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Monthly Archives

February 2023

Inhibition of mTOR improves malnutrition induced hepatic metabolic dysfunction

Abstract

Severe malnutrition accounts for half-a-million deaths annually in children under the age of five. Despite improved WHO guidelines, inpatient mortality remains high and is associated with metabolic dysfunction. Previous studies suggest a correlation between hepatic metabolic dysfunction and impaired autophagy. We aimed to determine the role of mTORC1 inhibition in a murine model of malnutrition-induced hepatic dysfunction. Wild type weanling C57/B6 mice were fed a 18 or 1% protein diet for two weeks. A third low-protein group received daily rapamycin injections, an mTORC1 inhibitor. Hepatic metabolic function was assessed by histology, immunofluorescence, gene expression, metabolomics and protein levels. Low protein-fed mice manifested characteristics of severe malnutrition, including weight loss, hypoalbuminemia, hypoglycemia, hepatic steatosis and cholestasis. Low protein-fed mice had fewer mitochondria and showed signs of impaired mitochondrial function. Rapamycin prevented hepatic steatosis, restored ATP levels and fasted plasma glucose levels compared to untreated mice. This correlated with increased content of LC3-II, and decreased content mitochondrial damage marker, PINK1. We demonstrate that hepatic steatosis and disturbed mitochondrial function in a murine model of severe malnutrition can be partially prevented through inhibition of mTORC1. These findings suggest that stimulation of autophagy could be a novel approach to improve metabolic function in severely malnourished children.

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Organoids as a model to study intestinal and liver dysfunction in severe malnutrition

ABSTRACT
Hospitalized children with severe malnutrition face high mortality rates and often suffer from hepatic and intestinal dysfunction, with negative impacts on their survival. New treatments cannot be developed without
understanding the underlying pathophysiology. We have established and characterized translational organoid
models of severe malnutrition of the liver and the intestine. In these models, amino acid starvation recapitulates
the expected organ-specific functional changes (e.g., hepatic steatosis, barrier dysfunction) accompanied by
reduced mitochondrial and peroxisomal proteins, and altered intestinal tight junction proteins. Resupplementation of amino acids or pharmacological interventions with rapamycin or fenofibrate lead to partial recovery. Restoration of protein levels aligned with signs of improved peroxisomal function in both organoids, and increased mitochondrial proteins and tight junction protein claudin-3 in intestinal organoids. We
present two organoid models as novel tools to gain mechanistic insights and to act as a testing platform for
potential treatments for intestinal and hepatic dysfunction in severe malnutrition.

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The role of the tryptophan-NAD + pathway in a mouse model of severe malnutrition induced liver dysfunction

Abstract

Mortality in children with severe malnutrition is strongly related to signs of metabolic dysfunction, such as hypoglycemia. Lower circulating tryptophan levels in children with severe malnutrition suggest a possible disturbance in the tryptophan-nicotinamide adenine dinucleotide (TRP-NAD+) pathway and subsequently in NAD+  dependent metabolism regulator sirtuin1 (SIRT1). Here we show that severe malnutrition in weanling mice, induced by 2-weeks of low protein diet feeding from weaning, leads to an impaired TRP-NAD+  pathway with decreased NAD+ levels and affects hepatic mitochondrial turnover and function. We demonstrate that stimulating the TRP-NAD+  pathway with NAD+  precursors improves hepatic mitochondrial and overall metabolic function through SIRT1 modulation. Activating SIRT1 is sufficient to induce improvement in metabolic functions. Our findings indicate that modulating the TRP-NAD+  pathway can improve liver metabolic function in a mouse model of severe malnutrition. These results could lead to the development of new interventions for children with severe malnutrition.

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Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

Background

A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC).

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