Novel biomarkers distinguishing active tuberculosis from latent infection identified by gene expression profile of peripheral blood mononuclear cells

TRAIL and IP-10 dynamics in pregnant women post COVID-19 vaccination: associations with neutralizing antibody potency

Introduction

The aim of this study is to investigate changes in TNF-related apoptosis-inducing ligand (TRAIL) and gamma interferon-induced protein 10 (IP-10) after COVID-19 vaccination in pregnant women and to explore their association with neutralizing antibody (Nab) inhibition.

Methods

The study evaluated 93 pregnant women who had previously received two (n=21), three (n=55) or four (n=17) doses of COVID-19 vaccine. Also we evaluated maternal blood samples that were collected during childbirth. The levels of TRAIL, IP-10 and Nab inhibition were measured using enzyme-linked immunosorbent assays (ELISA).

Results and discussion

Our study revealed four-dose group resulted in lower TRAIL levels when compared to the two-dose and three-dose groups (4.78 vs. 16.07 vs. 21.61 pg/ml, p = 0.014). The two-dose group had reduced IP-10 levels than the three-dose cohort (111.49 vs. 147.89 pg/ml, p=0.013), with no significant variation compared to the four-dose group. In addition, the four-dose group showed stronger Nab inhibition against specific strains (BA.2 and BA.5) than the three-dose group. A positive correlation was observed between TRAIL and IP-10 in the two-dose group, while this relationship was not found in other dose groups or between TRAIL/IP-10 and Nab inhibition. As the doses of the COVID-19 vaccine increase, the levels of TRAIL and IP-10 generally increase, only by the fourth dose, the group previously vaccinated with AZD1222 showed lower TRAIL but higher IP-10. Despite these changes, more doses of the vaccine consistently reinforced Nab inhibition, apparently without any relation to TRAIL and IP-10 levels. The variation may indicate the induction of immunological memory in vaccinated mothers, which justifies further research in the future.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

Identification of immune biomarkers in recent active pulmonary tuberculosis

Tuberculosis (TB) has remained an unsolved problem and a major public health issue, particularly in developing countries. Pakistan is one of the countries with the highest tuberculosis infection rates globally. However, methods or biomarkers to detect early signs of TB infection are limited. Here, we characterized the mRNA profiles of immune responses in unstimulated Peripheral blood mononuclear cells obtained from treatment naïve patients with early signs of active pulmonary tuberculosis without previous history of clinical TB. We identified a unique mRNA profile in active TB compared to uninfected controls, including cytokines such as IL-27, IL-15, IL-2RA, IL-24, and TGFβ, transcription factors such as STAT1 and NFATC1 and immune markers/receptors such as TLR4, IRF1, CD80, CD28, and PTGDR2 from an overall 84 different transcripts analyzed. Among 12 significant differentially expressed transcripts, we identified five gene signatures which included three upregulated IL-27, STAT1, TLR4 and two downregulated IL-24 and CD80 that best discriminate between active pulmonary TB and uninfected controls with AUC ranging from 0.9 to 1. Our data identified a molecular immune signature associated with the early stages of active pulmonary tuberculosis and it could be further investigated as a potential biomarker of pulmonary TB.

Evaluation of the IP-10 mRNA release assay for diagnosis of TB in HIV-infected individuals

HIV-infected individuals are susceptible to Mycobacterium tuberculosis (M.tb) infection and are at high risk of developing active tuberculosis (TB). Interferon-gamma release assays (IGRAs) are auxiliary tools in the diagnosis of TB. However, the performance of IGRAs in HIV-infected individuals is suboptimal, which limits clinical application. Interferon-inducible protein 10 (IP-10) is an alternative biomarker for identifying M.tb infection due to its high expression after stimulation with M.tb antigens. However, whether IP-10 mRNA constitutes a target for the diagnosis of TB in HIV-infected individuals is unknown. Thus, we prospectively enrolled HIV-infected patients with suspected active TB from five hospitals between May 2021 and May 2022, and performed the IGRA test (QFT-GIT) alongside the IP-10 mRNA release assay on peripheral blood. Of the 216 participants, 152 TB patients and 48 non-TB patients with a conclusive diagnosis were included in the final analysis. The number of indeterminate results of IP-10 mRNA release assay (13/200, 6.5%) was significantly lower than that of the QFT-GIT test (42/200, 21.0%) (P = 0.000026). IP-10 mRNA release assay had a sensitivity of 65.3% (95%CI 55.9% - 73.8%) and a specificity of 74.2% (95%CI 55.4% - 88.1%), respectively; while the QFT-GIT test had a sensitivity of 43.2% (95%CI 34.1% - 52.7%) and a specificity of 87.1% (95%CI 70.2% - 96.4%), respectively. The sensitivity of the IP-10 mRNA release assay was significantly higher than that of QFT-GIT test (P = 0.00062), while no significant difference was detected between the specificities of these two tests (P = 0.198). The IP-10 mRNA release assay showed a lower dependence on CD4⁺ T cells than that of QFT-GIT test. This was evidenced by the fact that the QFT-GIT test had a higher number of indeterminate results and a lower sensitivity when the CD4⁺ T cells counts were decreased (P < 0.05), while no significant difference in the number of indeterminate results and sensitivity were observed for the IP-10 mRNA release assay among HIV-infected individuals with varied CD4⁺T cells counts (P > 0.05). Therefore, our study suggested that M.tb specific IP-10 mRNA is a better biomarker for diagnosis of TB in HIV-infected individuals.

Management of Tuberculosis Infection: Current Situation, Recent Developments and Operational Challenges

Tuberculosis infection (TBI) is defined as a state of infection in which individuals host live Mycobacterium tuberculosis with or without clinical signs of active TB. It is now understood as a dynamic process covering a spectrum of responses to infection resulting from the interaction between the TB bacilli and the host immune system. The global burden of TBI is about one-quarter of the world’s population, representing a reservoir of approximately 2 billion people. On average, 5–10% of people who are infected will develop TB disease over the course of their lives, but this risk is enhanced in a series of conditions, such as co-infection with HIV. The End-TB strategy promotes the programmatic management of TBI as a crucial endeavor to achieving global targets to end the TB epidemic. The current development of new diagnostic tests capable of discriminating between simple TBI and active TB, combined with novel short-course preventive treatments, will help achieve this goal. In this paper, we present the current situation and recent developments of management of TBI and the operational challenges.

