Acinetobacter species in the skin microbiota protect against allergic sensitization and inflammation €ki, PhD,c Ville Veckman, PhD,a Nanna Fyhrquist, PhD,a Lasse Ruokolainen, PhD,b Alina Suomalainen, BSc,a Sari Lehtima a a a d Johanna Vendelin, PhD, Piia Karisola, PhD, Maili Lehto, PhD, Terhi Savinko, PhD, Hanna Jarva, MD,e Timo U. Kosunen, MD,e Jukka Corander, PhD,f Petri Auvinen, PhD,d Lars Paulin, PhD,d Leena von Hertzen, PhD,g €kela €, MD,g Tari Haahtela, MD,g Dario Greco, PhD,a Ilkka Hanski, PhD,b and Tiina Laatikainen, PhD,h,i Mika Ma a Harri Alenius, PhD Helsinki, Turku, and Kuopio, Finland Background: The human commensal microbiota interacts in a complex manner with the immune system, and the outcome of these interactions might depend on the immune status of the subject. Objective: Previous studies have suggested a strong allergy-protective effect for Gammaproteobacteria. Here we analyze the skin microbiota, allergic sensitization (atopy), and immune function in a cohort of adolescents, as well as the influence of Acinetobacter species on immune responses in vitro and in vivo. Methods: The skin microbiota of the study subjects was identified by using 16S rRNA sequencing. PBMCs were analyzed for baseline and allergen-stimulated mRNA expression. In in vitro assays human monocyte-derived dendritic cells and primary keratinocytes were incubated with Acinetobacter lwoffii. Finally, in in vivo experiments mice were injected intradermally with A lwoffii during the sensitization phase of the asthma protocol, followed by readout of inflammatory parameters.

From athe Unit of Systems Toxicology, Finnish Institute of Occupational Health, Helsinki; bthe Department of Biosciences and dthe Institute of Biotechnology, University of Helsinki; cthe Molecular Immunology Group, Turku Centre for Biotechnology; ethe Haartman Institute, Department of Bacteriology and Immunology and Research Programs Unit, Immunobiology, University of Helsinki, and Helsinki University Central Hospital Laboratory (HUSLAB); fthe Department of Mathematics and Statistics, University of Helsinki; gthe Allergy Department, Skin and Allergy Hospital, Helsinki University Hospital; hthe Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki; and ithe Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio. The research leading to these results has received funding from the Academy of Finland (grants nos. 138695, 255350, 121025, and 251170), the European Research Council (grant no. 239784), the Jane and Aatos Erkko Foundation, Helsinki University Hospital (HUS) (grant no. 8361), and by the European Union’s Seventh Framework Programme FP7/2007-2013 under grant agreements 261366 (MAARS) and 261357 (MeDALL). Disclosure of potential conflict of interest: This study was funded by the Academy of Finland (grants nos. 138695, 255350, 121025, and 251170), the European Research Council (grant no. 239784), the Jane and Aatos Erkko Foundation, the European Union’s Seventh Framework Programme (grant agreements 261366 and 261357), and the Helsinki University Hospital (grant no. 8361). T. Haahtela is on the board of the Global Initiative for Asthma, and has received payment for delivering lectures from OrionPharma and Merck. The rest of the authors declare that they have no relevant conflicts of interest. Received for publication April 28, 2014; revised July 1, 2014; accepted for publication July 7, 2014. Available online September 26, 2014. Corresponding author: Harri Alenius, PhD, Unit of Systems Toxicology, Finnish Institute of Occupational Health, Topeliuksenkatu 41 b, Helsinki-FIN 00250, Finland. E-mail: [email protected]. 0091-6749/$36.00 Ó 2014 American Academy of Allergy, Asthma & Immunology http://dx.doi.org/10.1016/j.jaci.2014.07.059

Results: In healthy subjects, but not in atopic ones, the relative abundance of Acinetobacter species was associated with the expression of anti-inflammatory molecules by PBMCs. Moreover, healthy subjects exhibited a robust balance between anti-inflammatory and TH1/TH2 gene expression, which was related to the composition of the skin microbiota. In cell assays and in a mouse model, Acinetobacter species induced strong TH1 and anti-inflammatory responses by immune cells and skin cells and protected against allergic sensitization and lung inflammation through the skin. Conclusion: These results support the hypothesis that skin commensals play an important role in tuning the balance of TH1, TH2, and anti-inflammatory responses to environmental allergens. (J Allergy Clin Immunol 2014;134:1301-9.) Key words: Atopy, Gammaproteobacteria, Acinetobacter species, PBMC, anti-inflammatory gene expression, dendritic cells, keratinocytes, mouse asthma model

The incidence of atopic disorders has increased steadily in developed countries for several decades,1 now affecting approximately 40% of children in the United Kingdom.2 This epidemic has been related to changes in lifestyle.3 The ‘‘old driends’’4-6 and biodiversity3 hypotheses postulate that the increase in chronic inflammatory disorders is caused by reduced exposure to environmental microbes, which in turn influences the composition of the human commensal microbiota and its interactions with the immune system. Microbes and vertebrates have coevolved over millennia,4,7 and the sudden altered microbial contact in urban societies in developed countries8 might lead to dysregulation of host immunity. Revealingly, children growing up on traditional farms have an especially low risk of allergic sensitization (atopy), with the protective phenotype sustained into adult life9; a farm environment exposes children to microbial pressure that has few equivalents in the developed world. In a study in eastern Finland, land use in the surroundings of children’s homes was significantly associated with the prevalence of atopy, with children living in homes surrounded by much forest and agricultural land showing less atopy.10 The causal factor might be microbial exposure because the generic diversity of Proteobacteria on the skin was significantly associated with environmental land use.10 Microorganisms inhabiting mammalian body surfaces have a highly coevolved relationship with the host’s immune system. Although the immune system is essential in maintaining homeostasis with resident microbes, the latter shape immunity by inducing protective and regulatory responses.11 The molecular and cellular pathways that sense and transduce signals leading to protection are still largely unknown, but they are likely to primarily target regulatory immune processes. Thus bacteria in the intestine can 1301

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Abbreviations used ACTB: b-Actin gene AHR: Aryl hydrocarbon receptor gene DC: Dendritic cell FOXP3: Forkhead box P3 gene GAPDH: Glyceraldeyde-3-phosphate dehydrogenase gene moDC: Monocyte-derived dendritic cell OVA: Ovalbumin PE: Phycoerythrin qPCR: Quantitative PCR TLR2: Toll-like receptor 2 Treg: Regulatory T

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institutional ethics committees approved the protocol. Atopy was defined as a specific IgE level to inhalant allergens of greater than 2.5 kUA/L. On the basis of the previous study,10 the cutoff value was located between 2 clear humps in the log-transformed distribution of IgE values for this cohort.

Skin microbiota The skin microbiota was analyzed by using 16s rRNA sequencing, as described previously.10

PBMCs PBMCs were separated from whole blood and stimulated with allergens for 6 or 24 hours in complete RPMI.

