30 research outputs found
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Targeting PI3Kδ function for amelioration of murine chronic graft-versus-host disease.
Chronic graft-versus-host disease (cGVHD) is a leading cause of morbidity and mortality following allotransplant. Activated donor effector T cells can differentiate into pathogenic T helper (Th)-17 cells and germinal center (GC)-promoting T follicular helper (Tfh) cells, resulting in cGVHD. Phosphoinositide-3-kinase-δ (PI3Kδ), a lipid kinase, is critical for activated T cell survival, proliferation, differentiation, and metabolism. We demonstrate PI3Kδ activity in donor T cells that become Tfh cells is required for cGVHD in a nonsclerodermatous multiorgan system disease model that includes bronchiolitis obliterans (BO), dependent upon GC B cells, Tfhs, and counterbalanced by T follicular regulatory cells, each requiring PI3Kδ signaling for function and survival. Although B cells rely on PI3Kδ pathway signaling and GC formation is disrupted resulting in a substantial decrease in Ig production, PI3Kδ kinase-dead mutant donor bone marrow-derived GC B cells still supported BO cGVHD generation. A PI3Kδ-specific inhibitor, compound GS-649443, that has superior potency to idelalisib while maintaining selectivity, reduced cGVHD in mice with active disease. In a Th1-dependent and Th17-associated scleroderma model, GS-649443 effectively treated mice with active cGVHD. These data provide a foundation for clinical trials of US Food and Drug Administration (FDA)-approved PI3Kδ inhibitors for cGVHD therapy in patients
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Regionally coupled atmosphere-ocean-sea ice-marine biogeochemistry model ROM: 1. Description and validation
The general circulation models used to simulate global climate typically feature resolution too coarse to reproduce many smaller-scale processes, which are crucial to determining the regional responses to climate change. A novel approach to downscale climate change scenarios is presented which includes the interactions between the North Atlantic Ocean and the European shelves as well as their impact on the North Atlantic and European climate. The goal of this paper is to introduce the global ocean-regional atmosphere coupling concept and to show the potential benefits of this model system to simulate present-day climate. A global ocean-sea ice-marine biogeochemistry model (MPIOM/HAMOCC) with regionally high horizontal resolution is coupled to an atmospheric regional model (REMO) and global terrestrial hydrology model (HD) via the OASIS coupler. Moreover, results obtained with ROM using NCEP/NCAR reanalysis and ECHAM5/MPIOM CMIP3 historical simulations as boundary conditions are presented and discussed for the North Atlantic and North European region. The validation of all the model components, i.e., ocean, atmosphere, terrestrial hydrology, and ocean biogeochemistry is performed and discussed. The careful and detailed validation of ROM provides evidence that the proposed model system improves the simulation of many aspects of the regional climate, remarkably the ocean, even though some biases persist in other model components, thus leaving potential for future improvement. We conclude that ROM is a powerful tool to estimate possible impacts of climate change on the regional scale
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Sensitivity of simulated regional Arctic climate to the choice of coupled model domain
The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an ‘internal mode’ of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to ‘internal variability’ within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on ‘geographic’ reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system
Quantifying the incremental value of deep learning: Application to lung nodule detection.
We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some domains; however, implementation of these methods is in early development. Often these methods are only directly compared to existing methods; here we present a framework for assessing the value of a machine learning model by assessing the incremental value. We developed a machine learning model to identify and classify lung nodules and assessed the incremental value added to existing risk prediction models. Multiple external datasets were used for validation. We found that our image model, trained on a dataset from The Cancer Imaging Archive (TCIA), improves upon existing models that are restricted to patient characteristics, but it was inconclusive about whether it improves on models that consider nodule features. Another interesting finding is the variable performance on different datasets, suggesting population generalization with machine learning models may be more challenging than is often considered
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Dendritic Cell Expression of Retinal Aldehyde Dehydrogenase-2 Controls Graft-versus-Host Disease Lethality
Recent studies have underscored the critical role of retinoic acid (RA) in the development of lineage-committed CD4 and CD8 T cells in vivo. We have shown that under acute graft-versus-host disease (GVHD) inflammatory conditions, RA is upregulated in the intestine and is proinflammatory, as GVHD lethality was attenuated when donor allogeneic T cells selectively expressed a dominant negative RA receptor α that blunted RA signaling. RA can function in an autocrine and paracrine fashion, and as such, the host cell lineage responsible for the production of RA metabolism and the specific RA-metabolizing enzymes that potentiate GVHD severity are unknown. In this study, we demonstrate that enhancing RA degradation in the host and to a lesser extent donor hematopoietic cells by overexpressing the RA-catabolizing enzyme CYP26A1 reduced GVHD. RA production is facilitated by retinaldehyde isoform-2 (RALDH2) preferentially expressed in dendritic cells (DCs). Conditionally deleted RA-synthesizing enzyme RALDH2 in host or to a lesser extent donor DCs reduced GVHD lethality. Improved survival in recipients with RALDH2-deleted DCs was associated with increased T cell death, impaired T effector function, increased regulatory T cell frequency, and augmented coinhibitory molecule expression on donor CD4+ T cells. In contrast, retinaldehydrogenase isoform-1 (RALDH1) is dominantly expressed in intestinal epithelial cells. Unexpectedly, conditional host intestinal epithelial cells RALDH1 deletion failed to reduce GVHD. These data demonstrate the critical role of both donor and especially host RALDH2+ DCs in driving murine GVHD and suggest RALDH2 inhibition or CYP26A1 induction as novel therapeutic strategies to prevent GVHD