METHOD FOR DIAGNOSING ESTHETIC DEGRADATIONS OF SKIN

Abstract

The present invention relates to a method for diagnosing esthetic degradations of skin, in particular linked to pollution, in a subject, comprising a step (a) of determining, in a skin sample of the subject, the level of at least one marker chosen from the group constituted of (i) bacteria of the species Propionibacterim acnes, bacteria of the family Micrococcaceae, bacteria of the genus Brachybacterium, bacteria of the genus Brevibacterium, bacteria of the order Burkholderiales, bacteria of the genus Parococcus, bacteria of the family Rhodobacteraceae and bacteria of the genus Fusobacterium, and (ii) metabolites of these bacteria chosen from 3-hydroxy-3-methylglutarate, 3-methylglutarate/2-methylglutarate, 4-guanidinobutanoate, 4-imidazoleacetate, 5-oxoproline, aconitrate, adipate, alanine, alpha-cetoglutarate, arabonate/xylonate, azelate, beta-citrylglutamate, choline, cis-urocanate, citraconate/glutaconate, fructose, fumarate, gamma-glutamylalanine, gamma-glutamylglutamine, gamma-glutamylglycine, gamma-glutamylisoleucine, gamma-glutamylleucine, gamma-glutamylsérine, gamma-glutamylthréonine, gamma-glutamyltryptophane, gamma-glutamylvaline, glutarate, glycerate, glycerol-3-phosphate, glycine, isovalerylglycine, kynurenate, lactate, linoleoyl ethanolamide, malate, maleate, malonate, maltose, methionine sulfoxide, methylsuccinate, N-acetylalanine, N-acetylarginine, N-acetylaspartate, N-acetylglycine, N-acetylhistidine, N-acetylphenylalanine, N-acetylthréonine, N-acetylvaline, oleamide, ornithine, palmitamide, pimelate, proline, salicylate, sebacate, serine, suberate, succinate, undecanedioate and S-amino-omega caprolactam.

Claims

1. A method for diagnosing esthetic degradations of skin in a subject, comprising a step (a) of determining, in a skin sample of the subject, the level of at least one marker chosen from the group constituted of (i) bacteria that comprise a nucleic acid encoding a 16S rRNA of sequence at least 90% identical to the sequence SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8 or SEQ ID NO: 9, and (ii) metabolites of these bacteria chosen from 3-hydroxy-3-methylglutarate, 3-methylglutarate/2-methylglutarate, 4-guanidinobutanoate, 4-imidazoleacetate, 5-oxoproline, aconitrate, adipate, alanine, alpha-cetoglutarate, arabonate/xylonate, azelate, beta-citrylglutamate, choline, cis-urocanate, citraconate/glutaconate, fructose, fumarate, gamma-glutamylalanine, gamma-glutamylglutamine, gamma-glutamylglycine, gamma-glutamylisoleucine, gamma-glutamylleucine, gamma-glutamylserine, gamma-glutamylthreonine, gamma-glutamyltryptophane, gamma-glutamylvaline, glutarate, glycerate, glycerol-3-phosphate, glycine, isovalerylglycine, kynurenate, lactate, linoleoyl ethanolamide, malate, maleate, malonate, maltose, methionine sulfoxide, methylsuccinate, N-acetylalanine, N-acetylaspartate, N-acetylarginine, N-acetylglycine, N-acetylhistidine, N-acetylphenylalanine, N-acetylthreonine, N-acetylvaline, oleamide, ornithine, palmitamide, pimelate, proline, salicylate, sebacate, serine, suberate, succinate, undecanedioate and S-amino-omega caprolactam.

