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Yashar WM, Estabrook J, Holly HD, Somers J, Nikolova O, Babur Ö, Braun TP, Demir E. Predicting transcription factor activity using prior biological information. iScience 2024; 27:109124. [PMID: 38455978 PMCID: PMC10918219 DOI: 10.1016/j.isci.2024.109124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/20/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.
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Affiliation(s)
- William M. Yashar
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joseph Estabrook
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Hannah D. Holly
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julia Somers
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Olga Nikolova
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Özgün Babur
- Computer Science Department, University of Massachusetts, Boston, MA 02125, USA
| | - Theodore P. Braun
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Emek Demir
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
- Pacific Northwest National Laboratories, Richland, WA 99354, USA
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52
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Yun HY. Leucine rich repeat LGI family member 3: Integrative analyses support its prognostic association with pancreatic adenocarcinoma. Medicine (Baltimore) 2024; 103:e37183. [PMID: 38394487 PMCID: PMC11309673 DOI: 10.1097/md.0000000000037183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/17/2024] [Indexed: 02/25/2024] Open
Abstract
Leucine rich repeat LGI family member 3 (LGI3) is a member of the LGI protein family. Previous studies of our group have reported that LGI3 is expressed in adipose tissue, skin and brain, and serves as a multifunctional cytokine. LGI3 may also be involved in cytokine networks in various cancers. This study aimed to analyze differentially expressed genes in pancreatic adenocarcinoma (PAC) tissues and PAC cohort data in order to evaluate the prognostic role of LGI3. The expression microarray and the PAC cohort data were analyzed by bioinformatic methods for differential expression, protein-protein interactions, functional enrichment and pathway analyses, gene co-expression network analysis, and prognostic association analysis. Results showed that LGI3 expression was significantly reduced in PAC tissues. Nineteen upregulated genes and 31 downregulated genes in PAC tissues were identified as LGI3-regulated genes. Protein-protein interaction network analysis demonstrated that 92% (46/50) of the LGI3-regulated genes that were altered in PACs belonged to a protein-protein interaction network cluster. Functional enrichment and gene co-expression network analyses demonstrated that these genes in the network cluster were associated with various processes including inflammatory and immune responses, metabolic processes, cell differentiation, and angiogenesis. PAC cohort analyses revealed that low expression levels of LGI3 were significantly associated with poor PAC prognosis. Analysis of favorable or unfavorable prognostic gene products in PAC showed that 93 LGI3-regulated genes were differentially associated with PAC prognosis. LGI3 expression was correlated with the tumor-infiltration levels of various immune cells. Taken together, these results suggested that LGI3 may be a potential prognostic marker of PAC.
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Affiliation(s)
- Hye-Young Yun
- Department of Biochemistry, Chung-Ang University, College of Medicine, Seoul, Republic of Korea
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53
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Winkler I, Tolkachov A, Lammers F, Lacour P, Daugelaite K, Schneider N, Koch ML, Panten J, Grünschläger F, Poth T, Ávila BMD, Schneider A, Haas S, Odom DT, Gonçalves Â. The cycling and aging mouse female reproductive tract at single-cell resolution. Cell 2024; 187:981-998.e25. [PMID: 38325365 DOI: 10.1016/j.cell.2024.01.021] [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: 07/25/2022] [Revised: 04/21/2023] [Accepted: 01/12/2024] [Indexed: 02/09/2024]
Abstract
The female reproductive tract (FRT) undergoes extensive remodeling during reproductive cycling. This recurrent remodeling and how it shapes organ-specific aging remains poorly explored. Using single-cell and spatial transcriptomics, we systematically characterized morphological and gene expression changes occurring in ovary, oviduct, uterus, cervix, and vagina at each phase of the mouse estrous cycle, during decidualization, and into aging. These analyses reveal that fibroblasts play central-and highly organ-specific-roles in FRT remodeling by orchestrating extracellular matrix (ECM) reorganization and inflammation. Our results suggest a model wherein recurrent FRT remodeling over reproductive lifespan drives the gradual, age-related development of fibrosis and chronic inflammation. This hypothesis was directly tested using chemical ablation of cycling, which reduced fibrotic accumulation during aging. Our atlas provides extensive detail into how estrus, pregnancy, and aging shape the organs of the female reproductive tract and reveals the unexpected cost of the recurrent remodeling required for reproduction.
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Affiliation(s)
- Ivana Winkler
- German Cancer Research Center (DKFZ), Division of Somatic Evolution and Early Detection, 69120 Heidelberg, Germany
| | - Alexander Tolkachov
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany
| | - Fritjof Lammers
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany
| | - Perrine Lacour
- German Cancer Research Center (DKFZ), Division of Somatic Evolution and Early Detection, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, 69117 Heidelberg, Germany
| | - Klaudija Daugelaite
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, 69117 Heidelberg, Germany
| | - Nina Schneider
- German Cancer Research Center (DKFZ), Division of Somatic Evolution and Early Detection, 69120 Heidelberg, Germany
| | - Marie-Luise Koch
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany
| | - Jasper Panten
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, 69117 Heidelberg, Germany; German Cancer Research Center (DKFZ), Division of Computational Genomics and Systems Genetics, 69120 Heidelberg, Germany
| | - Florian Grünschläger
- Heidelberg University, Faculty of Biosciences, 69117 Heidelberg, Germany; German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Division of Stem Cells and Cancer, 69120 Heidelberg, Germany; Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany
| | - Tanja Poth
- CMCP - Center for Model System and Comparative Pathology, Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | | | - Augusto Schneider
- Universidade Federal de Pelotas, Faculdade de Nutrição, 96010-610 Pelotas, RS, Brazil
| | - Simon Haas
- German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Division of Stem Cells and Cancer, 69120 Heidelberg, Germany; Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany; Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany; Charité - Universitätsmedizin Berlin, Department of Hematology, Oncology and Cancer Immunology, 10115 Berlin, Germany
| | - Duncan T Odom
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, 69120 Heidelberg, Germany; Cancer Research UK - Cambridge Institute, University of Cambridge, Cambridge, UK.
| | - Ângela Gonçalves
- German Cancer Research Center (DKFZ), Division of Somatic Evolution and Early Detection, 69120 Heidelberg, Germany.
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Leuzzi G, Vasciaveo A, Taglialatela A, Chen X, Firestone TM, Hickman AR, Mao W, Thakar T, Vaitsiankova A, Huang JW, Cuella-Martin R, Hayward SB, Kesner JS, Ghasemzadeh A, Nambiar TS, Ho P, Rialdi A, Hebrard M, Li Y, Gao J, Gopinath S, Adeleke OA, Venters BJ, Drake CG, Baer R, Izar B, Guccione E, Keogh MC, Guerois R, Sun L, Lu C, Califano A, Ciccia A. SMARCAL1 is a dual regulator of innate immune signaling and PD-L1 expression that promotes tumor immune evasion. Cell 2024; 187:861-881.e32. [PMID: 38301646 PMCID: PMC10980358 DOI: 10.1016/j.cell.2024.01.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 07/23/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024]
Abstract
Genomic instability can trigger cancer-intrinsic innate immune responses that promote tumor rejection. However, cancer cells often evade these responses by overexpressing immune checkpoint regulators, such as PD-L1. Here, we identify the SNF2-family DNA translocase SMARCAL1 as a factor that favors tumor immune evasion by a dual mechanism involving both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. Mechanistically, SMARCAL1 limits endogenous DNA damage, thereby suppressing cGAS-STING-dependent signaling during cancer cell growth. Simultaneously, it cooperates with the AP-1 family member JUN to maintain chromatin accessibility at a PD-L1 transcriptional regulatory element, thereby promoting PD-L1 expression in cancer cells. SMARCAL1 loss hinders the ability of tumor cells to induce PD-L1 in response to genomic instability, enhances anti-tumor immune responses and sensitizes tumors to immune checkpoint blockade in a mouse melanoma model. Collectively, these studies uncover SMARCAL1 as a promising target for cancer immunotherapy.
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Affiliation(s)
- Giuseppe Leuzzi
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alessandro Vasciaveo
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Angelo Taglialatela
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Xiao Chen
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | | | - Wendy Mao
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Tanay Thakar
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alina Vaitsiankova
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Jen-Wei Huang
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Raquel Cuella-Martin
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Samuel B Hayward
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Jordan S Kesner
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Ali Ghasemzadeh
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Tarun S Nambiar
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Patricia Ho
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alexander Rialdi
- Center for OncoGenomics and Innovative Therapeutics (COGIT), Center for Therapeutics Discovery, Department of Oncological Sciences and Pharmacological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maxime Hebrard
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (ASTAR), Singapore, Singapore
| | - Yinglu Li
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Jinmei Gao
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | | | | | | | - Charles G Drake
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Urology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Richard Baer
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Institute for Cancer Genetics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Benjamin Izar
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Ernesto Guccione
- Center for OncoGenomics and Innovative Therapeutics (COGIT), Center for Therapeutics Discovery, Department of Oncological Sciences and Pharmacological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Raphael Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Lu Sun
- EpiCypher Inc., Durham, NC 27709, USA
| | - Chao Lu
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Andrea Califano
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Institute for Cancer Genetics, Columbia University Irving Medical Center, New York, NY 10032, USA.
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55
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Savage TM, Fortson KT, de Los Santos-Alexis K, Oliveras-Alsina A, Rouanne M, Rae SS, Gamarra JR, Shayya H, Kornberg A, Cavero R, Li F, Han A, Haeusler RA, Adam J, Schwabe RF, Arpaia N. Amphiregulin from regulatory T cells promotes liver fibrosis and insulin resistance in non-alcoholic steatohepatitis. Immunity 2024; 57:303-318.e6. [PMID: 38309273 PMCID: PMC10922825 DOI: 10.1016/j.immuni.2024.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/20/2023] [Accepted: 01/10/2024] [Indexed: 02/05/2024]
Abstract
Production of amphiregulin (Areg) by regulatory T (Treg) cells promotes repair after acute tissue injury. Here, we examined the function of Treg cells in non-alcoholic steatohepatitis (NASH), a setting of chronic liver injury. Areg-producing Treg cells were enriched in the livers of mice and humans with NASH. Deletion of Areg in Treg cells, but not in myeloid cells, reduced NASH-induced liver fibrosis. Chronic liver damage induced transcriptional changes associated with Treg cell activation. Mechanistically, Treg cell-derived Areg activated pro-fibrotic transcriptional programs in hepatic stellate cells via epidermal growth factor receptor (EGFR) signaling. Deletion of Areg in Treg cells protected mice from NASH-dependent glucose intolerance, which also was dependent on EGFR signaling on hepatic stellate cells. Areg from Treg cells promoted hepatocyte gluconeogenesis through hepatocyte detection of hepatic stellate cell-derived interleukin-6. Our findings reveal a maladaptive role for Treg cell-mediated tissue repair functions in chronic liver disease and link liver damage to NASH-dependent glucose intolerance.
