Combination of gene expression patterns in whole blood discriminate between tuberculosis infection states

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness

Background

Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting.

Methods

A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462).

Findings

Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I² 77%) was present.

Interpretation

Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved.

Funding

No funding received.

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

Importance

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.

Objective

To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.

Design, Setting, and Participants

This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.

Interventions

The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.

Main Outcomes and Measures

Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement.

Results

Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.

Conclusions and Relevance

In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Revisiting tuberculosis screening: An insight to complementary diagnosis and prospective molecular approaches for the recognition of the dormant TB infection in human and cattle hosts

Tuberculosis (TB) is defined as a chronic infection in both human and cattle hosts and many subclinical cases remain undetected. After the pathogen is inhaled by a host, phagocyted bacilli can persist inside macrophages surviving intracellularly. Hosts develop granulomatous lesions in the lungs or lymph nodes, limiting infection. However, bacilli become persister cells. Immunological diagnosis of TB is performed basically by routine tuberculin skin test (TST), and in some cases, by ancillary interferon-gamma release assay (IGRA). The concept of human latent TB infection (LTBI) by M. tuberculosis is recognized in cohorts without symptoms by routine clinical diagnostic tests, and nowadays IGRA tests are used to confirm LTBI with either active or latent specific antigens of M. tuberculosis. On the other hand, dormant infection in cattle by M. bovis has not been described by TST or IGRA testing as complications occur by cross-reactive immune responses to homolog antigens of environmental mycobacteria or a false-negative test by anergic states of a wained bovine immunity, evidencing the need for deciphering more specific biomarkers by new-generation platforms of analysis for detection of M. bovis dormant infection. The study and description of bovine latent TB infection (boLTBI) would permit the recognition of hidden animal infection with an increase in the sensitivity of routine tests for an accurate estimation of infected dairy cattle. Evidence of immunological and experimental analysis of LTBI should be taken into account to improve the study and the description of the still neglected boLTBI.

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in most ruminants. The pathogen MAP causes Johne's disease (JD), a chronic, incurable, wasting disease. Weight loss, diarrhea, and a gradual drop in milk production characterize the disease's clinical phase, culminating in death. Several studies have characterized long non-coding RNA (lncRNA) in bovine tissues, and a previous study characterizes (lncRNA) in macrophages infected with MAP in vitro. In this study, we aim to characterize the lncRNA in macrophages from cows naturally infected with MAP. From 15 herds, feces and blood samples were collected for each cow older than 24 months, twice yearly over 3–5 years. Paired samples were analyzed by fecal PCR and blood ELISA. We used RNA-seq data to study lncRNA in macrophages from 33 JD(+) and 33 JD(–) dairy cows. We performed RNA-seq analysis using the “new Tuxedo” suite. We characterized lncRNA using logistic regression and multilayered neural networks and used DESeq2 for differential expression analysis and Panther and Reactome classification systems for gene ontology (GO) analysis. The study identified 13,301 lncRNA, 605 of which were novel lncRNA. We found seven genes close to differentially expressed lncRNA, including CCDC174, ERI1, FZD1, TWSG1, ZBTB38, ZNF814, and ZSCAN4. None of the genes associated with susceptibility to JD have been cited in the literature. LncRNA target genes were significantly enriched for biological process GO terms involved in immunity and nucleic acid regulation. These include the MyD88 pathway (TLR5), GO:0043312 (neutrophil degranulation), GO:0002446 (neutrophil-mediated immunity), and GO:0042119 (neutrophil activation). These results identified lncRNA with potential roles in host immunity and potential candidate genes and pathways through which lncRNA might function in response to MAP infection.

Screening of potential biomarkers for distinguishing between latent and active tuberculosis in children using bioinformatics analysis

Tuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis.

Gene expression pattern analysis using dual-color RT-MLPA and integrative genome-wide association studies of eQTL for tuberculosis suscepitibility

Background

When infected with Mycobacterium tuberculosis, only a small proportion of the population will develop active TB, and the role of host genetic factors in different TB infection status was not fully understood.

