Diagnosis of childhood tuberculosis and host RNA expression in Africa

Identification of MORN3 and LLGL2 as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification

BACKGROUND

Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible molecular mechanisms of LTBI pathogenesis.

METHODS

Three GEO datasets (GSE19439, GSE19444, and GSE62525) were utilized to analyze the differentially expressed genes (DEGs). Functional enrichment studies were then performed on these DEGs. To ascertain potential diagnostic biomarkers, we utilized two different machine learning techniques: LASSO and RF. ROC curves were constructed in both the training and validation datasets to assess the diagnostic efficacy. The expression of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT, we estimated the abundances of 22 immune cell types in LTBI group, and subsequently analyzed the relationship between biomarker expression and immune cell infiltration.

RESULTS

166 DEGs were identified between ATB and LTBI groups, which are primarily associated with immune responses, inflammatory signaling pathways, and infection factors. Following that, 22 candidate diagnostic biomarkers for LTBI were selected in the machine learning process. Three up-regulated genes, MORN3, LLGL2, and IFT140, whose expression levels were not previously reported in TB, were validated using the training and validation cohort datasets. In our own Chinese cohort, we also found that MORN3 and LLGL2 showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific infiltration features of immune cells in LTBI and observed a notable correlation between potential marker expression and immune cells.

CONCLUSIONS

MORN3 and LLGL2 emerged as candidate diagnostic biomarkers for LTBI, following the elucidation of the key immune cell types involved. Our findings will contribute to providing a potential target for early noninvasive diagnosis of LTBI patients.

Biomarkers in diagnosing and therapeutic monitoring of tuberculosis: a review

Tuberculosis (TB) continues to pose a significant health challenge worldwide, emphasizing the importance of prompt diagnosis and efficient monitoring of treatment outcomes for effective disease control. Biomarkers have become increasingly important in the realm of TB diagnoses and treatment. The objective of this comprehensive review is to examine the present state of biomarkers employed in the diagnosis of TB, monitoring the response to treatment, and predicting treatment outcomes. In this study, we undertake a comprehensive examination of the diverse biomarkers utilized in TB diagnoses, spanning molecular, immunological, and other novel methodologies. Furthermore, we examine the potential of biomarkers in the context of therapeutic monitoring, assessment of treatment effectiveness, and anticipation of drug resistance. Additionally, this paper presents future prospects regarding the utilization of biomarkers in the therapy of tuberculosis.

Diagnostic potential of combining plasma biomarkers of tissue damage and inflammation in pediatric TB

INTRODUCTION

Immune-based diagnostic tests for tuberculosis (TB) have suboptimal sensitivity in children and cannot differentiate between latent infection (LTBI) and active disease. This study evaluated the diagnostic potential of a broad range of biomarkers of tissue damage and inflammation in unstimulated plasma in children.

METHODS

We analyzed 17 biomarkers in 15 non-M. tuberculosis (MTB)-infected controls and 33 children with TB infection (LTBI, n = 8; probable TB, n = 19; confirmed TB, n = 6). Biomarker concentrations were measured using a Luminex magnetic bead-based platform and multiplex sandwich immunoassays. Concentrations, correlations and diagnostic accuracy assessments were conducted among patient groups.

RESULTS

Confirmed TB cases had significantly higher concentrations of IFN-γ and IL-2 and higher IFN-γ/MCP-1 and IL-2/MCP-1 ratios compared to LTBI and non-MTB-infected children. Among children with confirmed TB, there was a strong correlation between IFN-γ and IL-10 (r = 0.95; p < 0.001) and a significant correlation between IL-2 and IL-1ra (r = 0.92), IL-21 (r = 0.91), MCP-3 (r = 0.84), and MMP-1 (r = 0.85). The IFN-γ/MCP-1 ratio was the most accurate biomarker combination for differentiating between MTB-infected and non-MTB-infected children (AUC, 0.82; sensitivity, 87.9%; specificity, 66.6%; p < 0.001) and between active TB and non-MTB-infected children (AUC 0.82; sensitivity 88.0%; specificity 60.0%; p < 0.001). None of the biomarkers investigated were able to discriminate between LTBI and active TB.

CONCLUSION

Our data suggest that combining the analyses of multiple biomarkers in plasma has the potential to enhance diagnosis of TB in children and, thus, warrants additional investigation. In particular, the diagnostic potential of IFN-γ/MCP-1 ratios should be further explored in larger pediatric cohorts.

Assessment of transcriptional markers for the differentiation of Mycobacterium mungi infection status in free-ranging banded mongoose (Mungos mungo)

There is an increasingly urgent need to improve our ability to accurately forecast and control zoonotic diseases in wildlife reservoirs. We are confronted, however, with the continued challenge of accurately determining host infection status across space and time. This dilemma is epitomized with the Mycobacterium tuberculosis Complex (MTBC) pathogens and particularly in free-ranging wildlife, a critical global challenge for both human and animal health. In humans, transcriptional markers have been increasingly identified as a robust tool for diagnosing Mycobacterium tuberculosis (MTB) infection status but have rarely been utilized for diagnosing TB in free-ranging wildlife populations. Here, we report the first use of transcriptional markers to evaluate TB infection status in a free-ranging wildlife species, banded mongoose (Mungos mungo), infected with the MTBC pathogen, Mycobacterium mungi. In this study, we found that GBP5 and DUSP3 were significantly upregulated in free-ranging banded mongoose infected with M. mungi. These results provide the first step in developing an antemortem diagnostic tool for use in free-ranging wildlife species. Our results highlight the potential of transcriptional marker-based assays to advance our ability to detect and manage TB in free-ranging wildlife, especially in field studies and other scenarios when conventional diagnostics are not feasible.

