scholarly journals Finding branched pathways in metabolic network via atom group tracking

2021 ◽  
Vol 17 (2) ◽  
pp. e1008676
Author(s):  
Yiran Huang ◽  
Yusi Xie ◽  
Cheng Zhong ◽  
Fengfeng Zhou
Keyword(s):  
Metabolic Network ◽  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Source Code ◽  
Biologically Relevant ◽  
Link Type ◽  
Atom Group ◽  
Comprehensive Picture ◽  
Thermodynamic Feasibility ◽  
Made In

Finding non-standard or new metabolic pathways has important applications in metabolic engineering, synthetic biology and the analysis and reconstruction of metabolic networks. Branched metabolic pathways dominate in metabolic networks and depict a more comprehensive picture of metabolism compared to linear pathways. Although progress has been developed to find branched metabolic pathways, few efforts have been made in identifying branched metabolic pathways via atom group tracking. In this paper, we present a pathfinding method called BPFinder for finding branched metabolic pathways by atom group tracking, which aims to guide the synthetic design of metabolic pathways. BPFinder enumerates linear metabolic pathways by tracking the movements of atom groups in metabolic network and merges the linear atom group conserving pathways into branched pathways. Two merging rules based on the structure of conserved atom groups are proposed to accurately merge the branched compounds of linear pathways to identify branched pathways. Furthermore, the integrated information of compound similarity, thermodynamic feasibility and conserved atom groups is also used to rank the pathfinding results for feasible branched pathways. Experimental results show that BPFinder is more capable of recovering known branched metabolic pathways as compared to other existing methods, and is able to return biologically relevant branched pathways and discover alternative branched pathways of biochemical interest. The online server of BPFinder is available at http://114.215.129.245:8080/atomic/. The program, source code and data can be downloaded from https://github.com/hyr0771/BPFinder.

scholarly journals Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions

2019 ◽  
Author(s):  
Gregory L. Medlock ◽  
Jason A. Papin
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Biology ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Ensemble Simulations ◽  
Link Type ◽  
Genome Scale ◽  
Python Package

AbstractUncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles, perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org/) and from github (https://github.com/gregmedlock/Medusa/), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest/.

scholarly journals Metabolic network reductions

2018 ◽  
Author(s):  
Mojtaba Tefagh ◽  
Stephen P. Boyd
Keyword(s):  
Metabolic Network ◽  
Metabolic Pathways ◽  
Degrees Of Freedom ◽  
Metabolic Networks ◽  
System Level ◽  
Network Reduction ◽  
Computational Aspects ◽  
Sheer Size ◽  
Problem Instances ◽  
Genome Scale

AbstractGenome-scale metabolic networks are exceptionally huge and even efficient algorithms can take a while to run because of the sheer size of the problem instances. To address this problem, metabolic network reductions can substantially reduce the overwhelming size of the problem instances at hand. We begin by formulating some reasonable axioms defining what it means for a metabolic network reduction to be “canonical” which conceptually enforces reversibility without loss of any information on the feasible flux distributions. Then, we start to search for an efficient way to deduce some of the attributes of the original network from the reduced one in order to improve the performance. As the next step, we will demonstrate how to reduce a metabolic network repeatedly until no more reductions are possible. In the end, we sum up by pointing out some of the biological implications of this study apart from the computational aspects discussed earlier.Author summaryMetabolic networks appear at first sight to be nothing more than an enormous body of reactions. The dynamics of each reaction obey the same fundamental laws and a metabolic network as a whole is the melange of its reactions. The oversight in this kind of reductionist thinking is that although the behavior of a metabolic network is determined by the states of its reactions in theory, nevertheless it cannot be inferred directly from them in practice. Apart from the infeasibility of this viewpoint, metabolic pathways are what explain the biological functions of the organism and thus also what we are frequently concerned about at the system level.Canonical metabolic network reductions decrease the number of reactions substantially despite leaving the metabolic pathways intact. In other words, the reduced metabolic networks are smaller in size while retaining the same metabolic pathways. The possibility of such operations is rooted in the fact that the total degrees of freedom of a metabolic network in the steady-state conditions are significantly lower than the number of its reactions because of some emergent redundancies. Strangely enough, these redundancies turn out to be very well-studied in the literature.

