Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: a Case-Control Study with Systematic Cross-Classifier Evaluation

Backtranslation of human RNA biosignatures of tuberculosis disease risk into the preclinical pipeline is condition dependent

It is not clear whether human progression to active tuberculosis disease (TB) risk signatures are viable endpoint criteria for evaluations of treatments in clinical or preclinical development. TB is the deadliest infectious disease globally and more efficacious vaccines are needed to reduce this mortality. However, the immune correlates of protection for either preventing infection with Mycobacterium tuberculosis or preventing TB disease have yet to be completely defined, making the advancement of candidate vaccines through the pipeline slow, costly, and fraught with risk. Human-derived correlate of risk (COR) gene signatures, which identify an individual's risk to progressing to active TB disease, provide an opportunity for evaluating new therapies for TB with clear and defined endpoints. Though prospective clinical trials with longitudinal sampling are prohibitively expensive, characterization of COR gene signatures is practical with preclinical models. Using a 3Rs (Replacement, Reduction and Refinement) approach we reanalyzed heterogeneous publicly available transcriptional datasets to determine whether a specific set of COR signatures are viable endpoints in the preclinical pipeline. We selected RISK6, Sweeney3 and BATF2 human-derived blood-based RNA biosignatures because they require relatively few genes to assign a score and have been carefully evaluated across several clinical cohorts. Excitingly, these data provide proof-of-concept that human COR signatures seem to have high fidelity across several tissue types in the preclinical TB model pipeline and show best performance when the model most closely reflected human infection or disease conditions. Human-derived COR signatures offer an opportunity for high-throughput preclinical endpoint criteria of vaccine and drug therapy evaluations.

One Sentence Summary

Human-derived biosignatures of tuberculosis disease progression were evaluated for their predictive fidelity across preclinical species and derived tissues using available public data sets.

Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures

BACKGROUND

Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed.

METHODS

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models.

RESULTS

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

CONCLUSION

Gene set enrichment scoring of existing gene sets can distinguish patients with active TB disease from other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis

Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.

Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis

Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide, partly due to a lack of effective strategies to screen and triage individuals with potential TB. Whole blood RNA signatures have been tested as biomarkers for TB, but have failed to meet the World Health Organization's (WHO) optimal target product profiles (TPP). Here, we use RNA sequencing and machine-learning to investigate the utility of plasma cell-free RNA (cfRNA) as a host-response biomarker for TB in cohorts from Uganda, Vietnam and Philippines. We report a 6-gene cfRNA signature, which differentiates TB-positive and TB-negative individuals with AUC = 0.95, 0.92, and 0.95 in test, training and validation, respectively. This signature meets WHO TPPs (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) regardless of geographic location, sample collection method and HIV status. Overall, our results identify plasma cfRNA as a promising host response biomarker to diagnose TB.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

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.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Systematic review of diagnostic and prognostic host blood transcriptomic signatures of tuberculosis disease in people living with HIV

Background HIV-associated tuberculosis (TB) has high mortality; however, current triage and prognostic tools offer poor sensitivity and specificity, respectively. We conducted a systematic review of diagnostic and prognostic host-blood transcriptomic signatures of TB in people living with HIV (PLHIV). Methods We systematically searched online databases for studies published in English between 1990-2020. Eligible studies included PLHIV of any age in test or validation cohorts, and used microbiological or composite reference standards for TB diagnosis. Inclusion was not restricted by setting or participant age. Study selection, quality appraisal using the QUADAS-2 tool, and data extraction were conducted independently by two reviewers. Thereafter, narrative synthesis of included studies, and comparison of signatures performance, was performed. Results We screened 1,580 records and included 12 studies evaluating 31 host-blood transcriptomic signatures in 10 test or validation cohorts of PLHIV that differentiated individuals with TB from those with HIV alone, latent Mycobacterium tuberculosis infection, or other diseases (OD). Two (2/10; 20%) cohorts were prospective (29 TB cases; 51 OD) and 8 (80%) case-control (353 TB cases; 606 controls) design. All cohorts (10/10) were recruited in Sub-Saharan Africa and 9/10 (90%) had a high risk of bias. Ten signatures (10/31; 32%) met minimum WHO Target Product Profile (TPP) criteria for TB triage tests. Only one study (1/12; 8%) evaluated prognostic performance of a transcriptomic signature for progression to TB in PLHIV, which did not meet the minimum WHO prognostic TPP. Conclusions Generalisability of reported findings is limited by few studies enrolling PLHIV, limited geographical diversity, and predominantly case-control design, which also introduces spectrum bias. New prospective cohort studies are needed that include PLHIV and are conducted in diverse settings. Further research exploring the effect of HIV clinical, virological, and immunological factors on diagnostic performance is necessary for development and implementation of TB transcriptomic signatures in PLHIV.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures

Rationale

Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed.

Objectives

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use.

Methods

We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models.

