Detail
Článek
Článek online
FT
Medvik - BMČ
  • Je něco špatně v tomto záznamu ?

Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression

L. Vu, K. Garcia-Mansfield, A. Pompeiano, J. An, V. David-Dirgo, R. Sharma, V. Venugopal, H. Halait, G. Marcucci, YH. Kuo, L. Uechi, RC. Rockne, P. Pirrotte, R. Bowser

. 2023 ; 10 (11) : 2025-2042. [pub] 20230830

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc24000871

Grantová podpora
R56 NS061867 NINDS NIH HHS - United States
R01 NS061867 NINDS NIH HHS - United States
P30 CA033572 NCI NIH HHS - United States

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. METHODS: We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. RESULTS: We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. INTERPRETATION: We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc24000871
003      
CZ-PrNML
005      
20240213093430.0
007      
ta
008      
240109s2023 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1002/acn3.51890 $2 doi
035    __
$a (PubMed)37646115
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Vu, Lucas $u Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA
245    10
$a Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression / $c L. Vu, K. Garcia-Mansfield, A. Pompeiano, J. An, V. David-Dirgo, R. Sharma, V. Venugopal, H. Halait, G. Marcucci, YH. Kuo, L. Uechi, RC. Rockne, P. Pirrotte, R. Bowser
520    9_
$a OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. METHODS: We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. RESULTS: We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. INTERPRETATION: We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
650    _2
$a lidé $7 D006801
650    12
$a amyotrofická laterální skleróza $7 D000690
650    _2
$a proteom $x metabolismus $7 D020543
650    _2
$a proteomika $x metody $7 D040901
650    _2
$a biologické markery $x mozkomíšní mok $7 D015415
650    _2
$a progrese nemoci $7 D018450
650    _2
$a plazmatické proteiny vázající retinol $7 D054839
655    _2
$a časopisecké články $7 D016428
655    _2
$a Research Support, N.I.H., Extramural $7 D052061
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Garcia-Mansfield, Krystine $u Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA $u Integrated Mass Spectrometry, City of Hope Comprehensive Cancer Center, Duarte, California, 19050, USA
700    1_
$a Pompeiano, Antonio $u International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
700    1_
$a An, Jiyan $u Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA
700    1_
$a David-Dirgo, Victoria $u Integrated Mass Spectrometry, City of Hope Comprehensive Cancer Center, Duarte, California, 19050, USA
700    1_
$a Sharma, Ritin $u Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA $u Integrated Mass Spectrometry, City of Hope Comprehensive Cancer Center, Duarte, California, 19050, USA
700    1_
$a Venugopal, Vinisha $u Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA
700    1_
$a Halait, Harkeerat $u Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA
700    1_
$a Marcucci, Guido $u Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA
700    1_
$a Kuo, Ya-Huei $u Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA
700    1_
$a Uechi, Lisa $u Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA
700    1_
$a Rockne, Russell C $u Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California, 91010, USA
700    1_
$a Pirrotte, Patrick $u Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, Arizona, 85004, USA $u Integrated Mass Spectrometry, City of Hope Comprehensive Cancer Center, Duarte, California, 19050, USA
700    1_
$a Bowser, Robert $u Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA $1 https://orcid.org/0000000154047259
773    0_
$w MED00189500 $t Annals of clinical and translational neurology $x 2328-9503 $g Roč. 10, č. 11 (2023), s. 2025-2042
856    41
$u https://pubmed.ncbi.nlm.nih.gov/37646115 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20240109 $b ABA008
991    __
$a 20240213093427 $b ABA008
999    __
$a ok $b bmc $g 2049466 $s 1210565
BAS    __
$a 3
BAS    __
$a PreBMC-MEDLINE
BMC    __
$a 2023 $b 10 $c 11 $d 2025-2042 $e 20230830 $i 2328-9503 $m Annals of clinical and translational neurology $n Ann Clin Transl Neurol $x MED00189500
GRA    __
$a R56 NS061867 $p NINDS NIH HHS $2 United States
GRA    __
$a R01 NS061867 $p NINDS NIH HHS $2 United States
GRA    __
$a P30 CA033572 $p NCI NIH HHS $2 United States
LZP    __
$a Pubmed-20240109

Najít záznam

Citační ukazatele

Možnosti archivace