Meta-Analysis Identification of Highly Robust and Differential Immune-Metabolic Signatures of Systemic Host Response to Acute and Latent Tuberculosis in Children and Adults

Abstract

Background: Whole blood expression profiling is a mainstay for delineating differential diagnostic signatures of infection yet is subject to high variability that reduces power and complicates clinical usefulness. To date, confirmatory high confidence expression profiling signatures for clinical use remain uncertain. Here we have sought to evaluate the reproducibility and confirmatory nature of differential expression signatures, comprising molecular and cellular pathways, across multiple international clinical observational studies investigating children and adult whole blood transcriptome responses to tuberculosis (TB).Methods and findings: A systematic search and quality control assessment of gene expression repositories for human TB using whole blood resulted in 11 datasets with a total of 1073 patients from Africa, Europe, and South America. A non-parametric estimation of percentage of false prediction was used for meta-analysis of high confidence differential expression analysis. Deconvolution analysis was applied to infer changes in immune cell proportions and enrichment tests applied using pathway database resources. Meta-analysis identified high confidence differentially expressed genes, comprising 372 in adult active-TB versus latent-TB (LTBI), 332 in adult active-TB versus controls (CON), five in LTBI versus CON, and 415 in childhood active-TB versus LTBI. Notably, these confirmatory markers have low representation in published signatures for diagnosing TB. Pathway biology analysis of high confidence gene sets revealed dominant metabolic and innate-immune pathway signatures while suppressed signatures were enriched with adaptive signaling pathways and reduced proportions of T and B cells. Childhood TB showed uniquely strong inflammasome antagonist signature (IL1RN and ILR2), while adult TB patients exhibit a significant preponderance type I and type II IFN markers. Key limitations of the study include the paucity of data on potential confounders.Conclusion: Meta-analysis identified high confidence confirmatory immune-metabolic and cellular expression signatures across studies regardless of the population resource setting, HIV status and circulating endemic pathogens. Notably, previously identified diagnostic signature markers for TB show limited concordance with the confirmatory meta-analysis. Overall, our results support the use of the confirmatory expression signatures for guiding optimized diagnostic, prognostic, and therapeutic monitoring modalities in TB.

Novel functional sequences uncovered through a bovine multi-assembly graph

“…The top downregulated non-reference gene supported by the BLASTX query encodes a protein with high similarity (79.80%) to leukocyte immunoglobulin-like receptor A5 (LILRA5). LILRA5 triggers the strength of the innate immune response to Mycobacterium infections 30 and might serve as a target for pathogen-mediated immunomodulation. Many genes of the leukocyte receptor complex are missing in the assembled chromosomes of the ARS-UCD1.2 reference; instead, LILRA5 (LOC100139766) is annotated on a 236 kb long unplaced scaffold (NW_020190675).…”

“…LILRA5 triggers the strength of the innate immune response to Mycobacterium infections 30 and might serve as a target for pathogen-mediated immunomodulation. Many genes of the leukocyte receptor complex are missing in the assembled chromosomes of the ARS-UCD1.2 Table 3).…”

Immune Subtyping in Latent Tuberculosis

“…Although whole blood transcriptomic studies have previously been widely successful in identifying differential gene expression signatures for active TB ( 7 , 13 – 17 ), they have not been successful in differentiating between the HC and asymptomatic LTBI populations ( 7 , 13 ). All of these studies, along with other gene expression meta-analysis have clearly shown that there is no statistically significant differential gene expression between these two ( 20 ). Our independent analysis of the datasets here corroborates these findings.…”

“…Recently, Burel and co-workers used transcriptomics and protein profiling of CD4+ T cells and identified an LTBI specific signature to separate them from uninfected cases ( 19 ). However, accurately differentiating non-incipient and non-subclinical LTBI from uninfected individuals has remained an open question ( 20 ).…”

Tuberculosis and HIV—An Update on the “Cursed Duet” in Children

“…Progress has been made on identifying immuno-metabolic signatures in children and adults for M.tb infection and TB disease irrespective of HIV status (60).…”