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Ng, S. Q.

Publications and source records attributed to Ng, S. Q..

2 recordsLinked to original sources

Glucose and fructose differently mediate alcohol cocktail drinking in female and male rats: interaction of glucose and alcohol on post-ingestive behavior

Sweetened alcoholic beverages are thought to contribute to developing Alcohol Use Disorder by increasing palatability. One monosaccharide, glucose, readily enters the brain more than fructose and directly impacts the activity of central neurons. The objective of this study is to determine the impact of glucose versus fructose on alcohol drinking patterns in female and male rats. Rats drank alcohol cocktails (1.25%-10%) containing either glucose or fructose (10%) in 4-hour sessions. We sought to parse orosensory effects from post-ingestive central effects by analyzing drinking microstructure. We compared measures of palatability and post-ingestive feedback between early and later in the session when brain levels of alcohol and glucose are different. We found that rats of both sexes drank more low alcohol glucose cocktails than cocktails containing fructose by volume and by overall calories. When considering the dose of alcohol, glucose potentiated alcohol intake by shifting the dose-response curve leftward compared to similar fructose cocktails. We found that drinking patterns associated with palatability remained stable for both types of cocktails over the entire drinking session. In contrast, post-ingestive behavior related to brain mediated satiety or positive feedback showed a greater influence of the session time, as well as a greater interaction with sex. Overall, our results suggest that glucose and alcohol interact to impact central regulation of cocktail drinking. This highlights that the type of sugar within cocktails interacts and ultimately have different effects on brain regulated alcohol drinking.

pharmacology and toxicology↗

Discovery of multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution, where they represent important therapeutic targets. However, the full spectrum, characteristics and regulation of existing metabolic switches are unknown. We propose that metabolic switches can be recognised by locating large alternating gene expression patterns and associate them with specific metabolic states. We developed a method to identify interspersed genesets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on major breast cancer transcriptome datasets we discovered a series of gene sets with switch-like behaviour, predicting mitochondrial content, activity and central carbon fluxes in tumours associated with different switch positions. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates and were ultimately extended by geneset analysis to link metabolic alterations to cellular states, thus predicting tumour pathology, prognosis and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Statement of significanceWe present a novel method to identify the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation. We illustrate the power of the method by stratifying breast cancer into metabolic subtypes, predicting their biology, architecture and clinical outcome.

systems biology↗