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.
This SuperSeries is composed of the SubSeries listed below.
Overall design
Experiment subseries GSE11908 regroups the profiles that have been used to construct the modular transcriptional framework: 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.
Experiment subseries GSE11909 regroups the profiles that have been used to identify and validate biomarkers of SLE disease activity: 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.
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