Combining bioinformatics and biological detection to identify novel biomarkers for diagnosis and prognosis of pulmonary tuberculosis

Abstract

Objectives: To identify the novel and promising indicators for pulmonary tuberculosis (PTB) patients.

Methods: The study was carried out between June 2016 and June 2019. Three RNA sequencing or microarray datasets of TB infection were used to identify the potential genes showing a common expression trend. The expression level of screened targets was determined by reverse transcription polymerase chain reaction and ELISA using samples of whole blood and peripheral blood mononuclear cells (PBMCs) isolated from 69 PTB patients and 69 healthy volunteers. The potential of the identified targets to predict the treatment outcomes was further studied.

Results: Bioinformatics analysis demonstrated that a total of 91 genes were up-regulated in all the 3 datasets; among them, the expression of SLAMF8, LILRB4, and IL-10Ra was significantly increased at both the mRNA and protein levels in whole blood and PBMC samples of PTB patients compared with the healthy controls. The mortality rate increased significantly in SLAMF8 or LILRB4 high expression group compared with SLAMF8 or LILRB4 low expression group. Further, the decrease rate of bacteria in patients with SLAMF8 or LILRB4 high expression was slower than that in patients with SLAMF8 or LILRB4 low expression.

Conclusion: This study provides a promising way to identify novel indicators for PTB. Moreover, the LILRB4 expression may play a role in predicting the outcome of treatments on PTB patients.

Figure 1

Heatmap analysis displaying the overlapped targets which were dis-regulated under tuberculosis infections among the Gene Expression Omnibus public datasets GSE20050 (clinic), GSE57275 (in vivo), and GSE52819 (cultured cells based).

Figure 2

Venn diagram showing the distribution of the common A) up-regulated or B) down-regulated targets under Mycobacterium tuberculosis infections among the Gene Expression Omnibus public datasets.

Figure 3

Kyoto Encyclopedia of Genes and Genomes bioinformatics analysis determined the pathways related to the differentially expressed genes identified in Figure 1.

Figure 4

Gene Ontology bioinformatics analysis enriched the biological functions related to the differentially expressed genes in Figure 1. FDR: false discovery rate

Figure 5

Validated the enhancement of the targets that were involved in negative regulation of immune system process in A) whole and B) peripheral blood monocular cell between active pulmonary tuberculosis (PTB) patients and healthy controls using reverse transcription polymerase chain reaction. COPD: chronic obstructive pulmonary disease

Figure 6

The protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra in different subgroup of pulmonary tuberculosis patients (PTB) with COPD and control subjects by using ELISA assay. Determination of the relative of D) SLAMF8, E) LILRB4, and F) IL-10Ra expressions in smear negative and smear positive PTB patients by using enzyme-linked immunosorbent assays assay. HC: healthy control, SP: smear positive, SN: smear negative

Figure 7

The receiving operating curve curve for the expressions of SLAMF8, LILRB4, and IL-10Ra in relation to the pulmonary tuberculosis patients (PTB).

Figure 8

Kaplan-Meier curves of patients with tuberculosis produced according to the protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra expression. The p-values were determined by the log rank test.

Figure 9

Weekly bacteriologic sterilization according to the protein abundance of A) SLAMF8, B) LILRB4, and C) IL-10Ra expression, were evaluated as the colony forming units per milliliter sputum, in the first 8 weeks treatment of pulmonary tuberculosis patients.

Figure 10

Weekly bacteriologic sterilization according to the A) SLAMF8, B) LILRB4, and C) IL-10Ra expression, were evaluated as the smear conversation rate, in the first 8 weeks treatment of pulmonary tuberculosis patients.