Microbes for in vitro and in vivo experiments promote the activity of regulatory T (Treg) cells by inducing IL10 production. For instance, Bacteroides fragilis causes CD41 T cells to secrete IL-10 through the action of polysaccharide A on Toll-like receptor 2 (TLR2),12 and a mixture of Clostridium strains promotes intestinal Treg cell activity, possibly through the induction of TGF-b by the production of short-chain fatty acids.13 In germ-free mice IL-10 expression is markedly reduced and Treg cells in the colon are less abundant in comparison with those seen in normal mice.14 Microbes can also influence the immune system and protect against allergies through induction of TH1-type immune responses, which inhibit the development of TH2 cells. Endotoxins (constituents of the outer membrane of gram-negative bacteria) stimulate macrophages and antigenpresenting cells to produce IL-12, which triggers the development of TH1 immune responses. Microbe-induced immune programming might further involve epigenetic modifications at immunerelated genes. A recent study using a mouse model demonstrated modified histone acetylation of the IFN-g promoter of CD41 T cells in the offspring of A lwoffii–exposed mothers.15 The effect of microbes at other sites than the gut has been less extensively investigated, but there is evidence that skin commensals autonomously control local inflammatory responses16 and intranasal delivery of microbial material provides significant protection from experimental allergy.17,18 Here we analyze associations between the relative abundances of bacterial genera on the skin and expression of selected genes coding for proinflammatory and anti-inflammatory molecules in a cohort of adolescents. We report significantly dissimilar patterns of association between the microbes and gene expression in PBMCs of healthy versus atopic subjects, suggesting that the interactions between the immune system and the microbiota are significantly altered by atopy (allergic sensitization). The analysis highlights a robust positive association between the genus Acinetobacter (Gammaproteobacteria) and anti-inflammatory molecules in healthy but not atopic subjects. Having identified the special role for Acinetobacter species among some hundreds of bacterial genera, we investigated the effect of heat-inactivated A lwoffii on immune responses and report strong induction of anti-inflammatory gene expression in cell assays and protection against allergic sensitization and lung inflammation after exposure to microbial material through the skin in a mouse model.

METHODS Study subjects and atopic sensitization The study subjects (n 5 118) had been previously selected for a long-term allergy study.19 All subjects provided written informed consent, and

A lwoffii strains from blood culture isolates were identified by means if 16S sequencing or API 20NE strips (BioMerieux, Lyon, France), grown on chocolate agar plates, collected, and heat inactivated.

In vitro cell assays Monocytes were isolated from healthy donor buffy coats and differentiated into monocyte-derived dendritic cells (moDCs). The moDCs and human epidermal keratinocytes were stimulated for 6 hours with heat-killed A lwoffii (at 1:5 cell/bacteria ratio).

Real-time quantitative PCR and Luminex RNAwas extracted from PBMCs, moDCs, keratinocytes, and mouse tissue; reverse transcribed into cDNA; and analyzed by using real-time quantitative PCR (qPCR). As endogenous controls, we used 18S rRNA, b-actin gene (ACTB), and glyceraldeyde-3-phosphate dehydrogenase gene (GAPDH) for human targets and 18S and TATA-binding protein for mouse targets. The results are expressed as relative quantity, which was calculated by using the comparative cycle threshold method, according to the manufacturer’s instructions. Supernatants from the PBMC cultures were analyzed by using Luminex with Bio-Rad multiplex assays (Bio-Rad Laboratories, Hercules, Calif).

Animal model All animal experiments were approved by the Social and Health Care Department of the State Provincial Office of Southern Finland. Mice were shaved on the back, tape stripped 3 times, and injected intradermally with ovalbumin (OVA) and heat-killed A lwoffii, followed by intranasal OVA challenge and collection of samples. Lung, skin, and blood samples were prepared for histologic analysis, RNA extraction, and measurement of IgE serum levels.

Statistical analysis The association between and effect of gene expression, the relative abundance of Acinetobacter species on the skin, and atopy were examined by using Pearson correlation and analysis of covariance, respectively. Global associations of gene expression with the microbiota were inferred by using network analysis. For a detailed description of the methods, see the Methods section in this article’s Online repository at www.jacionline.org.

RESULTS Associations between the skin microbiota and PBMC gene expression The skin microbiota was identified to the genus level by sequencing the 16S rRNA gene from DNA samples obtained from the volar surface of the forearm. Altogether, 1017 bacterial genera were identified in the 118 study subjects. The PBMCs

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FIG 1. Network analysis of the relative abundance of skin microbial genera and PBMC gene expression in healthy (A) and atopic (B) subjects. Th1, T helper type 1 (blue); Th2, T helper type 2 (red); Th17, T helper type 17 (yellow); Treg, T regulatory/immunoregulatory (green) genes. Red and blue edges indicate positive and negative correlations, respectively, and the color hue indicates the strength of the correlation. Sample size is 46 healthy and 28 atopic subjects.

were analyzed for baseline and allergen-stimulated mRNA expression of inflammatory and regulatory genes. We used network-inferring algorithms to highlight patterns of association between the relative abundances of the microbial genera and gene expression. Microbial genera that were present in at least 75% of the study subjects were included in the analysis (see Table E1 in this article’s Online repository at www.jacionline. org), and those without direct edges to genes were removed from the final networks. The distribution of edges between microbes and gene expression in unstimulated 24-hour cultures of PBMCs deviated significantly between healthy and atopic subjects (defined by increased specific IgE levels to inhalant allergens). In healthy subjects the most significant direct links were between Acinetobacter species and IL-10 and between Diaphorobacter species (Proteobacteria) and TLR2, which is a strong inducer of IL-10 (Fig 1, A). IL-10 further bridged into a network of anti-inflammatory transcripts, including forkhead box P3 (FOXP3), TGFB, TLR2, and aryl hydrocarbon receptor gene (AHR; Fig 1, A). In the network for atopic subjects, Microbacterium and Alcaligenes species correlated negatively with the expression of IL13 and IL17, respectively (Fig 1, B). The positive correlation between Acinetobacter species and IL-10 in healthy subjects and the negative, although not significant, association in atopic subjects were similar at the protein level, as measured from the supernatants of the PBMCs (Fig 2, A). The protein concentrations corresponded closely to the level of RNA expression (see Fig E1 in this article’s Online repository at www.jacionline.org).

Association of anti-inflammatory responses with Acinetobacter species The network inference was carried out stringently. To facilitate interpretation, we highlight here the simple correlations behind the network results for the biologically most interesting relationships, considering data for all stimuli (Bet v 1, Phl p 1, and anti-human CD3/CD28) and time points (6 and

24 hours) used in the PBMC cultures. In healthy subjects Acinetobacter species influenced most strongly the expression of IL10 (r 5 0.51, P 5 .0004), FOXP3 (r 5 0.42, P 5 .004), and AHR (r 5 0.35, P 5 .018; Fig 2, B, and see Fig E2 in this article’s Online Repository at www.jacionline.org) in 24hour unstimulated PBMCs, as well as TGFB (r 5 0.40, P 5 .0053; Fig 2, B) and FOXP3 (r 5 0.30, P 5 .041) in Bet v 1–stimulated PBMCs. No such correlations were observed in atopic subjects (see Table E2 in this article’s Online Repository at www.jacionline.org). We used analysis of covariance to explain gene expression by diagnosis (healthy vs atopic), the relative abundance of Acinetobacter species, and their interaction. The interaction term between Acinetobacter species and atopy was significant for IL10 (24-hour unstimulated cultures, P 5 .005), FOXP3 (Phl p 1– and Bet v 1–stimulated and unstimulated cultures: P 5 .029, P 5 .031, and P 5 .042, respectively), TGFB (Bet v 1–stimulated 6-hour cultures, P 5 .031), and IL1B (6-hour unstimulated cultures, P 5.043), indicating opposite associations with Acinetobacter species in healthy and atopic subjects (see Table E3 in this article’s Online Repository at www.jacionline. org). As expected, the expression of IL4 (Bet v 1, P < .0001; Phl p 1, P 5 .006) and IL13 (Bet v 1, P 5 .0002) in allergenstimulated PBMCs is significantly different between atopic and healthy subjects, and atopy had a weak effect on the expression of IL10 and TGFB in unstimulated 24-hour PBMC cultures (P 5 .024 and P 5 .025, respectively; see Table E3). To find out the cellular source of the above genes, we isolated CD31 T cells and CD141 monocytes from PBMCs with magnetic beads, followed by RNA extraction from the different cell subsets. The magnetic bead–enriched CD141 monocytes expressed high levels of IL10 and, to some extent, TLR2 and AHR, whereas CD31 T cells contributed a small part of TGFB expression and most of the FOXP3 expression (see Fig E3 in this article’s Online Repository at www.jacionline.org). Given the high expression of TLR2, AHR, and TGFB in PBMCs, the analysis likely misses additional cellular sources, such as B cells, natural killer cells, and dendritic cells (DCs).

