Headway made towards biosignatures for incipient tuberculosis.
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ABSTRACT:
SUBMITTER: Scriba TJ
PROVIDER: S-EPMC8908387 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Headway made towards biosignatures for incipient tuberculosis.
Scriba Thomas J TJ Mendelsohn Simon C SC
The Lancet. Respiratory medicine 20200117 4
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Project description:Biomarkers for TB treatment response and outcome are needed. This study characterize changes in immune profiles during TB treatment, define biosignatures associated with treatment outcomes, and explore the feasibility of predictive models for relapse. Seventy-two markers were measured by multiplex cytokine array in serum samples from 78 cured, 12 relapsed and 15 failed treatment patients from South Africa before and during therapy for pulmonary TB. Promising biosignatures were evaluated in a second cohort from Uganda/Brazil consisting of 17 relapse and 23 cured patients. Thirty markers changed significantly with different response patterns during TB treatment in cured patients. The serum biosignature distinguished cured from relapse patients and a combination of two clinical (time to positivity in liquid culture and BMI) and four immunological parameters (TNF-β, sIL-6R, IL-12p40 and IP-10) at diagnosis predicted relapse with a 75% sensitivity (95%CI 0.38-1) and 85% specificity (95%CI 0.75-0.93). This biosignature was validated in an independent Uganda/Brazil cohort correctly classifying relapse patients with 83% (95%CI 0.58-1) sensitivity and 61% (95%CI 0.39-0.83) specificity. A characteristic biosignature with value as predictor of TB relapse was identified. The repeatability and robustness of these biomarkers require further validation in well-characterized cohorts.
| S-EPMC6839616 | biostudies-literature
Project description:Host markers to monitor the response to tuberculosis (TB) therapy hold some promise. We evaluated the changes in concentration of Mycobacterium tuberculosis (M.tb)-induced soluble biomarkers during early treatment for predicting short- and long-term treatment outcomes. Whole blood samples from 30 cured and 12 relapsed TB patients from diagnosis, week 1, 2, and 4 of treatment were cultured in the presence of live M.tb for seven days and patients followed up for 24 weeks after the end of treatment. 57 markers were measured in unstimulated and antigen-stimulated culture supernatants using Luminex assays. Top performing multi-variable models at diagnosis using unstimulated values predicted outcome at 24 months after treatment completion with a sensitivity of 75.0% (95% CI, 42.8-94.5%) and specificity of 72.4% (95% CI, 52.8-87.3%) in leave-one-out cross validation. Month two treatment responder classification was correctly predicted with a sensitivity of 79.2% (95% CI, 57.8-92.9%) and specificity of 92.3% (95% CI, 64.0-99.8%). This study provides evidence of the early M.tb-specific treatment response in TB patients but shows that the observed unstimulated marker models are not outperformed by stimulated marker models. Performance of unstimulated predictive host marker signatures is promising and requires validation in larger studies.
| S-EPMC8192498 | biostudies-literature
Project description:A large proportion of the global tuberculosis (TB) burden is asymptomatic and not detectable by symptom-based screening, driving the TB epidemic through continued M. tuberculosis transmission. Currently, no validated tools exist to diagnose incipient and subclinical TB. Nested within a large prospective study in household contacts of pulmonary TB cases in Southern India, we assessed 35 incipient TB and 12 subclinical TB cases, along with corresponding household active TB cases (n=11), and household controls (n=39) using high throughput methods for transcriptional and protein profiling. We split the data into training and test sets and applied a support vector machine classifier followed by a Lasso regression model to identify signatures. The Lasso regression model identified an 11-gene signature (ABLIM2, C20orf197, CTC-543D15.3, CTD-2503O16.3, HLADRB3, METRNL, RAB11B-AS1, RP4-614C10.2, RNA5SP345, RSU1P1, and UACA) that distinguished subclinical TB from incipient TB with a very good discriminatory power by AUCs in both training and test sets. Further, we identified an 8-protein signature comprising b-FGF, IFNγ, IL1RA, IL7, IL12p70, IL13, PDGF-BB, and VEGF that differentiated subclinical TB from incipient TB with good and moderate discriminatory power by AUCs in the training and test sets, respectively. The identified 11-gene signature discriminated well between the distinct stages of the TB disease spectrum, with very good discriminatory power, suggesting it could be useful for predicting TB progression in household contacts. However, the high discriminatory power could partly be due to over-fitting, and validation in other studies is warranted to confirm the potential of the immune biosignatures for identifying subclinical TB.
