of M2 and M3 subtypes of AML cells and leukemic stem-like cells by overcoming the differentiation blockade, which may offer a novel therapeutic strategy for AML to overcome relapse and drug resistance in patients with AML. Our findings highlight the possibility of using compounds like JOA as a promising differentiation-induced agent for the treatment of AML.
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Affiliation(s)
- Fahui Li
- School of Pharmacy, Weifang Medical University, Weifang, China
| | - Congying Gao
- School of Pharmacy, Weifang Medical University, Weifang, China
| | - Xueming Li
- School of Pharmacy, Weifang Medical University, Weifang, China
| | - Jiangyun Wang
- School of Pharmacy, Weifang Medical University, Weifang, China
| | - Yao Zhao
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Yu Ke
- School of Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Ying Liu
- School of Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Hong-Min Liu
- School of Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Zhenbo Hu
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang, China
- *Correspondence: Zhenbo Hu, ; Liuya Wei, ; Zhe-Sheng Chen,
| | - Liuya Wei
- School of Pharmacy, Weifang Medical University, Weifang, China
- *Correspondence: Zhenbo Hu, ; Liuya Wei, ; Zhe-Sheng Chen,
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John’s University, Queens, NY, United States
- *Correspondence: Zhenbo Hu, ; Liuya Wei, ; Zhe-Sheng Chen,
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10
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Cai Z, Xi D, Zhu X, Li R. Causal discoveries for high dimensional mixed data. Stat Med 2022; 41:4924-4940. [PMID: 35968913 DOI: 10.1002/sim.9544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/28/2022] [Accepted: 07/21/2022] [Indexed: 11/08/2022]
Abstract
Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the PC algorithm, to identify causal structures. The proposed algorithm, called the latent-PC algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent-PC algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the PC algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent-PC algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent-PC algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature.
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Affiliation(s)
- Zhanrui Cai
- Department of Statistics, Iowa State University, Ames, Iowa, USA
| | - Dong Xi
- Gilead Sciences, Foster City, California, USA
| | - Xuan Zhu
- Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Runze Li
- Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, USA
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11
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Danger R, Feseha Y, Brouard S. The Pseudokinase TRIB1 in Immune Cells and Associated Disorders. Cancers (Basel) 2022; 14:cancers14041011. [PMID: 35205759 PMCID: PMC8869936 DOI: 10.3390/cancers14041011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary TRIB1 is at the center of major cell signaling pathways. In this review, we describe its role in immune cells and highlight TRIB1 interacting partners which suggests cell-specific functions and that TRIB1 is involved in cellular homeostasis and also in different cancers and immune-related disorders. Abstract Research advances in Tribbles homolog (TRIB) genes have established the consensus that this protein family plays roles in diverse biological conditions and regulates intracellular signaling networks and several human diseases. In this review, we focus on one member of the family, TRIB1, and its role at the crossroads of immune signaling. TRIB1 directly interacts with transcription factors such as FOXP3 and C/EBPα, with several signaling molecules such as MEK1 and MALT1 and directly acts on key cell signaling pathways such as the MAPK and NF-κB pathways. Altogether, these interactions emphasize that TRIB1 is at the center of major cell signaling pathways while TRIB1 has cell-specific roles, potentially depending on the expressing cells and binding partners. In this review, we describe its roles in immune cells and highlight the interacting partners explaining these functions which suggests TRIB1 as a precise mediator of cellular homeostasis as well as in different cancers and immune-related disorders.
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Affiliation(s)
- Richard Danger
- CHU Nantes, Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology (CR2TI), UMR 1064, F-44000 Nantes, France; (R.D.); (Y.F.)
| | - Yodit Feseha
- CHU Nantes, Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology (CR2TI), UMR 1064, F-44000 Nantes, France; (R.D.); (Y.F.)
| | - Sophie Brouard
- CHU Nantes, Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology (CR2TI), UMR 1064, F-44000 Nantes, France; (R.D.); (Y.F.)
- LabEx IGO “Immunotherapy, Graft, Oncology”, F-44000 Nantes, France
- Correspondence: ; Tel.: +33-240-087-842
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12
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Hernández-Quiles M, Baak R, Borgman A, den Haan S, Sobrevals Alcaraz P, van Es R, Kiss-Toth E, Vos H, Kalkhoven E. Comprehensive Profiling of Mammalian Tribbles Interactomes Implicates TRIB3 in Gene Repression. Cancers (Basel) 2021; 13:6318. [PMID: 34944947 PMCID: PMC8699236 DOI: 10.3390/cancers13246318] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 12/30/2022] Open
Abstract
The three human Tribbles (TRIB) pseudokinases have been implicated in a plethora of signaling and metabolic processes linked to cancer initiation and progression and can potentially be used as biomarkers of disease and prognosis. While their modes of action reported so far center around protein-protein interactions, the comprehensive profiling of TRIB interactomes has not been reported yet. Here, we have developed a robust mass spectrometry (MS)-based proteomics approach to characterize Tribbles' interactomes and report a comprehensive assessment and comparison of the TRIB1, -2 and -3 interactomes, as well as domain-specific interactions for TRIB3. Interestingly, TRIB3, which is predominantly localized in the nucleus, interacts with multiple transcriptional regulators, including proteins involved in gene repression. Indeed, we found that TRIB3 repressed gene transcription when tethered to DNA in breast cancer cells. Taken together, our comprehensive proteomic assessment reveals previously unknown interacting partners and functions of Tribbles proteins that expand our understanding of this family of proteins. In addition, our findings show that MS-based proteomics provides a powerful tool to unravel novel pseudokinase biology.
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Affiliation(s)
- Miguel Hernández-Quiles
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (M.H.-Q.); (R.B.); (A.B.); (S.d.H.)
| | - Rosalie Baak
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (M.H.-Q.); (R.B.); (A.B.); (S.d.H.)
| | - Anouska Borgman
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (M.H.-Q.); (R.B.); (A.B.); (S.d.H.)
| | - Suzanne den Haan
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (M.H.-Q.); (R.B.); (A.B.); (S.d.H.)
| | - Paula Sobrevals Alcaraz
- Oncode Institute and Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (P.S.A.); (R.v.E.); (H.V.)
| | - Robert van Es
- Oncode Institute and Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (P.S.A.); (R.v.E.); (H.V.)
| | - Endre Kiss-Toth
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield S10 2TN, UK;
| | - Harmjan Vos
- Oncode Institute and Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (P.S.A.); (R.v.E.); (H.V.)
| | - Eric Kalkhoven
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584 CG Utrecht, The Netherlands; (M.H.-Q.); (R.B.); (A.B.); (S.d.H.)
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13
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Nakazawa MA, Tamada Y, Tanaka Y, Ikeguchi M, Higashihara K, Okuno Y. Novel cancer subtyping method based on patient-specific gene regulatory network. Sci Rep 2021; 11:23653. [PMID: 34880275 PMCID: PMC8654869 DOI: 10.1038/s41598-021-02394-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 11/12/2021] [Indexed: 12/11/2022] Open
Abstract
The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.
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Affiliation(s)
| | - Yoshinori Tamada
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
- Innovation Center for Health Promotion, Hirosaki University, Hirosaki, 036-8562, Japan.
| | - Yoshihisa Tanaka
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8507, Japan
- Biomedical Computational Intelligence Unit, HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe, 650-0047, Japan
| | - Marie Ikeguchi
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Kako Higashihara
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
- Biomedical Computational Intelligence Unit, HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe, 650-0047, Japan.
