Human skin CD141+ dendritic cells regulate cutaneous immunity via the neuropeptide urocortin 2.

Skin immune homeostasis is a multi-faceted process where dermal dendritic cells (DDCs) are key in orchestrating responses to environmental stressors. We have previously identified CD141+CD14+ DDCs as a skin-resident immunoregulatory population that is vitamin-D3 (VitD3) inducible from monocyte-derived DCs (moDCs), termed CD141hi VitD3 moDCs. We demonstrate that CD141+ DDCs and CD141hi VitD3 moDCs share key immunological features including cell surface markers, reduced T cell stimulation, IL-10 production, and a common transcriptomic signature. Bioinformatic analysis identified the neuroactive ligand receptor pathway and the neuropeptide, urocortin 2 (UCN2), as a potential immunoregulatory candidate molecule. Incubation with VitD3 upregulated UCN2 in CD141+ DCs and UVB irradiation induced UCN2 in CD141+ DCs in healthy skin in vivo. Notably, CD141+ DDC generation of suppressive Tregs was dependent upon the UCN2 pathway as in vivo administration of UCN2 reversed skin inflammation in humanized mice. We propose the neuropeptide UCN2 as a novel skin DC-derived immunoregulatory mediator with a potential role in UVB and VitD3-dependent skin immune homeostasis.

Transcriptomic profiling of recessive dystrophic epidermolysis bullosa wounded skin highlights drug repurposing opportunities to improve wound healing.

Chronic wounds present a major disease burden in people with recessive dystrophic epidermolysis bullosa (RDEB), an inherited blistering skin disorder caused by mutations in COL7A1 encoding type VII collagen, the major component of anchoring fibrils at the dermal-epidermal junction. Treatment of RDEB wounds is mostly symptomatic, and there is considerable unmet need in trying to improve and accelerate wound healing. In this study, we defined transcriptomic profiles and gene pathways in RDEB wounds and compared these to intact skin in RDEB and healthy control subjects. We then used a reverse transcriptomics approach to discover drugs or compounds, which might restore RDEB wound profiles towards intact skin. Differential expression analysis identified >2000 differences between RDEB wounds and intact skin, with RDEB wounds displaying aberrant cytokine-cytokine interactions, Toll-like receptor signalling, and JAK-STAT signalling pathways. In-silico prediction for compounds that reverse gene expression signatures highlighted methotrexate as a leading candidate. Overall, this study provides insight into the molecular profiles of RDEB wounds and underscores the possible clinical value of reverse transcriptomics data analysis in RDEB, and the potential of this approach in discovering or repurposing drugs for other diseases.

Enhanced NF-κB signaling in type-2 dendritic cells at baseline predicts non-response to adalimumab in psoriasis.

Biologic therapies have transformed the management of psoriasis, but clinical outcome is variable leaving an unmet clinical need for predictive biomarkers of response. Here we perform in-depth immunomonitoring of blood immune cells of 67 patients with psoriasis, before and during therapy with the anti-TNF drug adalimumab, to identify immune mediators of clinical response and evaluate their predictive value. Enhanced NF-κBp65 phosphorylation, induced by TNF and LPS in type-2 dendritic cells (DC) before therapy, significantly correlates with lack of clinical response after 12 weeks of treatment. The heightened NF-κB activation is linked to increased DC maturation in vitro and frequency of IL-17+ T cells in the blood of non-responders before therapy. Moreover, lesional skin of non-responders contains higher numbers of dermal DC expressing the maturation marker CD83 and producing IL-23, and increased numbers of IL-17+ T cells. Finally, we identify and clinically validate LPS-induced NF-κBp65 phosphorylation before therapy as a predictive biomarker of non-response to adalimumab, with 100% sensitivity and 90.1% specificity in an independent cohort. Our study uncovers important molecular and cellular mediators underpinning adalimumab mechanisms of action in psoriasis and we propose a blood biomarker for predicting clinical outcome.

Single-cell mutation analysis of clonal evolution in myeloid malignancies.

