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Razzaghi H, Forrest CB, Hirabayashi K, Wu Q, Allen AJ, Rao S, Chen Y, Bunnell HT, Chrischilles EA, Cowell LG, Cummins MR, Hanauer DA, Higginbotham M, Horne BD, Horowitz CR, Jhaveri R, Kim S, Mishkin A, Muszynski JA, Naggie S, Pajor NM, Paranjape A, Schwenk HT, Sills MR, Tedla YG, Williams DA, Bailey LC. Vaccine Effectiveness Against Long COVID in Children. Pediatrics 2024; 153:e2023064446. [PMID: 38225804 PMCID: PMC10979300 DOI: 10.1542/peds.2023-064446] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/15/2023] [Indexed: 01/17/2024] Open
Abstract
OBJECTIVES Vaccination reduces the risk of acute coronavirus disease 2019 (COVID-19) in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5 to 17 years. METHODS This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record program for visits after vaccine availability. We examined both probable (symptom-based) and diagnosed long COVID after vaccination. RESULTS The vaccination rate was 67% in the cohort of 1 037 936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, whereas diagnosed long COVID was 0.8%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5-44.7) against probable long COVID and 41.7% (15.0-60.0) against diagnosed long COVID. VE was higher for adolescents (50.3% [36.6-61.0]) than children aged 5 to 11 (23.8% [4.9-39.0]). VE was higher at 6 months (61.4% [51.0-69.6]) but decreased to 10.6% (-26.8% to 37.0%) at 18-months. CONCLUSIONS This large retrospective study shows moderate protective effect of severe acute respiratory coronavirus 2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including electronic health record sources and prospective data.
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Affiliation(s)
- Hanieh Razzaghi
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Christopher B. Forrest
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics
| | - Kathryn Hirabayashi
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Qiong Wu
- Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Andrea J. Allen
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Suchitra Rao
- Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, Colorado
| | - Yong Chen
- Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - H. Timothy Bunnell
- Biomedical Research Informatics Center, Nemours Children’s Health, Wilmington, Delaware
| | | | - Lindsay G. Cowell
- Peter O’Donnell Jr School of Public Health; Department of Immunology, School of Biomedical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan
| | - Miranda Higginbotham
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Benjamin D. Horne
- Intermountain Heart Institute, Intermountain Health, Salt Lake City, Utah
| | - Carol R. Horowitz
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Ravi Jhaveri
- Division of Infectious Diseases, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois
| | - Susan Kim
- Division of Rheumatology, Benioff Children’s Hospital, University of California, San Francisco, San Francisco, California
| | - Aaron Mishkin
- Section of Infectious Diseases, Temple University Lewis Katz School of Medicine, Philadelphia, Pennsylvania
| | - Jennifer A. Muszynski
- Division of Critical Care Medicine, Department of Pediatrics, Nationwide Children’s Hospital, Columbus, Ohio
| | - Susanna Naggie
- Division of Infectious Diseases, Duke University School of Medicine, Duke Clinical Research Institute, Durham, North Carolina
| | - Nathan M. Pajor
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Anuradha Paranjape
- Section of Infectious Diseases, Temple University Lewis Katz School of Medicine, Philadelphia, Pennsylvania
| | - Hayden T. Schwenk
- Division of Pediatric Infectious Diseases, Stanford School of Medicine, Palo Alto, California
| | | | - Yacob G. Tedla
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David A. Williams
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan
| | - L. Charles Bailey
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics
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Jia L, Cowell LG, Kapur P. Understanding Factors that Influence Prognosis and Response to Therapy in Clear Cell Renal Cell Carcinoma. Adv Anat Pathol 2024; 31:96-104. [PMID: 38179997 DOI: 10.1097/pap.0000000000000428] [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: 01/06/2024]
Abstract
In this review, we highlight and contextualize emerging morphologic prognostic and predictive factors in renal cell carcinoma. We focus on clear cell renal cell carcinoma (ccRCC), the most common histologic subtype. Our understanding of the molecular characterization of ccRCC has dramatically improved in the last decade. Herein, we highlight how these discoveries have laid the foundation for new approaches to prognosis and therapeutic decision-making for patients with ccRCC. We explore the clinical relevance of common mutations, established gene expression signatures, intratumoral heterogeneity, sarcomatoid/rhabdoid morphology and PD-L1 expression, and discuss their impact on predicting response to therapy.
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Affiliation(s)
| | - Lindsay G Cowell
- Peter O'Donnell School of Public Health
- Kidney Cancer Program at Simmons Comprehensive Cancer Center, Dallas, TX
| | - Payal Kapur
- Department of Pathology
- Department of Urology, University of Texas Southwestern Medical Center
- Kidney Cancer Program at Simmons Comprehensive Cancer Center, Dallas, TX
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Zhang Y, Romieu-Hernandez A, Boehmer TK, Azziz-Baumgartner E, Carton TW, Gundlapalli AV, Fearrington J, Nagavedu K, Dea K, Moyneur E, Cowell LG, Kaushal R, Mayer KH, Puro J, Rasmussen SA, Thacker D, Weiner MG, Saydah S, Block JP. Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults. BMC Infect Dis 2024; 24:181. [PMID: 38341566 PMCID: PMC10859007 DOI: 10.1186/s12879-024-09076-8] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results. METHODS We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020-May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test. RESULTS After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions. CONCLUSIONS Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection.
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Affiliation(s)
- Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA
| | | | - Tegan K Boehmer
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | | | - Adi V Gundlapalli
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Julia Fearrington
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, 401 Park Drive, Suite 401 East, Boston, MA, USA
| | - Kshema Nagavedu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, 401 Park Drive, Suite 401 East, Boston, MA, USA
| | | | | | - Lindsay G Cowell
- Peter O-Donnell Jr. School of Public Health, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA
| | - Kenneth H Mayer
- Fenway Institute, Fenway Health, Harvard Medical School, Boston, MA, USA
| | | | - Sonja A Rasmussen
- Department of Pediatrics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Deepika Thacker
- Nemours Cardiac Center, Nemours Children's Health, Wilmington, Delaware, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA
| | - Sharon Saydah
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, 401 Park Drive, Suite 401 East, Boston, MA, USA.
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Beverley J, Babcock S, Carvalho G, Cowell LG, Duesing S, He Y, Hurley R, Merrell E, Scheuermann RH, Smith B. Coordinating virus research: The Virus Infectious Disease Ontology. PLoS One 2024; 19:e0285093. [PMID: 38236918 PMCID: PMC10796065 DOI: 10.1371/journal.pone.0285093] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/12/2023] [Indexed: 01/22/2024] Open
Abstract
The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.
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Affiliation(s)
- John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
| | - Shane Babcock
- National Center for Ontological Research, Buffalo, NY, United States of America
- Air Force Research Laboratory, Wright Patterson Air Force Base, Riverside, OH, United States of America
| | - Gustavo Carvalho
- Department of Cognitive Science, Northwestern University, Evanston, IL, United States of America
| | - Lindsay G. Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Sebastian Duesing
- Department of Philosophy, Loyola University, Chicago, IL, United States of America
| | - Yongqun He
- Computational Medicine and Bioinformatics, University of Michigan Medical School, He Group, Ann Arbor, MI, United States of America
| | - Regina Hurley
- National Center for Ontological Research, Buffalo, NY, United States of America
- Department of Philosophy, Northwestern University, Evanston, IL, United States of America
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
| | - Richard H. Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
- Department of Pathology, University of California, San Diego, CA, United States of America
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States of America
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
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5
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Razzaghi H, Forrest CB, Hirabayashi K, Wu Q, Allen A, Rao S, Chen Y, Bunnell HT, Chrischilles EA, Cowell LG, Cummins MR, Hanauer DA, Higginbotham M, Horne BD, Horowitz CR, Jhaveri R, Kim S, Mishkin A, Muszynski JA, Naggie S, Pajor NM, Paranjape A, Schwenk HT, Sills MR, Tedla YG, Williams DA, Bailey C. Vaccine Effectiveness Against Long COVID in Children: A Report from the RECOVER EHR Cohort. medRxiv 2023:2023.09.27.23296100. [PMID: 37808803 PMCID: PMC10557822 DOI: 10.1101/2023.09.27.23296100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Objective Vaccination reduces the risk of acute COVID-19 in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5-17 years. Methods This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record (EHR) Program for visits between vaccine availability, and October 29, 2022. Conditional logistic regression was used to estimate VE against long COVID with matching on age group (5-11, 12-17) and time period and adjustment for sex, ethnicity, health system, comorbidity burden, and pre-exposure health care utilization. We examined both probable (symptom-based) and diagnosed long COVID in the year following vaccination. Results The vaccination rate was 56% in the cohort of 1,037,936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, while diagnosed long COVID was 0.7%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5 - 44.5) against probable long COVID and 41.7% (15.0 - 60.0) against diagnosed long COVID. VE was higher for adolescents 50.3% [36.3 - 61.0]) than children aged 5-11 (23.8% [4.9 - 39.0]). VE was higher at 6 months (61.4% [51.0 - 69.6]) but decreased to 10.6% (-26.8 - 37.0%) at 18-months. Discussion This large retrospective study shows a moderate protective effect of SARS-CoV-2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including EHR sources and prospective data. Article Summary Vaccination against COVID-19 has a protective effect against long COVID in children and adolescents. The effect wanes over time but remains significant at 12 months. What’s Known on This Subject Vaccines reduce the risk and severity of COVID-19 in children. There is evidence for reduced long COVID risk in adults who are vaccinated, but little information about similar effects for children and adolescents, who have distinct forms of long COVID. What This Study Adds Using electronic health records from US health systems, we examined large cohorts of vaccinated and unvaccinated patients <18 years old and show that vaccination against COVID-19 is associated with reduced risk of long COVID for at least 12 months. Contributors’ Statement Drs. Hanieh Razzaghi and Charles Bailey conceptualized and designed the study, supervised analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript.Drs. Christopher Forrest and Yong Chen designed the study and critically reviewed and revised the manuscript.Ms. Kathryn Hirabayashi, Ms. Andrea Allen, and Dr. Qiong Wu conducted analyses, and critically reviewed and revised the manuscript.Drs. Suchitra Rao, H Timothy Bunnell, Elizabeth A. Chrischilles, Lindsay G. Cowell, Mollie R. Cummins, David A. Hanauer, Benjamin D. Horne, Carol R. Horowitz, Ravi Jhaveri, Susan Kim, Aaron Mishkin, Jennifer A. Muszynski, Susanna Nagie, Nathan M. Pajor, Anuradha Paranjape, Hayden T. Schwenk, Marion R. Sills, Yacob G. Tedla, David A. Williams, and Ms. Miranda Higginbotham critically reviewed and revised the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Authorship statement Authorship has been determined according to ICMJE recommendations.