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14
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Hao R, Hu J, Liu Y, Liang D, Li YM, Wang R, Zhang S, Wang P, Li YJ, Xie S. RFWD2 Knockdown as a Blocker to Reverse the Oncogenic Role of TRIB2 in Lung Adenocarcinoma. Front Oncol 2021; 11:733175. [PMID: 34646775 PMCID: PMC8503262 DOI: 10.3389/fonc.2021.733175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
RFWD2, an E3 ubiquitin ligase, is overexpressed in numerous human cancers, including leukemia, lung cancer, breast cancer, renal cell carcinoma, and colorectal cancer. The roles of RFWD2 in cancer are related to the targeting of its substrates for ubiquitination and degradation. This study aimed to investigate the role of TRIB2 in relation to the regulation of protein degradation through RFWD2. inBio Discover™ results demonstrated that TRIB2 can perform its functions by interacting with RFWD2 or other factors. TRIB2 can interact with and regulate RFWD2, which further attends the proteasome-mediated degradation of the RFWD2 substrate p-IκB-α. TRIB2 colocalizes with RFWD2-related IκB-α to form a ternary complex and further affects the IκB-α degradation by regulating its phosphorylation. Specific domain analysis showed that TRIB2 may bind to RFWD2 via its C-terminus, whereas it binds to IκB via its pseudokinase domain. TRIB2 acts as an oncogene and promotes cancer cell proliferation and migration, whereas RFWD2 knockdown reversed the role of TRIB2 in promoting cancer cell growth and colony formation in vitro and in vivo. In summary, this study reveals that TRIB2 promotes the progression of cancer by affecting the proteasome-mediated degradation of proteins through the interaction with RFWD2.
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Affiliation(s)
- Ruimin Hao
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Jinxia Hu
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Yuemei Liu
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Dongmin Liang
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Yan-Mei Li
- Department of Immune Rheumatism, Yantaishan Hospital, Yantai, China
| | - Ranran Wang
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Shucui Zhang
- Key Laboratory of Cardiovascular Remodeling and Function Research, Qilu Hospital, Shandong University, Jinan, China
| | - Pingyu Wang
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - You-Jie Li
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
| | - Shuyang Xie
- Department of Biochemistry and Molecular Biology, Binzhou Medical University, Yantai, China
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15
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Nobile MS, Cazzaniga P, Ramazzotti D. Investigating the performance of multi-objective optimization when learning Bayesian Networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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16
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Abstract
Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.
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17
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Zhang X, Zhang B, Zhang C, Sun G, Sun X. Current Progress in Delineating the Roles of Pseudokinase TRIB1 in Controlling Human Diseases. J Cancer 2021; 12:6012-6020. [PMID: 34539875 PMCID: PMC8425202 DOI: 10.7150/jca.51627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 08/10/2021] [Indexed: 12/15/2022] Open
Abstract
Tribbles homolog 1 (TRIB1) is a member of the tribbles family of pseudoprotein kinases and is widely expressed in numerous tissues, such as bone marrow, skeletal muscle, liver, heart, and adipose tissue. It is closely associated with acute myeloid leukemia, prostate cancer, and tumor drug resistance, and can interfere with the hematopoietic stem cell cycle, promote tumor cell proliferation, and inhibit apoptosis. Recent studies have shown that TRIB1 can regulate acute and chronic inflammation by affecting the secretion of inflammatory factors, which is closely related to the occurrence of hyperlipidemia and cardiovascular diseases. Given the important biological functions of TRIB1, the reviews published till now are not sufficiently comprehensive. Therefore, this paper reviews the progress in TRIB1 research aimed at exploring its roles in cancer, hyperlipidemia, and cardiovascular disease, and providing a theoretical basis for further studies on the biological roles of TRIB1.
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Affiliation(s)
- Xuelian Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.,Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.,Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing 100193, China.,Key Laboratory of efficacy evaluation of Chinese Medicine against Glyeolipid Metabolism Disorder Disease, State Administration of Traditional Chinese Medicine, Beijing 100193, China
| | - Bin Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.,Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.,Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing 100193, China.,Key Laboratory of efficacy evaluation of Chinese Medicine against Glyeolipid Metabolism Disorder Disease, State Administration of Traditional Chinese Medicine, Beijing 100193, China
| | - Chenyang Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.,Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.,Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing 100193, China.,Key Laboratory of efficacy evaluation of Chinese Medicine against Glyeolipid Metabolism Disorder Disease, State Administration of Traditional Chinese Medicine, Beijing 100193, China
| | - Guibo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.,Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.,Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing 100193, China.,Key Laboratory of efficacy evaluation of Chinese Medicine against Glyeolipid Metabolism Disorder Disease, State Administration of Traditional Chinese Medicine, Beijing 100193, China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100193, China.,Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing 100193, China.,Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing 100193, China.,Key Laboratory of efficacy evaluation of Chinese Medicine against Glyeolipid Metabolism Disorder Disease, State Administration of Traditional Chinese Medicine, Beijing 100193, China
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18
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Beveridge DJ, Richardson KL, Epis MR, Brown RAM, Stuart LM, Woo AJ, Leedman PJ. The tumor suppressor miR-642a-5p targets Wilms Tumor 1 gene and cell-cycle progression in prostate cancer. Sci Rep 2021; 11:18003. [PMID: 34504167 PMCID: PMC8429423 DOI: 10.1038/s41598-021-97190-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 08/17/2021] [Indexed: 12/19/2022] Open
Abstract
RNA-based therapeutics are emerging as innovative options for cancer treatment, with microRNAs being attractive targets for therapy development. We previously implicated microRNA-642a-5p (miR-642a-5p) as a tumor suppressor in prostate cancer (PCa), and here we characterize its mode of action, using 22Rv1 PCa cells. In an in vivo xenograft tumor model, miR-642a-5p induced a significant decrease in tumor growth, compared to negative control. Using RNA-Sequencing, we identified gene targets of miR-642a-5p which were enriched for gene sets controlling cell cycle; downregulated genes included Wilms Tumor 1 gene (WT1), NUAK1, RASSF3 and SKP2; and upregulated genes included IGFBP3 and GPS2. Analysis of PCa patient datasets showed a higher expression of WT1, NUAK1, RASSF3 and SKP2; and a lower expression of GPS2 and IGFBP3 in PCa tissue compared to non-malignant prostate tissue. We confirmed the prostatic oncogene WT1, as a direct target of miR-642a-5p, and treatment of 22Rv1 and LNCaP PCa cells with WT1 siRNA or a small molecule inhibitor of WT1 reduced cell proliferation. Taken together, these data provide insight into the molecular mechanisms by which miR-642a-5p acts as a tumor suppressor in PCa, an effect partially mediated by regulating genes involved in cell cycle control; and restoration of miR-642-5p in PCa could represent a novel therapeutic approach.
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Affiliation(s)
- Dianne J Beveridge
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Kirsty L Richardson
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Michael R Epis
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Rikki A M Brown
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Lisa M Stuart
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Andrew J Woo
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Peter J Leedman
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, QEII Medical Centre, 6 Verdun St, Nedlands, 6009, Australia.
- Centre for Medical Research, The University of Western Australia, Crawley, WA, 6009, Australia.
- School of Medicine and Pharmacology, The University of Western Australia, Crawley, WA, 6009, Australia.
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19
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Dogan H, Hakguder Z, Madadjim R, Scott S, Pierobon M, Cui J. Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning. Brief Bioinform 2021; 22:6346341. [PMID: 34373890 DOI: 10.1093/bib/bbab270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/07/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. RESULTS In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. AVAILABILITY http://sbbi-panda.unl.edu/pin/.
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Affiliation(s)
- Haluk Dogan
- Department of Computer Science and Engineering (CSE) at the University of Nebraska- Lincoln (UNL), Lincoln, NE 68588-0115, USA
| | | | | | | | | | - Juan Cui
- CSE department at UNL, Lincoln, NE 68588-0115, USA
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20
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Huang VS, Morris K, Jain M, Ramesh BM, Kemp H, Blanchard J, Isac S, Sarkar B, Gothalwal V, Namasivayam V, Kumar P, Sgaier SK. Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health. BMJ Glob Health 2021; 5:bmjgh-2020-002340. [PMID: 33028696 PMCID: PMC7542627 DOI: 10.1136/bmjgh-2020-002340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 08/12/2020] [Accepted: 08/18/2020] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India. METHODS Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups. RESULTS Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning. CONCLUSION These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.