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FIG 2. The expression of IL10 RNA (***P 5 .0004) and IL-10 protein (P 5 .079; A) and FOXP3 RNA (**P 5 .004; B, left panel) in 24-hour unstimulated PBMC cultures and TGFB RNA (**P 5 .0053; B, right panel) in 6-hour Bet v 1–stimulated PBMC cultures correlated with the relative abundance of Acinetobacter species on the skin in healthy (open symbols) but not atopic (solid symbols) subjects. Sample size is 46 healthy and 28 atopic subjects. RQ, Relative quantity.

Balanced proinflammatory and anti-inflammatory responses in healthy subjects In atopy there is a lack of balance between TH1, TH2, and Treg cell responses. We characterized the expression of TH1, TH2, and anti-inflammatory genes in stimulated and unstimulated PBMCs with a principal component analysis and found that the first principal component of TH2 cytokines (accounting for 51% of the variance) correlated strongly with the first principal component of TH1 and anti-inflammatory molecules (47% of the variance) in healthy subjects (P 5 .021; Fig 3, A). For instance, IL13 and IFNG expression correlated in healthy subjects in both unstimulated (P 5 .0009; see Fig E4, A, in this article’s Online Repository at www.jacionline.org) and allergen-stimulated (Bet v 1, P 5 .0005; Fig 3, B) PBMCs. In striking contrast the atopic subjects lacked such a balance and had significantly lower expression of IFNG relative to IL13 (Fig 3, B). Similar patterns were observed for IL13 and IL4 against IL27, with a strong correlation in healthy subjects but none in atopic subjects (see Fig E4, B-D). Furthermore, IL4 expression correlated strongly with that of the Treg cell marker FOXP3 (P 5.0023; see Fig E4, E) and the anti-inflammatory cytokine TGFB (P 5 .0002; Fig 3, C) but again only in the healthy subjects. Finally, there was a highly

significant balance between IL10 and IL4 expression in unstimulated long-term PBMC cultures in healthy subjects (P < .0001) but not in atopic subjects (see Fig E4, F).

Acinetobacter species–induced immune responses in vitro To examine the influence of Acinetobacter species on the immune system, we incubated human moDCs and human keratinocytes with A lwoffii and 2 other skin commensals, Staphylococcus aureus and Staphylococcus epidermidis. Consistent with previous studies,18,20,21 we observed strong induction of IL12 (Fig 4, A), IL10, and IL-10–inducing genes (Delta-like-4 and IL27; Fig 4, B) in A lwoffii–stimulated moDCs. In keratinocytes A lwoffii–induced expression of TNF and IL10, but not thymic stromal lymphopoietin (TSLP), which was induced by S epidermidis (Fig 4, C). Acinetobacter species–induced protective immune responses in vivo The effect of microbial materials on the immune system has been studied in several mouse models by using the airways or the

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FIG 3. A, TH2-type responses correlated (*P 5 .021) with TH1/Treg cell–type responses in the healthy (open symbols) but not atopic (solid symbols) subjects. B and C, In the healthy subjects IFNG expression correlated with IL13 expression (***P 5 .0005) and was at a relatively higher level (**P < .01; Fig 3, B), and IL4 expression correlated with TGFB expression (**P 5 .0002; Fig 3, C). Sample size is 46 healthy and 28 atopic subjects. RQ, Relative quantity.

gut as exposure routes. To examine how Acinetobacter species influences the immune system through the skin, we injected mice intradermally with heat-inactivated A lwoffii or S epidermidis during the sensitization phase of the asthma protocol (Fig 5, A). Four injections of A lwoffii together with the allergen (OVA) resulted in reduced lung inflammation after allergen challenge through the airways compared with that seen in OVA-treated control mice or mice treated with OVA plus S epidermidis (Fig 5, B). The number of eosinophils in the bronchoalveolar lavage fluid, the expression of IL5 and IL13 mRNA in lung tissue (Fig 5, C, and see Fig E5 in this article’s Online Repository at www.jacionline.org), and the serum levels of OVA-specific IgE and IgG2a were significantly lower in the A lwoffii–treated mice (Fig 5, D). One week after the last exposure to microbes, the A lwoffii–treated mice had increased levels of IL-10 and IFN-g in the skin at the site of the injections (Fig 5, E). These results clearly demonstrate strong modulation of the local environment in the skin by A lwoffii toward TH1-polarized anti-inflammatory immune responses, resulting in reduced allergen-induced production of TH2 cell–associated cytokines and IgE production.

DISCUSSION Here we present evidence of a robust positive association between the relative abundance of Acinetobacter species on the

skin and the expression of anti-inflammatory genes in PBMCs in healthy subjects, an association that is strikingly lacking in atopic subjects. Furthermore, healthy subjects display a controlled balance between anti-inflammatory TH1 and TH2 gene transcription, which is related to the composition of the skin microbiota. Finally, we show that Acinetobacter species triggers anti-inflammatory and TH1-polarized immune responses in human keratinocytes and moDCs and inhibits the development of allergic airway inflammation through the skin in a mouse model. These observations indicate a critical role for the skin microbiota in tuning immune responses and inducing tolerance against allergic sensitization, with effects beyond the skin. The allergic phenotype is the product of both genetic predisposition and gene-environment interactions, with the latter heavily influencing the development of atopy and TH2dominated immunity.22 Adaptive immune responses are largely immature and suppressed in the newborn, being dependent on programming by environmental factors. Exposures promoting the development of TH1 and anti-inflammatory immune responses protect against atopy, and their timing is crucial, with the strongest effect early in life. Nonetheless, even in adolescents, the immune system is susceptible to lifestyle factors that provoke immune imbalance.23 In healthy subjects the relative amounts or balance of the TH1, TH2, and anti-inflammatory cytokines appear to be more

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FIG 4. Heat-inactivated Acinetobacter lwoffii (Al) induced the expression of IL12 (A) and IL10, Delta-like-4, and IL27 (B) in human moDCs and TNF and IL10, but not TSLP (C), in human primary keratinocytes. Sa, Staphylococcus aureus; Se, Staphylococcus epidermidis. *P < .05 and **P < .01. Bars represent means 6 SEMs (n 5 3-5 per group).

important than the absolute amounts, and the consistency of this balance suggests functional significance.24 The mechanisms that maintain the balance of immune responses are unknown but might include induction of IL-10. Considering the hypothesis that IL-10 plays a pivotal role, our results on the networks of microbial genera on the skin and PBMC gene expression point to Acinetobacter species as a potential causative factor. In the network for healthy subjects, the very strongest positive link among all the identified microbial genera and the set of 17 immune-related genes was between the relative abundance of Acinetobacter species and IL10, which was further related to a network of other antiinflammatory genes, including FOXP3 and TGFB. In the atopic subjects these correlations were lacking or even reversed. The anti-inflammatory genes further correlated strongly with TH2type cytokine expression in the healthy subjects, suggesting that

the immune balance might be directly linked to the composition of the skin microbiota. TH1-type responses were tightly correlated with TH2-type responses but only in healthy subjects. IL-10 is produced by many different immune cells.25 Of PBMCs, monocytes are the most efficient producers of IL-10. Our results point to the same conclusion. Thus the expression of IL-10 in unstimulated PBMCs, which correlated with the relative abundance of Acinetobacter species on the skin in the healthy subjects, is likely derived from monocytes. However, the potential role of T cells cannot be excluded because the production of IL-10 also correlated with Acinetobacter species in 24-hour PBMC cultures that were stimulated with anti-human CD3 and anti-human CD28 (see Fig E6 in this article’s Online Repository at www.jacionline.org). The involvement of T cells is further supported by the association of

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FIG 5. Intradermal injections of A lwoffii (Al; A) reduced lung inflammation (B), eosinophil counts in bronchoalveolar lavage fluid and TH2 cytokine expression in lung tissue (C), and OVA-specific IgE and IgG2a levels in serum (D). E, A lwoffii induced IL-10 and IFN-g in the skin. S epidermidis (Se) was used as a control. *P < .05, **P < .01, and ***P < .001. Bars represent means 6 SEMs (n 5 8 per group). i.d., Intradermal; i.n., intranasal; RQ, relative quantity.