Measurement and Main Results

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

Conclusion

Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

Circulating Cell-Free RNA in Blood as a Host Response Biomarker for the Detection of Tuberculosis

Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide. This is partly due to a lack of tools to effectively screen and triage individuals with potential TB. Whole blood RNA signatures have been extensively studied as potential biomarkers for TB, but they have failed to meet the World Health Organization's (WHOs) target product profiles (TPPs) for a non-sputum triage or diagnostic test. In this study, we investigated the utility of plasma cell-free RNA (cfRNA) as a host response biomarker for TB. We used RNA profiling by sequencing to analyze plasma samples from 182 individuals with a cough lasting at least two weeks, who were seen at outpatient clinics in Uganda, Vietnam, and the Philippines. Of these individuals, 100 were diagnosed with microbiologically-confirmed TB. Our analysis of the plasma cfRNA transcriptome revealed 541 differentially abundant genes, the top 150 of which were used to train 15 machine learning models. The highest performing model led to a 9-gene signature that had a diagnostic accuracy of 89.1% (95% CI: 83.6-93.4%) and an area under the curve of 0.934 (95% CI: 0.8674-1) for microbiologically-confirmed TB. This 9-gene signature exceeds the optimal WHO TPPs for a TB triage test (sensitivity: 96.2% [95% CI: 80.9-100%], specificity: 89.7% [95% CI: 72.4-100%]) and was robust to differences in sample collection, geographic location, and HIV status. Overall, our results demonstrate the utility of plasma cfRNA for the detection of TB and suggest the potential for a point-of-care, gene expression-based assay to aid in early detection of TB.

Systematic review of diagnostic and prognostic host blood transcriptomic signatures of tuberculosis disease in people living with HIV

Background HIV-associated tuberculosis (TB) has high mortality; however, current triage and prognostic tools offer poor sensitivity and specificity, respectively. We conducted a systematic review of diagnostic and prognostic host-blood transcriptomic signatures of TB in people living with HIV (PLHIV). Methods We systematically searched online databases for studies published in English between 1990-2020. Eligible studies included PLHIV of any age in test or validation cohorts, and used microbiological or composite reference standards for TB diagnosis. Inclusion was not restricted by setting or participant age. Study selection, quality appraisal using the QUADAS-2 tool, and data extraction were conducted independently by two reviewers. Thereafter, narrative synthesis of included studies, and comparison of signatures performance, was performed. Results We screened 1,580 records and included 12 studies evaluating 31 host-blood transcriptomic signatures in 10 test or validation cohorts of PLHIV that differentiated individuals with TB from those with HIV alone, latent Mycobacterium tuberculosis infection, or other diseases (OD). Two (2/10; 20%) cohorts were prospective (29 TB cases; 51 OD) and 8 (80%) case-control (353 TB cases; 606 controls) design. All cohorts (10/10) were recruited in Sub-Saharan Africa and 9/10 (90%) had a high risk of bias. Ten signatures (10/31; 32%) met minimum WHO Target Product Profile (TPP) criteria for TB triage tests. Only one study (1/12; 8%) evaluated prognostic performance of a transcriptomic signature for progression to TB in PLHIV, which did not meet the minimum WHO prognostic TPP. Conclusions Generalisability of reported findings is limited by few studies enrolling PLHIV, limited geographical diversity, and predominantly case-control design, which also introduces spectrum bias. New prospective cohort studies are needed that include PLHIV and are conducted in diverse settings. Further research exploring the effect of HIV clinical, virological, and immunological factors on diagnostic performance is necessary for development and implementation of TB transcriptomic signatures in PLHIV.

Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting

BACKGROUND

Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country.

METHOD

Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves.

RESULTS

A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%).

CONCLUSION

The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.

A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.

Gene expression profiling identifies candidate biomarkers for new latent tuberculosis infections. A cohort study

Objective

To determine the gene expression profile in individuals with new latent tuberculosis infection (LTBI), and to compare them with people with active tuberculosis (TB) and those exposed to TB but not infected.

Design

A prospective cohort study. Recruitment and follow-up were conducted between September 2016 to December 2018. Gene expression and data processing and analysis from April 2019 to April 2021.

Setting

Two male Colombian prisons.

Participants

15 new tuberculin skin test (TST) converters (negative TST at baseline that became positive during follow-up), 11 people that continued with a negative TST after two years of follow-up, and 10 people with pulmonary ATB.

Main outcome measures

Gene expression profile using RNA sequencing from PBMC samples. The differential expression was assessed using the DESeq2 package in Bioconductor. Genes with |logFC| >1.0 and an adjusted p-value < 0.1 were differentially expressed. We analyzed the differences in the enrichment of KEGG pathways in each group using InterMiner.

Results

The gene expression was affected by the time of incarceration. We identified group-specific differentially expressed genes between the groups: 289 genes in people with a new LTBI and short incarceration (less than three months of incarceration), 117 in those with LTBI and long incarceration (one or more years of incarceration), 26 in ATB, and 276 in the exposed but non-infected individuals. Four pathways encompassed the largest number of down and up-regulated genes among individuals with LTBI and short incarceration: cytokine signaling, signal transduction, neutrophil degranulation, and innate immune system. In individuals with LTBI and long incarceration, the only enriched pathway within up-regulated genes was Emi1 phosphorylation.

Conclusions

Recent infection with MTB is associated with an identifiable RNA pattern related to innate immune system pathways that can be used to prioritize LTBI treatment for those at greatest risk for developing active TB.

Are mRNA based transcriptomic signatures ready for diagnosing tuberculosis in the clinic? - A review of evidence and the technological landscape

Funding

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes

BACKGROUND

In vitro, animal model, and clinical evidence suggests that tuberculosis is not a monomorphic disease, and that host response to tuberculosis is protean with multiple distinct molecular pathways and pathologies (endotypes). We applied unbiased clustering to identify separate tuberculosis endotypes with classifiable gene expression patterns and clinical outcomes.

METHODS

A cohort comprised of microarray gene expression data from microbiologically confirmed tuberculosis patients were used to identify putative endotypes. One microarray cohort with longitudinal clinical outcomes was reserved for validation, as was two RNA-seq cohorts. Finally, a separate cohort of tuberculosis patients with functional immune responses was evaluated to clarify stimulated from unstimulated immune responses.

