Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome
There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we ev...
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-03-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fimmu.2019.00527/full |
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DOAJ |
| language |
English |
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Article |
| sources |
DOAJ |
| author |
Fergal J. Duffy January Weiner Scott Hansen David L. Tabb Sara Suliman Ethan Thompson Jeroen Maertzdorf Smitha Shankar Gerard Tromp Shreemanta Parida Shreemanta Parida Drew Dover Michael K. Axthelm Jayne S. Sutherland Hazel M. Dockrell Tom H. M. Ottenhoff Thomas J. Scriba Louis J. Picker Gerhard Walzl Stefan H. E. Kaufmann Daniel E. Zak The GC6-74 Consortium S. H. E. Kaufmann S. K. Parida R. Golinski J. Maertzdorf J. Weiner M. Jacobson G. McEwen G. Walzl G. F. Black G. van der Spuy K. Stanley M. Kriel N. Du Plessis N. Nene A. G. Loxton N. N. Chegou S. Suliman T. Scriba M. Fisher H. Mahomed J. Hughes K. Downing A. Penn-Nicholson H. Mulenga B. Abel M. Bowmaker B. Kagina W. Kwong C. W. Hanekom T. H. M. Ottenhoff M. R. Klein M. C. Haks K. L. Franken A. Geluk K. E. van Meijgaarden S. A. Joosten D. van Baarle F. Miedema W. H. Boom B. Thiel J. Sadoff D. Sizemore S. Ramachandran L. Barker M. Brennan F. Weichold S. Muller L. Geiter G. Schoolnik G. Dolganov T. Van H. Mayanja-Kizza M. Joloba S. Zalwango M. Nsereko B. Okwera H. Kisingo H. M. Dockrell S. Smith P. Gorak-Stolinska Y.-G. Hur M. Lalor J.-S. Lee A. C. Crampin N. French B. Ngwira A. B. Smith K. Watkins L. Ambrose F. Simukonda H. Mvula F. Chilongo J. Saul K. Branson D. Kassa A. Abebe T. Mesele B. Tegbaru R. Howe A. Mihret A. Aseffa Y. Bekele R. Iwnetu M. Tafesse L. Yamuah M. Ota J. Sutherland P. Hill R. Adegbola T. Corrah M. Antonio T. Togun I. Adetifa S. Donkor P. Andersen I. Rosenkrands M. Doherty K. Weldingh |
| spellingShingle |
Fergal J. Duffy January Weiner Scott Hansen David L. Tabb Sara Suliman Ethan Thompson Jeroen Maertzdorf Smitha Shankar Gerard Tromp Shreemanta Parida Shreemanta Parida Drew Dover Michael K. Axthelm Jayne S. Sutherland Hazel M. Dockrell Tom H. M. Ottenhoff Thomas J. Scriba Louis J. Picker Gerhard Walzl Stefan H. E. Kaufmann Daniel E. Zak The GC6-74 Consortium S. H. E. Kaufmann S. K. Parida R. Golinski J. Maertzdorf J. Weiner M. Jacobson G. McEwen G. Walzl G. F. Black G. van der Spuy K. Stanley M. Kriel N. Du Plessis N. Nene A. G. Loxton N. N. Chegou S. Suliman T. Scriba M. Fisher H. Mahomed J. Hughes K. Downing A. Penn-Nicholson H. Mulenga B. Abel M. Bowmaker B. Kagina W. Kwong C. W. Hanekom T. H. M. Ottenhoff M. R. Klein M. C. Haks K. L. Franken A. Geluk K. E. van Meijgaarden S. A. Joosten D. van Baarle F. Miedema W. H. Boom B. Thiel J. Sadoff D. Sizemore S. Ramachandran L. Barker M. Brennan F. Weichold S. Muller L. Geiter G. Schoolnik G. Dolganov T. Van H. Mayanja-Kizza M. Joloba S. Zalwango M. Nsereko B. Okwera H. Kisingo H. M. Dockrell S. Smith P. Gorak-Stolinska Y.-G. Hur M. Lalor J.-S. Lee A. C. Crampin N. French B. Ngwira A. B. Smith K. Watkins L. Ambrose F. Simukonda H. Mvula F. Chilongo J. Saul K. Branson D. Kassa A. Abebe T. Mesele B. Tegbaru R. Howe A. Mihret A. Aseffa Y. Bekele R. Iwnetu M. Tafesse L. Yamuah M. Ota J. Sutherland P. Hill R. Adegbola T. Corrah M. Antonio T. Togun I. Adetifa S. Donkor P. Andersen I. Rosenkrands M. Doherty K. Weldingh Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome Frontiers in Immunology rhesus macaque household contact biomarker transcriptomics metabolomics tuberculosis |
| author_facet |
Fergal J. Duffy January Weiner Scott Hansen David L. Tabb Sara Suliman Ethan Thompson Jeroen Maertzdorf Smitha Shankar Gerard Tromp Shreemanta Parida Shreemanta Parida Drew Dover Michael K. Axthelm Jayne S. Sutherland Hazel M. Dockrell Tom H. M. Ottenhoff Thomas J. Scriba Louis J. Picker Gerhard Walzl Stefan H. E. Kaufmann Daniel E. Zak The GC6-74 Consortium S. H. E. Kaufmann S. K. Parida R. Golinski J. Maertzdorf J. Weiner M. Jacobson G. McEwen G. Walzl G. F. Black G. van der Spuy K. Stanley M. Kriel N. Du Plessis N. Nene A. G. Loxton N. N. Chegou S. Suliman T. Scriba M. Fisher H. Mahomed J. Hughes K. Downing A. Penn-Nicholson H. Mulenga B. Abel M. Bowmaker B. Kagina W. Kwong C. W. Hanekom T. H. M. Ottenhoff M. R. Klein M. C. Haks K. L. Franken A. Geluk K. E. van Meijgaarden S. A. Joosten D. van Baarle F. Miedema W. H. Boom B. Thiel J. Sadoff D. Sizemore S. Ramachandran L. Barker M. Brennan F. Weichold S. Muller L. Geiter G. Schoolnik G. Dolganov T. Van H. Mayanja-Kizza M. Joloba S. Zalwango M. Nsereko B. Okwera H. Kisingo H. M. Dockrell S. Smith P. Gorak-Stolinska Y.-G. Hur M. Lalor J.-S. Lee A. C. Crampin N. French B. Ngwira A. B. Smith K. Watkins L. Ambrose F. Simukonda H. Mvula F. Chilongo J. Saul K. Branson D. Kassa A. Abebe T. Mesele B. Tegbaru R. Howe A. Mihret A. Aseffa Y. Bekele R. Iwnetu M. Tafesse L. Yamuah M. Ota J. Sutherland P. Hill R. Adegbola T. Corrah M. Antonio T. Togun I. Adetifa S. Donkor P. Andersen I. Rosenkrands M. Doherty K. Weldingh |
