1
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Huang KL, Scott AD, Zhou DC, Wang LB, Weerasinghe A, Elmas A, Liu R, Wu Y, Wendl MC, Wyczalkowski MA, Baral J, Sengupta S, Lai CW, Ruggles K, Payne SH, Raphael B, Fenyö D, Chen K, Mills G, Ding L. Spatially interacting phosphorylation sites and mutations in cancer. Nat Commun 2021; 12:2313. [PMID: 33875650 PMCID: PMC8055881 DOI: 10.1038/s41467-021-22481-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 02/17/2021] [Indexed: 11/18/2022] Open
Abstract
Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.
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Affiliation(s)
- Kuan-Lin Huang
- Department of Genetics and Genomics, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Adam D Scott
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Cui Zhou
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Liang-Bo Wang
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Amila Weerasinghe
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Abdulkadir Elmas
- Department of Genetics and Genomics, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruiyang Liu
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Yige Wu
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael C Wendl
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Jessika Baral
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Sohini Sengupta
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Chin-Wen Lai
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Kelly Ruggles
- Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Benjamin Raphael
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
| | - David Fenyö
- Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY, USA
| | - Ken Chen
- Departments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gordon Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Li Ding
- Department of Medicine, McDonnell Genome Institute, Department of Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA.
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2
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Bailey MH, Meyerson WU, Dursi LJ, Wang LB, Dong G, Liang WW, Weerasinghe A, Li S, Li Y, Kelso S, Saksena G, Ellrott K, Wendl MC, Wheeler DA, Getz G, Simpson JT, Gerstein MB, Ding L. Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. Nat Commun 2020; 11:6232. [PMID: 33257764 PMCID: PMC7705717 DOI: 10.1038/s41467-020-20128-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w.
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Affiliation(s)
- Matthew H Bailey
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - William U Meyerson
- Yale School of Medicine, Yale University, New Haven, CT, 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Lewis Jonathan Dursi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
- The Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada
| | - Liang-Bo Wang
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Guanlan Dong
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Wen-Wei Liang
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Amila Weerasinghe
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yize Li
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
| | - Sean Kelso
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Gordon Saksena
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kyle Ellrott
- Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Michael C Wendl
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Department of Mathematics, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Department of Genetics, Washington University School of Medicine, St.Louis, MO, 63110, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jared T Simpson
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S, Canada
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
| | - Li Ding
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA.
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Department of Medicine and Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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3
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Bailey MH, Meyerson WU, Dursi LJ, Wang LB, Dong G, Liang WW, Weerasinghe A, Li S, Li Y, Kelso S, Saksena G, Ellrott K, Wendl MC, Wheeler DA, Getz G, Simpson JT, Gerstein MB, Ding L. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. Nat Commun 2020; 11:4748. [PMID: 32958763 PMCID: PMC7505971 DOI: 10.1038/s41467-020-18151-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 07/28/2020] [Indexed: 02/03/2023] Open
Abstract
The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.
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Affiliation(s)
- Matthew H Bailey
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - William U Meyerson
- Yale School of Medicine, Yale University, New Haven, CT, 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Lewis Jonathan Dursi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
- The Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada
| | - Liang-Bo Wang
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Guanlan Dong
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Wen-Wei Liang
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Amila Weerasinghe
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yize Li
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
| | - Sean Kelso
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Gordon Saksena
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kyle Ellrott
- Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Michael C Wendl
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA
- Department of Mathematics, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Department of Genetics, Washington University School of Medicine, St.Louis, MO, 63110, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jared T Simpson
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S, Canada
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
| | - Li Ding
- The McDonnell Genome Institute at Washington University, St. Louis, MO, 63108, USA.
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Department of Medicine and Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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Scott AD, Huang KL, Weerasinghe A, Mashl RJ, Gao Q, Martins Rodrigues F, Wyczalkowski MA, Ding L. CharGer: clinical Characterization of Germline variants. Bioinformatics 2019; 35:865-867. [PMID: 30102335 DOI: 10.1093/bioinformatics/bty649] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 06/24/2018] [Accepted: 08/08/2018] [Indexed: 11/12/2022] Open
Abstract
SUMMARY CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User-designed pathogenicity criteria can be incorporated into CharGer's flexible framework, thereby allowing users to create a customized classification protocol. AVAILABILITY AND IMPLEMENTATION Source code is freely available at https://github.com/ding-lab/CharGer and is distributed under the GNU GPL-v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix-based operating systems. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Adam D Scott
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Kuan-Lin Huang
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Amila Weerasinghe
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - R Jay Mashl
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Qingsong Gao
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Fernanda Martins Rodrigues
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Matthew A Wyczalkowski
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Li Ding
- Oncology Division, Washington University School of Medicine, St. Louis, MO, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.,Siteman Cancer Center, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
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5
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Kankanamge D, Tennakoon M, Weerasinghe A, Cedeno-Rosario L, Chadee DN, Karunarathne A. G protein αq exerts expression level-dependent distinct signaling paradigms. Cell Signal 2019; 58:34-43. [PMID: 30849518 DOI: 10.1016/j.cellsig.2019.02.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 02/23/2019] [Accepted: 02/25/2019] [Indexed: 12/16/2022]
Abstract
G protein αq-coupled receptors (Gq-GPCRs) primarily signal through GαqGTP mediated phospholipase Cβ (PLCβ) stimulation and the subsequent hydrolysis of phosphatidylinositol 4, 5 bisphosphate (PIP2). Though Gq-heterotrimer activation results in both GαqGTP and Gβγ, unlike Gi/o-receptors, it is unclear if Gq-coupled receptors employ Gβγ as a major signal transducer. Compared to Gi/o- and Gs-coupled receptors, we observed that most cell types exhibit a limited free Gβγ generation upon Gq-pathway and Gαq/11 heterotrimer activation. We show that cells transfected with Gαq or endogenously expressing more than average-levels of Gαq/11 compared to Gαs and Gαi exhibit a distinct signaling regime primarily characterized by recovery-resistant PIP2 hydrolysis. Interestingly, the elevated Gq-expression is also associated with enhanced free Gβγ generation and signaling. Furthermore, the gene GNAQ, which encodes for Gαq, has recently been identified as a cancer driver gene. We also show that GNAQ is overexpressed in tumor samples of patients with Kidney Chromophobe (KICH) and Kidney renal papillary (KIRP) cell carcinomas in a matched tumor-normal sample analysis, which demonstrates the clinical significance of Gαq expression. Overall, our data indicates that cells usually express low Gαq levels, likely safeguarding cells from excessive calcium as wells as from Gβγ signaling.
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Affiliation(s)
- Dinesh Kankanamge
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH 43606, USA
| | - Mithila Tennakoon
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH 43606, USA
| | - Amila Weerasinghe
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Luis Cedeno-Rosario
- Department of Biological Sciences, The University of Toledo, Toledo, OH 43606, USA
| | - Deborah N Chadee
- Department of Biological Sciences, The University of Toledo, Toledo, OH 43606, USA
| | - Ajith Karunarathne
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, OH 43606, USA.