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Affiliation(s)
- Thomas M Savage
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | - Katherine T Fortson
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | | | | | - Mathieu Rouanne
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | - Sarah S Rae
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | | | - Hani Shayya
- Mortimer B. Zuckerman Mind, and Brain and Behavior Institute, Columbia University, New York, NY, USA
| | - Adam Kornberg
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA; Columbia Center for Translational Immunology, Columbia University, New York, NY, USA
| | - Renzo Cavero
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | - Fangda Li
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA
| | - Arnold Han
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA; Columbia Center for Translational Immunology, Columbia University, New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA
| | - Rebecca A Haeusler
- Naomi Berrie Diabetes Center, Columbia University, New York, NY, USA; Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Julien Adam
- Pathology Department, Hopital Paris Saint-Joseph, Paris, France; INSERM U1186, Gustave Roussy, Villejuif, France
| | | | - Nicholas Arpaia
- Department of Microbiology & Immunology, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
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56
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Lu C, Donners MMPC, de Baaij JBJ, Jin H, Otten JJT, Manca M, van Zonneveld AJ, Jukema JW, Kraaijeveld A, Kuiper J, Pasterkamp G, Mees B, Sluimer JC, Cavill R, Karel JMH, Goossens P, Biessen EAL. Identification of a gene network driving the attenuated response to lipopolysaccharide of monocytes from hypertensive coronary artery disease patients. Front Immunol 2024; 15:1286382. [PMID: 38410507 PMCID: PMC10894924 DOI: 10.3389/fimmu.2024.1286382] [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: 08/31/2023] [Accepted: 01/24/2024] [Indexed: 02/28/2024] Open
Abstract
Introduction The impact of cardiovascular disease (CVD) risk factors, encompassing various biological determinants and unhealthy lifestyles, on the functional dynamics of circulating monocytes-a pivotal cell type in CVD pathophysiology remains elusive. In this study, we aimed to elucidate the influence of CVD risk factors on monocyte transcriptional responses to an infectious stimulus. Methods We conducted a comparative analysis of monocyte gene expression profiles from the CTMM - CIRCULATING CELLS Cohort of coronary artery disease (CAD) patients, at baseline and after lipopolysaccharide (LPS) stimulation. Gene co-expression analysis was used to identify gene modules and their correlations with CVD risk factors, while pivotal transcription factors controlling the hub genes in these modules were identified by regulatory network analyses. The identified gene module was subjected to a drug repurposing screen, utilizing the LINCS L1000 database. Results Monocyte responsiveness to LPS showed a highly significant, negative correlation with blood pressure levels (ρ< -0.4; P<10-80). We identified a ZNF12/ZBTB43-driven gene module closely linked to diastolic blood pressure, suggesting that monocyte responses to infectious stimuli, such as LPS, are attenuated in CAD patients with elevated diastolic blood pressure. This attenuation appears associated with a dampening of the LPS-induced suppression of oxidative phosphorylation. Finally, we identified the serine-threonine inhibitor MW-STK33-97 as a drug candidate capable of reversing this aberrant LPS response. Conclusions Monocyte responses to infectious stimuli may be hampered in CAD patients with high diastolic blood pressure and this attenuated inflammatory response may be reversed by the serine-threonine inhibitor MW-STK33-97. Whether the identified gene module is a mere indicator of, or causal factor in diastolic blood pressure and the associated dampened LPS responses remains to be determined.
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Affiliation(s)
- Chang Lu
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University and Heidelberg University Hospital, Heidelberg, Germany
| | - Marjo M P C Donners
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
| | - Julius B J de Baaij
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
| | - Han Jin
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jeroen J T Otten
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
| | | | - Anton Jan van Zonneveld
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
- Netherlands Heart Institute, Utrecht, Netherlands
| | - Adriaan Kraaijeveld
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Johan Kuiper
- Division of BioTherapeutics, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | - Gerard Pasterkamp
- Circulatory Health Research Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Barend Mees
- Department of Vascular Surgery, Maastricht University Medical Center, Maastricht, Netherlands
| | - Judith C Sluimer
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- Centre for Cardiovascular Science (CVS), University of Edinburgh, Edinburgh, United Kingdom
| | - Rachel Cavill
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, Netherlands
| | - Joël M H Karel
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, Netherlands
| | - Pieter Goossens
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
| | - Erik A L Biessen
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- Institute for Molecular Cardiovascular Research, Klinikum RWTH Aachen, Aachen, Germany
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57
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Zhang H, Zhao Z, Wu Z, Xia Y, Zhao Y. Identifying interactions among air pollutant emissions on diabetes prevalence in Northeast China using a complex network. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:393-400. [PMID: 38110789 DOI: 10.1007/s00484-023-02597-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023]
Abstract
BACKGROUND Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes. METHODS After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China. RESULTS Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO. CONCLUSIONS Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions.
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Affiliation(s)
- Hehua Zhang
- Clinical Research Center, Shengjing Hospital of China Medical University, Sanhao Street, No. 36, Heping District, Shenyang, 110002, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, 110002, Liaoning Province, China
| | - Zhiying Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Sanhao Street, No. 36, Heping District, Shenyang, 110002, China
| | - Zhuo Wu
- Tianjin Third Central Hospital, No. 83, Jintang Road, Hedong District, Tianjin, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Sanhao Street, No. 36, Heping District, Shenyang, 110002, China
| | - Yuhong Zhao
- Clinical Research Center, Shengjing Hospital of China Medical University, Sanhao Street, No. 36, Heping District, Shenyang, 110002, China.
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Sanhao Street, No. 36, Heping District, Shenyang, 110002, China.
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, 110002, Liaoning Province, China.
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Lei W, Zhu H, Cao M, Zhang F, Lai Q, Lu S, Dong W, Sun J, Ru D. From genomics to metabolomics: Deciphering sanguinarine biosynthesis in Dicranostigma leptopodum. Int J Biol Macromol 2024; 257:128727. [PMID: 38092109 DOI: 10.1016/j.ijbiomac.2023.128727] [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: 06/15/2023] [Revised: 11/15/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023]
Abstract
Dicranostigma leptopodum (Maxim) Fedde (DLF) is a renowned medicinal plant in China, known to be rich in alkaloids. However, the unavailability of a reference genome has impeded investigation into its plant metabolism and genetic breeding potential. Here we present a high-quality chromosomal-level genome assembly for DLF, derived using a combination of Nanopore long-read sequencing, Illumina short-read sequencing and Hi-C technologies. Our assembly genome spans a size of 621.81 Mb with an impressive contig N50 of 93.04 Mb. We show that the species-specific whole-genome duplication (WGD) of DLF and Papaver somniferum corresponded to two rounds of WGDs of Papaver setigerum. Furthermore, we integrated comprehensive homology searching, gene family analyses and construction of a gene-to-metabolite network. These efforts led to the discovery of co-expressed transcription factors, including NAC and bZIP, alongside sanguinarine (SAN) pathway genes CYP719 (CFS and SPS). Notably, we identified P6H as a promising gene for enhancing SAN production. By providing the first reference genome for Dicranostigma, our study confirms the genomic underpinning of SAN biosynthesis and establishes a foundation for advancing functional genomic research on Papaveraceae species. Our findings underscore the pivotal role of high-quality genome assemblies in elucidating genetic variations underlying the evolutionary origin of secondary metabolites.
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Affiliation(s)
- Weixiao Lei
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Hui Zhu
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Man Cao
- Gansu Pharmacovigilance Center, Lanzhou 730070, China
| | - Feng Zhang
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Qing Lai
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Shengming Lu
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Wenpan Dong
- School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
| | - Jiahui Sun
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Dafu Ru
- State Key Laboratory of Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China.
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Li J, Meng Z, Cao Z, Lu W, Yang Y, Li Z, Lu S. ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy. Front Immunol 2024; 15:1304183. [PMID: 38343549 PMCID: PMC10853338 DOI: 10.3389/fimmu.2024.1304183] [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: 09/29/2023] [Accepted: 01/02/2024] [Indexed: 02/15/2024] Open
Abstract
Background Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need. Methods We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets. Results Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME. Conclusion Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.
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Affiliation(s)
| | | | | | | | | | - Ziming Li
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Shun Lu
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
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Xin J, Wang M, Qu L, Chen Q, Wang W, Wang Z. BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:188-199. [PMID: 38127613 DOI: 10.1109/tcbb.2023.3345317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.
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61
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Tu S, Zuo J. Systematic single cell RNA sequencing analysis reveals unique transcriptional regulatory networks of Atoh1-mediated hair cell conversion in adult mouse cochleae. PLoS One 2023; 18:e0284685. [PMID: 38079436 PMCID: PMC10712870 DOI: 10.1371/journal.pone.0284685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 04/05/2023] [Indexed: 12/18/2023] Open
Abstract
Regeneration of mammalian cochlear hair cells (HCs) by modulating molecular pathways or transcription factors is a promising approach to hearing restoration; however, immaturity of the regenerated HCs in vivo remains a major challenge. Here, we analyzed a single cell RNA sequencing (scRNA-seq) dataset during Atoh1-induced supporting cell (SC) to hair cell (HC) conversion in adult mouse cochleae (Yamashita et al. (2018)) using multiple high-throughput sequencing analytical tools (WGCNA, SCENIC, ARACNE, and VIPER). Instead of focusing on differentially expressed genes, we established independent expression modules and confirmed the existence of multiple conversion stages. Gene regulatory network (GRN) analysis uncovered previously unidentified key regulators, including Nhlh1, Lhx3, Barhl1 and Nfia, that guide converted HC differentiation. Comparison of the late-stage converted HCs with the scRNA-seq data from neonatal mouse cochleae (Kolla et al. (2020)) revealed that they closely resemble postnatal day 1 wild-type OHCs, in contrast to other developmental stages. Using ARACNE and VIPER, we discovered multiple key regulators likely to promote conversion to a more mature OHC-like state, including Zbtb20, Nfia, Zmiz1, Gm14418, Bhlhe40, Six2, Fosb and Klf9. Our findings provide insights into the regulation of HC regeneration in adult mammalian cochleae in vivo and demonstrate an approach for analyzing GRNs in large scRNA-seq datasets.
