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

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis

Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein-protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC-0.993, sensitivity-93.8%, specificity-100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

Type I interferons and Mycobacterium tuberculosis whole cell lysate induce distinct transcriptional responses in M. tuberculosis infection

Type I interferon (IFN)-induced genes have the potential for distinguishing active tuberculosis (ATB) from latent TB infection (LTBI) and healthy controls (HC), monitoring treatment, and detection of individuals at risk of progression to active disease. We examined the differential effects of IFN-α, IFN-β and Mycobacterium tuberculosis whole cell lysate (Mtb WCL) stimulation on the expression of selected IFN-stimulated genes in peripheral blood mononuclear cells from individuals with either LTBI, ATB, and healthy controls. Stimulation with IFN-α and IFN-β induced a higher expression of the interrogated genes while Mtb WCL stimulation induced expression similar to that observed at baseline, with the exception of IL-1A and IL-1B genes that were downregulated. The expression of IFN-α-induced FCGR1A gene, IFN-β-induced FCGR1A, FCGR1B, and SOCS3 genes, and Mtb WCL-induced IFI44, IFI44L, IFIT1, and IFITM3 genes differed significantly between LTBI and ATB. These findings suggest stimulation-driven gene expression patterns could potentially discriminate LTBI and ATB. Mechanistic studies are necessary to define the processes through which distinct type I IFNs and downstream ISGs determine infection outcomes and identify potential host-directed therapeutic strategies.

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.

Assessment of ferroptosis-associated gene signatures as potential biomarkers for differentiating latent from active tuberculosis in children

Ferroptotic cell death is a regulated process that is governed by iron-dependent membrane lipid peroxide accumulation that plays a pathogenic role in several disease-related settings. The use of ferroptosis-related genes (FRGs) to distinguish active tuberculosis (ATB) from latent tuberculosis infection (LTBI) among children, however, remains to be analysed. Tuberculosis-related gene expression data and FRG lists were obtained, respectively, from Gene Expression Omnibus (GEO) and FerrDb. Differentially expressed FRGs (DE-FRGs) detected when comparing samples from paediatric ATB and LTBI patients were explored using appropriate bioinformatics techniques, after which enrichment analyses were performed for these genes and hub genes were identified, with these genes then being used to explore potential drug interactions and construct competing endogenous RNA (ceRNA) networks. The GSE39939 dataset yielded 124 DE-FRGs that were primarily related to responses to oxidative, chemical and extracellular stimulus-associated stress. In total, the LASSO and SVM-RFE algorithms enabled the identification of nine hub genes (MAPK14, EGLN2, IDO1, USP11, SCD, CBS, PARP8, PARP16, CDC25A) that exhibited good diagnostic utility. Functional enrichment analyses of these genes suggested that they may govern ATB transition from LTBI through the control of many pathways, including the immune response, DNA repair, transcription, RNA degradation, and glycan and energy metabolism pathways. The CIBERSORT algorithm suggested that these genes were positively correlated with inflammatory and myeloid cell activity while being negatively correlated with the activity of lymphocytes. A total of 50 candidate drugs targeting 6 hub DE-FRGs were also identified, and a ceRNA network was used to explore the complex interplay among these hub genes. The nine hub FRGs defined in this study may serve as valuable biomarkers differentiating between ATB and LTBI in young patients.

Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection

Globally, it is estimated that one-quarter of the world's population is latently infected with Mycobacterium tuberculosis (Mtb), also known as latent tuberculosis infection (LTBI). Recently, this condition has been referred to as tuberculosis infection (TBI), considering the dynamic spectrum of the infection, as 5-10% of the latently infected population will develop active TB (ATB). The chances of TBI development increase due to close contact with index TB patients. The emergence of multidrug-resistant TB (MDR-TB) and the risk of development of latent MDR-TB has further complicated the situation. Detection of TBI is challenging as the infected individual does not present symptoms. Currently, there is no gold standard for TBI diagnosis, and the only screening tests are tuberculin skin test (TST) and interferon gamma release assays (IGRAs). However, these tests have several limitations, including the inability to differentiate between ATB and TBI, false-positive results in BCG-vaccinated individuals (only for TST), false-negative results in children, elderly, and immunocompromised patients, and the inability to predict the progression to ATB, among others. Thus, new host markers and Mtb-specific antigens are being tested to develop new diagnostic methods. Besides screening, TBI therapy is a key intervention for TB control. However, the long-course treatment and associated side effects result in non-adherence to the treatment. Additionally, the latent MDR strains are not susceptible to the current TBI treatments, which add an additional challenge. This review discusses the current situation of TBI, as well as the challenges and efforts involved in its control.

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.

Transcriptomics for child and adolescent tuberculosis

Tuberculosis (TB) in humans is caused by Mycobacterium tuberculosis (Mtb). It is estimated that 70 million children (<15 years) are currently infected with Mtb, with 1.2 million each year progressing to disease. Of these, a quarter die. The risk of progression from Mtb infection to disease and from disease to death is dependent on multiple pathogen and host factors. Age is a central component in all these transitions. The natural history of TB in children and adolescents is different to adults, leading to unique challenges in the development of diagnostics, therapeutics, and vaccines. The quantification of RNA transcripts in specific cells or in the peripheral blood, using high-throughput methods, such as microarray analysis or RNA-Sequencing, can shed light into the host immune response to Mtb during infection and disease, as well as understanding treatment response, disease severity, and vaccination, in a global hypothesis-free manner. Additionally, gene expression profiling can be used for biomarker discovery, to diagnose disease, predict future disease progression and to monitor response to treatment. Here, we review the role of transcriptomics in children and adolescents, focused mainly on work done in blood, to understand disease biology, and to discriminate disease states to assist clinical decision-making. In recent years, studies with a specific pediatric and adolescent focus have identified blood gene expression markers with diagnostic or prognostic potential that meet or exceed the current sensitivity and specificity targets for diagnostic tools. Diagnostic and prognostic gene expression signatures identified through high-throughput methods are currently being translated into diagnostic tests.

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

Background

Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting.

Methods

A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462).

Findings

Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I² 77%) was present.

Interpretation

Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved.

Funding

No funding received.

Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis.

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

BAG3, a co-chaperone protein with a Bcl-2-associated athanogene (BAG) domain, has diverse functionalities in protein-folding, apoptosis, inflammation, and cell cycle regulatory cross-talks. It has been well characterised in cardiac diseases, cancers, and viral pathogenesis. The multiple roles of BAG3 are attributed to its functional regions like BAG, Tryptophan-rich (WW), isoleucine-proline-valine-rich (IPV), and proline-rich (PXXP) domains. However, to study its structural impact on various functions, the experimental 3D structure of BAG3 protein was not available. Hence, the structure was predicted through in silico modelling and validated through computational tools and molecular dynamics simulation studies. To the best of our knowledge, the role of BAG3 in bacterial infections is not explicitly reported. We attempted to study them through an in-silico protein-protein interaction network and host-pathogen interaction analysis. From structure-function relationships, it was identified that the WW and PXXP domains were associated with cellular cytoskeleton rearrangement and adhesion-mediated response, which might be involved in BAG3-related intracellular bacterial proliferation. From functional enrichment analysis, Gene Ontology terms and topological matrices, 18 host proteins and 29 pathogen proteins were identified in the BAG3 interactome pertaining to Legionellosis, Tuberculosis, Salmonellosis, Shigellosis, and Pertussis through differential phosphorylation events associated with serine metabolism. Furthermore, it was evident that direct (MAPK8, MAPK14) and associated (MAPK1, HSPD1, NFKBIA, TLR2, RHOA) interactors of BAG3 could be considered as therapeutic markers to curb down intracellular bacterial propagation in humans.