Paper Info
Title
Model-driven analysis of mutant fitness experiments improves genome-scale metabolic models of Zymomonas mobilis ZM4.
Abstract
Author summary We have used several methods of analysis of a high-throughput transposon library dataset for the organismZymomonas mobilisin order to improve a newly developed genome-scale metabolic model. Data from individual experiments within this dataset were compared to predictions made by our draft model to determine areas of inaccuracies in the model. Correlations within the dataset were investigated in the framework of metabolic modules in the model enabling us to identify, and subsequently experimentally confirm, genes for several reactions in the model that previously lacked an associated gene. Finally, we looked at which metabolic modules correlate poorly, highlighting where metabolic knowledge gaps inZ.mobilismay still reside. Our metabolic model,iZM4_478, is one of the most complete forZ.mobilisZM4 to date, and we expect it will be a valuable tool for investigating the unique, streamlined metabolism of this organism. The data analysis approaches that we used can be easily applied by other investigators in the development of metabolic models for other organisms with similar datasets, leading to more complete assignment of genes to reactions and more accurate metabolic models. Genome-scale metabolic models have been utilized extensively in the study and engineering of the organisms they describe. Here we present the analysis of a published dataset from pooled transposon mutant fitness experiments as an approach for improving the accuracy and gene-reaction associations of a metabolic model forZymomonas mobilisZM4, an industrially relevant ethanologenic organism with extremely high glycolytic flux and low biomass yield. Gene essentiality predictions made by the draft model were compared to data from individual pooled mutant experiments to identify areas of the model requiring deeper validation. Subsequent experiments showed that some of the discrepancies between the model and dataset were caused by polar effects, mis-mapped barcodes, or mutants carrying both wild-type and transposon disrupted gene copies-highlighting potential limitations inherent to data from individual mutants in these high-throughput datasets. Therefore, we analyzed correlations in fitness scores across all 492 experiments in the dataset in the context of functionally related metabolic reaction modules identified within the model via flux coupling analysis. These correlations were used to identify candidate genes for a reaction in histidine biosynthesis lacking an annotated gene and highlight metabolic modules with poorly correlated gene fitness scores. Additional genes for reactions involved in biotin, ubiquinone, and pyridoxine biosynthesis inZ.mobiliswere identified and confirmed using mutant complementation experiments. These discovered genes, were incorporated into the final model,iZM4_478, which contains 747 metabolic and transport reactions (of which 612 have gene-protein-reaction associations), 478 genes, and 616 unique metabolites, making it one of the most complete models ofZ.mobilisZM4 to date. The methods of analysis that we applied here with theZ.mobilistransposon mutant dataset, could easily be utilized to improve future genome-scale metabolic reconstructions for organisms where these, or similar, high-throughput datasets are available.
Year
DOI
Venue
2020
10.1371/journal.pcbi.1008137
PLOS COMPUTATIONAL BIOLOGY
DocType
Volume
Issue
Journal
16
8
ISSN
Citations 
PageRank 
1553-734X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wai Kit Ong100.68
Dylan K Courtney200.34
Shu Pan300.34
Ramon Bonela Andrade400.34
Patricia Kiley510.71
Brian F Pfleger600.34
Jennifer L Reed719114.90