METHOD FOR DIAGNOSIS OF EARLY AGEING OF THE SKIN

Abstract

The present invention concerns a method for diagnosing early ageing of the skin in a subject, that in particular is pollution-related, comprising a step (a) of determining in a skin sample of the subject the level of at least one marker selected from the group constituted of fungi comprising a nucleic acid encoding an ITS1 («Internal Transcribed Spacer 1») region of sequence being at least 90% identical to sequence SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5 or SEQ ID NO: 6, and optionally fungi comprising a nucleic acid encoding an ITS1 region of sequence being at least 90% identical to sequence SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9 or SEQ ID NO: 10.

Claims

1. A method for diagnosis of early ageing of the skin in a subject, comprising a step (a) of determining in a skin sample of the subject the level of at least one marker selected from the group constituted of fungi comprising a nucleic acid encoding an ITS1 («Internal Transcribed Spacer 1») region of sequence at least 90% identical to sequence SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5 or SEQ ID NO: 6, and optionally fungi comprising a nucleic acid encoding an ITS1 region of sequence at least 90% identical to sequence SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9 or SEQ ID NO: 10.

2. The method according to claim 1, wherein said at least one marker is selected from the group constituted of fungi of genus Candida, fungi of the Sclerotiniaceae family, fungi of genus Emericella, fungi of order Hypocreales, fungi of genus Mucor, fungi of genus Sporobolomyces, and optionally fungi of genus Malassezia and fungi of genus Cryptococcus.

3. The method according to claim 1, wherein said at least one marker is selected from the group constituted of fungi of genus Candida, fungi of the Sclerotiniaceae family, fungi of genus Emericella, fungi of order Hypocreales, fungi of genus Mucor and fungi of genus Sporobolomyces.

4. The method according to claim 1, the method further comprising the steps consisting in: (b) comparing the level of said at least one marker measured at step (a) with a control; and (c) on the basis of the comparison at step (b), determining whether the skin of the subject shows early ageing.

5. The method according to claim 1, wherein the level of said at least one marker is determined by measuring the level of the corresponding ITS1 DNA region.

6. The method according to claim 5, wherein the level of said at least one marker is determined by PCR amplification combined with sequencing of the ITS1 DNA region.

7. The method according to claim 1, wherein the skin sample is taken by rubbing the surface of the skin.

8. The method according to claim 1, wherein early ageing of the skin is pollution-related.

9. The method according to claim 1, wherein the subject is aged between 25 and 45 years.

10. The method according to claim 1, wherein early ageing includes the presence of lines and/or wrinkles, large macules, lentigo simplex, red patches and/or a complexion that is dull and/or heterogeneous.

11. The method according to claim 2, the method further comprising the steps consisting in: (b) comparing the level of said at least one marker measured at step (a) with a control; and (c) on the basis of the comparison at step (b), determining whether the skin of the subject shows early ageing.

12. The method according to claim 3, the method further comprising the steps consisting in: (b) comparing the level of said at least one marker measured at step (a) with a control; and (c) on the basis of the comparison at step (b), determining whether the skin of the subject shows early ageing.

13. The method according to claim 2, wherein the level of said at least one marker is determined by measuring the level of the corresponding ITS1 DNA region.

14. The method according to claim 3, wherein the level of said at least one marker is determined by measuring the level of the corresponding ITS1 DNA region.

15. The method according to claim 4, wherein the level of said at least one marker is determined by measuring the level of the corresponding ITS1 DNA region.

16. The method according to claim 2, wherein the skin sample is taken by rubbing the surface of the skin.

17. The method according to claim 3, wherein the skin sample is taken by rubbing the surface of the skin.

18. The method according to claim 4, wherein the skin sample is taken by rubbing the surface of the skin.

19. The method according to claim 5, wherein the skin sample is taken by rubbing the surface of the skin.

20. The method according to claim 6, wherein the skin sample is taken by rubbing the surface of the skin.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0095] FIG. 1 shows clustering of fungi and early ageing. The grey circles correspond to individuals having wrinkles and hyperpigmented spots. The left-hand ellipse corresponds to the cluster: «Early ageing»: n (total)=90 (53 Baoding/37 Dalian) 59% vs 41%.

EXAMPLE

[0096] The example below shows the identification of a signature comprising 8 fungi which are significantly modulated in skin samples of individuals exposed to chronic pollution (on the basis of detection of high levels of pollutants in hair samples of the individuals).

Materials and Methods

Main Steps:

[0097] skin sampling [0098] profiling fungal ITS1 rDNA [0099] determining the relative abundance of microbiome markers [0100] statistical study [0101] diagnosis of early ageing of the skin.

