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The GlycoPaSER prototype as a real-time N-glycopeptide identification tool based on the PaSER parallel computing platform

ABSTRACT: Real-time database searching allows for simpler and automated proteomics workflows as it eliminates technical bottlenecks in high throughput experiments. Most importantly, it enables results dependent acquisition (RDA) where search results can be used to guide data acquisition during acquisition. This is especially beneficial for glycoproteomics since the wide range of physicochemical properties of glycopeptides lead to a wide range of optimal acquisition parameters. We established here the GlycoPaSER prototype by extending the Parallel Search Engine in Real-time (PaSER) functionality for real-time glycopeptide identification from fragmentation spectra. Glycopeptide fragmentation spectra were decomposed into peptide- and glycan-moiety spectra using common N-glycan fragments. Each moiety was subsequently identified by a specialized algorithm running in real-time. GlycoPaSER can keep up with the rate of data acquisition for real-time analysis with similar performance to other glycoproteomics software and produces results that are in line with literature reference data. The GlycoPaSER prototype presented here provides the first proof-of-concept for real-time glycopeptide identification that unlocks future development of RDA technology to transcend data acquisition.

INSTRUMENT(S): timsTOF Pro 2

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Gad Armony

LAB HEAD: Hans J.C.T. Wessels

PROVIDER: PXD040716 | Pride | 2023-03-13

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	Controle_19_Slot1-19_1_8827.d.zip	Other
	Controle_19_Slot1_19_1_8827_glycoPaSER_results.tsv	Tabular
	Controle_20_Slot1-20_1_8834.d.zip	Other
	Controle_20_Slot1_20_1_8834_glycoPaSER_results.tsv	Tabular
	Controle_25_Slot1-25_1_8698.d.zip	Other

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Project description:Background: The vascular wall of small arteries is heavily affected by high blood pressure. However, the underlying mechanisms causing vascular changes are not fully elucidated. Using a novel data-independent acquisition mass spectrometry (DIA-MS) approach, we aimed to determine the proteomic changes in small mesenteric arteries during early-onset high blood pressure in a rat model of hypertension. Methods: Snap frozen small mesenteric and renal arteries from the spontaneous hypertension rat (SHR) model and Wistar Kyoto (WKY) control rats were collected from two time points (6- and 12-weels of age) and analyzed by a label free quantitative DIA-MS workflow. Mesenteric arteries from Wister Hannover rats were included as an additional control to clarify genetic drift caused by selective inbreeding. Results: We identified a total of 3956 consistent proteins in the mesenteric artery wall and found that 286 proteins were significantly regulated in 12-weeks old SHRs compared to WKY controls. Comparing to an in silico matrisome database, we identified 38 extracellular matrix-associated proteins that could distinguish SHRs from WKY controls. Furthermore, when comparing the significantly regulated proteins identified in mesenteric and renal arteries, we identified 18 proteins, including Serpina3l, Igg-2a, ENSRNOG00000049829, Acyp2, Enpp3, Lss, Acaa1a, Basp1, an isoform of Basp1, Flot1, Flot2, Gstt1, Nit1, Ppid, Ikbkap, Poglut3, P4ha2 and Usp15, that were changed in both vascular beds. These proteins were associated with vital cellular processes, such as dyslipidemia, protease inhibition, remodeling and generation of reactive oxygen species. Majority of the identified proteins and pathways were associated with hypertension, and mapping the underlying changes help understanding the pathological processes occurring in the arterial wall during early-onset hypertension. Conclusions: Our data provides an in-depth analysis of the proteomic architecture of the mesenteric and renal artery wall from SHRs and WKY control rats. We identified 18 novel candidate proteins that highlights critical changes in small arteries of the SHR.

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The GlycoPaSER prototype as a real-time N-glycopeptide identification tool based on the PaSER parallel computing platform

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