A Modular Analysis Framework for Blood Genomics Studies: Application to Systemic Lupus Erythematosus
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE11907 | GEO | 2008/08/11
SECONDARY ACCESSION(S): PRJNA105749
REPOSITORIES: GEO
Similar Datasets
Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data.
2008-08-11 | GSE11908 | GEO
Project description:Transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus.
2008-08-11 | GSE11909 | GEO
Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.
2008-08-11 | E-GEOD-11908 | biostudies-arrayexpress
Project description:The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings. Experiment Overall Design: Experiment subseries GSE11908 regroups the profiles that have been used to construct the modular transcriptional framework: Experiment Overall Design: The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the âclustering behaviorâ of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is âdata-drivenâ and does not involve manual selection of genes by the investigator. Experiment Overall Design: We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). Experiment Overall Design: The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules. Experiment Overall Design: Experiment subseries GSE11909 regroups the profiles that have been used to identify and validate biomarkers of SLE disease activity: Experiment Overall Design: The proposed biomarker-selection strategy relies on modules for reducing highly dimensional microarray data sets in a stepwise manner. Starting from the full set of 28 modules, only those for which a set minimum proportion of transcripts are significantly changed between the study groups are selected (e.g., minimum proportion of differentially expressed transcripts at p < 0.05 = 15% overexpressed or underexpressed transcripts; in the example given, 11 SLE modules meet this criterion). This eliminates from the selection pool the modules registering fewer consistent changes that could be attributed to noise. Transcriptional vectors were derived for the entire cohort of 22 untreated pediatric SLE patients with the use of this set of 11 SLE modules. Patient profiles were also generated for an independent set of 31 children with SLE treated with steroids and/or cytotoxic drugs and/or hydroxychloroquine. A nonparametric method for analyzing multivariate ordinal data was used to score the patients. Lupus disease flares can lead to irreversible worsening of the patient's status. We tested the relevance of this multivariate transcriptional score for longitudinal monitoring of the disease activity in a cohort of 20 pediatric SLE patients (two to four time points/patient, intervals between each time point varied from one month to 18 months). Half of the patients had been included in our cross-sectional analysis before they were enrolled in this longitudinal study. Parallel trends were observed between multivariate transcriptional scores and a clinical severity score. The positive association was verified statistically with the use of a linear-regression model.
2008-10-04 | E-GEOD-11907 | biostudies-arrayexpress
Project description:Characterization of functionally distinct Dendritic Cell (DC) subsets in mice has fueled interest in whether analogous counterparts exist in humans. Transcriptional modules of coordinately expressed genes were utilized for defining shared functions between the species. Comparing modules derived for four human skin DC subsets, and modules derived from the Immunological Genome Project database for all mouse DC subsets, revealed that human Langerhans Cells (LCs) and the mouse XCR1+CD8α+CD103+ DCs shared the Class I-mediated antigen processing and cross-presentation transcriptional modules that, however, were not seen in mouse LCs. Furthermore, human LCs were enriched in a transcriptional signature specific to the blood cross-presenting CD141/BDCA-3+ DCs, the proposed equivalent to mouse CD8α+ DCs. Consistent with our analysis, LCs were highly adept at inducing primary CTL responses. Thus, our study suggests that the function of LCs may not be conserved between mouse and human and supports human LCs as an especially relevant therapeutic target. RNA from sorted populations was purified using TRIzol. RNA was processed, amplified, labeled and hybridized at Washington University Core Facility (GTAC) with Illumina HumanHT-12 v4 Expression BeadChip. Data were background subtracted and quantile normalized.
2015-03-27 | E-GEOD-66355 | biostudies-arrayexpress
Project description:Characterization of functionally distinct Dendritic Cell (DC) subsets in mice has fueled interest in whether analogous counterparts exist in humans. Transcriptional modules of coordinately expressed genes were utilized for defining shared functions between the species. Comparing modules derived for four human skin DC subsets, and modules derived from the Immunological Genome Project database for all mouse DC subsets, revealed that human Langerhans Cells (LCs) and the mouse XCR1+CD8α+CD103+ DCs shared the Class I-mediated antigen processing and cross-presentation transcriptional modules that, however, were not seen in mouse LCs. Furthermore, human LCs were enriched in a transcriptional signature specific to the blood cross-presenting CD141/BDCA-3+ DCs, the proposed equivalent to mouse CD8α+ DCs. Consistent with our analysis, LCs were highly adept at inducing primary CTL responses. Thus, our study suggests that the function of LCs may not be conserved between mouse and human and supports human LCs as an especially relevant therapeutic target.