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Affiliation(s)
- Shu Tu
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, United States of America
| | - Jian Zuo
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, United States of America
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62
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Chen CC, Tran W, Song K, Sugimoto T, Obusan MB, Wang L, Sheu KM, Cheng D, Ta L, Varuzhanyan G, Huang A, Xu R, Zeng Y, Borujerdpur A, Bayley NA, Noguchi M, Mao Z, Morrissey C, Corey E, Nelson PS, Zhao Y, Huang J, Park JW, Witte ON, Graeber TG. Temporal evolution reveals bifurcated lineages in aggressive neuroendocrine small cell prostate cancer trans-differentiation. Cancer Cell 2023; 41:2066-2082.e9. [PMID: 37995683 PMCID: PMC10878415 DOI: 10.1016/j.ccell.2023.10.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/25/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
Trans-differentiation from an adenocarcinoma to a small cell neuroendocrine state is associated with therapy resistance in multiple cancer types. To gain insight into the underlying molecular events of the trans-differentiation, we perform a multi-omics time course analysis of a pan-small cell neuroendocrine cancer model (termed PARCB), a forward genetic transformation using human prostate basal cells and identify a shared developmental, arc-like, and entropy-high trajectory among all transformation model replicates. Further mapping with single cell resolution reveals two distinct lineages defined by mutually exclusive expression of ASCL1 or ASCL2. Temporal regulation by groups of transcription factors across developmental stages reveals that cellular reprogramming precedes the induction of neuronal programs. TFAP4 and ASCL1/2 feedback are identified as potential regulators of ASCL1 and ASCL2 expression. Our study provides temporal transcriptional patterns and uncovers pan-tissue parallels between prostate and lung cancers, as well as connections to normal neuroendocrine cell states.
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Affiliation(s)
- Chia-Chun Chen
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Wendy Tran
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Kai Song
- Department of Bioengineering, UCLA, Los Angeles, CA, USA
| | - Tyler Sugimoto
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Matthew B Obusan
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Liang Wang
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Katherine M Sheu
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Donghui Cheng
- Eli and Edythe Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA
| | - Lisa Ta
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Grigor Varuzhanyan
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Arthur Huang
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Runzhe Xu
- Department of Biological Chemistry, UCLA, Los Angeles, CA, USA
| | - Yuanhong Zeng
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Amirreza Borujerdpur
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Nicholas A Bayley
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Miyako Noguchi
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Zhiyuan Mao
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Colm Morrissey
- Department of Urology, University of Washington School of Medicine, Seattle, WA, USA
| | - Eva Corey
- Department of Urology, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter S Nelson
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Yue Zhao
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA; Department of Pathology, College of Basic Medical Sciences and the First Hospital, China Medical University, Shenyang, China
| | - Jiaoti Huang
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Jung Wook Park
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Owen N Witte
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA; Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA; Eli and Edythe Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA; Molecular Biology Institute, UCLA, Los Angeles, CA, USA; Parker Institute for Cancer Immunotherapy, UCLA, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA.
| | - Thomas G Graeber
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA; Crump Institute for Molecular Imaging, UCLA, Los Angeles, CA, USA; California NanoSystems Institute, UCLA, Los Angeles, CA, USA; Metabolomics Center, UCLA, Los Angeles, CA, USA.
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Hicks AR, Reynolds RH, O’Callaghan B, García-Ruiz S, Gil-Martínez AL, Botía J, Plun-Favreau H, Ryten M. The non-specific lethal complex regulates genes and pathways genetically linked to Parkinson's disease. Brain 2023; 146:4974-4987. [PMID: 37522749 PMCID: PMC10689904 DOI: 10.1093/brain/awad246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/12/2023] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Genetic variants conferring risks for Parkinson's disease have been highlighted through genome-wide association studies, yet exploration of their specific disease mechanisms is lacking. Two Parkinson's disease candidate genes, KAT8 and KANSL1, identified through genome-wide studies and a PINK1-mitophagy screen, encode part of the histone acetylating non-specific lethal complex. This complex localizes to the nucleus, where it plays a role in transcriptional activation, and to mitochondria, where it has been suggested to have a role in mitochondrial transcription. In this study, we sought to identify whether the non-specific lethal complex has potential regulatory relationships with other genes associated with Parkinson's disease in human brain. Correlation in the expression of non-specific lethal genes and Parkinson's disease-associated genes was investigated in primary gene co-expression networks using publicly-available transcriptomic data from multiple brain regions (provided by the Genotype-Tissue Expression Consortium and UK Brain Expression Consortium), whilst secondary networks were used to examine cell type specificity. Reverse engineering of gene regulatory networks generated regulons of the complex, which were tested for heritability using stratified linkage disequilibrium score regression. Prioritized gene targets were then validated in vitro using a QuantiGene multiplex assay and publicly-available chromatin immunoprecipitation-sequencing data. Significant clustering of non-specific lethal genes was revealed alongside Parkinson's disease-associated genes in frontal cortex primary co-expression modules, amongst other brain regions. Both primary and secondary co-expression modules containing these genes were enriched for mainly neuronal cell types. Regulons of the complex contained Parkinson's disease-associated genes and were enriched for biological pathways genetically linked to disease. When examined in a neuroblastoma cell line, 41% of prioritized gene targets showed significant changes in mRNA expression following KANSL1 or KAT8 perturbation. KANSL1 and H4K8 chromatin immunoprecipitation-sequencing data demonstrated non-specific lethal complex activity at many of these genes. In conclusion, genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinson's disease. Overall, these findings reveal a potentially wider role for this protein complex in regulating genes and pathways implicated in Parkinson's disease.
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Affiliation(s)
- Amy R Hicks
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Regina H Reynolds
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, Bloomsbury, London WC1N 1EH, UK
| | - Benjamin O’Callaghan
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Sonia García-Ruiz
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, Bloomsbury, London WC1N 1EH, UK
| | - Ana Luisa Gil-Martínez
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, Bloomsbury, London WC1N 1EH, UK
- Department of Information and Communication Engineering, University of Murcia, Murcia 30100, Spain
| | - Juan Botía
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Information and Communication Engineering, University of Murcia, Murcia 30100, Spain
| | - Hélène Plun-Favreau
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Mina Ryten
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, Bloomsbury, London WC1N 1EH, UK
- NIHR GOSH Biomedical Research Centre, Great Ormond Street Institute of Child Health, Bloomsbury, London WC1N 1EH, UK
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64
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Tudose C, Bond J, Ryan CJ. Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity. NAR Cancer 2023; 5:zcad056. [PMID: 38035131 PMCID: PMC10683780 DOI: 10.1093/narcan/zcad056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.
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Affiliation(s)
- Cosmin Tudose
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
- The SFI Centre for Research Training in Genomics Data Science, Ireland
| | - Jonathan Bond
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
- Children's Health Ireland at Crumlin, Dublin, Ireland
| | - Colm J Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Computer Science, University College Dublin, Dublin, Ireland
- Conway Institute, University College Dublin, Dublin, Ireland
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65
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Arriojas A, Patalano S, Macoska J, Zarringhalam K. A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data. NAR Genom Bioinform 2023; 5:lqad106. [PMID: 38094309 PMCID: PMC10716740 DOI: 10.1093/nargab/lqad106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/12/2023] [Accepted: 11/24/2023] [Indexed: 12/20/2023] Open
Abstract
The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.
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Affiliation(s)
- Argenis Arriojas
- Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA
- Department of Physics, University of Massachusetts Boston, Boston, MA 02125, USA
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Susan Patalano
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Jill Macoska
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Kourosh Zarringhalam
- Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
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66
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Hawley JE, Obradovic AZ, Dallos MC, Lim EA, Runcie K, Ager CR, McKiernan J, Anderson CB, Decastro GJ, Weintraub J, Virk R, Lowy I, Hu J, Chaimowitz MG, Guo XV, Zhang Y, Haffner MC, Worley J, Stein MN, Califano A, Drake CG. Anti-PD-1 immunotherapy with androgen deprivation therapy induces robust immune infiltration in metastatic castration-sensitive prostate cancer. Cancer Cell 2023; 41:1972-1988.e5. [PMID: 37922910 PMCID: PMC11184948 DOI: 10.1016/j.ccell.2023.10.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/19/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
When compared to other malignancies, the tumor microenvironment (TME) of primary and castration-resistant prostate cancer (CRPC) is relatively devoid of immune infiltrates. While androgen deprivation therapy (ADT) induces a complex immune infiltrate in localized prostate cancer, the composition of the TME in metastatic castration-sensitive prostate cancer (mCSPC), and the effects of ADT and other treatments in this context are poorly understood. Here, we perform a comprehensive single-cell RNA sequencing (scRNA-seq) profiling of metastatic sites from patients participating in a phase 2 clinical trial (NCT03951831) that evaluated standard-of-care chemo-hormonal therapy combined with anti-PD-1 immunotherapy. We perform a longitudinal, protein activity-based analysis of TME subpopulations, revealing immune subpopulations conserved across multiple metastatic sites. We also observe dynamic changes in these immune subpopulations in response to treatment and a correlation with clinical outcomes. Our study uncovers a therapy-resistant, transcriptionally distinct tumor subpopulation that expands in cell number in treatment-refractory patients.
