Blockade of the CD47/SIRPα checkpoint axis potentiates the macrophage-mediated anti-tumor efficacy of tafasitamab.

Macrophages are one of the key mediators of the therapeutic effects exerted by monoclonal antibodies, such as the anti-CD19 antibody tafasitamab, approved in combination with lenalidomide for the treatment of relapsed or refractory (r/r) diffuse large B cell lymphoma (DLBCL). However, antibody-dependent cellular phagocytosis (ADCP) in the tumor microenvironment can be counteracted by increased expression of the inhibitory receptor SIRPα on macrophages and its ligand, the immune checkpoint molecule CD47 on tumor cells. The aim of this study was to investigate the impact of the CD47-SIRPα axis on tafasitamabmediated phagocytosis and explore the potential of anti-CD47 blockade to enhance its antitumor activity. Elevated expression of both SIRPα and CD47 was observed in DLBCL patient-derived lymph node biopsies compared to healthy controls. CRISPR-mediated CD47 overexpression impacted tafasitamab-mediated ADCP in vitro and increased expression of SIRPα on macrophages correlated with decreased ADCP activity of tafasitamab against DLBCL cell lines. Combination of tafasitamab and an anti-CD47 blocking antibody enhanced ADCP activity of in vitro generated macrophages. Importantly, tafasitamab-mediated phagocytosis was elevated in combination with CD47 blockade using primary DLBCL cells and patient-derived lymphoma-associated macrophages (LAMs) in an autologous setting. Furthermore, lymphoma cells with low CD19 expression were efficiently eliminated by the combination treatment. Finally, combined treatment of tafasitamab and an anti-CD47 antibody resulted in enhanced tumor volume reduction and survival benefit in lymphoma xenograft mouse models. These findings provide evidence that CD47 blockade can enhance the phagocytic potential of tumor targeting immunotherapies such as tafasitamab and suggest there is value in exploring the combination in the clinic.

Stringent monitoring can decrease mortality of immune checkpoint inhibitor induced cardiotoxicity.

Background: Immune checkpoint inhibitor (ICI)-induced myocarditis is a rare immune-related adverse event (irAE) with a fatality rate of 40%-46%. However, irMyocarditis can be asymptomatic. Thus, improved monitoring, detection and therapy are needed. This study aims to generate knowledge on pathogenesis and assess outcomes in cancer centers with intensified patient management. Methods: Patients with cardiac irAEs from the SERIO registry (www.serio-registry.org) were analyzed for demographics, ICI-related information (type of ICI, therapy line, combination with other drugs, onset of irAE, and tumor response), examination results, irAE treatment and outcome, as well as oncological endpoints. Cardiac biopsies of irMyocarditis cases (n = 12) were analyzed by Nanostring and compared to healthy heart muscle (n = 5) and longitudinal blood sampling was performed for immunophenotyping of irMyocarditis-patients (n = 4 baseline and n = 8 during irAE) in comparison to patients without toxicity under ICI-therapy (n = 4 baseline and n = 7 during ICI-therapy) using flow cytometry. Results: A total of 51 patients with 53 cardiac irAEs induced by 4 different ICIs (anti-PD1, anti-PD-L1, anti-CTLA4) were included from 12 centers in 3 countries. Altogether, 83.0% of cardiac irAEs were graded as severe or life-threatening, and 11.3% were fatal (6/53). Thus, in centers with established consequent troponin monitoring, work-up upon the rise in troponin and consequent treatment of irMyocarditis with corticosteroids and -if required-second-line therapy mortality rate is much lower than previously reported. The median time to irMyocarditis was 36 days (range 4-1,074 days) after ICI initiation, whereas other cardiotoxicities, e.g. asystolia or myocardiopathy, occurred much later. The cytokine-mediated signaling pathway was differentially regulated in myocardial biopsies as compared to healthy heart based on enrichment Gene Ontology analysis. Additionally, longitudinal peripheral blood mononuclear cell (PBMC) samples from irMyocarditis-patients indicated ICI-driven enhanced CD4+ Treg cells and reduced CD4+ T cells. Immunophenotypes, particularly effector memory T cells of irMyocarditis-patients differed from those of ICI-treated patients without side effects. LAG3 expression on T cells and PD-L1 expression on dendritic cells could serve as predictive indicators for the development of irMyocarditis. Conclusion: Interestingly, our cohort shows a very low mortality rate of irMyocarditis-patients. Our data indicate so far unknown local and systemic immunological patterns in cardiotoxicity.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

BACKGROUND: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers. METHODS: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics. RESULTS: In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline. CONCLUSIONS: In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Identifying biomarkers and novel therapeutic targets in uveal melanoma.