RESULTS

A discovery cohort, including 435 tuberculosis patients and 533 asymptomatic controls, identified two tuberculosis endotypes. Endotype A is characterised by increased expression of genes related to inflammation and immunity and decreased metabolism and proliferation; in contrast, endotype B has increased activity of metabolism and proliferation pathways. An independent RNA-seq validation cohort, including 118 tuberculosis patients and 179 controls, validated the discovery results. Gene expression signatures for treatment failure were elevated in endotype A in the discovery cohort, and a separate validation cohort confirmed that endotype A patients had slower time to culture conversion, and a reduced cure rate. These observations suggest that endotypes reflect functional immunity, supported by the observation that tuberculosis patients with a hyperinflammatory endotype have less responsive cytokine production upon stimulation.

CONCLUSION

These findings provide evidence that metabolic and immune profiling could inform optimisation of endotype-specific host-directed therapies for tuberculosis.

Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform

BACKGROUND

Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers.

METHODS

The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker.

RESULTS

Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB.

CONCLUSIONS

The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.

Past and Present Approaches to Diagnosis of Active Pulmonary Tuberculosis

Tuberculosis disease continues to contribute to the mortality burden globally. Due to the several shortcomings of the available diagnostic methods, tuberculosis disease continues to spread. The difficulty to obtain sputum among the very ill patients and the children also affects the quick diagnosis of tuberculosis disease. These challenges warrant investigating different sample types that can provide results in a short time. Highlighted in this review are the approved pulmonary tuberculosis diagnostic methods and ongoing research to improve its diagnosis. We used the PRISMA guidelines for systematic reviews to search for studies that met the selection criteria for this review. In this review we found out that enormous biosignature research is ongoing to identify host biomarkers that can be used as predictors of active PTB disease. On top of this, more research was also being done to improve already existing diagnostic tests. Host markers required more optimization for use in different settings given their varying sensitivity and specificity in PTB endemic and non-endemic settings.

Metatranscriptomic Characterization of Coronavirus Disease 2019 Identified a Host Transcriptional Classifier Associated With Immune Signaling

BACKGROUND

The recent identification of a novel coronavirus, also known as SARS-CoV-2, has caused a global outbreak of respiratory illnesses. The rapidly developing pandemic has posed great challenges to diagnosis of this novel infection. However, little is known about the metatranscriptomic characteristics of patients with Coronavirus Disease 2019 (COVID-19).

METHODS

We analyzed metatranscriptomics in 187 patients (62 cases with COVID-19 and 125 with non-COVID-19 pneumonia). Transcriptional aspects of three core elements – pathogens, the microbiome, and host responses – were interrogated. Based on the host transcriptional signature, we built a host gene classifier and examined its potential for diagnosing COVID-19 and indicating disease severity.

RESULTS

The airway microbiome in COVID-19 patients had reduced alpha diversity, with 18 taxa of differential abundance. Potentially pathogenic microbes were also detected in 47% of the COVID-19 cases, 58% of which were respiratory viruses. Host gene analysis revealed a transcriptional signature of 36 differentially expressed genes significantly associated with immune pathways such as cytokine signaling. The host gene classifier built on such a signature exhibited potential for diagnosing COVID-19 (AUC of 0.75-0.89) and indicating disease severity.

CONCLUSIONS

Compared to those with non-COVID-19 pneumonias, COVID-19 patients appeared to have a more disrupted airway microbiome with frequent potential concurrent infections, and a special trigger host immune response in certain pathways such as interferon gamma signaling. The immune-associated host transcriptional signatures of COVID-19 hold promise as a tool for improving COVID-19 diagnosis and indicating disease severity.

Gene Set Enrichment Analysis Reveals Individual Variability in Host Responses in Tuberculosis Patients

Group-aggregated responses to tuberculosis (TB) have been well characterized on a molecular level. However, human beings differ and individual responses to infection vary. We have combined a novel approach to individual gene set analysis (GSA) with the clustering of transcriptomic profiles of TB patients from seven datasets in order to identify individual molecular endotypes of transcriptomic responses to TB. We found that TB patients differ with respect to the intensity of their hallmark interferon (IFN) responses, but they also show variability in their complement system, metabolic responses and multiple other pathways. This variability cannot be sufficiently explained with covariates such as gender or age, and the molecular endotypes are found across studies and populations. Using datasets from a Cynomolgus macaque model of TB, we revealed that transcriptional signatures of different molecular TB endotypes did not depend on TB progression post-infection. Moreover, we provide evidence that patients with molecular endotypes characterized by high levels of IFN responses (IFN-rich), suffered from more severe lung pathology than those with lower levels of IFN responses (IFN-low). Harnessing machine learning (ML) models, we derived gene signatures classifying IFN-rich and IFN-low TB endotypes and revealed that the IFN-low signature allowed slightly more reliable overall classification of TB patients from non-TB patients than the IFN-rich one. Using the paradigm of molecular endotypes and the ML-based predictions allows more precisely tailored treatment regimens, predicting treatment-outcome with higher accuracy and therefore bridging the gap between conventional treatment and precision medicine.

Validation of Differentially Expressed Immune Biomarkers in Latent and Active Tuberculosis by Real-Time PCR

Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB), pulmonary (PTB)) and latent TB (LTBI) infection remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. Whole blood mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new TB diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs).

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

Background

Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition.

