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Zamecnik CR, Sowa GM, Abdelhak A, Dandekar R, Bair RD, Wade KJ, Bartley CM, Kizer K, Augusto DG, Tubati A, Gomez R, Fouassier C, Gerungan C, Caspar CM, Alexander J, Wapniarski AE, Loudermilk RP, Eggers EL, Zorn KC, Ananth K, Jabassini N, Mann SA, Ragan NR, Santaniello A, Henry RG, Baranzini SE, Zamvil SS, Sabatino JJ, Bove RM, Guo CY, Gelfand JM, Cuneo R, von Büdingen HC, Oksenberg JR, Cree BAC, Hollenbach JA, Green AJ, Hauser SL, Wallin MT, DeRisi JL, Wilson MR. An autoantibody signature predictive for multiple sclerosis. Nat Med 2024:10.1038/s41591-024-02938-3. [PMID: 38641750 DOI: 10.1038/s41591-024-02938-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. In this study, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster in approximately 10% of PwMS who share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active preclinical period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically or radiologically isolated neuroinflammatory syndromes.
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Affiliation(s)
- Colin R Zamecnik
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Gavin M Sowa
- University of California, San Francisco School of Medicine, San Francisco, CA, USA
- Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL, USA
| | - Ahmed Abdelhak
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Ravi Dandekar
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Rebecca D Bair
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kristen J Wade
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Christopher M Bartley
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kerry Kizer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Danillo G Augusto
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Asritha Tubati
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Refujia Gomez
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Camille Fouassier
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Chloe Gerungan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Colette M Caspar
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jessica Alexander
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Anne E Wapniarski
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Rita P Loudermilk
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Erica L Eggers
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kelsey C Zorn
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Kirtana Ananth
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Nora Jabassini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sabrina A Mann
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA
| | - Nicholas R Ragan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Adam Santaniello
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sergio E Baranzini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Scott S Zamvil
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Joseph J Sabatino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Riley M Bove
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Chu-Yueh Guo
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Richard Cuneo
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - H-Christian von Büdingen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jorge R Oksenberg
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce A C Cree
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jill A Hollenbach
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Ari J Green
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mitchell T Wallin
- Department of Veterans Affairs, Multiple Sclerosis Center of Excellence, Washington, DC, USA
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA
| | - Michael R Wilson
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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Chitnis T, Qureshi F, Gehman VM, Becich M, Bove R, Cree BAC, Gomez R, Hauser SL, Henry RG, Katrib A, Lokhande H, Paul A, Caillier SJ, Santaniello A, Sattarnezhad N, Saxena S, Weiner H, Yano H, Baranzini SE. Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect disease activity in multiple sclerosis. medRxiv 2023:2023.06.28.23291157. [PMID: 37461671 PMCID: PMC10350151 DOI: 10.1101/2023.06.28.23291157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Background/Objectives Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA. Funding Octave Bioscience, Inc (Menlo Park, CA).
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Zamecnik CR, Sowa GM, Abdelhak A, Dandekar R, Bair RD, Wade KJ, Bartley CM, Tubati A, Gomez R, Fouassier C, Gerungan C, Alexander J, Wapniarski AE, Loudermilk RP, Eggers EL, Zorn KC, Ananth K, Jabassini N, Mann SA, Ragan NR, Santaniello A, Henry RG, Baranzini SE, Zamvil SS, Bove RM, Guo CY, Gelfand JM, Cuneo R, von Büdingen HC, Oksenberg JR, Cree BAC, Hollenbach JA, Green AJ, Hauser SL, Wallin MT, DeRisi JL, Wilson MR. A Predictive Autoantibody Signature in Multiple Sclerosis. medRxiv 2023:2023.05.01.23288943. [PMID: 37205595 PMCID: PMC10187343 DOI: 10.1101/2023.05.01.23288943] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. Here, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster of PwMS that share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active prodromal period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid (CSF) and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically- or radiologically-isolated neuroinflammatory syndromes.
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Affiliation(s)
- Colin R. Zamecnik
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gavin M. Sowa
- Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL, USA
| | - Ahmed Abdelhak
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ravi Dandekar
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rebecca D. Bair
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kristen J. Wade
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Christopher M. Bartley
- UCSF Weill Institute for Neurosciences, Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Asritha Tubati
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Camille Fouassier
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Chloe Gerungan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jessica Alexander
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne E. Wapniarski
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rita P. Loudermilk
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Erica L. Eggers
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kelsey C. Zorn
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
| | - Kirtana Ananth
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Nora Jabassini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sabrina A. Mann
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
| | - Nicholas R. Ragan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Roland G. Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sergio E. Baranzini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Scott S. Zamvil
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Riley M. Bove
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Chu-Yueh Guo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jeffrey M. Gelfand
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Richard Cuneo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - H.-Christian von Büdingen
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jorge R. Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce AC Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jill A. Hollenbach
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA USA
| | - Ari J. Green
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Stephen L. Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Mitchell T. Wallin
- Veterans Affairs, Multiple Sclerosis Center of Excellence, Washington, DC and University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph L. DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Michael R. Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
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Servetto A, Salomone F, Napolitano F, Santaniello A, Formisano L, Bianco R. 59P Assessment of QoL results and correlation with survival outcomes in phase III clinical trials in metastatic NSCLC. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Salomone F, Napolitano F, Caltavituro A, Buonaiuto R, Pecoraro G, Isernia M, Santaniello A, Formisano L, Bianco R, Servetto A. 65P Correlation of overall survival and surrogate endpoints in advanced non-small cell lung cancer treated with immune checkpoint inhibitors: A trial-level analysis. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Bove R, Poole S, Cuneo R, Gupta S, Sabatino J, Harms M, Cooper T, Rowles W, Miller N, Gomez R, Lincoln R, McPolin K, Powers K, Santaniello A, Renschen A, Bevan CJ, Gelfand JM, Goodin DS, Guo CY, Romeo AR, Hauser SL, Campbell Cree BA. Remote Observational Research for Multiple Sclerosis: A Natural Experiment. Neurol Neuroimmunol Neuroinflamm 2023; 10:10/2/e200070. [PMID: 36585249 PMCID: PMC9808915 DOI: 10.1212/nxi.0000000000200070] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 04/10/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND OBJECTIVES Prospective, deeply phenotyped research cohorts monitoring individuals with chronic neurologic conditions, such as multiple sclerosis (MS), depend on continued participant engagement. The COVID-19 pandemic restricted in-clinic research activities, threatening this longitudinal engagement, but also forced adoption of televideo-enabled care. This offered a natural experiment in which to analyze key dimensions of remote research: (1) comparison of remote vs in-clinic visit costs from multiple perspectives and (2) comparison of the remote with in-clinic measures in cross-sectional and longitudinal disability evaluations. METHODS Between March 2020 and December 2021, 207 MS cohort participants underwent hybrid in-clinic and virtual research visits; 96 contributed 100 "matched visits," that is, in-clinic (Neurostatus-Expanded Disability Status Scale [NS-EDSS]) and remote (televideo-enabled EDSS [tele-EDSS]; electronic patient-reported EDSS [ePR-EDSS]) evaluations. Clinical, demographic, and socioeconomic characteristics of participants were collected. RESULTS The costs of remote visits were lower than in-clinic visits for research investigators (facilities, personnel, parking, participant compensation) but also for participants (travel, caregiver time) and carbon footprint (p < 0.05 for each). Median cohort EDSS was similar between the 3 modalities (NS-EDSS: 2, tele-EDSS: 1.5, ePR-EDSS: 2, range 0.6.5); the remote evaluations were each noninferior to the NS-EDSS within ±0.5 EDSS point (TOST for noninferiority, p < 0.01 for each). Furthermore, year to year, the % of participants with worsening/stable/improved EDSS scores was similar, whether each annual evaluation used NS-EDSS or whether it switched from NS-EDSS to tele-EDSS. DISCUSSION Altogether, the current findings suggest that remote evaluations can reduce the costs of research participation for patients, while providing a reasonable evaluation of disability trajectory longitudinally. This could inform the design of remote research that is more inclusive of diverse participants.
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Affiliation(s)
- Riley Bove
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA.
| | - Shane Poole
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Richard Cuneo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Sasha Gupta
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Joseph Sabatino
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Meagan Harms
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Tifffany Cooper
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - William Rowles
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Nicolette Miller
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Refujia Gomez
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Robin Lincoln
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Kira McPolin
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Kyra Powers
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Adam Santaniello
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Adam Renschen
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Carolyn J Bevan
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Jeffrey M Gelfand
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Douglas S Goodin
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Chu-Yueh Guo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Andrew R Romeo
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Stephen L Hauser
- From the UCSF Weill Institute for Neuroscience, Division of Neuroimmunology and Glial Biology, Department of Neurology, University of California San Francisco, San Francisco, CA
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7
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Shams H, Shao X, Santaniello A, Kirkish G, Harroud A, Ma Q, Isobe N, Schaefer CA, McCauley JL, Cree BAC, Didonna A, Baranzini SE, Patsopoulos NA, Hauser SL, Barcellos LF, Henry RG, Oksenberg JR. Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain 2023; 146:645-656. [PMID: 35253861 PMCID: PMC10169285 DOI: 10.1093/brain/awac092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 11/23/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.
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Affiliation(s)
- Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Xiaorong Shao
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adil Harroud
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Noriko Isobe
- Department of Neurology, Graduate School of medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | | | | | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA.,Dr. John T. Macdonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Department of Anatomy and Cell Biology, East Carolina University, Greenville, NC 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, 02115 MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lisa F Barcellos
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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8
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Cordano C, Nourbakhsh B, Yiu HH, Papinutto N, Caverzasi E, Abdelhak A, Oertel FC, Beaudry-Richard A, Santaniello A, Sacco S, Bennett DJ, Gomez A, Sigurdson CJ, Hauser SL, Magliozzi R, Cree BA, Henry RG, Green AJ. Differences in Age-related Retinal and Cortical Atrophy Rates in Multiple Sclerosis. Neurology 2022; 99:e1685-e1693. [PMID: 36038272 PMCID: PMC9559941 DOI: 10.1212/wnl.0000000000200977] [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] [Received: 02/23/2022] [Accepted: 06/01/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The timing of neurodegeneration in multiple sclerosis (MS) remains unclear. It is critical to understand the dynamics of neuroaxonal loss if we hope to prevent or forestall permanent disability in MS. We therefore used a deeply phenotyped longitudinal cohort to assess and compare rates of neurodegeneration in retina and brain throughout the MS disease course. METHODS We analyzed 597 patients with MS who underwent longitudinal optical coherence tomography imaging annually for 4.5 ± 2.4 years and 432 patients who underwent longitudinal MRI scans for 10 ± 3.4 years, quantifying macular ganglion cell-inner plexiform layer (GCIPL) volume and cortical gray matter (CGM) volume. The association between the slope of decline in the anatomical structure and the age of entry in the cohort (categorized by the MRI cohort's age quartiles) was assessed by hierarchical linear models. RESULTS The rate of CGM volume loss declined with increasing age of study entry (1.3% per year atrophy for the age of entry in the cohort younger than 35 years; 1.1% for older than 35 years and younger than 41; 0.97% for older than 41 years and younger than 49; 0.9% for older than 49 years) while the rate of GCIPL thinning was highest in patients in the youngest quartile, fell by more than 50% in the following age quartile, and then stabilized (0.7% per year thinning for the age of entry in the cohort younger than 35 years; 0.29% for age older than 35 and younger than 41 years; 0.34% for older than 41 and younger than 49 years; 0.33% for age older than 49 years). DISCUSSION An age-dependent reduction in retinal and cortical volume loss rates during relapsing-remitting MS suggests deceleration in neurodegeneration in the earlier period of disease and further indicates that the period of greatest adaptive immune-mediated inflammatory activity is also the period with the greatest neuroaxonal loss.
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Affiliation(s)
- Christian Cordano
- From the Department of Neurology (C.C., N.P., E.C., A.A., F.C.O., A.B.-R., A.S., S.S., D.J.B., A.G., S.L.H., B.A.C.C., R.G.H., A.J.G.), UCSF Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (B.N.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Biology (H.H.Y.), University of Maryland, College Park; Department of Pathology (C.J.S.), University of California, San Diego, La Jolla; and Department of Neurosciences (R.M.), Biomedicine and Movement Sciences, University of Verona, Italy.