IP10 is a predictor of successful vaccine protection against paratuberculosis infection in sheep

The cell mediated immune response and ability of immune cells to migrate to the site of infection are both key aspects of protection against many pathogens. Mycobacterium avium subsp. paratuberculosis (MAP) is an intracellular pathogen and the causative agent of paratuberculosis, a chronic wasting disease of ruminants. Current commercial vaccines for paratuberculosis reduce the occurrence of clinical disease but not all animals are protected from infection. Therefore, there is a need to understand the immune responses triggered by these vaccines at the site of infection, in circulating immune cells and their relationships to vaccine-mediated protection. The magnitude and location of gene expression related to the cell mediated immune response and cellular migration were studied in the ileum of sheep. In addition, longitudinal IP10 (also known as IP10) secretion by circulating immune cells was examined in the same sheep. Animals were grouped based on vaccination status (vaccinated vs non-vaccinated) and MAP exposure (experimentally exposed vs unexposed). Vaccination of unexposed sheep increased the expression of IP10, CCL5 and COR1c. Sheep that were successfully protected by vaccination (uninfected following experimental exposure) had significantly reduced expression of IP10 in the ileum at 12 months post exposure compared to vaccine non-responders (those that became infected) and non-vaccinated infected sheep. Successfully protected sheep also had significantly increased secretion of IP10 in in vitro stimulated immune cells from whole blood compared to vaccine non responders at 4 months post exposure. Therefore, the IP10 recall response has the potential to be used as marker for infection status in vaccinated sheep and could be a biomarker for a DIVA test in sheep.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

OBJECTIVE

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

METHODS

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

RESULTS

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

CONCLUSION

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Diagnostic Performance of a Novel CXCL10 mRNA Release Assay for Mycobacterium tuberculosis Infection

One-fourth of the world’s population has been infected with Mycobacterium tuberculosis (M.tb). Although interferon-gamma release assays (IGRAs) have been shown to be valid methods for identifying M.tb infection and auxiliary methods for diagnosis of active tuberculosis (TB), lower sensitivity and higher indeterminate rate were often detected among immunosuppressed patients. IP-10 was an alternative biomarker due to the higher expression level after M.tb antigen stimulation, but whether CXCL10 mRNA (the gene that transcribes for the IP-10 protein) can be used as a target for M.tb infection diagnosis was limited. Therefore, we aimed to evaluate the performance of a novel M.tb-specific CXCL10 mRNA release assay in diagnosis of M.tb infection. Suspected TB patients and healthy controls were prospectively recruited between March 2018 and November 2019 from three hospitals in China. CXCL10 mRNA release assay and traditional interferon-gamma release assay (T-SPOT.TB) were simultaneously performed on peripheral blood. Of the 1,479 participants enrolled in the study, 352 patients with definite TB and 153 healthy controls were analyzed. CXCL10 mRNA release assay provided a sensitivity of 93.9% (95% CI = 90.8–96.2%) and a specificity of 98.0% (95% CI = 94.3–99.6%) in the diagnosis of M.tb infection, respectively, while T-SPOT.TB gave a sensitivity of 94.5% (95% CI = 91.5–96.6%) and a specificity of 100% (95% CI = 97.6–100.0%) in the diagnosis of M.tb infection, respectively. The diagnostic performance of CXCL10 mRNA release assay was consistent with T-SPOT.TB, with a total coincidence rate of 95.0% (95% CI = 93.0–96.9%) and a Cohen’s kappa value of 0.89 (0.84–0.93, p < 0.001). However, among TB patients with HIV co-infection (n = 14), CXCL10 mRNA release assay presented significantly higher positive rate [92.9% (66.1–99.8%) vs. 61.5% (31.6–86.1%), p = 0.029] than those of T-SPOT.TB. These results suggested that M.tb-specific CXCL10 mRNA was a novel and useful target in the diagnosis of M.tb infection.

The Biological and Clinical Aspects of a Latent Tuberculosis Infection

Tuberculosis (TB), caused by bacilli from the Mycobacterium tuberculosis complex, remains a serious global public health problem, representing one of the main causes of death from infectious diseases. About one quarter of the world’s population is infected with Mtb and has a latent TB infection (LTBI). According to the World Health Organization (WHO), an LTBI is characterized by a lasting immune response to Mtb antigens without any TB symptoms. Current LTBI diagnoses and treatments are based on this simplified definition, although an LTBI involves a broad range of conditions, including when Mtb remains in the body in a persistent form and the immune response cannot be detected. The study of LTBIs has progressed in recent years; however, many biological and medical aspects of an LTBI are still under discussion. This review focuses on an LTBI as a broad spectrum of states, both of the human body, and of Mtb cells. The problems of phenotypic insusceptibility, diagnoses, chemoprophylaxis, and the necessity of treatment are discussed. We emphasize the complexity of an LTBI diagnosis and its treatment due to its ambiguous nature. We consider alternative ways of differentiating an LTBI from active TB, as well as predicting TB reactivation based on using mycobacterial “latency antigens” for interferon gamma release assay (IGRA) tests and the transcriptomic analysis of human blood cells.

Exaggerated IL-17A activity in human in vivo recall responses discriminates active tuberculosis from latent infection and cured disease

Host immune responses at the site of Mycobacterium tuberculosis (Mtb) infection can mediate pathogenesis of tuberculosis (TB) and onward transmission of infection. We hypothesized that pathological immune responses would be enriched at the site of host-pathogen interactions modelled by a standardized tuberculin skin test (TST) challenge in patients with active TB compared to those without disease, and interrogated immune responses by genome-wide transcriptional profiling. We show exaggerated interleukin (IL)-17A and Th17 responses among 48 individuals with active TB compared to 191 with latent TB infection, associated with increased neutrophil recruitment and matrix metalloproteinase-1 expression, both involved in TB pathogenesis. Curative antimicrobial treatment reversed these observed changes. Increased IL-1β and IL-6 responses to mycobacterial stimulation were evident in both circulating monocytes and in molecular changes at the site of TST in individuals with active TB, supporting a model in which monocyte-derived IL-1β and IL-6 promote Th17 differentiation within tissues. Modulation of these cytokine pathways may provide a rational strategy for host-directed therapy in active TB.

