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Transcriptomic signature for precise prediction of 3HP related systemic drug reaction for latent tuberculosis infection treatment: A prospective study

ABSTRACT: Background: Systemic drug reaction (SDR) is a major safety concern of weekly rifapentine-based treatment (known as 3HP) for latent tuberculosis infection (LTBI). Understanding predictors of SDR and identifying risk subjects before treatment can improve public acceptance and cost-effective implement of LTBI program. Methods: We first prospectively recruited 187 subjects receiving 3HP and randomly selected a pilot cohort for generating whole-blood transcriptomic data. After integrating the hierarchical systems biology model (HiSBiM) and a therapy-biomarker pathway approach, candidate genes were obtained and evaluated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Then, we developed interpretable machine learning models and established universal cut-off value (shapley additive explanations value) for each SDR prediction model. An independent cohort was used to evaluate the performance of these predictive models. Findings: Based on the transcriptomic profile of the pilot cohort, a total of 19 candidate genes were identified. Among them, six were sieved out by RT-qPCR results of a small number of samples from the training cohort. By applying the SHAP values of various combinations of the 6 gene expression signature, and the best model consisted of three genes, had an G-mean of 0.959, sensitivity of 0.972 and specificity of 0.947 for the joint of the two cohorts, and worked well across different subgroups.

ORGANISM(S): Homo sapiens

PROVIDER: GSE174552 | GEO | 2022/02/10

REPOSITORIES: GEO

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Project description:Saliva, a biofluid enriched in biological omic constituents, has emerged as a promising source for exosomal biomarkers due to its easy accessibility. Despite the understanding of the coronavirus disease-19 (COVID-19), the role of Salivary Extracellular Vesicles (sEVs) in COVID-19 remains poorly understood. Exploring the proteomic cargo of sEVs could prove valuable for diagnostic and prognostic purposes in assessing COVID-19. The proteomic cargo of sEVs from COVID-19 (+) subjects and their healthy close contacts (HCC) was explored. Nine COVID-19 positive (+) patients and eleven in-house close contact patients identified by real-time quantitative polymerase chain reaction (RT-qPCR) of nasopharyngeal swabs were included. In-house close contacts were defined as individuals with a negative RT-qPCR result sharing a residence with a confirmed COVID-19 case. sEVs were isolated by ultracentrifugation from unstimulated saliva samples, and subsequently characterized through nanoparticle tracking, transmission electron microscopy, and western-blot analyses. The proteomic cargo of sEVs was processed by LC-MS/MS. sEVs were morphologically compatible with EVs, with the presence of Syntenin-1 and CD81 EVs markers. The sEVs proteome showed 1,417 proteins: 1,288 in COVID-19 (+) cases and 1,382 in HCC. 35 proteins were found exclusively and 89 were more abundant in sEVs from COVID-19 (+) subjects. “Coronavirus disease response”, “complement and coagulation cascades”, and “PMN extracellular trap formation” were the most enriched KEGG pathways in COVID-19 (+) cases. The most represented biological processes were “Hemoglobin and haptoglobin binding” and “oxygen carrier activity”, and the best-denoted molecular functions were “regulated exocytosis and secretion” and “leucocyte and PMN mediated immunity”. We suggest that sEVs proteomic cargo in COVID-19 is related to immune response processes, oxygen transport, and antioxidant mechanisms. In contrast, in HCC, sEVs signature profiles are mainly associated with epithelial homeostasis.

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Transcriptomic signature for precise prediction of 3HP related systemic drug reaction for latent tuberculosis infection treatment: A prospective study

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