Explore the vast range of titles available.

Some pediatric tracheal pathologies remain therapeutic dead ends for which current palliative strategies are fraught with serious complications. With the aim of a tracheal replacement, our team has previously developed and patented a clinical grade partially decellularized trachea (PDT) from porcine tracheas. The aim of this work was to study and compare the biointegration mechanisms of this PDT in vivo , in a pig cervical muscle, with or without immunosuppressant. The secondary objective was to evaluate the optimal maturation time of the PDT in this heterotopic position. In total, 11 female Large White/Landrace pigs, weighing between 50 and 70 kg were included in this study. The mean age of the animals at the implantation was 4.8 months. The PDTs were implanted in a cervical muscle of pigs for either 28 days, with or without cyclosporin A treatment, or for 56 days without immunosuppression. Histological evaluation showed very good PDT biointegration, characterized by neovascularization and fibroblast colonization, and no detectabale infection. Additionally, tissue and blood analyses showed no signs of graft rejection or surrounding tissue necrosis. Immunosuppression did not show any superiority in terms of biointegration after 28 days of treatment. After 56 days of implantation, a more significant degradation of the cartilage. Therefore, the optimal condition for PDT maturation proved to be 28 days, without immunosuppression.

The complement system is crucial for defense against pathogens and the removal of dying cells or immune complexes. Thus, clinical indications for possible complete complement deficiencies include, among others, recurrent mild or serious bacterial infections as well as autoimmune diseases (AID). The diagnostic approach includes functional activity measurements of the classical (CH50) and alternative pathway (AP50) and the determination of the C3 and C4 levels, followed by the quantitative analysis of individual components or regulators. When biochemical analysis reveals the causal abnormality of the complement deficiency (CD), molecular mechanisms remains frequently undetermined. Here, using direct sequencing analysis of the coding region we report the pathogenic variants spectrum that underlie the total or subtotal complement deficiency in 212 patients. We identified 107 different hemizygous, homozygous, or compound heterozygous pathogenic variants in 14 complement genes [ β ( = 1), ( = 3), ( = 2), ( = 12), ( = 5), C5 ( = 12), ( = 9), ( = 17), β ( = 7), ( = 3), ( = 7), ( = 18), ( = 10), ( = 2)]. Molecular analysis identified 17 recurrent pathogenic variants in 6 genes ( , and ). More than half of the pathogenic variants identified in unrelated patients were also found in healthy controls from the same geographic area. Our study confirms the strong association of meningococcal infections with terminal pathway deficiency and highlights the risk of pneumococcal and auto-immune diseases in the classical and alternative pathways. Results from this large genetic investigation provide evidence of a restricted number of molecular mechanisms leading to complement deficiency and describe the clinical potential adverse events of anti-complement therapy.

Antibodies reactive to human leukocyte antigens (HLA) represent a barrier for patients awaiting transplantation. Based on reactivity patterns in single-antigen bead (SAB) assays, various epitope matching algorithms have been proposed to improve transplant outcomes. However, some antibody reactivities cannot be explained by amino acid motifs, leading to uncertainty about their clinical relevance. Antibodies against the HLA class II molecule, DQβ0603:DQα0103, present in some candidates, represent one such example. Here, we show that peptides derived from amino acids 119-148 of the HLA class I heavy chain are bound to DQβ0603:DQα0103 proteins and contribute to antibody reactivity through an HLA-DM-dependent process. Moreover, antibody reactivity is impacted by the specific amino acid sequence presented. In summary, we demonstrate that polymorphic HLA class I peptides, bound to HLA class II proteins, can directly or indirectly be part of the antibody binding epitope. Our findings have potential important implications for the field of transplant immunology and for our understanding of adaptive immunity. Bead-based assays to assess the donor-specific antibody profile of solid organ transplant patients often produce discordant results relative to cell-based alternatives. In this study, the authors demonstrate that, for some MHC class-II-specific antibodies, discordance can be attributed to recognition of the MHC class-I-derived peptides bound to MHC class-II molecules.

