Fukutani, E. | Search bioRxiv

bioRxiv2025

Two distinct cytokine response clusters identified in healthcare workers with apparent resistance to infection with Mycobacterium tuberculosis despite sustained occupational exposure

IntroductionEvidence exists that some individuals resist ("Resisters") Mycobacterium tuberculosis (Mtb) infection despite sustained exposure, but the protective mechanisms involved are not fully understood. We investigated immune responses induced by ex vivo stimulation of peripheral blood mononuclear cells (PBMC) with live Mtb in Resisters compared to those with latent TB infection (LTBI). MethodsHIV-uninfected healthcare workers working in high TB exposure healthcare facilities for over 5 years were screened for Mtb sensitization using the interferon-gamma release assay (IGRA) and the tuberculin skin test (TST). We identified Resisters (TST<10mm and IGRA<0.35 IU/mL, n=129) and those with LTBI (TST[≥]10mm and IGRA[≥]0.35 IU/mL n=145). We selected a subset of extreme resisters (TST=0mm and IGRA<0.2 IU/mL; n=26) and extreme LTBI (TST[≥]15mm and IGRA[≥]1 IU/mL; n=24) for these analyses. Blood was collected and PBMC isolated and cultured with live H37Rv Mtb for 18 hours. Supernatants were collected and used for measuring 65 secreted analytes by Luminex. We also evaluated cell-associated cytokine expression by CD4 T cells and monocytes in these participants using flow cytometry. ResultsUsing the feature selection in R, we identified a set of 4 cytokines: IL-17A, MCP-1, IL-8, and MDC, which collectively classified extreme Resisters from extreme LTBI with an area-under-the-curve (AUC) of 0.67 (0.54-0.85). Focusing only on the extreme Resisters, and using hierarchical clustering, participants in this group segregated into two main clusters (Resister_c1 and Resister_c2). Further analyses identified 37 cytokines that were significantly higher and 15 cytokines that were lower in Resister_c2 compared with Resister_c1. A set of 5 cytokines (TRAIL, MIP-1{beta}, Fractalkine, GRO- and IL-1) collectively classified these two clusters with an AUC of 1 (1-1). CD4 T cell and monocyte responses to Mtb did not significantly differ between the two clusters. ConclusionsMtb-specific immune responses segregate extreme Resisters into two distinct clusters in individuals with apparent resistance to Mtb infection and may allow discrimination of individuals with true resistance from those with infection but have alternative immune responses not detected by IGRA and TST.

immunology↗

bioRxiv2019

Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer

BackgroundCigarette smoking is associated with increased risk of developing respiratory diseases and various types of cancer. Early identification of such unfavorable outcomes in patients who smoke is critical for optimizing personalized medical care.\n\nMethodsHere, we perform a comprehensive analysis using Systems Biology tools of publicly available data from a total of six transcriptomic studies, which examined different specimens of lung tissue and/or cells of smokers and nonsmokers to identify potential markers associated with lung cancer.\n\nResultsExpression level of twenty-two genes was capable of classifying smokers from non-smokers. A machine learning algorithm revealed that AKR1B10 was the most informative gene among the 22 DEGs accounting for the classification of the clinical groups. AKR1B10 expression was higher in smokers compared to non-smokers in datasets examining small and large airway epithelia, but not in the data from a study of sorted alveolar macrophages. We next tested whether AKR1B10 expression could be useful in identification of cancer tissue in patients who were not exposed to smoking. AKR1B10 expression was substantially higher in lung cancer specimens compared to matched healthy tissue obtained from nonsmoking individuals (accuracy: 80%, p<0.0001). Finally, we searched the expression of 11 single nucleotide polymorphisms (SNPs) of AKR1B10 worldwide. We found that the SNP rs782881 was the most frequent mutant allele in the majority of continents. Africa was the continent which exhibited higher frequency of SNPs associated with lower AKR1B10 expression and displayed lower lung cancer incidence and deaths attributable to tobacco.\n\nConclusionThe systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 expression, smoking and occurrence of lung cancer.

genomics↗