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Chilimoniuk J, Erol A, Rödiger S, Burdukiewicz M. Challenges and opportunities in processing NanoString nCounter data. Comput Struct Biotechnol J 2024; 23:1951-1958. [PMID: 38736697 PMCID: PMC11087919 DOI: 10.1016/j.csbj.2024.04.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024] Open
Abstract
NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.
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Affiliation(s)
| | - Anna Erol
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Stefan Rödiger
- Institute of Biotechnology, Faculty Environment and Natural Sciences, Brandenburg University of Technology Cottbus - Senftenberg, Senftenberg, Germany
| | - Michał Burdukiewicz
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
- Institute of Biotechnology and Biomedicine, Autonomous University of Barcelona, Barcelona, Spain
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2
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Yu Y, Mai Y, Zheng Y, Shi L. Assessing and mitigating batch effects in large-scale omics studies. Genome Biol 2024; 25:254. [PMID: 39363244 PMCID: PMC11447944 DOI: 10.1186/s13059-024-03401-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/23/2024] [Indexed: 10/05/2024] Open
Abstract
Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.
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Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
- Cancer Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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3
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Johnson OD, Paul S, Gutierrez JA, Russell WK, Ward MC. DNA damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.14.607863. [PMID: 39185220 PMCID: PMC11343126 DOI: 10.1101/2024.08.14.607863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Cardiovascular disease (CVD) is associated with both genetic variants and environmental factors. One unifying consequence of the molecular risk factors in CVD is DNA damage, which must be repaired by DNA damage response proteins. However, the impact of DNA damage on global cardiomyocyte protein abundance, and its relationship to CVD risk remains unclear. We therefore treated induced pluripotent stem cell-derived cardiomyocytes with the DNA-damaging agent Doxorubicin (DOX) and a vehicle control, and identified 4,178 proteins that contribute to a network comprising 12 co-expressed modules and 403 hub proteins with high intramodular connectivity. Five modules correlate with DOX and represent distinct biological processes including RNA processing, chromatin regulation and metabolism. DOX-correlated hub proteins are depleted for proteins that vary in expression across individuals due to genetic variation but are enriched for proteins encoded by loss-of-function intolerant genes. While proteins associated with genetic risk for CVD, such as arrhythmia are enriched in specific DOX-correlated modules, DOX-correlated hub proteins are not enriched for known CVD risk proteins. Instead, they are enriched among proteins that physically interact with CVD risk proteins. Our data demonstrate that DNA damage in cardiomyocytes induces diverse effects on biological processes through protein co-expression modules that are relevant for CVD, and that the level of protein connectivity in DNA damage-associated modules influences the tolerance to genetic variation.
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Affiliation(s)
- Omar D. Johnson
- Biochemistry, Cellular and Molecular Biology Graduate Program, University of Texas Medical Branch, Galveston, Texas, USA
- MD-PhD Combined Degree Program, University of Texas Medical Branch, Galveston, Texas, USA
| | - Sayan Paul
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, USA
| | - Jose A. Gutierrez
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, USA
| | - William K. Russell
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, USA
| | - Michelle C. Ward
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, USA
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Hoch D, Majali-Martinez A, Bandres-Meriz J, Bachbauer M, Pöchlauer C, Kaudela T, Bankoglu EE, Stopper H, Glasner A, Hauguel-De Mouzon S, Gauster M, Tokic S, Desoye G. Obesity-associated non-oxidative genotoxic stress alters trophoblast turnover in human first-trimester placentas. Mol Hum Reprod 2024; 30:gaae027. [PMID: 39092995 PMCID: PMC11347397 DOI: 10.1093/molehr/gaae027] [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: 04/02/2024] [Revised: 05/31/2024] [Indexed: 08/04/2024] Open
Abstract
Placental growth is most rapid during the first trimester (FT) of pregnancy, making it vulnerable to metabolic and endocrine influences. Obesity, with its inflammatory and oxidative stress, can cause cellular damage. We hypothesized that maternal obesity increases DNA damage in the FT placenta, affecting DNA damage response and trophoblast turnover. Examining placental tissue from lean and obese non-smoking women (4-12 gestational weeks), we observed higher overall DNA damage in obesity (COMET assay). Specifically, DNA double-strand breaks were found in villous cytotrophoblasts (vCTB; semi-quantitative γH2AX immunostaining), while oxidative DNA modifications (8-hydroxydeoxyguanosine; FPG-COMET assay) were absent. Increased DNA damage in obese FT placentas did not correlate with enhanced DNA damage sensing and repair. Indeed, obesity led to reduced expression of multiple DNA repair genes (mRNA array), which were further shown to be influenced by inflammation through in vitro experiments using tumor necrosis factor-α treatment on FT chorionic villous explants. Tissue changes included elevated vCTB apoptosis (TUNEL assay; caspase-cleaved cytokeratin 18), but unchanged senescence (p16) and reduced proliferation (Ki67) of vCTB, the main driver of FT placental growth. Overall, obesity is linked to heightened non-oxidative DNA damage in FT placentas, negatively affecting trophoblast growth and potentially leading to temporary reduction in early fetal growth.
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Affiliation(s)
- Denise Hoch
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Alejandro Majali-Martinez
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
- Departamento de Medicina, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, Madrid, Spain
| | - Julia Bandres-Meriz
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Martina Bachbauer
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Caroline Pöchlauer
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Theresa Kaudela
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Ezgi Eyluel Bankoglu
- Institute of Pharmacology and Toxicology, University of Wuerzburg, Wuerzburg, Germany
| | - Helga Stopper
- Institute of Pharmacology and Toxicology, University of Wuerzburg, Wuerzburg, Germany
| | | | | | - Martin Gauster
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - Silvija Tokic
- Department of Paediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
- Research Unit of Analytical Mass Spectrometry, Cell Biology and Biochemistry of Inborn Errors of Metabolism, Medical University of Graz, Graz, Austria
| | - Gernot Desoye
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
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Montoto-Louzao J, Gómez-Carballa A, Bello X, Pardo-Seco J, Camino-Mera A, Viz-Lasheras S, Martín MJ, Martinón-Torres F, Salas A. GUANIN: an all-in-one GUi-driven analyzer for NanoString interactive normalization. Bioinformatics 2024; 40:btae462. [PMID: 39051707 PMCID: PMC11333565 DOI: 10.1093/bioinformatics/btae462] [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: 02/25/2024] [Revised: 05/20/2024] [Accepted: 07/24/2024] [Indexed: 07/27/2024] Open
Abstract
SUMMARY Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI). AVAILABILITY AND IMPLEMENTATION GUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license.
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Affiliation(s)
- Julián Montoto-Louzao
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
| | - Alberto Gómez-Carballa
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
| | - Xabier Bello
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
| | - Jacobo Pardo-Seco
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
| | - Alba Camino-Mera
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
| | - Sandra Viz-Lasheras
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
| | - María J Martín
- CITIC, Computer Architecture Group, Universidade da Coruña, Facultad de Informática, 15071, A Coruña, Spain
| | - Federico Martinón-Torres
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Choupana s/n, Santiago de Compostela, 15706, Spain
| | - Antonio Salas
- Unidade de Xenética, Instituto de Ciencias Forenses, Facultade de Medicina, Universidade de Santiago de Compostela, and Genética de Poblaciones en Biomedicina (GenPoB) Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706, Santiago de Compostela, Spain
- Genetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santigo de Compostela, 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Madrid, 28029, Spain
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Yaghoubi Naei V, Monkman J, Sadeghirad H, Mehdi A, Blick T, Mullally W, O'Byrne K, Warkiani ME, Kulasinghe A. Spatial proteomic profiling of tumor and stromal compartments in non-small-cell lung cancer identifies signatures associated with overall survival. Clin Transl Immunology 2024; 13:e1522. [PMID: 39026528 PMCID: PMC11257771 DOI: 10.1002/cti2.1522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/20/2024] Open
Abstract
Objectives Non-small-cell lung carcinoma (NSCLC) is the most prevalent and lethal form of lung cancer. The need for biomarker-informed stratification of targeted therapies has underpinned the need to uncover the underlying properties of the tumor microenvironment (TME) through high-plex quantitative assays. Methods In this study, we profiled resected NSCLC tissues from 102 patients by targeted spatial proteomics of 78 proteins across tumor, immune activation, immune cell typing, immune-oncology, drug targets, cell death and PI3K/AKT modules to identify the tumor and stromal signatures associated with overall survival (OS). Results Survival analysis revealed that stromal CD56 (HR = 0.384, P = 0.06) and tumoral TIM3 (HR = 0.703, P = 0.05) were associated with better survival in univariate Cox models. In contrast, after adjusting for stage, BCLXL (HR = 2.093, P = 0.02) and cleaved caspase 9 (HR = 1.575, P = 0.1) negatively influenced survival. Delta testing indicated the protective effect of TIM-3 (HR = 0.614, P = 0.04) on OS. In multivariate analysis, CD56 (HR = 0.172, P = 0.001) was associated with better survival in the stroma, while B7.H3 (HR = 1.72, P = 0.008) was linked to poorer survival in the tumor. Conclusions Deciphering the TME using high-plex spatially resolved methods is giving us new insights into compartmentalised tumor and stromal protein signatures associated with clinical endpoints in NSCLC.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical EngineeringUniversity of Technology SydneySydneyNSWAustralia
- Frazer Institute, Faculty of MedicineThe University of QueenslandBrisbaneQLDAustralia
| | - James Monkman
- Frazer Institute, Faculty of MedicineThe University of QueenslandBrisbaneQLDAustralia
| | - Habib Sadeghirad
- Frazer Institute, Faculty of MedicineThe University of QueenslandBrisbaneQLDAustralia
| | - Ahmed Mehdi
- Queensland Cyber Infrastructure Foundation (QCIF) LtdThe University of QueenslandBrisbaneQLDAustralia
| | - Tony Blick
- Frazer Institute, Faculty of MedicineThe University of QueenslandBrisbaneQLDAustralia
| | | | - Ken O'Byrne
- The Princess Alexandra HospitalBrisbaneQLDAustralia
| | | | - Arutha Kulasinghe
- Frazer Institute, Faculty of MedicineThe University of QueenslandBrisbaneQLDAustralia
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7
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Tsang E, Han VX, Flutter C, Alshammery S, Keating BA, Williams T, Gloss BS, Graham ME, Aryamanesh N, Pang I, Wong M, Winlaw D, Cardamone M, Mohammad S, Gold W, Patel S, Dale RC. Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome. EBioMedicine 2024; 104:105156. [PMID: 38768529 PMCID: PMC11134553 DOI: 10.1016/j.ebiom.2024.105156] [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: 01/10/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Kabuki syndrome (KS) is a genetic disorder caused by DNA mutations in KMT2D, a lysine methyltransferase that methylates histones and other proteins, and therefore modifies chromatin structure and subsequent gene expression. Ketones, derived from the ketogenic diet, are histone deacetylase inhibitors that can 'open' chromatin and encourage gene expression. Preclinical studies have shown that the ketogenic diet rescues hippocampal memory neurogenesis in mice with KS via the epigenetic effects of ketones. METHODS Single-cell RNA sequencing and mass spectrometry-based proteomics were used to explore molecular mechanisms of disease in individuals with KS (n = 4) versus controls (n = 4). FINDINGS Pathway enrichment analysis indicated that loss of function mutations in KMT2D are associated with ribosomal protein dysregulation at an RNA and protein level in individuals with KS (FDR <0.05). Cellular proteomics also identified immune dysregulation and increased abundance of other lysine modification and histone binding proteins, representing a potential compensatory mechanism. A 12-year-old boy with KS, suffering from recurrent episodes of cognitive decline, exhibited improved cognitive function and neuropsychological assessment performance after 12 months on the ketogenic diet, with concomitant improvement in transcriptomic ribosomal protein dysregulation. INTERPRETATION Our data reveals that lysine methyltransferase deficiency is associated with ribosomal protein dysfunction, with secondary immune dysregulation. Diet and the production of bioactive molecules such as ketone bodies serve as a significant environmental factor that can induce epigenetic changes and improve clinical outcomes. Integrating transcriptomic, proteomic, and clinical data can define mechanisms of disease and treatment effects in individuals with neurodevelopmental disorders. FUNDING This study was supported by the Dale NHMRC Investigator Grant (APP1193648) (R.D), Petre Foundation (R.D), and The Sydney Children's Hospital Foundation/Kids Research Early and Mid-Career Researcher Grant (E.T).
