Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis

Blood transcriptomic markers of Mycobacterium tuberculosis load in sputum

BACKGROUND

Peripheral blood transcriptome signatures that distinguish active pulmonary tuberculosis (TB) from control groups have been reported, but correlations of these signatures with sputum mycobacterial load are incompletely defined.

METHODS

We assessed the performance of published TB transcriptomic signatures in Haiti, and identified transcriptomic biomarkers of TB bacterial load in sputum as measured by Xpert® MTB/RIF molecular testing. People in Port au Prince, Haiti, with untreated pulmonary TB (n = 51) formed the study cohort: 19 people with low and 32 with high sputum Mycobacterium tuberculosis load. Peripheral whole blood transcriptomes were generated using RNA sequencing.

RESULTS

Twenty of the differentially expressed transcripts in TB vs. no TB were differentially expressed in people with low vs. high sputum mycobacterial loads. The difference between low and high bacterial load groups was independent of radiographic severity. In a published data set of transcriptomic response to anti-tuberculosis treatment, this 20-gene subset was more treatment-responsive at 6 months than the full active TB signature.

CONCLUSION

We identified genes whose transcript levels in the blood distinguish active TB with high vs. low M. tuberculosis loads in the sputum. These transcripts may reveal mechanisms of mycobacterial control of M. tuberculosis during active infection, as well as identifying potential biomarkers for bacterial response to anti-tuberculosis treatment.

Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection

Tuberculosis (TB) is the leading infectious cause of mortality worldwide, due in part to a limited understanding of its clinical pathogenic spectrum of infection and disease. Historically, scientific research, diagnostic testing, and drug treatment have focused on addressing one of two disease states: latent TB infection or active TB disease. Recent research has clearly demonstrated that human TB infection, from latent infection to active disease, exists within a continuous spectrum of metabolic bacterial activity and antagonistic immunological responses. This revised understanding leads us to propose two additional clinical states: incipient and subclinical TB. The recognition of incipient and subclinical TB, which helps divide latent and active TB along the clinical disease spectrum, provides opportunities for the development of diagnostic and therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli. In this report, we review the current understanding of the pathogenesis, immunology, clinical epidemiology, diagnosis, treatment, and prevention of both incipient and subclinical TB, two emerging clinical states of an ancient bacterium.

Identification of new diagnostic biomarkers for Mycobacterium tuberculosis and the potential application in the serodiagnosis of human tuberculosis

Mycobacterium tuberculosis (M. tuberculosis) regions of difference (RD) encode proteins which are potentially useful as diagnostic reagents for tuberculosis (TB). In this study, 75 genes from M. tuberculosis RD1‐RD16 were successfully cloned from which 68 proteins were expressed and purified. Three serum pools from patients with pulmonary TB (PTB), extra‐pulmonary tuberculosis (EPTB) and healthy controls (HC) were used to preliminarily screen individual RD proteins. The OD₆₃₀ ratio of the PTB or EPTB to the HC group ≥ 2‐fold was positive. As a result, 29 proteins were obtained. The serological response to the identified antigens was further verified using 58 PTB samples with 38 sera from smear‐positive PTB (PTB‐SP) patients and 20 sera from smear‐negative PTB (PTB‐SN) patients, 16 EPTB samples, 42 latent M. tuberculosis infection samples and 40 HCs by indirect ELISA. With respect to the PTB diagnosis, receiver operating characteristic analysis showed that Rv0222 [area under the curve (AUC), 0.8129; 95% confidence interval (CI), 0.7280–0.8979] and Rv3403c (AUC, 0.8537; 95% CI, 0.7779–0.9294) performed better than ESAT6/CFP10 (AUC, 0.7435; 95% CI, 0.6465–0.8406). Rv0222 and Rv3403c demonstrated the highest diagnostic ability in the PTB‐SP group (sensitivity, 86.8%; specificity, 80%), while Rv3403c demonstrated the highest diagnostic ability in the PTB‐SN group (sensitivity, 70%; specificity, 80%). With respect to the EPTB diagnosis, Rv0222 exhibited the highest diagnostic value (AUC, 0.7523; sensitivity, 68.8%; specificity, 87.5%). In addition, the combination of Rv0222 and Rv3403c improved the test for PTB‐SN. These results indicate that Rv0222 and Rv3403c would be potential diagnostic biomarkers for active TB serodiagnosis. Mouse experiments demonstrated that Rv0222 and Rv3403c elicited specific cellular and humoral responses which were characterized by production of IFN‐γ, IgG1, and IgG2a, but a higher level of IgG1 than IgG2a.

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise the information from the entire transcriptome, which includes overabundance of type I interferon-inducible genes and underabundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals or those with acute viral and bacterial infections. We suggest that methods targeting gene selection across multiple discriminant modules can improve the development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach, we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

Latent tuberculosis infection: Opportunities and challenges

Diagnosing and treating latent tuberculosis (TB) infection (LTBI) is recognized by the World Health Organization as an important strategy to accelerate the decline in global TB and achieve TB elimination. Even among low-TB burden countries that have achieved high rates of detection and successful treatment for active TB, a number of barriers have prevented implementing or expanding LTBI treatment programmes. Of those infected with TB, relatively few will develop active disease and the current diagnostic tests have a low predictive value. LTBI treatment using isoniazid (INH) has low completion rates due to the long duration of therapy and poor tolerability. Both patients and physicians often perceive the risk of toxicity to be greater than the risk of reactivation TB. As a result, LTBI treatment has had a limited or negligible role outside of countries with high resources and low burden of disease. New tools have emerged including the interferon-gamma release assays that more accurately diagnose LTBI, particularly in people vaccinated with Bacillus Calmette-Guerin (BCG). Shorter, better tolerated treatment using rifamycins are proving safe and effective alternatives to INH. While still imperfect, TB prevention using these new diagnostic and treatment tools appear cost effective in modelling studies in the United States and have the potential to improve TB prevention efforts globally. Continued research to understand the host-organism interactions within the spectrum of LTBI is needed to develop better tools. Until then, overcoming the barriers and optimizing our current tools is essential for progressing toward TB elimination.

