Common patterns and disease-related signatures in tuberculosis and sarcoidosis

Abstract

In light of the marked global health impact of tuberculosis (TB), strong focus has been on identifying biosignatures. Gene expression profiles in blood cells identified so far are indicative of a persistent activation of the immune system and chronic inflammatory pathology in active TB. Definition of a biosignature with unique specificity for TB demands that identified profiles can differentiate diseases with similar pathology, like sarcoidosis (SARC). Here, we present a detailed comparison between pulmonary TB and SARC, including whole-blood gene expression profiling, microRNA expression, and multiplex serum analytes. Our analysis reveals that previously disclosed gene expression signatures in TB show highly similar patterns in SARC, with a common up-regulation of proinflammatory pathways and IFN signaling and close similarity to TB-related signatures. microRNA expression also presented a highly similar pattern in both diseases, whereas cytokines in the serum of TB patients revealed a slightly elevated proinflammatory pattern compared with SARC and controls. Our results indicate several differences in expression between the two diseases, with increased metabolic activity and significantly higher antimicrobial defense responses in TB. However, matrix metallopeptidase 14 was identified as the most distinctive marker of SARC. Described communalities as well as unique signatures in blood profiles of two distinct inflammatory pulmonary diseases not only have considerable implications for the design of TB biosignatures and future diagnosis, but they also provide insights into biological processes underlying chronic inflammatory disease entities of different etiology.

Fig. 1.

Common and differential expression of genes, miRNAs, and serum analytes. Venn diagrams show the number of significantly (q < 0.01) differentially expressed genes, miRNAs, and cytokines (P < 0.01) between the disease groups and healthy controls. Red and green areas are disease-specific for TB and SARC, respectively; overlap in yellow represents differential expression in both diseases. Right shows the number of differentially expressed analytes in direct comparison between TB and SARC.

Fig. 2.

Expression differences between disease groups and controls. Hierarchical clustering illustrating differences in gene expression between TB, SARC, and healthy controls (CTRL) based on the top discriminating genes as identified by RF analysis. Color coding indicates ranked absolute expression values of each gene from low (blue) to high (red) expression. The gene names within the two clusters discriminating both diseases are given on the right; a full list of the most discriminating genes between all groups is supplied in Dataset S4.

Fig. 3.

Serum analytes in diseased and healthy individuals. Cytokine and chemokine levels in serum from diseased and healthy individuals. Bars indicate mean with SEM. *Significant difference between indicated groups at P < 0.01 (one-way ANOVA with Tukey a posteriori test).

Fig. 4.

Correlation clustering of genes and miRNAs. The figure illustrates the correlations in expression levels between gene transcripts (Top) and miRNAs (Middle) in the cluster containing miRNA-144. Bottom show the in-between correlations of miRNAs and genes. Heat map on Bottom Left shows the strength of these correlations (strongest correlation in red); the network in Bottom Right shows expression differences in correlating genes (ovals) and miRNAs (squares). Red color coding indicates increased expression in disease; blue indicates lower expression compared with controls. The correlations were based on data from TB and SARC patients only.