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Affiliation(s)
- Erica Tsang
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Velda X Han
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Chloe Flutter
- The Kabuki Syndrome Foundation - Volunteer, Northbrook, IL, USA
| | - Sarah Alshammery
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Brooke A Keating
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia
| | - Tracey Williams
- Kids Rehab, The Children's Hospital at Westmead, Sydney, NSW, Australia
| | - Brian S Gloss
- Westmead Research Hub, Westmead Institute for Medical Research, Westmead, NSW, Australia
| | - Mark E Graham
- Biomedical Proteomics, Children's Medical Research Institute, The University of Sydney, Australia
| | - Nader Aryamanesh
- Bioinformatics Group, Children's Medical Research Institute, Westmead, Sydney, NSW, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Ignatius Pang
- Bioinformatics Group, Children's Medical Research Institute, Westmead, Sydney, NSW, Australia
| | - Melanie Wong
- The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - David Winlaw
- Heart Centre, Ann and Robert H. Lurie Children's Hospital of Chicago and Feinberg School of Medicine, Northwestern University, USA
| | - Michael Cardamone
- Sydney Children's Hospital, Randwick, NSW, Australia; School of Clinical Medicine, University of New South Wales, NSW, Australia
| | - Shekeeb Mohammad
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Wendy Gold
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia; Molecular Neurobiology Research Laboratory, Kids Research, The Children's Hospital at Westmead & the Children's Medical Research Institute, NSW, Australia
| | - Shrujna Patel
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Russell C Dale
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Faculty of Medicine and Health, University of Sydney, NSW, Australia; The Children's Hospital at Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia; The Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
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8
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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9
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Glyn T, Williams S, Whitehead M, Eglinton T, West N, Purcell RV. Digital spatial profiling identifies molecular changes involved in development of colitis-associated colorectal cancer. Front Oncol 2024; 14:1247106. [PMID: 38505585 PMCID: PMC10949367 DOI: 10.3389/fonc.2024.1247106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 02/01/2024] [Indexed: 03/21/2024] Open
Abstract
Objective Chronic colonic inflammation seen in inflammatory bowel disease (IBD) is a risk factor for colorectal cancer (CRC). Colitis-associated cancers (CAC) are molecularly different from sporadic CRC. This study aimed to evaluate spatially defined molecular changes associated with neoplastic progression to identify mechanisms of action and potential biomarkers for prognostication. Design IBD patients who had undergone colectomy for treatment of their IBD or dysplasia were identified from an institutional database. Formalin-fixed paraffin embedded samples from areas of normal, inflamed, dysplastic and adenocarcinoma tissue were identified for digital spatial profiling using the Nanostring GeoMx™ Cancer Transcriptome Atlas. RNA expression and quantification of 1812 genes was measured and analysed in a spatial context to compare differences in gene expression. Results Sixteen patients were included, nine patients had CAC, two had dysplasia only and five had colitis only. Significant, step-wise differences in gene expression were seen between tissue types, mainly involving progressive over-expression of collagen genes associated with stromal remodelling. Similarly, MYC over-expression was associated with neoplastic progression. Comparison of normal and inflamed tissue from patients who progressed to those who did not also showed significant differences in immune-related genes, including under-expression of thte chemokines CCL18, CCL25 and IL-R7, as well as CD3, CD6 and lysozyme. The known oncogene CD24 was significantly overexpressed. Conclusion Both tissue types and patient groups are molecularly distinguishable on the basis of their gene expression patterns. Further prospective work is necessary to confirm these differences and establish their clinical significance and potential utility as biomarkers.
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Affiliation(s)
- Tamara Glyn
- Department of Surgery and Critical Care, University of Otago, Christchurch, New Zealand
| | - Sarah Williams
- Griffith Health, Griffith University, Gold Coast, QLD, Australia
| | - Martin Whitehead
- Department of Anatomical Pathology, Te Whatu Ora Waitaha, Christchurch, New Zealand
| | - Tim Eglinton
- Department of Surgery and Critical Care, University of Otago, Christchurch, New Zealand
| | - Nicholas West
- Griffith Health, Griffith University, Gold Coast, QLD, Australia
| | - Rachel V. Purcell
- Department of Surgery and Critical Care, University of Otago, Christchurch, New Zealand
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10
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Peltzer A, Mohr C, Stadermann KB, Zwick M, Schmid R. nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis. Bioinformatics 2024; 40:btae019. [PMID: 38212989 PMCID: PMC10805338 DOI: 10.1093/bioinformatics/btae019] [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: 06/23/2023] [Revised: 11/29/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. RESULTS Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. AVAILABILITY AND IMPLEMENTATION The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.
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Affiliation(s)
- Alexander Peltzer
- Clinical Bioinformatics and Systems Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach/Riss, Germany
| | - Christopher Mohr
- Clinical Bioinformatics and Systems Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach/Riss, Germany
| | - Kai B Stadermann
- Clinical Bioinformatics and Systems Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach/Riss, Germany
| | - Matthias Zwick
- Clinical Bioinformatics and Systems Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach/Riss, Germany
| | - Ramona Schmid
- Clinical Bioinformatics and Systems Pharmacology, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach/Riss, Germany
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11
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Desmond LW, Holbrook EM, Wright CTO, Zambrano CA, Stamper CE, Bohr AD, Frank MG, Podell BK, Moreno JA, MacDonald AS, Reber SO, Hernández-Pando R, Lowry CA. Effects of Mycobacterium vaccae NCTC 11659 and Lipopolysaccharide Challenge on Polarization of Murine BV-2 Microglial Cells. Int J Mol Sci 2023; 25:474. [PMID: 38203645 PMCID: PMC10779110 DOI: 10.3390/ijms25010474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
Previous studies have shown that the in vivo administration of soil-derived bacteria with anti-inflammatory and immunoregulatory properties, such as Mycobacterium vaccae NCTC 11659, can prevent a stress-induced shift toward an inflammatory M1 microglial immunophenotype and microglial priming in the central nervous system (CNS). It remains unclear whether M. vaccae NCTC 11659 can act directly on microglia to mediate these effects. This study was designed to determine the effects of M. vaccae NCTC 11659 on the polarization of naïve BV-2 cells, a murine microglial cell line, and BV-2 cells subsequently challenged with lipopolysaccharide (LPS). Briefly, murine BV-2 cells were exposed to 100 µg/mL whole-cell, heat-killed M. vaccae NCTC 11659 or sterile borate-buffered saline (BBS) vehicle, followed, 24 h later, by exposure to 0.250 µg/mL LPS (Escherichia coli 0111: B4; n = 3) in cell culture media vehicle (CMV) or a CMV control condition. Twenty-four hours after the LPS or CMV challenge, cells were harvested to isolate total RNA. An analysis using the NanoString platform revealed that, by itself, M. vaccae NCTC 11659 had an "adjuvant-like" effect, while exposure to LPS increased the expression of mRNAs encoding proinflammatory cytokines, chemokine ligands, the C3 component of complement, and components of inflammasome signaling such as Nlrp3. Among LPS-challenged cells, M. vaccae NCTC 11659 had limited effects on differential gene expression using a threshold of 1.5-fold change. A subset of genes was assessed using real-time reverse transcription polymerase chain reaction (real-time RT-PCR), including Arg1, Ccl2, Il1b, Il6, Nlrp3, and Tnf. Based on the analysis using real-time RT-PCR, M. vaccae NCTC 11659 by itself again induced "adjuvant-like" effects, increasing the expression of Il1b, Il6, and Tnf while decreasing the expression of Arg1. LPS by itself increased the expression of Ccl2, Il1b, Il6, Nlrp3, and Tnf while decreasing the expression of Arg1. Among LPS-challenged cells, M. vaccae NCTC 11659 enhanced LPS-induced increases in the expression of Nlrp3 and Tnf, consistent with microglial priming. In contrast, among LPS-challenged cells, although M. vaccae NCTC 11659 did not fully prevent the effects of LPS relative to vehicle-treated control conditions, it increased Arg1 mRNA expression, suggesting that M. vaccae NCTC 11659 induces an atypical microglial phenotype. Thus, M. vaccae NCTC 11659 acutely (within 48 h) induced immune-activating and microglial-priming effects when applied directly to murine BV-2 microglial cells, in contrast to its long-term anti-inflammatory and immunoregulatory effects observed on the CNS when whole-cell, heat-killed preparations of M. vaccae NCTC 11659 were given peripherally in vivo.
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Affiliation(s)
- Luke W. Desmond
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Evan M. Holbrook
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Caelan T. O. Wright
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Cristian A. Zambrano
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Christopher E. Stamper
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Adam D. Bohr
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
| | - Matthew G. Frank
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
- Center for Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Brendan K. Podell
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80523, USA;
| | - Julie A. Moreno
- Prion Research Center, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA;
- Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA
- Center for Healthy Aging, Colorado State University, Fort Collins, CO 80523, USA
| | - Andrew S. MacDonald
- Lydia Becker Institute of Immunology and Inflammation, University of Manchester, Manchester M13 9NT, UK;
| | - Stefan O. Reber
- Laboratory for Molecular Psychosomatics, Department of Psychosomatic Medicine and Psychotherapy, Ulm University Medical Center, 89081 Ulm, Germany;
| | - Rogelio Hernández-Pando
- Sección de Patología Experimental, Departamento de Patología, Instituto Nacional De Ciencias Médicas Y Nutrición Salvador Zubirán, Ciudad de México 14080, Mexico;
| | - Christopher A. Lowry
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (L.W.D.); (E.M.H.); (C.T.O.W.); (C.A.Z.); (C.E.S.); (A.D.B.); (M.G.F.)
