Gene expression changes in multiple sclerosis relapse suggest activation of T and non-T cells
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE19224 | GEO | 2010/06/30
SECONDARY ACCESSION(S): PRJNA120847
REPOSITORIES: GEO
Similar Datasets
Project description:paired comparison of RNA expression in peripheral blood mononuclear cells in the same group of 14 multiple sclerosis patients while stable and while in relapse. A defining feature of multiple sclerosis is the occurrence of clinical relapses separated by periods of clinical stability. Better understanding of the events underlying clinical relapse might suggest new approaches to treatment. We used microarrays to measure mRNA expression in the peripheral blood of 14 MS patients during clinical relapse and while stable. Seventy-one transcripts changed expression at the p<0.001 significance level. The most notable finding was decreased expression of transcripts with regulatory function, expressed primarily in non-T cells. Transcripts with increased expression were primarily expressed in T cells. Pathways analysis suggested involvement of the cytokine network, coagulation and complement cascades, IL-10 signaling, and NF-?B signaling. total RNA from PBMC in relapse and while stable from 14 multiple sclerosis patients
2010-07-30 | E-GEOD-19224 | biostudies-arrayexpress
Project description:Multiple sclerosis is a common inflammatory and degenerative disease that causes neurological disability. It affects young adults and its prevalence is higher in women. The most common form is manifested as a series of acute episodes of neurological disability (relapses) followed by a recovery phase (remission). Recently, non-coding RNAs have emerged as new players in transcriptome regulation, and in turn, they could have a significant role in MS pathogenesis. In this context, our aim was to investigate the involvement of microRNAs and snoRNAs in the relapse-remission dynamics of MS in peripheral blood leucocytes, to shed light on the molecular and regulatory mechanisms that underlie this complex process. With this approach, we found that a subset of small non-coding RNAs (sncRNA) is altered in relapse and remission, revealing unexpected opposite changes that are sex dependent. Furthermore, we found that a relapse-related miRNA signature regulated general metabolism processes in leucocytes, and miRNA altered in remission are involved in the regulation of innate immunity. We observed that sncRNA dysregulation is different in relapse and remission leading to differences in transcriptome regulation, and that this process is sex dependent. In conclusion, relapse and remission have a different molecular background in men and women. 24 multiple sclerosis patients with samples both in remission and relapse (2 samples for each patient; 48 blood samples in total) and 24 healthy controls were included in the study, for a total of 72 samples.
2015-11-03 | E-GEOD-74579 | biostudies-arrayexpress
Project description:Almost a quarter of pediatric patients with Acute Lymphoblastic Leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis relapse pairs of ALL patients using genomeâ??wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients very few differences between diagnosis and relapse samples were found (â??stable groupâ??), suggesting that mostly extra-leukemic factors (e.g., drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 samples with clear differences in gene expression (â??skewed groupâ??), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of relapses are due to selection or emergence of a clone with deregulated expression of a genes involved in pathways that regulate B cell signaling, development, cell cycle, cellular division and replication. The study contains 41 pairs of samples taken from patients at diagnosis and relapse (a total of 82 arrays).
2010-03-23 | E-GEOD-18497 | biostudies-arrayexpress
Project description:Despite the effectiveness of immuno-chemotherapy, 40% of patients with diffuse large B-cell lymphoma (DLBCL) experience relapse or refractory disease. Longitudinal studies have previously focused on the mutational landscape of relapse but falling short of providing a consistent relapse-specific genetic signature. In our study, we have focussed attention on the changes in gene expression profile accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo DLBCL patients. Cell of origin remained stable from diagnosis to relapse in 80% of patients, with only a single patient showing COO switching from ABC to GCB. Analysis of the transcriptomic changes that occur following relapse suggest ABC and GCB relapses are mediated via different mechanisms. We developed a 30-gene discriminator for ABC-DLBCLs derived from relapse-associated genes, that defined clinically distinct high and low risk subgroups in ABC-DLBCLs at diagnosis in datasets comprising both population-based and clinical trial cohorts. This signature also identified a population of <60-year-old patients with superior PFS and OS treated with Ibrutinib-R-CHOP as part of the PHOENIX trial. Altogether this new signature adds to the existing toolkit of putative genetic predictors now available in DLBCL that can be readily assessed as part of prospective clinical trials.
2023-03-08 | GSE193566 | GEO
Project description:Almost a quarter of pediatric patients with Acute Lymphoblastic Leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis relapse pairs of ALL patients using genome–wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients very few differences between diagnosis and relapse samples were found (“stable group”), suggesting that mostly extra-leukemic factors (e.g., drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 samples with clear differences in gene expression (“skewed group”), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of relapses are due to selection or emergence of a clone with deregulated expression of a genes involved in pathways that regulate B cell signaling, development, cell cycle, cellular division and replication.
2010-03-10 | GSE18497 | GEO
Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.
2009-07-15 | E-GEOD-15245 | biostudies-arrayexpress
Project description:Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells
| PRJNA116533 | ENA
Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction
2009-07-15 | GSE15245 | GEO
Project description:<p>During rheumatoid arthritis (RA), TNF activates fibroblast-like synoviocytes (FLS) inducing in a temporal order a constellation of genes, which perpetuate synovial inflammation. Although the molecular mechanisms regulating TNF-induced transcription are well characterized, little is known about the impact of mRNA stability on gene expression and the impact of TNF on decay rates of mRNA transcripts in FLS. To address these issues we performed RNA sequencing and genome-wide analysis of the mRNA stabilome in RA FLS. We found that TNF induces a biphasic gene expression program: initially, the inducible transcriptome consists primarily of unstable transcripts but progressively switches and becomes dominated by very stable transcripts. This temporal switch is due to: a) TNF-induced prolonged stabilization of previously unstable transcripts that enables progressive transcript accumulation over days and b) sustained expression and late induction of very stable transcripts. TNF- induced mRNA stabilization in RA FLS occurs during the late phase of TNF response, is MAPK-dependent, and involves several genes with pathogenic potential such as IL6, CXCL1, CXCL3, CXCL8/IL8, CCL2, and PTGS2. These results provide the first insights into genome-wide regulation of mRNA stability in RA FLS and highlight the potential contribution of dynamic regulation of the mRNA stabilome by TNF to chronic synovitis.</p>
| phs001371 | dbGaP
Project description:Multiple sclerosis is a common inflammatory and degenerative disease that causes neurological disability. It affects young adults and its prevalence is higher in women. The most common form is manifested as a series of acute episodes of neurological disability (relapses) followed by a recovery phase (remission). Recently, non-coding RNAs have emerged as new players in transcriptome regulation, and in turn, they could have a significant role in MS pathogenesis. In this context, our aim was to investigate the involvement of microRNAs and snoRNAs in the relapse-remission dynamics of MS in peripheral blood leucocytes, to shed light on the molecular and regulatory mechanisms that underlie this complex process. With this approach, we found that a subset of small non-coding RNAs (sncRNA) is altered in relapse and remission, revealing unexpected opposite changes that are sex dependent. Furthermore, we found that a relapse-related miRNA signature regulated general metabolism processes in leucocytes, and miRNA altered in remission are involved in the regulation of innate immunity. We observed that sncRNA dysregulation is different in relapse and remission leading to differences in transcriptome regulation, and that this process is sex dependent. In conclusion, relapse and remission have a different molecular background in men and women.
2015-11-03 | GSE74579 | GEO