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BACKGROUND

Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.

METHODS

We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.

RESULTS

Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).

CONCLUSIONS

Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Blood transcriptomic signatures for symptomatic tuberculosis in an African multicohort study

BACKGROUND

Multiple host blood transcriptional signatures have been developed as non-sputum triage tests for tuberculosis (TB). We aimed to compare the diagnostic performance of 20 blood transcriptomic TB signatures for differentiating between symptomatic patients who have TB versus other respiratory diseases (ORD).

METHODS

As part of a nested case-control study, individuals presenting with respiratory symptoms at primary healthcare clinics in Ethiopia, Malawi, Namibia, Uganda, South Africa and The Gambia were enrolled. TB was diagnosed based on clinical, microbiological and radiological findings. Transcriptomic signatures were measured in whole blood using microfluidic real-time quantitative PCR. Diagnostic performance was benchmarked against the World Health Organization Target Product Profile (TPP) for a non-sputum TB triage test.

RESULTS

Among 579 participants, 158 had definite, microbiologically confirmed TB, 32 had probable TB, while 389 participants had ORD. Nine signatures differentiated between ORD and TB with equivalent performance (Satproedprai7: area under the curve 0.83 (95% CI 0.79-0.87); Jacobsen3: 0.83 (95% CI 0.79-0.86); Suliman2: 0.82 (95% CI 0.78-0.86); Roe1: 0.82 (95% CI 0.78-0.86); Kaforou22: 0.82 (95% CI 0.78-0.86); Sambarey10: 0.81 (95% CI 0.77-0.85); Duffy9: 0.81 (95% CI 0.76-0.86); Gliddon3: 0.8 (95% CI 0.75-0.85); Suliman4 0.79 (95% CI 0.75-0.84)). Benchmarked against a 90% sensitivity, these signatures achieved specificities between 44% (95% CI 38-49%) and 54% (95% CI 49-59%), not meeting the TPP criteria. Signature scores significantly varied by HIV status and country. In country-specific analyses, several signatures, such as Satproedprai7 and Penn-Nicholson6, met the minimal TPP criteria for a triage test in Ethiopia, Malawi and South Africa.

CONCLUSION

No signatures met the TPP criteria in a pooled analysis of all countries, but several signatures met the minimum criteria for a non-sputum TB triage test in some countries.

Sex-specific blood-derived RNA biomarkers for childhood tuberculosis

Confirmatory diagnosis of childhood tuberculosis (TB) remains a challenge mainly due to its dependence on sputum samples and the paucibacillary nature of the disease. Thus, only ~ 30% of suspected cases in children are diagnosed and the need for minimally invasive, non-sputum-based biomarkers remains unmet. Understanding host molecular changes by measuring blood-based transcriptomic markers has shown promise as a diagnostic tool for TB. However, the implication of sex contributing to disease heterogeneity and therefore diagnosis remains to be understood. Using publicly available gene expression data (GSE39939, GSE39940; n = 370), we report a sex-specific RNA biomarker signature that could improve the diagnosis of TB disease in children. We found four gene biomarker signatures for male (SLAMF8, GBP2, WARS, and FCGR1C) and female pediatric patients (GBP6, CELSR3, ALDH1A1, and GBP4) from Kenya, South Africa, and Malawi. Both signatures achieved a sensitivity of 85% and a specificity of 70%, which approaches the WHO-recommended target product profile for a triage test. Our gene signatures outperform most other gene signatures reported previously for childhood TB diagnosis.

Differential Expression of lncRNAs in HIV Patients with TB and HIV-TB with Anti-Retroviral Treatment

Tuberculosis (TB) is the leading cause of death among people with HIV-1 infection. To improve the diagnosis and treatment of HIV-TB patients, it is important to understand the mechanisms underlying these conditions. Here, we used an integrated genomics approach to analyze and determine the lncRNAs that are dysregulated in HIV-TB patients and HIV-TB patients undergoing anti-retroviral therapy (ART) using a dataset available in the public domain. The analyses focused on the portion of the genome transcribed into non-coding transcripts, which historically have been poorly studied and received less focus. This revealed that Mtb infection in HIV prominently up-regulates the expression of long non-coding RNA (lncRNA) genes DAAM2-AS1, COL4A2-AS1, LINC00599, AC008592.1, and CLRN1-AS1 and down-regulates the expression of lncRNAs AC111000.4, AC100803.3, AC016168.2, AC245100.7, and LINC02073. It also revealed that ART down-regulates the expression of some lncRNA genes (COL4A2-AS1, AC079210.1, MFA-AS1, and LINC01993) that are highly up-regulated in HIV-TB patients. Furthermore, the interrogation of the genomic regions that are associated with regulated lncRNAs showed enrichment for biological processes linked to immune pathways in TB-infected conditions. However, intriguingly, TB patients treated with ART showed completely opposite and non-overlapping pathways. Our findings suggest that lncRNAs could be used to identify critical diagnostic, prognostic, and treatment targets for HIV-TB patients.