- Center for Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
- Center for Microbial Exploration, University of Colorado Boulder, Boulder, CO 80309, USA
- Department of Physical Medicine and Rehabilitation and Center for Neuroscience, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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12
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Park SL, Christo SN, Wells AC, Gandolfo LC, Zaid A, Alexandre YO, Burn TN, Schröder J, Collins N, Han SJ, Guillaume SM, Evrard M, Castellucci C, Davies B, Osman M, Obers A, McDonald KM, Wang H, Mueller SN, Kannourakis G, Berzins SP, Mielke LA, Carbone FR, Kallies A, Speed TP, Belkaid Y, Mackay LK. Divergent molecular networks program functionally distinct CD8 + skin-resident memory T cells. Science 2023; 382:1073-1079. [PMID: 38033053 DOI: 10.1126/science.adi8885] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023]
Abstract
Skin-resident CD8+ T cells include distinct interferon-γ-producing [tissue-resident memory T type 1 (TRM1)] and interleukin-17 (IL-17)-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations use common mechanisms to establish tissue residence is unknown. In this work, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in the skin. TRM1 cells depend on a T-bet-Hobit-IL-15 axis, whereas TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, by targeting this pathway, skin TRM17 cells can be ablated without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.
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Affiliation(s)
- Simone L Park
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Susan N Christo
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Alexandria C Wells
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Luke C Gandolfo
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, VIC, Australia
| | - Ali Zaid
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Yannick O Alexandre
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Thomas N Burn
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Jan Schröder
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Nicholas Collins
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Seong-Ji Han
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Stéphane M Guillaume
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Maximilien Evrard
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Clara Castellucci
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Brooke Davies
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Maleika Osman
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Andreas Obers
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Keely M McDonald
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Huimeng Wang
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Scott N Mueller
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - George Kannourakis
- Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, VIC, Australia
| | - Stuart P Berzins
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, VIC, Australia
| | - Lisa A Mielke
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Heidelberg, VIC, Australia
| | - Francis R Carbone
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Axel Kallies
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Terence P Speed
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, VIC, Australia
| | - Yasmine Belkaid
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
- NIAID Microbiome Program, NIAID, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Mackay
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
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13
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Rifai OM, O’Shaughnessy J, Dando OR, Munro AF, Sewell MDE, Abrahams S, Waldron FM, Sibley CR, Gregory JM. Distinct neuroinflammatory signatures exist across genetic and sporadic amyotrophic lateral sclerosis cohorts. Brain 2023; 146:5124-5138. [PMID: 37450566 PMCID: PMC10690026 DOI: 10.1093/brain/awad243] [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: 03/26/2023] [Revised: 05/31/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. ALS is on a pathogenetic disease spectrum with frontotemporal dementia, referred to as ALS-frontotemporal spectrum disorder (ALS-FTSD). For mutations associated with ALS-FTSD, such as the C9orf72 hexanucleotide repeat expansion, the molecular factors associated with heterogeneity along this spectrum require further characterization. Here, using a targeted NanoString molecular barcoding approach, we interrogate neuroinflammatory dysregulation and heterogeneity at the level of gene expression in post-mortem motor cortex tissue from a cohort of clinically heterogeneous C9-ALS-FTSD cases. We identified 20 dysregulated genes in C9-ALS-FTSD, with enrichment of microglial and inflammatory response gene sets. Two genes with significant correlations to available clinical metrics were selected for validation: FKBP5, a correlate of cognitive function, and brain-derived neurotrophic factor (BDNF), a correlate of disease duration. FKBP5 and its signalling partner, NF-κB, appeared to have a cell type-specific staining distribution, with activated (i.e. nuclear) NF-κB immunoreactivity in C9-ALS-FTSD. Expression of BDNF, a correlate of disease duration, was confirmed to be higher in individuals with long compared to short disease duration using BaseScope™ in situ hybridization. Our analyses also revealed two distinct neuroinflammatory panel signatures (NPS), NPS1 and NPS2, delineated by the direction of expression of proinflammatory, axonal transport and synaptic signalling pathways. We compared NPS between C9-ALS-FTSD cases and those from sporadic ALS and SOD1-ALS cohorts and identified NPS1 and NPS2 across all cohorts. Moreover, a subset of NPS was also able to separate publicly available RNA sequencing data from independent C9-ALS and sporadic ALS cohorts into two inflammatory subgroups. Importantly, NPS subgroups did not clearly segregate with available demographic, genetic, clinical or pathological features, highlighting the value of molecular stratification in clinical trials for inflammatory subgroup identification. Our findings thus underscore the importance of tailoring therapeutic approaches based on distinct molecular signatures that exist between and within ALS-FTSD cohorts.
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Affiliation(s)
- Olivia M Rifai
- Translational Neuroscience PhD Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Judi O’Shaughnessy
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Owen R Dando
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, EH8 9XF, UK
| | - Alison F Munro
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Michael D E Sewell
- Translational Neuroscience PhD Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Sharon Abrahams
- Human Cognitive Neuroscience-Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, EH8 9AD, UK
| | - Fergal M Waldron
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, AB25 2ZD, UK
| | - Christopher R Sibley
- Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, EH8 9XF, UK
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, The King’s Buildings, Edinburgh, EH9 3FF, UK
| | - Jenna M Gregory
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, AB25 2ZD, UK
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14
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Jung AM, Furlong MA, Goodrich JM, Cardenas A, Beitel SC, Littau SR, Caban-Martinez AJ, Gulotta JJ, Wallentine DD, Urwin D, Gabriel J, Hughes J, Graber JM, Grant C, Burgess JL. Associations Between Epigenetic Age Acceleration and microRNA Expression Among U.S. Firefighters. Epigenet Insights 2023; 16:25168657231206301. [PMID: 37953967 PMCID: PMC10634256 DOI: 10.1177/25168657231206301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/20/2023] [Indexed: 11/14/2023] Open
Abstract
Epigenetic changes may be biomarkers of health. Epigenetic age acceleration (EAA), the discrepancy between epigenetic age measured via epigenetic clocks and chronological age, is associated with morbidity and mortality. However, the intersection of epigenetic clocks with microRNAs (miRNAs) and corresponding miRNA-based health implications have not been evaluated. We analyzed DNA methylation and miRNA profiles from blood sampled among 332 individuals enrolled across 2 U.S.-based firefighter occupational studies (2015-2018 and 2018-2020). We considered 7 measures of EAA in leukocytes (PhenoAge, GrimAge, Horvath, skin-blood, and Hannum epigenetic clocks, and extrinsic and intrinsic epigenetic age acceleration). We identified miRNAs associated with EAA using individual linear regression models, adjusted for sex, race/ethnicity, chronological age, and cell type estimates, and investigated downstream effects of associated miRNAs with miRNA enrichment analyses and genomic annotations. On average, participants were 38 years old, 88% male, and 75% non-Hispanic white. We identified 183 of 798 miRNAs associated with EAA (FDR q < 0.05); 126 with PhenoAge, 59 with GrimAge, 1 with Horvath, and 1 with the skin-blood clock. Among miRNAs associated with Horvath and GrimAge, there were 61 significantly enriched disease annotations including age-related metabolic and cardiovascular conditions and several cancers. Enriched pathways included those related to proteins and protein modification. We identified miRNAs associated with EAA of multiple epigenetic clocks. PhenoAge had more associations with individual miRNAs, but GrimAge and Horvath had greater implications for miRNA-associated pathways. Understanding the relationship between these epigenetic markers could contribute to our understanding of the molecular underpinnings of aging and aging-related diseases.
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Affiliation(s)
- Alesia M Jung
- Department of Community, Environment & Policy, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
- Department of Pharmacology & Toxicology, R. Ken Coit College of Pharmacy, College of Public Health, Tucson, AZ, USA
| | - Melissa A Furlong
- Department of Community, Environment & Policy, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Jaclyn M Goodrich
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Andres Cardenas
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Shawn C Beitel
- Department of Community, Environment & Policy, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Sally R Littau
- Department of Community, Environment & Policy, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Alberto J Caban-Martinez
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
| | | | | | - Derek Urwin
- Los Angeles County Fire Department, Los Angeles, CA, USA
- Department of Chemistry & Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
- Division of Health Safety and Medicine, International Association of Fire Fighters, Washington, DC, USA
| | - Jamie Gabriel
- Los Angeles County Fire Department, Los Angeles, CA, USA
| | | | - Judith M Graber
- Department of Biostatistics & Epidemiology, School of Public Health, Rutgers University, Piscataway, NJ, USA
| | - Casey Grant
- Fire Protection Research Foundation, Quincy, MA, USA
| | - Jefferey L Burgess
- Department of Community, Environment & Policy, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
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15
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Torabi F, Vadakekolathu J, Wyatt R, Leete P, Tombs MA, Richardson CC, Boocock DJ, Turner MD, Morgan NG, Richardson SJ, Christie MR. Differential expression of genes controlling lymphocyte differentiation and migration in two distinct endotypes of type 1 diabetes. Diabet Med 2023; 40:e15155. [PMID: 37246834 DOI: 10.1111/dme.15155] [Citation(s) in RCA: 2] [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: 03/10/2023] [Revised: 05/04/2023] [Accepted: 05/26/2023] [Indexed: 05/30/2023]
Abstract
AIMS Morphological studies of pancreas samples obtained from young people with recent-onset type 1 diabetes have revealed distinct patterns of immune cell infiltration of the pancreatic islets suggestive of two age-associated type 1 diabetes endotypes that differ by inflammatory responses and rates of disease progression. The objective of this study was to investigate whether these proposed disease endotypes are associated with pathological differences in immune cell activation and cytokine secretion by applying multiplexed gene expression analysis to pancreatic tissue from recent-onset type 1 diabetes cases. METHODS RNA was extracted from samples of fixed, paraffin-embedded pancreas tissue from type 1 diabetes cases characterised by endotype and from controls without diabetes. Expression levels of 750 genes associated with autoimmune inflammation were determined by hybridisation to a panel of capture and reporter probes and these were counted as a measure of gene expression. Normalised counts were analysed for differences in expression between 29 type 1 diabetes cases and 7 controls without diabetes, and between the two type 1 diabetes endotypes. RESULTS Ten inflammation-associated genes, including INS, were significantly under-expressed in both endotypes and 48 genes were more highly expressed. A different set of 13 genes associated with the development, activation and migration of lymphocytes was uniquely overexpressed in the pancreas of people developing diabetes at younger age. CONCLUSIONS The results provide evidence that histologically defined type 1 diabetes endotypes differ in their immunopathology and identify inflammatory pathways specifically involved in disease developing at a young age, essential for a better understanding of disease heterogeneity.
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Affiliation(s)
- Forough Torabi
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | | | - Rebecca Wyatt
- Islet Biology Exeter (IBEx), Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Pia Leete
- Islet Biology Exeter (IBEx), Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | | | | | - David J Boocock
- John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK
| | - Mark D Turner
- Centre for Diabetes, Chronic Diseases and Ageing, Nottingham Trent University, Nottingham, UK
| | - Noel G Morgan
- Islet Biology Exeter (IBEx), Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
| | - Sarah J Richardson
- Islet Biology Exeter (IBEx), Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
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16
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Leach DT, Stratton KG, Irvahn J, Richardson R, Webb-Robertson BJM, Bramer LM. malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data. Anal Chem 2023; 95:12195-12199. [PMID: 37551970 DOI: 10.1021/acs.analchem.3c01289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.
