You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


accepted manuscript online 30 September 2015

Introduction {#sec1}
============

In cells of the outermost epidermal layers, the plasma membrane is replaced by a cornified envelope, formed by transglutaminase-1-mediated crosslinking of proteins and lipids ([@bib17]). This insoluble structure is a key component of the epidermal barrier that protects the body from environmental assaults ([@bib3]). Defects in the epidermal barrier are linked to atopic dermatitis in humans and mice ([@bib17]). Mice triply deficient in three cornified envelope precursors---*envoplakin*, *periplakin*, and *involucrin* (EPI-/- mice)---have epidermal barrier defects, a reduction in epidermal γδ T cells, and an increase in dermal CD4^+^ T cells ([@bib24]).

EPI-/- mice are highly resistant to developing skin tumors when treated with chemical carcinogens ([@bib5]). This is due, at least in part, to an exaggerated atopic response to the tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA), including upregulation of the keratinocyte stress-associated antigen Rae-1, which binds natural killer group 2D (NKG2D) on immune cells. NKG2D ligands are currently being evaluated as therapeutic targets in a variety of cancer types ([@bib27]).

Rae-1--NKG2D interactions are not only implicated in cancer ([@bib5], [@bib28]) but also mediate host responses to bacterial and viral infection ([@bib9], [@bib15], [@bib28], [@bib29]). Given the strong evidence linking the gut microbiota, chronic inflammation, and cancer ([@bib25]), we speculated that the skin microbiome might be altered in EPI-/- mice. Human skin is covered by wide variety of commensal microbiota, which vary in composition according to body site, age, and health status ([@bib8]). In addition, studies suggest a regulatory role for skin commensal bacteria in skin infection and inflammation ([@bib14], [@bib16]). We therefore investigated whether the skin microbiome differs between EPI-/- and wild-type (WT) mice and whether it influences keratinocyte expression of Rae-1 and antimicrobial proteins (AMPs), natural antibiotics that are effectors of the innate immune system ([@bib7]).

Results {#sec2}
=======

Altered abundance and distribution of bacteria in the skin of EPI-/- mice {#sec2.1}
-------------------------------------------------------------------------

To determine whether EPI-/- mice had an altered skin microbiome, we measured the quantity and diversity of microbes on the ear skin of six EPI-/- and six WT control mice on the same genetic background. Quantitative PCR (qPCR) of the 16S rRNA gene revealed that the bacterial load of EPI-/- mice skin was significantly higher than that of control mice ([Figure 1](#fig1){ref-type="fig"}a). However, pyrosequencing of 16S rRNA gene PCR products showed that the composition of the bacterial flora of EPI-/- and control skin did not differ significantly ([Figure 1](#fig1){ref-type="fig"}b and c).

The microbiota of EPI-/- skin were visualized by whole-mount fluorescence in situ hybridization (FISH) of the epidermis with a universal bacterial probe. There was punctate labeling on the surface of both EPI-/- and control skin ([Figure 2](#fig2){ref-type="fig"}a). A FISH probe for *Candida dubulinienesis* served as a negative control ([@bib2]) ([Figure 2](#fig2){ref-type="fig"}b). In WT epidermis, bacteria were largely confined to the cornified layers, which can be distinguished in vertical sections because the cells lack nuclei ([Figure 2](#fig2){ref-type="fig"}c). However, bacteria in EPI-/- epidermis were also localized between keratinocytes in the living layers, extending in some cases into the basal layer (arrowheads, [Figure 2](#fig2){ref-type="fig"}c). Quantitation of the label confirmed that bacteria in EPI-/- epidermis were located significantly deeper than in control epidermis ([Figure 2](#fig2){ref-type="fig"}d; WT vs. EPI-/- : 17.13 ± 0.04 vs. 15.89 ± 0.02 μm to the median line of epidermis).

Atopic features of EPI-/- mouse skin are unaffected by skin microbiota {#sec2.2}
----------------------------------------------------------------------

To determine whether the increased bacterial load influenced the atopic phenotype of EPI-/- mice, we used two strategies to reduce the bacterial content of the skin. One was to breed EPI-/- mice for multiple generations under specific-pathogen-free (SPF) barrier conditions (flora-deficient EPI-/- mice). The other was to treat them systemically for 2 weeks with the broad-spectrum antibiotic enrofloxacin, a time frame chosen on the basis that the exaggerated atopic response to TPA we described previously ([@bib5]) is induced within 9 days. Both treatments reduced the bacterial load of EPI-/- skin to that of WT control mice ([Figure 3](#fig3){ref-type="fig"}a).

