Schalk, E. | Search bioRxiv

bioRxiv2024

Multiple Unfolded Protein Response pathways cooperate to link cytosolic dsDNA release to Stimulator of Interferon Gene (STING) activation

The double-stranded DNA (dsDNA) sensor STING has been increasingly implicated in responses to "sterile" endogenous threats and pathogens without nominal DNA or cyclic di-nucleotide stimuli. Previous work showed an endoplasmic reticulum (ER) stress response, known as the unfolded protein response (UPR), activates STING. Herein, we sought to determine if ER stress generated a STING ligand, and to identify the UPR pathways involved. Induction of IFN-{beta} expression following stimulation with the UPR inducer thapsigargin (TPG) or oxygen glucose deprivation required both STING and the dsDNA-sensing cyclic GMP-AMP synthase (cGAS). Furthermore, TPG increased cytosolic mitochondrial DNA, and immunofluorescence visualized dsDNA punctae in murine and human cells, providing a cGAS stimulus. N-acetylcysteine decreased IFN-{beta} induction by TPG, implicating reactive oxygen species (ROS). However, mitoTEMPO, a mitochondrial oxidative stress inhibitor did not impact TPG-induced IFN. On the other hand, inhibiting the inositol requiring enzyme 1 (IRE1) ER stress sensor and its target transcription factor XBP1 decreased the generation of cytosolic dsDNA. iNOS upregulation was XBP1-dependent, and an iNOS inhibitor decreased cytosolic dsDNA and IFN-{beta}, implicating ROS downstream of the IRE1-XBP1 pathway. Inhibition of the PKR-like ER kinase (PERK) pathway also attenuated cytoplasmic dsDNA release. The PERK-regulated apoptotic factor Bim was required for both dsDNA release and IFN-{beta} mRNA induction. Finally, XBP1 and PERK pathways contributed to cytosolic dsDNA release and IFN-induction by the RNA virus, Vesicular Stomatitis Virus (VSV). Together, our findings suggest that ER stressors, including viral pathogens without nominal STING or cGAS ligands such as RNA viruses, trigger multiple canonical UPR pathways that cooperate to activate STING and downstream IFN-{beta} via mitochondrial dsDNA release.

immunology↗

bioRxiv2018

Mathematical Models for the Influence of Cytarabine on White Blood Cell Dynamics in Acute Myeloid Leukemia

We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment.\n\nWe extend the gold-standard model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-Cs pharmacodynamic effects. We cross-validate 12 mathematical models using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracies are similarly good despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate how personalized predictions of the impact of treatment timing on subsequent nadir values could be used for clinical decision support.\n\nAuthor summaryThe major obstacle in accurately predicting the outcome of a medical therapy is the vast variation in individual response patterns. It concerns both the subjective experience of the patient and the objectively measurable achievement of a clinical remission with restoration of normal blood counts. Here, we address acute myeloid leukemia (AML)-chemotherapy using cytarabine (Ara-C) as this drug is this most important component of AML-treatment. In addition to the wide spectrum of genetic aberrations involved in pathogenesis leading to variations in patient response patterns, another facet of personalised medicine awaits exploration of its full potential: a systematic, mathematical approach to understand and manipulate the dynamics of relevant biomarkers. We use personalised mathematical models to describe and predict white blood cell (WBC) counts during AML consolidation treatment. We analyse why and to what extent low WBC counts, a serious adverse event during therapy, occur. In a comprehensive approach we investigate published models, compare them with our extended models and outline the impact of modelling assumptions and varying chemotherapy schedules on prediction accuracy and model discrimination. Our numerical results confirm the clinical finding that a newly proposed schedule is superior with respect to WBC recovery and shed new light on the reasons why.

systems biology↗