Description of the main S&T results / foregrounds

1. Work progress and achievements.......................................................................................................... 11.1. WP1 Clinical studies.......................................................................................................................... 11.2. WP2 Microbiome analysis................................................................................................................. 31.3. WP3 Transcriptomics…………......................................................................................................... 41.4. WP4 Bioinformatics and systems biology......................................................................................... 61.5. WP5 Molecular and cellular networks.............................................................................................. 71.6. WP6 Animal models…………........................................................................................................... 9

1. Work progress and achievements

In WP1 tasks 1 and 2 (collection of patient swab samples in skin biopsies) were finalized during the 2nd reporting period. A total of 134 psoriasis (PSO) patients, 91 atopic dermatitis (AD) patients and 126 healthy volunteers were recruited, samples were collected and submitted to analysis in WP2 and WP3. Task 3 (Patient stratification) was a central part of WP1 activities during the 2nd and 3rd reporting period, providing additional clinical information to aid data analysis, and patient genotypes will soon be available for genetic stratification. For Task 4, (Skin explants) WP1 has established the production of skin equivalents and an an ex vivo skin organ culture model, for further analysis and validation of the cutaneous response to microbes. In WP2 the MAARS patient skin microbiome has been characterized by 16S rRNA sequencing and metagenomics shotgun sequencing (Task 1), and patient blood samples have been subjected to the analysis of virus content (Task 2), in addition to more in depth analysis of the microbiome data (Task 3). In WP3 RNA has been extracted from all patient skin biopsies (Task 1) and the gene expression has been profiled by using DNA microarrays and high-throughput sequencing (Task 2). Task 3 was modified into combining data from microbiome and transcriptome analyses. In WP4 emphasis has been on data management (task 1) and analysis strategies (task 2-4) and patient stratification. We have set up a pipe-line that involves pre-processing steps and more advanced statistical approaches, and introduced models of data integration, and we have developed an information system KDS2 to centralize and retrieve all the data from the MAARS project. During the 3rd period of the project emphasis has been on data integration, and biological interpretation in order to draw important biomedical and conceptual conclusions from the large amounts of high quality data generated throughout the project, and the first set of results from WP1-WP4 are currently under review at the journal Science. WP5 objectives included the production of protein, antibody and cell-based tools (Task 1) and in vitro investigation of microbial targets identified by WP4 (Tasks 2-5), and WP6 objectives included validation of microbial targets in experimental AD and PSO (Tasks 1-3). According to the colonization pattern of microbes identified by WP1-WP4, C. simulans and C. kroppenstedtii are thought to be pro-inflammatory in PSO, whereas L. crispatus might have an anti-inflammatory role in both, AD and PSO. Indeed our in vitro and in vivo data confirm immune-modulatory, protective roles of lactobacilli and a pro-inflammatory function of Corynebacteria spp, supporting WP1-4 conclusions.

1.1. WP1 Clinical studies

To evaluate differences in the cutaneous microbial colonization and transcriptional profile in atopy- and autoimmune-type skin diseases, adult patients (18-70 years) with mild-to-severe chronic AD (SCORAD score > 25, n=91) and plaque-type PSO (PASI score >7, n=134) as well as healthy volunteers (n=126) were recruited from three Depts. of Dermatology, at University Hospitals located in Duesseldorf (HHU, Germany), London (KINGS, Great Britain) and Helsinki (UH, Finland) (table 1). Each subject underwent a physical examination by a dermatologist and the medical history was recorded. The diagnoses were made by a dermatologist based on clinical presentation, personal history, laboratory findings and the criteria of Hanifin and Rajka. The exclusion criteria included concomitant autoimmune diseases (e.g. rheumatoid arthritis, diabetes, alopecia areata, etc.) the use of systemic antibiotics within 2 weeks and systemic immunosuppressive therapy or phototherapy or systemic biologic agents within the previous 12 weeks prior to screening.