Vascular Ehlers–Danlos syndrome (vEDS) is a rare inherited autosomal dominant disorder caused by COL3A1 pathogenic variants. A high percentage of de novo cases has been suggested. Part of it could be due to parental mosaicism, but its frequency is unknown. This retrospective study included a large series of COL3A1-confirmed vEDS probands with family information. The frequency of de novo cases was evaluated and the distribution of the type of variants was compared according to the mode of inheritance. The COL3A1 mosaicism was studied by deep targeted next- generation sequencing (NGS) from parental blood DNA. Out of 177 vEDS probands, 90 had a negative family history, suggesting a high rate (50.8%) of de novo pathogenic variants, enriched in the more severe COL3A1 variants (no null variant). Among those, both parental DNA were available in 36 cases and one parental DNA in 18 cases. NGS detected only one mosaicism from maternal blood DNA (allelic ratio 18%), which was confirmed in saliva (allelic ratio 22%). vEDS is characterized by a high frequency of de novo pathogenic variants. Parental mosaicism is rare (2–3%), but should be systematically searched with targeted NGS, taking into account its importance in genetic counseling.

The human leukocyte antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLAs present on the graft by donor-specific antibodies raised by the recipient. It is therefore of key importance to identify all potentially immunogenic B-cell epitopes on HLAs in order to refine organ allocation. Such HLAs epitopes are currently characterized by the presence of polymorphic residues called \"eplets\". However, many polymorphic positions in HLAs sequences are not yet experimentally confirmed as eplets associated with a HLA epitope. Moreover, structural studies of these epitopes only consider 3D static structures. We present here a machine-learning approach for predicting HLA epitopes, based on 3D-surface patches and molecular dynamics simulations. A collection of 3D-surface patches labeled as Epitope (2117) or Nonepitope (4769) according to Human Leukocyte Antigen Eplet Registry information was derived from 207 HLAs (61 solved and 146 predicted structures). Descriptors derived from static and dynamic patch properties were computed and three tree-based models were trained on a reduced non-redundant dataset. HLA-Epicheck is the prediction system formed by the three models. It leverages dynamic descriptors of 3D-surface patches for more than half of its prediction performance. Epitope predictions on unconfirmed eplets (absent from the initial dataset) are compared with experimental results and notable consistency is found. Structural data and MD trajectories are deposited as open data under doi: 10.57745/GXZHH8. In-house scripts and machine-learning models for HLA-EpiCheck are available from https://gitlab.inria.fr/capsid.public_codes/hla-epicheck.

The Human Leukocyte Antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLA proteins by Donor-Specific Antibodies (DSA). Therefore, the identification of potentially immunogenic epitopes is a key task to refine organ allocation and then to improve the survival of transplanted organs. Here, we present HLA-EpiCheck, a machine learning predictor for B-cell epitopes on HLA proteins that leverages an unprecedented dataset of high-quality molecular dynamics simulations of 207 HLA proteins. Candidate epitopes are represented as surface patches centered on solvent-accessible residues and described by a set of 18 descriptors. The descriptors include both static and dynamic properties, such as hydrophobicity, electrostatic charges, relative solvent-accessible surface area and side-chain flexibility. The HLA-EpiCheck was trained using an Extra Trees ensemble learning method and was compared to DiscoTope-3.0, a state-of-the-art B-cell epitope predictor. HLA-EpiCheck largely outperformed DiscoTope-3.0 in the task of predicting HLA epitopes. HLA-EpiCheck was also used to assess the epitope status of a subset of non-confirmed eplets. The predictions were compared to experimental data and a notable consistency was found. These results suggest that HLA-EpiCheck could be used to better define HLA matching between donor and recipient to reduce de novo DSA formation and graft rejection.Competing Interest StatementThe authors have declared no competing interest.