A computational framework to improve cross-platform implementation of transcriptomics signatures

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

A blood-based 3-gene signature score for therapeutic monitoring in patients with pulmonary tuberculosis

OBJECTIVE

To assess the validity of Xpert Tuberculosis Fingerstick score for monitoring treatment response and analyze factors influencing its performance.

METHODS

122 adults with pulmonary tuberculosis were recruited and stratified into three cohorts: Diabetic-drug-susceptible-TB (DM-TB), Non-diabetic-drug-susceptible-TB (NDM-TB) and Non-diabetic Multidrug-resistant TB (MDR-TB). Fingerstick blood specimens were tested at treatment initiation (M0) and the end of the first (M1), second (M2), and sixth month (M6) to generate a TB-score.

RESULTS

The TB-score in all participants yielded an AUC of 0.707 (95% CI: 0.579-0.834) at M2 when its performance was evaluated against sputum culture conversion. In all non-diabetes patients, the AUC reached 0.88 (95% CI: 0.756-1.000) with an optimal cut-off value of 1.95 at which sensitivity was 90.0% (95% CI: 59.6-98.2%) and specificity was 81.3% (95% CI: 70.0-88.9%). The mean TB score was higher in patients with low bacterial loads (n = 31) than those with high bacterial loads (n = 91) at M0, M1, M2, and M6, and was higher in non-cavitary patients (n = 71) than those with cavitary lesions (n = 51) at M0, M1, and M2.

CONCLUSION

Xpert TB-score shows promising predictive value for culture conversion in non-diabetic TB patients. Sputum bacterial load and lung cavitation status have an influence on the value of TB score.

What is New in the Diagnosis of Childhood Tuberculosis?

The fact that almost half of the 1 million cases of childhood tuberculosis (TB) globally remain undiagnosed jeopardizes the TB elimination goal. Fortunately, there are new advances in this field which have the potential to bridge this diagnostic gap. Advances in imaging include computer assisted interpretation of chest X-rays (CXRs), point of care ultrasound (POCUS) and faster and superior computed tomography/ magnetic resonance imaging (CT/ MRI) protocols. The urine lipoarabinomannan test has proved to be a good point of care test for diagnosing TB in Human immunodeficiency virus (HIV) infected children. Stool and nasopharyngeal aspirates are emerging as acceptable alternatives for gastric lavage and induced sputum for diagnosing intrathoracic tuberculosis. Xpert MTB/RIF Ultra has improved sensitivity compared to Xpert MTB/RIF for diagnosing both pulmonary/ extrapulmonary TB. Xpert XDR is another commercially available accurate point of care test for detecting resistance to drugs other than rifampicin in smear positive samples. Other molecular methods including new line probe assays, pyrosequencing, whole genome sequencing, and targeted next generation sequencing are extremely promising but not available commercially at present. The C-Tb skin test is an acceptable alternative to the tuberculin skin test and interferon gamma release assays for diagnosis of latent infection. There is an urgent need to incorporate some of these advances in the existing diagnostic algorithms of childhood TB.

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.

Host RNA Expression Signatures in Young Infants with Urinary Tract Infection: A Prospective Study

Early diagnosis of infections in young infants remains a clinical challenge. Young infants are particularly vulnerable to infection, and it is often difficult to clinically distinguish between bacterial and viral infections. Urinary tract infection (UTI) is the most common bacterial infection in young infants, and the incidence of associated bacteremia has decreased in the recent decades. Host RNA expression signatures have shown great promise for distinguishing bacterial from viral infections in young infants. This prospective study included 121 young infants admitted to four pediatric emergency care departments in the capital region of Denmark due to symptoms of infection. We collected whole blood samples and performed differential gene expression analysis. Further, we tested the classification performance of a two-gene host RNA expression signature approaching clinical implementation. Several genes were differentially expressed between young infants with UTI without bacteremia and viral infection. However, limited immunological response was detected in UTI without bacteremia compared to a more pronounced response in viral infection. The performance of the two-gene signature was limited, especially in cases of UTI without bloodstream involvement. Our results indicate a need for further investigation and consideration of UTI in young infants before implementing host RNA expression signatures in clinical practice.

The malarial blood transcriptome: translational applications

The blood transcriptome of malaria patients has been used extensively to elucidate the pathophysiological mechanisms and host immune responses to disease, identify candidate diagnostic and prognostic biomarkers, and reveal new therapeutic targets for drug discovery. This review gives a high-level overview of the three main translational applications of these studies (diagnostics, prognostics, and therapeutics) by summarising recent literature and outlining the main limitations and future directions of each application. It highlights the need for consistent and accurate definitions of disease states and subject groups and discusses how prognostic studies must distinguish clearly between analyses that attempt to predict future disease states and those which attempt to discriminate between current disease states (classification). Lastly it examines how many promising therapeutics fail due to the choice of imperfect animal models for pre-clinical testing and lack of appropriate validation studies in humans, and how future transcriptional studies may be utilised to overcome some of these limitations.

