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. 2018 Apr 30;8(1):6758.
doi: 10.1038/s41598-018-24509-6.

Transcriptional Landscape of Mycobacterium Tuberculosis Infection in Macrophages

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Free PMC article

Transcriptional Landscape of Mycobacterium Tuberculosis Infection in Macrophages

Sugata Roy et al. Sci Rep. .
Free PMC article

Abstract

Mycobacterium tuberculosis (Mtb) infection reveals complex and dynamic host-pathogen interactions, leading to host protection or pathogenesis. Using a unique transcriptome technology (CAGE), we investigated the promoter-based transcriptional landscape of IFNγ (M1) or IL-4/IL-13 (M2) stimulated macrophages during Mtb infection in a time-kinetic manner. Mtb infection widely and drastically altered macrophage-specific gene expression, which is far larger than that of M1 or M2 activations. Gene Ontology enrichment analysis for Mtb-induced differentially expressed genes revealed various terms, related to host-protection and inflammation, enriched in up-regulated genes. On the other hand, terms related to dis-regulation of cellular functions were enriched in down-regulated genes. Differential expression analysis revealed known as well as novel transcription factor genes in Mtb infection, many of them significantly down-regulated. IFNγ or IL-4/IL-13 pre-stimulation induce additional differentially expressed genes in Mtb-infected macrophages. Cluster analysis uncovered significant numbers, prolonging their expressional changes. Furthermore, Mtb infection augmented cytokine-mediated M1 and M2 pre-activations. In addition, we identified unique transcriptional features of Mtb-mediated differentially expressed lncRNAs. In summary we provide a comprehensive in depth gene expression/regulation profile in Mtb-infected macrophages, an important step forward for a better understanding of host-pathogen interaction dynamics in Mtb infection.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Experimental design and PCA analysis. (a) Schematic representation of the experimental design and comparisons to characterize the transcriptional landscape of Mtb-infected macrophages. The first comparison was performed between Mtb-infected macrophage and M1 (IFNγ)- or M2 (IL-4/IL-13)-stimulated BMDMs. The second comparison was performed to analyze the effect of pre-stimulation among Mtb, IFNγ_Mtb and IL-4/IL-13_Mtb. (b) Principal component analysis (PCA) was performed between Mtb, IFNγ-treated M1 macrophages, IL-4/IL-13-treated M2 macrophages. (c) PCA was performed between Mtb, IFNγ_Mtb and IL-4/IL-13_Mtb stimulated macrophages. Each number in the PCA plots represents the average expression of each sample for the indicated time point. Each condition is depicted with a different color. The PCA analysis using all replicates was shown in Supplementary Fig. 6a and b.
Figure 2
Figure 2
Global effect of Mtb infection in macrophages. (a) and (b) Heat map of enriched GO terms for up-regulated (a) and down-regulated (b) genes. Results of top ten gene ontology were shown. Because we did not find significant GO terms for down-regulated genes at 48 h, we showed them up to 24 h. (c) and (d) Box plot analysis of time course log fold-change expression of differentially up-regulated (c) and down-regulated (d) TF genes in Mtb-infected macrophages. Boxes show median and interquartile ranges and whiskers show the 10th and 90th percentile values.
Figure 3
Figure 3
Effect of IFNγ and IL-4/IL-13 pre-stimulation in Mtb-infected macrophages. (a) Venn diagram analysis of differentially expressed genes in Mtb, IFNγ_Mtb and IL-4/IL-13_Mtb. The overall landscape of differentially expressed (up and down) genes regardless of time was shown. There are same gene up and down regulated at different time point for the same condition (11 in IFNγ_Mtb and 3 in IL-4/IL-13_Mtb) which were excluded from the venn diagram analysis. (b) Number of up-regulated (left panel) and down-regulated (right panel) genes at each time point in Mtb and IFNγ_Mtb. Open column indicates commonly regulated genes and red and green column indicate specifically regulated genes in IFNγ_Mtb and Mtb, respectively. (c) Number of up-regulated (left panel) and down-regulated (right panel) genes at each time point in Mtb and IL-4/IL-13_Mtb. Open column indicates commonly regulated genes and blue and green column indicate specifically regulated genes inIL-4/IL-13_Mtb and Mtb, respectively. (d) Seven clusters of the k-means clustering analysis for differentially expressed genes in Mtb infection are shown. We visualized the time-dependent pattern of expression change by plotting the expression of the genes that were centers of each cluster (thick black line for control and thick blue line for the condition). The remaining genes were plotted in fine lines, grey for control and light blue for the condition. The control is the mean expression of all replicates of unstimulated non-infected BMDM at each time points. Thus the controls average/center around zero (the average fold change of controls vs mean control is zero).
