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David SP, Dunnenberger HM, Choi S, DePersia A, Ilbawi N, Ward C, Wake DT, Khandekar JD, Shannon Y, Hughes K, Miller N, Mangold KA, Sabatini LM, Helseth DL, Xu J, Sanders A, Kaul KL, Hulick PJ. Personalized medicine in a community health system: the NorthShore experience. Front Genet 2023; 14:1308738. [PMID: 38090148 PMCID: PMC10713750 DOI: 10.3389/fgene.2023.1308738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/06/2023] [Indexed: 02/01/2024] Open
Abstract
Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.
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Affiliation(s)
- Sean P. David
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Family Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Outcomes Research Network, NorthShore University HealthSystem, Evanston, IL, United States
| | - Henry M. Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Sarah Choi
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Allison DePersia
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Outcomes Research Network, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Nadim Ilbawi
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Family Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Christopher Ward
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Dyson T. Wake
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Janardan D. Khandekar
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Kellogg Cancer Center, NorthShore University HealthSystem, Evanston, IL, United States
| | - Yvette Shannon
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Kristen Hughes
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Nicholas Miller
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Kathy A. Mangold
- Department of Pathology, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Linda M. Sabatini
- Department of Pathology, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Donald L. Helseth
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Jianfeng Xu
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
| | - Alan Sanders
- Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, NorthShore University HealthSystem, Evanston, IL, United States
- Departments of Psychiatry and Behavioral Neuroscience, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
| | - Karen L. Kaul
- Outcomes Research Network, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Pathology, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
- Department of Pathology, NorthShore University HealthSystem, Evanston, IL, United States
| | - Peter J. Hulick
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Outcomes Research Network, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, United States
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, United States
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Tartanian AC, Mulroney N, Poselenzny K, Akroush M, Unger T, Helseth DL, Sabatini LM, Bouma M, Larkin PMK. Corrigendum: NGS implementation for monitoring SARS-CoV-2 variants in Chicagoland: an institutional perspective, successes and challenges. Front Public Health 2023; 11:1234900. [PMID: 37441637 PMCID: PMC10335793 DOI: 10.3389/fpubh.2023.1234900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 07/15/2023] Open
Abstract
[This corrects the article DOI: 10.3389/fpubh.2023.1177695.].
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Affiliation(s)
| | - Nicole Mulroney
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Michael Akroush
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Trevor Unger
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Linda M. Sabatini
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
| | - Michael Bouma
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Paige M. K. Larkin
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
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3
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Tartaninan AC, Mulroney N, Poselenzny K, Akroush M, Unger T, Helseth DL, Sabatini LM, Bouma M, Larkin PM. NGS implementation for monitoring SARS-CoV-2 variants in Chicagoland: An institutional perspective, successes and challenges. Front Public Health 2023; 11:1177695. [PMID: 37151582 PMCID: PMC10157391 DOI: 10.3389/fpubh.2023.1177695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 05/09/2023] Open
Abstract
Identification of SARS-CoV-2 lineages has shown to provide invaluable information regarding treatment efficacy, viral transmissibility, disease severity, and immune evasion. These benefits provide institutions with an expectation of high informational upside with little insight in regards to practicality with implementation and execution of such high complexity testing in the midst of a pandemic. This article details our institution's experience implementing and using Next Generation Sequencing (NGS) to monitor SARS-CoV-2 lineages in the northern Chicagoland area throughout the pandemic. To date, we have sequenced nearly 7,000 previously known SARS-CoV-2 positive samples from various patient populations (e.g., outpatient, inpatient, and outreach sites) to reduce bias in sampling. As a result, our hospital was guided while making crucial decisions about staffing, masking, and other infection control measures during the pandemic. While beneficial, establishing this NGS procedure was challenging, with countless considerations at every stage of assay development and validation. Reduced staffing prompted transition from a manual to automated high throughput workflow, requiring further validation, lab space, and instrumentation. Data management and IT security were additional considerations that delayed implementation and dictated our bioinformatic capabilities. Taken together, our experience highlights the obstacles and triumphs of SARS-CoV-2 sequencing.