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Osazuwa-Peters OL, Wilson LE, Check DK, Roberts MC, Srinivasan S, Clark AG, Crawford J, Chrischilles E, Carnahan RM, Campbell WS, Cowell LG, Greenlee R, Abbott AM, Mosa ASM, Mandhadi V, Stoddard A, Dinan MA. Factors Associated With Receipt of Molecular Testing and its Impact on Time to Initial Systemic Therapy in Metastatic Non-Small Cell Lung Cancer. Clin Lung Cancer 2023; 24:305-312. [PMID: 37055337 DOI: 10.1016/j.cllc.2023.03.001] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/13/2023] [Accepted: 03/10/2023] [Indexed: 04/15/2023]
Abstract
BACKGROUND Despite recommendations for molecular testing irrespective of patient characteristics, differences exist in receipt of molecular testing for oncogenic drivers amongst metastatic non-small cell lung cancer (mNSCLC) patients. Exploration into these differences and their effects on treatment is needed to identify opportunities for improvement. PATIENTS AND METHODS We conducted a retrospective cohort study of adult patients diagnosed with mNSCLC between 2011 and 2018 using PCORnet's Rapid Cycle Research Project dataset (n = 3600). Log-binomial, Cox proportional hazards (PH), and time-varying Cox regression models were used to ascertain whether molecular testing was received, and time from diagnosis to molecular testing and/or initial systemic treatment in the context of patient age, sex, race/ethnicity, and multiple comorbidities status. RESULTS The majority of patients in this cohort were ≤ 65 years of age (median [25th, 75th]: 64 [57, 71]), male (54.3%), non-Hispanic white individuals (81.6%), with > 2 comorbidities in addition to mNSCLC (54.1%). About half the cohort received molecular testing (49.9%). Patients who received molecular testing had a 59% higher probability of initial systemic treatment than patients who were yet to receive testing. Multiple comorbidity status was positively associated with receipt of molecular testing (RR, 1.27; 95% CI 1.08, 1.49). CONCLUSION Receipt of molecular testing in academic centers was associated with earlier initiation of systemic treatment. This finding underscores the need to increase molecular testing rates amongst mNSCLC patients during a clinically relevant period. Further studies to validate these findings in community centers are warranted.
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Affiliation(s)
| | - Lauren E Wilson
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC
| | - Devon K Check
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC
| | - Megan C Roberts
- Division of Pharmaceutical Outcomes and Policy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC
| | - Swetha Srinivasan
- Division of Pharmaceutical Outcomes and Policy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC
| | - Amy G Clark
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC
| | - Jeffrey Crawford
- Department of Medicine, Duke University School of Medicine, Durham, NC
| | | | - Ryan M Carnahan
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA
| | - W Scott Campbell
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE
| | - Lindsay G Cowell
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Robert Greenlee
- Center for Clinical Epidemiology & Population Health, Marshfield Clinical Research Institute, Marshfield, WI
| | - Andrea M Abbott
- Department of Surgery, Medical University of South Carolina, Clinical Sciences, Charleston, SC
| | - Abu S M Mosa
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, MO
| | - Vasanthi Mandhadi
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, MO
| | - Alexander Stoddard
- Biomedical Informatics, Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI
| | - Michaela A Dinan
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.
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Lees WD, Christley S, Peres A, Kos JT, Corrie B, Ralph D, Breden F, Cowell LG, Yaari G, Corcoran M, Karlsson Hedestam GB, Ohlin M, Collins AM, Watson CT, Busse CE. AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets. Immunoinformatics (Amst) 2023; 10:100025. [PMID: 37388275 PMCID: PMC10310305 DOI: 10.1016/j.immuno.2023.100025] [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] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.
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Affiliation(s)
- William D. Lees
- Institute of Structural and Molecular Biology, Birkbeck College, London, England
- Human-Centered Computing and Information Science, Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal
| | - Scott Christley
- Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ayelet Peres
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, KY, USA
| | - Brian Corrie
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Duncan Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Lindsay G. Cowell
- Peter O’Donnell Jr. School of Public Health, Department of Immunology, School of Biomedical Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Gur Yaari
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Martin Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Swede
| | | | - Mats Ohlin
- Department of Immunotechnology and SciLifeLab, Lund University, Lund, Sweden
| | - Andrew M. Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, KY, USA
| | - Christian E. Busse
- Division of B Cell Immunology, German Cancer Research Center, Heidelberg, Germany
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Abstract
Purpose of Review The goal of this narrative review is to educate clinicians regarding the foundational concepts, efficacy, and future directions of therapeutic vaccines for human papillomavirus (HPV)-mediated cancers. Recent Findings Therapeutic HPV vaccines deliver tumor antigens to stimulate an immune response to eliminate tumor cells. Vaccine antigen delivery platforms are diverse and include DNA, RNA, peptides, proteins, viral vectors, microbial vectors, and antigen-presenting cells. Randomized, controlled trials have demonstrated that therapeutic HPV vaccines are efficacious in patients with cervical intraepithelial neoplasia. In patients with HPV-mediated malignancies, evidence of efficacy is limited. However, numerous ongoing studies evaluating updated therapeutic HPV vaccines in combination with immune checkpoint inhibition and other therapies exhibit significant promise. Summary Therapeutic vaccines for HPV-mediated malignancies retain a strong biological rationale, despite their limited efficacy to date. Investigators anticipate they will be most effectively used in combination with other regimens, such as immune checkpoint inhibition.
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Affiliation(s)
- Flora Yan
- Department of Otolaryngology-Head and Neck Surgery, Temple University, Philadelphia, PA USA
| | - Lindsay G Cowell
- Peter O'Donnell Jr. School of Public Health, Department of Immunology, UT Southwestern Medical Center, Dallas, TX USA
| | - Anna Tomkies
- Department of Otolaryngology-Head and Neck Surgery, UT Southwestern Medical Center, 2001 Inwood Blvd, Dallas, TX 75390-9035 USA
| | - Andrew T Day
- Department of Otolaryngology-Head and Neck Surgery, UT Southwestern Medical Center, 2001 Inwood Blvd, Dallas, TX 75390-9035 USA
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9
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Corrie BD, Christley S, Busse CE, Cowell LG, Neller KCM, Rubelt F, Schwab N. Data Sharing and Reuse: A Method by the AIRR Community. Methods Mol Biol 2022; 2453:447-476. [PMID: 35622339 DOI: 10.1007/978-1-0716-2115-8_23] [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] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.
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Affiliation(s)
- Brian D Corrie
- Biological Sciences, Simon Fraser University, Burnaby, BC, Canada.
| | - Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
| | | | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.,Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kira C M Neller
- Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | | | - Nicholas Schwab
- Department of Neurology with Institute of Translational Neurology, University of Muenster, Muenster, Germany
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10
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Block JP, Boehmer TK, Forrest CB, Carton TW, Lee GM, Ajani UA, Christakis DA, Cowell LG, Draper C, Ghildayal N, Harris AM, Kappelman MD, Ko JY, Mayer KH, Nagavedu K, Oster ME, Paranjape A, Puro J, Ritchey MD, Shay DK, Thacker D, Gundlapalli AV. Cardiac Complications After SARS-CoV-2 Infection and mRNA COVID-19 Vaccination - PCORnet, United States, January 2021-January 2022. MMWR Morb Mortal Wkly Rep 2022; 71:517-523. [PMID: 35389977 PMCID: PMC8989373 DOI: 10.15585/mmwr.mm7114e1] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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11
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Hernandez-Romieu AC, Carton TW, Saydah S, Azziz-Baumgartner E, Boehmer TK, Garret NY, Bailey LC, Cowell LG, Draper C, Mayer KH, Nagavedu K, Puro JE, Rasmussen SA, Trick WE, Wanga V, Chevinsky JR, Jackson BR, Goodman AB, Cope JR, Gundlapalli AV, Block JP. Prevalence of Select New Symptoms and Conditions Among Persons Aged Younger Than 20 Years and 20 Years or Older at 31 to 150 Days After Testing Positive or Negative for SARS-CoV-2. JAMA Netw Open 2022; 5:e2147053. [PMID: 35119459 PMCID: PMC8817203 DOI: 10.1001/jamanetworkopen.2021.47053] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
IMPORTANCE New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear. OBJECTIVE To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing. MAIN OUTCOMES AND MEASURES International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated). RESULTS A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01]; <20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%. CONCLUSIONS AND RELEVANCE In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.
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Affiliation(s)
- Alfonso C Hernandez-Romieu
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Sharon Saydah
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Tegan K Boehmer
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nedra Y Garret
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - L Charles Bailey
- Applied Clinical Research Center, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lindsay G Cowell
- Department of Population and Data Sciences, Department of Immunology, University of Texas Southwestern Medical Center, Dallas
| | - Christine Draper
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
| | | | - Kshema Nagavedu
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
| | | | - Sonja A Rasmussen
- Department of Pediatrics, University of Florida College of Medicine, Gainesville
| | - William E Trick
- Health Research & Solutions, Cook County Health, Chicago, Illinois
| | - Valentine Wanga
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jennifer R Chevinsky
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Brendan R Jackson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Alyson B Goodman
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jennifer R Cope
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Adi V Gundlapalli
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
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12
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Christley S, Stervbo U, Cowell LG. Immune Repertoire Analysis on High-Performance Computing Using VDJServer V1: A Method by the AIRR Community. Methods Mol Biol 2022; 2453:439-446. [PMID: 35622338 PMCID: PMC9761903 DOI: 10.1007/978-1-0716-2115-8_22] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
AIRR-seq data sets are usually large and require specialized analysis methods and software tools. A typical Illumina MiSeq sequencing run generates 20-30 million 2 × 300 bp paired-end sequence reads, which roughly corresponds to 15 GB of sequence data to be processed. Other platforms like NextSeq, which is useful in projects where the full V gene is not needed, create about 400 million 2 × 150 bp paired-end reads. Because of the size of the data sets, the analysis can be computationally expensive, particularly the early analysis steps like preprocessing and gene annotation that process the majority of the sequence data. A standard desktop PC may take 3-5 days of constant processing for a single MiSeq run, so dedicated high-performance computational resources may be required.VDJServer provides free access to high-performance computing (HPC) at the Texas Advanced Computing Center (TACC) through a graphical user interface (Christley et al. Front Immunol 9:976, 2018). VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provides access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene assignment, repertoire characterization, and repertoire comparison. Furthermore, VDJServer has parallelized execution for tools such as IgBLAST, so more compute resources are utilized as the size of the input data grows. Analysis that takes days on a desktop PC might take only a few hours on VDJServer. VDJServer is a free, publicly available, and open-source licensed resource. Here, we describe the workflow for performing immune repertoire analysis on VDJServer's high-performance computing.
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Affiliation(s)
- Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA.
