Gene expression profiles classifying clinical stages of tuberculosis and monitoring treatment responses in Ethiopian HIV-negative and HIV-positive cohorts

Pathogen recognition pathway gene variants and inflammasome sensors gene expression in tuberculosis patients under treatment

BACKGROUND

Several epidemiological studies have suggested that genetic variations in encoding pattern recognition receptors (PRRs) genes such as Toll Like Receptors (TLRs) and their signaling products, may influence the susceptibility, severity and outcome of tuberculosis (TB). After sensing a pathogen, the cell responds producing an inflammatory response, to restrain the pathogen's successful course of infection. Herein we assessed single nucleotide polymorphisms (SNP) and gene expression from pathogen recognition and inflammasome pathways in Brazilian TB patients.

METHODS AND RESULTS

For genetic association analysis we included MYD88 and TLR4, PRRs sensing proteins. Allele distribution for MYD88 rs6853 (A > G) and TLR4 rs7873784 (C > G) presented conserved among the tested samples with statistically differential distribution in TB patients versus controls. However, when testing according to sample ethnicity (African or Caucasian-derived individuals) we identified that the rs6853 G/G genotype was associated with a lower susceptibility to TB in Caucasian population. Meanwhile, the rs7873784 G/G genotype was associated with a higher TB susceptibility in Afro-descendant ethnicity individuals. We also aimed to verify MYD88 and the inflammasome genes NLRP1 and NLRC4 expression in order to connect to active TB and/or clinical aspects.

CONCLUSIONS

We identified that inflammasome gene expression in TB patients under treatment display a similar pattern as in healthy controls, indicating that TB treatment impairs NLRP1 inflammasome activation.

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.

Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment

BACKGROUND

Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success is lower among people with diabetes mellitus (DM). Predicting treatment outcome early after diagnosis, especially in TB-DM patients, would allow early treatment adaptation for individuals and may improve global TB control.

METHODS

Samples were collected in a longitudinal cohort study of adult TB patients from South Africa (n  =  94) and Indonesia (n  =  81), who had concomitant DM (n  =  59), intermediate hyperglycaemia (n  =  79) or normal glycaemia/no DM (n  =  37). Treatment outcome was monitored, and patients were categorized as having a good (cured) or poor (failed, recurrence, died) outcome during treatment and 12 months follow-up. Whole blood transcriptional profiles before, during and at the end of TB treatment were characterized using unbiased RNA-Seq and targeted gene dcRT-MLPA.

FINDINGS

We report differences in whole blood transcriptome profiles, which were observed before initiation of treatment and throughout treatment, between patients with a good versus poor TB treatment outcome. An eight-gene and a 22-gene blood transcriptional signature distinguished patients with a good TB treatment outcome from patients with a poor TB treatment outcome at diagnosis (AUC = 0·815) or two weeks (AUC = 0·834) after initiation of TB treatment, respectively. High accuracy was obtained by cross-validating this signature in an external cohort (AUC = 0·749).

INTERPRETATION

These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM.

FUNDING

The research leading to these results, as part of the TANDEM Consortium, received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 305279) and the Netherlands Organization for Scientific Research (NWO-TOP Grant Agreement No. 91214038). The research leading to the results presented in the Indian validation cohort was supported by Research Council of Norway Global Health and Vaccination Research (GLOBVAC) projects: RCN 179342, 192534, and 248042, the University of Bergen (Norway).