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Affiliation(s)
| | - Nicole Mulroney
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Michael Akroush
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Trevor Unger
- NorthShore University HealthSystem, Evanston, IL, United States
| | | | - Linda M. Sabatini
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
| | - Michael Bouma
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Paige M.K. Larkin
- NorthShore University HealthSystem, Evanston, IL, United States
- Pritzker School of Medicine, The University of Chicago, Chicago, IL, United States
- *Correspondence: Paige M.K. Larkin,
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Helseth DL, Gulukota K, Miller N, Yang M, Werth T, Sabatini LM, Bouma M, Dunnenberger HM, Wake DT, Hulick PJ, Kaul KL, Khandekar JD. Flype: Software for enabling personalized medicine. Am J Med Genet C Semin Med Genet 2020; 187:37-47. [PMID: 33270363 PMCID: PMC7984435 DOI: 10.1002/ajmg.c.31867] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 02/02/2023]
Abstract
The advent of next generation DNA sequencing (NGS) has revolutionized clinical medicine by enabling wide‐spread testing for genomic anomalies and polymorphisms. With that explosion in testing, however, come several informatics challenges including managing large amounts of data, interpreting the results and providing clinical decision support. We present Flype, a web‐based bioinformatics platform built by a small group of bioinformaticians working in a community hospital setting, to address these challenges by allowing us to: (a) securely accept data from a variety of sources, (b) send orders to a variety of destinations, (c) perform secondary analysis and annotation of NGS data, (d) provide a central repository for all genomic variants, (e) assist with tertiary analysis and clinical interpretation, (f) send signed out data to our EHR as both PDF and discrete data elements, (g) allow population frequency analysis and (h) update variant annotation when literature knowledge evolves. We discuss the multiple use cases Flype supports such as (a) in‐house NGS tests, (b) in‐house pharmacogenomics (PGX) tests, (c) dramatic scale‐up of genomic testing using an external lab, (d) consumer genomics using two external partners, and (e) a variety of reporting tools. The source code for Flype is available upon request to the authors.
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Affiliation(s)
- Donald L Helseth
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Kamalakar Gulukota
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Nicholas Miller
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Mathew Yang
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Tom Werth
- Health Information Technology, NorthShore University HealthSystem, Skokie, Illinois, USA
| | - Linda M Sabatini
- Department of Pathology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Mike Bouma
- Department of Pathology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Dyson T Wake
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Peter J Hulick
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA.,Center for Medical Genetics, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Karen L Kaul
- Department of Pathology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Janaradan D Khandekar
- Mark R. Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA.,Kellogg Cancer Center, NorthShore University HealthSystem, Evanston, Illinois, USA
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Xu W, Yang Y, Hu Z, Head M, Mangold KA, Sullivan M, Wang E, Saha P, Gulukota K, Helseth DL, Guise T, Prabhkar BS, Kaul K, Schreiber H, Seth P. LyP-1-Modified Oncolytic Adenoviruses Targeting Transforming Growth Factor β Inhibit Tumor Growth and Metastases and Augment Immune Checkpoint Inhibitor Therapy in Breast Cancer Mouse Models. Hum Gene Ther 2020; 31:863-880. [PMID: 32394753 DOI: 10.1089/hum.2020.078] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
