So, what does a healthy microbiome look like? This is complicated by large intrapersonal and interpersonal variation, where every human has a unique ecosystem of microbes inhabiting their body [4,22,23]. As discussed in our previous work , changes in skin microbial abundances have been associated with disease states, including acne  and psoriasis . However, despite attempts at finding definitive biomarkers for skin health using species or clades, and years of research, we are still very far off [7,27–29], and due to the very nature and complexity of ecosystems which operate using non-linear physics principles, it may not be unreasonable to suggest it could be an academic dead-end for at least the near future. Conversely, biodiversity as a biomarker for healthy or damaged skin is far more conclusive, which led us to our 2017 discovery of what was called ‘the first clear mechanism for measuring skin health’ . We noticed the same phenomenon that occurred across nature, was no different on the skin: an increase in biodiversity equated to a healthier ecosystem [31–36]. Therefore damaged or diseased skin displays a reduced diversity when compared to healthy skin on the same subject and same area of the body [29,37–42], and studies have observed that with higher bacterial diversity, the immune system works more effectively to protect us [10,11,43]. Dysbiosis and decreased skin microbiome biodiversity has been linked with the majority of skin ailments, including eczema , psoriasis , dermatitis , skin cancer , and many more [40,42,47–54], but more work would need to be done to properly determine if low skin
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Although many different substrates are consumed by skin bacteria, several stand out as being particularly important for success. Bacterial use of organic and amino acids, for example, shows enrichment in abun- dant skin bacteria. Interestingly, all eight of the amino acids that we find used significantly more by successful skin species have been positively identified in fingerprint samples . This is consistent with our conclusion that these are important skin nutrients. Similar to amino acids, many of the organic acids that are used by a greater fraction of abundant skin taxa also appear commonly on human skin. This includes lactate, pyruvate , formate , caprate, and valerate . In other cases, nutrients whose use is overrepresented among abundant taxa may not be produced by hu- man skin, but rather, by dominant skin constituents. Succinate, for example, is a skin fermentation product of Staphylococcus epidermidis, meaning that it is likely widely available on the skin surface . Further analysis of the chemical composition of skin secretions, not only by the human host but also by the entire skin microbiome, will help elucidate our findings regarding preferential substrate use.
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communities. Here we present a comprehensive analysis of the temporal dynamics of the skin microbiome in various diseases. In the first section, we characterize the diversity and dynamics of both bacterial and fungal communities colonizing chronic wounds and its associations with clinical outcomes. In a study of 100 subjects with diabetic foot ulcers, we sampled the wound microbiota in 2-week intervals until healing, amputation of 26 weeks of follow-up. We demonstrate the high levels of community instability in chronic wounds and expose the positive association between wound healing community instability. We also reveal the effect of antibiotic perturbation on the microbiota. The fungal component was found to have associations with various bacteria and clinical outcomes. Our results should inform the design of future studies and provides evidence that microbial dynamics may be an effective biomarker for identifying high-risk ulcers. The second section investigates the body-site specific effects of psoriasis on the skin microbiome and how it responds to therapy. We reveal these patterns in a study of 114 subjects, across 6 body sites, and over 112 weeks of follow-up. The effect of psoriatic lesions was found to be mild and body-site specific. In contrast, ustekinumab treatment was found to induce moderate shifts in microbial composition, including an increase in atypical skin bacteria and inter-individual heterogeneity. These results suggest that the effect of psoriasis lesions is secondary to the effect the broad effects of the immune environment. Together the work presented in this thesis represents a significant advancement in our understanding of the microbial dynamics of the skin and their associations with human health.