Infected juvenile salmon can experience increased predation during freshwater migration

Predation risk for animal migrants can be impacted by physical condition. Although size- or condition-based selection is often observed, observing infection-based predation is rare due to the difficulties in assessing infectious agents in predated samples. We examined predation of outmigrating sockeye salmon (Oncorhynchus nerka) smolts by bull trout (Salvelinus confluentus) in south-central British Columbia, Canada. We used a high-throughput quantitative polymerase chain reaction (qPCR) platform to screen for the presence of 17 infectious agents found in salmon and assess 14 host genes associated with viral responses. In one (2014) of the two years assessed (2014 and 2015), the presence of infectious haematopoietic necrosis virus (IHNv) resulted in 15-26 times greater chance of predation; in 2015 IHNv was absent among all samples, predated or not. Thus, we provide further evidence that infection can impact predation risk in migrants. Some smolts with high IHNv loads also exhibited gene expression profiles consistent with a virus-induced disease state. Nine other infectious agents were observed between the two years, none of which were associated with increased selection by bull trout. In 2014, richness of infectious agents was also associated with greater predation risk. This is a rare demonstration of predator consumption resulting in selection for prey that carry infectious agents. The mechanism by which this selection occurs is not yet determined. By culling infectious agents from migrant populations, fish predators could provide an ecological benefit to prey.

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.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Characterization of cytokine profile to distinguish latent tuberculosis from active tuberculosis and healthy controls

BACKGROUND

Tuberculosis (TB) is an infectious disease and its mortality rate ranks first. Latent tuberculosis infection (LTBI) means that a patient is infected with Mycobacterium tuberculosis, but has no relative clinical symptoms. It has been estimated that approximately 10% of patients with LTBI would develop into active tuberculosis. Therefore, it was urgent to search for more efficient biomarkers to discriminate LTBI from healthy population.

METHODS

The Luminex assay was employed to detect the quantity of cytokines secreted by mononuclear cells from peripheral blood stimulated with the ESAT6 protein among TB, LTBI and healthy controls. The cytokine profile was analyzed by principal components analysis and the receiver operating characteristic curve analysis.

RESULTS

The principal components analysis indicated that LTBI and TB were clearly separated from healthy controls, and that LTBI was also successfully differentiated from healthy controls. The cytokine profiling method to distinguish LTBI from healthy controls has a sensitivity and specificity of 100%. Nine potential biomarkers, including IL-23, IL-21, HGF, Bngf, IL-27, IL-31, IL-1β, IL-22 and IL-18, were identified, and these cytokines were considered as a potential cytokine complex for more effectively discriminating LTBI from healthy controls.

CONCLUSION

IL-23, IL-21, HGF, Bngf, IL-27, IL-31, IL-1β, IL-22 and IL-18 were demonstrated to be the potential cytokine complex for the assessment between LTBI and healthy controls.

Transcriptional Profiling of Human Peripheral Blood Mononuclear Cells Identifies Diagnostic Biomarkers That Distinguish Active and Latent Tuberculosis

Mycobacterium tuberculosis (M. tuberculosis) infection in humans can cause active disease or latent infection. However, the factors contributing to the maintenance of latent infection vs. disease progression are poorly understood. In this study, we used a genome-wide RNA sequencing (RNA-seq) approach to identify host factors associated with M. tuberculosis infection status and a novel gene signature that can distinguish active disease from latent infection. By RNA-seq, we characterized transcriptional differences in purified protein derivative (PPD)-stimulated peripheral blood mononuclear cells (PBMCs) among three groups: patients with active tuberculosis (ATB), individuals with latent TB infection (LTBI), and TB-uninfected controls (CON). A total of 401 differentially expressed genes enabled grouping of individuals into three clusters. A validation study by quantitative real-time PCR (qRT-PCR) confirmed the differential expression of TNFRSF10C, IFNG, PGM5, EBF3, and A2ML1 between the ATB and LTBI groups. Additional clinical validation was performed to evaluate the diagnostic performance of these five biomarkers using 130 subjects. The 3-gene signature set of TNFRSF10C, EBF3, and A2ML1 enabled correct classification of 91.5% of individuals, with a high sensitivity of 86.2% and specificity of 94.9%. Diagnostic performance of the 3-gene signature set was validated using a clinical cohort of 147 subjects with suspected ATB. The sensitivity and specificity of the 3-gene set for ATB were 82.4 and 92.4%, respectively. In conclusion, we detected distinct gene expression patterns in PBMCs stimulated by PPD depending on the status of M. tuberculosis infection. Furthermore, we identified a 3-gene signature set that could distinguish ATB from LTBI, which may facilitate rapid diagnosis and treatment for more effective disease control.

Biomarkers for Detecting Resilience against Mycobacterial Disease in Animals

Paratuberculosis and bovine tuberculosis are two mycobacterial diseases of ruminants which have a considerable impact on livestock health, welfare, and production. These are chronic "iceberg" diseases which take years to manifest and in which many subclinical cases remain undetected. Suggested biomarkers to detect infected or diseased animals are numerous and include cytokines, peptides, and expression of specific genes; however, these do not provide a strong correlation to disease. Despite these advances, disease detection still relies heavily on dated methods such as detection of pathogen shedding, skin tests, or serology. Here we review the evidence for suitable biomarkers and their mechanisms of action, with a focus on identifying animals that are resilient to disease. A better understanding of these factors will help establish new strategies to control the spread of these diseases.

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.