We report here the development of oncolytic adenoviruses (Ads) that have reduced toxicity, enhanced tumor tropism, produce strong antitumor response, and can overcome resistance to immune checkpoint inhibitor therapy in breast cancer. We have shown that LyP-1 receptor (p32) is highly expressed on the surface of breast cancer cells and tumors from cancer patients, and that increased stromal expression of transforming growth factor β-1 (TGFβ-1) is associated with triple-negative breast cancer. Therefore, we constructed oncolytic Ads, AdLyp.sT and mHAdLyp.sT, in which the p32-binding LyP-1 peptide was genetically inserted into the adenoviral fiber protein. Both AdLyp.sT and mHAdLyp.sT express sTGFβRIIFc, a TGFβ decoy that can inhibit TGFβ pathways. mHAdLyp.sT is an Ad5/48 chimeric hexon virus in which hypervariable regions (HVRs 1-7) of Ad5 are replaced with the corresponding Ad48 HVRs. AdLyp.sT and mHAdLyp.sT exhibited better binding, replication, and produced higher sTGFβRIIFc protein levels in breast cancer cell lines compared with Ad.sT or mHAd.sT control viruses without LyP-1 peptide modification. Systemic delivery of mHAdLyp.sT in mice resulted in reduced hepatic/systemic toxicity compared with Ad.sT and AdLyp.sT. Intravenous delivery of AdLyp.sT and mHAdLyp.sT elicited a strong antitumor response in a human MDA-MB-231 bone metastasis model in mice, as indicated by bioluminescence imaging, radiographic tumor burden, serum TRACP 5b and calcium, and body weight analyses. Furthermore, intratumoral delivery of AdLyp.sT in 4T1 model in immunocompetent mice inhibited tumor growth and metastases, and augmented anti-PD-1 and anti-CTLA-4 therapy. Based on these studies, we believe that AdLyp.sT and mHAdLyp.sT can be developed as potential targeted immunotherapy agents for the treatment of breast cancer.
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Affiliation(s)
- Weidong Xu
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, An Affiliate of the University of Chicago, Evanston, Illinois, USA
| | - Yuefeng Yang
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, An Affiliate of the University of Chicago, Evanston, Illinois, USA.,Department of Experimental Medical Science and Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, P.R. China
| | - Zebin Hu
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, An Affiliate of the University of Chicago, Evanston, Illinois, USA.,National Institutes for Food and Drug Control (NIFDC), Beijing, P.R. China
| | - Maria Head
- Department of Pathology and Laboratory Medicine
| | | | | | - Edward Wang
- Biostatistics and Clinical Research Informatics, Department of Surgery
| | | | - Kamalakar Gulukota
- Center for Personalized Medicine; NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Donald L Helseth
- Center for Personalized Medicine; NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Theresa Guise
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bellur S Prabhkar
- Department of Microbiology and Immunology, University of Illinois College of Medicine, Chicago, Illinois, USA
| | - Karen Kaul
- Department of Pathology and Laboratory Medicine
| | - Hans Schreiber
- Department of Pathology, The University of Chicago, Chicago, Illinois, USA
| | - Prem Seth
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, An Affiliate of the University of Chicago, Evanston, Illinois, USA
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6
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Yang Y, Xu W, Peng D, Wang H, Zhang X, Wang H, Xiao F, Zhu Y, Ji Y, Gulukota K, Helseth DL, Mangold KA, Sullivan M, Kaul K, Wang E, Prabhakar BS, Li J, Wu X, Wang L, Seth P. An Oncolytic Adenovirus Targeting Transforming Growth Factor β Inhibits Protumorigenic Signals and Produces Immune Activation: A Novel Approach to Enhance Anti-PD-1 and Anti-CTLA-4 Therapy. Hum Gene Ther 2019; 30:1117-1132. [PMID: 31126191 DOI: 10.1089/hum.2019.059] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [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: 01/03/2023] Open
Abstract