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These solutions were applied on 18 healthy subjects with ages ranging from 22 to 42. Eight different skin areas were defined for application, whereof three were on the chest and five were located along the spine (Fig. 1a). These areas were chosen due to their typically high abundance of sebaceous glands. To get an understanding about the dose response of applied bacterial strains, three different concentrations were chosen (10 4 , 10 6 , and 10 8 CFU/mL) and applied on the different areas. One area (area 4) was used as a negative control (i.e., no application). To better understand synergistic effects, different strain combina- tions were used. One mixture contained only strain H1 (H1), a second was spiked with small amounts of A1 (H1+A1), and a third consisting of nearly equal amounts of H1 and D1 and small amounts of A1 (H1+D1+A1). H1 is a type IB strain; A1 and D1 are type IA strains (Add- itional file 1: Table S2). To circumvent biases on each sub- ject area, a different concentration was applied and rotated along the different individuals. We rotated site ap- plication for a given solution to prevent potential specific site biases. Initially assigned treatments were maintained for the rest of the study. All test areas except area 4 (con- trol) were sterilized before application. Probiotic solutions were applied every day during days 1, 2, and 3. Skin microbiome samples were taken with commercial skin stripping method (3S-Biokit, C+K electronic) based on fast hardening cyanoacrylate glue at 16 time points (0, 1,
treatments. At present the ruling consensus is that micro- organisms have a secondary role, but only little is under- stood about their contribution and the pathogenesis. Many important questions are rising about the host- microorganism relationship and its relevance. Atopic dermatitis often occurs in characteristic areas of the skin such as the elbows and knee folds, which could be partly explained by differences in the skin microbiome. Whether these specificities are driven by the endogen- ous microbial community structure or is only an epi- phenomenon due to secondary changes, e.g. changes in the skin barrier in atopic dermatitis (e.g. flaggrin mutations) or immunologic factors (e.g. Th2-shift), remains to be determined. However the characteristic changes in microbial colonization have awoken an interest in developing new diagnostic methods and treatments .
microbiome of other vertebrates, including teleostean fishes, at both interspecific [7, 11, 46–48] and intraspecific scales [7, 49–51], this is the first report of an effect of spe- cies diet on the skin microbiome. An explanation could be an indirect transfer from fishes’ feces to their skin. However, the gut microbiome of the thousands of coral reef fishes [21, 52] is still largely unknown (but see  for Acanthuri- dae from the Red Sea). Another explanation would be that fishes having different diets produce different surface mucus. Accordingly, one study showed that different but- terflyfishes produce distinct metabolites in their gill mucus, and that diet was the predominant factor explaining such differences . Another study focusing on tropical reef fish also showed that gill microbiome was partially influ- enced by diet . These findings suggest that the different metabolites present in fish alimentation sources may alter the mucus composition of the consumer, by modification of its physiology and/or by assimilation of certain metabo- lites and exudation in mucus, which would in turn alter mi- crobial community composition in fish gills. Gill and skin mucus are both produced by goblet cells, share several similar components, and may thus be altered by similar pathways [56, 57]. Therefore, as in the case of gill mucus, diet may induce the production of distinct skin mucus, which may drive skin microbiome structure. Assessment of the metabolites present in skin mucus and the effect of fish diet at both inter- and intraspecific scales are now needed to confirm such hypothesis.
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P. acnes strains and general genome features. To understand the genomic diversity of this important skin commensal at the strain level, we analyzed the genomes of 69 P. acnes strains that we se- quenced. Among them, 67 P. acnes strains were isolated from the skin of healthy individuals and acne patients (4) and two P. acnes strains, HL201PA1 and HL202PA1, were isolated from refractory endodontic lesions (23) (Table 1). These 69 strains cover all the known P. acnes lineages isolated to date. We classified the strains based on their 16S rRNA sequences. Each unique 16S rRNA se- quence was defined as a ribotype (RT). All the sequenced P. acnes genomes had three identical copies of 16S rRNA. Based on our metagenomic study of the skin microbiome associated with acne (4), among the top ten major ribotypes, RT1, RT2, and RT3 were the most abundant and found in both healthy individuals and acne patients with no significant differences. RT4, RT5, and RT8, however, were enriched in acne patients, while RT6 was found mostly in healthy individuals. Our 69 strains included 19 RT1 strains, five RT2 strains, 15 RT3 strains, eight RT4 strains, seven RT5 strains, four RT6 strains, six RT8 strains, four strains of mi- nor ribotypes, and one type III strain. The sequence types of all the strains were assigned based on two published MLST schemes (11– 13) and are shown in Table S1 in the supplemental material. The average genome size was 2.50 Mb (ranging from 2.46 to 2.58 Mb), and the GC content was 60%. On average, each genome encoded 2,626 ORFs (ranging from 2,393 to 2,806) (Table 1).