2. The method for diagnosing according to claim 1, wherein said at least one marker is chosen from the group constituted of (i) bacteria of the species Propionibacterim acnes, bacteria of the family Micrococcaceae, bacteria of the genus Brachybacterium, bacteria of the genus Brevibacterium, bacteria of the order Burkholderiales, bacteria of the genus Parococcus, bacteria of the family Rhodobacteraceae and bacteria of the genus Fusobacterium, and (ii) metabolites of these bacteria chosen from 3-hydroxy-3-methylglutarate, 3-methylglutarate/2-methylglutarate, 4-guanidinobutanoate, 4-imidazoleacetate, 5-oxoproline, aconitrate, adipate, alanine, alpha-cetoglutarate, arabonate/xylonate, azelate, beta-citrylglutamate, choline, cis-urocanate, citraconate/glutaconate, fructose, fumarate, gamma-glutamylalanine, gamma-glutamylglutamine, gamma-glutamylglycine, gamma-glutamylisoleucine, gamma-glutamylleucine, gamma-glutamylserine, gamma-glutamylthreonine, gamma-glutamyltryptophane, gamma-glutamylvaline, glutarate, glycerate, glycerol-3-phosphate, glycine, isovalerylglycine, kynurenate, lactate, linoleoyl ethanolamide, malate, maleate, malonate, maltose, methionine sulfoxide, methylsuccinate, N-acetylalanine, N-acetylaspartate, N-acetylarginine, N-acetylglycine, N-acetylhistidine, N-acetylphenylalanine, N-acetylthreonine, N-acetylvaline, oleamide, ornithine, palmitamide, pimelate, proline, salicylate, sebacate, serine, suberate, succinate, undecanedioate and S-amino-omega caprolactam.

3. The method for diagnosing according to claim 1, wherein said at least one marker is chosen from the group constituted of (i) bacteria of the species Micrococcus luteus and bacteria of the species Paracoccus sp., and (ii) metabolites of these bacteria chosen from kynurenate, 4-imidazoleacetate, maleate, ornithine, 4-guanidinobutanaoate, cis-urocanate, malonate, gamma-glutamylleucine, N-acetylarginine and glycerol-3-phosphate.

4. The method for diagnosing according to claim 1, the method further comprising the steps consisting of: (b) comparing the level of said at least one marker measured in step (a) with a control, and (c) based on the comparison of step (b), determining if the skin of the subject displays esthetic degradations.

5. The method for diagnosing according to claim 1, wherein said at least one marker is a bacterium, and the level of said at least one marker is determined by measuring the level of the corresponding 16S rRNA gene.

6. The method for diagnosing according to claim 1, wherein said at least one marker is a metabolite, and the level of said at least one marker is determined by liquid chromatography-mass spectrometry.

7. The method for diagnosing according to claim 1, wherein the skin sample is taken using a D-squame® disc.

8. The method for diagnosing according to claim 1, wherein the esthetic degradation of the skin is linked to pollution.

9. The method for diagnosing according to claim 1, wherein the esthetic degradation of the skin is selected from esthetic pigmentary disorders, lack of radiance and heterogeneity in the complexion.

10. A method of evaluating the cutaneous exposure of a subject to pollution, comprising a step (a) of determining, in a skin sample of the subject, the level of at least one marker chosen from the group constituted of (i) bacteria that comprise a nucleic acid encoding a 16S rRNA of sequence at least 90% identical to the sequence SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8 or SEQ ID NO: 9, and (ii) metabolites of these bacteria chosen from 3-hydroxy-3-methylglutarate, 3-methylglutarate/2-methylglutarate, 4-guanidinobutanoate, 4-imidazoleacetate, 5-oxoproline, aconitrate, adipate, alanine, alpha-cetoglutarate, arabonate/xylonate, azelate, beta-citrylglutamate, choline, cis-urocanate, citraconate/glutaconate, fructose, fumarate, gamma-glutamylalanine, gamma-glutamylglutamine, gamma-glutamylglycine, gamma-glutamylisoleucine, gamma-glutamylleucine, gamma-glutamylserine, gamma-glutamylthreonine, gamma-glutamyltryptophane, gamma-glutamylvaline, glutarate, glycerate, glycerol-3-phosphate, glycine, isovalerylglycine, kynurenate, lactate, linoleoyl ethanolamide, malate, maleate, malonate, maltose, methionine sulfoxide, methylsuccinate, N-acetylalanine, N-acetylaspartate, N-acetylarginine, N-acetylglycine, N-acetylhistidine, N-acetylphenylalanine, N-acetylthreonine, N-acetylvaline, oleamide, ornithine, palmitamide, pimelate, proline, salicylate, sebacate, serine, suberate, succinate, undecanedioate and S-amino-omega caprolactam.

