mjenior / klebsiella_2021

Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae

Abstract

Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of ~50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected from in vitro characterization of both laboratory and clinical isolates revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and GENREs have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased consumption of L-valine in clinical isolate-specific growth simulations. Importantly, valine has been shown to augment macrophage phagocytosis, and this result could be indicative of an immunosuppressive strategy K. pneumoniae evolved for survival during infection. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.

Overview

project
|- README          		# description of content
|- LICENSE         		# the license for this project
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|- doc/					# additional documents associated with the study
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|- data/          		# raw and primary data
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|- code/				# all programmatic code (python & R)
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|- results/				# all output from workflows and analyses
| |- figures/			# manuscript figures
| +- tables/			# supplementary tables
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|- notebooks/			# jupyter notebooks for the analyses performed during this study