Bovine tuberculosis at a cattle-small ruminant-human interface in Meskan, Gurage region, Central Ethiopia

ABSTRACT: BACKGROUND: Bovine tuberculosis (BTB) is endemic in Ethiopian cattle. The aim of this study was to assess BTB prevalence at an intensive contact interface in Meskan Woreda (district) in cattle, small ruminants and suspected TB-lymphadenitis (TBLN) human patients. METHODS: The comparative intradermal test (CIDT) was carried out for all animals involved in the cross-sectional study and results interpreted using a < 4 mm and a < 2 mm cut-off. One PPD positive goat was slaughtered and lymph nodes subjected to culture and molecular typing. In the same villages, people with lymphadenitis were subjected to clinical examination. Fine needle aspirates (FNA) were taken from suspected TBLN and analyzed by smear microscopy and molecular typing. RESULTS: A total of 1214 cattle and 406 small ruminants were tested for BTB. In cattle, overall individual prevalence (< 2 mm cut-off) was 6.8% (CI: 5.4-8.5%) with 100% herd prevalence. Only three small ruminants (2 sheep and 1 goat) were reactors. The overall individual prevalence in small ruminants (< 2 mm cut-off) was 0.4% (CI: 0.03-5.1%) with 25% herd prevalence. Cattle from owners with PPD positive small ruminants were all PPD negative. 83% of the owners kept their sheep and goats inside their house at night and 5% drank regularly goat milk.FNAs were taken from 33 TBLN suspected cases out of a total of 127 screened individuals with lymph node swellings. Based on cytology results, 12 were confirmed TBLN cases. Nine out of 33 cultures were AFB positive. Culture positive samples were subjected to molecular typing and they all yielded M. tuberculosis. M. tuberculosis was also isolated from the goat that was slaughtered. CONCLUSIONS: This study highlighted a low BTB prevalence in sheep and goats despite intensive contact with cattle reactors. TBLN in humans was caused entirely by M. tuberculosis, the human pathogen. M. tuberculosis seems to circulate also in livestock but their role at the interface is unknow

Metabolite changes in blood predict the onset of tuberculosis

Data availability: Metabolomic data have been deposited to Metabolomic Workbench48, ID PR000666 and are accessible under http://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR000666. Data used to generate the figures 2–5 are available as a Source Data file.Electronic supplementary material is available online at: https://www.nature.com/articles/s41467-018-07635-7#Sec23 .Source data are available online at: https://www.nature.com/articles/s41467-018-07635-7#Sec24 .New biomarkers of tuberculosis (TB) risk and disease are critical for the urgently needed control of the ongoing TB pandemic. In a prospective multisite study across Subsaharan Africa, we analyzed metabolic profiles in serum and plasma from HIV-negative, TB-exposed individuals who either progressed to TB 3–24 months post-exposure (progressors) or remained healthy (controls). We generated a trans-African metabolic biosignature for TB, which identifies future progressors both on blinded test samples and in external data sets and shows a performance of 69% sensitivity at 75% specificity in samples within 5 months of diagnosis. These prognostic metabolic signatures are consistent with development of subclinical disease prior to manifestation of active TB. Metabolic changes associated with pre-symptomatic disease are observed as early as 12 months prior to TB diagnosis, thus enabling timely interventions to prevent disease progression and transmission.The Bill & Melinda Gates Foundation Grand Challenges in Global Health Program (BMGF GC6-74, #37772 and GC6-2013, OPP1055806), National Institutes of Health Contract No. HHSN266200700022C/NO1-AI-70022 for the Tuberculosis Research Unit (TBRU)

Immunometabolic signatures predict risk of progression to active tuberculosis and disease outcome

Data Availability: All datasets generated for this study are included in the manuscript with the exception of the rhesus macaque metabolics data which is included as Table S6 [https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2019.00527/full#SM14].Supplementary Material is available online at: https://www.frontiersin.org/articles/10.3389/fimmu.2019.00527/full#supplementary-material .There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.This work was supported by the Bill & Melinda Gates Foundation (BMGF) Grand Challenges in Global Health (GC6-74 grant 37772, OPP1055806 and OPP1087783 in conjunction with AERAS). This work was also supported by a Strategic Health Innovation Partnership grant from the South African Medical Research Council and Department of Science and Technology/South African Tuberculosis Bioinformatics Initiative. Additional support was provided by the European Union FP7 (ADITEC, 280873 and TBVAC2020, 643381) and the National Institutes of Health [U19 AI106761 and U19 AI135976]. FD was supported by the NCDIR (National Institutes of Health [P41 GM109824]). DT and GT were supported by South African Medical Research Council SHIP funding for the South African Tuberculosis Bioinformatics Initiative to GW