| S-EPMC9876034 | biostudies-literature
Project description: Not available
| S-EPMC7159435 | biostudies-literature
Project description:Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.
| S-EPMC11222504 | biostudies-literature
Project description:ObjectiveIncipient Tuberculosis (ITB) refers to Mycobacterium tuberculosis infection that is likely to progress to active disease in the absence of treatment, but without clinical signs, symptoms, radiographic or microbiological evidence of disease. Biomarker-based tests to diagnose incipient TB hold promise for better prediction and, through TB preventive therapy, prevention of disease. This study explored current and future framing and prioritisation of ITB.MethodsTwenty-two interviews across eight countries were conducted. A modified Shiffman & Smith Framework, containing four categories-Ideas, Issue Characteristics, Actor Power, and Political Contexts-was used to analyse the current landscape and potential for prioritisation of diagnosis and treatment of ITB.ResultsLatent TB policy implementation has been slow due to technical, logistical and financial challenges, and because it has been framed in a manner non-conducive to gaining political priority. Framing ITB testing as 'early detection' rather than 'prediction', and its management as 'treatment' rather than 'preventive therapy', may help raise its importance in policies, and its acceptance among actors.ConclusionConsensus surrounding the framing of ITB will be crucial for the successful adoption of ITB diagnostics and treatment. When designing ITB tools and policies, it will be important to address challenges that pertain to latent TB policies.
| S-EPMC9306665 | biostudies-literature
Project description:It is unclear whether human progression to active tuberculosis disease (TB) risk signatures are viable endpoint criteria for evaluations of treatments in development. TB is the deadliest infectious disease globally and more efficacious vaccines are needed to reduce this mortality. However, the immune correlates of protection for either preventing infection with Mycobacterium tuberculosis or preventing TB disease have yet to be completely defined, making the advancement of candidate vaccines through the pipeline slow, costly, and fraught with risk. Human-derived correlate of risk (COR) gene signatures, which identify an individual's risk of progressing to active TB disease, provide an opportunity for evaluating new therapies for TB with clear and defined endpoints. Though prospective clinical trials with longitudinal sampling are prohibitively expensive, the characterization of COR gene signatures is practical with preclinical models. Using a 3Rs (replacement, reduction, and refinement) approach we reanalyzed heterogeneous publicly available transcriptional data sets to determine whether a specific set of COR signatures are viable endpoints in the preclinical pipeline. We selected RISK6, Sweeney3, and BATF2 human-derived blood-based RNA biosignatures because they require relatively few genes and have been carefully evaluated across several clinical cohorts. These data suggest that in certain experimental designs and in several tissue types, human COR signatures correlate with disease progression as measured by the bacterial burden in the preclinical TB model pipeline. We observed the best performance when the model most closely reflected human infection or disease conditions. Human-derived COR signatures offer an opportunity for high-throughput preclinical endpoint criteria of vaccine and drug therapy evaluations.ImportanceUnderstanding the strengths or limitations of back-translating human-derived correlate of risk (COR) RNA signatures into the preclinical pipeline may help streamline down-selection of therapeutic vaccine and drug candidates and better align preclinical models with proposed clinical trial efficacy endpoints.