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Affiliation(s)
- Jessica E Hawley
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Aleksandar Z Obradovic
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Matthew C Dallos
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Emerson A Lim
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Karie Runcie
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Casey R Ager
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - James McKiernan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Christopher B Anderson
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Guarionex J Decastro
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Urology, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Joshua Weintraub
- Department of Interventional Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Renu Virk
- Department of Pathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Israel Lowy
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Jianhua Hu
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Matthew G Chaimowitz
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Xinzheng V Guo
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Ya Zhang
- Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Michael C Haffner
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA; Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jeremy Worley
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mark N Stein
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biochemistry & Molecular Biophysics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032 USA; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032 USA; Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY 10032 USA; J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY 10032 USA.
| | - Charles G Drake
- Division of Hematology and Oncology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA; Department of Interventional Radiology, Columbia University Irving Medical Center, New York, NY, USA.
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67
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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68
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Ji P, Liu Y, Yan L, Jia Y, Zhao M, Lv D, Yao Y, Ma W, Yin D, Liu F, Gao S, Wusiman A, Yang K, Zhang L, Liu G. Melatonin improves the vitrification of sheep morulae by modulating transcriptome. Front Vet Sci 2023; 10:1212047. [PMID: 37920328 PMCID: PMC10619913 DOI: 10.3389/fvets.2023.1212047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/03/2023] [Indexed: 11/04/2023] Open
Abstract
Embryo vitrification technology is widely used in livestock production, but freezing injury has been a key factor hindering the efficiency of embryo production. There is an urgent need to further analyze the molecular mechanism of embryo damage by the vitrification process. In the study, morulae were collected from Hu sheep uterine horns after superovulation and sperm transfusion. Morulae were Cryotop vitrified and warmed. Nine morulae were in the vitrified control group (frozen), and seven morulae were vitrified and warmed with 10-5 M melatonin (melatonin). Eleven non-frozen morulae were used as controls (fresh). After warming, each embryo was sequenced separately for library construction and gene expression analysis. p < 0.05 was used to differentiate differentially expressed genes (DEG). The results showed that differentiated differentially expressed genes (DEG) in vitrified morulae were mainly enriched in protein kinase activity, adhesion processes, calcium signaling pathways and Wnt, PI3K/AKT, Ras, ErbB, and MAPK signaling pathways compared to controls. Importantly, melatonin treatment upregulated the expression of key pathways that increase the resistance of morulae against vitrification induced damage. These pathways include kinase activity pathway, ErbB, and PI3K/Akt signaling pathway. It is worth mentioning that melatonin upregulates the expression of XPA, which is a key transcription factor for DNA repair. In conclusion, vitrification affected the transcriptome of in vivo-derived Hu sheep morulae, and melatonin had a protective effect on the vitrification process. For the first time, the transcriptome profiles caused by vitrification and melatonin in sheep morulae were analyzed in single embryo level. These data obtained from the single embryo level provide an important molecular mechanism for further optimizing the cryopreservation of embryos or other cells.
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Affiliation(s)
- Pengyun Ji
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yunjie Liu
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Laiqing Yan
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | | | - Mengmeng Zhao
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Dongying Lv
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yujun Yao
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Wenkui Ma
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Depeng Yin
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Fenze Liu
- Inner Mongolia Golden Grassland Ecological Technology Group Co., Ltd., Inner Mongolia, China
| | - Shuai Gao
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Abulizi Wusiman
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Kailun Yang
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Lu Zhang
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guoshi Liu
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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69
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Hsieh PH, Lopes-Ramos CM, Zucknick M, Sandve GK, Glass K, Kuijjer ML. Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinformatics 2023; 39:btad610. [PMID: 37802917 PMCID: PMC10598588 DOI: 10.1093/bioinformatics/btad610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 08/05/2023] [Accepted: 10/05/2023] [Indexed: 10/08/2023] Open
Abstract
MOTIVATION Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. RESULTS We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. AVAILABILITY AND IMPLEMENTATION The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.
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Affiliation(s)
- Ping-Han Hsieh
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo 0317, Norway
| | | | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Marieke Lydia Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway
- Department of Pathology, Leiden University Medical Center, Leiden 2300RC, The Netherlands
- Leiden Center of Computational Oncology, Leiden University Medical Center,Leiden 2300RC, The Netherlands
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70
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Ketteler A, Blumenthal DB. Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer. Brief Bioinform 2023; 24:bbad413. [PMID: 37985453 DOI: 10.1093/bib/bbad413] [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: 06/16/2023] [Revised: 09/19/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.
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Affiliation(s)
- Anna Ketteler
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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71
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Henao JD, Lauber M, Azevedo M, Grekova A, Theis F, List M, Ogris C, Schubert B. Multi-omics regulatory network inference in the presence of missing data. Brief Bioinform 2023; 24:bbad309. [PMID: 37670505 PMCID: PMC10516394 DOI: 10.1093/bib/bbad309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/06/2023] [Accepted: 05/29/2023] [Indexed: 09/07/2023] Open
Abstract
A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.
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Affiliation(s)
- Juan D Henao
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Michael Lauber
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising
| | - Manuel Azevedo
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Anastasiia Grekova
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Fabian Theis
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising
| | - Christoph Ogris
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Benjamin Schubert
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
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72
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Hoskins JW, Christensen TA, Amundadottir LT. Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data. STAR Protoc 2023; 4:102362. [PMID: 37330907 PMCID: PMC10285694 DOI: 10.1016/j.xpro.2023.102362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/26/2023] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs). We describe steps for tissue-/cell-type-specific co-expression network modeling, expression regulator activity inference, and identification of representative phenotypic master regulators. Finally, we detail activity QTL and eQTL analyses. This protocol requires genotype, expression, and relevant covariables and phenotype data from existing eQTL datasets. For complete details on the use and execution of this protocol, please refer to Hoskins et al.1.
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Affiliation(s)
- Jason W Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MA 20892, USA.
| | - Trevor A Christensen
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MA 20892, USA
| | - Laufey T Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MA 20892, USA.
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Theofilatos K, Stojkovic S, Hasman M, van der Laan SW, Baig F, Barallobre-Barreiro J, Schmidt LE, Yin S, Yin X, Burnap S, Singh B, Popham J, Harkot O, Kampf S, Nackenhorst MC, Strassl A, Loewe C, Demyanets S, Neumayer C, Bilban M, Hengstenberg C, Huber K, Pasterkamp G, Wojta J, Mayr M. Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes. Circ Res 2023; 133:542-558. [PMID: 37646165 PMCID: PMC10498884 DOI: 10.1161/circresaha.123.322590] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. METHODS Two hundred nineteen carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. Proteomic signatures were integrated with bulk, single-cell, and spatial RNA-sequencing data, and validated in 200 patients from the Athero-Express Biobank study. RESULTS This extensive proteomics analysis identified plaque inflammation and calcification signatures, which were inversely correlated and validated using targeted proteomics. The inflammation signature was characterized by the presence of neutrophil-derived proteins, such as S100A8/9 (calprotectin) and myeloperoxidase, whereas the calcification signature included fetuin-A, osteopontin, and gamma-carboxylated proteins. The proteomics data also revealed sex differences in atherosclerosis, with large-aggregating proteoglycans versican and aggrecan being more abundant in females and exhibiting an inverse correlation with estradiol levels. The integration of RNA-sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages, and the calcification and sex signatures to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in plaques, such as fetuin-A. Dimensionality reduction and machine learning techniques were applied to identify 4 distinct plaque phenotypes based on proteomics data. A protein signature of 4 key proteins (calponin, protein C, serpin H1, and versican) predicted future cardiovascular mortality with an area under the curve of 75% and 67.5% in the discovery and validation cohort, respectively, surpassing the prognostic performance of imaging and histology. CONCLUSIONS Plaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.
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Affiliation(s)
- Konstantinos Theofilatos
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Stefan Stojkovic
- Division of Cardiology, Department of Internal Medicine II (S.S., O.H., C.H., J.W., M.M.), Medical University of Vienna, Austria
| | - Maria Hasman
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Sander W. van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, the Netherlands (S.W.v.d.L., G.P.)
| | - Ferheen Baig
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Javier Barallobre-Barreiro
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Lukas Emanuel Schmidt
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Siqi Yin
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Xiaoke Yin
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Sean Burnap
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Bhawana Singh
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Jude Popham
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
| | - Olesya Harkot
- Division of Cardiology, Department of Internal Medicine II (S.S., O.H., C.H., J.W., M.M.), Medical University of Vienna, Austria
| | - Stephanie Kampf
- Division of Vascular Surgery, Department of Surgery (S.K., C.N.), Medical University of Vienna, Austria
| | | | - Andreas Strassl
- Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-Guided Therapy (A.S., C.L.), Medical University of Vienna, Austria
| | - Christian Loewe
- Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-Guided Therapy (A.S., C.L.), Medical University of Vienna, Austria
| | - Svitlana Demyanets
- Department of Laboratory Medicine (S.D.), Medical University of Vienna, Austria
| | - Christoph Neumayer
- Division of Vascular Surgery, Department of Surgery (S.K., C.N.), Medical University of Vienna, Austria
| | - Martin Bilban
- Core Facilities (M.B.), Medical University of Vienna, Austria
| | - Christian Hengstenberg
- Division of Cardiology, Department of Internal Medicine II (S.S., O.H., C.H., J.W., M.M.), Medical University of Vienna, Austria
| | - Kurt Huber
- Third Medical Department, Wilhelminenspital, and Sigmund Freud University, Medical Faculty, Vienna, Austria (K.H.)
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, the Netherlands (S.W.v.d.L., G.P.)
| | - Johann Wojta
- Division of Cardiology, Department of Internal Medicine II (S.S., O.H., C.H., J.W., M.M.), Medical University of Vienna, Austria
- Ludwig Boltzmann Institute for Cardiovascular Research, Vienna, Austria (J.W.)
| | - Manuel Mayr
- King’s British Heart Foundation Centre, Kings College London, United Kingdom (K.T., M.H., F.B., J.B.B., L.E.S., S.Y., X.Y., S.B., B.S., J.P., M.M.)