As previously reported ([@bib5], [@bib24]), EPI-/- mice have more dermal CD4^+^CD3^+^ lymphocytes than control mice ([Figure 3](#fig3){ref-type="fig"}b). This difference persisted when EPI-/- mice were depleted of bacteria ([Figure 3](#fig3){ref-type="fig"}b). The number of epidermal γδTCR^+^CD3^+^ lymphocytes (dendritic epidermal T cells) is reduced in EPI-/- mice ([@bib5], [@bib24]), and again this was not affected by reducing the number of skin bacteria, when evaluated by expression of a pan-γδTCR marker ([Figure 3](#fig3){ref-type="fig"}c) or Vγ3, a specific marker of epidermal dendritic epidermal T cells ([Figure 3](#fig3){ref-type="fig"}d).

Dendritic epidermal T cells are involved in the lymphoid stress-surveillance response, in which tissue damage upregulates "stress-associated" genes, including *Rae-1*, in keratinocytes, leading to the activation of immunoreceptor NKG2D of immune cells ([@bib29]). *Rae-1* expression was increased in EPI-/- mice, as described previously ([@bib5]), and was not reduced by depletion of the skin microbiota ([Figure 3](#fig3){ref-type="fig"}e).

Neutrophil infiltration, indicative of chronic inflammation, is highly elevated in TPA-treated EPI-/- skin ([@bib5]). There was a slight (but not statistically significant) increase in the level of myeloperoxidase (MPO), a marker of neutrophil activation, in untreated EPI-/- skin ([Figure 3](#fig3){ref-type="fig"}f). MPO activity was not, however, affected by depletion of the skin microbiota ([Figure 3](#fig3){ref-type="fig"}f). These findings were supported by quantitating the number of Ly6G/Gr-1 positive cells (neutrophils) in the dermis of WT, EPI-/-, flora-deficient EPI-/-, and antibiotic-treated EPI-/- mice (see [Supplementary Figure 1](#appsec1){ref-type="sec"}, online).

Histologically, untreated adult dorsal skin of EPI-/- mice showed mild hyperkeratosis ([Figure 4](#fig4){ref-type="fig"}a) and an exaggerated epidermal response to TPA that included hyperkeratosis, parakeratosis, epidermal thickening, and spongiosis ([Figure 4](#fig4){ref-type="fig"}b and c), as reported previously ([@bib5], [@bib24]). Depletion of the skin microbiota by antibiotic treatment had no effect on these features ([Figure 4](#fig4){ref-type="fig"}a--c). In addition, antibiotics did not reduce spleen size in EPI-/- mice ([Figure 4](#fig4){ref-type="fig"}d).

EPI-/- mice have an increased level of epidermal AMPs, proteases, and protease inhibitors, independent of skin microbiota {#sec2.3}
-------------------------------------------------------------------------------------------------------------------------

Because bacterial load was not responsible for the skin inflammatory phenotype or keratinocyte-specific stress response of EPI-/- mice, we next investigated whether the expression of AMPs was influenced by skin bacteria. Skin AMPs belong to several distinct protein families that target different types of bacteria ([@bib7]). We examined expression of representatives of three of the families by qPCR. EPI-/- epidermis had significantly elevated levels of *Camp* (cathelicidin-related antimicrobial peptide; gram-positive and -negative bacteria). *Defb1*, *Defb2*, *Defb3*, and *Defb6* (β-defensins; gram-positive and -negative bacteria) and *S100A9* (calprotectins; *Staphylococcus aureus*) levels were similar to control mice ([Figure 5](#fig5){ref-type="fig"}a--f). Expression of AMP genes was not affected by depletion of the skin microbiota ([Figure 5](#fig5){ref-type="fig"}a--f). Immunofluorescence labeling for β3-defensin and *Camp* in sections of mouse back skin suggested that both EPI-/- and flora-deficient EPI-/- epidermis had more abundant β3-defensin and *Camp* than WT epidermis (see [Supplementary Figure 2](#appsec1){ref-type="sec"}, online).

Antibiotic treatment reduces expression of intestinal AMPs, including *Pla2g2a*, *Reg3b*, and *Reg3g* ([@bib22]). Intriguingly, these genes were not upregulated in EPI-/- skin and were not affected by the depletion of skin microbiota ([Figure 5](#fig5){ref-type="fig"}g--i). This suggests that the AMPs of the gut and skin respond differently to bacterial load.