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Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PKS, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortés-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018; 174:1034-1035. [PMID: 30096302 PMCID: PMC8045146 DOI: 10.1016/j.cell.2018.07.034] [Citation(s) in RCA: 290] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Sengupta S, Scott A, Weerasinghe A, Zhou DC, Wyczalkowski MA, Jayasinghe RG, Chen K, Mills G, Wendl MC, Dipersio J, Ding L. Abstract 2357: Utilizing biological and protein structure-guided features to improve driver mutation discovery. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-2357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Distinguishing between driver and passenger somatic mutations to pinpoint genetic alterations leading to oncogenesis still presents significant challenges. To meet these challenges, computational tools have been developed as effective filters, pruning most of the somatic mutations to a shortlist of high-priority, functional candidates for experimental validation. Most tools include searching for genes or pathways having mutation rates higher than explained by chance, mutations in conserved regions, or genes with neighboring mutations on the linear DNA or protein sequence. Recently, there has been a shift to utilize tertiary/quaternary protein structures to identify mutations clustering proximal to each other in 3D space. Such enrichment of mutations can indicate specific domains critical to normal protein function and when mutated, can drive tumor initiation and progression. HotSpot3D, a protein structure-based tool, identifies clusters enriched with proximal mutations within proteins. Though HotSpot3D has been valuable in identifying clusters of residues that are important to cancer, it does not distinguish the driving potential or structural impact of different mutations within a cluster nor does it consider the physical impact of different amino acid substitutions at the same site. The prediction power of HotSpot3D in distinguishing driver mutations from passenger mutations can be improved if spatial clustering considers physical/biological features proximal to mutations in significant clusters as well as the specific amino acid substitutions of mutations. We have created a machine learning algorithm that further prioritizes putative driver mutations found in HotSpot3D clusters by incorporating structural/biological features such as proximity of mutations to functional sites (active sites, phosphorylation sites, disulfide bonds, etc.), solvent accessibility, physiochemical property change of mutations, free energy change of mutations, conservation of residue sites, secondary structure state of residue sites, and expression/phosphorylation changes of samples containing mutations. We have curated experimentally validated mutations identified as neutral or oncogenic from various databases to serve as our training sets. This algorithm can be trained on the curated mutations in various protein subclasses such as homologous proteins, oncogenes, tumor suppressors, etc. to identify distinct structural feature signatures per subclass specific to driver mutations. This tool will aid in revealing putative driver mutations in genes not previously linked with cancer and help pinpoint mutations in known cancer genes that are driving cancer. Specifically, we are interested in applying the algorithm to druggable protein families such as G-Protein Coupled Receptors, Kinases, and Nuclear Hormone Receptors to better understand their role in tumor initiation and progression.
Citation Format: Sohini Sengupta, Adam Scott, Amila Weerasinghe, Dan C. Zhou, Matthew A. Wyczalkowski, Reyka G. Jayasinghe, Ken Chen, Gordon Mills, Mike C. Wendl, John Dipersio, Li Ding. Utilizing biological and protein structure-guided features to improve driver mutation discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2357.
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Affiliation(s)
| | - Adam Scott
- 1Washington Univ. St. Louis, Saint Louis, MO
| | | | - Dan C. Zhou
- 1Washington Univ. St. Louis, Saint Louis, MO
| | | | | | - Ken Chen
- 2MD Anderson Cancer Center, Houston, TX
| | | | | | | | - Li Ding
- 1Washington Univ. St. Louis, Saint Louis, MO
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8
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Huang KL, Weerasinghe A, Wu Y, Liang WW, Mashl RJ, Reynolds S, Houlahan KE, Oak N, Atlas TCG, Lazar AJ, Wendel MC, Khurana E, Plon S, Chen F, Gerstein M, Shmulevich I, Ding L. Abstract 5359: Regulatory germline variants in 10,389 adult cancers. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-5359] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Previous studies of rare germline variants in cancer has largely been limited to the coding regions of known predisposition genes. The TCGA PanCanAtlas Germline Working Group is analyzing germline predisposing variants of 10,389 cases in 33 cancer types. We deployed more than 121,000 virtual machines running for over 600,000 hours on the ISB Cancer Genome Cloud to conduct massively parallel variant calling and analyses, and the resulting data are shared with scientists across institutions worldwide. Carriers of the functional regulatory variants add on to the 8.9% of cases carrying known pathogenic variants. Burden analyses reveal enrichment of rare variants in the 3'UTR region of NHP2 and POLH. Further, we observed variants aggregating in conserved regions of selected microRNA families that are also affected by somatic mutations, including mir-17 and mir-29. We nominate regulatory variants by using GWAVA and FunSeq2 corroborated with their enrichment in cancer. The prioritized variants are then further evaluated by further co-occurrence of two-hit events and expression changes in their respective tumor samples. Finally, we examine ancestries, familial history and age at onset for carriers of these variants. Overall, we aim to discover and establish the role of regulatory germline variants in oncogenesis.
Citation Format: Kuan-lin Huang, Amila Weerasinghe, Yige Wu, Wen-wei Liang, R. Jay Mashl, Sheila Reynolds, Kathleen E. Houlahan, Ninad Oak, The Cancer Genome Atlas, Alexander J. Lazar, Michael C. Wendel, Ekta Khurana, Sharon Plon, Feng Chen, Mark Gerstein, Ilya Shmulevich, Li Ding. Regulatory germline variants in 10,389 adult cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5359.
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Affiliation(s)
| | | | - Yige Wu
- 1Washington Univ. St. Louis, Saint Louis, MO
| | | | | | | | | | | | | | | | | | | | | | - Feng Chen
- 1Washington Univ. St. Louis, Saint Louis, MO
| | | | | | - Li Ding
- 1Washington Univ. St. Louis, Saint Louis, MO
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9
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Weerasinghe A, Sengupta S, Scott AD, Bailey MH, Wendl MC, Chen K, Mills G, Ding L. Abstract 1306: Density-based mutation clustering in 3D space. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-1306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Cancer driver mutation and gene discovery has been a major challenge in cancer research. Several computational tools focus on clustering mutations on 3D protein structures to identify cancer drivers, including HotSpot3D, CLUMPS, HotMAPS, 3DHotSpots.org and e-Driver. While other tools provide a single snapshot of mutation clusters on the 3D structure, HotSpot3D utilizes a unique density-based clustering module capable of providing a full dynamical profile of clusters with varying densities. The density module (DM) is capable of detecting subtle variation in the density of mutations in 3D structures with little computation time and complexity. DM gives users the ability to detect the bridging effect, where more dense subclusters form a less dense supercluster due to a small number of mutations in between the subclusters connecting them. In addition to clustering based on physical density of mutations, DM is capable of clustering by additional biologic properties, including mutation recurrence and pathogenicity. Moreover, the underlying OPTICS algorithm utilized in the DM is inherently a “fuzzy” clustering algorithm, allowing slight variation of clusters based on the start point of the clustering algorithm from one run to another. Therefore, the DM provides a cluster membership probability measure for each mutation, giving the user the ability to choose the preferred stringency. Additionally, DM allows users to visualize the density clusters using Pymol. Since the whole dynamical set of clusters is produced by a single run, the visualization is performed so that one can reveal other clusters with higher densities by zooming in and out. Using a curated set of experimentally validated oncogenic mutations, we evaluated the performance of DM in detecting functionally activating mutations; our tool performed well in identifying driver mutations at high density thresholds. Moreover, the DM was able to identify mutations clustering at high density in BRAF, PIK3CA and KEAP1, which were not detected by other 3D clustering tools and sequence-based tools (SIFT, PolyPhen2, CHASM, etc.). The DM was also better at detecting clusters along the interface of protein-protein complexes (e.g., BRAF-KEAP1) compared to other 3D tools. In summary, HotSpot3D-DM allows for dynamical clustering, improved visualization, and identifies novel driver mutations missed by previous tools.
