Biology subjects

Mitton, J.

Publications and source records attributed to Mitton, J..

3 recordsLinked to original sources

bioRxiv2023

Endothelial-specific reduction in Arf6 impairs insulin-stimulated vasodilation and skeletal muscle blood flow resulting in systemic insulin resistance

BackgroundMuch of what we know about insulin resistance is based on studies from metabolically active tissues such as liver, adipose tissue, and skeletal muscle. Emerging evidence suggests that the vascular endothelium plays a crucial role in systemic insulin resistance, however, the underlying mechanisms remain incompletely understood. ADP ribosylation factor 6 (Arf6) is a small GTPase that plays a critical role in endothelial cell (EC) function. Here, we tested the hypothesis that the deletion of endothelial Arf6 will result in systemic insulin resistance. MethodsWe used mouse models of constitutive EC-specific Arf6 deletion (Arf6f/- Tie2Cre) and tamoxifen inducible Arf6 knockout (Arf6f/f Cdh5Cre). Endothelium-dependent vasodilation was assessed using pressure myography. Metabolic function was assessed using a battery of metabolic assessments including glucose- and insulin-tolerance tests and hyperinsulinemic-euglycemic clamps. A fluorescence microsphere-based technique was used to measure tissue blood flow. Intravital microscopy was used to assess skeletal muscle capillary density. ResultsEndothelial Arf6 deletion impaired insulin-stimulated vasodilation in white adipose tissue (WAT) and skeletal muscle feed arteries. The impairment in vasodilation was primarily due to attenuated insulin-stimulated nitric oxide (NO) bioavailability but independent of altered acetylcholine- or sodium nitroprusside-mediated vasodilation. In vitro Arf6 inhibition resulted in suppressed insulin stimulated phosphorylation of Akt and endothelial NO synthase. Endothelial cell-specific deletion of Arf6 also resulted in systematic insulin resistance in normal chow fed mice and glucose intolerance in high fat diet fed obese mice. The underlying mechanisms of glucose intolerance were reductions in insulin-stimulated blood flow and glucose uptake in the skeletal muscle and were independent of changes in capillary density or vascular permeability. ConclusionResults from this study support the conclusion that endothelial Arf6 signaling is essential for maintaining insulin sensitivity. Reduced expression of endothelial Arf6 impairs insulin-mediated vasodilation and results in systemic insulin resistance. These results have therapeutic implications for diseases that are associated with endothelial cell dysfunction and insulin resistance such as diabetes.

physiology↗

bioRxiv2022

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra

Accurate prediction of mosquito population age structures can improve the evaluation of mosquito-targeted interventions since old mosquitoes are more likely to transmit malaria than young ones. Mid-infrared spectroscopy (MIRS) reveals age-associated variation in the biochemical composition of the mosquito cuticle, which can then be used to train machine learning (ML) models to predict mosquito ages. However, these MIRS-ML models are not always generalisable across different mosquito populations. Here, we investigated whether dimensionality reduction applied to the MIRS input data and transfer learning could improve the generalisability of MIRS-ML predictions for mosquito ages. We reared adults of the malaria vector, Anopheles arabiensis, in two insectaries (Ifakara, Tanzania and Glasgow, UK). The heads and thoraces of female mosquitoes of two age classes (1-9 day-olds and 10-17 day-olds) were scanned using an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer (4000 cm-1 to 400 cm-1). The dimensionality of the spectra data was reduced using unsupervised principal component analysis (PCA) or t-distributed stochastic neighbour embedding (t-SNE), and then the spectra were used to train deep learning (DL) and standard machine learning (ML) classifiers. Transfer learning was also evaluated for improving the computational cost of the models when predicting mosquito age classes from new populations. Model accuracies for predicting the age of test mosquitoes from the same insectary as the training samples reached 99% for DL and 92% for ML, but did not generalise to a different insectary, achieving only 46% and 48% for ML for DL, respectively. Dimensionality reduction did not improve the model generalisability between locations but reduced computational time up to 5-fold. However, transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate location brought both DL and standard ML model performance to ~98% accuracy for predicting mosquito age classes in the alternative insectary. Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies could investigate the optimal quantities and diversity of training data necessary for transfer learning, and implications for broader generalisability to unseen datasets.

bioinformatics↗

bioRxiv2020

Rapid ageing and species identification of natural mosquitoes for malaria surveillance

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of a mosquito. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquitoes cuticle that simultaneously identifies the species and the age of three main malaria vectors, in natural populations. Using over 40,000 ecologically and genetically diverse females, we could speciate and age grade An. gambiae, An. arabiensis, and An. coluzzii with up to 95% accuracy. Further, our model learned the age of new populations with minimal sampling effort and detected the impact of control interventions on simulated mosquito populations, measured as a shift in their age structures. We anticipate our method to be applied to other arthropod vector-borne diseases.

zoology↗