CoA protects against the deleterious effects of caloric overload in Drosophila

Musselman, Laura Palanker; Fink, Jill L.; Baranski, Thomas J.

doi:10.1194/jlr.m062976

Cited by 26 publications

(19 citation statements)

References 54 publications

(43 reference statements)

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“…Diet-induced insulin resistance can be produced in Drosophila by chronic overfeeding using high-calorie diets (28)(29)(30)(31)(32). High-sugar (HS)-fed larvae and adult flies develop obesity, dyslipidemia, insulin resistance, decreased fertility, and cardiovascular disease (29)(30)(31)(32)(33)(34). Previous studies showed a role for the fat body, a lipid storage tissue akin to mammalian adipose tissue, especially in animals on HS diets.…”

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confidence: 99%

“…Fat bodies increased lipid storage and developed insulin resistance after HS feeding, compared with control-fed animals. Fat body lipid storage is required for tolerance of HS feeding; genetically lean animals typically exhibit poor survival on HS diets (33,34). However, roles for the fat body other than lipid storage have been poorly characterized in the context of overnutrition.…”

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confidence: 99%

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A Complex Relationship between Immunity and Metabolism in Drosophila Diet-Induced Insulin Resistance

Musselman

Fink

Grant

et al. 2018

Molecular and Cellular Biology

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Systemic and tissue-specific insulin resistance have been described in , and are accompanied by many indicators of metabolic disease. The downstream mediators of insulin-resistant pathophysiology remain unclear. We analyzed insulin signaling in the fat body using loss- and gain-of-function. When expression of the sole Insulin receptor (InR) was reduced in larval fat bodies, animals exhibited developmental delay and reduced size in a diet-dependent manner. Fat body InR knockdown also led to reduced survival on high-sugar diets. To look downstream of InR at potential mediators of insulin resistance, RNA-seq studies in insulin-resistant fat bodies revealed differential expression of genes, including those involved in innate immunity. Obesity-associated insulin resistance led to increased susceptibility of flies to infection, as in humans. Reduced innate immunity was dependent on fat body InR expression. The peptidoglycan recognition proteins (PGRPs) PGRP-SB2 and PGRP-SC2 were selected for further study based on differential expression studies. Downregulating PGRP-SB2 selectively in the fat body protected animals from the deleterious effects of overnutrition, whereas downregulating PGRP-SC2 produced InR-like phenotypes. These studies extend earlier work linking the immune and insulin signaling pathways and identify new targets of insulin signaling that could serve as potential drug targets to treat type 2 diabetes.

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confidence: 99%

A Complex Relationship between Immunity and Metabolism in Drosophila Diet-Induced Insulin Resistance

Musselman

Fink

Grant

et al. 2018

Molecular and Cellular Biology

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“…A nutrient produced by the rumen microbiota is pantothenate. Pantothenate plays a significant role in the metabolism of fatty acids in ruminants and other species 18,19 . In this study, pantothenate was identified as a potential biomarker of RFI and was shown to be significantly enriched in low-RFI compared to high-RFI steers.…”

Section: Discussionmentioning

confidence: 99%

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Clemmons

Martino

Powers

et al. 2019

Preprint

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The rumen microbiome is critical in ruminant nutrition and contributes to nutrient utilization and feed efficiency in cattle. Therefore, the objective of this study was to interrogate microbial and biochemical factors affecting divergences in feed efficiency in Angus steers using 16S amplicon sequencing and untargeted metabolomics. Average residual feed intake (RFI) was calculated, and steers were divided into low-and high-RFI groups. Features were ranked in relation to RFI through supervised machine learning on microbial and metabolite compositions. Residual feed intake was associated with several attributes of the rumen bacterial community. Low-RFI steers were associated with decreased bacterial α -(P=0.03) and β -diversity (P<0.001). Several serum metabolites were associated with RFI. Based on fold change (high/low RFI), low-RFI steers had greater abundances of pantothenate (P=0.02). Machine learning on RFI was predictive of both serum metabolomic signature and rumen bacterial composition (AUC ≥ 0.7). Log-ratio proportions of the bacterial classes Flavobacteriia over Fusobacteriia were enriched in low-RFI steers (F=6.8, P=.01). Greater proportions of pantothenate-producing bacteria, such as Flavobacteriia, and/or reductions in Fusobacteriia may result in improved nutrient utilization in low-RFI steers. Pantothenate and Flavobacteriia may serve as potentially novel biomarkers to assess or predict feed efficiency in Angus steers.

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“…Moreover, the power provided by measuring multiple metabolites and entire pathways can allow the discovery of important aspects of regulation that might be missed otherwise. For example, metabolomic analysis allowed for the identification of coenzyme A as protecting flies against the toxicity of a high sugar diet (Palanker Musselman et al 2016). Metabolomics can also be invaluable for pinpointing the metabolic defects in a mutant.…”

Section: Stable Isotope Tracer Analysis 1179mentioning

confidence: 99%

“…Superimposed on these genotypic backgrounds are the impact of many environmental factors that can affect metabolism (Reed et al 2014;Williams et al 2015). Some of these, such as diet, have been studied using metabolomic approaches in Drosophila, whereas others, such as the effect of epigenetic inheritance or the gut microbiome on the metabolome, remain to be characterized (Heinrichsen et al 2014;Laye et al 2015;Williams et al 2015;Palanker Musselman et al 2016). Thus, close attention needs to be paid to genetic background when designing metabolomic studies of mutants, ideally using multiple control genotypes for internal confirmation.…”

Section: Stable Isotope Tracer Analysis 1179mentioning

confidence: 99%

Metabolomic Studies in Drosophila

2017

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Metabolomic analysis provides a powerful new tool for studies of physiology. This approach allows investigators to detect thousands of chemical compounds in a single sample, representing the combined contributions of gene expression, enzyme activity, and environmental context. Metabolomics has been used for a wide range of studies in, often providing new insights into gene function and metabolic state that could not be obtained using any other approach. In this review, we survey the uses of metabolomic analysis since its entry into the field. We also cover the major methods used for metabolomic studies in and highlight new directions for future research.

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CoA protects against the deleterious effects of caloric overload in Drosophila

Cited by 26 publications

References 54 publications

A Complex Relationship between Immunity and Metabolism in Drosophila Diet-Induced Insulin Resistance

A Complex Relationship between Immunity and Metabolism in Drosophila Diet-Induced Insulin Resistance

Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Metabolomic Studies in Drosophila

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