Citation Format: Amila Weerasinghe, Sohini Sengupta, Adam D. Scott, Maththew H. Bailey, Michael C. Wendl, Ken Chen, Gordon Mills, Li Ding. Density-based mutation clustering in 3D space [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1306.
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Affiliation(s)
| | | | | | | | | | - Ken Chen
- 2The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gordon Mills
- 2The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Li Ding
- 1Washington University in St. Louis, St. Louis, MO
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Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PKS, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortés-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018; 173:371-385.e18. [PMID: 29625053 PMCID: PMC6029450 DOI: 10.1016/j.cell.2018.02.060] [Citation(s) in RCA: 1155] [Impact Index Per Article: 192.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Revised: 11/22/2017] [Accepted: 02/23/2018] [Indexed: 12/19/2022]
Abstract
Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.
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Affiliation(s)
- Matthew H Bailey
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Collin Tokheim
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Eduard Porta-Pardo
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Sohini Sengupta
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Denis Bertrand
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672
| | - Amila Weerasinghe
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Antonio Colaprico
- Interuniversity Institute of Bioinformatics in Brussels (IB2), 1050 Brussels, Belgium; Machine Learning Group (MLG), Département d'Informatique, Université Libre de Bruxelles (ULB), Boulevard du Triomphe, CP212, 1050 Bruxelles, Belgium; Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jaegil Kim
- The Broad Institute, Cambridge, MA 02142, USA
| | - Brendan Reardon
- The Broad Institute, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Patrick Kwok-Shing Ng
- Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kang Jin Jeong
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Song Cao
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Zixing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjiong Gao
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Qingsong Gao
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Fang Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eric Minwei Liu
- Meyer Cancer Center and Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Loris Mularoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Carlota Rubio-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Niranjan Nagarajan
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672
| | - Isidro Cortés-Ciriano
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Boston, MA 02115, USA; Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Daniel Cui Zhou
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Wen-Wei Liang
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | | | - Venkata D Yellapantula
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - David Tamborero
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Chayaporn Suphavilai
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672
| | - Jia Yu Ko
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672
| | - Ekta Khurana
- Meyer Cancer Center and Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Boston, MA 02115, USA
| | - Eliezer M Van Allen
- The Broad Institute, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Han Liang
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Michael S Lawrence
- The Broad Institute, Cambridge, MA 02142, USA; Department of Pathology, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114, USA
| | - Adam Godzik
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Josh Stuart
- University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
| | - David Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gad Getz
- The Broad Institute, Cambridge, MA 02142, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Alexander J Lazar
- Departments of Pathology, Genomic Medicine, & Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Gordon B Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.
| | - Li Ding
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA.
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Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, Gibbs DL, Weerasinghe A, Huang KL, Tokheim C, Cortés-Ciriano I, Jayasinghe R, Chen F, Yu L, Sun S, Olsen C, Kim J, Taylor AM, Cherniack AD, Akbani R, Suphavilai C, Nagarajan N, Stuart JM, Mills GB, Wyczalkowski MA, Vincent BG, Hutter CM, Zenklusen JC, Hoadley KA, Wendl MC, Shmulevich L, Lazar AJ, Wheeler DA, Getz G. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 2018; 173:305-320.e10. [PMID: 29625049 PMCID: PMC5916814 DOI: 10.1016/j.cell.2018.03.033] [Citation(s) in RCA: 210] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 02/20/2018] [Accepted: 03/13/2018] [Indexed: 12/21/2022]
Abstract
The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
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Affiliation(s)
- Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA.
| | - Matthew H Bailey
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Eduard Porta-Pardo
- Barcelona Supercomputing Centre, 08034 Barcelona, Spain; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | | | - Antonio Colaprico
- Machine Learning Group (MLG), Département d'Informatique, Université Libre de Bruxelles, 1050 Brussels, Belgium; Department of Human Genetics, University of Miami, Miami, FL 33136, USA
| | - Denis Bertrand
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 13862
| | - David L Gibbs
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Amila Weerasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Kuan-Lin Huang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Collin Tokheim
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Isidro Cortés-Ciriano
- Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Boston, MA 02115, USA; Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Reyka Jayasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Lihua Yu
- H3 Biomedicine Inc., Cambridge, MA 02139, USA
| | - Sam Sun
- Department of Radiation Oncology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Catharina Olsen
- Machine Learning Group (MLG), Département d'Informatique, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jaegil Kim
- Broad Institute, Cambridge, MA 02142, USA
| | - Alison M Taylor
- Broad Institute, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Andrew D Cherniack
- Broad Institute, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77498, USA
| | - Chayaporn Suphavilai
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 13862
| | - Niranjan Nagarajan
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 13862
| | - Joshua M Stuart
- Baskin School of Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Gordon B Mills
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77498, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Benjamin G Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Carolyn M Hutter
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | | | - Katherine A Hoadley
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Michael C Wendl
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA
| | | | - Alexander J Lazar
- Departments of Pathology, Genomic Medicine, and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77498, USA
| | - David A Wheeler
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Gad Getz
- Harvard Medical School, Boston, MA 02115, USA; Broad Institute, Cambridge, MA 02142, USA; Massachusetts General Hospital, Boston, MA 02114, USA.
