RESUMEN
Here, we show that Porphyromonas gingivalis (Pg), an endogenous oral pathogen, dampens all aspects of interferon (IFN) signaling in a manner that is strikingly similar to IFN suppression employed by multiple viral pathogens. Pg suppressed IFN production by down-regulating several IFN regulatory factors (IRFs 1, 3, 7, and 9), proteolytically degrading STAT1 and suppressing the nuclear translocation of the ISGF3 complex, resulting in profound and systemic repression of multiple interferon-stimulated genes. Pg-induced IFN paralysis was not limited to murine models but was also observed in the oral tissues of human periodontal disease patients, where overabundance of Pg correlated with suppressed IFN generation. Mechanistically, multiple virulence factors and secreted proteases produced by Pg transcriptionally suppressed IFN promoters and also cleaved IFN receptors, making cells refractory to exogenous IFN and inducing a state of broad IFN paralysis. Thus, our data show a bacterial pathogen with equivalence to viruses in the down-regulation of host IFN signaling.
Asunto(s)
Encía/inmunología , Interacciones Huésped-Patógeno/inmunología , Interferones/metabolismo , Interleucinas/metabolismo , Microbiota , Porphyromonas gingivalis/fisiología , Animales , Línea Celular , Encía/metabolismo , Humanos , Ratones , Cultivo Primario de CélulasRESUMEN
The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.
Asunto(s)
Inmunoterapia , Neoplasias , Humanos , Neoplasias/inmunología , Neoplasias/terapia , Neoplasias/sangre , Inmunoterapia/métodos , Citometría de Flujo/métodos , Transcriptoma , Pronóstico , Perfilación de la Expresión Génica/métodos , Femenino , Biomarcadores de Tumor/sangre , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/inmunologíaRESUMEN
Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.