NEW BIOMARKERS OF HUMAN SKIN AGING
20210109111 · 2021-04-15
Inventors
- Sandrine BOURGOIN-VOILLARD (GRENOBLE, FR)
- Sylvia Marie-Louise LEHMANN (GRENOBLE, FR)
- Walid RACHIDI (Meylan, FR)
- Michel SEVE (MEYLAN, FR)
Cpc classification
C12Q1/6809
CHEMISTRY; METALLURGY
C12Q1/6876
CHEMISTRY; METALLURGY
C12Q1/6883
CHEMISTRY; METALLURGY
International classification
Abstract
The present invention refers to an in vitro method to determine if the skin of a subject presents signs of physiological aging, a method of cosmetic and a method to identify a substance that is capable of reducing or reversing the visible signs of physiological skin aging. The invention further refers to a kit comprising capture ligands and a use of said kit for determining in a skin sample the expression level of the markers of skin aging that are identified in context of the present invention.
Claims
1. An in vitro method to determine if the skin of a subject presents signs of physiological skin aging comprising the steps of a) determining in a skin sample of said subject the expression level of a first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1 and the expression level of at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7, TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13, b) determining if the skin presents signs of physiological skin aging.
2. Method according to claim 1, further comprising a step of comparing the expression level of said first protein with a reference level and comparing the expression level of the at least one further protein with a reference level.
3. A method of cosmetic treatment capable of reducing or reversing the visible signs of physiological skin aging on a subject comprising the steps of a) determining in a skin sample of said subject the expression level of a first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1 and the expression level of at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7, TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13, b) deducing from the expression level of said first protein and the expression level of said at least one further protein determined in step a) if the skin presents signs of physiological skin aging, and c) if the skin is determined as presenting signs of physiological aging, treating said subject with a cosmetic composition that reduces or reverses the visible signs of physiological skin aging.
4. A method to identify a substance that is capable of reducing or reversing the visible signs of physiological skin aging comprising the steps of a) treating a skin sample with a candidate substance, b) determining in the skin sample of step a) the expression level of a first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1 and the expression level of at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7, TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13, c) comparing the expression level of said first protein and said at least one further protein with the expression level of said first protein and said at least one further protein in a skin sample that has not been treated with said candidate substance, d) identifying the candidate substance as a substance that reduces or reverses the visible signs of physiological skin aging.
5. Method according to claim 4, wherein the skin sample of step a) and the skin sample that has not been treated with said candidate substance of step c) are exposed to an environment that induces skin aging.
6. Method according to any one of claims 1 to 5, wherein said skin sample comprises keratinocytes, preferably primary keratinocytes.
7. Method according to any one of claims 1 to 6, wherein said subject is caucasian.
8. Method according to any one of claims 1 to 7 wherein said subject is a woman.
9. Method according to any one of claims 1 to 2 and 4 to 8, wherein the expression level of a first protein and at least one further protein is compared with a reference expression level by determining a ratio of expression of said first protein by dividing the expression level of said first protein through a reference expression level of said first protein and by determining a ratio of expression of said at least one further protein by dividing the expression level of said at least one further protein through a reference expression level of said at least one further protein.
10. Method according to claim 9, wherein the skin of said subject presents signs of physiological aging, when the ratio of expression is higher or lower than 1 for the first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1.
11. Method according to claim 9 or 10, wherein the skin of said subject presents signs of physiological aging, when the ratio of expression is higher than 1 for the at least one further protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13.
12. Method according to claim 9 or 10, wherein the skin of said subject presents signs of physiological aging, when the ratio of expression is less than 1 for the at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7.
13. A kit comprising at least one capture ligand for determining the expression level of a first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1, and at least one capture ligand for determining the expression level of at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7, TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13.
14. A kit according to claim 13, wherein the capture ligand is an antibody.
15. Use of a kit as defined in claims 13 and 14 for determining in a skin sample the expression level of one first protein encoded by a gene selected from the group of genes consisting of TUBB3, HMGA2 and HMGN1, and the expression level of at least one further protein encoded by a gene selected from the group of genes consisting of HMGN1, HIST1H2BK, PPFIA2, COXSA, MT1E, HMGN2, EEA1, CDV3, ZC3H11A, HMGA1, PTMA, SERBP1, PDAP1, TMSB10, PSMD9, CALM1, MT1G, TPM4, SPRR1B, GBF1, HDGF, HSPE1, DBI, TRIM6, PTMS, IMUP, SH3BGRL3, RPS28, STMN1, AHSG, PFDN6, SUMO2, PFDN2, NEB, HMGB1, TMSB4X, CLTB, MYH11, SRSF7, TUBB3, HMGA2, ATP6V1A, SQRDL, IDH3A, PFKP, PRDX3, RPS13, PDIA4, GSTO1, GSTP1, ACTR3, SLC2A1, CCT5, PSMB2, PLS3, PSMD2, IGHG4, RPL13.