scholarly journals Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

2020 ◽  
Author(s):  
Jasmin Hafner ◽  
Vassily Hatzimanikatis
Keyword(s):  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Search Algorithm ◽  
Biochemical Networks ◽  
Biochemical Network ◽  
Graph Search ◽  
Enzymatic Reactions ◽  
Graph Representations ◽  
Biologically Relevant ◽  
Product Molecule

AbstractFinding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, especially with the integration of big data, the efficient analysis and navigation of metabolic networks remains a challenge. Here, we propose the construction of searchable graph representations of metabolic networks. Éach reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6,546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant-product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks.

scholarly journals Dynamic histone acetylation in floral volatile synthesis and emission in petunia flowers

2021 ◽  
Author(s):  
Ryan M Patrick ◽  
Xing-Qi Huang ◽  
Natalia Dudareva ◽  
Ying Li
Keyword(s):  
Transcriptional Activation ◽  
Metabolic Pathways ◽  
Transcriptional Repression ◽  
Metabolic Networks ◽  
Underlying Mechanism ◽  
Expression Of Genes ◽  
Genome Wide ◽  
Chemical Inhibitors ◽  
Volatile Organic ◽  
Floral Volatile

Abstract Biosynthesis of secondary metabolites relies on primary metabolic pathways to provide precursors, energy, and cofactors, thus requiring coordinated regulation of primary and secondary metabolic networks. However, to date, it remains largely unknown how this coordination is achieved. Using Petunia hybrida flowers, which emit high levels of phenylpropanoid/benzenoid volatile organic compounds (VOCs), we uncovered genome-wide dynamic deposition of histone H3 lysine 9 acetylation (H3K9ac) during anthesis as an underlying mechanism to coordinate primary and secondary metabolic networks. The observed epigenome reprogramming is accompanied by transcriptional activation at gene loci involved in primary metabolic pathways that provide precursor phenylalanine, as well as secondary metabolic pathways to produce volatile compounds. We also observed transcriptional repression among genes involved in alternative phenylpropanoid branches that compete for metabolic precursors. We show that GNAT family histone acetyltransferase(s) (HATs) are required for the expression of genes involved in VOC biosynthesis and emission, by using chemical inhibitors of HATs, and by knocking down a specific HAT gene, ELP3, through transient RNAi. Together, our study supports that regulatory mechanisms at chromatin level may play an essential role in activating primary and secondary metabolic pathways to regulate VOC synthesis in petunia flowers.

scholarly journals OP0277-PARE METABERN, THE EUROPEAN REFERENCE NETWORK FOR RARE HEREDITARY DISEASES: STRUCTURE, OBJECTIVES, METABOLIC RMDS AND THE ROLES OF EUROPEAN PATIENT ADVOCACY GROUPS (EPAGS)

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 168.2-168
Author(s):  
L. Wagner ◽  
S. Sestini ◽  
C. Brown ◽  
A. Finglas ◽  
R. Francisco ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Social Science ◽  
Metabolic Pathways ◽  
Metabolic Disorders ◽  
Clinical Care ◽  
Single Point ◽  
Link Type ◽  
The Impact