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13
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Kalot MA, Dahm P, Cowell LG, Noureddine L, Mustafa RA. Burden of Renal Cysts Imaging: A Survey of Patients among the Greater Plains Collaborative. Urol Int 2021; 106:693-699. [PMID: 34525470 DOI: 10.1159/000517791] [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: 03/29/2021] [Accepted: 06/02/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Renal cysts are a frequent incidental finding on cross-sectional radiographic imaging. While most cysts are indolent, individuals with such cysts are frequently monitored for interval growth and potential malignant transformation, which is ultimately rare. In this study, we aimed to assess patients' values and preferences (believes and attitudes) about renal cysts. METHODS We deployed a cross-sectional survey to a random sample of patients with a diagnosis of renal cysts who were identified by billing code and self-identification. We collected data about demographics, insurance status, family history and overall health, and characteristics of patients with renal cysts. We performed a binary regression analysis (adjusted for age, gender, family history of cancer and kidney disease, and treatment plan for renal cysts) to determine anxiety predictors in patients with renal cysts. RESULTS We included 301 respondents in whom billing code and self-identification corresponded; of these, 138 had renal cysts and 163 did not. In an adjusted regression analysis, there was a suggestion that a clear management plan (OR = 0.49, 95% CI [0.22-1.11]) (p value 0.08) may be associated with less anxiety and a family history of renal disease may be associated with more anxiety (OR = 1.94 [0.76-4.94]) (p value 0.17). Family history of cancer also did not significantly predict anxiety (OR = 0.54 [0.24-1.19]) (p value 0.13). All these results were not statistically significant and had wide confidence intervals of the effect estimates make the results imprecise. CONCLUSION Findings of this pilot study suggest a clear management plan for the renal cyst(s) management may be associated with a lower level of anxiety, thereby by emphasizing the importance of good communication, patient engagement and evidence-based guidance. More definitive, adequately powered studies are needed to evaluate this finding further. In addition, further studies exploring differences in imaging practices, patient symptomatology and patient engagement by different provider types would be insightful. Ultimately, tools to improve shared decision-making are needed to provide more patient-centered care.
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Affiliation(s)
- Mohamad A Kalot
- Department of Internal Medicine, State University of New York at Buffalo, Buffalo, New York, USA
| | - Philipp Dahm
- Urology Section, Minneapolis VAMC and Department of Urology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Lama Noureddine
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Reem A Mustafa
- Division of Nephrology and Hypertension, Department of Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
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14
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Abstract
BACKGROUND Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.
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Affiliation(s)
- Shane Babcock
- Department of Philosophy, Niagara University, Lewiston, NY, USA.
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA.
| | - John Beverley
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, Northwestern University, Evanston, IL, USA
| | - Lindsay G Cowell
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Cowell Lab, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Barry Smith
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, University at Buffalo, Buffalo, NY, USA
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15
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Furmanchuk A, Liu M, Song X, Waitman LR, Meurer JR, Osinski K, Stoddard A, Chrischilles E, McClay JC, Cowell LG, Tachinardi U, Embi PJ, Mosa ASM, Mandhadi V, Shah RC, Garcia D, Angulo F, Patino A, Trick WE, Markossian TW, Rasmussen-Torvik LJ, Kho AN, Black BS. Effect of the Affordable Care Act on diabetes care at major health centers: newly detected diabetes and diabetes medication management. BMJ Open Diabetes Res Care 2021; 9:9/Suppl_1/e002205. [PMID: 34187842 PMCID: PMC8245434 DOI: 10.1136/bmjdrc-2021-002205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/13/2021] [Indexed: 12/04/2022] Open
Affiliation(s)
- Al'ona Furmanchuk
- Division of General Internal Medicine and Geriatrics, Northwestern University, Chicago, Illinois, USA
| | - Mei Liu
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Xing Song
- Division of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Lemuel R Waitman
- Division of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
| | - John R Meurer
- Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Kristen Osinski
- Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Alexander Stoddard
- Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Elizabeth Chrischilles
- Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, Iowa, USA
| | - James C McClay
- Department of Emergency Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Lindsay G Cowell
- Division of Biomedical Informatics, Department of Population and Data Sciences, Department of Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Umberto Tachinardi
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Peter J Embi
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Abu Saleh Mohammad Mosa
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Vasanthi Mandhadi
- Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Raj C Shah
- Department of Family Medicine and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Diana Garcia
- Health Research and Solutions Unit, Cook County Bureau of Health Services, Chicago, Illinois, USA
| | - Francisco Angulo
- Health Research and Solutions Unit, Cook County Bureau of Health Services, Chicago, Illinois, USA
| | - Alejandro Patino
- Health Research and Solutions Unit, Cook County Bureau of Health Services, Chicago, Illinois, USA
| | - William E Trick
- Department of Medicine, Cook County Bureau of Health Services, Chicago, Illinois, USA
| | - Talar W Markossian
- Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Northwestern University, Chicago, Illinois, USA
| | - Bernard S Black
- Pritzker School of Law, Kellogg School of Management, Northwestern University, Chicago, Illinois, USA
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16
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Christley S, Ostmeyer J, Quirk L, Zhang W, Sirak B, Giuliano AR, Zhang S, Monson N, Tiro J, Lucas E, Cowell LG. T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates. Front Immunol 2021; 12:624230. [PMID: 33868241 PMCID: PMC8050337 DOI: 10.3389/fimmu.2021.624230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/09/2021] [Indexed: 12/24/2022] Open
Abstract
Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor β chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.
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Affiliation(s)
- Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Jared Ostmeyer
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Lisa Quirk
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Wei Zhang
- Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Bradley Sirak
- Center for Immunization and Infection Research, Moffitt Cancer Center, Tampa, FL, United States
| | - Anna R Giuliano
- Center for Immunization and Infection Research, Moffitt Cancer Center, Tampa, FL, United States
| | - Song Zhang
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Nancy Monson
- Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Jasmin Tiro
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Elena Lucas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, United States.,Department of Pathology, Parkland Health and Hospital System, Dallas, TX, United States
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States.,Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
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17
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Balachandra S, Kusin SB, Lee R, Blackwell JM, Tiro JA, Cowell LG, Chiang CM, Wu SY, Varma S, Rivera EL, Mayo HG, Ding L, Sumer BD, Lea JS, Bagrodia A, Farkas LM, Wang R, Fakhry C, Dahlstrom KR, Sturgis EM, Day AT. Blood-based biomarkers of human papillomavirus-associated cancers: A systematic review and meta-analysis. Cancer 2021; 127:850-864. [PMID: 33270909 PMCID: PMC8135101 DOI: 10.1002/cncr.33221] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [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: 04/15/2020] [Revised: 06/06/2020] [Accepted: 06/14/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite the significant societal burden of human papillomavirus (HPV)-associated cancers, clinical screening interventions for HPV-associated noncervical cancers are not available. Blood-based biomarkers may help close this gap in care. METHODS Five databases were searched, 5687 articles were identified, and 3631 unique candidate titles and abstracts were independently reviewed by 2 authors; 702 articles underwent a full-text review. Eligibility criteria included the assessment of a blood-based biomarker within a cohort or case-control study. RESULTS One hundred thirty-seven studies were included. Among all biomarkers assessed, HPV-16 E seropositivity and circulating HPV DNA were most significantly correlated with HPV-associated cancers in comparison with cancer-free controls. In most scenarios, HPV-16 E6 seropositivity varied nonsignificantly according to tumor type, specimen collection timing, and anatomic site (crude odds ratio [cOR] for p16+ or HPV+ oropharyngeal cancer [OPC], 133.10; 95% confidence interval [CI], 59.40-298.21; cOR for HPV-unspecified OPC, 25.41; 95% CI, 8.71-74.06; cOR for prediagnostic HPV-unspecified OPC, 59.00; 95% CI, 15.39-226.25; cOR for HPV-unspecified cervical cancer, 12.05; 95% CI, 3.23-44.97; cOR for HPV-unspecified anal cancer, 73.60; 95% CI, 19.68-275.33; cOR for HPV-unspecified penile cancer, 16.25; 95% CI, 2.83-93.48). Circulating HPV-16 DNA was a valid biomarker for cervical cancer (cOR, 15.72; 95% CI, 3.41-72.57). In 3 cervical cancer case-control studies, cases exhibited unique microRNA expression profiles in comparison with controls. Other assessed biomarker candidates were not valid. CONCLUSIONS HPV-16 E6 antibodies and circulating HPV-16 DNA are the most robustly analyzed and most promising blood-based biomarkers for HPV-associated cancers to date. Comparative validity analyses are warranted. Variations in tumor type-specific, high-risk HPV DNA prevalence according to anatomic site and world region highlight the need for biomarkers targeting more high-risk HPV types. Further investigation of blood-based microRNA expression profiling appears indicated.
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Affiliation(s)
| | | | - Rebecca Lee
- Department of Otolaryngology–Head and Neck Surgery, UT Southwestern Medical Center, Dallas, Texas
| | | | - Jasmin A. Tiro
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
| | - Lindsay G. Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas
- Department of Immunology, UT Southwestern Medical Center, Dallas, Texas
| | - Cheng-Ming Chiang
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
- Department of Biochemistry, UT Southwestern Medical Center, Dallas, Texas
- Department of Pharmacology, UT Southwestern Medical Center, Dallas, Texas
| | - Shwu-Yuan Wu
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
- Department of Biochemistry, UT Southwestern Medical Center, Dallas, Texas
| | - Sanskriti Varma
- Department of Internal Medicine, NewYork-Presbyterian Hospital–Columbia Campus, New York, New York
| | - Erika L. Rivera
- Department of General Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Helen G. Mayo
- Digital Library and Learning Center, UT Southwestern Medical Center, Dallas, Texas
| | - Lianghao Ding
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Baran D. Sumer
- Department of Otolaryngology–Head and Neck Surgery, UT Southwestern Medical Center, Dallas, Texas
| | - Jayanthi S. Lea
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, Texas
| | - Aditya Bagrodia
- Department of Urology, UT Southwestern Medical Center, Dallas, Texas
| | - Linda M. Farkas
- Department of Surgery, UT Southwestern Medical Center, Dallas, Texas
| | - Richard Wang
- Department of Dermatology, UT Southwestern Medical Center, Dallas, Texas
| | - Carole Fakhry
- Department of Otolaryngology–Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kristina R. Dahlstrom
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Erich M. Sturgis
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Andrew T. Day
- Department of Otolaryngology–Head and Neck Surgery, UT Southwestern Medical Center, Dallas, Texas
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
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18
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Chronister WD, Crinklaw A, Mahajan S, Vita R, Koşaloğlu-Yalçın Z, Yan Z, Greenbaum JA, Jessen LE, Nielsen M, Christley S, Cowell LG, Sette A, Peters B. TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors. Front Immunol 2021; 12:640725. [PMID: 33777034 PMCID: PMC7991084 DOI: 10.3389/fimmu.2021.640725] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 02/22/2021] [Indexed: 12/12/2022] Open
Abstract
The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.