Whole blood RNA signatures in tuberculosis patients receiving H56:IC31 vaccine as adjunctive therapy

Introduction

Therapeutic vaccination in tuberculosis (TB) represents a Host Directed Therapy strategy which enhances immune responses in order to improve clinical outcomes and shorten TB treatment. Previously, we have shown that the subunit H56:IC31 vaccine induced both humoral and cellular immune responses when administered to TB patients adjunctive to standard TB treatment (TBCOX2 study, NCT02503839). Here we present the longitudinal whole blood gene expression patterns in H56:IC31 vaccinated TB patients compared to controls receiving standard TB treatment only.

Methods

The H56:IC31 group (N=11) and Control group (N=7) underwent first-line TB treatment for 182 days. The H56:IC31 group received 5 micrograms of the H56:IC31 vaccine (Statens Serum Institut; SSI, Valneva Austria GmbH) intramuscularly at day 84 and day 140. Total RNA was extracted from whole blood samples collected in PAXgene tubes on days 0, 84, 98, 140, 154, 182 and 238. The expression level of 183 immune-related genes was measured by high-throughput microfluidic qPCR (Biomark HD system, Standard BioTools).

Results

The targeted gene expression profiling unveiled the upregulation of modules such as interferon (IFN) signalling genes, pattern recognition receptors and small nucleotide guanosine triphosphate (GTP)-ases in the vaccinated group compared to controls two weeks after administration of the first H56:IC31 vaccine. Additionally, the longitudinal analysis of the Adolescent Cohort Study-Correlation of Risk (ACS-COR) signature showed a progressive downregulation in both study arms towards the end of TB treatment, in congruence with reported treatment responses and clinical improvements. Still, two months after the end of TB treatment, vaccinated patients, and especially those developing both cellular and humoral vaccine responses, showed a lower expression of the ACS-COR genes compared to controls.

Discussion

Our data report gene expression patterns following H56:IC31 vaccination which might be interpreted as a lower risk of relapse in therapeutically vaccinated patients. Further studies are needed to conclude if these gene expression patterns could be used as prognostic biosignatures for therapeutic TB vaccine responses.

The elusive allure of a rapid host blood signature for tuberculosis disease

A rapid host transcriptional signature cartridge could be a major advancement for tuberculosis diagnosis and treatment monitoring. In a recent study, M. Li, Y. Qiu, M. Guo, R. Qu, et al. (J Clin Microbiol 61:e00911-23, 2023, https://doi.org/10.1128/jcm.00911-23) conducted an evaluation of the Cepheid 3-gene assay (Xpert-MTB-HR) within a diagnostic case-control study in China. While the study provides a strong contribution for determining the value of the Xpert-MTB-HR assay for diagnostic accuracy and treatment response, further assay optimization and more prospective studies are necessary before adaptation into clinical practice.

Whole-Blood Gene Expression Profile After Hypoxic-Ischemic Encephalopathy

Importance

Induced hypothermia, the standard treatment for hypoxic-ischemic encephalopathy (HIE) in high-income countries (HICs), is less effective in the low-income populations in South Asia, who have the highest disease burden.

Objective

To investigate the differences in blood genome expression profiles of neonates with HIE from an HIC vs neonates with HIE from South Asia.

Design, Setting, and Participants

This case-control study analyzed data from (1) a prospective observational study involving neonates with moderate or severe HIE who underwent whole-body hypothermia between January 2017 and June 2019 and age-matched term healthy controls in Italy and (2) a randomized clinical trial involving neonates with moderate or severe HIE in India, Sri Lanka, and Bangladesh recruited between August 2015 and February 2019. Data were analyzed between October 2020 and August 2023.

Exposure

Whole-blood RNA that underwent next-generation sequencing.

Main Outcome and Measures

The primary outcomes were whole-blood genome expression profile at birth associated with adverse outcome (death or disability at 18 months) after HIE in the HIC and South Asia cohorts and changes in whole-genome expression profile during the first 72 hours after birth in neonates with HIE and healthy controls from the HIC cohort. Blood samples for RNA extraction were collected before whole-body hypothermia at 4 time points (6, 24, 48, and 72 hours after birth) for the HIC cohort. Only 1 blood sample was drawn within 6 hours after birth for the South Asia cohort.

Results

The HIC cohort was composed of 35 neonates (21 females [60.0%]) with a median (IQR) birth weight of 3.3 (3.0-3.6) kg and gestational age of 40.0 (39.0-40.6) weeks. The South Asia cohort consisted of 99 neonates (57 males [57.6%]) with a median (IQR) birth weight of 2.9 (2.7-3.3) kg and gestational age of 39.0 (38.0-40.0) weeks. Healthy controls included 14 neonates (9 females [64.3%]) with a median (IQR) birth weight of 3.4 (3.2-3.7) kg and gestational age of 39.2 (38.9-40.4) weeks. A total of 1793 significant genes in the HIC cohort and 99 significant genes in the South Asia cohort were associated with adverse outcome (false discovery rate <0.05). Only 11 of these genes were in common, and all had opposite direction in fold change. The most significant pathways associated with adverse outcome were downregulation of eukaryotic translation initiation factor 2 signaling in the HIC cohort (z score = -4.56; P < .001) and aldosterone signaling in epithelial cells in the South Asia cohort (z score = null; P < .001). The genome expression profile of neonates with HIE (n = 35) at birth, 24 hours, 48 hours, and 72 hours remained significantly different from that of age-matched healthy controls in the HIC cohort (n = 14).

