Biology subjects

Widder, S.

Publications and source records attributed to Widder, S..

3 recordsLinked to original sources

bioRxiv2025

Linking Transcriptional and Cellular Responses of Human and Murine Pneumococcal Pneumonia

Community-acquired pneumonia (CAP), often caused by Streptococcus pneumoniae, poses a significant global health challenge, especially among high-risk populations. This study investigates the temporal dynamics of pulmonary gene expression during S. pneumoniae-induced pneumonia using a murine model to elucidate host-pathogen interactions and identify potential biomarkers of disease severity. Using bulk RNA sequencing, we analyzed lung tissues at early (1h, 8h) and acute infection (2d, 3d), as well as post-resolution (30d) time-points. At 2 days post-infection, differentially expressed genes (DEGs) revealed heightened innate immune responses, including chemokine and interferon signaling pathways. By 30 days post-infection, gene expression profiles and histological changes normalized, reflecting resolution of inflammation. Stratifying mice into recovered, sick, and moribund phenotypes during the acute infection phase highlighted significant transcriptional distinctions, including upregulated pro-inflammatory genes and Schlafen family members. Arginase 1 emerged as a predictive marker of disease severity, with elevated expression detectable as early as 8 hours post-infection. Comparative analyses revealed significant overlap between transcriptional responses in our model and those from a Gram-negative Acinetobacter baumannii pneumonia model, implicating conserved pathways, such as IL-17 and Toll-like receptor signaling. Additionally, murine DEGs correlated with human plasma proteins associated with severe CAP, including CCL8 and CD14, suggesting translational relevance. This study underscores phase-specific transcriptional reprogramming during pneumococcal pneumonia and identifies potential biomarkers and therapeutic targets for improving outcomes in severe CAP cases.

microbiology↗

bioRxiv2023

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD). Intermittent pulmonary exacerbations (PEx) in these conditions are associated with lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed 880 near-daily CF sputum samples and developed non-standard microbiome descriptors to model community reorganization prior and during 18 PEx. We identified two communal microbial regimes with opposing ecology and dynamics. Whereas pathogen-governed dysbiosis showed hierarchical community organization and reduced diversity, anaerobic bloom dysbiosis displayed stochasticity and increased diversity. Microbiome organization modulated the relevance of pathogens and a simulation of antimicrobial treatment predicted better efficacy for hierarchically organized microbiota. This causal link between PEx, microbiome organization, and treatment success advances the development of personalized dysbiosis management in CF and, potentially, other obstructive lung diseases.

systems biology↗

bioRxiv2022

Forecasting of a complex microbial community using meta-omics

Microbial communities are complex assemblages whose dynamics are shaped by abiotic and biotic factors. A major challenge concerns correctly forecasting the community behaviour in the future. In this context, communities in biological wastewater treatment plants (BWWTPs) represent excellent model systems, because forecasting them is required to ultimately control and operate the plants in a sustainable manner. Here, we forecast the microbial community from the water-air interface of the anaerobic tank of a BWWTP via longitudinal meta-omics (metagenomics, metatranscriptomics and metaproteomics) data covering 14 months at weekly intervals. We extracted all the available time-dependent information, summarised it in 17 temporal signals (explaining 91.1% of the temporal variance) and linked them over time to rebuild the sequence of ecological phenomena behind the community dynamics. We forecasted the signals over the following five years and tested the predictions with 21 extra samples. We were able to correctly forecast five signals accounting for 22.5% of the time-dependent information in the system and generate mechanistic predictions on the ecological events in the community (e.g. a predation cycle involving bacteria, viruses and amoebas). Through the forecasting of the 17 signals and the environmental variables readings we reconstructed the gene abundance and expression for the following 5 years, showing a nearly perfect trend prediction (coefficient of determination [≥] 0.97) for the first 2 years. The study demonstrates the maturity of microbial ecology to forecast composition and gene expression of open microbial ecosystems using year-spanning interactions between community cycles and environmental parameters.

microbiology↗