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

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

Latent tuberculosis infection: Opportunities and challenges

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

Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection

The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB patients (n = 15), compared with LTBI individuals (n = 15) and healthy controls (HCs, n = 15). Eight differentially expressed proteins were verified using western blot analysis, which was 100% consistent with the proteomics results. Statistically significant differences of six proteins were further validated in the PTB group compared with the LTBI and HC groups in the training set (n = 240), using ELISA. Classification and regression tree (CART) analysis was employed to determine the ideal protein combination for discriminating PTB from LTBI and HC. A diagnostic model consisting of alpha-1-antichymotrypsin (ACT), alpha-1-acid glycoprotein 1 (AGP1), and E-cadherin (CDH1) was established and presented a sensitivity of 81.2% (69/85) and a specificity of 95.2% (80/84) in discriminating PTB from LTBI, and a sensitivity of 81.2% (69/85) and a specificity of 90.1% (64/81) in discriminating PTB from HCs. Additional validation was performed by evaluating the diagnostic model in blind testing set (n = 113), which yielded a sensitivity of 75.0% (21/28) and specificity of 96.1% (25/26) in PTB vs. LTBI, 75.0% (21/28) and 92.3% (24/26) in PTB vs. HCs, and 75.0% (21/28) and 81.8% (27/33) in PTB vs. lung cancer (LC), respectively. This study obtained the plasma proteomic profiles of different M.TB infection statuses, which contribute to a better understanding of the pathogenesis involved in the transition from latent infection to TB activation and provide new potential diagnostic biomarkers for distinguishing PTB and LTBI.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Genome-wide transcriptional profiling identifies potential signatures in discriminating active tuberculosis from latent infection

To better understand the host immune response involved in the progression from latent tuberculosis infection (LTBI) to active tuberculosis (TB) and identify the potential signatures for discriminating TB from LTBI, we performed a genome-wide transcriptional profile of Mycobacterium tuberculosis (M.TB)–specific antigens-stimulated peripheral blood mononuclear cells (PBMCs) from patients with TB, LTBI individuals and healthy controls (HCs). A total of 209 and 234 differentially expressed genes were detected in TB vs. LTBI and TB vs. HCs, respectively. Nineteen differentially expressed genes with top fold change between TB and the other 2 groups were validated using quantitative real-time PCR (qPCR), and showed 94.7% consistent expression pattern with microarray test. Six genes were selected for further validation in an independent sample set of 230 samples. Expression of the resistin (RETN) and kallikrein 1 (KLK1) genes showed the greatest difference between the TB and LTBI or HC groups (P < 0.0001). Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUC) for RETN and KLK1 were 0.844 (0.783–0.904) and 0.833 (0.769–0.897), respectively, when discriminating TB from LTBI. The combination of these two genes achieved the best discriminative capacity [AUC = 0.916 (0.872–0.961)], with a sensitivity of 71.2% (58.7%–81.7%) and a specificity of 93.6% (85.7%–97.9%). Our results provide a new potentially diagnostic signature for discriminating TB and LTBI and have important implications for better understanding the pathogenesis involved in the transition from latent infection to TB activation.

Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted protein-protein interaction network and attract algorithm

About 90% of tuberculosis (TB) patients latently infected with Mycobacterium tuberculosis (Mtb) show no symptoms, yet have a 10% chance in lifetime to progress active TB. Nevertheless, current diagnosis approaches need improvement in efficiency and sensitivity. The objective of this work was to detect potential signatures for active TB to further improve the understanding of the biological roles of functional modules involved in this disease. First, targeted networks of active TB and control groups were established via re-weighting protein-protein interaction (PPI) networks using Pearson's correlation coefficient (PCC). Candidate modules were detected from the targeted networks, and the modules with Jaccard score >0.7 were defined as attractors. After that, identification of dys-regulated modules was conducted from the attractors using attract method, Subsequently, gene oncology (GO) enrichment analyses were implemented for genes in the dys-regulated modules. We obtained 33 and 65 candidate modules from the targeted networks of control and active TB groups, respectively. Overall, 13 attractors were identified. Using the cut-off criteria of false discovery rate <0.05, there were 4 dys-regulated modules (Module 1, 2, 3, and 4). Based on the GO annotation results, genes in Modules 1, 2 and 4 were only involved in translation. Most genes in Module 1, 2 and 4 were associated with ribosomes. Accordingly, these dys-regulated modules might serve as potential biomarkers of active TB, facilitating the development for a more efficient, and sensitive diagnostic assay for active TB.

Molecular indices of viral disease development in wild migrating salmon†

Infectious diseases can impact the physiological performance of individuals, including their mobility, visual acuity, behavior and tolerance and ability to effectively respond to additional stressors. These physiological effects can influence competitiveness, social hierarchy, habitat usage, migratory behavior and risk to predation, and in some circumstances, viability of populations. While there are multiple means of detecting infectious agents (microscopy, culture, molecular assays), the detection of infectious diseases in wild populations in circumstances where mortality is not observable can be difficult. Moreover, if infection-related physiological compromise leaves individuals vulnerable to predation, it may be rare to observe wildlife in a late stage of disease. Diagnostic technologies designed to diagnose cause of death are not always sensitive enough to detect early stages of disease development in live-sampled organisms. Sensitive technologies that can differentiate agent carrier states from active disease states are required to demonstrate impacts of infectious diseases in wild populations. We present the discovery and validation of salmon host transcriptional biomarkers capable of distinguishing fish in an active viral disease state [viral disease development (VDD)] from those carrying a latent viral infection, and viral versus bacterial disease states. Biomarker discovery was conducted through meta-analysis of published and in-house microarray data, and validation performed on independent datasets including disease challenge studies and farmed salmon diagnosed with various viral, bacterial and parasitic diseases. We demonstrate that the VDD biomarker panel is predictive of disease development across RNA-viral species, salmon species and salmon tissues, and can recognize a viral disease state in wild-migrating salmon. Moreover, we show that there is considerable overlap in the biomarkers resolved in our study in salmon with those based on similar human viral influenza research, suggesting a highly conserved suite of host genes associated with viral disease that may be applicable across a broad range of vertebrate taxa.

