Bacterial Microbiota Composition in Hidradenitis Suppurativa Differs per Skin Layer

Abstract

Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disease in which a shift in skin microbiota composition plays a role in the initiation and/or maintenance of the disease (Mintoff et al., 2021Mintoff D. Borg I. Pace N.P. The clinical relevance of the microbiome in hidradenitis suppurativa: a systematic review.Vaccines (Basel). 2021; 9: 1076Crossref Scopus (10) Google Scholar; van Straalen et al., 2022van Straalen K.R. Prens E.P. Gudjonsson J.E. Insights into hidradenitis suppurativa.J Allergy Clin Immunol. 2022; 149: 1150-1161Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar; Wark and Cains, 2021Wark K.J.L. Cains G.D. The microbiome in hidradenitis suppurativa: a review.Dermatol Ther (Heidelb). 2021; 11: 39-52Crossref PubMed Scopus (27) Google Scholar). Differences across studies regarding the relevant bacteria in HS persist owing to the use of different sampling techniques (swabs, scrapings, or biopsies). This affects the comparability of current HS microbiome studies (Nakatsuji et al., 2013Nakatsuji T. Chiang H.I. Jiang S.B. Nagarajan H. Zengler K. Gallo R.L. The microbiome extends to subepidermal compartments of normal skin.Nat Commun. 2013; 4: 1431Crossref PubMed Scopus (333) Google Scholar; Prast-Nielsen et al., 2019Prast-Nielsen S. Tobin A.M. Adamzik K. Powles A. Hugerth L.W. Sweeney C. et al.Investigation of the skin microbiome: swabs vs. biopsies.Br J Dermatol. 2019; 181: 572-579Crossref PubMed Scopus (36) Google Scholar). Therefore, in this study, we analyzed the microbiota composition captured at different depths of HS lesional skin using swabs, superficial biopsies, and deep biopsies in different disease stages. In total, 96 samples were collected from the skin of 32 patients with HS (Hurley 1: n = 10; Hurley II: n = 12; and Hurley III: n = 10) collected during routine surgery. The institutional review board of the Erasmus University Medical Center (Rotterdam, The Netherlands) allows the use of surgical discarded tissue for research purposes under an opt-out principle; therefore, no additional informed consent was required for this study. Fifteen patients were sampled in the groin, 13 were sampled in the axilla, and 4 were sampled in other areas (n = 3 for buttock, and n = 1 for neck). The mean body mass index was 29.1 kg/m2 (±6.3), and 78% (25 of 32 patients) were current smokers. Detailed patient characteristics, sample collection, methods, and statistical analyses can be found in Supplementary Table S1. After quality control, 585 amplicon sequence variants were identified in 84 remaining samples (swabs: n = 32; superficial biopsies: n = 28; deep biopsies: n = 24). After adjusting for age, body mass index, smoking status, anatomical location, and Hurley stage, both superficial and deep biopsies were significantly associated with a decreased richness (P < 0.001 and P < 0.001, respectively) and Shannon diversity (P < 0.001 and P = 0.03, respectively) compared with swabs. In addition, adjusted for all other factors, Hurley stage III was associated with a decreased Chao1 index (P = 0.02). These results show that the deeper layers of the skin, captured by biopsies, carry specific microbiota niches colonized by a limited subset of bacteria compared with the external epidermis. The 585 amplicon sequence variants remaining after filtering belonged to Actinobacteria (147), Bacteroidetes (251), Firmicutes (184), Fusobacteria (7), and Proteobacteria (15) (Supplementary Figure S1). Although prominent in HS swabs, Firmicutes and Bacteroidetes were found to be significantly less abundant in deep biopsies (P = 0.020 and P = 0.027, respectively) (Figure 1a and b and Supplementary Table S2). This was accompanied by an increase in Proteobacteria (P < 0.001) in biopsies. Multivariable analysis showed that sampling technique explained 14% of the overall microbiota composition at the phylum level, whereas Hurley stage and body mass index accounted for 3% (P < 0.001) and 2% (P < 0.001), respectively. Of note, there was an increased relative abundance of Proteobacteria from swabs to superficial to deep biopsies in all Hurley