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9
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Zhou X, Baumann R, Gao X, Mendoza M, Singh S, Sand IK, Xia Z, Cox LM, Chitnis T, Yoon H, Moles L, Caillier SJ, Santaniello A, Ackermann G, Harroud A, Lincoln R, Gomez R, Peña AG, Digga E, Hakim DJ, Vazquez-Baeza Y, Soman K, Warto S, Humphrey G, Farez M, Gerdes LA, Oksenberg JR, Zamvil SS, Chandran S, Connick P, Otaegui D, Castillo-Triviño T, Hauser SL, Gelfand JM, Weiner HL, Hohlfeld R, Wekerle H, Graves J, Bar-Or A, Cree BA, Correale J, Knight R, Baranzini SE. Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course. Cell 2022; 185:3467-3486.e16. [PMID: 36113426 PMCID: PMC10143502 DOI: 10.1016/j.cell.2022.08.021] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [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: 10/23/2021] [Revised: 04/21/2022] [Accepted: 08/18/2022] [Indexed: 02/07/2023]
Abstract
Changes in gut microbiota have been associated with several diseases. Here, the International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of 576 MS patients (36% untreated) and genetically unrelated household healthy controls (1,152 total subjects). We observed a significantly increased proportion of Akkermansia muciniphila, Ruthenibacterium lactatiformans, Hungatella hathewayi, and Eisenbergiella tayi and decreased Faecalibacterium prausnitzii and Blautia species. The phytate degradation pathway was over-represented in untreated MS, while pyruvate-producing carbohydrate metabolism pathways were significantly reduced. Microbiome composition, function, and derived metabolites also differed in response to disease-modifying treatments. The therapeutic activity of interferon-β may in part be associated with upregulation of short-chain fatty acid transporters. Distinct microbial networks were observed in untreated MS and healthy controls. These results strongly support specific gut microbiome associations with MS risk, course and progression, and functional changes in response to treatment.
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Affiliation(s)
- Xiaoyuan Zhou
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Ryan Baumann
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Xiaohui Gao
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Myra Mendoza
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Sneha Singh
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Ilana Katz Sand
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lau M. Cox
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Tanuja Chitnis
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hongsup Yoon
- Institute of Clinical Neuroimmunology, Biomedical Center and University Hospitals, Ludwig-Maximilians-Universität München, and Munich Cluster of Systems Neurology (SyNergy), München, Germany
- Department Neuroimmunology, Max Planck Institute (MPI) of Neurobiology, Munich, Germany
| | - Laura Moles
- Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Stacy J. Caillier
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Adil Harroud
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Robin Lincoln
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | | | | | - Elise Digga
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel Joseph Hakim
- Department of Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA
| | - Yoshiki Vazquez-Baeza
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Karthik Soman
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Shannon Warto
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Greg Humphrey
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Mauricio Farez
- Department of Neurology, Institute for Neurological Research Dr. Raul Carrea (FLENI), Buenos Aires, Argentina
| | - Lisa Ann Gerdes
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jorge R. Oksenberg
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Scott S. Zamvil
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Peter Connick
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - David Otaegui
- Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Tamara Castillo-Triviño
- Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
- Department of Neurology, Hospital Universitario Donostia and Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Stephen L. Hauser
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Jeffrey M. Gelfand
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Howard L. Weiner
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Reinhard Hohlfeld
- Institute of Clinical Neuroimmunology, Biomedical Center and University Hospitals, Ludwig-Maximilians-Universität München, and Munich Cluster of Systems Neurology (SyNergy), München, Germany
| | - Hartmut Wekerle
- Department Neuroimmunology, Max Planck Institute (MPI) of Neurobiology, Munich, Germany
| | - Jennifer Graves
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Pennsylvania, PA, USA
| | - Bruce A.C. Cree
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
| | - Jorge Correale
- Department of Neurology, Institute for Neurological Research Dr. Raul Carrea (FLENI), Buenos Aires, Argentina
| | - Rob Knight
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Sergio E. Baranzini
- Weill Institute for Neurosciences. Department of Neurology, University of California, San Francisco, CA, USA
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10
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Kaplan TB, Gopal A, Block VJ, Suskind AM, Zhao C, Polgar-Turcsanyi M, Saraceno TJ, Gomez R, Santaniello A, Consortium SUMMIT, Ayoubi NE, Cree BA, Hauser SL, Weiner H, Chitnis T, Khoury S, Bove R. Challenges to Longitudinal Characterization of Lower Urinary Tract Dysfunction in Multiple Sclerosis. Mult Scler Relat Disord 2022; 62:103793. [DOI: 10.1016/j.msard.2022.103793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/01/2022] [Accepted: 04/03/2022] [Indexed: 11/24/2022]
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11
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Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, Mallott JM, Asteggiano C, Sacco S, Gundel TJ, Zhao C, Stern WA, Caverzasi E, Zhou Y, Gomez R, Ragan NR, Santaniello A, Zhu AH, Juwono J, Bevan CJ, Bove RM, Crabtree E, Gelfand JM, Goodin DS, Graves JS, Green AJ, Oksenberg JR, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Reply to "Spinal cord atrophy is a preclinical marker of progressive MS". Ann Neurol 2022; 91:735-736. [PMID: 35233827 PMCID: PMC9511767 DOI: 10.1002/ana.26340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Antje Bischof
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA.,Department of Neurology with Institute for Translational Neurology, University Hospital Münster, Germany
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anisha Keshavan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anand Rajesh
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Xinheng Zhang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jacob M Mallott
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carlo Asteggiano
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Tristan J Gundel
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Chao Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Yifan Zhou
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Refujia Gomez
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nicholas R Ragan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Alyssa H Zhu
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeremy Juwono
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carolyn J Bevan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Riley M Bove
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Elizabeth Crabtree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeffrey M Gelfand
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Douglas S Goodin
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jennifer S Graves
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Ari J Green
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
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- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Bruce A Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
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12
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Bischof A, Papinutto N, Keshavan A, Rajesh A, Kirkish G, Zhang X, Mallott JM, Asteggiano C, Sacco S, Gundel TJ, Zhao C, Stern WA, Caverzasi E, Zhou Y, Gomez R, Ragan NR, Santaniello A, Zhu AH, Juwono J, Bevan CJ, Bove RM, Crabtree E, Gelfand JM, Goodin DS, Graves JS, Green AJ, Oksenberg JR, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Spinal cord atrophy predicts progressive disease in relapsing multiple sclerosis. Ann Neurol 2021; 91:268-281. [PMID: 34878197 PMCID: PMC8916838 DOI: 10.1002/ana.26281] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [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: 06/24/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 11/06/2022]
Abstract
Objective A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS). Methods From a single‐center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12‐year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion. Results Patients who developed SPMS showed faster cord atrophy rates (−2.19%/yr) at least 4 years before conversion compared to their RRMS matches (−0.88%/yr, p < 0.001). Spinal cord atrophy rates decelerated after conversion (−1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (−1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p < 0.0001) and 53% (p < 0.0001) shorter time to silent progression and SPMS conversion, respectively. Interpretation Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268–281
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Affiliation(s)
- Antje Bischof
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nico Papinutto
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anisha Keshavan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Anand Rajesh
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Xinheng Zhang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jacob M Mallott
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carlo Asteggiano
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Simone Sacco
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Tristan J Gundel
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Chao Zhao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - William A Stern
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Yifan Zhou
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Refujia Gomez
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Nicholas R Ragan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Alyssa H Zhu
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeremy Juwono
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Carolyn J Bevan
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Riley M Bove
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Elizabeth Crabtree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jeffrey M Gelfand
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Douglas S Goodin
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jennifer S Graves
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Ari J Green
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Emmanuelle Waubant
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Michael R Wilson
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Scott S Zamvil
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | -
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Bruce A Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, 675, Nelson Rising Lane, 94158, San Francisco, California, USA
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Osoegawa K, Creary LE, Montero-Martín G, Mallempati KC, Gangavarapu S, Caillier SJ, Santaniello A, Isobe N, Hollenbach JA, Hauser SL, Oksenberg JR, Fernández-Viňa MA. High Resolution Haplotype Analyses of Classical HLA Genes in Families With Multiple Sclerosis Highlights the Role of HLA-DP Alleles in Disease Susceptibility. Front Immunol 2021; 12:644838. [PMID: 34211458 PMCID: PMC8240666 DOI: 10.3389/fimmu.2021.644838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 12/22/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) susceptibility shows strong genetic associations with HLA alleles and haplotypes. We genotyped 11 HLA genes in 477 non-Hispanic European MS patients and their 954 unaffected parents using a validated next-generation sequencing (NGS) methodology. HLA haplotypes were assigned unequivocally by tracing HLA allele transmissions. We explored HLA haplotype/allele associations with MS using the genotypic transmission disequilibrium test (gTDT) and multiallelic TDT (mTDT). We also conducted a case-control (CC) study with all patients and 2029 healthy unrelated ethnically matched controls. We performed separate analyses of 54 extended multi-case families by reviewing transmission of haplotype blocks. The haplotype fragment including DRB5*01:01:01~DRB1*15:01:01:01 was significantly associated with predisposition (gTDT: p < 2.20e-16; mTDT: p =1.61e-07; CC: p < 2.22e-16) as reported previously. A second risk allele, DPB1*104:01 (gTDT: p = 3.69e-03; mTDT: p = 2.99e-03; CC: p = 1.00e-02), independent from the haplotype bearing DRB1*15:01 was newly identified. The allele DRB1*01:01:01 showed significant protection (gTDT: p = 8.68e-06; mTDT: p = 4.50e-03; CC: p = 1.96e-06). Two DQB1 alleles, DQB1*03:01 (gTDT: p = 2.86e-03; mTDT: p = 5.56e-02; CC: p = 4.08e-05) and DQB1*03:03 (gTDT: p = 1.17e-02; mTDT: p = 1.16e-02; CC: p = 1.21e-02), defined at two-field level also showed protective effects. The HLA class I block, A*02:01:01:01~C*03:04:01:01~B*40:01:02 (gTDT: p = 5.86e-03; mTDT: p = 3.65e-02; CC: p = 9.69e-03) and the alleles B*27:05 (gTDT: p = 6.28e-04; mTDT: p = 2.15e-03; CC: p = 1.47e-02) and B*38:01 (gTDT: p = 3.20e-03; mTDT: p = 6.14e-03; CC: p = 1.70e-02) showed moderately protective effects independently from each other and from the class II associated factors. By comparing statistical significance of 11 HLA loci and 19 haplotype segments with both untruncated and two-field allele names, we precisely mapped MS candidate alleles/haplotypes while eliminating false signals resulting from ‘hitchhiking’ alleles. We assessed genetic burden for the HLA allele/haplotype identified in this study. This family-based study including the highest-resolution of HLA alleles proved to be powerful and efficient for precise identification of HLA genotypes associated with both, susceptibility and protection to development of MS.
