Zhaobin Xu, Xin Fang, Thomas K. Wood, Zuyi Jacky Huang
Abstract
Prevention of the initiation of biofilm formation is the most important step for combating biofilm-associated pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once biofilms are formed. Genes essential to bacterial growth in the planktonic state are potential targets to treat biofilm-associated pathogens. However, the biofilm formation capability of strains with mutations in these essential genes must be evaluated, since the pathogen might form a biofilm before it is eliminated. In order to address this issue, this work proposes a systems-level approach to quantifying the biofilm formation capability of mutants to determine target genes that are essential for bacterial metabolism in the planktonic state but do not induce biofilm formation in their mutants. The changes of fluxes through the reactions associated with the genes positively related to biofilm formation are used as soft sensors in the flux balance analysis to quantify the trend of biofilm formation upon the mutation of an essential gene. The essential genes whose mutants are predicted not to induce biofilm formation are regarded as gene targets. The proposed approach was applied to identify target genes to treat Pseudomonas aeruginosa infections. It is interesting to find that most essential gene mutants exhibit high potential to induce the biofilm formation while most non-essential gene mutants do not. Critically, we identified four essential genes, lysC, cysH, adk, and galU, that constitute gene targets to treat P. aeruginosa. They have been suggested by existing experimental data as potential drug targets for their crucial role in the survival or virulence of P. aeruginosa. It is also interesting to find that P. aeruginosa tends to survive the essential-gene mutation treatment by mainly enhancing fluxes through 8 metabolic reactions that regulate acetate metabolism, arginine metabolism, and glutamate metabolism.
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 February 2020.
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#6,254,926
of 22,699,621 outputs
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#74,868
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#51,938
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Outputs of similar age from PLOS ONE
#1,660
of 5,396 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 193,796 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has gotten more attention than average, scoring higher than 60% of its peers.
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We're also able to compare this research output to 5,396 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