Figure 4
Figure 4
Mtb infection augments M1 gene activation in M1 pre-activated macrophages. Using the differential expressed genes, qualitative analysis was performed in Mtb, IFNγ_Mtb, IL-4/IL-13_Mtb samples. Several fold elevated or suppressed genes were identified by calculating TMP expression fold change of IFNγ_Mtb in comparison with Mtb, IL-4/IL-13_Mtb samples at each time point. IFNγ pre-stimulation-mediated several fold up regulated (a) and suppressed or down regulated (b) non TF genes (10-fold respectively) and TF genes (3-fold respectively) was selected as seen in the heatmap cluster in each time point. Both heatmaps shows un-stimulated control which indicate the basal level of gene expression. (c) Expression profiles of representative M1 key effector genes, Nos2, Cxcl10, Cxcl9 and Cxcl11. (d) Expression profiles of representative M1 key TF genes, Batf2 and Irf1.
Figure 5
Figure 5
Mtb infection augments M2 activation in M2 pre-activated macrophages. Qualitative analysis was performed to obtain several fold elevated or suppressed genes in IL-4/IL-13_Mtb by calculating TPM expression fold change comparing expression TPM value of IL-4/IL-13_Mtb with Mtb, IFNγ_Mtb samples at each time point. IL-4/IL-13 pre-stimulation-mediated several fold up regulated (a) and suppressed or down regulated (b) non TF genes (more and less than 10-fold respectively) and TF genes (more and less than 3-fold respectively) was selected as seen in the heatmap cluster in each time point. Both heat maps shows un-stimulated control which indicate the basal level of gene expression. (c) Expression profiles of representative M2 key effector genes, Arg1 and Ccl24. (d) Expression profiles of representative M2 key TF genes, Irf4 and Myc.
Figure 6
Figure 6
Differentially expressed lncRNAs in Mtb infected macrophages shows unique transcriptional features. (a) Venn diagram analysis of differentially expressed lncRNAs in Mtb, IFNγ_Mtb and IL-4/IL-13_Mtb. The overall landscape of differentially expressed (up and down) genes regardless of time was shown. (b) Number of up-regulated (left panel) and down-regulated (right panel) lncRNAs at each time point in Mtb and IFNγ_Mtb. Open column indicates commonly regulated lncRNAs and red and green column indicate specifically regulated lncRNAs in IFNγ_Mtb and Mtb, respectively. (c) Number of up-regulated (left panel) and down-regulated (right panel) lncRNAs at each time point in Mtb andIL-4/IL-13_Mtb. Open column indicates commonly regulated lncRNAs and blue and green column indicate specifically regulated lncRNAs in IL-4/IL-13_Mtb and Mtb, respectively. (d) Expression correlation between differentially expressed lncRNAs and their nearest protein coding genes. Boxes show median and interquartile ranges and whiskers show the 10th and 90th percentile values.

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References

    1. World Health Organisation, G. T. R. W. R. Who., Geneva, Switzerland, WHO/HTM/TB/2016.13 2016.
    1. Houben RM, Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS medicine. 2016;13:e1002152. doi: 10.1371/journal.pmed.1002152. - DOI - PMC - PubMed
    1. Zuniga J, et al. Cellular and humoral mechanisms involved in the control of tuberculosis. Clinical & developmental immunology. 2012;2012:193923. doi: 10.1155/2012/193923. - DOI - PMC - PubMed
    1. Ehrt S, et al. Reprogramming of the macrophage transcriptome in response to interferon-gamma and Mycobacterium tuberculosis: signaling roles of nitric oxide synthase-2 and phagocyte oxidase. The Journal of experimental medicine. 2001;194:1123–1140. doi: 10.1084/jem.194.8.1123. - DOI - PMC - PubMed
    1. Nathan CF, Murray HW, Wiebe ME, Rubin BY. Identification of interferon-gamma as the lymphokine that activates human macrophage oxidative metabolism and antimicrobial activity. The Journal of experimental medicine. 1983;158:670–689. doi: 10.1084/jem.158.3.670. - DOI - PMC - PubMed

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