Transcriptomic differences in MSA clinical variants

Background: Multiple system atrophy (MSA) is a rare oligodendroglial synucleinopathy of unknown etiopathogenesis including two major clinical variants with predominant parkinsonism (MSA-P) or cerebellar dysfunction (MSA-C). Objective: To identify novel disease mechanisms we performed a blood transcriptomic study investigating differential gene expression changes and biological process alterations in MSA and its clinical subtypes. Methods: We compared the transcriptome from rigorously gender and age-balanced groups of 10 probable MSA-P, 10 probable MSA-C cases, 10 controls from the Catalan MSA Registry (CMSAR), and 10 Parkinson Disease (PD) patients. Results: Gene set enrichment analyses showed prominent positive enrichment in processes related to immunity and inflammation in all groups, and a negative enrichment in cell differentiation and development of the nervous system in both MSA-P and PD, in contrast to protein translation and processing in MSA-C. Gene set enrichment analysis using expression patterns in different brain regions as a reference also showed distinct results between the different synucleinopathies. Conclusions: In line with the two major phenotypes described in the clinic, our data suggest that gene expression and biological processes might be differentially affected in MSA-P and MSA-C. Future studies using larger sample sizes are warranted to confirm these results

Differentiation of multiple system atrophy subtypes by gray matter atrophy

Background and purpose: Multiple system atrophy(MSA) is a rare adult-onset synucleinopathy that can be divided in two subtypes depending on whether the prevalence of its symptoms is more parkinsonian or cerebellar (MSA-P and MSA-C, respectively). The aim of this work is to investigate the structural MRI changes able to discriminate MSA phenotypes. Methods: The sample includes 31 MSA patients (15 MSA-C and 16 MSA-P) and 39 healthy controls. Participants underwent a comprehensive motor and neuropsychological battery. MRI data were acquired with a 3T scanner (MAGNETOM Trio, Siemens, Germany). FreeSurfer was used to obtain volumetric and cortical thickness measures. A Support Vector Machine (SVM) algorithm was used to assess the classification between patients' group using cortical and subcortical structural data. Results: After correction for multiple comparisons, MSA-C patients had greater atrophy than MSA-P in the left cerebellum, whereas MSA-P showed reduced volume bilaterally in the pallidum and putamen. Using deep gray matter volume ratios and mean cortical thickness as features, the SVM algorithm provided a consistent classification between MSA-C and MSA-P patients (balanced accuracy 74.2%, specificity 75.0%, and sensitivity 73.3%). The cerebellum, putamen, thalamus, ventral diencephalon, pallidum, and caudate were the most contributing features to the classification decision (z > 3.28; p < .05 [false discovery rate]). Conclusions: MSA-C and MSA-P with similar disease severity and duration have a differential distribution of gray matter atrophy. Although cerebellar atrophy is a clear differentiator between groups, thalamic and basal ganglia structures are also relevant contributors to distinguishing MSA subtypes. Keywords: cognition; cortical thickness; machine learning; multiple system atrophy; neuroimaging

Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level

Background: Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients. Objectives: Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level. Methods: Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven restingstate networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients. Results: MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified. Conclusion: Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than difusion tensor-derived metrics for the detection of MSA

Transcriptomic differences in MSA clinical variants

Background: Multiple system atrophy (MSA) is a rare oligodendroglial synucleinopathy of unknown etiopathogenesis including two major clinical variants with predominant parkinsonism (MSA-P) or cerebellar dysfunction (MSA-C). Objective: To identify novel disease mechanisms we performed a blood transcriptomic study investigating differential gene expression changes and biological process alterations in MSA and its clinical subtypes. Methods: We compared the transcriptome from rigorously gender and age-balanced groups of 10 probable MSA-P, 10 probable MSA-C cases, 10 controls from the Catalan MSA Registry (CMSAR), and 10 Parkinson Disease (PD) patients. Results: Gene set enrichment analyses showed prominent positive enrichment in processes related to immunity and inflammation in all groups, and a negative enrichment in cell differentiation and development of the nervous system in both MSA-P and PD, in contrast to protein translation and processing in MSA-C. Gene set enrichment analysis using expression patterns in different brain regions as a reference also showed distinct results between the different synucleinopathies. Conclusions: In line with the two major phenotypes described in the clinic, our data suggest that gene expression and biological processes might be differentially affected in MSA-P and MSA-C. Future studies using larger sample sizes are warranted to confirm these results.We would like to thank all the patients for their always willing and generous collaboration. This project has been possible thanks to the funding from the Fundació Marató TV3 and CERCA Programme from Generalitat de Catalunya. We also thank the European Research Council RIBOMYLOME_309545 and Spanish Ministry of Economy and Competitiveness (BFU2017-86970-P). A.P.-S. was funded by a PHD4MD grant, which is a collaborative research training program for medical doctors. R.F.-S. was supported by a Jóvenes Investigadores (JIN) grant of the Spanish Ministry of Economy and Competitiveness (MINECO) and the Agencia Estatal de Investigación (AEI) (AEI/FEDER/UE) (grant # SAF2015-73508-JIN), and a Miguel Servet grant from the Instituto de Salud Carlos III (grant # CP19/00048). M.F. was funded by María de Maeztu programme (grant # MDM-2017-0729). Thanks to Lara Nonell, head of the Human Computational Biology group in IMIM, for offering the computational resources in the institution and the help with data management

Cerebrospinal fluid cytokines in multiple system atrophy: A cross-sectional Catalan MSA registry study

Introduction: Neuroinflammation is a potential player in neurodegenerative conditions, particularly the aggressive ones, such as multiple system atrophy (MSA). Previous reports on cytokine levels in MSA using serum or cerebrospinal fluid (CSF) have been inconsistent, including small samples and a limited number of cytokines, often without comparison to Parkinson's disease (PD), a main MSA differential diagnosis. Methods: Cross-sectional study of CSF levels of 38 cytokines using a multiplex assay in 73 participants: 39 MSA patients (19 with parkinsonian type [MSAp], 20 with cerebellar type [MSAc]; 31 probable, 8 possible), 19 PD patients and 15 neurologically unimpaired controls. None of the participants was under non-steroidal anti-inflammatory drugs at the time of the lumbar puncture. Results: There were not significant differences in sex and age among participants. In global non-parametric comparisons FDR-corrected for multiple comparisons, CSF levels of 5 cytokines (FGF-2, IL-10, MCP-3, IL-12p40, MDC) differed among the three groups. In pair-wise FDR-corrected non-parametric comparisons 12 cytokines (FGF-2, eotaxin, fractalkine, IFN-α2, IL-10, MCP-3, IL-12p40, MDC, IL-17, IL-7, MIP-1β, TNF-α) were significantly higher in MSA vs. non-MSA cases (PD + controls pooled together). Of these, MCP-3 and MDC were the most significant ones, also differed in MSA vs. PD, and were significant MSA-predictors in binary logistic regression models and ROC curves adjusted for age. CSF levels of fractalkine and MIP-1α showed a strong and significant positive correlation with UMSARS-2 scores. Conclusion: Increased CSF levels of cytokines such as MCP-3, MDC, fractalkine and MIP-1α deserve consideration as potential diagnostic or severity biomarkers of MSA