The wondrous and worrying diversity of the N-glycans of Chlorella food supplements
Mocsai, R.; Helm, J.; Polacsek, K.; Stadlmann, J.; Altmann, F.
Show abstract
N-glycans have recently emerged as highly varied elements of Chlorella strains and products. Four years and many samples later, the ever-growing N-glycan diversity shall be revisited in the light of concepts of species definition and product authenticity. N-glycans of commercial products were analyzed by matrix-assisted time-of-flight mass spectrometry (MALDI-TOF MS) supported by chromatography on porous graphitic carbon with mass spectrometric detection. While 36% of 172 products were labeled C. vulgaris, only few had matching N-glycan patterns. 5 and 20 % of the products matched with C. sorokiniana strains SAG 211-8k and SAG 211-34, respectively, which, however, carry entirely different structures. 41 % presented with four frequently occurring glyco-types while 26 % of the samples showed unique or rare N-glycan patterns. The rest presented what could be taken as a C. vulgaris type N-glycan pattern. Identical masses derive from different structures in many cases. By no means do we want to question the presumed health benefits of the products or the honest intentions of manufacturers. We rather wish to raise awareness for the fascinating but also worrying variety of microalgal N-glycans and suggest it as a means for defining product identity and taxonomic assignments.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Leveraging immonium ions for identifying and targeting acyl-lysine modifications in proteomic datasets 92%
- LC-MS/MS characterization of SOBERANA(R)02, a receptor binding domain-tetanus toxoid conjugate vaccine against SARS-CoV-2 92%
- Monitoring Functional Post-Translational Modifications Using a Data-Driven Proteome Informatic Pipeline 92%
"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.