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Affiliation(s)
- Kazutoyo Osoegawa
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Lisa E Creary
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Gonzalo Montero-Martín
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Kalyan C Mallempati
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Sridevi Gangavarapu
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States
| | - Stacy J Caillier
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Noriko Isobe
- Department of Neurology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Jill A Hollenbach
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Marcelo A Fernández-Viňa
- Histocompatibility & Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, United States.,Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, United States
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Romeo AR, Rowles WM, Schleimer ES, Barba P, Hsu WY, Gomez R, Santaniello A, Zhao C, Pearce JR, Jones JB, Cree BC, Hauser SL, Gelfand JM, Stewart WF, Goodin DS, Bove RM. An electronic, unsupervised patient-reported Expanded Disability Status Scale for multiple sclerosis. Mult Scler 2020; 27:1432-1441. [PMID: 33236967 DOI: 10.1177/1352458520968814] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 11/17/2022]
Abstract
BACKGROUND In persons with multiple sclerosis (MS), the Expanded Disability Status Scale (EDSS) is the criterion standard for assessing disability, but its in-person nature constrains patient participation in research and clinical assessments. OBJECTIVE The aim of this study was to develop and validate a scalable, electronic, unsupervised patient-reported EDSS (ePR-EDSS) that would capture MS-related disability across the spectrum of severity. METHODS We enrolled 136 adult MS patients, split into a preliminary testing Cohort 1 (n = 50), and a validation Cohort 2 (n = 86), which was evenly distributed across EDSS groups. Each patient completed an ePR-EDSS either immediately before or after a MS clinician's Neurostatus EDSS evaluation. RESULTS In Cohort 2, mean age was 50.6 years (range = 26-80) and median EDSS was 3.5 (interquartile range (IQR) = [1.5, 5.5]). The ePR-EDSS and EDSS agreed within 1-point for 86% of examinations; kappa for agreement within 1-point was 0.85 (p < 0.001). The correlation coefficient between the two measures was 0.91 (<0.001). DISCUSSION The ePR-EDSS was highly correlated with EDSS, with good agreement even at lower EDSS levels. For clinical care, the ePR-EDSS could enable the longitudinal monitoring of a patient's disability. For research, it provides a valid and rapid measure across the entire spectrum of disability and permits broader participation with fewer in-person assessments.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - J B Jones
- Sutter Health, Palo Alto Medical Foundation Research Institute, Walnut Creek, CA, USA
| | | | | | | | | | - Douglas S Goodin
- UCSF MS and Neuroinflammation Center, Weill Institute for Neurosciences, Department of Neurology, Division of Neuroinflammation and Glial Biology, University of California San Francisco, San Francisco, CA, USA
| | - Riley M Bove
- UCSF MS and Neuroinflammation Center, Weill Institute for Neurosciences, Department of Neurology, Division of Neuroinflammation and Glial Biology, University of California San Francisco, San Francisco, CA, USA
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15
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Beecham AH, Amezcua L, Chinea A, Manrique CP, Rubi C, Isobe N, Lund BT, Santaniello A, Beecham GW, Burchard EG, Comabella M, Patsopoulos N, Fitzgerald K, Calabresi PA, De Jager P, Conti DV, Delgado SR, Oksenberg JR, McCauley JL. The genetic diversity of multiple sclerosis risk among Hispanic and African American populations living in the United States. Mult Scler 2020; 26:1329-1339. [PMID: 31368393 PMCID: PMC6994382 DOI: 10.1177/1352458519863764] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Substantial progress has been made toward unraveling the genetic architecture of multiple sclerosis (MS) within populations of European ancestry, but few genetic studies have focused on Hispanic and African American populations within the United States. OBJECTIVE We sought to test the relevance of common European MS risk variants outside of the major histocompatibility complex (n = 200) within these populations. METHODS Genotype data were available on 2652 Hispanics (1298 with MS, 1354 controls) and 2435 African Americans (1298 with MS, 1137 controls). We conducted single variant, pathway, and cumulative genetic risk score analyses. RESULTS We found less replication than statistical power suggested, particularly among African Americans. This could be due to limited correlation between the tested and causal variants within the sample or alternatively could indicate allelic and locus heterogeneity. Differences were observed between pathways enriched among the replicating versus all 200 variants. Although these differences should be examined in larger samples, a potential role exists for gene-environment or gene-gene interactions which alter phenotype differentially across racial and ethnic groups. Cumulative genetic risk scores were associated with MS within each study sample but showed limited diagnostic capability. CONCLUSION These findings provide a framework for fine-mapping efforts in multi-ethnic populations of MS.
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Affiliation(s)
- A H Beecham
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA/The Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - L Amezcua
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A Chinea
- San Juan MS Center, Guaynabo, Puerto Rico, USA; Universidad Central del Caribe, Bayamon, Puerto Rico, USA
| | - C P Manrique
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - C Rubi
- San Juan MS Center, Guaynabo, Puerto Rico, USA; Universidad Central del Caribe, Bayamon, Puerto Rico, USA
| | - N Isobe
- Department of Neurological Therapeutics, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - B T Lund
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A Santaniello
- Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
| | - G W Beecham
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA/The Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - E G Burchard
- Departments of Medicine and Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, USA
| | - M Comabella
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d'Hebron (VHIR), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - N Patsopoulos
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - K Fitzgerald
- Department of Neurology and The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - P A Calabresi
- Department of Neurology and The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - P De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - D V Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - S R Delgado
- Multiple Sclerosis Division, Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - J R Oksenberg
- Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
| | - J L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA/The Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
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16
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Schleimer E, Pearce J, Barnecut A, Rowles W, Lizee A, Klein A, Block VJ, Santaniello A, Renschen A, Gomez R, Keshavan A, Gelfand JM, Henry RG, Hauser SL, Bove R. A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development. J Med Internet Res 2020; 22:e15605. [PMID: 32628124 PMCID: PMC7381029 DOI: 10.2196/15605] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/16/2019] [Accepted: 02/04/2020] [Indexed: 01/11/2023] Open
Abstract
Background Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. Results This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.
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Affiliation(s)
- Erica Schleimer
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | | | - Andrew Barnecut
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - William Rowles
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Antoine Lizee
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Arno Klein
- Child Mind Institute, New York, NY, United States
| | - Valerie J Block
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Santaniello
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Renschen
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Refujia Gomez
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Anisha Keshavan
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
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17
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Zhou X, Singh S, Baumann R, Barba P, Landefeld J, Casaccia P, Sand IK, Xia Z, Weiner H, Chitnis T, Chandran S, Connick P, Otaegui D, Castillo-Triviño T, Caillier SJ, Santaniello A, Ackermann G, Humphrey G, Negrotto L, Farez M, Hohlfeld R, Pröbstel AK, Jia X, Graves J, Bar-or A, Oksenberg JR, Gelfand J, Wilson MR, Crabtree E, Zamvil SS, Correale J, Cree BA, Hauser SL, Knight R, Baranzini SE. Household paired design reduces variance and increases power in multi-city gut microbiome study in multiple sclerosis. Mult Scler 2020; 27:1352458520924594. [PMID: 33115343 PMCID: PMC7968892 DOI: 10.1177/1352458520924594] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Evidence for a role of human gut microbiota in multiple sclerosis (MS) risk is mounting, yet large variability is seen across studies. This is, in part, due to the lack of standardization of study protocols, sample collection methods, and sequencing approaches. OBJECTIVE This study aims to address the effect of a household experimental design, sample collection, and sequencing approaches in a gut microbiome study in MS subjects from a multi-city study population. METHODS We analyzed 128 MS patient and cohabiting healthy control pairs from the International MS Microbiome Study (iMSMS). A total of 1005 snap-frozen or desiccated Q-tip stool samples were collected and evaluated using 16S and shallow whole-metagenome shotgun sequencing. RESULTS The intra-individual variance observed by different collection strategies was dramatically lower than inter-individual variance. Shallow shotgun highly correlated with 16S sequencing. Participant house and recruitment site accounted for the two largest sources of microbial variance, while higher microbial similarity was seen in household-matched participants as hypothesized. A significant proportion of the variance in dietary intake was also dominated by geographic distance. CONCLUSION A household pair study largely overcomes common inherent limitations and increases statistical power in population-based microbiome studies.
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18
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Santaniello A, Bellocchi C, Bettolini L, Cassavia M, Montanelli G, Severino A, Caronni M, Campochiaro C, De Lorenzis E, Natalello G, Delvino P, Tirelli C, Cavagna L, De Luca G, Bosello SL, Beretta L. OP0009 DERIVATION AND VALIDATION OF THE SCLERODERMA LUNG 3-STAGE INDEX (SL3SI), A NEW FUNCTIONAL INDEX FOR INTERSTITIAL LUNG DISEASE WITH PROGNOSTIC IMPLICATIONS. Ann Rheum Dis 2020. [DOI: 10.1136/annrheumdis-2020-eular.5788] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Background:The staging of interstitial lung disease (ILD) is important to monitor disease progression and for prognostication. A disease severity scale of Systemic Sclerosis (SSc)-related lung disease has long been proposed (i.e. Medsger’s severity scale). This scale was mostly developed by discussion and consensus and stage thresholds were not computed by a data-driven approach. Hidden Markov models (HMM) are methods to estimate population quantities for chronic diseases with a staged interpretation which are diagnosed by markers measured at irregular intervals.Objectives:To build a SSc-ILD specific disease severity scale with prognostic relevance via HMM modeling.Methods:A total of 358 SSc patients at risk for or with ILD were enrolled in a discovery (207 cases, Milan1) and in a validation (151 cases, Milan2, Pavia and Rome) cohort. Patients were included if satisfied the following criteria: 1) Diagnosis of SSc according to the EULAR/ACR 2013 criteria, 2) absence of anticentromere antibodies, 3) dcSSc subset or 4) other subsets with either 4a) ILD-related antibodies (Scl70, PmScl, Ku) or 4b) evidence of ILD on HRCT, 5) disease duration < 5 years at the time of the first pulmonary function test (PFT). Serial PFTs were retrieved and the time up to the last available visit -if the patient alive-, or to death due to pulmonary complications, was recorded. HMM were used to estimate the threshold of a 3-stage model (SL3SI, Scleroderma Lung 3-Stage Index) based on PFT functional values (normal/mild, moderate, severe involvement) in the discovery cohort. Survival estimates of the SL3SI model were compared to Medsger’s severity classes estimates and their predictive capability evaluated via the explained residual variation (R2) of prediction errors (the higher the better). One-hundred random replicates were generated to simulate the prediction effort in patients with different disease duration and lung severity.Results:Patients characteristics are summarized in the Table. Fifteen-years survival estimates for Mesdger’s classes in the discovery set were: normal=0.88, mild=0.86, moderate=0.84 and severe=0.71. The SL3SI was defined by the following thresholds: normal/mild, FVC and DLco >=75%; moderate FVC or DLco 74-55%; severe, FVC or DLco <55%. SL3SI 15-yrs survival estimates were: normal/mild=0.89, moderate=0.82 and severe=0.63. Prediction analysis showed a higher R2values at 15 yrs for the SL3SI compared to Medsger’s classes, providing evidence for a better predictive capability of the former (discovery: 0.31 vs 0.25; validation: 0.28 vs 0.19).Conclusion:The SL3SI, a simplified 3-stage functional model of SSc-ILD, yields better survival estimates and long-term prognostic information than Medsger’s classes. Its reproducibility and ease of use make it a useful tool for the functional and prognostic evaluation of SSc patients at risk for or with ILD.Table:VariablesDiscovery (n=207)Replication (n=151)DcSSc62 (30%)98 (64%)Age at first PFR48.6±1249.1±14.4Disease duration at first PFR1.7±1.61.3±2.4FVC90.5±18.191.1±20.2DLco70.7±19.861.3±20.1ILD on HRCT179 (86%)125 (80%)Scl70157 (76%)153 (78%)SSA63 (30%)32 (21%)n of visits38571473Follow-up time, yrs11±5.610.6±5.7Deaths27 (13%)23 (15%)Disclosure of Interests:Alessandro Santaniello: None declared, Chiara Bellocchi: None declared, Luca Bettolini: None declared, Marcello Cassavia: None declared, Gaia Montanelli: None declared, Adriana Severino: None declared, Monica Caronni: None declared, Corrado Campochiaro Speakers bureau: Novartis, Pfizer, Roche, GSK, SOBI, Enrico De Lorenzis: None declared, Gerlando Natalello: None declared, Paolo Delvino: None declared, Claudio Tirelli: None declared, Lorenzo Cavagna: None declared, Giacomo De Luca Speakers bureau: SOBI, Novartis, Celgene, Pfizer, MSD, Silvia Laura Bosello: None declared, Lorenzo Beretta Grant/research support from: Pfizer
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19
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Varriale L, Russo TP, Pace A, Mediatore S, Borrelli L, Santaniello A, Menna LF, Fioretti A, Dipineto L. Microbiological survey of sugar gliders (Petaurus breviceps) kept as pets in Italy. Lett Appl Microbiol 2020; 69:399-402. [PMID: 31618795 DOI: 10.1111/lam.13233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 08/08/2019] [Revised: 10/03/2019] [Accepted: 10/06/2019] [Indexed: 12/29/2022]
Abstract
The sugar glider (Petaurus breviceps) is a small, arboreal, nocturnal, gliding mammalian possum belonging to the marsupial infraclass. Exotic marsupials, including sugar gliders, are becoming popular companion pets and, consequently, the risk of potential infections that can be transmitted to humans should be investigated. Data on the role of the sugar glider as a possible carrier of pathogenic and zoonotic bacteria are scarce and fragmentary. Therefore, this study is aimed at evaluating the prevalence of potentially zoonotic bacteria (Salmonella spp., Escherichia coli, Campylobacter spp., Pseudomonas spp., Klebsiella spp., Listeria monocytogenes and Yersinia enterocolitica) in 64 sugar gliders kept as pets in Italy. The highest prevalence of infection pertained to members of the family Enterobacteriaceae, in particular Citrobacter spp. (50%), Enterobacter spp. (28·1%) and Klebsiella pneumoniae (15·6%); Pseudomonas aeruginosa was isolated from 10 out of 64 samples (15·6%). All strains of Klebsiella pneumoniae exhibited some level of resistance to multiple antimicrobials (ampicillin, amoxicillin-clavulanic acid and doxycycline). SIGNIFICANCE AND IMPACT OF THE STUDY: The results of this study show that sugar gliders may act as carriers of potentially pathogenic agents for humans and other animal species, therefore caution should be exercised in the handling and contact with these animals.
