Arachidonic acid supplementation transiently augments the acute inflammatory response to resistance exercise in trained men

Strenuous exercise can result in skeletal muscle damage, leading to the systemic mobilization, activation and intramuscular accumulation of blood leukocytes. Eicosanoid metabolites of arachidonic acid (ARA) are potent inflammatory mediators, but whether changes in dietary ARA intake influence exercise-induced inflammation is not known. This study investigated the effect of 4 weeks of dietary supplementation with 1.5 g/day ARA (n=9, 24 {plus minus} 1.5 years) or corn-soy oil placebo (n=10, 26 {plus minus} 1.3 years) on systemic and intramuscular inflammatory responses to an acute bout of resistance exercise (8 sets each of leg press and extension at 80% 1RM) in previously trained men. Whole EDTA blood, serum, peripheral blood mononuclear cells (PMBCs), and skeletal muscle biopsies were collected prior to exercise, immediately post-exercise and at 2, 4 and 48 h of recovery. ARA supplementation resulted in higher exercise-stimulated serum creatine kinase activity (incremental area under the curve (iAUC) p=0.046) and blood leukocyte counts (iAUC for total white cells p<0.001, neutrophils p=0.007, monocytes p=0.015). The exercise-induced fold change in PBMC mRNA expression of interleukin-1 beta (IL1B), CD11b (ITGAM) and neutrophil elastase (ELANE), as well as muscle mRNA expression of the chemokines interleukin-8 (CXCL8) and monocyte chemoattractant protein 1 (CCL2) was also greater in the ARA group than placebo. Despite this, ARA supplementation did not influence the histological presence of leukocytes within muscle, perceived muscle soreness or the extent and duration of muscle force loss. These data show that ARA supplementation transiently increased the inflammatory response to acute resistance exercise, but did not impair recovery

Metabolic Disease Risk Alters Circulating Peripheral Blood Mononuclear Cell microRNAs in Response to A High Glycemic Meal

Background: High glycemic diets have been shown to exacerbate the risk of cardio-metabolicdisease in individuals with pre-existing disease risk, including obesity and insulin resistance,common to the Metabolic Syndrome (MetS). [...

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.

Funder: Alzheimer's Society; Id: http://dx.doi.org/10.13039/501100000320Funder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100007155Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations

Effect of a Tailored Dietary Intervention with High or Standard Protein Intake on B-Vitamin and One Carbon Metabolism Status in Healthy Older Males: A 10 Week Randomised Controlled Trial

Background: Maintaining optimal status of folate and metabolically [...

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Additional file 2: Figure S1. of Dietary supplementation with bovine-derived milk fat globule membrane lipids promotes neuromuscular development in growing rats

Real time RT‐PCR analysis of Agrn and Z+ Agrn expression in rat soleus muscle. Figure S2. Real time RT‐PCR analysis of synaptophysin expression in rat soleus muscle. A (PDF 369 kb