Biology subjects

Davis, R. C.

Publications and source records attributed to Davis, R. C..

3 recordsLinked to original sources

bioRxiv2025

Glo1 reduction in mice results in age- and sex-dependent metabolic dysfunction

ObjectivesAdvanced glycation end products (AGEs) have been implicated as an important mediator of metabolic disorders including obesity, insulin resistance, and coronary artery disease. Glyoxalase 1 (Glo1) is a critical enzyme in the clearance of toxic dicarbonyl such as methylglyoxal, precursors of AGEs. The role of AGE-independent mechanisms that underly Glo1-induced metabolic disorders have yet to be elucidated. MethodsWe performed a longitudinal study of female and male Glo1 heterozygous knockdown (Glo1+/-) mice with [~]50% gene expression and screened metabolic phenotypes such as body weight, adiposity, glycemic control and plasma lipids. We also evaluated atherosclerotic burden, AGE levels, and gene expression profiles across cardiometabolic tissues (liver, adipose, muscle, kidney and aorta) to identify pathway perturbations and potential regulatory genes of Glo1 actions. ResultsPartial loss of Glo1 resulted in obesity, hyperglycemia, dyslipidemia, and alterations in lipid metabolism in metabolic tissues in an age- and sex-dependent manner. Glo1+/- females displayed altered glycemic control and increased plasma triglycerides, which aligned with significant perturbations in genes involved in adipogenesis, PPARg, insulin signaling, and fatty acid metabolism pathways in liver and adipose tissues. Conversely, Glo1+/- males developed increased skeletal muscle mass and visceral adipose depots along with changes in lipid metabolism pathways. For both cohorts, most phenotypes manifested after 14 weeks of age. Evaluation of methylglyoxal-derived AGEs demonstrated changes in only male skeletal muscle but not in female tissues, which cannot explain the broad metabolic changes observed in Glo1+/-mice. Transcriptional profiles suggest that altered glucose and lipid metabolism may be partially explained by alternative detoxification of methylglyoxal to metabolites such as pyruvate. Moreover, transcription factor (TF) analysis of the tissue-specific gene expression data identified TFs involved in cardiometabolic diseases such as Hnf4a (all tissues) and Arntl (aorta, liver, and kidney) which are female-biased regulators and whose targets are altered in response to Glo1+/-. ConclusionsOur results indicate that Glo1 reduction perturbs metabolic health and metabolic pathways in a sex- and age-dependent manner without significant changes in AGEs across metabolic tissues. Rather, tissue-specific gene expression analysis suggests that key transcription factors such as Hfn4a and Arntl as well as metabolite changes from alternative methylglyoxal detoxification such as pyruvate, likely contribute to metabolic dysregulation in Glo1+/- mice.

molecular biology↗

bioRxiv2023

The Genetic Architecture of Dietary Iron Overload and Associated Pathology in Mice

Tissue iron overload is a frequent pathologic finding in multiple disease states including non-alcoholic fatty liver disease (NAFLD), neurodegenerative disorders, cardiomyopathy, diabetes, and some forms of cancer. The role of iron, as a cause or consequence of disease progression and observed phenotypic manifestations, remains controversial. In addition, the impact of genetic variation on iron overload related phenotypes is unclear, and the identification of genetic modifiers is incomplete. Here, we used the Hybrid Mouse Diversity Panel (HMDP), consisting of over 100 genetically distinct mouse strains optimized for genome-wide association studies and systems genetics, to characterize the genetic architecture of dietary iron overload and pathology. Dietary iron overload was induced by feeding male mice (114 strains, 6-7 mice per strain on average) a high iron diet for six weeks, and then tissues were collected at 10-11 weeks of age. Liver metal levels and gene expression were measured by ICP-MS/ICP-AES and RNASeq, and lipids were measured by colorimetric assays. FaST-LMM was used for genetic mapping, and Metascape, WGCNA, and Mergeomics were used for pathway, module, and key driver bioinformatics analyses. Mice on the high iron diet accumulated iron in the liver, with a 6.5 fold difference across strain means. The iron loaded diet also led to a spectrum of copper deficiency and anemia, with liver copper levels highly positively correlated with red blood cell count, hemoglobin, and hematocrit. Hepatic steatosis of various severity was observed histologically, with 52.5 fold variation in triglyceride levels across the strains. Liver triglyceride and iron mapped most significantly to an overlapping locus on chromosome 7 that has not been previously associated with either trait. Based on network modeling, significant key drivers for both iron and triglyceride accumulation are involved in cholesterol biosynthesis and oxidative stress management. To make the full data set accessible and useable by others, we have made our data and analyses available on a resource website. Author summaryThe response to a high iron diet is determined in part by genetic factors. We now report the responses to such a diet in a diverse set of inbred strains of mice, known as the Hybrid Mouse Diversity Panel, that enables high resolution genetic mapping and systems genetics analyses. The levels of iron in the liver varied about >5 fold across the strains, with genetic variation explaining up to 74% of the variation in liver iron. Pathologies included copper deficiency, anemia, and fatty liver, with liver triglycerides varying over 50 fold among the strains. Genetic mapping and network modeling identified significant genetic loci and pathways underlying the response to diet.

genetics↗

bioRxiv2020

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

Gastric cancer is among the most malignant tumours with the highest incidence and mortality rates. The early detection and accurate histopathological diagnosis of gastric cancer are essential factors that can help increase the chances of successful treatment. While the worldwide shortage of pathologists has imposed burdens on current histopathology services, it also offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. To the best of our knowledge, there has not been a clinically applicable histopathological assistance system with high accuracy that can generalize to whole slide images created with diverse digital scanner models from different hospitals. Here, we report a clinically applicable artificial intelligence assistance system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieved a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset, which included 3,212 whole slide images digitalized with three scanner models. We showed that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrated that our system could perform robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.Competing Interest StatementXiaoqing Zhang is the founder of Thorough Images. Shuhao Wang is the co-founder and chief technology officer (CTO) of Thorough Images. Calvin Ku, Cancheng Liu, Zhuo Sun, Gang Xu, Yuefeng Wang are algorithm researchers of Thorough Images. All remaining authors have declared no conflicts of interest.View Full Text

pathology↗