Epidermal proteases are known to process native AMPs to their active forms ([@bib21]), and expression of one of the major skin serine protease inhibitors, *Serpina1b*, is increased in EPI-/- skin ([@bib24]). In contrast to AMPs, *Serpina1b* levels were affected by depletion of skin bacteria in EPI-/- mice, showing a significant increase in antibiotic-treated and SPF-housed animals ([Figure 5](#fig5){ref-type="fig"}j). However, when WT mice were treated with antibiotics, epidermal *Serpina1b* expression was not altered (see [Supplementary Figure 3](#appsec1){ref-type="sec"}, online).

In contrast to our earlier report ([@bib24]), total epidermal protease activity was increased in EPI-/- mice. However, total protease activity was not affected by bacterial depletion ([Figure 5](#fig5){ref-type="fig"}k). Casein gel electrophoresis revealed two predominant proteins with protease activity, which, based on electrophoretic mobility, likely correspond to elastase 2 and kallikrein 7 ([Figure 5](#fig5){ref-type="fig"}l). Both bands were more abundant in EPI-/- than WT skin but were not reduced by bacterial depletion ([Figure 5](#fig5){ref-type="fig"}l). These data indicate that, like AMP expression, total proteases are unaffected by bacterial load in the skin of EPI-/- mice, notwithstanding the increase in *Serpina1b* levels.

Discussion {#sec3}
==========

Our study demonstrates that the epidermal barrier defect of EPI-/- mice results in an increased bacterial load and deeper bacterial penetration of the epidermis. Both phenomena are most likely attributable to the previously reported fragility of the cornified envelope and abnormalities in the intercellular desmosomal junctions ([@bib24]). The increase in bacteria does not, however, impact on the atopic phenotype of EPI-/- skin, including inflammation, upregulation of *Rae-1*, AMPs, and epidermal protease activity, and does not reflect any major changes in skin bacterial phyla.

The only exception to these observations was *Serpina1b*, which, as reported previously ([@bib24]), was elevated in EPI-/- skin relative to WT. *Serpina1b* levels were further increased by bacterial depletion in EPI-/- but not WT skin. Although the underlying mechanism and biological significance of the *Serpina1b* findings remain to be explored, it is worth noting that elevated expression of Serpins contributes to barrier dysfunction and inflammation in allergen-treated mouse skin ([@bib26]). It would be interesting to examine whether EPI-/- mice exhibit increased allergic contact dermatitis, because Filaggrin-null mice, which also have a defective barrier, display an enhanced cutaneous response to ovalbumin sensitization ([@bib13]).

Our observations indicate that the major tumor-protective mechanism identified in EPI-/- mice, namely signaling via Rae-1 and NKG2D, is attributable to the inherent structural defects in the epidermal barrier and not to the increase in the skin microbiota. This is in marked contrast to the protumorigenic effect of flagellated bacteria that colonize skin wounds and signal via Toll-like receptor-5 ([@bib10], [@bib11]).

Our study distinguishes phenotypes resulting from changes to the epidermal barrier from microbiota-induced phenotypes. Genetic ablation of the serine protease matripase, which processes Filaggrin and is required for normal cornified envelope assembly, results in changes in the composition of the skin microbiota and changes in AMP levels, but whether the two phenomena are related has not been explored ([@bib23]). The AMP β-defensin-2 is upregulated in the skin of patients with atopic dermatitis ([@bib1], [@bib20]), and *S. aureus* infections are common in these patients ([@bib18]). However, the skin inflammation that is a hallmark of atopic dermatitis and other epidermal barrier disorders, such as congenital ichthyosis ([@bib12], [@bib19]), is not alleviated by antibiotic treatment. We propose that the altered skin microbiome associated with atopic dermatitis is a consequence, not a cause, of the disease.

Methods {#sec4}
=======

Mice {#sec4.1}
----

EPI-/- mice were generated as described previously on a mixed genetic background ([@bib24]). Genome scanning performed by the Jackson Laboratory (Bar Harbor, ME; [@bib5]) established that their genetic background comprises Sv129 (40.98% ± 1.52%), C57Bl/6 (51.39% ± 1.62%), and BALB/c (4.7% ± 1.24%). On that basis we selected the F2 generation of Sv129 and C57BL/6J crosses (51.82% ± 3.35% Sv129; 49.92% ± 1.98% C57Bl/6) as the control for all experiments, because their genetic background was 95% identical to that of EPI-/- mice. The F2 control mice were previously used in studies of the susceptibility of EPI-/- mice to chemical carcinogenesis ([@bib5]). Matched control and EPI-/- mice were housed in the same environments and had the same diet.