Myeloid malignancies, including acute myeloid leukaemia (AML), arise from the expansion of haematopoietic stem and progenitor cells that acquire somatic mutations. Bulk molecular profiling has suggested that mutations are acquired in a stepwise fashion: mutant genes with high variant allele frequencies appear early in leukaemogenesis, and mutations with lower variant allele frequencies are thought to be acquired later1-3. Although bulk sequencing can provide information about leukaemia biology and prognosis, it cannot distinguish which mutations occur in the same clone(s), accurately measure clonal complexity, or definitively elucidate the order of mutations. To delineate the clonal framework of myeloid malignancies, we performed single-cell mutational profiling on 146 samples from 123 patients. Here we show that AML is dominated by a small number of clones, which frequently harbour co-occurring mutations in epigenetic regulators. Conversely, mutations in signalling genes often occur more than once in distinct subclones, consistent with increasing clonal diversity. We mapped clonal trajectories for each sample and uncovered combinations of mutations that synergized to promote clonal expansion and dominance. Finally, we combined protein expression with mutational analysis to map somatic genotype and clonal architecture with immunophenotype. Our findings provide insights into the pathogenesis of myeloid transformation and how clonal complexity evolves with disease progression.

The Tumor Suppressor CYLD Inhibits Mammary Epithelial to Mesenchymal Transition by the Coordinated Inhibition of YAP/TAZ and TGF Signaling.

Downregulation of the cylindromatosis (CYLD) tumor suppressor has been associated with breast cancer development and progression. Here, we report a critical role for CYLD in maintaining the phenotype of mammary epithelial cells in vitro and in vivo. CYLD downregulation or inactivation induced an epithelial to mesenchymal transition of mammary epithelial cells that was dependent on the concomitant activation of the transcription factors Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) and transforming growth factor beta (TGF)signaling. CYLD inactivation enhanced the nuclear localization of YAP/TAZ and the phosphorylation of Small Mothers Against Decapentaplegic (SMAD)2/3 proteins in confluent cell culture conditions. Consistent with these findings were the hyperplastic alterations of CYLD-deficient mouse mammary epithelia, which were associated with enhanced nuclear expression of the YAP/TAZ transcription factors. Furthermore, in human breast cancer samples, downregulation of CYLD expression correlates with enhanced YAP/TAZ-regulated target gene expression. Our results identify CYLD as a critical regulator of a signaling node that prevents the coordinated activation of YAP/TAZ and the TGF pathway in mammary epithelial cells, in order to maintain their phenotypic identity and homeostasis. Consequently, they provide a novel conceptual framework that supports and explains a causal implication of deficient CYLD expression in aggressive human breast cancers.

Phase I/II open-label trial of intravenous allogeneic mesenchymal stromal cell therapy in adults with recessive dystrophic epidermolysis bullosa.

BACKGROUND: Recessive dystrophic epidermolysis bullosa (RDEB) is a hereditary blistering disorder due to a lack of type VII collagen. At present, treatment is mainly supportive. OBJECTIVES: To determine whether intravenous allogeneic bone marrow-derived mesenchymal stromal/stem cells (BM-MSCs) are safe in RDEB adults and if the cells improve wound healing and quality of life. METHODS: We conducted a prospective, phase I/II, open-label study recruiting 10 RDEB adults to receive 2 intravenous infusions of BM-MSCs (on day 0 and day 14; each dose 2-4 × 106 cells/kg). RESULTS: BM-MSCs were well tolerated with no serious adverse events to 12 months. Regarding efficacy, there was a transient reduction in disease activity scores (8/10 subjects) and a significant reduction in itch. One individual showed a transient increase in type VII collagen. LIMITATIONS: Open-label trial with no placebo. CONCLUSIONS: MSC infusion is safe in RDEB adults and can have clinical benefits for at least 2 months.

Genome-wide association study in frontal fibrosing alopecia identifies four susceptibility loci including HLA-B*07:02.