Description
FIGURES
[0261]
[0262]
[0263]
[0264]
[0265]
[0266]
Prediction=83.76−(992.91×BETA TUBULINE)−(4936.61×HMGA2)+(273.40×HMGN1)
EXAMPLES
Example 1
1. Material and Methods
1.1 Cell Culture
[0267] Isolation and culture of primary keratinocytes: Skin biopsies were obtained after plastic mammary surgery following healthy person's written consent. Donors were European Caucasian women aged of 60 and 65 years (n=2) and 27 and 32 years (n=2) classified in two age groups designed hereafter elderly and young respectively (agreement No. DC-2008-444 from Codecoh (Conservation D'Eléments du Corps Humain)). Skin biopsies were sun protected non-exposed skin. Human primary keratinocytes were cultured in KSFM medium supplemented with 25 μg/mL BPE and 0.9 ng/mL EGF.
1.2 Protein Extraction
[0268] Frozen cells pellets were lysed for 30 minutes at 4° in a solution containing 40 mM HEPES ph 7.4, 100 mM NaCl, 1 mM EDTA, 0.02% Triton, 0.02% Sodium Deoxycholate, 0.2 mM TCEP, and protease and phosphatase inhibitor cocktail (PhosSTOP) from Roche. Lysis was achieved by short sonication on ice and the lysates were cleared by centrifugation at 14,000 rpm for 20 minutes at 4°. The concentration of the protein extract was determined using BCA protein assay kit (Thermo Fisher Scientific, IL, USA).
1.3 Protein Digestion and iTRAQ Labeling
[0269] Protein samples were labeled with iTRAQ reagents in a 8-plex set according to the manufacturer's instructions (iTRAQ Reagents 8 plex Applications kit; AB Sciex, Framingham, Mass., USA). Briefly, equal amount of protein extract obtained from cells originated from young donors were pooled in order to achieve a total of 100 rig. The same procedure was applied for cells from elderly donors. The samples were reduced in 20 mM of TCEP (tris-(2-carboxyethyl)phosphine) at 37° C. for 1 h, cysteine-residues were blocked in 10 mM of MMTS (methyl methanethiosulfonate) at room temperature for 10 min, followed by trypsin (Promega) digestion at a ratio of 1:10 (trypsin:protein) at 37° C. overnight. Each peptide solution was labeled with one iTRAQ reagent:iTRAQ reporter ions of m/z 113.1 for young and m/z 117.1 for elderly. iTRAQ labeling was verified for all reaction and the samples were pooled in a ration 1:1 and dried by vacuum centrifugation prior to the OFFGEL peptides fractionation.
1.4 Peptide OFFGEL Isoelectrofocusing
[0270] Peptide fractionation according to their pI was performed with 3100 OFFGEL Fractionator and the OFFGEL Kit linear pH 3-10 (Agilent Technology) in a 24-well setup following the manufacturer's instructions. The device was set up for the 24 fractions separation by using 24-cm-long IPG gel strip with a linear pH gradient ranging at 3-10. iTRAQ labeled peptide mix was dried by vacuum centrifugation and resuspended in focusing OFFGEL buffer prior loading in each of the 24 wells. Peptides were focused with a constant current of 50 μA until 50 kVh was reached. After complete fractionation, peptides samples were recovered from each well, dried in a vacuum concentrator and then desalted using C18 ZipTips (Millipore, MA, USA).
1.5 Reversed Phase Nano-Liquid Chromatography
[0271] Further peptide separation was performed on an Ultimate 3000 C18 reversed-phase nano liquid chromatography (RP-nanoLC) system (Ultimate 3000, Dionex/Thermo Scientific) controlled by Chromeleon v. 6.80 software (Dionex/Thermo Scientific/LC Packings, Amsterdam, The Netherlands) and coupled to a PROBOT MALDI spotting device controlled by the μCarrier 2.0 software (Dionex/Thermo Scientific/LC Packings, Amsterdam, The Netherlands).
Vacuum dried fractions were resuspended in buffer A (98% water, 2% ACN and 0.05% TFA) before injection on a nano-trapping column (C18, 3 μm, 100A pore size; LC Packing) in 2% ACN and 0.05% TFA at a flow rate of 20 μL/min for 5 min. Then, trapped peptides were separated by reversed phase chromatography (Acclaim PepMap300 75 μm, 15 cm, nanoViper C18, 3 μm, 100 Å pore size; Thermo Scientific) with a binary gradient of buffer A (2% ACN and 0.05% TFA) and buffer B (80% ACN and 0.04% TFA) at a flow rate of 0.3 μL/min. The entire run lasted 60 min and the nanoLC gradient was set up as follows: 5-35 min, 8-42% B; 35-40 min, 42-58% B; 40-50 min, 58-90% B and 50-60 min, 90% B. Fractions from eluted solution were collected and spotted on a MALDI sample plate (AB Sciex, Les Ulis, France) at a frequency of one spot per 15 seconds. The α-cyano-4-hydroxy-cinnamic acid matrix (HCCA, 2 mg/mL in 70% ACN and 0.1% TFA) was continuously added to the column effluent at a flow rate of 0.9 μL/min, and therefore, integrated in each spot of MALDI sample plate.
1.6 MALDI-TOF/TOF Analysis
[0272] MS and MS/MS analysis of nanoLC-off-line spotted peptide samples were performed using the 4800 MALDI-TOF/TOF Analyzer (AB Sciex, Les Ulis, France) controlled by the 4000 Series Explorer software v. 3.5. The mass spectrometer was operated in positive reflector mode. Each spectrum was externally calibrated using the Peptide Calibration Standard II (Bruker Daltonics, Bremen, Germany) and the peptide mass tolerance was set to 50 ppm. MS spectra were acquired in a m/z 700-4000 range. Up to 30 of the most intense ions per spot position characterized by a S/N (signal/noise) ratio higher than 40 were chosen for MS/MS analysis. Selected ions were fragmented by using CID (Collision Induced Dissociation) activation mode in order to obtain the corresponding MS/MS spectrum that is necessary to determine the sequence of these peptides and quantify them.
1.7 Analysis of iTRAQ Data
MS and MS/MS spectra were used for identification and relative quantitation by using ProteinPilot™ software v 4.0 with the Paragon™ Algorithm (AB Sciex, Les Ulis, France) and Mascot. The analysis was performed with the human database of UniProtKB release 2015_06—June, 2015/Swiss-Prot (European Bioinformatics Institute, Hinxton, UK). Concerning ProteinPilot search, the search effort was set to ‘Thorough ID’ and the False Discovery Rate Analysis (FDR) of 1% was applied. For quantification, bias and background correction was applied and only quantified proteins with at least 1 peptide at the 95% peptide confidence level were included. For Mascot search the FDR was set lower than 1% and only peptides with a score higher than 30 was considered. Data were merged at the peptide level after ProteinPilot and Mascot analysis. In order to obtain high quality in quantitative analysis, the inventors analyzed the data with the R package Isobar (Breitwieser et al., 2011, J. Proteome Res. 10, 2758-2766) which allows the determination of statistical significance of protein/peptide regulation. A normal fit was used and only proteins which ratio had a pValueRatio and a pValueSample <0.05 are then considered as significantly differentially expressed depending on age. For output of our quantitative iTRAQ results, all protein ratios were expressed as elderly over young (117:113) to present relative protein quantification ratios. A summary of the parameters applied for the mass data analysis is presented in Table 1 herein below.
TABLE-US-00002 TABLE 1 Summary of the parameters applied for the bioinformatic analysis Parameters for peptides and proteins identification Analysis with Ppilot and Mascot Database: uniprot/Swissprot with variants Ppilot settings : Biological modifications Mascot settings: Precursor 50 ppm; MS/MS: 0.6 Da; Fixed modif: MMTS; variable modif: deamidation (NQ) Oxidation (M) Data merged at peptide level Global FDR: 1% (protein level) Peptide confidence >=95% Mascot score >30 Number of validated peptides identified # peptides identified: 57339 (with variants) # Peptides identified by Mascot: 54513 # peptides identified by Ppilot: 13891 # peptides identified by Mascot and Ppilot: 11065 Parameters and results of the annalysis with the isobar R package Isobar for statistical analysis using a Normal fit model # protein identified: 517 (groups of proteins) # proteins identified and quantified: 446 # proteins identified not quantified: 71 # Dysregulated proteins: 58 # Upregulated proteins (Elderly vs Young): 18 # Downregulated protéins (Elderly vs Young): 40
1.8 Gene Ontology and Pathway Analysis
[0273] Gene ontology and pathway analysis were performed using PANTHER (http://www.pantherdb.org/) (Mi et al., 2013, Nucleic Acids Res. 41, D377-D386) by importing the list of dysregulated proteins and the proteins were classifiied in one or several categories regarding PANTHER Family; Protein class; GO-Slim Molecular function, Biological Process and Cellular Component, and Pathway (data not shown).
1.9 Western Blot Analysis
[0274] Human primary keratinocytes was harvested and cultivated from skin biopsies of 8 young (Age: 18; 21; 24; 26; 27 (2 donors); 30; 32) and 10 elderly donors (57; 59; 60; 62 (2); 65 (2); 66; 68; 71). At early passages (2 or 3, when cells are still proliferating), cells were lysed by vortexing in RIPA Buffer (Sigma-Aldrich) containing protease inhibitors (Complete Mini protease inhibitor cocktail, Roche, Switzerland), 1 mM DTT and 100 μM PMSF. Samples were then centrifuged for 15 minutes at 14,000 rpm and the supernatants collected. Protein concentration was determined with MicroBC Assay (Interchim) and 20 μg of total protein was loaded on TGX Stain-Free™ FastCast™ 12% Acrylamide gels (Biorad). Proteins were transferred onto a nitrocellulose membrane using Trans-Blot® Turbo™ Transfert System (Biorad). Membranes were blocked with TBS-Tween 0.5% containing 5% non-fat milk. Primary antibodies were incubated at the following dilution in TBS-Tween 0.5% containing 5% non-fat milk overnight at 4° C.: 1/1000 for Tubulin beta-3 chain antibody (MA1-118; Thermoscientific) and 1/1000 for Cornifin-B antibody (PA5-26062; Thermoscientific). After washing in TBS-Tween 0.5%, membranes were incubated with HRP conjugated secondary antibodies (Amersham ECL anti-mouse and anti-rabbit IgG HRP-linked, whole antibody, GE Healthcare) for 1 hour at RT. Membranes were then washed in TBS-Tween 0.5% and blot images were acquired on Molecular Imager Gel Doc XR+ and Chemidoc XRS+ Systems (Biorad). Specific detected bands were quantified with Image LAb 2.0 Software (Biorad) and corresponding intensities were normalized with total protein content and expressed as a ratio.
Western blot results of TUBB3 and HMGA2 were illustrated by box plots and receiver operating characteristic curves (ROC curve) was created by using GraphPad Prism version 7.00 for Windows, (GraphPad Software, La Jolla Calif. USA, www.graphpad.com).
2. Results
2.1 Identification of Fifty Eight Proteins Differentially Expressed Depending on Age Status by Proteomic Analysis
[0275] In order to obtain a quantitative proteomic map of elderly and young donors derived keratinocytes cells, an iTRAQ labeling coupled with OFFGEL fractionation and off-line nanoLC/MS/MS was used as previously described (Martin-Bernabé et al., 2014, J. Proteome Res. 13, 4695-470). The bioinformatic analysis with ProteinPilot and Mascot resulted in the identification of 517 unique proteins using a 1% FDR and considering only proteins with at least 1 peptide with >95% confidence level and score >30. A statistical analysis with the isobar package and quantified 446 proteins was performed. Elderly cells were labeled with iTRAQ m/z 117 tag and young cells with iTRAQ m/z 113 tag. Thus, the ratio 117:113 (Elderly:Young) indicates the relative protein abundance between elderly and young cell samples. The complete list of identified proteins, including the UniProtKB accession number, ID, protein and gene name, peptide count, spectral count, sequence coverage, iTRAQ ratios and p-values ratio and p-values sample for elderly versus young cells are provided in supplemental data (Data not shown).
[0276] When the pValue ratio and the pValue sample were both <0.05, proteins were considered significantly differently expressed. Applying these criteria, 58 proteins significantly differentially expressed were identified depending on age status. From them, 40 were downregulated and 18 were upregulated with aging (Table 2 and Table 3).
[0277] Table 2: List of proteins significantly downregulated in elderly cells versus young cells (iTRAQ ratio 117/113). Statistically significant iTRAQ ratios (p-value ratio and p-value sample 0.05) for proteins down-regulated
TABLE-US-00003 TABLE 2 List of proteins significantly downregulated in elderly cells versus young cells (iTRAQ ratio 117/113). Statistically significant iTRAQ ratios (p-value ratio and p-value sample ≤ 0.05) for proteins down-regulated Sequence Ratio Accession/ Coverage [Elderly/ P Value P Value Log10 variants ID Description Gene Count Count (%) Young] Rat Sample Ratio O60814 H2B1K_HUMAN Histone H2B HIST1H2BK 9 131 4.76 0.170 6.27E−05 4.36E−19 −0.769 type 1-K O75334- LIPA2_HUMAN Liprin-alpha-2 PPFIA2 2 2 0.64 0.295 3.83E−02 5.16E−10 −0.531 [2-6] P20674 COX5A_HUMAN Cytochrome COX5A 2 3 9.33 0.309 1.56E−02 2.23E−09 −0.510 c oxidase subunit 5A, mitochondrial P04732 MT1E_HUMAN Metallothionein-1E MT1E 1 9 16.39 0.310 7.22E−13 2.40E−09 −0.509 P05204 HMGN2_HUMAN Non-histone HMGN2 4 11 8.89 0.330 1.89E−02 1.50E−08 −0.482 chromosomal protein HMG-17 Q15075 EEA1_HUMAN Early endosome EEA1 2 3 0.64 0.331 1.71E−02 1.61E−08 −0.481 antigen 1 Q9UKY7- CDV3_HUMAN Protein CDV3 CDV3 3 16 11.63 0.342 1.18E−03 4.11E−08 −0.466 [2] homolog O75152 ZC11A_HUMAN Zinc finger CCCH ZC3H11A 1 1 1.48 0.349 2.87E−02 7.18E−08 −0.457 domain-containing protein 11A P17096 HMGA1_HUMAN High mobility HMGA1 1 19 7.48 0.352 1.56E−02 8.83E−08 −0.454 group protein HMG-I/HMG-Y P06454- PTMA_HUMAN Prothymosin alpha PTMA 5 24 12.61 0.357 4.01E−02 1.34E−07 −0.447 [2] [Cleaved into: Prothymosin alpha, N- terminally processed; Thymosin alpha-1] Q8NC51- PAIRB_HUMAN Plasminogen SERBP1 11 90 4.66 0.361 1.56E−02 1.82E−07 −0.442 [3] activator inhibitor 1 RNA-binding protein Q13442 HAP28_HUMAN 28 kDa heat- PDAP1 1 1 7.18 0.363 1.23E−02 2.09E−07 −0.440 and acid- stable phosphoprotein P63313 TYB10_HUMAN Thymosin beta-10 TMSB10 2 35 13.64 0.364 1.30E−02 2.21E−07 −0.439 O00233 PSMD9_HUMAN 26S proteasome PSMD9 1 6 5.38 0.383 2.34E−04 8.33E−07 −0.416 non-ATPase regulatory subunit 9 P05114 HMGN1_HUMAN Non-histone HMGN1 3 13 8.00 0.384 1.20E−02 8.80E−07 −0.415 chromosomal protein HMG-14 P62158 CALM_HUMAN Calmodulin CALM1 10 147 8.72 0.385 2.89E−02 9.13E−07 −0.415 P02795, MT1G_HUMAN, Metallothionein-1G, MT1G, 1 20 16.29 0.392 3.61E−03 1.47E−06 −0.406 P13640- MT1X_HUMAN, Metallothionein-1X, MT1X, [2], MT2_HUMAN Metallothionein-2 MT2A P80297 P67936 TPM4_HUMAN Tropomyosin TPM4 5 48 3.23 0.401 