Characterization of promoter of the tuberculosis-resistant gene intracellular pathogen resistance 1

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis, which most commonly affects the lungs and causes over 1.3 million people die annually. Variation in host genes is known to influence susceptibility to tuberculosis. Expression of the intracellular pathogen resistance 1 (Ipr1) gene could enhance the host resistance to mycobacterium. Here, we analyzed the coding region sequence and promoter of Ipr1 gene of mouse strains C57BL/6 and BALB/c. We found that the coding sequences of Ipr1 gene both in C57BL/6 and in BALB/c mice encode the same protein, while the Ipr1 promoter of BALB/c exists a short deletion and showed a slight of decreased transcriptional activity when compared with C57BL/6. Moreover, the optimal and minimal Ipr1 promoter was identified by luciferase assays using truncated reporter constructs, and the region from -293 to +95 bp showed the highest transcriptional activity and responsible for IFN-γ stimulation. Furthermore, the results showed that IFN-γ activates JAK/STAT and NF-κB signaling pathways to induce Ipr1 expression, and the signal transducer and activator of transcription 1 (Stat1) are critical for IFN-γ-induced Ipr1 expression, because overexpression of Stat1 promotes Ipr1 transcription, but knockdown of Stat1 reduced Ipr1 expression. Collectively, for the first time, our study characterizes Ipr1 promoter and investigates the positive and negative regulation of Ipr1 expression, providing basic data for application of Ipr1 in animal breeding.

Transcriptomic Biomarkers for Tuberculosis: Evaluation of DOCK9. EPHA4, and NPC2 mRNA Expression in Peripheral Blood

Lately, much effort has been made to find mRNA biomarkers for tuberculosis (TB) disease/infection with microarray-based approaches. In a pilot investigation, through RNA sequencing technology, we observed a prominent modulation of DOCK9, EPHA4, and NPC2 mRNA abundance in the blood of TB patients. To corroborate these findings, independent validations were performed in cohorts from different areas. Gene expression levels in blood were evaluated by quantitative real-time PCR (Brazil, n = 129) or reanalysis of public microarray data (UK: n = 96; South Africa: n = 51; Germany: n = 26; and UK/France: n = 63). In the Brazilian cohort, significant modulation of all target-genes was observed comparing TB vs. healthy recent close TB contacts (rCt). With a 92% specificity, NPC2 mRNA high expression (NPC2^high) showed the highest sensitivity (85%, 95% CI 65%–96%; area under the ROC curve [AUROC] = 0.88), followed by EPHA4 (53%, 95% CI 33%–73%, AUROC = 0.73) and DOCK9 (19%, 95% CI 7%–40%; AUROC = 0.66). All the other reanalyzed cohorts corroborated the potential of NPC2^high as a biomarker for TB (sensitivity: 82–100%; specificity: 94–97%). An NPC2^high profile was also observed in 60% (29/48) of the tuberculin skin test positive rCt, and additional follow-up evaluation revealed changes in the expression levels of NPC2 during the different stages of Mycobacterium tuberculosis infection, suggesting that further studies are needed to evaluate modulation of this gene during latent TB and/or progression to active disease. Considering its high specificity, our data indicate, for the first time, that NPC2^high might serve as an accurate single-gene biomarker for TB.

The Transcriptional Signature of Active Tuberculosis Reflects Symptom Status in Extra-Pulmonary and Pulmonary Tuberculosis

Background

Mycobacterium tuberculosis infection is a leading cause of infectious death worldwide. Gene-expression microarray studies profiling the blood transcriptional response of tuberculosis (TB) patients have been undertaken in order to better understand the host immune response as well as to identify potential biomarkers of disease. To date most of these studies have focused on pulmonary TB patients with gene-expression profiles of extra-pulmonary TB patients yet to be compared to those of patients with pulmonary TB or sarcoidosis.

Methods

A novel cohort of patients with extra-pulmonary TB and sarcoidosis was recruited and the transcriptional response of these patients compared to those with pulmonary TB using a variety of transcriptomic approaches including testing a previously defined 380 gene meta-signature of active TB.

Results

The 380 meta-signature broadly differentiated active TB from healthy controls in this new dataset consisting of pulmonary and extra-pulmonary TB. The top 15 genes from this meta-signature had a lower sensitivity for differentiating extra-pulmonary TB from healthy controls as compared to pulmonary TB. We found the blood transcriptional responses in pulmonary and extra-pulmonary TB to be heterogeneous and to reflect the extent of symptoms of disease.

Conclusions

The transcriptional signature in extra-pulmonary TB demonstrated heterogeneity of gene expression reflective of symptom status, while the signature of pulmonary TB was distinct, based on a higher proportion of symptomatic individuals. These findings are of importance for the rational design and implementation of mRNA based TB diagnostics.

Combined Expression of IFN-γ, IL-17, and IL-4 mRNA by Recall PBMCs Moderately Discriminates Active Tuberculosis from Latent Mycobacterium tuberculosis Infection in Patients with Miscellaneous Inflammatory Underlying Conditions

New biomarkers are needed for discriminating active tuberculosis (TB) from latent TB infection (LTBI), especially in vulnerable groups representing the major diagnostic challenge. This pilot study was carried out to explore the diagnostic potential of selected genes, IFN-γ, IL-17, IL-4, and FoxP3, associated with TB immunity and immunopathology. IFN-γ, IL-17, IL-4, and FoxP3 mRNA expression levels were measured by quantitative reverse transcription PCR (RT-qPCR) from antigen-stimulated peripheral blood mononuclear cells of patients with active TB (n = 25); patients with miscellaneous inflammatory disorders and concomitant LTBI (n = 20), rheumatoid arthritis (RA) being the most predominant in the group (n = 11); and in healthy Bacillus Calmette–Guérin (BCG) vaccinees (n = 8). While the levels of FoxP3 mRNA did not differ between the tested groups, the cumulative expression levels of purified protein derivative-stimulated IFN-γ, IL-17, and IL-4 mRNAs were found to distinguish active TB from the whole group of LTBI with 48% sensitivity and 85% specificity. When restricting the LTBI group to RA cases only, the sensitivity was 56% and specificity 100%. When interpreting the result as positive in at least one of the mRNAs IFN-γ, IL-17, or IL-4, sensitivity of 64% and specificities of 75% (heterogeneous group of LTBI) or 100% (LTBI with RA) were achieved. Moderate discrimination of active TB from LTBI with miscellaneous inflammatory underlying conditions by using combined quantitative expression of IFN-γ, IL-17, and IL-4 mRNA seems not to be of high diagnostic potential.

