CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes.
Ontology highlight
ABSTRACT:
SUBMITTER: Elkjaer ML
PROVIDER: S-EPMC7892884 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:OBJECTIVE:Here, we applied a multi-omics approach (i) to examine molecular pathways related to de- and remyelination in multiple sclerosis (MS) lesions; and (ii) to translate these findings to the CSF proteome in order to identify molecules that are differentially expressed among MS subtypes. METHODS:To relate differentially expressed genes in MS lesions to de- and remyelination, we compared transcriptome of MS lesions to transcriptome of cuprizone (CPZ)-induced de- and remyelination. Protein products of the overlapping orthologous genes were measured within the CSF by quantitative proteomics, parallel reaction monitoring (PRM). Differentially regulated proteins were correlated with molecular markers of inflammation by using MesoScale multiplex immunoassay. Expression kinetics of differentially regulated orthologous genes and proteins were examined in the CPZ model. RESULTS:In the demyelinated and remyelinated corpus callosum, we detected 1239 differentially expressed genes; 91 orthologues were also differentially expressed in MS lesions. Pathway analysis of these orthologues suggested that the TYROBP (DAP12)-TREM2 pathway, TNF-receptor 1, CYBA and the proteasome subunit PSMB9 were related to de- and remyelination. We designed 129 peptides representing 51 orthologous proteins, measured them by PRM in 97 individual CSF, and compared their levels between relapsing (n = 40) and progressive MS (n = 57). Four proteins were differentially regulated among relapsing and progressive MS: tyrosine protein kinase receptor UFO (UFO), TIMP-1, apolipoprotein C-II (APOC2), and beta-2-microglobulin (B2M). The orthologous genes/proteins in the mouse brain peaked during acute remyelination. UFO, TIMP-1 and B2M levels correlated inversely with inflammation in the CSF (IL-6, MCP-1/CCL2, TARC/CCL17). APOC2 showed positive correlation with IL-2, IL-16 and eotaxin-3/CCL26. CONCLUSIONS:Pathology-based multi-omics identified four CSF markers that were differentially expressed in MS subtypes. Upregulated TIMP-1, UFO and B2M orthologues in relapsing MS were associated with reduced inflammation and reflected reparatory processes, in contrast to the upregulated orthologue APOC2 in progressive MS that reflected changes in lipid metabolism associated with increased inflammation.
| S-EPMC6095600 | biostudies-literature
Project description:Multiple sclerosis (MS) is an immune-mediated, neuro-inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS) with a heterogeneous clinical presentation and course. There is a remarkable phenotypic heterogeneity in MS, and the molecular mechanisms underlying it remain unknown. We aimed to investigate further the etiopathogenesis related molecular pathways in subclinical types of MS using proteomic and bioinformatics approaches in cerebrospinal fluids of patients with clinically isolated syndrome, relapsing remitting MS and progressive MS (n=179). Comparison of disease groups with controls revealed a total of 151 proteins that are differentially expressed in clinically different MS subtypes. KEGG analysis using PANOGA tool revealed the disease related pathways including aldosterone-regulated sodium reabsorption (p=8.02x10-5) which is important in the immune cell migration, renin-angiotensin (p=6.88x10-5) system that induces Th17 dependent immunity, notch signaling (p=1.83x10-10) pathway indicating the activated remyelination and vitamin digestion and absorption pathways (p=1.73x10-5). An emerging theme from our studies is that whilst all MS clinical forms share common biological pathways, there are also clinical subtypes specific and pathophysiology related pathways which may have further therapeutic implications.
| S-EPMC4420287 | biostudies-literature
Project description:Introduction: Multiple sclerosis (MS) is a chronic disorder of the central nervous system with an untreatable late progressive phase. Molecular maps of different stages of brain lesion evolution in patients with progressive multiple sclerosis (PMS) are missing but critical for understanding disease development and to identify novel targets to halt progression. Materials and Methods: The MS Atlas database comprises comprehensive high-quality transcriptomic profiles of 98 white matter (WM) brain samples of different lesion types (normal-appearing WM [NAWM], active, chronic active, inactive, remyelinating) from ten progressive MS patients and 25 WM areas from five non-neurological diseased cases. Results: We introduce the first MS brain lesion atlas (msatlas.dk), developed to address the current challenges of understanding mechanisms driving the fate on a lesion basis. The MS Atlas gives means for testing research hypotheses, validating biomarkers and drug targets. It comes with a user-friendly web interface, and it fosters bioinformatic methods for de novo network enrichment to extract mechanistic markers for specific lesion types and pathway-based lesion type comparison. We describe examples of how the MS Atlas can be used to extract systems medicine signatures and demonstrate the interface of MS Atlas. Conclusion: This compendium of mechanistic PMS WM lesion profiles is an invaluable resource to fuel future MS research and a new basis for treatment development.