Methods

Forty-three patients with active tuberculosis and 49 with latent tuberculosis were enrolled in the prospective cohort. Expressing levels of 27 candidate mRNAs, which were previously demonstrated to differentially expressed in latent and active TB, were measured by dual color reverse transcription multiplex ligation dependent probe amplification assay (dcRT-MLPA). Using expression levels of these mRNAs as quantitative traits, associations between expression abundance and genome-wild single nucleotide polymorphisms (SNPs) were calculated. Finally, identified candidate SNPs were further assessed for their associations with TB infection status in a validation cohort with 313 Chinese Han cases.

Results

We identified 9 differentially expressed mRNAs including il7r, il4, il8, tnfrsf1b, pgm5, ccl19, il2ra, marco and fpr1 in the prospective cohort. Through expression quantitative trait loci mapping, we screened out 8 SNPs associated with these mRNAs. Then, CG genotype of the SNP rs62292160 was finally verified to be significantly associated with higher transcription levels of IL4 in LTBI than in TB patients.

Conclusion

We reported that the SNP rs62292160 in Chinese Han population may link to higher expression of il4 in latent tuberculosis. Our findings provided a new genetic variation locus for further exploration of the mechanisms of TB and a possible target for TB genetic susceptibility studies, which might aid the clinical decision to precision treatment of TB.

Differential Gene Expression of ASUN, NEMF, PTPRC and DHX29: Candidate Biomarkers for the Diagnosis of Active and Latent Tuberculosis

Tuberculosis (TB) remains one of the most important infectious causes of death throughout the world. A wide range of technologies have been used for the diagnosis of TB. However, current diagnostic tests are inadequate. The aim of this study was to evaluate the expression of four genes, namely ASUN, NEMF, PTPRC and DHX29 as candidate biomarkers for the diagnosis of Latent tuberculosis infection (LTBI) and active TB and discrimination of active TB and LTBI. ; Materials and Methods: The expression of the mentioned four genes as well as ACTB as a housekeeping gene was evaluated by real-time PCR. Receiver operating characteristic (ROC) curve analysis was conducted to assess the specificity and sensitivity of each validated biomarker. ; Results: Our results showed that the expression of theASUN gene could discriminate between active TB cases and healthy BCG vaccinated volunteers with an AUC value of 0.76, combing with a sensitivity of 68% and a specificity of 67%. It should be noted that the PTPRC gene also has the potential for the diagnosis of active TB with an AUC value of 0.67 and a sensitivity of 64.5% and a specificity of 70%. The curve revealed that cases with LTBI could be distinguished from healthy BCG vaccinated volunteers according to their expression of the ASUN gene with an AUC value of 0.81. The cut-off value for diagnosing was 11, with a sensitivity of 73% and a specificity of 79%. Moreover, the expression of the NEMF gene might be considered as a diagnostic tool for the diagnosis of LTBI. The analysis showed an AUC value of 0.75. The highest sensitivity (60%) and specificity (81%) were obtained with a cut off value of 12. ; Conclusion: According to our results, the expression of ASUN and NEMF genes might be considered as a diagnostic tool for the diagnosis of LTBI. Our study showed that the expression of ASUN and PTPRC was obviously higher in active TB patients than those in healthy BCG vaccinated controls. On the other hand, DHX29 and PTPRC genes might be helpful in differentiating active TB and LTBI. However, our findings deserve further validation in larger studies.

Human Alveolar and Splenic Macrophage Populations Display a Distinct Transcriptomic Response to Infection With Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) infects alveolar macrophages (AMs), causing pulmonary tuberculosis (PTB), the most common form of the disease. Less frequently, Mtb is disseminated to many other organs and tissues, resulting in different extrapulmonary forms of TB. Nevertheless, very few studies have addressed the global mRNA response of human AMs, particularly from humans with the active form of the disease. Strikingly, almost no studies have addressed the response of human extrapulmonary macrophages to Mtb infection. In this pilot study, using microarray technology, we examined the transcriptomic ex vivo response of AMs from PTB patients (AMTBs) and AMs from control subjects (AMCTs) infected with two clinical isolates of Mtb. Furthermore, we also studied the infection response of human splenic macrophages (SMs) to Mtb isolates, as a model for extrapulmonary infection, and compared the transcriptomic response between AMs and SMs. Our results showed a striking difference in global mRNA profiles in response to infection between AMs and SMs, implicating a tissue-specific macrophage response to Mtb.

