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 Gene Expression Kinetics During Treatment of Tuberculosis in HIV-Coinfected Individuals Is Independent of Highly Active Antiretroviral Therapy.

Background: Limitations in diagnostic tools to discriminate between active tuberculosis and latent Mycobacterium tuberculosis infection and for monitoring antituberculosis treatment responses are major challenges in tuberculosis control, especially in human immunodeficiency virus (HIV)-coinfected individuals. Methods: Expression levels of 105 immune-related genes were determined in 131 HIV-infected patients with active tuberculosis (n = 48), patients with latent M. tuberculosis infection (LTBI; n = 37), and controls with no M. tuberculosis infection (n = 46) in Addis Ababa, Ethiopia, using focused gene expression profiling with a dual-color reverse-transcription multiplex ligation-dependent probe amplification assay. Results: Within the cohort of HIV-positive subjects, the expression profiles of 7 genes at baseline (FCGR1A, RAB24, TLR1, TLR4, MMP9, NLRC4, and IL1B) could accurately discriminate between active tuberculosis and both latent and no M. tuberculosis infection, largely independently of (in)eligibility for highly active antiretroviral therapy (HAART). Six months after antituberculosis treatment, biomarker profiles of patients with tuberculosis became indistinguishable from those of patients with LTBI and controls. Importantly, host gene expression kinetics during antituberculosis treatment in HIV-coinfected individuals was found to be independent of HAART use. Conclusions: Blood transcriptomic profiles can potentially be used as biomarkers to discriminate the different clinical stages of tuberculosis in HIV-coinfected individuals and to monitor tuberculosis treatment responses in both HAART recipients and untreated individuals

Lipid Profile in Tuberculosis Patients with and without Human Immunodeficiency Virus Infection

Background. Understanding whether the preceding low lipid profile leads to active tuberculosis (TB) or active TB leads to low lipid profile is crucial. Methods. Lipid profile concentrations were determined from 159 study participants composed of 93 active TB patients [44 HIV coinfected (HIV+TB+) and 49 HIV negative (HIV−TB+)], 41 tuberculin skin test (TST) positive cases [17 HIV coinfected (HIV+TST+) and 24 HIV negative (HIV−TST+)], and 25 healthy controls (HIV−TST−). Cobas Integra 400 Plus was used to determine lipid profiles concentration level. Results. The concentrations of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in HIV−TB+ patients were significantly lower compared to HIV−TST+ and to HIV−TST− individuals. Similarly, the concentrations of the TC, LDL-C, and HDL-C in HIV+TB+ were significantly lower compared to HIV−TB+ patients. After the 6 months of anti-TB treatment (ATT), the concentration levels of TC, LDL-C, and HDL-C in HIV−TB+ patients were higher compared to the baseline concentration levels, while they were not significantly different compared to that of HIV−TST+ concentration. Conclusion. The low concentration of lipid profiles in TB patients may be a consequence of the disease and significantly increased in TB patients after treatment

Host gene expression kinetics during treatment of tuberculosis in HIV-coinfected individuals is independent of HAART therapy.

Limitations in diagnostic tools to discriminate between active and latent tuberculosis (TB) and for monitoring TB treatment responses are major challenges in TB control, especially in HIV-coinfected individuals

Genomic transmission clusters and circulating lineages of Mycobacterium tuberculosis among refugees residing in refugee camps in Ethiopia

Abstract: Background: Understanding the transmission dynamics of Mycobacterium tuberculosis (Mtb) could benefit the design of tuberculosis (TB) prevention and control strategies for refugee populations. Whole Genome Sequencing (WGS) has not yet been used to document the Mtb transmission dynamics among refugees in Ethiopia. We applied WGS to accurately identify transmission clusters and Mtb lineages among TB cases in refugee camps in Ethiopia. Method and design: We conducted a cross-sectional study of 610 refugees in refugee camps in Ethiopia presenting with symptoms of TB. WGS data of 67 isolates was analyzed using the Maximum Accessible Genome for Mtb Analysis (MAGMA) pipeline; iTol and FigTree were used to visualize phylogenetic trees, lineages, and the presence of transmission clusters. Results: Mtb culture-positive refugees originated from South Sudan (52/67, 77.6%), Somalia (9/67, 13.4%). Eritrea (4/67, 6%), and Sudan (2/67, 3%). The majority (52, 77.6%) of the isolates belonged to Mtb lineage (L) 3, and one L9 was identified from a Somalian refugee. The vast majority (82%) of the isolates were pan-susceptible Mtb, and none were multi-drug-resistant (MDR)-TB. Based on the 5-single nucleotide polymorphisms cutoff, we identified eight potential transmission clusters containing 23.9% of the isolates. Contact investigation confirmed epidemiological links with either family or social interaction within the refugee camps or with neighboring refugee camps. Conclusion: Four lineages (L1, L3, L4, and L9) were identified, with the majority of strains being L3, reflecting the Mtb L3 dominance in South Sudan, where the majority of refugees originated from. Recent transmission among refugees was relatively low (24%), likely due to the short study period. The improved understanding of the Mtb transmission dynamics using WGS in refugee camps could assist in designing effective TB control programs for refugees

The performance of BD FACSPresto™ for CD4 T-cell count, CD4% and hemoglobin concentration test in Ethiopia - Fig 1

<p>Bland-Altman comparisons between the BD FACSPresto™ with capillary blood (a-c) and venous blood (d-f) samples with the BD FACSCalibur™ reference standard. The corresponding graphs show the absolute bias between the FACSPresto™ and FACSCalibur represented in the Bland-Altman plots for CD4 T-cell testing with capillary blood (a) and venous blood (d); Bland-Altman plots for CD4% testing with capillary blood (b), venous blood (e) samples; Bland-Altman plots for Hgb testing with capillary blood (c), venous blood (f)., The solid green lines represent the mean bias and the solid deep red lines represent the upper and lower limits of agreement (LOA = mean ± 1.96SD).</p

BD FACSPresto™ comparing with BD FACSCalibur™ on the CD4% testing.

<p>BD FACSPresto™ comparing with BD FACSCalibur™ on the CD4% testing.</p

Precision of the BD FACSPresto™ technology.

<p>Precision of the BD FACSPresto™ technology.</p