11. The method for diagnosing according to claim 2, the method further comprising the steps consisting of: (b) comparing the level of said at least one marker measured in step (a) with a control, and (c) based on the comparison of step (b), determining if the skin of the subject displays esthetic degradations.

12. The method for diagnosing according to claim 3, the method further comprising the steps consisting of: (b) comparing the level of said at least one marker measured in step (a) with a control, and (c) based on the comparison of step (b), determining if the skin of the subject displays esthetic degradations.

13. The method for diagnosing according to claim 2, wherein said at least one marker is a bacterium, and the level of said at least one marker is determined by measuring the level of the corresponding 16S rRNA gene.

14. The method for diagnosing according to claim 3, wherein said at least one marker is a bacterium, and the level of said at least one marker is determined by measuring the level of the corresponding 16S rRNA gene.

15. The method for diagnosing according to claim 4, wherein said at least one marker is a bacterium, and the level of said at least one marker is determined by measuring the level of the corresponding 16S rRNA gene.

16. The method for diagnosing according to claim 2, wherein said at least one marker is a metabolite, and the level of said at least one marker is determined by liquid chromatography-mass spectrometry.

17. The method for diagnosing according to claim 3, wherein said at least one marker is a metabolite, and the level of said at least one marker is determined by liquid chromatography-mass spectrometry.

18. The method for diagnosing according to claim 4, wherein said at least one marker is a metabolite, and the level of said at least one marker is determined by liquid chromatography-mass spectrometry.

19. The method for diagnosing according to claim 2, wherein the esthetic degradation of the skin is linked to pollution.

20. The method for diagnosing according to claim 3, wherein the esthetic degradation of the skin is linked to pollution.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0315] FIG. 1: Appearance and severity of the extended maculae on the cheek and the forehead of women in the groups GP1 and GP2 defined in the examples (mean scores).

[0316] FIG. 2: Severity of the extended maculae over a clinical score from 1 to 4 (4 being the most severe) within each group.

EXAMPLE

[0317] The example hereinbelow shows the identification of signatures comprising 60 metabolites and 8 microbes which are significantly modulated in skin samples of individuals exposed to chronic pollution (based on the detection of high levels of pollutants in the samples of their hair).

[0318] Methods and Results

[0319] identification of the “polluted” vs. “non-polluted” groups

[0320] Two groups of individuals, called Group 1 (GP1) and Group 2 (GP2) in the text hereinbelow, were derived from an integrative unsupervised multivariate analysis of blocks of PAH data (polycyclic aromatic hydrocarbons), metabolites and bacterial microbiome.

[0321] Skin samples (D-Squame®) from 42 women (GP1) exposed to a high level of pollution were compared with those of 45 women (GP2) exposed to a relatively low level of pollution. The women of the two groups were in good health, and aged 25-45 years. 30/42 women in Group 1 (GP1) lived in a polluted city in China for at least 15 years. 42/45 women in Group 2 (GP2) lived in a less polluted city for at least 15 years.

[0322] The polluted and non-polluted cities were selected based on a significantly discriminating Air Quality Index (AQI) over a period of one year.

[0323] Here, pollution is defined as exposure to particles of matter and was established in these women by analyzing the PAHs and metabolites of PAHs in hair samples (Palazzi et al. (2018) Environment international 121:1341-1354).

[0324] The PAHs and metabolites of PAHs were quantified in pg/ml and compared between the two groups. A V-test analysis was conducted and the Fold Change (F.C.) was calculated. The statistical analysis is described in a separate section.

TABLE-US-00002 TABLE F.C. PAH and PAH metabolite, GP1 versus GP2 PAH and PAH Log2 F.C. F.C. No. metabolites (GP1 vs GP2) (GP1 vs GP2) p value 1 In_2OHPhenanth 0.872588218 1.8309447 0.000738291 2 In_3OHFluorene 0.730025913 1.658668884 0.001879259 3 In_3OHPhenanth 0.782246456 1.719806742 0.002666112 4 In_Bbfluora 0.900554433 1.866783257 6.19299 × 10.sup.−7 5 In_Benzoghipery 0.812510839 1.756265356 8.39888 × 10.sup.−8 6 In_fluoranthene 1.061596344 2.087239778 2.70474 × 10.sup.−14 7 In_pyrene 0.757862725 1.690983667 2.03191 × 10.sup.−11

[0325] The women in GP1 have the clinical sign “Extended maculae or inhomogeneous skin color” on the cheek and on the forehead in a higher way, with respect to the women in GP2 (see FIG. 1). The severity of this clinical sign, evaluated over a score ranging from 1 to 4, is j higher in GP1 with respect to GP2 (see FIG. 2).

[0326] Analysis of the Metabolites

[0327] An analysis of the metabolites was conducted on D-Squame® samples of GP1 and GP2. All the samples were analyzed using a non-targeted approach.

[0328] In sum, the samples were extracted and divided into equal parts for an analysis on liquid chromatography-mass spectrometry (LC/MS/MS) and polar liquid chromatography platforms. A suitable software was used to have the ions correspond to a customized library of standards for the identification of metabolites and for the quantification of metabolites by integration of the area of the peak.

[0329] The biochemical identifications are therefore based on 3 criteria: the retention index within a narrow retention window of the identification proposed, a precise correspondence of the mass with the library ±10 ppm, and the “forward” and inverse MS/MS scores between the experimental data and the authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum and the ions present in the spectrum of the library. Although there may be similarities between the molecules based on one of these factors, using three data points makes it possible to distinguish and to differentiate the biochemical molecules.

[0330] More than 3300 commercially-available purified standard compounds were acquired and recorded in the LIMS for analysis on all the platforms in order to determine their analytical characteristics. Additional entries of mass spectra were created for the structurally unnamed biochemical molecules, which were identified through their recurring nature (both chromatographically and mass spectrum). These compounds have the potential of being identified by future acquisition of a corresponding purified standard or by conventional structural analysis. The peaks were quantified by using the area under the curve of the primary MS ions.

[0331] Conventional T-tests were used to determine the statistical significance.

[0332] A variety of treatment procedures was implemented in order to ensure that a high-quality dataset was made available for the statistical analyses and data interpretation. The quality control and treatment processes were designed to ensure precise and coherent identification of the true chemical entities, and to eliminate those representing artifacts of the system, incorrect assignments and background noise.