3.64E−02 2.51E−06 −0.397 alpha-4 chain P22528 SPR1B_HUMAN Cornifin-B SPRR1B 4 56 8.99 0.405 2.54E−02 3.18E−06 −0.392 Q92538- GBF1_HUMAN Golgi-specific GBF1 1 1 0.32 0.406 4.48E−02 3.34E−06 −0.391 [2, 3] brefeldin A-resistance guanine nucleotide exchange factor 1 P51858 HDGF_HUMAN Hepatoma-derived HDGF 4 12 3.75 0.429 2.37E−02 1.17E−05 −0.368 growth factor P61604 CH10_HUMAN 10 kDa heat HSPE1 12 121 7.84 0.455 2.20E−02 4.16E−05 −0.342 shock protein, mitochondrial P07108- ACBP_HUMAN Acyl-CoA-binding DBI 4 50 9.20 0.460 1.86E−02 5.21E−05 −0.337 [2-5] protein Q9C030- TRIM6_HUMAN Tripartite TRIM6 2 8 1.23 0.463 1.10E−02 6.03E−05 −0.334 [2] motif-containing protein 6 P20962 PTMS_HUMAN Parathymosin PTMS 4 11 8.82 0.468 1.44E−02 7.30E−05 −0.330 Q9GZP8 IMUP_HUMAN Immortalization IMUP 3 5 9.43 0.476 8.05E−03 1.05E−04 −0.322 up-regulated protein Q9H299 SH3L3_HUMAN SH3 SH3BGRL3 3 36 10.75 0.490 1.62E−02 1.82E−04 −0.310 domain-binding glutamic acid-rich-like protein 3 P62857 RS28_HUMAN 40S ribosomal RPS28 3 20 17.39 0.491 9.21E−03 1.93E−04 −0.309 protein S28 P16949- STMN1_HUMAN Stathmin STMN1 3 24 8.72 0.492 2.51E−02 2.00E−04 −0.308 [2] P02765 FETUA_HUMAN Alpha-2-HS- AHSG 4 61 3.27 0.507 4.77E−02 3.38E−04 −0.295 glycoprotein O15212 PFD6_HUMAN Prefoldin subunit 6 PFDN6 1 9 9.30 0.519 1.72E−02 5.20E−04 −0.285 P52926 HMGA2_HUMAN High mobility group HMGA2 3 8 11.93 protein HMGI-C P61956 SUMO2_HUMAN Small SUMO2 1 7 12.63 0.534 1.89E−02 8.65E−04 −0.272 ubiquitin-related modifier 2 Q9UHV9 PFD2_HUMAN Prefoldin subunit 2 PFDN2 1 5 9.09 0.544 1.81E−02 1.16E−03 −0.265 P20929- NEBU_HUMAN Nebulin NEB 4 4 0.13 0.563 5.23E−06 2.03E−03 −0.250 [2, 3] P09429 HMGB1_HUMAN High mobility group HMGB1 5 33 5.58 0.563 9.96E−07 2.07E−03 −0.249 protein B1 P62328 TYB4_HUMAN Thymosin beta-4 TMSB4X 1 16 15.91 0.582 4.60E−03 3.41E−03 −0.235 P09497- CLCB_HUMAN Clathrin light CLTB 7 24 3.49 0.593 1.87E−03 4.46E−03 −0.227 [2] chain B P35749- MYH11_HUMAN Myosin-11 MYH11 1 5 0.56 0.649 4.13E−02 1.53E−02 −0.188 [2-4] Q16629- SRSF7_HUMAN Serine/arginine-rich SRSF7 2 13 3.78 0.651 3.09E−02 1.59E−02 −0.187 [2-4] splicing factor 7
[0278] Table 3: List of proteins significantly up-regulated in elderly cells versus young cells (iTRAQ ratio 117/113). Statistically significant iTRAQ ratios (p-value ratio and p-value sample 0.05) for proteins up-regulated
TABLE-US-00004 TABLE 3 List of proteins significantly up-regulated in elderly cells versus young cells (iTRAQ ratio 117/113). Statistically significant iTRAQ ratios (p-value ratio and p-value sample ≤ 0.05) for proteins up-regulated Ratio Accession/ Peptide Coverage [Elderly/ P Value P Value Log10 variants ID Description Gene Count Count (%) Young] Ratio Sample Ratio P26373 RL13_HUMAN 60S ribosomal RPL13 4 15 4.27 1.455 2.54E−03 3.04E−02 0.163 protein L13 P01861 IGHG4_HUMAN Ig gamma-4 IGHG4 