Multiple cytokine responses in discriminating between active tuberculosis and latent tuberculosis infection

BACKGROUND

Cytokines play an important role in cell-mediated immune responses against Mycobacterium tuberculosis (Mtb) infection. Cytokine profile specifically associated with active tuberculosis (ATB) patients, subjects with latent tuberculosis infection (LTBI) and non-infected individuals remains to be determined.

METHODS

We enrolled a total of 92 subjects including patients with ATB (n = 25), LTBI (n = 36) and healthy controls (HC, n = 31) to investigate the cytokine production by peripheral blood mononuclear cells after Mtb purified protein derivative (PPD) stimulation which was evaluated by a beads-based multiplex assay system.

RESULTS

The production of IL-1β, IL-2, IL-6, IL-10, IL-17, G-CSF, IFN-γ, IP-10, MIP-1α and TNF-α was abundantly induced by PPD in all three groups. The levels of IL-2, IL-10, IFN-γ, IP-10 and TNF-α were significantly higher in LTBI group than in ATB group. The combination of PPD-stimulated IL-2 and IL-10 accurately identified 84.0% of ATB and 88.9% of LTBI. We validated the use of PPD-stimulated IL-2 and IL-10 test combined with T-SPOT.TB test in a cohort of 44 subjects with TB suspicion. The sensitivity and specificity of the combined test were 83.3% and 92.3%, respectively. The PPD-stimulated IL-2/IFN-γ ratio (p < 0.001) in LTBI subjects was significantly higher than in active TB patients.

CONCLUSION

Our study identified cytokine patterns characteristic of ATB and LTBI. Cytokines such as IL-2 and IL-10 may serve as biomarkers for distinguishing ATB from LTBI and healthy control and may contribute to intervention and improvement in TB diagnosis.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis

Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy toward this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of hematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time reverse transcription polymerase chain reaction (RT-PCR) assay and we also found a down-regulation of S100B in A. fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.

Diagnostic 'omics' for active tuberculosis

The decision to treat active tuberculosis (TB) is dependent on microbiological tests for the organism or evidence of disease compatible with TB in people with a high demographic risk of exposure. The tuberculin skin test and peripheral blood interferon-γ release assays do not distinguish active TB from a cleared or latent infection. Microbiological culture of mycobacteria is slow. Moreover, the sensitivities of culture and microscopy for acid-fast bacilli and nucleic acid detection by PCR are often compromised by difficulty in obtaining samples from the site of disease. Consequently, we need sensitive and rapid tests for easily obtained clinical samples, which can be deployed to assess patients exposed to TB, discriminate TB from other infectious, inflammatory or autoimmune diseases, and to identify subclinical TB in HIV-1 infected patients prior to commencing antiretroviral therapy. We discuss the evaluation of peripheral blood transcriptomics, proteomics and metabolomics to develop the next generation of rapid diagnostics for active TB. We catalogue the studies published to date seeking to discriminate active TB from healthy volunteers, patients with latent infection and those with other diseases. We identify the limitations of these studies and the barriers to their adoption in clinical practice. In so doing, we aim to develop a framework to guide our approach to discovery and development of diagnostic biomarkers for active TB.

A review of clinical models for the evaluation of human TB vaccines

While much progress has been made in the fight against the scourge of tuberculosis (TB), we are still some way from reaching the ambitious targets of eliminating it as a global public health problem by the mid twenty-first century. A new and effective vaccine that protects against pulmonary TB disease will be an essential element of any control strategy. Over a dozen vaccines are currently in development, but recent efficacy trial data from one of the most advanced candidates have been disappointing. Limitations of current preclinical animal models exist, together with a lack of a complete understanding of host immunity to TB or robust correlates of disease risk and protection. Therefore, in the context of such obstacles, we discuss the lessons identified from recent efficacy trials, current concepts of biomarkers and correlates of protection, the potential of innovative clinical models such as human challenge and conducting trials in high-incidence settings to evaluate TB vaccines in humans, and the use of systems vaccinology and novel technologies including transcriptomics and metabolomics, that may facilitate their utility.

Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

BACKGROUND

Tuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population.

RESULTS

Bioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance.

CONCLUSIONS

Our study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.

Tuberculosis vaccines: barriers and prospects on the quest for a transformative tool

The road to a more efficacious vaccine that could be a truly transformative tool for decreasing tuberculosis morbidity and mortality, along with Mycobacterium tuberculosis transmission, is quite daunting. Despite this, there are reasons for optimism. Abetted by better conceptual clarity, clear acknowledgment of the degree of our current immunobiological ignorance, the availability of powerful new tools for dissecting the immunopathogenesis of human tuberculosis, the generation of more creative diversity in tuberculosis vaccine concepts, the development of better fit-for-purpose animal models, and the potential of more pragmatic approaches to the clinical testing of vaccine candidates, the field has promise for delivering novel tools for dealing with this worldwide scourge of poverty.

Transformative tools for tackling tuberculosis

The world is in need of more effective approaches to controlling tuberculosis. The development of improved control strategies has been hampered by deficiencies in the tools available for detecting Mycobacterium tuberculosis and defining the dynamic consequences of the interaction of M. tuberculosis with its human host. Key needs include a highly sensitive, specific nonsputum diagnostic; biomarkers predictive of responses to therapy; correlates of risk for disease development; and host response-independent markers of M. tuberculosis infection. Tools able to sensitively detect and quantify total body M. tuberculosis burden might well be transformative across many needed use cases. Here, we review the current state of the field, paying particular attention to needed changes in experimental paradigms that would facilitate the discovery, validation, and development of such tools.