Preventive therapy for tuberculosis in rheumatological patients undergoing therapy with biological drugs

INTRODUCTION

Latent tuberculosis infection (LTBI) accounts for almost a quarter of the world population, and, in 5-10% of the subjects with impaired immune-response against M. tuberculosis growth, it may progress to active tuberculosis (TB). In this review, we focus on the need to propose a screening for LTBI including preventive therapy offer in rheumatic patients undergoing therapy with biological drugs. Areas covered: We report on evidence that biologics are associated with an increased risk of active TB reactivation. This effect seems to be mainly limited to treatment with anti-tumor necrosis factor (TNF) agents, while non-anti-TNF-targeted biologics are not likely associated to any increased risk. We introduce the concept that the patients' coexisting host-related risk factors, such as comorbidities, are crucial to identify those at higher risk to reactivate TB. We report that preventive TB therapy is well tolerated in patients treated with biological drugs. Expert commentary: Availability of non-anti-TNF targeted biologics, that are not associated with an increased risk of TB reactivation, offers a great opportunity to tailor a therapeutic intervention at low/absent TB risk. After proper LTBI screening investigations, preventive TB therapy has been demonstrated to be effective and well-tolerated to reduce the risk of TB reactivation in rheumatic patients requiring biological drugs.

RNA-Seq Meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers

Background

Feed intake and body weight gain are economically important inputs and outputs of beef production systems. The purpose of this study was to discover differentially expressed genes that will be robust for feed intake and gain across a large segment of the cattle industry. Transcriptomic studies often suffer from issues with reproducibility and cross-validation. One way to improve reproducibility is by integrating multiple datasets via meta-analysis. RNA sequencing (RNA-Seq) was performed on longissimus dorsi muscle from 80 steers (5 cohorts, each with 16 animals) selected from the outside fringe of a bivariate gain and feed intake distribution to understand the genes and pathways involved in feed efficiency. In each cohort, 16 steers were selected from one of four gain and feed intake phenotypes (n = 4 per phenotype) in a 2 × 2 factorial arrangement with gain and feed intake as main effect variables. Each cohort was analyzed as a single experiment using a generalized linear model and results from the 5 cohort analyses were combined in a meta-analysis to identify differentially expressed genes (DEG) across the cohorts.

Results

A total of 51 genes were differentially expressed for the main effect of gain, 109 genes for the intake main effect, and 11 genes for the gain x intake interaction (P_corrected < 0.05). A jackknife sensitivity analysis showed that, in general, the meta-analysis produced robust DEGs for the two main effects and their interaction. Pathways identified from over-represented genes included mitochondrial energy production and oxidative stress pathways for the main effect of gain due to DEG including GPD1, NDUFA6, UQCRQ, ACTC1, and MGST3. For intake, metabolic pathways including amino acid biosynthesis and degradation were identified, and for the interaction analysis the pathways identified included GADD45, pyridoxal 5’phosphate salvage, and caveolar mediated endocytosis signaling.

Conclusions

Variation among DEG identified by cohort suggests that environment and breed may play large roles in the expression of genes associated with feed efficiency in the muscle of beef cattle. Meta-analyses of transcriptome data from groups of animals over multiple cohorts may be necessary to elucidate the genetics contributing these types of biological phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4769-8) contains supplementary material, which is available to authorized users.

Perspectives for personalized therapy for patients with multidrug-resistant tuberculosis

According to the World Health Organization (WHO), tuberculosis is the leading cause of death attributed to a single microbial pathogen worldwide. In addition to the large number of patients affected by tuberculosis, the emergence of Mycobacterium tuberculosis drug-resistance is complicating tuberculosis control in many high-burden countries. During the past 5 years, the global number of patients identified with multidrug-resistant tuberculosis (MDR-TB), defined as bacillary resistance at least against rifampicin and isoniazid, the two most active drugs in a treatment regimen, has increased by more than 20% annually. Today we experience a historical peak in the number of patients affected by MDR-TB. The management of MDR-TB is characterized by delayed diagnosis, uncertainty of the extent of bacillary drug-resistance, imprecise standardized drug regimens and dosages, very long duration of therapy and high frequency of adverse events which all translate into a poor prognosis for many of the affected patients. Major scientific and technological advances in recent years provide new perspectives through treatment regimens tailor-made to individual needs. Where available, such personalized treatment has major implications on the treatment outcomes of patients with MDR-TB. The challenge now is to bring these adances to those patients that need them most.

"Tuberculosis in advanced HIV infection is associated with increased expression of IFNγ and its downstream targets"

BACKGROUND

Tuberculosis (TB) is the major cause of death in Human Immunodeficiency Virus (HIV)-infected individuals. However, diagnosis of TB in HIV remains challenging particularly when HIV infection is advanced. Several gene signatures and serum protein biomarkers have been identified that distinguish active TB from latent infection. Our study was designed to assess if gene expression signatures and cytokine levels would distinguish active TB in advanced HIV.