In an effort to develop a new therapy for cancer and to improve antiprogrammed death inhibitor-1 (anti-PD-1) and anticytotoxic T lymphocyte-associated protein (anti-CTLA-4) responses, we have created a telomerase reverse transcriptase promoter-regulated oncolytic adenovirus rAd.sT containing a soluble transforming growth factor receptor II fused with human IgG Fc fragment (sTGFβRIIFc) gene. Infection of breast and renal tumor cells with rAd.sT produced sTGFβRIIFc protein with dose-dependent cytotoxicity. In immunocompetent mouse 4T1 breast tumor model, intratumoral delivery of rAd.sT inhibited both tumor growth and lung metastases. rAd.sT downregulated the expression of several transforming growth factor β (TGFβ) target genes involved in tumor growth and metastases, inhibited Th2 cytokine expression, and induced Th1 cytokines and chemokines, and granzyme B and perforin expression. rAd.sT treatment also increased the percentage of CD8+ T lymphocytes, promoted the generation of CD4+ T memory cells, reduced regulatory T lymphocytes (Tregs), and reduced bone marrow-derived suppressor cells. Importantly, rAd.sT treatment increased the percentage of CD4+ T lymphocytes, and promoted differentiation and maturation of antigen-presenting dendritic cells in the spleen. In the immunocompetent mouse Renca renal tumor model, similar therapeutic effects and immune activation results were observed. In the 4T1 mammary tumor model, rAd.sT improved the inhibition of tumor growth and lung and liver metastases by anti-PD-1 and anti-CTLA-4 antibodies. Analysis of the human breast and kidney tumors showed that a significant number of tumor tissues expressed high levels of TGFβ and TGFβ-inducible genes. Therefore, rAd.sT could be a potential enhancer of anti-PD-1 and anti-CTLA-4 therapy for treating breast and kidney cancers.
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Affiliation(s)
- Yuefeng Yang
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, an Affiliate of the University of Chicago, Evanston, Illinois
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Weidong Xu
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, an Affiliate of the University of Chicago, Evanston, Illinois
| | - Di Peng
- Department of Urology, The Third Medical Center of Chinese People's Liberation Army, Beijing, China
| | - Hao Wang
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiaoyan Zhang
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Hua Wang
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Fengjun Xiao
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Yitan Zhu
- Program of Computational Genomics and Medicine, Department of Surgery; NorthShore University HealthSystem, Evanston, Illinois
| | - Yuan Ji
- Program of Computational Genomics and Medicine, Department of Surgery; NorthShore University HealthSystem, Evanston, Illinois
| | - Kamalakar Gulukota
- Center for Personalized Medicine, Department of Surgery; NorthShore University HealthSystem, Evanston, Illinois
| | - Donald L Helseth
- Center for Personalized Medicine, Department of Surgery; NorthShore University HealthSystem, Evanston, Illinois
| | - Kathy A Mangold
- Department of Pathology and Laboratory Medicine, NorthShore University HealthSystem, Evanston, Illinois
| | - Megan Sullivan
- Department of Pathology and Laboratory Medicine, NorthShore University HealthSystem, Evanston, Illinois
| | - Karen Kaul
- Department of Pathology and Laboratory Medicine, NorthShore University HealthSystem, Evanston, Illinois
| | - Edward Wang
- Biostatistics and Clinical Research Informatics, Department of Surgery; NorthShore University HealthSystem, Evanston, Illinois
| | - Bellur S Prabhakar
- Department of Microbiology and Immunology, University of Illinois College of Medicine, Chicago, Illinois
| | - Jinnan Li
- Department of Urology, The Third Medical Center of Chinese People's Liberation Army, Beijing, China
| | - Xuejie Wu
- Department of Urology, The Third Medical Center of Chinese People's Liberation Army, Beijing, China
| | - Lisheng Wang
- Department of Experimental Hematology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Prem Seth
- Gene Therapy Program, Department of Medicine, NorthShore Research Institute, an Affiliate of the University of Chicago, Evanston, Illinois
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Zhu Y, Xu Y, Helseth DL, Gulukota K, Yang S, Pesce LL, Mitra R, Müller P, Sengupta S, Guo W, Silverstein JC, Foster I, Parsad N, White KP, Ji Y. Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data. J Natl Cancer Inst 2015; 107:djv129. [PMID: 25956356 DOI: 10.1093/jnci/djv129] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 04/10/2015] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. METHODS We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes "Prior interaction map + TCGA data → Posterior interaction map." RESULTS Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. CONCLUSIONS Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.