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Affiliation(s)
| | | | | | - Banadakoppa Manjappa Ramesh
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - James Blanchard
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shajy Isac
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,India Health Action Trust, New Delhi, India
| | - Bidyut Sarkar
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,India Health Action Trust, Lucknow, India
| | - Vikas Gothalwal
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,India Health Action Trust, Lucknow, India
| | - Vasanthakumar Namasivayam
- Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pankaj Kumar
- National Health Mission, Government of Uttar Pradesh, Lucknow, India
| | - Sema K Sgaier
- Surgo Foundation, Washington, DC, USA .,Department of Global Health, University of Washington, Seattle, WA, USA.,Department of Global Health and Population, Harvard University T.H. Chan School of Public Health, Boston, Massachusetts, USA
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21
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McMillan HD, Keeshan K, Dunbier AK, Mace PD. Structure vs. Function of TRIB1-Myeloid Neoplasms and Beyond. Cancers (Basel) 2021; 13:3060. [PMID: 34205360 PMCID: PMC8235551 DOI: 10.3390/cancers13123060] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 12/31/2022] Open
Abstract
The Tribbles family of proteins-comprising TRIB1, TRIB2, TRIB3 and more distantly related STK40-play important, but distinct, roles in differentiation, development and oncogenesis. Of the four Tribbles proteins, TRIB1 has been most well characterised structurally and plays roles in diverse cancer types. The most well-understood role of TRIB1 is in acute myeloid leukaemia, where it can regulate C/EBP transcription factors and kinase pathways. Structure-function studies have uncovered conformational switching of TRIB1 from an inactive to an active state when it binds to C/EBPα. This conformational switching is centred on the active site of TRIB1, which appears to be accessible to small-molecule inhibitors in spite of its inability to bind ATP. Beyond myeloid neoplasms, TRIB1 plays diverse roles in signalling pathways with well-established roles in tumour progression. Thus, TRIB1 can affect both development and chemoresistance in leukaemia; glioma; and breast, lung and prostate cancers. The pervasive roles of TRIB1 and other Tribbles proteins across breast, prostate, lung and other cancer types, combined with small-molecule susceptibility shown by mechanistic studies, suggests an exciting potential for Tribbles as direct targets of small molecules or biomarkers to predict treatment response.
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Affiliation(s)
- Hamish D McMillan
- Biochemistry Department, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand; (H.D.M.); (A.K.D.)
| | - Karen Keeshan
- Paul O’Gorman Leukaemia Research Centre, Institute of Cancer Sciences, University of Glasgow, Scotland G12 0YN, UK;
| | - Anita K Dunbier
- Biochemistry Department, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand; (H.D.M.); (A.K.D.)
| | - Peter D Mace
- Biochemistry Department, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand; (H.D.M.); (A.K.D.)
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22
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Zhang Q, Wei H, Liu J, Zhao Z, Ran Q, Gu W. A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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23
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Becker AK, Dörr M, Felix SB, Frost F, Grabe HJ, Lerch MM, Nauck M, Völker U, Völzke H, Kaderali L. From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach. PLoS Comput Biol 2021; 17:e1008735. [PMID: 33577591 PMCID: PMC7906470 DOI: 10.1371/journal.pcbi.1008735] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 02/25/2021] [Accepted: 01/22/2021] [Indexed: 01/26/2023] Open
Abstract
In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network. High-dimensional and heterogeneous healthcare data, such as electronic health records or epidemiological study data, contain much information on yet unknown risk factors that are associated with disease development. The identification of these risk factors may help to improve prevention, diagnosis, and therapy. Bayesian networks are powerful statistical models that can decipher these complex relationships. However, high dimensionality and heterogeneity of data, together with missing values and high feature correlation, make it difficult to automatically learn a good model from data. To facilitate the use of network models, we present a novel, fully automated workflow that combines network learning with hierarchical clustering. The algorithm reveals groups of strongly related features and models the interactions among those groups. It results in simpler network models that are easier to analyze. We introduce a method of adaptive refinement of such models to ensure that disease-relevant parts of the network are modeled in great detail. Our approach makes it easy to learn compact, accurate, and easily interpretable biomarker interaction networks. We test our method extensively on simulated data as well as data from the Study of Health in Pomerania (SHIP-Trend) by learning models of hypertension and non-alcoholic fatty liver disease.
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Affiliation(s)
- Ann-Kristin Becker
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Stephan B. Felix
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Fabian Frost
- Department of Internal Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry, University Medicine Greifswald, Greifswald, Germany
| | - Markus M. Lerch
- Department of Internal Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute of Community Medicine, SHIP/KEF, University Medicine Greifswald, Greifswald, Germany
| | - Lars Kaderali
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
- * E-mail:
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24
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Niespolo C, Johnston JM, Deshmukh SR, Satam S, Shologu Z, Villacanas O, Sudbery IM, Wilson HL, Kiss-Toth E. Tribbles-1 Expression and Its Function to Control Inflammatory Cytokines, Including Interleukin-8 Levels are Regulated by miRNAs in Macrophages and Prostate Cancer Cells. Front Immunol 2020; 11:574046. [PMID: 33329538 PMCID: PMC7728618 DOI: 10.3389/fimmu.2020.574046] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 10/27/2020] [Indexed: 12/21/2022] Open
Abstract
The pseudokinase TRIB1 controls cell function in a range of contexts, by regulating MAP kinase activation and mediating protein degradation via the COP1 ubiquitin ligase. TRIB1 regulates polarization of macrophages and dysregulated Trib1 expression in murine models has been shown to alter atherosclerosis burden and adipose homeostasis. Recently, TRIB1 has also been implicated in the pathogenesis of prostate cancer, where it is often overexpressed, even in the absence of genetic amplification. Well described TRIB1 effectors include MAP kinases and C/EBP transcription factors, both in immune cells and in carcinogenesis. However, the mechanisms that regulate TRIB1 itself remain elusive. Here, we show that the long and conserved 3’untranslated region (3’UTR) of TRIB1 is targeted by miRNAs in macrophage and prostate cancer models. By using a systematic in silico analysis, we identified multiple “high confidence” miRNAs potentially binding to the 3’UTR of TRIB1 and report that miR-101-3p and miR-132-3p are direct regulators of TRIB1 expression and function. Binding of miR-101-3p and miR-132-3p to the 3’UTR of TRIB1 mRNA leads to an increased transcription and secretion of interleukin-8. Our data demonstrate that modulation of TRIB1 by miRNAs alters the inflammatory profile of both human macrophages and prostate cancer cells.
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Affiliation(s)
- Chiara Niespolo
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Jessica M Johnston
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Sumeet R Deshmukh
- Department of Molecular Biology and Biotechnology, Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, United Kingdom
| | - Swapna Satam
- Institute for Diabetes and Cancer IDC, Helmholtz Center, Munich, Germany
| | - Ziyanda Shologu
- Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal
| | | | - Ian M Sudbery
- Department of Molecular Biology and Biotechnology, Sheffield Institute for Nucleic Acids, University of Sheffield, Sheffield, United Kingdom
| | - Heather L Wilson
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Endre Kiss-Toth
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom
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25
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Shi M, Tan S, Xie XP, Li A, Yang W, Zhu T, Wang HQ. Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data. BMC Genomics 2020; 21:711. [PMID: 33054712 PMCID: PMC7559338 DOI: 10.1186/s12864-020-07079-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.
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Affiliation(s)
- Ming Shi
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.,Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sheng Tan
- The CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Xin-Ping Xie
- School of Mathematics and Physics, Anhui Jianzhu University, 856 Jinzhai Road, Hefei, Anhui, 230022, P. R. China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Wulin Yang
- Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China
| | - Tao Zhu
- Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Hong-Qiang Wang
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China. .,Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.
