Comprehensive analyses of potential key genes in active tuberculosis: A systematic review

Construction of ceRNA regulatory networks for active pulmonary tuberculosis

Delayed diagnosis in patients with pulmonary tuberculosis (PTB) often leads to serious public health problems. High throughput sequencing was used to determine the expression levels of lncRNAs, mRNAs, and miRNAs in the lesions and adjacent health lung tissues of patients with PTB. Their differential expression profiles between the two groups were compared, and 146 DElncRs, 447 DEmRs, and 29 DEmiRs were obtained between lesions and adjacent health tissues in patients with PTB. Enrichment analysis for mRNAs showed that they were mainly involved in Th1, Th2, and Th17 cell differentiation. The lncRNAs, mRNAs with target relationship with miRNAs were predicted respectively, and correlation analysis was performed. The ceRNA regulatory network was obtained by comparing with the differentially expressed transcripts (DElncRs, DEmRs, DEmiRs), then 2 lncRNAs mediated ceRNA networks were established. The expression of genes within the network was verified by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis revealed that the proportion of Th1 cells and Th17 cells was lower in PTB than in controls, while the proportion of Th2 cells increased. Our results provide rich transcriptome data for a deeper investigation of PTB. The ceRNA regulatory network we obtained may be instructive for the diagnosis and treatment of PTB.

Evaluation of the Xpert MTB Host Response assay for the triage of patients with presumed pulmonary tuberculosis: a prospective diagnostic accuracy study in Viet Nam, India, the Philippines, Uganda, and South Africa

BACKGROUND

Non-sputum-based triage tests for tuberculosis are a priority for ending tuberculosis. We aimed to evaluate the diagnostic accuracy of the late-prototype Xpert MTB Host Response (Xpert HR) blood-based assay.

METHODS

We conducted a prospective diagnostic accuracy study among outpatients with presumed tuberculosis in outpatient clinics in Viet Nam, India, the Philippines, Uganda, and South Africa. Eligible participants were aged 18 years or older and reported cough lasting at least 2 weeks. We excluded those receiving tuberculosis treatment in the preceding 12 months and those who were unwilling to consent. Xpert HR was performed on capillary or venous blood. Reference standard testing included sputum Xpert MTB/RIF Ultra and mycobacterial culture. We performed receiver operating characteristic (ROC) analysis to identify the optimal cutoff value for the Xpert HR to achieve the target sensitivity of 90% or more while maximising specificity, then calculated diagnostic accuracy using this cutoff value. This study was prospectively registered with ClinicalTrials.gov, NCT04923958.

FINDINGS

Between July 13, 2021, and Aug 15, 2022, 2046 adults with at least 2 weeks of cough were identified, of whom 1499 adults (686 [45·8%] females and 813 [54·2%] males) had valid Xpert HR and reference standard results. 329 (21·9%) had microbiologically confirmed tuberculosis. Xpert HR had an area under the ROC curve of 0·89 (95% CI 0·86-0·91). The optimal cutoff value was less than or equal to -1·25, giving a sensitivity of 90·3% (95% CI 86·5-93·3; 297 of 329) and a specificity of 62·6% (95% CI 59·7-65·3; 732 of 1170). Sensitivity was similar across countries, by sex, and by subgroups, although specificity was lower in people living with HIV (45·1%, 95% CI 37·8-52·6) than in those not living with HIV (65·9%, 62·8-68·8; difference of 20·8%, 95% CI 13·0-28·6; p<0·0001). Xpert HR had high negative predictive value (95·8%, 95% CI 94·1-97·1), but positive predictive value was only 40·1% (95% CI 36·8-44·1). Using the Xpert HR as a triage test would have reduced confirmatory sputum testing by 57·3% (95% CI 54·2-60·4).

INTERPRETATION

Xpert HR did not meet WHO minimum specificity targets for a non-sputum-based triage test for pulmonary tuberculosis. Despite promise as a rule-out test that could reduce confirmatory sputum testing, further cost-effectiveness modelling and data on acceptability and usability are needed to inform policy recommendations.

FUNDING

National Institute of Allergy and Infectious Diseases of the US National Institutes of Health.

TRANSLATIONS

For the Vietnamese and Tagalog translations of the abstract see Supplementary Materials section.

Correlation between the risk of lymph node metastasis and the expression of GBP1 in breast cancer patients

Objective

To explore the prognostic value and correlation between the risk of lymph node metastasis (LNM) and Guanylate-binding Protein 1 (GBP1) in breast cancer (BC) patients.

Methods

In this retrospective study, the clinical data of 150 patients with BC who were surgically resected in The Affiliated Qingdao Central Hospital of Qingdao University from January 2019 to December 2021 were included. Patients were divided into metastasis group (n=110) or non-metastasis group (n=40) according to whether there was LNM post-surgery. Logistic regression was used to analyze the risk factors for LNM in BC, and Kaplan-Meier was used to assess the risk of disease progression 12 months post-operation in both groups. Patients were divided into a GBP1 low expression-group (n=75) or a GBP1 high expression-group (n=75). The risk of disease progression, one-year post-surgery was analyzed, and the predictive value of GBP1 in BC tissue was assessed by the receiver operating characteristics (ROC) curve.

