Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases

Abstract

Current diagnostic tests for tuberculosis (TB) are not able to predict reactivation disease progression from latent TB infection (LTBI). The main barrier to predicting reactivation disease is the lack of our understanding of host biomarkers associated with progression from latent infection to active disease. Here, we applied an immune-based gene expression profile by NanoString platform to identify whole blood markers that can distinguish active TB from other lung diseases (OPD), and that could be further evaluated as a reactivation TB predictor. Among 23 candidate genes that differentiated patients with active TB from those with OPD, nine genes (CD274, CEACAM1, CR1, FCGR1A/B, IFITM1, IRAK3, LILRA6, MAPK14, PDCD1LG2) demonstrated sensitivity and specificity of 100%. Seven genes (C1QB, C2, CCR2, CCRL2, LILRB4, MAPK14, MSR1) distinguished TB from LTBI with sensitivity and specificity between 82 and 100%. This study identified single gene candidates that distinguished TB from OPD and LTBI with high sensitivity and specificity (both > 82%), which may be further evaluated as diagnostic for disease and as predictive markers for reactivation TB.

In silico structure evaluation of BAG3 and elucidating its association with bacterial infections through protein–protein and host‐pathogen interaction analysis

“…The highest interactions were recorded for the direct interactors of BAG3, such as HSP90AA1, MAPK8, and MAPK14, having 13 interactions each with overall connectivity of 72.22%. Both MAPK8 and MAPK14 are markers for tuberculosis and play active roles in proinflammatory responses and immune cell proliferation 51,52 . HSP90AA1, a molecular chaperone, is involved in signal transduction and cell‐cycle control, promoting maturation and structural maintenance, while HSPA1A indulges in checking correct protein folding, proteolysis or refolding of misfolded proteins 53,54 .…”

“…Both MAPK8 and MAPK14 are markers for tuberculosis and play active roles in proinflammatory responses and immune cell proliferation. 51,52 HSP90AA1, a molecular chaperone, is involved in signal transduction and cell-cycle control, promoting maturation and structural maintenance, while HSPA1A indulges in checking correct protein folding, proteolysis or refolding of misfolded proteins. 53,54 Other significant proteins TLR2 and MAPK1, were also found to have been involved in BAG3-mediated response possessing identical connectivity parameters as other direct interactors.…”

Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness

“… Sens 0.92 (95%CI 0.73-0.99) Spec 0.96 (95%CI 0.78-1.0) dcRT-MLPA Healthy adults Miotto 2013 74 Case control San Raffaele Hospital (Milano, Italy), Ifakara Health Institute, Tanzania, St. Francis Nsambya Hospital (Kampala, Uganda) 19-90 years 10 miRNA signature (European) Sens 0.78 (95%CI 0.52-0.94) Spec 0.89 (95%CI 0.65-0.99) Microarray Healthy adults 12 miRNA signature (African-specific sig.) Sens 1.0 (95%CI 0.69-1.0) Spec 0.90 (95%CI 0.55-1.0) Ndzi 2019 75 Case control Jamot hospital Yaounde, Cameroon 16-76 years miR-29a-3p Sens 0.80 (95%CI 0.70-0.88) Spec 0.72 (95%CI 0.56-0.85) RT-qPCR Healthy adults MiR-155-5p Sens 0.80 (95%CI 0.70-0.88) Spec 0.50 (95%CI 0.35-0.65) MiR-361-5p Sens 0.88 (95%CI 0.79-0.94) Spec 0.58 (95%CI 0.42-0.73) Perumal 2021 76 Case control All India Institute of Medical Sciences, New Delhi and The Jawaharlal Institute of Postgraduate Medical Education & Research Puducherry Guy's and St Thomas’, Royal Free London >18 years GBP1, IFIT3, IFITM3, SAMD9L Sens 0.80 (95%CI 0.73-0.86) Spec 0.94 (0.89-0.98) qPCR Healthy adults Petrilli 2020 77 Case control Instituto Brasileiro para Investigação de Tuberculose and 2°Centro de Saúde Rodrigo Argolo, Bahia, Brazil. 25 to 60 years CEACAM1 Sens 1.0 (95%CI 0.80-1.0) Spec 1.0 (95%CI 0.48-1.0) NanoString platform Healthy adults CR1 Sens 1.0 (95%CI 0.80-1.0) Spec 1.0 (95%CI 0.48-1.0) FCGR1A/B Sens 1.0 (95%CI 0.80-1.0) Spec 1.0 (95%CI 0.48-1.0) Poore 2018 78 Case control Emergency Dept at Duke Hospital, UNC-Chapel Hill, and Henry Ford Hospital 19 to 76 years Bacterial vs viral miRNA signature (40 DE miRNA) Sens 1.0 (95%CI 0.69-1.0) Spec 0.85 (95%CI 0.55-0.98) Microarray ...…”

Transcriptomics for child and adolescent tuberculosis*

“…67 It can be used for quantification of custom genes or inventoried gene panels according to application area or biological process of interest. 68 Transcriptome-wide profiling methods allow for systematic analysis of thousands of RNA molecules simultaneously in a highthroughput manner and provide a global quantitative profile of the cell or tissue of interest, without the need of a pre-existing research hypothesis. Microarray technology exploits the principles of specific hybridization between two DNA strands and the emission and detection of fluorescence proportional to the amount of nucleic acid that is bound.…”

“…The relatively new NanoString nCounter gene expression system first reported in 2008 (Nanostring Technologies, WA, USA) has introduced a new method for targeted RNA quantification based on color‐coded probe pairs' ability to hybridize with complementary mRNA and fluorescence 67 . It can be used for quantification of custom genes or inventoried gene panels according to application area or biological process of interest 68 …”