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Acinetobacter species with the transcription of genes (eg, FOXP3) that are expressed by T cells. These results suggest that the tuning of immune responses, which appears to be influenced by Acinetobacter species, occurs through both the innate and adaptive arms of immunity. Our present and previous results10 suggest a protective role for Gammaproteobacteria and especially for Acinetobacter species on the skin against the risk of allergic sensitization. Previous studies have identified Acinetobacter species from mattress dust and found its relative abundance to be inversely related to the risk of atopic disease in children.26 A lwoffii isolated from cowsheds induces tolerogenic21 and TH1-polarizing18 programming of DCs and reduce allergic reactions in mice, acting through TLR-mediated signaling17 and epigenetic modifications.15 In our experiments A lwoffii induced TH1 programming and expression of IL10 by moDCs and TNF (which is a known promoter of Treg cells27) and IL10 by keratinocytes. These results suggest that both immune cells and skin-resident cells actively contribute to the induction of tolerance. It is noteworthy that the skin commensal S epidermidis, but not A lwoffii, triggered TSLP expression in the keratinocytes. Skin-derived TSLP is a key initiator of TH2 immune responses, activating DCs, which in turn induce the expression of TH2type cytokines by CD41 T cells.28 The atopic march in human subjects often starts with sensitization of the skin, progressing through food allergies to the development of rhinitis or asthma later on in life.29 To imitate this process in a mouse model, we sensitized mice through the skin, followed by allergen challenge through the airways. The sensitizing process is orchestrated by DCs, which, after internalizing and processing the allergen, mature and migrate to local lymph nodes, where they present the processed antigen to naive T cells. The phenotype of the DC is flavored by danger signals, cytokines, and chemokines of the local environment, which in turn influence the programming of T cells into distinct subsets of effector or regulatory cells. Naik et al16 recently demonstrated that skin commensals are critical for the tuning of skin immune responses by balancing effector and Treg cells, controlling skin cytokine expression, and driving skin immunity against pathogens in a manner dependent on IL-1 and TLR signaling. A large part of the skin microbiota resides in subepidermal compartments, with Gammaproteobacteria accumulating in the deeper layers of the skin,30 where their influence on the immune system is likely to be more profound. To mimic the localization of bacteria in the skin, we injected mice intradermally with heat-inactivated A lwoffii together with the allergen. Treatment with A lwoffii resulted in significantly reduced lung inflammation in terms of decreased infiltration of eosinophils and expression of TH2 cytokines in the lung tissue, as well as lower levels of OVA IgE in the serum. Remarkably, the injection of A lwoffii produced long-lasting effects in the skin, inducing long-term expression of IL-10 and IFN-g, with likely consequences for the sensitization process. We conclude that A lwoffii has a strong modulatory effect of the immune system through the skin, producing a local TH1- and IL-10–polarized environment, which is translated into systemic protection from allergic sensitization and inflammation on allergen challenge. In conclusion, our results indicate a significant immunomodulatory and protective role for Acinetobacter species in the skin microbiota, as supported by results from an epidemiologic study of a human study cohort, cell assays, and an animal model. In the

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study cohort the healthy subjects displayed a robust balance between anti-inflammatory and TH1/TH2 gene expression by PBMCs, which was lacking in the atopic subjects; we suggest that induction of IL-10 by Acinetobacter species contributes to this balance in healthy subjects. We found significant induction of TH1 and protective immune responses in vitro, as well as protection against allergic sensitization and lung inflammation in vivo by Acinetobacter species. Our experimental results add credence to the observed negative correlation between the diversity and abundance of Gammaproteobacteria on the skin and the incidence of atopy.10 Given that high relative abundances of Gammaproteobacteria and other Proteobacteria are associated with much forest and agricultural land in the environment,10 these results support the general notion that interactions with a biologically rich and diverse natural environment might enrich the commensal microbiota, with far-reaching consequences for public health. We thank Ms Katriina Rossi, Ms Sari Tillander, and Mr Sauli Savukoski for technical assistance.

Key messages d

Abundance of allergy-protective Gammaproteobacteria on the skin strongly associates with anti-inflammatory gene expression in human PBMCs.

d

Acinetobacter species induce anti-inflammatory and TH1type gene expression in DCs and keratinocytes in vitro and protect against allergic sensitization (atopy) and lung inflammation in a mouse model when applied intradermally.

d

Skin commensals play a key role in the tuning of immune responses to environmental allergens.

REFERENCES 1. Bach JF. The effect of infections on susceptibility to autoimmune and allergic diseases. N Engl J Med 2002;347:911-20. 2. Gupta R, Sheikh A, Strachan DP, Anderson HR. Burden of allergic disease in the UK: secondary analyses of national databases. Clin Exp Allergy 2004;34:520-6. 3. von Hertzen L, Hanski I, Haahtela T. Natural immunity. Biodiversity loss and inflammatory diseases are two global megatrends that might be related. EMBO Rep 2011;12:1089-93. 4. Rook GA. 99th Dahlem conference on infection, inflammation and chronic inflammatory disorders: darwinian medicine and the ‘‘hygiene’’ or ‘‘old friends’’ hypothesis. Clin Exp Immunol 2010;160:70-9. 5. Rook GA. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc Natl Acad Sci U S A 2013;110:18360-7. 6. Rook GA, Raison CL, Lowry CA. Microbial ‘‘old friends,’’ immunoregulation and socio-economic status. Clin Exp Immunol 2014;177:1-12. 7. Rook GA. Review series on helminths, immune modulation and the hygiene hypothesis: the broader implications of the hygiene hypothesis. Immunology 2009;126:3-11. 8. Schram ME, Tedja AM, Spijker R, Bos JD, Williams HC, Spuls PI. Is there a rural/ urban gradient in the prevalence of eczema? A systematic review. Br J Dermatol 2010;162:964-73. 9. Ege MJ, Mayer M, Normand AC, Genuneit J, Cookson WO, Braun-Fahrlander C, et al. Exposure to environmental microorganisms and childhood asthma. N Engl J Med 2011;364:701-9. 10. Hanski I, von Hertzen L, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci U S A 2012;109:8334-9. 11. Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science 2012;336:1268-73.

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12. Round JL, Mazmanian SK. Inducible Foxp31 regulatory T-cell development by a commensal bacterium of the intestinal microbiota. Proc Natl Acad Sci U S A 2010; 107:12204-9. 13. Atarashi K, Tanoue T, Oshima K, Suda W, Nagano Y, Nishikawa H, et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 2013;500:232-6. 14. Atarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 2011;331:337-41. 15. Brand S, Teich R, Dicke T, Harb H, Yildirim AO, Tost J, et al. Epigenetic regulation in murine offspring as a novel mechanism for transmaternal asthma protection induced by microbes. J Allergy Clin Immunol 2011;128:618-25, e1-7. 16. Naik S, Bouladoux N, Wilhelm C, Molloy MJ, Salcedo R, Kastenmuller W, et al. Compartmentalized control of skin immunity by resident commensals. Science 2012;337:1115-9. 17. Conrad ML, Ferstl R, Teich R, Brand S, Blumer N, Yildirim AO, et al. Maternal TLR signaling is required for prenatal asthma protection by the nonpathogenic microbe Acinetobacter lwoffii F78. J Exp Med 2009;206:2869-77. 18. Debarry J, Garn H, Hanuszkiewicz A, Dickgreber N, Blumer N, von Mutius E, et al. Acinetobacter lwoffii and Lactococcus lactis strains isolated from farm cowsheds possess strong allergy-protective properties. JAllergy Clin Immunol 2007;119:1514-21. 19. von Hertzen L, Makela MJ, Petays T, Jousilahti P, Kosunen TU, Laatikainen T, et al. Growing disparities in atopy between the Finns and the Russians: a comparison of 2 generations. J Allergy Clin Immunol 2006;117:151-7. 20. Hessle C, Andersson B, Wold AE. Gram-positive bacteria are potent inducers of monocytic interleukin-12 (IL-12) while gram-negative bacteria preferentially stimulate IL-10 production. Infect Immun 2000;68:3581-6.