Description of Subjects

[0102] All the subjects in each city came to the facilities in Baoding and Dalian (China). Skin samples were collected in 204 Chinese women in good health, being 25 to 45 years-old, among whom 102 lived in the relatively rural and industrial city of Baoding, a city in the north of China in the Hebei province recording high levels of air pollution (about 90 μg PM.sub.2.5/m.sup.3 air), and 102 living in Dalian, a city in the north of China, urbanised and modern in the province of Liaoning with a lower degree of recorded air pollution (about 30 μg PM.sub.2.5/m.sup.3 air). These cities are located at the same latitude and have shared a similar climate and equivalent UV exposure (UV index) over the last 15 years.

[0103] The participants living in the two cities were assessed for their exposure to PAHs (polycyclic aromatic hydrocarbons) in 12 cm hair samples (reflecting the extent of exposure over a one-year period) (Palazzi et al. (2018) Env. Int. 121:1341-1354). Specifically, in Baoding, the median concentration was 1.5 to 2.8 times higher for parent PAHs and 1.1 to 2.3 times higher for PAH metabolites than the concentration in Dalian. Among quantified parent PAHs, higher levels were observed for phenanthrene, fluoranthene, pyrene, fluorene, acenaphthylene and anthracene, whereas for the PAH metabolites the levels of 9-OH fluorene, 2-OH-naphthalene and 1-OH-anthracene were higher (Palazzi et al. (2018) Env. Int. 121:1341-1354).

[0104] On clinical level, increased severity was observed for almost all facial signs including wrinkles and pigmentary disorders in individuals living in Baoding. In addition, discriminant analysis of the subjects was conducted using dermatological evaluation data (Bourokba et al., poster presented at the 76.sup.th annual conference of the Society for Investigative Dermatology, Portland, Ore. 26-29 Apr. 2017). This analysis led to the definition of an «early ageing» cluster, corresponding to n=90 women out of 204 (53 from Baoding and 37 from Dalian). The average age of these women was 36 years and they showed increased levels of wrinkles and pigmentary disorders (large macules, lentigo simplex, red patches).

Subjects and Sample Collection

[0105] None of the participants received systemic antibiotics or antifungals for one month before sampling, none had a severe skin disorder or had used skin or systemic depigmenting/whitening treatments for three months before sampling, or an exfoliative product one month before sampling. They were requested to use a provided neutral soap not containing any antibacterial compounds for face washing for 3 days (once a day) before sampling. The last shampoo and last soap were applied respectively 48 and 24 h before sampling. No other product was authorised on the scalp, hair and face until samples had been taken.

[0106] Microbiota sampling was performed in a room with controlled atmosphere at 22° C. and 60% humidity. The samples for microbiome analysis were collected using dry, sterile cotton buds which were heated to 150° C. and pre-moistened with ST solution (0.15 M NaCl with 0.1% Tween 20). For cheek samples, the swabs were immersed in collection buffer and firmly rubbed 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, immediately frozen in liquid nitrogen, and stored at −80° C. before extracting genomic DNA (gDNA).

Profiling of Fungal ITS1 rDNA [0107] Preparation of an amplicon sample for ITS sequencing.

[0108] The gDNA was extracted using the PowerSoil DNA® isolating kit (MO BIO Laboratories, Carlsbad, Calif., USA) following the manufacturer's instructions with the modifications described in Leung et al. (2014) Appl. Environ. Microbiol. 80:6760-6770. In addition, after C6 elution, the eluate was passed an additional time through the same column filter to increase yield. Negative water controls without DNA were extracted in parallel. Each gDNA sample was subjected to PCR in triplicate with primers targeting the ITS1 region as described in Leung et al. (2016) Microbiome 4:46. For analysis of the ITS1 region, the amplicon PCR and indexing PCR were prepared 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). Preparation of the library and paired-end sequencing of the fungal nucleic acids of 250 bp on Illumina Miseq® platform were performed by SeqMatic LLC (Fremont, Calif., USA). [0109] Processing of the ITS sequence and bioinformatic analysis

[0110] Fungal reads respectively paired in.fastq format were merged using the command «-fastq_mergeairs» in USEARCH. The merged reads were filtered for quality control using the command «-fastq_filter» in USEARCH, with a maximum expected error rate of 0.01.

[0111] The merged reads were cut-off at 450 bp and the shortest reads were discarded. The filtered reads were subjected to OTU grouping with 97% sequence identity using the UPARSE algorithm (Edgar (2013) Nature Methods 10:996-998). The fungal OTUs were interrogated against an organized fungal database designed for monitoring the skin microbiome (Findley et al. (2013) Nature 498:367-370). Detection of chimera was performed using UCHIME2 (Edgar (2016) bioRxiv 074252) under high confidence mode. The OTUs in the taxonomic lines present in more than 5% of negative controls were considered to be potential contaminants (Leung et al. (2018) Microbiome 6:26), and were removed from the dataset. In addition, chimeric OTUs, OTUs of chloroplasts and of mitochondria were also removed. After quality control and removal of undesirable reads, a total of 14649172 fungal reads was retained.

