Inflammatory signaling in human tuberculosis granulomas is spatially organized

Abstract

Granulomas are the pathological hallmark of tuberculosis (TB). However, their function and mechanisms of formation remain poorly understood. To understand the role of granulomas in TB, we analyzed the proteomes of granulomas from subjects with tuberculosis in an unbiased manner. Using laser-capture microdissection, mass spectrometry and confocal microscopy, we generated detailed molecular maps of human granulomas. We found that the centers of granulomas have a pro-inflammatory environment that is characterized by the presence of antimicrobial peptides, reactive oxygen species and pro-inflammatory eicosanoids. Conversely, the tissue surrounding the caseum has a comparatively anti-inflammatory signature. These findings are consistent across a set of six human subjects and in rabbits. Although the balance between systemic pro- and anti-inflammatory signals is crucial to TB disease outcome, here we find that these signals are physically segregated within each granuloma. From the protein and lipid snapshots of human and rabbit lesions analyzed here, we hypothesize that the pathologic response to TB is shaped by the precise anatomical localization of these inflammatory pathways during the development of the granuloma.

Figure 1. High resolution mass-spectrometry profiling of granuloma composition

(a) The three types of granulomas sampled in the study: solid, caseous and cavitary granulomas, scale bar = 1 mm. (b) Experimental workflow: laser capture micro-dissection was used to dissect, process and analyze multiple samples (n = 50 to 100, depending on the lesion compartment) in the caseous and cellular regions of each granuloma type. Dissected samples from each of the 5 regions were pooled and engaged in proteome-wide LC/MS-MS analysis. (c) A total of 4,406 proteins were identified across all granulomas and an average of ~95% protein identifications were shared between at least two proteomes. Five regions were sampled in three granuloma types from three different subjects. The proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD003646

Figure 2. Quantitative proteome analysis reveals spatially distinct protein signatures in granuloma

(a) Hierarchical clustering analysis of 2,529 LFQ-protein intensities (log₂) that were quantified in at least two of the five granuloma proteomes and showed at least a 1.5-fold difference in their abundance between at least two granuloma regions. Heatmap of z score- and log₂-transformed LFQ protein intensities where proteins and granuloma regions were grouped using unsupervised hierarchical clustering. (b) Principal component analysis (PCA) separates necrotic caseum (orange circles) from cellular components (green squares) of granuloma. The first two components, which account for 52% and 26.5% of data variability from all granuloma regions, are shown. (c) Annotation terms (KEGG, GO, CORUM) of proteins significantly different along component 1 that separates necrotic caseum from cellular components. The scatter plot depicts the difference score of these terms versus the −log₁₀-transformed P value. Annotation terms most associated with the necrotic caseum (orange circles) and cellular components (green circles) of granuloma are color coded. (d) Unsupervised hierarchical clustering based heat map of z score- and log₂- transformed LFQ protein intensities for the indicated example pro-inflammatory proteins (labelled in orange font) and anti-inflammatory proteins (labelled in green). (e) Proteomic profiling of the leukotriene and lipoxin synthetic pathway. For statistical analyses, we tested and computed the difference between medians of the two following groups: proteins corresponding to an annotation category versus all proteins in the dataset. We did this for every annotation term using two-sided Wilcoxon-Mann-Whitney test where the multiple hypothesis testing is adjusted by applying a Benjamini-Hochberg FDR threshold of 0.05.

Figure 3. The eicosanoid precursor arachidonic acid (AA) is abundant and synthesized diffusely within all granulomas

(a) Workfhow of MALDI MS imaging and quantitation by LC/MS of membrane phospholipids and AA in dissected lesions. (b) MALDI MS imaging of AA-containing lipids and AA in human tissues. Three cavities and three caseous granulomas from six different subjects were imaged, and one representative lesion of each type is shown. (c) MALDI MS imaging of AA-containing lipids in rabbit tissues. Two solid and two caseous granulomas from four rabbits were imaged, and one representative lesion of each type is shown. Scale bar = 5 mm. Color bars show the image intensity ranging from blue (lowest) to red (highest).

Figure 4. Leukotriene biosynthesis is enriched at in the caseum and the cellular layer directly adjacent to the caseum

(a) AA is the precursor for pro-inflammatory leukotrienes (left) or less inflammatory prostanoids (right). (b) H&E (upper left) and IHC staining of LTA4H (green), nuclei using DAPI (blue), and macrophages using IBA1 (cyan). (c) Average quantitation of LTA4H fluorescence in granulomas (n = 5). (d) Quantitation of LTA4H fluorescence as a function of distance from the macrophage border. (e) Relative intensity of LTA4H fluorescence in cellular subtypes within a typical caseous granuloma. Refer to Methods (Immunohistochemistry section) for explanations on how regions were delineated. All scale bars represent 350 um. For quantification of fluorescence average intensities in IHC images (n = 22), a student’s t-test was performed for single comparisons. For multiple comparisons, mean differences were tested by non-parametric Kruskal-Wallis analysis and adjusted by use of the Bonferoni-Dunn correction. P values of <0.05 were considered significant.

Figure 5. TNF-α and LTA4H are more abundant in the caseum and its margins

(a) IHC staining for TNF-α (red), LTA4H (green), nuclei using DAPI (blue), and macrophages using IBA1 (cyan) in a solid granuloma (n = 1). (b) IHC staining of one representative caseous granuloma (n=4). (c) IHC staining of one representative cavitary granuloma (n= 3). (d) IHC staining for TNF-α (red), LTA4H (green), nuclei using DAPI (blue), and macrophages using IBA1 (cyan) of a representative rabbit caseous granuloma (n = 2). All scale bars represent 350 um.

Figure 6. COX1/2 are diffusely expressed throughout all granuloma regions

(a) COX1/2 are the key enzymes in prostanoid synthesis from AA. (b) Average quantitation of COX1/2 fluorescence in granulomas (n = 5). (c) Lipid quantitation for 6-keto-PGF1-α, a prostanoid, in the cellular regions of granulomas, mirroring COX1 and COX2 distribution (n = 5). For quantification of fluorescence average intensities in IHC images and for lipid quantification, a student’s t-test was performed for single comparisons. For multiple comparisons, mean differences were tested by non-parametric Kruskal-Wallis analysis and adjusted by use of the Bonferoni-Dunn correction. * indicates a P value < 0.05 and ** indicate a P value < 0.01. (d) H&E (upper left) and IHC staining of COX1 (left) and COX2 (right) in green fluorescence, DAPI (blue) and IBA1 (cyan). (e) Quantitation of COX1 and COX2 fluorescence as a function of distance from the macrophage border. (f) COX1 and COX2 average fluorescence intensity in cellular subtypes within granulomas (n = 7). (g) IHC staining of COX1 in cellular and necrotic regions of a rabbit caseous granuloma. TNF-α (red), COX1 (green), nuclei using DAPI (blue), and macrophages using IBA1 (cyan). (h) H&E and IHC staining of COX1 in cellular regions of a rabbit solid granuloma. TNF-α (red), COX1 (green), nuclei using DAPI (blue), and macrophages using IBA1 (cyan). All scale bars represent 350 um.