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Wynne, K.

Publications and source records attributed to Wynne, K..

20 records · Page 2Linked to original sources

Triphenyl phosphate is a selective PPARγ modulator that does not induce brite adipogenesis in vitro and in vivo

Triphenyl phosphate (TPhP) is an environmental PPAR{gamma} ligand, and growing evidence suggests that it is a metabolic disruptor. We have shown previously that the structurally similar ligand, tributyltin, does not induce brite adipocyte gene expression. Here, using in vivo and in vitro models, we tested the hypothesis that TPhP is a selective PPAR{gamma} ligand, which fails to induce brite adipogenesis. C57BL/6J male mice were fed either a low or very high fat diet for 13 weeks. From weeks 7-13, mice were injected intraperitoneally, daily, with vehicle, rosiglitazone (Rosi), or TPhP (10 mg/kg). Compared to Rosi, TPhP did not induce expression of browning-related genes (e.g. Elovl3, Cidea, Acaa2, CoxIV) in mature adipocytes isolated from inguinal adipose. To determine if this resulted from an effect directly on the adipocytes, 3T3-L1 cells and primary human preadipocytes were differentiated into adipocytes in the presence of Rosi or TPhP. Rosi, but not TPhP, induced expression of brite adipocyte genes, mitochondrial biogenesis and cellular respiration. Further, Rosi and TPhP induced distinct proteomes and phosphoproteomes; Rosi enriched more regulatory pathways related to fatty acid oxidation and mitochondrial proteins. We assessed the role of phosphorylation of PPAR{gamma} in these differences in 3T3-L1 cells. Only Rosi protected PPAR{gamma} from phosphorylation at Ser273. TPhP gained the ability to stimulate brite adipocyte gene expression in the presence of the CDK5 inhibitor and in 3T3-L1 cells expressing alanine at position 273. We conclude that TPhP is a selective PPAR{gamma} modulator that fails to protect PPAR{gamma} from phosphorylation at ser273.

pharmacology and toxicology↗

Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 12 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

bioinformatics↗