Conclusions and Relevance

This case-control study found that disease mechanisms underlying HIE were primarily associated with acute hypoxia in the HIC cohort and nonacute hypoxia in the South Asia cohort. This finding might explain the lack of hypothermic neuroprotection.

Diagnostic accuracy of a three-gene Mycobacterium tuberculosis host response cartridge using fingerstick blood for childhood tuberculosis: a multicentre prospective study in low-income and middle-income countries

BACKGROUND

Childhood tuberculosis remains a major cause of morbidity and mortality in part due to missed diagnosis. Diagnostic methods with enhanced sensitivity using easy-to-obtain specimens are needed. We aimed to assess the diagnostic accuracy of the Cepheid Mycobacterium tuberculosis Host Response prototype cartridge (MTB-HR), a candidate test measuring a three-gene transcriptomic signature from fingerstick blood, in children with presumptive tuberculosis disease.

METHODS

RaPaed-TB was a prospective diagnostic accuracy study conducted at four sites in African countries (Malawi, Mozambique, South Africa, and Tanzania) and one site in India. Children younger than 15 years with presumptive pulmonary or extrapulmonary tuberculosis were enrolled between Jan 21, 2019, and June 30, 2021. MTB-HR was performed at baseline and at 1 month in all children and was repeated at 3 months and 6 months in children on tuberculosis treatment. Accuracy was compared with tuberculosis status based on standardised microbiological, radiological, and clinical data.

FINDINGS

5313 potentially eligible children were screened, of whom 975 were eligible. 784 children had MTB-HR test results, of whom 639 had a diagnostic classification and were included in the analysis. MTB-HR differentiated children with culture-confirmed tuberculosis from those with unlikely tuberculosis with a sensitivity of 59·8% (95% CI 50·8-68·4). Using any microbiological confirmation (culture, Xpert MTB/RIF Ultra, or both), sensitivity was 41·6% (34·7-48·7), and using a composite clinical reference standard, sensitivity was 29·6% (25·4-34·2). Specificity for all three reference standards was 90·3% (95% CI 85·5-94·0). Performance was similar in different age groups and by malnutrition status. Among children living with HIV, accuracy against the strict reference standard tended to be lower (sensitivity 50·0%, 15·7-84·3) compared with those without HIV (61·0%, 51·6-69·9), although the difference did not reach statistical significance. Combining baseline MTB-HR result with one Ultra result identified 71·2% of children with microbiologically confirmed tuberculosis.

INTERPRETATION

MTB-HR showed promising diagnostic accuracy for culture-confirmed tuberculosis in this large, geographically diverse, paediatric cohort and hard-to-diagnose subgroups.

FUNDING

European and Developing Countries Clinical Trials Partnership, UK Medical Research Council, Swedish International Development Cooperation Agency, Bundesministerium für Bildung und Forschung; German Center for Infection Research (DZIF).

Assessing the Influence of Climate Change and Environmental Factors on the Top Tick-Borne Diseases in the United States: A Systematic Review

In the United States (US), tick-borne diseases (TBDs) have more than doubled in the past fifteen years and are a major contributor to the overall burden of vector-borne diseases. The most common TBDs in the US-Lyme disease, rickettsioses (including Rocky Mountain spotted fever), and anaplasmosis-have gradually shifted in recent years, resulting in increased morbidity and mortality. In this systematic review, we examined climate change and other environmental factors that have influenced the epidemiology of these TBDs in the US while highlighting the opportunities for a One Health approach to mitigating their impact. We searched Medline Plus, PUBMED, and Google Scholar for studies focused on these three TBDs in the US from January 2018 to August 2023. Data selection and extraction were completed using Covidence, and the risk of bias was assessed with the ROBINS-I tool. The review included 84 papers covering multiple states across the US. We found that climate, seasonality and temporality, and land use are important environmental factors that impact the epidemiology and patterns of TBDs. The emerging trends, influenced by environmental factors, emphasize the need for region-specific research to aid in the prediction and prevention of TBDs.

Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

Introduction

Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.

Methodology

Utilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.

Results

In executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.

Conclusion

The present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis.

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.

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND

Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.

METHODS

A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.

FINDINGS

We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.

CONCLUSIONS

Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.

FUNDING

European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.

MicroRNA hsa-miR-320a-3p and Its Targeted mRNA FKBP5 Were Differentially Expressed in Patients with HIV/TB Co-Infection

