Control of blood vessel tone is central to vascular homeostasis. Here, we show that metabolism of tryptophan to kynurenine by indoleamine 2,3-dioxygenase (IDO) expressed in endothelial cells contributes to arterial vessel relaxation and the control of blood pressure. Infection of mice with malarial parasites (Plasmodium berghei), and experimental induction of endotoxemia, caused endothelial expression of IDO, resulting in decreased plasma tryptophan, increased kynurenine, and hypotension. Pharmacological inhibition of IDO increased blood pressure in systemically inflamed mice, but not in mice deficient for IDO or interferon-γ, which is required for IDO induction. Tryptophan dilated pre-constricted porcine coronary arteries only if active IDO and an intact endothelium were both present. Kynurenine dose-dependently decreased blood pressure in spontaneously hypertensive rats, inhibited contraction of arteries, and relaxed pre-constricted rings endothelium-independently. Arterial relaxation by kynurenine was mediated by activation of the adenylate and soluble guanylate cyclase pathways.

We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the least absolute shrinkage and selection operator (Lasso) may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman-Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-ofmeans estimator. Simulation and data examples show that Lassobased adjustment can be advantageous even when the number of covariates is less than the number of observations. Specifically, a variant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly well, and it chooses a smoothing parameter based on combined performance of Lasso and OLS.randomized experiment | Neyman-Rubin model | average treatment effect | high-dimensional statistics | Lasso R andomized experiments are widely used to measure the efficacy of treatments. Randomization ensures that treatment assignment is not influenced by any potential confounding factors, both observed and unobserved. Experiments are particularly useful when there is no rigorous theory of a system's dynamics, and full identification of confounders would be impossible. This advantage was cast elegantly in mathematical terms in the early 20th century by Jerzy Neyman, who introduced a simple model for randomized experiments, which showed that the difference of average outcomes in the treatment and control groups is statistically unbiased for the average treatment effect (ATE) over the experimental sample (1).However, no experiment occurs in a vacuum of scientific knowledge. Often, baseline covariate information is collected about individuals in an experiment. Even when treatment assignment is not related to these covariates, analyses of experimental outcomes often take them into account with the goal of improving the accuracy of treatment effect estimates. In modern randomized experiments, the number of covariates can be very large-sometimes even larger than the number of individuals in the study. In clinical trials overseen by regulatory bodies like the Food and Drug Administration and the Medicines and Healthcare products Regulatory Agency, demographic and genetic information may be recorded about each patient. In applications in the tech industry, where randomization is often called A/B testing, there is often a huge amount of behavioral data ...

These results demonstrate that IDO prevents acute lung allograft injury through augmenting the local antioxidant defense system and inhibiting alloreactive T-cell responses.