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26
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Genomic and Functional Regulation of TRIB1 Contributes to Prostate Cancer Pathogenesis. Cancers (Basel) 2020; 12:cancers12092593. [PMID: 32932846 PMCID: PMC7565426 DOI: 10.3390/cancers12092593] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 08/26/2020] [Accepted: 09/04/2020] [Indexed: 12/17/2022] Open
Abstract
Prostate cancer is the most frequent malignancy in European men and the second worldwide. One of the major oncogenic events in this disease includes amplification of the transcription factor cMYC. Amplification of this oncogene in chromosome 8q24 occurs concomitantly with the copy number increase in a subset of neighboring genes and regulatory elements, but their contribution to disease pathogenesis is poorly understood. Here we show that TRIB1 is among the most robustly upregulated coding genes within the 8q24 amplicon in prostate cancer. Moreover, we demonstrate that TRIB1 amplification and overexpression are frequent in this tumor type. Importantly, we find that, parallel to its amplification, TRIB1 transcription is controlled by cMYC. Mouse modeling and functional analysis revealed that aberrant TRIB1 expression is causal to prostate cancer pathogenesis. In sum, we provide unprecedented evidence for the regulation and function of TRIB1 in prostate cancer.
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27
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Liu CZ, Guo WP, Peng JB, Chen G, Lin P, Huang XL, Liu XF, Yang H, He Y, Pang YY, Ma W. Clinical significance of CCNE2 protein and mRNA expression in thyroid cancer tissues. Adv Med Sci 2020; 65:442-456. [PMID: 33059229 DOI: 10.1016/j.advms.2020.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 06/22/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Thyroid carcinoma (TC) is the most common endocrinal malignancy worldwide. Cyclin E2 (CCNE2), a member of the cyclin family, acts as a regulatory subunit of cyclin-dependent kinases (CDKs). It controls the transition of quiescent cells into the cell cycle, regulates the G1/S transition, promotes DNA replication, and activates CDK2. This study explored the role and potential molecular mechanisms of CCNE2 expression in TC tissues. MATERIAL/METHODS Immunohistochemistry was used to evaluate the CCNE2 protein expression levels in TC. High-throughput data on CCNE2 in TC were obtained from RNA sequencing (RNA-seq), microarray, and literature data. The CCNE2 expression levels in TC were comprehensively assessed through an integrated analysis. Analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPIs) data facilitated the investigation of the relative molecular mechanisms of CCNE2 in TC. RESULTS The immunohistochemical experiment showed a significant increase in the expression of CCNE2 in the TC tissues. For 505 TC and 59 non-cancerous samples from RNA-seq data, the area under the curve (AUC) was 0.8016 (95% confidence interval [CI] 0.742-0.8612; p<0.001). With another 14 microarrays, the pool standard mean difference [SMD] was 1.01 (95% CI [0.82-1.19]). The pooled SMD of CCNE2 was 1.12 (95% CI [0.60-1.64]), and the AUC was 0.87 (95% CI [0.84-0.90]) for 1157 TC samples and 366 non-cancerous thyroid samples from all possible sources. Nine hub genes were upregulated in TC. CONCLUSIONS A high expression of CCNE2 may lead to carcinogenesis and the development of TC.
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MESH Headings
- Adenocarcinoma, Follicular/genetics
- Adenocarcinoma, Follicular/metabolism
- Adenocarcinoma, Follicular/pathology
- Apoptosis
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Carcinoma, Papillary/genetics
- Carcinoma, Papillary/metabolism
- Carcinoma, Papillary/pathology
- Cell Proliferation
- Cyclins/genetics
- Cyclins/metabolism
- Female
- Gene Expression Regulation, Neoplastic
- Humans
- Male
- Middle Aged
- Prognosis
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
- Survival Rate
- Thyroid Neoplasms/genetics
- Thyroid Neoplasms/metabolism
- Thyroid Neoplasms/pathology
- Tumor Cells, Cultured
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Affiliation(s)
- Cui-Zhen Liu
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Wan-Ping Guo
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Jin-Bo Peng
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Peng Lin
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Xiao-Li Huang
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Xiao-Fan Liu
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Hong Yang
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Yun He
- Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Yu-Yan Pang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Wei Ma
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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28
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Maleknia S, Salehi Z, Rezaei Tabar V, Sharifi-Zarchi A, Kavousi K. An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus. Arthritis Res Ther 2020; 22:156. [PMID: 32576231 PMCID: PMC7310461 DOI: 10.1186/s13075-020-02239-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/05/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. METHODS In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls. RESULTS In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway). CONCLUSIONS Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.
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Affiliation(s)
- Samaneh Maleknia
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Rezaei Tabar
- Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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29
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Trilla-Fuertes L, Gámez-Pozo A, Arevalillo JM, López-Vacas R, López-Camacho E, Prado-Vázquez G, Zapater-Moros A, Díaz-Almirón M, Ferrer-Gómez M, Navarro H, Nanni P, Zamora P, Espinosa E, Maín P, Fresno Vara JÁ. Bayesian networks established functional differences between breast cancer subtypes. PLoS One 2020; 15:e0234752. [PMID: 32525929 PMCID: PMC7289386 DOI: 10.1371/journal.pone.0234752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 06/01/2020] [Indexed: 12/15/2022] Open
Abstract
Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.
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Affiliation(s)
| | - Angelo Gámez-Pozo
- Biomedica Molecular Medicine SL, Madrid, Spain
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | - Jorge M. Arevalillo
- Operational Research and Numerical Analysis, National Distance Education University (UNED), Madrid, Spain
| | - Rocío López-Vacas
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | | | - Guillermo Prado-Vázquez
- Biomedica Molecular Medicine SL, Madrid, Spain
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | - Andrea Zapater-Moros
- Biomedica Molecular Medicine SL, Madrid, Spain
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | | | - María Ferrer-Gómez
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | - Hilario Navarro
- Operational Research and Numerical Analysis, National Distance Education University (UNED), Madrid, Spain
| | - Paolo Nanni
- Functional Genomics Centre Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Pilar Zamora
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Madrid, Spain
| | - Enrique Espinosa
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Madrid, Spain
- Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain
| | - Paloma Maín
- Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain
| | - Juan Ángel Fresno Vara
- Biomedica Molecular Medicine SL, Madrid, Spain
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Madrid, Spain
- Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Madrid, Spain
- * E-mail:
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30
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Wei L, Yang Y, Gupta P, Wang A, Zhao M, Zhao Y, Qu M, Ke Y, Liu Y, Liu HM, Xu X, Sun Y, Chen ZS, Hu Z. A Small Molecule Inhibitor, OGP46, Is Effective against Imatinib-Resistant BCR-ABL Mutations via the BCR-ABL/JAK-STAT Pathway. MOLECULAR THERAPY-ONCOLYTICS 2020; 18:137-148. [PMID: 32671189 PMCID: PMC7341061 DOI: 10.1016/j.omto.2020.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 06/03/2020] [Indexed: 12/21/2022]
Abstract
Chronic myeloid leukemia (CML) is caused by the Philadelphia (Ph+) chromosome carrying the BCR-ABL oncogene, a constitutively active tyrosine kinase. The discovery of imatinib represents a major success story in the treatment against CML. However, mutations in the BCR-ABL kinase domain are a major cause of resistance to imatinib, demonstrating that BCR-ABL remains a critical drug target. Here, we investigate a novel small molecule inhibitor, OGP46, for its inhibitory activity against K562, a panel of murine BaF3 cell lines stably expressing either wild-type BCR-ABL or its mutant forms, including T315I. OGP46 exhibits potent activity against imatinib-resistant BCR-ABL mutations, including T315I. OGP46 induced cell differentiation accompanied by G0/G1 cell-cycle arrest and suppressed the colony formation capacity of cells. Treatment with OGP46 significantly decreased the mRNA and protein expression of BCR-ABL in K562 and BaF3-p210-T315I cells. Mechanistically, the anti-cancer activity of OGP46 induced by cell differentiation is likely through the BCR-ABL/JAK-STAT pathway in native BCR-ABL and mutant BCR-ABL, including T315I, of CML cells. Our findings highlight that OGP46 is active against not only native BCR-ABL but also 11 clinically relevant BCR-ABL mutations, including T315I mutation, which are resistant to imatinib. Thus, OGP46 may be a novel strategy for overcoming imatinib-resistance BCR-ABL mutations, including T315I.