Before skin sampling, the biopsy sites were left untreated for at least 2 weeks and cleansing with only the non-antibacterial Dove soap was allowed and washing was avoided for 24 hours prior to sampling. The patients or healthy volunteers who did not match these clinical exclusion criteria were removed from the study. The following biological samples were then obtained and submitted to analysis: 1) microbiome samples from upper/lower back, posterior thigh or buttocks (PSO, AD, healthy volunteers) with no prior cleaning or preparation of the skin surface using sterile gloves to prevent cross-contamination, were obtained placing a sterile ring (2.5 cm diameter) onto the appropriate skin area, 1.5 ml PBS was supplemented into the ring and the area sampled scraping a glass rod in a circular motion 10 times to the left and to the right. Subsequently, the microbiome-enriched PBS was harvested and stored. 2) 6 mm punch biopsies from skin at the “microbiome” sites were taken in local anaesthesia. Subsequently, samples were stored in RNAlater (Sigma-Aldrich) and subjected to further analyses (Fig. 1). The study was approved by the appropriate local Institutional Review Boards, and all subjects provided written informed consent before participation.

Figure 1. Flow chart of study design and samples included in the study.Punch biopsies (6 mm) were taken after obtaining microbiome samples, from the same skin sites as the swab samples, from lesional and non-lesional skin of AD patients (n=91, 2 biopsies per patient), PSO patients (n=134, 2 biopsies per patient) or from normal healthy individuals (n=126, 1-3 biopsies per patient). Samples were stored in RNAlater (Sigma-Aldrich) and subjected to further analyses, as described above.In addition, heparinized blood was drawn from each patient (in CPT tubes), as well as from the healthy donors, and PBMC were isolated locally using standard protocols and either used fresh or frozen for later studies (for details, see WPs 3, 4 and 5). The plasma collected from these blood samples has been shipped to Partner 8 for viral DNA analyses.

10 ml of EDTA blood was drawn from each participant for genomic analyses (for details, see WPs 3, 4 and 5). The samples have been shipped to Partner 8 for standardized DNA extraction.

Related to milestone 4 (MS4) Stratification of patients for high-throughput transcriptomics and microbiome studies, WP1 provided clinical stratifiers for psoriasis and atopic dermatitis. For genetic stratification of the patients, central extraction of the patient DNA at KINGS (P8) is under progress. The three most common filaggrin mutations (in AD) are being analyzed in Stephan Weidinger’s laboratory in Kiel and HLA Cw6 variations will be detected by DNA microarrays (Affymetrix, Illumina chip version II).

Related to Task 4, (Skin explants), HHU (P5) has established the production of skin equivalents and ex vivo skin organ culture models, setting up important tools for further analysis and validation of the cutaneous response to microbes in vitro and ex vivo in genetically-defined and disease-related settings.

1.2. WP2 Microbiome analysis

The microbiome sequencing of patient samples has proceeded at a satisfactory pace and, as previously reported, we have extracted DNA from all samples. PCR amplification has been carried out and 16S sequence data has been obtained for all samples. The analysis of these sequences showed that clear identification of bacterial taxa has been achieved (Task 1) (Fig.2).

Figure 2. Characterization of the skin microbiome in patients with atopic dermatitis (AD), psoriasis (PSO) and healthy volunteers (HV) by 16S rRNA sequencing.

The data have been analyzed further and followed up with data integration with other

datasets. The results will be submitted for publication shortly.We have also completed the shotgun sequencing task. The setting up of the methodology for this task was time consuming, but successful. Thanks to lower sequencing costs and efficient novel library preparation protocols, we managed to produce shotgun data from all samples instead of a subset, which has resulted in data of tremendous importance for the study (Task 1). A bioinformatics pipeline has been established for primary analysis of the shotgun data and the analysis is ongoing. The initial results indicate a deep sampling of the bacteria, fungal and viral skin microbiomes.

