Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy - PubMed (original) (raw)

. 2016 Sep 22;1(15):e88843.

doi: 10.1172/jci.insight.88843.

Rosane Mb Teles 2, Delila Pouldar 2, Priscila R Andrade 2, Cressida A Madigan 2, David Lopez 1, Mike Ambrose 1, Mahdad Noursadeghi 3, Euzenir N Sarno 4, Thomas H Rea 5, Maria T Ochoa 5, M Luisa Iruela-Arispe 1, William R Swindell 6, Tom Hm Ottenhoff 7, Annemieke Geluk 7, Barry R Bloom 8, Matteo Pellegrini 1, Robert L Modlin 2 9

Affiliations

Cell-type deconvolution with immune pathways identifies gene networks of host defense and immunopathology in leprosy

Megan S Inkeles et al. JCI Insight. 2016.

Abstract

Transcriptome profiles derived from the site of human disease have led to the identification of genes that contribute to pathogenesis, yet the complex mixture of cell types in these lesions has been an obstacle for defining specific mechanisms. Leprosy provides an outstanding model to study host defense and pathogenesis in a human infectious disease, given its clinical spectrum, which interrelates with the host immunologic and pathologic responses. Here, we investigated gene expression profiles derived from skin lesions for each clinical subtype of leprosy, analyzing gene coexpression modules by cell-type deconvolution. In lesions from tuberculoid leprosy patients, those with the self-limited form of the disease, dendritic cells were linked with MMP12 as part of a tissue remodeling network that contributes to granuloma formation. In lesions from lepromatous leprosy patients, those with disseminated disease, macrophages were linked with a gene network that programs phagocytosis. In erythema nodosum leprosum, neutrophil and endothelial cell gene networks were identified as part of the vasculitis that results in tissue injury. The present integrated computational approach provides a systems approach toward identifying cell-defined functional networks that contribute to host defense and immunopathology at the site of human infectious disease.

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Figures

Figure 1

Figure 1. Differential distribution of leprosy subtypes.

(A) Unsupervised principal component analysis (PCA) of 29 leprosy biopsy specimens. Frozen RMA–normalized and filtered gene expression profiles from 29 leprosy biopsy specimens were characterized using PCA and displayed, with each profile displayed as a colored sphere and individual identification. Ellipsoids represent the 95% confidence interval for sample distribution. Total 3-dimensional PCA mapping represents 56% of variance (PC1 = 29.5%; PC2 = 16.1%, and PC3 = 10.3%). (B) Unsupervised hierarchical clustering of leprosy biopsy specimens. Gene expression profiles from A were clustered using average Pearson correlation and displayed in a tree, with each terminal leaf representing a biopsy sample. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 2

Figure 2. Cell-type–specific enrichment (deconvolution).

(A) Cell-type–specific deconvolution of all leprosy clinical subtypes. For each of 19 immune cell–type–specific signatures, signature enrichment scores were calculated using average gene expression for each leprosy subtype and normalized to Z scores. Each Z score represents the enrichment for a particular immune cell–type signature in the gene expression profile of one leprosy subtype relative to the other subtypes. Enrichment profiles for each condition were clustered using Euclidean distance and displayed in a heatmap, for which columns correspond to leprosy subtypes and rows correspond to cell types. Each individual square corresponds to the enrichment for one immune cell type in a specific leprosy subtype, with darker squares indicating higher enrichment. (B) Cell-type deconvolution of the proportional median lists for all leprosy subtypes. The gene count represents the number of genes in the proportional median list that overlapped with the specific cell-type list. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 3

Figure 3. Identification of clinical subtype gene modules in leprosy patients.

Correlation of WGCNA modules to leprosy subtypes was calculated by correlating module eigengene expression to a binary matrix representation of sample membership for each particular subtype. Module eigengenes, as well as the corresponding number of genes in each module, are labeled on the y axis, and leprosy subtypes are labeled on the x axis. Each square in the heatmap is colored according to correlation, with P values for correlation given in parentheses. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 4

Figure 4. Integration of WGCNA gene modules with cell-type–specific gene signatures.