| S-EPMC7500075 | biostudies-literature
Project description:Effective biomarkers for multiple sclerosis diagnosis, assessment of prognosis, and treatment responses, in particular those measurable in blood, are largely lacking. We have investigated a broad set of protein biomarkers in cerebrospinal fluid (CSF) and plasma using a highly sensitive proteomic immunoassay. Cases from two independent cohorts were compared with healthy controls and patients with other neurological diseases. We identified and replicated 10 cerebrospinal fluid proteins including IL-12B, CD5, MIP-1a, and CXCL9 which had a combined diagnostic efficacy similar to immunoglobulin G (IgG) index and neurofilament light chain (area under the curve [AUC] = 0.95). Two plasma proteins, OSM and HGF, were also associated with multiple sclerosis in comparison to healthy controls. Sensitivity and specificity of combined CSF and plasma markers for multiple sclerosis were 85.7% and 73.5%, respectively. In the discovery cohort, eotaxin-1 (CCL11) was associated with disease duration particularly in patients who had secondary progressive disease (P CSF < 4 × 10-5, P plasma < 4 × 10-5), and plasma CCL20 was associated with disease severity (P = 4 × 10-5), although both require further validation. Treatment with natalizumab and fingolimod showed different compartmental changes in protein levels of CSF and peripheral blood, respectively, including many disease-associated markers (e.g., IL12B, CD5) showing potential application for both diagnosing disease and monitoring treatment efficacy. We report a number of multiple sclerosis biomarkers in CSF and plasma for early disease detection and potential indicators for disease activity. Of particular importance is the set of markers discovered in blood, where validated biomarkers are lacking.
| S-EPMC7293699 | biostudies-literature
Project description:Multiple sclerosis (MS) is an inflammatory and demyelinating neurological disease of the central nervous system. Image-based biomarkers, such as lesions defined on magnetic resonance imaging (MRI), play an important role in MS diagnosis and patient monitoring. The detection of newly formed lesions provides crucial information for assessing disease progression and treatment outcome. Here, we propose a deep learning-based pipeline for new MS lesion detection and segmentation, which is built upon the nnU-Net framework. In addition to conventional data augmentation, we employ imaging and lesion-aware data augmentation methods, axial subsampling and CarveMix, to generate diverse samples and improve segmentation performance. The proposed pipeline is evaluated on the MICCAI 2021 MS new lesion segmentation challenge (MSSEG-2) dataset. It achieves an average Dice score of 0.510 and F 1 score of 0.552 on cases with new lesions, and an average false positive lesion number n FP of 0.036 and false positive lesion volume V FP of 0.192 mm 3 on cases with no new lesions. Our method outperforms other participating methods in the challenge and several state-of-the-art network architectures.
| S-EPMC9633994 | biostudies-literature
Project description:Brain magnetic resonance imaging is widely used as a diagnostic and monitoring tool in multiple sclerosis and provides a non-invasive, sensitive and reproducible way to track the disease. Topological characteristics relating to the distribution and shape of lesions are recognized as important neuroradiological markers in the diagnosis of multiple sclerosis, although these have been much less well characterized quantitatively than have traditional measures such as T2 hyperintense or T1 hypointense lesion volumes. Here, we used voxel-level 3 T magnetic resonance imaging T1-weighted scans to reconstruct the 3D topology of lesions in 284 subjects with multiple sclerosis and tested whether this is a heritable phenotype. To this end, we extracted the genotypes from a published genome-wide association study on these same individuals and searched for genetic associations with lesion load, shape and topological distribution. Lesion probability maps were created to identify frequently affected areas and to assess the overall distribution of T1 lesions in the subject population as a whole. We then developed an original algorithm to cluster adjacent lesional voxels (cluxels) in each subject and tested whether cluxel topology was significantly associated with any single-nucleotide polymorphism in our data set. To focus on patterns of lesion distribution, we computed the first 10 principal components. Although principal component 1 correlated with lesion load, none of the remaining orthogonal components correlated with any other known variable. We then conducted genome-wide association studies on each of these and found 31 significant associations (false discovery rate <0.01) with principal component 8, which represents a mode of variation of lesion topology in the population. The majority of the loci can be linked to genes related to immune cell function and to myelin and neural growth; some (SYK, MYT1L, TRAPPC9, SLITKR6 and RIC3) have been previously associated with the distribution of white matter lesions in multiple sclerosis. Finally, we used a bioinformatics approach to identify a network of 48 interacting proteins showing genetic associations (P < 0.01) with cluxel topology in multiple sclerosis. This network also contains proteins expressed in immune cells and is enriched in molecules expressed in the central nervous system that contribute to neural development and regeneration. Our results show how quantitative traits derived from brain magnetic resonance images of patients with multiple sclerosis can be used as dependent variables in a genome-wide association study. With the widespread availability of powerful computing and the availability of genotyped populations, integration of imaging and genetic data sets is likely to become a mainstream tool for understanding the complex biological processes of multiple sclerosis and other brain disorders.
