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Schäuble, S.

Publications and source records attributed to Schäuble, S..

2 recordsLinked to original sources

Integrative phenotypic and transcriptomic validation of an alveolar-like macrophage model reveals early host-pathogen dynamics during Aspergillus fumigatus infection

Primary alveolar macrophages (pAMs) are essential for the rapid clearance of conidia and maintenance of pulmonary homeostasis. However, Aspergillus fumigatus remains the leading cause of invasive pulmonary aspergillosis in immunocompromised patients, and the mechanisms governing fungal clearance versus invasion remain poorly understood. Although, pAMs can be isolated from human donors, their broader application in in vitro infection studies is limited by their low availability and technical challenges with their maintenance in culture. In this study, we successfully adapted a previously established monocyte-derived alveolar-like macrophage (ALM) model to investigate early host-pathogen interactions upon A. fumigatus challenge. Given the requirement of GM-CSF for maintaining alveolar macrophage identity and function, we included GM-CSF differentiated macrophages (GM-M), as a widely employed reference model. Primary alveolar macrophages (pAM), isolated from human lung biopsies were utilized to validate the physiological relevance of the ALM model. Combined phenotypic, transcriptomic and functional analyses demonstrated that ALMs closely resemble pAMs under both steady-state and infection conditions across multiple time points and fungal burdens. Notably, fungal dual RNA-sequencing revealed a significant upregulation of fungal virulence-associated factors during interaction with ALM, which was not observed in our GM-M co-cultures. Collectively, these findings support the use of ALMs as a robust, experimentally accessible and physiologically relevant in vitro model for investigating early A. fumigatus infection, providing new insights into host-pathogen dynamics at the alveolar interface.

microbiology↗

GePI: Retrieval of fully automated recognition and extraction of gene and protein interaction mentions from unstructured literature

MotivationKnowledge about interactions between genes and proteins is vital for bio-molecular research. A large part of this knowledge is published in written text and not accessible in a structured way. To remedy this situation, several repositories of automatically extracted interaction facts were proposed over the years. However, existing solutions lack key features such as permanently updated data resources, easy accessibility and structured result generation ready to be used for downstream analyses. ResultsWe propose GePI, a database portal for fully automated extraction and presentation of molecular interaction facts from scientific literature. GePI offers batch queries, immediate inspection of textual evidence and full text filters. To this end, GePI leverages two gene recognition and normalization approaches as well as optimized runtime for molecular event extraction. The resulting natural language processing pipeline is applied to the full set of publicly available documents from PubMed and the PubMed Central open access subset accounting for more than 33M abstracts and 4.2M complete articles as of 2022. To accommodate the rapid growth of the scientific literature, the fact database is automatically updated several times per week. In summary, our web application GePI allows for the first time a free and easy-to-use investigation of gene and protein interaction information as soon as they are published with unique query possibilities. Availability and ImplementationThe GePI web interface is available at http://gepi.coling.uni-jena.de. Contacterik.faessler@uni-jena.de

bioinformatics↗