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In addition to host physiology, extrinsic perturbations also likely shape the skin microbiome. Skin microbes are thought to remain stable once intrinsic factors, such as the period leading to adolescence, stabilize (8). However, it is possible that age- independent extrinsic factors, including medications and lifestyle, are associated with shifts in the skin microbiome. It is well known that older people are frequently predisposed to the development of inﬂammatory conditions (9), and this may facilitate colonization by speciﬁc microbial taxa. In addition, the skin’s chemical landscape can be strongly impacted by skin care products and cleansers (10): certain compounds present in these products may also contribute to skin microbiome variation, particularly if repeated exposure has a cumulative impact on skin properties (11). For instance, N-acetylglucosamine, commonly found in skin care products, was recently implicated as a driver of the skin microbiome (12). This compound is a precursor to hyaluronic acid, a major dermal and epidermal constituent whose biosynthetic pathway responds to UV irradiation (13), the major driver of accelerated skin aging. The investigation of micro- biome composition determinants is critical to understanding whether speciﬁc factors contribute to a healthy skin microbial community or whether they indirectly inﬂuence host skin health via microbiome alterations.
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What makes S. aureus so dangerous? The answer lies in the variety of factors S. aureus strains produce to exploit a weakened skin barrier and activate deleterious host immune reactions (Fig- ure 1B). For instance, S. aureus produces a protease that enables it to penetrate into the dermis of AD patients or mice with filaggrin loss-of function mutations (53, 54). S. aureus penetration results in increased production of type 2 cytokines such as TSLP, IL-4, and IL-13 (53). S. aureus strains also produce a number of molecules that induce skin inflammation and exacerbation of AD. Among these are α-toxin, which lyses keratinocytes, especially in the presence of type 2 cytokines (55, 56). Local concentrations of type 2 cytokines are increased by δ-toxin, which induces mast cell degranulation (48), as well as by lipoproteins, S. aureus cell wall components that signal through TLR2/6 and induce TSLP production in keratinocytes (57). Staphylococcal enterotoxin B (SEB) is a superantigen that induces a mixed Th1/Th2 response after application to tape-stripped mouse skin and results in specific IgE responses to both SEB and the coap- plied model antigen OVA (58). When applied to human skin, SEB led to skin inflammation in both healthy subjects and those with AD; in three of six participants with AD, the application of SEB led to a disease flare (59). All of these factors contribute to the exacer- bation of AD by creating local inflammation and inducing a further breakdown of the skin barrier. In future studies, it will be interest- ing to further clarify the role of commensal bacteria in the develop- ment of AD. What other factors might contribute to disease in those children who did not exhibit dysbiosis or an overgrowth of S. aureus before the development of AD? Current treatment regimens still mainly focus on suppressing inflammation and supporting the epi- dermal barrier with emollients (15). Combining these with therapies targeting the skin microbiome may be beneficial.