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21. Zhang M, Liu M, Luther J, Kao JY. Helicobacter pylori directs tolerogenic programming of dendritic cells. Gut Microbes 2010;1:325-9. 22. Romagnani S. Coming back to a missing immune deviation as the main explanatory mechanism for the hygiene hypothesis. J Allergy Clin Immunol 2007;119:1511-3. 23. von Mutius E, Weiland SK, Fritzsch C, Duhme H, Keil U. Increasing prevalence of hay fever and atopy among children in Leipzig, East Germany. Lancet 1998;351: 862-6. 24. Halonen M, Lohman IC, Stern DA, Spangenberg A, Anderson D, Mobley S, et al. Th1/Th2 patterns and balance in cytokine production in the parents and infants of a large birth cohort. J Immunol 2009;182:3285-93. 25. Ouyang W, Rutz S, Crellin NK, Valdez PA, Hymowitz SG. Regulation and functions of the IL-10 family of cytokines in inflammation and disease. Ann Rev Immunol 2011;29:71-109. 26. Ege MJ, Mayer M, Schwaiger K, Mattes J, Pershagen G, van Hage M, et al. Environmental bacteria and childhood asthma. Allergy 2012;67:1565-71. 27. Chen X, Baumel M, Mannel DN, Howard OM, Oppenheim JJ. Interaction of TNF with TNF receptor type 2 promotes expansion and function of mouse CD41CD251 T regulatory cells. J Immunol 2007;179:154-61. 28. Leyva-Castillo JM, Hener P, Michea P, Karasuyama H, Chan S, Soumelis V, et al. Skin thymic stromal lymphopoietin initiates Th2 responses through an orchestrated immune cascade. Nat Commun 2013;4:2847. 29. Spergel JM. From atopic dermatitis to asthma: the atopic march. Ann Allergy Asthma Immunol 2010;105:99-109, 117. 30. Nakatsuji T, Chiang HI, Jiang SB, Nagarajan H, Zengler K, Gallo RL. The microbiome extends to subepidermal compartments of normal skin. Nat Commun 2013;4:1431.

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METHODS Study subjects and atopic sensitization The study subjects were a random sample of 13- to 20-year-old adolescents (n 5 118) selected for a long-term allergy study among school children in 2003E1 and living within an area of 100 3 150 km in eastern Finland. Samples were collected in September 2010 and 2011. The homes of the study subjects varied from flats in apartment buildings to row and individual houses in the town of Joensuu (a small town of 73,000 inhabitants) to villages of different sizes and isolated houses in the sparsely populated rural area. For further description of the study cohort, see Hanski et al.E2 Atopy was defined by a specific IgE level to inhalant allergens of greater than 2.5 kUA/L (see Hanski et alE2).

Skin microbiota The skin microbiota was analyzed, as described previously.E2 In brief, study subjects were sampled with sterile nylon swabs, DNA was extracted from the swabs with the FastDNA spin kit for soil (MP Biomedicals, Solon, Ohio), and the V1-V3 region of the 16s rRNA gene was amplified in a PTC-225 thermal cycler (MJ Research, Bruno, Canada) and sequenced by using the 454-GS FLX Titanium protocol with an average read length of approximately 400 bp (Roche Diagnostics, Mannheim, Germany). After filtering out low-quality data, sequences were aligned and clustered, and operational taxonomical units were defined. Altogether, 1017 bacterial genera were identified in the 118 study subjects.

PBMCs PBMCs were separated from whole blood by using BD Vacutainer CPT tubes (#362780; BD Biosciences PharMingen, San Jose, Calif), frozen, and shipped to the site of analysis. The thawed PBMCs were cultured in 24-well plates at 1 3 106/mL in complete RPMI-1640 medium (Gibco, Life Technologies, Carlsbad, Calif) with 50 U/mL penicillin, 50 mg/mL streptomycin (PEST, Life Technologies), and 10% heat-inactivated FBS (Gibco, Life Technologies) at 378C in a 5% CO2 atmosphere and stimulated with Bet v 1 or Phl p 1 at 10 mg/mL (Indoor Biotechnologies, Charlottesville, Va) or soluble mouse anti-human CD3 (1 mg/mL; R&D Systems, Minneapolis, Minn) and anti-human CD28 (1 mg/mL, BD) for 6 or 24 hours. CD31 T cells and CD141 monocytes were isolated from PBMCs of 1 healthy donor by means of positive selection with human CD31 and CD141 selection kits (STEMCELL Technologies, Grenoble, France), respectively. The original PBMC population consisted of 82% lymphocytes and 14% monocytes according to the cell counter (Beckman Coulter, Fullerton, Calif). The lymphocyte population consisted of approximately 69% CD31 cells, and the monocyte population consisted of approximately 90% CD141 cells. Thus the PBMCs contained approximately 12.6% CD141 monocytes and approximately 56% CD31 T lymphocytes based on cell counter values and flow cytometric analysis (Fig E3, A). The purity of the cell populations was analyzed by means of flow cytometry with mouse anti-human allophycocyanin-CD14, phycoerythrin (PE)–CD11c, and A488-CD3 antibodies (BD; Fig E3, B and C). The cell fraction values were used for extrapolation of gene expression levels of the 2 cell subsets relative to those of the mixed PBMC population.

RNA isolation and cDNA synthesis After 6 or 24 hours of culture, total RNA was extracted from the PBMCs by using TRIsure reagent (Bioline, London, United Kingdom) and from human primary keratinocytes and human moDCs by using the Exiqon kit (Vedbaek, Denmark), according to the manufacturer’s instructions. Mouse lung and skin tissue was homogenized in TRIsure by using the FastPrep instrument (Thermo Fisher Scientific, Waltham, Mass), and RNA was extracted according to the manufacturer’s instructions. The purity of RNA was analyzed by using the NanoDrop ND-1000 (Thermo Fisher Scientific, Wilmington, Del), wherein a A260/A280 ratio of greater than 1.8 was considered pure. Three hundred nanograms of RNA was reverse transcribed into cDNA by using the High Capacity cDNA Reverse

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Transcription kit (Life Technologies), according to the manufacturer’s instructions. A reaction was performed in 25 mL at 258C for 20 minutes, followed by 378C for 120 minutes. For mouse lung and skin specimens, 0.5 mg of RNA was used for cDNA synthesis.

Real-time quantitative PCR analysis mRNA levels of IFNG, IL1B, IL4, IL5, IL10, IL12A, IL12B, IL13, IL17, IL27, AHR, ALDH1A1, CYP1A1, CYP1B1, Delta-like-4, ENTPD1, FOXP3, GZMB, MAF, TGFB, TNF, TSLP, and TLR2 were analyzed by means of quantitative RT-PCR with TaqMan chemistry and the 7500 Fast Real-Time PCR System (Applied Biosystems, Life Technologies, Foster City, Calif). Reactions were performed in 1 cycle of 2 minutes at 508C and 30 seconds at 958C followed by 40 cycles of 3 seconds at 958C and 30 seconds at 608C. PCR amplification of the endogenous 18S rRNA, ACTB, and GAPDH for human targets and 18S and TATA-binding protein for mouse targets was performed for each sample to control sample loading and to allow normalization between samples. Probe and primer sets were purchased from Life Technologies. The results are expressed as relative units, which were calculated by using the comparative cycle threshold method, according to the manufacturer’s instructions.E3

PBMC cytokine protein quantification Supernatants from 24-hour PBMC cultures (unstimulated or anti-human CD3 [R&D Systems] and anti-human CD28 [BD] stimulated) were analyzed for cytokine production by using Luminex with Bio-Rad multiplex assays (Bio-Rad Laboratories).