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Affiliation(s)
- Liuya Wei
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang 261042, China.,School of Pharmacy, Weifang Medical University, Weifang 261053, China.,Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Yang Yang
- School of Pharmacy, Weifang Medical University, Weifang 261053, China
| | - Pranav Gupta
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Aihong Wang
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang 261042, China
| | - Min Zhao
- School of Pharmacy, Weifang Medical University, Weifang 261053, China
| | - Yao Zhao
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang 261042, China
| | - Mei Qu
- School of Pharmacy, Weifang Medical University, Weifang 261053, China
| | - Yu Ke
- School of Pharmacy, Zhengzhou University, Zhengzhou 450052, China
| | - Ying Liu
- School of Pharmacy, Zhengzhou University, Zhengzhou 450052, China
| | - Hong-Min Liu
- School of Pharmacy, Zhengzhou University, Zhengzhou 450052, China
| | - Xin Xu
- College of Bioscience and Technology, Weifang Medical University, Weifang 261053, China
| | - Yanli Sun
- Department of Laboratory Medicine, Weifang Medical University, Weifang 261053, China
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Zhenbo Hu
- Laboratory for Stem Cell and Regenerative Medicine, Affiliated Hospital of Weifang Medical University, Weifang 261042, China
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31
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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Hastings JF, O'Donnell YEI, Fey D, Croucher DR. Applications of personalised signalling network models in precision oncology. Pharmacol Ther 2020; 212:107555. [PMID: 32320730 DOI: 10.1016/j.pharmthera.2020.107555] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/07/2020] [Indexed: 02/07/2023]
Abstract
As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.
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Affiliation(s)
- Jordan F Hastings
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
| | | | - Dirk Fey
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - David R Croucher
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland; St Vincent's Hospital Clinical School, University of New South Wales, Sydney, NSW 2052, Australia.
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Xie X, Yang X, Wu J, Ma J, Wei W, Fei X, Wang M. Trib1 Contributes to Recovery From Ischemia/Reperfusion-Induced Acute Kidney Injury by Regulating the Polarization of Renal Macrophages. Front Immunol 2020; 11:473. [PMID: 32265926 PMCID: PMC7098949 DOI: 10.3389/fimmu.2020.00473] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 02/28/2020] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence suggests that macrophage polarization is involved in the recovery from ischemia-reperfusion (I/R)-induced acute kidney injury (AKI), implying that the regulation of macrophage polarization homeostasis might mediate AKI recovery. Trib1 is a key regulator of macrophage differentiation, but its role in AKI remains unclear. Here, we aimed to investigate the role of Trib1 and its link with the macrophage phenotype in the process of adaptive recovery from I/R-induced renal injury. Lentiviral vector-mediated RNA interference (RNAi) was used to knock down Trib1 expression in vitro and in vivo, and a mouse model of moderate AKI was established by the induction of I/R injury. Renal function measurements and inflammatory factors were determined by the corresponding kits. Histomorphology was assessed by hematoxylin-eosin, Masson and PAS staining. Western blot and flow cytometry were employed for the analysis of signal transduction, cell apoptosis and macrophage phenotypes. Trib1 knockdown inhibited cell viability of tubular epithelial cells (TECs) by inhibiting proliferation and enhancing apoptosis in vitro. I/R-induced AKI significantly impaired renal function in mice via increasing the levels of BUN, Scr, NGAL and renal tubular damage, leading to renal fibrosis from days 1 to 3. Through the adaptive self-repair mechanism, renal dysfunction recovered over time and returned to almost normal levels on day 28 after I/R intervention. However, Trib1 depletion worsened renal damage on day 3 and blunted the adaptive repair process of the renal tissue. Mechanistically, Trib1 inhibition suppressed renal tubular cell proliferation under adaptive self-repair conditions by affecting the expression of the proliferation-related proteins cyclin D1, cyclin B, p21, and p27, the apoptosis-related proteins Bcl-2 and Bax, and the fibrosis-related proteins collagen I and III. Furthermore, the M1/M2 macrophage ratio increased in the first 3 days and decreased from day 7 to day 28, consistent with changes in the expression of inflammatory factors, including TNFα, IL-6, IL-12, IL-10, and IL-13. Trib1 inhibition blocked macrophage polarization during adaptive recovery from I/R-induced moderate AKI. Our results show that Trib1 plays a role in kidney recovery and regeneration via the regulation of renal tubular cell proliferation by affecting macrophage polarization. Thus, Trib1 might be a viable therapeutic target to improve renal adaptive repair following I/R injury.
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Affiliation(s)
- Xiangcheng Xie
- Department of Nephrology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiu Yang
- Department of Nephrology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junxia Wu
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jilin Ma
- Division of Nephrology, Zhejiang Traditional Chinese Medicine and Western Medicine Hospital, Hangzhou, China
| | - Wei Wei
- Department of Nephrology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Fei
- Department of Nephrology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ming Wang
- Department of Nephrology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Wen X, Gao L, Hu Y. LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation. Front Genet 2020; 11:235. [PMID: 32256525 PMCID: PMC7093494 DOI: 10.3389/fgene.2020.00235] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/27/2020] [Indexed: 12/14/2022] Open
Abstract
Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.
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Affiliation(s)
- Xiao Wen
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork. Biomolecules 2020; 10:biom10020306. [PMID: 32075209 PMCID: PMC7072632 DOI: 10.3390/biom10020306] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/03/2020] [Accepted: 02/08/2020] [Indexed: 12/18/2022] Open
Abstract
Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.
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de Miguel-Gómez L, Ferrero H, López-Martínez S, Campo H, López-Pérez N, Faus A, Hervás D, Santamaría X, Pellicer A, Cervelló I. Stem cell paracrine actions in tissue regeneration and potential therapeutic effect in human endometrium: a retrospective study. BJOG 2020; 127:551-560. [PMID: 31876085 DOI: 10.1111/1471-0528.16078] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2019] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Determining genetic and paracrine mechanisms behind endometrial regeneration in Asherman's syndrome and endometrial atrophy (AS/EA) patients after autologous CD133+ bone marrow-derived stem cell (CD133+ BMDSC) transplantation. DESIGN Retrospective study using human endometrial biopsies and mouse models. SETTING Fundación-IVI, IIS-La Fe, Valencia, Spain. SAMPLES Endometrial biopsies collected before and after CD133+ BMDSC therapy, from eight women with AS/EA (NCT02144987) from the uterus of five mice with only left horns receiving CD133+ BMDSC therapy. METHODS In human samples, haematoxylin and eosin (H&E) staining, RNA arrays, PCR validation, and neutrophil elastase (NE) immunohistochemistry (IHQ). In mouse samples, PCR validation and protein immunoarrays. MAIN OUTCOME MEASURES H&E microscopic evaluation, RNA expression levels, PCR, and growth/angiogenic factors quantification, NE IHQ signal. RESULTS Treatment improved endometrial morphology and thickness for all patients. In human samples, Jun, Serpine1, and Il4 were up-regulated whereas Ccnd1 and Cxcl8 were down-regulated after treatment. The significant decrease of NE signal corroborated Cxcl8 expression. Animal model analysis confirmed human results and revealed a higher expression of pro-angiogenic cytokines (IL18, HGF, MCP-1, MIP2) in treated uterine horns. CONCLUSIONS CD133+ BMDSC seems to activate several factors through a paracrine mechanism to help tissue regeneration, modifying endometrial behaviour through an immunomodulatory milieu that precedes proliferation and angiogenic processes. Insight into these processes could bring us one step closer to a non-invasive treatment for AS/EA patients. TWEETABLE ABSTRACT CD133+ BMDSC therapy regenerates endometrium, modifying the immunological milieu that precedes proliferation and angiogenesis.