Type I, II, and III Interferons: Regulating Immunity to Mycobacterium tuberculosis Infection

Interferons (IFNs) are cytokines released by host cells in response to the presence of pathogens or tumor cells. The aim of this review was to present the previously known and new findings about the role of interferons type I and II, and recently discovered type III in Mycobacterium tuberculosis (M. tuberculosis) infection control. Infection of various cell types with M. tuberculosis induce both IFN-α and IFN-β synthesis. The majority of the studies support the findings that IFN type I actually promotes infection with M. tuberculosis. It has been well establish that IFN-γ has protective function against M. tuberculosis and the other mycobacteria and that the primary source of this cytokine are CD4(+) and CD8(+) T cells. Recently, it has been shown that also the innate lymphocytes, γδ T cells, natural killer (NK) T cells, and NK cells can also be the source of IFN-γ in response to mycobacterial infection. Several studies have shown that CD4(+) T cells protect mice against M. tuberculosis independently of IFN-γ. The balance between IFN-γ and different cytokines such as IL-10 and other Th2 cell cytokines is likely to influence disease outcome. Type I IFN appears to be detrimental through at least three separate, but overlapping, type I IFN-mediated mechanisms: induction of excessive apoptosis, specific suppression of Th1 and IFN-γ responses, and dampening of the immune response by strong IL-10 induction. Recently it has been found that M. tuberculosis infection in A549 lung epithelial cells stimulate up-regulation of IFN-λ genes in vitro. IFN-λs also have a role in modulation of Th1/Th2 response. IFN-λs are not essential for M. tuberculosis infection control, but can give some contribution in immune response to this pathogen.

Host Protein Biomarkers Identify Active Tuberculosis in HIV Uninfected and Co-infected Individuals

Biomarkers for active tuberculosis (TB) are urgently needed to improve rapid TB diagnosis. The objective of this study was to identify serum protein expression changes associated with TB but not latent Mycobacterium tuberculosis infection (LTBI), uninfected states, or respiratory diseases other than TB (ORD). Serum samples from 209 HIV uninfected (HIV⁻) and co-infected (HIV⁺) individuals were studied. In the discovery phase samples were analyzed via liquid chromatography and mass spectrometry, and in the verification phase biologically independent samples were analyzed via a multiplex multiple reaction monitoring mass spectrometry (MRM-MS) assay. Compared to LTBI and ORD, host proteins were significantly differentially expressed in TB, and involved in the immune response, tissue repair, and lipid metabolism. Biomarker panels whose composition differed according to HIV status, and consisted of 8 host proteins in HIV⁻ individuals (CD14, SEPP1, SELL, TNXB, LUM, PEPD, QSOX1, COMP, APOC1), or 10 host proteins in HIV⁺ individuals (CD14, SEPP1, PGLYRP2, PFN1, VASN, CPN2, TAGLN2, IGFBP6), respectively, distinguished TB from ORD with excellent accuracy (AUC = 0.96 for HIV⁻ TB, 0.95 for HIV⁺ TB). These results warrant validation in larger studies but provide promise that host protein biomarkers could be the basis for a rapid, blood-based test for TB.

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Active tuberculosis leads to the differential expression of serum proteins involved in associated host processes.

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Serum protein expression changes in tuberculosis involve the immune response, tissue repair, and lipid metabolism.

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Panels of 8–10 host proteins can distinguish active tuberculosis from latent infection, and other respiratory diseases.

Accurate biomarkers for active tuberculosis (TB) are urgently needed to improve rapid diagnosis. Current diagnostics for TB rely on microbiologic or molecular confirmation of M. tuberculosis, and are therefore dependent on a specimen from the site of disease which is not always accessible. This study demonstrates that human host proteins are differentially expressed in TB compared to latent M. tuberculosis infection, or respiratory diseases other than TB. Our data thus provide promise that host proteins have the potential to become the basis of rapid blood tests that do not require a sample from the site of disease.

New Diagnostics for Childhood Tuberculosis

The challenge of diagnosing childhood tuberculosis (TB) results from its paucibacillary nature and the difficulties of sputum collection in children. Mycobacterial culture, the diagnostic gold standard, provides microbiological confirmation in only 30% to 40% of childhood pulmonary TB cases and takes up to 6 weeks to result. Conventional drug susceptibility testing requires an additional 2 to 4 weeks after culture confirmation. In response to the low sensitivity and long wait time of the traditional diagnostic approach, many new assays have been developed. These new tools have shortened time to result; however, none of them offer greater sensitivity than culture.

A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases

The goal of this study was to identify a host gene signature that can distinguish tuberculosis (TB) from other pulmonary diseases (OPD). We conducted real-time PCR on whole blood samples from patients in Brazil. TB and OPD patients (asthma and non-TB pneumonia) differentially expressed granzyme A (GZMA), guanylate binding protein 5 (GBP5) and Fc gamma receptor 1A (CD64). Receiver operating characteristic, tree classification and random forest analyses were applied to evaluate the discriminatory power of the three genes and find the gene panel most predictive of patients' disease classification. Tree classification produced a model based on GBP5 and CD64 expression. In random forest analysis, the combination of the three genes provided a robust biosignature to distinguish TB from OPD with 95% specificity and 93% sensitivity. Our results suggest that GBP5 and CD64 in tandem may be the most predictive combination. However, GZMA contribution to the prediction model requires further investigation. Regardless, these three genes show promise as a rapid diagnostic marker separating TB from OPD.

Systematic expression profiling analysis identifies specific microRNA-gene interactions that may differentiate between active and latent tuberculosis infection

Tuberculosis (TB) is the second most common cause of death from infectious diseases. About 90% of those infected are asymptomatic—the so-called latent TB infections (LTBI), with a 10% lifetime chance of progressing to active TB. To further understand the molecular pathogenesis of TB, several molecular studies have attempted to compare the expression profiles between healthy controls and active TB or LTBI patients. However, the results vary due to diverse genetic backgrounds and study designs and the inherent complexity of the disease process. Thus, developing a sensitive and efficient method for the detection of LTBI is both crucial and challenging. For the present study, we performed a systematic analysis of the gene and microRNA profiles of healthy individuals versus those affected with TB or LTBI. Combined with a series of in silico analysis utilizing publicly available microRNA knowledge bases and published literature data, we have uncovered several microRNA-gene interactions that specifically target both the blood and lungs. Some of these molecular interactions are novel and may serve as potential biomarkers of TB and LTBI, facilitating the development for a more sensitive, efficient, and cost-effective diagnostic assay for TB and LTBI for the Taiwanese population.