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Tzanidakis K, Choudhury N, Bhat S, Weerasinghe A, Marais J. Evaluation of disinfection of flexible nasendoscopes using Tristel wipes: a prospective single blind study. Ann R Coll Surg Engl 2012; 94:185-8. [PMID: 22507724 DOI: 10.1308/003588412x13171221589937] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION The otorhinolaryngology department at Northwick Park Hospital uses the Tristel wipes system for cleaning nasendoscopes in the outpatient clinics. This system uses chlorine dioxide as its only disinfectant. The manufacturer claims the system provides safe sterilisation of nasendoscopes. However, there appear to be no reports in the literature to date that evaluate the efficacy of this system in a clinical setting. The aim of this study was to evaluate the 'in use' efficacy of Tristel wipes in decontaminating nasendoscopes and to identify any significant contamination between cleaning and usage. METHODS A total of 31 cleaning episodes were performed. Each cleaning episode included two swabs after cleaning the scopes, one from the tip and the other from the handle. Another two swabs from the same areas were also taken before application to the patient. The microbiology unit evaluated all swabs for bacterial, fungal and mycobacterial growth. RESULTS Overall, 123 swabs from 31 cleaning episodes were tested. None of the swabs taken from the tips (n=31) or handles (n=31) after cleaning with Tristel wipes developed any organism growth. Furthermore, none of the swabs taken from the tip of the scopes before using on patients (n=31) developed any growth. Of the 31 swabs taken from the handle before use, 3 developed significant staphylococcal growth. CONCLUSIONS In our study, the 'in use' efficacy of Tristel wipes in cleaning the scopes of bacteria, fungi and mycobacteria was 100%. Attention to hand hygiene and the use of gloves should be considered when handling the cleaned scopes to minimise the risk of contamination between cleaning and application to patients.
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Tzanidakis K, Choudhury N, Marais J, Bhat S, Weerasinghe A. Evaluation of ‘in use’ efficacy of cleaning nasendoscopes with Tristel Wipes system. Br J Oral Maxillofac Surg 2011. [DOI: 10.1016/j.bjoms.2011.03.237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Weerasinghe A, Ha H, Hartley D, Khan AA. The effect of community interventions in reducing burns and scalds in children. Inj Prev 2010. [DOI: 10.1136/ip.2010.029215.188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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De Silva K, Gamage R, Dunuwille J, Gunarathna D, Sirisena D, Weerasinghe A, Amarasinghe P, Hosomi A, Mizuno T. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL): A patient from Sri Lanka. J Clin Neurosci 2009; 16:1492-3. [DOI: 10.1016/j.jocn.2009.01.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2008] [Revised: 01/15/2009] [Accepted: 01/16/2009] [Indexed: 10/20/2022]
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De Silva R, Gamage R, Dunuwille J, Gunarathna D, Sirisena D, Weerasinghe A, Amarasinghe P, Hosomi A, Toshiki T. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), first reported case from Sri Lanka. Neurosci Res 2009. [DOI: 10.1016/j.neures.2009.09.580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Moattar* H, Foteinos G, Mandel K, Zal B, Weerasinghe A, Fredricks S, Jahangiri M, Carter N, Afzal A. P.047 CCR5-DEL32 GENOTYPE MODIFIES PRO-INFLAMMATORY//ANTI-INFLAMMATORY CYTOKINE RATIO; POSSIBLE ROLE IN ATHEROGENESIS. Artery Res 2007. [DOI: 10.1016/s1872-9312(07)70070-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Day JRS, Malik IS, Weerasinghe A, Poullis M, Nadra I, Haskard DO, Taylor KM, Landis RC. Distinct yet complementary mechanisms of heparin and glycoprotein IIb/IIIa inhibitors on platelet activation and aggregation: implications for restenosis during percutaneous coronary intervention. Heart 2004; 90:794-9. [PMID: 15201252 PMCID: PMC1768310 DOI: 10.1136/hrt.2003.017749] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/13/2003] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE To study the effect of unfractionated heparin (UFH) versus low molecular weight heparin (LMWH) in combination with glycoprotein (Gp) IIb/IIIa blockers on platelet activation and aggregation. METHODS Washed platelets were stimulated with thrombin in the presence or absence of UFH (monoparin), LMWH (enoxaparin), and a Gp IIb/IIIa blocker (abciximab, eptifibatide, or tirofiban). RESULTS Although Gp IIb/IIIa antagonists blocked the final common pathway of thrombin induced platelet aggregation, UFH and LMWH were better at blocking upstream platelet activation. UFH was significantly more effective than LMWH at inhibiting P selectin expression (p = 0.001) and platelet derived growth factor release from thrombin activated platelets (p = 0.012). CONCLUSIONS UFH and LMWH exert complementary effects to Gp IIb/IIIa blockers by inhibiting afferent pathways of platelet activation. Coadministration of heparin with Gp IIb/IIIa blockers provides improved protection against persistent platelet activation, thereby improving outcome after percutaneous coronary intervention. Judging from these data, UFH may be more effective in this regard than LMWH, at least in vitro. The use of LMWH in preference to UFH during percutaneous coronary intervention, although initially attractive, may inadequately protect against platelet activation despite the presence of Gp IIb/IIIa blockers.
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Affiliation(s)
- J R S Day
- British Heart Foundation Cardiovascular Medicine and Cardiac Surgery Unit, National Heart and Lung Institute, Imperial College School of Medicine, Hammersmith Hospital, Du Cane road, London W12 0NN, UK.
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Day J, Malik I, Weerasinghe A, Nadra I, Haskard D, Taylor K, Landis C. W01.16 Distinct yet complimentary roles of IIbIIIa inhibitors and heparin during platelet activation: Implications for restenosis following PCI. ATHEROSCLEROSIS SUPP 2004. [DOI: 10.1016/s1567-5688(04)90016-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Affiliation(s)
- K S Nair
- Department of Cardiothoracic Surgery, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
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Mannoor MK, Weerasinghe A, Halder RC, Reza S, Morshed M, Ariyasinghe A, Watanabe H, Sekikawa H, Abo T. Resistance to malarial infection is achieved by the cooperation of NK1.1(+) and NK1.1(-) subsets of intermediate TCR cells which are constituents of innate immunity. Cell Immunol 2001; 211:96-104. [PMID: 11591113 DOI: 10.1006/cimm.2001.1833] [Citation(s) in RCA: 41] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We previously reported that the major expanding lymphocytes were intermediate TCR (TCR(int)) cells (mainly NK1.1(-)) during malarial infection in mice. Cell transfer experiments of TCR(int) cells indicated that these T cells mediated resistance to malaria. However, TCR(int) cells always contain NK1.1(+)TCR(int) cells (i.e., NKT cells) and controversial results (NKT cells were effective or not for resistance to malaria) have been reported by different investigators. In this study, we used CD1d((-/-)) mice, which almost completely lack NKT cells in the liver and other immune organs. Parasitemia was prolonged in the blood of CD1d((-/-)) mice and the expansion of lymphocytes in the liver of these mice was more prominent after an injection of Plasmodium yoelii-infected erythrocytes. However, these mice finally recovered from malaria. In contrast to B6 mice, CD4(-)8(-) NKT cells as well as NK1.1(-)CD3(int) cells expanded in CD1d((-/-)) mice after malarial infection, instead of CD4(+) (and CD8(+)) NKT cells. These newly generated CD4(-)8(-)NKT cells in CD1d((-/-)) mice did not use an invariant chain of Valpha14Jalpha281 for TCRalpha. Other evidence was that severe thymic atrophy and autoantibody production were accompanied by malarial infection, irrespective of the mice used. These results suggest that both NK1.1(-) and NK1.1(+) subsets of TCR(int) cells (i.e., constituents of innate immunity) are associated with resistance to malaria and that an autoimmune-like state is induced during malarial infection.