Among the PLWH (people living with HIV) population, the risk of developing active tuberculosis (TB) is increasing. Active TB also accelerates the deterioration of PLWH's immune function and is one of the leading causes of death in the PLWH population. So far, accurate diagnosis of active TB in the PLWH population remains challenging. Through data analysis of HIV/TB co-infection in the GEO database, the differentially expressed genes as well as their related microRNA (miRNA) were acquired and were further verified through clinical blood samples. Dual-luciferase assay was used to verify the mechanism of miRNA on mRNA. The enrichment of immune cells in database patient samples was analyzed by bioinformatics and finally verified by blood routine data. Our study found that FKBP5 (FK506 binding protein 5) was highly expressed in the HIV/TB co-infection group; hsa-miR-320a-3p was highly expressed in the HIV infection group but decreased in the HIV/TB co-infection group. Dual-luciferase assay results showed that hsa-miR-320a-3p mimics significantly reduced the relative luciferase activity of the WT-FKBP5 group; however, this phenomenon was not observed in the MUT-FKBP5 group. At the same time, as a key molecule of the immune-related pathway, FKBP5 is highly correlated with the amount of neutrophils, which provides a new suggestion for the treatment of the HIV/TB co-infection population. Our study found that hsa-miR-320a-3p can decrease FKBP5 expression, suggesting a potential regulatory role for FKBP5. The involvement of FKBP5 and its related molecule hsa-miR-320a-3p in HIV/TB co-infection proposes them as potential biomarkers for the diagnosis of active TB in the PLWH population.

Gene Expression in Cord Blood and Tuberculosis in Early Childhood: A Nested Case-Control Study in a South African Birth Cohort

BACKGROUND

Transcriptomic profiling of adults with tuberculosis (TB) has become increasingly common, predominantly for diagnostic and risk prediction purposes. However, few studies have evaluated signatures in children, particularly in identifying those at risk for developing TB disease. We investigated the relationship between gene expression obtained from umbilical cord blood and both tuberculin skin test conversion and incident TB disease through the first 5 years of life.

METHODS

We conducted a nested case-control study in the Drakenstein Child Health Study, a longitudinal, population-based birth cohort in South Africa. We applied transcriptome-wide screens to umbilical cord blood samples from neonates born to a subset of selected mothers (N = 131). Signatures identifying tuberculin conversion and risk of subsequent TB disease were identified from genome-wide analysis of RNA expression.

RESULTS

Gene expression signatures revealed clear differences predictive of tuberculin conversion (n = 26) and TB disease (n = 10); 114 genes were associated with tuberculin conversion and 30 genes were associated with the progression to TB disease among children with early infection. Coexpression network analysis revealed 6 modules associated with risk of TB infection or disease, including a module associated with neutrophil activation in immune response (P < .0001) and defense response to bacterium (P < .0001).

CONCLUSIONS

These findings suggest multiple detectable differences in gene expression at birth that were associated with risk of TB infection or disease throughout early childhood. Such measures may provide novel insights into TB pathogenesis and susceptibility.

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.

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

BACKGROUND

To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections.

METHODS

Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39).

RESULTS

In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV.

CONCLUSIONS

MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

Genetic epidemiology of resistance to M. tuberculosis Infection: importance of study design and recent findings

Resistance to M. tuberculosis, often referred to as "RSTR" in the literature, is being increasingly studied because of its potential relevance as a clinical outcome in vaccine studies. This review starts by addressing the importance of epidemiological characterization of this phenotype, and ongoing challenges in that characterization. Then, this review summarizes the extant genetic and genomic studies of this phenotype, including heritability studies, candidate gene studies, and genome-wide association studies, as well as whole transcriptome studies. Findings from recent studies that used longitudinal characterization of the RSTR phenotype are compared to those using a cross-sectional definition, and the challenges of using tuberculin skin test and interferon-gamma release assay are discussed. Finally, future directions are proposed. Since this is a rapidly evolving area of public health significance, this review will help frame future research questions and study designs.

Rapid and Accurate Diagnosis of Pediatric Tuberculosis Disease: A Diagnostic Accuracy Study for Pediatric Tuberculosis

INTRODUCTION

An estimated 1.2 million children develop tuberculosis (TB) every year with 240,000 dying because of missed diagnosis. Existing tools suffer from lack of accuracy and are often unavailable. Here, we describe the scientific and clinical methodology applied in RaPaed-TB, a diagnostic accuracy study.

METHODS

This prospective diagnostic accuracy study evaluating several candidate tests for TB was set out to recruit 1000 children <15 years with presumptive TB in 5 countries (Malawi, Mozambique, South Africa, Tanzania, India). Assessments at baseline included documentation of TB signs and symptoms, TB history, radiography, tuberculin skin test, HIV testing and spirometry. Respiratory samples for reference standard testing (culture, Xpert Ultra) included sputum (induced/spontaneous) or gastric aspirate, and nasopharyngeal aspirate (if <5 years). For novel tests, blood, urine and stool were collected. All participants were followed up at months 1 and 3, and month 6 if on TB treatment or unwell. The primary endpoint followed NIH-consensus statements on categorization of TB disease status for each participant. The study was approved by the sponsor's and all relevant local ethics committees.

DISCUSSION

As a diagnostic accuracy study for a disease with an imperfect reference standard, Rapid and Accurate Diagnosis of Pediatric Tuberculosis Disease (RaPaed-TB) was designed following a rigorous and complex methodology. This allows for the determination of diagnostic accuracy of novel assays and combination of testing strategies for optimal care for children, including high-risk groups (ie, very young, malnourished, children living with HIV). Being one of the largest of its kind, RaPaed-TB will inform the development of improved diagnostic approaches to increase case detection in pediatric TB.

From tuberculosis bedside to bench: UBE2B splicing as a potential biomarker and its regulatory mechanism

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology 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.

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.