Statistical Analysis

[0112] Two statistical approaches were used: a hypothesis test on OTU abundance and multivariate analysis using hierarchical multiblock analysis (MAXVAR-A).

[0113] The result of these tests is qualitative: normal skin or skin showing early ageing. [0114] Method 1: comparison of OTU abundance per marker or in combination.

[0115] With regard to the processing of relative abundance data, pre-filtering was performed on OTU relative abundance. OTUs with relative abundance lower than 0.1% among all the individuals were discarded. Also, CSS («Cumulative sum scaling») standardisation was applied which corrects biases in the assessment of differential abundance induced by TSS («Total sum scaling») standardisation. CSS standardisation of data was applied using the R package metagenomeSeq (http://www.cbcb.umd.edu/software/metagenomeSeq).

[0116] The mean difference between the two groups in terms of OTU abundance was evaluated by statistical test procedure using the v-test. V-test values correspond to the comparison between the mean OTU abundance in the group of interest (i.e. early ageing) (column: Mean.in.category) and the mean OTU abundance of the total population (column: Overall.mean). A positive value Indicates that OTU abundance is higher in the group of interest and a negative value indicates lower abundance. A p-value associated with each v-test value was also calculated (column: p.value).

[0117] The mean OTU abundance in subjects other than in the «early ageing» group was calculated (column: mean.in.cluster1.3.4). OTU abundance, after CSS standardisation, per group, and the threshold values are given in the table below.

TABLE-US-00002 R1 = OTUs Genus N v.test Mean.in.category Overall.mean F11231 Malassezia 90 −3.217047857 5.344342385 5.843403089 F14 Candida 90 2.313612741 8.221231167 7.507521539 F26 Emericella 90 2.079601491 4.908542505 4.358347787 F28 Cryptococcus 90 2.432235172 6.430968209 5.768489628 F37 unclassified_Hypocreales_genus 90 2.011446181 4.145301468 3.542348539 F4665 Malassezia 90 −2.046548822 6.894411374 7.089832626 F495 Malassezia 90 2.492228817 6.592514446 5.947327592 F67 unclassified_Sclerotiniaceae_genus 90 2.252923104 4.297122808 3.658265936 F70 Sporobolomyces 90 1.967496681 2.894134872 2.405374295 F89 Mucor 90 1.992780973 1.767717962 1.378751049 OTU threshold R2 = value for OTUs mean.in.cluster1.3.4 sd.in.category Overall.sd p.value early ageing F11231 6.240885065 1.843859765 1.967675918 0.001295170 <R2 F14 6.939080243 3.699983995 3.912808610 0.020688974 >R2 F26 3.920139604 3.251421014 3.355784120 0.037562100 >R2 F28 5.240851820 3.052527138 3.454808623 0.015005961 >R2 F37 3.062120543 3.947335323 3.802180320 0.044278350 >R2 F4665 7.245477872 1.204036406 1.211176404 0.040702404 <R2 F495 5.433461956 3.129962031 3.283638313 0.012694423 >R2 F67 3.149441879 3.484592943 3.596788655 0.024263998 >R2 F70 2.016095960 3.437818152 3.150938145 0.049125978 >R2 F89 1.068954393 2.601905214 2.475772927 0.046285436 >R2 [0118] Method 2: Comparison of fungal relative abundance profile in relation to historical grouping of subjects versus PAH compounds (Baoding/Dalian database).

[0119] With regard to analysis of global correlation with PAH, pre-filtering was performed on OTU relative abundance. OTUs with relative abundance lower than 0.1% among all individuals were removed. Also, CSS standardisation («Cumulative sum scaling u) was applied which corrects biases in the assessment of differential abundance induced by TSS («Total sum scaling») standardisation. Among the OTUs, 69 were selected for the fungi. PAH measurements were log-transformed to follow Gaussian distribution. A total of 202 individuals with OTU and PAH data were included in the analysis. With a view to variable selection, Sparse Canonical Correlation Analysis (sCCA) was performed to select the OTU and PAH descriptors which were active in relationships between blocks. The sparsity parameters of sCCA were selected via permutation procedure using the MutiCCA.permute function of the PMD R package (Witten et al. (2009) Biostatistics 10:515-534; Tenenhaus et al. (2014) Biostatistics 15:569-583). Finally, to obtain common representation of individuals in the 2 blocks, hierarchical multi-block analysis (MAXVAR-A) was performed using the RGCCA R package (Tenenhaus et al. (2017) Psychometrika 82:737-777). Subjects showing signs of early ageing are surrounded by a circle in FIG. 1.

[0120] For diagnosis, the new profile must be compared with the grouping. If it is contained within the left-hand ellipse, the subject tests positive and shows early ageing of the skin.