2015-03-27 | GSE66355 | GEO
Project description:Differentiation of CD4+T-cells into effector subsets is a critical component of the adaptive immune system and an incorrect response can lead to autoimmunity or immune deficiency. Cellular differentiation including T-cell differentiation is accompanied by large-scale transcriptional changes, including expression of microRNAs involved in suppressing mRNA expression. We combined time-series microRNA and mRNA expression to construct a CD4+T-cell microRNA/mRNA regulatory network and identified 25 subnetworks (modules) which were associated with multiple T-cell related diseases. This dataset was designed to validate the disease association of the constructed microRNA/mRNA regulatory network and identified modules. Matching mRNA expression profiling was originally submitted to NCBI Gene Expression Omnibus and has been imported into ArrayExpress under accession E-GEOD-60677.
2022-05-29 | E-MTAB-4900 | biostudies-arrayexpress
Project description:A Unified Framework for Systematic Identification of Post-Transcriptional Regulatory Modules
| PRJNA937646 | ENA
Project description:Transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Experiment Overall Design: The proposed biomarker-selection strategy relies on modules for reducing highly dimensional microarray data sets in a stepwise manner. Starting from the full set of 28 modules, only those for which a set minimum proportion of transcripts are significantly changed between the study groups are selected (e.g., minimum proportion of differentially expressed transcripts at p < 0.05 = 15% overexpressed or underexpressed transcripts; in the example given, 11 SLE modules meet this criterion). This eliminates from the selection pool the modules registering fewer consistent changes that could be attributed to noise. Transcriptional vectors were derived for the entire cohort of 22 untreated pediatric SLE patients with the use of this set of 11 SLE modules. Patient profiles were also generated for an independent set of 31 children with SLE treated with steroids and/or cytotoxic drugs and/or hydroxychloroquine. A nonparametric method for analyzing multivariate ordinal data was used to score the patients. Lupus disease flares can lead to irreversible worsening of the patient's status. We tested the relevance of this multivariate transcriptional score for longitudinal monitoring of the disease activity in a cohort of 20 pediatric SLE patients (two to four time points/patient, intervals between each time point varied from one month to 18 months). Half of the patients had been included in our cross-sectional analysis before they were enrolled in this longitudinal study. Parallel trends were observed between multivariate transcriptional scores and a clinical severity score. The positive association was verified statistically with the use of a linear-regression model. Experiment Overall Design:
2008-10-01 | E-GEOD-11909 | biostudies-arrayexpress
Project description:<p>Our goals are to develop a comprehensive understanding of the genomics of transcription in a population based unselected sample and to discover DNA and RNA biomarkers for major depressive disorder (MDD). This work is essential to developing a more complete understanding of the biological basis of MDD, a common complex trait associated with considerable morbidity, mortality, and personal/societal cost. All biological samples have been collected from well-defined populations, and are now available.</p> <p>First, we conduct a "genetical genomics" or eQTL study of ~800 MZ and ~800DZ twin pairs. Each subject has been assayed for genome-wide SNPs and CNVs and gene expression from peripheral blood sampled under standardized conditions. We determine the genetic architecture (genetic and non-genetic proportions of variance via twin analyses) for every transcript, and the genome-wide associations (i.e., SNP-transcript eQTL pairs). These analyses will be expanded to consider transcriptional modules. The key deliverable is a detailed catalogue of the general and specific architecture of transcription plus raw intensity files.</p> <p>Second, we seek to discover DNA and RNA biomarkers relevant to MDD, capitalizing on the results of a large MDD study with repeated clinical and biological assessments; we have previously shown that PB is a reasonable proxy for CNS expression and employ an advanced modelling framework: (a) Using baseline data, we identify biomarkers for MDD by comparing ~1000 controls with ~1400 MDD cases via comparisons of SNP, CNV, expression transcripts, and transcriptional modules. (b) Using longitudinal data, we contrast gene expression signatures assessed at baseline and two years later in ~200 controls and ~500 MDD cases.</p>
| phs000486 | dbGaP