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Affiliation(s)
- Damon T Leach
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Jan Irvahn
- Artificial Intelligence and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Rachel Richardson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
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17
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Abstract
Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
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Affiliation(s)
- Sophia M Guldberg
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
| | - Trine Line Hauge Okholm
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Elizabeth E McCarthy
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Institute for Human Genetics; Division of Rheumatology, Department of Medicine; Medical Scientist Training Program; and Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, USA
| | - Matthew H Spitzer
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
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18
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Lin Y, Cao Y, Willie E, Patrick E, Yang JYH. Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2. Nat Commun 2023; 14:4272. [PMID: 37460600 DOI: 10.1038/s41467-023-39923-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.
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Affiliation(s)
- Yingxin Lin
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Yue Cao
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Elijah Willie
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia
| | - Ellis Patrick
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- The Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Jean Y H Yang
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
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19
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Steinberg C, Gaudreault N, Papadakis AI, Henry C, Champagne J, Philippon F, O’Hara G, Blier L, Plourde B, Nault I, Roy K, Sarrazin JF, Spatz A, Bossé Y. Leucocyte-derived micro-RNAs as candidate biomarkers in Brugada syndrome. Europace 2023; 25:euad145. [PMID: 37314195 PMCID: PMC10265963 DOI: 10.1093/europace/euad145] [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: 03/09/2023] [Accepted: 05/02/2023] [Indexed: 06/15/2023] Open
Abstract
AIMS Risk stratification for sudden cardiac death in patients with Brugada syndrome remains a major challenge. Contemporary risk prediction models have only modest predictive value. The aim of this study was to assess the role of micro-RNAs from peripheral blood as candidate biomarkers in Brugada syndrome. METHODS AND RESULTS In this prospective study, Brugada patients and unaffected control individuals were enrolled for analysis of leucocyte-derived microRNAs (miRNAs) levels. Expression levels of 798 different circulating miRNAs were analysed on the NanoString® nCounter platform. All results were cross-validated by using a quantitative polymerase chain reaction. Micro-RNA expression levels of Brugada patients were compared with clinical data. A total of 21 definite Brugada patients (38% with a history of ventricular arrhythmia or cardiac arrest) and 30 unaffected control individuals were included in the study. Micro-RNA analysis showed a distinct expression profile in Brugada patients with 42 differentially expressed markers (38 up-regulated, 4 down-regulated miRNAs). The symptom status of Brugada patients was associated with a distinct miRNA signature. Micro-RNAs 145-5p and 585-3p were significantly up-regulated in symptomatic Brugada patients (P = 0.04). Incorporating miRNAs 145-5p and 585-3p into a multivariable model demonstrated significantly increased symptom prediction (area under the curve = 0.96; 95% confidence interval: 0.88-1.00). CONCLUSION Brugada patients display a distinct miRNA expression profile compared with unaffected control individuals. There is also evidence that certain miRNAs (miR-145-5p and miR-585-3p) are associated with the symptom status of Brugada patients. The results suggest the principal utility of leucocyte-derived miRNAs as prognostic biomarkers for Brugada syndrome.
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Affiliation(s)
- Christian Steinberg
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Nathalie Gaudreault
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Andreas I Papadakis
- Lady Davis Institute for Medical Research, Montreal Jewish Hospital, McGill University, Montreal, Canada
| | - Cyndi Henry
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Jean Champagne
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - François Philippon
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Gilles O’Hara
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Louis Blier
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Benoit Plourde
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Isabelle Nault
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Karine Roy
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Jean-François Sarrazin
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
| | - Alan Spatz
- Lady Davis Institute for Medical Research, Montreal Jewish Hospital, McGill University, Montreal, Canada
| | - Yohan Bossé
- Institut universitaire de cardiologie et pneumologie de Québec, Université Laval, 2725, Chemin Ste-Foy, Quebec City, Canada G1V 4G5
- Department of Molecular Medicine, Laval University, Quebec City, Canada
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20
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Li Mow Chee F, Beernaert B, Griffith BGC, Loftus AEP, Kumar Y, Wills JC, Lee M, Valli J, Wheeler AP, Armstrong JD, Parsons M, Leigh IM, Proby CM, von Kriegsheim A, Bickmore WA, Frame MC, Byron A. Mena regulates nesprin-2 to control actin-nuclear lamina associations, trans-nuclear membrane signalling and gene expression. Nat Commun 2023; 14:1602. [PMID: 36959177 PMCID: PMC10036544 DOI: 10.1038/s41467-023-37021-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/21/2023] [Indexed: 03/25/2023] Open
Abstract
Interactions between cells and the extracellular matrix, mediated by integrin adhesion complexes, play key roles in fundamental cellular processes, including the sensing and transduction of mechanical cues. Here, we investigate systems-level changes in the integrin adhesome in patient-derived cutaneous squamous cell carcinoma cells and identify the actin regulatory protein Mena as a key node in the adhesion complex network. Mena is connected within a subnetwork of actin-binding proteins to the LINC complex component nesprin-2, with which it interacts and co-localises at the nuclear envelope. Moreover, Mena potentiates the interactions of nesprin-2 with the actin cytoskeleton and the nuclear lamina. CRISPR-mediated Mena depletion causes altered nuclear morphology, reduces tyrosine phosphorylation of the nuclear membrane protein emerin and downregulates expression of the immunomodulatory gene PTX3 via the recruitment of its enhancer to the nuclear periphery. We uncover an unexpected role for Mena at the nuclear membrane, where it controls nuclear architecture, chromatin repositioning and gene expression. Our findings identify an adhesion protein that regulates gene transcription via direct signalling across the nuclear envelope.
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Affiliation(s)
- Frederic Li Mow Chee
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Bruno Beernaert
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, OX3 7DQ, UK
| | - Billie G C Griffith
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Alexander E P Loftus
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Yatendra Kumar
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Jimi C Wills
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Martin Lee
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Jessica Valli
- Edinburgh Super Resolution Imaging Consortium, Institute of Biological Chemistry, Biophysics and Bioengineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK
| | - Ann P Wheeler
- Advanced Imaging Resource, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - J Douglas Armstrong
- Simons Initiative for the Developing Brain, School of Informatics, University of Edinburgh, Edinburgh, EH8 9LE, UK
| | - Maddy Parsons
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, SE1 1UL, UK
| | - Irene M Leigh
- Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
- Institute of Dentistry, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
| | - Charlotte M Proby
- Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Alex von Kriegsheim
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Wendy A Bickmore
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Margaret C Frame
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK
| | - Adam Byron
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, UK.
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PT, UK.
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21
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Virassamy B, Caramia F, Savas P, Sant S, Wang J, Christo SN, Byrne A, Clarke K, Brown E, Teo ZL, von Scheidt B, Freestone D, Gandolfo LC, Weber K, Teply-Szymanski J, Li R, Luen SJ, Denkert C, Loibl S, Lucas O, Swanton C, Speed TP, Darcy PK, Neeson PJ, Mackay LK, Loi S. Intratumoral CD8 + T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer. Cancer Cell 2023; 41:585-601.e8. [PMID: 36827978 DOI: 10.1016/j.ccell.2023.01.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/17/2022] [Accepted: 01/13/2023] [Indexed: 02/25/2023]
Abstract
CD8+ tumor-infiltrating lymphocytes with a tissue-resident memory T (TRM) cell phenotype are associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, the relative contribution of CD8+ TRM cells to anti-tumor immunity and immune checkpoint blockade efficacy in breast cancer remains unknown. Here, we show that intratumoral CD8+ T cells in murine mammary tumors transcriptionally resemble those from TNBC patients. Phenotypic and transcriptional studies established two intratumoral sub-populations: one more enriched in markers of terminal exhaustion (TEX-like) and the other with a bona fide resident phenotype (TRM-like). Treatment with anti-PD-1 and anti-CTLA-4 therapy resulted in expansion of these intratumoral populations, with the TRM-like subset displaying significantly enhanced cytotoxic capacity. TRM-like CD8+ T cells could also provide local immune protection against tumor rechallenge and a TRM gene signature extracted from tumor-free tissue was significantly associated with improved clinical outcomes in TNBC patients treated with checkpoint inhibitors.
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Affiliation(s)
- Balaji Virassamy
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Franco Caramia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Peter Savas
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sneha Sant
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Jianan Wang
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia
| | - Susan N Christo
- Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Ann Byrne
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Kylie Clarke
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Emmaline Brown
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Zhi Ling Teo
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Bianca von Scheidt
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David Freestone
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Luke C Gandolfo
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Karsten Weber
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Julia Teply-Szymanski
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany; Department of Pathology, University Marburg-Giessen, Campus Marburg, Germany
| | - Ran Li
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephen J Luen
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Carsten Denkert
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany; Department of Pathology, University Marburg-Giessen, Campus Marburg, Germany
| | - Sibylle Loibl
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Olivia Lucas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Phillip K Darcy
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
| | - Paul J Neeson
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
| | - Laura K Mackay
- Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
| | - Sherene Loi
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
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22
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Holbrook EM, Zambrano CA, Wright CTO, Dubé EM, Stewart JR, Sanders WJ, Frank MG, MacDonald AS, Reber SO, Lowry CA. Mycobacterium vaccae NCTC 11659, a Soil-Derived Bacterium with Stress Resilience Properties, Modulates the Proinflammatory Effects of LPS in Macrophages. Int J Mol Sci 2023; 24:ijms24065176. [PMID: 36982250 PMCID: PMC10049321 DOI: 10.3390/ijms24065176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/15/2023] [Accepted: 02/20/2023] [Indexed: 03/10/2023] Open
Abstract
Inflammatory conditions, including allergic asthma and conditions in which chronic low-grade inflammation is a risk factor, such as stress-related psychiatric disorders, are prevalent and are a significant cause of disability worldwide. Novel approaches for the prevention and treatment of these disorders are needed. One approach is the use of immunoregulatory microorganisms, such as Mycobacterium vaccae NCTC 11659, which have anti-inflammatory, immunoregulatory, and stress-resilience properties. However, little is known about how M. vaccae NCTC 11659 affects specific immune cell targets, including monocytes, which can traffic to peripheral organs and the central nervous system and differentiate into monocyte-derived macrophages that, in turn, can drive inflammation and neuroinflammation. In this study, we investigated the effects of M. vaccae NCTC 11659 and subsequent lipopolysaccharide (LPS) challenge on gene expression in human monocyte-derived macrophages. THP-1 monocytes were differentiated into macrophages, exposed to M. vaccae NCTC 11659 (0, 10, 30, 100, 300 µg/mL), then, 24 h later, challenged with LPS (0, 0.5, 2.5, 250 ng/mL), and assessed for gene expression 24 h following challenge with LPS. Exposure to M. vaccae NCTC 11659 prior to challenge with higher concentrations of LPS (250 ng/mL) polarized human monocyte-derived macrophages with decreased IL12A, IL12B, and IL23A expression relative to IL10 and TGFB1 mRNA expression. These data identify human monocyte-derived macrophages as a direct target of M. vaccae NCTC 11659 and support the development of M. vaccae NCTC 11659 as a potential intervention to prevent stress-induced inflammation and neuroinflammation implicated in the etiology and pathophysiology of inflammatory conditions and stress-related psychiatric disorders.
