Summary The analysis of patient blood transcriptional profiles offers a means to investigate 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 datasets. Mapping changes in gene expression at the module-level generated disease-specific transcriptional fingerprints which 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.

Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.

Summary Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by breakdown of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA) and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis, and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, which were supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases.

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