Low transcriptomic of PTPRCv1 and CD3E is an independent predictor of mortality in HIV and tuberculosis co-infected patient

A comprehensive assessment of immunological profiles during HIV-TB co-infection is essential to predict mortality, and facilitate the development of effective diagnostic assays, therapeutic agents, and vaccines. Expression levels of 105 immune-related genes were measured at enrolment and 6th month follow-up from 9 deceased HIV and TB coinfected patients who died between 3 and 7th months follow-up and at enrolment, 6th and 18th month from 18 survived matched controls groups for 2 years. Focused gene expression profiling was assessed from peripheral whole blood using a dual-color Reverse-Transcription Multiplex Ligation-dependent Probe Amplification assay. Eleven of the 105 selected genes were differentially expressed between deceased individuals and survivor-matched controls at baseline. At baseline, IL4δ2 was significantly more highly expressed in the deceased group than survivor matched controls, whereas CD3E, IL7R, PTPRCv1, CCL4, GNLY, BCL2, CCL5, NOD1, TLR3, and NLRP13 had significantly lower expression levels in the deceased group compared to survivor matched controls. At baseline, a non-parametric receiver operator characteristic curve was conducted to determine the prediction of mortality of single genes identified CCL5, PTPRCv1, CD3E, and IL7R with Area under the Curve of 0.86, 0.86, 0.86, and 0.85 respectively. The expression of these genes in the survived control was increased at the end of TB treatment from that at baseline, while decreased in the deceased group. The expression of PTPRCv1, CD3E, CCL5, and IL7R host genes in peripheral blood of patients with TB-HIV coinfected can potentially be used as a predictor of mortality in the Ethiopian setting. Anti-TB treatment might be less likely to restore gene expression in the level expression of the deceased group. Therefore, other new therapeutics that can restore these genes (PTPRCv1, CD3E, IL7R, and CCL5) in the deceased groups at baseline might be needed to save lives.

Biomarkers that correlate with active pulmonary tuberculosis treatment response: a systematic review and meta-analysis

Current WHO recommendations for monitoring treatment response in adult pulmonary tuberculosis (TB) are sputum smear microscopy and/or culture conversion at the end of the intensive phase of treatment. These methods either have suboptimal accuracy or a long turnaround time. There is a need to identify alternative biomarkers to monitor TB treatment response. We conducted a systematic review of active pulmonary TB treatment monitoring biomarkers. We screened 9,739 articles published between 1 January 2008 and 31 December 2020, of which 77 met the inclusion criteria. When studies quantitatively reported biomarker levels, we meta-analyzed the average fold change in biomarkers from pretreatment to week 8 of treatment. We also performed a meta-analysis pooling the fold change since the previous time point collected. A total of 81 biomarkers were identified from 77 studies. Overall, these studies exhibited extensive heterogeneity with regard to TB treatment monitoring study design and data reporting. Among the biomarkers identified, C-reactive protein (CRP), interleukin-6 (IL-6), interferon gamma-induced protein 10 (IP-10), and tumor necrosis factor alpha (TNF-α) had sufficient data to analyze fold changes. All four biomarker levels decreased during the first 8 weeks of treatment relative to baseline and relative to previous time points collected. Based on limited data available, CRP, IL-6, IP-10, and TNF-α have been identified as biomarkers that should be further explored in the context of TB treatment monitoring. The extensive heterogeneity in TB treatment monitoring study design and reporting is a major barrier to evaluating the performance of novel biomarkers and tools for this use case. Guidance for designing and reporting treatment monitoring studies is urgently needed.

Validation of a host gene expression test for bacterial/viral discrimination in immunocompromised hosts

BACKGROUND

Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host response tests remains unknown. We evaluated a host response test discriminating bacterial, viral, and non-infectious conditions in immunocompromised subjects.

METHODS

An 81-gene signature was measured using RT-PCR in subjects with immunocompromise (chemotherapy, solid organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects.

RESULTS

Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial vs. non-bacterial discrimination and 80.8% for viral vs. non-viral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (p=0.04 relative to immunocompetent subjects) and 75.4% for viral infection (p=0.30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions.

CONCLUSION

A host gene expression test discriminated bacterial, viral, and non-infectious etiologies at a lower overall accuracy in immunocompromised patients compared to immunocompetent patients, though this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host response strategy may offer clinically useful diagnostic information for patients with immunocompromise.