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Affiliation(s)
- L Varriale
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - T P Russo
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - A Pace
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - S Mediatore
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - L Borrelli
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - A Santaniello
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - L F Menna
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - A Fioretti
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
| | - L Dipineto
- Department of Veterinary Medicine and Animal Productions, Università degli Studi di Napoli Federico II, Napoli, Italy
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20
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Renschen A, Matsunaga A, Oksenberg JR, Santaniello A, Didonna A. TopoDB: a novel multifunctional management system for laboratory animal colonies. Database (Oxford) 2020; 2020:5989500. [PMID: 33206961 PMCID: PMC7673335 DOI: 10.1093/database/baaa098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/22/2020] [Accepted: 10/23/2020] [Indexed: 11/14/2022]
Abstract
Animal models are widely employed in basic research to test mechanistic hypotheses in a complex biological environment as well as to evaluate the therapeutic potential of candidate compounds in preclinical settings. Rodents, and in particular mice, represent the most common in vivo models for their small size, short lifespan and possibility to manipulate their genome. Over time, a typical laboratory will develop a substantial number of inbred strains and transgenic mouse lines, requiring a substantial effort, in both logistic and economic terms, to maintain an animal colony for research purposes and to safeguard the integrity of results. To meet this need, here we present TopoDB, a robust and extensible web-based platform for the rational management of laboratory animals. TopoDB allows an easy tracking of individual animals within the colony and breeding protocols as well as the convenient storage of both genetic and phenotypic data generated in the different experiments. Altogether, these features facilitate and enhance the design of in vivo research, thus reducing the number of necessary animals and the housing costs. In summary, TopoDB represents a novel valuable tool in modern biomedical research. Database URL: https://github.com/UCSF-MS-DCC/TopoDB.
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Affiliation(s)
- Adam Renschen
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Atsuko Matsunaga
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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21
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Cantó E, Barro C, Zhao C, Caillier SJ, Michalak Z, Bove R, Tomic D, Santaniello A, Häring DA, Hollenbach J, Henry RG, Cree BAC, Kappos L, Leppert D, Hauser SL, Benkert P, Oksenberg JR, Kuhle J. Association Between Serum Neurofilament Light Chain Levels and Long-term Disease Course Among Patients With Multiple Sclerosis Followed up for 12 Years. JAMA Neurol 2019; 76:1359-1366. [PMID: 31403661 DOI: 10.1001/jamaneurol.2019.2137] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Importance Blood sample-based biomarkers that are associated with clinically meaningful outcomes for patients with multiple sclerosis (MS) have not been developed. Objective To evaluate the potential of serum neurofilament light chain (sNFL) measurements as a biomarker of disease activity and progression in a longitudinal MS data set. Design, Setting, and Participants Single-center, ongoing, prospective observational cohort study of 607 patients with MS from the longitudinal EPIC (Expression, Proteomics, Imaging, Clinical) study at the University of California, San Francisco from July 1, 2004, through August 31, 2017. Clinical evaluations and sample collection were performed annually for 5 years, then at different time points for up to 12 years, with a median follow-up duration of 10 (interquartile range, 7-11) years. Serum NFL levels were measured using a sensitive single molecule array platform and compared with clinical and magnetic resonance imaging variables with the use of univariable and multivariable analyses. Main Outcomes and Measures The main outcomes were disability progression defined as clinically significant worsening on the Expanded Disability Status Scale (EDSS) score and brain fraction atrophy. Results Mean (SD) age of the 607 study participants at study entry was 42.5 (9.8) years; 423 (69.7%) were women; and all participants were of non-Hispanic European descent. Of 3911 samples sequentially collected, 3904 passed quality control for quantification of sNFL. Baseline sNFL levels showed significant associations with EDSS score (β, 1.080; 95% CI, 1.047-1.114; P < .001), MS subtype (β, 1.478; 95% CI, 1.279-1.707; P < .001), and treatment status (β, 1.120; 95% CI, 1.007-1.245; P = .04). A significant interaction between EDSS worsening and change in levels of sNFL over time was found (β, 1.015; 95% CI, 1.007-1.023; P < .001). Baseline sNFL levels alone were associated with approximately 11.6% of the variance in brain fraction atrophy at year 10. In a multivariable analysis that considered sex, age, and disease duration, baseline sNFL levels were associated with 18.0% of the variance in brain fraction atrophy at year 10. After 5 years' follow-up, active treatment was associated with lower levels of sNFL, with high-potency treatments associated with the greater decreases in sNFL levels compared with platform therapies (high-potency vs untreated: β, 0.946; 95% CI, 0.915-0.976; P < .001; high-potency vs platform: β, 0.972; 95% CI, 0.948-0.998; P = .04). Conclusions and Relevance This study found that statistically significant associations of sNFL with relevant clinical and neuroimaging outcomes in MS were confirmed and extended, supporting the potential of sNFL as an objective surrogate of ongoing MS disease activity. In this data set of patients with MS who received early treatment, the prognostic power of sNFL for relapse activity and long-term disability progression was limited. Further prospective studies are necessary to assess the assay's utility for decision-making in individual patients.
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Affiliation(s)
- Ester Cantó
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Christian Barro
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Chao Zhao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Stacy J Caillier
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Zuzanna Michalak
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Riley Bove
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | | | - Adam Santaniello
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | | | - Jill Hollenbach
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Roland G Henry
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Bruce A C Cree
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.,Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - David Leppert
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Stephen L Hauser
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Pascal Benkert
- Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jorge R Oksenberg
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine, and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
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22
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Krysko KM, Henry RG, Cree BAC, Lin J, Caillier S, Santaniello A, Zhao C, Gomez R, Bevan C, Smith DL, Stern W, Kirkish G, Hauser SL, Oksenberg JR, Graves JS. Telomere Length Is Associated with Disability Progression in Multiple Sclerosis. Ann Neurol 2019; 86:671-682. [PMID: 31486104 DOI: 10.1002/ana.25592] [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: 04/05/2019] [Revised: 08/23/2019] [Accepted: 09/01/2019] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To assess whether biological aging as measured by leukocyte telomere length (LTL) is associated with clinical disability and brain volume loss in multiple sclerosis (MS). METHODS Adults with MS/clinically isolated syndrome in the University of California, San Francisco EPIC cohort study were included. LTL was measured on DNA samples by quantitative polymerase chain reaction and expressed as telomere to somatic DNA (T/S) ratio. Expanded Disability Status Scale (EDSS) and 3-dimensional T1-weighted brain magnetic resonance imaging were performed at baseline and follow-up. Associations of baseline LTL with cross-sectional and longitudinal outcomes were assessed using simple and mixed effects linear regression models. A subset (n = 46) had LTL measured over time, and we assessed the association of LTL change with EDSS change with mixed effects models. RESULTS Included were 356 women and 160 men (mean age = 43 years, median disease duration = 6 years, median EDSS = 1.5 [range = 0-7], mean T/S ratio = 0.97 [standard deviation = 0.18]). In baseline analyses adjusted for age, disease duration, and sex, for every 0.2 lower LTL, EDSS was 0.27 higher (95% confidence interval [CI] = 0.13-0.42, p < 0.001) and brain volume was 7.4mm3 lower (95% CI = 0.10-14.7, p = 0.047). In longitudinal adjusted analyses, those with lower baseline LTL had higher EDSS and lower brain volumes over time. In adjusted analysis of the subset, LTL change was associated with EDSS change over 10 years; for every 0.2 LTL decrease, EDSS was 0.34 higher (95% CI = 0.08-0.61, p = 0.012). INTERPRETATION Shorter telomere length was associated with disability independent of chronological age, suggesting that biological aging may contribute to neurological injury in MS. Targeting aging-related mechanisms is a potential therapeutic strategy against MS progression. ANN NEUROL 2019;86:671-682.
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Affiliation(s)
- Kristen M Krysko
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jue Lin
- Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA
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- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stacy Caillier
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Chao Zhao
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- Department of Neurology, Northwestern University, Evanston, IL
| | - Dana L Smith
- Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA
| | - William Stern
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jorge R Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer S Graves
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA.,Department of Neurosciences, University of California, San Diego, San Diego, CA
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23
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Foschini F, Formisano L, Marciano R, Mozzillo E, Carratù A, Napolitano F, Santaniello A, De Placido P, Cascetta P, Servetto A, Bianco R. FOLFIRINOX after first-line gemcitabine-based chemotherapy in metastatic pancreatic cancer: a mono-institutional experience. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz155.192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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24
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Mitrovič M, Patsopoulos NA, Beecham AH, Dankowski T, Goris A, Dubois B, D’hooghe MB, Lemmens R, Van Damme P, Søndergaard HB, Sellebjerg F, Sorensen PS, Ullum H, Thørner LW, Werge T, Saarela J, Cournu-Rebeix I, Damotte V, Fontaine B, Guillot-Noel L, Lathrop M, Vukusik S, Gourraud PA, Andlauer TF, Pongratz V, Buck D, Gasperi C, Bayas A, Heesen C, Kümpfel T, Linker R, Paul F, Stangel M, Tackenberg B, Bergh FT, Warnke C, Wiendl H, Wildemann B, Zettl U, Ziemann U, Tumani H, Gold R, Grummel V, Hemmer B, Knier B, Lill CM, Luessi F, Dardiotis E, Agliardi C, Barizzone N, Mascia E, Bernardinelli L, Comi G, Cusi D, Esposito F, Ferrè L, Comi C, Galimberti D, Leone MA, Sorosina M, Mescheriakova J, Hintzen R, van Duijn C, Teunissen CE, Bos SD, Myhr KM, Celius EG, Lie BA, Spurkland A, Comabella M, Montalban X, Alfredsson L, Stridh P, Hillert J, Jagodic M, Piehl F, Jelčić I, Martin R, Sospedra M, Ban M, Hawkins C, Hysi P, Kalra S, Karpe F, Khadake J, Lachance G, Neville M, Santaniello A, Caillier SJ, Calabresi PA, Cree BA, Cross A, Davis MF, Haines JL, de Bakker PI, Delgado S, Dembele M, Edwards K, Fitzgerald KC, Hakonarson H, Konidari I, Lathi E, Manrique CP, Pericak-Vance MA, Piccio L, Schaefer C, McCabe C, Weiner H, Goldstein J, Olsson T, Hadjigeorgiou G, Taylor B, Tajouri L, Charlesworth J, Booth DR, Harbo HF, Ivinson AJ, Hauser SL, Compston A, Stewart G, Zipp F, Barcellos LF, Baranzini SE, Martinelli-Boneschi F, D’Alfonso S, Ziegler A, Oturai A, McCauley JL, Sawcer SJ, Oksenberg JR, De Jager PL, Kockum I, Hafler DA, Cotsapas C. Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk. Cell 2019; 178:262. [PMID: 31251915 PMCID: PMC6602362 DOI: 10.1016/j.cell.2019.06.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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25
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Madireddy L, Patsopoulos NA, Cotsapas C, Bos SD, Beecham A, McCauley J, Kim K, Jia X, Santaniello A, Caillier SJ, Andlauer TFM, Barcellos LF, Berge T, Bernardinelli L, Martinelli-Boneschi F, Booth DR, Briggs F, Celius EG, Comabella M, Comi G, Cree BAC, D’Alfonso S, Dedham K, Duquette P, Dardiotis E, Esposito F, Fontaine B, Gasperi C, Goris A, Dubois B, Gourraud PA, Hadjigeorgiou G, Haines J, Hawkins C, Hemmer B, Hintzen R, Horakova D, Isobe N, Kalra S, Kira JI, Khalil M, Kockum I, Lill CM, Lincoln M, Luessi F, Martin R, Oturai A, Palotie A, Pericak-Vance MA, Henry R, Saarela J, Ivinson A, Olsson T, Taylor BV, Stewart GJ, Harbo HF, Compston A, Hauser SL, Hafler DA, Zipp F, De Jager P, Sawcer S, Oksenberg JR, Baranzini SE. A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis. Nat Commun 2019; 10:2236. [PMID: 31110181 PMCID: PMC6527683 DOI: 10.1038/s41467-019-09773-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 03/26/2019] [Indexed: 02/02/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.