METHODS

We conducted a case-control study of whole blood RNA-Seq and plasma cytokine/chemokine analysis in HIV-infected with CD4+ T cell count of ≤ 100 cells/μl, with and without active TB. Next, the overlap of the differentially expressed genes (DEG) with the published signatures was performed and then receiver operator characteristic (ROC) analysis was done on small gene discriminators to determine their performance in distinguishing TB in advanced HIV. ELISA was performed on plasma to evaluate cytokine and chemokine levels.

RESULTS

Hierarchical clustering of the transcriptional profiles showed that, in general, HIV-infected individuals with TB (TB-HIV) clustered separately from those without TB. IPA indicated that the TB-HIV signature was characterized by an increase in inflammatory signaling pathways. Analysis of overlaps between DEG in our data set with published TB signatures revealed that significant overlap was seen with one TB signature and one TB-IRIS signature. ROC analysis revealed that transcript levels of FcGR1A (AUC = 0.85) and BATF2 (AUC = 0.82), previously reported as consistent single gene classifiers of active TB irrespective of HIV status, performed successfully even in advanced HIV. Plasma protein levels of IFNγ, a stimulator of FcGR1A and BATF2, and CXCL10, also up-regulated by IFNγ, accurately classified active TB (AUC = 0.98 and 0.91, respectively) in advanced HIV. Neither of these genes nor proteins distinguished between TB and TB-IRIS.

CONCLUSIONS

Gene expression of FcGR1A and BATF2, and plasma protein levels of IFNγ and CXCL10 have the potential to independently detect TB in advanced HIV. However, since other lung diseases were not included in this study, these final candidates need to be validated as specific to TB in the advanced HIV population with TB.

The Expanding Diversity of Mycobacterium tuberculosis Drug Targets

After decades of relative inactivity, a large increase in efforts to discover antitubercular therapeutics has brought insights into the biology of Mycobacterium tuberculosis (Mtb) and promising new drugs such as bedaquiline, which inhibits ATP synthase, and the nitroimidazoles delamanid and pretomanid, which inhibit both mycolic acid synthesis and energy production. Despite these advances, the drug discovery pipeline remains underpopulated. The field desperately needs compounds with novel mechanisms of action capable of inhibiting multi- and extensively drug -resistant Mtb (M/XDR-TB) and, potentially, nonreplicating Mtb with the hope of shortening the duration of required therapy. New knowledge about Mtb, along with new methods and technologies, has driven exploration into novel target areas, such as energy production and central metabolism, that diverge from the classical targets in macromolecular synthesis. Here, we review new small molecule drug candidates that act on these novel targets to highlight the methods and perspectives advancing the field. These new targets bring with them the aspiration of shortening treatment duration as well as a pipeline of effective regimens against XDR-TB, positioning Mtb drug discovery to become a model for anti-infective discovery.

Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth

Preterm birth (PTB) is the leading cause of newborn deaths around the world. Spontaneous preterm birth (sPTB) accounts for two-thirds of all PTBs; however, there remains an unmet need of detecting and preventing sPTB. Although the dysregulation of the immune system has been implicated in various studies, small sizes and irreproducibility of results have limited identification of its role. Here, we present a cross-study meta-analysis to evaluate genome-wide differential gene expression signals in sPTB. A comprehensive search of the NIH genomic database for studies related to sPTB with maternal whole blood samples resulted in data from three separate studies consisting of 339 samples. After aggregating and normalizing these transcriptomic datasets and performing a meta-analysis, we identified 210 genes that were differentially expressed in sPTB relative to term birth. These genes were enriched in immune-related pathways, showing upregulation of innate immunity and downregulation of adaptive immunity in women who delivered preterm. An additional analysis found several of these differentially expressed at mid-gestation, suggesting their potential to be clinically relevant biomarkers. Furthermore, a complementary analysis identified 473 genes differentially expressed in preterm cord blood samples. However, these genes demonstrated downregulation of the innate immune system, a stark contrast to findings using maternal blood samples. These immune-related findings were further confirmed by cell deconvolution as well as upstream transcription and cytokine regulation analyses. Overall, this study identified a strong immune signature related to sPTB as well as several potential biomarkers that could be translated to clinical use.

Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis

Rationale: Contacts of patients with tuberculosis (TB) constitute an important target population for preventive measures because they are at high risk of infection with Mycobacterium tuberculosis and progression to disease.Objectives: We investigated biosignatures with predictive ability for incident TB.Methods: In a case-control study nested within the Grand Challenges 6-74 longitudinal HIV-negative African cohort of exposed household contacts, we employed RNA sequencing, PCR, and the pair ratio algorithm in a training/test set approach. Overall, 79 progressors who developed TB between 3 and 24 months after diagnosis of index case and 328 matched nonprogressors who remained healthy during 24 months of follow-up were investigated.Measurements and Main Results: A four-transcript signature derived from samples in a South African and Gambian training set predicted progression up to two years before onset of disease in blinded test set samples from South Africa, the Gambia, and Ethiopia with little population-associated variability, and it was also validated in an external cohort of South African adolescents with latent M. tuberculosis infection. By contrast, published diagnostic or prognostic TB signatures were predicted in samples from some but not all three countries, indicating site-specific variability. Post hoc meta-analysis identified a single gene pair, C1QC/TRAV27 (complement C1q C-chain / T-cell receptor-α variable gene 27) that would consistently predict TB progression in household contacts from multiple African sites but not in infected adolescents without known recent exposure events.Conclusions: Collectively, we developed a simple whole blood-based PCR test to predict TB in recently exposed household contacts from diverse African populations. This test has potential for implementation in national TB contact investigation programs.

