Gene Ontology (GO)-Driven Inference of Candidate Proteomic Markers Associated with Muscle Atrophy Conditions.

Skeletal muscle homeostasis is essential for the maintenance of a healthy and active lifestyle. Imbalance in muscle homeostasis has significant consequences such as atrophy, loss of muscle mass, and progressive loss of functions. Aging-related muscle wasting, sarcopenia, and atrophy as a consequence of disease, such as cachexia, reduce the quality of life, increase morbidity and result in an overall poor prognosis. Investigating the muscle proteome related to muscle atrophy diseases has a great potential for diagnostic medicine to identify (i) potential protein biomarkers, and (ii) biological processes and functions common or unique to muscle wasting, cachexia, sarcopenia, and aging alone. We conducted a meta-analysis using gene ontology (GO) analysis of 24 human proteomic studies using tissue samples (skeletal muscle and adipose biopsies) and/or biofluids (serum, plasma, urine). Whilst there were few similarities in protein directionality across studies, biological processes common to conditions were identified. Here we demonstrate that the GO analysis of published human proteomics data can identify processes not revealed by single studies. We recommend the integration of proteomics data from tissue samples and biofluids to yield a comprehensive overview of the human skeletal muscle proteome. This will facilitate the identification of biomarkers and potential pathways of muscle-wasting conditions for use in clinics.

Gas chromatography tandem mass spectrometry offers advantages for urinary steroids analysis.

Gas chromatography mass spectrometry has been the lynchpin of clinical assessment of steroid profiles for ∼3 decades. The improvements in assay performance offered by tandem mass spectrometry were assessed. Across the spectrum of glucocorticoid and androgen analytes tested, limits of detection and quantitation were ∼20 fold lower with triple than single quadrupole systems, but the more noticeable improvement was that signal to noise was substantially improved and the linear range wider. These benefits allowed more reliable and concomitant measurement of steroids with substantially different abundances and in smaller volumes of urine.

An in vitro study to determine the impact of lipid emulsion on partitioning of a broad spectrum of drugs associated with overdose.

Background: Intravenous lipid emulsion is recognised as a therapy for rescue in cases of local anaesthetic toxicity, but its use in reversing overdose or toxicity related to other drugs remains the subject of debate. This in vitro study sought to expand our understanding of the importance of partitioning in determining the impact of intravenous lipid emulsion on aqueous free drug concentrations. Methods: Twenty-seven drugs and associated metabolites were screened for the ability of intravenous lipid emulsion to reduce the amount of free drug in the aqueous phase, using specialised cassettes designed for this purpose. The relative amount of drug equilibrating across the membrane from plasma to phosphate-buffered saline was measured, using liquid chromatography-mass spectrometry, at a 6 h timepoint in plasma samples treated with intravenous lipid emulsion and paired, untreated controls. Results: The data obtained were plotted against measures of partition (LogP and cLogD7.4) and with log-transformed non-protein bound drug. There were significant inverse correlations between the capacity for intravenous lipid emulsion to reduce drug detected in the phosphate-buffered saline compartment and LogP and cLogD7.4, and a direct association with log [non-protein-bound drug]. However, a number of drugs showed substantial variance between different plasma samples. Conclusions: Modulation of free drug in the aqueous compartment is broadly predictable by the partition coefficient, although ramipril was identified to be an outlier in this regard. Further mechanistic and clinical exploration is merited to establish a standardised protocol for lipid emulsion therapy.