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MESH Headings
- Animals
- Antigens/immunology
- Antigens, CD1/genetics
- Antigens, CD1/immunology
- Antigens, CD1d
- Antigens, Differentiation, B-Lymphocyte
- Antigens, Ly
- Antigens, Surface
- CD3 Complex/immunology
- Disease Models, Animal
- Histocompatibility Antigens Class II
- Immunity, Innate/immunology
- Immunophenotyping
- Interferon-gamma/analysis
- Interleukin-4/analysis
- Killer Cells, Natural/immunology
- Kinetics
- Lectins, C-Type
- Liver/injuries
- Liver/pathology
- Lymphocyte Count
- Malaria/immunology
- Mice
- Mice, Inbred C57BL
- Mice, Knockout
- Mice, Nude
- NK Cell Lectin-Like Receptor Subfamily B
- Plasmodium yoelii/immunology
- Proteins/immunology
- Receptors, Antigen, T-Cell, alpha-beta/immunology
- T-Lymphocytes/immunology
- Time Factors
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Affiliation(s)
- M K Mannoor
- Department of Immunology, Niigata University School of Medicine, Niigata, 951-8510, Japan
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22
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Weerasinghe A, Hornick P, Smith P, Taylor K, Ratnatunga C. Coronary artery bypass grafting in non-dialysis-dependent mild-to-moderate renal dysfunction. J Thorac Cardiovasc Surg 2001; 121:1083-9. [PMID: 11385375 DOI: 10.1067/mtc.2001.113022] [Citation(s) in RCA: 65] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
OBJECTIVES The effect of mild-to-moderate elevation of preoperative serum creatinine levels on morbidity and mortality from coronary artery bypass grafting has not been investigated in a large multivariable model incorporating preoperative and intraoperative variables. Our first objective was to ascertain the effect of a mild-to-moderate elevation in the preoperative serum creatinine level on the need for mechanical renal support; the duration of special care and total postoperative stay; the occurrence of infective, respiratory, and neurologic complications; and hospital mortality. Our second objective was to ascertain which patient variables contributed to an increase in the serum creatinine level in association with coronary artery bypass grafting. METHODS A total of 1427 patients who had no known pre-existing renal disease and who were undergoing first-time coronary artery bypass grafting with cardiopulmonary bypass were recruited for the study. Patients were divided, on the basis of preoperative serum creatinine level, into 3 groups as follows: creatinine level of less than 130 micromol. L(-1); creatinine level of 130 to 149 micromol. L(-1); and creatinine level of 150 micromol. L(-1) or greater. A multivariable stepwise logistic regression analysis was used, and variables significant at the 5% level were included when developing the final multivariable models. RESULTS Multivariable analysis showed that elevation of the preoperative serum creatinine level to 130 micromol. L(-1) or greater increased the likelihood of needing mechanical renal support postoperatively (P <.001), as well as the need for postoperative special care (P <.001) and total hospital stay (P <.001). In-hospital mortality was also significantly elevated as the preoperative creatinine level rose to 130 to 149 micromol. L(-1) (P =.045) and to 150 micromol. L(-1) or greater (P <.001). It was further observed that patients with preoperative serum creatinine levels of 130 to 149 micromol. L(-1) (P =.02), patients with preoperative serum creatinine levels of 150 micromol. L(-1) or greater (P =.001), hypertensive patients (P =.007), patients with angina of New York Heart Association class III or greater (P =.001), patients having a nonelective operation (P =.002), and patients having a prolonged cardiopulmonary bypass time (P =.008) had a significantly greater increase in the serum creatinine level as a result of coronary artery bypass grafting. Of particular note was the finding that the method of myocardial protection (cardioplegia or crossclamp fibrillation) did not significantly influence in-hospital mortality, need for mechanical renal support, or special care or total postoperative hospital stay. CONCLUSIONS A mild elevation (130-149 micromol. L(-1)) in the preoperative serum creatinine level significantly increases the need for mechanical renal support, the duration of special care and total postoperative stay, and the in-hospital mortality. As the preoperative serum creatinine level increases further (> or =150 micromol. L(-1)), this effect is more pronounced. No significant difference in outcome was observed between the use of cardioplegia or crossclamp fibrillation for myocardial protection.
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Affiliation(s)
- A Weerasinghe
- Department of Cardiothoracic Surgery, Imperial College School of Medicine, University of London, Hammersmith Hospital, London W12 0HS, United Kingdom.
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23
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Weerasinghe A, Sekikawa H, Watanabe H, Mannoor K, Morshed SR, Halder RC, Kawamura T, Kosaka T, Miyaji C, Kawamura H, Seki S, Abo T. Association of intermediate T cell receptor cells, mainly their NK1.1(-) subset, with protection from malaria. Cell Immunol 2001; 207:28-35. [PMID: 11161450 DOI: 10.1006/cimm.2000.1737] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Mice were infected with Plasmodium (P.) yoelii blood-stage parasites. Both the liver and spleen were the sites of inflammation during malarial infection at the beginning of day 7. The major expanding cells were found to be NK1.1(-) intermediate alphabetaTCR (alphabetaTCR(int)) in the liver and spleen, although the population of NK1.1(+) alphabetaTCR(int) cells remained constant or slightly increased. These TCR(int) cells are of extrathymic origin or are generated by an alternative intrathymic pathway and are distinguished from conventional T cells of thymic origin. During malarial infection, the population of conventional T cells did not increase at all. TCR(int) cells purified from the liver of mice which had recovered from P. yoelii infection protected mice from malaria when they were transferred into 6.5-Gy-irradiated mice. Interestingly, the immunity against malaria seemed to disappear as a function of time after recovery, namely, mice which had recovered from malaria 1 year previously again became susceptible to malarial infection. The present results suggest that TCR(int) cells are intimately associated with protection against malarial infection and, therefore, that mice which had recovered from malaria 1 year previously lost such immunity.
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Affiliation(s)
- A Weerasinghe
- Department of Immunology, Niigata University School of Medicine, Niigata, 951-8510, Japan
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24
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Cockerill GW, Huehns TY, Weerasinghe A, Stocker C, Lerch PG, Miller NE, Haskard DO. Elevation of plasma high-density lipoprotein concentration reduces interleukin-1-induced expression of E-selectin in an in vivo model of acute inflammation. Circulation 2001; 103:108-12. [PMID: 11136694 DOI: 10.1161/01.cir.103.1.108] [Citation(s) in RCA: 189] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although there is strong evidence that plasma HDL levels correlate inversely with the incidence of coronary artery disease, the precise mechanism(s) for the protective effect of HDLs remains unclear. We recently showed that HDLs inhibit endothelial cell expression of cytokine-induced leukocyte adhesion molecules in vitro. Our study therefore sought to test the hypothesis that elevating the level of circulating HDLs would inhibit endothelial cell activation in vivo. METHODS AND RESULTS We used a porcine model of inflammation previously established in our laboratory, in which the level of vascular endothelial cell expression of E-selectin in interleukin (IL)-1alpha-induced skin lesions was measured by the uptake of a radiolabeled anti-E-selectin antibody (1.2B6). Porcine plasma HDL levels were elevated by use of a bolus injection of reconstituted discoidal HDL (recHDL). These particles resemble nascent HDL particles in shape and contain apolipoprotein A-I as the sole protein and soybean phosphatidylcholine as the sole phospholipid. We found that recHDLs inhibited the expression of IL-1alpha-induced E-selectin by porcine aortic endothelial cells in vitro, confirming that the inhibitory effect is conserved with synthetic HDLs and demonstrating that the phenomenon is not restricted to human endothelial cells. In vivo, elevating the circulating level of HDLs approximately 2-fold led to significant inhibition of basal and IL-1alpha-induced E-selectin expression by porcine microvascular endothelial cells. CONCLUSIONS These observations demonstrate the potential anti-inflammatory action of HDLs and provide support for the further investigation of the mechanisms underlying the inhibitory effects of HDLs on endothelial cell activation.