Transcriptomic Analysis of CD4+ T Cells Reveals Novel Immune Signatures of Latent Tuberculosis

In the context of infectious diseases, cell population transcriptomics are useful to gain mechanistic insight into protective immune responses, which is not possible using traditional whole-blood approaches. In this study, we applied a cell population transcriptomics strategy to sorted memory CD4 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and gain insight into the phenotype of tuberculosis (TB)-specific CD4 T cells. We found a 74-gene signature that could discriminate between memory CD4 T cells from healthy latently Mycobacterium tuberculosis-infected subjects and noninfected controls. The gene signature presented a significant overlap with the gene signature of the Th1* (CCR6⁺CXCR3⁺CCR4^-) subset of CD4 T cells, which contains the majority of TB-specific reactivity and is expanded in LTBI. In particular, three Th1* genes (ABCB1, c-KIT, and GPA33) were differentially expressed at the RNA and protein levels in memory CD4 T cells of LTBI subjects compared with controls. The 74-gene signature also highlighted novel phenotypic markers that further defined the CD4 T cell subset containing TB specificity. We found the majority of TB-specific epitope reactivity in the CD62L^-GPA33^- Th1* subset. Thus, by combining cell population transcriptomics and single-cell protein-profiling techniques, we identified a CD4 T cell immune signature of LTBI that provided novel insights into the phenotype of TB-specific CD4 T cells.

Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis — the ontology and the annotations — evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be a strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

Rebooting Human Immunology

Recent progress in both conceptual and technological approaches to human immunology have rejuvenated a field that has long been in the shadow of the inbred mouse model. This is a healthy development both for the clinical relevance of immunology and for the fact that it is a way to gain access to the wealth of phenomenology in the many human diseases that involve the immune system. This is where we are likely to discover new immunological mechanisms and principals, especially those involving genetic heterogeneity or environmental influences that are difficult to model effectively in inbred mice. We also suggest that there are likely to be novel immunological mechanisms in long-lived, less fecund mammals such as human beings since they must remain healthy far longer than short-lived rodents in order for the species to survive.

Update on tuberculosis biomarkers: From correlates of risk, to correlates of active disease and of cure from disease

Tuberculosis (TB) remains a devastating disease, yet despite its enormous toll on global health, tools to control TB are insufficient and often outdated. TB Biomarkers (TB-BM) would constitute extremely useful tools to measure infection status and predict outcome of infection, vaccination or therapy. There are several types of TB-BM: Correlate of Infection; Correlate of TB Disease; Correlate of Increased Risk of Developing Active TB Disease; Correlate of the Curative Response to Therapy; and Correlate of Protection (CoP). Most TB-BM currently studied are host-derived BM, and consist of transcriptomic, proteomic, metabolomic, cellular markers or marker combinations ('signatures'). In particular, vaccine-inducible CoP are expected to be transformative in developing new TB vaccines as they will de-risk vaccine research and development (R&D) as well as human testing at an early stage. In addition, CoP could also help minimizing the need for preclinical studies in experimental animals. Of key importance is that TB-BM are tested and validated in different well-characterized human TB cohorts, preferably with complementary profiles and geographically diverse populations: genetic and environmental factors such as (viral) coinfections, exposure to non-tuberculous mycobacteria, nutritional status, metabolic status, age (infants vs children vs adolescents vs adults) and other factors impact host immune set points and host responses across different populations. In this study, we review the most recent advances in research into TB-BM for the diagnosis of active TB, risk of TB development and treatment-induced TB cure.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Multicohort Analysis of Whole-Blood Gene Expression Data Does Not Form a Robust Diagnostic for Acute Respiratory Distress Syndrome

OBJECTIVES

To identify a novel, generalizable diagnostic for acute respiratory distress syndrome using whole-blood gene expression arrays from multiple acute respiratory distress syndrome cohorts of varying etiologies.

DATA SOURCES

We performed a systematic search for human whole-blood gene expression arrays of acute respiratory distress syndrome in National Institutes of Health Gene Expression Omnibus and ArrayExpress. We also included the Glue Grant gene expression cohorts.

STUDY SELECTION

We included investigator-defined acute respiratory distress syndrome within 48 hours of diagnosis and compared these with relevant critically ill controls.

DATA EXTRACTION

We used multicohort analysis of gene expression to identify genes significantly associated with acute respiratory distress syndrome, both with and without adjustment for clinical severity score. We performed gene ontology enrichment using Database for Annotation, Visualization and Integrated Discovery and cell type enrichment tests for both immune cells and pneumocyte gene expression. Finally, we selected a gene set optimized for diagnostic power across the datasets and used leave-one-dataset-out cross validation to assess robustness of the model.

DATA SYNTHESIS

We identified datasets from three adult cohorts with sepsis, one pediatric cohort with acute respiratory failure, and two datasets of adult patients with trauma and burns, for a total of 148 acute respiratory distress syndrome cases and 268 critically ill controls. We identified 30 genes that were significantly associated with acute respiratory distress syndrome (false discovery rate < 20% and effect size >1.3), many of which had been previously associated with sepsis. When metaregression was used to adjust for clinical severity scores, none of these genes remained significant. Cell type enrichment was notable for bands and neutrophils, suggesting that the gene expression signature is one of acute inflammation rather than lung injury per se. Finally, an attempt to develop a generalizable diagnostic gene set for acute respiratory distress syndrome showed a mean area under the receiver-operating characteristic curve of only 0.63 on leave-one-dataset-out cross validation.

CONCLUSIONS

The whole-blood gene expression signature across a wide clinical spectrum of acute respiratory distress syndrome is likely confounded by systemic inflammation, limiting the utility of whole-blood gene expression studies for uncovering a generalizable diagnostic gene signature.

Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India

Several studies have identified blood transcriptomic signatures that can distinguish active from latent Tuberculosis (TB). The purpose of this study was to assess how well these existing gene profiles classify TB disease in a South Indian population. RNA sequencing was performed on whole blood PAXgene samples collected from 28 TB patients and 16 latently TB infected (LTBI) subjects enrolled as part of an ongoing household contact study. Differential gene expression and clustering analyses were performed and compared with explicit predictive testing of TB and LTBI individuals based on established gene signatures. We observed strong predictive performance of TB disease states based on expression of known gene sets (ROC AUC 0.9007-0.9879). Together, our findings indicate that previously reported classifiers generated from different ethnic populations can accurately discriminate active TB from LTBI in South Indian patients. Future work should focus on converting existing gene signatures into a universal TB gene signature for diagnosis, monitoring TB treatment, and evaluating new drug regimens.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

Gene annotation bias impedes biomedical research

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets

Background

With the global emergence of multi-drug resistant strains of Mycobacterium tuberculosis, new strategies to treat tuberculosis are urgently needed such as therapeutics targeting potential human host factors.

Results

Here we performed a statistical meta-analysis of human gene expression in response to both latent and active pulmonary tuberculosis infections from nine published datasets. We found 1655 genes that were significantly differentially expressed during active tuberculosis infection. In contrast, no gene was significant for latent tuberculosis. Pathway enrichment analysis identified 90 significant canonical human pathways, including several pathways more commonly related to non-infectious diseases such as the LRRK2 pathway in Parkinson’s disease, and PD-1/PD-L1 signaling pathway important for new immuno-oncology therapies. The analysis of human genome-wide association studies datasets revealed tuberculosis-associated genetic variants proximal to several genes in major histocompatibility complex for antigen presentation. We propose several new targets and drug-repurposing opportunities including intravenous immunoglobulin, ion-channel blockers and cancer immuno-therapeutics for development as combination therapeutics with anti-mycobacterial agents.

Conclusions

Our meta-analysis provides novel insights into host genes and pathways important for tuberculosis and brings forth potential drug repurposing opportunities for host-directed therapies.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12918-017-0524-z) contains supplementary material, which is available to authorized users.

Potential Diagnostic Power of Blood Circular RNA Expression in Active Pulmonary Tuberculosis

BACKGROUND

Circular RNAs (circRNAs) are a class of novel RNAs with important biological functions, and aberrant expression of circRNAs has been implicated in human diseases. However, the feasibility of using blood circRNAs as disease biomarkers is largely unknown.

METHODS

We explored the potential of using human peripheral blood mononuclear cell (PBMC) circRNAs as marker molecules to diagnose active pulmonary tuberculosis (TB).

FINDINGS

First, we demonstrated that circRNAs are widely expressed in human PBMCs and that many are abundant enough to be detected. Second, we found that the magnitude of PBMC circRNAs in TB patients was higher than that in the paired healthy controls. Compared with host linear transcripts, the circRNAs within several pathways are disproportionately upregulated in active TB patients, including "Cytokine-cytokine receptor interaction", "Chemokine signaling pathway", "Neurotrophin signaling pathway", and "Bacterial invasion of epithelial cells". Based on the differentially expressed circRNAs within these pathways, we developed a PBMC circRNA-based molecular signature differentiating active TB patients from healthy controls. We validated the classification power of the PBMC circRNA signature in an independent cohort with the area under the receiver operating characteristic curve (AUC) at 0.946.

INTERPRETATION

Our results suggest that PBMC circRNAs are potentially reliable marker molecules in TB diagnosis.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Type I Interferons in the Pathogenesis of Tuberculosis: Molecular Drivers and Immunological Consequences

Tuberculosis (TB) remains a major global health threat. Urgent needs in the fight against TB include improved and innovative treatment options for drug-sensitive and -resistant TB as well as reliable biological indicators that discriminate active from latent disease and enable monitoring of treatment success or failure. Prominent interferon (IFN) inducible gene signatures in TB patients and animal models of Mycobacterium tuberculosis infection have drawn significant attention to the roles of type I IFNs in the host response to mycobacterial infections. Here, we review recent developments in the understanding of the innate immune pathways that drive type I IFN responses in mycobacteria-infected host cells and the functional consequences for the host defense against M. tuberculosis, with a view that such insights might be exploited for the development of targeted host-directed immunotherapies and development of reliable biomarkers.

Genome-wide host RNA signatures of infectious diseases: discovery and clinical translation

The use of whole blood gene expression to derive diagnostic biomarkers capable of distinguishing between phenotypically similar diseases holds great promise but remains a challenge. Differential gene expression analysis is used to identify the key genes that undergo changes in expression relative to healthy individuals, as well as to patients with other diseases. These key genes can act as diagnostic, prognostic and predictive markers of disease. Gene expression ‘signatures’ in the blood hold the potential to be used for the diagnosis of infectious diseases, where current diagnostics are unreliable, ineffective or of limited potential. For diagnostic tests based on RNA signatures to be useful clinically, the first step is to identify the minimum set of gene transcripts that accurately identify the disease in question. The second requirement is rapid and cost‐effective detection of the gene expression levels. Signatures have been described for a number of infectious diseases, but ‘clinic‐ready’ technologies for RNA detection from clinical samples are limited, though existing methods such as RT‐PCR are likely to be superseded by a number of emerging technologies, which may form the basis of the translation of gene expression signatures into routine diagnostic tests for a range of disease states.

Diagnostic accuracy of a selected signature gene set that discriminates active pulmonary tuberculosis and other pulmonary diseases

OBJECTIVE

We validated the accuracy of host selected signature gene set using unstimulated whole blood (WB), and peripheral blood mononuclear cells (PBMC) in the diagnosis of tuberculosis (TB).

METHODS

The unstimulated WB and PBMC from 1417 individuals with active pulmonary TB patients, other lung diseases and healthy participants were analyzed using real time polymerase chain reaction (RT-PCR).

RESULTS

The WB cohort test demonstrates that the combination of GBP5 and KLF2 can differentiate active TB versus HC with sensitivity and specificity of 77.8% and 87.1%, respectively; but most importantly active TB versus OD with sensitivity and specificity of 96.1% and 85.2%, respectively. Again during treatment course, the TB score of GBP5 and KLF2, analytes secretion and clinical parameters were found to be associated in disease progression. In the PBMC cohort test, we found that the only and best discriminatory combination was GBP5, DUSP3 and KLF2 inthe active TB versus HC with a sensitivity and specificity of 76.4% and 85.9%, respectively.

CONCLUSIONS

Our study reveals that GBP5 and KLF2 may be useful as a diagnostic tool for active TB, also the two-gene set may serve as surrogate biomarkers for monitoring TB therapy.

Systems immunology: just getting started

Systems-biology approaches in immunology take various forms, but here we review strategies for measuring a broad swath of immunological functions as a means of discovering previously unknown relationships and phenomena and as a powerful way of understanding the immune system as a whole. This approach has rejuvenated the field of vaccine development and has fostered hope that new ways will be found to combat infectious diseases that have proven refractory to classical approaches. Systems immunology also presents an important new strategy for understanding human immunity directly, taking advantage of the many ways the immune system of humans can be manipulated.

Tuberculosis Diagnostics: State of the Art and Future Directions

Rapid and accurate diagnosis is critical for timely initiation of anti-tuberculosis (TB) treatment, but many people with TB (or TB symptoms) do not have access to adequate initial diagnosis. In many countries, TB diagnosis is still reliant on sputum microscopy, a test with known limitations. However, new diagnostics are starting to change the landscape. Stimulated, in part, by the success and rollout of Xpert MTB/RIF, an automated, molecular test, there is now considerable interest in new technologies. The landscape looks promising with a pipeline of new tools, particularly molecular diagnostics, and well over 50 companies actively engaged in product development, and many tests have been reviewed by WHO for policy endorsement. However, new diagnostics are yet to reach scale, and there needs to be greater convergence between diagnostics development and the development of shorter TB drug regimens. Another concern is the relative absence of non-sputum-based diagnostics in the pipeline for children, and of biomarker tests for triage, cure, and latent TB progression. Increased investments are necessary to support biomarker discovery, validation, and translation into clinical tools. While transformative tools are being developed, high-burden countries will need to improve the efficiency of their health care delivery systems, ensure better uptake of new technologies, and achieve greater linkages across the TB and HIV care continuum. While we wait for next-generation technologies, national TB programs must scale up the best diagnostics currently available, and use implementation science to get the maximum impact.

Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

Host blood RNA signatures predict the outcome of tuberculosis treatment

Biomarkers for tuberculosis treatment outcome will assist in guiding individualized treatment and evaluation of new therapies. To identify candidate biomarkers, RNA sequencing of whole blood from a well-characterized TB treatment cohort was performed. Application of a validated transcriptional correlate of risk for TB revealed symmetry in host gene expression during progression from latent TB infection to active TB disease and resolution of disease during treatment, including return to control levels after drug therapy. The symmetry was also seen in a TB disease signature, constructed from the TB treatment cohort, that also functioned as a strong correlate of risk. Both signatures identified patients at risk of treatment failure 1–4 weeks after start of therapy. Further mining of the transcriptomes revealed an association between treatment failure and suppressed expression of mitochondrial genes before treatment initiation, leading to development of a novel baseline (pre-treatment) signature of treatment failure. These novel host responses to TB treatment were integrated into a five-gene real-time PCR-based signature that captures the clinically relevant responses to TB treatment and provides a convenient platform for stratifying patients according to their risk of treatment failure. Furthermore, this 5-gene signature is shown to correlate with the pulmonary inflammatory state (as measured by PET-CT) and can complement sputum-based Gene Xpert for patient stratification, providing a rapid and accurate alternative to current methods.

Diagnosis of Tuberculosis in HIV Co-infected Individuals: Current Status, Challenges and Opportunities for the Future

Tuberculosis (TB) remains one of the most important causes of death among people co-infected with human immunodeficiency virus (HIV). The diagnosis of TB remains challenging in HIV co-infected individuals, due to a high frequency of smear-negative disease and high rates of extrapulmonary TB. Accurate, ease of use and rapid diagnosis of active TB are critical to the World Health Organization (WHO) End TB Strategy by 2050. Traditional laboratory techniques do not provide rapid and accurate results to effectively manage HIV co-infected patients. Over the last decade, molecular methods have provided significant steps in the fight against TB. However, many HIV co-infected patients do not have access to these molecular diagnostic tests. Given the costs closely related with confirming a TB diagnosis in HIV patients, an overtreatment for TB is used in this patient population. Nowadays, an estimated US $8 billion a year is required to provide TB treatment, which is very high compared with making an important strategy to improve the current diagnostic tests. This review focuses on current advances in diagnosing active TB with an emphasis on the diagnosis of HIV-associated TB. Also discussed are the main challenges that need to be overcome for improving an adequate initial diagnosis of active TB in HIV-positive patients.

