Defining tissue proteomes by systematic literature review

Hibbert, Sarah; Ozols, Matiss; Griffiths, Christopher E.M.; Watson, Rachel; Bell, Mike; Sherratt, Michael J.

doi:10.1038/s41598-017-18699-8

Cited by 12 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite this, the number of proteins detected overall using Mascot and Scaffold in this study was broadly comparable to previous studies that used whole biopsies and that would have had more tissue available for analysis . Proteomics studies on the skin are therefore still far short of the 825 epidermal, 1 636 dermal and epidermal, 95 dermal and 32 DEJ/basement membrane proteins now known, as reported by a systematic review on proteins present in the skin .…”

Section: Discussionsupporting

confidence: 78%

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Newton

Riba-Garcia

Griffiths

et al. 2019

Intern J of Cosmetic Sci

View full text Add to dashboard Cite

Objective With increasing age, skin is subject to alterations in its organization, which impact on its function as well as having clinical consequences. Proteomics is a useful tool for non‐targeted, semi‐quantitative simultaneous investigation of high numbers of proteins. In the current study, we utilize proteomics to characterize and contrast age‐associated differences in photoexposed and photoprotected skin, with a focus on the epidermis, dermal–epidermal junction and papillary dermis. Methods Skin biopsies from buttock (photoprotected) and forearm (photoexposed) of healthy volunteers (aged 18–30 or ≥65 years) were transversely sectioned from the stratum corneum to a depth of 250 μm. Following SDS‐PAGE, each sample lane was segmented prior to analysis by liquid chromatography‐mass spectrometry/mass spectrometry. Pathway analysis was carried out using Ingenuity IPA. Results Comparison of skin proteomes at buttock and forearm sites revealed differences in relative protein abundance. Ageing in skin on the photoexposed forearm resulted in 80% of the altered proteins being increased with age, in contrast to the photoprotected buttock where 74% of altered proteins with age were reduced. Functionally, age‐altered proteins in the photoexposed forearm were associated with conferring structure, energy and metabolism. In the photoprotected buttock, proteins associated with gene expression, free‐radical scavenging, protein synthesis and protein degradation were most frequently altered. Conclusion This study highlights the necessity of not considering photoageing as an accelerated intrinsic ageing, but as a distinct physiological process.

show abstract

Section: Discussionsupporting

confidence: 78%

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Newton

Riba-Garcia

Griffiths

et al. 2019

Intern J of Cosmetic Sci

View full text Add to dashboard Cite

show abstract

“…Skin proteins identified (Scaffold 4; peptide probability threshold = 50%, protein probability threshold = 99%, minimum two peptides per protein) in the LC-MS/MS data generated in this study were used to expand the existing Manchester Proteome database with new protein entries (70) COL18A1, COL3A1, COL4A2, COL6A1, COL6A6, COL6A2, COL6A3, LAMA3, LAMA5, LAMC1 COL12A1, FBLN1, FBLN2, LGALS7, LTBP4, NID2, Table S1. Full list of LC-MS/MS-identified exclusive peptides in dermis samples used for structural mapping and fingerprinting.…”

Section: Expansion Of the Manchester Proteomementioning

confidence: 99%

Peptide location fingerprinting reveals modification-associated biomarkers of ageing in human tissue proteomes

Ozols

Eckersley

Mellody

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Although dysfunctional protein homeostasis (proteostasis) is a key factor in many age-related diseases, the untargeted identification of structural modifications in proteins remains challenging. Peptide location fingerprinting is a proteomic analysis technique capable of identifying structural modification-associated differences in mass spectrometry (MS) datasets of complex biological samples. A new webtool (Manchester Peptide Location Fingerprinter), applied to photoaged and intrinsically aged skin proteomes, can relatively quantify peptides (spectral counting) and map statistically significant differences to regions within protein structures. New photoageing biomarkers were identified in multiple proteins including matrix components (collagens and proteoglycans), oxidation and protease modulators (peroxiredoxins and SERPINs) and cytoskeletal proteins (keratins). Crucially, for many extracellular biomarkers, structural modification-associated differences were not correlated with relative abundance (by ion intensity). By applying peptide location fingerprinting to published MS datasets, (identifying biomarkers including collagen V and versican in ageing tendon) we demonstrate the potential of the MPLF webtool to discover novel biomarkers.