Methods

We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing.

Results

The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662–0.989. Three gene sets were not significantly predictive.

Conclusion

Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05598-z.

Systematic evaluation, verification and comparison of tuberculosis-related non-coding RNA diagnostic panels

We systematically summarized tuberculosis (TB)‐related non‐coding RNA (ncRNA) diagnostic panels, validated and compared panel performance. We searched TB‐related ncRNA panels in PubMed, OVID and Web of Science up to 28 February 2020, and available datasets in GEO, SRA and EBI ArrayExpress up to 1 March 2020. We rebuilt models and synthesized the results of each model in validation sets by bivariate mixed models. Specificity at 90% sensitivity, area under curve (AUC) and inconsistence index (I²) were calculated. NcRNA biofunctions were analysed. Nineteen models based on 18 ncRNA panels (miRNA, lncRNA, circRNA and snoRNA panels) and 18 datasets were included. Limited available datasets only allowed to evaluate miRNA panels further. Cui 2017 and Latorre 2015 exhibited specificity >70% at 90% sensitivity and AUC >80% in all validation sets. Cui 2017 showed higher specificity at 90% sensitivity (92%) and AUC (95%) and lower heterogeneity (I² = 0%) in ethological‐confirmation validation sets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that most ncRNAs in panels involved in immune cell activation, oxidative stress, and Wnt and MAPK signalling pathway. Cui 2017 outperformed other models in both all available and aetiological‐confirmed validation sets, meeting the criteria of target product profile of WHO. This work provided a basis for clinical choice of TB‐related ncRNA diagnostic panels to a certain extent.

Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis

INTRODUCTION

Host blood transcriptomic biomarkers have potential as rapid point-of-care triage, diagnostic, and predictive tests for Tuberculosis disease. We aimed to summarise the performance of host blood transcriptomic signatures for diagnosis of and prediction of progression to Tuberculosis disease; and compare their performance to the recommended World Health Organisation target product profile.

METHODS

A systematic review and meta-analysis of the performance of host blood mRNA signatures for diagnosing and predicting progression to Tuberculosis disease in HIV-negative adults and adolescents, in studies with an independent validation cohort. Medline, Scopus, Web of Science, and EBSCO libraries were searched for articles published between January 2005 and May 2019, complemented by a search of bibliographies. Study selection, data extraction and quality assessment were done independently by two reviewers. Meta-analysis was performed for signatures that were validated in ≥3 comparable cohorts, using a bivariate random effects model.

RESULTS

Twenty studies evaluating 25 signatures for diagnosis of or prediction of progression to TB disease in a total of 68 cohorts were included. Eighteen studies evaluated 24 signatures for TB diagnosis and 17 signatures met at least one TPP minimum performance criterion. Three diagnostic signatures were validated in clinically relevant cohorts to differentiate TB from other diseases, with pooled sensitivity 84%, 87% and 90% and pooled specificity 79%, 88% and 74%, respectively. Four studies evaluated signatures for progression to TB disease and performance of one signature, assessed within six months of TB diagnosis, met the minimal TPP for a predictive test for progression to TB disease.

CONCLUSION

Host blood mRNA signatures hold promise as triage tests for TB. Further optimisation is needed if mRNA signatures are to be used as standalone diagnostic or predictive tests for therapeutic decision-making.

Identification of Mycobacterium tuberculosis Peptides in Serum Extracellular Vesicles from Persons with Latent Tuberculosis Infection

Identification of biomarkers for latent Mycobacterium tuberculosis infection and risk of progression to tuberculosis (TB) disease are needed to better identify individuals to target for preventive therapy, predict disease risk, and potentially predict preventive therapy efficacy. Our group developed multiple reaction monitoring mass spectrometry (MRM-MS) assays that detected M. tuberculosis peptides in serum extracellular vesicles from TB patients. We subsequently optimized this MRM-MS assay to selectively identify 40 M. tuberculosis peptides from 19 proteins that most commonly copurify with serum vesicles of patients with TB. Here, we used this technology to evaluate if M. tuberculosis peptides can also be detected in individuals with latent TB infection (LTBI). Serum extracellular vesicles from 74 individuals presumed to have latent M. tuberculosis infection (LTBI) based on close contact with a household member with TB or a recent tuberculin skin test (TST) conversion were included in this study. Twenty-nine samples from individuals with no evidence of TB infection by TST and no known exposure to TB were used as controls to establish a threshold to account for nonspecific/background signal. We identified at least one of the 40 M. tuberculosis peptides in 70 (95%) individuals with LTBI. A single peptide from the glutamine synthetase (GlnA1) enzyme was identified in 61/74 (82%) individuals with LTBI, suggesting peptides from M. tuberculosis proteins involved in nitrogen metabolism might be candidates for pathogen-specific biomarkers for detection of LTBI. The detection of M. tuberculosis peptides in serum extracellular vesicles from persons with LTBI represents a potential advance in the diagnosis of LTBI.

Tryptophan catabolism reflects disease activity in human tuberculosis

There is limited understanding of the role of host metabolism in the pathophysiology of human tuberculosis (TB). Using high-resolution metabolomics with an unbiased approach to metabolic pathway analysis, we discovered that the tryptophan pathway is highly regulated throughout the spectrum of TB infection and disease. This regulation is characterized by increased catabolism of tryptophan to kynurenine, which was evident not only in active TB disease but also in latent TB infection (LTBI). Further, we found that tryptophan catabolism is reversed with effective treatment of both active TB disease and LTBI in a manner commensurate with bacterial clearance. Persons with active TB and LTBI also exhibited increased expression of indoleamine 2,3-dioxygenase-1 (IDO-1), suggesting IDO-1 mediates observed increases in tryptophan catabolism. Together, these data indicate IDO-1-mediated tryptophan catabolism is highly preserved in the human response to Mycobacterium tuberculosis and could be a target for biomarker development as well as host-directed therapies.