The role of human host genetics in tuberculosis resistance

INTRODUCTION

Tuberculosis (TB) remains a public health problem: the latest estimate of new incident cases per year is a staggering 10.4 million. Despite this overwhelming number, the majority of the immunocompetent population can control infection with Mycobacterium tuberculosis. The human genome underlies the immune response and contributes to the outcome of TB infection. Areas covered: Investigations of TB resistance in the general population have closely mirrored those of other infectious diseases and initially involved epidemiological observations. Linkage and association studies, including studies of VDR, SLC11A1 and HLA-DRB1 followed. Genome-wide association studies of common variants, not necessarily sufficient for disease, became possible after technological advancements. Other approaches involved the identification of those individuals with rare disease-causing mutations that strongly predispose to TB, epistasis and the role of ethnicity in disease. Despite these efforts, infection outcome, on an individual basis, cannot yet be predicted. Expert commentary: The early identification of future disease progressors is necessary to stem the TB epidemic. Human genetics may contribute to this endeavour and could in future suggest pathways to target for disease prevention. This will however require concerted efforts to establish large, well-phenotyped cohorts from different ethnicities, improved genomic resources and a better understanding of the human genome architecture.

Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children

Pediatric tuberculosis (TB) is challenging to diagnose, confirmed by growth of Mycobacterium tuberculosis at best in 40% of cases. The WHO has assigned high priority to the development of non-sputum diagnostic tools. We therefore sought to identify transcriptional signatures in whole blood of Indian children, capable of discriminating intra-thoracic TB disease from other symptomatic illnesses. We investigated the expression of 198 genes in a training set, comprising 47 TB cases (19 definite/28 probable) and 36 asymptomatic household controls, and identified a 7- and a 10-transcript signature, both including NOD2, GBP5, IFITM1/3, KIF1B and TNIP1. The discriminatory abilities of the signatures were evaluated in a test set comprising 24 TB cases (17 definite/7 probable) and 26 symptomatic non-TB cases. In separating TB-cases from symptomatic non-TB cases, both signatures provided an AUC of 0.94 (95%CI, 0.88–1.00), a sensitivity of 91.7% (95%CI, 71.5–98.5) regardless of culture status, and 100% sensitivity for definite TB. The 7-transcript signature provided a specificity of 80.8% (95%CI, 60.0–92.7), and the 10-transcript signature a specificity of 88.5% (95%CI, 68.7–96.9%). Although warranting exploration and validation in other populations, our findings are promising and potentially relevant for future non-sputum based POC diagnostic tools for pediatric TB.

Quantification of circulating Mycobacterium tuberculosis antigen peptides allows rapid diagnosis of active disease and treatment monitoring

Tuberculosis (TB) is a major global health threat, resulting in an urgent unmet need for a rapid, non-sputum-based quantitative test to detect active Mycobacterium tuberculosis (Mtb) infections in clinically diverse populations and quickly assess Mtb treatment responses for emerging drug-resistant strains. We have identified Mtb-specific peptide fragments and developed a method to rapidly quantify their serum concentrations, using antibody-labeled and energy-focusing porous discoidal silicon nanoparticles (nanodisks) and high-throughput mass spectrometry (MS) to enhance sensitivity and specificity. NanoDisk-MS diagnosed active Mtb cases with high sensitivity and specificity in a case-control study with cohorts reflecting the complexity of clinical practice. Similar robust sensitivities were obtained for cases of culture-positive pulmonary TB (PTB; 91.3%) and extrapulmonary TB (EPTB; 92.3%), and the sensitivities obtained for culture-negative PTB (82.4%) and EPTB (75.0%) in HIV-positive patients significantly outperformed those reported for other available assays. NanoDisk-MS also exhibited high specificity (87.1-100%) in both healthy and high-risk groups. Absolute quantification of serum Mtb antigen concentration was informative in assessing responses to antimycobacterial treatment. Thus, a NanoDisk-MS assay approach could significantly improve the diagnosis and management of active TB cases, and perhaps other infectious diseases as well.

A tuberculosis biomarker database: the key to novel TB diagnostics

New diagnostic innovations for tuberculosis (TB), including point-of-care solutions, are critical to reach the goals of the End TB Strategy. However, despite decades of research, numerous reports on new biomarker candidates, and significant investment, no well-performing, simple and rapid TB diagnostic test is yet available on the market, and the search for accurate, non-DNA biomarkers remains a priority. To help overcome this 'biomarker pipeline problem', FIND and partners are working on the development of a well-curated and user-friendly TB biomarker database. The web-based database will enable the dynamic tracking of evidence surrounding biomarker candidates in relation to target product profiles (TPPs) for needed TB diagnostics. It will be able to accommodate raw datasets and facilitate the verification of promising biomarker candidates and the identification of novel biomarker combinations. As such, the database will simplify data and knowledge sharing, empower collaboration, help in the coordination of efforts and allocation of resources, streamline the verification and validation of biomarker candidates, and ultimately lead to an accelerated translation into clinically useful tools.

Tuberculosis: Is the landscape changing?

Robert Heinrich Herman Koch, a German physician and microbiologist, received Nobel Prize in 1905 for identifying the specific causative agent of tuberculosis (TB). During his time it was believed that TB was an inherited disease. However he was convinced that the disease was caused by a bacterium and was infectious, tested his postulates using guinea pigs, and found the causative agent to be slow growing mycobacterium tuberculosis. TB is the second most common cause of death from infectious diseases after HIV/AIDS. Drug-resistant TB poses serious challenge to effective management of TB worldwide. Multidrug-resistant TB accounted for about half a million new cases and over 200,000 deaths in 2013. Whole-genome sequencing (first done in 1998) technologies have provided new insight into the mechanism of drug resistance. For the first time in 50 y, new anti TB drugs have been developed. The World Health Organization (WHO) has recently revised their treatment guidelines based on 32 studies. In United States, latent TB affects between 10 and 15 million people, 10% of whom may develop active TB disease. QuantiFERON TB Gold and T-SPOT.TB test are used for diagnosis. Further research will look into the importance of newly discovered gene mutations in causing drug resistance.

