Dataset Information

Proteomics Characterization of Longitudinal CSF from ALS Fast and Slow Progressors

ABSTRACT: Each CSF sample was concentrated and added to a multiple affinity removal spin cartridge .The eluates were subjected to a buffer exchange and proteins were reduced using DTT and alkylated using iodoacetamide. Digestions were performed using trypsin. CSF from patients with amyotrophic lateral sclerosis (ALS) was characterized via LC-MS/MS. Biomarkers were identified to differentiate fast and slow progressors.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

DISEASE(S): Amyotrophic Lateral Sclerosis

SUBMITTER: Krystine Mansfield

LAB HEAD: Patrick Pirrotte

PROVIDER: PXD035026 | Pride | 2024-01-26

REPOSITORIES: Pride

ACCESS DATA

Publications

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

Vu Lucas L Garcia-Mansfield Krystine K Pompeiano Antonio A An Jiyan J David-Dirgo Victoria V Sharma Ritin R Venugopal Vinisha V Halait Harkeerat H Marcucci Guido G Kuo Ya-Huei YH Uechi Lisa L Rockne Russell C RC Pirrotte Patrick P Bowser Robert R

Annals of clinical and translational neurology 20230830 11

<h4>Objective</h4>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.<h4>Methods</h4>We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a ...[more]

PMID: 37646115

Similar Datasets

Project description:Human T-lymphotropic virus type 1 (HTLV-1)-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is an inflammatory neurodegenerative disease that affects motor, urinary, intestinal, and sensory functions. Typically, HAM/TSP is slowly progressive, but it may vary from limited motor disability after decades (very slow progression) to loss of motor function in a few years from disease onset (rapid). In this study, we aimed to identify prognostic biomarkers for HAM/TSP to support patient management. Thus, proteomic analysis of the cerebrospinal fluid (CSF) was performed with samples from HTLV-1 asymptomatic carriers (AC) (n=13) and HAM/TSP patients (n=21) with rapid, typical, and very slow progression using quantitative label-free liquid chromatography/tandem mass spectrometry. Enrichment analyses were also carried out to identify key biological processes associated with distinct neurological conditions in HTLV-1 infection. Candidate biomarkers were validated by ELISA in paired CSF and serum samples, and samples from HTLV-1-seronegative individuals (n=9) were used as controls. CSF analysis identified 602 proteins. Leukocyte/cell activation, immune response processes and neurodegeneration pathways were enriched in rapid progressors. Conversely, HTLV-1 AC and HAM/TSP patients with typical and very slow progression had enriched processes for nervous system development. Differential expression analysis showed that soluble vascular cell adhesion molecule 1 (sVCAM-1), chitotriosidase 1 (CHIT1), and cathepsin C (CTSC) were upregulated in HAM/TSP. However, only CHIT1 was significantly elevated after validation, particularly in HAM/TSP rapid progressors. In contrast, none of these biomarkers were altered in serum. Additionally, CSF CHIT1 levels in HAM/TSP patients positively correlated with the speed of HAM/TSP progression, defined as points in the IPEC-2 HAM/TSP disability scale per year of disease, and with CSF levels of phosphorylated neurofilament heavy chain, neopterin, CXCL5, CXCL10, and CXCL11. In conclusion, higher CSF levels of CHIT1 were associated with HAM/TSP rapid progression and correlated with other biomarkers of neuroinflammation and neurodegeneration. Therefore, we propose CHIT1 as a surrogate CSF biomarker to identify HAM/TSP patients with a worse prognosis.

Project description:The Rationale: Molecular markers of disease activity that are predictive of forced vital capacity (FVC) progression in idiopathic pulmonary fibrosis are needed. Objectives: Develop a predictor using longitudinal within-patient gene expression differences (ΔGE) in peripheral blood mononuclear cells (PBMC) to predict of FVC progression. Methods: Patients in the training cohort (n=74) experiencing ≥10% relative reduction in FVC% of predicted over 12 months were categorized as progressors in contrast to the remaining stable patients. Baseline to 4-month within-patient ΔGE were correlated with FVC status. FVC-predictor genes were prioritized by two-group comparison with FDR<5%, logistic LASSO regression with p<0.05, and 10-Fold Cross-Validation with ≥50% support. Receiver operating characteristic with area under the curve (AUC) analyses were conducted in training subsets and independent validation cohorts from UChicago (n=27), UPMC (n=35), and Imperial (n=24) where different transcriptome assay platforms and varying transcriptome sampling times were used to derive ΔGE. Results: Intra-subject Compared to cross-sectional analysis of baseline GE, our longitudinal ΔGE variation approach demonstratedlargely reduced within-group sample variation and increased statistic power fin progressor and stable groups for prediction model development. A 25-gene FVC-predictor separated “progressors” from “stable” by Principal Component Analysis in the training and subsets of the training cohort. The resulting FVC-predictor consistently demonstrated high discriminatory performance independent of transcriptome assay platforms and varying sampling times in validation cohorts (AUC= 0.77-0.80). TGF beta was the highest-ranking canonical pathway by Gene Set Enrichment Analysis. Conclusions: Our novel short-term longitudinal within-patient ΔGE approach identified a FVC-predictor which may reflect disease activity and prove to be a reliable biomarker predictive of future FVC decline.

Dataset Information

Proteomics Characterization of Longitudinal CSF from ALS Fast and Slow Progressors

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure