The Combination of Vascular Endothelial Growth Factor A (VEGF-A) and Fibroblast Growth Factor 1 (FGF1) Modified mRNA Improves Wound Healing in Diabetic Mice: An Ex Vivo and In Vivo Investigation.

BACKGROUND: Diabetic foot ulcers (DFU) pose a significant health risk in diabetic patients, with insufficient revascularization during wound healing being the primary cause. This study aimed to assess microvessel sprouting and wound healing capabilities using vascular endothelial growth factor (VEGF-A) and a modified fibroblast growth factor (FGF1). METHODS: An ex vivo aortic ring rodent model and an in vivo wound healing model in diabetic mice were employed to evaluate the microvessel sprouting and wound healing capabilities of VEGF-A and a modified FGF1 both as monotherapies and in combination. RESULTS: The combination of VEGF-A and FGF1 demonstrated increased vascular sprouting in the ex vivo mouse aortic ring model, and topical administration of a combination of VEGF-A and FGF1 mRNAs formulated in lipid nanoparticles (LNPs) in mouse skin wounds promoted faster wound closure and increased neovascularization seven days post-surgical wound creation. RNA-sequencing analysis of skin samples at day three post-wound creation revealed a strong transcriptional response of the wound healing process, with the combined treatment showing significant enrichment of genes linked to skin growth. CONCLUSION: f-LNPs encapsulating VEGF-A and FGF1 mRNAs present a promising approach to improving the scarring process in DFU.

Protein interaction, monocyte toxicity and immunogenic properties of cerium oxide crystals with 5% or 14% gadolinium, cobalt oxide and iron oxide nanoparticles - an interdisciplinary approach.

Metal oxide nanoparticles are widely used in both consumer products and medical applications, but the knowledge regarding exposure-related health effects is limited. However, it is challenging to investigate nanoparticle interaction processes with biological systems. The overall aim of this project was to improve the possibility to predict exposure-related health effects of metal oxide nanoparticles through interdisciplinary collaboration by combining workflows from the pharmaceutical industry, nanomaterial sciences, and occupational medicine. Specific aims were to investigate nanoparticle-protein interactions and possible adverse immune reactions. Four different metal oxide nanoparticles; CeOx nanocrystals with 5% or 14% Gd, Co3O4, and Fe2O3, were characterized by dynamic light scattering and high-resolution transmission electron microscopy. Nanoparticle-binding proteins were identified and screened for HLA-binding peptides in silico. Monocyte interaction with nanoparticle-protein complexes was assessed in vitro. Herein, for the first time, immunogenic properties of nanoparticle-binding proteins have been characterized. The present study indicates that especially Co3O4-protein complexes can induce both 'danger signals', verified by the production of inflammatory cytokines and simultaneously bind autologous proteins, which can be presented as immunogenic epitopes by MHC class II. The clinical relevance of these findings should be further evaluated to investigate the role of metal oxide nanoparticles in the development of autoimmune disease. The general workflow identified experimental difficulties, such as nanoparticle aggregate formation and a lack of protein-free buffers suitable for particle characterization, protein analyses, as well as for cell studies. This confirms the importance of future interdisciplinary collaborations.

Transcriptional sex and regional differences in paired human atrial and ventricular cardiac biopsies collected in vivo.

Transcriptional studies of the human heart provide insight into physiological and pathophysiological mechanisms, essential for understanding the fundamental mechanisms of normal cardiac function and how they are altered by disease. To improve the understanding of why men and women may respond differently to the same therapeutic treatment it is crucial to learn more about sex-specific transcriptional differences. In this study the transcriptome of right atrium and left ventricle was compared across sex and regional location. Paired biopsies from five male and five female patients undergoing aortic valve replacement or coronary artery bypass grafting were included. Gene expression analysis identified 620 differentially expressed transcripts in atrial and ventricular tissue in men and 471 differentially expressed transcripts in women. In total 339 of these transcripts overlapped across sex but notably, 281 were unique in the male tissue and 162 in the female tissue, displaying marked sex differences in the transcriptional machinery. The transcriptional activity was significantly higher in atrias than in ventricles as 70% of the differentially expressed genes were upregulated in the atrial tissue. Furthermore, pathway- and functional annotation analyses performed on the differentially expressed genes showed enrichment for a more heterogeneous composition of biological processes in atrial compared with the ventricular tissue, and a dominance of differentially expressed genes associated with infection disease was observed. The results reported here provide increased insights about transcriptional differences between the cardiac atrium and ventricle but also reveal transcriptional differences in the human heart that can be attributed to sex.