TABLE-US-00003 TABLE F.C. Metabolite, GP1 versus GP2 Log2 F.C. F.C. No. Metabolite (GP1 vs GP2) (GP1 vs GP2) p value  1 3-hydroxy-3-methylglutarate 0.412283837 1.330790837 0.000141773  2 3-methylglutarate/2-methylglutarate 0.419605244 1.337561516 2.01884E−05  3 4-guanidinobutanoate 0.493307598 1.407668473 0.013184653  4 4-imidazoleacetate 0.561001295 1.475292782 0.00068275  5 5-oxoproline 0.434527232 1.351467887 4.12627E−06  6 aconitate [cis or trans] 0.523460601 1.437399013 0.000720695  7 adipate (C6-DC) 0.293464336 1.225579725 0.021999483  8 alanine 0.463441718 1.378827252 9.22971E−06  9 alpha-ketoglutarate 0.223848168 1.167844482 0.126047656 10 arabonate/xylonate 0.544364971 1.45837827 0.042519418 11 azelate (C9-DC) 0.351317553 1.275725162 0.005087577 12 beta-citryglutamate 0.467446649 1.382660203 0.016141134 13 choline 0.283321874 1,216993847 0.015545954 14 cis-urocanate 0.639671208 1,557974054 2.33224E−06 15 citraconate/glutaconate 0.460210012 1.375742069 0.000116885 16 fructose 0.225626142 1.169284618 0.288871001 17 fumarate 0.34431474 1.269547822 0.027794183 18 gamma-glutamylalanine 0.424110429 1.341744923 0.045952367 19 gamma-glutamylglutamine 0.309613559 1.239375676 0.096328655 20 gamma-glutamylglycine 0.544635809 1.458652079 0.00281599 21 gamma-glutamylisoleucine 0.452863724 1.368754516 0.001564193 22 gamma-glutamylleucine 0.507610478 1,421693508 0.00197086 23 gamma-glutamylserine 0.502571602 1.416736644 0.001120818 24 gamma-glutamylthreonine 0.475991305 1.390873589 0.0014828 25 gamma-glutamyltryptophane 0.500982815 1.415177303 0.004224649 26 gamma-glutamylvaline 0.487642689 1.402151938 0.000258416 27 glutarate (C5-DC) 0.23915041 1.18029739 0.032509061 28 glycerate 0.26215793 1.199271189 0.031130415 29 glycerol 3-phosphate 0.733675298 1.662869899 0.000458764 30 glycine 0.454261383 1.370081184 0.00046025 31 isovalerylglycine 0.416550126 1.33473203 0.006928031 32 kynurenate 1.222806378 2.33400294 0.004630418 33 lactate 0.356163108 1.280017122 0.038356821 34 linoleoyl ethanolamide 0.460563945 1.376079618 0.02239249 35 malate 0.36932128 1,291744983 0.004359211 36 maleate 0.612820901 1.529246422 6.74611E−06 37 malonate 0.527575029 1.441504188 7.05402E−06 38 maltose 0.70983322 1.635615024 0.055872613 39 methionine sulfoxide 0.292698208 1.224929067 0.02369457 40 methylsuccinate 0.427132207 1.344558202 5.33979E−05 41 N-acetylarginine 0.723274866 1.650925326 0.007175595 42 N-acetylalanine 0.263559676 1.200436987 0.018347588 43 N-acetylaspartate (NAA) 0.537552815 1.451508294 0.015127349 44 N-acetylglycine 0.39320947 1.313311796 0.004112891 45 N-acetylhistidine 0.33993252 1.265697391 0.009064176 46 N-acetylphenylalanine 0.418235714 1.33629239 0.02762647 47 N-acetylthreonine 0.193190462 1.143289261 0.051522665 48 N-acetylvaline 0.378148188 1.299672553 0.018932275 49 oleamide 0.482884878 1.397535449 0.004567401 50 ornithine 0.400608101 1.320064205 0.0030726 51 palmitamide (16:0) 0.574514155 1.489175881 0.000267398 52 pimelate (C7-DC) 0.264673385 1.20136404 0.021751008 53 proline 0.386162431 1.306912389 0.000143603 54 salicylate 0.647492446 1.566443188 0.001580432 55 sebacate (C10-DC) 0.304064132 1.234617488 0.003014042 56 serine 0.425638239 1.34316658 6.74308E−05 57 suberate (C8-DC) 0.33608662 1.262327821 0.004204843 58 succinate 0.306667853 1.236847691 0.082821617 59 undecanedioate (C11-DC) 0.414292193 1.332644702 0.000297138 60 S-amino-omegacaprolactam 1.251717593 2.381247524 0.000567402 (isomer)

[0333] Evaluation of the Bacterial Microbiome

[0334] None of the participants received any antibiotics or systemic antifungals one month before sampling, had any severe skin disorder, and had used any skin or systemic treatments for depigmentation/whitening three months before sampling, or any exfoliating product one month before sampling.

[0335] They were asked to wash their face using a neutral soap provided without antibacterial compounds for 3 days (once a day) before sampling. The last shampoo and the last soap were applied respectively 48 and 24 h before sampling. No other product was authorized on the scalp, the hair and the face until the sampling was complete.

[0336] The sampling of microbiota was carried out in a controlled atmosphere at 22° C. and 60% humidity. The samples for the analysis of the microbiome were collected using sterile dry cotton buds that were heated to 150° C. and pre-moistened with an ST solution (0.15 M NaCl with 0.1% tween 20). For the cheek samples, the swabs were soaked in a collection buffer and rubbed firmly on the cheek for 60 seconds to cover a surface of 1 cm×2 cm. After sampling, each cotton bud was placed in a microtube and immediately frozen in liquid nitrogen, and stored at ˜80° C. before extraction of the genomic DNA (DNAg).

