Nature CommunicationsImmunologyPeer-reviewed journal article
Inflammatory Biomarkers of Asymptomatic and Symptomatic Tuberculosis
Denis AwanyiD, Dominique T Ariefdien, Simon C. MendelsohniD, Virginie RozotiD, Humphrey MulengaiD, Sarah NyanguiD, Michele D. TamerisiD, Tumelo MoloantoaiD, Austin Katona, Fernanda MaruriiD, Firdows NooriD, Ravindre PanchiaiD, Khuthadzo E. HlongwaneiD, Kim StanleyiD, Yuri F. van der HeijdeniD, Katie Hadley, Andrew J. Fiore-GartlandiD, Craig InnesiD, William BrumskineiD, Keertan U.J. DhedaiD, Shameem Z. JaumdallyiD, Tahlia PerumaliD, Neil Martinson, Alasdair J. LeslieiD, Bernard P. FourieiD, , Petrus Tyambetyuv
Abstract
A large proportion of individuals with tuberculosis (TB) are asymptomatic. The biological and inflammatory underpinnings of asymptomatic TB are unknown and may differ from symptomatic TB. We characterised blood transcriptomic and proteomic profiles in South African community screening vs. health facility-based triage cohorts. Asymptomatic TB shared core transcriptomic and proteomic features with symptomatic TB, including upregulation of innate, interferon and inflammatory pathways and downregulation of T and B cell pathways. Integration of transcriptomic and proteomic data from asymptomatic TB individuals identified two distinct sub-clusters characterized by higher or lower bacterial burden, blood IFN-{gamma} responses, BMI, and chest radiographic abnormalities, suggesting different disease severity. We identified a new blood transcriptomic signature of asymptomatic TB. However, diagnostic performance of transcriptomic and proteomic markers was weaker for asymptomatic TB than symptomatic TB, suggesting that policy development for community-based, asymptomatic TB screening should not adopt biomarkers developed for symptomatic TB triage without further optimization.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 0th percentile
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