Incidence and risk factors for adalimumab and infliximab anti-drug antibodies in rheumatoid arthritis: A European retrospective multicohort analysis.

OBJECTIVES: To evaluate the incidence of anti-drug antibody (ADA) occurrences and ADA-related risk factors under adalimumab and infliximab treatment in rheumatoid arthritis (RA) patients. METHODS: The study combined retrospective cohorts from the ABIRISK project totaling 366 RA patients treated with adalimumab (n = 240) or infliximab (n = 126), 92.4% of them anti-TNF naive (n = 328/355) and 96.6% of them co-treated with methotrexate (n = 341/353) with up to 18 months follow-up. ADA positivity was measured by enzyme-linked immunosorbent assay. The cumulative incidence of ADA was estimated, and potential bio-clinical factors were investigated using a Cox regression model on interval-censored data. RESULTS: ADAs were detected within 18 months in 19.2% (n = 46) of the adalimumab-treated patients and 29.4% (n = 37) of the infliximab-treated patients. The cumulative incidence of ADA increased over time. In the adalimumab and infliximab groups, respectively, the incidence was 15.4% (5.2-20.2) and 0% (0-5.9) at 3 months, 17.6% (11.4-26.4) and 0% (0-25.9) at 6 months, 17.7% (12.6-37.5) and 34.1% (11.4-46.3) at 12 months, 50.0% (25.9-87.5) and 37.5% (25.9-77.4) at 15 months and 50.0% (25.9-87.5) and 66.7% (37.7-100) at 18 months. Factors associated with a higher risk of ADA development were: longer disease duration (1-3 vs. < 1 year; adalimumab: HR 3.0, 95% CI 1.0-8.7; infliximab: HR 2.7, 95% CI 1.1-6.8), moderate disease activity (DAS28 3.2-5.1 vs. < 3.2; adalimumab: HR 6.6, 95% CI 1.3-33.7) and lifetime smoking (infliximab: HR 2.7, 95% CI 1.2-6.3). CONCLUSIONS: The current study focusing on patients co-treated with methotrexate for more than 95% of them found a late occurrence of ADAs not previously observed, whereby the risk continued to increase over 18 months. Disease duration, DAS28 and lifetime smoking are clinical predictors of ADA development.

Methotrexate and BAFF interaction prevents immunization against TNF inhibitors.

OBJECTIVES: TNF inhibitors (TNFi) can induce anti-drug antibodies (ADA) in patients with autoimmune diseases (AID) leading to clinical resistance. We explored a new way of using methotrexate (MTX) to decrease this risk of immunisation. METHODS: We treated BAFF transgenic (BAFFtg) mice, a model of AID in which immunisation against biologic drugs is high, with different TNFi. We investigated the effect of a single course of MTX during the first exposure to TNFi. Wild-type (WT) and BAFFtg mice were compared for B-Cell surface markers involved in MTX-related purinergic metabolism, adenosine production and regulatory B-cells (Bregs).We translated the study to macaques and patients with rheumatoid arthritis from the ABIRISK cohort to determine if there was an interaction between serum BAFF levels and MTX that prevented immuniation. RESULTS: In BAFFtg but not in WT mice or macaques, a single course of MTX prevented immunisation against TNFi and maintained drug concentration for over 52 weeks. BAFFtg mice B-cells expressed more CD73 and CD39 compared to WT mice. MTX induced adenosine release from B cells and increased Bregs and precursors. Use of CD73 blocking antibodies reversed MTX-induced tolerance. In patients from the ABIRISK cohort treated with TNFi for chronic inflammatory diseases, high BAFF serum level correlated with absence of ADA to TNFi only in patients cotreated with MTX but not in patients on TNFi monotherapy. CONCLUSION: MTX and BAFF interact in mice where CD73, adenosine and regulatory B cells were identified as key actors in this phenomenon. MTX and BAFF also interact in patients to prevent ADA formation.

WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome.

Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WD-repeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1-4, in autophagy. This article is part of a Focus on Autophagosome biogenesis. For further reading, please see related articles: 'ERES: sites for autophagosome biogenesis and maturation?' by Jana Sanchez-Wandelmer et al. (J. Cell Sci. 128, 185-192) and 'Membrane dynamics in autophagosome biogenesis' by Sven R. Carlsson and Anne Simonsen (J. Cell Sci. 128, 193-205).

Fluorescence-based imaging of autophagy progression by human WIPI protein detection.

