Loss of CD4+ T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection

Arginase 1 (Arg1), the enzyme catalyzing the conversion of arginine to ornithine, is a hallmark of IL-10-producing immunoregulatory M2 macrophages. However, its expression in T cells is disputed. Here, we demonstrate that induction of Arg1 expression is a key feature of lung CD4+ T cells during mouse in vivo influenza infection. Conditional ablation of Arg1 in CD4+ T cells accelerated both virus-specific T helper 1 (Th1) effector responses and its resolution, resulting in efficient viral clearance and reduced lung pathology. Using unbiased transcriptomics and metabolomics, we found that Arg1-deficiency was distinct from Arg2-deficiency and caused altered glutamine metabolism. Rebalancing this perturbed glutamine flux normalized the cellular Th1 response. CD4+ T cells from rare ARG1-deficient patients or CRISPR-Cas9-mediated ARG1-deletion in healthy donor cells phenocopied the murine cellular phenotype. Collectively, CD4+ T cell-intrinsic Arg1 functions as an unexpected rheostat regulating the kinetics of the mammalian Th1 lifecycle with implications for Th1-associated tissue pathologies

Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis

Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m-2 y-1 during the spring transition period, and +75 ± 130 g C m-2 y-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology, and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately, and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models.Engineering and Applied SciencesOrganismic and Evolutionary Biolog

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Drug discovery in advanced prostate cancer: translating biology into therapy.

Castration-resistant prostate cancer (CRPC) is associated with a poor prognosis and poses considerable therapeutic challenges. Recent genetic and technological advances have provided insights into prostate cancer biology and have enabled the identification of novel drug targets and potent molecularly targeted therapeutics for this disease. In this article, we review recent advances in prostate cancer target identification for drug discovery and discuss their promise and associated challenges. We review the evolving therapeutic landscape of CRPC and discuss issues associated with precision medicine as well as challenges encountered with immunotherapy for this disease. Finally, we envision the future management of CRPC, highlighting the use of circulating biomarkers and modern clinical trial designs