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

Background

Genetic factors are involved in susceptibility or protection to tuberculosis (TB). Apart from gene polymorphisms and mutations, changes in levels of gene expression, induced by non-genetic factors, may also determine whether individuals progress to active TB.

Methods

We analysed the expression level of 45 genes in a total of 47 individuals (23 healthy household contacts and 24 new smear-positive pulmonary TB patients) in Addis Ababa using a dual colour multiplex ligation-dependent probe amplification (dcRT-MLPA) technique to assess gene expression profiles that may be used to distinguish TB cases and their contacts and also latently infected (LTBI) and uninfected household contacts.

Results

The gene expression level of BLR1, Bcl2, IL4d2, IL7R, FCGR1A, MARCO, MMP9, CCL19, and LTF had significant discriminatory power between sputum smear-positive TB cases and household contacts, with AUCs of 0.84, 0.81, 0.79, 0.79, 0.78, 0.76, 0.75, 0.75 and 0.68 respectively. The combination of Bcl2, BLR1, FCGR1A, IL4d2 and MARCO identified 91.66% of active TB cases and 95.65% of household contacts without active TB. The expression of CCL19, TGFB1, and Foxp3 showed significant difference between LTBI and uninfected contacts, with AUCs of 0.85, 0.82, and 0.75, respectively, whereas the combination of BPI, CCL19, FoxP3, FPR1 and TGFB1 identified 90.9% of QFT^- and 91.6% of QFT⁺ household contacts.

Conclusions

Expression of single and especially combinations of host genes can accurately differentiate between active TB cases and healthy individuals as well as between LTBI and uninfected contacts.

The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis

Despite advances in antimicrobials, vaccination and public health measures, infectious diseases remain a leading cause of morbidity and mortality worldwide. With the increase in antimicrobial resistance and the emergence of new pathogens, there remains a need for new and more accurate diagnostics, the ability to monitor adequate treatment response as well as the ability to predict prognosis for an individual. Transcriptional approaches using blood signatures have enabled a better understanding of the host response to diseases, leading not only to new avenues of basic research, but also to the identification of potential biomarkers for use in diagnosis, prognosis and treatment monitoring.

Diagnosis of childhood tuberculosis and host RNA expression in Africa

BACKGROUND

Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV).

METHODS

The study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood.

RESULTS

We identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%.

CONCLUSIONS

RNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).

A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection

A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization–tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind testing set, LTBI subjects could be identified with sensitivities and specificities of 85.20% to 88.90% and 85.7% to 100%, respectively. Additionally, 14 potential LTBI biomarkers were identified, and all proteins were identified for the first time through proteomics in the plasma of healthy, latently infected individuals. In all, proteomic pattern analyses can increase the accuracy of LTBI diagnosis, and the data presented here provide novel insights into potential mechanisms involved in LTBI.

CXCL10 controls inflammatory pain via opioid peptide-containing macrophages in electroacupuncture

Acupuncture is widely used for pain treatment in patients with osteoarthritis or low back pain, but molecular mechanisms remain largely enigmatic. In the early phase of inflammation neutrophilic chemokines direct opioid-containing neutrophils in the inflamed tissue and stimulate opioid peptide release and antinociception. In this study the molecular pathway and neuroimmune connections in complete Freund's adjuvant (CFA)-induced hind paw inflammation and electroacupuncture for peripheral pain control were analyzed. Free moving Wistar rats with hind paw inflammation were treated twice with electroacupuncture at GB30 (Huan Tiao - gall bladder meridian) (day 0 and 1) and analyzed for mechanical and thermal nociceptive thresholds. The cytokine profiles as well as the expression of opioid peptides were quantified in the inflamed paw. Electroacupuncture elicited long-term antinociception blocked by local injection of anti-opioid peptide antibodies (beta-endorphin, met-enkephalin, dynorphin A). The treatment altered the cytokine profile towards an anti-inflammatory pattern but augmented interferon (IFN)-gamma and the chemokine CXCL10 (IP-10: interferon gamma-inducible protein) protein and mRNA expression with concomitant increased numbers of opioid peptide-containing CXCR3⁺ macrophages. In rats with CFA hind paw inflammation without acupuncture repeated injection of CXCL10 triggered opioid-mediated antinociception and increase opioid-containing macrophages. Conversely, neutralization of CXCL10 time-dependently decreased electroacupuncture-induced antinociception and the number of infiltrating opioid peptide-expressing CXCR3⁺ macrophages. In summary, we describe a novel function of the chemokine CXCL10 - as a regulator for an increase of opioid-containing macrophages and antinociceptive mediator in inflammatory pain and as a key chemokine regulated by electroacupuncture.

Identification of a 251 gene expression signature that can accurately detect M. tuberculosis in patients with and without HIV co-infection

Background

Co-infection with tuberculosis (TB) is the leading cause of death in HIV-infected individuals. However, diagnosis of TB, especially in the presence of an HIV co-infection, can be limiting due to the high inaccuracy associated with the use of conventional diagnostic methods. Here we report a gene signature that can identify a tuberculosis infection in patients co-infected with HIV as well as in the absence of HIV.

Methods

We analyzed global gene expression data from peripheral blood mononuclear cell (PBMC) samples of patients that were either mono-infected with HIV or co-infected with HIV/TB and used support vector machines to identify a gene signature that can distinguish between the two classes. We then validated our results using publically available gene expression data from patients mono-infected with TB.

Results

Our analysis successfully identified a 251-gene signature that accurately distinguishes patients co-infected with HIV/TB from those infected with HIV only, with an overall accuracy of 81.4% (sensitivity = 76.2%, specificity = 86.4%). Furthermore, we show that our 251-gene signature can also accurately distinguish patients with active TB in the absence of an HIV infection from both patients with a latent TB infection and healthy controls (88.9–94.7% accuracy; 69.2–90% sensitivity and 90.3–100% specificity). We also demonstrate that the expression levels of the 251-gene signature diminish as a correlate of the length of TB treatment.

Conclusions

A 251-gene signature is described to (a) detect TB in the presence or absence of an HIV co-infection, and (b) assess response to treatment following anti-TB therapy.