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The skin is an ecosystem at the interface with the environment and its primary role is to serve as a physical barrier, cooperating with a chemical defense (pH, antimicrobial peptides synthesis…) to protect the body from external aggressions. Recent studies revealed that the outer layer of the skin is intimately bound to its microbial communities, the skin microbiome, representing effectively the first line of skin defense; disruptions in its balance can result in skin disorders or infections (Grice & Segre, 2011). The human skin microbiome hosts fungi, viruses and bacteria, in particular four major phyla: Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes. The microbiome is essential for the skin equilibrium. Indeed, the cutaneous microbiome protects the skin from pathogens colonization (competition and surface occupation, acidic pH, antimicrobial peptides synthesis), and interacts with the cutaneous immune system in order to educate it tolerating its environment (Chen & Tsao, 2013). The skin microbiome composition is highly heterogeneous, as it shows intrapersonal variation depending on the localization on the body, the gender, the age, the ethnicity and the environment (climate, stress, diet, life style, drugs, hygiene habits…) (Schommer & Gallo, 2013; Baldwin et al., 2017; Dimitriu et al., 2019). Concerning the variability depending on the body sites, the microbial composition is influenced by physiological characteristics (Grice et al., 2009; Grice & Segre, 2011). Three typical broad microenvironment types were identified: sebaceous sites (as the forehead, the face or the back) mainly
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ABSTRACT Distinct microbial communities inhabit individuals as part of the human skin microbiome and are continually shed to the surrounding environment. Micro- bial communities from 17 skin sites of 10 sexually active cohabiting couples (20 indi- viduals) were sampled to test whether cohabitation impacts an individual’s skin mi- crobiome, leading to shared skin microbiota among partner pairs. Ampliﬁed 16S rRNA genes of bacteria and archaea from a total of 340 skin swabs were analyzed by high-throughput sequencing, and the results demonstrated that cohabitation was signiﬁcantly associated with microbial community composition, although this associ- ation was greatly exceeded by characteristics of body location and individuality. Random forest modeling demonstrated that the partners could be predicted 86% of the time (P ⬍ 0.001) based on their skin microbiome proﬁles, which was always greater than combinations of incorrectly matched partners. Cohabiting couples had the most similar overall microbial skin communities on their feet, according to Bray- Curtis distances. In contrast, thigh microbial communities were strongly associated with biological sex rather than cohabiting partner. Additional factors that were asso- ciated with the skin microbiome of speciﬁc body locations included the use of skin care products, pet ownership, allergies, and alcohol consumption. These baseline data identiﬁed links between the skin microbiome and daily interactions among co- habiting individuals, adding to known factors that shape the human microbiome and, by extension, its relation to human health.
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Infection outcome also played a role in community composi- tion; the microbiomes differed between resolvers and pustule formers at the endpoint. In contrast to what was found at prein- fection, at the endpoint the microbiomes in pustule sites clustered together. Furthermore, 10 signature bacteria were significantly overrepresented in pustule-forming sites at the endpoint. These data suggest that pustule formation, which coincides with an in- effective hyperinflammatory immune response, is associated with a shift toward a more similar community composition. Similarly, Horton and colleagues found that in patients with abscesses caused by S. aureus, the microbiomes of periabscess and contralat- eral skin were more similar to each other than to the microbiomes of control samples obtained from uninfected volunteers (41). Ad- ditionally, natural and experimental infection with the fungal pathogen Batrachochytrium dendrobatidis drives common changes in the skin bacterial communities of frogs (42). We also found that the resolved sites of pustule formers maintained bac- terial communities similar to adjacent pustule sites, suggesting that host factors have a strong influence on the microbiome. Con- sistent with this host effect, the microbiome of the skin adjacent to S. aureus abscesses resembles the microbiome of the uninfected contralateral arm within the same person (41). Taken together, these results suggest that the community structure of the skin microbiome is associated with susceptibility to infection and that the skin microbiomes of infected hosts converge toward a similar composition, perhaps driven by the persistence of the host im- mune response that failed to clear the pathogen. These observa- tions may have important implications regarding the host- microbiome interactions in our understanding the susceptibility and treatment of skin infections.