| S-EPMC3613709 | biostudies-literature
Project description:Background and purposeIn MS, the total brain lesion volume and spatial distribution of lesions across the brain vary widely among individual patients. We hypothesized that spatial distribution may be partially driven by genetic predisposition, and we aimed to explore relations among candidate genes and the spatial distribution of white matter brain lesions in MS.Material and methodsGenotypes of 69 SNPs in 208 patients with MS were related to the spatial distribution of T2 brain lesions. Lesions were manually outlined on MR images, and binary lesion masks were produced and registered to a common space. With Randomise software, the lesion masks were related to genotype by using a voxelwise nonparametric GLM approach, followed by clusterwise analysis. We used a DNA chip with SNPs selected from the literature on MS susceptibility, severity, and phenotypes.ResultsFor 11 of these SNPs, 1 of the genotypes expressed significant clusters of increased or decreased lesion probability in varying, predominantly periventricular, brain regions. When we statistically controlled the voxelwise analyses for effects of total brain lesion volume, only 1 SNP remained significant: rs2227139, located within the MHC class II region. This SNP retained its periventricular cluster of significantly increased lesion probability for the heterozygote genotype.ConclusionsHeterozygosity of rs2227139 (MHC class II region) is associated with increased right frontal periventricular lesion probability (P<.01). Ten other SNPs showed associations between genotype and spatial lesion distribution that are partly explained by total lesion volume.
| S-EPMC7965886 | biostudies-literature
Project description:BackgroundCigarette smoking has been linked to higher susceptibility and increased risk of progressive multiple sclerosis (MS). The effects of smoking on MRI characteristics of patients with MS have not been evaluated.ObjectivesTo compare the MRI characteristics in cigarette smoker and nonsmoker patients with MS.MethodsWe studied 368 consecutive patients with MS (age 44.0 +/-SD 10.2 years, disease duration 12.1 +/- 9.1 years) comprising 240 never-smokers and 128 (34.8%) ever-smokers (currently active and former smokers). The average number of packs per day smoked (+/-SD) was 0.95 +/- 0.65, and the mean duration of smoking was 18.0 +/- 9.5 years. All patients obtained full clinical and quantitative MRI evaluation. MRI measures included T1, T2, and gadolinium contrast-enhancing (CE) lesion volumes (LVs) and measures of central, global, and tissue-specific brain atrophy. The associations between smoking status and MRI measurements were assessed in regression analysis.ResultsSmoking was associated with increased Expanded Disability Status Scale (EDSS) scores (p = 0.004). The median EDSS scores (interquartile range) in the ever-smoker group and the active-smoker group were both 3.0 (2.0), compared with 2.5 (2.5) in never-smokers. There were adverse associations between smoking and the lesion measures including increased number of CE lesions (p < 0.001), T2 LV (p = 0.009), and T1 LV (p = 0.003). Smoking was associated with decreased brain parenchymal fraction (p = 0.047) and with increases in the lateral ventricle volume (p = 0.001) and third ventricle width (p = 0.023).ConclusionsSmoking is associated with increased blood-brain barrier disruption, higher lesion volumes, and greater atrophy in multiple sclerosis.
| S-EPMC2833095 | biostudies-literature
Project description:We investigated the overlap shared between the immunoglobulin (Ig) proteome of the cerebrospinal fluid (CSF) and the B cell Ig-transcriptome of CSF and the central nervous system (CNS) tissue of three patients with multiple sclerosis. We determined the IgG-proteomes of CSF by mass spectrometry, and compared them to the IgG-transcriptomes from CSF and brain lesions, which were analyzed by cDNA cloning. Characteristic peptides that were identified in the CSF-proteome could also be detected in the transcriptomes of both, brain lesions and CSF, providing evidence for a strong overlap of the IgG repertoires in brain lesions and in the CSF.
| S-EPMC3090654 | biostudies-literature
Project description:Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.
| S-EPMC7856304 | biostudies-literature