Immunologic, metabolic and genetic impact of diabetes on tuberculosis susceptibility

Due to the increasing prevalence of diabetes mellitus (DM) globally, the interaction between DM and major global diseases like tuberculosis (TB) is of great public health significance, with evidence of DM having about a three-fold risk for TB disease. TB defense may be impacted by diabetes-related effects on immunity, metabolism, and gene transcription. An update on the epidemiological aspects of DM and TB, and the recent trends in understanding the DM-associated immunologic, metabolic, and genetic mechanisms of susceptibility to TB will be discussed in this review. This review highlights gaps in the incomplete understanding of the mechanisms that may relate to TB susceptibility in type 2 DM (T2DM). Understanding these three main domains regarding mechanisms of TB susceptibility in T2DM patients can help us build practical treatment plans to lessen the combined burden of the diseases in rampant areas.

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.

Functions of exosomal non-coding RNAs to the infection with Mycobacterium tuberculosis

Tuberculosis (TB) is a major infectious disease induced by Mycobacterium tuberculosis (M. tb) which causes the world's dominant fatal bacterial contagious disease. Increasing studies have indicated that exosomes may be a novel option for the diagnosis and treatment of TB. Exosomes are nanovesicles (30-150 nm) containing lipids, proteins and non-coding RNAs (ncRNAs) released from various cells, and can transfer their cargos and communicate between cells. Furthermore, exosomal ncRNAs exhibit diagnosis potential in bacterial infections, including TB. Additionally, differential exosomal ncRNAs regulate the physiological and pathological functions of M. tb-infected cells and act as diagnostic markers for TB. This current review explored the potential biological roles and the diagnostic application prospects of exosomal ncRNAs, and included recent information on their pathogenic and therapeutic functions in TB.

Evaluation of IFIT3 and ORM1 as Biomarkers for Discriminating Active Tuberculosis from Latent Infection

OBJECTIVE

Current commercially available immunological tests cannot be used for discriminating active tuberculosis (TB) from latent TB infection. To evaluate the value of biomarker candidates in the diagnosis of active TB, this study aimed to identify differentially expressed genes in peripheral blood mononuclear cells (PBMCs) between patients with active TB and individuals with latent TB infection by transcriptome sequencing.

METHODS

The differentially expressed genes in unstimulated PBMCs and in Mycobacterium tuberculosis (Mtb) antigen-stimulated PBMCs from patients with active TB and individuals with latent TB infection were identified by transcriptome sequencing. Selected candidate genes were evaluated in cohorts consisting of 110 patients with TB, 30 individuals with latent TB infections, and 50 healthy controls by quantitative real-time RT-PCR. Receiver operating characteristic (ROC) curve analysis was performed to calculate the diagnostic value of the biomarker candidates.

RESULTS

Among the differentially expressed genes in PBMCs without Mtb antigen stimulation, interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) had the highest area under curve (AUC) value (0.918, 95% CI: 0.852-0.984, P<0.0001) in discriminating patients with active TB from individuals with latent TB infection, with a sensitivity of 91.86% and a specificity of 84.00%. In Mtb antigen-stimulated PBMCs, orosomucoid 1 (ORM1) had a high AUC value (0.833, 95% CI: 0.752-0.915, P<0.0001), with a sensitivity of 81.94% and a specificity of 70.00%.

CONCLUSION

IFIT3 and ORM1 might be potential biomarkers for discriminating active TB from latent TB infection.

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.

Patterns of Childhood Tuberculosis Diagnosis in Hawassa University Comprehensive Specialized Hospital, Hawassa, Sidama Regional State, Ethiopia

Background

Because of the pauci bacillary nature of childhood tuberculosis and the difficulties in obtaining proper sputum samples from young children, diagnosing childhood tuberculosis (TB) is difficult. Childhood TB needs early identification and care since it advances swiftly to more advanced stages. This study was aimed to determine the patterns of all forms of childhood tuberculosis diagnosis at Hawassa University Comprehensive Specialized Hospital in Hawassa, Ethiopia.

Methods

A retrospective cross-sectional study was conducted from February 1, 2017 to January 30, 2021 among 175 children diagnosed and treated for tuberculosis in the pediatric ward. Children medical charts and pediatrics ward logbook were used to extract pertinent data by structured checklists. SPSS version 23.0 was used for data entry and statistical analysis.

Results

Of 175 children, fever was the leading clinical symptoms and diagnosed in 166 (94.9%) children followed by weight loss (154, 88%), and cough (136, 77.7%). In twenty seven out of 88 (30.6%) children, gastric aspirate was positive for TB infection by Xpert MTB/Rif, while 3/40 (7.5%) were positive for TB using fine needle aspiration cytology (FNAC), 19/66 (28.8%) had suggestive TB by cerebrospinal fluid analysis (CSF), 10/29 (34.5%) were smear positive for TB and 70/162 (43.2%) were suspected for TB by chest X-ray.

Conclusion

Despite recent breakthroughs in quick microbiological detection, such as Xpert MTB/Rif, this study revealed that more than half of the children, 89/175 (51%), were treated for TB diseases solely based on clinical criteria. This will significantly underestimate the true nature of the illness or disease and make them vulnerable to mistreatment. As a result, in order to appropriately treat the disease and manage patients in our settings, getting a microbiological diagnosis of childhood tuberculosis requires improvement, and we call for expanded availability and use of a more sensitive and specific diagnostic technique to circumvent these concerns.