Neutrophil degranulation, NETosis and platelet degranulation pathway genes are co-induced in whole blood up to six months before tuberculosis diagnosis

Data Availability: The full datasets can be obtained from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) as GSE94438 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94438) and GSE89403 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89403). These details are also provided in the Methods. Additional data on PET-CT scores accompany the revised document as S1 Table (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278295#pone.0278295.s001).The authors acknowledge the Centre for High Performance Computing (CHPC), South Africa, for providing computational resources to this research project.Mycobacterium tuberculosis (M.tb) causes tuberculosis (TB) and remains one of the leading causes of mortality due to an infectious pathogen. Host immune responses have been implicated in driving the progression from infection to severe lung disease. We analyzed longitudinal RNA sequencing (RNAseq) data from the whole blood of 74 TB progressors whose samples were grouped into four six-month intervals preceding diagnosis (the GC6-74 study). We additionally analyzed RNAseq data from an independent cohort of 90 TB patients with positron emission tomography-computed tomography (PET-CT) scan results which were used to categorize them into groups with high and low levels of lung damage (the Catalysis TB Biomarker study). These groups were compared to non-TB controls to obtain a complete whole blood transcriptional profile for individuals spanning from early stages of M.tb infection to TB diagnosis. The results revealed a steady increase in the number of genes that were differentially expressed in progressors at time points closer to diagnosis with 278 genes at 13–18 months, 742 at 7–12 months and 5,131 detected 1–6 months before diagnosis and 9,205 detected in TB patients. A total of 2,144 differentially expressed genes were detected when comparing TB patients with high and low levels of lung damage. There was a large overlap in the genes upregulated in progressors 1–6 months before diagnosis (86%) with those in TB patients. A comprehensive pathway analysis revealed a potent activation of neutrophil and platelet mediated defenses including neutrophil and platelet degranulation, and NET formation at both time points. These pathways were also enriched in TB patients with high levels of lung damage compared to those with low. These findings suggest that neutrophils and platelets play a critical role in TB pathogenesis, and provide details of the timing of specific effector mechanisms that may contribute to TB lung pathology.SM, EM, GT and GW were supported by the South African Tuberculosis Bioinformatics Initiative (SATBBI), a Strategic Health Innovation Partnership grant from the South African Medical Research Council (https://www.samrc.ac.za/) and South African Department of Science and Innovation (https://www.dst.gov.za/); no grant number. STM received funding from the EDCTP2 program (Grant Number CDF1576) supported by the European Union (http://www.edctp.org/projects-2/#). GW received funding from the South African National Research Foundation (SARChI TB Biomarkers #86535) and the South African Medical Research Council (https://www.samrc.ac.za/). SHEK, TJS and GW received funding from the Bill and Melinda Gates Foundation (Grant Numbers OPP37772 & OPP1055806), (https://www.gatesfoundation.org/) GW received funding from the Bill and Melinda Gates Foundation (Grant Number OPP51919) (https://www.gatesfoundation.org/) through the Catalysis Foundation for Health (https://catalysisfoundation.org/) AGL is supported by the NRF-CSUR (Grant Number CSUR60502163639) and by the Centre for Tuberculosis Research from the South African Medical Research Council (https://www.samrc.ac.za/). JAS is supported by a Clinician Scientist Fellowship (Grant Number MR/R007942/1) jointly funded by the UK Medical Research Council (MRC; https://www.ukri.org/about-us/mrc/) and the UK Department for International Development [DFID; replaced by Foreign, Commonwealth & Development Office (FCDO); https://www.gov.uk/government/organisations/foreign-commonwealth-development-office] under the MRC/DFID Concordat agreement

T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection

Antigen-specific, MHC-restricted αβ T cells are necessary for protective immunity against Mycobacterium tuberculosis, but the ability to broadly study these responses has been limited. In the present study, we used single-cell and bulk T cell receptor (TCR) sequencing and the GLIPH2 algorithm to analyze M. tuberculosis-specific sequences in two longitudinal cohorts, comprising 166 individuals with M. tuberculosis infection who progressed to either tuberculosis (n = 48) or controlled infection (n = 118). We found 24 T cell groups with similar TCR-β sequences, predicted by GLIPH2 to have common TCR specificities, which were associated with control of infection (n = 17), and others that were associated with progression to disease (n = 7). Using a genome-wide M. tuberculosis antigen screen, we identified peptides targeted by T cell similarity groups enriched either in controllers or in progressors. We propose that antigens recognized by T cell similarity groups associated with control of infection can be considered as high-priority targets for future vaccine development