A transcriptional blood signature distinguishes early tuberculosis disease from latent tuberculosis infection and uninfected individuals in a Vietnamese cohort

OBJECTIVES

Global tuberculosis (TB) control is restricted by the failure to detect an estimated 3.3 million TB cases annually. In the majority of TB endemic settings, sputum smear microscopy is used to diagnose TB, but this test is insensitive for TB in its early stages. The objective of this study is to establish a concise gene signature that discriminates between individuals with early TB disease, latent TB infection (LTBI) and those without infection.

METHODS

This is a case control study nested within a cluster-randomised trial of population screening for active TB using Xpert MTB/RIF. Whole blood samples from 303 participants with active TB (97), LTBI (92) and uninfected individuals (114) were subject to transcriptomic analysis of selected target genes based on a systematic review of previous studies.

RESULTS

Analysis of 82 genes identified a pattern of differentially expressed genes in TB disease. A seven gene signature was identified that distinguished between TB disease and no TB disease with an AUC of 0.86 (95% CI: 0.80-0.91), and between TB disease from LTBI with an AUC of 0.88 (95% CI: 0.82-0.93).

CONCLUSION

This gene signature accurately distinguishes early TB disease from those without TB disease or infection, in the context of community-wide TB screening. It could be used as a non-sputum based screening tool or triage test to detect prevalent cases of TB in the community.

M. Ottenhoff T, M. Cliff J, C. Haks M. Gene expression profiles classifying clinical stages of tuberculosis and monitoring treatment responses in Ethiopian HIV-negative and HIV-positive cohorts

Background

Validation of previously identified candidate biomarkers and identification of additional candidate gene expression profiles to facilitate diagnosis of tuberculosis (TB) disease and monitoring treatment responses in the Ethiopian context is vital for improving TB control in the future.

Methods

Expression levels of 105 immune-related genes were determined in the blood of 80 HIV-negative study participants composed of 40 active TB cases, 20 latent TB infected individuals with positive tuberculin skin test (TST+), and 20 healthy controls with no Mycobacterium tuberculosis (Mtb) infection (TST-), using focused gene expression profiling by dual-color Reverse-Transcription Multiplex Ligation-dependent Probe Amplification assay. Gene expression levels were also measured six months after anti-TB treatment (ATT) and follow-up in 38 TB patients.

Results

The expression of 15 host genes in TB patients could accurately discriminate between TB cases versus both TST+ and TST- controls at baseline and thus holds promise as biomarker signature to classify active TB disease versus latent TB infection in an Ethiopian setting. Interestingly, the expression levels of most genes that markedly discriminated between TB cases versus TST+ or TST- controls did not normalize following completion of ATT therapy at 6 months (except for PTPRCv1, FCGR1A, GZMB, CASP8 and GNLY) but had only fully normalized at the 18 months follow-up time point. Of note, network analysis comparing TB-associated host genes identified in the current HIV-negative TB cohort to TB-associated genes identified in our previously published Ethiopian HIV-positive TB cohort, revealed an over-representation of pattern recognition receptors including TLR2 and TLR4 in the HIV-positive cohort which was not seen in the HIV-negative cohort. Moreover, using ROC cutoff ≥ 0.80, FCGR1A was the only marker with classifying potential between TB infection and TB disease regardless of HIV status.

Conclusions

Our data indicate that complex gene expression signatures are required to measure blood transcriptomic responses during and after successful ATT to fully diagnose TB disease and characterise drug-induced relapse-free cure, combining genes which resolve completely during the 6-months treatment phase of therapy with genes that only fully return to normal levels during the post-treatment resolution phase.