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Affiliation(s)
| | - Austin Crinklaw
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Swapnil Mahajan
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Randi Vita
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | | | - Zhen Yan
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Jason A Greenbaum
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Leon E Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,Department of Medicine, University of California, San Diego, San Diego, CA, United States
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19
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Greiff V, Yaari G, Cowell LG. Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.coisb.2020.10.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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20
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Christley S, Aguiar A, Blanck G, Breden F, Bukhari SAC, Busse CE, Jaglale J, Harikrishnan SL, Laserson U, Peters B, Rocha A, Schramm CA, Taylor S, Vander Heiden JA, Zimonja B, Watson CT, Corrie B, Cowell LG. The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons. Front Big Data 2020; 3:22. [PMID: 33693395 PMCID: PMC7931935 DOI: 10.3389/fdata.2020.00022] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 05/18/2020] [Indexed: 11/13/2022] Open
Abstract
The Adaptive Immune Receptor Repertoire (AIRR) Community is a research-driven group that is establishing a clear set of community-accepted data and metadata standards; standards-based reference implementation tools; and policies and practices for infrastructure to support the deposit, curation, storage, and use of high-throughput sequencing data from B-cell and T-cell receptor repertoires (AIRR-seq data). The AIRR Data Commons is a distributed system of data repositories that utilizes a common data model, a common query language, and common interoperability formats for storage, query, and downloading of AIRR-seq data. Here is described the principal technical standards for the AIRR Data Commons consisting of the AIRR Data Model for repertoires and rearrangements, the AIRR Data Commons (ADC) API for programmatic query of data repositories, a reference implementation for ADC API services, and tools for querying and validating data repositories that support the ADC API. AIRR-seq data repositories can become part of the AIRR Data Commons by implementing the data model and API. The AIRR Data Commons allows AIRR-seq data to be reused for novel analyses and empowers researchers to discover new biological insights about the adaptive immune system.
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Affiliation(s)
- Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Ademar Aguiar
- Centre for Information Systems and Computer Graphics, Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.,Department of Informatics Engineering, Faculty of Engineering, University of Porto, Porto, Portugal
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Syed Ahmad Chan Bukhari
- Division of Computer Science, Mathematics and Science (Healthcare Informatics), College of Professional Studies, St. John's University, New York, NY, United States
| | - Christian E Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jerome Jaglale
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | | | - Uri Laserson
- Department of Genetics and Genome Sciences, Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bjoern Peters
- Division of Vaccine Discover, La Jolla Institute for Immunology, La Jolla, CA, United States.,Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Artur Rocha
- Centre for Information Systems and Computer Graphics, Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, United States
| | | | - Jason Anthony Vander Heiden
- Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA, United States
| | - Bojan Zimonja
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
| | - Brian Corrie
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
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21
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Ostmeyer JL, Cowell LG, Christley S. Developing and validating an approach for diagnosing and prognosticating cancer from biochemical motifs in T-cell receptors. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.15_suppl.e15260] [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: 11/20/2022] Open
Abstract
e15260 Background: Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate cancer by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Methods: Our goal is to develop a method to diagnose and prognosticate cancer using sequenced T-cell receptors. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a machine learning based approach. Each predictive model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Results: Using a patient-holdout cross-validation, we fit predictive models to distinguish: 1. colorectal tumors from healthy tissue matched controls with 93% accuracy, 2. breast tumors from healthy tissue matched controls with 94% accuracy, 3. ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort) 4. and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. Conclusions: Immune repertoires can be used to diagnose and prognosticate cancer.
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22
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Ostmeyer JL, Cowell LG, Christley S. Developing & Validating an Approach for Diagnosing and Prognosticating Cancer from Biochemical Motifs in T-cell Receptors. The Journal of Immunology 2020. [DOI: 10.4049/jimmunol.204.supp.145.33] [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] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate diseases by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Our study objective is to develop a method to diagnose and prognosticate cancer using T-cell receptors sequenced from tissue biopsies. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a logistic regression model. Each logistic regression model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Using a patient-holdout cross-validation, we fit logistic regression models to distinguish colorectal tumors from healthy tissue matched controls with 93% accuracy, breast tumors from healthy tissue matched controls with 94% accuracy, ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort), and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. In conclusion, immune repertoires can be used to diagnose and prognosticate disease.
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23
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Ostmeyer J, Lucas E, Christley S, Lea J, Monson N, Tiro J, Cowell LG. Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma. PLoS One 2020; 15:e0229569. [PMID: 32134923 PMCID: PMC7058380 DOI: 10.1371/journal.pone.0229569] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 02/09/2020] [Indexed: 11/18/2022] Open
Abstract
We previously showed, in a pilot study with publicly available data, that T cell receptor (TCR) repertoires from tumor infiltrating lymphocytes (TILs) could be distinguished from adjacent healthy tissue repertoires by the presence of TCRs bearing specific, biophysicochemical motifs in their antigen binding regions. We hypothesized that such motifs might allow development of a novel approach to cancer detection. The motifs were cancer specific and achieved high classification accuracy: we found distinct motifs for breast versus colorectal cancer-associated repertoires, and the colorectal cancer motif achieved 93% accuracy, while the breast cancer motif achieved 94% accuracy. In the current study, we sought to determine whether such motifs exist for ovarian cancer, a cancer type for which detection methods are urgently needed. We made two significant advances over the prior work. First, the prior study used patient-matched TILs and healthy repertoires, collecting healthy tissue adjacent to the tumors. The current study collected TILs from patients with high-grade serous ovarian carcinoma (HGSOC) and healthy ovary repertoires from cancer-free women undergoing hysterectomy/salpingo-oophorectomy for benign disease. Thus, the classification task is distinguishing women with cancer from women without cancer. Second, in the prior study, classification accuracy was measured by patient-hold-out cross-validation on the training data. In the current study, classification accuracy was additionally assessed on an independent cohort not used during model development to establish the generalizability of the motif to unseen data. Classification accuracy was 95% by patient-hold-out cross-validation on the training set and 80% when the model was applied to the blinded test set. The results on the blinded test set demonstrate a biophysicochemical TCR motif found overwhelmingly in women with HGSOC but rarely in women with healthy ovaries, strengthening the proposal that cancer detection approaches might benefit from incorporation of TCR motif-based biomarkers. Furthermore, these results call for studies on large cohorts to establish higher classification accuracies, as well as for studies in other cancer types.
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Affiliation(s)
- Jared Ostmeyer
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Elena Lucas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Jayanthi Lea
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Nancy Monson
- Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Jasmin Tiro
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Lindsay G. Cowell
- Department of Population and Data Sciences, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
- * E-mail:
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24
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Cowell LG. The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer. Cancer Res 2019; 80:643-654. [PMID: 31888887 DOI: 10.1158/0008-5472.can-19-1457] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 10/14/2019] [Accepted: 12/17/2019] [Indexed: 11/16/2022]
Abstract
Lymphocytes play a critical role in antitumor immune responses. They are directly targeted by some therapies, and the composition and spatial organization of intratumor T-cell populations is prognostic in some cancer types. A better understanding of lymphocyte population dynamics over the course of disease and in response to therapy is urgently needed to guide therapy decisions and to develop new therapy targets. Deep sequencing of the repertoire of antigen receptor-encoding genes expressed in a lymphocyte population has become a widely used approach for profiling the population's immune status. Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richness and diversity of lymphocyte populations; to track clone members over time, between tissues, and across lymphocyte subsets; to detect clonal expansion; and to detect the recruitment of new clones into a tissue. Repertoire sequencing is thus a critical complement to other methods of lymphocyte and immune profiling in cancer. This review describes the current state of knowledge based on repertoire sequencing studies conducted on human cancer patients, with a focus on studies of the T-cell receptor beta chain locus. The review then outlines important questions left unanswered and suggests future directions for the field.
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Affiliation(s)
- Lindsay G Cowell
- Department of Population and Data Sciences, Department of Immunology, UT Southwestern Medical Center, Dallas, Texas.
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25
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Rubelt F, Busse CE, Bukhari SAC, Bürckert JP, Mariotti-Ferrandiz E, Cowell LG, Watson CT, Marthandan N, Faison WJ, Hershberg U, Laserson U, Corrie BD, Davis MM, Peters B, Lefranc MP, Scott JK, Breden F, Luning Prak ET, Kleinstein SH. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat Immunol 2019; 18:1274-1278. [PMID: 29144493 DOI: 10.1038/ni.3873] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Florian Rubelt
- Department of Microbiology and Immunology and Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA
| | - Christian E Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Jean-Philippe Bürckert
- Department of Infection and Immunity, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Encarnita Mariotti-Ferrandiz
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, Kentucky, USA
| | - Nishanth Marthandan
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - William J Faison
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Uri Hershberg
- School of Biomedical Engineering, Science & Health Systems, and Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, Pennsylvania, USA
| | - Uri Laserson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Brian D Corrie
- iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Mark M Davis
- Department of Microbiology and Immunology and Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA.,Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, USA
| | - Marie-Paule Lefranc
- IMGT, the international ImMunoGeneTics information system, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Jamie K Scott
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada.,iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Felix Breden
- iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | | | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Immunobiology, Yale School of Medicine, and Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
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26
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Corrie BD, Marthandan N, Zimonja B, Jaglale J, Zhou Y, Barr E, Knoetze N, Breden FMW, Christley S, Scott JK, Cowell LG, Breden F. iReceptor: A platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol Rev 2019; 284:24-41. [PMID: 29944754 DOI: 10.1111/imr.12666] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [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: 12/14/2022]
Abstract
Next-generation sequencing allows the characterization of the adaptive immune receptor repertoire (AIRR) in exquisite detail. These large-scale AIRR-seq data sets have rapidly become critical to vaccine development, understanding the immune response in autoimmune and infectious disease, and monitoring novel therapeutics against cancer. However, at present there is no easy way to compare these AIRR-seq data sets across studies and institutions. The ability to combine and compare information for different disease conditions will greatly enhance the value of AIRR-seq data for improving biomedical research and patient care. The iReceptor Data Integration Platform (gateway.ireceptor.org) provides one implementation of the AIRR Data Commons envisioned by the AIRR Community (airr-community.org), an initiative that is developing protocols to facilitate sharing and comparing AIRR-seq data. The iReceptor Scientific Gateway links distributed (federated) AIRR-seq repositories, allowing sequence searches or metadata queries across multiple studies at multiple institutions, returning sets of sequences fulfilling specific criteria. We present a review of the development of iReceptor, and how it fits in with the general trend toward sharing genomic and health data, and the development of standards for describing and reporting AIRR-seq data. Researchers interested in integrating their repositories of AIRR-seq data into the iReceptor Platform are invited to contact support@ireceptor.org.