For some experiments multiple generations of EPI-/- and control mice were housed under SPF conditions. Analysis was performed on mice that had been born in pathogen free conditions in an SPF barrier facility. Entry into the facility required staff to change into scrubs and protective footwear, to take an air shower, and to use hand disinfectant. Mice, whether behind the barrier or in the conventional facility, were kept in individually ventilated cages that were only opened in a laminar flow cabinet and were only handled with gloved hands. For antibiotic treatment oral enrofloxacin (5 mg/kg/day) was given to mice kept in the conventional facility for 2 weeks.

For TPA treatment, 6 nmol (3.7 μg) of TPA (Sigma-Aldrich, Gillingham, UK) in 200 μl acetone was applied to mouse dorsal skin three times on alternating days ([@bib5]). Skin, and in some cases spleen, was harvested 48 hours after the last TPA treatment. Epidermal thickness was assessed on hematoxylin and eosin-stained sections of TPA-treated skin using ImageJ software (NIH, Bethesda, MD). At least six fields were analyzed per skin section. All experiments were performed under the terms of a UK government Home Office project license following institutional ethical review.

Analysis of bacterial load {#sec4.2}
--------------------------

Extraction of skin bacterial DNA was performed as described previously ([@bib23]). Briefly, 5-mm diameter skin biopsies were collected from the ears of age- and sex-matched mice using sterile instruments. DNA was extracted with the PureLink Genome DNA Mini Kit (Life Technologies, Grand Island, NY) following the bead-beating step using Precellys 24 (Bertin Technologies, Montigny le Bretonneux, France). 16S rRNA gene qPCR was carried out using specific primers (Bact-8F: AGAGTTTGATCCTGGCTCAG, Bact--338R: CTGCTGCCTCCCGTAGGAGT) with fast SYBR Green detection (Applied Biosystems, Foster City, CA) and run on an ABI Prism 7900HT Sequence detection system (Applied Biosystems). Standard curves were prepared by amplifying serial dilutions of known quantities of *E. coli* cells. The V1-V2 region of the 16S rRNA gene was amplified using barcoded fusion primers, and PCR products were sequenced on a 454GS FLSGX platform (Roche, Branford, CT). Pyrosequencing reads were uploaded into QIIME ([qiime.org/](http://qiime.org/){#interref0015}) and processed as described previously ([@bib4]).

Whole-mount FISH {#sec4.3}
----------------

Pieces of ear skin were collected with a sterilized 3-mm biopsy punch. Skin samples were fixed in 1:1 acetone-to-methanol and incubated with PNA FISH probes at 55 °C. A universal bacteria probe (BacUni; AdvanDx, Woburn, MA) and probe for *C. dubuliniensis* (AdvanDx) were used. DAPI was used for nuclear staining. Images and Z-stacks of whole-mount FISH were obtained using a TCS SP5 Tandem Scanner confocal microscope (Leica, Mannheim, Germany). Optical sectioning and Z-stack maximum projection images of whole-mount preparations were produced using LAS AF software (Leica). Evaluation of bacterial location was performed on three-dimensional reconstructed skin using Volocity software (PerkinElmer, Wellesley, MA). All the BacUni DAPI signals were detected using the software. The median epidermal line was calculated using the localization of the center of epidermal DAPI signals. The distance of each BacUni signal from the median epidermal line was plotted. Positive values indicate signals above the median line, whereas negative values correspond to signals below the median line.

Immunofluorescence {#sec4.4}
------------------

Frozen sections were fixed with 4% paraformaldehyde/phosphate-buffered saline and stained with the following antibodies conjugated with Alexa 488 or FITC or Alexa 633: anti-CD3 (BD Pharmingen, San Diego, CA, clone 17A2), anti-CD4 (eBioscience, San Diego, CA, clone RM4-5), anti-γδ TCR (BD Pharmingen, clone GL3), anti-Vγ3 TCR (BD Pharmingen, clone 536), or anti-Ly6G/Gr-1 (Beckman Coulter, Miami, FL, clone RB6-8C5). Formalin-fixed paraffin sections were treated with citrate buffer for antigen retrieval and incubated with anti-Camp (Abcam, Cambridge, UK) or anti-β3 defensin (Alpha Diagnostic, San Antonio, TX) followed by Alexa 488-conjugated donkey anti-rabbit antibody (Life Technologies). DAPI or PI was used for nuclear staining. Images were acquired using a TCS SP5 Tandem Scanner or an FV1000 confocal microscope (Olympus, Tokyo, Japan). When quantifying cells that expressed a particular marker, at least six fields were analyzed per skin section.

