RESUMEN
The Microbiome Protocols eBook (MPB) serves as a crucial bridge, filling gaps in microbiome protocols for both wet experiments and data analysis. The first edition, launched in 2020, featured 152 meticulously curated protocols, garnering widespread acclaim. We now extend a sincere invitation to researchers to participate in the upcoming 2nd version of MPB, contributing their valuable protocols to advance microbiome research.
RESUMEN
Comorbidities are prevalent in digestive cancers, intensifying patient discomfort and complicating prognosis. Identifying potential comorbidities and investigating their genetic connections in a systemic manner prove to be instrumental in averting additional health challenges during digestive cancer management. Here, we investigated 150 diseases across 18 categories by collecting and integrating various factors related to disease comorbidity, such as disease-associated SNPs or genes from sources like MalaCards, GWAS Catalog and UK Biobank. Through this extensive analysis, we have established an integrated pleiotropic gene set comprising 548 genes in total. Particularly, there enclosed the genes encoding major histocompatibility complex or related to antigen presentation. Additionally, we have unveiled patterns in protein-protein interactions and key hub genes/proteins including TP53, KRAS, CTNNB1 and PIK3CA, which may elucidate the co-occurrence of digestive cancers with certain diseases. These findings provide valuable insights into the molecular origins of comorbidity, offering potential avenues for patient stratification and the development of targeted therapies in clinical trials.
Asunto(s)
Comorbilidad , Humanos , Estudio de Asociación del Genoma Completo , Pleiotropía Genética , Neoplasias del Sistema Digestivo/genética , Neoplasias del Sistema Digestivo/epidemiología , Polimorfismo de Nucleótido Simple , Predisposición Genética a la Enfermedad , Mapas de Interacción de Proteínas/genéticaRESUMEN
Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.