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Affiliation(s)
- Yitan Zhu
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Yanxun Xu
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Donald L Helseth
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Kamalakar Gulukota
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Shengjie Yang
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Lorenzo L Pesce
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Riten Mitra
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Peter Müller
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Subhajit Sengupta
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Wentian Guo
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Jonathan C Silverstein
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Ian Foster
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Nigel Parsad
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Kevin P White
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL
| | - Yuan Ji
- Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL.
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8
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Song Y, Kirkpatrick LL, Schilling AB, Helseth DL, Chabot N, Keillor JW, Johnson GVW, Brady ST. Transglutaminase and polyamination of tubulin: posttranslational modification for stabilizing axonal microtubules. Neuron 2013; 78:109-23. [PMID: 23583110 DOI: 10.1016/j.neuron.2013.01.036] [Citation(s) in RCA: 143] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2013] [Indexed: 11/30/2022]
Abstract
Neuronal microtubules support intracellular transport, facilitate axon growth, and form a basis for neuronal morphology. While microtubules in nonneuronal cells are depolymerized by cold, Ca(2+), or antimitotic drugs, neuronal microtubules are unusually stable. Such stability is important for normal axon growth and maintenance, while hyperstability may compromise neuronal function in aging and degeneration. Though mechanisms for stability are unclear, studies suggest that stable microtubules contain biochemically distinct tubulins that are more basic than conventional tubulins. Transglutaminase-catalyzed posttranslational incorporation of polyamines is one of the few modifications of intracellular proteins that add positive charges. Here we show that neuronal tubulin can be polyaminated by transglutaminase. Endogenous brain transglutaminase-catalyzed polyaminated tubulins have the biochemical characteristics of neuronal stable microtubules. Inhibiting polyamine synthesis or transglutaminase activity significantly decreases microtubule stability in vitro and in vivo. Together, these findings suggest that transglutaminase-catalyzed polyamination of tubulins stabilizes microtubules essential for unique neuronal structures and functions.
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Affiliation(s)
- Yuyu Song
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612, USA
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9
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Sabherwal Y, Mahajan N, Helseth DL, Gassmann M, Shi H, Zhang M. PDEF downregulates stathmin expression in prostate cancer. Int J Oncol 2012; 40:1889-99. [PMID: 22378487 DOI: 10.3892/ijo.2012.1392] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2011] [Accepted: 01/30/2012] [Indexed: 11/05/2022] Open
Abstract
The Ets proteins are a family of transcription factors characterized by an evolutionarily conserved DNA binding domain that controls key cellular processes. Prostate-derived Ets transcription factor (PDEF), a member of the Ets family, is reported to be present in tissues with high epithelial content, notably breast and prostate. However, the role of PDEF in cancer development is not fully understood. To gain insight into the molecular mechanisms associated with prostate cancer progression, we employed iTRAQ labeling followed by mass spectrometric (MS) analysis to identify candidate proteins that are differentially expressed in prostate cancer cells with or without PDEF. To this end, we overexpressed PDEF in PC3 human prostate cells using a tetracycline inducible system (Tet-On). Many differentially expressed proteins which play important roles in various cellular and biological processes were identified. Among them, stathmin (STMN), which is a microtubule (MT)-destabilizing protein, was found to be downregulated in multiple analyses. We demonstrated that re-expression of STMN reversed the antitumor properties of PDEF in PDEF-overexpressing PC3 cells. Using in vitro functional assays, we showed that STMN overexpression counteracted PDEF's effects against cell proliferation, colony formation and tumor migration. Similar results were further confirmed with the prostate cancer cell line CWR22rv1. In conclusion, many differentially expressed proteins were identified and STMN was found to be downregulated by PDEF. These results suggest that PDEF may inhibit prostate cancer progression by transcriptional downregulation of oncogenic STMN expression. Analyzing the association among differentially expressed proteins may provide a basis to better understand the molecular mechanisms underlying the process of cancer progression and development and further aid in designing therapeutics in the future.