Central to the process of macroautophagy (hereafter autophagy) is the formation of autophagosomes, double-membrane vesicles that sequester cytoplasmic cargo, including proteins, lipids and organelles, for lysosomal degradation and macromolecule recycling. Tight regulation of both autophagic activity and capacity is crucial to secure cellular homeostasis and aberrant autophagy is tightly linked to the development of many human diseases. Hence it is of great importance to accurately measure autophagy progression in health and disease. Members of the human WIPI ß-propeller proteins associate with autophagosomal membranes due to specific phosphatidylinositol 3-phosphate (PtdIns3P) binding at the onset of autophagy. The specific autophagosomal localization of both WIPI1 and WIPI2 (refered to as WIPI puncta) has been employed to assess autophagy using fluorescence microscopy methods, such as confocal and live-cell video microscopy and was extended for automated high-throughput image acquisition and analyses procedures. We here provide an overview on the employment of human WIPI members for the assessment of autophagy in higher eukaryotic cells, suitable for systems biology approaches such as mathematical modelling.

miHA-Match: computational detection of tissue-specific minor histocompatibility antigens.

Allogenic stem cell transplantation has shown considerable success in a number of hematological malignancies, in particular in leukemia. The beneficial effect is mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use. Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.

A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility complex (MHC) binding these epitopes is highly polymorphic, every patient possesses a set of MHC class I and class II molecules of differing specificities. A peptide combination effective for one person can thus be completely ineffective for another. This renders the optimal selection of these epitopes an important and interesting optimization problem. In this work we present a mathematical framework based on integer linear programming (ILP) that allows the formulation of various flavors of the vaccine design problem and the efficient identification of optimal sets of epitopes. Out of a user-defined set of predicted or experimentally determined epitopes, the framework selects the set with the maximum likelihood of eliciting a broad and potent immune response. Our ILP approach allows an elegant and flexible formulation of numerous variants of the EV design problem. In order to demonstrate this, we show how common immunological requirements for a good EV (e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a set, or avoidance of epitopes with high mutation rates) can be translated into constraints or modifications of the objective function within the ILP framework. An implementation of the algorithm outperforms a simple greedy strategy as well as a previously suggested evolutionary algorithm and has runtimes on the order of seconds for typical problem sizes.

Support vector machine-based prediction of MHC-binding peptides.

The use of major histocompatibility complex (MHC) class I binding peptides for immunotherapeutic purposes has shown promising results in recent years. The identification of such peptides mostly starts with predicting MHC-binding peptides, given a protein of interest. An accurate prediction method can reduce the number of peptides that needs to be tested experimentally. This protocol describes in this describes how support vector machines (SVMs) can be used for predicting MHC class I binding peptides. Focus is given on data representation, the concept of cross-validation, and how optimal SVM-specific parameters are obtained.

SVMHC: a server for prediction of MHC-binding peptides.

Identification of MHC-binding peptides is a prerequisite in rational design of T-cell based peptide vaccines. During the past decade a number of computational approaches have been introduced for the prediction of MHC-binding peptides, efficiently reducing the number of candidate binders that need to be experimentally verified. Here the SVMHC server for prediction of both MHC class I and class II binding peptides is presented. SVMHC offers fast analysis of a wide range of alleles and prediction results are given in several comprehensive formats. The server can be used to find the most likely binders in a protein sequence and to investigate the effects of single nucleotide polymorphisms in terms of MHC-peptide binding. The SVMHC server is accessible at http://www-bs.informatik.uni-tuebingen.de/SVMHC/.

Integrative analysis of cancer-related data using CAP.

The development of human cancer is a highly complex process and can be considered the result of several combined events, such as genetic alterations, disturbance of signal transduction, or failure of immunological surveillance. Cancer-related databases usually focus on specific fields of research, e.g., cancer genetics or cancer immunology, whereas the complexity of cancer genesis requires an integrated analysis of heterogeneous data from several sources. Here we present the cancer-associated protein database (CAP), a novel analysis system for cancer-related data. CAP integrates data from multiple external databases, augments these data with functional annotations, and offers tools for statistical analysis of these data. We have employed CAP to analyze genes that have been found to cause an autoimmune response in cancer. In particular, we explored the connection between the autoimmune response, mutations, and overexpression of these genes. Our preliminary results suggest that mutations are not significant contributors to raising an antibody response against tumor antigens, whereas overexpression seems to play a more important role. We hereby demonstrate how different types of data can be integrated and analyzed successfully, providing interesting results. As the amount of available data is growing rapidly, a combined analysis will play an important role in exploring the genetic and immunological basis of cancer. CAP is freely available at the following web site: http://www.bioinf.uni-sb.de/CAP/.

Prediction of MHC class I binding peptides, using SVMHC.

BACKGROUND: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. RESULTS: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.