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26
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Cree BAC, Hollenbach JA, Bove R, Kirkish G, Sacco S, Caverzasi E, Bischof A, Gundel T, Zhu AH, Papinutto N, Stern WA, Bevan C, Romeo A, Goodin DS, Gelfand JM, Graves J, Green AJ, Wilson MR, Zamvil SS, Zhao C, Gomez R, Ragan NR, Rush GQ, Barba P, Santaniello A, Baranzini SE, Oksenberg JR, Henry RG, Hauser SL. Silent progression in disease activity-free relapsing multiple sclerosis. Ann Neurol 2019; 85:653-666. [PMID: 30851128 PMCID: PMC6518998 DOI: 10.1002/ana.25463] [Citation(s) in RCA: 241] [Impact Index Per Article: 48.2] [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: 09/26/2018] [Revised: 03/05/2019] [Accepted: 03/06/2019] [Indexed: 12/17/2022]
Abstract
Objective Rates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow‐up. Notably, “no evidence of disease activity” at 2 years did not predict long‐term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long‐term disability accumulation. Methods Disability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0–5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA‐DRB1*15:01 as covariates. Results Relapses were associated with a transient increase in disability over 1‐year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05). Interpretation Long‐term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing–remitting MS. Ann Neurol 2019;85:653–666
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Affiliation(s)
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jill A Hollenbach
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Riley Bove
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gina Kirkish
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Simone Sacco
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Eduardo Caverzasi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Antje Bischof
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Tristan Gundel
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alyssa H Zhu
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - William A Stern
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Andrew Romeo
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Douglas S Goodin
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jeffrey M Gelfand
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer Graves
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ari J Green
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Michael R Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Scott S Zamvil
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Chao Zhao
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nicholas R Ragan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gillian Q Rush
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Patrick Barba
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Sergio E Baranzini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jorge R Oksenberg
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA
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27
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Mitrovič M, Patsopoulos NA, Beecham AH, Dankowski T, Goris A, Dubois B, D’hooghe MB, Lemmens R, Van Damme P, Søndergaard HB, Sellebjerg F, Sorensen PS, Ullum H, Thørner LW, Werge T, Saarela J, Cournu-Rebeix I, Damotte V, Fontaine B, Guillot-Noel L, Lathrop M, Vukusik S, Gourraud PA, Andlauer TF, Pongratz V, Buck D, Gasperi C, Bayas A, Heesen C, Kümpfel T, Linker R, Paul F, Stangel M, Tackenberg B, Bergh FT, Warnke C, Wiendl H, Wildemann B, Zettl U, Ziemann U, Tumani H, Gold R, Grummel V, Hemmer B, Knier B, Lill CM, Luessi F, Dardiotis E, Agliardi C, Barizzone N, Mascia E, Bernardinelli L, Comi G, Cusi D, Esposito F, Ferrè L, Comi C, Galimberti D, Leone MA, Sorosina M, Mescheriakova J, Hintzen R, van Duijn C, Teunissen CE, Bos SD, Myhr KM, Celius EG, Lie BA, Spurkland A, Comabella M, Montalban X, Alfredsson L, Stridh P, Hillert J, Jagodic M, Piehl F, Jelčić I, Martin R, Sospedra M, Ban M, Hawkins C, Hysi P, Kalra S, Karpe F, Khadake J, Lachance G, Neville M, Santaniello A, Caillier SJ, Calabresi PA, Cree BA, Cross A, Davis MF, Haines JL, de Bakker PI, Delgado S, Dembele M, Edwards K, Fitzgerald KC, Hakonarson H, Konidari I, Lathi E, Manrique CP, Pericak-Vance MA, Piccio L, Schaefer C, McCabe C, Weiner H, Goldstein J, Olsson T, Hadjigeorgiou G, Taylor B, Tajouri L, Charlesworth J, Booth DR, Harbo HF, Ivinson AJ, Hauser SL, Compston A, Stewart G, Zipp F, Barcellos LF, Baranzini SE, Martinelli-Boneschi F, D’Alfonso S, Ziegler A, Oturai A, McCauley JL, Sawcer SJ, Oksenberg JR, De Jager PL, Kockum I, Hafler DA, Cotsapas C. Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk. Cell 2018; 175:1679-1687.e7. [PMID: 30343897 PMCID: PMC6269166 DOI: 10.1016/j.cell.2018.09.049] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 08/08/2018] [Accepted: 09/24/2018] [Indexed: 12/21/2022]
Abstract
Multiple sclerosis is a complex neurological disease, with ∼20% of risk heritability attributable to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that much of the remaining heritability is also due to additive effects of common variants rather than epistasis between these variants or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that up to 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common-variant signals, highlighting key pathogenic roles for regulatory T cell homeostasis and regulation, IFNγ biology, and NFκB signaling. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS.
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Napolitano F, Di Mauro C, Pesapane A, Rosa R, D'Amato V, Santaniello A, Servetto A, Formisano L, Marciano R, Bianco R. Hedgehog pathway influence in the immune escape of tumor cells through PDL-1 modulation. Ann Oncol 2018. [DOI: 10.1093/annonc/mdy315.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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29
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Jia X, Madireddy L, Caillier S, Santaniello A, Esposito F, Comi G, Stuve O, Zhou Y, Taylor B, Kilpatrick T, Martinelli-Boneschi F, Cree BAC, Oksenberg JR, Hauser SL, Baranzini SE. Genome sequencing uncovers phenocopies in primary progressive multiple sclerosis. Ann Neurol 2018; 84:51-63. [PMID: 29908077 PMCID: PMC6119489 DOI: 10.1002/ana.25263] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [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: 12/29/2017] [Revised: 05/16/2018] [Accepted: 05/17/2018] [Indexed: 01/06/2023]
Abstract
Objective Primary progressive multiple sclerosis (PPMS) causes accumulation of neurological disability from disease onset without clinical attacks typical of relapsing multiple sclerosis (RMS). However, whether genetic variation influences the disease course remains unclear. We aimed to determine whether mutations causative of neurological disorders that share features with multiple sclerosis (MS) contribute to risk for developing PPMS. Methods We examined whole‐genome sequencing (WGS) data from 38 PPMS and 81 healthy subjects of European ancestry. We selected pathogenic variants exclusively found in PPMS patients that cause monogenic neurological disorders and performed two rounds of replication genotyping in 746 PPMS, 3,049 RMS, and 1,000 healthy subjects. To refine our findings, we examined the burden of rare, potentially pathogenic mutations in 41 genes that cause hereditary spastic paraplegias (HSPs) in PPMS (n = 314), secondary progressive multiple sclerosis (SPMS; n = 587), RMS (n = 2,248), and healthy subjects (n = 987) genotyped using the MS replication chip. Results WGS and replication studies identified three pathogenic variants in PPMS patients that cause neurological disorders sharing features with MS: KIF5A p.Ala361Val in spastic paraplegia 10; MLC1 p.Pro92Ser in megalencephalic leukodystrophy with subcortical cysts, and REEP1 c.606 + 43G>T in Spastic Paraplegia 31. Moreover, we detected a significant enrichment of HSP‐related mutations in PPMS patients compared to controls (risk ratio [RR] = 1.95; 95% confidence interval [CI], 1.27–2.98; p = 0.002), as well as in SPMS patients compared to controls (RR = 1.57; 95% CI, 1.18–2.10; p = 0.002). Importantly, this enrichment was not detected in RMS. Interpretation This study provides evidence to support the hypothesis that rare Mendelian genetic variants contribute to the risk for developing progressive forms of MS. Ann Neurol 2018;83:51–63
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Affiliation(s)
- Xiaoming Jia
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Lohith Madireddy
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Stacy Caillier
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Adam Santaniello
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Federica Esposito
- Laboratory of Human Genetics of Neurological Disorders, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.,Department of Neurology and Neuro-rehabilitation, San Raffaele Scientific Institute, Milan, Italy
| | - Giancarlo Comi
- Laboratory of Human Genetics of Neurological Disorders, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.,Department of Neurology and Neuro-rehabilitation, San Raffaele Scientific Institute, Milan, Italy
| | - Olaf Stuve
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical, Dallas, TX
| | - Yuan Zhou
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Bruce Taylor
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Trevor Kilpatrick
- Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, VIC, Australia
| | - Filippo Martinelli-Boneschi
- Laboratory of Genomics of Neurological Diseases and Department of Neurology, Policlinico San Donato Hospital and Scientific Institute, San Donato Milanese, Italy.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.,Laboratory of Human Genetics of Neurological Disorders, Institute of Experimental Neurology (INSpe), Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Jorge R Oksenberg
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA
| | - Sergio E Baranzini
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA.,Department of Neurology, University of California San Francisco, San Francisco, CA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA.,Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA
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Albini A, Casella R, Santaniello A, Beretta L, Bollati V, Rota F, Cantone L, Dioni L, Lombardi F, Vicenzi M. 30Extracellular vesicles in systemic sclerosis as potential mediator for pulmonary vascular disease. Cardiovasc Res 2018. [DOI: 10.1093/cvr/cvy060.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- A Albini
- Fondazione IRCCS Cà Granda, Cardiovascular Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - R Casella
- Fondazione IRCCS Cà Granda, Cardiovascular Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - A Santaniello
- Fondazione IRCCS Cà Granda, Immunological Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - L Beretta
- Fondazione IRCCS Cà Granda, Immunological Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - V Bollati
- University of Milan, EPIGET Lab, Department of Clinical Sciences and Community, Fondazione IRCCS Ca' Granda, Milan, Italy
| | - F Rota
- University of Milan, EPIGET Lab, Department of Clinical Sciences and Community, Fondazione IRCCS Ca' Granda, Milan, Italy
| | - L Cantone
- University of Milan, EPIGET Lab, Department of Clinical Sciences and Community, Fondazione IRCCS Ca' Granda, Milan, Italy
| | - L Dioni
- University of Milan, EPIGET Lab, Department of Clinical Sciences and Community, Fondazione IRCCS Ca' Granda, Milan, Italy
| | - F Lombardi
- Fondazione IRCCS Cà Granda, Cardiovascular Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - M Vicenzi
- Fondazione IRCCS Cà Granda, Cardiovascular Unit, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
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Napolitano F, Rosa R, D'Amato V, Orsini R, Ciciola P, Di Mauro C, Santaniello A, Marciano R, De Placido S, Bianco R. Investigating the role of nuclear sphingosine kinase 1 (SphK1) in lung cancer. Ann Oncol 2018. [DOI: 10.1093/annonc/mdy047.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Damotte V, Lizée A, Tremblay M, Agrawal A, Khankhanian P, Santaniello A, Gomez R, Lincoln R, Tang W, Chen T, Lee N, Villoslada P, Hollenbach JA, Bevan CD, Graves J, Bove R, Goodin DS, Green AJ, Baranzini SE, Cree BAC, Henry RG, Hauser SL, Gelfand JM, Gourraud PA. Harnessing electronic medical records to advance research on multiple sclerosis. Mult Scler 2018; 25:408-418. [DOI: 10.1177/1352458517747407] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [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
Background: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. Objectives: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. Methods: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. Results: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing–remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. Conclusion: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.
