Tricks to translating TB transcriptomics.

Transcriptomics and other high-throughput methods are increasingly applied to questions relating to tuberculosis (TB) pathogenesis. Whole blood transcriptomics has repeatedly been applied to define correlates of TB risk and has produced new insight into the late stage of disease pathogenesis. In a novel approach, authors of a recently published study in Science Translational Medicine applied complex data analysis of existing TB transcriptomic datasets, and in vitro models, in an attempt to identify correlates of protection in TB, which are crucially required for the development of novel TB diagnostics and therapeutics to halt this global epidemic. Utilizing latent TB infection (LTBI) as a surrogate of protection, they identified IL-32 as a mediator of interferon gamma (IFNγ)-vitamin D dependent antimicrobial immunity and a marker of LTBI. Here, we provide a review of all TB whole-blood transcriptomic studies to date in the context of identifying correlates of protection, discuss potential pitfalls of combining complex analyses originating from such studies, the importance of detailed metadata to interpret differential patient classification algorithms, the effect of differing circulating cell populations between patient groups on the interpretation of resulting biomarkers and we decipher weighted gene co-expression network analysis (WGCNA), a recently developed systems biology tool which holds promise of identifying novel pathway interactions in disease pathogenesis. In conclusion, we propose the development of an integrated OMICS platform and open access to detailed metadata, in order for the TB research community to leverage the vast array of OMICS data being generated with the aim of unraveling the holy grail of TB research: correlates of protection

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

BACKGROUND. Tuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma. / METHODS. We applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203). / RESULTS. We generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low–molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81). / CONCLUSION. We report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test. / FUNDING. Colciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research

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

BACKGROUND: Validation of previously identified candidate biomarkers and identification of additional candidate gene expression profiles to facilitate diagnosis of tuberculosis (TB) disease and monitoring treatment responses in the Ethiopian context is vital for improving TB control in the future. METHODS: Expression levels of 105 immune-related genes were determined in the blood of 80 HIV-negative study participants composed of 40 active TB cases, 20 latent TB infected individuals with positive tuberculin skin test (TST+), and 20 healthy controls with no Mycobacterium tuberculosis (Mtb) infection (TST-), using focused gene expression profiling by dual-color Reverse-Transcription Multiplex Ligation-dependent Probe Amplification assay. Gene expression levels were also measured six months after anti-TB treatment (ATT) and follow-up in 38 TB patients. RESULTS: The expression of 15 host genes in TB patients could accurately discriminate between TB cases versus both TST+ and TST- controls at baseline and thus holds promise as biomarker signature to classify active TB disease versus latent TB infection in an Ethiopian setting. Interestingly, the expression levels of most genes that markedly discriminated between TB cases versus TST+ or TST- controls did not normalize following completion of ATT therapy at 6 months (except for PTPRCv1, FCGR1A, GZMB, CASP8 and GNLY) but had only fully normalized at the 18 months follow-up time point. Of note, network analysis comparing TB-associated host genes identified in the current HIV-negative TB cohort to TB-associated genes identified in our previously published Ethiopian HIV-positive TB cohort, revealed an over-representation of pattern recognition receptors including TLR2 and TLR4 in the HIV-positive cohort which was not seen in the HIV-negative cohort. Moreover, using ROC cutoff ≥ 0.80, FCGR1A was the only marker with classifying potential between TB infection and TB disease regardless of HIV status. CONCLUSIONS: Our data indicate that complex gene expression signatures are required to measure blood transcriptomic responses during and after successful ATT to fully diagnose TB disease and characterise drug-induced relapse-free cure, combining genes which resolve completely during the 6-months treatment phase of therapy with genes that only fully return to normal levels during the post-treatment resolution phase

Serum microRNAs as biomarker for active and latent tuberculosis infection in immunocompetent and immunodeficient hosts

Background: Expression patterns of microRNAs in body fluids show potential to be used as noninvasive rapid and accurate biomarkers for various diseases.The study aimed to (i) identify patterns of microRNA signatures for diagnosis of tuberculosis (TB) and (ii) assess significance of a patient’s genetic background on signature composition and diagnostic performance. Patients and Methods: The study enrolled consented participants from Europe and Africa. Circulating miRNAs were measured and compared between patients belonging to the following categories; (i) active pulmonary tuberculosis (PTB), (ii) healthy individuals (H), (iii) active pulmonary TB co-infected with HIV (PTB/HIV), (iv) latent TB infection (LTBI) and (v) other pulmonary infection (OPI). As a first step, pooled sera of 10 participants from each category and region of enrolment were measured by TaqMan low-density arrays. Secondly, the identified significant miRNA signatures were applied to 56 individual sera aiming to discriminate between H and PTB patients. Next, the identified miRNA signatures were analysed for their diagnostic performances using multivariate logistic analysis, and Relevance Vector Machine (RVM). The diagnostic performance of both models was evaluated by a leave-one-out-cross-validation (LOOCV).Results: Significant miRNA signatures that discriminated patient categories were selected from the pooled samples. After validation of these in 56 individual participants (36 from the European cohort and 20 from the African population); a signature of 15 miRNAs was observed to be significantly differently expressed between categories, and able to differentiate healthy individuals and from individuals with PTB with a diagnostic accuracy of 82% (CI 70.2-90.0) in the RVM and 77% (CI 64.2-85.9) in the logistic classification model. The analysis based on genetic background identified a signature of 10 miRNAs that was specific for the European cohort with a diagnostic accuracy of 83% (CI 68.1-92.1) in RVM, and 81% (65.0-90.3) in the logistic model. Whereas a signature of 12 miRNAs was specific to the African cohort and the diagnostic accuracy increased up to 95% (CI 76.4-99.1) and 100% (83.9-100.0) in RVM and logistic model, respectively.Conclusion: This proof-of-concept study showed that miRNA levels were significantly higher in patient with TB than in those without TB. miRNAs are a promising diagnostic candidate for TB, therefore further prospective evaluation of this diagnostic seems warranted

