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Mitochondria and endoplasmic reticulum (ER) are physically and functionally connected. This close interaction, via mitochondria-associated membranes, is increasingly explored and supports the importance of studying these two organelles as a whole. Metabolomics and lipidomics are powerful approaches for the exploration of metabolic pathways that may be useful to provide deeper information on these organelles' functions, dysfunctions, and interactions. We developed a quick and simple experimental procedure for the purification of a mitochondria-ER fraction from human fibroblasts. We applied combined metabolomics and lipidomics analyses by mass spectrometry with excellent reproducibility. Seventy-two metabolites and 418 complex lipids were detected with a mean coefficient of variation around 12%, among which many were specific to the mitochondrial metabolism. Thus this strategy based on robust mitochondria-ER extraction and "omics" combination will be useful for investigating the pathophysiology of complex diseases.

In recent years, the number of investigations based on nontargeted metabolomics has increased, although often without a thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated nontargeted metabolomics strategy with pipeline for unequivocal identification of metabolites using the MSMLS molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS, and MS/MS spectra. A UHPLC-HRMS Q-Exactive method was developed, and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from remote ischemic pre-conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy, and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites' VIP values, and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the metabolomics standard initiative and confirmed by both RPLC and HILIC-HRMS.

The use of anabolic agents in livestock production is a subject of much concern. Although prohibited for more than 20 years within the EU, growth promoting practices are still widely suspected. To meet the current challenges for detecting illicit practices, 'omics' strategies have recently been demonstrated as important new investigative tools. These investigations, based on the observation of physiological disturbances, mainly in urine, demonstrated the possibility to monitor biomarkers enabling high throughput determination of animal status in terms of hormonal treatment. In this context, serum was investigated for the first time as an alternative and potential complementary sample type. A metabolomic approach based on liquid chromatography coupled to high resolution mass spectrometry, was exploited in order to, highlight metabolic perturbations in serum of Revalor-XS (trenbolone acetate/estradiol) implanted bovines. Univariate and multivariate statistical analyses were carried out to establish descriptive and predictive models. These models enabled the discrimination of anabolised from control animals, and highlighted a number of metabolites which contributed the most in the observed discrimination. Further, a screening model combining a set of selected markers intensities was generated and it successfuly classified animals according to their status, up to 4 weeks post Revalor-XS implant. This research indicates, for the first time, that serum metabolomics has an important role in screening to detect for anabolic misuse in bovines. [ABSTRACT FROM AUTHOR]