In-silico modeling of granulomatous diseases

Abstract

Purpose of review: The pathogenesis of genetically complex granulomatous diseases, such as sarcoidosis and latent tuberculosis, remains largely unknown. With the recent advent of more powerful research tools, such as genome-wide expression platforms, comes the challenge of making sense of the enormous data sets so generated. This manuscript will provide demonstrations of how in-silico (computer) analysis of large research data sets can lead to novel discoveries in the field of granulomatous lung disease.

Recent findings: The application of in-silico research tools has led to novel discoveries in the fields of noninfectious (e.g., sarcoidosis) and infectious granulomatous diseases. Computer models have identified novel disease mechanisms and can be used to perform 'virtual' experiments rapidly and at low cost compared with conventional laboratory techniques.

Summary: Granulomatous lung diseases are extremely complex, involving dynamic interactions between multiple genes, cells, and molecules. In-silico interpretation of large data sets generated from new research platforms that are capable of comprehensively characterizing and quantifying pools of biological molecules promises to rapidly accelerate the rate of scientific discovery in the field of granulomatous lung disorders.

Figure 1. Comparison of lung gene expression profiles in patients with sarcoidosis and granulomatous fungal infections

Gene expression was compared in lung tissue derived from sarcoidosis (n=6), infectious granulomatous disease (n=6; 4 atypical mycobacterium, 2 histoplasmosis), and disease-free controls (n=6) using the Affymetrix Human U133 Plus 2.0 gene array platform using fold difference of >2, p<0.005, and false discovery rate of 3% as the criterion. Differentially expressed genes were then analyzed by Ingenuity Pathway Analysis [Ingenuity Systems (a QIAGEN company)]. Panel a: principal component analysis shows similar gene expression profiles in sarcoidosis (green) and infection (blue), which were distinct from controls (red). Panel b: expression of a gene network that is predicted to be regulated by transcription factors PAX3 and SIX1 were higher (darker shades of red = higher expression) in sarcoidosis compared to infection. Panel c: expression of genes regulated by the “B Cell Receptor” was reduced in sarcoidosis compared to infection.

Figure 2. Predicted interactions between genes and microRNA (miRNA) in pulmonary sarcoidosis

Genes that were differentially expressed in lung tissues of patients with pulmonary sarcoidosis compared to controls (as described in Figure 1) are depicted according to the function of their protein products. The predicted targets of differentially expressed miRNA transcripts, which were identified in pulmonary sarcoidosis in a previously published report (23) were then analyzed using curated online miRNA target prediction resources (microRNA.org, targetscan.org). The predicted interactions among gene products are represented by black arrows, and the predicted interactions of specific miRNA with specific genes are represented by color-coded triangles. The sum total of these interactions is designated the “interactome”.

Figure 3. A mathematical model of pulmonary sarcoidosis

Panel a: a basic schematic network of sarcoidosis: arrowhead means production or activation, block head means inhibition, and diamond means chemoattraction. Panel b: comparison of math model simulations of chemokine and cytokine profiles in sarcoidosis with actual human sarcoidosis clinical data [from Ref (8)], showing nearly identical results. Panel c: math model predictions of TNFα concentrations in sarcoidosis tissues over time, and matching granuloma size simulations (wherein “R” refers to granuloma radius), showing the effects of “anti-TNFα treatment” rendered 15 weeks after the onset of sarcoidosis. Suppression of TNFα is shown to create a new steady state associated with reduced granuloma radius around week 20. (Reproduced with permission from Hao W, Crouser ED, Freidman A. Mathematical model of sarcoidosis. Proc Natl Acad Sci USA 2014;111:16065-16070.).