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Mouse transcriptome reveals potential signatures of protection and pathogenesis in human tuberculosis

Nature Immunology volume 21, pages 464–476 (2020)Cite this article

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Abstract

Although mouse infection models have been extensively used to study the host response to Mycobacterium tuberculosis, their validity in revealing determinants of human tuberculosis (TB) resistance and disease progression has been heavily debated. Here, we show that the modular transcriptional signature in the blood of susceptible mice infected with a clinical isolate of M. tuberculosis resembles that of active human TB disease, with dominance of a type I interferon response and neutrophil activation and recruitment, together with a loss in B lymphocyte, natural killer and T cell effector responses. In addition, resistant but not susceptible strains of mice show increased lung B cell, natural killer and T cell effector responses in the lung upon infection. Notably, the blood signature of active disease shared by mice and humans is also evident in latent TB progressors before diagnosis, suggesting that these responses both predict and contribute to the pathogenesis of progressive M. tuberculosis infection.

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Fig. 1: Human TB blood transcriptional signature is preserved in blood of TB-susceptible mice.
Fig. 2: Mouse lung disease modules tested in lungs from diverse mouse TB models.
Fig. 3: Histological analysis of mouse lungs from M. tuberculosis-infected mice.
Fig. 4: Gene networks of specific TB modules in human blood from patients with TB and blood and lung from M. tuberculosis-infected mice.
Fig. 5: Histological analysis of mouse lungs from M. tuberculosis-infected mice for neutrophils, T cells and B cells.
Fig. 6: Quantitation of specific blood modular signatures against extent of lung pathology in mouse models and human TB.
Fig. 7: Quantitation of specific blood modular signatures in blood of healthy controls, LTBI, LTBI-progressors and patients with active TB.
Fig. 8: Quantitation of IFN-, neutrophil- and lymphocyte-specific gene expression in blood of healthy controls, LTBI, LTBI-progressors and patients with active TB.

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Data availability

The materials, data and any associated protocols that support the findings of this study are available from the corresponding author upon request. The RNA-seq datasets have been deposited in the NCBI Gene Expression Omnibus database with the primary accession number GSE140945 (TB mouse blood and lung). Publicly available datasets used in this study include GSE107995 (human TB datasets from Singhania et al.16) and GSE79362 (human TB dataset from Zak et al.38).

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Acknowledgements

The authors thank S. Hadebe (Francis Crick Institute UK, now UCT, South Africa) and J. Pitt (Medical Research Council (MRC), National Institute for Medical Research (NIMR), UK) for some of the early mouse M. tuberculosis infections and samples and sample processing leading up to the current study and K. Potempa (MRC, NIMR, UK) for early analysis of some of the microarray data leading up to the current study. The authors also thank L. Gabryšová (Francis Crick Institute UK, now Novartis, Basel, Switzerland) for her intellectual contribution to discussion of the project. The authors thank X. Wu for her help in organization of mice for TB experiments. The authors thank the Francis Crick Institute Science Technology Platforms: Biological Services for breeding and maintenance of the mice used for the early mouse M. tuberculosis infections and samples leading up to the current study; Advanced Sequencing Facility, Bioinformatics and Biostatistics Science Technology Platforms for their contribution to our sequencing processing and R. Goldstone for excellent project management of sequencing and D. Jackson for support of sequencing; and Experimental Histopathology for their excellent work in preparing lung sections for histological analyses. This study was funded by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001126), the UK MRC (FC001126) and the Wellcome Trust (FC001126); before that by the UK MRC (U117565642); and by the European Research Council (294682-TB-PATH). A.O’G., L.M-T., O.T., C.M.G., A. Singhania and E.S. were supported by the Francis Crick Institute, which receives its core funding as described above; L.M.-T., and A. Singhania, were additionally funded by the European Research Council (294682-TB-PATH). S.L.P., A.S.-B. and E.H. were funded by the Royal Veterinary College and the Francis Crick Institute. K.D.M.-B. and A. Sher were funded by the Intramural Research Program of the National Institutes of Allergy and Infectious Disease. M.S. was funded by FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020 – Operational Programme for Competitiveness and Internationalization (POCI), Portugal 2020 and by Portuguese funds through Fundação para a Ciência e a Tecnologia (FCT) in the framework of the project ‘Institute for Research and Innovation in Health Sciences’ (POCI-01-0145-FEDER-007274) and by FCT through Estimulo Individual ao Emprego Científico. K.L.F. and B.C. are funded by FCT PhD scholarships SFRH/BD/114405/2016 and SFRH/BD/114403/2016, respectively. P.H. and R.V. were supported by NIHR Leicester Biomedical Research Centre and the University of Leicester.