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Affiliation(s)
- Brian D Corrie
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | - Nishanth Marthandan
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada.,Deptartment of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Bojan Zimonja
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | - Jerome Jaglale
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | - Yang Zhou
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | - Emily Barr
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | - Nicole Knoetze
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada
| | | | - Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jamie K Scott
- Deptartment of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Felix Breden
- The IRMACS Centre, Simon Fraser University, Burnaby, BC, Canada.,Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
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27
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Ostmeyer J, Christley S, Toby IT, Cowell LG. Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue. Cancer Res 2019; 79:1671-1680. [PMID: 30622114 DOI: 10.1158/0008-5472.can-18-2292] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/16/2018] [Accepted: 01/03/2019] [Indexed: 12/19/2022]
Abstract
Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.
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Affiliation(s)
- Jared Ostmeyer
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Inimary T Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.
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28
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Christley S, Scarborough W, Salinas E, Rounds WH, Toby IT, Fonner JM, Levin MK, Kim M, Mock SA, Jordan C, Ostmeyer J, Buntzman A, Rubelt F, Davila ML, Monson NL, Scheuermann RH, Cowell LG. VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements. Front Immunol 2018; 9:976. [PMID: 29867956 PMCID: PMC5953328 DOI: 10.3389/fimmu.2018.00976] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/19/2018] [Indexed: 11/13/2022] Open
Abstract
Background Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation. Results VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity. Conclusion VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.
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Affiliation(s)
- Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Walter Scarborough
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Eddie Salinas
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - William H. Rounds
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Inimary T. Toby
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - John M. Fonner
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | | | - Min Kim
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Stephen A. Mock
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Christopher Jordan
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Jared Ostmeyer
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Adam Buntzman
- Bio5 Institute, University of Arizona, Tucson, AZ, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Marco L. Davila
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Nancy L. Monson
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, TX, United States,Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Richard H. Scheuermann
- J. Craig Venter Institute, La Jolla, CA, United States,Department of Pathology, University of California, San Diego, San Diego, CA, United States,La Jolla Institute for Allergy & Immunology, La Jolla, CA, United States
| | - Lindsay G. Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States,*Correspondence: Lindsay G. Cowell,
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Breden F, Luning Prak ET, Peters B, Rubelt F, Schramm CA, Busse CE, Vander Heiden JA, Christley S, Bukhari SAC, Thorogood A, Matsen Iv FA, Wine Y, Laserson U, Klatzmann D, Douek DC, Lefranc MP, Collins AM, Bubela T, Kleinstein SH, Watson CT, Cowell LG, Scott JK, Kepler TB. Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. Front Immunol 2017; 8:1418. [PMID: 29163494 PMCID: PMC5671925 DOI: 10.3389/fimmu.2017.01418] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 10/12/2017] [Indexed: 12/22/2022] Open
Abstract
High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1–3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community’s founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).
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Affiliation(s)
- Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Christian E Busse
- Division of B Cell Immunology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Jason A Vander Heiden
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Adrian Thorogood
- entre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Frederick A Matsen Iv
- Public Health Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Yariv Wine
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
| | - Uri Laserson
- Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Klatzmann
- Immunology-Immunopathology-Immunotherapy (i3 & i2B), Sorbonne Université, Paris, France
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Marie-Paule Lefranc
- IMGT, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Andrew M Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Tania Bubela
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jamie K Scott
- Faculty of Health Sciences, Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Thomas B Kepler
- Department of Microbiology, Boston University School of Medicine, Boston, MA, United States.,Department of Mathematics and Statistics, Boston University, Boston, MA, United States
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Christley S, Levin MK, Toby IT, Fonner JM, Monson NL, Rounds WH, Rubelt F, Scarborough W, Scheuermann RH, Cowell LG. VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data. BMC Bioinformatics 2017; 18:448. [PMID: 29020925 PMCID: PMC5637252 DOI: 10.1186/s12859-017-1853-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 10/02/2017] [Indexed: 12/20/2022] Open
Abstract
Background Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files. Results Processing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5′ and 3′ PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO. Conclusions VDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.
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Affiliation(s)
- Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | | | - Inimary T Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - John M Fonner
- Texas Advanced Computing Center, Austin, TX, 78758-4497, USA
| | - Nancy L Monson
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, 75390, USA.,Department of Immunology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - William H Rounds
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Florian Rubelt
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, 92037, USA.,Department of Pathology, University of California, San Diego, CA, 92093, USA.,La Jolla Institute for Allergy & Immunology, La Jolla, CA, 92037, USA
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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Ostmeyer J, Christley S, Rounds WH, Toby I, Greenberg BM, Monson NL, Cowell LG. Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis. BMC Bioinformatics 2017; 18:401. [PMID: 28882107 PMCID: PMC5588725 DOI: 10.1186/s12859-017-1814-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 08/29/2017] [Indexed: 12/29/2022] Open
Abstract
Background Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose. Results We use the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function, which is fit to give the correct diagnosis for each patient using maximum likelihood optimization methods. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102). Conclusions Our method is the first to apply statistical learning to immune repertoires to aid disease diagnosis, learning repertoire-level labels from the set of individual immune repertoire sequences. This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert. Additionally, this method points to a diagnostic biochemical motif in the antibodies of RRMS patients, which may offer insight into the disease process. Electronic supplementary material The online version of this article (10.1186/s12859-017-1814-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jared Ostmeyer
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - William H Rounds
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Inimary Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Benjamin M Greenberg
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA
| | - Nancy L Monson
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.
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Toby I, Christley S, Scarborough W, Rounds WH, Fonner J, Mock S, Monson N, Scheuermann RH, Cowell LG. VDJServer – a web-accessible analysis portal for immune repertoire sequencing analysis. The Journal of Immunology 2017. [DOI: 10.4049/jimmunol.198.supp.55.49] [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] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Abstract
VDJServer is a comprehensive, web-accessible system for analysis of immune repertoire sequencing data. VDJServer provides a complete analysis workflow from pre-processing of sequence reads, to V(D)J assignment, to repertoire characterization and comparison. Recent enhancements in VDJServer include: --Automatic parallelization of analysis tools to handle very large data sets running on a high-performance supercomputer--Import and export subject and sample metadata. User-defined sample groups allows for sophisticated group analysis and comparison.--Extensive analysis functionality such as gene segment usage, CDR3 patterns, clonality, diversity measures, somatic mutation patterns, B cell lineage trees, and quantification of selection pressure. Analysis is performed for both samples and sample groups.--Interactive charting of analysis data provides exploratory visualization for ad-hoc comparison of samples and sample groups. Charts can be downloaded as image files for use in presentations and publications. All analysis data can be downloaded in standard TSV format for use with external tools.--Novel process workflow metadata that is automatically captured by VDJServer. Hiding the complexities of command line tools and their parameters, yet providing complete transparency of the analysis workflow for reproducibility.
VDJServer allows users to upload antigen receptor repertoire sequences and execute a customizable workflow for all steps in the analysis. Data and analysis results can be privately shared with other users for collaborative projects. VDJServer is funded by the NIAID and is freely available.
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Affiliation(s)
| | | | | | | | - John Fonner
- 2Texas Advanced Computing Center at UT Austin
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Breden F, Luning Prak ET, Peters B, Rubelt F, Schramm CA, Busse CE, Vander Heiden JA, Christley S, Bukhari SAC, Thorogood A, Matsen Iv FA, Wine Y, Laserson U, Klatzmann D, Douek DC, Lefranc MP, Collins AM, Bubela T, Kleinstein SH, Watson CT, Cowell LG, Scott JK, Kepler TB. Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. Front Immunol 2017. [PMID: 29163494 DOI: 10.3389/fimmu.2017.01418/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1-3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community's founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).
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Affiliation(s)
- Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Christian E Busse
- Division of B Cell Immunology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Jason A Vander Heiden
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Adrian Thorogood
- entre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Frederick A Matsen Iv
- Public Health Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Yariv Wine
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
| | - Uri Laserson
- Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Klatzmann
- Immunology-Immunopathology-Immunotherapy (i3 & i2B), Sorbonne Université, Paris, France
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Marie-Paule Lefranc
- IMGT, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Andrew M Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Tania Bubela
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jamie K Scott
- Faculty of Health Sciences, Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Thomas B Kepler
- Department of Microbiology, Boston University School of Medicine, Boston, MA, United States
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
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Rivas JR, Ireland SJ, Chkheidze R, Rounds WH, Lim J, Johnson J, Ramirez DMO, Ligocki AJ, Chen D, Guzman AA, Woodhall M, Wilson PC, Meffre E, White C, Greenberg BM, Waters P, Cowell LG, Stowe AM, Monson NL. Peripheral VH4+ plasmablasts demonstrate autoreactive B cell expansion toward brain antigens in early multiple sclerosis patients. Acta Neuropathol 2017; 133:43-60. [PMID: 27730299 DOI: 10.1007/s00401-016-1627-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 05/03/2016] [Revised: 09/23/2016] [Accepted: 09/23/2016] [Indexed: 11/24/2022]
Abstract
Plasmablasts are a highly differentiated, antibody secreting B cell subset whose prevalence correlates with disease activity in Multiple Sclerosis (MS). For most patients experiencing partial transverse myelitis (PTM), plasmablasts are elevated in the blood at the first clinical presentation of disease (known as a clinically isolated syndrome or CIS). In this study we found that many of these peripheral plasmablasts are autoreactive and recognize primarily gray matter targets in brain tissue. These plasmablasts express antibodies that over-utilize immunoglobulin heavy chain V-region subgroup 4 (VH4) genes, and the highly mutated VH4+ plasmablast antibodies recognize intracellular antigens of neurons and astrocytes. Most of the autoreactive, highly mutated VH4+ plasmablast antibodies recognize only a portion of cortical neurons, indicating that the response may be specific to neuronal subgroups or layers. Furthermore, CIS-PTM patients with this plasmablast response also exhibit modest reactivity toward neuroantigens in the plasma IgG antibody pool. Taken together, these data indicate that expanded VH4+ peripheral plasmablasts in early MS patients recognize brain gray matter antigens. Peripheral plasmablasts may be participating in the autoimmune response associated with MS, and provide an interesting avenue for investigating the expansion of autoreactive B cells at the time of the first documented clinical event.
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Affiliation(s)
- Jacqueline R Rivas
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Sara J Ireland
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Rati Chkheidze
- Department of Pathology, UT Southwestern, Dallas, TX, USA
| | - William H Rounds
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Joseph Lim
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Jordan Johnson
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Denise M O Ramirez
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Ann J Ligocki
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Ding Chen
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Alyssa A Guzman
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Mark Woodhall
- Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Patrick C Wilson
- Department of Biomedical Sciences, University of Chicago, Chicago, IL, USA
| | - Eric Meffre
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Charles White
- Department of Pathology, UT Southwestern, Dallas, TX, USA
| | | | - Patrick Waters
- Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Lindsay G Cowell
- Department of Clinical Science, UT Southwestern, Dallas, TX, USA
| | - Ann M Stowe
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA
| | - Nancy L Monson
- Department of Neurology and Neurotherapeutics, UT Southwestern, Dallas, TX, USA.
- Department of Immunology, UT Southwestern, Dallas, TX, USA.