MPO ELISA {#sec4.5}
---------

For MPO quantification, an MPO ELISA kit (Hycult Biotech, Uden, Netherlands) was used according to the manufacturer's protocol. A 3-mm punched ear skin punch biopsy was homogenized in lysis buffer (200 mM NaCl, 5 mM EDTA, 10 mM Tris, 10% glycerin, 1 mM phenylmethylsulfonyl fluoride) and centrifuged. The supernatants were used for MPO ELISA. The absorbance was measured using a Sunrise spectrophotometer (Tecan, Männedorf, Switzerland).

Quantitative reverse transcriptase PCR {#sec4.6}
--------------------------------------

RNA was extracted from murine whole skin or from epidermis that had been scraped from the skin after incubation in phosphate-buffered saline for 30 seconds at 56 °C in phosphate-buffered saline. The RNeasy mini kit (Qiagen, West Sussex, UK) was used according to the manufacturer's instructions. RNA concentration was measured using an ND-100 NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE). cDNA was synthesized using a Superscript III First-Strand Synthesis Supermix for quantitative reverse transcriptase PCR (qRT-PCR) kit (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. qRT-PCR was carried out using specific primers and fast SYBR Green and run on an ABI Prism 7900HT Sequence detection system. All samples were compared relative to mouse Gapdh as a housekeeping control.

The following primers were used. *Rae-1*: CTAGTGCCACCTGGGAATTCA (forward), CATCATTAGCTGATCTCCAGCTCA (reverse); *Camp*: CTTCAACCAGCAGTCCCTAGACA (forward), TCCAGGTCCAGGAGACGGTA (reverse); *Defb2*: GCCATGAGACTCTCTGCTC (forward), TGCAACAGGGGTTCTTCTCT (reverse); *Defb3*: ATTTCTCCTGGTGCTCGTGT (forward), GGAACTCCACAACTGCCAAT (reverse); *Pla2g2a*: CTGTTGCTACAAGAGCCTGG (forward), TTTTCTTGTTCCGGGCGAAA (reverse); *Reg3b*: CCTTAGACCGTGCTTTCTGTG (forward), GTCCATGATGCTCTTCAAGACA (reverse); *Reg3g*: ACATCAACTGGGAGACGAATC (forward), TTTGGGATCTTGCTTGTGGCTA (reverse); *Serpina1b*: CATAGGAACGGCTTCAAAGA (forward), TCCAGATCCATATCCCCAGA (reverse); *Gapdh*: AACATCAAATGGGGTGAGGCC (forward), GTTGTCATGGATGACCTTGGC (reverse).

Epidermal protease activity {#sec4.7}
---------------------------

Epidermal lysates were obtained as described previously ([@bib6]). Briefly, epidermis was homogenized in 1 M acetic acid using Precellys 24 (Bertin Technologies). After overnight extraction at 4 °C, soluble proteins were lyophilized and resuspended in MiliQ water (Millipore, Bedford, MA). After acetone precipitation the BCA protein assay kit (Pierce, Rockford, IL) was used to quantify protein concentration. The soluble fraction, 2 μg, was monitored for protease activity using the EnzChek Protease Assay Kit (Life Technologies), according to the manufacturer's instructions; 5 μg of the soluble fraction was loaded onto Novex Zymogram (casein) Gels (Life Technologies) for electrophoresis. Gels were stained with Coomassie brilliant blue.

Statistics {#sec4.8}
----------

Statistical analysis was performed using GraphPad Prism (GraphPad Software, San Diego, CA). Probability values were determined with the unpaired Student's *t*-test or one-way ANOVA followed by Tukey's test. For analysis of bacterial depth in the skin, a nonparametric Mann-Whitney test was performed.

Conflict of Interest {#sec5}
====================

The authors state no conflict of interest.

Supplementary Material {#appsec1}
======================

Supplementary Figures 1--3

KN was the recipient of an overseas postdoctoral fellowship from the Japan Society for the Promotion of Science. SC was the recipient of a Federation of European Biochemical Societies Fellowship. FMW gratefully acknowledges the financial support of the Wellcome Trust and MRC. We thank Esther Hoste for her input and the core facilities of the Cancer Research UK Cambridge Research Institute, where some of this work was performed, for superb technical assistance.