- Division of Cardiology, Department of Internal Medicine II (S.S., O.H., C.H., J.W., M.M.), Medical University of Vienna, Austria
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74
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Li X, Lappalainen T, Bussemaker HJ. Identifying genetic regulatory variants that affect transcription factor activity. CELL GENOMICS 2023; 3:100382. [PMID: 37719147 PMCID: PMC10504674 DOI: 10.1016/j.xgen.2023.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 05/19/2023] [Accepted: 07/21/2023] [Indexed: 09/19/2023]
Abstract
Genetic variants affecting gene expression levels in humans have been mapped in the Genotype-Tissue Expression (GTEx) project. Trans-acting variants impacting many genes simultaneously through a shared transcription factor (TF) are of particular interest. Here, we developed a generalized linear model (GLM) to estimate protein-level TF activity levels in an individual-specific manner from GTEx RNA sequencing (RNA-seq) profiles. It uses observed differential gene expression after TF perturbation as a predictor and, by analyzing differential expression within pairs of neighboring genes, controls for the confounding effect of variation in chromatin state along the genome. We inferred genotype-specific activities for 55 TFs across 49 tissues. Subsequently performing genome-wide association analysis on this virtual trait revealed TF activity quantitative trait loci (aQTLs) that, as a set, are enriched for functional features. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omics approach. The transparent peer review record is available.
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Affiliation(s)
- Xiaoting Li
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Harmen J. Bussemaker
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
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75
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Jethalia M, Jani SP, Ceccarelli M, Mall R. Pancancer network analysis reveals key master regulators for cancer invasiveness. J Transl Med 2023; 21:558. [PMID: 37599366 PMCID: PMC10440887 DOI: 10.1186/s12967-023-04435-6] [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: 03/23/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. MATERIALS AND METHODS We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. RESULTS Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. CONCLUSION Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.
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Affiliation(s)
- Mahesh Jethalia
- Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Siddhi P Jani
- Centre of Brain Research, Indian Institute of Sciences, Bangalore, Karnataka, India
- Institute of Science, Nirma University, Ahmedabad, India
| | - Michele Ceccarelli
- Department of Public Health Sciences, University of Miami, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Raghvendra Mall
- St. Jude Children's Hospital, Memphis, TN, USA.
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates.
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76
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Jiang Z, Wu F, Laise P, Takayuki T, Na F, Kim W, Kobayashi H, Chang W, Takahashi R, Valenti G, Sunagawa M, White RA, Macchini M, Renz BW, Middelhoff M, Hayakawa Y, Dubeykovskaya ZA, Tan X, Chu TH, Nagar K, Tailor Y, Belin BR, Anand A, Asfaha S, Finlayson MO, Iuga AC, Califano A, Wang TC. Tff2 defines transit-amplifying pancreatic acinar progenitors that lack regenerative potential and are protective against Kras-driven carcinogenesis. Cell Stem Cell 2023; 30:1091-1109.e7. [PMID: 37541213 PMCID: PMC10414754 DOI: 10.1016/j.stem.2023.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 04/06/2023] [Accepted: 07/07/2023] [Indexed: 08/06/2023]
Abstract
While adult pancreatic stem cells are thought not to exist, it is now appreciated that the acinar compartment harbors progenitors, including tissue-repairing facultative progenitors (FPs). Here, we study a pancreatic acinar population marked by trefoil factor 2 (Tff2) expression. Long-term lineage tracing and single-cell RNA sequencing (scRNA-seq) analysis of Tff2-DTR-CreERT2-targeted cells defines a transit-amplifying progenitor (TAP) population that contributes to normal homeostasis. Following acute and chronic injury, Tff2+ cells, distinct from FPs, undergo depopulation but are eventually replenished. At baseline, oncogenic KrasG12D-targeted Tff2+ cells are resistant to PDAC initiation. However, KrasG12D activation in Tff2+ cells leads to survival and clonal expansion following pancreatitis and a cancer stem/progenitor cell-like state. Selective ablation of Tff2+ cells prior to KrasG12D activation in Mist1+ acinar or Dclk1+ FP cells results in enhanced tumorigenesis, which can be partially rescued by adenoviral Tff2 treatment. Together, Tff2 defines a pancreatic TAP population that protects against Kras-driven carcinogenesis.
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Affiliation(s)
- Zhengyu Jiang
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Feijing Wu
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Pasquale Laise
- Department of Systems Biology, College of Physicians and Surgeons, Columbia University, New York, NY, USA; DarwinHealth Inc., New York, NY, USA
| | - Tanaka Takayuki
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Fu Na
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; Department of Traditional and Western Medical Hepatology, Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Woosook Kim
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Hiroki Kobayashi
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Wenju Chang
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ryota Takahashi
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Giovanni Valenti
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Masaki Sunagawa
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Ruth A White
- Division of Hematology and Oncology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Marina Macchini
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Bernhard W Renz
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; Department of General, Visceral, and Transplantation Surgery, LMU University Hospital, LMU Munich, Germany
| | - Moritz Middelhoff
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; Division of Digestive and Liver Diseases, CU and Klinikum rechts der Isar, Technical University, Munich, Germany
| | - Yoku Hayakawa
- Graduate School of Medicine, Department of Gastroenterology, The University of Tokyo, Tokyo, Japan
| | - Zinaida A Dubeykovskaya
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Xiangtian Tan
- Department of Systems Biology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Timothy H Chu
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Karan Nagar
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Yagnesh Tailor
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Bryana R Belin
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Akanksha Anand
- Division of Digestive and Liver Diseases, Department of Medicine and Department of Gastroenterology II, Klinikum rechts der Isar, Technical University, Munich, Germany
| | - Samuel Asfaha
- Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Michael O Finlayson
- Department of Systems Biology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Alina C Iuga
- Department of Pathology and Cell Biology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Andrea Califano
- Department of Systems Biology, College of Physicians and Surgeons, Columbia University, New York, NY, USA; DarwinHealth Inc., New York, NY, USA
| | - Timothy C Wang
- Division of Digestive and Liver Diseases, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
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77
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Hasman M, Mayr M, Theofilatos K. Uncovering Protein Networks in Cardiovascular Proteomics. Mol Cell Proteomics 2023; 22:100607. [PMID: 37356494 PMCID: PMC10460687 DOI: 10.1016/j.mcpro.2023.100607] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023] Open
Abstract
Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
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Affiliation(s)
- Maria Hasman
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
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78
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Liu Q, Wei F, Wang J, Liu H, Zhang H, Liu M, Liu K, Ye Z. Molecular mechanisms regulating natural menopause in the female ovary: a study based on transcriptomic data. Front Endocrinol (Lausanne) 2023; 14:1004245. [PMID: 37564980 PMCID: PMC10411606 DOI: 10.3389/fendo.2023.1004245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 07/03/2023] [Indexed: 08/12/2023] Open
Abstract
Introduction Natural menopause is an inevitable biological process with significant implications for women's health. However, the molecular mechanisms underlying menopause are not well understood. This study aimed to investigate the molecular and cellular changes occurring in the ovary before and after perimenopause. Methods Single-cell sequencing data from the GTEx V8 cohort (30-39: 14 individuals; 40-49: 37 individuals; 50-59: 61 individuals) and transcriptome sequencing data from ovarian tissue were analyzed. Seurat was used for single-cell sequencing data analysis, while harmony was employed for data integration. Cell differentiation trajectories were inferred using CytoTrace. CIBERSORTX assessed cell infiltration scores in ovarian tissue. WGCNA evaluated co-expression network characteristics in pre- and post-perimenopausal ovarian tissue. Functional enrichment analysis of co-expression modules was conducted using ClusterprofileR and Metascape. DESeq2 performed differential expression analysis. Master regulator analysis and signaling pathway activity analysis were carried out using MsViper and Progeny, respectively. Machine learning models were constructed using Orange3. Results We identified the differentiation trajectory of follicular cells in the ovary as ARID5B+ Granulosa -> JUN+ Granulosa -> KRT18+ Granulosa -> MT-CO2+ Granulosa -> GSTA1+ Granulosa -> HMGB1+ Granulosa. Genes driving Granulosa differentiation, including RBP1, TMSB10, SERPINE2, and TMSB4X, were enriched in ATP-dependent activity regulation pathways. Genes involved in maintaining the Granulosa state, such as DCN, ARID5B, EIF1, and HSP90AB1, were enriched in the response to unfolded protein and chaperone-mediated protein complex assembly pathways. Increased contents of terminally differentiated HMGB1+ Granulosa and GSTA1+ Granulosa were observed in the ovaries of individuals aged 50-69. Signaling pathway activity analysis indicated a gradual decrease in TGFb and MAPK pathway activity with menopause progression, while p53 pathway activity increased. Master regulator analysis revealed significant activation of transcription factors FOXR1, OTX2, MYBL2, HNF1A, and FOXN4 in the 30-39 age group, and GLI1, SMAD1, SMAD7, APP, and EGR1 in the 40-49 age group. Additionally, a diagnostic model based on 16 transcription factors (Logistic Regression L2) achieved reliable performance in determining ovarian status before and after perimenopause. Conclusion This study provides insights into the molecular and cellular mechanisms underlying natural menopause in the ovary. The findings contribute to our understanding of perimenopausal changes and offer a foundation for health management strategies for women during this transition.
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Affiliation(s)
- Quan Liu
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Fangqin Wei
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Jiannan Wang
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Haiyan Liu
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Hua Zhang
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Min Liu
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Kaili Liu
- Binhu Hospital, Hefei First People’s Hospital, Hefei, Anhui, China
| | - Zheng Ye
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
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79
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Ceglia N, Sethna Z, Freeman SS, Uhlitz F, Bojilova V, Rusk N, Burman B, Chow A, Salehi S, Kabeer F, Aparicio S, Greenbaum BD, Shah SP, McPherson A. Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector. Nat Commun 2023; 14:4400. [PMID: 37474509 PMCID: PMC10359421 DOI: 10.1038/s41467-023-39985-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/04/2023] [Indexed: 07/22/2023] Open
Abstract
Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.