For the identification of virus in serum samples, a complete set of protocols for virus enrichment, library preparation and sequencing were established and tested extensively using different types of samples. In addition an efficient bioinformatics pipeline for the subsequent analysis was established. Illumina Miseq data was produced for all MAARS patient samples, as pools representing the two diseases and the healthy subjects. The resulting data have been analyzed and multiple viral species were identified (Task 2).The identification of interesting candidates has progressed along with the data analysis. Bacteria selected for further study include Lactobacillus and Corynebacterium, and fungal and viral species will also be studied further (Task 3).

1.3. WP3 Transcriptomics

In WP3 task 1, biopsy samples from 91 AD patients, 134 PSO patients, and 126 healthy volunteers were used for total RNA extraction. At least 250 ng of high quality total RNA per sample was sent to Institute Curie (P6) Affymetrix platform for generation of transcriptional profiles by usage of Affymetrix Chip Human Gene ST2.1 array. Quality controls at the cRNA and cDNA levels were satisfactory, except for 3 samples out of 600 for which no amplification product was detected. This corresponds to a 0.5% failure, which is very satisfactory for such a large number of samples. There was no outlier chip after Gene PLIER normalization, confirming a good quality in the hybridization procedure. After preprocessing and normalization of the raw data by WP4, a PCA on the 1000 most variant genes revealed good separation between the groups (Fig. 3).

Figure 3. Transcriptomics analysis of the three patient groups. (A) Projection of AD (red), PSO (orange) and HV (green) transcriptome profiles in the subspace spanned by the 2 first components of the Principal Component Analysis (PCA) performed on the 1.000 most variant genes.

In task 2, 60 ng of the same samples were delivered to KI (P2) sequencing facility for generation of transcriptional profile by high-throughput sequencing analysis by STRT method. STRT sequencing libraries were prepared and a commercial control RNA (10 ng) that was received from Curie (P6) was added to each batch to control the variation between the libraries. The quality and amount of libraries were checked before sequencing and when not adequate, the library was re-prepared. After the raw data was received from the sequencing facility, it was transferred to Curie for storage, and can be accessed by MAARS members.

1.4. WP4 Bioinformatics and systems biology

WP4 is a central work package which includes partners of diverse and complementary expertise in data management, bioinformatics and systems biology. Connections with other WPs are crucial to ensure a smooth and efficient processing and analysis of the data in order to move towards the global objectives of the MAARS project. During the first reporting period, emphasis was given to the data management, in order to implement the most appropriate solutions to address data storage and handling and ensure success of research tasks as well as clinical practice. Frequent interactions with WP1 and WP2 were important to ensure that clinical information/annotation was made available in the right format for subsequent biostatistics/bioinformatics analysis. An electronic CRF was built in accordance

with the paper CRF and clinical input from WP2 partners. Downstream interactions with WP5 were established in order to prepare the biological database (BIRD). Interactions with WP3 were critical to link RNA samples to data generation. Several important issues were also addressed regarding the choice of transcriptomics methods, future analysis strategies, and task-sharing among partners. In particular, a meta-analysis of public transcriptomic data was performed (FIOS GENOMICS (P10)) in order to establish some biological benchmarks for comparison with our own data. By the end of the first reporting period, everything was in place to store, annotate and analyze data according to state of the art bioinformatics methods.

During the second reporting period, emphasis was maintained on the data management, in order to ensure continued quality of the data, manage transfer of new large-scale data, and perform all required annotations and quality controls. In parallel, data analysis gradually increased as the data became available. Frequent interactions with WP1 and WP2 were important to ensure that clinical information/annotation was made available in the right format for subsequent biostatistics/bioinformatics analysis. Downstream interactions with WP5 were continued as in first reporting period, in order to finalize the biological result database (BIRD), and to discuss molecular targets to focus on within WP4. Interactions with WP3 were critical to link RNA samples to data generation for the final large transcriptome dataset. By the end of the 2nd reporting period, transcriptomics and microbiome data on the final large dataset of 600 samples had been generated, and were available to all consortium members.

