An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

Abstract

Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.

mergem: merging and comparing genome-scale metabolic models using universal identifiers

“…Using mergem with GEMs of such organisms can aid in globally comparing the metabolisms between related species. To illustrate this case, we compared a GEM for Pseudomonas aeruginosa iPau21 [48] with two GEM versions of Pseudomonas putida (iJN1463 [46] and iJN746 [47]). iJN746 is the first published model for P. putida, while iJN1463 is a refinement of iJN746 and thus contains updated reaction and metabolite information for P. putida .…”

“…Comparing metabolic networks to find commonalities can facilitate the discovery of potential broadly-distributed targets, such as for developing new antimicrobial drugs [49]. To illustrate this application, we used mergem in Fluxer to visualize the commonalities between the curated GEMs for three gram-negative pathogens: Acinetobacter baumanii [50] , Klebsiella pneumoniae [51], and Pseudomonas aeruginosa [48]. Figure 11 shows the visualization of the resultant merged models in Fluxer.…”

Nutritional Interactions between Bacterial Species Colonising the Human Nasal Cavity: Current Knowledge and Future Prospects

“…Accordingly, it is possible to model how well organisms might grow under defined nutritional conditions by determining which precursors and essential nutrients are needed and which metabolic end products would be produced. Hundreds of genome-scale metabolic models are currently available, including those of nasopharyngeal commensals and pathogens such as Haemophilus influenzae [108,109], Klebsiella pneumoniae [110], Micrococcus luteus [111], Pseudomonas aeruginosa [112], Staphylococcus aureus [113] and Dolosigranulum pigrum [114].…”

Transcriptome guided metabolic network analysis reveals rearrangements of carbon flux distribution inNeisseria gonorrhoeaeduring neutrophil co-culture

“…To accomplish this goal, we applied the RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) algorithm (45), which uses RNA-seq data to identify the most cost-effective usage of metabolism while also reflecting the organism's transcriptional investment. RIPTiDE has been used successfully with models of Pseudomonas aeruginosa and Clostridioides difficile to uncover metabolic contributors to virulence in the context of mucin degradation, biofilm formation, murine infection 13 models, and co-culture with other microbes (19,21,46). We reasoned this approach would generate context-specific models of the metabolism of Gc when grown with and without PMN co-culture and would identify those reactions that are likely to be differentially active in each condition.…”

“…GENREs enable large-scale, in silico manipulations of bacterial metabolism and have been used in a variety of applications including genome wide-knockout screens, synthetic lethal studies, and metabolic engineering that would otherwise be time-consuming and labor intensive to conduct (18). More recently, these tools have been used for the integration and interpretation of multi-omics data and applied to studies of human health and disease, including modeling of the metabolism of prominent human pathogens including Mycobacterium tuberculosis, Staphylococcus aureus, Pseudomonas aeruginosa, Clostridioides difficile, and Salmonella typhimurium (19)(20)(21). In contrast, there is no published model of Gc metabolism; while there is a GENRE for the related N. meningitidis (22), these two species are known to have key differences in their metabolism, for instance in sugar utilization (23).…”