stages, whereas the relative abundance of this phylum was low in Hurley stage III regardless of sampling technique (Figure 1c and d and Supplementary Figure S2). Both Hurley stages II and III showed a prominent decrease in Bacteroidetes from swabs to deep biopsies, whereas Hurley stage I showed an increase in abundance. In line with previous studies, Prevotella, Corynebacterium_1, Ezakiella, Porphyromonas, and Peptoniphilus, together accounting for approximately 80% of the total relative abundance, were the most common bacteria found in swabs (Figure 2a) (Schell et al., 2021Schell S.L. Schneider A.M. Nelson A.M. Yin and Yang: a disrupted skin microbiome and an aberrant host immune response in hidradenitis suppurativa.Exp Dermatol. 2021; 30: 1453-1470Crossref PubMed Scopus (17) Google Scholar). Prevotella, Ezakiella, Porphyromonas, and Peptoniphilus were also consistently found among the top 10 most abundant genera of superficial and deep biopsies, which is consistent with previous data from HS tunnel content (Ring et al., 2017Ring H.C. Thorsen J. Saunte D.M. Lilje B. Bay L. Riis P.T. et al.The follicular skin microbiome in patients with hidradenitis suppurativa and healthy controls.JAMA Dermatol. 2017; 153: 897-905Crossref PubMed Scopus (201) Google Scholar). Univariable analysis revealed that the relative abundance of Corynebacterium_1, Peptonipihilus, Porphyromonas, and Finegoldia was significantly higher in swabs than in deep biopsies in contrast to those of Pelomonas and Sphingomonas, which were increased in deep biopsies (Supplementary Table S3 and Figure 2b). Only Corynebacterium_1, Peptonipihilus, and Pelomonas remained significant after multiple testing. Corynebacterium_1 and Peptonipihilus have consistently been found to be enriched in HS lesional skin (Schell et al., 2021Schell S.L. Schneider A.M. Nelson A.M. Yin and Yang: a disrupted skin microbiome and an aberrant host immune response in hidradenitis suppurativa.Exp Dermatol. 2021; 30: 1453-1470Crossref PubMed Scopus (17) Google Scholar). The fact that Pelomonas has not been found in HS before could be due to its existence deep in the dermis, which is not captured with more superficial sampling techniques (Bay et al., 2020Bay L. Barnes C.J. Fritz B.G. Thorsen J. Restrup M.E.M. Rasmussen L. et al.Universal dermal microbiome in human skin.mBio. 2020; 11e02945-19Crossref PubMed Scopus (65) Google Scholar; Schell et al., 2021Schell S.L. Schneider A.M. Nelson A.M. Yin and Yang: a disrupted skin microbiome and an aberrant host immune response in hidradenitis suppurativa.Exp Dermatol. 2021; 30: 1453-1470Crossref PubMed Scopus (17) Google Scholar). Multivariate analysis (permutational ANOVA) revealed that sampling technique explained 9% of the genera variation (P < 0.001), followed by Hurley severity (4%; P < 0.001) and body mass index (3%, P < 0.001). When splitting the samples per Hurley stage, Peptonipihilus was found to be primarily present in swabs from Hurley stage I and to a much lesser extent in Hurley stages II–III (Figure 2c). In contrast, Hurley stages II–III swabs showed the greatest contribution of Corynebacterium_1. Hurley stage III samples showed a higher relative abundance of Prevotella than Hurley stages I–II samples. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes using PICRUSt2 (Douglas et al., 2020Douglas G.M. Maffei V.J. Zaneveld J.R. Yurgel S.N. Brown J.R. Taylor C.M. et al.PICRUSt2 for prediction of metagenome functions.Nat Biotechnol. 2020; 38: 685-688Crossref PubMed Scopus (1940) Google Scholar), adjusted for multiple testing, identified polycyclic aromatic hydrocarbon degradation (ko00624, P = 0.014) and benzoate degradation (ko00362, P = 0.038) pathways to be significantly enriched in deep biopsies compared with those in swabs (Figure 2d). In this study, we demonstrate that significantly different microbiome results are found when analyzing different Hurley stages with different sampling techniques. In line with previous studies, we identified Prevotella and Porphyromonas to be the most prominent genera in HS lesional skin, with the highest abundance in Hurley III samples (Schell et al., 2021Schell S.L. Schneider A.M. Nelson A.M. Yin and Yang: a disrupted skin microbiome and an aberrant host immune response in hidradenitis suppurativa.Exp Dermatol. 