Targeting metabolism with arsenic trioxide and dichloroacetate in breast cancer cells

Background: Cancer cells have a different metabolic profile compared to normal cells. The Warburg effect (increased aerobic glycolysis) and glutaminolysis (increased mitochondrial activity from glutamine catabolism) are well known hallmarks of cancer an

Oral Gavage Delivery of Stable Isotope Tracer for in Vivo Metabolomics

Stable isotope-resolved metabolomics (SIRM) is a powerful tool for understanding disease. Advances in SIRM techniques have improved isotopic delivery and expanded the workflow from exclusively in vitro applications to in vivo methodologies to study systemic metabolism. Here, we report a simple, minimally-invasive and cost-effective method of tracer delivery to study SIRM in vivo in laboratory mice. Following a brief fasting period, we orally administered a solution of [U-13C] glucose through a blunt gavage needle without anesthesia, at a physiological dose commonly used for glucose tolerance tests (2 g/kg bodyweight). We defined isotopic enrichment in plasma and tissue at 15, 30, 120, and 240 min post-gavage. 13C-labeled glucose peaked in plasma around 15 min post-gavage, followed by period of metabolic decay and clearance until 4 h. We demonstrate robust enrichment of a variety of central carbon metabolites in the plasma, brain and liver of C57/BL6 mice, including amino acids, neurotransmitters, and glycolytic and tricarboxylic acid (TCA) cycle intermediates. We then applied this method to study in vivo metabolism in two distinct mouse models of diseases known to involve dysregulation of glucose metabolism: Alzheimer’s disease and type II diabetes. By delivering [U-13C] glucose via oral gavage to the 5XFAD Alzheimer’s disease model and the Lepob/ob type II diabetes model, we were able to resolve significant differences in multiple central carbon pathways in both model systems, thus providing evidence of the utility of this method to study diseases with metabolic components. Together, these data clearly demonstrate the efficacy and efficiency of an oral gavage delivery method, and present a clear time course for 13C enrichment in plasma, liver and brain of mice following oral gavage of [U-13C] glucose—data we hope will aid other researchers in their own 13C-glucose metabolomics study design

Clinical Features, Survival and Prognostic Factors of Glycogen-Rich Clear Cell Carcinoma (GRCC) of the Breast in the U.S. Population

The World Health Organization (WHO) defines glycogen-rich clear cell carcinoma (GRCC) of the breast as a carcinoma with glycogen accumulation in more than 90% of its tumor cells. Due to the rarity of this disease, its reported survival and clinical associations have been inconsistent due to reliance on case reports and limited case series. As a result, the prognostic implication of this cancer subtype remains unclear. Using the U.S. Surveillance, Epidemiology, and End Results (SEER) program database, we compared the incidence, demographics and prognostic factors of 155 cases of GRCC of the breast to 1,251,584 cases of other (non-GRCC) breast carcinomas. We demonstrate that GRCC is more likely to be identified as high grade, advanced stage, and more likely to have triple negative receptor status. GRCC cases display a poorer prognosis than non-GRCC carcinomas of the breast irrespective of age, AJCC staging, tumor grade, joint hormone receptor/human epidermal growth factor receptor 2 (HER2) status, and treatment. Similar to non-GRCC carcinomas, older age and higher American Joint Committee on Cancer (AJCC)/TNM staging were associated with poorer prognosis for GRCC, while treatment with surgery and radiation were associated with improved survival. Radiation, specifically in the setting of breast-conserving surgery, further improved survival compared to surgery alone. Our study highlights the poorer prognosis associated with glycogen accumulation in breast cancers and hence stresses the importance of identifying this more aggressive tumor type

Noninvasive Liquid Diet Delivery of Stable Isotopes into Mouse Models for Deep Metabolic Network Tracing

Delivering isotopic tracers for metabolic studies in rodents without overt stress is challenging. Current methods achieve low label enrichment in proteins and lipids. Here, we report noninvasive introduction of 13C6-glucose via a stress-free, ad libitum liquid diet. Using NMR and ion chromatography-mass spectrometry, we quantify extensive 13C enrichment in products of glycolysis, the Krebs cycle, the pentose phosphate pathway, nucleobases, UDP-sugars, glycogen, lipids, and proteins in mouse tissues during 12 to 48 h of 13C6-glucose feeding. Applying this approach to patient-derived lung tumor xenografts (PDTX), we show that the liver supplies glucose-derived Gln via the blood to the PDTX to fuel Glu and glutathione synthesis while gluconeogenesis occurs in the PDTX. Comparison of PDTX with ex vivo tumor cultures and arsenic-transformed lung cells versus xenografts reveals differential glucose metabolism that could reflect distinct tumor microenvironment. We further found differences in glucose metabolism between the primary PDTX and distant lymph node metastases