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Affiliation(s)
- L de Miguel-Gómez
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - H Ferrero
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - S López-Martínez
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - H Campo
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - N López-Pérez
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - A Faus
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - D Hervás
- Data Science, Biostatistics and Bioinformatics, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - X Santamaría
- Igenomix Academy, Valencia, Spain.,IVIRMA, Barcelona, Barcelona, Spain
| | - A Pellicer
- IVIRMA Valencia, Valencia, Spain.,Reproductive Medicine Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - I Cervelló
- Fundación Instituto Valenciano de Infertilidad (FIVI), Instituto de Investigación Sanitaria La Fe, Valencia, Spain
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Richmond L, Keeshan K. Pseudokinases: a tribble-edged sword. FEBS J 2019; 287:4170-4182. [PMID: 31621188 DOI: 10.1111/febs.15096] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/29/2019] [Accepted: 10/14/2019] [Indexed: 12/20/2022]
Abstract
Advances in the understanding of the Tribbles family of pseudokinases (TRIB1, TRIB2 and TRIB3) reveal these proteins as potentially valuable biomarkers of disease diagnosis, prognosis, prediction and clinical strategy. In their role as signalling mediators and scaffolding proteins, TRIBs lead to changes in protein stability and activity, which impact on diverse cellular processes such as proliferation, differentiation, cell cycle and cell death. We review the role of TRIB proteins as promising therapeutic targets, with an emphasis on their role in cancer, and as biomarkers, with potential application across diverse pathological processes.
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Affiliation(s)
- Laura Richmond
- Paul O'Gorman Leukaemia Research Centre, Institute of Cancer Sciences, University of Glasgow, UK
| | - Karen Keeshan
- Paul O'Gorman Leukaemia Research Centre, Institute of Cancer Sciences, University of Glasgow, UK
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The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro. Arch Toxicol 2019; 93:3067-3098. [PMID: 31586243 DOI: 10.1007/s00204-019-02585-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 09/25/2019] [Indexed: 12/13/2022]
Abstract
Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.
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Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data. Genes (Basel) 2019; 10:genes10100753. [PMID: 31557971 PMCID: PMC6826985 DOI: 10.3390/genes10100753] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 12/19/2022] Open
Abstract
Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.
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Reactive Oxygen Species Are Involved in the Development of Gastric Cancer and Gastric Cancer-Related Depression through ABL1-Mediated Inflammation Signaling Pathway. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:5813985. [PMID: 31396300 PMCID: PMC6664690 DOI: 10.1155/2019/5813985] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Background The mechanisms of crosstalk between depression and gastric cancer (GC) remain ill defined. Given that reactive oxygen species (ROS) is involved in the pathophysiology of both GC and depression, we try to explore the activities of ROS in the development of GC and GC-related depression. Methods 110 patients with newly diagnosed GC were recruited in our study. The clinical characteristics of these patients were recorded. Inflammation and oxidative stress markers were detected by ELISA. The depression status of patients with GC was assessed during follow-up. The association between ROS, ABL1, and inflammation factors was evaluated in H2O2-treated GC cell lines and The Cancer Genome Atlas (TCGA) database. The effect of ABL1 on inflammation was detected with Imatinib/Nilotinib-treated GC cell lines. A chronic mild stress- (CMS-) induced patient-derived xenograft (PDX) mice model was established to assess the crosstalk between depression and GC. Results Depression was correlated with poor prognosis of patients with GC. GC patients with depression were under a high level of oxidative status as well as dysregulated inflammation. In the CMS-induced GC PDX mice model, CMS could facilitate the development of GC. Additionally, tumor bearing could induce depressive-like behaviors of mice. With the treatment of ROS, the activities of ABL1 and inflammatory signaling were enhanced both in vitro and in vivo, and blocking the activities of ABL1 inhibited inflammatory signaling. Conclusions ROS-activated ABL1 mediates inflammation through regulating NF-κB1 and STAT3, which subsequently leads to the development of GC and GC-related depression.
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Liu ZZ, Han ZD, Liang YK, Chen JX, Wan S, Zhuo YJ, Cai ZD, Deng YL, Lin ZY, Mo RJ, He HC, Zhong WD. TRIB1 induces macrophages to M2 phenotype by inhibiting IKB-zeta in prostate cancer. Cell Signal 2019; 59:152-162. [PMID: 30926388 DOI: 10.1016/j.cellsig.2019.03.017] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 03/18/2019] [Accepted: 03/19/2019] [Indexed: 11/27/2022]
Abstract
Immunotherapy has made great breakthroughs in the field of cancer. However, the immunotherapeutic effect of prostate cancer is unsatisfactory. We found that the expression of TRIB1 was significantly correlated with the infiltration of CD163+ macrophages in prostate cancer. This study focused on the effects of TRIB1 on macrophage polarization in the immune microenvironment of prostate cancer. RNA sequencing analysis demonstrated that TRIB1 has significant effects on the regulation of the nuclear factor (NF)-κB signaling pathway and downstream cytokines. Flow cytometry and enzyme-linked immunosorbent assay were used to examine THP-1 cells cultured in conditioned medium from prostate cancer cells overexpressing TRIB1 and showed that overexpression of TRIB1 promoted the secretion of CXCL2 and interleukin (IL)8 by PC3 cells, which increased the secretion of IL12 by THP-1 cells as well as the expression of CD163 on THP-1 cells. IKB-zeta, regulated by TRIB1, was expressed in PC3 cells but was barely detectable in DU145 cells. The reductions in CXCL2 and IL8 by the inhibition of TRIB1 were rescued by the deletion of IKB-zeta. Here we showed that TRIB1 promoted the secretion of cytokines from prostate cancer cells and induced the differentiation of monocytes/macrophages into M2 macrophages.
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Affiliation(s)
- Ze-Zhen Liu
- Department of Urology, Huadu District People's Hospital, Southern Medical University, Guangzhou 510800, China; Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Zhao-Dong Han
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Ying-Ke Liang
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Jun-Xu Chen
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Song Wan
- Department of Urology, Huadu District People's Hospital, Southern Medical University, Guangzhou 510800, China
| | - Yang-Jia Zhuo
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Zhi-Duan Cai
- Department of Urology, Huadu District People's Hospital, Southern Medical University, Guangzhou 510800, China; Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Yu-Lin Deng
- Department of Urology, Huadu District People's Hospital, Southern Medical University, Guangzhou 510800, China; Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Zhuo-Yuan Lin
- Department of Urology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510260, China
| | - Ru-Jun Mo
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China
| | - Hui-Chan He
- Urology Key Laboratory of Guangdong Province, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510230, China
| | - Wei-De Zhong
- Department of Urology, Huadu District People's Hospital, Southern Medical University, Guangzhou 510800, China; Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Liu J, Feng X, Tian Y, Wang K, Gao F, Yang L, Li H, Tian Y, Yang R, Zhao L, Miao X, Huang J, Liu Q, Zhang W, Li Y, Wang C, Duan H, Liu S. Knockdown of TRIM27 expression suppresses the dysfunction of mesangial cells in lupus nephritis by FoxO1 pathway. J Cell Physiol 2019; 234:11555-11566. [PMID: 30648253 DOI: 10.1002/jcp.27810] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 11/05/2018] [Indexed: 12/29/2022]
Abstract
TRIM27 (tripartite motif-containing 27) is a member of the TRIM (tripartite motif) protein family and participates in a variety of biological processes. Some research has reported that TRIM27 was highly expressed in certain kinds of carcinoma cells and tissues and played an important role in the proliferation of carcinoma cells. However, whether TRIM27 takes part in the progression of lupus nephritis (LN) especially in cells proliferation remains unclear. Our study revealed that the overexpression of TRIM27 was observed in the kidneys of patients with LN, lupus mice and mesangial cells exposed to LN plasma which correlated with the proliferation of mesangial cells and ECM (extracellular matrix) deposition. Downregulation of TRIM27 expression suppressed the proliferation of mesangial cells and ECM accumulation in MRL/lpr mice and cultured human mesangial cells (HMCs) by regulating the FoxO1 pathway. Furthermore, the overexpression of FoxO1 remarkably decreased HMCs proliferation level and ECM accumulation in LN plasma-treated HMCs. In addition, the protein kinase B (Akt) signal pathway inhibitor LY294002 significantly reduced the expression of TRIM27 and inhibited the dysfunction of mesangial cells. These above data suggested that TRIM27 mediated abnormal mesangial cell proliferation in kidney of lupus and might be the potential target for treating mesangial cell proliferation of lupus nephritis.