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Affiliation(s)
- G W Cockerill
- British Heart Foundation Cardiovascular Research Unit, National Heart and Lung Institute, Imperial College School of Medicine, Hammersmith Hospital Campus, London, UK.
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25
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Yamagiwa S, Yoshida Y, Halder RC, Weerasinghe A, Sugahara S, Asakura H, Abo T. Mechanisms involved in enteropathy induced by administration of nonsteroidal antiinflammatory drugs (NSAIDS). Dig Dis Sci 2001; 46:192-9. [PMID: 11270786 DOI: 10.1023/a:1005678312885] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Mice received oral indomethacin (1 mg/mouse) daily for five days. It was found that severe gastroenteropathy (ie, paralytic stomach and necrotic intestine) was induced on the sixth day. Ulcer formation was also seen at many sites in the digestive tract, especially in the colon. In parallel with the increase in the number of leukocytes in the digestive tract, the proportion of granulocytes increased at various sites, for example, in the intraepithelium and lamina propria of the colon and the lamina propria of the appendix. The number of extrathymic T cells at these sites in the digestive tract, especially gammadelta T cells in the colon, increased. A functional assay revealed that granulocytes isolated from mice injected with indomethacin were activated in terms of their superoxide production upon stimulation. In conjunction with the data on the simultaneous activation of granulocytes in the liver and blood, the present results suggest that nonsteroidal antiinflammatory drugs (NSAIDs) have the potential to induce severe granulocytosis in specific sites of the body, possibly via their stimulatory effect on the sympathetic nervous system (ie, granulocytes bear adrenergic receptors on their surface).
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Affiliation(s)
- S Yamagiwa
- Department of Immunology, Niigata University School of Medicine, Japan
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26
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de Silva NR, Gunawardena S, Ratnayake H, Weerasinghe A. The pattern of hypogammaglobulinaemia in Sri Lankan children. Ceylon Med J 2000; 45:58-60. [PMID: 11051701 DOI: 10.4038/cmj.v45i2.8001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To evaluate the prevalence of hypogammaglobulinaemia in Sri Lankan children who present with recurrent or severe bacterial infections. DESIGN A descriptive study. SETTING Medical Research Institute (MRI), Colombo. SUBJECTS 100 children between the ages of four months to twelve years referred to the Department of Immunology, MRI, for evaluation of immune status during four years from 1993 to 1997. MEASUREMENTS Immunoglobulin G, A and M levels were measured using radial immunodiffusion technique. RESULTS 22 out of 100 children had an underlying antibody deficiency, of whom IgA deficiency was the commonest (18 patients). Two patients had low IgG and A and elevated IgM levels, and they were diagnosed as having X linked-hyper-IgM syndrome. One patient had deficient IgA and IgM, and all three immunoglobulins were deficient in another. CONCLUSIONS Results indicate that IgA deficiency is the commonest immunodeficiency in Sri Lanka, which is comparable with studies done in the West. This study also shows the need to improve the standard of care in patients with immunodeficiency.
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Affiliation(s)
- N R de Silva
- Department of Immunology, Medical Research Institute, Colombo
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27
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Shimizu T, Kawamura T, Miyaji C, Oya H, Bannai M, Yamamoto S, Weerasinghe A, Halder RC, Watanabe H, Hatakeyama K, Abo T. Resistance of extrathymic T cells to stress and the role of endogenous glucocorticoids in stress associated immunosuppression. Scand J Immunol 2000; 51:285-92. [PMID: 10736098 DOI: 10.1046/j.1365-3083.2000.00695.x] [Citation(s) in RCA: 43] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
When mice were exposed to restraint stress for 12 or 24 h, severe lymphopenia was induced in all immune system organs, including the liver and the thymus. However, in adrenalectomized mice, this response was completely absent. Phenotypic characterization revealed that interleukin (IL)-2Rbeta+CD3int cells (i.e. extrathymic T cells) with CD4+ phenotype and the NK1.1+ subset of CD3int cells (i.e. NKT cells) in the liver as well as the mature conventional T cells in the thymus were resistant to such stress. In adrenalectomized mice, there was no significant change in the distribution of lymphocyte subsets in all tested organs before stress. Interestingly, the number of lymphocytes in the liver and spleen and the proportion of NKT cells in the liver rather increased after stress in these adrenalectomized mice. Therefore, endogenous steroid hormones were indicated to be important in the induction of immunosuppressive states after stress. Among stress associated cytokines, the secretion of tumour necrosis factor (TNF)-alpha was completely suppressed while that of IL-6 was partially suppressed in adrenalectomized mice. These results suggest that endogenous steroid hormones are important for the induction of the stress associated immunosuppression and that NKT cells are resistant to stress, namely, resistant to exposure to endogenous steroid hormones.
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Affiliation(s)
- T Shimizu
- Department of Immunology, and; First Department of Surgery, Niigata University School of Medicine, Niigata 951-8510, Japan
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28
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Maruyama S, Minagawa M, Shimizu T, Oya H, Yamamoto S, Musha N, Abo W, Weerasinghe A, Hatakeyama K, Abo T. Administration of glucocorticoids markedly increases the numbers of granulocytes and extrathymic T cells in the bone marrow. Cell Immunol 1999; 194:28-35. [PMID: 10357878 DOI: 10.1006/cimm.1999.1492] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Glucocorticoids, steroid hormones, are widely used as an anti-inflammatory drug. However, clinicians have sometimes encountered adverse drug reactions such as ulcers and tissue damage. In this study, we investigated how such adverse reactions of glucocorticoids are evoked, using an experimental mice model. When hydrocortisone (0.5 or 1.0 mg/day/mouse) was administered daily for 2 weeks, severe leukocytopenia was induced in all immune system organs. However, granulocytes (Gr-1(+)Mac-1(+)) were increased in number in the bone marrow and peripheral blood. This seemed to be due to an elevated level of myelopoiesis in the bone marrow. As well as increasing in number, granulocytes were functionally activated as estimated by the Ca2+ influx and superoxide production. The proportion of primordial T cells (CD3(int)IL-2Rbeta+) in the thymus and the number of primordial T cells in the bone marrow also increased. Mice administered hydrocortisone became susceptible to stress. Thus, these mice showed gastric ulcers when they were exposed to restraint stress for 12 h. These results suggest that activated granulocytes and primordial T cells might provide a mechanism involved in steroid ulcers and tissue damage, possibly through the superoxide production of granulocytes and the autoreactivity of primordial T cells.