2021; 30: 1453-1470Crossref PubMed Scopus (17) Google Scholar; Wark and Cains, 2021Wark K.J.L. Cains G.D. The microbiome in hidradenitis suppurativa: a review.Dermatol Ther (Heidelb). 2021; 11: 39-52Crossref PubMed Scopus (27) Google Scholar). The relative abundance of Corynebacterium_1 and Staphylococcus, which are the main components of skin swabs, decreased significantly in deep biopsies. In contrast, Porphyromonas and Prevotella_6 increased in deep biopsies relative to their levels in swabs. Superficial biopsies seemed to have a microbiota composition between swabs and deep biopsies. Studies of Prevotella and Porphyromonas in HS-associated diseases demonstrate that they can drive T helper 17 immune responses, leading to an upregulation of key HS cytokines (e.g., IL-23A, IL-17, and IL-1), and can stimulate epithelial cells to promote neutrophil recruitment through IL-8 and IL-6 secretion (Larsen, 2017Larsen J.M. The immune response to Prevotella bacteria in chronic inflammatory disease.Immunology. 2017; 151: 363-374Crossref PubMed Scopus (645) Google Scholar; van Straalen et al., 2022van Straalen K.R. Prens E.P. Gudjonsson J.E. Insights into hidradenitis suppurativa.J Allergy Clin Immunol. 2022; 149: 1150-1161Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). Potentially, this microbiota-driven effect could be an understudied factor in the poorer clinical response of patients with Hurley stage III to adalimumab treatment (Kimball et al., 2016Kimball A.B. Okun M.M. Williams D.A. Gottlieb A.B. Papp K.A. Zouboulis C.C. et al.Two Phase 3 trials of adalimumab for hidradenitis suppurativa.N Engl J Med. 2016; 375: 422-434Crossref PubMed Scopus (470) Google Scholar). As such, combining antibiotics with biologics might be needed to achieve higher response rates. Using enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways, we found not previously described enrichment of pathways associated with xenobiotic metabolism in deep biopsies (Ring et al., 2017Ring H.C. Thorsen J. Saunte D.M. Lilje B. Bay L. Riis P.T. et al.The follicular skin microbiome in patients with hidradenitis suppurativa and healthy controls.JAMA Dermatol. 2017; 153: 897-905Crossref PubMed Scopus (201) Google Scholar; Schneider et al., 2020Schneider A.M. Cook L.C. Zhan X. Banerjee K. Cong Z. Imamura-Kawasawa Y. et al.Loss of skin microbial diversity and alteration of bacterial metabolic function in hidradenitis suppurativa.J Invest Dermatol. 2020; 140: 716-720Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar). These pathways have previously been found in the skin microbiota of individuals exposed to polycyclic aromatic hydrocarbons, which can be found in pollution and tobacco smoke (Leung et al., 2020Leung M.H.Y. Tong X. Bastien P. Guinot F. Tenenhaus A. Appenzeller B.M.R. et al.Changes of the human skin microbiota upon chronic exposure to polycyclic aromatic hydrocarbon pollutants.Microbiome. 2020; 8: 100Crossref PubMed Scopus (55) Google Scholar). Enrichment of these pathways could be driven by the higher number of smokers in our study (78.1 vs. 67 and 45.5%) or be a result of our deep sampling technique (Ring et al., 2017Ring H.C. Thorsen J. Saunte D.M. Lilje B. Bay L. Riis P.T. et al.The follicular skin microbiome in patients with hidradenitis suppurativa and healthy controls.JAMA Dermatol. 2017; 153: 897-905Crossref PubMed Scopus (201) Google Scholar; Schneider et al., 2020Schneider A.M. Cook L.C. Zhan X. Banerjee K. Cong Z. Imamura-Kawasawa Y. et al.Loss of skin microbial diversity and alteration of bacterial metabolic function in hidradenitis suppurativa.J Invest Dermatol. 2020; 140: 716-720Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar). A major strength of our study is that we used the recommended hypervariable regions for skin microbiota studies (V1–V3) in contrast to regions used in many previous studies (Mintoff et al., 2021Mintoff D. Borg I. Pace N.P. The clinical relevance of the microbiome in hidradenitis suppurativa: a systematic review.Vaccines (Basel). 