During the third reporting period emphasis was on data integration, and biological interpretation in order to draw important biomedical and conceptual conclusions from the large amounts of high quality data. We found that AD (n=88) and PSO (PSO, n=129) are classified by distinct microbes, which differ from healthy volunteers (n=117) microbiome in composition and relative abundance. AD is dominated by a single microbe (S. aureus), associated to a specific disease relevant transcriptomic signature (Fig. 4). In contrast, PSO is characterized by co-occurring communities of microbes impacting psoriasis core gene transcripts. Our work underscores the importance of distinct microbial classes in tissue inflammation and provides a basis for discovery of biomarkers and targeted therapies in skin dysbiosis. These results are currently under review at the journal Science.

Figure 4. Differential analysis between healthy and AD samples revealed 16,716 genes (FDR < 0.05), for which we created an AD gene co-expression network by Pearson correlation analysis, followed by partitioning of the network into modules using network community detection (network on the left). Hypergeometric tests revealed significant enrichment in S. aureus-associated genes (coloured red) that exclusively mapped to module 4 (FDR<0.05) (network on the right). The functional characterization of this module using Ingenuity Pathway Analysis identified 78 significantly enriched pathways (FDR < 0.05) (Fig. 3D). Top enriched pathways were predominantly related to cytokines/cytokine signaling and antimicrobial peptides.

1.5. WP5 Molecular and cellular networks

Cutaneous surfaces are the first sites that encounter microbial threats and therefore may initiate and shape the innate and adaptive immune response to invading pathogens through activation of pathogen recognition receptors ultimately leading to diseases, like atopic dermatitis (AD) and psoriasis (PSO). Conversely, commensals colonize the skin and critically contribute to tissue homeostasis. Indeed, the MAARS consortium (WP1-WP4) identified a specific colonization pattern in healthy skin that differs significantly from that observed in AD and PSO (as described above). Among those, L. crispatus, C. simulans and C. kroppenstedtii are of special interest and, therefore, have been cultured in vitro. According to the colonization pattern C. simulans and C. kroppenstedtii are thought to be pro-inflammatory in PSO, whereas L. crispatus might have an anti-inflammatory role in both, AD and PSO.

In WP5 we have focused on five different aspects: 1) production of relevant protein, antibody and cell-based tools, 2) dysregulated responses at cutaneous surfaces, 3) the micromilieu and its effect on DC function and T cell priming, 4) novel effector pathways of memory T cells within the skin, and 5) the role of the allergic effector unit. Concerning task 1, we have successfully produced the following tools: human 2B4 rabbit polyclonal antibodies, human TSLP rabbit polyclonal antibodies, ELISA tests for detection of human 2B4, mouse 2B4, human CD48 and mouse CD68, and ELISA tests for human RNase7.

In work related to tasks 2-5, we observed that L. crispatus extracts induce regulatory cytokines and decrease Th2 type responses in human PBMCs and primary keratinocytes (Fig.

5), and diminish IgE mediated degranulation of mast cells, but have no effect on Th1 type inflammation. The data indicate that L. crispatus enhances the immune-modulatory and protective function of immune cells, antagonizes Th2 type inflammatory responses, and may be involved in the maintenance of skin homeostasis.

Figure 5. L. crispatus culture supernatant decreases TH2-, but not TH1-inflammation. Primary human keratinocytes were pre-treated with L. crispatus culture supernatant (A-579-1 in a 1:10 dilution) for 24 hrs followed by a stimulation with IL-4 or IFN-gamma for 24 hrs. Cells were harvested, RNA was prepared and subjected to qPCR analysis (n=8). **, p<0.01; ***, p<0.001.

Lactobacilli and other skin commensals produce short chain fatty acids (SCFAs) such as butyrate and propionate, which might harbor immune-modulatory properties. We found that human primary keratinocytes express SCFA receptors GPR109A and 109B, indicating that microbes that produce these compounds have direct effects on keratinocytes. We observed that sodium butyrate (BA) enhances Th2 responses, downregulates FceRI expression on mast cells, and interferes with Th1 responses in human primary keratinocytes. Further, BA interferes with Th1 responses in dendritic cells (DCs), and reduces the expression of MHC-II, suggesting that BA down-regulates antigen-specific activation of T-cells. BA also differentially regulates psoriasis-related chemokines in DCs, suggesting a role for BA in the regulation of leukocyte trafficking towards the inflammatory site.

