Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics

Abstract

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

Keywords: autoimmunity; autoinflammation; mechanisms; patient stratification; therapeutic targets; transcriptomics.

Figure 1

A module framework to biologically interpret the blood transcriptome. (a) Reference whole-blood data sets are selected to represent a large spectrum of immune conditions, including autoimmunity, infectious diseases, cancer, and immunodeficiency. (b) Sets of coexpressed genes are extracted (69). (c) Modules are functionally annotated using a combination of knowledge-based (e.g., pathway enrichment analysis) and data-driven (e.g., hierarchical clustering, module enrichment in isolated leukocyte populations) methods. (d ) Module fingerprints can be derived for disease groups or for individual samples and mapped as module grids. The color represents the directionality of the module (red, up; blue, down) when compared to the reference control, and the intensity of the color represents the proportion of module probes that pass the significance threshold. (e) The modules can also be used as regular gene sets for gene set enrichment analyses (e.g., GSEA, QuSAGE, Q-Gen). Modified from Reference with permission.

Figure 2

The interferon (IFN) signature in systemic lupus erythematosus (SLE). (a) Unsupervised hierarchical clustering of the data reveals a prominent type I IFN signature in ~85% of SLE samples. (b) The module fingerprint of SLE, normalized to a group of reference healthy controls, reduces data dimensionality and enables rapid biological interpretation, revealing overexpression of the three IFN modules. (c) Gene-level network displaying the connectivity between IFN-inducible transcripts (circles) that positively correlate with disease activity. Modules are represented as squares. Nodes are colored according to the normalized expression of the transcripts in the high disease activity sample group. Significant transcripts were selected with a linear mixed model that compared high and low disease activity samples while accounting for patient ethnicity and treatment. Modified from Reference with permission.

Figure 3

Five applications of transcriptomics for the clinic. ❶ Disease diagnosis: Disease-specific signatures can help assign patients to disease A, B, or C. ❷ Disease molecular stratification: Patients with the same clinical disease may exhibit distinct transcriptional signatures, suggesting distinct underlying mechanisms of pathogenesis. The level of molecular heterogeneity can vary from disease to disease. ❸ Disease prognosis: A transcriptional signature can help predict the course of disease, including onset, flare, or prolonged remission. ❹ Treatment response monitoring: Transcriptional fingerprints obtained at regular intervals after initiation of treatment can help segregate responders from nonresponders and characterize the mechanisms of response to treatment. ❺ Treatment response prediction: A signature obtained ideally before initiation of treatment can predict the long-term response to therapy and help customize therapeutic regimen accordingly.

Figure 4

Stratification of SLE patients through individual immunoprofiling. (a) Modules of coexpressed genes over time are identified and a module/trait correlation matrix is generated for each patient. (b) The module that best correlates with a clinical trait of interest, such as SLEDAI, is selected for each individual. (c) Patients are stratified according to the immune network that best correlates with the trait of interest. Patients are genotyped and SNP analysis is conducted for each patient group. (d ) Definition of tailored treatment based on genotype and early clinical assessment of SLE patients. Abbreviations: CBC, complete blood count; C3, complement component 3; SLE, systemic lupus erythematosus; SLEDAI, SLE disease activity index;WBC, white blood cell count. Modified from Reference with permission.