Back

Simple, rapid, and sensitive quantification of dolichyl phosphates using phosphate methylation and reverse-phase liquid chromatography-high resolution mass spectrometry

Kale, D.; Kikul, F.; Phapale, P.; Beedgen, L.; Thiel, C.; Bruegger, B.

2022-09-15 biochemistry
10.1101/2022.09.14.504994 bioRxiv
Show abstract

Dolichyl monophosphates (DolPs) are essential lipids in glycosylation pathways that are highly conserved across almost all domains of life. The availability of DolP is crucial for all glycosylation processes, as these lipids serve as membrane-anchored building blocks that various types of glycosyltransferases use to generate complex post-translational modifications of proteins and lipids. Analysis of DolP species by reverse-phase liquid chromatography-mass spectrometry (RPLC-MS) has remained challenging due to their very low abundance and wide range of lipophilicities. Until now, a method for the simultaneous qualitative and quantitative assessment of DolP species from biological membranes has been lacking. Here we describe a novel approach based on simple sample preparation, rapid and efficient trimethylsilyl diazomethane (TMSD)-dependent phosphate methylation and RPLC-MS analysis for quantification of DolP species with different isoprene chain lengths. We used this workflow to selectively quantify DolP species from lipid extracts derived of Saccharomyces cerevisiae, HeLa and human skin fibroblasts from steroid 5--reductase 3-congenital disorders of glycosylation (SRD5A3-CDG) patients and healthy controls. Integration of this workflow with global lipidomics analyses will be a powerful tool to further our understanding of the role of DolPs in pathophysiological alterations of metabolic pathways downstream of HMG-CoA reductase, associated with CDGs, hypercholesterolemia, neurodegeneration, and cancer.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.