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Several studies have focused on microorganisms that are causative agents of reptile skin diseases. Reptiles are prone to infection by a variety of predominately Gram-negative commensal bacteria, including Aeromo- nas, Klebsiella, Proteus, Pseudomonas, and Salmonella . Fungal dermatitis in the USA has affected numer- ous reptilian species, including dusky pigmy rattlesnakes (Sistrurus miliarius barbouri), garter snakes (Thamnophis sirtalis), and ribbon snakes (Thamnophis sauritis) . The fungus Ophidiomyces ophiodiicola currently causes high mortality in snakes across Europe and North Amer- ica . A study of Eastern Massasauga snakes (Sis- trurus catenatus) determined that infected snakes were more likely to have high populations of Serratia and Janthinobacterium. In contrast, Janthinobacterium has been associated beneficially with salamander populations to prevent Batrachochytrium dendrobatidis infections , whereas Serratia has been observed in the skin microbiome of immunocompromised human patients . Moreover, a subset of OTUs such as Xylanimicro- bium and Cellulosimicrobium were reduced in infected snakes, further indicating that snake fungal disease shifts the skin microbiome . Another study determined that microbial communities did not differ signifi- cantly between snake populations of timber rattlesnakes (Crotalus horridus) and black racers (Coluber con- strictor), indicating that snake fungal disease studies on model organisms may widely apply to multiple snake species . Future studies will be able to leverage these findings to investigate whether a “ protective microbiome ” may help conservation efforts. For ex- ample, it may be possible to create a skin probiotic cul- ture from microorganisms that have been experimentally determined to be protective against skin diseases. Devel- oping a stable topical treatment may prove useful to shift the microbiome, just as this strategy has been mod- erately successful with human gut probiotics .
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Resident skin bacteria are influenced by topological and endogenous factors of skin and can be modulated by exter- nal factors such as clothing, hygiene, topical treatments and skin care products (Table 1). There are gender differences in skin microbiome as well. Skin microbiomes differ between children and adults (described in the following). Bacteria are not uniformly distributed in skin. There is a superficial and a deeper compartment in the human stratum corneum. After injury, a neo-microbiome is produced from the deeper compartment, which can be regarded as the indigenous microbiome. Furthermore, bacteria are consistently detect- able also in deeper skin layers such as the dermis and the subcutaneous adipose tissue. A balanced resident skin flora is a protective measure. 15,16
beyond that of past studies, we filtered the blastx hits to keep only those with > 75% identity. Viromes contained 29 unique antibiotic resistance gene (ARG) groups, which were related to antibiotic efflux, and resistance to beta-lactamases, rifampin, tetracycline, and elfamycin (Fig 4A). Tetracyclines are commonly used to treat dermatological conditions such as acne, and elfamycins are naturally occurring antibiotics with strong activity against Propionibacterium acnes . To confirm the identified ARGs are associated with the virome and not cellular contamination or artifacts, we demonstrated ~50% of ARGs co-localized on contigs with other annotated phage genes, or are themselves known phage-associated antibiotic resistance genes (Fig 4B). ARGs were primarily associated with “multiple hit”, Bacillus, and Streptococcus phages (Fig 4B). We also identified potential virulence factors (VFs) associated with the skin virome using the Virulence Factor Database (VFDB)  with the same blastx parameters and filtering as described for antibiotic resistance analysis above. We identified 122 unique VF genes and >1/3 of the VF contigs were either known phage-associated genes or co-localized with phage genes (Fig 4C). These findings together indicate that bacteriophages of the skin microbiome may be a significant source of transmissible genes associated with antibiotic resistance, virulence, and pathogenicity.
