A blood RNA transcript signature for TB exposure in household contacts

Latent Tuberculosis: Challenges in Diagnosis and Treatment, Perspectives, and the Crucial Role of Biomarkers

Tuberculosis (TB) is the major cause of morbidity and mortality globally, which is caused by a single infectious agent Mycobacterium tuberculosis. For years, many TB control programmes are established for effective diagnosis and treatment of active TB cases, but these approaches alone are insufficient for TB eradication. This review aims to discourse on the crucial management of latent tuberculosis infection. This review will first summarize the current status, and methods for diagnosing latent tuberculosis then describes the challenges involved in the diagnosis and treatment of latent tuberculosis, and finally encounters the purpose of biomarkers as predicting tool in latent tuberculosis.

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.

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.

Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection

To determine the mechanisms that mediate resistance to Mycobacterium tuberculosis (M. tuberculosis) infection in household contacts (HHCs) of patients with tuberculosis (TB), we followed 452 latent TB infection-negative (LTBI-) HHCs for 2 years. Those who remained LTBI- throughout the study were identified as nonconverters. At baseline, nonconverters had a higher percentage of CD14+ and CD3-CD56+CD27+CCR7+ memory-like natural killer (NK) cells. Using a whole-transcriptome and metabolomic approach, we identified deoxycorticosterone acetate as a metabolite with elevated concentrations in the plasma of nonconverters, and further studies showed that this metabolite enhanced glycolytic ATP flux in macrophages and restricted M. tuberculosis growth by enhancing antimicrobial peptide production through the expression of the surface receptor sialic acid binding Ig-like lectin-14. Another metabolite, 4-hydroxypyridine, from the plasma of nonconverters significantly enhanced the expansion of memory-like NK cells. Our findings demonstrate that increased levels of specific metabolites can regulate innate resistance against M. tuberculosis infection in HHCs of patients with TB who never develop LTBI or active TB.

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.

Evaluation of Host Protein Biomarkers by ELISA From Whole Lysed Peripheral Blood for Development of Diagnostic Tests for Active Tuberculosis

Tuberculosis (TB) remains a significant global health crisis and the number one cause of death for an infectious disease. The health consequences in high-burden countries are significant. Barriers to TB control and eradication are in part caused by difficulties in diagnosis. Improvements in diagnosis are required for organisations like the World Health Organisation (WHO) to meet their ambitious target of reducing the incidence of TB by 50% by the year 2025, which has become hard to reach due to the COVID-19 pandemic. Development of new tests for TB are key priorities of the WHO, as defined in their 2014 report for target product profiles (TPPs). Rapid triage and biomarker-based confirmatory tests would greatly enhance the diagnostic capability for identifying and diagnosing TB-infected individuals. Protein-based test methods e.g. lateral flow devices (LFDs) have a significant advantage over other technologies with regard to assay turnaround time (minutes as opposed to hours) field-ability, ease of use by relatively untrained staff and without the need for supporting laboratory infrastructure. Here we evaluate the diagnostic performance of nine biomarkers from our previously published biomarker qPCR validation study; CALCOCO2, CD274, CD52, GBP1, IFIT3, IFITM3, SAMD9L, SNX10 and TMEM49, as protein targets assayed by ELISA. This preliminary evaluation study was conducted to quantify the level of biomarker protein expression across latent, extra-pulmonary or pulmonary TB groups and negative controls, collected across the UK and India, in whole lysed blood samples (WLB). We also investigated associative correlations between the biomarkers and assessed their suitability for ongoing diagnostic test development, using receiver operating characteristic/area under the curve (ROC) analyses, singly and in panel combinations. The top performing single biomarkers for pulmonary TB versus controls were CALCOCO2, SAMD9L, GBP1, IFITM3, IFIT3 and SNX10. TMEM49 was also significantly differentially expressed but downregulated in TB groups. CD52 expression was not highly differentially expressed across most of the groups but may provide additional patient stratification information and some limited use for incipient latent TB infection. These show therefore great potential for diagnostic test development either in minimal configuration panels for rapid triage or more complex formulations to capture the diversity of disease presentations.

The effect of host factors on discriminatory performance of a transcriptomic signature of tuberculosis risk

Background

We aimed to understand host factors that affect discriminatory performance of a transcriptomic signature of tuberculosis risk (RISK11).

Methods

HIV-negative adults aged 18–60 years were evaluated in a prospective study of RISK11 and surveilled for tuberculosis through 15 months. Generalised linear models and receiver-operating characteristic (ROC) regression were used to estimate effect of host factors on RISK11 score (%marginal effect) and on discriminatory performance for tuberculosis disease (area under the curve, AUC), respectively.