Results

Independent risk factors for BC with LNM were GBP1, CEA and TNM stage (P<0.05). There is a linear relationship between GBP1 expression and LNM risk in BC (χ2=0.88, P<0.05). Patients with high expression of GBP1 had a higher risk of LNM (χ2=3.204, P<0.001) and early postoperative progression (χ2=7.412, P<0.05). The AUC of GBP1 in predicting the risk of LNM was 0.840.

Conclusions

Patients with BC and a higher expression of GBP1 could be at an increased risk of LNM. Elevations in GBP1 expression can also suggest a poor prognosis for patients with BC.

Identification of immune biomarkers in recent active pulmonary tuberculosis

Tuberculosis (TB) has remained an unsolved problem and a major public health issue, particularly in developing countries. Pakistan is one of the countries with the highest tuberculosis infection rates globally. However, methods or biomarkers to detect early signs of TB infection are limited. Here, we characterized the mRNA profiles of immune responses in unstimulated Peripheral blood mononuclear cells obtained from treatment naïve patients with early signs of active pulmonary tuberculosis without previous history of clinical TB. We identified a unique mRNA profile in active TB compared to uninfected controls, including cytokines such as IL-27, IL-15, IL-2RA, IL-24, and TGFβ, transcription factors such as STAT1 and NFATC1 and immune markers/receptors such as TLR4, IRF1, CD80, CD28, and PTGDR2 from an overall 84 different transcripts analyzed. Among 12 significant differentially expressed transcripts, we identified five gene signatures which included three upregulated IL-27, STAT1, TLR4 and two downregulated IL-24 and CD80 that best discriminate between active pulmonary TB and uninfected controls with AUC ranging from 0.9 to 1. Our data identified a molecular immune signature associated with the early stages of active pulmonary tuberculosis and it could be further investigated as a potential biomarker of pulmonary TB.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

OBJECTIVE

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

METHODS

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

RESULTS

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

CONCLUSION

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis

Methods

Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA).

Results

In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity.

Conclusion

The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.

Mycobacterium tuberculosis Affects Protein and Lipid Content of Circulating Exosomes in Infected Patients Depending on Tuberculosis Disease State

Tuberculosis (TB), which is caused by the bacterium Mycobacterium tuberculosis (Mtb), is still one of the deadliest infectious diseases. Understanding how the host and pathogen interact in active TB will have a significant impact on global TB control efforts. Exosomes are increasingly recognized as a means of cell-to-cell contact and exchange of soluble mediators. In the case of TB, exosomes are released from the bacillus and infected cells. In the present study, a comprehensive lipidomics and proteomics analysis of size exclusion chromatography-isolated plasma-derived exosomes from patients with TB lymphadenitis (TBL) and treated as well as untreated pulmonary TB (PTB) was performed to elucidate the possibility to utilize exosomes in diagnostics and knowledge building. According to our findings, exosome-derived lipids and proteins originate from both the host and Mtb in the plasma of active TB patients. Exosomes from all patients are mostly composed of sphingomyelins (SM), phosphatidylcholines, phosphatidylinositols, free fatty acids, triacylglycerols (TAG), and cholesterylesters. Relative proportions of, e.g., SMs and TAGs, vary depending on the disease or treatment state and could be linked to Mtb pathogenesis and dormancy. We identified three proteins of Mtb origin: DNA-directed RNA polymerase subunit beta (RpoC), Diacyglycerol O-acyltransferase (Rv2285), and Formate hydrogenase (HycE), the latter of which was discovered to be differently expressed in TBL patients. Furthermore, we discovered that Mtb infection alters the host protein composition of circulating exosomes, significantly affecting a total of 37 proteins. All TB patients had low levels of apolipoproteins, as well as the antibacterial proteins cathelicidin, Scavenger Receptor Cysteine Rich Family Member (SSC5D), and Ficolin 3 (FCN3). When compared to healthy controls, the protein profiles of PTB and TBL were substantially linked, with 14 proteins being co-regulated. However, adhesion proteins (integrins, Intercellular adhesion molecule 2 (ICAM2), CD151, Proteoglycan 4 (PRG4)) were shown to be more prevalent in PTB patients, while immunoglobulins, Complement component 1r (C1R), and Glutamate receptor-interacting protein 1 (GRIP1) were found to be more abundant in TBL patients, respectively. This study could confirm findings from previous reports and uncover novel molecular profiles not previously in focus of TB research. However, we applied a minimally invasive sampling and analysis of circulating exosomes in TB patients. Based on the findings given here, future studies into host–pathogen interactions could pave the way for the development of new vaccines and therapies.