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Affiliation(s)
- Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Zachary Sethna
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel S Freeman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Florian Uhlitz
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoria Bojilova
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bharat Burman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Chow
- Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Farhia Kabeer
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Benjamin D Greenbaum
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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80
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Li JJ, Vasciaveo A, Karagiannis D, Sun Z, Chen X, Socciarelli F, Frankenstein Z, Zou M, Pannellini T, Chen Y, Gardner K, Robinson BD, de Bono J, Abate-Shen C, Rubin MA, Loda M, Sawyers CL, Califano A, Lu C, Shen MM. NSD2 maintains lineage plasticity and castration-resistance in neuroendocrine prostate cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.549585. [PMID: 37502956 PMCID: PMC10370123 DOI: 10.1101/2023.07.18.549585] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The clinical use of potent androgen receptor (AR) inhibitors has promoted the emergence of novel subtypes of metastatic castration-resistant prostate cancer (mCRPC), including neuroendocrine prostate cancer (CRPC-NE), which is highly aggressive and lethal 1 . These mCRPC subtypes display increased lineage plasticity and often lack AR expression 2-5 . Here we show that neuroendocrine differentiation and castration-resistance in CRPC-NE are maintained by the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2) 6 , which catalyzes histone H3 lysine 36 dimethylation (H3K36me2). We find that organoid lines established from genetically-engineered mice 7 recapitulate key features of human CRPC-NE, and can display transdifferentiation to neuroendocrine states in culture. CRPC-NE organoids express elevated levels of NSD2 and H3K36me2 marks, but relatively low levels of H3K27me3, consistent with antagonism of EZH2 activity by H3K36me2. Human CRPC-NE but not primary NEPC tumors expresses high levels of NSD2, consistent with a key role for NSD2 in lineage plasticity, and high NSD2 expression in mCRPC correlates with poor survival outcomes. Notably, CRISPR/Cas9 targeting of NSD2 or expression of a dominant-negative oncohistone H3.3K36M mutant results in loss of neuroendocrine phenotypes and restores responsiveness to the AR inhibitor enzalutamide in mouse and human CRPC-NE organoids and grafts. Our findings indicate that NSD2 inhibition can reverse lineage plasticity and castration-resistance, and provide a potential new therapeutic target for CRPC-NE.
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81
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Laber S, Strobel S, Mercader JM, Dashti H, dos Santos FR, Kubitz P, Jackson M, Ainbinder A, Honecker J, Agrawal S, Garborcauskas G, Stirling DR, Leong A, Figueroa K, Sinnott-Armstrong N, Kost-Alimova M, Deodato G, Harney A, Way GP, Saadat A, Harken S, Reibe-Pal S, Ebert H, Zhang Y, Calabuig-Navarro V, McGonagle E, Stefek A, Dupuis J, Cimini BA, Hauner H, Udler MS, Carpenter AE, Florez JC, Lindgren C, Jacobs SB, Claussnitzer M. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler. CELL GENOMICS 2023; 3:100346. [PMID: 37492099 PMCID: PMC10363917 DOI: 10.1016/j.xgen.2023.100346] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 08/22/2022] [Accepted: 05/26/2023] [Indexed: 07/27/2023]
Abstract
A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.
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Affiliation(s)
- Samantha Laber
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Sophie Strobel
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Hesam Dashti
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Felipe R.C. dos Santos
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Phil Kubitz
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Maya Jackson
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alina Ainbinder
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Julius Honecker
- Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
| | - Saaket Agrawal
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Garrett Garborcauskas
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David R. Stirling
- Imaging Platform, Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aaron Leong
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Katherine Figueroa
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nasa Sinnott-Armstrong
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Stanford University, San Francisco, CA, USA
| | - Maria Kost-Alimova
- Imaging Platform, Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Giacomo Deodato
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alycen Harney
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gregory P. Way
- Imaging Platform, Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alham Saadat
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sierra Harken
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Saskia Reibe-Pal
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Hannah Ebert
- Institute of Nutritional Science, University Hohenheim, 70599 Stuttgart, Germany
| | - Yixin Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Virtu Calabuig-Navarro
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute of Nutritional Science, University Hohenheim, 70599 Stuttgart, Germany
| | - Elizabeth McGonagle
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Adam Stefek
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada
| | - Beth A. Cimini
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hans Hauner
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
- Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Anne E. Carpenter
- Imaging Platform, Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Cecilia Lindgren
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Suzanne B.R. Jacobs
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Melina Claussnitzer
- Programs in Metabolism and Medical and Population Genetics, Type 2 Diabetes Systems Genomics Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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82
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Misetic H, Keddar MR, Jeannon JP, Ciccarelli FD. Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment. Genome Med 2023; 15:40. [PMID: 37277866 PMCID: PMC10240791 DOI: 10.1186/s13073-023-01197-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. METHODS Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. RESULTS The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. CONCLUSIONS Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .
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Affiliation(s)
- Hrvoje Misetic
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Mohamed Reda Keddar
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK
| | - Jean-Pierre Jeannon
- Department of Head & Neck Surgery, Great Maze Pond, Guy's Hospital, London, SE1 9RT, UK
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.
- School of Cancer and Pharmaceutical Sciences, King's College London, London, SE11UL, UK.
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83
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Ding J, Lee SJ, Vlahos L, Yuki K, Rada CC, van Unen V, Vuppalapaty M, Chen H, Sura A, McCormick AK, Tomaske M, Alwahabi S, Nguyen H, Nowatzke W, Kim L, Kelly L, Vollrath D, Califano A, Yeh WC, Li Y, Kuo CJ. Therapeutic blood-brain barrier modulation and stroke treatment by a bioengineered FZD 4-selective WNT surrogate in mice. Nat Commun 2023; 14:2947. [PMID: 37268690 PMCID: PMC10238527 DOI: 10.1038/s41467-023-37689-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/27/2023] [Indexed: 06/04/2023] Open
Abstract
Derangements of the blood-brain barrier (BBB) or blood-retinal barrier (BRB) occur in disorders ranging from stroke, cancer, diabetic retinopathy, and Alzheimer's disease. The Norrin/FZD4/TSPAN12 pathway activates WNT/β-catenin signaling, which is essential for BBB and BRB function. However, systemic pharmacologic FZD4 stimulation is hindered by obligate palmitoylation and insolubility of native WNTs and suboptimal properties of the FZD4-selective ligand Norrin. Here, we develop L6-F4-2, a non-lipidated, FZD4-specific surrogate which significantly improves subpicomolar affinity versus native Norrin. In Norrin knockout (NdpKO) mice, L6-F4-2 not only potently reverses neonatal retinal angiogenesis deficits, but also restores BRB and BBB function. In adult C57Bl/6J mice, post-stroke systemic delivery of L6-F4-2 strongly reduces BBB permeability, infarction, and edema, while improving neurologic score and capillary pericyte coverage. Our findings reveal systemic efficacy of a bioengineered FZD4-selective WNT surrogate during ischemic BBB dysfunction, with potential applicability to adult CNS disorders characterized by an aberrant blood-brain barrier.
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Affiliation(s)
- Jie Ding
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Sung-Jin Lee
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Lukas Vlahos
- Department of Systems Biology, Columbia University, Columbia, NY, 10032, USA
| | - Kanako Yuki
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Cara C Rada
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Vincent van Unen
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Hui Chen
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Asmiti Sura
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Aaron K McCormick
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Madeline Tomaske
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Samira Alwahabi
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Huy Nguyen
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - William Nowatzke
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Lily Kim
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lisa Kelly
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Douglas Vollrath
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University, Columbia, NY, 10032, USA
| | - Wen-Chen Yeh
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Yang Li
- Surrozen, Inc. South San Francisco, South San Francisco, CA, 94080, USA
| | - Calvin J Kuo
- Department of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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84
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Pan TC, Chockalingam SP, Aluru M, Aluru S. MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework. Bioinformatics 2023; 39:btad373. [PMID: 37289522 PMCID: PMC10287961 DOI: 10.1093/bioinformatics/btad373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 04/06/2023] [Accepted: 06/06/2023] [Indexed: 06/10/2023] Open
Abstract
MOTIVATION Gene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. RESULTS We developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene-gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements. AVAILABILITY AND IMPLEMENTATION Source code freely available for download at https://doi.org/10.5281/zenodo.6499747 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux.
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Affiliation(s)
- Tony C Pan
- Department of Biomedical Informatics, Emory University, Woodruff Memorial Research Building 101 Woodruff Circle, 4th Floor East, Atlanta, GA 30322, United States
- Institute for Data Engineering and Science, Georgia Institute of Technology, 756 W Peachtree St NW, 12th Floor, Atlanta, GA 30332, United States
| | - Sriram P Chockalingam
- Institute for Data Engineering and Science, Georgia Institute of Technology, 756 W Peachtree St NW, 12th Floor, Atlanta, GA 30332, United States
| | - Maneesha Aluru
- School of Biological Sciences, Georgia Institute of Technology, 310 Ferst Dr NW, Atlanta, GA 30332, United States
| | - Srinivas Aluru
- Institute for Data Engineering and Science, Georgia Institute of Technology, 756 W Peachtree St NW, 12th Floor, Atlanta, GA 30332, United States
- School of Computational Science and Engineering, Georgia Institute of Technology, 756 W Peachtree St NW, 13th Floor, Atlanta, GA 30332, United States
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85
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Obradovic A, Ager C, Turunen M, Nirschl T, Khosravi-Maharlooei M, Iuga A, Jackson CM, Yegnasubramanian S, Tomassoni L, Fernandez EC, McCann P, Rogava M, DeMarzo AM, Kochel CM, Allaf M, Bivalacqua T, Lim M, Realubit R, Karan C, Drake CG, Califano A. Systematic elucidation and pharmacological targeting of tumor-infiltrating regulatory T cell master regulators. Cancer Cell 2023; 41:933-949.e11. [PMID: 37116491 PMCID: PMC10193511 DOI: 10.1016/j.ccell.2023.04.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/13/2022] [Accepted: 04/06/2023] [Indexed: 04/30/2023]
Abstract
Due to their immunosuppressive role, tumor-infiltrating regulatory T cells (TI-Tregs) represent attractive immuno-oncology targets. Analysis of TI vs. peripheral Tregs (P-Tregs) from 36 patients, across four malignancies, identified 17 candidate master regulators (MRs) as mechanistic determinants of TI-Treg transcriptional state. Pooled CRISPR-Cas9 screening in vivo, using a chimeric hematopoietic stem cell transplant model, confirmed the essentiality of eight MRs in TI-Treg recruitment and/or retention without affecting other T cell subtypes, and targeting one of the most significant MRs (Trps1) by CRISPR KO significantly reduced ectopic tumor growth. Analysis of drugs capable of inverting TI-Treg MR activity identified low-dose gemcitabine as the top prediction. Indeed, gemcitabine treatment inhibited tumor growth in immunocompetent but not immunocompromised allografts, increased anti-PD-1 efficacy, and depleted MR-expressing TI-Tregs in vivo. This study provides key insight into Treg signaling, specifically in the context of cancer, and a generalizable strategy to systematically elucidate and target MR proteins in immunosuppressive subpopulations.
