Plasma proteome in multiple sclerosis disease progression

Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sens...
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Autor*in:	Arjan Malekzadeh [verfasserIn] Cyra Leurs [verfasserIn] Wessel vanWieringen [verfasserIn] Martijn D. Steenwijk [verfasserIn] Menno M. Schoonheim [verfasserIn] Michael Amann [verfasserIn] Yvonne Naegelin [verfasserIn] Jens Kuhle [verfasserIn] Joep Killestein [verfasserIn] Charlotte E. Teunissen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: Annals of Clinical and Translational Neurology - Wiley, 2015, 6(2019), 9, Seite 1582-1594
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:9 ; pages:1582-1594

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/acn3.771

Katalog-ID:	DOAJ00153758X

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520			\|a Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.
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700	0		\|a Menno M. Schoonheim \|e verfasserin \|4 aut
700	0		\|a Michael Amann \|e verfasserin \|4 aut
700	0		\|a Yvonne Naegelin \|e verfasserin \|4 aut
700	0		\|a Jens Kuhle \|e verfasserin \|4 aut
700	0		\|a Joep Killestein \|e verfasserin \|4 aut
700	0		\|a Charlotte E. Teunissen \|e verfasserin \|4 aut
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matchkey_str	article:23289503:2019----::lsartoenutpeceoids
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allfields	10.1002/acn3.771 doi (DE-627)DOAJ00153758X (DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Arjan Malekzadeh verfasserin aut Plasma proteome in multiple sclerosis disease progression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Cyra Leurs verfasserin aut Wessel vanWieringen verfasserin aut Martijn D. Steenwijk verfasserin aut Menno M. Schoonheim verfasserin aut Michael Amann verfasserin aut Yvonne Naegelin verfasserin aut Jens Kuhle verfasserin aut Joep Killestein verfasserin aut Charlotte E. Teunissen verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 6(2019), 9, Seite 1582-1594 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:6 year:2019 number:9 pages:1582-1594 https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b kostenfrei https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 9 1582-1594
spelling	10.1002/acn3.771 doi (DE-627)DOAJ00153758X (DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Arjan Malekzadeh verfasserin aut Plasma proteome in multiple sclerosis disease progression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Cyra Leurs verfasserin aut Wessel vanWieringen verfasserin aut Martijn D. Steenwijk verfasserin aut Menno M. Schoonheim verfasserin aut Michael Amann verfasserin aut Yvonne Naegelin verfasserin aut Jens Kuhle verfasserin aut Joep Killestein verfasserin aut Charlotte E. Teunissen verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 6(2019), 9, Seite 1582-1594 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:6 year:2019 number:9 pages:1582-1594 https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b kostenfrei https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 9 1582-1594
allfields_unstemmed	10.1002/acn3.771 doi (DE-627)DOAJ00153758X (DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Arjan Malekzadeh verfasserin aut Plasma proteome in multiple sclerosis disease progression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Cyra Leurs verfasserin aut Wessel vanWieringen verfasserin aut Martijn D. Steenwijk verfasserin aut Menno M. Schoonheim verfasserin aut Michael Amann verfasserin aut Yvonne Naegelin verfasserin aut Jens Kuhle verfasserin aut Joep Killestein verfasserin aut Charlotte E. Teunissen verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 6(2019), 9, Seite 1582-1594 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:6 year:2019 number:9 pages:1582-1594 https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b kostenfrei https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 9 1582-1594
allfieldsGer	10.1002/acn3.771 doi (DE-627)DOAJ00153758X (DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Arjan Malekzadeh verfasserin aut Plasma proteome in multiple sclerosis disease progression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Cyra Leurs verfasserin aut Wessel vanWieringen verfasserin aut Martijn D. Steenwijk verfasserin aut Menno M. Schoonheim verfasserin aut Michael Amann verfasserin aut Yvonne Naegelin verfasserin aut Jens Kuhle verfasserin aut Joep Killestein verfasserin aut Charlotte E. Teunissen verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 6(2019), 9, Seite 1582-1594 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:6 year:2019 number:9 pages:1582-1594 https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b kostenfrei https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 9 1582-1594
allfieldsSound	10.1002/acn3.771 doi (DE-627)DOAJ00153758X (DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Arjan Malekzadeh verfasserin aut Plasma proteome in multiple sclerosis disease progression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Cyra Leurs verfasserin aut Wessel vanWieringen verfasserin aut Martijn D. Steenwijk verfasserin aut Menno M. Schoonheim verfasserin aut Michael Amann verfasserin aut Yvonne Naegelin verfasserin aut Jens Kuhle verfasserin aut Joep Killestein verfasserin aut Charlotte E. Teunissen verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 6(2019), 9, Seite 1582-1594 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:6 year:2019 number:9 pages:1582-1594 https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b kostenfrei https://doi.org/10.1002/acn3.771 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 9 1582-1594
language	English
source	In Annals of Clinical and Translational Neurology 6(2019), 9, Seite 1582-1594 volume:6 year:2019 number:9 pages:1582-1594