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Toby IT, Levin MK, Salinas EA, Christley S, Bhattacharya S, Breden F, Buntzman A, Corrie B, Fonner J, Gupta NT, Hershberg U, Marthandan N, Rosenfeld A, Rounds W, Rubelt F, Scarborough W, Scott JK, Uduman M, Vander Heiden JA, Scheuermann RH, Monson N, Kleinstein SH, Cowell LG. VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements. BMC Bioinformatics 2016; 17:333. [PMID: 27766961 PMCID: PMC5073965 DOI: 10.1186/s12859-016-1214-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Background The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses. Results To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format. Conclusions The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/. We welcome participation from the community in developing the file format standard, as well as code contributions.
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Affiliation(s)
- Inimary T Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Mikhail K Levin
- Bank of America Corporate Center, 100 North Tryon Street, Charlotte, NC, 28202, USA
| | | | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Sanchita Bhattacharya
- Institute for Computational Health Sciences, University of California San Francisco, Mission Hall, 550 16th Street, 4th Floor, Box 0110, San Francisco, CA, 94158, USA
| | - Felix Breden
- Department of Biological Sciences and The IRMACS Centre, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6, British Columbia, Canada
| | - Adam Buntzman
- Department of Immunobiology, University of Arizona School of Medicine, 1656 E. Mabel Street, P.O. Box 245221, Tucson, AZ, 85724-5221, USA
| | - Brian Corrie
- New Zealand eScience Infrastructure, University of Auckland, Level 10, 49 Symonds Street, Auckland, New Zealand
| | - John Fonner
- Texas Advanced Computing Center, Research Office Complex 1.101, J.J. Pickle Research Campus, Building 196, 10100 Burnet Road (R8700), Austin, TX, 78758-4497, USA
| | - Namita T Gupta
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, USA
| | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems and Department of Microbiology and Immunology, College of Medicine, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA
| | - Nishanth Marthandan
- The IRMACS Centre (ASB 10905), Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Aaron Rosenfeld
- School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA
| | - William Rounds
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA
| | - Florian Rubelt
- Stanford University School of Medicine, 279 Campus Drive, Stanford, CA, 94305-5101, USA
| | - Walter Scarborough
- Texas Advanced Computing Center, Research Office Complex 1.101, J.J. Pickle Research Campus, Building 196, 10100 Burnet Road (R8700), Austin, TX, 78758-4497, USA
| | - Jamie K Scott
- Department of Molecular Biology and Biochemistry and Faculty of Health Sciences, Simon Fraser University, Blusson Hall, Room 11300, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Mohamed Uduman
- Department of Pathology, Yale School of Medicine, 300 George Street, Suite 505, New Haven, CT, 06511, USA
| | - Jason A Vander Heiden
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.,Department of Pathology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.,Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA
| | - Nancy Monson
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9036, USA
| | - Steven H Kleinstein
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 505, New Haven, CT, 06511, USA.,Department of Pathology, Yale School of Medicine, 300 George Street, Suite 505, New Haven, CT, 06511, USA.,Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.
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Christley S, Levin M, Fonner J, Monson N, Rounds WH, Rubelt F, Scarborough W, Scheuermann RH, Toby I, Cowell LG. VDJPipe: a pre-processing pipeline for immune repertoire sequencing data. The Journal of Immunology 2016. [DOI: 10.4049/jimmunol.196.supp.209.26] [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] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass of the data. These tasks include base composition statistics, read quality statistics, numerous quality filters, homopolymer filtering, length and nucleotide filtering, barcode demultiplexing, 5′ and 3′ PCR primer matching, and filtering of duplicate reads. VDJPipe utilizes a “pipeline” approach whereby multiple processing steps are described in a sequential workflow, with the output of each step passed as input to the next step. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. VDJPipe is an integral part of the VDJServer (http://www.vdjserver.org) web resource for performing immune repertoire analysis and can be accessed via the VDJServer Software page.
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Toby I, Breden F, Buntzman A, Christley S, Corrie B, Fonner J, Gupta N, Hershberg U, Jordan C, Kim MS, Kleinstein S, Marthandan N, Mock S, Monson N, Rounds WH, Rojas M, Rosenfeld A, Rubelt F, Scarborough W, Scheuermann RH, Scott J, Uduman M, Heiden JV, Cowell LG. VDJML – tools for capturing the results of inferring immune receptor rearrangements. The Journal of Immunology 2016. [DOI: 10.4049/jimmunol.196.supp.209.24] [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] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
Despite the widespread use of immune repertoire profiling, there is currently no standardized format for output files from VDJ analysis. Researchers utilize software such as IgBlast and IMGT/High V-Quest to perform VDJ analysis and infer germline rearrangements. Each of these software tools produces results in a different file format, and can identify the same result using different labels. These differences make it challenging for users to perform additional analysis using the output file from one software to the next. We have addressed this problem by developing a standardized file format for representing results. The purpose of VDJML is to provide a common standardized format for different VDJ analysis applications and to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format is accompanied by a suite of analysis tools, which are accessible via command line and written in C++ and python. The VDJML suite will allow users to streamline their VDJ analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://www.vdjserver.org/software. We welcome participation from others in developing the file format standard, as well as code contributions.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Min S Kim
- 1Univ. of Texas Southwestern Med. Ctr
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Monson N, Rivas J, Ireland S, Rounds W, Ligocki A, Ma L, Frohman E, Greenberg B, Cowell LG, Stowe AM. The auto-reactive profile of B cells from early MS patients. The Journal of Immunology 2016. [DOI: 10.4049/jimmunol.196.supp.124.44] [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] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
My lab’s recent work has led to the discovery that antibodies expressed by memory B cells in the cerebrospinal fluid (CSF) of patients with Multiple Sclerosis recognize neurons and astrocytes in the brain. Genetic analysis further revealed that only certain heavy chain genes accommodate binding to these brain targets, and that memory B cells from control cohorts rarely express these antibody genes. In fact, in a recent clinical trial, we demonstrated that the prevalence of these antibody genes expressed by memory B cells in the CSF support identification of patients who either have MS or will develop MS in the future with 89–94% accuracy. This Antibody Gene Signature (AGS) is a novel biomarker that heralds the advent of antibody genetics as a means to support categorization of patients early in their disease course. Our goal for this project was to determine whether antibodies expressed by activated memory B cells that have initiated antibody production (called “plasmablasts”) in the peripheral blood of MS patients also recognize neurons and astrocytes. We cloned and expressed more than 50 antibodies expressed by peripheral plasmablasts of early MS patients and control cohorts. We found that antibodies expressed by peripheral plasmablasts from patients who develop MS bind to neurons and astrocytes in the brain. Those plasmablasts that did not express antibody heavy chains associated with the AGS did not bind to brain targets. Interestingly, only those patients in our cohort who have an elevated frequency of peripheral plasmablasts continue to accumulate brain MRI activity. It is possible that brain-reactive peripheral plasmablasts contribute to the brain damage associated with MS.
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Affiliation(s)
| | | | | | | | | | - Lisha Ma
- 1Univ. of Texas Southwestern Med. Ctr
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Rubelt F, Bolen CR, McGuire HM, Vander Heiden JA, Gadala-Maria D, Levin M, Euskirchen GM, Mamedov MR, Swan GE, Dekker CL, Cowell LG, Kleinstein SH, Davis MM. Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells. Nat Commun 2016; 7:11112. [PMID: 27005435 PMCID: PMC5191574 DOI: 10.1038/ncomms11112] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 02/20/2016] [Indexed: 01/10/2023] Open
Abstract
The adaptive immune system's capability to protect the body requires a highly diverse lymphocyte antigen receptor repertoire. However, the influence of individual genetic and epigenetic differences on these repertoires is not typically measured. By leveraging the unique characteristics of B, CD4+ T and CD8+ T-lymphocyte subsets from monozygotic twins, we quantify the impact of heritable factors on both the V(D)J recombination process and on thymic selection. We show that the resulting biases in both V(D)J usage and N/P addition lengths, which are found in naïve and antigen experienced cells, contribute to significant variation in the CDR3 region. Moreover, we show that the relative usage of V and J gene segments is chromosomally biased, with ∼1.5 times as many rearrangements originating from a single chromosome. These data refine our understanding of the heritable mechanisms affecting the repertoire, and show that biases are evident on a chromosome-wide level. The diversity of antigen receptor specificities is largely generated by random recombination of its segments. Here the authors show, by genetic comparison of monozygotic twin lymphocyte subsets, that individual genetic and epigenetic biases also contribute to the shape of the B and T cell repertoires.
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Affiliation(s)
- Florian Rubelt
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Christopher R Bolen
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Helen M McGuire
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Jason A Vander Heiden
- Interdepartmental Program in Computational Biology and Bioinformatics, Deaptment of Computational Biology &Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
| | - Daniel Gadala-Maria
- Interdepartmental Program in Computational Biology and Bioinformatics, Deaptment of Computational Biology &Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
| | - Mikhail Levin
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Ghia M Euskirchen
- Department of Genetics, Stanford University School of Medicine, Palo Alto, California 94304, USA
| | - Murad R Mamedov
- Program in Immunology, Department of Microbiology and Immunology, Stanford University, Stanford, California 94305, USA
| | - Gary E Swan
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Cornelia L Dekker
- Department of Pediatrics (Infectious Diseases), Stanford University School of Medicine, Stanford, California 94305, USA
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Steven H Kleinstein
- Interdepartmental Program in Computational Biology and Bioinformatics, Deaptment of Computational Biology &Bioinformatics, Yale University, New Haven, Connecticut 06520, USA.,Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, 06520, USA.,Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut 06520, USA
| | - Mark M Davis
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA.,Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California 94305, USA.,Institute of Immunity, Department of Microbiology and Immunology, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA
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Rounds WH, Salinas EA, Wilks TB, Levin MK, Ligocki AJ, Ionete C, Pardo CA, Vernino S, Greenberg BM, Bigwood DW, Eastman EM, Cowell LG, Monson NL. MSPrecise: A molecular diagnostic test for multiple sclerosis using next generation sequencing. Gene 2015; 572:191-7. [PMID: 26172868 PMCID: PMC4702260 DOI: 10.1016/j.gene.2015.07.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [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: 04/11/2015] [Revised: 06/11/2015] [Accepted: 07/03/2015] [Indexed: 12/25/2022]
Abstract
BACKGROUND We have previously demonstrated that cerebrospinal fluid-derived B cells from early relapsing-remitting multiple sclerosis (RRMS) patients that express a VH4 gene accumulate specific replacement mutations. These mutations can be quantified as a score that identifies such patients as having or likely to convert to RRMS. Furthermore, we showed that next generation sequencing is an efficient method for obtaining the sequencing information required by this mutation scoring tool, originally developed using the less clinically viable single-cell Sanger sequencing. OBJECTIVE To determine the accuracy of MSPrecise, the diagnostic test that identifies the presence of the RRMS-enriched mutation pattern from patient cerebrospinal fluid B cells. METHODS Cerebrospinal fluid cell pellets were obtained from RRMS and other neurological disease (OND) patient cohorts. VH4 gene segments were amplified, sequenced by next generation sequencing and analyzed for mutation score. RESULTS The diagnostic test showed a sensitivity of 75% on the RRMS cohort and a specificity of 88% on the OND cohort. The accuracy of the test in identifying RRMS patients or patients that will develop RRMS is 84%. CONCLUSION MSPrecise exhibits good performance in identifying patients with RRMS irrespective of time with RRMS.