Frontal fibrosing alopecia (FFA) is a recently described inflammatory and scarring type of hair loss affecting almost exclusively women. Despite a dramatic recent increase in incidence the aetiopathogenesis of FFA remains unknown. We undertake genome-wide association studies in females from a UK cohort, comprising 844 cases and 3,760 controls, a Spanish cohort of 172 cases and 385 controls, and perform statistical meta-analysis. We observe genome-wide significant association with FFA at four genomic loci: 2p22.2, 6p21.1, 8q24.22 and 15q2.1. Within the 6p21.1 locus, fine-mapping indicates that the association is driven by the HLA-B*07:02 allele. At 2p22.1, we implicate a putative causal missense variant in CYP1B1, encoding the homonymous xenobiotic- and hormone-processing enzyme. Transcriptomic analysis of affected scalp tissue highlights overrepresentation of transcripts encoding components of innate and adaptive immune response pathways. These findings provide insight into disease pathogenesis and characterise FFA as a genetically predisposed immuno-inflammatory disorder driven by HLA-B*07:02.

Aptamer selection by direct microfluidic recovery and surface plasmon resonance evaluation.

A surface plasmon resonance (SPR)-based SELEX approach has been used to raise RNA aptamers against a structured RNA, derived from XBP1 pre-mRNA, that folds as two contiguous hairpins. Thanks to the design of the internal microfluidic cartridge of the instrument, the selection was performed during the dissociation phase of the SPR analysis by recovering the aptamer candidates directly from the target immobilized onto the sensor chip surface. The evaluation of the pools was performed by SPR, simultaneously, during the association phase, each time the amplified and transcribed candidates were injected over the immobilized target. SPR coupled with SELEX from the first to the last round allowed identifying RNA aptamers that formed highly stable loop-loop complexes (KD equal to 8nM) with the hairpin located on the 5' side of the target. High throughput sequencing of two key rounds confirmed the evolution observed by SPR and also revealed the selection of hairpins displaying a loop not fully complementary to the loop of its target. These candidates were selected mainly because they bound 79 times faster to the target than those having a complementary loop. SELEX coupled with SPR is expected to speed up the selection process because selection and evaluation are performed simultaneously.

Pathway-level disease data mining through hyper-box principles.

In microarray data analysis, traditional methods that focus on single genes are increasingly replaced by methods that analyse functional units corresponding to biochemical pathways, as these are considered to offer more insight into gene expression and disease associations. However, the development of robust pipelines to relate genotypic functional modules to disease phenotypes through known molecular interactions is still at its early stages. In this article we first discuss methodologies that employ groups of genes in disease classification tasks that aim to link gene expression patterns with disease outcome. Then we present a pathway-based approach for disease classification through a mathematical programming model based on hyper-box principles. Association rules derived from the model are extracted and discussed with respect to pathway-specific molecular patterns related to the disease. Overall, we argue that the use of gene sets corresponding to disease-relevant pathways is a promising route to uncover expression-to-phenotype relations in disease classification and we illustrate the potential of hyper-box classification in assessing the predictive power of functional pathways and uncover the effect of specific genes in the prediction of disease phenotypes.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.

BACKGROUND: Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. RESULTS: A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. CONCLUSIONS: The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

Integrative biology approach identifies cytokine targeting strategies for psoriasis.

Cytokines are critical checkpoints of inflammation. The treatment of human autoimmune disease has been revolutionized by targeting inflammatory cytokines as key drivers of disease pathogenesis. Despite this, there exist numerous pitfalls when translating preclinical data into the clinic. We developed an integrative biology approach combining human disease transcriptome data sets with clinically relevant in vivo models in an attempt to bridge this translational gap. We chose interleukin-22 (IL-22) as a model cytokine because of its potentially important proinflammatory role in epithelial tissues. Injection of IL-22 into normal human skin grafts produced marked inflammatory skin changes resembling human psoriasis. Injection of anti-IL-22 monoclonal antibody in a human xenotransplant model of psoriasis, developed specifically to test potential therapeutic candidates, efficiently blocked skin inflammation. Bioinformatic analysis integrating both the IL-22 and anti-IL-22 cytokine transcriptomes and mapping them onto a psoriasis disease gene coexpression network identified key cytokine-dependent hub genes. Using knockout mice and small-molecule blockade, we show that one of these hub genes, the so far unexplored serine/threonine kinase PIM1, is a critical checkpoint for human skin inflammation and potential future therapeutic target in psoriasis. Using in silico integration of human data sets and biological models, we were able to identify a new target in the treatment of psoriasis.

