A synthetic agent ameliorates polycystic kidney disease by promoting apoptosis of cystic cells through increased oxidative stress.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of chronic kidney disease and the fourth leading cause of end-stage kidney disease, accounting for over 50% of prevalent cases requiring renal replacement therapy. There is a pressing need for improved therapy for ADPKD. Recent insights into the pathophysiology of ADPKD revealed that cyst cells undergo metabolic changes that up-regulate aerobic glycolysis in lieu of mitochondrial respiration for energy production, a process that ostensibly fuels their increased proliferation. The present work leverages this metabolic disruption as a way to selectively target cyst cells for apoptosis. This small-molecule therapeutic strategy utilizes 11beta-dichloro, a repurposed DNA-damaging anti-tumor agent that induces apoptosis by exacerbating mitochondrial oxidative stress. Here, we demonstrate that 11beta-dichloro is effective in delaying cyst growth and its associated inflammatory and fibrotic events, thus preserving kidney function in perinatal and adult mouse models of ADPKD. In both models, the cyst cells with homozygous inactivation of Pkd1 show enhanced oxidative stress following treatment with 11beta-dichloro and undergo apoptosis. Co-administration of the antioxidant vitamin E negated the therapeutic benefit of 11beta-dichloro in vivo, supporting the conclusion that oxidative stress is a key component of the mechanism of action. As a preclinical development primer, we also synthesized and tested an 11beta-dichloro derivative that cannot directly alkylate DNA, while retaining pro-oxidant features. This derivative nonetheless maintains excellent anti-cystic properties in vivo and emerges as the lead candidate for development.

Effects of High Temperature and Heavy Precipitation on Drinking Water Quality and Child Hand Contamination Levels in Rural Kenya.

Climate change may impact human health through the influence of weather on environmental transmission of diarrhea. Previous studies have found that high temperatures and heavy precipitation are associated with increased diarrhea prevalence, but the underlying causal mechanisms have not been tested and validated. We linked measurements of Escherichia coli in source water (n = 1673), stored drinking water (n = 9692), and hand rinses from children <2 years old (n = 2634) with publicly available gridded temperature and precipitation data (at ≤0.2 degree spatial resolution and daily temporal resolution) by the GPS coordinates and date of sample collection. Measurements were collected over a 3-year period across a 2500 km2 area in rural Kenya. In drinking water sources, high 7-day temperature was associated with a 0.16 increase in log10 E. coli levels (p < 0.001, 95% CI: 0.07, 0.24), while heavy 7-day total precipitation was associated with a 0.29 increase in log10 E. coli levels (p < 0.001, 95% CI: 0.13, 0.44). In household stored drinking water, heavy 7-day precipitation was associated with a 0.079 increase in log10 E. coli levels (p = 0.042, 95% CI: 0.07, 0.24). Heavy precipitation did not increase E. coli levels among respondents who treated their water, suggesting that water treatment can mitigate effects on water quality. On child hands, high 7-day temperature was associated with a 0.39 decrease in log10 E. coli levels (p < 0.001, 95% CI: -0.52, -0.27). Our findings provide insight on how climate change could impact environmental transmission of bacterial pathogens in Kenya. We suggest water treatment is especially important after heavy precipitation (particularly when preceded by dry periods) and high temperatures.

A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil.

Large-scale agriculture in the state of Mato Grosso, Brazil is a major contributor to global food supplies, but its continued productivity is vulnerable to contracting wet seasons and increased exposure to extreme temperatures. Sowing dates serve as an effective adaptation strategy to these climate perturbations. By controlling the weather experienced by crops and influencing the number of successive crops that can be grown in a year, sowing dates can impact both individual crop yields and cropping intensities. Unfortunately, the spatiotemporally resolved crop phenology data necessary to understand sowing dates and their relationship to crop yield are only available over limited years and regions. To fill this data gap, we produce a 500 m rainfed soy (Glycine max) sowing and harvest date dataset for Mato Grosso from 2004 to 2014 using a novel time series analysis method for Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, adapted for implementation in Google Earth Engine (GEE). Our estimates reveal that soy sowing and harvest dates varied widely (about 2 months) from field to field, confirming the need for spatially resolved crop timing information. An interannual trend toward earlier sowing dates occurred independently of variations in wet season onset, and may be attributed to an improvement in logistic or economic constraints that previously hampered early sowing. As anticipated, double cropped fields in which two crops are grown in succession are planted earlier than single cropped fields. This difference shrank, however, as sowing of single cropped fields occurred closer to the wet season onset in more recent years. The analysis offers insights about sowing behavior in response to historical climate variations which could be extended to understand sowing response under climate change in Mato Grosso.