| author_sort |
Fergal J. Duffy |
| title |
Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome |
| title_short |
Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome |
| title_full |
Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome |
| title_fullStr |
Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome |
| title_full_unstemmed |
Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome |
| title_sort |
immunometabolic signatures predict risk of progression to active tuberculosis and disease outcome |
| publisher |
Frontiers Media S.A. |
| series |
Frontiers in Immunology |
| issn |
1664-3224 |
| publishDate |
2019-03-01 |
| description |
There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes. |
| topic |
rhesus macaque household contact biomarker transcriptomics metabolomics tuberculosis |
| url |
https://www.frontiersin.org/article/10.3389/fimmu.2019.00527/full |
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doaj-1eba36dc045c44aa95b786e37918a63e2020-11-24T22:00:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242019-03-011010.3389/fimmu.2019.00527438472Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease OutcomeFergal J. Duffy0January Weiner1Scott Hansen2David L. Tabb3Sara Suliman4Ethan Thompson5Jeroen Maertzdorf6Smitha Shankar7Gerard Tromp8Shreemanta Parida9Shreemanta Parida10Drew Dover11Michael K. Axthelm12Jayne S. Sutherland13Hazel M. Dockrell14Tom H. M. Ottenhoff15Thomas J. Scriba16Louis J. Picker17Gerhard Walzl18Stefan H. E. Kaufmann19Daniel E. Zak20The GC6-74 ConsortiumS. H. E. KaufmannS. K. ParidaR. GolinskiJ. MaertzdorfJ. WeinerM. JacobsonG. McEwenG. WalzlG. F. BlackG. van der SpuyK. StanleyM. KrielN. Du PlessisN. NeneA. G. LoxtonN. N. ChegouS. SulimanT. ScribaM. FisherH. MahomedJ. HughesK. DowningA. Penn-NicholsonH. MulengaB. AbelM. BowmakerB. KaginaW. KwongC. W. HanekomT. H. M. OttenhoffM. R. KleinM. C. HaksK. L. FrankenA. GelukK. E. van MeijgaardenS. A. JoostenD. van BaarleF. MiedemaW. H. BoomB. ThielJ. SadoffD. SizemoreS. RamachandranL. BarkerM. BrennanF. WeicholdS. MullerL. GeiterG. SchoolnikG. DolganovT. VanH. Mayanja-KizzaM. JolobaS. ZalwangoM. NserekoB. OkweraH. KisingoH. M. DockrellS. SmithP. Gorak-StolinskaY.-G. HurM. LalorJ.-S. LeeA. C. CrampinN. FrenchB. NgwiraA. B. SmithK. WatkinsL. AmbroseF. SimukondaH. MvulaF. ChilongoJ. SaulK. BransonD. KassaA. AbebeT. MeseleB. TegbaruR. HoweA. MihretA. AseffaY. BekeleR. IwnetuM. TafesseL. YamuahM. OtaJ. SutherlandP. HillR. AdegbolaT. CorrahM. AntonioT. TogunI. AdetifaS. DonkorP. AndersenI. RosenkrandsM. DohertyK. WeldinghCenter for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United StatesMax Planck Institute for Infection Biology, Berlin, GermanyOregon Health and Science University, Portland, OR, United StatesDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South AfricaDepartment of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South AfricaCenter for Infectious Disease Research, Seattle, WA, United StatesMax Planck Institute for Infection Biology, Berlin, GermanyCenter for Infectious Disease Research, Seattle, WA, United StatesDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South AfricaMax Planck Institute for Infection Biology, Berlin, GermanyTranslational Medicine & Global Health Consulting, Berlin, GermanyCenter for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United StatesOregon Health and Science University, Portland, OR, United StatesVaccines & Immunity Theme, Medical Research Council Unit, Fajara, GambiaDepartment of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom0Department of Infectious Diseases, Leiden University Medical Center, Leiden, NetherlandsDepartment of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South AfricaOregon Health and Science University, Portland, OR, United StatesDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South AfricaMax Planck Institute for Infection Biology, Berlin, GermanyCenter for Infectious Disease Research, Seattle, WA, United StatesThere remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.https://www.frontiersin.org/article/10.3389/fimmu.2019.00527/fullrhesus macaquehousehold contactbiomarkertranscriptomicsmetabolomicstuberculosis |