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Thompson, J. A.

Publications and source records attributed to Thompson, J. A..

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The damage signal IL-33 promotes a focal protective myeloid cell response to Toxoplasma gondii in the brain

Understanding how invading pathogens are sensed within the brain is necessary to uncover how effective immune response are mounted in immunoprivileged sites. The eukaryotic parasite Toxoplasma gondii colonizes the brain of its hosts and initiates robust immune cell recruitment, but little is known about innate recognition of T. gondii within brain tissue. The host damage signal IL-33 is one protein that has been implicated in control of chronic T. gondii infection, but the specific impact of IL-33 signaling within the brain is unclear. Here, we show that IL-33 is expressed by oligodendrocytes and astrocytes during T. gondii infection, is released into the cerebrospinal fluid of T. gondii-infected animals, and is required for control of infection. IL-33 signaling promotes chemokine expression within brain tissue and is required for the recruitment of peripheral anti-parasitic immune cells, including IFN-{gamma}-expressing T cells and iNOS-expressing monocytes. Importantly, we find that the beneficial effects of IL-33 during chronic infection are not a result of signaling on infiltrating immune cells, but rather on radio-resistant responders, and specifically, astrocytes. Mice with IL-33R-deficient astrocytes fail to promote an adaptive immune response in the CNS and control parasite burden, demonstrating that astrocytes can directly respond to IL-33 in vivo. Together, these results indicate a brain-specific mechanism by which IL-33 is released and sensed locally, to engage the peripheral immune system in controlling a neurotropic pathogen.

immunology

Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

BackgroundApproaches that capitalize on the benefits of multi-omic data integration in invasive breast carcinoma to define prognostic biomarkers for precision medicine have been slow to emerge. In this work, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors as part of a single analysis to create prognostic risk scores for overall survival, distant metastasis, and chemosensitivity.\n\nMethodsGene expression and DNA methylation values as well as clinical variables were integrated via M2EFM to build prognostic models of overall survival using 1028 breast tumor samples and further applied to external validation cohorts of 61 and 327 samples. Data-integrated prognostic models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples of external validation data. Additionally, we compared the discrimination and calibration of M2EFM models to other approaches.\n\nResultsDespite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC [≥] .7) for all outcomes in all validation data. M2EFM models had the most consistent performance overall and superior calibration, suggesting a greater likelihood of clinical utility. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.\n\nConclusionM2EFM uses multiple levels of genomic data to infer disrupted regulatory patterns, thus providing a gene signature that connects loss of regulatory control with cancer prognosis.\n\nFundingThe analyses described in this report were supported by NIH grants R01ES022222, P30CA138292, P30ES019776, and R01DE022772.\n\nConflicts of InterestThe authors declare no potential conflicts of interest.

bioinformatics