For each of the 17 modules of related genes derived from WGCNA analysis, enrichment for cell-type–specific gene signatures for 18 cell types with immune or structural functions were calculated and displayed in a heatmap of Z scores. Z scores were calculated from log2 fold change enrichment scores, using average gene expression for each leprosy subtype for each cell type. Cell-type names are provided in rows, and WGCNA module names from Figure 2 are provided in the column. Modules that were significantly associated with one leprosy subtype are labeled. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 5

Figure 5. Specific networks for clinical subtypes of leprosy (L-lep and ENL).

Gene ontology, KEGG pathway, and Reactome analysis were performed with ClueGO (Cytoscape software) for the most relevant WGCNA modules and the top 250 genes of the proportional median (PM) list for each leprosy clinical subtype. Genes selected from gene sets composed of either PM lists or WGCNA modules were overlapped with the top 250 genes in the specific cell-type signatures related with (A) L-lep and (B) ENL. Connections were visualized by Gephi software. Blue circles represent genes, red circles denote immune functions, yellow circles show cell type, and gray lines represent connections between genes and immune functions and/or cell types. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy.

Figure 6

Figure 6. Specific networks for clinical subtypes of leprosy (T-lep and RR).

Gene ontology, KEGG pathway, and Reactome analysis were performed with ClueGO (Cytoscape software) for the most relevant WGCNA modules and the top 250 genes of the proportional median (PM) list for each leprosy clinical subtype. Genes selected from gene sets composed of either PM lists or WGCNA modules were overlapped with the top 250 genes in the specific cell-type signatures related with (A) RR and (B) T-lep. Connections were visualized by Gephi software. Blue circles represent genes, red circles denote immune functions, yellow circles show cell type, and gray lines represent connections between genes and immune functions and/or cell types. (A) RR function analysis shows genes (blue circles) connected with immune and neural biology (red) functions. RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 7

Figure 7. MMP12 regulation in leprosy.

Distribution of MMP12 mRNA by microarray analysis of skin lesions shown in arbitrary units (AU). The graph shows the total number of samples per clinical subtype (mean and SEM). Data represent mean ± SEM, RR = 10 (457.2 ± 207.2 AU), T-lep = 13 (732.7 ± 186.3 AU), L-lep = 16 (143.2 ± 54.41 AU), and ENL = 12 (186.3 ± 138.6 AU). Significance was determined by Kruskal-Wallis test (nonparametric ANOVA style test) using GraphPad Prism software and post-hoc analysis (Dunn’s multiple comparison test). **P < 0.01. ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

Figure 8

Figure 8. MMP-12 expression in leprosy lesions.

MMP-12 protein expression was evaluated in leprosy lesions (T-lep, L-lep, RR, and ENL). (A) CD3 expression was used as a marker for T cells, and IgG1 was used as a negative control; 1 representative labeled section is shown of at least 5 (each obtained from a different patient). Scale bar: 40 μm. Original magnification: ×100. (B) Automated image analysis of MMP12 protein expression. Each dot represents the percentage of MMP-12–stained area (diaminobenzidine [DAB]) per nuclear area for each individual photomicrograph (n = 5 for each group). Data represent mean ± SEM, T-lep (77.5 ± 10.7), L-lep (17.8 ± 8), RR (54.76 ± 10.2), and ENL 13 (32.14 ± 10.2). One-way ANOVA analysis was performed (P = 0.0004) using GraphPad Prism software, and post-hoc analysis (Bonferroni test) is indicated (*P < 0.05, **P < 0.01, ***P < 0.001). ENL, erythema nodosum leprosum; L-lep, lepromatous leprosy; RR, reversal reaction; T-lep, tuberculoid leprosy.

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