PXD047529-1

PXD047529 is an original dataset announced via ProteomeXchange.

Title	Mass-Spectrometry-based proteomics on cerebrospinal fluid combined with artificial intelligence approach identified novel potential biomarkers for Spinal Muscular Atrophy
Description	The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need to identify reliable biomarkers to monitor disease progression, therapeutic response and classify patients according to disease severity. Objectives of this study were to identify potential biomarkers for disease severity, and to describe changes in the proteomic profile after 302 days of nusinersen administration (T302). In this multicenter retrospective longitudinal study, we employed an untargeted non-targeted mass spectrometry-based proteomic approach (LC-MS) on cerebrospinal fluid (CSF) samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at baseline and T302. A machine learning classifier approach (Random Forest, RF) was applied to exploit proteins able to stratify disease severity at baseline. Bioinformatics analysis was performed to investigate Gene Ontology (GO) functional annotation of differentially expressed proteins (DEPs) at T302. The RF algorithm identified CNTN1 and NRXN3 as new potential biomarkers of disease severity based on their expression at baseline. Analysis of changes in proteomic profiles identified 147 DEPs after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3. Overall, Nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in all SMA types (i.e. regulation of axonogenesis), and ii) disease severity-specific changes, namely regulation of glucose metabolism in SMA1, of coagulation processes in SMA2, and of complement cascade in SMA3. By analyzing a large cohort of CSF samples from SMA patients, and applying cutting-edge bioinformatic analysis and artifical intelligence alghorithms, this study has identified new potential biomarkers of disease severity, and provided new insights on biological processes modulation after 302 days of nusinersen treatment.
HostingRepository	PRIDE
AnnounceDate	2024-10-02
AnnouncementXML	Submission_2024-10-02_03:43:05.242.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Martina Bartolucci
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	acetylated residue; monohydroxylated residue; deamidated residue; iodoacetamide derivatized residue
Instrument	Orbitrap Fusion

Revision	Datetime	Status	ChangeLog Entry
0	2023-12-05 03:33:08	ID requested
⏵ 1	2024-10-02 03:43:05	announced
2	2024-10-22 07:00:24	announced	2024-10-22: Updated project metadata.

10.1007/S00018-024-05426-6;

submitter keyword: cerebrospinal fluid, proteomics,spinal muscular atrophy, biomarkers

Andrea Petretto
contact affiliation	IRCCS Istituto G Gaslini
contact email	a.petretto@gmail.com
lab head
Martina Bartolucci
contact affiliation	IRCCS Gaslini
contact email	smartibartolucci@gmail.com
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD047529
     1. Label: PRIDE project
     2. Name: Mass-Spectrometry-based proteomics on cerebrospinal fluid combined with artificial intelligence approach identified novel potential biomarkers for Spinal Muscular Atrophy