DREAM: an R package for druggability evaluation of human complex diseases.

MOTIVATION: De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. RESULTS: In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. AVAILABILITY AND IMPLEMENTATION: DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

Integrative network analysis suggests prioritised drugs for atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery. METHODS: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD. RESULTS: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD. CONCLUSIONS: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis.

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

Blood transcriptome profiling identifies 2 candidate endotypes of atopic dermatitis.

BACKGROUND: Few studies have analyzed the blood transcriptome in atopic dermatitis (AD). OBJECTIVE: We explored blood transcriptomic features of moderate to severe AD. METHODS: Blood messenger RNA sequencing on 60 adults from the TREATgermany registry including 49 patients before and after dupilumab treatment, as well as from an independent cohort of 31 patients and 43 controls was performed. Patient clustering, differential expression, correlation and coexpression network analysis, and unsupervised learning were conducted. RESULTS: AD patients showed pronounced inflammatory expression signatures with increased myeloid and IL-5-related patterns, and clearly segregated into 2 distinct clusters, with striking differences in particular for transcripts involved in eosinophil signaling. The eosinophil-high endotype showed a more pronounced global dysregulation, a positive correlation between disease activity and signatures related to IL-5 signaling, and strong correlations with several target proteins of antibodies or small molecules under development for AD. In contrast, the eosinophil-low endotype showed little transcriptomic dysregulation and no association between disease activity and gene expression. Clinical improvement with receipt of dupilumab was accompanied by a decrease of innate immune responses and an increase of lymphocyte signatures including B-cell activation and natural killer cell composition and/or function. The proportion of super responders was higher in the eosinophil-low endotype (32% vs 11%). Continued downregulation of IL18RAP, IFNG, and granzyme A in the eosinophil-high endotype suggests a residual disturbance of natural killer cell function despite clinical improvement. CONCLUSION: AD can be stratified into eosinophilic and noneosinophilic endotypes; such stratification may be useful when assessing stratified trial designs and treatment strategies.

Atopic dermatitis displays stable and dynamic skin transcriptome signatures.

BACKGROUND: Skin transcriptome studies in atopic dermatitis (AD) showed broad dysregulation as well as "improvement" under therapy. These observations were mainly made in trials and based on microarray data. OBJECTIVES: Our aim was to explore the skin transcriptome and the impact of systemic treatment in patients of the TREATgermany registry. METHODS: Biopsy specimens from 59 patients with moderate-to-severe AD before and 30 patients 12 weeks after start of systemic treatment (dupilumab [n = 22] or cyclosporine [n = 8]) and from 31 healthy controls were subjected to mRNA sequencing. Differential expression, pathway enrichment, correlation, and coexpression network analysis were conducted. RESULTS: Both lesional and nonlesional skin showed a stable "core" signature characterized by disturbed epidermal differentiation and activation of IL-31/IL-1 signaling. A second dynamic signature showed progressive enrichment for type 2 inflammation, TH17 signaling, and natural killer cell function. Markers correlated with disease activity have functions in epidermal barrier properties and immune modulation. IL4RA was among the top 3 central dysregulated genes. Cyclosporine led to a more pronounced global transcriptome reversion and normalized TH17 cell/IL23 signaling, whereas dupilumab led to a stronger increase in level of epidermal differentiation markers. Both treatments strongly decreased levels of type 2 markers, but overall the residual profile was still profoundly different from that of healthy skin. Lower levels of IL4RA and IL13 and high IL36A expression were related to a stronger clinical response to dupilumab. CONCLUSION: The AD core signature is characterized by dysregulation of genes related to keratinocyte differentiation and itch signaling. A dynamic signature reflects progressive immune responses dominated by type 2 cytokines with an additional role of TH17 and natural killer cell signaling.

Elevated NK-cell transcriptional signature and dysbalance of resting and activated NK cells in atopic dermatitis.