Microbes for in vitro and in vivo experiments A lwoffii strains were blood culture isolates from the Helsinki University Central Hospital Laboratory that were identified by means of 16S rRNA sequencing or API 20NE strips. A lwoffii, S epidermidis (ATCC 12228), and S aureus (ATCC 25923) were grown over night at 1378C on chocolate agar plates. Bacteria were collected and washed with sterile 0.9% NaCl. The microbial concentration was adjusted to 1.8 3 108 cells/mL by using OD600. The microbes were killed by means of boiling for 15 minutes, placed in aliquots, and stored at 2208C until use.

In vitro cell assays Monocytes were isolated from buffy coats of healthy donors (Red Cross Blood Transfusion Service, Helsinki, Finland) by using standard Ficoll density gradient centrifugation, further enriched with the Monocyte enrichment kit (STEMCELL Technologies), and allowed to adhere on 6-well plates at 1378C for 1 hour. Adherent cells were cultured in DC medium (complete RPMI-1640, PEST, 10% FBS, GM-CSF [10 ng/mL, Sigma-Aldrich, St Louis, Mo], and IL-4 [20 ng/mL, Immunotools, Friesoythe, Germany]) for 8 days and analyzed by using flow cytometry (with mouse anti-human fluorescein isothiocyanate–CD209, PE-CD11c, PE-Cy5–CD1a, and allophycocyaninCD14 antibodies) to ensure proper differentiation. DCs were stimulated with heat-killed microbes at a 1:5 ratio (DC/microbe) for 6 hours. Pooled neonatal human epidermal keratinocytes were purchased from Invitrogen Life Technologies and cultured in Epilife medium supplemented with Human Keratinocyte Growth Supplement and PEST (Invitrogen, Life Technologies). Cells were grown until 30% confluence, seeded into 25-cm2 cultures vials, and left to adhere for 24 hours. The adhered cells were stimulated with microbes (2.5 3 105/mL) for 6 hours. RNA was isolated from DCs and keratinocytes by using an RNA isolation kit from Exiqon, followed by real-time qPCR, as described above.

Murine asthma model BALB/c mice (Scanbur, Karlslunde, Denmark) aged 6 to 8 weeks were maintained on OVA-free diets and water ad libitum. All animal experiments were approved by the Social and Health Care Department of the State

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Provincial Office of Southern Finland. The mice were anesthetized and their backs were shaved and tape stripped 3 times to introduce skin injury, followed by intradermal injection of 50 mg of OVA in 100 mL of PBS in the tape-stripped area. The mice were sensitized to OVA by a total of 4 injections during a period of 4 weeks. Two groups of mice received 5 3 106 heatinactivated A lwoffii or S epidermidis in addition to OVA in the injections. Control mice received OVA or PBS only. On days 30, 31, and 32, the mice were challenged with intranasally administered OVA (50 mg of OVA in 50 mL of PBS), followed by collection of samples on day 33. Inflammatory cells in the bronchoalveolar lavage fluid were handled and counted, as previously described.E4 Skin or lung tissue was fixed in 10% formalin and embedded in paraffin. Multiple 4-mm-thick sections were stained with the hematoxylin and eosin protocol. Skin from the injection area or lung tissue was homogenized in TRIsure, and RNA was extracted according to the manufacturer’s instructions. Real-time qPCR was performed, as described above. For measurement of OVA-specific IgE and IgG2a levels, the plate was coated with rat anti-mouse IgE/IgG2a mAb (BD Biosciences). Diluted sera were allowed to bind overnight, and bound IgE/IgG2a was detected with biotinylated OVA, Streptavidin-HRP (BD Biosciences), and peroxidase substrate reagents (Kirkegaard & Perry Laboratories, Gaithersburg, Md). Absorbance at 405 nm was read with an automated ELISA reader (Titertek Multiscan; Eflab, Turku, Finland).

Statistical analyses The association between the expression of each gene and the relative abundance of Acinetobacter species on the skin of healthy and atopic subjects was examined by using the Pearson correlation test. Analysis of covariance was used to examine the effects of the relative abundance of Acinetobacter species on the skin (ac), atopy status (at), and their interaction on the expression of each gene separately, as follows:

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correlation matrix between cytokines and microbes was used as the starting point for the algorithm, as implemented in the package minet in R.E6 The convex optimization algorithm searches for a model that maximizes a penalized log-likelihood for the concentration matrix (inverse of covariance matrix) of the data (K).E7 This method was applied as implemented in the package glasso in R,E8 with a penalization parameter r of 0.4 for healthy subjects (0 < r < 1; higher values lead to sparser networks) and r of 0.6 for atopic subjects (higher penalty for atopic individuals because of smaller sample size). Finally, we used forward selection of network edges (starting from the minimal BIC forest for the data in the package gRapHDE9 by using BIC as the selection criterion [function stepw in package gRapHDE7]). These algorithms were used to construct cytokine-microbe networks for both healthy and atopic subjects. The credibility of each network was assessed with random permutations. For this, the ordering of both rows and columns in the data matrix were randomized while retaining the original labels (Patients 3 [Cytokines 1 Microbes]), and the randomized data were in turn used to construct a network graph. This procedure was repeated 9999 times for each network-building algorithm. Calculating the mean over the adjacency matrices (a square matrix in which the presence of an edge between 2 nodes is indicated with 0/1) for these 9999 networks gave the probability of detecting an edge by chance, which could be compared with the networks inferred from the original data. On the basis of these permutation analyses, we decided to trust those edges that were recovered by at least 2 different algorithms with 90% probability. This high probability was chosen because high correlations arise relatively easily at random in small data sets. However, because a node is kept only if recovered with at least 2 independent methods, the type I error probability is a P value of .01 or less. Because we were mainly interested in the links between cytokines and microbes, we removed all microbial genera without direct edges to cytokines from the final networks.

yi ;ac1at1ac  at1error; where y is the normalized and standardized PBMC expression. This analysis was performed with R software, version 3.0.0 (http://cran.r-project.org).

Network analysis Global associations of gene expression with the microbiota were inferred by means of network analysis. The data were filtered as follows before analysis. We selected only those genera that were present in at least 75% of all samples (n 5 65). To simplify the analysis, we further removed all genera with a Pearson correlation coefficient of less than 0.2 for healthy subjects and 0.4 for atopic subjects with all cytokines. This step was repeated for the 7 different stimuli for cytokine expression Healthy and atopic subjects were treated separately because of a smaller sample size for atopic (n 5 22) than healthy (n 5 43) subjects; a smaller sample size increases the likelihood of spurious correlations. Patterns of association between cytokine expression and the relative abundances of microbial genera were sought by using 3 different networkbuilding algorithms: ARACNE, convex optimization, and stepwise selection. ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) is an information-theoretic algorithm that seeks to eliminate indirect interactions from a network.E5 In the present case a Spearman

REFERENCES E1. von Hertzen L, M€akal€a MJ, Pet€ays H, Jousilahti P, Kosunen TU, Laatikainen T, et al. Growing disparities in atopy between the Finns and the Russians: a comparison of 2 generations. J Allergy Clin Immunol 2006;117:151-7. E2. Hanski I, von Hertzen L, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci U S A 2012;109:8334-9. E3. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 2008;3:1101-8. E4. Haapakoski R, Karisola P, Fyhrquist N, Savinko T, Wolff H, Turjanmaa K, et al. Intradermal cytosine-phosphate-guanosine treatment reduces lung inflammation but induces IFN-g-mediated airway hyperreactivity in a murine model of natural rubber latex allergy. Am J Respir Cell Mol Biol 2011;44:639-47. E5. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006;7(suppl 1):S7. E6. Meyer PE, Lafitte F, Bontempi G. MINET: An open source R/Bioconductor package for mutual information based network inference. 2008 E7. H€ojsgaard S, Edwards D, Lauritzen S. Graphical models with R, use R!. Springer New York Dordrecht Heidelberg London; 2012. E8. Friedman J, Hastie T, Tibshirani R. glasso: Graphical lasso-estimation of Gaussian graphical models. R package version 1.7. 2011. E9. Abreu GCG, Edwards D, Labouriau R. High-dimensional graphical model search with the gRapHD R package. J Stat Softw 2010;37:1-18.

