Biology subjects

Stoner, M.

Publications and source records attributed to Stoner, M..

5 recordsLinked to original sources

bioRxiv2026

Bioinformatic Characterization of Regulated IRE1a-Dependent Decay (RIDD) in Heart Failure

Inositol-requiring enzyme 1a (IRE1a) is a canonical signaling factor in the unfolded protein response (UPR). In addition to this essential role (which prevents the accumulation of misfolded proteins in the endoplasmic reticulum), the endoribonuclease activity of IRE1a targets multiple mRNAs for degradation through a process called Regulated IRE1a-Dependent Decay (RIDD). The products of over 50 genes have been identified as RIDD targets; however, the biological significance of this process remains underexplored. Using publicly available datasets, we examined the fate of 27 well-characterized RIDD targets in the septal wall of heart failure patients, and in mice subject to pressure overload-induced heart failure. We show that decreased mRNA abundance from these RIDD substrate genes - an outcome consistent with RIDD induction - is commonly observed in heart failure.

cell biology↗

bioRxiv2024

TCA Cycle Dysfunction and Amino Acid Catabolism Drive Hepatic Steatosis in Mice with HFpEF

The prevalence of cardiometabolic heart failure with preserved ejection fraction (HFpEF) continues to grow worldwide, and now represents over half of current heart failure cases in the United States (1). Due to a lack of specific approved therapies, current treatment guidelines focus on the management of comorbidities related to metabolic syndrome (e.g. obesity, diabetes, hypertension) that promote HFpEF progression (1). The same comorbidities also drive cardiometabolic disease in non-cardiac tissues, and links between disease presentations in different organs are increasingly being recognized in the clinic. However, mechanistic studies examining the underlying pathophysiological connections have not kept pace, particularly in the cardio-hepatic disease axis (2). To address this, we used a recently developed and validated preclinical model of HFpEF (3) to examine how this disease impacts the liver. The development of HFpEF in mice leads to the simultaneous development of widespread hepatic steatosis that is consistent with human non-alcoholic fatty liver disease (NAFLD). Mechanistically, we show that the liver steatosis observed is driven by excess glucogenic amino acid entry into the TCA cycle, which promotes hepatic glucose production and de novo lipogenesis. Our findings suggest that HFpEF development is a multi-organ event, with implications for both preclinical and translational research.

physiology↗

bioRxiv2024

GCN5L1 regulates glucose and ketone body metabolism during cardiac ischemia-reperfusion injury

Myocardial infarction remains one of the leading causes of mortality. Reperfusion of the infarcted myocardium restores blood flow and reduces primary ischemic injury. However, despite its protective function, reperfusion is also associated with several deleterious outcomes that can result in ischemia-reperfusion (I/R) injury to cardiac tissue. While negative outcomes such as reactive oxygen species generation are strongly associated with I/R injury, cardiac energy metabolism is also greatly disrupted. Furthermore, previous studies have shown that the restoration of normal fuel oxidation in the myocardium regulates the extent of contractile recovery. A better understanding of the pathophysiological mechanisms underlying I/R injury may allow us to develop new treatments that limit the negative aspects of the process. In this study, we examined the role played by GCN5L1, a protein implicated in the regulation of energy metabolism, in I/R injury. We demonstrate that cardiac-specific loss of GCN5L1 promotes the inhibitory phosphorylation of pyruvate dehydrogenase in vitro and in vivo, a process likely to inhibit glucose oxidation, and that this corresponds to increased myocardial damage following ischemia-reperfusion (I/R) injury.

physiology↗

bioRxiv2023

Validation of GCN5L1/BLOC1S1/BLOS1 Antibodies Using Knockout Cells and Tissue

GCN5L1, also known as BLOC1S1 and BLOS1, is a small intracellular protein involved in a number of key biological processes. Over the last decade, GCN5L1 has been implicated in the regulation of protein lysine acetylation, energy metabolism, endo-lysosomal function, and cellular immune pathways. An increasing number of published papers have used commercially-available reagents to interrogate GCN5L1 function. However, in many cases these reagents have not been rigorously validated, leading to potentially misleading results. In this report we tested several commercially-available antibodies for GCN5L1, and found that two-thirds of those available did not unambiguously detect the protein by western blot in cultured mouse cells or ex vivo liver tissue. These data suggest that previously published studies which used these unverified antibodies to measure GCN5L1 protein abundance, in the absence of other independent methods of corroboration, should be interpreted with appropriate caution.

cell biology↗

bioRxiv2018

Improving preterm newborn identification in low-resource settings with machine learning

BackgroundGlobally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.\n\nMethods and FindingsThis study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3 - 40.3). Among assessed newborns with complete data included in this analysis (n = 458), the median GA by ultrasound was 39.6 weeks (IQR: 38.4 - 40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796.\n\nConclusionsWe identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA.

bioinformatics↗