BACKGROUND: Altered quantities, activity, and composition of natural killer (NK) cells in blood as well as expression changes of genes involved in NK-cell function in skin lesions of patients with atopic dermatitis (AD) were recently reported. OBJECTIVES: We sought to comprehensively analyze cutaneous NK-cell transcriptomic signatures in AD, and to examine changes under treatment. METHODS: We analyzed NK-cell signatures in skin transcriptome data from 57 patients with moderate to severe AD and 31 healthy controls. In addition, changes after 12 weeks of systemic treatment (dupilumab n = 21, cyclosporine n = 8) were analyzed. Deconvolution of leucocyte fractions was conducted. Immunofluorescence staining of NK cells was performed on paraffin-embedded skin sections. RESULTS: Immunofluorescence staining revealed a relatively high abundance of both NK cells and CD3+CD56+ cells in lesional as compared with nonlesional and healthy skin. Lesional and to a lesser extent nonlesional skin showed a strong upregulation of NK-cell markers together with a dysbalanced expression of inhibitory and activating receptors, which was not reverted under treatment. Digital cytometry showed a decrease in activated and an increase in resting NK cells in both lesional and nonlesional skin, which was reverted by both treatment with dupilumab and cyclosporine. The NK-cell transcriptomic signature remained upregulated after treatment, but there was a shift on the qualitative level, indicating a compositional change in NK-cell subsets toward CD56bright NK cells. CONCLUSIONS: Lesional AD skin shows a NK-cell dysregulation, which despite clinical improvement under systemic therapy was only partially reverted, and which may represent a yet underappreciated disease mechanism.

Epigenetic factors involved in the pathophysiology of inflammatory skin diseases.

Epigenetics has been discussed as a potential factor influencing the pathophysiology and severity of inflammatory skin diseases. In recent years, emerging evidence suggests that epigenetic mechanisms are involved in the pathophysiology of not only atopic dermatitis (AD) and psoriasis (PSO) but also lupus erythematosus and oral lichen. A systematic review of the literature was undertaken to provide an unbiased and comprehensive update on the involvement of methylation patterns in inflammatory skin disease. In addition to reviewing the contribution of epigenetic mechanisms regulating the development of inflammatory skin diseases, this review aimed to discern the overlap of epigenetic risk factors of the 2 most common inflammatory skin diseases, AD and PSO. Although AD and PSO are both inflammatory skin diseases, both show a distinct genetic profile. Herein, we give evidence that both AD and PSO share epigenetic risk factors that might contribute to disease characteristics. We identify a core subset of inflammation-associated differentially methylated genes in both AD and PSO and discuss the association in other inflammatory diseases.

Platelet-Released Growth Factors Induce Genes Involved in Extracellular Matrix Formation in Human Fibroblasts.

Platelet concentrate products are increasingly used in many medical disciplines due to their regenerative properties. As they contain a variety of chemokines, cytokines, and growth factors, they are used to support the healing of chronic or complicated wounds. To date, underlying cellular mechanisms have been insufficiently investigated. Therefore, we analyzed the influence of Platelet-Released Growth Factors (PRGF) on human dermal fibroblasts. Whole transcriptome sequencing and gene ontology (GO) enrichment analysis of PRGF-treated fibroblasts revealed an induction of several genes involved in the formation of the extracellular matrix (ECM). Real-time PCR analyses of PRGF-treated fibroblasts and skin explants confirmed the induction of ECM-related genes, in particular transforming growth factor beta-induced protein (TGFBI), fibronectin 1 (FN1), matrix metalloproteinase-9 (MMP-9), transglutaminase 2 (TGM2), fermitin family member 1 (FERMT1), collagen type I alpha 1 (COL1A1), a disintegrin and metalloproteinase 19 (ADAM19), serpin family E member 1 (SERPINE1) and lysyl oxidase-like 3 (LOXL3). The induction of these genes was time-dependent and in part influenced by the epidermal growth factor receptor (EGFR). Moreover, PRGF induced migration and proliferation of the fibroblasts. Taken together, the observed effects of PRGF on human fibroblasts may contribute to the underlying mechanisms that support the beneficial wound-healing effects of thrombocyte concentrate products.