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Affiliation(s)
- Aleksandar Obradovic
- Columbia Center for Translational Immunology, Irving Medical Center, New York, NY, USA; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Casey Ager
- Columbia Center for Translational Immunology, Irving Medical Center, New York, NY, USA; Department of Hematology Oncology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Thomas Nirschl
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Alina Iuga
- Department of Pathology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Christopher M Jackson
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Lorenzo Tomassoni
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Ester Calvo Fernandez
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick McCann
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Meri Rogava
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Angelo M DeMarzo
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Urology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christina M Kochel
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mohamad Allaf
- Department of Urology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Trinity Bivalacqua
- Department of Urology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Lim
- Department of Neurosurgery, Stanford School of Medicine, Palo Alto, CA, USA
| | - Ronald Realubit
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles G Drake
- Columbia Center for Translational Immunology, Irving Medical Center, New York, NY, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA; Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
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86
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Arriojas A, Patalano S, Macoska J, Zarringhalam K. A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539308. [PMID: 37205561 PMCID: PMC10187261 DOI: 10.1101/2023.05.03.539308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.
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Affiliation(s)
- Argenis Arriojas
- Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA
- Department of Physics, University of Massachusetts Boston, Boston, MA 02125, USA
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Susan Patalano
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Jill Macoska
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Kourosh Zarringhalam
- Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA
- Center for Personalized Cancer Therapy, University of Massachusetts Boston, Boston, MA 02125, USA
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87
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Wang Q, Guo M, Chen J, Duan R. A gene regulatory network inference model based on pseudo-siamese network. BMC Bioinformatics 2023; 24:163. [PMID: 37085776 PMCID: PMC10122305 DOI: 10.1186/s12859-023-05253-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 03/24/2023] [Indexed: 04/23/2023] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) arise from the intricate interactions between transcription factors (TFs) and their target genes during the growth and development of organisms. The inference of GRNs can unveil the underlying gene interactions in living systems and facilitate the investigation of the relationship between gene expression patterns and phenotypic traits. Although several machine-learning models have been proposed for inferring GRNs from single-cell RNA sequencing (scRNA-seq) data, some of these models, such as Boolean and tree-based networks, suffer from sensitivity to noise and may encounter difficulties in handling the high noise and dimensionality of actual scRNA-seq data, as well as the sparse nature of gene regulation relationships. Thus, inferring large-scale information from GRNs remains a formidable challenge. RESULTS This study proposes a multilevel, multi-structure framework called a pseudo-Siamese GRN (PSGRN) for inferring large-scale GRNs from time-series expression datasets. Based on the pseudo-Siamese network, we applied a gated recurrent unit to capture the time features of each TF and target matrix and learn the spatial features of the matrices after merging by applying the DenseNet framework. Finally, we applied a sigmoid function to evaluate interactions. We constructed two maize sub-datasets, including gene expression levels and GRNs, using existing open-source maize multi-omics data and compared them to other GRN inference methods, including GENIE3, GRNBoost2, nonlinear ordinary differential equations, CNNC, and DGRNS. Our results show that PSGRN outperforms state-of-the-art methods. This study proposed a new framework: a PSGRN that allows GRNs to be inferred from scRNA-seq data, elucidating the temporal and spatial features of TFs and their target genes. The results show the model's robustness and generalization, laying a theoretical foundation for maize genotype-phenotype associations with implications for breeding work.
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Affiliation(s)
- Qian Wang
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
| | - Jian Chen
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Ran Duan
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
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88
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Blatti C, de la Fuente J, Gao H, Marín-Goñi I, Chen Z, Zhao SD, Tan W, Weinshilboum R, Kalari KR, Wang L, Hernaez M. Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer. Cancer Res 2023; 83:1361-1380. [PMID: 36779846 PMCID: PMC10102853 DOI: 10.1158/0008-5472.can-22-1910] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/29/2022] [Accepted: 02/08/2023] [Indexed: 02/14/2023]
Abstract
Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. SIGNIFICANCE The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.
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Affiliation(s)
- Charles Blatti
- NCSA, University of Illinois at Urbana-Champaign, Champaign, Illinois
| | | | - Huanyao Gao
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Irene Marín-Goñi
- Computational Biology Program, CIMA University of Navarra, Navarra, Spain
| | - Zikun Chen
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois
| | - Sihai D. Zhao
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois
| | - Winston Tan
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Krishna R. Kalari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Mikel Hernaez
- Computational Biology Program, CIMA University of Navarra, Navarra, Spain
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois
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89
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Neyret-Kahn H, Fontugne J, Meng XY, Groeneveld CS, Cabel L, Ye T, Guyon E, Krucker C, Dufour F, Chapeaublanc E, Rapinat A, Jeffery D, Tanguy L, Dixon V, Neuzillet Y, Lebret T, Gentien D, Davidson I, Allory Y, Bernard-Pierrot I, Radvanyi F. Epigenomic mapping identifies an enhancer repertoire that regulates cell identity in bladder cancer through distinct transcription factor networks. Oncogene 2023; 42:1524-1542. [PMID: 36944729 PMCID: PMC10162941 DOI: 10.1038/s41388-023-02662-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/23/2023]
Abstract
Muscle-invasive bladder cancer (BLCA) is an aggressive disease. Consensus BLCA transcriptomic subtypes have been proposed, with two major Luminal and Basal subgroups, presenting distinct molecular and clinical characteristics. However, how these distinct subtypes are regulated remains unclear. We hypothesized that epigenetic activation of distinct super-enhancers could drive the transcriptional programs of BLCA subtypes. Through integrated RNA-sequencing and epigenomic profiling of histone marks in primary tumours, cancer cell lines, and normal human urothelia, we established the first integrated epigenetic map of BLCA and demonstrated the link between subtype and epigenetic control. We identified the repertoire of activated super-enhancers and highlighted Basal, Luminal and Normal-associated SEs. We revealed super-enhancer-regulated networks of candidate master transcription factors for Luminal and Basal subgroups including FOXA1 and ZBED2, respectively. FOXA1 CRISPR-Cas9 mutation triggered a shift from Luminal to Basal phenotype, confirming its role in Luminal identity regulation and induced ZBED2 overexpression. In parallel, we showed that both FOXA1 and ZBED2 play concordant roles in preventing inflammatory response in cancer cells through STAT2 inhibition. Our study furthers the understanding of epigenetic regulation of muscle-invasive BLCA and identifies a co-regulated network of super-enhancers and associated transcription factors providing potential targets for the treatment of this aggressive disease.
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Affiliation(s)
- Hélène Neyret-Kahn
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France.
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France.
- INSERM U830, Equipe Labellisée LNCC, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, SIREDO Oncology Center, Institut Curie Research Center, Paris, France.
| | - Jacqueline Fontugne
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Department of Pathology, Institut Curie, Saint-Cloud, France
- Université Versailles St-Quentin, Université Paris-Saclay, F-78180, Montigny-le-Bretonneux, France
| | - Xiang Yu Meng
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
- College of Basic Medical Sciences, Medical School, Hubei Minzu University, Enshi, 445000, China
| | - Clarice S Groeneveld
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
- Université de Paris, Centre de Recherche des Cordeliers, Paris, France
| | - Luc Cabel
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
| | - Tao Ye
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U1258, Centre National de Recherche Scientifique (CNRS) UMR7104, Université de Strasbourg,1 rue Laurent Fries, 67404, Illkirch, France
| | - Elodie Guyon
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Department of Pathology, Institut Curie, Paris, France
| | - Clémentine Krucker
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
- Department of Pathology, Institut Curie, Saint-Cloud, France
| | - Florent Dufour
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
| | - Elodie Chapeaublanc
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
| | - Audrey Rapinat
- Department of Translational Research, Genomics Platform, Institut Curie, PSL Research University, Paris, France
| | - Daniel Jeffery
- Urology Medico-Scientific Program, Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Laura Tanguy
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
| | - Victoria Dixon
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Department of Pathology, Institut Curie, Saint-Cloud, France
| | - Yann Neuzillet
- Université Versailles St-Quentin, Université Paris-Saclay, F-78180, Montigny-le-Bretonneux, France
- Department of Urology, Hôpital Foch, Suresnes, France
| | - Thierry Lebret
- Université Versailles St-Quentin, Université Paris-Saclay, F-78180, Montigny-le-Bretonneux, France
- Department of Urology, Hôpital Foch, Suresnes, France
| | - David Gentien
- Department of Translational Research, Genomics Platform, Institut Curie, PSL Research University, Paris, France
| | - Irwin Davidson
- Department of Functional Genomics and Cancer, Institut de Genétique et de Biologie Moleculaire et Cellulaire, CNRS/INSERM/UDS, 67404, Illkirch Cedex, France
| | - Yves Allory
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Department of Pathology, Institut Curie, Saint-Cloud, France
- Université Versailles St-Quentin, Université Paris-Saclay, F-78180, Montigny-le-Bretonneux, France
| | - Isabelle Bernard-Pierrot
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
| | - François Radvanyi
- Molecular Oncology, PSL Research University, CNRS, UMR 144, Institut Curie, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, 75005, Paris, France
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90
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Griffin AT, Vlahos LJ, Chiuzan C, Califano A. NaRnEA: An Information Theoretic Framework for Gene Set Analysis. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25030542. [PMID: 36981431 PMCID: PMC10048242 DOI: 10.3390/e25030542] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/03/2023] [Accepted: 03/13/2023] [Indexed: 05/26/2023]
Abstract
Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.