Blood transcriptional biomarkers for active pulmonary tuberculosis in a high-burden setting: a prospective, observational, diagnostic accuracy study

BACKGROUND

Blood transcriptional signatures are candidates for non-sputum triage or confirmatory tests of tuberculosis. Prospective head-to-head comparisons of their diagnostic accuracy in real-world settings are necessary to assess their clinical use. We aimed to compare the diagnostic accuracy of candidate transcriptional signatures identified by systematic review, in a setting with a high burden of tuberculosis and HIV.

METHODS

We did a prospective observational study nested within a diagnostic accuracy study of sputum Xpert MTB/RIF (Xpert) and Xpert MTB/RIF Ultra (Ultra) tests for pulmonary tuberculosis. We recruited consecutive symptomatic adults aged 18 years or older self-presenting to a tuberculosis clinic in Cape Town, South Africa. Participants provided blood for RNA sequencing, and sputum samples for liquid culture and molecular testing using Xpert and Ultra. We assessed the diagnostic accuracy of candidate blood transcriptional signatures for active tuberculosis (including those intended to distinguish active tuberculosis from other diseases) identified by systematic review, compared with culture or Xpert MTB/RIF positivity as the standard reference. In our primary analysis, patients with tuberculosis were defined as those with either a positive liquid culture or Xpert result. Patients with missing blood RNA or sputum results were excluded. Our primary objective was to benchmark the diagnostic accuracy of candidate transcriptional signatures against the WHO target product profile (TPP) for a tuberculosis triage test.

FINDINGS

Between Feb 12, 2016, and July 18, 2017, we obtained paired sputum and RNA sequencing data from 181 participants, 54 (30%) of whom had confirmed pulmonary tuberculosis. Of 27 eligible signatures identified by systematic review, four achieved the highest diagnostic accuracy with similar area under the receiver operating characteristic curves (Sweeney3: 90·6% [95% CI 85·6-95·6]; Kaforou25: 86·9% [80·9-92·9]; Roe3: 86·9% [80·3-93·5]; and BATF2: 86·8% [80·6-93·1]), independent of age, sex, HIV status, previous tuberculosis, or sputum smear result. At test thresholds that gave 70% specificity (the minimum WHO TPP specificity for a triage test), these four signatures achieved sensitivities between 83·3% (95% CI 71·3-91·0) and 90·7% (80·1-96·0). No signature met the optimum criteria, of 95% sensitivity and 80% specificity proposed by WHO for a triage test, or the minimum criteria (of 65% sensitivity and 98% specificity) for a confirmatory test, but all four correctly identified Ultra-positive, culture-negative patients.

INTERPRETATION

Selected blood transcriptional signatures met the minimum WHO benchmarks for a tuberculosis triage test but not for a confirmatory test. Further development of the signatures is warranted to investigate their possible effects on clinical and health economic outcomes as part of a triage strategy, or when used as add-on confirmatory test in conjunction with the highly sensitive Ultra test for Mycobacterium tuberculosis DNA.

FUNDING

Royal Society Newton Advanced Fellowship, Wellcome Trust, National Institute of Health Research, and UK Medical Research Council.

Concise whole blood transcriptional signatures for incipient tuberculosis: a systematic review and patient-level pooled meta-analysis

BACKGROUND

Multiple blood transcriptional signatures have been proposed for identification of active and incipient tuberculosis. We aimed to compare the performance of systematically identified candidate signatures for incipient tuberculosis and to benchmark these against WHO targets.

METHODS

We did a systematic review and individual participant data meta-analysis. We searched Medline and Embase for candidate whole blood mRNA signatures discovered with the primary objective of diagnosis of active or incipient tuberculosis, compared with controls who were healthy or had latent tuberculosis infection. We tested the performance of eligible signatures in whole blood transcriptomic datasets, in which sampling before tuberculosis diagnosis was done and time to disease was available. Culture-confirmed and clinically or radiologically diagnosed pulmonary or extrapulmonary tuberculosis cases were included. Non-progressor (individuals who remained tuberculosis-free during follow-up) samples with less than 6 months of follow-up from the date of sample collection were excluded, as were participants with prevalent tuberculosis and those who received preventive therapy. Scores were calculated for candidate signatures for each participant in the pooled dataset. Receiver operating characteristic curves, sensitivities, and specificities were examined using prespecified intervals to tuberculosis (<3 months, <6 months, <1 year, and <2 years) from sample collection. This study is registered with PROSPERO, number CRD42019135618.