Uveal melanoma (UM) is an orphan cancer despite being the most common eye tumor in adults. Patients often present to skin cancer centers for treatment of metastatic disease although there are significant genetic, biological, and clinical differences from cutaneous melanoma. The treatments most commonly used for metastatic UM are tebentafusp and combined immune checkpoint blockade, both of which yield low response rates and may be accompanied by high treatment costs and significant immune-related toxicities. Thus, it is of paramount importance to identify biomarkers and clinical profiles predictive of treatment response and to find novel therapeutic targets. The use of immune checkpoint blockade showed more favorable outcomes in patients with extrahepatic disease and normal levels of serum lactate dehydrogenase in a panel of retrospective studies, making its use more reasonable in this subgroup. To identify novel drug targets, we will analyze the expression and relevance of neural crest transcription factors in patient bio-specimens using next-generation nanopore sequencing. Computer algorithms and network-based analysis will facilitate the identification of druggable targets which will subsequently be validated in patient-derived short-term cell cultures. This approach will help to find novel and personalized treatments for UM.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

Is tebentafusp superior to combined immune checkpoint blockade and other systemic treatments in metastatic uveal melanoma? A comparative efficacy analysis with population adjustment.

BACKGROUND: Distinct systemic treatments exist for metastatic uveal melanoma. Tebentafusp and combined immune checkpoint blockade (ICB) with ipilimumab plus anti-PD-1 antibodies are the most commonly used treatment options but their comparative efficacy is unclear. The aim of this study is to compare currently available systemic treatments regarding overall survival (OS) and progression-free survival (PFS) with a focus on the comparison of tebentafusp versus combined ICB. METHODS: The protocol for this study was defined a priori and registered online in the PROSPERO international prospective register of systematic reviews (CRD42022308356, date of registration: 7.2.2022). We performed a systematic literature search in Medline, Embase, and Central to identify eligible studies reporting Kaplan-Meier curves or individual-level survival data showing OS and PFS for metastatic uveal melanoma patients treated with systemic treatments. Kaplan-Meier curves were digitized using the "WebPlotDigitizer" program. Individual-level survival data were subsequently remodelled and pooled for distinct treatment groups. To compare the OS of tebentafusp versus combined ICB, we used matching-adjusted indirect comparison (MAIC), two-stage MAIC (2SMAIC), and simulated treatment comparison (STC) together with digitized individual-level survival data as population-adjusted models. RESULTS: Overall, 55 independent studies were included of which 2,682 patients were evaluable for OS and 2,258 for PFS. Tebentafusp showed the highest median OS (mOS) of 22.4 months (95% confidence interval (CI): 19.9-29.6) compared to combined ICB (mOS: 15.7 months (95% CI: 14.4-17.9)), anti-PD-(L)1 antibody (mOS: 10.9 months (95% CI: 9.8-13.4)), chemotherapy (mOS: 9.95 months (95% CI: 8.9-11.2)), targeted therapies (mOS: 8.86 months (95% CI: 7.5-10.8)), and anti-CTLA-4 antibody (mOS: 7.8 months (95% CI: 6.8-9.3). The median PFS (mPFS) was similar among the treatment groups ranging from 2.7 months to 3.4 months. For the comparison of tebentafusp versus combined ICB, the hazard ratio (HR) was 0.641 (95% CI: 0.449-0.915) in the unadjusted model, whereas the population-adjusted models showed a HR of 0.386 (95% CI: 0.236-0.631) using MAIC, 0.378 (95% CI: 0.234-0.612) applying 2SMAIC and 0.284 (95% CI: 0.184-0.440) using STC. CONCLUSIONS: Tebentafusp achieved the best results compared to combined ICB and other systemic treatments, although these results have to be interpreted with caution due to the approximative methodical approach and high heterogeneity of included studies.

Integration of transcriptomics data into agent-based models of solid tumor metastasis.

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer.

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Glaucoma-TrEl: A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma.

