Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysis

Abstract

Background Blood transcriptomics can be used for confirmation of tuberculosis diagnosis or sputumless triage, and a comparison of their practical diagnostic accuracy is needed to assess their usefulness. In this study, we investigated potential biomarkers to improve our understanding of the pathogenesis of active pulmonary tuberculosis (PTB) using bioinformatics methods. Methods Differentially expressed genes (DEGs) were analyzed between PTB and healthy controls (HCs) based on two microarray datasets. Pathways and functional annotation of DEGs were identified and ten hub genes were selected. They were further analyzed and selected, then verified with an independent sample set. Finally, their diagnostic power was further evaluated between PTB and HCs or other diseases. Results 62 DEGs mostly related to type I IFN pathway, IFN-γ-mediated pathway, etc. in GO term and immune process, and especially RIG-I-like receptor pathway were acquired. Among them, OAS1, IFIT1 and IFIT3 were upregulated and were the main risk factors for predicting PTB, with adjusted risk ratios of 1.36, 3.10, and 1.32, respectively. These results further verified that peripheral blood mRNA expression levels of OAS1, IFIT1 and IFIT3 were significantly higher in PTB patients than HCs (all P < 0.01). The performance of a combination of these three genes (three-gene set) had exceeded that of all pairwise combinations of them in discriminating TB from HCs, with mean AUC reaching as high as 0.975 with a sensitivity of 94.4% and a specificity of 100%. The good discernibility capacity was evaluated d via 7 independent datasets with an AUC of 0.902, as well as mean sensitivity of 87.9% and mean specificity of 90.2%. In regards to discriminating PTB from other diseases (i.e., initially considered to be possible TB, but rejected in differential diagnosis), the three-gene set equally exhibited an overall strong ability to separate PTB from other diseases with an AUC of 0.999 (sensitivity: 99.0%; specificity: 100%) in the training set, and 0.974 with a sensitivity of 96.4% and a specificity of 98.6% in the test set. Conclusion The described commonalities and unique signatures in the blood profiles of PTB and the other control samples have considerable implications for PTB biosignature design and future diagnosis, and provide insights into the biological processes underlying PTB.

Identification of immune biomarkers in recent active pulmonary tuberculosis

“…Further, TGFβ is a potent immune suppressive cytokine, but in our study, TGFβ was downregulated in active TB. Our data differ from most of the data, including TB-infected individuals with a strong immune response, probably due to our cohort’s early stages of infection 36 , 37 . In addition, our data lack subsequent cellular activation or stimulation, the use of naïve PBMCs, providing an easy test to detect the early stages of TB in countries with limited access to clinical and molecular tools.…”

“…Only a few transcriptional studies were performed on unstimulated PBMCs. Most of these studies used cohorts with active, latent or drug-treated Mtb cases without a proper selection of early cases of infection 35 – 37 . Hence, limited data is available on the transcriptional profiling of pro and anti-inflammatory markers from unstimulated (naïve) PBMCs of patients with an early and treatment naïve Mtb infection 38 – 40 .…”

Integrated bioinformatics analysis of dendritic cells hub genes reveal potential early tuberculosis diagnostic markers

“…In a multicohort analysis, DEGs were analyzed between active pulmonary tuberculosis (PTB) and healthy controls (HCs) using blood transcriptional datasets. 62 DEGs including unregulated IFIT1 and IFIT3 mostly related to IFN-γ-mediated pathway, which were also in accordance with our results [ 31 ]. IFN-γ was also considered as diagnostic marker of MTB-specific cytokine responses to distinguish LTBI, active TB from health control [ 32 ].…”

Inhibition of host PARP1 contributes to the anti-inflammatory and antitubercular activity of pyrazinamide

“…On the other hand, C3HeB/FeJ mice displaying the least lung pathology (RIF + Tp, RIF + PZA) had dramatic reductions in both lung and granuloma neutrophils. PARP1 inhibition also potently suppressed the expression of IFNβ and the type I IFN-inducible genes IFIT1 and IFIT3, which were recently identified as biomarkers predictive of active TB 57 . Type I IFNs drive TB susceptibility and pathogenesis by inducing neutrophilic inflammation which promotes disease progression 68 .…”

“…In addition, PARP1 inhibition had remarkable effects on interferon (IFN) signaling: while Type I IFN ( Ifnb ) expression was modestly reduced by RIF or Tp alone and potently by PZA or RIF + Tp, the IFN-simulated genes interferon-induced protein with tetratricopeptide repeats 1 ( Ifit1 ) and 3 ( Ifit3 ) were significantly inhibited by Tp, PZA or RIF + Tp while RIF alone had minimal effects on their expression. Recent blood transcriptomic analysis identified IFIT1 and IFIT3 as two of the few main risk factors predictive for TB progression in humans 57 . In contrast, the expression of monocyte chemoattractant protein-1 ( Mcp-1 ) and C–X–C motif chemokine receptor 5 ( Cxcr5 ) were repressed or stimulated, respectively, in direct correlation with bacterial burden and showed minimal effects of PARP1 inhibition.…”