RESULTS

We tested 17 candidate mRNA signatures in a pooled dataset from four eligible studies comprising 1126 samples. This dataset included 183 samples from 127 incipient tuberculosis cases in South Africa, Ethiopia, The Gambia, and the UK. Eight signatures (comprising 1-25 transcripts) that predominantly reflect interferon and tumour necrosis factor-inducible gene expression, had equivalent diagnostic accuracy for incipient tuberculosis over a 2-year period with areas under the receiver operating characteristic curves ranging from 0·70 (95% CI 0·64-0·76) to 0·77 (0·71-0·82). The sensitivity of all eight signatures declined with increasing disease-free time interval. Using a threshold derived from two SDs above the mean of uninfected controls to prioritise specificity and positive-predictive value, the eight signatures achieved sensitivities of 24·7-39·9% over 24 months and of 47·1-81·0% over 3 months, with corresponding specificities of more than 90%. Based on pre-test probability of 2%, the eight signatures achieved positive-predictive values ranging from 6·8-9·4% over 24 months and 11·2-14·4% over 3 months. When using biomarker thresholds maximising sensitivity and specificity with equal weighting to both, no signature met the minimum WHO target product profile parameters for incipient tuberculosis biomarkers over a 2-year period.

INTERPRETATION

Blood transcriptional biomarkers reflect short-term risk of tuberculosis and only exceed WHO benchmarks if applied to 3-6-month intervals. Serial testing among carefully selected target groups might be required for optimal implementation of these biomarkers.

FUNDING

Wellcome Trust and National Institute for Health Research.

Expression and production of the SERPING1-encoded endogenous complement regulator C1-inhibitor in multiple cohorts of tuberculosis patients

BACKGROUND

To facilitate better discrimination between patients with active tuberculosis (TB) and latent TB infection (LTBI), whole blood transcriptomic studies have been performed to identify novel candidate host biomarkers. SERPING1, which encodes C1-inhibitor (C1-INH), the natural inhibitor of the C1-complex has emerged as candidate biomarker. Here we collated and analysed SERPING1 expression data and subsequently determined C1-INH protein levels in four cohorts of patients with TB.

METHODS

SERPING1 expression data were extracted from online deposited datasets. C1-INH protein levels were determined by ELISA in sera from individuals with active TB, LTBI as well as other disease controls in geographically diverse cohorts.

FINDINGS

SERPING1 expression was increased in patients with active TB compared to healthy controls (8/11 cohorts), LTBI (13/14 cohorts) and patients with other (non-TB) lung-diseases (7/7 cohorts). Serum levels of C1-INH were significantly increased in The Gambia and Italy in patients with active TB relative to the endemic controls but not in South Africa or Korea. In the largest cohort (n = 50), with samples collected longitudinally, normalization of C1-INH levels following successful TB treatment was observed. This cohort, also showed the most abundant increase in C1-INH, and a positive correlation between C1q and C1-INH levels. Combined presence of increased levels of both C1q and C1-INH had high specificity for active TB (96 %) but only very modest sensitivity 38 % compared to the endemic controls.

INTERPRETATION

SERPING1 transcript expression is increased in TB patients, while serum protein levels of C1-INH were increased in half of the cohorts analysed.

Host-Based Diagnostics for Acute Respiratory Infections

PURPOSE

The inappropriate use of antimicrobials, especially in acute respiratory infections (ARIs), is largely driven by difficulty distinguishing bacterial, viral, and noninfectious etiologies of illness. A new frontier in infectious disease diagnostics looks to the host response for disease classification. This article examines how host response-based diagnostics for ARIs are being used in clinical practice, as well as new developments in the research pipeline.

METHODS

A limited search was conducted of the relevant literature, with emphasis placed on literature published in the last 5 years (2014-2019).

FINDINGS

Advances are being made in all areas of host response-based diagnostics for ARIs. Specifically, there has been significant progress made in single protein biomarkers, as well as in various "omics" fields (including proteomics, metabolomics, and transcriptomics) and wearable technologies. There are many potential applications of a host response-based approach; a few key examples include the ability to discriminate bacterial and viral disease, presymptomatic diagnosis of infection, and pathogen-specific host response diagnostics, including modeling disease progression.

IMPLICATIONS

As biomarker measurement technologies continue to improve, host response-based diagnostics will increasingly be translated to clinically available platforms that can generate a holistic characterization of an individual's health. This knowledge, in the hands of both patient and provider, can improve care for the individual patient and help fight rising rates of antibiotic resistance.

Host Transcriptomics as a Tool to Identify Diagnostic and Mechanistic Immune Signatures of Tuberculosis

Tuberculosis (TB) is a major infectious disease worldwide, and is associated with several challenges for control and eradication. First, more accurate diagnostic tools that better represent the spectrum of infection states are required; in particular, identify the latent TB infected individuals with high risk of developing active TB. Second, we need to better understand, from a mechanistic point of view, why the immune system is unsuccessful in some cases for control and elimination of the pathogen. Host transcriptomics is a powerful approach to identify both diagnostic and mechanistic immune signatures of diseases. We have recently reported that optimal study design for these two purposes should be guided by different sets of criteria. Here, based on already published transcriptomics signatures of tuberculosis, we further develop these guidelines and identify additional factors to consider for obtaining diagnostic vs. mechanistic signatures in terms of cohorts, samples, data generation and analysis. Diagnostic studies should aim to identify small disease signatures with high discriminatory power across all affected populations, and against similar pathologies to TB. Specific focus should be made on improving the diagnosis of infected individuals at risk of developing active disease. Conversely, mechanistic studies should focus on tissues biopsies, immune relevant cell subsets, state of the art transcriptomic techniques and bioinformatics tools to understand the biological meaning of identified gene signatures that could facilitate therapeutic interventions. Finally, investigators should ensure their data are made publicly available along with complete annotations to facilitate metadata and cross-study analyses.