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Affiliation(s)
- Vincent Damotte
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Antoine Lizée
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France
| | - Matthew Tremblay
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Department of Neurology, John Dempsey Hospital, University of Connecticut Health Center, Farmington, CT, USA
| | - Alisha Agrawal
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pouya Khankhanian
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Santaniello
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Refujia Gomez
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Robin Lincoln
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Wendy Tang
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Tiffany Chen
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Nelson Lee
- Information Technology, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pablo Villoslada
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/IDIBAPS—Hospital Clinic of Barcelona, Barcelona, Spain
| | - Jill A Hollenbach
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Carolyn D Bevan
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Jennifer Graves
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Riley Bove
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Douglas S Goodin
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Ari J Green
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Sergio E Baranzini
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Bruce AC Cree
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Roland G Henry
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Stephen L Hauser
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Jeffrey M Gelfand
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Pierre-Antoine Gourraud
- MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France
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Dicé F, Santaniello A, Gerardi F, Menna L, Freda M. Meeting the emotion! Application of the Federico II Model for pet therapy to an experience of Animal Assisted Education (AAE) in a primary school. PRAT PSYCHOL 2017. [DOI: 10.1016/j.prps.2017.03.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Isobe N, Keshavan A, Gourraud PA, Zhu AH, Datta E, Schlaeger R, Caillier SJ, Santaniello A, Lizée A, Himmelstein DS, Baranzini SE, Hollenbach J, Cree BAC, Hauser SL, Oksenberg JR, Henry RG. Association of HLA Genetic Risk Burden With Disease Phenotypes in Multiple Sclerosis. JAMA Neurol 2017; 73:795-802. [PMID: 27244296 DOI: 10.1001/jamaneurol.2016.0980] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
IMPORTANCE Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive. OBJECTIVES To investigate whether MS risk-associated HLA alleles also affect disease phenotypes. DESIGN, SETTING, AND PARTICIPANTS A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015. MAIN OUTCOMES AND MEASURES Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set. RESULTS Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [-0.3 to 0.5], respectively; P = 1.8 × 10-27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = -1.20 × 10-1; P = 1.7 × 10-2 and standard β = -1.67 × 10-1; P = 2.3 × 10-4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = -1.28 × 10-1; P = 5.1 × 10-3 and standard β = 9.52 × 10-2; P = 3.6 × 10-2, respectively). CONCLUSIONS AND RELEVANCE We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.
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Affiliation(s)
- Noriko Isobe
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Anisha Keshavan
- Department of Neurology, School of Medicine, University of California, San Francisco
| | | | - Alyssa H Zhu
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Esha Datta
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Regina Schlaeger
- Department of Neurology, School of Medicine, University of California, San Francisco2Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Stacy J Caillier
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Adam Santaniello
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Antoine Lizée
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Daniel S Himmelstein
- Department of Neurology, School of Medicine, University of California, San Francisco3Biological and Medical Informatics, University of California, San Francisco
| | - Sergio E Baranzini
- Department of Neurology, School of Medicine, University of California, San Francisco3Biological and Medical Informatics, University of California, San Francisco
| | - Jill Hollenbach
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Bruce A C Cree
- Department of Neurology, School of Medicine, University of California, San Francisco
| | - Stephen L Hauser
- Department of Neurology, School of Medicine, University of California, San Francisco4Institute of Human Genetics, University of California, San Francisco
| | - Jorge R Oksenberg
- Department of Neurology, School of Medicine, University of California, San Francisco4Institute of Human Genetics, University of California, San Francisco
| | - Roland G Henry
- Department of Neurology, School of Medicine, University of California, San Francisco5Bioengineering Graduate Group, University of California, San Francisco and Berkeley6Department of Radiology and Biomedical Imaging, University of California, San Francisc
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Cree BAC, Gourraud PA, Oksenberg JR, Bevan C, Crabtree-Hartman E, Gelfand JM, Goodin DS, Graves J, Green AJ, Mowry E, Okuda DT, Pelletier D, von Büdingen HC, Zamvil SS, Agrawal A, Caillier S, Ciocca C, Gomez R, Kanner R, Lincoln R, Lizee A, Qualley P, Santaniello A, Suleiman L, Bucci M, Panara V, Papinutto N, Stern WA, Zhu AH, Cutter GR, Baranzini S, Henry RG, Hauser SL. Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 2016; 80:499-510. [PMID: 27464262 PMCID: PMC5105678 DOI: 10.1002/ana.24747] [Citation(s) in RCA: 271] [Impact Index Per Article: 33.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 07/12/2016] [Accepted: 07/24/2016] [Indexed: 12/20/2022]
Abstract
Objective To characterize the accrual of long‐term disability in a cohort of actively treated multiple sclerosis (MS) patients and to assess whether clinical and magnetic resonance imaging (MRI) data used in clinical trials have long‐term prognostic value. Methods This is a prospective study of 517 actively managed MS patients enrolled at a single center. Results More than 91% of patients were retained, with data ascertained up to 10 years after the baseline visit. At this last assessment, neurologic disability as measured by the Expanded Disability Status Scale (EDSS) was stable or improved compared to baseline in 41% of patients. Subjects with no evidence of disease activity (NEDA) by clinical and MRI criteria during the first 2 years had long‐term outcomes that were no different from those of the cohort as a whole. 25‐OH vitamin D serum levels were inversely associated with short‐term MS disease activity; however, these levels had no association with long‐term disability. At a median time of 16.8 years after disease onset, 10.7% (95% confidence interval [CI] = 7.2–14%) of patients reached an EDSS ≥ 6, and 18.1% (95% CI = 13.5–22.5%) evolved from relapsing MS to secondary progressive MS (SPMS). Interpretation Rates of worsening and evolution to SPMS were substantially lower when compared to earlier natural history studies. Notably, the NEDA 2‐year endpoint was not a predictor of long‐term stability. Finally, the data call into question the utility of annual MRI assessments as a treat‐to‐target approach for MS care. Ann Neurol 2016;80:499–510
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Affiliation(s)
| | - Bruce A C Cree
- Department of Neurology, University of California, San Francisco, San Francisco, CA.
| | | | - Jorge R Oksenberg
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | | | - Jeffrey M Gelfand
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Douglas S Goodin
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer Graves
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ari J Green
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ellen Mowry
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Darin T Okuda
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Daniel Pelletier
- Department of Neurology, University of Southern California, Los Angeles, CA
| | | | - Scott S Zamvil
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alisha Agrawal
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stacy Caillier
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Caroline Ciocca
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Rachel Kanner
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Robin Lincoln
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Pamela Qualley
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Leena Suleiman
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Monica Bucci
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Valentina Panara
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nico Papinutto
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - William A Stern
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alyssa H Zhu
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gary R Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Sergio Baranzini
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, San Francisco, CA
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Santaniello A, Quaranta F, Strigari L, Sportelli L. Dose profiles of low energy beams for dosimeter calibration. Phys Med 2016. [DOI: 10.1016/j.ejmp.2016.01.206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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Abstract
iCTNet (integrated Complex Traits Networks) version 2 is a Cytoscape app and database that allows researchers to build heterogeneous networks by integrating a variety of biological interactions, thus offering a systems-level view of human complex traits. iCTNet2 is built from a variety of large-scale biological datasets, collected from public repositories to facilitate the building, visualization and analysis of heterogeneous biological networks in a comprehensive fashion via the Cytoscape platform. iCTNet2 is freely available at the Cytoscape app store.
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Affiliation(s)
- Lili Wang
- School of Computing, Queen's University, Kingston, Ontario, K7L 3N6, Canada
| | - Daniel S Himmelstein
- Graduate Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, 94143-0523, USA
| | - Adam Santaniello
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Mousavi Parvin
- School of Computing, Queen's University, Kingston, Ontario, K7L 3N6, Canada
| | - Sergio E Baranzini
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA; Graduate Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, 94143-0523, USA
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Tranah GJ, Santaniello A, Caillier SJ, D'Alfonso S, Martinelli Boneschi F, Hauser SL, Oksenberg JR. Mitochondrial DNA sequence variation in multiple sclerosis. Neurology 2015; 85:325-30. [PMID: 26136518 DOI: 10.1212/wnl.0000000000001744] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 04/07/2015] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To assess the influence of common mitochondrial DNA (mtDNA) sequence variation on multiple sclerosis (MS) risk in cases and controls part of an international consortium. METHODS We analyzed 115 high-quality mtDNA variants and common haplogroups from a previously published genome-wide association study among 7,391 cases from the International Multiple Sclerosis Genetics Consortium and 14,568 controls from the Wellcome Trust Case Control Consortium 2 project from 7 countries. Significant single nucleotide polymorphism and haplogroup associations were replicated in 3,720 cases and 879 controls from the University of California, San Francisco. RESULTS An elevated risk of MS was detected among haplogroup JT carriers from 7 pooled clinic sites (odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.07-1.24, p = 0.0002) included in the discovery study. The increased risk of MS was observed for both haplogroup T (OR = 1.17, 95% CI = 1.06-1.29, p = 0.002) and haplogroup J carriers (OR = 1.11, 95% CI = 1.01-1.22, p = 0.03). These haplogroup associations with MS were not replicated in the independent sample set. An elevated risk of primary progressive (PP) MS was detected for haplogroup J participants from 3 European discovery populations (OR = 1.49, 95% CI = 1.10-2.01, p = 0.009). This elevated risk was borderline significant in the US replication population (OR = 1.43, 95% CI = 0.99-2.08, p = 0.058) and remained significant in pooled analysis of discovery and replication studies (OR = 1.43, 95% CI = 1.14-1.81, p = 0.002). No common individual mtDNA variants were associated with MS risk. CONCLUSIONS Identification and validation of mitochondrial genetic variants associated with MS and PPMS may lead to new targets for treatment and diagnostic tests for identifying potential responders to interventions that target mitochondria.
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Affiliation(s)
- Gregory J Tranah
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy.
| | - Adam Santaniello
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
| | - Stacy J Caillier
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
| | - Sandra D'Alfonso
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
| | - Filippo Martinelli Boneschi
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
| | - Stephen L Hauser
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
| | - Jorge R Oksenberg
- From the California Pacific Medical Center Research Institute (G.J.T.), San Francisco, CA; Department of Neurology (A.S., S.J.C., S.L.H., J.R.O.), University of California, San Francisco; Department of Health Sciences (S.D.), UPO and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Avogadro, Novara, Italy; and Department of Neuro-rehabilitation and INSPE (Institute of Experimental Neurology) (F.M.B.), Scientific Institute San Raffaele, Milan, Italy
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Cossu M, Andracco R, Santaniello A, Caronni M, Marchini M, Severino A, Radstake T, Beretta L. AB0204 Serum Levels of Vascular Markers Reflect Disease Severity and Stage in Systemic Sclerosis. Ann Rheum Dis 2015. [DOI: 10.1136/annrheumdis-2015-eular.6347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Isobe N, Madireddy L, Khankhanian P, Matsushita T, Caillier SJ, Moré JM, Gourraud PA, McCauley JL, Beecham AH, Piccio L, Herbert J, Khan O, Cohen J, Stone L, Santaniello A, Cree BAC, Onengut-Gumuscu S, Rich SS, Hauser SL, Sawcer S, Oksenberg JR. An ImmunoChip study of multiple sclerosis risk in African Americans. Brain 2015; 138:1518-30. [PMID: 25818868 DOI: 10.1093/brain/awv078] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [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: 11/20/2014] [Accepted: 01/26/2015] [Indexed: 12/27/2022] Open
Abstract
The aims of this study were: (i) to determine to what degree multiple sclerosis-associated loci discovered in European populations also influence susceptibility in African Americans; (ii) to assess the extent to which the unique linkage disequilibrium patterns in African Americans can contribute to localizing the functionally relevant regions or genes; and (iii) to search for novel African American multiple sclerosis-associated loci. Using the ImmunoChip custom array we genotyped 803 African American cases with multiple sclerosis and 1516 African American control subjects at 130 135 autosomal single nucleotide polymorphisms. We conducted association analysis with rigorous adjustments for population stratification and admixture. Of the 110 non-major histocompatibility complex multiple sclerosis-associated variants identified in Europeans, 96 passed stringent quality control in our African American data set and of these, >70% (69) showed over-representation of the same allele amongst cases, including 21 with nominally significant evidence for association (one-tailed test P < 0.05). At a further eight loci we found nominally significant association with an alternate correlated risk-tagging single nucleotide polymorphism from the same region. Outside the regions known to be associated in Europeans, we found seven potentially associated novel candidate multiple sclerosis variants (P < 10(-4)), one of which (rs2702180) also showed nominally significant evidence for association (one-tailed test P = 0.034) in an independent second cohort of 620 African American cases and 1565 control subjects. However, none of these novel associations reached genome-wide significance (combined P = 6.3 × 10(-5)). Our data demonstrate substantial overlap between African American and European multiple sclerosis variants, indicating common genetic contributions to multiple sclerosis risk.