Findings

Among 2923 participants including 74 prevalent and 56 incident tuberculosis cases, percentage marginal effects on RISK11 score were increased among those with prevalent tuberculosis (+18·90%, 95%CI 12·66−25·13), night sweats (+14·65%, 95%CI 5·39−23·91), incident tuberculosis (+7·29%, 95%CI 1·46−13·11), flu-like symptoms (+5·13%, 95%CI 1·58−8·68), and smoking history (+2·41%, 95%CI 0·89−3·93) than those without; and reduced in males (−6·68%, 95%CI −8·31−5·04) and with every unit increase in BMI (−0·13%, −95%CI −0·25−0·01). Adjustment for host factors affecting controls did not change RISK11 discriminatory performance. Cough was associated with 72·55% higher RISK11 score in prevalent tuberculosis cases. Stratification by cough improved diagnostic performance from AUC = 0·74 (95%CI 0·67−0·82) overall, to 0·97 (95%CI 0·90−1·00, p < 0·001) in cough-positive participants. Combining host factors with RISK11 improved prognostic performance, compared to RISK11 alone, (AUC = 0·76, 95%CI 0·69−0·83 versus 0·56, 95%CI 0·46−0·68, p < 0·001) over a 15-month predictive horizon.

Interpretation

Several host factors affected RISK11 score, but only adjustment for cough affected diagnostic performance. Combining host factors with RISK11 should be considered to improve prognostic performance.

Funding

Bill and Melinda Gates Foundation, South African Medical Research Council.

Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing

Tuberculosis (TB), is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), and presents with high morbidity and mortality. Alveolar macrophages play an important role in TB pathogenesis although there is heterogeneity and functional plasticity. This study aimed to show the characteristics of alveolar macrophages from bronchioalveolar lavage fluid (BALF) in active TB patients. Single-cell RNA sequencing (scRNA-seq) was performed on BALF cells from three patients with active TB and additional scRNA-seq data from three healthy adults were established as controls. Transcriptional profiles were analyzed and compared by differential geneexpression and functional enrichment analysis. We applied pseudo-temporal trajectory analysis to investigate correlations and heterogeneity within alveolar macrophage subclusters. Alveolar macrophages from active TB patients at the single-cell resolution are described. We found that TB patients have higher cellular percentages in five macrophage subclusters. Alveolar macrophage subclusters with increased percentages were involved in inflammatory signaling pathways as well as the basic macrophage functions. The TB-increased alveolar macrophage subclusters might be derived from M1-like polarization state, before switching to an M2-like polarization state with the development ofM. tuberculosis infection. Cell-cell communications of alveolar macrophages also increased and enhanced in active TB patients. Overall, our study demonstrated the characteristics of alveolar macrophages from BALF in active TB patients by using scRNA-seq.

A blood RNA transcriptome signature for COVID-19

Background

COVID-19 is a respiratory viral infection with unique features including a more chronic course and systemic disease manifestations including multiple organ involvement; and there are differences in disease severity between ethnic groups. The immunological basis for disease has not been fully characterised. Analysis of whole-blood RNA expression may provide valuable information on disease pathogenesis.

Methods

We studied 45 patients with confirmed COVID-19 infection within 10 days from onset of illness and a control group of 19 asymptomatic healthy volunteers with no known exposure to COVID-19 in the previous 14 days. Relevant demographic and clinical information was collected and a blood sample was drawn from all participants for whole-blood RNA sequencing. We evaluated differentially-expressed genes in COVID-19 patients (log2 fold change ≥ 1 versus healthy controls; false-discovery rate < 0.05) and associated protein pathways and compared these to published whole-blood signatures for respiratory syncytial virus (RSV) and influenza. We developed a disease score reflecting the overall magnitude of expression of internally-validated genes and assessed the relationship between the disease score and clinical disease parameters.

Results

We found 135 differentially-expressed genes in the patients with COVID-19 (median age 35 years; 82% male; 36% Chinese, 53% South Asian ethnicity). Of the 117 induced genes, 14 were found in datasets from RSV and 40 from influenza; 95 genes were unique to COVID-19. Protein pathways were mostly generic responses to viral infections, including apoptosis by P53-associated pathway, but also included some unique pathways such as viral carcinogenesis. There were no major qualitative differences in pathways between ethnic groups. The composite gene-expression score was correlated with the time from onset of symptoms and nasal swab qPCR CT values (both p < 0.01) but was not related to participant age, gender, ethnicity or the presence or absence of chest X-ray abnormalities (all p > 0.05).

Conclusions

The whole-blood transcriptome of COVID-19 has overall similarity with other respiratory infections but there are some unique pathways that merit further exploration to determine clinical relevance. The approach to a disease score may be of value, but needs further validation in a population with a greater range of disease severity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01006-w.

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).