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Qin H, Peng M, Cheng J, Wang Z, Cui Y, Huang Y, Gui Y, Sun Y, Xiang W, Huang X, Huang T, Wang L, Chen J, Hou Y. A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells. Cell Death Dis 2024; 15:482. [PMID: 38965225 PMCID: PMC11224233 DOI: 10.1038/s41419-024-06865-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/18/2024] [Accepted: 06/25/2024] [Indexed: 07/06/2024]
Abstract
Leukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8+ T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML.
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Affiliation(s)
- Huanhuan Qin
- The First Clinical Institute, Zunyi Medical University, Zunyi, 563006, China
| | - Meixi Peng
- Department of Radiological Medicine, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, China
| | - Jingsong Cheng
- The Second Clinical College, Chongqing Medical University, Chongqing, 400016, China
| | - Zhenyu Wang
- Guizhou Provincial College-Based Key Lab for Tumor Prevention and Treatment with Distinctive Medicines, Zunyi Medical University, Zunyi, 563006, China
| | - Yinghui Cui
- Department of Hematology/Oncology, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Yongxiu Huang
- Department of Radiological Medicine, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, China
- Department of Hematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Yaoqi Gui
- Department of Radiological Medicine, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, China
| | - Yanni Sun
- Department of Hematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
- Medical School of Guizhou University, Guiyang, 550025, China
| | - Wenqiong Xiang
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaomei Huang
- Obstetrics and Gynecology Department, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Ting Huang
- Department of Gynecology and Obstetrics, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Li Wang
- Department of Hematology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Jieping Chen
- Department of Hematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
| | - Yu Hou
- Department of Radiological Medicine, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, China.
- Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing Medical University, Chongqing, 400016, China.
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2
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Katebi A, Chen X, Ramirez D, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. NPJ Syst Biol Appl 2024; 10:38. [PMID: 38594351 PMCID: PMC11003984 DOI: 10.1038/s41540-024-00366-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Daniel Ramirez
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA.
- The Jackson Laboratory Cancer Center, Bar Harbor, ME, USA.
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA.
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3
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Lan G, Wu X, Zhao A, Lan J, Guo Q, Wang B, Shen F, Yu X, Zhao Y, Gao R, Xu T. The miR-146b-3p/TNFAIP2 axis regulates cell differentiation in acute myeloid leukaemia. Aging (Albany NY) 2024; 16:1496-1515. [PMID: 38271140 PMCID: PMC10866442 DOI: 10.18632/aging.205441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/01/2023] [Indexed: 01/27/2024]
Abstract
Our purpose is to verify that miR-146b-3p targets the downstream transcript TNFAIP2 in order to reveal the machinery underlying the miR-146b-3p/TNFAIP2 axis regulating acute myeloid leukaemia (AML) differentiation. Bioinformatics analyses were performed using multiple databases and R packages. The CD11b+ and CD14+ cell frequencies were detected using flow cytometry and immunofluorescence staining. The TNFAIP2 protein expression was evaluated using western blotting, immunocytochemistry and immunofluorescence staining. The qRT-PCR was conducted to detect the expression of TNFAIP2 and miR-146b-3p. TNFAIP2 and its correlated genes were enriched in multiple cell differentiation pathways. TNFAIP2 was upregulated upon leukaemic cell differentiation. miR-146b-3p directly targeted TNFAIP2, resulting in a decrease in TNFAIP2 expression. Forced expression of TNFAIP2 or knockdown of miR-146b-3p significantly induced the differentiation of MOLM-13 cells. In this study, we demonstrated that TNFAIP2 is a critical driver in inducing differentiation and that the miR-146b-3p/TNFAIP2 axis involves in regulating cell differentiation in AML.
