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1.
Sci Immunol ; 7(67): eabe2634, 2022 01 28.
Artículo en Inglés | MEDLINE | ID: mdl-35089814

RESUMEN

Tissue-resident memory T cells (TRM) have recently emerged as crucial cellular players for host defense in a wide variety of tissues and barrier sites. Insights into the maintenance and regulatory checkpoints of human TRM cells remain scarce, especially due to the difficulties associated with tracking T cells through time and space in humans. We therefore sought to identify and characterize skin-resident T cells in humans defined by their long-term in situ lodgment. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) preceded by myeloablative chemotherapy unmasked long-term sequestration of host T cell subsets in human skin despite complete donor T cell chimerism in the blood. Single-cell chimerism analysis paired with single-cell transcriptional profiling comprehensively characterized these bona fide long-term skin-resident T cells and revealed differential tissue maintenance for distinct T cell subsets, specific TRM cell markers such as galectin-3, but also tissue exit potential with retention of the transcriptomic TRM cell identity. Analysis of 26 allo-HSCT patients revealed profound interindividual variation in the tissue maintenance of host skin T cells. The long-term persistence of host skin T cells in a subset of these patients did not correlate with the development of chronic GvHD. Our data exemplify the power of exploiting a clinical situation as a proof of concept for the existence of bona fide human skin TRM cells and reveal long-term persistence of host T cells in a peripheral tissue but not in the circulation or bone marrow in a subset of allo-HSCT patients.


Asunto(s)
Enfermedad Injerto contra Huésped/inmunología , Trasplante de Células Madre Hematopoyéticas , Piel/inmunología , Linfocitos T/inmunología , Femenino , Humanos , Masculino , Acondicionamiento Pretrasplante
2.
Nucleic Acids Res ; 47(D1): D559-D563, 2019 01 08.
Artículo en Inglés | MEDLINE | ID: mdl-30357367

RESUMEN

CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (67%), mouse (15%) and rat (10%). Given the vital functions of these macromolecular machines, their identification and functional characterization is foundational to our understanding of normal and disease biology. The new CORUM 3.0 release encompasses 4274 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 4473 different genes, representing 22% of the protein coding genes in humans. Protein complexes are described by a protein complex name, subunit composition, cellular functions as well as the literature references. Information about stoichiometry of subunits depends on availability of experimental data. Recent developments include a graphical tool displaying known interactions between subunits. This allows the prediction of structural interconnections within protein complexes of unknown structure. In addition, we present a set of 58 protein complexes with alternatively spliced subunits. Those were found to affect cellular functions such as regulation of apoptotic activity, protein complex assembly or define cellular localization. CORUM is freely accessible at http://mips.helmholtz-muenchen.de/corum/.


Asunto(s)
Bases de Datos de Proteínas , Complejos Multiproteicos/química , Complejos Multiproteicos/metabolismo , Empalme Alternativo , Animales , Humanos , Ratones , Complejos Multiproteicos/genética , Conformación Proteica , Mapeo de Interacción de Proteínas , Isoformas de Proteínas/genética , Isoformas de Proteínas/metabolismo , Subunidades de Proteína/química , Subunidades de Proteína/metabolismo , Ratas
3.
Orphanet J Rare Dis ; 13(1): 22, 2018 01 25.
Artículo en Inglés | MEDLINE | ID: mdl-29370821

RESUMEN

BACKGROUND: Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases. RESULTS: PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of 'cardiovascular abnormality' and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database ( http://mips.helmholtz-muenchen.de/phenodis/ ) with multiple search options and provide the complete dataset for download. CONCLUSION: PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes.


Asunto(s)
Cardiopatías/genética , Cardiopatías/patología , Enfermedades Raras/genética , Enfermedades Raras/patología , Biología Computacional/métodos , Bases de Datos Genéticas , Variación Genética/genética , Genómica/métodos , Cardiopatías/metabolismo , Humanos , Fenotipo , Medicina de Precisión/métodos , Enfermedades Raras/metabolismo
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