Platelet-Released Growth Factors and Platelet-Rich Fibrin Induce Expression of Factors Involved in Extracellular Matrix Organization in Human Keratinocytes

Platelet-released growth factor (PRGF) is a thrombocyte concentrate lysate which, like its clinically equivalent variations (e.g., Vivostat PRF® (platelet-rich fibrin)), is known to support the healing of chronic and hard-to-heal wounds. However, studies on the effect of PRGF on keratinocytes remain scarce. This study aims to identify genes in keratinocytes that are significantly influenced by PRGF. Therefore, we performed a whole transcriptome and gene ontology (GO) enrichment analysis of PRGF-stimulated human primary keratinocytes. This revealed an increased expression of genes involved in extracellular matrix (ECM) organization. Real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) analysis confirmed the PRGF-mediated induction of selected ECM-related factors such as transforming growth factor beta-induced protein, fibronectin 1, matrix metalloproteinase-9, transglutaminase 2, fermitin family member 1, collagen type I alpha 1 and collagen type XXII alpha 1. PRGF-induced expression of the above factors was influenced by blockade of the epidermal growth factor receptor (EGFR), a receptor playing a crucial role in wound healing. A differential induction of the investigated factors was also detected in skin explants exposed to PRGF and in experimentally generated in vivo wounds treated with Vivostat PRF®. Together, our study indicates that the induction of ECM-related factors may contribute to the beneficial wound-healing effects of PRGF-based formulations.

Molecular targets and signaling pathways regulated by nuclear translocation of syndecan-1.

BACKGROUND: The cell-surface heparan sulfate proteoglycan syndecan-1 is important for tumor cell proliferation, migration, and cell cycle regulation in a broad spectrum of malignancies. Syndecan-1, however, also translocates to the cell nucleus, where it might regulate various molecular functions. RESULTS: We used a fibrosarcoma model to dissect the functions of syndecan-1 related to the nucleus and separate them from functions related to the cell-surface. Nuclear translocation of syndecan-1 hampered the proliferation of fibrosarcoma cells compared to the mutant lacking nuclear localization signal. The growth inhibitory effect of nuclear syndecan-1 was accompanied by significant accumulation of cells in the G0/G1 phase, which indicated a possible G1/S phase arrest. We implemented multiple, unsupervised global transcriptome and proteome profiling approaches and combined them with functional assays to disclose the molecular mechanisms that governed nuclear translocation and its related functions. We identified genes and pathways related to the nuclear compartment with network enrichment analysis of the transcriptome and proteome. The TGF-ß pathway was activated by nuclear syndecan-1, and three genes were significantly altered with the deletion of nuclear localization signal: EGR-1 (early growth response 1), NEK11 (never-in-mitosis gene a-related kinase 11), and DOCK8 (dedicator of cytokinesis 8). These candidate genes were coupled to growth and cell-cycle regulation. Nuclear translocation of syndecan-1 influenced the activity of several other transcription factors, including E2F, NFκß, and OCT-1. The transcripts and proteins affected by syndecan-1 showed a striking overlap in their corresponding biological processes. These processes were dominated by protein phosphorylation and post-translation modifications, indicative of alterations in intracellular signaling. In addition, we identified molecules involved in the known functions of syndecan-1, including extracellular matrix organization and transmembrane transport. CONCLUSION: Collectively, abrogation of nuclear translocation of syndecan-1 resulted in a set of changes clustering in distinct patterns, which highlighted the functional importance of nuclear syndecan-1 in hampering cell proliferation and the cell cycle. This study emphasizes the importance of the localization of syndecan-1 when considering its effects on tumor cell fate.

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

CD109 drives pro-tumorigenic cell properties in human non-small cell lung cancer through interaction with desmoglein-2.

Cluster of differentiation 109 (CD109) is a glycosylphosphatidylinositol (GPI) anchored cell surface protein, expressed on epithelial and endothelial cells, CD4+ and CD8+ T-cells, and premature lymphocytes. CD109 interacts with different cell surface receptors and thereby modulates intracellular signaling pathways, which ultimately changes cellular functions. One well-studied example is the interaction of CD109 with the TGFß/TGFß-receptor complex at the cell surface. CD109 silences intracellular SMAD2/3 signaling and targets TGFß/TGFß-receptor to the endosomal/lysosomal compartment. In recent years, CD109 emerged as a tumor marker for different tumor entities and expression of CD109 could be linked to adverse outcome in patients. In this study, we show that silencing of CD109 in human non-small cell lung cancer (NSCLC) cells, returns these cells to an epithelial like growth phenotype. On the transcriptional level, we describe changes in cell-cell contact and epithelial-mesenchymal transition associated gene clusters. At the cell surface, we identify desmoglein-2 (DSG2) as a new interaction partner of CD109 and demonstrate CD109 dependent targeting of DSG2 to the apical cell surface, where it forms desmosomes between apical and basal cell poles. Both, CD109 and DSG2 are genetic risk factors, linked to reduced overall survival in lung adenocarcinoma patients (subtype of NSCLC). In this study, we show the expression of both proteins in the same tumor and suggest a new CD109-DSG2 axis in NSCLC patients that could present a targetable therapeutic option in the future.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