Paradigm changing evidence that alter tuberculosis perception and detection: Focus on latency

Tuberculosis remains a devastating disease to Mankind, ranking as the ninth cause of death worldwide. Eliminating tuberculosis as proven much more difficult than once anticipated. In addition to the delay in diagnosis and drug resistance problems that compromise the efficacy of treatment, the enormous reservoir of latently infected individuals continuously feeds the epidemics. However, targeting latency with prophylactic antibiotic administration is not possible at the populational level. Together, these issues call for a better understanding of latency, as well as for a more precise identification of individuals at high risk of reactivation. For this, recent paradigm changing evidence need to be taken into account, most notably, the existence of a tuberculosis spectrum; the genetic diversity of both humans and tuberculosis-causing bacteria; and the changes in the human population that interfere with tuberculosis. Here we discuss latency in the light of these variables and how that understanding can move forward tuberculosis research and elimination.

Complement Component C1q as Serum Biomarker to Detect Active Tuberculosis

Background: Tuberculosis (TB) remains a major threat to global health. Currently, diagnosis of active TB is hampered by the lack of specific biomarkers that discriminate active TB disease from other (lung) diseases or latent TB infection (LTBI). Integrated human gene expression results have shown that genes encoding complement components, in particular different C1q chains, were expressed at higher levels in active TB compared to LTBI.

Methods: C1q protein levels were determined using ELISA in sera from patients, from geographically distinct populations, with active TB, LTBI as well as disease controls.

Results: Serum levels of C1q were increased in active TB compared to LTBI in four independent cohorts with an AUC of 0.77 [0.70; 0.83]. After 6 months of TB treatment, levels of C1q were similar to those of endemic controls, indicating an association with disease rather than individual genetic predisposition. Importantly, C1q levels in sera of TB patients were significantly higher as compared to patients with sarcoidosis or pneumonia, clinically important differential diagnoses. Moreover, exposure to other mycobacteria, such as Mycobacterium leprae (leprosy patients) or BCG (vaccinees) did not result in elevated levels of serum C1q. In agreement with the human data, in non-human primates challenged with Mycobacterium tuberculosis, increased serum C1q levels were detected in animals that developed progressive disease, not in those that controlled the infection.

Conclusions: In summary, C1q levels are elevated in patients with active TB compared to LTBI in four independent cohorts. Furthermore, C1q levels from patients with TB were also elevated compared to patients with sarcoidosis, leprosy and pneumonia. Additionally, also in NHP we observed increased C1q levels in animals with active progressive TB, both in serum and in broncho-alveolar lavage. Therefore, we propose that the addition of C1q to current biomarker panels may provide added value in the diagnosis of active TB.

The value of transcriptomics in advancing knowledge of the immune response and diagnosis in tuberculosis

Blood transcriptomics analysis of tuberculosis has revealed an interferon-inducible gene signature that diminishes in expression after successful treatment; this promises improved diagnostics and treatment monitoring, which are essential for the eradication of tuberculosis. Sensitive radiography revealing lung abnormalities and blood transcriptomics have demonstrated heterogeneity in patients with active tuberculosis and exposed asymptomatic people with latent tuberculosis, suggestive of a continuum of infection and immune states. Here we describe the immune response to infection with Mycobacterium tuberculosis revealed through the use of transcriptomics, as well as differences among clinical phenotypes of infection that might provide information on temporal changes in host immunity associated with evolving infection. We also review the diverse blood transcriptional signatures, composed of small sets of genes, that have been proposed for the diagnosis of tuberculosis and the identification of at-risk asymptomatic people and suggest novel approaches for the development of such biomarkers for clinical use.

Meta-Analysis Identification of Highly Robust and Differential Immune-Metabolic Signatures of Systemic Host Response to Acute and Latent Tuberculosis in Children and Adults

Background: Whole blood expression profiling is a mainstay for delineating differential diagnostic signatures of infection yet is subject to high variability that reduces power and complicates clinical usefulness. To date, confirmatory high confidence expression profiling signatures for clinical use remain uncertain. Here we have sought to evaluate the reproducibility and confirmatory nature of differential expression signatures, comprising molecular and cellular pathways, across multiple international clinical observational studies investigating children and adult whole blood transcriptome responses to tuberculosis (TB). Methods and findings: A systematic search and quality control assessment of gene expression repositories for human TB using whole blood resulted in 11 datasets with a total of 1073 patients from Africa, Europe, and South America. A non-parametric estimation of percentage of false prediction was used for meta-analysis of high confidence differential expression analysis. Deconvolution analysis was applied to infer changes in immune cell proportions and enrichment tests applied using pathway database resources. Meta-analysis identified high confidence differentially expressed genes, comprising 372 in adult active-TB versus latent-TB (LTBI), 332 in adult active-TB versus controls (CON), five in LTBI versus CON, and 415 in childhood active-TB versus LTBI. Notably, these confirmatory markers have low representation in published signatures for diagnosing TB. Pathway biology analysis of high confidence gene sets revealed dominant metabolic and innate-immune pathway signatures while suppressed signatures were enriched with adaptive signaling pathways and reduced proportions of T and B cells. Childhood TB showed uniquely strong inflammasome antagonist signature (IL1RN and ILR2), while adult TB patients exhibit a significant preponderance type I and type II IFN markers. Key limitations of the study include the paucity of data on potential confounders. Conclusion: Meta-analysis identified high confidence confirmatory immune-metabolic and cellular expression signatures across studies regardless of the population resource setting, HIV status and circulating endemic pathogens. Notably, previously identified diagnostic signature markers for TB show limited concordance with the confirmatory meta-analysis. Overall, our results support the use of the confirmatory expression signatures for guiding optimized diagnostic, prognostic, and therapeutic monitoring modalities in TB.