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Affiliation(s)
- Gaochen Lan
- Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Xiaolong Wu
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Aiyue Zhao
- Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Jinjian Lan
- The First Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qiusheng Guo
- Department of Oncology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Bolin Wang
- Institute of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Fenglin Shen
- The First Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaoling Yu
- Institute of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yanna Zhao
- Institute of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ruilan Gao
- Institute of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Tianwen Xu
- Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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4
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Wu Y, Zhang W, Wang Y, Lu Q, Zhou J, Chen Z, Yang Z, Loor JJ. Molecular mechanisms in the miR-33a/LPPR4 pathway regulating unsaturated fatty acid synthesis in bovine mammary epithelial cells. Anim Biotechnol 2023; 34:2636-2648. [PMID: 35984635 DOI: 10.1080/10495398.2022.2111308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
The regulatory mechanisms governing metabolism of fatty acids in cow mammary gland are crucial for establishing relationships between milk quality and fatty acid content. Both, microRNAs (miRNAs) and protein-coding genes are important factors involved in the regulation of milk fat synthesis. In this study, high-throughput sequencing of miRNAs and mRNAs in bovine mammary gland tissue was performed during peak lactation (3 samples) and late lactation (3 samples) periods to characterize expression profiles. Differential expression (DE) analyses of miRNA and mRNA and miRNA-mRNA regulatory pathway screening were performed. Two-hundred eighty regulatory miRNA-mRNA pairs were identified, including the miR-33a-lipid phosphate phosphatase-related protein type 4 (LPPR4) pathway. Bioinformatics prediction, dual-luciferase reporter system detection, qRT-PCR, and Western blotting revealed that miR-33a can directly target LPPR4 and inhibit its expression. Experiments also revealed that miR-33a promotes the synthesis of triglycerides and increases the content of unsaturated fatty acids (UFAs) in bovine mammary epithelial cells (BMECs). These results indicate that miR-33a via LPPR4 plays an important role in the regulation of milk fat synthesis and UFA levels.
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Affiliation(s)
- Yanni Wu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Wei Zhang
- Guangdong Haid Group Co., Ltd, Guangzhou, China
| | - Yuhao Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Qinyue Lu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Jingpeng Zhou
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zhi Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zhangping Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, PR China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, China
| | - Juan J Loor
- Mammalian Nutrition Physiology Genomics, Department of Animal Sciences and Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA
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5
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Katebi A, Chen X, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.29.551111. [PMID: 37577526 PMCID: PMC10418072 DOI: 10.1101/2023.07.29.551111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
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6
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Zuccherato LW, Machado CMT, Magalhães WCS, Martins PR, Campos LS, Braga LC, Teixeira-Carvalho A, Martins-Filho OA, Franco TMRF, Paula SOC, da Silva IT, Drummond R, Gollob KJ, Salles PGO. Cervical Cancer Stem-Like Cell Transcriptome Profiles Predict Response to Chemoradiotherapy. Front Oncol 2021; 11:639339. [PMID: 34026616 PMCID: PMC8138064 DOI: 10.3389/fonc.2021.639339] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
Cervical cancer (CC) represents a major global health issue, particularly impacting women from resource constrained regions worldwide. Treatment refractoriness to standard chemoradiotheraphy has identified cancer stem cells as critical coordinators behind the biological mechanisms of resistance, contributing to CC recurrence. In this work, we evaluated differential gene expression in cervical cancer stem-like cells (CCSC) as biomarkers related to intrinsic chemoradioresistance in CC. A total of 31 patients with locally advanced CC and referred to Mário Penna Institute (Belo Horizonte, Brazil) from August 2017 to May 2018 were recruited for the study. Fluorescence-activated cell sorting was used to enrich CD34+/CD45- CCSC from tumor biopsies. Transcriptome was performed using ultra-low input RNA sequencing and differentially expressed genes (DEGs) using Log2 fold differences and adjusted p-value < 0.05 were determined. The analysis returned 1050 DEGs when comparing the Non-Responder (NR) (n=10) and Responder (R) (n=21) groups to chemoradiotherapy. These included a wide-ranging pattern of underexpressed coding genes in the NR vs. R patients and a panel of lncRNAs and miRNAs with implications for CC tumorigenesis. A panel of biomarkers was selected using the rank-based AUC (Area Under the ROC Curve) and pAUC (partial AUC) measurements for diagnostic sensitivity and specificity. Genes overlapping between the 21 highest AUC and pAUC loci revealed seven genes with a strong capacity for identifying NR vs. R patients (ILF2, RBM22P2, ACO16722.1, AL360175.1 and AC092354.1), of which four also returned significant survival Hazard Ratios. This study identifies DEG signatures that provide potential biomarkers in CC prognosis and treatment outcome, as well as identifies potential alternative targets for cancer therapy.
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Affiliation(s)
| | | | | | | | - Larissa S. Campos
- Núcleo de Ensino e Pesquisa - Instituto Mário Penna, Belo Horizonte, Brazil
| | - Letícia C. Braga
- Núcleo de Ensino e Pesquisa - Instituto Mário Penna, Belo Horizonte, Brazil
| | | | | | | | | | | | - Rodrigo Drummond
- International Research Center, A.C. Camargo Cancer Center, São Paulo, Brazil
| | - Kenneth J. Gollob
- Núcleo de Ensino e Pesquisa - Instituto Mário Penna, Belo Horizonte, Brazil
- Translational Immuno-Oncology Laboratory, International Research Center, A.C. Camargo Cancer Center, São Paulo, Brazil
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