Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

Abstract

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs’ efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Deciphering two decades of cellular reprogramming in cancer: A bibliometric analysis of evolving trends and research frontiers

“…The cluster that exhibited the greatest significance was designated as “reprogramming” [ 21 ] thus, establishing the foundation for the research theme. Following closely were the clusters emphasizing the “metabolic modeling” [ 22 ] (cluster #1), “induced pluripotent stem cells” [ 23 ] (cluster #2), and “pancreatic cancer” [ 24 ] (cluster #3). Delving deeper into the research landscape, other notable clusters that emerged included H + -ATP synthase, induced cancer stem cells, metabolic reprogramming, metabolic immunotherapy, and differentiation.…”

A reconstructed genome‐scale metabolic model of Helicobacter pylori for predicting putative drug targets in clarithromycin and rifampicin resistance conditions

“…16 GSMMs represent a valuable approach for unraveling the metabolism of pathogens and are widely employed for this purpose. 16,56 To gain a comprehensive understanding of the metabolic capabilities of H. pylori, we have updated the iIT341 model. 18 This effort was essential for improving the quality of model, and shed light on the intricate metabolic processes of H. pylori.…”

“…These context-specific models can reveal the cellular metabolism of an organism, which is useful for developing new therapeutical approaches. 15,16 The initial GSMM of H. pylori was reconstructed in 2002 by Schilling et al. 17 This study introduced a minimal medium for H. pylori growth and performed deletion experiments on central metabolism reactions and associated genes for predicting essential ones.…”

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

“…Moreover, metabolic network analysis has been applied to identify effective drug targets and develop novel antimicrobials. One antimicrobial drug development strategy involves determining inhibitors of key biological pathways in pathogens [119,120]. Bacterial metabolism presents many potential drug targets.…”