| S-EPMC11774039 | biostudies-literature
Project description:BackgroundPrecise designation of high risk forms of latent Mycobacterium tuberculosis-M.tb infections (LTBI) is impossible. Delineation of high-risk LTBI can, however, allow for chemoprophylaxis and curtail majority cases of active tuberculosis (ATB). There is epidemiological evidence to support the view that LTBI in context of HIV-1 co-infection is high-risk for progression to ATB relative to LTBI among HIV-ve persons. We recently showed that assays of M.tb thymidylate kinase (TMKmt) antigen and host specific IgG can differentiate ATB from LTBI and or no TB (NTB, or healthy controls). In this study, we aimed to expose the differential levels of TMKmt Ag among HIV+ve co-infected LTBI relative to HIV-ve LTBI as a strategy to advance these assays for designating incipient LTBI.MethodsTMKmt host specific IgM and IgG detection Enzyme Immuno-Assays (EIA) were conducted on 40 TB exposed house-hold contacts (22 LTBI vs. 18 no TB (NTB) by QunatiFERON-TB GOLD®); and TMKmt Ag detection EIA done on 82 LTBI (46 HIV+ve vs 36 HIV-ve) and 9 NTB (American donors). Purified recombinant TMKmt protein was used as positive control for the Ag assays.ResultsIgM levels were found to be equally low across QuantiFERON-TB GOLD® prequalified NTB and TB exposed house-hold contacts. Higher TMKmt host specific IgG trends were found among TB house-hold contacts relative to NTB controls. TMKmt Ag levels among HIV+ve LTBI were 0.2676 ± 0.0197 (95% CI: 0.2279 to 0.3073) relative to 0.1069 ± 0.01628 (95% CI: 0.07385 to 0.14) for HIV-ve LTBI (supporting incipient nature of LTBI in context of HIV-1 co-infection). NTB had TMKmt Ag levels of 0.1013 ± 0.02505 (5% CI: 0.0421 to 0.1606) (intimating that some were indeed LTBI).ConclusionsTMKmt Ag levels represent a novel surrogate biomarker for high-risk LTBI, while host-specific IgG can be used to designate NTB from LTBI.
| S-EPMC5857104 | biostudies-literature
Project description:Mycobacterium tuberculosis is a highly infectious pathogen that is still responsible for millions of deaths annually. Effectively treating this disease typically requires a course of antibiotics, most of which were developed decades ago. These drugs are, however, not effective against persistent tubercle bacilli and the emergence of drug-resistant stains threatens to make many of them obsolete. The identification of new drug targets, allowing the development of new potential drugs, is therefore imperative. Both proteomics and structural biology have important roles to play in this process, the former as a means of identifying promising drug targets and the latter allowing understanding of protein function and protein-drug interactions at atomic resolution. The determination of M. tuberculosis protein structures has been a goal of the scientific community for the last decade, who have aimed to supply a large amount of structural data that can be used in structure-based approaches for drug discovery and design. Only since the genome sequence of M. tuberculosis has been available has the determination of large numbers of tuberculosis protein structures been possible. Currently, the molecular structures of 8.5% of all the pathogen's protein-encoding ORFs have been determined. In this review, we look at the progress made in determining the M. tuberculosis structural proteome and the impact this has had on the development of potential new drugs, as well as the discovery of the function of crucial mycobaterial proteins.
| S-EPMC3345573 | biostudies-literature
Project description:Surface electromyography is used for non-invasive evaluations of the neuromuscular system and conventionally involves electrodes placed on the skin to collect electrical signals associated with muscle activity. Recently, embroidered electrodes have been presented as a low-cost alternative to the current commercial solutions. However, the high cost of equipment used in their fabrication forms a barrier to deployment. To address this, this paper presents the first study into the hand-sewing of electrodes for surface electromyography to assess its feasibility as an affordable, alternative means of production. In experiments reported here, batches of hand-sewn electrodes from six novice embroiderers are tested for (i) manufacturing consistency, and (ii) myographic data acquisition against conventional gelled and machine-sewn electrodes. First, the electrical properties of the created electrodes are assessed through simple resistance measurements. Then, linear regression is performed using electromyography data to test if force-variation detection is feasible. The results demonstrate that hand-sewn electrodes provide similar sensitivity to force variation as their machine-sewn counterparts according to the linear regression gradients calculated ( 8.84 using the hand-sewn electrodes and 9.38 using the machine-sewn electrodes, on the flexor muscles of the forearm). This suggests that hand-made, low-cost textile interfaces could be deployed using local production in developing economies.
| S-EPMC7349794 | biostudies-literature