2021; 9: 1076Crossref Scopus (10) Google Scholar; Wark and Cains, 2021Wark K.J.L. Cains G.D. The microbiome in hidradenitis suppurativa: a review.Dermatol Ther (Heidelb). 2021; 11: 39-52Crossref PubMed Scopus (27) Google Scholar). Limitations include the relatively small sample size and the absence of healthy control skin samples. In conclusion, our data demonstrate a marked shift at both phylum and genus levels between swabs and deep biopsies, further diversified by different patterns in different Hurley stages. Therefore, we recommend that in future HS microbiota studies, both swabs and (deep) biopsy samples should be assessed in patients with HS and that analyses should be stratified for Hurley stages. Raw fastq files containing the 16S RNA microbiome data were uploaded to the Mendeley Data and Digital Commons Data and will be publicly available in January 2024. The data can be downloaded from 10.17632/knwhjtwb46. Luba M. Pardo: http://orcid.org/0000-0003-0684-3175 Cong Wang: http://orcid.org/0000-0001-7083-2767 Christine B. Ardon: http://orcid.org/0000-0002-2303-4604 Robert Kraaij: http://orcid.org/0000-0003-0416-999X Errol P. Prens: http://orcid.org/0000-0002-8158-660X Kelsey R. van Straalen: http://orcid.org/0000-0003-3305-3814 LMP is a consultant at Centogene GmbH. EPP is a consultant, speaker, and principal investigator at or received grants from AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Janssen-Cilag, Novartis, Pfizer, and UCB. KRvS is a consultant at and/or received honoraria from Novartis, UCB, and Boehringer-Ingelheim. The remaining authors state no conflict of interest. LMP and KRvS had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. LMP is the guarantor for this work. This work was presented at the 69th annual Montagna Symposium on the Biology of Skin, “Microbes, Autoimmunity & Cancer,” held on October 20–24, 2022. Conceptualization: EPP; Data Curation: LMP; Formal Analysis: LMP; Investigation: CBA, RK, KRvS; Methodology: RK, EPP; Resources: RK; Visualization: LMP, KRvS; Writing – Original Draft Preparation: CBA, KRvS; Writing – Review and Editing: LMP, CW, CBA, RK, EPP, KRvS A total of 96 samples were analyzed from 32 patients with hidradenitis suppurativa. The total number of reads was 1,007,659 with a median count of 9,068 (range = 257–22,629) and an average number of 10,282 reads per sample. The proportion of singletons was 29%. After quality control, 585 amplicon sequence variants (ASVs) remained for analysis. A swab, superficial biopsy, and deep biopsy were collected from a single lesion before the start of routine surgery in 32 patients with hidradenitis suppurativa at the Department of Dermatology of the Erasmus University Medical Center (Rotterdam, The Netherlands) between January and September 2018 Inclusion criteria were adult patients with physician diagnoses of hidradenitis suppurativa undergoing routine excision of active inflamed lesions at the Erasmus University Medical Center between January and September 2018. Exclusion criteria were oral and topical antibiotic use for at least 2 weeks before surgery or immunosuppressive or immunomodulatory therapies including biologics for at least 3 months before surgery. The Erasmus University Medical Center has an opt-out principle for the use of surgical discard for research. As such, this study required no required no additional informed consent. One swab, one superficial biopsy, and one deep biopsy were taken sequentially from the exact same location. Swabs were collected using a premoistened (0.9% sterile sodium chloride) cotton swabs, which were rubbed on the skin for 30 seconds. Subsequently, a superficial 4-mm punch biopsy was taken from the same lesion as the swab. The deep biopsy was taken from the skin opening formed by the first superficial biopsy, obtaining the deeper dermal tissue exposed by the superficial biopsy. The skin swabs and biopsies were stored in empty 1.5 ml sterile tubes and snap frozen in liquid nitrogen. All samples were stored at −80 °C until further analysis. DNA was extracted using the DNA Extraction Kit on the Arrow pipetting instrument (DiaSorin S.P.A., Saluggia, Italy). Swabs and biopsies were treated with DNA Pretreatment Buffer 2 and Proteinase K for 30 minutes at 56 °C. Subsequent DNA isolation was performed in the arrow instrument in batches of 12 samples per run, according to the manufacturer's instructions. DNA concentration was measured using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA). All extracted DNA was stored at −20 °C. The V1 to V3 variable regions of the 16S ribosomal RNA gene were amplified using the 27F−519R primer pair and dual indexing as previously described (Fadrosh et al., 2014Fadrosh D.W. Ma B. Gajer P. Sengamalay N. Ott S. Brotman R.M. et al.An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform.Microbiome. 