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Affiliation(s)
- William H Rounds
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Edward A Salinas
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Mikhail K Levin
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ann J Ligocki
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Carolina Ionete
- Department of Neurology, UMass Memorial Medical Center, Worcester, MA, USA
| | - Carlos A Pardo
- Department of Neurology and Neurosurgery, John Hopkins University, Baltimore, MD, USA
| | - Steven Vernino
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin M Greenberg
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | | | | | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Nancy L Monson
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA; Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA.
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Abstract
Background The increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms. Results We developed owlcpp, a library for storing and searching RDF triples, parsing RDF/XML documents, converting triples into OWL axioms, and reasoning. The library is written in ISO-compliant C++ to facilitate efficiency, portability, and accessibility from other programming languages. Internally, owlcpp uses the Raptor RDF Syntax library for parsing RDF/XML and the FaCT++ library for reasoning. The current version of owlcpp is supported under Linux, OSX, and Windows platforms and provides an API for Python. Conclusions The results of our evaluation show that, compared to other commonly used libraries, owlcpp is significantly more efficient in terms of memory usage and searching RDF triple stores. owlcpp performs strict parsing and detects errors ignored by other libraries, thus reducing the possibility of incorrect semantic interpretation of ontologies. owlcpp is available at http://owl-cpp.sf.net/ under the Boost Software License, Version 1.0. Electronic supplementary material The online version of this article (doi:10.1186/s13326-015-0035-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mikhail K Levin
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX USA
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX USA
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Teng G, Maman Y, Resch W, Kim M, Yamane A, Qian J, Kieffer-Kwon KR, Mandal M, Ji Y, Meffre E, Clark MR, Cowell LG, Casellas R, Schatz DG. RAG Represents a Widespread Threat to the Lymphocyte Genome. Cell 2015; 162:751-65. [PMID: 26234156 DOI: 10.1016/j.cell.2015.07.009] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Revised: 04/14/2015] [Accepted: 06/02/2015] [Indexed: 11/26/2022]
Abstract
The RAG1 endonuclease, together with its cofactor RAG2, is essential for V(D)J recombination but is a potent threat to genome stability. The sources of RAG1 mis-targeting and the mechanisms that have evolved to suppress it are poorly understood. Here, we report that RAG1 associates with chromatin at thousands of active promoters and enhancers in the genome of developing lymphocytes. The mouse and human genomes appear to have responded by reducing the abundance of "cryptic" recombination signals near RAG1 binding sites. This depletion operates specifically on the RSS heptamer, whereas nonamers are enriched at RAG1 binding sites. Reversing this RAG-driven depletion of cleavage sites by insertion of strong recombination signals creates an ectopic hub of RAG-mediated V(D)J recombination and chromosomal translocations. Our findings delineate rules governing RAG binding in the genome, identify areas at risk of RAG-mediated damage, and highlight the evolutionary struggle to accommodate programmed DNA damage in developing lymphocytes.
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Affiliation(s)
- Grace Teng
- Department of Immunobiology, Yale University School of Medicine, 300 Cedar Street, Box 208011, New Haven, CT 06520-8011, USA
| | - Yaakov Maman
- Department of Immunobiology, Yale University School of Medicine, 300 Cedar Street, Box 208011, New Haven, CT 06520-8011, USA
| | - Wolfgang Resch
- Genomics and Immunity, NIAMS, Center of Cancer Research, NCI, National Institutes of Health, Bethesda, MD 20892, USA
| | - Min Kim
- Division of Biomedical Informatics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Box 9066, Dallas, TX 75390-9066, USA
| | - Arito Yamane
- Genomics and Immunity, NIAMS, Center of Cancer Research, NCI, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jason Qian
- Genomics and Immunity, NIAMS, Center of Cancer Research, NCI, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kyong-Rim Kieffer-Kwon
- Genomics and Immunity, NIAMS, Center of Cancer Research, NCI, National Institutes of Health, Bethesda, MD 20892, USA
| | - Malay Mandal
- Department of Medicine, Section of Rheumatology and Gwen Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL 60637, USA
| | - Yanhong Ji
- Department of Immunology and Microbiology, College of Medicine, Xi'an Jiao Tong University, 76 Yan Ta West Road, Box 37, Xian, Shaanxi 710061, PRC
| | - Eric Meffre
- Department of Immunobiology, Yale University School of Medicine, 300 Cedar Street, Box 208011, New Haven, CT 06520-8011, USA
| | - Marcus R Clark
- Department of Medicine, Section of Rheumatology and Gwen Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL 60637, USA
| | - Lindsay G Cowell
- Division of Biomedical Informatics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Box 9066, Dallas, TX 75390-9066, USA
| | - Rafael Casellas
- Genomics and Immunity, NIAMS, Center of Cancer Research, NCI, National Institutes of Health, Bethesda, MD 20892, USA.
| | - David G Schatz
- Department of Immunobiology, Yale University School of Medicine, 300 Cedar Street, Box 208011, New Haven, CT 06520-8011, USA; Howard Hughes Medical Institute, 295 Congress Avenue, New Haven, CT 06511, USA.
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Rounds WH, Ligocki AJ, Levin MK, Greenberg BM, Bigwood DW, Eastman EM, Cowell LG, Monson NL. The antibody genetics of multiple sclerosis: comparing next-generation sequencing to sanger sequencing. Front Neurol 2014; 5:166. [PMID: 25278930 PMCID: PMC4165282 DOI: 10.3389/fneur.2014.00166] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 08/19/2014] [Indexed: 11/29/2022] Open
Abstract
We previously identified a distinct mutation pattern in the antibody genes of B cells isolated from cerebrospinal fluid (CSF) that can identify patients who have relapsing-remitting multiple sclerosis (RRMS) and patients with clinically isolated syndromes who will convert to RRMS. This antibody gene signature (AGS) was developed using Sanger sequencing of single B cells. While potentially helpful to patients, Sanger sequencing is not an assay that can be practically deployed in clinical settings. In order to provide AGS evaluations to patients as part of their diagnostic workup, we developed protocols to generate AGS scores using next-generation DNA sequencing (NGS) on CSF-derived cell pellets without the need to isolate single cells. This approach has the potential to increase the coverage of the B-cell population being analyzed, reduce the time needed to generate AGS scores, and may improve the overall performance of the AGS approach as a diagnostic test in the future. However, no investigations have focused on whether NGS-based repertoires will properly reflect antibody gene frequencies and somatic hypermutation patterns defined by Sanger sequencing. To address this issue, we isolated paired CSF samples from eight patients who either had MS or were at risk to develop MS. Here, we present data that antibody gene frequencies and somatic hypermutation patterns are similar in Sanger and NGS-based antibody repertoires from these paired CSF samples. In addition, AGS scores derived from the NGS database correctly identified the patients who initially had or subsequently converted to RRMS, with precision similar to that of the Sanger sequencing approach. Further investigation of the utility of the AGS in predicting conversion to MS using NGS-derived antibody repertoires in a larger cohort of patients is warranted.
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Affiliation(s)
- William H Rounds
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - Ann J Ligocki
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - Mikhail K Levin
- Department of Clinical Sciences, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - Benjamin M Greenberg
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | | | | | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - Nancy L Monson
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center , Dallas, TX , USA ; Department of Immunology, University of Texas Southwestern Medical Center , Dallas, TX , USA
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Nelson CL, Pelak K, Podgoreanu MV, Ahn SH, Scott WK, Allen AS, Cowell LG, Rude TH, Zhang Y, Tong A, Ruffin F, Sharma-Kuinkel BK, Fowler VG. A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting. BMC Infect Dis 2014; 14:83. [PMID: 24524581 PMCID: PMC3928605 DOI: 10.1186/1471-2334-14-83] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Accepted: 02/06/2014] [Indexed: 01/10/2023] Open
Abstract
Background Humans vary in their susceptibility to acquiring Staphylococcus aureus infection, and research suggests that there is a genetic basis for this variability. Several recent genome-wide association studies (GWAS) have identified variants that may affect susceptibility to infectious diseases, demonstrating the potential value of GWAS in this arena. Methods We conducted a GWAS to identify common variants associated with acquisition of S. aureus bacteremia (SAB) resulting from healthcare contact. We performed a logistic regression analysis to compare patients with healthcare contact who developed SAB (361 cases) to patients with healthcare contact in the same hospital who did not develop SAB (699 controls), testing 542,410 SNPs and adjusting for age (by decade), sex, and 6 significant principal components from our EIGENSTRAT analysis. Additionally, we evaluated the joint effect of the host and pathogen genomes in association with severity of SAB infection via logistic regression, including an interaction of host SNP with bacterial genotype, and adjusting for age (by decade), sex, the 6 significant principal components, and dialysis status. Bonferroni corrections were applied in both analyses to control for multiple comparisons. Results Ours is the first study that has attempted to evaluate the entire human genome for variants potentially involved in the acquisition or severity of SAB. Although this study identified no common variant of large effect size to have genome-wide significance for association with either the risk of acquiring SAB or severity of SAB, the variant (rs2043436) most significantly associated with severity of infection is located in a biologically plausible candidate gene (CDON, a member of the immunoglobulin family) and may warrant further study. Conclusions The genetic architecture underlying SAB is likely to be complex. Future investigations using larger samples, narrowed phenotypes, and advances in both genotyping and analytical methodologies will be important tools for identifying causative variants for this common and serious cause of healthcare-associated infection.
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Affiliation(s)
- Charlotte L Nelson
- Duke Clinical Research Institute, Duke University Medical Center, 2400 Pratt Street, Room 0311 Terrace Level, Durham, NC 27705, USA.
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Gordon CL, Pouch S, Cowell LG, Boland MR, Platt HL, Goldfain A, Weng C. Design and evaluation of a bacterial clinical infectious diseases ontology. AMIA Annu Symp Proc 2013; 2013:502-511. [PMID: 24551353 PMCID: PMC3900194] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
With antimicrobial resistance increasing worldwide, there is a great need to use automated antimicrobial decision support systems (ADSSs) to lower antimicrobial resistance rates by promoting appropriate antimicrobial use. However, they are infrequently used mostly because of their poor interoperability with different health information technologies. Ontologies can augment portable ADSSs by providing an explicit knowledge representation for biomedical entities and their relationships, helping to standardize and integrate heterogeneous data resources. We developed a bacterial clinical infectious diseases ontology (BCIDO) using Protégé-OWL. BCIDO defines a controlled terminology for clinical infectious diseases along with domain knowledge commonly used in hospital settings for clinical infectious disease treatment decision-making. BCIDO has 599 classes and 2355 object properties. Terms were imported from or mapped to Systematized Nomenclature of Medicine, Unified Medical Language System, RxNorm and National Center for Bitechnology Information Organismal Classification where possible. Domain expert evaluation using the "laddering" technique, ontology visualization, and clinical notes and scenarios, confirmed the correctness and potential usefulness of BCIDO.