1 4 4.89 1.482 2.00E−02 2.46E−02 0.171 chain C region Q13200 PSMD2_HUMAN 26S proteasome PSMD2 1 5 1.65 1.511 4.13E−02 1.96E−02 0.179 non-ATPase regulatory subunit 2 P13797 PLST_HUMAN Plastin-3 PLS3 5 13 1.90 1.584 2.13E−02 1.08E−02 0.200 P49721 PSB2_HUMAN Proteasome PSMB2 1 22 5.47 1.635 1.82E−03 7.01E−03 0.214 subunit beta type-2 P48643 TCPE_HUMAN T-complex CCT5 6 35 1.29 1.696 2.17E−03 4.15E−03 0.229 protein 1 subunit epsilon P11166 GTR1_HUMAN Solute carrier SLC2A1 4 46 2.03 1.891 2.60E−02 7.28E−04 0.277 family 2, facilitated glucose transporter member 1 P61158 ARP3_HUMAN Actin-related ACTR3 6 26 2.63 2.040 3.21E−02 1.84E−04 0.310 protein 3 P09211 GSTP1_HUMAN Glutathione GSTP1 10 117 7.62 2.308 3.65E−02 1.47E−05 0.363 S-transferase P P78417 GSTO1_HUMAN Glutathione GSTO1 4 25 5.81 2.482 2.89E−10 2.77E−06 0.395 S-transferase omega-1 P13667 PDIA4_HUMAN Protein PDIA4 3 4 1.09 2.633 2.99E−02 6.60E−07 0.420 disulfide-isomerase A4 P62277 RS13_HUMAN 40S ribosomal RPS13 1 1 7.95 2.638 2.88E−02 6.27E−07 0.421 protein S13 Q13509 TBB3_HUMAN Tubulin beta-3 TUBB3 4 19 4.00 2.664 4.21E−04 4.87E−07 0.426 chain P30048 PRDX3_HUMAN Thioredoxin-dependent PRDX3 1 1 5.47 2.769 3.86E−02 1.80E−07 0.442 peroxide reductase, mitochondrial Q01813- PFKAP_HUMAN ATP-dependent 6- PFKP 2 3 2.68 3.116 3.23E−02 6.75E−09 0.494 [2] phosphofructokinase, platelet type P50213 IDH3A_HUMAN Isocitrate IDH3A 3 3 2.19 3.239 6.67E−03 2.14E−09 0.510 dehydrogenase [NAD] subunit alpha, mitochondrial Q9Y6N5 SQRD_HUMAN Sulfide:quinone SQRDL 1 2 2.22 3.487 8.97E−03 2.17E−10 0.543 oxidoreductase, mitochondrial P38606- VATA_HUMAN V-type proton ATP6V1A 2 2 2.43 4.833 1.38E−02 1.75E−15 0.684 [2] ATPase catalytic subunit A
2.2 Gene Ontology Analysis
[0279] The 58 proteins previously identified were analyzed using PANTHER (Mi et al., 2013, Nucleic Acids Res. 41, D377-D386) and classifiied into the following gene ontology and PANTHER categories: Protein Family; Protein class; Molecular function; Biological process; Cellular Component and Pathway (data not shown). The main represented biological process categories are metabolism (30%), cellular process (21%), cellular component organization and biological regulation (10%), localization and developmental process (8%), response to stimuli (4%), multicellular organismal process and immune system process (3%) and biological adhesion (1%). Concerning Protein Class, dysregulated proteins belongs to the main following protein classes: Nucleic acid binding (25%), Cytoskeletal Protein (13%), enzyme modulator (12%), Oxidoreductase and signaling molecules (8%), Chaperone (6%), transferase and transcription factor (4%), extracellular matrix protein, hydrolase, carrier protein, membrane traffic protein, cell junction protein, kinase, isomerase and receptor (2%) (Graphical representation not shown).