Regional N-Glycan and Lipid Analysis from Tissues Using MALDI-Mass Spectrometry Imaging

N-glycans and lipids are structural metabolites that play important roles in cellular processes. Both show unique regional distribution in tissues; therefore, spatial analyses of these metabolites are crucial to our understanding of cellular physiology. Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is an innovative technique that enables in situ detection of analytes with spatial distribution. This workflow details a MALDI-MSI protocol for the spatial profiling of N-glycans and lipids from tissues following application of enzyme and MALDI matrix. For complete details on the use and execution of this protocol, please refer to Drake et al. (2018) and Andres et al. (2020)

Evaluation of Glutaminase Expression in Prostate Adenocarcinoma and Correlation with Clinicopathologic Parameters

High Glutaminase (GLS1) expression may have prognostic implications in colorectal and breast cancers; however, high quality data for expression in prostate cancer (PCa) are lacking. The purpose of this study is to investigate the status of GLS1 expression in PCa and correlated expression levels with clinicopathologic parameters. This study was conducted in two phases: an exploratory cohort analyzing RNA-Seq data for GLS1 from The Cancer Genome Atlas (TCGA) data portal (246 PCa samples) and a GLS1 immunohistochemical protein expression cohort utilizing a tissue microarray (TMA) (154 PCa samples; 41 benign samples) for correlation with clinicopathologic parameters. In the TCGA cohort, GLS1 mRNA expression did not show a statistically significant difference in disease-free survival (DFS) but did show a small significant difference in overall survival (OS). In the TMA cohort, there was no correlation between GLS1 expression and stage, Gleason score, DFS and OS. GLS1 expression did not significantly correlate with the clinical outcomes measured; however, GLS1 expression was higher in PCa cells compared to benign epithelium. Future studies are warranted to evaluate expression levels in greater numbers of high-grade and advanced PCa samples to investigate whether there is a rational basis for GLS1 targeted therapy in a subset of patients with prostate cancer

Astrocytic glycogen accumulation drives the pathophysiology of neurodegeneration in Lafora disease

The hallmark of Lafora disease, a fatal neurodegenerative disorder, is the accumulation of intracellular glycogen aggregates, called Lafora bodies. Until recently, it was widely believed that brain Lafora bodies were present exclusively in neurons and thus that Lafora disease pathology derived from their accumulation in this cell population. However, recent evidence indicates that Lafora bodies are also present in astrocytes. To define the role of astrocytic Lafora bodies in Lafora disease pathology, we deleted glycogen synthase specifically from astrocytes in a mouse model of the disease (malinKO). Strikingly, blocking glycogen synthesis in astrocytes-thus impeding Lafora bodies accumulation in this cell type-prevented the increase in neurodegeneration markers, autophagy impairment, and metabolic changes characteristic of the malinKO model. Conversely, mice that overaccumulate glycogen in astrocytes showed an increase in these markers. These results unveil the deleterious consequences of the deregulation of glycogen metabolism in astrocytes and change the perspective that Lafora disease is caused solely by alterations in neuron

A psycho-Geoinformatics approach for investigating older adults’ driving behaviours and underlying cognitive mechanisms

Introduction: Safe driving constantly challenges the driver’s ability to respond to the dynamic traffic scene under space and time constraints. It is of particular importance for older drivers to perform sufficient visual and motor actions with effective coordination due to the fact of age-related cognitive decline. However, few studies have been able to integrate drivers’ visual-motor behaviours with environmental information in a spatial-temporal context and link to the cognitive conditions of individual drivers. Little is known about the mechanisms that underpin the deterioration in visual-motor coordination of older drivers. Development: Based on a review of driving-related cognitive decline in older adults and the context of driver-vehicle-environment interactions, this paper established a conceptual framework to identify the parameters of driver’s visual and motor behaviour, and reveal the cognitive process from visual search to vehicle control in driving. The framework led to a psycho-geoinformatics approach to measure older drivers’ driving behaviours and investigate the underlying cognitive mechanisms. The proposed data collection protocol and the analysis and assessments depicted the psycho-geoinformatics approach on obtaining quantified variables and the key means of analysis, as well as outcome measures. Conclusions: Recordings of the driver and their interactions with the vehicle and environment at a detailed scale give a closer assessment of the driver’s behaviours. Using geoinformatics tools in driving behaviours assessment opens a new era of research with many possible analytical options, which do not have to rely on human observations. Instead, it receives clear indicators of the individual drivers’ interactions with the vehicle and the traffic environment. This approach should make it possible to identify lower-performing older drivers and problematic visual and motor behaviours, and the cognitive predictors of risky driving behaviours. A better targeted regulation and tailored intervention programs for older can be developed by further research