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Affiliation(s)
- Noriko Isobe
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA 2 Division of Neurology, Department of Internal Medicine, Saga University Faculty of Medicine, Saga, Saga 849-8501, Japan
| | - Lohith Madireddy
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Pouya Khankhanian
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Takuya Matsushita
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA 3 Department of Neurological Therapeutics, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka 812-8582, Japan
| | - Stacy J Caillier
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Jayaji M Moré
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Pierre-Antoine Gourraud
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Jacob L McCauley
- 4 John P. Hussman Institute for Human Genomics and The Dr John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Ashley H Beecham
- 4 John P. Hussman Institute for Human Genomics and The Dr John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | | | - Laura Piccio
- 5 Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Joseph Herbert
- 6 Department of Neurology, New York University School of Medicine, New York, NY 10016, USA
| | - Omar Khan
- 7 Multiple Sclerosis Centre and The Sastry Foundation Advanced Imaging Laboratory, Department of Neurology, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Jeffrey Cohen
- 8 Mellen Centre for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lael Stone
- 8 Mellen Centre for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Adam Santaniello
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Bruce A C Cree
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Suna Onengut-Gumuscu
- 9 Centre for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Stephen S Rich
- 9 Centre for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Stephen L Hauser
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
| | - Stephen Sawcer
- 10 Department of Clinical Neurosciences, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Jorge R Oksenberg
- 1 Department of Neurology, School of Medicine, University of California, San Francisco, CA 94158, USA
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Vigone B, Santaniello A, Marchini M, Montanelli G, Caronni M, Severino A, Beretta L. Role of class II human leucocyte antigens in the progression from early to definite systemic sclerosis. Rheumatology (Oxford) 2014; 54:707-11. [DOI: 10.1093/rheumatology/keu381] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Márquez A, Hernández-Rodríguez J, Cid MC, Solans R, Castañeda S, Fernández-Contreras ME, Ramentol M, Morado IC, Narváez J, Gómez-Vaquero C, Martínez-Taboada VM, Ortego-Centeno N, Sopeña B, Monfort J, García-Villanueva MJ, Caminal-Montero L, de Miguel E, Blanco R, Palm O, Molberg O, Latus J, Braun N, Moosig F, Witte T, Beretta L, Santaniello A, Pazzola G, Boiardi L, Salvarani C, González-Gay MA, Martín J. Influence of theIL17A locusin giant cell arteritis susceptibility. Ann Rheum Dis 2014; 73:1742-5. [DOI: 10.1136/annrheumdis-2014-205261] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Santaniello A, Giorgi F, Di Tommaso D, Di Tommaso G, Piaggesi A, Perata P. GENOMIC APPROACHES TO UNVEIL THE PHYSIOLOGICAL PATHWAYS ACTIVATED IN ARABIDOPSIS TREATED WITH PLANT-DERIVED RAW EXTRACTS. ACTA ACUST UNITED AC 2013. [DOI: 10.17660/actahortic.2013.1009.20] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Isobe N, Gourraud PA, Harbo HF, Caillier SJ, Santaniello A, Khankhanian P, Maiers M, Spellman S, Cereb N, Yang S, Pando MJ, Piccio L, Cross AH, De Jager PL, Cree BAC, Hauser SL, Oksenberg JR. Genetic risk variants in African Americans with multiple sclerosis. Neurology 2013; 81:219-27. [PMID: 23771490 PMCID: PMC3770164 DOI: 10.1212/wnl.0b013e31829bfe2f] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [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: 11/30/2012] [Accepted: 04/04/2013] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVES To assess the association of established multiple sclerosis (MS) risk variants in 3,254 African Americans (1,162 cases and 2,092 controls). METHODS Human leukocyte antigen (HLA)-DRB1, HLA-DQB1, and HLA-A alleles were typed by molecular techniques. Single nucleotide polymorphism (SNP) genotyping was conducted for 76 MS-associated SNPs and 52 ancestry informative marker SNPs selected throughout the genome. Self-declared ancestry was refined by principal component analysis of the ancestry informative marker SNPs. An ancestry-adjusted multivariate model was applied to assess genetic associations. RESULTS The following major histocompatibility complex risk alleles were replicated: HLA-DRB1*15:01 (odds ratio [OR] = 2.02 [95% confidence interval: 1.54-2.63], p = 2.50e-07), HLA-DRB1*03:01 (OR = 1.58 [1.29-1.94], p = 1.11e-05), as well as HLA-DRB1*04:05 (OR = 2.35 [1.26-4.37], p = 0.007) and the African-specific risk allele of HLA-DRB1*15:03 (OR = 1.26 [1.05-1.51], p = 0.012). The protective association of HLA-A*02:01 was confirmed (OR = 0.72 [0.55-0.93], p = 0.013). None of the HLA-DQB1 alleles were associated with MS. Using a significance threshold of p < 0.01, outside the major histocompatibility complex region, 8 MS SNPs were also found to be associated with MS in African Americans. CONCLUSION MS genetic risk in African Americans only partially overlaps with that of Europeans and could explain the difference of MS prevalence between populations.
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Affiliation(s)
- Noriko Isobe
- Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
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Nickles D, Chen HP, Li MM, Khankhanian P, Madireddy L, Caillier SJ, Santaniello A, Cree BAC, Pelletier D, Hauser SL, Oksenberg JR, Baranzini SE. Blood RNA profiling in a large cohort of multiple sclerosis patients and healthy controls. Hum Mol Genet 2013; 22:4194-205. [PMID: 23748426 DOI: 10.1093/hmg/ddt267] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). It is characterized by the infiltration of autoreactive immune cells into the CNS, which target the myelin sheath, leading to the loss of neuronal function. Although it is accepted that MS is a multifactorial disorder with both genetic and environmental factors influencing its development and course, the molecular pathogenesis of MS has not yet been fully elucidated. Here, we studied the longitudinal gene expression profiles of whole-blood RNA from a cohort of 195 MS patients and 66 healthy controls. We analyzed these transcriptomes at both the individual transcript and the biological pathway level. We found 62 transcripts to be significantly up-regulated in MS patients; the expression of 11 of these genes was counter-regulated by interferon treatment, suggesting partial restoration of a 'healthy' gene expression profile. Global pathway analyses linked the proteasome and Wnt signaling to MS disease processes. Since genotypes from a subset of individuals were available, we were able to identify expression quantitative trait loci (eQTL), a number of which involved two genes of the MS gene signature. However, all these eQTL were also present in healthy controls. This study highlights the challenge posed by analyzing transcripts from whole blood and how these can be mitigated by using large, well-characterized cohorts of patients with longitudinal follow-up and multi-modality measurements.
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Prithviraj GK, Sommers SR, Jump RL, Halmos B, Chambless LB, Parker SL, Hassam-Malani L, McGirt MJ, Thompson RC, Chambless LB, Parker SL, Hassam-Malani L, McGirt MJ, Thompson RC, Hunter K, Chamberlain MC, Le EM, Lee ELT, Chamberlain MC, Sadighi ZS, Pearlman ML, Slopis JM, Vats TS, Khatua S, DeVito NC, Yu M, Chen R, Pan E, Cloughesy T, Raizer J, Drappatz J, Gerena-Lewis M, Rogerio J, Yacoub S, Desjardin A, Groves MD, DeGroot J, Loghin M, Conrad CA, Hess K, Ni J, Ictech S, Hunter K, Yung WA, Porter AB, Dueck AC, Karlin NJ, Chamberlain MC, Olson J, Silber J, Reiner AS, Panageas KS, Iwamoto FM, Cloughesy TF, Aldape KD, Rivera AL, Eichler AF, Louis DN, Paleologos NA, Fisher BJ, Ashby LS, Cairncross JG, Roldan GB, Wen PY, Ligon KL, Shiff D, Robins HI, Rocque BG, Chamberlain MC, Mason WP, Weaver SA, Green RM, Kamar FG, Abrey LE, DeAngelis LM, Jhanwar SC, Rosenblum MK, Lassman AB, Cachia D, Alderson L, Moser R, Smith T, Yunus S, Saito K, Mukasa A, Narita Y, Tabei Y, Shinoura N, Shibui S, Saito N, Flechl B, Ackerl M, Sax C, Dieckmann K, Crevenna R, Widhalm G, Preusser M, Marosi C, Marosi C, Ay C, Preusser M, Dunkler D, Widhalm G, Pabinger I, Dieckmann K, Zielinski C, Belongia M, Jogal S, Schlingensiepen KH, Bogdahn U, Stockhammer G, Mahapatra AK, Venkataramana NK, Oliushine V, Parfenov V, Poverennova I, Hau P, Jachimczak P, Heinrichs H, Mammoser AG, Shonka NA, de Groot JF, Shibahara I, Sonoda Y, Kumabe T, Saito R, Kanamori M, Yamashita Y, Watanabe M, Ishioka C, Tominaga T, Silvani A, Gaviani P, Lamperti E, Botturi A, DiMeco F, Broggi G, Fariselli L, Solero CL, Salmaggi A, Green RM, Woyshner EA, Cloughesy TF, Shu F, Oh YS, Iganej S, Singh G, Vemuri SL, Theeler BJ, Ellezam B, Gilbert MR, Aoki T, Kobayashi H, Takano S, Nishikawa R, Shinoura N, Nagane M, Narita Y, Muragaki Y, Sugiyama K, Kuratsu J, Matsutani M, Sadighi ZS, Khatua S, Langford LA, Puduvalli VK, Shen D, Chen ZP, Zhang JP, Chen ZP, Bedekar D, Rand S, Connelly J, Malkin M, Paulson E, Mueller W, Schmainda K, Gallego O, Benavides M, Segura PP, Balana C, Gil M, Berrocal A, Reynes G, Garcia JL, Murata P, Bague S, Quintana MJ, Vasishta VG, Nagane M, Kobayashi K, Tanaka M, Tsuchiya K, Shiokawa Y, Bavle AA, Ayyanar K, Puduvalli VK, Prado MP, Hess KR, Hunter K, Ictech S, Groves MD, Gilbert MR, Liu V, Conrad CA, de Groot J, Loghin ME, Colman H, Levin VA, Alfred Yung WK, Hackney JR, Palmer CA, Markert JM, Cure J, Riley KO, Fathallah-Shaykh H, Nabors LB, Saria MG, Corle C, Hu J, Rudnick J, Phuphanich S, Mrugala MM, Lee LK, Fu BD, Bota DA, Kim RY, Brown T, Feely H, Hu A, Drappatz J, Wen PY, Lee JW, Carter B, Kesari S, Fu BD, Kong XT, Bota DA, Fu BD, Bota DA, Sparagana S, Belousova E, Jozwiak S, Korf B, Frost M, Kuperman R, Kohrman M, Witt O, Wu J, Flamini R, Jansen A, Curtalolo P, Thiele E, Whittemore V, De Vries P, Ford J, Shah G, Cauwel H, Edrich P, Sahmoud T, Franz D, Khasraw M, Brown C, Ashley DM, Rosenthal MA, Jiang X, Mou YG, Chen ZP, Oh M, kim E, Chang J, Juratli TA, Kirsch M, Schackert G, Krex D, Gilbert MR, Wang M, Aldape KD, Stupp R, Hegi M, Jaeckle KA, Armstrong TS, Wefel JS, Won M, Blumenthal DT, Mahajan A, Schultz CJ, Erridge SC, Brown PD, Chakravarti A, Curran WJ, Mehta