Synthesis of Uronic Acid 1-Azasugars as Putative Inhibitors of α-Iduronidase, ß-Glucuronidase and Heparanase.

1-Azasugar analogues of l-iduronic acid (l-IdoA) and d-glucuronic acid (d-GlcA) and their corresponding enantiomers have been synthesized as potential pharmacological chaperones for mucopolysaccharidosis I (MPS I), a lysosomal storage disease caused by mutations in the gene encoding α-iduronidase (IDUA). The compounds were efficiently synthesized in nine or ten steps from d- or l-arabinose, and the structures were confirmed by X-ray crystallographic analysis of key intermediates. All compounds were inactive against IDUA, although l-IdoA-configured 8 moderately inhibited ß-glucuronidase (ß-GLU). The d-GlcA-configured 9 was a potent inhibitor of ß-GLU and a moderate inhibitor of the endo-ß-glucuronidase heparanase. Co-crystallization of 9 with heparanase revealed that the endocyclic nitrogen of 9 forms close interactions with both the catalytic acid and catalytic nucleophile.

Satellite SST-Based Coral Disease Outbreak Predictions for the Hawaiian Archipelago.

Predicting wildlife disease risk is essential for effective monitoring and management, especially for geographically expansive ecosystems such as coral reefs in the Hawaiian archipelago. Warming ocean temperature has increased coral disease outbreaks contributing to declines in coral cover worldwide. In this study we investigated seasonal effects of thermal stress on the prevalence of the three most widespread coral diseases in Hawai'i: Montipora white syndrome, Porites growth anomalies and Porites tissue loss syndrome. To predict outbreak likelihood we compared disease prevalence from surveys conducted between 2004 and 2015 from 18 Hawaiian Islands and atolls with biotic (e.g., coral density) and abiotic (satellite-derived sea surface temperature metrics) variables using boosted regression trees. To date, the only coral disease forecast models available were developed for Acropora white syndrome on the Great Barrier Reef (GBR). Given the complexities of disease etiology, differences in host demography and environmental conditions across reef regions, it is important to refine and adapt such models for different diseases and geographic regions of interest. Similar to the Acropora white syndrome models, anomalously warm conditions were important for predicting Montipora white syndrome, possibly due to a relationship between thermal stress and a compromised host immune system. However, coral density and winter conditions were the most important predictors of all three coral diseases in this study, enabling development of a forecasting system that can predict regions of elevated disease risk up to six months before an expected outbreak. Our research indicates satellite-derived systems for forecasting disease outbreaks can be appropriately adapted from the GBR tools and applied for a variety of diseases in a new region. These models can be used to enhance management capacity to prepare for and respond to emerging coral diseases throughout Hawai'i and can be modified for other diseases and regions around the world.