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Sampling methodologies for studying the acne microbiome The choice of sampling method, anatomical location, and sequencing approach are important factors in any skin microbiome study . The most common methods for sampling the skin microbiome include non-invasive methods such as swab, scrub, and tape stripping that re- cover microbes residing on the skin surface and within the stratum corneum. These methods have the most relevance for microbiome analysis of superficial skin diseases like psoriasis and atopic dermatitis [38, 39], and when adopted for acne studies, distinct lineages of C. acnes were found to be more associated with health or disease [43, 44]. However, acne vulgaris is generally considered a disease of the pilosebaceous unit, and an accurate representation of the acne microbiome may require sampling of the follicu- lar environment. Common “invasive” sampling techniques include pore strips and cyanoacrylate gel biopsy that cap- ture individual hairs or follicular “casts,” respectively. The former approach was utilized by Fitz-Gibbon et al. to characterize the microbiome of acne patients sampled from the follicular contents of the nose . Crucially, to collect enough follicular material for sequencing, the au- thors pooled all pilosebaceous units within the same skin site, sacrificing sensitivity since uninvolved follicles vastly outnumber acne-affected follicles . Many issues in- volving bias in sampling methods, anatomical choice, and sequencing were subject of interesting debate following publication of this study [46–48]. In light of such contro- versies, a recent study compared the skin microbiome of acne patients using three different sampling techniques: swab, pore strips, and gel biopsy combined with multiple sequencing approaches . While greater bacterial diver- sity was discovered on the surface skin, the overall com- position of the surface and follicular environment was comparable for the most abundant species, particularly C. acnes, with several strain-types represented in both niches. Overall, the authors concluded that surface or follicular sampling were both suitable approaches for accurate ana- lysis of the skin microbiome in acne research, particularly in the context of C. acnes association.
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The most striking observation from the community composition data of the rinse experiment is the domination during early recovery (Figure 3, days 1–3) of the fish skin microbiome by one taxa, identified at the family level as Aeromonadaceae. Unfortunately, given the vagueness of the family-level identification, prediction of biochemical activi- ties and thus being able to integrate the 16S results and bio- chemical community profiles from the rinse experiment are not possible. This OTU is observed in PT control fish as the ninth most abundant, at 2.1%. It rises during recovery after the rinsing, being the most abundant taxa in the microbiome (peaking at 87.3% in the 2-day sample) at every sample until day 5 of recovery (where it is still the third-most abundant). This is perhaps an example of a pioneer species in secondary succession following a severe disruption. The high abundance of Aeromonadaceae is apparently not stable, as it falls to 1.0% abundance by day 8 of recovery. The abundance of this taxa has an inverse relationship to the community richness of the microbiome, which reaches a minimum of 23 detected taxa on day 2, less than half of the PT microbiome (compare Figure 3 and Table 12). The number of taxa that dominate the com- munity, as measured by the taxa ≥ 1% or ≥ 0.1% abundance, follows the overall richness, with the final community at 8 days (39 of 59 community members being at or above 0.1% abundance) being more even than the PT community (32 of 59 being ≥ 0.1%). So, when Aeromonadaceae is most domi- nant, the community is least even. Three other genera exhibit similar patterns of a strong rise in abundance during recovery, followed by a decline. These may be influencing or influenced by Aeromonadaceae. Flavobacterium is a dominant member of the PT skin microbiome, at 3.2% abundance. This OTU
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sembled virome contigs to the Comprehensive Antibiotic Resis- tance Database (CARD) (39) (BLASTx E value, ⬍10 ⫺5 ). To fur- ther increase our confidence in the annotations beyond that of past studies, we filtered the BLASTx hits to keep only those with ⬎ 75% identity. Viromes contained 29 unique antibiotic resis- tance gene (ARG) groups, which were related to antibiotic efflux and resistance to beta-lactamases, rifampin, tetracycline, and el- famycin (Fig. 4A). Tetracyclines are commonly used to treat der- matological conditions such as acne, and elfamycins are naturally occurring antibiotics with strong activity against P. acnes (40). To confirm that the ARGs identified are associated with the virome and not cellular contamination or artifacts, we demonstrated that ~50% of the ARGs are colocalized on contigs with other annotated phage genes or are themselves known phage-associated ARGs (Fig. 4B). ARGs were associated primarily with “multiple-hit,” Bacillus, and Streptococcus phages (Fig. 4B). We also identified potential VFs associated with the skin virome by using the VF database (VFDB) (41) with the same BLASTx parameters and fil- tering as described for antibiotic resistance analysis above. We identified 122 unique VF genes, and ⬎1/3 of the VF contigs were either known phage-associated genes or colocalized with phage genes (Fig. 4C). These findings together indicate that bacterio- phages of the skin microbiome may be a significant source of transmissible genes associated with antibiotic resistance, viru- lence, and pathogenicity.