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Affiliation(s)
- S Maruyama
- Department of Immunology, Niigata University School of Medicine, Niigata, Japan
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29
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Kawamura T, Kawachi Y, Kuwano Y, Sugahara S, Weerasinghe A, Kosaka T, Seki S, Abo T. Mechanisms involved in graft-versus-host disease induced by the disparity of minor histocompatibility M1s antigens. Scand J Immunol 1999; 49:258-68. [PMID: 10102643 DOI: 10.1046/j.1365-3083.1999.00497.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
In this study we investigated which type of T cells: high T-cell receptor (TCRhigh, cells of thymic origin) or intermediate TCR (TCRint, cells of extrathymic origin), expanded in the liver and other organs, resulting in the induction of graft-versus-host disease (GVHD) with minor lymphocyte stimulating (M1s) disparity. When 6.5 Gy-irradiated BALB/c (H-2d M1s-1b2a) mice were injected with interleukin-2 receptor beta-chain(-) (IL-2Rbeta(-)) CD3high cells purified from the spleen of B10.D2 (H-2d M1s-1b2b) mice, IL-2Rbeta(+)CD3high cells expanded in the liver and other organs of recipient mice. The majority of these cells were found to be IL-2Ralpha(-)Mel-14(-)CD4(+)Vbeta3(+) in GVHD mice. The CDR3 region in their TCR-alphabeta (i.e. N-Dbeta-N) was polyclonal, although there were skewed usages of Vbeta3 and Jbeta2.4. The majority of cells were confirmed to be of donor origin by the individual discrimination method, namely, they originated from isolated IL-2Rbeta(-)CD3high cells. Interestingly, these T cells lacked cytotoxicity against both a natural killer (NK)-sensitive target and thymocytes with M1s disparity and nondisparity. Another important finding was that activated granulocytes expanded at generalized sites in GVHD mice. The present results raise the possibility that M1s disparity is mainly recognized by TCRhigh cells with unique properties but that direct effector cells that induce GVHD might not be such T cells but rather accompanied granulocytes.
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Affiliation(s)
- T Kawamura
- Department of Immunology, Niigata University School of Medicine, Japan
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30
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Abstract
BACKGROUND The United Kingdom Heart Valve Registry (UKHVR) has recently completed collecting information on 52 659 heart valve replacements (in 47 718 patients) performed during the period 1986 to 1995 in the whole of the United Kingdom. Information stored in the UKHVR's computer database was used for this study. Factors affecting the time from first prosthesis to first redo prosthesis were analyzed and provided useful predictive information. The association between prosthesis-induced local pathological processes and redo valve size was investigated. METHODS AND RESULTS This is a retrospective study of 43 301 patients (from among 47 718 in the database) undergoing single-site replacement of a diseased native mitral or aortic valve over a 10-year period from January 1986 to December 1995 in the United Kingdom. Of these patients, 1051 (2.43%) went on to have a first redo heart valve replacement. Valve survival analysis (Cox regression and Kaplan-Meier curves) was used to study the natural progression to the first redo heart valve replacement. Female sex and having a replacement at the aortic rather than the mitral position were both associated with a longer interval to the first redo operation. Regression analysis showed the size of the redo valve to be influenced by the interoperative time. This effect was more pronounced at the mitral position. CONCLUSIONS Females and patients having an aortic valve replacement exhibit a longer interval to the first redo operation than do males and patients having mitral valve replacements, respectively. The time from the first replacement to the first redo operation significantly affects the size of the first redo valve.
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Affiliation(s)
- A Weerasinghe
- Department of Cardiothoracic Surgery, United Kingdom Heart Valve Registry, Hammersmith Hospital, London, UK
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Abstract
Platelets are the smallest of the blood cells and are known to be activated during cardiopulmonary bypass. They play a role in many associated complications. Both quantitative and qualitative platelet defects have been demonstrated, resulting in microvascular hemorrhage and thromboembolism. As their interactions with endothelium and other blood cells are unraveled, the important contribution they make toward the systemic inflammatory response to operation seen in cardiopulmonary bypass is increasingly evident. In this review, we consider platelet activation during cardiopulmonary bypass, the resultant clinical effects, and potential approaches to therapy and prevention.
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Affiliation(s)
- A Weerasinghe
- Department of Cardiothoracic Surgery, Imperial College of Science, Technology and Medicine, University of London, Hammersmith Hospital, England
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Halder RC, Seki S, Weerasinghe A, Kawamura T, Watanabe H, Abo T. Characterization of NK cells and extrathymic T cells generated in the liver of irradiated mice with a liver shield. Clin Exp Immunol 1998; 114:434-47. [PMID: 9844055 PMCID: PMC1905136 DOI: 10.1046/j.1365-2249.1998.00726.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
We previously reported that c-kit+ stem cells which give rise to extrathymic T cells are present in the liver of adult mice. Further characterization of extrathymic T cells in the liver of adult mice is conducted here. When mice with a liver shield were lethally (9.5 Gy) irradiated, all mice survived. All tested organs showed a distribution pattern of hepatic lymphocytes on day 7. The distribution pattern in the liver was characterized by an abundance of NK (CD3- IL-2Rbeta+) and extrathymic T cells (CD3int IL-2Rbeta+) before and after irradiation. To determine their function, post-irradiation allogeneic bone marrow transplantation (BMT) was performed in mice with or without a liver shield. Allogeneic BM cells were rejected in mice with a liver shield and specific activation of CD8+ CD3int IL-2Rbeta+ cells was induced. At that time, potent cytotoxicity of liver mononuclear cells (MNC) against allogeneic thymocytes was induced. Both NK1.1+ and NK1.1- subsets of CD3int cells expanded in these mice. An in vivo elimination experiment of the subsets indicated that the NK1.1+ subset of CD3int cells (i.e. NK T cells) was much more associated with the rejection of allogeneic BM cells. However, even after the elimination of NK T cells, allogeneic BM cells were rejected. In this case, granulocytes expanded in parallel with NK1.1- subsets. Granulocytes may also be associated with the rejection of allogeneic BM cells. These results suggest that the liver is an important haematopoietic organ even in adult life.