C. simulans and C. kroppenstedtii are over-represented in psoriasis. We found that sera from both psoriatic and healthy subjects contain specific IgG against C. simulans, but the magnitude and pattern of antigen recognition differ between psoriasis patients and healthy subjects. A psoriasis specific band was identified, cut out and sequenced, and several peptide fragments of hornerin and filaggrin were identified. The current hypothesis is that C. simulans might contribute to the generation of autoantigens in psoriasis, via triggering of supramolecular forms of hornerin. Moreover, C. simulans significantly up-regulates the expression of PSO-related genes (CCL20, IL36G, S100A7) in human primary keratinocytes, and synergizes with IL-17.

Finally, the fungus Malassezia which is known from the literature to play a role in both AD and PSO, induces the expression of pro-inflammatory cytokines and chemokines in human primary keratinocytes, and synergizes with psoriasis related cytokines IFN-gamma and IL-17 to further drive the expression of pro-inflammatory genes. To conclude, our data suggest immune-modulatory, protective roles of lactobacilli and disease promoting roles of Corynebacteria spp, supporting WP1-4 conclusions.

1.6. WP6 Animal models

Cutaneous surfaces are the first sites that encounter microbial threats and therefore may initiate and shape the innate and adaptive immune response to invading pathogens through activation of pathogen recognition receptors ultimately leading to diseases, like atopic dermatitis (AD) and psoriasis (PSO). Conversely, commensals colonize the skin and critically contribute to tissue homeostasis. The MAARS consortium (WP1-4) identified specific colonization pattern in healthy skin that differs significantly from that observed in AD and PSO. Among those, Lactobacillus crispatus, Acinetobacter lwoffii, Candida albicans and Staphylococcus aureus are of special interest and, therefore, have been cultured in vitro. L. crispatus and A. lwoffii were abundant on the skin in healthy volunteers and lost in AD and PSO lesions, and L. crispatus is suggested to have an anti-inflammatory role in both, AD and PSO. In contrast, according to the colonization pattern seen in patient samples C. albicans and S. aureus are thought to be pro-inflammatory in AD.

In the mouse model of AD (FIOH, P1), L. crispatus antagonized Th2 type allergic responses by reducing the expression of IL-4 and IL-13 mRNA in the skin and by lowering the level of OVA-specific IgE in the serum (Fig. 6). Similarly in the IMQ-model (KINGS, P8) pretreatment of the skin with L. crispatus extracts reduced skin thickness and macrophage infiltration.

Figure 5. a) Exposure to L. crispatus during the AD model protocol resulted in reduced b) inflammation in the skin, including c) lower numbers of eosinophils in the skin, reduced expression of Th2 type cytokines and lower levels of OVA specific IgE in the serum.

A. lwoffii is also present at a higher abundance in the skin in healthy individuals compared with AD patients, and is associated with protection against atopy and allergies in previous studies. Topical exposure of the mouse AD model to A lwoffii extracts significantly diminished the inflammatory response in wild type mice, including decreased numbers of eosinophils and lower expression of Th1, Th2 and Th17 related genes in the skin. In reverse, the yeast C. albicans, which is more abundantly present in AD lesions compared with healthy skin, substantially exacerbated skin inflammation in the mouse AD model through the amplification of Th2 components in addition to Th1 and Th17 type responses.

To validate the role of microbial targets in driving disease pathways in PSO and skin inflammation, KINGS (P8) optimized two in vivo models, human skin transplantion model and psoriasiform with imiquimod, and showed that L. crispatus ameliorated local skin inflammation associated with decreased skin thickness and macrophage infiltration.

To conclude, our results suggest that microbes identified by the MAARS study have a strong impact on the cutaneous inflammatory responses in vivo. Understanding of these processes and interactions will open new avenues to treat and even prohibit inflammatory skin diseases.

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