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Affiliation(s)
- Claire L Gordon
- Department of Biomedical Informatics, Columbia University, New York ; Division of Infectious Diseases, Department of Medicine, Columbia University, New York ; Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Stephanie Pouch
- Division of Infectious Diseases, Department of Medicine, Columbia University, New York
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas
| | | | - Heather L Platt
- Division of Infectious Diseases, Department of Medicine, Columbia University, New York
| | | | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York
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Brentjens RJ, Davila ML, Riviere I, Park J, Wang X, Cowell LG, Bartido S, Stefanski J, Taylor C, Olszewska M, Borquez-Ojeda O, Qu J, Wasielewska T, He Q, Bernal Y, Rijo IV, Hedvat C, Kobos R, Curran K, Steinherz P, Jurcic J, Rosenblat T, Maslak P, Frattini M, Sadelain M. CD19-targeted T cells rapidly induce molecular remissions in adults with chemotherapy-refractory acute lymphoblastic leukemia. Sci Transl Med 2013; 5:177ra38. [PMID: 23515080 DOI: 10.1126/scitranslmed.3005930] [Citation(s) in RCA: 1542] [Impact Index Per Article: 140.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Adults with relapsed B cell acute lymphoblastic leukemia (B-ALL) have a dismal prognosis. Only those patients able to achieve a second remission with no minimal residual disease (MRD) have a hope for long-term survival in the context of a subsequent allogeneic hematopoietic stem cell transplantation (allo-HSCT). We have treated five relapsed B-ALL subjects with autologous T cells expressing a CD19-specific CD28/CD3ζ second-generation dual-signaling chimeric antigen receptor (CAR) termed 19-28z. All patients with persistent morphological disease or MRD(+) disease upon T cell infusion demonstrated rapid tumor eradication and achieved MRD(-) complete remissions as assessed by deep sequencing polymerase chain reaction. Therapy was well tolerated, although significant cytokine elevations, specifically observed in those patients with morphologic evidence of disease at the time of treatment, required lymphotoxic steroid therapy to ameliorate cytokine-mediated toxicities. Indeed, cytokine elevations directly correlated to tumor burden at the time of CAR-modified T cell infusions. Tumor cells from one patient with relapsed disease after CAR-modified T cell therapy, who was ineligible for additional allo-HSCT or T cell therapy, exhibited persistent expression of CD19 and sensitivity to autologous 19-28z T cell-mediated cytotoxicity, which suggests potential clinical benefit of additional CAR-modified T cell infusions. These results demonstrate the marked antitumor efficacy of 19-28z CAR-modified T cells in patients with relapsed/refractory B-ALL and the reliability of this therapy to induce profound molecular remissions, forming a highly effective bridge to potentially curative therapy with subsequent allo-HSCT.
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Affiliation(s)
- Renier J Brentjens
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
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Meehan TF, Masci AM, Abdulla A, Cowell LG, Blake JA, Mungall CJ, Diehl AD. Logical development of the cell ontology. BMC Bioinformatics 2011; 12:6. [PMID: 21208450 PMCID: PMC3024222 DOI: 10.1186/1471-2105-12-6] [Citation(s) in RCA: 104] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2010] [Accepted: 01/05/2011] [Indexed: 12/03/2022] Open
Abstract
Background The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL. Results Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types. Conclusion Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.
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Affiliation(s)
- Terrence F Meehan
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA.
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Harp CT, Ireland S, Davis LS, Remington G, Cassidy B, Cravens PD, Stuve O, Lovett-Racke AE, Eagar TN, Greenberg BM, Racke MK, Cowell LG, Karandikar NJ, Frohman EM, Monson NL. Memory B cells from a subset of treatment-naïve relapsing-remitting multiple sclerosis patients elicit CD4(+) T-cell proliferation and IFN-γ production in response to myelin basic protein and myelin oligodendrocyte glycoprotein. Eur J Immunol 2010; 40:2942-56. [PMID: 20812237 DOI: 10.1002/eji.201040516] [Citation(s) in RCA: 89] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Recent evidence suggests that B- and T-cell interactions may be paramount in relapsing-remitting MS (RRMS) disease pathogenesis. We hypothesized that memory B-cell pools from RRMS patients may specifically harbor a subset of potent neuro-APC that support neuro-Ag reactive T-cell proliferation and cytokine secretion. To test this hypothesis, we compared CD80 and HLA-DR expression, IL-10 and lymphotoxin-α secretion, neuro-Ag binding capacity, and neuro-Ag presentation by memory B cells from RRMS patients to naïve B cells from RRMS patients and to memory and naïve B cells from healthy donors (HD). We identified memory B cells from some RRMS patients that elicited CD4(+) T-cell proliferation and IFN-γ secretion in response to myelin basic protein and myelin oligodendrocyte glycoprotein. Notwithstanding the fact that the phenotypic parameters that promote efficient Ag presentation were observed to be similar between RRMS and HD memory B cells, a corresponding capability to elicit CD4(+) T-cell proliferation in response to myelin basic protein and myelin oligodendrocyte glycoprotein was not observed in HD memory B cells. Our results demonstrate for the first time that the memory B-cell pool in RRMS harbors neuro-Ag specific B cells that can activate T cells.
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Affiliation(s)
- Christopher T Harp
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Ahn SH, Deshmukh H, Johnson N, Cowell LG, Rude TH, Scott WK, Nelson CL, Zaas AK, Marchuk DA, Keum S, Lamlertthon S, Sharma-Kuinkel BK, Sempowski GD, Fowler VG. Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses. PLoS Pathog 2010; 6:e1001088. [PMID: 20824097 PMCID: PMC2932726 DOI: 10.1371/journal.ppat.1001088] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2009] [Accepted: 08/04/2010] [Indexed: 11/18/2022] Open
Abstract
Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N2 backcross mice (F1 [C18A]×C57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus–challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 β and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies. Staphylococcus aureus has a wide spectrum of human infection, ranging from asymptomatic nasal carriage to overwhelming sepsis and death. Mouse models offer an attractive strategy for investigating complex diseases such as S. aureus infections. A/J mice are highly susceptible to S. aureus infection compared with C57BL/6J mice. We showed that genes on chromosomes 8, 11, and 18 in A/J are responsible for susceptibility to S. aureus by using chromosome substitution strains (CSS). From the ∼4200 genes on these three chromosomes, we identified 191 which were differentially expressed between A/J and C57BL/6J when challenged with S. aureus. Next, we identified two significant QTLs on chromosome 18 that are associated with susceptibility to S. aureus infection in N2 backcross mice. Ten genes (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTLs and were differentially expressed between A/J and C57BL/6J. One gene on each QTL, Tnfaip8 and Seh1l, affected expression of cytokines in mouse macrophages exposed to S. aureus. These cytokine response patterns were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18, but not C57BL/6J. Tnfaip8 and Seh1l are strong candidates for genes influencing susceptibility to S. aureus of A/J mice.
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MESH Headings
- Animals
- Apoptosis Regulatory Proteins/antagonists & inhibitors
- Apoptosis Regulatory Proteins/genetics
- Apoptosis Regulatory Proteins/metabolism
- Biomarkers/metabolism
- Blotting, Western
- Chemokines/metabolism
- Chromosome Mapping
- Chromosomes, Mammalian/genetics
- Cytokines/metabolism
- Enzyme-Linked Immunosorbent Assay
- Flow Cytometry
- Gene Expression Profiling
- Genetic Predisposition to Disease
- Humans
- Macrophages, Peritoneal/cytology
- Macrophages, Peritoneal/metabolism
- Macrophages, Peritoneal/microbiology
- Male
- Mice
- Mice, Inbred A
- Mice, Inbred C57BL
- Neutrophils/cytology
- Neutrophils/metabolism
- Neutrophils/microbiology
- Oligonucleotide Array Sequence Analysis
- Phenotype
- Polymorphism, Single Nucleotide/genetics
- Quantitative Trait Loci/genetics
- RNA, Messenger/genetics
- RNA, Small Interfering/pharmacology
- Reverse Transcriptase Polymerase Chain Reaction
- Sepsis/genetics
- Sepsis/microbiology
- Sepsis/pathology
- Staphylococcal Infections/genetics
- Staphylococcal Infections/microbiology
- Staphylococcal Infections/pathology
- Staphylococcus aureus/genetics
- Staphylococcus aureus/pathogenicity
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Affiliation(s)
- Sun-Hee Ahn
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Hitesh Deshmukh
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Nicole Johnson
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Lindsay G. Cowell
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Thomas H. Rude
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - William K. Scott
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
| | - Charlotte L. Nelson
- Duke Clinical Research Institute, Durham, North Carolina, United States of America
| | - Aimee K. Zaas
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Douglas A. Marchuk
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Sehoon Keum
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Supaporn Lamlertthon
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Batu K. Sharma-Kuinkel
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | | | - Vance G. Fowler
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
- Duke Clinical Research Institute, Durham, North Carolina, United States of America
- * E-mail:
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50
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Goldfain A, Smith B, Cowell LG. Towards an ontological representation of resistance: the case of MRSA. J Biomed Inform 2010; 44:35-41. [PMID: 20206294 DOI: 10.1016/j.jbi.2010.02.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2009] [Revised: 02/21/2010] [Accepted: 02/24/2010] [Indexed: 11/19/2022]
Abstract
This paper addresses a family of issues surrounding the biological phenomenon of resistance and its representation in realist ontologies. The treatments of resistance terms in various existing ontologies are examined and found to be either overly narrow, internally inconsistent, or otherwise problematic. We propose a more coherent characterization of resistance in terms of what we shall call blocking dispositions, which are collections of mutually coordinated dispositions which are of such a sort that they cannot undergo simultaneous realization within a single bearer. A definition of 'protective resistance' is proposed for use in the Infectious Disease Ontology (IDO) and we show how this definition can be used to characterize the antibiotic resistance in Methicillin-Resistant Staphylococcus aureus (MRSA). The ontological relations between entities in our MRSA case study are used alongside a series of logical inference rules to illustrate logical reasoning about resistance. A description logic representation of blocking dispositions is also provided. We demonstrate that our characterization of resistance is sufficiently general to cover two other cases of resistance in the infectious disease domain involving HIV and malaria.
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Affiliation(s)
- Albert Goldfain
- Blue Highway, 2-212 Center for Science & Technology Syracuse, NY 13244-4100, USA.
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