Platelet integrity and function critically depend on lipid composition. However, the lipid inventory in platelets was hitherto not quantified. Here, we examined the lipidome of murine platelets using lipid-category tailored protocols on a quantitative lipidomics platform. We could show that the platelet lipidome comprises almost 400 lipid species and covers a concentration range of 7 orders of magnitude. A systematic comparison of the lipidomics network in resting and activated murine platelets, validated in human platelets, revealed that <20% of the platelet lipidome is changed upon activation, involving mainly lipids containing arachidonic acid. Sphingomyelin phosphodiesterase-1 (Smpd1) deficiency resulted in a very specific modulation of the platelet lipidome with an order of magnitude upregulation of lysosphingomyelin (SPC), and subsequent modification of platelet activation and thrombus formation. In conclusion, this first comprehensive quantitative lipidomic analysis of platelets sheds light on novel mechanisms important for platelet function, and has therefore the potential to open novel diagnostic and therapeutic opportunities.
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Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
In addition, the alignment with the ␣/-hydrolase fold region indicated that PhaG belongs to ␣/-hydrolase superfamily. Accordingly, CD analysis suggested a secondary structure composition of 29% ␣؊helix, 22% -sheet, 18% -turn, and 31% random coil. Site-specific mutagenesis of seven highly conserved amino acid residues (Asp-60, Ser-102, His-177, Asp-182, His-192, Asp-223, His-251) was used to validate the protein model and to investigate organization of the transacylase active site. Only the D182(A/E) mutation was permissive with about 30% specific activity of the wild type enzyme. Furthermore, this mutation caused a change in substrate specificity, indicating a functional role in substrate binding. The serine-specific agent phenylmethylsulfonyl fluoride (PMSF) or the histidinespecific agent diethylpyrocarbonate (DEPC) caused inhibition of 3-hydroxyacyl transfer to holo-ACP, and the S102(A/T) or H251(A/R) PhaG mutant was incapable of catalyzing 3-hydroxyacyl transfer, suggesting that these residues are part of a catalytic triad.
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