MP, Hofland KF, Hansen S, Sorensen M, Schultz H, Muhic A, Engelholm S, Ask A, Kristiansen C, Thomsen C, Poulsen HS, Lassen UN, Zalatimo O, Weston C, Zoccoli C, Glantz M, Rahmanuddin S, Shiroishi MS, Cen SY, Jones J, Chen T, Pagnini P, Go J, Lerner A, Gomez J, Law M, Ram Z, Wong ET, Gutin PH, Bobola MS, Alnoor M, Silbergeld DL, Rostomily RC, Chamberlain MC, Silber JR, Martha N, Jacqueline S, Thaddaus G, Daniel P, Hans M, Armin M, Eugen T, Gunther S, Hutterer M, Tseng HM, Zoccoli CM, Glantz M, Zalatimo O, Patel A, Rizzo K, Sheehan JM, Sumrall AL, Vredenburgh JJ, Desjardins A, Reardon DA, Friiedman HS, Peters KB, Taylor LP, Stewart M, Blondin NA, Baehring JM, Foote T, Laack N, Call J, Hamilton MG, Walling S, Eliasziw M, Easaw J, Shirsat NV, Kundar R, Gokhale A, Goel A, Moiyadi AA, Wang J, Mutlu E, Oyan A, Yan T, Tsinkalovsky O, Jacobsen HK, Talasila KM, Sleire L, Pettersen K, Miletic H, Andersen S, Mitra S, Weissman I, Li X, Kalland KH, Enger PO, Sepulveda J, Belda C, Balana C, Segura PP, Reynes G, Gil M, Gallego O, Berrocal A, Blumenthal DT, Sitt R, Phishniak L, Bokstein F, Philippe M, Carole C, Andre MDP, Marylin B, Olivier C, L'Houcine O, Dominique FB, Philippe M, Isabelle NM, Olivier C, Frederic F, Stephane F, Henry D, Marylin B, L'Houcine O, Dominique FB, Errico MA, Kunschner LJ, Errico MA, Kunschner LJ, Soffietti R, Trevisan E, Ruda R, Bertero L, Bosa C, Fabrini MG, Lolli I, Jalali R, Julka PK, Anand AK, Bhavsar D, Singhal N, Naik R, John S, Mathew BS, Thaipisuttikul I, Graber J, DeAngelis LM, Shirinian M, Fontebasso AM, Jacob K, Gerges N, Montpetit A, Nantel A, Albrecht S, Jabado N, Mammoser AG, Shah K, Conrad CA, Di K, Linskey M, Bota DA, Thon N, Eigenbrod S, Kreth S, Lutz J, Tonn JC, Kretzschmar H, Peraud A, Kreth FW, Muggeri AD, Alderuccio JP, Diez BD, Jiang P, Chao Y, Gallagher M, Kim R, Pastorino S, Fogal V, Kesari S, Rudnick JD, Bresee C, Rogatko A, Sakowsky S, Franco M, Hu J, Lim S, Lopez A, Yu L, Ryback K, Tsang V, Lill M, Steinberg A, Sheth R, Grimm S, Helenowski I, Rademaker A, Raizer J, Nunes FP, Merker V, Jennings D, Caruso P, Muzikansky A, Stemmer-Rachamimov A, Plotkin S, Spalding AC, Vitaz TW, Sun DA, Parsons S, Welch MR, Omuro A, DeAngelis LM, Omuro A, Beal K, Correa D, Chan T, DeAngelis L, Gavrilovic I, Nolan C, Hormigo A, Lassman AB, Kaley T, Mellinghoff I, Grommes C, Panageas K, Reiner A, Barradas R, Abrey L, Gutin P, Lee SY, Slagle-Webb B, Glantz MJ, Sheehan JM, Connor JR, Schlimper CA, Schlag H, Stoffels G, Weber F, Krueger DA, Care MM, Holland K, Agricola K, Tudor C, Byars A, Sahmoud T, Franz DN, Raizer J, Rice L, Rademaker A, Chandler J, Levy R, Muro K, Grimm S, Nayak L, Iwamoto FM, Rudnick JD, Norden AD, Omuro A, Kaley TJ, Thomas AA, Fadul CE, Meyer LP, Lallana EC, Colman H, Gilbert M, Alfred Yung WK, Aldape K, De Groot J, Conrad C, Levin V, Groves M, Loghin M, Chris P, Puduvalli V, Nagpal S, Feroze A, Recht L, Rangarajan HG, Kieran MW, Scott RM, Lew SM, Firat SY, Segura AD, Jogal SA, Kumthekar PU, Grimm SA, Avram M, Patel J, Kaklamani V, McCarthy K, Cianfrocca M, Gradishar W, Mulcahy M, Von Roenn J, Helenowski I, Rademaker A, Raizer J, Galanis E, Anderson SK, Lafky JM, Kaufmann TJ, Uhm JH, Giannini C, Kumar SK, Northfelt DW, Flynn PJ, Jaeckle KA, Buckner JC, Omar AI, Panageas KS, Iwamoto FM, Cloughesy TF, Aldape KD, Rivera AL, Eichler AF, Louis DN, Paleologos NA, Fisher BJ, Ashby LS, Cairncross JG, Roldan GB, Wen PY, Ligon KL, Schiff D, Robins HI, Rocque BG, Chamberlain MC, Mason WP, Weaver SA, Green RM, Kamar FG, Abrey LE, DeAngelis LM, Jhanwar SC, Rosenblum MK, Lassman AB, Delios A, Jakubowski A, DeAngelis L, Grommes C, Lassman AB, Theeler BJ, Melguizo-Gavilanes I, Shonka NA, Qiao W, Wang X, Mahajan A, Puduvalli V, Hashemi-Sadraei N, Bawa H, Rahmathulla G, Patel M, Elson P, Stevens G, Peereboom D, Vogelbaum M, Weil R, Barnett G, Ahluwalia MS, Alvord EC, Rockne RC, Rockhill JK, Mrugala MM, Rostomily R, Lai A, Cloughesy T, Wardlaw J, Spence AM, Swanson KR, Zadeh G, Alahmadi H, Wilson J, Gentili F, Lassman AB, Wang M, Gilbert MR, Aldape KD, Beumer JJ, Wright J, Takebe N, Puduvalli VK, Hormigo A, Gaur R, Werner-Wasik M, Mehta MP, Gupta AJ, Campos-Gines A, Le K, Arango C, Richards M, Landeros M, Juan H, Chang JH, Kim JS, Cho JH, Seo CO, Baldock AL, Rockne R, Canoll P, Born D, Yagle K, Swanson KR, Alexandru D, Bota D, Linskey ME, Nabeel S, Raval SN, Raizer J, Grimm S, Rice L, Rosenow J, Levy R, Bredel M, Chandler J, New PZ, Plotkin SR, Supko JG, Curry WT, Chi AS, Gerstner ER, Stemmer-Rachamimov A, Batchelor TT, Ahluwalia MS, Hashemi N, Rahmathulla G, Patel M, Chao ST, Peereboom D, Weil RJ, Suh JH, Vogelbaum MA, Stevens GH, Barnett GH, Corwin D, Holdsworth C, Stewart R, Rockne R, Swanson K, Graber JJ, Kaley T, Rockne RC, Anderson AR, Swanson KR, Jeyapalan S, Goldman M, Boxerman J, Donahue J, Elinzano H, Evans D, O'Connor B, Puthawala MY, Oyelese A, Cielo D, Blitstein M, Dargush M, Santaniello A, Constantinou M, DiPetrillo T, Safran H, Plotkin SR, Halpin C, Merker V, Barker FG, Maher EA, Ganji S, DeBerardinis R, Hatanpaa K, Rakheja D, Yang XL, Mashimo T, Raisanen J, Madden C, Mickey B, Malloy C, Bachoo R, Choi C, Ranjan T, Yono N, Zalatimo O, Zoccoli C, Glantz M, Han SJ, Sun M, Berger MS, Aghi M, Gupta N, Parsa AT. MEDICAL AND NEURO-ONCOLOGY. Neuro Oncol 2011. [DOI: 10.1093/neuonc/nor152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Jeyapalan SA, Goldmann M, Donahue J, Elinzano H, Evans DL, O'Connor BM, Puthawala MA, Oyelese A, Cielo D, Blitstein M, Dargush M, Santaniello A, Constantinou M, Dipetrillo T, Safran H. A phase II study of paclitaxel poliglumex (PPX), temozolamide (TMZ), and radiation (RT) for newly diagnosed high-grade gliomas. J Clin Oncol 2011. [DOI: 10.1200/jco.2011.29.15_suppl.2036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Gourraud PA, McElroy JP, Caillier SJ, Johnson BA, Santaniello A, Hauser SL, Oksenberg JR. Aggregation of multiple sclerosis genetic risk variants in multiple and single case families. Ann Neurol 2011; 69:65-74. [PMID: 21280076 DOI: 10.1002/ana.22323] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Multiple sclerosis (MS) is a multifactorial neurologic disease characterized by modest but tractable heritability. Genome-wide association studies have identified and/or validated multiple polymorphisms in approximately 16 genes associated with susceptibility. We aimed at investigating the aggregation of genetic MS risk markers in individuals by comparing multiple- and single-case families. METHODS A weighted log-additive integrative approach termed MS genetic burden (MSGB) was used to account for the well-established genetic variants from previous association studies and meta-analyses. The corresponding genetic burden and its transmission was analyzed in 1,213 independent MS families (810 sporadic and 403 multicase families). RESULTS MSGB analysis demonstrated a higher aggregation of susceptibility variants in multicase compared to sporadic MS families. In addition, the aggregation of non-major histocompatibility complex single nucleotide polymorphisms depended neither on gender nor on the presence or absence of HLA-DRB1*15:01 alleles. Interestingly, although a greater MSGB in siblings of MS patients was associated with an increased risk of MS (odds ratio, 2.1; p = 0.001), receiver operating characteristic curves of MSGB differences between probands and sibs (area under the receiver operator curves, 0.57 [95% confidence interval, 0.53-0.61]) show that case-control status prediction of MS cannot be achieved with the currently available genetic data. INTERPRETATION The primary interest in the MSGB concept resides in its capacity to integrate cumulative genetic contributions to MS risk. This analysis underlines the high variability of family load with known common variants. This novel approach can be extended to other genetically complex diseases. Despite the emphasis on assembling large case-control datasets, multigenerational, multiaffected families remain an invaluable resource for advancing the understanding of the genetic architecture of complex traits.
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Affiliation(s)
- Pierre-Antoine Gourraud
- Department of Neurology, School of Medicine, University of California at San Francisco, San Francisco, CA, USA
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Khankhanian P, Gourraud PA, Caillier SJ, Santaniello A, Hauser SL, Baranzini SE, Oksenberg JR. Genetic variation in the odorant receptors family 13 and the mhc loci influence mate selection in a multiple sclerosis dataset. BMC Genomics 2010; 11:626. [PMID: 21067613 PMCID: PMC3091764 DOI: 10.1186/1471-2164-11-626] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [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: 05/04/2010] [Accepted: 11/10/2010] [Indexed: 12/17/2022] Open
Abstract
Background When selecting mates, many vertebrate species seek partners with major histocompatibility complex (MHC) genes different from their own, presumably in response to selective pressure against inbreeding and towards MHC diversity. Attempts at replication of these genetic results in human studies, however, have reached conflicting conclusions. Results Using a multi-analytical strategy, we report validated genome-wide relationships between genetic identity and human mate choice in 930 couples of European ancestry. We found significant similarity between spouses in the MHC at class I region in chromosome 6p21, and at the odorant receptor family 13 locus in chromosome 9. Conversely, there was significant dissimilarity in the MHC class II region, near the HLA-DQA1 and -DQB1 genes. We also found that genomic regions with significant similarity between spouses show excessive homozygosity in the general population (assessed in the HapMap CEU dataset). Conversely, loci that were significantly dissimilar among spouses were more likely to show excessive heterozygosity in the general population. Conclusions This study highlights complex patterns of genomic identity among partners in unrelated couples, consistent with a multi-faceted role for genetic factors in mate choice behavior in human populations.
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Affiliation(s)
- Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, CA 94143-0435, USA
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Rinaldi L, Maurelli M, Musella V, Santaniello A, Coles G, Cringoli G. FLOTAC: An improved method for diagnosis of lungworm infections in sheep. Vet Parasitol 2010; 169:395-8. [DOI: 10.1016/j.vetpar.2010.01.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2009] [Revised: 11/30/2009] [Accepted: 01/05/2010] [Indexed: 11/30/2022]
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