[0337] The profiling of the bacterial 16S rDNA was carried out as follows: [0338] Preparation of the amplicon sample for the sequencing of the 16$ rRNA gene

[0339] the DNAg was extracted using the PowerSoil DNA® isolation kit (MO BIO Laboratories, Carlsbad, Calif., USA) by following the manufacturer's instructions with the modifications described in Leung et at (2014) Appl. Environ. Microbiol 80: 6760-6770. In addition, following elution C6, the eluate was passed through the same column filter an additional time in order to increase the yield. Negative controls of water without DNA were extracted in parallel. Each sample of DNAg was subjected to a PCR in triplicate with primers targeting the region V1-3 of the bacterial 16S rRNA, which is more precise for obtaining an image of the bacterial community of the skin (Meisel et al. (2016) J. Invest. Dermatol. 136:947-956). For the analysis of the 16S rRNA, the amplicon PCR and the indexing PCR were conducted on a PCR 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, Calif., USA), and the amplicons were purified with DNA/RNA purification beads (SeqMatic, Fremont, Calif., USA). The preparation of the library and the paired-end sequencing of the bacterial nucleic acids of 300 bp on the Illumina Miseq® platform were carried out by SeqMatic LLC (Fremont, Calif., USA). [0340] Processing of the sequence of the rRNA 16S gene and bioinformatics analysis

[0341] The bacterial and fungal readings paired respectively in .fastq format were merged by using the “-fastq_mergeairs” command in USEARCH. The merged readings were filtered for quality control using the “-fastq_filter” command in USEARCH, with a maximum expected error rate of 0.01. The merged readings were cut at 45 bp and the shorter readings were eliminated. The filtered readings were subjected to a OTU grouping at 97% sequence identity using the UPARSE algorithm (Edgar (2013) Nature Methods 10:996-998), and the taxonomic information was provided for the sequences representative of bacterial OTUs by using the “assign_taxonomy.py” command in QIIME (version 1.9) against the SILVA database (128 outputs). The OTUs in the taxonomic lines present in more than 5% of the negative controls were considered as potential contaminants (Leung et al. (2018) Microbiome 6: 26), and were eliminated from the dataset. In addition, the chimeric, chloroplast and mitochondria OTUs were also eliminated. Following the quality control and the elimination of undesirable readings, a total of 9,656,916 bacterial readings was retained.

TABLE-US-00004 TABLE F.C. bacterial microbiome, GP1 versus GP2 Log2 F.C. F.C. No. Microbe (GP1 vs GP2) (GP1 vs GP2) p value 1 Propionibactenum acnes (species) −0.123218238 0.918137264 9.1793E−06 2 Micrococcaceae (family)   1.180033301 2.265820071 6.38354E−05 3 Brachybacterium (genus)   0.790931581 1.730191326 0.000383131 4 Brevibacterium (genus)   1.277515888 2.424212032 3.71944E−06 5 Burkholderiales (order)   0.865917746 1.82249865 1.16516E−06 6 Paracoccus (genus)   0.566027163 1.480441171 0.001358025 7 Rhodobacteraceae (family)   0.514561357 1.428559728 0.000100992 8 Fusobacterium (genus) −0.349593237 0.78480534 0.028291382 9 Micrococcus luteus (species)   0.017750243 1.01237953 0.794723678

[0342] Statistical Analysis

[0343] In the original scale, the measurements of metabolites are standardized in terms of raw surface counts, each metabolite is then brought to scale in order to obtain a median equal to 1 and the missing values are imputed with the minimum. The statistical analyses are conducted on transformed values.

[0344] A multi-block statistical analysis was conducted in order put into relation in the same model the metabolites, the microbiome data sampled on the cheek and the PAH data. This analysis allowed the inventors to identify relevant groups of individuals and to characterize these groups with clinical variables using approaches of X.sup.2 test or variance analysis according to the type of clinical scores.

[0345] More precisely, a sparse generalized canonical correlation analysis (Witten et al. (200$) Biostatistics 10: 515-534) was conducted in order to select the descriptors significantly associated with the covariance/correlation structure between the blocks. Then, a regularized PCA-consensus, called MAXVAR-A (Tenenhaus et aL (2017) Psychometrika 82: 737-777), based on the selected descriptors, was implemented in order to construct a consensus space. Finally, a hierarchical grouping on the consensus space made it possible to reveal the two characteristic groups GP1 and GP2.

[0346] The comparisons between groups, based on the block descriptors, were carried out using Student's T test.