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Affiliation(s)
- Jinxi Liu
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Xiaojuan Feng
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Yu Tian
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Kexin Wang
- Clinical Medicine, College of Basic Medicine,Hebei Medical University, Shijiazhuang, China
| | - Fan Gao
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Lin Yang
- Department of Nephrology, the Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hongbo Li
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Yuexin Tian
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Ran Yang
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Lu Zhao
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Xinyan Miao
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Jie Huang
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Qingjuan Liu
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Wei Zhang
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
| | - Yuzhe Li
- Clinical Medicine, College of Basic Medicine,Hebei Medical University, Shijiazhuang, China
| | - Chunlin Wang
- Department of Pathology, Hebei Medical University, Key Laboratory of Kidney Diseases of Hebei Province, Shijiazhuang, China
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Lin S, Liu Y, Goldin LR, Lyu C, Kong X, Zhang Y, Caporaso NE, Xiang S, Gao Y. Sex-related DNA methylation differences in B cell chronic lymphocytic leukemia. Biol Sex Differ 2019; 10:2. [PMID: 30616686 PMCID: PMC6322323 DOI: 10.1186/s13293-018-0213-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 12/04/2018] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Men are at higher risk of developing chronic lymphocytic leukemia (CLL) than women. DNA methylation has been shown to play important roles in a number of cancers. There are differences in the DNA methylation pattern between men and women. In this study, we investigated whether this contributes to the sex-related difference of B cell CLL risk. METHODS Using the HumanMethylation450 BeadChip, we profiled the genome-wide DNA methylation pattern of CD19+ B cells from 48 CLL patients (29 female patients and 19 male patients) and 28 healthy people (19 women and 9 men). RESULTS We identified 1043 sex-related differentially methylated positions (DMPs) related to CLL, 56 of which are located on autosomes and 987 on the X chromosome. Using published B cell RNA-sequencing data, we found 18 genes covered by the DMPs also have different expression levels in male and female CLL patients. Among them, TRIB1, an autosome gene, has been shown to promote tumor growth by suppressing apoptosis. CONCLUSIONS Our study represents the first epigenome-wide association study (EWAS) that investigates the sex-related differences in cancer, and indicated that DNA methylation differences might contribute to the sex-related difference in CLL risk.
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Affiliation(s)
- Shuchun Lin
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yun Liu
- The MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Lynn R Goldin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chen Lyu
- Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, IN, USA
| | - Xiangyin Kong
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yan Zhang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong Province, China
| | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Song Xiang
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ying Gao
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
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Ueda T, Kohama Y, Sakurai H. IER family proteins are regulators of protein phosphatase PP2A and modulate the phosphorylation status of CDC25A. Cell Signal 2018; 55:81-89. [PMID: 30599213 DOI: 10.1016/j.cellsig.2018.12.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/25/2018] [Accepted: 12/29/2018] [Indexed: 01/09/2023]
Abstract
Proteins encoded by immediate-early response (IER) family genes, IER2, IER5, and IER5L, share homology at their N-terminal regions. IER5 binds to protein phosphatase 2A (PP2A) and enhances dephosphorylation of PP2A target proteins such as heat shock factor HSF1. Here, we show the expression of IER family genes and the target protein-specific function of IER proteins. The IER homology regions of IER2 and IER5L are required for the interaction with PP2A. Expression of IER2 and IER5L in cells leads to reduced phosphorylation of HSF1 and derepression of its transcriptional activity. Although IER5 and IER5L enhance dephosphorylation of ribosomal protein S6 kinase, IER2 fails to do so. IER2, IER5, and IER5L all bind to the cell cycle regulator CDC25A and convert it to the hypophosphorylated form, which causes dissociation from 14-3-3 regulatory protein. IER5 differentially regulates CDC25A levels in cells under normal and thermal stress conditions. These results suggest that IER proteins are target protein-specific regulators of PP2A activity and modulate cell proliferation through CDC25A activity.
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Affiliation(s)
- Takumi Ueda
- Division of Health Sciences, Kanazawa University Graduate School of Medical Science, 5-11-80 Kodatsuno, Kanazawa, Ishikawa 920-0942, Japan
| | - Yuri Kohama
- Division of Health Sciences, Kanazawa University Graduate School of Medical Science, 5-11-80 Kodatsuno, Kanazawa, Ishikawa 920-0942, Japan
| | - Hiroshi Sakurai
- Division of Health Sciences, Kanazawa University Graduate School of Medical Science, 5-11-80 Kodatsuno, Kanazawa, Ishikawa 920-0942, Japan.
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Xie XP, Xie YF, Liu YT, Wang HQ. Adaptively capturing the heterogeneity of expression for cancer biomarker identification. BMC Bioinformatics 2018; 19:401. [PMID: 30390627 PMCID: PMC6215657 DOI: 10.1186/s12859-018-2437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/15/2018] [Indexed: 11/25/2022] Open
Abstract
Background Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. Results In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer. Conclusions Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use. Software availability The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP. Electronic supplementary material The online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xin-Ping Xie
- School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China
| | - Yu-Feng Xie
- School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China.,Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanhu Road, P.O.Box 1130, Hefei, 230031, Anhui, China.,Present Address: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710100, China
| | - Yi-Tong Liu
- School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China.,Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanhu Road, P.O.Box 1130, Hefei, 230031, Anhui, China
| | - Hong-Qiang Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanhu Road, P.O.Box 1130, Hefei, 230031, Anhui, China.
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Moya L, Lai J, Hoffman A, Srinivasan S, Panchadsaram J, Chambers S, Clements JA, Batra J. Association Analysis of a Microsatellite Repeat in the TRIB1 Gene With Prostate Cancer Risk, Aggressiveness and Survival. Front Genet 2018; 9:428. [PMID: 30337939 PMCID: PMC6180282 DOI: 10.3389/fgene.2018.00428] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 09/10/2018] [Indexed: 01/24/2023] Open
Abstract
With an estimated 1.1 million men worldwide diagnosed with prostate cancer yearly, effective and more specific biomarkers for early diagnosis could lead to better patient outcome. As such, novel genetic markers are sought for this purpose. The tribbles homologue 1 gene (TRIB1) has recently shown to have a role in prostate tumorigenesis and data-mining of prostate cancer expression data confirmed clinical significance of TRIB1 in prostate cancer. For the first time, a polymorphic microsatellite in this gene was studied for its potential association with prostate cancer risk and aggressiveness. Genomic DNA was extracted from a cohort of 1,152 prostate cancer patients and 1,196 cancer-free controls and the TTTTG-TRIB1 microsatellite was genotyped. The socio-demographic and clinical characteristics were analyzed using the non-parametric t-test and two-way ANOVA. Association of the TTTTG-TRIB1 microsatellite and prostate cancer risk and aggressiveness were analyzed by binary logistic regression and confirmed by bootstrapping. Total and prostate cancer mortality was analyzed using the Kaplan Meier test. Genotype and allele correlation with TRIB1 mRNA levels was analyzed using the non-parametric Kolmogorov-Smirnov test. To predict the effect that the TTTTG-TRIB1 polymorphisms had on the mRNA structure, the in silico RNA folding predictor tool, mfold, was used. By analyzing the publicly available data, we confirmed a significant over-expression of TRIB1 in prostate cancer compared to other cancer types, and an over-expression in prostate cancerous tissue compared to adjacent benign. Three alleles (three-five repeats) were observed for TTTTG-TRIB1. The three-repeat allele was associated with prostate cancer risk at the allele (OR = 1.16; P = 0.044) and genotypic levels (OR = 1.70; P = 0.006) and this association was age-independent. The four-repeat allele was inversely associated with prosatet cancer risk (OR = 0.57; P < 0.0001). TRIB1 expression was upregulated in tumors when compared to adjacent cancer-free tissue but was not allele specific. In silico analysis suggested that the TTTTG-TRIB1 alleles may alter TRIB1 mRNA structure. In summary, the three-repeat allele was significantly associated with prostate cancer risk, suggesting a biomarker potential for this microsatellite to predict prostate cancer. Further studies are needed to elucidate the functional role of this microsatellite in regulating TRIB1 expression, perhaps by affecting the TRIB1 mRNA structure and stability.