High-throughput and computational approaches for diagnostic and prognostic host tuberculosis biomarkers

High-throughput techniques strive to identify new biomarkers that will be useful for the diagnosis, treatment, and prevention of tuberculosis (TB). However, their analysis and interpretation pose considerable challenges. Recent developments in the high-throughput detection of host biomarkers in TB are reported in this review

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas

Comparative miRNA Expression Profiles in Individuals with Latent and Active Tuberculosis

The mechanism of latent tuberculosis (TB) infection remains elusive. Several host factors that are involved in this complex process were previously identified. Micro RNAs (miRNAs) are endogenous ∼22 nt RNAs that play important regulatory roles in a wide range of biological processes. Several studies demonstrated the clinical usefulness of miRNAs as diagnostic or prognostic biomarkers in various malignancies and in a few nonmalignant diseases. To study the role of miRNAs in the transition from latent to active TB and to discover candidate biomarkers of this transition, we used human miRNA microarrays to probe the transcriptome of peripheral blood mononuclear cells (PBMCs) in patients with active TB, latent TB infection (LTBI), and healthy controls. Using the software package BRB Array Tools for data analyses, 17 miRNAs were differentially expressed between the three groups (P<0.01). Hierarchical clustering of the 17 miRNAs expression profiles showed that individuals with active TB clustered independently of individuals with LTBI or from healthy controls. Using the predicted target genes and previously published genome-wide transcriptional profiles, we constructed the regulatory networks of miRNAs that were differentially expressed between active TB and LTBI. The regulatory network revealed that several miRNAs, with previously established functions in hematopoietic cell differentiation and their target genes may be involved in the transition from latent to active TB. These results increase the understanding of the molecular basis of LTBI and confirm that some miRNAs may control gene expression of pathways that are important for the pathogenesis of this infectious disease

Diagnostic 'omics' for active tuberculosis

The decision to treat active tuberculosis (TB) is dependent on microbiological tests for the organism or evidence of disease compatible with TB in people with a high demographic risk of exposure. The tuberculin skin test and peripheral blood interferon-γ release assays do not distinguish active TB from a cleared or latent infection. Microbiological culture of mycobacteria is slow. Moreover, the sensitivities of culture and microscopy for acid-fast bacilli and nucleic acid detection by PCR are often compromised by difficulty in obtaining samples from the site of disease. Consequently, we need sensitive and rapid tests for easily obtained clinical samples, which can be deployed to assess patients exposed to TB, discriminate TB from other infectious, inflammatory or autoimmune diseases, and to identify subclinical TB in HIV-1 infected patients prior to commencing antiretroviral therapy. We discuss the evaluation of peripheral blood transcriptomics, proteomics and metabolomics to develop the next generation of rapid diagnostics for active TB. We catalogue the studies published to date seeking to discriminate active TB from healthy volunteers, patients with latent infection and those with other diseases. We identify the limitations of these studies and the barriers to their adoption in clinical practice. In so doing, we aim to develop a framework to guide our approach to discovery and development of diagnostic biomarkers for active TB

Identification of host gene expression biomarkers for tuberculosis

The presence of disease, including infectious disease, has been observed to give rise to specific patterns of gene expression in peripheral whole blood, regardless of disease site. These gene expression signatures allow for distinction between diseases and have the potential to reform diagnostics, particularly in diseases and patient groups for whom current diagnostics are unreliable, like Tuberculosis (TB). Although TB is a treatable infectious disease, it has high morbidity and mortality, especially in low resource countries and HIV infected patients. In this thesis, I propose a bioinformatics toolbox that derives minimal transcriptomic signatures from microarray datasets acquired from heterogeneous groups regardless of underlying co-infections and geographic locations. The transcripts’ expression values are then aggregated into a single value disease risk score (DRS) for every patient, that allows for classification between the disease groups in a binary manner. The toolbox was employed to analyse an adult and a paediatric TB transcriptomic study, comprising HIV infected and uninfected patients from sub-Saharan Africa. In the adult study, the DRS based on a 27-transcript signature distinguished culture confirmed TB from latent TB infection (LTBI), while 44 transcripts distinguished TB from other diseases phenotypically similar to TB (OD), with high sensitivity and specificity. Out-of-sample validation was performed using a publicly available dataset. In the paediatric study, a 51-transcript signature distinguished TB from OD and a 42-transcript signature from LTBI. The signatures were validated out-of-sample using an independent cohort and benchmarked against culture-negative TB patients and Xpert® MTB/RIF, currently used for detection of M. tuberculosis. This thesis provides proof of principle that minimal host blood transcriptional signatures are able to distinguish TB from LTBI and OD regardless of HIV infection. The subsequent transformation of the signatures into a score for every patient may facilitate disease categorisation and potentially development of diagnostic tools.Open Acces