Transcriptional landscape of Mycobacterium tuberculosis infection in macrophages

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Type
Article
Authors
Roy, Sugata
Schmeier, Sebastian cc
Kaczkowski, Bogumil cc
Arner, Erik
Alam, Tanvir cc
Ozturk, Mumin cc
Tamgue, Ousman
Parihar, Suraj P.
Kawaji, Hideya cc
Itoh, Masayoshi
Lassmann, Timo
Carninci, Piero
Hayashizaki, Yoshihide
Forrest, Alistair R. R.
Guler, Reto
Bajic, Vladimir B. cc
Brombacher, Frank
Suzuki, Harukazu
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Date
2018-04-24
Permanent link to this record
http://hdl.handle.net/10754/627770

Metadata
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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.
Citation
Roy S, Schmeier S, Kaczkowski B, Arner E, Alam T, et al. (2018) Transcriptional landscape of Mycobacterium tuberculosis infection in macrophages. Scientific Reports 8. Available: http://dx.doi.org/10.1038/s41598-018-24509-6.
Sponsors
This work is supported by Research Grants for the Special Coordination Funds for Promoting Science and Technology from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government (MEXT), and Strategic International Research Cooperative Program (JST) to HS, grants from the South African National Research Foundation (NRF) and South Africa Medical Research Council (SAMRC) to FB, the JSPS NRF grant to HS and FB, grants from the Wellcome Trust CIDRI-Africa (203135Z/16/Z) for contributing investigator to FB and RG, grants from the South African National Research Foundation (NRF) Competitive Programme for Unrated Researchers (CSUR) and Department of Science and Technology (DST)/South African National Research Foundation (NRF) Collaborative Postgraduate Training Programme to RG, a grant from the King Abdullah University of Science and Technology (KAUST) to VBB, a grant for RIKEN Center for Life Science Technologies, and grants for RIKEN Preventive Medicine and Diagnosis Innovation Program, the Innovation Cell Biology by Innovative Technology (Cell Innovation Program) and RIKEN Omics Science Center to Y.H. We would like to thank the FANTOM5 consortium members and the RIKEN CLST/DGT members for contributing analysis of the data-set and thank GeNAS for data production.
Publisher
Springer Nature
Journal
Scientific Reports
ISSN
2045-2322
DOI
10.1038/s41598-018-24509-6
PubMed ID
29712924
Additional Links
https://www.nature.com/articles/s41598-018-24509-6
Collections
Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.