2014; 2: 6Crossref PubMed Scopus (1154) Google Scholar). The pools were purified using Agencourt AMPure XP (Beckman Coulter Life Science, Indianapolis, IN), and the quantity of the pool was assessed using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Waltham, MA). PhiX Control v3 library (Illumina, San Diego, CA) was spiked into (∼10%) the pool before sequencing on an Illumina MiSeq sequencer (MiSeq Reagent Kit, version 3, 2 × 300 bp). Amplicons were normalized and pooled in one batch. Details of the procedure of amplicon purification are presented in the All samples were in one batch. were to control for Raw reads from Illumina MiSeq were and an et al., sequence variants should in data J. 2017; 11: PubMed Scopus Google Scholar; et al., T. J. R. of bacterial microbiome in a 2021; Scopus Google Scholar). files were further using the et al., sequence variants should in data J. 2017; 11: PubMed Scopus Google Scholar). filtering was performed in using the = = = and = reads were through the and were a from the ribosomal RNA version et al., C. P. J. T. P. et al.The ribosomal RNA gene improved data and 2013; PubMed Scopus Google Scholar), using the et al., G.M. J.M. J.R. for of rRNA into the bacterial PubMed Scopus Google Scholar). Data were into a using and S. an for analysis and of microbiome 2013; PubMed Scopus Google Scholar). Data quality control was on the microbiome first and we bacteria that were present in at least of the we samples with at least were using for all used the of bacteria the different layers of the skin, the diversity was using Chao1 and Shannon Chao1 is an of the richness of the whereas Shannon an of the relative Differences in Chao1 and Shannon diversity between sampling were using to the of the data sampling techniques from the same and the were adjusted for used the and J. 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S. et and for microbiome studies using and PubMed Scopus Google was used to for in microbiome composition and adjusted for body mass index, severity (Hurley stages and and anatomical of sampling using as variable to for This was at both the phylum and genus the we the into genus levels using from the in R. In total, we analyzed data analysis was performed to for abundance between specific using the et al., J.M. analysis for 2013; Scopus Google Scholar). this we on the composition between swabs and deep biopsies using and for multiple testing. were with a of P = et al., J.M. analysis for 2013; Scopus Google Scholar). were adjusted using a All the were in the version we a analysis of the 16S RNA to pathways from the using PICRUSt2 (Douglas et al., 2020Douglas G.M. Maffei V.J. Zaneveld J.R. Yurgel S.N. Brown J.R. Taylor C.M. et al.PICRUSt2 for prediction of metagenome functions.Nat Biotechnol. 2020; 38: 685-688Crossref PubMed Scopus Google with on the Kyoto Encyclopedia of Genes and Genomes pathways previous studies used this for in analysis. used reads to after we for in the between swabs and deep biopsies using The analysis was using these are also Figure composition of sample stratified for Hurley of the relative abundance of phylum stratified for Hurley Figure Table with HS (n = n mean mean n stage Hurley stage n Hurley stage n Hurley stage n n n (n = and (n = n body mass hidradenitis (n = and (n = in a Supplementary Table that and Hurley severity Hurley Hurley Hurley statistical body mass in a Supplementary Table at between and statistical in a body mass hidradenitis suppurativa. statistical body mass statistical

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