Author information

Author notes

  1. Paul S. Redford

    Present address: GSK R&D, Medicines Research Centre, Stevenage, UK

  2. These authors contributed equally: Lúcia Moreira-Teixeira, Olivier Tabone, Christine M. Graham.

Authors and Affiliations

  1. Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK

    Lúcia Moreira-Teixeira, Olivier Tabone, Christine M. Graham, Akul Singhania, Evangelos Stavropoulos, Paul S. Redford & Anne O’Garra

  2. Bioinformatics Core, The Francis Crick Institute, London, UK

    Probir Chakravarty

  3. Department of Pathobiology & Population Sciences, Royal Veterinary College, London, UK

    Simon L. Priestnall, Alejandro Suarez-Bonnet & Eleanor Herbert

  4. Experimental Histopathology Team, The Francis Crick Institute, London, UK

    Simon L. Priestnall, Alejandro Suarez-Bonnet & Eleanor Herbert

  5. Inflammation and Innate Immunity Unit, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Katrin D. Mayer-Barber

  6. Immunobiology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Alan Sher

  7. i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal

    Kaori L. Fonseca, Jeremy Sousa, Baltazar Cá & Margarida Saraiva

  8. IBMC - Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal

    Kaori L. Fonseca, Jeremy Sousa, Baltazar Cá & Margarida Saraiva

  9. ICBAS - Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal

    Kaori L. Fonseca, Jeremy Sousa & Baltazar Cá

  10. Programa de Pós-Graduação Ciência para o Desenvolvimento (PGCD), Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal

    Kaori L. Fonseca & Baltazar Cá

  11. Department of Respiratory Sciences, National Institute for Health Research Respiratory Biomedical Research Centre, University of Leicester, Leicester, UK

    Raman Verma & Pranabashis Haldar

  12. National Heart and Lung Institute, Imperial College London, London, UK

    Anne O’Garra

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  1. Lúcia Moreira-Teixeira
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Contributions

A.O’G. conceived and designed the study with input from L.M.-T., M.S., C.M.G. and P.S.R. and led and coordinated the study with help from L.M.-T. A.O’G. cowrote the manuscript together with L.M.-T. O.T. performed the major part of the bioinformatics analyses with considerable input from A. Singhania, supervised by A.O’G. P.C. provided technical bioinformatics support. L.M.-T. coordinated the logistics of the study. C.M.G. isolated RNA and prepared sequencing libraries from the TB mouse models. E.S. assisted in TB mouse model experiments executed by K.L.F., J.S., B.C. and P.S.R. and designed by A.O’G., L.M.-T. and M.S. E.S. contributed to all early mouse model TB experiments executed with P.S.R. and L.M.-T., supervised by A.O’G., L.M.-T. and M.S. S.L.P., A.S.-B. and E.H. performed histopathological analysis and interpretation. K.D.M.-B. and A.Sh. provided TB samples from early mouse TB experiments leading up to the current study. M.S., K.D.M.-B. and A. Sher also provided important discussion for the project throughout and critical feedback on the manuscript. R.V. and P.H. provided clinical analysis of patients with TB and also provided critical feedback on the manuscript and important discussion for the project. All coauthors read, reviewed and approved the manuscript.

Corresponding author

Correspondence to Anne O’Garra.

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Competing interests

The authors declare no competing interests.

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Peer review information Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–9.

Reporting Summary

Supplementary Table 1

Human blood TB modules annotation. Annotation and number of genes of each human blood TB module (HB1–HB23) from Singhania et al.16. Module name indicates biological processes associated with the genes included in the module. Gene of interest lists a subset of genes of interest included in each module, Canonical Pathway (IPA) shows the top five canonical pathways found with IPA and Pathways (Acumenta) shows the top five pathways found with Acumenta (Singhania et al.16).

