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

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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Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions

10.21203/rs.3.rs-448829/v1 ◽

2021 ◽

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 BackgroundGenome-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.ResultsBased 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 B values, predicting the differential expression of enzymes under different growth conditions. ConclusionsThis 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 Aspergillus 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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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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Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition

Scientific Reports ◽

10.1038/s41598-018-32341-1 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 12

Author(s):

Hongzhong Lu ◽

Weiqiang Cao ◽

Xiaoyun Liu ◽

Yufei Sui ◽

Liming Ouyang ◽

...

Keyword(s):

Aspergillus Niger ◽

Enzyme Production ◽

Model Simulation ◽

Production Condition ◽

Metabolic Model ◽

Integrative Analysis ◽

Cellular Adaptation ◽

Industrial Enzyme ◽

Genome Scale

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Integrating –omics data into genome-scale metabolic network models: principles and challenges

Essays in Biochemistry ◽

10.1042/ebc20180011 ◽

2018 ◽

Vol 62 (4) ◽

pp. 563-574 ◽

Cited By ~ 10

Author(s):

Charlotte Ramon ◽

Mattia G. Gollub ◽

Jörg Stelling

Keyword(s):

Data Integration ◽

Large Scale ◽

Network Models ◽

Omics Data ◽

Scale Models ◽

Common Framework ◽

Genome Scale ◽

Constraint Based Models ◽

Omics Data Integration

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.

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On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

10.1101/593277 ◽

2019 ◽

Author(s):

Miguel Ponce-de-León ◽

Iñigo Apaolaza ◽

Alfonso Valencia ◽

Francisco J. Planes

Keyword(s):

Gene Expression ◽

Metabolic Model ◽

Specific Model ◽

Omics Data ◽

Model Reconstruction ◽

Reconstruction Algorithms ◽

Reconstruction Methods ◽

Metabolic Models ◽

Context Specific ◽

Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

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Gsmodutils: a python based framework for test-driven genome scale metabolic model development

Bioinformatics ◽

10.1093/bioinformatics/btz088 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3397-3403 ◽

Cited By ~ 2

Author(s):

James Gilbert ◽

Nicole Pearcy ◽

Rupert Norman ◽

Thomas Millat ◽

Klaus Winzer ◽

...

Keyword(s):

Open Source ◽

Large Scale ◽

Model Development ◽

Metabolic Model ◽

Supplementary Information ◽

Validation Data ◽

Engineering Research ◽

Modelling Framework ◽

Wide Range ◽

Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.Availability and implementationThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.Supplementary informationSupplementary data are available at Bioinformatics online.

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Gsmodutils: A python based framework for test-driven genome scale metabolic model development

10.1101/430116 ◽

2018 ◽

Author(s):

James P Gilbert ◽

Nicole Pearcy ◽

Rupert Norman ◽

Thomas Millat ◽

Klaus Winzer ◽

...

Keyword(s):

Open Source ◽

Large Scale ◽

Model Development ◽

Metabolic Model ◽

Validation Data ◽

Engineering Research ◽

Modelling Framework ◽

Wide Range ◽

Scale Models ◽

Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed thegsmodutilsmodelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.AvailabilityThe software framework described within this paper is open source and freely available fromhttp://github.com/SBRCNottingham/gsmodutils

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Genome-scale modeling specifies the metabolic capabilities of Rhizophagus irregularis

10.1101/2021.10.07.463607 ◽

2021 ◽

Author(s):

Philipp Wendering ◽

Zoran Nikoloski

Keyword(s):

Mycorrhizal Fungi ◽

Molecular Mechanisms ◽

Arbuscular Mycorrhizal ◽

Dry Weight ◽

Metabolic Model ◽

Rhizophagus Irregularis ◽

Plant Performance ◽

System Level ◽

Cofactor Biosynthesis ◽

Genome Scale

Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here we used all published genomics, transcriptomics, and metabolomics resources to gain insights in the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to: (1) accurately predict increased growth of R. irregularis on myristate with minimal medium; (2) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (= 0.74) to measured hyphal dry weight; and (3) simulated growth rate increases with tighter association of this AMF with the host plant across three fungal structures. Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis. Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance.

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What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

10.1101/2020.04.26.057943 ◽

2020 ◽

Cited By ~ 2

Author(s):

Anne Richelle ◽

Benjamin P. Kellman ◽

Alexander T. Wenzel ◽

Austin W.T. Chiang ◽

Tyler Reagan ◽

...

Keyword(s):

Mammalian Cells ◽

Large Scale ◽

Metabolic Networks ◽

Omics Data ◽

Web Based ◽

Cell Functions ◽

Metabolic Functions ◽

Biological Studies ◽

Biological Entities ◽

Genome Scale

AbstractLarge-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

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