Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica

Photosynthesis is an essential biological process and a key approach for raising crop yield. However, photosynthesis in rice is not fully investigated. This study reported the photosynthetic properties and transcriptomic profiles of chlorophyll (Chl) b-deficient mutant (ch11) and wild-type rice (Oryza sativa L.). Chl b-deficient rice revealed irregular chloroplast development (indistinct membranes, loss of starch granules, thinner grana, and numerous plastoglobuli). Next-generation sequencing approach application revealed that the differential expressed genes were related to photosynthesis machinery, Chl-biosynthesis, and degradation pathway in ch11. Two genes encoding PsbR (PSII core protein), FtsZ1, and PetH genes, were found to be down-regulated. The expression of the FtsZ1 and PetH genes resulted in disrupted chloroplast cell division and electron flow, respectively, consequently reducing Chl accumulation and the photosynthetic capacity of Chl b-deficient rice. Furthermore, this study found the up-regulated expression of the GluRS gene, whereas the POR gene was down-regulated in the Chl biosynthesis and degradation pathways. The results obtained from RT-qPCR analyses were generally consistent with those of transcription analysis, with the exception of the finding that MgCH genes were up-regulated which enhance the important intermediate products in the Mg branch of Chl biosynthesis. These results indicate a reduction in the accumulation of both Chl a and Chl b. This study suggested that a decline in Chl accumulation is caused by irregular chloroplast formation and down-regulation of POR genes; and Chl b might be degraded via the pheophorbide b pathway, which requires further elucidation.

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Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

Toxicology and Applied Pharmacology ◽

10.1016/j.taap.2020.115390 ◽

2021 ◽

Vol 412 ◽

pp. 115390

Author(s):

Kristopher D. Rawls ◽

Bonnie V. Dougherty ◽

Kalyan C. Vinnakota ◽

Venkat R. Pannala ◽

Anders Wallqvist ◽

...

Keyword(s):

Metabolic Model ◽

Renal Metabolism ◽

A Genome ◽

Compound Exposure ◽

Genome Scale

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Expression dynamics indicate the role of Jasmonic acid biosynthesis pathway in regulating macronutrient (N, P and K+) deficiency tolerance in rice (Oryza sativa L.)

Plant Cell Reports ◽

10.1007/s00299-021-02721-5 ◽

2021 ◽

Author(s):

Deepika ◽

Amarjeet Singh

Keyword(s):

Oryza Sativa ◽

Jasmonic Acid ◽

Oryza Sativa L ◽

Biosynthesis Pathway ◽

Acid Biosynthesis ◽

Jasmonic Acid Biosynthesis ◽

K Deficiency

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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jingru Zhou ◽

Yingping Zhuang ◽

Jianye Xia

Keyword(s):

Aspergillus Niger ◽

Large Scale ◽

Measurement Techniques ◽

Metabolic Model ◽

System Level ◽

Metabolic Phenotype ◽

Omics Data ◽

Prediction Ability ◽

Phenotype Prediction ◽

Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression

Metabolites ◽

10.3390/metabo11070456 ◽

2021 ◽

Vol 11 (7) ◽

pp. 456

Author(s):

Pejman Salahshouri ◽

Modjtaba Emadi-Baygi ◽

Mahdi Jalili ◽

Faiz M. Khan ◽

Olaf Wolkenhauer ◽

...

Keyword(s):

Colorectal Cancer ◽

Cancer Progression ◽

Gut Microbiome ◽

Metabolic Model ◽

System Level ◽

Mathematical Framework ◽

Bacterial Populations ◽

Colorectal Cancer Progression ◽

Genome Scale ◽

High Throughput Data Analysis

The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA, as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.

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A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Metabolites ◽

10.3390/metabo11030168 ◽

2021 ◽

Vol 11 (3) ◽

pp. 168

Author(s):

John I. Hendry ◽

Hoang V. Dinh ◽

Debolina Sarkar ◽

Lin Wang ◽

Anindita Bandyopadhyay ◽

...

Keyword(s):

Electron Transport ◽

Pyruvate Decarboxylase ◽

Metabolic Model ◽

Cell Types ◽

Economic Feasibility ◽

Nitrogen Fixing ◽

Design Algorithm ◽

A Genome ◽

Anabaena Sp ◽

Genome Scale

Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors.

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