Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

Abstract

Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.

Pulmonary Surfactant Promotes Virulence Gene Expression and Biofilm Formation in Klebsiella pneumoniae

“…Due to the historic usage of KPPR1 as the model for Klebsiella lung infection, gene deletions were engineered into KPPR1 (ATCC 43816) (1, 54-58). KPPR1 and MGH 78578 share a high level of gene conservation, with 88% of their open reading frames being considered orthologous (59). More importantly, this genetic similarly is reflected within the surfactant microarray data, where 81% of transcripts expressed by MGH 78578 under these conditions are also encoded within the genome of KPPR1.…”

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

“…Genome-scale metabolic networks of different K. pneumoniae strains have been developed so far (Liao et al, 2011;Henry et al, 2017;Ramos et al, 2018;Norsigian et al, 2019), but to our knowledge they were not used for drug target discovery via the constraint-based analysis coupled to bioinformatic prioritization steps. This prompted us to investigate candidate drug targets for K. pneumoniae comprehensively via a network-based metabolism-centered approach.…”

“…To date, this approach has been commonly used in drug target discovery process at systems-level for different pathogens (Raman et al, 2008;Plata et al, 2010;Perumal et al, 2011;Ahn et al, 2014;Larocque et al, 2014;Presta et al, 2017). GMN models are available for different K. pneumoniae strains (Liao et al, 2011;Henry et al, 2017;Ramos et al, 2018;Norsigian et al, 2019). The first K. pneumoniae model at the genome level, called iYL1228, appeared in 2011 for the MGH 78578 strain (Liao et al, 2011).…”

“…They have also identified essential genes in the organism in silico, in aerobic minimal medium. The second model was developed for rifampin-resistant highly virulent strain KPPR1 using the GMN of the MGH 78578 strain as a reference (Henry et al, 2017). Similar validation analyses were performed using the model, termed iKp1289, and essential genes were identified in silico in aerobic rich media.…”

The DOE Systems Biology Knowledgebase (KBase)

“…KBase also has powerful comparative genomics tools, including newly developed apps for analyzing phenotype data. These tools are highlighted in a recent publication in which two phylogenetically close genomes are systematically compared, identifying how changes in gene content result in changes in growth phenotypes 30 (Table 2, Narrative 6) 31 . In this analysis, an existing metabolic model 32 is propagated to another strain of the same species.…”