sourceStr	In Annals of Clinical and Translational Neurology 6(2019), 9, Seite 1582-1594 volume:6 year:2019 number:9 pages:1582-1594
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system
isfreeaccess_bool	true
container_title	Annals of Clinical and Translational Neurology
authorswithroles_txt_mv	Arjan Malekzadeh @@aut@@ Cyra Leurs @@aut@@ Wessel vanWieringen @@aut@@ Martijn D. Steenwijk @@aut@@ Menno M. Schoonheim @@aut@@ Michael Amann @@aut@@ Yvonne Naegelin @@aut@@ Jens Kuhle @@aut@@ Joep Killestein @@aut@@ Charlotte E. Teunissen @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	77139649X
id	DOAJ00153758X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ00153758X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309163350.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/acn3.771</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00153758X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC321-571</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Arjan Malekzadeh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Plasma proteome in multiple sclerosis disease progression</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. Neuropsychiatry</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. 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callnumber-first	R - Medicine
author	Arjan Malekzadeh
spellingShingle	Arjan Malekzadeh misc RC321-571 misc RC346-429 misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system Plasma proteome in multiple sclerosis disease progression
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topic_title	RC321-571 RC346-429 Plasma proteome in multiple sclerosis disease progression
topic	misc RC321-571 misc RC346-429 misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system
topic_unstemmed	misc RC321-571 misc RC346-429 misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system
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title	Plasma proteome in multiple sclerosis disease progression
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title_full	Plasma proteome in multiple sclerosis disease progression
author_sort	Arjan Malekzadeh
journal	Annals of Clinical and Translational Neurology
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author_browse	Arjan Malekzadeh Cyra Leurs Wessel vanWieringen Martijn D. Steenwijk Menno M. Schoonheim Michael Amann Yvonne Naegelin Jens Kuhle Joep Killestein Charlotte E. Teunissen
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doi_str_mv	10.1002/acn3.771
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title_sort	plasma proteome in multiple sclerosis disease progression
callnumber	RC321-571
title_auth	Plasma proteome in multiple sclerosis disease progression
abstract	Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.
abstractGer	Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.
abstract_unstemmed	Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.
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title_short	Plasma proteome in multiple sclerosis disease progression
url	https://doi.org/10.1002/acn3.771 https://doaj.org/article/2dcba6eb08e94bdeb938fb2fc1264d0b https://doaj.org/toc/2328-9503
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up_date	2024-07-03T21:01:44.118Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ00153758X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309163350.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/acn3.771</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00153758X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2dcba6eb08e94bdeb938fb2fc1264d0b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC321-571</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Arjan Malekzadeh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Plasma proteome in multiple sclerosis disease progression</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background The pathophysiology of multiple sclerosis disease progression remains undetermined. The aim of this study was to identify differences in plasma proteome during different stages of MS disease progression. Methods We used a multiplex aptamer proteomics platform (Somalogic) for sensitive detection of 1129 proteins in plasma. MS patients were selected and categorized based on baseline and a 4‐year follow‐up EDSS (delta EDSS) scores; relapse‐onset (RO) slow progression (n = 31), RO with rapid progression (n = 29), primary progressive (n = 30), and healthy controls (n = 20). The relation of baseline plasma protein levels with delta EDSS and different MRI progression parameters were assessed using linear regression models. Results Regression analyses of plasma proteins with delta EDSS showed six significant associations. Strong associations were found for the proteins LGLAS8 (P = 7.64 × 10−5, q = 0.06), CCL3 (P = 0.0001, q = 0.06), and RGMA (P = 0.0005, q = 0.09). In addition, associations of plasma proteins were found with percentage brain volume for C3 (P = 2,08 × 10−9, q = 1,70 × 10−6), FGF9 (P = 3,42 × 10−9, q = 1,70 × 10−6), and EHMT2 (P = 0.0007, q = 0.01). Most of the significant markers were associated with cell‐cell and cell‐extracellular matrix adhesion, immune system communication, immune system activation, and complement pathways. Conclusions Our results revealed eight novel biomarkers related to clinical and radiological progression in MS. These results indicate that changes in immune system, complement pathway and ECM remodeling proteins contribute to MS progression and may therefore be further explored for use in prognosis of MS.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. Neuropsychiatry</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. 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Plasma proteome in multiple sclerosis disease progression

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