Dietary protein insufficiency has been linked to excessive TAG storage and non-alcoholic fatty liver disease (NAFLD) in developing countries. Hepatic TAG accumulation following a low-protein diet may be due to altered peroxisomal, mitochondrial and gut microbiota function. Hepatic peroxisomes and mitochondria normally mediate metabolism of nutrients to provide energy and substrates for lipogenesis. Peroxisome biogenesis and activities can be modulated by odd-chain fatty acids (OCFA) and SCFA that are derived from gut bacteria, for example, propionate and butyrate. Also produced during amino acid metabolism by peroxisomes and mitochondria, propionate and butyrate concentrations correlate inversely with risk of obesity, insulin resistance and NAFLD. In this horizon-scanning review, we have compiled available evidence on the effects of protein malnutrition on OCFA production, arising from loss in mitochondrial, peroxisomal and gut microbiota function, and its association with lipid accumulation in the liver. The methyl donor amino acid composition of dietary protein is an important contributor to liver function and lipid storage; the presence and abundance of dietary branched-chain amino acids can modulate the composition and metabolic activity of the gut microbiome and, on the other hand, can affect protective OCFA and SCFA production in the liver. In preclinical animal models fed with low-protein diets, specific amino acid supplementation can ameliorate fatty liver disease. The association between low dietary protein intake and fatty liver disease is underexplored and merits further investigation, particularly in vulnerable groups with dietary protein restriction in developing countries.

Background: Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues, and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms. Summary: Metabolomics provides a unique opportunity to discover novel biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing, and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acylcarnitines, succinic acid, malate, methylhistidine, aspartate, methionine, and phenylalanine. In terms of differentiating DCM from ischemic cardiomyopathy, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acylcarnitines, isoleucine and linoleic acid, and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of branch-chain amino acid, glycolysis, tricarboxylic acid cycle, and triacylglycerol and pentose phosphate metabolism, which have the therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies, and the fact that studies have so far been limited to case-control study designs. Key Messages: Metabolites have close proximity to disease phenotype. With recent advances in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multicenter studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

Oxysterols are critical regulators of inflammation and cholesterol metabolism in cells. They are oxidation products of cholesterol and may be differentially metabolised in subcellular compartments and in biological fluids. New analytical methods are needed to improve our understanding of oxysterol trafficking and the molecular interplay between the cellular compartments required to maintain cholesterol/oxysterol homeostasis. Here we describe a method for isolation of oxysterols using solid phase extraction and quantification by liquid chromatography-mass spectrometry, applied to tissue, cells and mitochondria.

The link between male diet and sperm quality has received significant investigation. However, the impact diet and dietary supplements have on the testicular environment has been examined to a lesser extent. Here, we establish the impact of a sub-optimal low protein diet (LPD) on testicular morphology, apoptosis and serum fatty acid profiles. Furthermore, we define whether supplementing a LPD with specific methyl donors abrogates any detrimental effects of the LPD. Male C57BL6 mice were fed either a control normal protein diet (NPD; 18% protein; n = 8), an isocaloric LPD (LPD; 9% protein; n = 8) or an LPD supplemented with methyl donors (MD-LPD; choline chloride, betaine, methionine, folic acid, vitamin B12; n = 8) for a minimum of 7 weeks. Analysis of male serum fatty acid profiles by gas chromatography revealed elevated levels of saturated fatty acids and lower levels of mono- and polyunsaturated fatty acids in MD-LPD males when compared to NPD and/or LPD males. Testes of LPD males displayed larger seminiferous tubule cross section area when compared to NPD and MD-LPD males, while MD-LPD tubules displayed a larger luminal area. Furthermore, TUNNEL staining revealed LPD males possessed a reduced number of tubules positive for apoptosis, while gene expression analysis showed MD-LPD testes displayed decreased expression of the pro-apoptotic genes Bax, Csap1 and Fas when compared to NPD males. Finally, testes from MD-LPD males displayed a reduced telomere length but increased telomerase activity. These data reveal the significance of sub-optimal nutrition for paternal metabolic and reproductive physiology.

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age‐related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well‐established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post‐pubertal offspring (3–6.5 y) of MO mothers ( n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography‐mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue‐specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue‐specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age‐related metabolic diseases later in life.

Gas chromatography–coupled mass spectrometry (GC–MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC–MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.