liputils: a Python module to manage individual fatty acid moieties from complex lipids

Effect of Diets on Plasma and Aorta Lipidome: A Study in the apoE Knockout Mouse Model

SCOPE

Specific lipid molecules circulating in plasma at low concentrations have emerged as biomarkers of atherosclerotic risk. The aim of the present study is that of evaluating, in an athero-prone mouse model, how different diets can affect plasma and aorta lipidome.

METHODS AND RESULTS

Thirty-six apoE knockout mice are divided in three groups and feed 12 weeks with diets differing for cholesterol and fatty acid content. Atherosclerosis is measured at the aortic sinus and aorta. Lipids are quantified in plasma and aorta with mass spectrometry. The cholesterol content of the diets is the main driver of lipid accumulation in plasma and aorta. The fatty acid composition of the diets affects plasma levels both of essential (linoleic acid) and nonessential (myristic and arachidonic acid) ones. Lipidomics show a comparable distribution, in plasma and aorta, of the main lipid components of oxidized LDL, including cholesteryl esters and lysophosphatidylcholines. Interestingly, lactosylceramide, glucosyl/galactosylceramide, and individual ceramide species are found to accumulate in diseased aortic segments.

CONCLUSION

Both the cholesterol and fatty acid content of the diets profoundly affect plasma lipidome. Aorta lipidome is likewise affected with the accumulation of specific lipids known as markers of atherosclerosis.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

reString: an open-source Python software to perform automatic functional enrichment retrieval, results aggregation and data visualization

Functional enrichment analysis is an analytical method to extract biological insights from gene expression data, popularized by the ever-growing application of high-throughput techniques. Typically, expression profiles are generated for hundreds to thousands of genes/proteins from samples belonging to two experimental groups, and after ad-hoc statistical tests, researchers are left with lists of statistically significant entities, possibly lacking any unifying biological theme. Functional enrichment tackles the problem of putting overall gene expression changes into a broader biological context, based on pre-existing knowledge bases of reference: database collections of known expression regulation, relationships and molecular interactions. STRING is among the most popular tools, providing both protein-protein interaction networks and functional enrichment analysis for any given set of identifiers. For complex experimental designs, manually retrieving, interpreting, analyzing and abridging functional enrichment results is a daunting task, usually performed by hand by the average wet-biology researcher. We have developed reString, a cross-platform software that seamlessly retrieves from STRING functional enrichments from multiple user-supplied gene sets, with just a few clicks, without any need for specific bioinformatics skills. Further, it aggregates all findings into human-readable table summaries, with built-in features to easily produce user-customizable publication-grade clustermaps and bubble plots. Herein, we outline a complete reString protocol, showcasing its features on a real use-case.