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Affiliation(s)
- R C Halder
- Department of Immunology, Niigata University School of Medicine, Niigata, Japan
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33
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Watanabe H, Weerasinghe A, Miyaji C, Sekikawa H, De Silva NR, Gunawardena S, Ratnayake H, Kobayashi J, Thoma H, Sato Y, Abo T. Extrathymic T cells in human malaria patients. Parasitol Int 1998. [DOI: 10.1016/s1383-5769(98)81079-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Weerasinghe A, Kawamura T, Moroda T, Seki S, Watanabe H, Abo T. Intermediate TCR cells can induce graft-versus-host disease after allogeneic bone marrow transplantation. Cell Immunol 1998; 185:14-29. [PMID: 9636679 DOI: 10.1006/cimm.1998.1263] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Mice fall victim to GVHD when subjected to immunosuppressive treatment and injected with allogeneic bone marrow cells. A major population of cells associated with GVHD is known to be T cells. However, whether such T cells are of thymic or extrathymic origin is obscure. We applied two immunosuppressive conditions, 9 and 6.5 Gy irradiation, to C3H/He mice (H-2k). Bone marrow cells for injection were obtained from C57BL/6 (B6) mice (H-2b). The 9-Gy mice were reconstituted by lymphocytes of donor origin and showed GVHD, whereas 6.5-Gy mice were reconstituted by lymphocytes of recipient origin and showed mild GVHD. The liver was the organ where the reconstitution of lymphocytes occurred efficiently, and a major lymphocyte subset was intermediate (int) CD3 cells (i.e., CD3int cells) in both mice. CD3int cells had the properties of extrathymic T cells, showing the phenotype of NK1.1 + CD3int using invariant V alpha 14 chain. In 6.5-Gy mice, allogeneic cells were rejected by extrathymic T cells of recipient origin. The stored CD3int cells from the liver of 9-Gy mice evoked similar GVHD when transferred into 6.5-Gy irradiated C3H/He mice. These results suggest that CD3int cells of extrathymic origin are a major population for the induction of GVHD under immunosuppressive conditions.
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MESH Headings
- Adoptive Transfer
- Animals
- Bone Marrow Cells/immunology
- Bone Marrow Transplantation/immunology
- Bone Marrow Transplantation/pathology
- CD3 Complex/analysis
- Cell Movement/immunology
- Cell Separation
- Cytotoxicity, Immunologic
- Flow Cytometry
- Gamma Rays
- Gene Rearrangement, alpha-Chain T-Cell Antigen Receptor
- Gene Rearrangement, beta-Chain T-Cell Antigen Receptor
- Graft vs Host Disease/etiology
- Graft vs Host Disease/immunology
- Graft vs Host Disease/pathology
- Immunophenotyping
- Leukocytes, Mononuclear/immunology
- Leukocytes, Mononuclear/transplantation
- Lymphoid Tissue/immunology
- Lymphoid Tissue/pathology
- Mice
- Mice, Inbred C3H
- Mice, Inbred C57BL
- Radiation Chimera
- Receptors, Antigen, T-Cell/analysis
- T-Lymphocyte Subsets/classification
- T-Lymphocyte Subsets/immunology
- T-Lymphocyte Subsets/metabolism
- Time Factors
- Transplantation, Homologous
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Affiliation(s)
- A Weerasinghe
- Department of Immunology, Niigata University School of Medicine, Japan
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Shirai K, Watanabe H, Weerasinghe A, Sakai T, Sekikawa H, Abo T. A monoclonal antibody, DL10, which recognizes a sugar moiety of MHC class I antigens expressed on NK cells, NK+ T cells, and granulocytes in humans. J Clin Immunol 1997; 17:510-23. [PMID: 9418192 DOI: 10.1023/a:1027379929042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
One mAb, DL10, was established from mice injected with dolphin lymphocytes. In addition to its reactivity against all dolphin lymphocytes, it reacted with some human leukocytes, including NK cells, NK+ T cells, and granulocytes. When its reactivity was examined in various animals, bovine, ovine, and equine leukocytes were DL10+. Murine, rat, and canine leukocytes were DL10-. Although the reactivity of DL10+ was similar to those of CD56 and CD57 antigens in humans, the actual molecules it recognized were different. Thus, all reactivity of DL10 disappeared after treatment of cells with glycopeptidase or after culture of cells with tunicamycin. Furthermore, the immunoprecipitation method revealed that DL10 indirectly recognized the heavy chain (45kD) of MHC class I antigen in humans and animals. Considering data from analysis of the N-terminal amino acid sequence of the DL10 molecule and the HLA typing of reactive cells, DL10 recognized a sugar moiety of some monomorphic MHC antigens and polymorphic MHC antigens such as HLA-B60 and -B61. If the donors are HLA-B60- and -B61 (> 80% in Japan and > 95% in the United States), DL10 would appear to be a very useful agent for the detection of pan-NK+ T cells.
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Affiliation(s)
- K Shirai
- Department of Preventive Veterinary Medicine and Animal Health, Nihon University School of Veterinary Medicine, Nihon University, Kanagawa, Japan
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Kawamura T, Kawachi Y, Moroda T, Weerasinghe A, Iiai T, Seki S, Tazawa Y, Takada G, Abo T. Cytotoxic activity against tumour cells mediated by intermediate TCR cells in the liver and spleen. Immunol Suppl 1996; 89:68-75. [PMID: 8911142 PMCID: PMC1456657 DOI: 10.1046/j.1365-2567.1996.d01-719.x] [Citation(s) in RCA: 40] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Morphological and phenotypic characterization in previous studies has indicated that intermediate (int) T-cell receptor (TCR) cells or T natural killer (TNK) cells may stand at an intermediate position between NK cells and high TCR cells of thymic origin in phylogenetic development. In this study, a functional study on cytotoxic activity against various tumour targets was performed in each purified subset. When a negative selection method entailing in vivo injection of anti-asialo GM, antibody or anti-interleukin (IL)-2R beta monoclonal antibody (mAb) was applied, IL-2R beta 1 CD3 NK cells were found to have the highest NK activity while IL-2R beta 1 int CD3 (or TCR) cells had a lower level of the NK activity. High CD3 cells (freshly isolated) did not have any such activity. Sorting experiments further revealed that the NK function mediated by int CD3 cells was augmented when they were exposed to anti-CD3 mAb. anti-TCR alpha beta, or anti-TCR-delta mAb. This phenomenon was not observed in NK cells and high CD3 cells. More importantly, when anti-CD3 mAb (or anti-TCR mAb) was added to the assay culture, int CD3 cells became cytotoxic against even NK-resistant tumour (Fc gamma R-. Fas+) targets. Liver mononuclear cells or int CD3 cells exposed to anti-CD3 mAb for 6 hr showed an elevated level of perforin in their cytoplasms. The present results suggest that int CD3 cells are usually non-cytotoxic against various tumours but become functional after being stimulated via the TCR CD3 complex.
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Affiliation(s)
- T Kawamura
- Department of Pediatrics, Akita University School of Medicine, Japan
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Sheriffdeen AH, de Abrew K, Jayasekera G, Nanayakkara S, de Mel CP, Jayasinghe S, Fernando H, Rajakanthan K, Weerasinghe A, Kuruppruarchchi L. Sri Lankan experience with three immunosuppressive protocols in living related donor kidney transplantation. Transplant Proc 1992; 24:1818. [PMID: 1412862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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