Toward prediction of immune mechanisms and design of immunotherapies in melanoma.

Malignant melanoma, the most lethal skin cancer, is considered as a representative model for cross talk between immune responses and malignancy. Efforts to elucidate the nature of these interactions have translated into immunotherapeutic strategies. Adjuvant therapeutics such as IL-2 and IFNα2b have reached clinical application, and emerging therapies targeting key immunomodulatory molecules such as CTLA-4 have renewed excitement in the field, highlighting the potential of manipulating immune responses in the clinical setting, but also the merits for further elucidating complex underlying immunological pathways. Screening technologies have yielded new insights leading to identification of biomarkers for disease prognosis and applied clinical immunotherapies. The promise of systems biology is to integrate diverse biomedical characterizations into detailed models of underlying mechanisms and therapies through suitable computational and mathematical formalisms. In this review, we discuss recent developments in dissecting the complex and diverse immune responses associated with melanoma through both computational and experimental means. We show the significance of devising new, improved approaches that can better serve as models of immune interactions and therapies. We propose that efforts in this direction may realize the potential of personalized medicine and facilitate development of the next generation of efficacious tools to treat patients.

Transcriptome classification reveals molecular subtypes in psoriasis.

BACKGROUND: Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. RESULTS: We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. CONCLUSION: Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.

Mapping cis- and trans-regulatory effects across multiple tissues in twins.

Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.

Protein coalitions in a core mammalian biochemical network linked by rapidly evolving proteins.

BACKGROUND: Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine metabolic responses, such as glucose-stimulated insulin secretion (GSIS) from the ß-cells of pancreatic islets. We describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways: glycolysis, the TCA cycle and oxidative phosphorylation. RESULTS: We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway members and may serve as rapidly evolving adapters, linking groups of co-evolving genes. CONCLUSIONS: Our results provide insights into the evolution of the involved proteins. We find evidence for two coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future research within a functional context.

Metabolic homeostasis is maintained in myocardial hibernation by adaptive changes in the transcriptome and proteome.

A transgenic mouse model for conditional induction of long-term hibernation via myocardium-specific expression of a VEGF-sequestering soluble receptor allowed the dissection of the hibernation process into an initiation and a maintenance phase. The hypoxic initiation phase was characterized by peak levels of K(ATP) channel and glucose transporter 1 (GLUT1) expression. Glibenclamide, an inhibitor of K(ATP) channels, blocked GLUT1 induction. In the maintenance phase, tissue hypoxia and GLUT1 expression were reduced. Thus, we employed a combined "-omics" approach to resolve this cardioprotective adaptation process. Unguided bioinformatics analysis on the transcriptomic, proteomic and metabolomic datasets confirmed that anaerobic glycolysis was affected and that the observed enzymatic changes in cardiac metabolism were directly linked to hypoxia-inducible factor (HIF)-1 activation. Although metabolite concentrations were kept relatively constant, the combination of the proteomic and transcriptomic dataset improved the statistical confidence of the pathway analysis by 2 orders of magnitude. Importantly, proteomics revealed a reduced phosphorylation state of myosin light chain 2 and cardiac troponin I within the contractile apparatus of hibernating hearts in the absence of changes in protein abundance. Our study demonstrates how combining different "-omics" datasets aids in the identification of key biological pathways: chronic hypoxia resulted in a pronounced adaptive response at the transcript and the protein level to keep metabolite levels steady. This preservation of metabolic homeostasis is likely to contribute to the long-term survival of the hibernating myocardium.