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CSF prognostic markers for amyotrophic lateral sclerosis: a proteomic study

ABSTRACT: The rate of disease progression widely varies between patients with amyotrophic lateral sclerosis (ALS). Prognostic assessment using biomarkers is highly preferred for planning medical care and improving the design of clinical trials. Therefore, the current study aimed to assess prognostic biomarkers for predicting future functional decline in patients with ALS. The prospective progression rate was calculated using the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) at CSF collection and in 6 months. The ALS patients were classified into slow, intermediate, and fast progression groups. We performed comprehensive proteomic analyses of the CSF samples. In total, 26 proteins changed significantly (p < 0.05 and q < 0.10), with levels varying within a large dynamic range (fold change of > 1.5 or < 0.5). A receiver operating characteristic curve analyses showed that the following proteins showed high discrimination power between slow and fast progression groups: GPNMB, GFAP, GPC1, MGAT2, and CHI3L2. Of these, GPNMB, GPC1, and CHI3L2 were significantly correlated to prognostic progression rate. This study demonstrated that CSF levels of neuroinflammation and glycosylation-related proteins were significantly correlated with prospective progression rates in patients with ALS. These proteins could be useful prognostic biomarkers for ALS.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

DISEASE(S): Amyotrophic Lateral Sclerosis

SUBMITTER: Kimie Nakamura

LAB HEAD: Koji Fujita

PROVIDER: PXD057276 | Pride | 2024-10-31

REPOSITORIES: Pride

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Project description:Unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of Amyotrophic Lateral Sclerosis (ALS). However, high abundance proteins mask the presence of lower abundance proteins that may hold valuable diagnostic and prognostic use. Developments in mass spectrometry proteomic data acquisition methods therefore hold promise for improved protein depth and more sensitive biomarker discovery. In this study, mass spectrometry with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of 40 people with ALS, 15 healthy controls and 8 people with mimicking conditions. Qthen quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins significantly upregulated in ALS vs healthy controls, and 9 with altered abundance in ALS vs mimicking conditions (FDR <0.1). Elevated Chitotriosidase 1 (CHIT1) was common to both comparisons and showed strong correlation with disease progression rate (Pearson r =0.41, FDR-adjusted p = 0.035), but no association with survival (Pearson correlation and Kaplan-Meier survival analysis). Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS vs healthy controls) showed a stronger correlation with disease progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and significant association with survival in Kaplan Meier analysis (log rank p = 0.013) but no independent effect in proportional hazards models. Weighted correlation network analysis was then used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was strongly associated with age at disease onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, negatively correlated with disease progression rate (r = -0.42, FDR-adjusted p =0.109). This study confirms that UCHL1 is strongly associated with disease progression and survival, and highlights potential of inflammatory and endoplasmic reticulum proteins to provide prognostic biomarker potential.

Project description:Extracellular vesicles (EVs) hold the potential for elucidating the pathogenesis and serving as biomarkers of amyotrophic lateral sclerosis (ALS). Notably, the comparative and longitudinal alterations in the protein profiles of EVs in serum (sEVs) and CSF (cEVs) of sporadic ALS (SALS) patients remain unexplored. This study sought to reveal such changes by collecting serum and CSF at fixed intervals from 10 controls and 20 SALS patients participating in the Ropinirole Hydrochloride Remedy for Amyotrophic Lateral Sclerosis trial. We also aimed to reveal longitudinal changes with disease progression and the effects of ropinirole hydrochloride (ROPI, a dopamine D2 receptor agonist identified as an anti-ALS drug candidate using induced pluripotent stem cell drug discovery) on protein profiles of EVs. Comprehensive proteomic analyses of EVs extracted from these samples were conducted using liquid chromatography-mass spectrometry to trace longitudinal shifts linked to disease progression and the influence of ROPI on EV protein profiles. The findings revealed notable disparities but high congruity in sEV and cEV protein profiles concerning disease status, time, and ROPI administration. Moreover, the sEV and cEV protein profiles converged over time in SALS patients, at least in part, suggestive of a loosening of the blood–brain barrier. In SALS patients, both sEVs and cEVs presented elevated inflammation-related protein levels but reduced levels of proteins associated with the unfolded protein response. These results mirrored the longitudinal changes after disease onset and correlated with the revised ALS Functional Rating Scale at sampling time, suggesting a link to the onset and progression of SALS. ROPI appeared to counteract these changes, attenuating inflammation-related protein levels and boosting those tied to the unfolded protein response in SALS, suggesting an anti-ALS effect on EV protein profiles. Reverse translational research using induced pluripotent stem cell-derived astrocytes indicated that these changes may partly reflect the DRD2-dependent neuroinflammatory inhibitory effects of ROPI. Baseline levels of OGN in sEVs and FRMPD1 in cEVs correlated with subsequent disease progression, indicating their potential as prognostic biomarkers for SALS. Machine learning-driven biomarker searches and diagnostic classification yielded an accuracy of 90.4% for sEVs and 80.3% for cEVs. Despite the limited sample size, this study is the first to report time-series proteomic alterations in sEVs and cEVs from SALS patients, offering comprehensive insights into SALS pathogenesis, ROPI-induced changes, and potential prognostic and diagnostic biomarkers.

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CSF prognostic markers for amyotrophic lateral sclerosis: a proteomic study

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