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Affiliation(s)
- Evan M. Holbrook
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Cristian A. Zambrano
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Caelan T. O. Wright
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Elizabeth M. Dubé
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Jessica R. Stewart
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
| | - William J. Sanders
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Matthew G. Frank
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Andrew S. MacDonald
- Lydia Becker Institute of Immunology and Inflammation, University of Manchester, Manchester M13 9NT, UK
| | - Stefan O. Reber
- Laboratory for Molecular Psychosomatics, Department of Psychosomatic Medicine and Psychotherapy, Ulm University Medical Center, 89081 Ulm, Germany
| | - Christopher A. Lowry
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
- Center for Neuroscience and Center for Microbial Exploration, University of Colorado, Boulder, CO 80309, USA
- Department of Physical Medicine and Rehabilitation and Center for Neuroscience, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Veterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center (MIRECC), The Rocky Mountain Regional Veterans Affairs Medical Center (RMRVAMC), Aurora, CO 80045, USA
- Military and Veteran Microbiome Consortium for Research and Education (MVM-CoRE), Aurora, CO 80045, USA
- Senior Fellow, inVIVO Planetary Health, of the Worldwide Universities Network (WUN), West New York, NJ 07093, USA
- Correspondence: ; Tel.: +1-303-492-6029; Fax: +1-303-492-0811
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23
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Ryu Y, Han GH, Jung E, Hwang D. Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods. Mol Cells 2023; 46:106-119. [PMID: 36859475 PMCID: PMC9982060 DOI: 10.14348/molcells.2023.0009] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 03/03/2023] Open
Abstract
With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.
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Affiliation(s)
- Yeonjae Ryu
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Geun Hee Han
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Eunsoo Jung
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Daehee Hwang
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
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24
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Xiao D, Chen C, Yang P. Computational systems approach towards phosphoproteomics and their downstream regulation. Proteomics 2023; 23:e2200068. [PMID: 35580145 DOI: 10.1002/pmic.202200068] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/26/2022] [Accepted: 05/03/2022] [Indexed: 11/07/2022]
Abstract
Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.
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Affiliation(s)
- Di Xiao
- Computational Systems Biology Group, Children's Medical Research Institute, The University of Sydney, Westmead, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Carissa Chen
- Computational Systems Biology Group, Children's Medical Research Institute, The University of Sydney, Westmead, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Pengyi Yang
- Computational Systems Biology Group, Children's Medical Research Institute, The University of Sydney, Westmead, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
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25
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Yasa J, Reed CE, Bournazos AM, Evesson FJ, Pang I, Graham ME, Wark JR, Nijagal B, Kwan KH, Kwiatkowski T, Jung R, Weisleder N, Cooper ST, Lemckert FA. Minimal expression of dysferlin prevents development of dysferlinopathy in dysferlin exon 40a knockout mice. Acta Neuropathol Commun 2023; 11:15. [PMID: 36653852 PMCID: PMC9847081 DOI: 10.1186/s40478-022-01473-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/03/2022] [Indexed: 01/19/2023] Open
Abstract
Dysferlin is a Ca2+-activated lipid binding protein implicated in muscle membrane repair. Recessive variants in DYSF result in dysferlinopathy, a progressive muscular dystrophy. We showed previously that calpain cleavage within a motif encoded by alternatively spliced exon 40a releases a 72 kDa C-terminal minidysferlin recruited to injured sarcolemma. Herein we use CRISPR/Cas9 gene editing to knock out murine Dysf exon 40a, to specifically assess its role in membrane repair and development of dysferlinopathy. We created three Dysf exon 40a knockout (40aKO) mouse lines that each express different levels of dysferlin protein ranging from ~ 90%, ~ 50% and ~ 10-20% levels of wild-type. Histopathological analysis of skeletal muscles from all 12-month-old 40aKO lines showed virtual absence of dystrophic features and normal membrane repair capacity for all three 40aKO lines, as compared with dysferlin-null BLAJ mice. Further, lipidomic and proteomic analyses on 18wk old quadriceps show all three 40aKO lines are spared the profound lipidomic/proteomic imbalance that characterises dysferlin-deficient BLAJ muscles. Collective results indicate that membrane repair does not depend upon calpain cleavage within exon 40a and that ~ 10-20% of WT dysferlin protein expression is sufficient to maintain the muscle lipidome, proteome and membrane repair capacity to crucially prevent development of dysferlinopathy.
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Affiliation(s)
- Joe Yasa
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.414235.50000 0004 0619 2154Functional Neuromics, Children’s Medical Research Institute, Westmead, Sydney, NSW Australia
| | - Claudia E. Reed
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.1013.30000 0004 1936 834XDiscipline of Child and Adolescent Health, Faculty of Medicine, University of Sydney, Sydney, NSW Australia
| | - Adam M. Bournazos
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.1013.30000 0004 1936 834XDiscipline of Child and Adolescent Health, Faculty of Medicine, University of Sydney, Sydney, NSW Australia
| | - Frances J. Evesson
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.414235.50000 0004 0619 2154Functional Neuromics, Children’s Medical Research Institute, Westmead, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XDiscipline of Child and Adolescent Health, Faculty of Medicine, University of Sydney, Sydney, NSW Australia
| | - Ignatius Pang
- grid.414235.50000 0004 0619 2154Synapse Proteomics, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW Australia
| | - Mark E. Graham
- grid.414235.50000 0004 0619 2154Synapse Proteomics, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW Australia
| | - Jesse R. Wark
- grid.1013.30000 0004 1936 834XOperations, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW Australia
| | - Brunda Nijagal
- grid.1008.90000 0001 2179 088XMetabolomics Australia, Bio21 Institute, The University of Melbourne, Victoria, Australia
| | - Kim H. Kwan
- grid.1008.90000 0001 2179 088XMetabolomics Australia, Bio21 Institute, The University of Melbourne, Victoria, Australia
| | - Thomas Kwiatkowski
- grid.268132.c0000 0001 0701 2416West Chester University, West Chester, PA 19383 USA
| | - Rachel Jung
- grid.412332.50000 0001 1545 0811Department of Physiology and Cell Biology, Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210-1252 USA
| | - Noah Weisleder
- grid.412332.50000 0001 1545 0811Department of Physiology and Cell Biology, Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210-1252 USA
| | - Sandra T. Cooper
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.414235.50000 0004 0619 2154Functional Neuromics, Children’s Medical Research Institute, Westmead, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XDiscipline of Child and Adolescent Health, Faculty of Medicine, University of Sydney, Sydney, NSW Australia
| | - Frances A. Lemckert
- grid.413973.b0000 0000 9690 854XKids Neuroscience Centre, The Children’s Hospital at Westmead, Cnr Hawkesbury Road, Hainsworth Street, Westmead, Sydney, NSW 2145 Australia ,grid.414235.50000 0004 0619 2154Functional Neuromics, Children’s Medical Research Institute, Westmead, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XDiscipline of Child and Adolescent Health, Faculty of Medicine, University of Sydney, Sydney, NSW Australia
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26
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Normalizing cancer RNA-seq data for library size, tumor purity and batch effects. Nat Biotechnol 2023; 41:27-28. [PMID: 36151469 DOI: 10.1038/s41587-022-01441-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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27
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Molania R, Foroutan M, Gagnon-Bartsch JA, Gandolfo LC, Jain A, Sinha A, Olshansky G, Dobrovic A, Papenfuss AT, Speed TP. Removing unwanted variation from large-scale RNA sequencing data with PRPS. Nat Biotechnol 2023; 41:82-95. [PMID: 36109686 PMCID: PMC9849124 DOI: 10.1038/s41587-022-01440-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 06/30/2022] [Indexed: 01/22/2023]
Abstract
Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.
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Affiliation(s)
- Ramyar Molania
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Momeneh Foroutan
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
| | | | - Luke C Gandolfo
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Aryan Jain
- Department of Economics and Statistics, Monash University, Melbourne, Victoria, Australia
| | - Abhishek Sinha
- Department of Economics and Statistics, Monash University, Melbourne, Victoria, Australia
| | - Gavriel Olshansky
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alexander Dobrovic
- Department of Surgery, The University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
| | - Anthony T Papenfuss
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia.
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia.
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28
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Class CA, Lukan CJ, Bristow CA, Do KA. Easy NanoString nCounter data analysis with the NanoTube. Bioinformatics 2023; 39:6849516. [PMID: 36440915 PMCID: PMC9805552 DOI: 10.1093/bioinformatics/btac762] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/21/2022] [Accepted: 11/24/2022] [Indexed: 11/30/2022] Open
Abstract
SUMMARY The NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise. AVAILABILITY AND IMPLEMENTATION The NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Caiden J Lukan
- Department of Pharmaceutical Sciences, Butler University, Indianapolis, IN 46208, USA
| | | | - Kim-Anh Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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29
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Fachrul M, Méric G, Inouye M, Pamp SJ, Salim A. Assessing and removing the effect of unwanted technical variations in microbiome data. Sci Rep 2022; 12:22236. [PMID: 36564466 PMCID: PMC9789116 DOI: 10.1038/s41598-022-26141-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze-thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.
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Affiliation(s)
- Muhamad Fachrul
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC, 3010, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Sünje Johanna Pamp
- National Food Institute, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Agus Salim
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3010, Australia.
- Department of Population Health, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia.
- Department Mathematics and Statistics, La Trobe University, Bundoora, VIC, 3086, Australia.
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30
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Winship AL, Alesi LR, Sant S, Stringer JM, Cantavenera A, Hegarty T, Requesens CL, Liew SH, Sarma U, Griffiths MJ, Zerafa N, Fox SB, Brown E, Caramia F, Zareie P, La Gruta NL, Phillips KA, Strasser A, Loi S, Hutt KJ. Checkpoint inhibitor immunotherapy diminishes oocyte number and quality in mice. NATURE CANCER 2022; 3:1-13. [PMID: 36008687 DOI: 10.1038/s43018-022-00413-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 06/23/2022] [Indexed: 06/15/2023]
Abstract
Loss of fertility is a major concern for female reproductive-age cancer survivors, since a common side-effect of conventional cytotoxic cancer therapies is permanent damage to the ovary. While immunotherapies are increasingly becoming a standard of care for many cancers-including in the curative setting-their impacts on ovarian function and fertility are unknown. We evaluated the effect of immune checkpoint inhibitors blocking programmed cell death protein ligand 1 and cytotoxic T lymphocyte-associated antigen 4 on the ovary using tumor-bearing and tumor-free mouse models. We find that immune checkpoint inhibition increases immune cell infiltration and tumor necrosis factor-α expression within the ovary, diminishes the ovarian follicular reserve and impairs the ability of oocytes to mature and ovulate. These data demonstrate that immune checkpoint inhibitors have the potential to impair both immediate and future fertility, and studies in women should be prioritized. Additionally, fertility preservation should be strongly considered for women receiving these immunotherapies, and preventative strategies should be investigated in future studies.