Diagnostic biomarkers for active tuberculosis: progress and challenges

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker- or biosignature-based diagnostics using point-of-care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen- and host-based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake.

Pediatric Tuberculosis Diagnostics: Present and Future

The current diagnostic abilities for the detection of pediatric tuberculosis are suboptimal. Multiple factors contribute to the under-diagnosis of intrathoracic tuberculosis in children, namely the absence of pathognomonic features of the disease, low bacillary loads in respiratory specimens, challenges in sample collection, and inadequate access to diagnostic tools in high-burden settings. Nonetheless, the 2020s have witnessed encouraging progress in the area of novel diagnostics. Recent WHO-endorsed rapid molecular assays hold promise for use in service decentralization strategies, and new policy recommendations include stools as an alternative, child-friendly specimen for testing with the GeneXpert assay. The pipeline of promising assays in mid/late-stage development is expanding, and novel pediatric candidate biomarkers based on the host immune response are being identified for use in diagnostic and triage tests. For a new test to meet the pediatric target product profiles prioritized by the WHO, it is key that the peculiarities and needs of the hard-to-reach pediatric population are considered in the early planning phases of discovery, validation, and implementation studies.

Transcriptional Biomarkers for Treatment Monitoring of Pulmonary Drug-Resistant Tuberculosis: Protocol for a Prospective Observational Study in Indonesia

Many blood-based gene expression biomarkers for monitoring tuberculosis (TB) treatment have been suggested so far, but promising biomarker results for drug-resistant TB treatment response have not been studied. This protocol presents a prospective observational study in Indonesia to profile the human blood transcriptome for predicting the response to drug-resistant TB treatment, focusing on pulmonary TB, and to adapt the specific RNA signature to the qRT-PCR platform. Longitudinal blood samples will be collected from 44 subjects with rifampicin resistant TB, confirmed by Xpert MTB/RIF, and 52 healthy controls. RNA-Seq will be performed to identify changes in the transcriptome following TB treatment. A discriminative RNA signature will be chosen and translated into a score for use in a quantitative PCR-based assay. This study will provide crucial information to guide the discovery and design of a clinically implementable tool to monitor the response of TB treatment.

Aberrant expression of long non-coding RNAs in peripheral blood mononuclear cells response to tuberculosis in children

We aimed to identify long non-coding RNAs (lncRNAs) aberrantly expressed in peripheral blood mononuclear cells (PBMCs) triggered by active tuberculosis (ATB), latent tuberculosis infection (LTBI), and healthy controls (HC). We examined lncRNAs expression in PBMCs isolated from children with ATB and LTBI, and from HC using RNA sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to explore the biological processes and signaling pathways of aberrantly expressed mRNAs. A total of 348 and 205 lncRNAs were differentially expressed in the ATB and LTBI groups, respectively, compared to the HC group. Compared to the LTBI group, 125 lncRNAs were differentially expressed in the ATB group. Compared to the HC group, 2317 mRNAs were differentially expressed in the ATB group, and 1093 mRNAs were differentially expressed in the LTBI group. Compared to the LTBI group, 2328 mRNAs were differentially expressed in the ATB group. The upregulated mRNAs were mainly enriched in neutrophil activation, neutrophil-mediated biological processes, and positive regulation of immune response in tuberculosis (TB), whereas the downregulated mRNAs were enriched in signaling pathways and structural processes, such as the Wnt signaling pathway and rDNA heterochromatin assembly. This is the first study on the differential expression of lncRNAs in PBMCs of children with TB. We identified significant differences in the expression profiles of lncRNAs and mRNAs in the PBMCs of children with ATB, LTBI, and HC, which has important implications for exploring lncRNAs as novel biomarkers for the diagnosis of TB. In addition, further experimental identification and validation of lncRNA roles could help elucidate the underlying mechanisms of Mycobacterium tuberculosis infection in children.

Comparative transcriptomic analysis of whole blood mycobacterial growth assays and tuberculosis patients' blood RNA profiles

In vitro whole blood infection models are used for elucidating the immune response to Mycobacterium tuberculosis (Mtb). They exhibit commonalities but also differences, to the in vivo blood transcriptional response during natural human Mtb disease. Here, we present a description of concordant and discordant components of the immune response in blood, quantified through transcriptional profiling in an in vitro whole blood infection model compared to whole blood from patients with tuberculosis disease. We identified concordantly and discordantly expressed gene modules and performed in silico cell deconvolution. A high degree of concordance of gene expression between both adult and paediatric in vivo-in vitro tuberculosis infection was identified. Concordance in paediatric in vivo vs in vitro comparison is largely characterised by immune suppression, while in adults the comparison is marked by concordant immune activation, particularly that of inflammation, chemokine, and interferon signalling. Discordance between in vitro and in vivo increases over time and is driven by T-cell regulation and monocyte-related gene expression, likely due to apoptotic depletion of monocytes and increasing relative fraction of longer-lived cell types, such as T and B cells. Our approach facilitates a more informed use of the whole blood in vitro model, while also accounting for its limitations.

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation.

Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability.

Results: We identified CD4⁺ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.

Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.

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

Funding

Transcriptomics for child and adolescent tuberculosis

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