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N-linked glycosylation of the M-protein variable region: Glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma.

ABSTRACT: Multiple Myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity for a site specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to healthy individuals, pointing towards an association. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias and possibly other fields.

INSTRUMENT(S): timsTOF Pro 2

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

DISEASE(S): Multiple Myeloma

SUBMITTER: Hans Wessels

LAB HEAD: Hans Wessels

PROVIDER: PXD040718 | Pride | 2023-06-09

REPOSITORIES: Pride

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Project description:we aimed to provide an objective roadmap for histone sample preparation wherein the most notable findings and most important metrics were highlighted. This roadmap included a Histone Coverage Tool that allows researchers to make a snapshot of public or in-house data in search of a protocol that suits their specific needs. However, despite the fact that the coverage provided by a sample preparation workflow is pivotal to answer specific research questions, it should be noted that other metrics such as enzyme specificity and workflow variability can be equally important. Moreover, when it comes to histone sample preparation there are multiple additional challenges and pitfalls compared to regular bottom-up proteomics workflows. First there is the abundance and unequal distribution of arginine and lysine cleavage sites, which makes it virtually impossible to cover the entire histone code with a single bottom-up workflow. The most widely spread workaround for the abundance of cleavage sites is derivatization of lysine residues used to block tryptic digest and create longer peptides. However, this also introduces chemical noise, which obscures biological PTMs, while lowering sensitivity due to diluted signal. The most obvious alternative is an arginine specific digest. However, on the one hand, derivatization of lysine residues not only increases peptide length but also increases retention on the LC-system, while decreasing charge state distribution of the precursor ions. The latter presumably reduces instrumental variation of the workflow, which is remarkable since this source of variation is generally assumed to be constant in the field of LC-MS/MS based proteomics. On the other hand, lysine derivatization also increases the singly charged precursor fraction, which is usually not fragmented in regular data dependent acquisitions, thus increasing the risk of losing annotations. It is clear that not one histone sample preparation workflow will fit all research questions. However, we hope that this manuscript will guide researchers in making informed decisions about the many pitfalls and trade-offs involved in optimizing or assessing histone sample preparation workflows.

Dataset Information

N-linked glycosylation of the M-protein variable region: Glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma.

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