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Affiliation(s)
- Yamini Sabherwal
- Department of Molecular Pharmacology and Biological Chemistry, Robert H. Lurie Cancer Center, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
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10
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Avner BS, Shioura KM, Scruggs SB, Grachoff M, Geenen DL, Helseth DL, Farjah M, Goldspink PH, Solaro RJ. Myocardial infarction in mice alters sarcomeric function via post-translational protein modification. Mol Cell Biochem 2011; 363:203-15. [PMID: 22160857 DOI: 10.1007/s11010-011-1172-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Accepted: 11/23/2011] [Indexed: 01/17/2023]
Abstract
Myocardial physiology in the aftermath of myocardial infarction (MI) before remodeling is an under-explored area of investigation. Here, we describe the effects of MI on the cardiac sarcomere with focus on the possible contributions of reactive oxygen species. We surgically induced MI in 6-7-month-old female CD1 mice by ligation of the left anterior descending coronary artery. Data were collected 3-4 days after MI or sham (SH) surgery. MI hearts demonstrated ventricular dilatation and systolic dysfunction upon echo cardiographic analysis. Sub-maximum Ca-activated tension in detergent-extracted fiber bundles from papillary muscles increased significantly in the preparations from MI hearts. Ca(2+) sensitivity increased after MI, whereas cooperativity of activation decreased. To assess myosin enzymatic integrity we measured splitting of Ca-ATP in myofibrillar preparations, which demonstrated a decline in Ca-ATPase activity of myofilament myosin. Biochemical analysis demonstrated post-translational modification of sarcomeric proteins. Phosphorylation of cardiac troponin I and myosin light chain 2 was reduced after MI in papillary samples, as measured using a phospho-specific stain. Tropomyosin was oxidized after MI, forming disulfide products detectable by diagonal non-reducing-reducing SDS-PAGE. Our analysis of myocardial protein oxidation post-MI also demonstrated increased S-glutathionylation. We functionally linked protein oxidation with sarcomere function by treating skinned fibers with the sulfhydryl reducing agent dithiothreitol, which reduced Ca(2+) sensitivity in MI, but not SH, samples. Our data indicate important structural and functional alterations to the cardiac sarcomere after MI, and the contribution of protein oxidation to this process.
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Affiliation(s)
- Benjamin S Avner
- Department of Physiology and Biophysics, (M/C 901), College of Medicine, University of Illinois at Chicago, 835 S. Wolcott Ave., Chicago, IL 60612-7342, USA
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11
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Warren CM, Geenen DL, Helseth DL, Solaro RJ. Sub-Proteomic Fractionation of Rat Cardiac Tissue: Comparing Ischemic Vs Normal Remote Region with In-Solution Based Proteomics. Biophys J 2010. [DOI: 10.1016/j.bpj.2009.12.3942] [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: 11/29/2022] Open
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Parker L, Engel-Hall A, Drew K, Steinhardt G, Helseth DL, Jabon D, McMurry T, Angulo DS, Kron SJ. Investigating quantitation of phosphorylation using MALDI-TOF mass spectrometry. J Mass Spectrom 2008; 43:518-527. [PMID: 18064576 PMCID: PMC2874747 DOI: 10.1002/jms.1342] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Despite advances in methods and instrumentation for analysis of phosphopeptides using mass spectrometry, it is still difficult to quantify the extent of phosphorylation of a substrate because of physiochemical differences between unphosphorylated and phosphorylated peptides. Here we report experiments to investigate those differences using MALDI-TOF mass spectrometry for a set of synthetic peptides by creating calibration curves of known input ratios of peptides/phosphopeptides and analyzing their resulting signal intensity ratios. These calibration curves reveal subtleties in sequence-dependent differences for relative desorption/ionization efficiencies that cannot be seen from single-point calibrations. We found that the behaviors were reproducible with a variability of 5-10% for observed phosphopeptide signal. Although these data allow us to begin addressing the issues related to modeling these properties and predicting relative signal strengths for other peptide sequences, it is clear that this behavior is highly complex and needs to be further explored.
