Visceral adipose tissue, liver, soleus muscle and plasma lipids were measured by mass spectrometry at the Lipotype shotgun lipidomics platform. Samples processing, lipid extraction, spectra acquisition and data processing and normalization were as described in Surma et al. 2015. The internal standard mixture contained: cholesterol D6 (chol), cholesterol ester 20:0 (CE), ceramide 18:1;2/17:0 (Cer), diacylglycerol 17:0/17:0 (DAG), phosphatidylcholine 17:0/17:0 (PC), phosphatidylethanolamine 17:0/17:0 (PE), lysophosphatidylcholine 12:0, (LPC) lysophosphatidylethanolamine 17:1 (LPE), triacylglycerol 17:0/17:0/17:0 (TAG) and sphingomyelin 18:1;2/12:0 (SM). Samples were analyzed by direct infusion in a QExactive mass spectrometer (Thermo Scientific) in a single acquisition. Tandem mass-spectrometry (MS/MS) was triggered by an inclusion list encompassing corresponding MS mass ranges scanned in 1 Da increments. MS and MS/MS data were combined to monitor CE, DAG and TAG ions as ammonium adducts; PC, PC O-, as acetate adducts; and PE, PE O- and PI as deprotonated anions. MS only was used to monitor LPE as deprotonated anion; Cer, SM and LPC as acetate adducts and cholesterol as ammonium adduct. Data post-processing and normalization were performed using an in-house developed data management system. Only lipid identifications with a signal-to-noise ratio >5 and a signal intensity 5-fold higher than in corresponding blank samples were considered for further analysis. The median coefficient of lipid subspecies variation (RSD), as accessed by the repeated analysis of reference samples, was 7.5%. Lipid species with ≥25% missing values across all available plasma samples were removed from the data set. For the lipids that remained in the data sets, missing values were imputed using a random forest approach, applying the function missForest from the R package missForest, with default parameters. Data were then normalized to the total signal (data = data / rowsums(data) *100). Data were not log transformed or further normalized. As for transcriptomics data, a WGCNA was run using signed network, Pearson correlation, soft thresholding power of 20 was used for plasma, liver and muscle, soft power of 12 was used for adipose, minimum module size of 5 for all tissues. To be consistent with transcriptomics data, 18 mice groups were defined by the three strains, two diets and three time points of harvesting. Module eigenvalues were summarized per mouse group using the mean.

Mehl, Thorens et al present a multiomics study aimiing to identify the pathways that are coordinately regulated in pancreatic b-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis.

Additional data sets (transcriptomics) pertaining to the same study have been deposited in other public data bases: in the Gene Expression Omnibus (NCBI GEO) with the accession number GSE164672, GSE140369 and GSE164673.