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Affiliation(s)
- Amy L Winship
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Lauren R Alesi
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Sneha Sant
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica M Stringer
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Aldana Cantavenera
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Teharn Hegarty
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Carolina Lliberos Requesens
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Seng H Liew
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Urooza Sarma
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Meaghan J Griffiths
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Nadeen Zerafa
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Stephen B Fox
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Emmaline Brown
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Franco Caramia
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Pirooz Zareie
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Nicole L La Gruta
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Kelly-Anne Phillips
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Andreas Strasser
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
| | - Sherene Loi
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
| | - Karla J Hutt
- Development and Stem Cell Program and Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.
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31
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Wang Y, Sun F, Lin W, Zhang S. AC-PCoA: Adjustment for confounding factors using principal coordinate analysis. PLoS Comput Biol 2022; 18:e1010184. [PMID: 35830390 PMCID: PMC9278763 DOI: 10.1371/journal.pcbi.1010184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/08/2022] [Indexed: 12/01/2022] Open
Abstract
Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data. In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification. With today’s unprecedented amount of data, researchers are challenged by the need to enhance meaningful signals without the interference of unwanted confounders hidden inside the data. Data visualization is an important step toward exploring and explaining data in order to intuitively identify the dominant patterns. Principal coordinate analysis (PCoA), as a visualization tool, allows flexible ways to define pairwise distances and project the samples into lower dimensions without changing the distances. However, when visualizing large-scale biological datasets, the true patterns are often hindered by unwanted confounding variations, either biologically or technically in origin. To eliminate these confounding factors and recover underlying signals, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, and showed that it significantly outperforms existing methods in visualization through three simulation studies and five real datasets. We further showed that the low-dimensional representations given by AC-PCoA provide promising results in statistical testing, clustering, and classification as well.
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Affiliation(s)
- Yu Wang
- School of Mathematical Sciences, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
| | - Fengzhu Sun
- Quantitative and Computational Biology Department, University of Southern California, Los Angeles, California, United States of America
| | - Wei Lin
- School of Mathematical Sciences, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, and Institutes of Brain Science, Fudan University, Shanghai, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- Key Laboratory of Mathematics for Nonlinear Science (Fudan University), Ministry of Education, Shanghai, China
- Shanghai Key Laboratory for Contemporary Applied Mathematics (Fudan University), Shanghai, China
| | - Shuqin Zhang
- School of Mathematical Sciences, Fudan University, Shanghai, China
- Key Laboratory of Mathematics for Nonlinear Science (Fudan University), Ministry of Education, Shanghai, China
- Shanghai Key Laboratory for Contemporary Applied Mathematics (Fudan University), Shanghai, China
- * E-mail:
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32
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Olshansky G, Giles C, Salim A, Meikle PJ. Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies. Prog Lipid Res 2022; 87:101177. [PMID: 35780914 DOI: 10.1016/j.plipres.2022.101177] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/19/2022] [Accepted: 06/26/2022] [Indexed: 10/17/2022]
Abstract
Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.
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Affiliation(s)
- Gavriel Olshansky
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Corey Giles
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3010, Australia; School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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Salim A, Molania R, Wang J, De Livera A, Thijssen R, Speed TP. RUV-III-NB: normalization of single cell RNA-seq data. Nucleic Acids Res 2022; 50:e96. [PMID: 35758618 PMCID: PMC9458465 DOI: 10.1093/nar/gkac486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 05/18/2022] [Accepted: 05/27/2022] [Indexed: 12/01/2022] Open
Abstract
Normalization of single cell RNA-seq data remains a challenging task. The performance of different methods can vary greatly between datasets when unwanted factors and biology are associated. Most normalization methods also only remove the effects of unwanted variation for the cell embedding but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. We propose RUV-III-NB, a method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. Using pseudo-replicates, RUV-III-NB explicitly takes into account potential association with biology when removing unwanted variation. The method can be used for both UMI or read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using published datasets with different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve DE analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent and is not sensitive to the number of factors assumed to contribute to the unwanted variation.
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Affiliation(s)
- Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, VIC 3053, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,School of Mathematics and Statistics, University of Melbourne, VIC 3010, Australia.,Baker Heart and Diabetes Institute Melbourne, VIC 3004, Australia.,Department of Mathematics and Statistics, La Trobe University, VIC 3086, Australia
| | - Ramyar Molania
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia
| | - Jianan Wang
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,Department of Medical Biology, University of Melbourne, VIC 3010, Australia
| | - Alysha De Livera
- Melbourne School of Population and Global Health, University of Melbourne, VIC 3053, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,Baker Heart and Diabetes Institute Melbourne, VIC 3004, Australia.,Department of Mathematics and Statistics, La Trobe University, VIC 3086, Australia.,School of Science, RMIT University, Melbourne VIC 3000, Australia
| | - Rachel Thijssen
- Blood Cells and Blood Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville VIC 3052, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,School of Mathematics and Statistics, University of Melbourne, VIC 3010, Australia
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34
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Manuck TA, Eaves LA, Rager JE, Sheffield-abdullah K, Fry RC. Nitric oxide-related gene and microRNA expression in peripheral blood in pregnancy vary by self-reported race. Epigenetics 2022; 17:731-745. [PMID: 34308756 PMCID: PMC9336489 DOI: 10.1080/15592294.2021.1957576] [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: 03/23/2021] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022] Open
Abstract
Adverse pregnancy outcomes disproportionately affect non-Hispanic (NH) Black patients in the United States. Structural racism has been associated with increased psychosocial distress and inflammation and may trigger oxidative stress. Thus, the nitric oxide (NO) pathway (involved in the regulation of inflammation and oxidative stress) may partly explain the underlying disparities in obstetric outcomes.Cohort study of 154 pregnant patients with high-risk obstetric histories; n = 212 mRNAs and n = 108 microRNAs (miRNAs) in the NO pathway were evaluated in circulating white blood cells. NO pathway mRNA and miRNA transcript counts were compared by self-reported race; NH Black patients were compared with women of other races/ethnicities. Finally, miRNA-mRNA expression levels were correlated.Twenty-two genes (q < 0.10) were differentially expressed in self-identified NH Black individuals. Superoxide dismutase 1 (SOD1), interleukin-8 (IL-8), dynein light chain LC8-type 1 (DYNLL1), glutathione peroxidase 4 (GPX4), and glutathione peroxidase 1 (GPX1) were the five most differentially expressed genes among NH Black patients compared to other patients. There were 63 significantly correlated miRNA-mRNA pairs (q < 0.10) demonstrating potential miRNA regulation of associated target mRNA expression. Ten miRNAs that were identified as members of significant miRNA-mRNA pairs were also differentially expressed among NH Black patients (q < 0.10).These findings support an association between NO pathway and inflammation and infection-related mRNA and miRNA expression in blood drawn during pregnancy and patient race/ethnicity. These findings may reflect key differences in the biology of inflammatory gene dysregulation that occurs in response to the stress of systemic racism and that underlies disparities in pregnancy outcomes.
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Affiliation(s)
- Tracy A. Manuck
- Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Lauren A. Eaves
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Julia E Rager
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
| | | | - Rebecca C. Fry
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC
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Sadeghirad H, Monkman J, Mehdi AM, Ladwa R, O’Byrne K, Hughes BGM, Kulasinghe A. Dissecting Tissue Compartment-Specific Protein Signatures in Primary and Metastatic Oropharyngeal Squamous Cell Carcinomas. Front Immunol 2022; 13:895513. [PMID: 35651606 PMCID: PMC9149425 DOI: 10.3389/fimmu.2022.895513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) often presents with locoregional or distant disease, despite multimodal therapeutic approaches, which include surgical resection, chemoradiotherapy, and more recently, immunotherapy for metastatic or recurrent HNSCC. Therapies often target the primary and nodal regional HNSCC sites, and their efficacy at controlling occult distant sites remains poor. While our understanding of the tumor microenvironment conducive to effective therapies is increasing, the biology underpinning locoregional sites remains unclear. Here, we applied targeted spatial proteomic approaches to primary and lymph node metastasis from an oropharyngeal SCC (OPSCC) cohort to understand the expression of proteins within tumors, and stromal compartments of the respective sites in samples of both matched and unmatched patients. In unmatched analyses of n = 43 primary and 11 nodal metastases, our data indicated that tumor cells in nodal metastases had higher levels of Ki-67, PARP, BAD, and cleaved caspase 9, suggesting a role for increased proliferation, DNA repair, and apoptosis within these metastatic cells. Conversely, in matched analyses (n = 7), pro-apoptotic markers BIM and BAD were enriched in the stroma of primary tumors. Univariate, overall survival (OS) analysis indicated CD25 in tumor regions of primary tumors to be associated with reduced survival (HR = 3.3, p = 0.003), while progesterone receptor (PR) was associated with an improved OS (HR = 0.33, p = 0.015). This study highlights the utility of spatial proteomics for delineating the tumor and stromal compartment composition, and utility toward understanding these properties in locoregional metastasis. These findings indicate unique biological properties of lymph node metastases that may elucidate further understanding of distant metastatic in OPSCC.
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Affiliation(s)
- Habib Sadeghirad
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | - James Monkman
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | - Ahmed M. Mehdi
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Queensland Cyber Infrastructure Foundation Ltd., QCIF Facility for Advanced Bioinformatics, Brisbane, QLD, Australia
| | - Rahul Ladwa
- Princess Alexandra Hospital, Woolloongabba, QLD, Australia
- Faculty of Medicine, University of Queensland, Herston, QLD, Australia
| | - Ken O’Byrne
- Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Brett G. M. Hughes
- Faculty of Medicine, University of Queensland, Herston, QLD, Australia
- Cancer Care Services, Royal Brisbane and Women’s Hospital, Herston, QLD, Australia
| | - Arutha Kulasinghe
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Faculty of Medicine, University of Queensland, Herston, QLD, Australia
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36
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Xu C, Wang X, Lim J, Xiao G, Xie Y. RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data. Stat Med 2022; 41:665-680. [PMID: 34773277 PMCID: PMC8795478 DOI: 10.1002/sim.9250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 08/23/2021] [Accepted: 10/16/2021] [Indexed: 11/05/2022]
Abstract
The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.
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Affiliation(s)
- Can Xu
- Department of Statistical Science, Southern Methodist University, Texas, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Texas, USA,Correspondence: Xinlei Wang, Department of Statistical Science, Southern Methodist University, Dallas, TX 75275.
| | - Johan Lim
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Guanghua Xiao
- Department of Population & Data Sciences and Department of Bioinformatics, University of Texas Southwestern Medical Center, Texas, USA
| | - Yang Xie
- Department of Population & Data Sciences and Department of Bioinformatics, University of Texas Southwestern Medical Center, Texas, USA
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37
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Johnson BE, Creason AL, Stommel JM, Keck JM, Parmar S, Betts CB, Blucher A, Boniface C, Bucher E, Burlingame E, Camp T, Chin K, Eng J, Estabrook J, Feiler HS, Heskett MB, Hu Z, Kolodzie A, Kong BL, Labrie M, Lee J, Leyshock P, Mitri S, Patterson J, Riesterer JL, Sivagnanam S, Somers J, Sudar D, Thibault G, Weeder BR, Zheng C, Nan X, Thompson RF, Heiser LM, Spellman PT, Thomas G, Demir E, Chang YH, Coussens LM, Guimaraes AR, Corless C, Goecks J, Bergan R, Mitri Z, Mills GB, Gray JW. An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer. Cell Rep Med 2022; 3:100525. [PMID: 35243422 PMCID: PMC8861971 DOI: 10.1016/j.xcrm.2022.100525] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/15/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022]
Abstract
Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.
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Affiliation(s)
- Brett E. Johnson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Allison L. Creason
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jayne M. Stommel
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jamie M. Keck
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Swapnil Parmar
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Courtney B. Betts
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aurora Blucher
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher Boniface
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Elmar Bucher
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Erik Burlingame
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Todd Camp
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Koei Chin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jennifer Eng
- 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 & Science University, Portland, OR 97239, USA
| | - Heidi S. Feiler
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Michael B. Heskett
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Zhi Hu
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Annette Kolodzie
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ben L. Kong
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pharmacy Services, Oregon Health & Science University, Portland, OR 97239, USA
| | - Marilyne Labrie
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jinho Lee
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Patrick Leyshock
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Souraya Mitri
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Janice Patterson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica L. Riesterer
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Multiscale Microscopy Core, Oregon Health & Science University, Portland, OR 97239, USA
| | - Shamilene Sivagnanam
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julia Somers
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR 97239, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Benjamin R. Weeder
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christina Zheng
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Xiaolin Nan
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Reid F. Thompson
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, OR 97239, USA
| | - Laura M. Heiser
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Paul T. Spellman
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - George Thomas
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Emek Demir
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Lisa M. Coussens
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexander R. Guimaraes
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher Corless
- Department of Pharmacy Services, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jeremy Goecks
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Raymond Bergan
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Zahi Mitri
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Medicine, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Gordon B. Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joe W. Gray
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
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Kulasinghe A, Monkman J, Shah ET, Matigian N, Adams MN, O’Byrne K. Spatial Profiling Identifies Prognostic Features of Response to Adjuvant Therapy in Triple Negative Breast Cancer (TNBC). Front Oncol 2022; 11:798296. [PMID: 35083152 PMCID: PMC8784863 DOI: 10.3389/fonc.2021.798296] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/20/2021] [Indexed: 12/25/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that has few effective treatment options due to its lack of targetable hormone receptors. Whilst the degree of tumour infiltrating lymphocytes (TILs) has been shown to associate with therapy response and prognosis, deeper characterization of the molecular diversity that may mediate chemotherapeutic response is lacking. Here we applied targeted proteomic analysis of both chemotherapy sensitive and resistant TNBC tissue samples by the Nanostring GeoMx Digital Spatial Platform (DSP). By quantifying 68 targets in the tumour and tumour microenvironment (TME) compartments and performing differential expression analysis between responsive and non-responsive tumours, we show that increased ER-alpha expression and decreased 4-1BB and MART1 within the stromal compartments is associated with adjuvant chemotherapy response. Similarly, higher expression of GZMA, STING and fibronectin and lower levels of CD80 were associated with response within tumour compartments. Univariate overall-survival (OS) analysis of stromal proteins supported these findings, with ER-alpha expression (HR=0.19, p=0.0012) associated with better OS while MART1 expression (HR=2.3, p=0.035) was indicative of poorer OS. Proteins within tumour compartments consistent with longer OS included PD-L1 (HR=0.53, p=0.023), FOXP3 (HR=0.5, p=0.026), GITR (HR=0.51, p=0.036), SMA (HR=0.59, p=0.043), while EPCAM (HR=1.7, p=0.045), and CD95 (HR=4.9, p=0.046) expression were associated with shorter OS. Our data provides early insights into the levels of these markers in the TNBC tumour microenvironment, and their association with chemotherapeutic response and patient survival.
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Affiliation(s)
- Arutha Kulasinghe
- University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - James Monkman
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Esha T. Shah
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Nicholas Matigian
- QFAB Bioinformatics, The University of Queensland, Brisbane, QLD, Australia
| | - Mark N. Adams
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ken O’Byrne
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
- Princess Alexandra Hospital, Brisbane, QLD, Australia
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Gheinani AH, Akshay A, Besic M, Kuhn A, Keller I, Bruggmann R, Rehrauer H, Adam RM, Burkhard FC, Monastyrskaya K. Integrated mRNA-miRNA transcriptome analysis of bladder biopsies from patients with bladder pain syndrome identifies signaling alterations contributing to the disease pathogenesis. BMC Urol 2021; 21:172. [PMID: 34876093 PMCID: PMC8653529 DOI: 10.1186/s12894-021-00934-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022] Open
Abstract
Background Interstitial cystitis, or bladder pain syndrome (IC/BPS), is a chronic bladder disorder characterized by lower abdominal pain associated with the urinary bladder and accompanied by urinary frequency and urgency in the absence of identifiable causes. IC/PBS can be separated into the classic Hunner’s ulcerative type and the more prevalent non-ulcerative disease. Our aim was to unravel the biological processes and dysregulated cell signaling pathways leading to the bladder remodeling in non-ulcerative bladder pain syndrome (BPS) by studying the gene expression changes in the patients’ biopsies.
Methods We performed paired microRNA (miRNA) and mRNA expression profiling in the bladder biopsies of BPS patients with non-Hunner interstitial cystitis phenotype, using comprehensive Next-generation sequencing (NGS) and studied the activated pathways and altered biological processes based on the global gene expression changes. Paired mRNA-miRNA transcriptome analysis delineated the regulatory role of the dysregulated miRNAs by identifying their targets in the disease-induced pathways. Results EIF2 Signaling and Regulation of eIF4 and p70S6K Signaling, activated in response to cellular stress, were among the most significantly regulated processes during BPS. Leukotriene Biosynthesis nociceptive pathway, important in inflammatory diseases and neuropathic pain, was also significantly activated. The biological processes identified using Gene Ontology over-representation analysis were clustered into six main functional groups: cell cycle regulation, chemotaxis of immune cells, muscle development, muscle contraction, remodeling of extracellular matrix and peripheral nervous system organization and development. Compared to the Hunner’s ulcerative type IC, activation of the immune pathways was modest in non-ulcerative BPS, limited to neutrophil chemotaxis and IFN-γ-mediated signaling. We identified 62 miRNAs, regulated and abundant in BPS and show that they target the mRNAs implicated in eIF2 signalling pathway. Conclusions The bladders of non-ulcerative BPS patients recruited in this study had alterations consistent with a strong cell proliferative response and an up-regulation of smooth muscle contractility, while the contribution of inflammatory processes was modest. Pathway analysis of the integrated mRNA-miRNA NGS dataset pinpointed important regulatory miRNAs whose dysregulation might contribute to the pathogenesis. Observed molecular changes in the peripheral nervous system organization and development indicate the potential role of local bladder innervation in the pain perceived in this type of BPS. Supplementary Information The online version contains supplementary material available at 10.1186/s12894-021-00934-0.
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Affiliation(s)
- Ali Hashemi Gheinani
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland.,Urological Diseases Research Center, Boston Children's Hospital, Boston, MA, USA.,Department of Surgery, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akshay Akshay
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland
| | - Mustafa Besic
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland
| | - Annette Kuhn
- Department of Gynaecology, Inselspital University Hospital, 3010, Bern, Switzerland
| | - Irene Keller
- Interfaculty Bioinformatics Unit, University of Bern, Bern, Switzerland
| | - Rémy Bruggmann
- Interfaculty Bioinformatics Unit, University of Bern, Bern, Switzerland
| | - Hubert Rehrauer
- Functional Genomics Center Zurich, ETH Zurich/University of Zurich, Zurich, Switzerland
| | - Rosalyn M Adam
- Urological Diseases Research Center, Boston Children's Hospital, Boston, MA, USA.,Department of Surgery, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fiona C Burkhard
- Department of Urology, Inselspital University Hospital, 3010, Bern, Switzerland
| | - Katia Monastyrskaya
- Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland.
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40
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Bright F, Katzeff JS, Hodges JR, Piguet O, Kril JJ, Halliday GM, Kim WS. Glycoprotein Pathways Altered in Frontotemporal Dementia With Autoimmune Disease. Front Immunol 2021; 12:736260. [PMID: 34539672 PMCID: PMC8440893 DOI: 10.3389/fimmu.2021.736260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/16/2021] [Indexed: 12/02/2022] Open
Abstract
Behavioral variant frontotemporal dementia (bvFTD) is a younger onset form of neurodegeneration initiated in the frontal and/or temporal lobes with a slow clinical onset but rapid progression. bvFTD is highly complex biologically with different pathological signatures and genetic variants that can exhibit a spectrum of overlapping clinical manifestations. Although the role of innate immunity has been extensively investigated in bvFTD, the involvement of adaptive immunity in bvFTD pathogenesis is poorly understood. We analyzed blood serum proteomics to identify proteins that are associated with autoimmune disease in bvFTD. Eleven proteins (increased: ATP5B, CALML5, COLEC11, FCGBP, PLEK, PLXND1; decreased: APOB, ATP8B1, FAM20C, LOXL3, TIMD4) were significantly altered in bvFTD with autoimmune disease compared to those without autoimmune disease. The majority of these proteins were enriched for glycoprotein-associated proteins and pathways, suggesting that the glycome is targeted in bvFTD with autoimmune disease.
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Affiliation(s)
- Fiona Bright
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Jared S Katzeff
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - John R Hodges
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Olivier Piguet
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.,School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - Jillian J Kril
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Glenda M Halliday
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Woojin Scott Kim
- School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
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41
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Jung AM, Zhou J, Beitel SC, Littau SR, Gulotta JJ, Wallentine DD, Moore PK, Burgess JL. Longitudinal evaluation of whole blood miRNA expression in firefighters. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2021; 31:900-912. [PMID: 33603099 PMCID: PMC8445815 DOI: 10.1038/s41370-021-00306-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/18/2021] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Dysregulated microRNA (miRNA) expression could provide a mechanism linking firefighter exposure to increased cancer risk. OBJECTIVE To determine if changes in longitudinal miRNA expression in firefighters are associated with occupational exposures. METHODS Whole blood MiRNA was evaluated in 52 new recruits prior to live-fire training and 20-37 months later. Linear mixed effects models adjusted for age, ethnicity, BMI, and batch effects were used to determine associations separately for all fires and structure fires only between employment duration, cumulative fire-hours and fire-runs, and time since most recent fire with (1) nine a priori and (2) the full array of 799 miRNAs. RESULTS For multivariable models including all fires, two a priori miRNAs were associated with employment duration and four with time since most recent fire. For multivariable models restricted to structure fires, three a priori miRNAs were associated with employment duration and one with fire-runs. Additional miRNAs from the full array were associated with employment duration for all fires and/or structure fires. In general, tumor suppressive miRNAs decreased and oncogenic miRNAs increased with exposure. SIGNIFICANCE Changes in miRNAs may serve as biomarkers of exposure effects and a mechanism for increased cancer risk in firefighters.
