A helicopter perspective on TB biomarkers: pathway and process based analysis of gene expression data provides new insight into TB pathogenesis

Abstract

Biomarker host genetic signatures are considered key tools for improved early diagnosis of tuberculosis (TB) disease (development). The analysis of gene expression changes based on a limited number of genes or single study designs, however, may not be sufficient for the identification of universal diagnostic biomarker profiles. Here we propose that biological pathway and process based analyses from multiple data sets may be more relevant for identification of key pathways in TB pathogenesis, and may reveal novel candidate diagnostic TB biomarkers. A number of independent genome-wide gene expression studies have recently been performed to study expression of biomarkers for TB disease. We have integrated the results from these independent studies and performed pathway- as well as biological process-based analysis on the total data set. Interestingly, IFNα/β signalling is not the single dominant pathway in the analysis of the total dataset, but combined, functional, analysis of biomarkers suggests a strong dominant role for myeloid cell involvement in inflammation.

Figure 1. Ingenuity pathway analysis of all genes identified by unbiased methods related to TB disease.

All 409 biomarkers were analysed by integrated pathway analysis using Ingenuity and the most dominant network is depicted here. Signalling pathways were coloured according to functional classification into myeloid cells, T cells and B cells and type I interferon related genes.

Figure 3. Genes identified by more than 1 independent study.

Genes identified by more than 1 independent global genome-wide gene expression analysis. Manuscript numbers refer to Table 2. Classification into modules, functional groups according to Ingenuity and GSEA was performed according to Tables 3 & 4 and identical to Genes identified by more than 1 independent global genome-wide gene expression analysis. Manuscript numbers refer to Table 2. Classification into modules, functional groups according to Ingenuity and GSEA was performed according to Tables 3 & 4 and identical to Table S1.

Figure 2. Functional classification of individual genes identified by gene expression analysis on TB patients.

Categories have been based on combined output from Ingenuity and GSEA software modules and may include multiple canonical pathways or cell processes. Myeloid cells includes the following canonical pathways: role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis; Fcg Receptor mediated phagocytosis in macrophages and monocytes; role of pattern recognition receptors in recognition of bacteria and viruses; IL12 signaling and production in macrophages; Dendritic cell maturation; production of Nitrox Oxide and Reactive Oxygen Species in Macrophages; Toll like receptor signaling. T cells includes: T cell receptor signaling; CD28 signaling in T helper cells; iCOS-iCOSL signaling in T helper cells. B cells includes: B cell receptor signaling; PI3K signaling in B lymphocytes. Interferon related pathways include: Interferon signaling, role of jak1, jak2 and tyk2 in interferon signaling, role of PKR in interferon induction and antiviral response. Inflammation includes: IL-8 signaling; NF-kB signaling; altered T cell and B cell signaling in Rheumatoid Arthritis; systemic lupus erythematosus signaling; chemokine signaling; IL-6 signaling. TREM1 includes specifically TREM1 signaling and mitochondrial dysfunction also only contains mitochondrial dysfunction. Finally, hematopoiesis includes: erythropoietin signaling; IL-3 signaling; FLT3 signaling in hematopoietic progenitor cells; prolactin signaling; HGF signaling.

Figure 4. Schematic representation of events during active TB Disease.

Pathway and process based analysis suggests that these processes are key players in TB disease pathogenesis.