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Taxonomic abundance was strongly associated with the molecular processes of immune activation, lipid me- tabolism, cell proliferation, and Notch/Wnt signaling (Fig. 4a). Clustering of these processes was strongly cor- related with differences in microbial abundance between SSc and controls, with statistically significant differences evident in 5 of 7 clusters (paired t-test, p < 0.05 for all; Fig. 4a, b). Among the most significant clusters was clus- ter 1, dominated by major lipophilic taxa, such as Malassezia and Propionibacterium, along with numerous Gram-positive Actinobacteria species (Additional file 9: Figure S4). These organisms were significantly more abun- dant in healthy controls (p < 0.001 for Actinobacteria and Propionibacterium by paired t-test) and exhibited strong positive correlations to lipid metabolism and cell prolifera- tion KEGG pathways (Fig. 4a, b). In contrast, cluster 5 exhibits substantial increases in a wide range of Proteo- bacteria and other Gram-negative taxa in SSc patients (p < 0.001 by paired t-test) and is strongly correlated with KEGG immune activation pathways, including Toll-like receptor (TLR) and TGFβ signaling (Fig. 4a, d). Cluster 3 shows strong, positive correlations with immune activation, lipid metabolism, and Notch/Wnt signaling and is associated with statistically significant decreases in Bacteroidetes levels in SSc patients (p = 0.028 by paired t-test), combined with modest increases in Proteobacteria, relative to controls (p = 0.085 by paired t-test; Fig. 4a, e). These data demonstrate a strong association between underlying gene expression and the composition of the skin microbiome in SSc.
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A comparison of the skin microbiome of AD and healthy pediatric cohorts from three cities, two Chinese and one American, revealed that, healthy and diseased skin microbiota from each city carried both city-speciﬁc signature (Fig. 3e) and AD- associated biomarkers (Fig. 4a), and there were signiﬁcant overlaps among the healthy and diseased biomarkers. Although the two Chinese cities shared more disease bio- markers than did the intercontinental pairs, a signiﬁcant core of AD-associated micro- biota was present, with its size and membership independent of geographic distances among populations. Therefore, despite the differences among pediatric cohorts, an AD diagnosis model built from a single city can be applied across the three cities with acceptable accuracy. As a result, despite the large effect size of city and individual variation, the MiSH model consisting of the top 25 bacterial skin genera can diagnose AD with 86.4% accuracy (AUC, 0.90) across cities and continents, and it offers high sensitivity in assessing the efﬁcacy of treatment products. Notably, although the body location is one of the most important factors to the skin microbiome (54, 55), applica- tion of MiSH (which was generated based on all samples from the three cities) on the Beijing samples of various body locations revealed that, in each of the six body locations that include both moist (antecubital fossa and popliteal fossa) and dry (arm, knee, neck, and shank) ones, MiSH can reliably distinguish their health status (Fig. S6d). Moreover, for nonlesional skin sites in AD-active children, the MiSH model revealed a distinct state of skin microbiota called suboptimal health, which is intermediate between those of lesional sites and healthy children, yet more similar to the former, and carries a level of Staphylococcus spp. higher than that in healthy hosts but lower than in lesional sites. This state was converted to a healthier state on the MiSH scale after topical medication. However, the degree of dysbiosis or its remediation does not correlate with physical distance to the lesional site. Although initial evidence for the alteration of microbiota on apparent healthy skin zones physically adjacent to the lesional sites has emerged (59, 60), the extent to which the skin microbiota respond across the whole body is not known. Our ﬁndings here support AD as a topical effect but with an underpinning microbiota dysbiosis that extends across the body (61, 62), and they underscore the dynamic interactions between global host immune response and local skin microbiota (63). Therefore, MiSH can be used not only for AD severity measurement but also for assessing the healthy state and the risk-prone state of skin in AD-free children (whether this is applicable in adults is unknown, as AD skin microbiome is affected by age ).
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