Molecular Signatures of Immunity and Immunogenicity in Infection and Vaccination

Vaccinology aims to understand what factors drive vaccine-induced immunity and protection. For many vaccines, however, the mechanisms underlying immunity and protection remain incompletely characterized at best, and except for neutralizing antibodies induced by viral vaccines, few correlates of protection exist. Recent omics and systems biology big data platforms have yielded valuable insights in these areas, particularly for viral vaccines, but in the case of more complex vaccines against bacterial infectious diseases, understanding is fragmented and limited. To fill this gap, the EC supported ADITEC project (http://www.aditecproject.eu/; http://stm.sciencemag.org/content/4/128/128cm4.full) featured a work package on “Molecular signatures of immunity and immunogenicity,” aimed to identify key molecular mechanisms of innate and adaptive immunity during effector and memory stages of immune responses following vaccination. Specifically, technologies were developed to assess the human immune response to vaccination and infection at the level of the transcriptomic and proteomic response, T-cell and B-cell memory formation, cellular trafficking, and key molecular pathways of innate immunity, with emphasis on underlying mechanisms of protective immunity. This work intersected with other efforts in the ADITEC project. This review summarizes the main achievements of the work package.

The role of human host genetics in tuberculosis resistance

INTRODUCTION

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

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

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

Temporal Expression of Peripheral Blood Leukocyte Biomarkers in a Macaca fascicularis Infection Model of Tuberculosis; Comparison with Human Datasets and Analysis with Parametric/Non-parametric Tools for Improved Diagnostic Biomarker Identification

A temporal study of gene expression in peripheral blood leukocytes (PBLs) from a Mycobacterium tuberculosis primary, pulmonary challenge model Macaca fascicularis has been conducted. PBL samples were taken prior to challenge and at one, two, four and six weeks post-challenge and labelled, purified RNAs hybridised to Operon Human Genome AROS V4.0 slides. Data analyses revealed a large number of differentially regulated gene entities, which exhibited temporal profiles of expression across the time course study. Further data refinements identified groups of key markers showing group-specific expression patterns, with a substantial reprogramming event evident at the four to six week interval. Selected statistically-significant gene entities from this study and other immune and apoptotic markers were validated using qPCR, which confirmed many of the results obtained using microarray hybridisation. These showed evidence of a step-change in gene expression from an ‘early’ FOS-associated response, to a ‘late’ predominantly type I interferon-driven response, with coincident reduction of expression of other markers. Loss of T-cell-associate marker expression was observed in responsive animals, with concordant elevation of markers which may be associated with a myeloid suppressor cell phenotype e.g. CD163. The animals in the study were of different lineages and these Chinese and Mauritian cynomolgous macaque lines showed clear evidence of differing susceptibilities to Tuberculosis challenge. We determined a number of key differences in response profiles between the groups, particularly in expression of T-cell and apoptotic makers, amongst others. These have provided interesting insights into innate susceptibility related to different host `phenotypes. Using a combination of parametric and non-parametric artificial neural network analyses we have identified key genes and regulatory pathways which may be important in early and adaptive responses to TB. Using comparisons between data outputs of each analytical pipeline and comparisons with previously published Human TB datasets, we have delineated a subset of gene entities which may be of use for biomarker diagnostic test development.

Transcriptomics: the key to biomarker discovery during tuberculosis?

Tuberculosis is a global threat affecting millions of people and requires more efficient methods of diagnosis, monitoring treatment response and the development of more efficacious drug therapies and new vaccines. The use of transcriptomic approaches and gene expression techniques have contributed to the elucidation of these aspects concerning the study of tuberculosis, and more specifically, the utilization of transcriptional profiles to identify biomarkers. These markers are the key to developing tools required to improve diagnosis and treatment of tuberculosis. Several studies have led to the identification of markers able to distinguish between different infection states, as well as other pulmonary diseases. Utilizing a systems biology approach will assist in obtaining more reliable results, leading to the implementation of significant findings.