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Affiliation(s)
- Leire Moya
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - John Lai
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Andrea Hoffman
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Srilakshmi Srinivasan
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Janaththani Panchadsaram
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Suzanne Chambers
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
- Cancer Research Centre, Cancer Council Queensland, Brisbane, QLD, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre – Queensland, Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
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Shi W, Zhai C, Feng W, Wang J, Zhu Y, Li S, Wang Q, Zhang Q, Yan X, Chai L, Liu P, Chen Y, Li M. Resveratrol inhibits monocrotaline-induced pulmonary arterial remodeling by suppression of SphK1-mediated NF-κB activation. Life Sci 2018; 210:140-149. [PMID: 30179628 DOI: 10.1016/j.lfs.2018.08.071] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 08/23/2018] [Accepted: 08/31/2018] [Indexed: 12/21/2022]
Abstract
AIMS This study aims to explore the molecular mechanisms underlying sphingosine kinase 1 (SphK1) inducing pulmonary vascular remodeling and resveratrol suppressing pulmonary arterial hypertension (PAH). MATERIAL AND METHODS monocrotaline (MCT) was used to induce PAH in rats. The right ventricular systolic pressure (RVSP), right ventricle hypertrophy index (RVHI) and histological analyses including hematoxylin and eosin staining, the percentage of medial wall thickness (%MT), α-SMA staining and Ki67 staining were performed to evaluate the development of PAH. Protein levels of SphK1, nuclear factor-kappaB (NF-κB)-p65 and cyclin D1 were determined using immunoblotting. Sphingosine-1-phosphate (S1P) concentration was measured using enzyme-linked immunosorbent assay. KEY FINDINGS SphK1 protein level, S1P production, NF-κB activation and cyclin D1 expression were significantly increased in MCT-induced PAH rats. Inhibition of SphK1 by PF543 suppressed S1P synthesis and NF-κB activation and down-regulated cyclin D1 expression in PAH rats. Suppression of NF-κB by pyrrolidine dithiocarbamate (PDTC) also reduced cyclin D1 expression in PAH model. Treatment of PAH rats with either PF543 or PDTC dramatically decreased RVSP, RVHI and %MT and reduced pulmonary arterial smooth muscle cells proliferation and pulmonary vessel muscularization. In addition, resveratrol effectively inhibited the development of PAH by suppression of SphK1/S1P-mediated NF-κB activation and subsequent cyclin D1 expression. SIGNIFICANCE SphK1/S1P signaling induces the development of PAH by activation of NF-κB and subsequent up-regulation of cyclin D1 expression. Resveratrol inhibits the MCT-induced PAH by targeting on SphK1 and reverses the downstream changes of SphK1, indicating that resveratrol might be a therapeutic agent for the prevention of PAH.
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Affiliation(s)
- Wenhua Shi
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China; National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Xi'an, Shaanxi 710061, China; Shaanxi Province Center for Regenerative Medicine and Surgery Engineering Research, Xi'an, Shaanxi 710061, China
| | - Cui Zhai
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Wei Feng
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China; National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Xi'an, Shaanxi 710061, China; Shaanxi Province Center for Regenerative Medicine and Surgery Engineering Research, Xi'an, Shaanxi 710061, China
| | - Jian Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China; National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Xi'an, Shaanxi 710061, China; Shaanxi Province Center for Regenerative Medicine and Surgery Engineering Research, Xi'an, Shaanxi 710061, China
| | - Yanting Zhu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Shaojun Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Qingting Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Qianqian Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xin Yan
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Limin Chai
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Pengtao Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Yuqian Chen
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Manxiang Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
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McGeachie MJ, Clemmer GL, Hayete B, Xing H, Runge K, Wu AC, Jiang X, Lu Q, Church B, Khalil I, Tantisira K, Weiss S. Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator. J Allergy Clin Immunol 2018; 142:1479-1488.e12. [PMID: 29410046 DOI: 10.1016/j.jaci.2017.11.059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 10/03/2017] [Accepted: 11/01/2017] [Indexed: 12/19/2022]
Abstract
BACKGROUND Variation in response to the most commonly used class of asthma controller medication, inhaled corticosteroids, presents a serious challenge in asthma management, particularly for steroid-resistant patients with little or no response to treatment. OBJECTIVE We applied a systems biology approach to primary clinical and genomic data to identify and validate genes that modulate steroid response in asthmatic children. METHODS We selected 104 inhaled corticosteroid-treated asthmatic non-Hispanic white children and determined a steroid responsiveness endophenotype (SRE) using observations of 6 clinical measures over 4 years. We modeled each subject's cellular steroid response using data from a previously published study of immortalized lymphoblastoid cell lines under dexamethasone (DEX) and sham treatment. We integrated SRE with immortalized lymphoblastoid cell line DEX responses and genotypes to build a genome-scale network using the Reverse Engineering, Forward Simulation modeling framework, identifying 7 genes modulating SRE. RESULTS Three of these genes were functionally validated by using a stable nuclear factor κ-light-chain-enhancer of activated B cells luciferase reporter in A549 human lung epithelial cells, IL-1β cytokine stimulation, and DEX treatment. By using small interfering RNA transfection, knockdown of family with sequence similarity 129 member A (FAM129A) produced a reduction in steroid treatment response (P < .001). CONCLUSION With this systems-based approach, we have shown that FAM129A is associated with variation in clinical asthma steroid responsiveness and that FAM129A modulates steroid responsiveness in lung epithelial cells.
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Affiliation(s)
- Michael J McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass.
| | - George L Clemmer
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass
| | | | - Heming Xing
- Novartis Institute for Biomedical Research, Cambridge, Mass
| | | | - Ann Chen Wu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass; Precision Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass
| | - Xiaofeng Jiang
- Program in Molecular and Integrative Physiological Sciences, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Mass
| | - Quan Lu
- Program in Molecular and Integrative Physiological Sciences, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Mass
| | | | | | - Kelan Tantisira
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass
| | - Scott Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Harvard Medical School, Boston, Mass
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Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer. Pharmacol Res 2017; 124:20-33. [PMID: 28735000 DOI: 10.1016/j.phrs.2017.07.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/09/2017] [Accepted: 07/14/2017] [Indexed: 12/12/2022]
Abstract
Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.
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Hayete B, Wuest D, Laramie J, McDonagh P, Church B, Eberly S, Lang A, Marek K, Runge K, Shoulson I, Singleton A, Tanner C, Khalil I, Verma A, Ravina B. A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease. PLoS One 2017; 12:e0178982. [PMID: 28604798 PMCID: PMC5467836 DOI: 10.1371/journal.pone.0178982] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 05/22/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research. OBJECTIVE To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD. METHODS Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III. RESULTS The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study. CONCLUSIONS Baseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.
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Affiliation(s)
- Boris Hayete
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Diane Wuest
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Jason Laramie
- Novartis, Cambridge, Massachusetts, United States of America
| | - Paul McDonagh
- Alexion Pharmaceuticals, Cambridge, Massachusetts, United States of America
| | - Bruce Church
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Shirley Eberly
- University of Rochester, Rochester, New York, United States of America
| | - Anthony Lang
- Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson’s Disease, Toronto Western Hospital and the University of Toronto, Toronto, Ontario, Canada
| | - Kenneth Marek
- Institute for Neurodegenerative Disorders, New Haven, Connecticut, United States of America
| | - Karl Runge
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Ira Shoulson
- Georgetown University, Washington, DC, United States of America
| | - Andrew Singleton
- National Institute on Aging, NIH, Bethesda, Maryland, United States of America
| | - Caroline Tanner
- University of San Francisco & San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America
| | - Iya Khalil
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Ajay Verma
- Biogen Idec, Cambridge, Massachusetts, United States of America
| | - Bernard Ravina
- Voyager Therapeutics, Cambridge, Massachusetts, United States of America
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