Peptide Location Fingerprinting Reveals Tissue Region-Specific Differences in Protein Structures in an Ageing Human Organ

Abstract

In ageing tissues, long-lived extracellular matrix (ECM) proteins are susceptible to the accumulation of structural damage due to diverse mechanisms including glycation, oxidation and protease cleavage. Peptide location fingerprinting (PLF) is a new mass spectrometry (MS) analysis technique capable of identifying proteins exhibiting structural differences in complex proteomes. PLF applied to published young and aged intervertebral disc (IVD) MS datasets (posterior, lateral and anterior regions of the annulus fibrosus) identified 268 proteins with age-associated structural differences. For several ECM assemblies (collagens I, II and V and aggrecan), these differences were markedly conserved between degeneration-prone (posterior and lateral) and -resistant (anterior) regions. Significant differences in peptide yields, observed within collagen I α2, collagen II α1 and collagen V α1, were located within their triple-helical regions and/or cleaved C-terminal propeptides, indicating potential accumulation of damage and impaired maintenance. Several proteins (collagen V α1, collagen II α1 and aggrecan) also exhibited tissue region (lateral)-specific differences in structure between aged and young samples, suggesting that some ageing mechanisms may act locally within tissues. This study not only reveals possible age-associated differences in ECM protein structures which are tissue-region specific, but also highlights the ability of PLF as a proteomic tool to aid in biomarker discovery.

Time-lapsed proteomics reveals a role for the novel protein, SNED1, in modulating ECM composition and protein folding

“…The copyright holder for this preprint this version posted January 14, 2022. ; https://doi.org/10.1101/2022.01.13.476092 doi: bioRxiv preprint biochemical (e.g., glycation [53]) or structural (e.g., degradation, cross-linking) post-translational modifications, leading to alteration of the structure of individual ECM proteins and that of the ECM meshwork. Bottom-up proteomic and peptide mapping have contributed to appreciate and evaluate the extent of these changes for example during aging and photoaging of the skin [19][20][21]27,54], fibrosis [11,26,27], or cancer progression [55], but much more remains to be discovered.…”

“…This method infers protein presence based on the identification of the most abundant peptides in any given sample. Proteomics has revealed that the ECM of any given tissue is made of well over 100 distinct proteins and has allowed the identification of ECM protein signatures characteristic of physiological processes (e.g., development [18], aging [19][20][21][22]) and pathological states (e.g., cardiovascular diseases [23], cancer [24], fibrosis [25][26][27]). However, existing proteomic approaches present limitations.…”

Peptide location fingerprinting identifies species- and tissue-conserved structural remodelling of proteins as a consequence of ageing and disease

“…PLF analyses of all datasets, and associated protein schematics will be interactively viewable and downloadable via the MPLF webtool at https://www.manchesterproteome.manchester.ac.uk/#/MPLF under the "Location Fingerprinter" tab [26,27] upon journal acceptance.…”

“…The LC-MS/MS datasets analysed by PLF in this paper were downloaded from the PRIDE repository: human IVD ageing - PXD017740; mouse lung ageing - PXD012307; human arterial atherosclerosis - PXD003930. PLF analyses of all datasets, and associated protein schematics will be interactively viewable and downloadable via the MPLF webtool at https://www.manchesterproteome.manchester.ac.uk/#/MPLF under the “Location Fingerprinter” tab [26, 27] upon journal acceptance.…”

“…Recently, through the development of the Manchester PLF webtool (MPLF), we applied PLF as a proteomic biomarker discovery tool to both self-generated human LC-MS/MS datasets of skin photoageing [26] and to historical tendon [26] and IVD [27] ageing datasets, sourced from a public database (PRIDE). In these studies, we identified potential photoageing- and ageing-affected proteins which were independent of changes in whole protein abundance, demonstrating that PLF identifies structure-associated differences that are unique to the methodology.…”