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Modelling HIV and MTB co-infection including combined treatment strategies.

ABSTRACT: A new host-pathogen model is described that simulates HIV-MTB co-infection and treatment, with the objective of testing treatment strategies. The model includes CD4+ and CD8+ T cells, resting and activated macrophages, HIV and Mycobacterium tuberculosis (MTB). For TB presentation at various stages of HIV disease in a co-infected individual, combined treatment strategies were tested with different relative timings of treatment for each infection. The stages were early HIV disease, late HIV disease and AIDS. The main strategies were TB treatment followed by anti-retroviral therapy (ART) after delays of 15 days, 2 months and 6 months. ART followed by TB treatment was an additional strategy that was tested. Treatment was simulated with and without drug interaction. Simulation results were that TB treatment first followed by ART after a stage-dependent delay has the best outcome. During early HIV disease a 6 month delay is acceptable. During late HIV disease, a 2 month delay is best. During AIDS it is better to start ART after 15 days. However, drug interaction works against the benefits of early ART. These results agree with expert reviews and clinical trials.

SUBMITTER: Ramkissoon S

PROVIDER: S-EPMC3509125 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Modelling HIV and MTB co-infection including combined treatment strategies.

Ramkissoon Santosh S Mwambi Henry G HG Matthews Alan P AP

PloS one 20121128 11

A new host-pathogen model is described that simulates HIV-MTB co-infection and treatment, with the objective of testing treatment strategies. The model includes CD4+ and CD8+ T cells, resting and activated macrophages, HIV and Mycobacterium tuberculosis (MTB). For TB presentation at various stages of HIV disease in a co-infected individual, combined treatment strategies were tested with different relative timings of treatment for each infection. The stages were early HIV disease, late HIV diseas ...[more]

PMID: 23209581

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Project description:Mycobacterium tuberculosis (Mtb) has developed specialized mechanisms to parasitize its host cell, the macrophage. These mechanisms allow it to overcome killing by oxidative burst and persist in the wake of an inflammatory response. Mtb infection in the majority of those exposed is controlled in an asymptomatic form referred to as latent tuberculosis infection (LTBI). HIV is a well-known catalyst of reactivation of LTBI to active TB infection (ATB). Through the use of nonhuman primates (NHPs) co-infected with Mtb and Simian Immunodeficiency Virus (Mtb/SIV), we are able to simulate human progression of TB/AIDS comorbidity. The advantage of NHP models is that they recapitulate the breadth of human TB outcomes, including immune control of infection, and loss of this control due to SIV co-infection. Identifying correlates of immune control of infection is important for both vaccine and therapeutics development. Using macaques infected with Mtb or Mtb/SIV and with different clinical outcomes we attempted to identify signatures between those that progress to active infection after SIV challenge (reactivators) and those that control the infection (non-reactivators). We particularly focused on pathways relevant to myeloid origin cells such as macrophages, as these innate immunocytes have an important contribution to the initial control or the lack thereof, following Mtb infection. Using bacterial burden, C-reactive protein (CRP), and other clinical indicators of disease severity as a guide, we were able to establish gene signatures of host disease state and progression. In addition to gene signatures, clustering algorithms were used to differentiate between host disease states and identify relationships between genes. This allowed us to identify clusters of genes which exhibited differential expression profiles between the three groups of macaques: ATB, LTBI and Mtb/SIV. The gene signatures were associated with pathways relevant to apoptosis, ATP production, phagocytosis, cell migration, and Type I interferon (IFN), which are related to macrophage function. Our results suggest novel macrophage functions that may play roles in the control of Mtb infection with and without co-infection with SIV. These results particularly point towards an interplay between Type I IFN signaling and IFN-γ signaling, and the resulting impact on lung macrophages as an important determinant of progression to TB.

Project description:BACKGROUND:Current diagnostics are inadequate to meet the challenges presented by co-infection with Mycobacterium tuberculosis (Mtb) and HIV, the leading cause of death for HIV-infected individuals. Improved characterization of Mtb/HIV coinfection as a distinct disease state may lead to better identification and treatment of affected individuals. METHODS:Four previously-published TB and HIV co-infection related datasets were used to train and validate multinomial machine learning classifiers that simultaneously predict TB and HIV status. Classifier predictive performance was measured using leave-one-out cross validation on the training set and blind predictive performance on multiple test sets using area under the ROC curve (AUC) as the performance metric. Linear modelling of signature gene expression was applied to systematically classify genes as TB-only, HIV-only or combined TB/HIV. RESULTS:The optimal signature discovered was a 10-gene random forest multinomial signature that robustly discriminated active tuberculosis (TB) from other non-TB disease states with improved performance compared with previously published signatures (AUC: 0.87), and specifically discriminated active TB/HIV co-infection from all other conditions (AUC: 0.88). Signature genes exhibited a variety of transcriptional patterns including both TB-only and HIV-only response genes and genes with expression patterns driven by interactions between HIV and TB infection states, including the CD8+ T-cell receptor LAG3 and the apoptosis-related gene CERKL. CONCLUSIONS:By explicitly including distinct disease states within the machine learning analysis framework, we developed a compact and highly diagnostic signature that simultaneously discriminates multiple disease states associated with Mtb/HIV co-infection. Examination of the expression patterns of signature genes suggests mechanisms underlying the unique inflammatory conditions associated with active TB in the presence of HIV. In particular, we observed that dysregulation of CD8+ effector T-cell and NK-cell associated genes may be an important feature of Mtb/HIV co-infection.

Dataset Information

Modelling HIV and MTB co-infection including combined treatment strategies.

Publications

Modelling HIV and MTB co-infection including combined treatment strategies.

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