Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

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Genome-scale metabolic network reconstruction of the chloroform-respiring Dehalobacter restrictus strain CF

10.1101/375063 ◽

2018 ◽

Cited By ~ 3

Author(s):

Kevin Correia ◽

Hanchen Ho ◽

Radhakrishnan Mahadevan

Keyword(s):

Metabolic Network ◽

Formate Dehydrogenase ◽

Tca Cycle ◽

Metabolic Model ◽

Network Reconstruction ◽

Acetyl Coa ◽

Metabolic Network Reconstruction ◽

Future Studies ◽

Transport Chain ◽

Genome Scale

ABSTRACTBackgroundOrganohalide-respiring bacteria (OHRB) play an important role in the global halogen cycle and bioremediation of industrial sites contaminated with chlorinated organics. One notable OHRB is Dehalobacter restrictus strain CF, which is capable of respiring chloroform to dichloromethane. Improved bioremediation strategies could be employed with a greater understanding of D. restrictus’ metabolism in isolate and community cultures. To this end, we reconstructed the genome-scale metabolic network of D. restrictus to study its metabolism in future studies using flux balance analysis.MethodThe RAST annotation server and Model SEED framework were used to obtain a draft metabolic network reconstruction. Additional curation was required for its acetyl-CoA sources, the Wood-Ljungdahl pathway, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes.ResultsiHH623 is the first curated genome-scale metabolic model in the Peptococcaceae family. It spans 1087 reactions and 983 metabolites, covering 623 genes (21% of all ORF’s). Its potential sources of acetyl-CoA are pyruvate ferredoxin oxidoreductase, pyruvate formate lyase, acetyl-CoA synthetase, phosphate acetyltransferase, and CO-methylating acetyl-CoA synthase. NADPH may be regenerated by isocitrate dehydrogenase, malic enzyme, NADP-reducing hydrogenase, cytosolic formate dehydrogenase, ferredoxin-dependent bifurcating transhydrogenase, 5-methyltetrahydrofolate dehydrogenase, and 5-10-methylenetetrahydrofolate. Additional reactions that were added or removed to the D. restrictus reconstruction are discussed.ConclusionsWe reconstructed the genome-scale metabolic network of D. restricus by obtaining an initial draft with the RAST server and Model SEED framework. Curation was required for D. restricus’ acetyl-CoA sources, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes. This metabolic model can be used to decipher D. restrictus’ metabolism in isolate and community cultures in future studies, or as a template to reconstruct the metabolic network of other Peptococcaceae species. The extensive curation of the draft metabolic network reconstruction highlights the need to be cautious of automated metabolic network reconstruction.

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A genome‐scale metabolic network reconstruction of tomato ( Solanum lycopersicum L.) and its application to photorespiratory metabolism

The Plant Journal ◽

10.1111/tpj.13075 ◽

2016 ◽

Vol 85 (2) ◽

pp. 289-304 ◽

Cited By ~ 32

Author(s):

Huili Yuan ◽

C.Y. Maurice Cheung ◽

Mark G. Poolman ◽

Peter A. J. Hilbers ◽

Natal A. W. Riel

Keyword(s):

Metabolic Network ◽

Solanum Lycopersicum ◽

Network Reconstruction ◽

Metabolic Network Reconstruction ◽

Solanum Lycopersicum L ◽

A Genome ◽

Genome Scale

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RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006541 ◽

2018 ◽

Vol 14 (10) ◽

pp. e1006541 ◽

Cited By ~ 50

Author(s):

Hao Wang ◽

Simonas Marcišauskas ◽

Benjamín J. Sánchez ◽

Iván Domenzain ◽

Daniel Hermansson ◽

...

Keyword(s):

Metabolic Network ◽

Streptomyces Coelicolor ◽

Network Reconstruction ◽

Metabolic Network Reconstruction

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mmnet: An R Package for Metagenomics Systems Biology Analysis

BioMed Research International ◽

10.1155/2015/167249 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 7

Author(s):

Yang Cao ◽

Xiaofei Zheng ◽

Fei Li ◽

Xiaochen Bo

Keyword(s):

Metabolic Network ◽

Human Microbiome ◽

R Package ◽

Network Reconstruction ◽

Community Level ◽

System Level ◽

Metagenomic Data ◽

Pure Species ◽

Integrated Network ◽

Metabolic Network Reconstruction

The human microbiome plays important roles in human health and disease. Previous microbiome studies focused mainly on single pure species function and overlooked the interactions in the complex communities on system-level. A metagenomic approach introduced recently integrates metagenomic data with community-level metabolic network modeling, but no comprehensive tool was available for such kind of approaches. To facilitate these kinds of studies, we developed an R package,mmnet, to implement community-level metabolic network reconstruction. The package also implements a set of functions for automatic analysis pipeline construction including functional annotation of metagenomic reads, abundance estimation of enzymatic genes, community-level metabolic network reconstruction, and integrated network analysis. The result can be represented in an intuitive way and sent to Cytoscape for further exploration. The package has substantial potentials in metagenomic studies that focus on identifying system-level variations of human microbiome associated with disease.

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