Approaching a diagnostic point-of-care test for pediatric tuberculosis through evaluation of immune biomarkers across the clinical disease spectrum

The World Health Organization (WHO) calls for an accurate, rapid, and simple point-of-care (POC) test for the diagnosis of pediatric tuberculosis (TB) in order to make progress "Towards Zero Deaths". Whereas the sensitivity of a POC test based on detection of Mycobacterium tuberculosis (MTB) is likely to have poor sensitivity (70-80% of children have culture-negative disease), host biomarkers reflecting the on-going pathological processes across the spectrum of MTB infection and disease may hold greater promise for this purpose. We analyzed transcriptional immune biomarkers direct ex-vivo and translational biomarkers in MTB-antigen stimulated whole blood in 88 Indian children with intra-thoracic TB aged 6 months to 15 years, and 39 asymptomatic siblings. We identified 12 biomarkers consistently associated with either clinical groups "upstream" towards culture-positive TB on the TB disease spectrum (CD14, FCGR1A, FPR1, MMP9, RAB24, SEC14L1, and TIMP2) or "downstream" towards a decreased likelihood of TB disease (BLR1, CD3E, CD8A, IL7R, and TGFBR2), suggesting a correlation with MTB-related pathology and high relevance to a future POC test for pediatric TB. A biomarker signature consisting of BPI, CD3E, CD14, FPR1, IL4, TGFBR2, TIMP2 and TNFRSF1B separated children with TB from asymptomatic siblings (AUC of 88%).

Focused human gene expression profiling using dual-color reverse transcriptase multiplex ligation-dependent probe amplification

To investigate the human immune response to newly developed or existing vaccines, or during infection/disease on a population scale, we have recently developed a dual-color Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (dcRT-MLPA) assay, which can rapidly profile mRNA expression of multiple host genes. dcRT-MLPA has a dynamic range and sensitivity comparable to real-time QPCR and RNA-Sequencing. Since this assay is high-throughput, it is an exceptionally suitable technique for monitoring host biomarkers in semi-large scale human cohorts, such as cross sectional studies with multiple groups, or longitudinal studies with multiple time points. Multicomponent host biomarker signatures with excellent predictive values can easily be identified using lasso regression analysis, while exploring additional data adjustment methods like RUV-2 may further optimize the identification of informative host biomarker signatures. dcRT-MLPA also allows comparisons of gene expression patterns across different human populations to explore the impact of geographical diversity on for example vaccine induced responses. The use of dcRT-MLPA is not limited to peripheral blood but can be adapted to analyze host biomarkers derived from any tissue or body fluids, further demonstrating the versatility of the dcRT-MLPA platform. Several examples will be given and discussed.

Broad adaptive immune responses to M. tuberculosis antigens precede TST conversion in tuberculosis exposed household contacts in a TB-endemic setting

Background

The identification of Mycobacterium^-tuberculosis (Mtb) infected individuals remains a challenge due to an insufficient understanding of immune responses detected with the current diagnostic tests for latent tuberculosis i.e. the tuberculin skin test (TST) or IFN–γ release assays (IGRAs) and an inability to distinguish infection stages with current immunologic assays. Further classification based on markers other than IFN–γ may help to define markers of early Mtb infection.

Methods

We assessed the TST status of Mtb^-exposed household contacts at baseline and at 6 months. Contacts were classified into those with initial positive TST (TST⁺); those with baseline negative TST but TST conversion at 6 months (TST converters, TSTC) and those with persistently negative TST (PTST⁻). We assessed their short^- and long^-term immune responses to PPD and ESAT–6/CFP–10 (EC) via IFN–γ ELISPOT and a multiplex cytokine array in relation to TST status and compared them to those of TB cases to identify immune profiles associated with a spectrum of infection stages.

Results

After 1 and 6 days stimulation with EC, 12 cytokines (IFN–γ, IL–2, IP–10, TNF–α, IL–13, IL–17, IL–10, GMCSF, MIP–1β, MCP–3, IL–2RA and IL–1A) were not different in TSTC compared to TST⁺ suggesting that robust adaptive Mtb^-specific immune responses precede TST conversion. Stratifying contacts by baseline IFN–γ ELISPOT to EC in combination with TST results revealed that IP–10 and IL–17 were highest in the group of TST converters with positive baseline ELISPOT, suggesting they might be markers for recent infection.

Conclusion

We describe a detailed analysis of Mtb^-specific biomarker profiles in exposed household contacts in a TB endemic area that provides insights into the dynamic immune responses to Mtb infection and may help to identify biomarkers for ‘at^-risk’ populations beyond TST and IGRA.