OBJECTIVE: Glaucoma is a chronic neurological disease that is associated with high intraocular pressure (IOP), causes gradual damage to retinal ganglion cells, and often culminates in vision loss. Recent research suggests that glaucoma is a complex multifactorial disease in which multiple interlinked genes and pathways play a role during onset and development. Also, differential availability of trace elements seems to play a role in glaucoma pathophysiology, although their mechanism of action is unknown. The aim of this work is to disseminate a web-based repository on interactions between trace elements and protein-coding genes linked to glaucoma pathophysiology. RESULTS: In this study, we present Glaucoma-TrEl, a web database containing information about interactions between trace elements and protein-coding genes that are linked to glaucoma. In the database, we include interactions between 437 unique genes and eight trace elements. Our analysis found a large number of interactions between trace elements and protein-coding genes mutated or linked to the pathophysiology of glaucoma. We associated genes interacting with multiple trace elements to pathways known to play a role in glaucoma. The web-based platform provides an easy-to-use and interactive tool, which serves as an information hub facilitating future research work on trace elements in glaucoma.

The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective.

Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their cellular function has progressed continuously. Today, we have a good understanding of miRNA-mediated gene regulation, miRNA-mediated cross talk between genes including competing endogenous RNAs, and miRNA-mediated signaling transduction both in normal human physiology and in diseases.Besides, these noncoding RNAs have shown their value for clinical applications, especially in an oncological context. They can be used as reliable biomarkers for cancer diagnosis and prognosis and attract increasing attention as potential therapeutic targets. Many achievements made in the miRNA field are based on joint efforts from computational and molecular biologists. Systems biology approaches, which integrate computational and experimental methods, have played a fundamental role in uncovering the cellular functions of miRNAs.In this chapter, we review and discuss the role of miRNAs in oncology from a system biology perspective. We first describe biological facts about miRNA genetics and function. Next, we discuss the role of miRNAs in cancer progression and review the application of miRNAs in cancer diagnostics and therapy. Finally, we elaborate on the role that miRNAs play in cancer gene regulatory networks. Taken together, we emphasize the importance of systems biology approaches in our continued efforts to study miRNA cancer regulation.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Artificial intelligence in cancer target identification and drug discovery.

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

Consenso peruano para el uso de la resonancia magnética en el diagnóstico y seguimiento de pacientes con esclerosis múltiple / Peruvian consensus for the use of Magnetic Resonance Imaging in the diagnosis and follow-up of patients with Multiple Sclerosis

RESUMEN El uso de la resonancia magnética (RM) en el diagnóstico y seguimiento de pacientes con esclerosis múltiple (EM) ha optimizado el cuidado de los pacientes afectados. Diversos grupos internacionales de trabajo han intentado clarificar y normatizar el uso global de la RM pero, en muchas ocasiones, se extrapolan datos de otras regiones que no contemplan la realidad de cada lugar o son difíciles de implementar. Objetivo: Consensuar aspectos relacionados con el uso de RM en el diagnóstico y seguimiento de pacientes con EM en el Perú. Material y Métodos: Un grupo de expertos peruanos, conformado por neurólogos y radiólogos, condujo la elaboración del consenso mediante metodología de ronda de encuestas a la distancia. Resultados: Las recomendaciones, basadas en la evidencia publicada y en el criterio de los expertos, enfocaron tanto el rol de las técnicas convencionales de RM como el de la medición de la atrofia cerebral en pacientes con EM al momento del diagnóstico y durante el periodo de seguimiento. Conclusiones: Las recomendaciones del consenso podrán potencialmente homogenizar y optimizar el cuidado y seguimiento de pacientes con EM en nuestro país.

SUMMARY The use of Magnetic Resonance Imaging (MRI) in the diagnosis and follow-up of patients with Multiple Sclerosis (MS) has optimized the care of the affected patients. Several international working groups have tried to clarify and standardize the global use of MRI but, on many occasions, data are extrapolated from other regions, do not contemplate local realities or are difficult to implement. Objective: To reach a consensus on aspects related to the use of MRI in the diagnosis and follow-up of patients with MS in Peru. Material and Methods: A group of Peruvian experts (neurologists and radiologists) worked on the elaboration of the consensus using a remote survey round methodology. Results: The recommendations, established on the basis of published evidence and on the experts' criteria, focused on the role of both, the conventional MRI techniques and the measurement of brain atrophy in MS patients both at the time of diagnosis and during the follow-up period Conclusions: The consensual recommendations could potentially assist in the standardization and optimization of the care and follow-up of patients with MS in our country.

Clinical determinants of long-term survival in metastatic uveal melanoma.