2.3 Western Blot Analysis of Candidate Proteins Validates the Proteomic Analysis
[0280] Two candidate proteins of interest were further analyzed by western blot on human primary keratinocytes cells from the same donors but also from ten other donors in order to validate the proteomic results on more donors to exclude the inter-individual variability. The selected proteins with the corresponding ratio obtained by proteomic experiment: Tubulin beta-3 chain (TBB3_HUMAN) ratio 2.6 and High mobility group protein HMGI-C (HMGA2_HUMAN) ratio 0.52. Western blots images and result of quantification are shown in
3. Discussion
[0281] Skin aging is a complex process with multifactorial origins that can be decipher using new technological approach such as quantitative proteomics. An iTRAQ-MALDI-TOF/TOF MS and MS/MS analysis was carried out to identify and quantify changes in human primary keratinocytes proteomes from young and elderly donors. 517 proteins were identified including proteins found mainly in keratinocytes such as Cornifin-B and Keratin-2e which is associated with keratinocyte activation, proliferation and keratinization (Collin et al., 1992, Exp. Cell Res. 202, 132-141). After applying robust statistical analysis, 58 proteins were found significantly differentially expressed depending on age status with 40 that were downregulated and 18 upregulated with aging.
[0282] The inventors found that more proteins are downregulated (40) than upregulated (18) with aging which is consistent with previous results from a gene expression study in women (Makrantonaki et al., 2012, PLoS ONE 7). The majority of proteins which expression is affected by age are involved in metabolism (30%) and nucleic acid binding (25%). Similar results have been observed in a previous transcriptomic study (Lener et al., 2006, Exp. Gerontol. 41, 387-397).
[0283] In this work, Cornifin-B has been found downregulated with aging as previously reported in two transcriptomic studies using women epidermis (Raddatz et al., 2013, Epigenetics Chromatin 6, 36) and skin biopsies (McGrath et al., 2012, Br. J. Dermatol. 166 Suppl 2, 9-15). Cornifin-B is a marker of keratinocyte differentiation (Tesfaigzi and Carlson, 1999, Cell Biochem. Biophys. 30, 243-265) and a downregulation of keratinocytes differentiation was already observed with aging (Raddatz et al., 2013, Epigenetics Chromatin 6, 36).
[0284] Peroxiredoxin 3 (PrxIII), a mitochondrial member of the antioxidant family of thioredoxin (Trx) peroxidases was found upregulated with aging in our study. Two other family members, Peroxiredoxin 1 and 2 were also upregulated in a previous report (Laimer et al., 2010, Exp. Dermatol. 19, 912-918). Peroxiredoxins are important cellular antioxidant, indeed they act as hydrogen peroxide and organic hydroperoxide scavengers (Nystrom et al., 2012, Genes Dev. 26, 2001-2008). It is well establish that with age, there is an increase in reactive oxygen species (ROS) production and a decrease in antioxidant activity both contributing to chronological aging (Poljšak et al., 2012, Acta Dermatovenerol. Alp. Pannonica Adriat. 21, 33-36). In oxidative stress conditions, PrxIII undergo overoxidation and subsequent irreversible inactivation. And it has been shown that in rats, this modified PrxIII form accumulate with aging (Musicco et al., 2009). In the present analysis, the peptide containing the cysteine that is overoxidized to sulfonic acid was not identified and therefore the two forms were not discriminated, explaining why in consequence a global upregulation of said protein was observed.
[0285] 6-phosphofructokinase, platelet type (PFKAP_HUMAN) is upregulated with aging in our study. This enzyme catalyzes the phosphorylation of D-fructose 6-phosphate to fructose 1,6-bisphosphate by ATP, the first committing step of glycolysis. It has been shown that human primary keratinocytes derived from elderly donors show higher glucose uptake and increased lactate production, which are the indicators of a shift in metabolism towards increased glycolysis (Prahl et al., 2008, BioFactors Oxf. Engl. 32, 245-255). Thus the observed upregulation of PFKAP is correlated with the increased glycolysis in primary keratinocytes.
[0286] Comparing our results with other studies aiming at identify biomarker of skin aging show some differences that may be explained by different type/origin of skin samples, gender, difference in sample processing all along the workflow and the variable correlation between mRNA and protein expression levels (Schwanhäusser et al., 2011, Nature 473, 337-342).
[0287] Defining the differential protein signature with aging even if these changes could be initiating, adaptive or compensatory events is crucial to further our knowledge of skin aging. This study brings a new effort to reach a better understanding of the biology of skin aging and to identify new and specific targets that could help to diagnose, prevent and treat skin aging and associated pathologies.
Example 2
[0288] Using the methods disclosed in Example 1 above, the inventors further demonstrated the improved sensitivity and specificity obtained when using a combination of markers of the invention.
[0289]
[0290] However, as shown in
Prediction=83.76−(992.91×TUBULIN)−(4936.61×HMGA2)+(273.40×HMGN1)