META-ANALYSIS OF CONTINUOUS PHENOTYPES IDENTIFIES A GENE SIGNATURE THAT CORRELATES WITH COPD DISEASE STATUS

The utility of multi-cohort two-class meta-analysis to identify robust differentially expressed gene signatures has been well established. However, many biomedical applications, such as gene signatures of disease progression, require one-class analysis. Here we describe an R package, MetaCorrelator, that can identify a reproducible transcriptional signature that is correlated with a continuous disease phenotype across multiple datasets. We successfully applied this framework to extract a pattern of gene expression that can predict lung function in patients with chronic obstructive pulmonary disease (COPD) in both peripheral blood mononuclear cells (PBMCs) and tissue. Our results point to a disregulation in the oxidation state of the lungs of patients with COPD, as well as underscore the classically recognized inammatory state that underlies this disease.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

Systemic sclerosis (SSc) is a rare autoimmune disease with the highest case-fatality rate of all connective tissue diseases. Current efforts to determine patient response to a given treatment using the modified Rodnan skin score (mRSS) are complicated by interclinician variability, confounding, and the time required between sequential mRSS measurements to observe meaningful change. There is an unmet critical need for an objective metric of SSc disease severity. Here, we performed an integrated, multicohort analysis of SSc transcriptome data across 7 datasets from 6 centers composed of 515 samples. Using 158 skin samples from SSc patients and healthy controls recruited at 2 centers as a discovery cohort, we identified a 415-gene expression signature specific for SSc, and validated its ability to distinguish SSc patients from healthy controls in an additional 357 skin samples from 5 independent cohorts. Next, we defined the SSc skin severity score (4S). In every SSc cohort of skin biopsy samples analyzed in our study, 4S correlated significantly with mRSS, allowing objective quantification of SSc disease severity. Using transcriptome data from the largest longitudinal trial of SSc patients to date, we showed that 4S allowed us to objectively monitor individual SSc patients over time, as (a) the change in 4S of a patient is significantly correlated with change in the mRSS, and (b) the change in 4S at 12 months of treatment could predict the change in mRSS at 24 months. Our results suggest that 4S could be used to distinguish treatment responders from nonresponders prior to mRSS change. Our results demonstrate the potential clinical utility of a novel robust molecular signature and a computational approach to SSc disease severity quantification.

Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.

BLR1 and FCGR1A transcripts in peripheral blood associate with the extent of intrathoracic tuberculosis in children and predict treatment outcome

Biomarkers reflecting the extent of Mycobacterium tuberculosis-induced pathology and normalization during anti-tuberculosis treatment (ATT) would considerably facilitate trials of new treatment regimens and the identification of patients with treatment failure. Therefore, in a cohort of 99 Indian children with intrathoracic tuberculosis (TB), we performed blood transcriptome kinetic analysis during ATT to explore 1) the association between transcriptional biomarkers in whole blood (WB) and the extent of TB disease at diagnosis and treatment outcomes at 2 and 6 months, and 2) the potential of the biomarkers to predict treatment response at 2 and 6 months. We present the first data on the association between transcriptional biomarkers and the extent of TB disease as well as outcome of ATT in children: Expression of three genes down-regulated on ATT (FCGR1A, FPR1 and MMP9) exhibited a positive correlation with the extent of TB disease, whereas expression of eight up-regulated genes (BCL, BLR1, CASP8, CD3E, CD4, CD19, IL7R and TGFBR2) exhibited a negative correlation with the extent of disease. Baseline levels of these transcripts displayed an individual capacity >70% to predict the six-month treatment outcome. In particular, BLR1 and FCGR1A seem to have a potential in monitoring and perhaps tailoring future antituberculosis therapy.

Correlates of tuberculosis risk: predictive biomarkers for progression to active tuberculosis

New approaches to control the spread of tuberculosis (TB) are needed, including tools to predict development of active TB from latent TB infection (LTBI). Recent studies have described potential correlates of risk, in order to inform the development of prognostic tests for TB disease progression. These efforts have included unbiased approaches employing “omics” technologies, as well as more directed, hypothesis-driven approaches assessing a small set or even individual selected markers as candidate correlates of TB risk. Unbiased high-throughput screening of blood RNAseq profiles identified signatures of active TB risk in individuals with LTBI, ≥1 year before diagnosis. A recent infant vaccination study identified enhanced expression of T-cell activation markers as a correlate of risk prior to developing TB; conversely, high levels of Ag85A antibodies and high frequencies of interferon (IFN)-γ specific T-cells were associated with reduced risk of disease. Others have described CD27⁻IFN-γ⁺CD4⁺ T-cells as possibly predictive markers of TB disease. T-cell responses to TB latency antigens, including heparin-binding haemagglutinin and DosR-regulon-encoded antigens have also been correlated with protection.

Further studies are needed to determine whether correlates of risk can be used to prevent active TB through targeted prophylactic treatment, or to allow targeted enrolment into efficacy trials of new TB vaccines and therapeutic drugs.

Promising biomarkers may allow accurate prediction of progression from infection to active TB diseasehttp://ow.ly/OzCL304ezfk