Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments

Ding, Tong; Case, Kyle A.; Omolo, Morrine; Reiland, Holly A.; Metz, Zachary P.; Diao, Xinyu

doi:10.1371/journal.pone.0149423

Cited by 14 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such screens typically generate a growth fitness score for each strain. This information has successfully been used to understand gene function and chromosome organization and to provide insight to the mechanism of certain drugs (16,27,30).…”

mentioning

confidence: 99%

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

Tong

French

Zahed

et al. 2020

mBio

View full text Add to dashboard Cite

Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux. IMPORTANCE While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3–5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

show abstract

mentioning

confidence: 99%

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

Tong

French

Zahed

et al. 2020

mBio

View full text Add to dashboard Cite

show abstract

“…Several genome-scale models have been developed for evaluating the mechanistic details of gut-microbe interactions ( Ding et al., 2016 , Gao, Zhao and Huang, 2014 , Sadhukhan and Raghunathan, 2014 , Shoaie and Nielsen, 2014 , Shoaie et al., 2013 , Shoaie, Ghaffari, Kovatcheva-Datchary and Mardinoglu, 2015 ) and host-microbe metabolic symbiosis ( Heinken, Sahoo, Fleming and Thiele, 2013 , Ji and Nielsen, 2015 , Singer, 2010 ). PHIDIAS ( Xiang, Tian, & He, 2007 ), HPIDB ( Kumar & Nanduri, 2010 ), PHISTO ( Durmus et al., 2013 ), PATRIC ( Wattam, Gabbard, Shukla, & Sobral, 2014 ), PHI-base ( Urban, Irvine, Cuzick, & Hammond-Kosack, 2015 ), CASINO ( Shoaie et al., 2015 ), HMI™ module ( Marzorati et al., 2014 ), and NetCooperate ( Levy, Carr, Kreimer, Freilich, & Borenstein, 2015 ) are web-based tools and databases accessible for studying the microbe-microbe, diet-microbe and microbe-host interactions.…”

Section: Systems Biology Paradigmmentioning

confidence: 99%

Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review

Chellapandi

Bharathi

Sangavai

et al. 2018

Veterinary and Animal Science

View full text Add to dashboard Cite

Methanobacterium formicicum (Methanobacteriaceae family) is an endosymbiotic methanogenic Archaean found in the digestive tracts of ruminants and elsewhere. It has been significantly implicated in global CH 4 emission during enteric fermentation processes. In this review, we discuss current genomic and metabolic aspects of this microorganism for the purpose of the discovery of novel veterinary therapeutics. This microorganism encompasses a typical H 2 scavenging system, which facilitates a metabolic symbiosis across the H 2 producing cellulolytic bacteria and fumarate reducing bacteria. To date, five genome-scale metabolic models (iAF692, iMG746, iMB745, iVS941 and iMM518) have been developed. These metabolic reconstructions revealed the cellular and metabolic behaviors of methanogenic archaea. The characteristics of its symbiotic behavior and metabolic crosstalk with competitive rumen anaerobes support understanding of the physiological function and metabolic fate of shared metabolites in the rumen ecosystem. Thus, systems biological characterization of this microorganism may provide a new insight to realize its metabolic significance for the development of a healthy microbiota in ruminants. An in-depth knowledge of this microorganism may allow us to ensure a long term sustainability of ruminant-based agriculture.

show abstract

“…Prediction of essential genes for enterobacterial human pathogens (three E. coli and one Salmonella strains) was performed in three different host niches including human bloodstream, urinary tract and macrophage (Ding et al, 2016 ). Together with E.coli pangenome metabolic model (iEco1712_pan) containing all metabolic reactions and associated genes from 16 E. coli genome, the experimentally-based nutrient compositions were used as simulation constraints to mimic these three host environments (Keiter et al, 1955 ; Putnam, 1971 ; Raghunathan et al, 2009 ; Baumler et al, 2011 ).…”

Section: Pathogen-host Modelingmentioning

confidence: 99%

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Sertbaş

Ülgen

2020

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

show abstract

Predicting Essential Metabolic Genome Content of Niche-Specific Enterobacterial Human Pathogens during Simulation of Host Environments

Cited by 14 publications

References 16 publications

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Contact Info

Product

Resources

About