show abstract

“…In a parallel set of experiments, we generated a complex ECM proteome by decellularizing a postconfluent culture of HDFs and digesting the deposited matrix with MMP9. In the baseline (non-MMP9 digested) matrix we identified over 60 proteins, many of which (COL6A1, COL6A2, COL6A3, EMILIN1, COL1A1, COL1A2) are found in human dermis (36,49). LC-MS/MS identified putative MMP9 cleavage sites in 40 out of 60 proteins including (COL6A1, EMILIN1, FBLN2, COL1A2) resulting in 390 putative MMP9 cleavage sites (Fig.2.d).…”

Section: Accurate Prediction Of Protease Cleavage Sites In Native Ecmmentioning

confidence: 99%

“…We next applied MPSC webtool calculations to every ECM and extracellular protein in the Manchester Skin Proteome (36) in order to predict their susceptibility to UVR/ROS and to UV-upregulated skin proteases (interstitial collagenase (MMP1), 92kDa gelatinase (MMP9), stromelysin-1 (MMP3),…”

Section: Figure 2 Prediction and Validation Of Protease Cleavage Sitementioning

confidence: 99%

Predicting and validating protein degradation in proteomes using deep learning

Ozols

Eckersley

Platt

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Age, disease, and exposure to environmental factors can induce tissue remodelling and alterations in protein structure and abundance. In the case of human skin, ultraviolet radiation (UVR)-induced photo-ageing has a profound effect on dermal extracellular matrix (ECM) proteins. We have previously shown that ECM proteins rich in UV-chromophore amino acids are differentially susceptible to UVR. However, this UVR-mediated mechanism alone does not explain the loss of UV-chromophore-poor assemblies such as collagen. Here, we aim to develop novel bioinformatics tools to predict the relative susceptibility of human skin proteins to not only UVR and photodynamically produced ROS but also to endogenous proteases. We test the validity of these protease cleavage site predictions against experimental datasets (both previously published and our own, derived by exposure of either purified ECM proteins or a complex cell-derived proteome, to matrix metalloproteinase [MMP]-9). Our deep Bidirectional Recurrent Neural Network (BRNN) models for cleavage site prediction in nine MMPs, four cathepsins, elastase-2, and granzyme-B perform better than existing models when validated against both simple and complex protein mixtures. We have combined our new BRNN protease cleavage prediction models with predictions of relative UVR/ROS susceptibility (based on amino acid composition) into the Manchester Proteome Susceptibility Calculator (MPSC) webapp http://www.manchesterproteome.manchester.ac.uk/#/MPSC (or http://130.88.96.141/#/MPSC). Application of the MPSC to the dermal proteome suggests that fibrillar collagens and elastic fibres will be preferentially degraded by proteases alone and by UVR/ROS and protease in combination, respectively. We also identify novel targets of oxidative damage and protease activity including dermatopontin (DPT), fibulins (EFEMP-1,-2, FBLN-1,-2,-5), defensins (DEFB1, DEFA3, DEFA1B, DEFB4B), proteases and protease inhibitors themselves (CTSA, CTSB, CTSZ, CTSD, TIMPs-1,-2,-3, SPINK6, CST6, PI3, SERPINF1, SERPINA-1,-3,-12). The MPSC webapp has the potential to identify novel protein biomarkers of tissue damage and to aid the characterisation of protease degradomics leading to improved identification of novel therapeutic targets.

show abstract

Defining tissue proteomes by systematic literature review

Cited by 12 publications

References 32 publications

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Mass spectrometry‐based proteomics reveals the distinct nature of the skin proteomes of photoaged compared to intrinsically aged skin

Peptide location fingerprinting reveals modification-associated biomarkers of ageing in human tissue proteomes

Predicting and validating protein degradation in proteomes using deep learning

Contact Info

Product

Resources

About