Supplementary Table 2

Human blood data in human blood TB modules, from Singhania et al.16, separated in 12 tabs depicting: London individuals raw, the raw read counts for all genes in all individual samples from the London dataset; London groups raw, the mean raw counts per group from the London dataset for all genes; London individuals norm, the normalized read counts of filtered genes in all individual samples from the London dataset; London groups norm, the mean normalized read counts per group from the London dataset for filtered genes; SA individuals raw, the raw read counts of all genes in all individual samples from the South Africa (SA) dataset; SA groups raw, the mean raw counts per group from the SA dataset for all genes; SA individuals norm, the normalized read counts of filtered genes in all individual samples of SA dataset; SA groups norm, the mean normalized read counts per group from the SA dataset for filtered genes; Leicester individuals raw, the raw read counts of all genes in all individual samples from the Leicester dataset; Leicester groups raw, the mean raw counts per group from the Leicester dataset for all genes; Leicester individuals norm, the normalized read counts of filtered genes in all individual samples from the Leicester dataset; Leicester groups norm, the mean normalized read counts per group from the Leicester dataset for filtered genes. For each gene, the corresponding human blood TB module is detailed if applicable. Normalized read counts have been processed using the variance stabilizing transformation from DESeq2 R library, and genes with low read counts have been filtered out, as described in Singhania et al.16.

Supplementary Table 3

Mouse blood data in human blood TB modules, from Singhania et al.16, separated in six tabs depicting: mouse blood individuals raw, the raw read counts of all genes in all individual samples of mouse blood RNA-seq dataset; mouse blood groups raw, the mean raw counts per group in the mouse blood RNA-seq dataset for all genes; mouse blood individuals norm, the normalized read counts of all genes in all individual samples of mouse blood RNA-seq dataset; mouse blood groups norm, the mean normalized read counts per group in the mouse blood RNA-seq dataset for all genes; Supplementary Fig. 2a mouse blood microarray, the mouse blood microarray data corresponding to Supplementary Fig. 2a; Supplementary Fig. 3a mouse blood RNA-seq, the mouse blood normalized RNA-seq data corresponding to Supplementary Fig. 3a. For each gene, the corresponding human blood TB module is detailed if applicable. Normalized read counts from RNA-seq data represent read counts processed using the variance stabilizing transformation from DESeq2 R library.

Supplementary Table 4

Mouse lung data in mouse lung disease modules, from Singhania et al.31, separated in six tabs depicting: mouse lung individuals raw, the raw read counts of all genes in all individual samples of mouse lung RNA-seq dataset; mouse lung groups raw, the mean raw counts per group in the mouse lung RNA-seq dataset for all genes; mouse lung individuals norm, the normalized read counts of all genes in all individual samples of lung blood RNA-seq dataset; mouse lung groups norm, the mean normalized read counts per group in the mouse lung RNA-seq dataset for all genes; Supplementary Fig. 2b mouse lung microarray, the mouse lung microarray data corresponding to Supplementary Fig. 2b; Supplementary Fig. 3b mouse lung RNA-seq, the mouse lung normalized RNA-seq data corresponding to Supplementary Fig. 3b. For each gene, the corresponding disease lung module is detailed if applicable. Normalized read counts from RNA-seq data represent read counts processed using the variance stabilizing transformation from DESeq2 R library.

Supplementary Table 5

Mouse lung TB modules annotation. Annotation and the number of genes for each mouse lung TB module (ML1–ML27). Module name indicates biological processes associated with the genes within the module. Gene of interest lists a subset of genes of interest included in each module and all the other columns represent an output from either IPA or Metacore.

Supplementary Table 6

Mouse lung data in mouse lung TB modules, separated in four tabs depicting: mouse lung individuals raw, the raw read counts of all genes in all individual samples of mouse lung RNA-seq dataset; Mouse lung groups raw, the mean raw counts per group in the mouse lung RNA-seq dataset for all genes; Mouse lung individuals norm, the normalized read counts of all genes in all samples of mouse lung RNA-seq dataset; Mouse lung groups norm, the mean normalized read counts per group in the mouse lung RNA-seq dataset for all genes. For each gene, the corresponding TB lung module is detailed if applicable. Normalized read counts from RNA-seq data represent read counts processed using the variance stabilizing transformation from DESeq2 R library.

Supplementary Table 7

X-ray scores for human TB cohort from Leicester. X-ray classification (CXR classification) for each patient evaluated from the Leicester cohort and their associated disease group (TB subgroup).

Supplementary Table 8

Mouse to human gene mapping. The table lists the correspondence between mouse Ensembl gene ID and human Ensembl gene ID (ortholog genes), found using biomaRt package in R.

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Moreira-Teixeira, L., Tabone, O., Graham, C.M. et al. Mouse transcriptome reveals potential signatures of protection and pathogenesis in human tuberculosis. Nat Immunol 21, 464–476 (2020). https://doi.org/10.1038/s41590-020-0610-z

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