Tricks to translating TB transcriptomics.

Transcriptomics and other high-throughput methods are increasingly applied to questions relating to tuberculosis (TB) pathogenesis. Whole blood transcriptomics has repeatedly been applied to define correlates of TB risk and has produced new insight into the late stage of disease pathogenesis. In a novel approach, authors of a recently published study in Science Translational Medicine applied complex data analysis of existing TB transcriptomic datasets, and in vitro models, in an attempt to identify correlates of protection in TB, which are crucially required for the development of novel TB diagnostics and therapeutics to halt this global epidemic. Utilizing latent TB infection (LTBI) as a surrogate of protection, they identified IL-32 as a mediator of interferon gamma (IFNγ)-vitamin D dependent antimicrobial immunity and a marker of LTBI. Here, we provide a review of all TB whole-blood transcriptomic studies to date in the context of identifying correlates of protection, discuss potential pitfalls of combining complex analyses originating from such studies, the importance of detailed metadata to interpret differential patient classification algorithms, the effect of differing circulating cell populations between patient groups on the interpretation of resulting biomarkers and we decipher weighted gene co-expression network analysis (WGCNA), a recently developed systems biology tool which holds promise of identifying novel pathway interactions in disease pathogenesis. In conclusion, we propose the development of an integrated OMICS platform and open access to detailed metadata, in order for the TB research community to leverage the vast array of OMICS data being generated with the aim of unraveling the holy grail of TB research: correlates of protection

The bacillary and macrophage response to hypoxia in tuberculosis and the consequences for T cell antigen recognition

M. tuberculosis is a facultative anaerobe and its characteristic pathological hallmark, the granuloma, exhibits hypoxia in humans and in most experimental models. Thus the host and bacillary adaptation to hypoxia is of central importance in understanding pathogenesis and thereby to derive new drug treatments and vaccines

Effect of prednisolone on inflammatory markers in pericardial tuberculosis: A pilot study

Background: Pericardial disorders are a common cause of heart disease, and the most common cause of pericarditis in developing countries is tuberculous (TB) pericarditis. It has been shown that prednisolone added to standard anti-TB therapy leads to a lower rate of constrictive pericarditis. We conducted a pilot study to evaluate the effect of adjunctive prednisolone treatment on the concentration of inflammatory markers in pericardial tuberculosis, in order to inform immunological mechanisms at the disease site. Methods: Pericardial fluid, plasma and saliva samples were collected from fourteen patients with pericardial tuberculosis, at multiple time points. Inflammatory markers were measured using multiplex luminex analysis and ELISA. Results: In samples from 14 patients we confirmed a strongly compartmentalized immune response at the disease site and found that prednisolone significantly reduced IL-6 concentrations in plasma by 8 hours of treatment, IL-1beta concentrations in saliva, as well as IL-8 concentrations in both pericardial fluid and saliva by 24 hours. Conclusion: Monitoring the early effect of adjunctive immunotherapy in plasma or saliva is a possibility in pericarditis

Assessment of treatment response in tuberculosis

Antibiotic treatment of tuberculosis has a duration of several months. There is significant variability of the host immune response and the pharmacokinetic-pharmacodynamic properties of Mycobacterium tuberculosis sub-populations at the site of disease. A limitation of sputum-based measures of treatment response may be sub-optimal detection and monitoring of Mycobacterium tuberculosis sub-populations. Potential biomarkers and surrogate endpoints should be benchmarked against hard clinical outcomes (failure/relapse/death) and may need tailoring to specific patient populations. Here, we assess the evidence supporting currently utilized and future potential host and pathogen-based models and biomarkers for monitoring treatment response in active and latent tuberculosis. Biomarkers for monitoring treatment response in extrapulmonary, pediatric and drug resistant tuberculosis are research priorities

Inference of Gene Flow in the Process of Speciation: An Efficient Maximum-Likelihood Method for the Isolation-with-Initial-Migration Model.

The isolation-with-migration (IM) model is commonly used to make inferences about gene flow during speciation, using polymorphism data. However, Becquet and Przeworski (2009) report that the parameter estimates obtained by fitting the IM model are very sensitive to the model's assumptions, including the assumption of constant gene flow until the present. This paper is concerned with the isolation-with-initial-migration (IIM) model, which drops precisely this assumption. In the IIM model, one ancestral population divides into two descendant subpopulations, between which there is an initial period of gene flow and a subsequent period of isolation. We derive a very fast method of fitting an extended version of the IIM model, which also allows for asymmetric gene flow and unequal population sizes. This is a maximum-likelihood method, applicable to data on the number of segregating sites between pairs of DNA sequences from a large number of independent loci. In addition to obtaining parameter estimates, our method can also be used, by means of likelihood ratio tests, to distinguish between alternative models representing the following divergence scenarios: a) divergence with potentially asymmetric gene flow until the present; b) divergence with potentially asymmetric gene flow until some point in the past and in isolation since then; c) divergence in complete isolation. We illustrate the procedure on pairs of Drosophila sequences from approximately 30,000 loci. The computing time needed to fit the most complex version of the model to this data set is only a couple of minutes. The R code to fit the IIM model can be found in the supplementary files of this paper