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Affiliation(s)
- Aaron T. Griffin
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lukas J. Vlahos
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Codruta Chiuzan
- Department of Biostatistics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- JP Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
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91
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Flores-Díaz A, Escoto-Sandoval C, Cervantes-Hernández F, Ordaz-Ortiz JJ, Hayano-Kanashiro C, Reyes-Valdés H, Garcés-Claver A, Ochoa-Alejo N, Martínez O. Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper ( Capsicum annuum L.). PLANTS (BASEL, SWITZERLAND) 2023; 12:1148. [PMID: 36904008 PMCID: PMC10005043 DOI: 10.3390/plants12051148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package "Salsa" (version 1.0).
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Affiliation(s)
- Alan Flores-Díaz
- Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
| | - Christian Escoto-Sandoval
- Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
| | - Felipe Cervantes-Hernández
- Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
| | - José J. Ordaz-Ortiz
- Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
| | - Corina Hayano-Kanashiro
- Departamento de Investigaciones Científicas y Tecnológicas de la Universidad de Sonora, Hermosillo 83000, Mexico
| | - Humberto Reyes-Valdés
- Department of Plant Breeding, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
| | - Ana Garcés-Claver
- Unidad de Hortofruticultura, Centro de Investigación y Tecnología Agroalimentaria de Aragón, Instituto Agroalimentario de Aragón-IA2 (CITA-Universidad de Zaragoza), 50059 Zaragoza, Spain
| | - Neftalí Ochoa-Alejo
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
| | - Octavio Martínez
- Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato 36824, Mexico
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92
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Lachmann A, Rizzo KA, Bartal A, Jeon M, Clarke DJB, Ma’ayan A. PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices. PeerJ 2023; 11:e14927. [PMID: 36874981 PMCID: PMC9979837 DOI: 10.7717/peerj.14927] [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: 10/06/2022] [Accepted: 01/30/2023] [Indexed: 03/03/2023] Open
Abstract
Background Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Results Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. Conclusions By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.
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Affiliation(s)
- Alexander Lachmann
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kaeli A. Rizzo
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alon Bartal
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Minji Jeon
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Daniel J. B. Clarke
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Avi Ma’ayan
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
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93
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528736. [PMID: 36824919 PMCID: PMC9949144 DOI: 10.1101/2023.02.15.528736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
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94
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The role of non-additive gene action on gene expression variation in plant domestication. EvoDevo 2023; 14:3. [PMID: 36765382 PMCID: PMC9912502 DOI: 10.1186/s13227-022-00206-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/05/2022] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Plant domestication is a remarkable example of rapid phenotypic transformation of polygenic traits, such as organ size. Evidence from a handful of study cases suggests this transformation is due to gene regulatory changes that result in non-additive phenotypes. Employing data from published genetic crosses, we estimated the role of non-additive gene action in the modulation of transcriptional landscapes in three domesticated plants: maize, sunflower, and chili pepper. Using A. thaliana, we assessed the correlation between gene regulatory network (GRN) connectivity properties, transcript abundance variation, and gene action. Finally, we investigated the propagation of non-additive gene action in GRNs. RESULTS We compared crosses between domesticated plants and their wild relatives to a set of control crosses that included a pair of subspecies evolving under natural selection and a set of inbred lines evolving under domestication. We found abundance differences on a higher portion of transcripts in crosses between domesticated-wild plants relative to the control crosses. These transcripts showed non-additive gene action more often in crosses of domesticated-wild plants than in our control crosses. This pattern was strong for genes associated with cell cycle and cell fate determination, which control organ size. We found weak but significant negative correlations between the number of targets of trans-acting genes (Out-degree) and both the magnitude of transcript abundance difference a well as the absolute degree of dominance. Likewise, we found that the number of regulators that control a gene's expression (In-degree) is weakly but negatively correlated with the magnitude of transcript abundance differences. We observed that dominant-recessive gene action is highly propagable through GRNs. Finally, we found that transgressive gene action is driven by trans-acting regulators showing additive gene action. CONCLUSIONS Our study highlights the role of non-additive gene action on modulating domestication-related traits, such as organ size via regulatory divergence. We propose that GRNs are shaped by regulatory changes at genes with modest connectivity, which reduces the effects of antagonistic pleiotropy. Finally, we provide empirical evidence of the propagation of non-additive gene action in GRNs, which suggests a transcriptional epistatic model for the control of polygenic traits, such as organ size.
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95
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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96
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Sebastian S, Roy S, Kalita J. A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network. Brief Bioinform 2023; 24:6868522. [PMID: 36534961 DOI: 10.1093/bib/bbac482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/14/2022] [Accepted: 10/11/2022] [Indexed: 12/23/2022] Open
Abstract
The inference of large-scale gene regulatory networks is essential for understanding comprehensive interactions among genes. Most existing methods are limited to reconstructing networks with a few hundred nodes. Therefore, parallel computing paradigms must be leveraged to construct large networks. We propose a generic parallel framework that enables any existing method, without re-engineering, to infer large networks in parallel, guaranteeing quality output. The framework is tested on 15 inference methods (not limited to) employing in silico benchmarks and real-world large expression matrices, followed by qualitative and speedup assessment. The framework does not compromise the quality of the base serial inference method. We rank the candidate methods and use the top-performing method to infer an Alzheimer's Disease (AD) affected network from large expression profiles of a triple transgenic mouse model consisting of 45,101 genes. The resultant network is further explored to obtain hub genes that emerge functionally related to the disease. We partition the network into 41 modules and conduct pathway enrichment analysis, revealing that a good number of participating genes are collectively responsible for several brain disorders, including AD. Finally, we extract the interactions of a few known AD genes and observe that they are periphery genes connected to the network's hub genes. Availability: The R implementation of the framework is downloadable from https://github.com/Netralab/GenericParallelFramework.
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Affiliation(s)
- Softya Sebastian
- Network Reconstruction and Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, 6th Mile, Gangtok, 737102, Sikkim, India
| | - Swarup Roy
- Network Reconstruction and Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, 6th Mile, Gangtok, 737102, Sikkim, India
| | - Jugal Kalita
- Department of Computer Science, University of Colorado at Colorado Springs, CO, 80918 USA
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97
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Zhang Y, Zhang Y, Song C, Zhao X, Ai B, Wang Y, Zhou L, Zhu J, Feng C, Xu L, Wang Q, Sun H, Fang Q, Xu X, Li E, Li C. CRdb: a comprehensive resource for deciphering chromatin regulators in human. Nucleic Acids Res 2023; 51:D88-D100. [PMID: 36318256 PMCID: PMC9825595 DOI: 10.1093/nar/gkac960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/04/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022] Open
Abstract
Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.
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Affiliation(s)
- Yimeng Zhang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
| | | | | | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liwei Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liyan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Hong Sun
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Qiaoli Fang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Xiaozheng Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Enmin Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South
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98
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Wang YQ, Wu DH, Wei D, Shen JY, Huang ZW, Liang XY, Cho WC, Ma J, Lv J, Chen YP. TEAD4 is a master regulator of high-risk nasopharyngeal carcinoma. SCIENCE ADVANCES 2023; 9:eadd0960. [PMID: 36608137 PMCID: PMC9821866 DOI: 10.1126/sciadv.add0960] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
The molecular basis underlying nasopharyngeal carcinoma (NPC) remains unclear. Recent progress in transcriptional regulatory network analysis helps identify the master regulator (MR) proteins that transcriptionally define malignant tumor phenotypes. Here, we investigated transcription factor-target interactions and identified TEA domain transcription factor 4 (TEAD4) as an MR of high-risk NPC. Precisely, TEAD4 promoted NPC migration, invasion and cisplatin resistance, depending on its autopalmitoylation. Mechanistically, YTHDF2 (YTH domain family 2) recognized WTAP (Wilms tumor 1-associating protein)-mediated TEAD4 m6A methylation to facilitate its stability and led to aberrant up-regulation of TEAD4. Up-regulated TEAD4 further drove NPC progression by transcriptionally activating BZW2 (basic leucine zipper and W2 domains 2) to induce the oncogenic AKT pathway. Moreover, the transcriptional activity of TEAD4 was independent of its canonical coactivators YAP/TAZ. Clinically, TEAD4 serves as an independent predictor of unfavorable prognosis and cisplatin response in NPC. Our data revealed the crucial role of TEAD4 in driving tumor malignancy, thus, may provide therapeutic vulnerability in NPC.
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Affiliation(s)
- Ya-Qin Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Dong-Hong Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Denghui Wei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Jia-Yi Shen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Zi-Wei Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Xiao-Yu Liang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - William C.S. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong Special Administrative Region, Hong Kong, P.R. China
| | - Jun Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Jiawei Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
| | - Yu-Pei Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Center for Precision Medicine of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
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99
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Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients. Cancer Genet 2023; 270-271:1-11. [PMID: 36410105 DOI: 10.1016/j.cancergen.2022.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/25/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes. MATERIALS AND METHODS Using Standard SQL, we queried ISB-CGC hosted Google BigQuery tables storing TCGA BrCA gene expression, methylation, and somatic mutation data and analyzed the combined multi-omics results using a variety of methods. RESULTS Among Stage II patients 50 years or younger, genes PIK3CA and CDH1 are more frequently mutated in White (W50) than in Black or African American patients (BAA50), while HUWE1, HYDIN, and FBXW7 mutations are more frequent in BAA50. Over-representation analysis (ORA) and Gene Set Enrichment Analysis (GSEA) results indicate that, among others, the Reactome Signaling by ROBO Receptors gene set is enriched in BAA50. Using the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm, putative top 20 master regulators identified include NUPR1, NFKBIL1, ZBTB17, TEAD1, EP300, TRAF6, CACTIN, and MID2. CACTIN and MID2 are of prognostic value. We identified driver genes, such as OTUB1, with suppressed expression whose DNA methylation status were inversely correlated with gene expression. Networks capturing microRNA and gene expression correlations identified notable microRNA hubs, such as miR-93 and miR-92a-2, expressed at higher levels in BAA50 than in W50. DISCUSSION/CONCLUSION The results point to several driver genes as being involved in the observed differences between the cohorts. The findings here form the basis for further mechanistic exploration.
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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