Biology subjects

Richardson, B. S.

Publications and source records attributed to Richardson, B. S..

3 recordsLinked to original sources

bioRxiv2024

White matter microglia morphological changes with aging in guinea pig offspring born growth restricted

Fetal growth restriction is implicated in the programming of later-life neurodegeneration. We hypothesized that growth restricted offspring would show accelerated changes to microglial white matter morphology, relative to controls. Control guinea pig sows were fed ad libitum, while maternal nutrient restriction sows received 70% of control diet switched to 90% from mid-gestation. Offspring were sacrificed at [~]26 days (neonate) or [~]110 days (adult) postpartum. Coronal brain sections from the frontal cortex were subject to IBA1-staining for microglial detection and analyzed by machine learning software. At birth, total body weight of growth restricted offspring was reduced relative to control (p<0.0001) with postnatal catch-up growth observed. Microglial density was reduced in the corpus callosum of control (p<0.05) and growth restricted (p=0.13) adults, relative to neonates. Adults from both groups showed greater IBA1-positive area in the cingulum and periventricular white matter (p<0.05) and increased microglial fractal dimension in the corpus callosum (p<0.10) and periventricular white matter (p<0.05), relative to neonates. At the timepoints studied, we report age-related changes in white matter microglial morphology. However, maternal nutrient restriction leading to fetal growth restriction in guinea pigs does not appear to exacerbate these white matter microglia morphological changes as a marker for later-life neurodegeneration.

developmental biology↗

bioRxiv2021

Differential and synergistic effects of low birth weight and Western diet on skeletal muscle vasculature, mitochondrial lipid metabolism and insulin signaling in male guinea pigs

Low birth weight (LBW) offspring are at increased risk for developing insulin resistance, a key precursor in metabolic syndrome and type 2 diabetes mellitus. Altered skeletal muscle vasculature, extracellular matrix, amino acid and mitochondrial lipid metabolism, and insulin signaling are implicated in this pathogenesis. Using uteroplacental insufficiency (UPI) to induce intrauterine growth restriction (IUGR) and LBW in the guinea pig, we investigated the relationship between UPI-induced IUGR/LBW and later life skeletal muscle arteriole density, fibrosis, amino acid and mitochondrial lipid metabolism, markers of insulin signaling and glucose uptake, and how a postnatal high-fat, high-sugar "Western" diet (WD) modulates these changes. Muscle of 145-day-old male LBW glucose tolerant offspring displayed diminished vessel density and altered acylcarnitine levels. Disrupted muscle insulin signaling despite maintained whole-body glucose homeostasis also occurred in both LBW and WD-fed male lean offspring. Additionally, postnatal WD unmasked LBW-induced impairment of mitochondrial lipid metabolism as reflected by increased acylcarnitine accumulation. This study provides evidence that early markers of skeletal muscle metabolic dysfunction appear to be influenced by the in utero environment and interact with a high fat-sugar postnatal environment to exacerbate altered mitochondrial lipid metabolism promoting mitochondrial overload.

physiology↗

bioRxiv2017

Real-time changepoint detection for noisy biological time series

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods.\n\nWe propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labour, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing pointwise detection methods.

bioinformatics↗