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

Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq

BACKGROUND

Tuberculosis (TB) continues to be a major cause of morbidity and mortality worldwide. However, the molecular mechanism underlying immune response to human infection with Mycobacterium tuberculosis (Mtb) remains unclear. Assessing changes in transcript abundance in blood between health and disease on a genome-wide scale affords a comprehensive view of the impact of Mtb infection on the host defense and a reliable way to identify novel TB biomarkers.

METHODS

We combined expression profiling by array and single cell RNA-sequencing (scRNA-seq) via 10X Genomics platform to better illustrate the immuno-related transcriptional signature of TB and explore potential diagnostic markers for differentiating TB from latent tuberculosis infection (LTBI) and healthy control (HC).

FINDINGS

Pathway analysis based on differential expressed genes (DEGs) revealed that immune transcriptional profiling could effectively differ TB with LTBI and HC. Following WGCNA and PPI network analysis based on DEGs, we screened out three key immuno-related hub genes (ADM, IFIT3 and SERPING1) highly associated with TB. Further validation found only ADM expression significantly increased in TB patients in both adult and children's datasets. By comparing the scRNA-seq datasets from TB, LTBI and HC, we observed a remarkable elevated expression level and proportion of ADM in TB Myeloid cells, further supporting that ADM expression changes could distinguish patients with TB from LTBI and HC. Besides, the hsa-miR-24-3p-NEAT1-ADM-CEBPB regulation pathway might be one of the critical networks regulating the pathogenesis of TB. Although further investigation in a larger cohort is warranted, we provide useful and novel insight to explore the potential candidate genes for TB diagnosis and intervention.

INTERPRETATION

We propose that the expression of ADM in peripheral blood could be used as a novel biomarker for differentiating TB with LTBI and HC.

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

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

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

Rhesus Macaques Are More Susceptible to Progressive Tuberculosis than Cynomolgus Macaques: a Quantitative Comparison

In the past 2 decades, it has become increasingly clear that nonhuman primates, specifically macaques, are useful models for human tuberculosis (TB). Several macaque species have been used for TB studies, and questions remain about the similarities and differences in TB pathogenesis among macaque species, which can complicate decisions about the best species for a specific experiment. Here we provide a quantitative assessment, using serial positron emission tomography and computed tomography (PET-CT) imaging and precise quantitative determination of bacterial burdens of low-dose Mycobacterium tuberculosis infection in cynomolgus macaques of Chinese origin, rhesus macaques of Chinese origin, and Mauritian cynomolgus macaques. This comprehensive study demonstrates that there is substantial variability in the outcome of infection within and among species. Overall, rhesus macaques have higher rates of disease progression, more lung, lymph node, and extrapulmonary involvement, and higher bacterial burdens than Chinese cynomolgus macaques. The small cohort of Mauritian cynomolgus macaques assessed here indicates that this species is more similar to rhesus macaques than to Chinese cynomolgus macaques in terms of M. tuberculosis infection outcome. These data provide insights into the differences among species, providing valuable data to the field for assessing macaque studies of TB.

Concordant and discordant gene expression patterns in mouse strains identify best-fit animal model for human tuberculosis

Immunity in infection, inflammation and malignancy differs markedly in man and mouse. Still, we learn about human immunity in large extent from experimental mouse models. We propose a novel data integration approach which identifies concordant and discordant gene expression patterns of the immune responses in heterologous data sets. We have conducted experiments to compare human and murine transcriptional responses to Mycobacterium tuberculosis (Mtb) infection in whole blood (WB) as well as macrophages and compared them with simulated as well as publicly available data. Our results indicate profound differences between patterns of gene expression in innate and adaptive immunity in man and mouse upon Mtb infection. We characterized differential expression of T-cell related genes corresponding to the differences in phenotype between tuberculosis (TB) highly and low susceptible mouse strains. Our approach is general and facilitates the choice of optimal animal model for studies of the human immune response to a particular disease.

Quantitative and qualitative iNKT repertoire associations with disease susceptibility and outcome in macaque tuberculosis infection

Correlates of immune protection that reliably predict vaccine efficacy against Mycobacterium tuberculosis (Mtb) infection are urgently needed. Invariant NKT cells (iNKTs) are CD1d-dependent innate T cells that augment host antimicrobial immunity through production of cytokines, including interferon (IFN)-γ and tumour necrosis factor (TNF)-α. We determined peripheral blood iNKT numbers, their proliferative responses and iNKT subset proportions after in vitro antigen expansion by α-galactosylceramide (αGC) in a large cohort of mycobacteria-naïve non-human primates, and macaques from Bacillus Calmette-Guerin (BCG) vaccine and Mtb challenge studies. Animals studied included four genetically distinct groups of macaques within cynomolgus and rhesus species that differ in their susceptibility to Mtb infection. We demonstrate significant differences in ex vivo iNKT frequency between groups, which trends towards an association with susceptibility to Mtb, but no significant difference in overall iNKT proliferative responses. Susceptible animals exhibited a skewed CD4+/CD8+ iNKT subset ratio in comparison to more Mtb-resistant groups. Correlation of iNKT subsets post BCG vaccination with clinical disease manifestations following Mtb challenge in the Chinese cynomolgus and Indian rhesus macaques identified a consistent trend linking increased CD8+ iNKTs with favourable disease outcome. Finally, a similar iNKT profile was conferred by BCG vaccination in rhesus macaques. Our study provides the first detailed characterisation of iNKT cells in macaque tuberculosis infection, suggesting that iNKT repertoire differences may impact on disease outcome, which warrants further investigation.

Using Data from Macaques To Predict Gamma Interferon Responses after Mycobacterium bovis BCG Vaccination in Humans: a Proof-of-Concept Study of Immunostimulation/Immunodynamic Modeling Methods

Macaques play a central role in the development of human tuberculosis (TB) vaccines. Immune and challenge responses differ across macaque and human subpopulations. We used novel immunostimulation/immunodynamic modeling methods in a proof-of-concept study to determine which macaque subpopulations best predicted immune responses in different human subpopulations. Data on gamma interferon (IFN-γ)-secreting CD4⁺ T cells over time after recent Mycobacterium bovis BCG vaccination were available for 55 humans and 81 macaques. Human population covariates were baseline BCG vaccination status, time since BCG vaccination, gender, and the monocyte/lymphocyte cell count ratio. The macaque population covariate was the colony of origin. A two-compartment mathematical model describing the dynamics of the IFN-γ T cell response after BCG vaccination was calibrated to these data using nonlinear mixed-effects methods. The model was calibrated to macaque and human data separately. The association between subpopulations and the BCG immune response in each species was assessed. The macaque subpopulations that best predicted immune responses in different human subpopulations were identified using Bayesian information criteria. We found that the macaque colony and the human baseline BCG status were significantly (P < 0.05) associated with the BCG-induced immune response. For humans who were BCG naïve at baseline, Indonesian cynomolgus macaques and Indian rhesus macaques best predicted the immune response. For humans who had already been BCG vaccinated at baseline, Mauritian cynomolgus macaques best predicted the immune response. This work suggests that the immune responses of different human populations may be best modeled by different macaque colonies, and it demonstrates the potential utility of immunostimulation/immunodynamic modeling to accelerate TB vaccine development.