scholarly journals Plant Metabolic Network: A multi-species resource of plant metabolic information

2021 ◽  
Author(s):  
Charles Hawkins ◽  
Daniel Ginzburg ◽  
Kangmei Zhao ◽  
William Dwyer ◽  
Bo Xue ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Multiple Correspondence Analysis ◽  
Model Organisms ◽  
Plant Metabolism ◽  
Online Resource ◽  
Transcriptomics Data ◽  
Cell Type Specific ◽  
Genome Scale ◽  
Metabolic Information

AbstractPlant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

scholarly journals Genome-scale metabolic network reconstruction of model animals as a platform for translational research

2021 ◽  
Vol 118 (30) ◽  
pp. e2102344118
Author(s):  
Hao Wang ◽  
Jonathan L. Robinson ◽  
Pinar Kocabas ◽  
Johan Gustafsson ◽  
Mihail Anton ◽  
...  
Keyword(s):  
Transgenic Mice ◽  
Metabolic Network ◽  
Model Organisms ◽  
Protein Overexpression ◽  
Sequencing Data ◽  
Proteomics Data ◽  
Gm2 Ganglioside ◽  
Species Specific ◽  
Specific Reactions ◽  
Genome Scale

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

scholarly journals Managing Uncertainty in Metabolic Network Structure and Improving Predictions Using EnsembleFBA

2016 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Small Molecule ◽  
Essential Genes ◽  
Model Organisms ◽  
Network Structures ◽  
Major Barrier ◽  
Metabolic Systems ◽  
Species Specific ◽  
Genome Scale

AbstractGenome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel ensemble approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs and is compatible with many automated reconstruction approaches. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contribute disproportionately to the set of predicted essential reactions in a way that is unique to each Streptococcus species. These species-specific network structures lead to species-specific outcomes from small molecule interactions. Through these analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.Author SummaryMetabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a biochemical system will behave as well as to design biochemical systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which overcomes uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, we open the door to studying systems which would otherwise be infeasible.

scholarly journals MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

2020 ◽  
Vol 49 (D1) ◽  
pp. D570-D574
Author(s):  
Sébastien Moretti ◽  
Van Du T Tran ◽  
Florence Mehl ◽  
Mark Ibberson ◽  
Marco Pagni
Keyword(s):  
Metabolic Network ◽  
Intrinsic Properties ◽  
Biochemical Reactions ◽  
Online Services ◽  
Major Improvement ◽  
Sparql Endpoint ◽  
Cross Links ◽  
Metabolic Models ◽  
Genome Scale

Abstract MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

scholarly journals MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

2020 ◽  
Author(s):  
Sébastien Moretti ◽  
Van Du T. Tran ◽  
Florence Mehl ◽  
Mark Ibberson ◽  
Marco Pagni
Keyword(s):  
Metabolic Network ◽  
Intrinsic Properties ◽  
Biochemical Reactions ◽  
Online Services ◽  
Major Improvement ◽  
Sparql Endpoint ◽  
Link Type ◽  
Cross Links ◽  
Genome Scale

ABSTRACTMetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

scholarly journals Developing an evidence assessment framework and appraising the academic literature on migrant health in Malaysia: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041379
Author(s):  
Allard Willem de Smalen ◽  
Zhie X Chan ◽  
Claudia Abreu Lopes ◽  
Michaella Vanore ◽  
Tharani Loganathan ◽  
...  
Keyword(s):  
Scoping Review ◽  
Multiple Correspondence Analysis ◽  
Population Data ◽  
Good Health ◽  
Migrant Health ◽  
High Quality ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Academic Literature

BackgroundA large number of international migrants in Malaysia face challenges in obtaining good health, the extent of which is still relatively unknown. This study aims to map the existing academic literature on migrant health in Malaysia and to provide an overview of the topical coverage, quality and level of evidence of these scientific studies.MethodsA scoping review was conducted using six databases, including Econlit, Embase, Global Health, Medline, PsycINFO and Social Policy and Practice. Studies were eligible for inclusion if they were conducted in Malaysia, peer-reviewed, focused on a health dimension according to the Bay Area Regional Health Inequities Initiative (BARHII) framework, and targeted the vulnerable international migrant population. Data were extracted by using the BARHII framework and a newly developed decision tree to identify the type of study design and corresponding level of evidence. Modified Joanna Briggs Institute checklists were used to assess study quality, and a multiple-correspondence analysis (MCA) was conducted to identify associations between different variables.Results67 publications met the selection criteria and were included in the study. The majority (n=41) of studies included foreign workers. Over two-thirds (n=46) focused on disease and injury, and a similar number (n=46) had descriptive designs. The average quality of the papers was low, yet quality differed significantly among them. The MCA showed that high-quality studies were mostly qualitative designs that included refugees and focused on living conditions, while prevalence and analytical cross-sectional studies were mostly of low quality.ConclusionThis study provides an overview of the scientific literature on migrant health in Malaysia published between 1965 and 2019. In general, the quality of these studies is low, and various health dimensions have not been thoroughly researched. Therefore, researchers should address these issues to improve the evidence base to support policy-makers with high-quality evidence for decision-making.

scholarly journals Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

scholarly journals Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 221
Author(s):  
Ozlem Altay ◽  
Cheng Zhang ◽  
Hasan Turkez ◽  
Jens Nielsen ◽  
Mathias Uhlén ◽  
...  
Keyword(s):  
Systems Biology ◽  
Alternative Pathway ◽  
Growth Conditions ◽  
Pentose Phosphate ◽  
Burkholderia Cenocepacia ◽  
Bacterial Cells ◽  
Metabolic Remodeling ◽  
Transcriptomics Data ◽  
Biofilm Development ◽  
Genome Scale

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

scholarly journals Small-Scale Coastal Fisheries in the Midst of Adaptation and Diversification: Insights from Southern Italy

2021 ◽  
Vol 13 (13) ◽  
pp. 7393
Author(s):  
Agata Nicolosi ◽  
Donatella Di Gregorio ◽  
Giuseppe Arena ◽  
Valentina Rosa Laganà ◽  
Donatella Privitera
Keyword(s):  
Southern Italy ◽  
Multiple Correspondence Analysis ◽  
Marketing Strategies ◽  
Small Scale ◽  
Artisanal Fishing ◽  
Economic Activities ◽  
Coastal Fisheries ◽  
Cluster Identification ◽  
The Eu

The study looks at the problems facing coastal fishing communities. It highlights the impacts that, in the complex framework of the EU reforms, have manifested themselves on economic activities and on society. The aim of the paper is twofold: to examine small-scale artisanal fishing in an area of Southern Italy in order to develop resilience and diversification and at the same time to outline the profiles of local bluefish buyers to highlight development strategies for the sector. The research carried out through a direct survey by administering a questionnaire to fishermen operating in areas of Southern Italy and the data cross-referenced with the opinions of local consumers. A conjoint experiment, followed by a multiple correspondence analysis and cluster identification, was used to outline the profiles of local bluefish buyers. The results of the analysis reveal that the fish market and the restaurant sector are the main distribution channels preferred by fishermen. Furthermore, fishermen are very sensitive to environmental issues and are willing to collaborate and actively participate in the environmental protection of the sea. Consumers recognise the quality of local bluefish, and they implicitly perceive the sustainability of the method of capture. The results show the need to undertake synergistic actions for the fishing industry, capable of activating marketing strategies adequately to support, promote and develop the sector. The results of the study provide helpful information for food companies in order to better segment their market and target their consumers, as well as to effectively promote their product using brands, certifications and traceability.

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