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Affiliation(s)
- Alesia M Jung
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Jin Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Shawn C Beitel
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Sally R Littau
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | | | | | | | - Jefferey L Burgess
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
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42
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Foroutan M, Molania R, Pfefferle A, Behrenbruch C, Scheer S, Kallies A, Speed TP, Cursons J, Huntington ND. The Ratio of Exhausted to Resident Infiltrating Lymphocytes Is Prognostic for Colorectal Cancer Patient Outcome. Cancer Immunol Res 2021; 9:1125-1140. [PMID: 34413087 DOI: 10.1158/2326-6066.cir-21-0137] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/14/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
Immunotherapy success in colorectal cancer is mainly limited to patients whose tumors exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumor-infiltrating immune cells. Indeed, the presence of specific infiltrating immune-cell subsets has been shown to correlate with immunotherapy response and is in many cases prognostic of treatment outcome. Tumor-infiltrating lymphocytes (TIL) can undergo distinct differentiation programs, acquiring features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors, such as PD-1, and lose functionality. Although residency and exhaustion programs of CD8+ T cells are relatively well studied, these programs have only recently been appreciated in CD4+ T cells and remain largely unknown in tumor-infiltrating natural killer (NK) cells. In this study, we used single-cell RNA sequencing (RNA-seq) data to identify signatures of residency and exhaustion in colorectal cancer-infiltrating lymphocytes, including CD8+, CD4+, and NK cells. We then tested these signatures in independent single-cell data from tumor and normal tissue-infiltrating immune cells. Furthermore, we used versions of these signatures designed for bulk RNA-seq data to explore tumor-intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic datasets from patients with colon adenocarcinoma, we showed that combinations of these signatures, in particular combinations of NK-cell activity signatures, together with tumor-associated signatures, such as TGFβ signaling, were associated with distinct survival outcomes in patients with colon adenocarcinoma.
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Affiliation(s)
- Momeneh Foroutan
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia.
| | - Ramyar Molania
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Aline Pfefferle
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia.,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
| | - Corina Behrenbruch
- University of Melbourne Centre for Cancer Research, Parkville, Victoria, Australia
| | - Sebastian Scheer
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
| | - Axel Kallies
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, Victoria, Australia
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,School of Mathematics & Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Joseph Cursons
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia. .,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
| | - Nicholas D Huntington
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia. .,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
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43
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A hierarchical approach to removal of unwanted variation for large-scale metabolomics data. Nat Commun 2021; 12:4992. [PMID: 34404777 PMCID: PMC8371158 DOI: 10.1038/s41467-021-25210-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/23/2021] [Indexed: 01/13/2023] Open
Abstract
Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies. Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.
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44
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Kim HJ, Kim T, Xiao D, Yang P. Protocol for the processing and downstream analysis of phosphoproteomic data with PhosR. STAR Protoc 2021; 2:100585. [PMID: 34151303 PMCID: PMC8190506 DOI: 10.1016/j.xpro.2021.100585] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021). This protocol describes how to run and interpret the results of PhosR PhosR performs filtering, imputation, and normalization of phosphoproteomic data PhosR enables kinase-substrate predictions and signalome construction The step-by-step protocol provides a comprehensive introduction to phospho-data analysis
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Affiliation(s)
- Hani Jieun Kim
- Charles Perkins Centre, School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
- Corresponding author
| | - Taiyun Kim
- Charles Perkins Centre, School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Di Xiao
- Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Pengyi Yang
- Charles Perkins Centre, School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
- Corresponding author
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45
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Bhattacharya A, Hamilton AM, Furberg H, Pietzak E, Purdue MP, Troester MA, Hoadley KA, Love MI. An approach for normalization and quality control for NanoString RNA expression data. Brief Bioinform 2021; 22:bbaa163. [PMID: 32789507 PMCID: PMC8138885 DOI: 10.1093/bib/bbaa163] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 01/10/2023] Open
Abstract
The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString's commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.
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Affiliation(s)
| | | | | | | | - Mark P Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute
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46
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von Siebenthal M, Besic M, Gheinani AH, Akshay A, Lizun-Platoni S, Kunz N, Burkhard FC, Monastyrskaya K. Urinary miRNA profiles discriminate between obstruction-induced bladder dysfunction and healthy controls. Sci Rep 2021; 11:10204. [PMID: 33986358 PMCID: PMC8119692 DOI: 10.1038/s41598-021-89535-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/28/2021] [Indexed: 02/07/2023] Open
Abstract
Urgency, frequency and incomplete emptying are the troublesome symptoms often shared between benign prostatic obstruction-induced (BLUTD) and neurogenic (NLUTD) lower urinary tract dysfunction. Previously, using bladder biopsies, we suggested a panel of miRNA biomarkers for different functional phenotypes of the bladder. Urine is a good source of circulating miRNAs, but sex- and age-matched controls are important for urinary metabolite comparison. In two groups of healthy subjects (average age 32 and 57 years old, respectively) the total protein and RNA content was very similar between age groups, but the number of secreted extracellular vesicles (uEVs) and expression of several miRNAs were higher in the young healthy male volunteers. Timing of urine collection was not important for these parameters. We also evaluated the suitability of urinary miRNAs for non-invasive diagnosis of bladder outlet obstruction (BOO). A three urinary miRNA signature (miR-10a-5p, miR-301b-3p and miR-363-3p) could discriminate between controls and patients with LUTD (BLUTD and NLUTD). This panel of representative miRNAs can be further explored to develop a non-invasive diagnostic test for BOO. The age-related discrepancy in the urinary miRNA content observed in this study points to the importance of selecting appropriate, age-matched controls.
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Affiliation(s)
- Michelle von Siebenthal
- Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland
| | - Mustafa Besic
- Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland
| | - Ali Hashemi Gheinani
- Urological Diseases Research Center, Boston Children's Hospital, Harvard Medical School, Boston, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akshay Akshay
- Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland
| | | | - Nadine Kunz
- Department of Urology, Inselspital University Hospital, 3010, Bern, Switzerland
| | - Fiona C Burkhard
- Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland.,Department of Urology, Inselspital University Hospital, 3010, Bern, Switzerland
| | - Katia Monastyrskaya
- Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern, Bern, Switzerland. .,Department of Urology, Inselspital University Hospital, 3010, Bern, Switzerland.
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47
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Manuck TA, Eaves LA, Rager JE, Fry RC. Mid-pregnancy maternal blood nitric oxide-related gene and miRNA expression are associated with preterm birth. Epigenomics 2021; 13:667-682. [PMID: 33890487 DOI: 10.2217/epi-2020-0346] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: The nitric oxide (NO) pathway modulates inflammation and may influence birth timing. Patients & methods: Case-control analysis of 136 pregnant women with RNA obtained <28 weeks; n = 212 mRNAs and n = 108 miRNAs in the NO pathway were evaluated. NO-pathway mRNA and miRNA transcript counts in women delivering preterm versus at term were compared, miRNA-mRNA expression levels correlated and prediction models generated. Results: Fourteen genes were differentially expressed in women delivering <37 weeks; 13/14 were also differentially expressed in those delivering <34 weeks (q <0.10) versus term births. Multiple miRNA-mRNA pairs were correlated. Models with gene expression better predicted prematurity than models with only clinical or nongenomic predictors. Conclusion: Maternal blood NO pathway-related mRNA and miRNA expression is associated with prematurity.
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Affiliation(s)
- Tracy A Manuck
- Department of Obstetrics & Gynecology, Division of Maternal Fetal Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA.,Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lauren A Eaves
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Julia E Rager
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca C Fry
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
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48
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Ehteda A, Simon S, Franshaw L, Giorgi FM, Liu J, Joshi S, Rouaen JRC, Pang CNI, Pandher R, Mayoh C, Tang Y, Khan A, Ung C, Tolhurst O, Kankean A, Hayden E, Lehmann R, Shen S, Gopalakrishnan A, Trebilcock P, Gurova K, Gudkov AV, Norris MD, Haber M, Vittorio O, Tsoli M, Ziegler DS. Dual targeting of the epigenome via FACT complex and histone deacetylase is a potent treatment strategy for DIPG. Cell Rep 2021; 35:108994. [PMID: 33852836 DOI: 10.1016/j.celrep.2021.108994] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/24/2020] [Accepted: 03/24/2021] [Indexed: 12/13/2022] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is an aggressive and incurable childhood brain tumor for which new treatments are needed. CBL0137 is an anti-cancer compound developed from quinacrine that targets facilitates chromatin transcription (FACT), a chromatin remodeling complex involved in transcription, replication, and DNA repair. We show that CBL0137 displays profound cytotoxic activity against a panel of patient-derived DIPG cultures by restoring tumor suppressor TP53 and Rb activity. Moreover, in an orthotopic model of DIPG, treatment with CBL0137 significantly extends animal survival. The FACT subunit SPT16 is found to directly interact with H3.3K27M, and treatment with CBL0137 restores both histone H3 acetylation and trimethylation. Combined treatment of CBL0137 with the histone deacetylase inhibitor panobinostat leads to inhibition of the Rb/E2F1 pathway and induction of apoptosis. The combination of CBL0137 and panobinostat significantly prolongs the survival of mice bearing DIPG orthografts, suggesting a potential treatment strategy for DIPG.
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Affiliation(s)
- Anahid Ehteda
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Sandy Simon
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Laura Franshaw
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Jie Liu
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Swapna Joshi
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Jourdin R C Rouaen
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Chi Nam Ignatius Pang
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Ruby Pandher
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia; School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia
| | - Yujie Tang
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Department of Pathophysiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Aaminah Khan
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Caitlin Ung
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Ornella Tolhurst
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Anne Kankean
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Elisha Hayden
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Rebecca Lehmann
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Sylvie Shen
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Anjana Gopalakrishnan
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Peter Trebilcock
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Katerina Gurova
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Andrei V Gudkov
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Murray D Norris
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia; Centre for Childhood Cancer Research, University of New South Wales, Sydney, NSW, Australia
| | - Michelle Haber
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Orazio Vittorio
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia; School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia
| | - Maria Tsoli
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia; School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia.
| | - David S Ziegler
- Children's Cancer Institute, Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW, Australia; School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia; Kid's Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia.
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PhosR enables processing and functional analysis of phosphoproteomic data. Cell Rep 2021; 34:108771. [PMID: 33626354 DOI: 10.1016/j.celrep.2021.108771] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 12/07/2020] [Accepted: 01/28/2021] [Indexed: 02/08/2023] Open
Abstract
Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.
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Bhuva DD, Cursons J, Davis MJ. Stable gene expression for normalisation and single-sample scoring. Nucleic Acids Res 2020; 48:e113. [PMID: 32997146 PMCID: PMC7641762 DOI: 10.1093/nar/gkaa802] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 09/08/2020] [Accepted: 09/29/2020] [Indexed: 12/22/2022] Open
Abstract
Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.
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Affiliation(s)
- Dharmesh D Bhuva
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Joseph Cursons
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC 3052, Australia.,Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Melissa J Davis
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC 3052, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia.,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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