Staphylococcus aureus Activates the Aryl Hydrocarbon Receptor in Human Keratinocytes.

Staphylococcus aureus is an important pathogen causing various infections, including - as most frequently isolated bacterium - cutaneous infections. Keratinocytes as the first barrier cells of the skin respond to S. aureus by the release of defense molecules such as cytokines and antimicrobial peptides. Although several pattern recognition receptors expressed in keratinocytes such as Toll-like and NOD-like receptors have been reported to detect the presence of S. aureus, the mechanisms underlying the interplay between S. aureus and keratinocytes are still emerging. Here, we report that S. aureus induced gene expression of CYP1A1 and CYP1B1, responsive genes of the aryl hydrocarbon receptor (AhR). AhR activation by S. aureus was further confirmed by AhR gene reporter assays. AhR activation was mediated by factor(s) <2 kDa secreted by S. aureus. Whole transcriptome analyses and real-time PCR analyses identified IL-24, IL-6, and IL-1beta as cytokines induced in an AhR-dependent manner in S. aureus-treated keratinocytes. AhR inhibition in a 3D organotypic skin equivalent confirmed the crucial role of the AhR in mediating the induction of IL-24, IL-6, and IL-1beta upon stimulation with living S. aureus. Taken together, we further highlight the important role of the AhR in cutaneous innate defense and identified the AhR as a novel receptor mediating the sensing of the important skin pathogen S. aureus in keratinocytes.

Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.

Host genetic factors related to innate immunity, environmental sensing and cellular functions are associated with human skin microbiota.

Despite the increasing knowledge about factors shaping the human microbiome, the host genetic factors that modulate the skin-microbiome interactions are still largely understudied. This contrasts with recent efforts to characterize host genes that influence the gut microbiota. Here, we investigated the effect of genetics on skin microbiota across three different skin microenvironments through meta-analyses of genome-wide association studies (GWAS) of two population-based German cohorts. We identified 23 genome-wide significant loci harboring 30 candidate genes involved in innate immune signaling, environmental sensing, cell differentiation, proliferation and fibroblast activity. However, no locus passed the strict threshold for study-wide significance (P < 6.3 × 10-10 for 80 features included in the analysis). Mendelian randomization (MR) analysis indicated the influence of staphylococci on eczema/dermatitis and suggested modulating effects of the microbiota on other skin diseases. Finally, transcriptional profiles of keratinocytes significantly changed after in vitro co-culturing with Staphylococcus epidermidis, chosen as a representative of skin commensals. Seven candidate genes from the GWAS were found overlapping with differential expression in the co-culturing experiments, warranting further research of the skin commensal and host genetic makeup interaction.

Ancient DNA study reveals HLA susceptibility locus for leprosy in medieval Europeans.

Leprosy, a chronic infectious disease caused by Mycobacterium leprae (M. leprae), was very common in Europe till the 16th century. Here, we perform an ancient DNA study on medieval skeletons from Denmark that show lesions specific for lepromatous leprosy (LL). First, we test the remains for M. leprae DNA to confirm the infection status of the individuals and to assess the bacterial diversity. We assemble 10 complete M. leprae genomes that all differ from each other. Second, we evaluate whether the human leukocyte antigen allele DRB1*15:01, a strong LL susceptibility factor in modern populations, also predisposed medieval Europeans to the disease. The comparison of genotype data from 69 M. leprae DNA-positive LL cases with those from contemporary and medieval controls reveals a statistically significant association in both instances. In addition, we observe that DRB1*15:01 co-occurs with DQB1*06:02 on a haplotype that is a strong risk factor for inflammatory diseases today.