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Affiliation(s)
- Laurie Parker
- Ludwig Center for Metastasis Research, University of Chicago, Knapp R322, 924 E. 57th Street, Chicago, IL 60637, USA.
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Helseth DL, Tolwin TM, Kaminski EJ, Osetek EM. Incomplete polymerization of Cavalite with the use of recommended photopolymerization times: a warning of possible cytotoxic effects. Oral Surg Oral Med Oral Pathol 1989; 68:223-5. [PMID: 2550871 DOI: 10.1016/0030-4220(89)90197-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
As part of a study of the suitability of new materials for use as a retrofilling material, we examined the polymerization properties of Cavalite, a light-cured, hydroxyapatite and glass ionomer-containing cavity liner. By varying the time of photopolymerization, it was found that polymerization for 20 to 30 seconds according to the manufacturer's recommendations is not sufficient to ensure complete polymerization. The implications of this incomplete polymerization are discussed in terms of possible cytotoxic effects on tissues exposed to unpolymerized Cavalite, both when used in retrofilling situations and as a deep cavity liner.
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Affiliation(s)
- D L Helseth
- Department of Oral Biology, Northwestern University Dental School, Chicago, Ill
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Helseth DL, Veis A. Cathepsin D-mediated processing of procollagen: lysosomal enzyme involvement in secretory processing of procollagen. Proc Natl Acad Sci U S A 1984; 81:3302-6. [PMID: 6587351 PMCID: PMC345495 DOI: 10.1073/pnas.81.11.3302] [Citation(s) in RCA: 36] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The proteolytic removal of the extension COOH-terminal propeptide from procollagen has been examined in vitro. A crude enzyme activity was identified in a whole-chicken-embryo extract that acted at acid pH and appeared to be similar to one identified previously [Davidson, J. M., McEneany , L. S. G. & Bornstein , P. (1979) Eur. J. Biochem. 100, 551-558]. This activity was inhibitable by pepstatin but not by leupeptin, suggesting that it might be cathepsin D. Cathepsin D was purified 907-fold from chicken livers by affinity chromatography on pepstatin-aminohexyl-Sepharose 4B and was found to remove the COOH propeptides from procollagen. At pH 6.0, the site of cleavage appeared to shift from the COOH telopeptide to the COOH telopeptide/propeptide junction, based upon the difference in electrophoretic migration of the cleavage products, although determining the actual cleavage site will require end-group analysis. A model for the involvement of cathepsin D in the in vivo processing of procollagen is presented.
