Chiara Panicucci, Eray Sahin, Martina Bartolucci, Sara Casalini, Noemi Brolatti, Marina Pedemonte, Serena Baratto, Sara Pintus, Elisa Principi, Adele D’Amico, Marika Pane, Marina Sframeli, Sonia Messina, Emilio Albamonte, Valeria A. Sansone, Eugenio Mercuri, Enrico Bertini, Ugur Sezerman, Andrea Petretto, Claudio Bruno
{"title":"对接受过纽西奈森治疗的脊髓性肌萎缩症患者进行蛋白质组学分析和机器学习","authors":"Chiara Panicucci, Eray Sahin, Martina Bartolucci, Sara Casalini, Noemi Brolatti, Marina Pedemonte, Serena Baratto, Sara Pintus, Elisa Principi, Adele D’Amico, Marika Pane, Marina Sframeli, Sonia Messina, Emilio Albamonte, Valeria A. Sansone, Eugenio Mercuri, Enrico Bertini, Ugur Sezerman, Andrea Petretto, Claudio Bruno","doi":"10.1007/s00018-024-05426-6","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Aim</h3><p>The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3.</p><p>Overall, nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in allSMA 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.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. Further confirmatory studies are needed to validate these results in larger number of patients and over abroader timeframe.</p>","PeriodicalId":10007,"journal":{"name":"Cellular and Molecular Life Sciences","volume":"7 1","pages":""},"PeriodicalIF":6.2000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy\",\"authors\":\"Chiara Panicucci, Eray Sahin, Martina Bartolucci, Sara Casalini, Noemi Brolatti, Marina Pedemonte, Serena Baratto, Sara Pintus, Elisa Principi, Adele D’Amico, Marika Pane, Marina Sframeli, Sonia Messina, Emilio Albamonte, Valeria A. Sansone, Eugenio Mercuri, Enrico Bertini, Ugur Sezerman, Andrea Petretto, Claudio Bruno\",\"doi\":\"10.1007/s00018-024-05426-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Aim</h3><p>The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302).</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3.</p><p>Overall, nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in allSMA 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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusions</h3><p>This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. 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Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy
Aim
The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302).
Methods
In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis.
Results
The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins 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 allSMA 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.
Conclusions
This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. Further confirmatory studies are needed to validate these results in larger number of patients and over abroader timeframe.
期刊介绍：
Journal Name: Cellular and Molecular Life Sciences (CMLS)
Location: Basel, Switzerland
Focus:
Multidisciplinary journal
Publishes research articles, reviews, multi-author reviews, and visions & reflections articles
Coverage:
Latest aspects of biological and biomedical research
Areas include:
Biochemistry and molecular biology
Cell biology
Molecular and cellular aspects of biomedicine
Neuroscience
Pharmacology
Immunology
Additional Features:
Welcomes comments on any article published in CMLS
Accepts suggestions for topics to be covered
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