This study aimed to identify prognostic factors in patients with metastatic uveal melanoma (UM) that were associated with long-term survival in a real-world setting. A total of 94 patients with metastatic UM were included from German skin cancer centers and the German national skin cancer registry (ADOReg). Data were analyzed for the response to treatment, progression-free survival, and overall survival (OS). Prognostic factors were explored with univariate Cox regression, log-rank, and χ2-tests. Identified factors were subsequently validated after the population was divided into two cohorts of short-term survival (< 2 years OS, cohort A, n = 50) and long-term survival (> 2 years OS, cohort B, n = 44). A poor ECOG performance status (hazard ratio [HR] 2.0, 95% confidence interval [CI] 1.0-3.9) and elevated serum LDH (HR 2.0, 95% CI 1.0-3.8) were associated with a poor OS, whereas a good response to immune checkpoint blockade (ICB, p < 0.001), radiation therapy (p < 0.001), or liver-directed treatments (p = 0.01) were associated with a prolonged OS. Long-term survivors (cohort B) showed a higher median number of organs affected by metastasis (p < 0.001), while patients with liver metastases only were more common in cohort A (40% vs. 9%; p = 0.002). A partial response to ICB was observed in 16% (12/73), being 21% (8/38) for combined ICB, 17% (1/6) for single CTLA4 inhibition, and 10% (3/29) for single PD1 inhibition. One complete response occurred in cohort B with combined ICB. We conclude that the response to ICB and the presence of extrahepatic disease were favorable prognostic factors for long-term survival.

A disease network-based deep learning approach for characterizing melanoma.

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

Plasma-derived extracellular vesicles discriminate type-1 allergy subjects from non-allergic controls.

BACKGROUND: Allergies are on the rise globally, with an enormous impact on affected individuals' quality of life as well as health care resources. They cause a wide range of symptoms, from slightly inconvenient to potentially fatal immune reactions. While allergies have been described and classified phenomenologically, there is an unmet need for easily accessible biomarkers to stratify the severity of clinical symptoms. Furthermore, biomarkers marking the success of specific immunotherapy are urgently needed. OBJECTIVES: Plasma extracellular vesicles (pEV) play a role in coordinating the immune response and may be useful future biomarkers. A pilot study on differences in pEV content was carried out between patients with type I allergy, suffering from rhinoconjunctivitis with or without asthma, and voluntary non-allergic donors. METHODS: We examined pEV from 38 individuals (22 patients with allergies and 16 controls) for 38 chemokines, cytokines, and soluble factors using high-throughput data mining approaches. RESULTS: Patients with allergies had a distinct biomarker pattern, with 7 upregulated (TNF-alpha, IL-4, IL-5, IL-6, IL-17F, CCL2, and CCL17) and 3 downregulated immune mediators (IL-11, IL-27, and CCL20) in pEV compared to controls. This reduced set of 10 factors was able to discriminate controls and allergic patients better than the total array. CONCLUSIONS: The content of pEV showed potential as a target for biomarker research in allergies. Plasma EV, which are readily measurable via blood test, may come to play an important role in allergy diagnosis. In this proof-of-principle study, it could be shown that pEV's discriminate patients with allergies from controls. Further studies investigating whether the content of pEVs may predict the severity of allergic symptoms or even the induction of tolerance to allergens are needed.

IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation.

Alternatively activated macrophages (AAMs) contribute to the resolution of inflammation and tissue repair. However, molecular pathways that govern their differentiation have remained incompletely understood. Here, we show that uncoupling protein-2-mediated mitochondrial reprogramming and the transcription factor GATA3 specifically controlled the differentiation of pro-resolving AAMs in response to the alarmin IL-33. In macrophages, IL-33 sequentially triggered early expression of pro-inflammatory genes and subsequent differentiation into AAMs. Global analysis of underlying signaling events revealed that IL-33 induced a rapid metabolic rewiring of macrophages that involved uncoupling of the respiratory chain and increased production of the metabolite itaconate, which subsequently triggered a GATA3-mediated AAM polarization. Conditional deletion of GATA3 in mononuclear phagocytes accordingly abrogated IL-33-induced differentiation of AAMs and tissue repair upon muscle injury. Our data thus identify an IL-4-independent and GATA3-dependent pathway in mononuclear phagocytes that results from mitochondrial rewiring and controls macrophage plasticity and the resolution of inflammation.