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Helseth DL, Veis A. Collagen self-assembly in vitro. Differentiating specific telopeptide-dependent interactions using selective enzyme modification and the addition of free amino telopeptide. J Biol Chem 1981; 256:7118-28. [PMID: 7251588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
The thermally induced in vitro self-assembly of collagen molecules to form active fibrils illustrates that collagen molecules themselves contain all of the structural information necessary for assembly. The molecule contains three structural domains, the NH2 and carboxyl-terminal extra helical regions (the telopeptides) and the major triple helical rod-like domain. Proteolytic removal of the short telopeptide domains drastically alters the in vitro self-assembly process. We have examined the specific contributions of each telopeptide to the initiation ("nucleation") and growth stages of self-assembly in collagens modified by selective proteinase treatment and by isolating a peptide containing the amino telopeptide and adding this to both normal and proteinase-modified collagen self-assembly systems. Pronase-modified collagen, devoid of both telopeptides, initiated self-assembly very poorly. Addition of small amounts of intact collagen accelerated the rate of nucleation of pronase-modified collagen. Addition of carboxypeptidase-modified collagen also accelerated the nucleation of pronase-modified collagen, suggesting that the remaining amino telopeptide was involved in nucleation. This was confirmed by isolating the cyanogen bromide fragment of the alpha 1(I) subunit containing the amino telopeptide and finding that it specifically accelerated the nucleation of intact pepsin- and pronase-modified to collagens. The amino telopeptide appears to bind to a specific region within the collagen triple helical domain. The isolated peptide requires thermal pretreatment to be active; hence, this interaction must involve a unique telopeptide conformation. This behavior is compatible with the recent model (Helseth, D. L., Jr., Lechner, J. H., and Veis, A. (1979) Biopolymers 18, 3005-3014) proposed for the conformation of the amino telopeptide and its interaction with a helical receptor site as a step in nucleation. Comparison of the behavior of leucine aminopeptidase- and carboxypeptidase-modified collagens suggests that the carboxyl telopeptide has its major role in the growth stages of self-assembly.
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Helseth DL, Lechner JH, Veis A. Role of the amino-terminal extrahelical region of type I collagen in directing the 4D overlap in fibrillogenesis. Biopolymers 1979. [DOI: 10.1002/bip.1979.360181208] [Citation(s) in RCA: 96] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Babitch JA, Helseth DL, Chiu TC. On using cis-Pt (II)-uracil as a stain for nucleic acids in brain slices and subcellular fractions. Histochemistry 1976; 49:253-61. [PMID: 62738 DOI: 10.1007/bf00492381] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Chick brain cortical slices and crude mitochondrial fractions were fixed with glutaraldehyde, stained only with cis-Pt (II)-uracil and processed for electron microscopy. The optimal time of staining was determined to be 10 min. Results show that this platinum-pyrimidine complex is a relatively specific stain for the nucleic acids of brain slices. However, staining of crude mitochondrial fractions apparently resulted in some protein staining and other artifacts. The method should be helpful identifying ribosomal contamination of subcellular preparations and if its specificity can be increased it may prove a useful addition to staining methods of the electron microscopist.
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Babitch JA, Breithaupt TB, Chiu TC, Garadi R, Helseth DL. Preparation of chick brain synaptosomes and synaptosomal membranes. Biochim Biophys Acta 1976; 433:75-89. [PMID: 1260063 DOI: 10.1016/0005-2736(76)90179-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
A method is described for the preparation of synaptosomes and synaptosomal membranes from chicken brain. Procedures for isolating rat synaptosomal membranes could not be used directly; several modifications of existing procedures are reported. Purity of the subcellular and subsynaptosomal fractions was monitored by electron microscopy and measurements of ferrocytochrome c: oxygen oxidoreductase (EC 1.9.3.)), monoamine: oxygen oxidoreductase (deaminating) EC 1.4.3.4), rotenone-insensitive NADH: cytochrome c oxidoreductase (EC 1.6.99.3), NADPH: cytochrome c oxidoreductase (EC 1.6.99.1), orthophosphoric monoester phosphohydrolase (EC 3.1.3.2), ATP phosphohydrolase (EC 3.6.1.4), and levels of RNA. Microsomes are the main contaminant of the synaptosomal membrane fraction. Mitochondrial and lysosomal enzymes occur in lesser amounts. No myelin contamination was observed. Marker enzymes for contaminants suggest that these synaptosomal membranes are as pure as membranes described by others, and the specific activity of a neuronal membrane marker, (Na+ -K+)-activated ATPase, is as high as other preparations. Levels of this enzyme in the membrane fraction are enriched 13-fold over homogenate ATPase levels.
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