التفاصيل البيبلوغرافية
| العنوان: |
Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment |
| المؤلفون: |
Kuper, Tracy J., Islam, Mohammad Mazharul, Peirce-Cottler, Shayn M., Papin, Jason A., Ford, Roseanne M |
| المساهمون: |
Scott, Matthew, National Institute of Allergy and Infectious Diseases, National Institutes of Health |
| المصدر: |
PLOS Computational Biology ; volume 20, issue 4, page e1012031 ; ISSN 1553-7358 |
| بيانات النشر: |
Public Library of Science (PLoS) |
| سنة النشر: |
2024 |
| المجموعة: |
PLOS Publications (via CrossRef) |
| الوصف: |
With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P . aeruginosa PA14 biofilm spatial transcriptomic data into a P . aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent’s local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm’s spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1371/journal.pcbi.1012031 |
| الإتاحة: |
https://doi.org/10.1371/journal.pcbi.1012031Test |
| حقوق: |
http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.D93E1FA3 |
| قاعدة البيانات: |
BASE |