Supplementary material is linked to the online version of the article at [www.jidonline.org](http://www.jidonline.org){#intref0010}, and at [doi:10.1038/JID.2015.383](10.1038/JID.2015.383){#interref0010}.

![**Characterization of skin microbiota in EPI-/- and WT mice.** (**a**) qPCR of 16sRNA gene in EPI-/- and WT ear skin. Data are means ± SEM from six mice per group. \*\**P* \< 0.01. (**b** and **c**) Microbial diversity of EPI-/- and WT ear skin obtained by 16s RNA gene pyrosequencing from six mice per group. (**b**) Data are presented as means of relative abundance ± SEM. (**c**) Each column shows relative abundance of the bacterial phyla in a single mouse. There were no statistically significant differences between WT and EPI-/- skin microbiota phyla.](gr1){#fig1}

![**Epidermal penetration by bacteria.** (**a** and **b**) Whole-mount FISH of ear skin using BacUni (**a**) or *C. dubuliniensis* (**b**) probes (green), with DAPI counterstain (blue). The images are Z-stack maximum projections of the epidermis. Scale bars = 20 μm. (**c**) Optical sectioning of 3D reconstructed whole-mount FISH (BacUni). Arrowheads show bacterial penetration of the basal epidermal layer. Dotted line indicates position of basement membrane. Scale bars = 10 μm. (**d**) Quantitation of location of individual BacUni signals relative to the median thickness (dotted line) of the epidermis. The boxes extend from the 25th to 75th percentiles. Whiskers indicate minimum to maximum. \*\*\*\**P* \< 0.0001.](gr2){#fig2}

![**Microbiota independent inflammatory and stress response phenotypes of EPI-/- skin.** (**a**) qPCR of 16s RNA gene in WT, EPI-/-, antibiotic-treated EPI-/-, and flora-deficient EPI-/- ear skin samples. (**b**) Number of CD4^+^CD3^+^ cells per mm^2^ dermis. (**c**) Number of γδTCR^+^ cells per mm epidermis. (**d**) Number of Vγ3^+^ cells per mm epidermis. (**e**) qRT-PCR of *Rae-1* in WT, EPI-/-, antibiotic-treated EPI-/-, and flora-deficient EPI-/- epidermis. (**f**) Quantification of MPO in WT, EPI-/-, and flora-deficient skin lysates. Data are means ± SEM from at least four (**a**) or three (**b--f**) mice per group. \**P* \< 0.05, \*\*\*\**P* \< 0.001; unless indicated no significant differences were found (one-way ANOVA followed by Tukey's test).](gr3){#fig3}

![**Effect of antibiotic treatment on skin histology.** (**a**) Hematoxylin and eosin-stained skin sections of WT, EPI-/-, and antibiotic-treated EPI-/- mice at steady state. Arrowheads, mild hyperkeratosis. (**b**) Hematoxylin and eosin-stained sections of WT, EPI-/-, and antibiotic-treated EPI-/- skin after three applications of TPA. White arrowheads, severe hyperkeratosis; asterisks, parakeratosis; black arrows, spongiosis. Scale bars = 100 μm. (**c** and **d**) Epidermal thickness (**c**) and spleen mass (**d**) of WT, EPI-/-, and antibiotic-treated EPI-/- mice. TPA was applied three times on alternating days on all mice (n = 3 or 4 per condition). \*\**P* \< 0.01, \*\*\**P* \< 0.001.](gr4){#fig4}

![**AMP expression and protease activity in EPI-/- epidermis after microbiota modulation.** (**a--k**) qRT-PCR of *Camp* (**a**) , *Defb1* (**b**), *Defb2* (**c**), *Defb3* (**d**), *Defb6* (**e**), *S100A9* (**f**), *Pla2g2a* (**g**), *Reg3b* (**h**), *Reg3g* (**i**), and *Serpina1b* (**j**) in WT, EPI-/-, antibiotic-treated EPI-/-, and flora-deficient EPI-/- epidermis. Data are means ± SEM from at least four mice per group. \**P* \< 0.05, \*\**P* \< 0.01, \*\*\**P* \< 0.001. (**k**) Total epidermal protease activities of WT, EPI-/-, antibiotic-treated EPI-/-, and flora-deficient EPI-/- skin. Data are means ± SEM from three to four mice per group. (**l**) Casein gel electrophoresis of epidermal lysates. Each lane is lysate from a different mouse. Positions of molecular mass markers (31 kDa, 17 kDa) are shown. The two major bands of proteolytic activity are indicated by arrowheads.](gr5){#fig5}