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.

Transcriptomic profiling reveals gene expression kinetics in patients with hypoxia and high altitude pulmonary edema

OBJECTIVE

High altitude pulmonary edema (HAPE) is a life threatening condition occurring in otherwise healthy individuals who rapidly ascend to high altitude. However, the molecular mechanisms of its pathophysiology are not well understood. The objective of this study is to evaluate differential gene expression in patients with HAPE during acute illness and subsequent recovery.

METHODS

Twenty-one individuals who ascended to an altitude of 3780 m were studied, including 12 patients who developed HAPE and 9 matched controls without HAPE. Whole-blood samples were collected during acute illness and subsequent recovery for analysis of the expression of hypoxia-related genes, and physiologic and laboratory parameters, including mean pulmonary arterial pressure (mPAP), heart rate, blood pressure, and arterial oxygen saturation (SpO₂), were also measured.

RESULTS

Compared with control subjects, numerous hypoxia-related genes were up-regulated in patients with acute HAPE. Gene network analyses suggested that HIF-1α played a central role in acute HAPE by affecting a variety of hypoxia-related genes, including BNIP3L, VEGFA, ANGPTL4 and EGLN1. Transcriptomic profiling revealed the expression of most HAPE-induced genes was restored to a normal level during the recovery phase except some key hypoxia response factors, such asBNIP3L, EGR1, MMP9 and VEGF, which remained persistently elevated.

CONCLUSIONS

Differential expression analysis of hypoxia-related genes revealed distinct molecular signatures of HAPE during acute and recovery phases. This study may help us to better understand HAPE pathogenesis and putative targets for further investigation and therapeutic intervention.

Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis

New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (P < 10⁻²⁰) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.

Unique Chemokine Profiles of Lung Tissues Distinguish Post-chemotherapeutic Persistent and Chronic Tuberculosis in a Mouse Model

There is a substantial need for biomarkers to distinguish latent stage from active Mycobacterium tuberculosis infections, for predicting disease progression. To induce the reactivation of tuberculosis, we present a new experimental animal model modified based on the previous model established by our group. In the new model, the reactivation of tuberculosis is induced without administration of immunosuppressive agents, which might disturb immune responses. To identify the immunological status of the persistent and chronic stages, we analyzed immunological genes in lung tissues from mice infected with M. tuberculosis. Gene expression was screened using cDNA microarray analysis and confirmed by quantitative RT-PCR. Based on the cDNA microarray results, 11 candidate cytokines genes, which were obviously up-regulated during the chronic stage compared with those during the persistent stage, were selected and clustered into three groups: (1) chemokine genes, except those of monocyte chemoattractant proteins (MCPs; CXCL9, CXCL10, CXCL11, CCL5, CCL19); (2) MCP genes (CCL2, CCL7, CCL8, CCL12); and (3) TNF and IFN-γ genes. Results from the cDNA microarray and quantitative RT-PCR analyses revealed that the mRNA expression of the selected cytokine genes was significantly higher in lung tissues of the chronic stage than of the persistent stage. Three chemokines (CCL5, CCL19, and CXCL9) and three MCPs (CCL7, CCL2, and CCL12) were noticeably increased in the chronic stage compared with the persistent stage by cDNA microarray (p < 0.01, except CCL12) or RT-PCR (p < 0.01). Therefore, these six significantly increased cytokines in lung tissue from the mouse tuberculosis model might be candidates for biomarkers to distinguish the two disease stages. This information can be combined with already reported potential biomarkers to construct a network of more efficient tuberculosis markers.

Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis

BACKGROUND. Novel rapid diagnostics for active tuberculosis (TB) are required to overcome the time delays and inadequate sensitivity of current microbiological tests that are critically dependent on sampling the site of disease. Multiparametric blood transcriptomic signatures of TB have been described as potential diagnostic tests. We sought to identify the best transcript candidates as host biomarkers for active TB, extend the evaluation of their specificity by comparison with other infectious diseases, and to test their performance in both pulmonary and extrapulmonary TB.

METHODS. Support vector machine learning, combined with feature selection, was applied to new and previously published blood transcriptional profiles in order to identify the minimal TB‑specific transcriptional signature shared by multiple patient cohorts including pulmonary and extrapulmonary TB, and individuals with and without HIV-1 coinfection.

RESULTS. We identified and validated elevated blood basic leucine zipper transcription factor 2 (BATF2) transcript levels as a single sensitive biomarker that discriminated active pulmonary and extrapulmonary TB from healthy individuals, with receiver operating characteristic (ROC) area under the curve (AUC) scores of 0.93 to 0.99 in multiple cohorts of HIV-1–negative individuals, and 0.85 in HIV-1–infected individuals. In addition, we identified and validated a potentially novel 4-gene signature comprising CD177, haptoglobin, immunoglobin J chain, and galectin 10 that discriminated active pulmonary and extrapulmonary TB from other febrile infections, giving ROC AUCs of 0.94 to 1.

CONCLUSIONS. Elevated blood BATF2 transcript levels provide a sensitive biomarker that discriminates active TB from healthy individuals, and a potentially novel 4-gene transcriptional signature differentiates between active TB and other infectious diseases in individuals presenting with fever.

FUNDING. MRC, Wellcome Trust, Rosetrees Trust, British Lung Foundation, NIHR.

Blood BATF2 transcripts provide a single biomarker for active tuberculosis and a novel four-gene transcriptional signature differentiates active TB from other infectious diseases with fever.

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.