Fergal J. Duffy, January Weiner, Scott Hansen, David L. Tabb, Sara Suliman, Ethan Thompson, Jeroen Maertzdorf, Smitha Shankar, Gerard Tromp, 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
Abstract
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.
This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2021.
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#2,895,337
of 26,414,132 outputs
Outputs from Frontiers in immunology
#3,041
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#60,595
of 366,858 outputs
Outputs of similar age from Frontiers in immunology
#92
of 651 outputs
Altmetric has tracked 26,414,132 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 33,172 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has done particularly well, scoring higher than 90% of its peers.
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We're also able to compare this research output to 651 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.