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FIG E1. Expression of IL10 at the protein level corresponds closely to the level of RNA transcription in PBMCs in healthy (left panel) and atopic (right panel) subjects. Sample size is 46 healthy and 28 atopic subjects. RQ, Relative quantity.

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FIG E2. Expression of AHR in PBMCs correlates with the abundance of Acinetobacter species on the skin of healthy (green circles) but not atopic (violet squares) subjects. Sample size is 46 healthy and 28 atopic subjects. RQ, Relative quantity.

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FIG E3. A, The original PBMC population consisted of 56% CD31 T lymphocytes (middle panel) and 12.6% CD141 monocytes (right panel). FSC, Forward scatter; SSC, side scatter. B and C, After cell separation, CD31 cells were 98% pure (Fig E3, B), and the CD141 cell subset was 97% pure (Fig E3, C; isotype control is shown at right). D, The relative expression of cytokines was extrapolated based on the fraction of each cell type in donors’ PBMCs. Bars represent mean 6 SEM (n 5 3 per group). RQ, Relative quantity.

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FIG E4. Linear regression plots of gene expression levels of IL-13 versus IFN-g (6-hour unstimulated PBMCs; A), IL-13 versus IL-27 (6-hour Bet v 1–stimulated PBMCs; B), IL-13 versus IL-27 (6-hour Phl p 1–stimulated PBMCs; C), IL-4 versus IL-27 (6-hour Bet v 1–stimulated PBMCs; D), IL-4 versus Foxp3 (6-hour unstimulated PBMCs; E), and IL-4 versus IL-10 (24-hour unstimulated PBMCs; F). Green open symbols, Healthy; violet solid symbols, atopic. Sample size is 46 healthy and 28 atopic subjects. RQ, Relative quantity.

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FIG E5. Representative images of immune cells in bronchoalveolar lavage fluid cytospin preparations in control (A) and OVA-sensitized (B) mice and in OVA-sensitized mice that were treated intradermally with A lwoffii (C) or S epidermidis (D).

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FIG E6. The production of IL-10 protein by anti-human CD3– and anti-human CD28–stimulated PBMCs correlates (P 5 .031) with the relative abundance of Acinetobacter species on the skin in healthy (green open symbols) but not atopic (violet solid symbols) subjects. Sample size is 46 healthy and 28 atopic subjects.

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TABLE E1. Bacterial genera on the skin of the study subjects All Class

Acidobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Actinobacteria Alphaproteobacteria Alphaproteobacteria Alphaproteobacteria Alphaproteobacteria Alphaproteobacteria Bacilli Bacilli Bacilli Bacilli Bacilli Bacilli Bacteroidia Bacteroidia Betaproteobacteria Betaproteobacteria Betaproteobacteria Betaproteobacteria Betaproteobacteria Clostridia Clostridia Clostridia Deinococci Deltaproteobacteria Flavobacteria Fusobacteria Gammaproteobacteria Gammaproteobacteria Gammaproteobacteria Gammaproteobacteria Gammaproteobacteria Negativicutes Total

Healthy

Atopic

Genus

Mean

SD

Mean

SD

Mean

SD

Gp2 Actinomyces Brachybacterium Corynebacterium Dermacoccus Kocuria Microbacterium Micrococcus Nocardioides Propionibacterium Bradyrhizobium Brevundimonas Methylobacterium Paracoccus Sphingomonas Bacillus Gemella Granulicatella Lactobacillus Staphylococcus Streptococcus Porphyromonas Prevotella Alcaligenes Burkholderia Diaphorobacter Neisseria Ralstonia Anaerococcus Finegoldia Peptoniphilus Deinococcus Desulfocurvus Chryseobacterium Fusobacterium Acinetobacter Enhydrobacter Haemophilus Pseudomonas Stenotrophomonas Veillonella

0.34 1.61 0.39 13.63 0.95 1.94 0.45 13.37 0.18 17.66 0.12 0.49 0.30 0.77 0.50 0.24 0.48 0.43 1.65 5.17 7.82 0.48 1.31 0.94 0.55 0.14 0.17 6.94 0.63 0.96 0.71 0.54 0.29 0.33 0.18 0.56 1.31 0.28 0.12 0.15 0.74 85.81

0.48 1.98 0.66 12.09 2.72 3.99 0.59 12.24 0.35 18.39 0.15 0.95 1.00 1.08 0.90 0.43 0.70 0.68 3.30 6.16 9.66 0.83 2.74 1.61 0.76 0.20 0.29 12.73 1.04 1.28 1.36 1.56 0.39 1.12 0.23 1.71 3.47 0.47 0.27 0.24 1.96

0.29 1.38 0.37 12.99 0.93 1.96 0.45 11.52 0.12 18.91 0.13 0.54 0.34 0.80 0.52 0.16 0.49 0.43 1.62 4.03 8.17 0.54 1.62 1.07 0.63 0.14 0.18 8.58 0.75 1.14 0.89 0.27 0.31 0.22 0.18 0.37 1.21 0.29 0.14 0.15 0.55 85.39

0.39 1.50 0.74 11.76 1.95 4.60 0.62 11.87 0.14 20.94 0.16 0.86 1.24 1.06 0.99 0.24 0.69 0.72 3.60 3.60 9.77 0.99 3.38 1.84 0.84 0.21 0.33 14.39 1.12 1.39 1.63 0.68 0.41 0.45 0.27 0.72 2.76 0.44 0.34 0.20 0.67

0.42 2.00 0.43 14.66 0.97 1.91 0.44 16.37 0.27 15.64 0.09 0.41 0.24 0.71 0.46 0.37 0.46 0.43 1.69 7.01 7.25 0.40 0.80 0.72 0.42 0.13 0.16 4.29 0.44 0.66 0.42 0.97 0.26 0.50 0.16 0.87 1.47 0.26 0.08 0.15 1.06 86.47

0.59 2.56 0.51 12.75 3.68 2.82 0.55 12.44 0.53 13.35 0.11 1.09 0.40 1.12 0.75 0.62 0.73 0.61 2.80 8.64 9.63 0.47 0.90 1.13 0.61 0.18 0.20 9.06 0.89 1.03 0.66 2.33 0.35 1.73 0.14 2.60 4.44 0.53 0.10 0.29 3.06

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TABLE E2. Correlation analysis of gene expression in PBMCs in relation to the relative abundance of Acinetobacter species on the skin of healthy and atopic subjects Healthy subjects Gene and stimulus

IL10, 24 h unstimulated FOXP3, 24 h unstimulated TGFB, Bet v 1 AHR, 24 h unstimulated ENTPD1, 24 h unstimulated GZMB, 24 h unstimulated CYP1A1, Bet v 1 IL13, 24 h unstimulated IL4, 24 h unstimulated TGFB, 24 h unstimulated FOXP3, Bet v 1 IL1B, Bet v 1 TLR2, 24 h unstimulated TLR2, Phl p 1 IFNG, Bet v 1 FOXP3, Phl p 1 ALDH1A1, 24 h unstimulated AHR, Bet v 1 CYP1B1, 24 h unstimulated ENTPD1, Bet v 1 ALDH1A1, Bet v 1 TGFB, Phl p 1 IFNG, 24 h unstimulated TLR2, Bet v 1 ALDH1A1, Phl p 1 IL27, Phl p 1 GZMB, 6 h unstimulated IL13, Bet v 1 AHR, Phl p 1 MAF, Bet v 1 IL4, Bet v 1 IL10, Phl p 1 IL1B, Phl p 1 IL1B, 24 h unstimulated MAF, 24 h unstimulated IL1B, 6 h unstimulated TGFB, 6 h unstimulated GZMB, Bet v 1 IL27, Bet v 1 IL17, 6 h unstimulated CYP1A1, Phl p 1 CYP1B1, Phl p 1 CYP1B1, Bet v 1 IL13, Phl p 1 IL4, Phl p 1 GZMB, Phl p 1 ALDH1A1, 6 h unstimulated IL10, 6 h unstimulated IL17, 24 h unstimulated IFNG, 6 h unstimulated IL17, Bet v 1 MAF, Phl p 1 IL27, 24 h unstimulated IL10, Bet v 1 AHR, 6 h unstimulated ENTPD1, 6 h unstimulated CYP1A1, 6 h unstimulated ENTPD1, Phl p 1 FOXP3, 6 h unstimulated

Atopic subjects

r

P value

r

P value

0.509 0.425 0.400 0.351 0.351 0.351 0.337 0.336 0.325 0.315 0.299 0.295 0.303 0.315 0.270 0.297 0.258 0.238 0.242 0.230 0.229 0.260 0.226 0.216 0.245 0.230 20.196 0.191 0.219 0.179 0.173 0.201 0.200 0.168 0.167 0.156 0.148 0.146 0.145 0.128 0.147 0.144 0.122 0.136 0.130 0.130 0.106 0.101 0.098 0.090 0.088 0.101 0.089 0.084 0.081 0.069 0.068 0.072 0.054

4.24E-04 4.04E-03 5.31E-03 .018 .018 .020 .021 .026 .029 .037 .041 .044 .046 .066 .067 .083 .091 .108 .109 .120 .121 .132 .141 .145 .156 .184 .193 .199 .206 .228 .244 .246 .250 .275 .277 .300 .327 .328 .329 .397 .399 .410 .413 .435 .455 .455 .484 .504 .527 .552 .554 .562 .571 .575 .593 .649 .654 .681 .721

20.193 20.125 20.126 0.038 0.119 20.067 0.002 0.245 0.026 0.169 20.220 0.000 20.078 0.173 0.009 20.299 0.234 20.105 0.010 20.039 20.008 20.145 20.185 20.062 0.178 20.302 20.168 20.083 0.079 20.261 20.222 20.243 20.157 20.221 0.170 20.319 20.048 20.029 0.038 20.280 0.197 0.029 20.010 0.005 20.032 0.124 20.016 20.308 20.247 20.205 20.221 0.051 0.117 20.074 20.029 0.058 0.094 20.094 20.136

.367 .562 .516 .861 .579 .756 .993 .248 .903 .430 .251 .998 .718 .454 .964 .188 .272 .587 .962 .842 .968 .531 .386 .751 .440 .184 .383 .668 .732 .172 .247 .288 .497 .300 .426 .092 .805 .883 .846 .141 .393 .902 .958 .982 .890 .602 .933 .104 .245 .286 .250 .825 .576 .703 .880 .766 .634 .684 .483

(Continued)

TABLE E2. (Continued) Healthy subjects Gene and stimulus

MAF, 6 h unstimulated IL13, 6 h unstimulated CYP1A1, 24 h unstimulated IFNG, Phl p 1 IL27, 6 h unstimulated IL17, Phl p 1 IL4, 6 h unstimulated CYP1B1, 6 h unstimulated TLR2, 6 h unstimulated

Atopic subjects

r

P value

r

P value

0.051 20.049 20.046 0.026 0.020 0.017 20.009 20.003 0.000

.739 .746 .765 .883 .893 .924 .954 .984 .998

20.156 20.089 20.549 0.027 20.318 0.177 0.170 0.009 20.117

.420 .648 .005 .907 .093 .443 .377 .965 .546

1309.e11 FYHRQUIST ET AL

J ALLERGY CLIN IMMUNOL DECEMBER 2014

TABLE E3. Analysis of covariance of gene expression in PBMCs in relation to the relative abundance of Acinetobacter species on the skin and diagnosis

Gene and stimulus

IL4, Bet v 1 IL13, Bet v 1 IL4, Phl p 1 IL10, 24 h unstimulated TGFB, 24 h unstimulated IL17, Bet v 1 IL4, 24 h unstimulated GZMB, 24 h unstimulated ENTPD1, 24 h unstimulated FOXP3, 6 h unstimulated IL1B, Phl p 1 IL10, Phl p 1 IL1B, 6 h unstimulated ALDH1A, Bet v 1 FOXP3, Bet v 1 MAF, 24 h unstimulated IL27, Bet v 1 TLR2, 24 h unstimulated AHR, 24 h unstimulated CYP1B1, 24 h unstimulated FOXP3, 24 h unstimulated ALDH1A1, 24 h unstimulated CYP1B1, Bet v 1 TLR2, Phl p 1 AHR, Bet v 1 GZMB, Phl p 1 FOXP3, Phl p 1 MAF, Phl p 1 IFNG, Bet v 1 IL17, Phl p 1 IL13, 24 h unstimulated AHR, 6 h unstimulated IL13, Phl p 1 CYP1A1, 24 h unstimulated IL1B, Bet v 1 IL4, 6 h unstimulated CYP1A1, unstimulated TGFB, Bet v 1 IL17, 24 h unstimulated CYP1B1, 6 h unstimulated CYP1A1, Phl p 1 IL10, Bet v 1 MAF, 6 h unstimulated IL27, 6 h unstimulated IL17, 6 h unstimulated IL27, 24 h unstimulated ENTPD1, 6 h unstimulated IFNG, 6 h unstimulated ALDH1A1, 6 h unstimulated ENTPD1, Phl p 1 ENTPD1, Bet v 1 IL10, 6 h unstimulated CYP1A1, Bet v 1 TLR2, Bet v 1 CYP1B1, Phl p 1 TGFB, 6 h unstimulated IL13, 6 h unstimulated IFNG, Phl p 1

Acinetobacter species, P value

.832 .329 .491 .022 .061 .898 .100 .074 .043 .926 .680 .834 .655 .336 .321 .255 .288 .126 .067 .239 .111 .057 .537 .058 .500 .388 .775 .557 .113 .732 .009 .690 .452 .195 .108 .556 .492 .068 .978 .953 .225 .904 .959 .425 .950 .448 .559 .873 .641 .923 .336 .536 .052 .326 .526 .515 .576 .844

Acinetobacter species Diagnosis, 3 diagnosis, P value P value

5.81E-06 2.10E-04 6.21E-03 .024 .025 .031 .043 .048 .053 .060 .063 .070 .093 .119 .121 .162 .173 .259 .290 .290 .420 .421 .433 .442 .459 .483 .489 .509 .577 .586 .589 .601 .602 .603 .614 .633 .646 .647 .675 .676 .735 .738 .744 .753 .762 .775 .794 .802 .811 .815 .818 .824 .850 .866 .873 .880 .897 .923

.077 .302 .618 .005 .643 .154 .245 .089 .388 .363 .225 .121 .043 .536 .031 .748 .704 .133 .264 .400 .042 .831 .650 .903 .194 .947 .029 .739 .297 .698 .609 .663 .659 .179 .225 .419 .945 .031 .243 .959 .684 .514 .453 .171 .124 .859 .979 .230 .638 .568 .314 .066 .156 .267 .814 .438 .839 .984 (Continued)

TABLE E3. (Continued)

Gene and stimulus

TLR2, 6 h unstimulated MAF, Bet v 1 AHR, Phl p 1 GZMB, Bet v 1 IL27, Phl p 1 IL1B, 24 h unstimulated ALDH1A, Phl p 1 IFNG, 24 h unstimulated GZMB, 6 h unstimulated

Acinetobacter species, P value

Diagnosis, P value

Acinetobacter species 3 diagnosis, P value

.737 .535 .274 .499 .587 .560 .105 .389 .120

.929 .929 .935 .958 .964 .967 .975 .980 .996

.640 .124 .842 .497 .077 .186 .909 .133 .916