Background:Inborn metabolic disorders (IMDs) currently encompass more than 1,500 diseases with new ones still to be identified1. Each of them is characterised by a genetic defect affecting a metabolic pathway. Only few of them have curative treatments, that target the respective metabolic pathway. Commonly, treatment examples include diet, substrate reduction therapies, enzyme replacement therapies, gene therapy and biologicals, enabling IMD-patient now to survive to adulthood. About 30 % of all IMDs involve the musculoskeletal system and are here referred to as rare metabolic RMDs. Generally, IMDs are very heterogenous with respect to symptoms and severity, often being systemic and affecting more children than adults. Thus, challenges include certified advanced training of adult metabolic experts, standardised transition plans, social support and development of therapies for diseases that do not have any cure yet.Objectives:Introduction of MetabERN, its structure and objectives, highlighting on the unique features and challenges of metabolic RMDs and describing the involvement of patient representation in MetabERN.Methods:MetabERN is stratified in 7 subnetworks (SNW) according to the respective metabolic pathways and 9 work packages (WP), including administration, dissemination, guidelines, virtual counselling framework, research/clinical trials, continuity of care, education and patient involvement. The patient board involves a steering committee and single point of contacts for each subnetwork and work package, respectively2. Projects include identifying the need of implementing social science to assess the psycho-socio-economic burden of IMDs, webinars on IMDs and their transition as well as surveys on the impact of COVID-193 on IMD-patients and health care providers (HCPs), social assistance for IMD-patients and analysing the transition landscape within Europe.Results:The MetabERN structure enables bundling of expertise, capacity building and knowledge transfer for faster diagnosis and better health care. Rare metabolic RMDs are present in all SNWs that require unique treatments according to their metabolic pathways. Implementation of social science to assess the psycho-socio-economic burden of IMDs is still underused. Involvement of patient representatives is essential for a holistic healthcare not only focusing on clinical care, but also on the quality of life for IMD-patients. Surveys identified unmet needs of patient care, patients having little information on national support systems and structural deficits of healthcare systems to ensure HCP can provide adequate clinical care during transition phases. These results are collected by MetabERN and forwarded to the Directorate-General for Health and Food Safety (DG SANTE) of the European Commission (EC) to be addressed further.Conclusion:MetabERN offers an infrastructure of virtual healthcare for patients with IMDs. Thus, in collaboration with ERN ReCONNET, MetabERN can assist in identifying rare metabolic disorders of RMDs to shorten the odyssey of diagnosis and advise on their respective therapies. On the other hand, MetabERN can benefit from EULAR’s longstanding experience regarding issues affecting the quality of life, all RMD patients are facing, such as pain, stiffness, fatigue, rehabilitation, maintaining work and disability claims.References:[1]IEMbase - Inborn Errors of Metabolism Knowledgebase http://www.iembase.org/ (accessed Jan 29, 2021).[2]MetabERN: European Refence Network for Hereditary Metabolic Disorders https://metab.ern-net.eu/ (accessed Jan 29, 2021).[3]Lampe, C.; Dionisi-Vici, C.; Bellettato, C. M.; Paneghetti, L.; van Lingen, C.; Bond, S.; Brown, C.; Finglas, A.; Francisco, R.; Sestini, S.; Heard, J. M.; Scarpa, M.; MetabERN collaboration group. The Impact of COVID-19 on Rare Metabolic Patients and Healthcare Providers: Results from Two MetabERN Surveys. Orphanet J. Rare Dis.2020, 15 (1), 341. https://doi.org/10.1186/s13023-020-01619-x.Acknowledgements:The authors thank the MetabERN collaboration group, the single point of contacts (SPOC) of the MetabERN patient board and the Transition Project Working Group (TPWG)Disclosure of Interests:None declared

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 Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2634
Author(s):  
Beatriz Soldevilla ◽  
Angeles López-López ◽  
Alberto Lens-Pardo ◽  
Carlos Carretero-Puche ◽  
Angeles Lopez-Gonzalvez ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Tca Cycle ◽  
Metabolomic Profile ◽  
Diagnostic Potential ◽  
The Tca Cycle ◽  
Novel Biomarkers ◽  
Potential Methods ◽  
Clinical Potential ◽  
First Time

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

scholarly journals Intracellular Fate of Universally Labelled 13C Isotopic Tracers of Glucose and Xylose in Central Metabolic Pathways of Xanthomonas oryzae

Metabolites ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 66 ◽  
Author(s):  
Manu Shree ◽  
Shyam K. Masakapalli
Keyword(s):  
Amino Acids ◽  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Tca Cycle ◽  
Xanthomonas Oryzae ◽  
Flux Analysis ◽  
Isotopic Tracers ◽  
Feeding Experiments ◽  
The Tca Cycle ◽  
Metabolic Route

The goal of this study is to map the metabolic pathways of poorly understood bacterial phytopathogen, Xanthomonas oryzae (Xoo) BXO43 fed with plant mimicking media XOM2 containing glutamate, methionine and either 40% [13C5] xylose or 40% [13C6] glucose. The metabolic networks mapped using the KEGG mapper and the mass isotopomer fragments of proteinogenic amino acids derived from GC-MS provided insights into the activities of Xoo central metabolic pathways. The average 13C in histidine, aspartate and other amino acids confirmed the activities of PPP, the TCA cycle and amino acid biosynthetic routes, respectively. The similar labelling patterns of amino acids (His, Ala, Ser, Val and Gly) from glucose and xylose feeding experiments suggests that PPP would be the main metabolic route in Xoo. Owing to the lack of annotated gene phosphoglucoisomerase in BXO43, the 13C incorporation in alanine could not be attributed to the competing pathways and hence warrants additional positional labelling experiments. The negligible presence of 13C incorporation in methionine brings into question its potential role in metabolism and pathogenicity. The extent of the average 13C labelling in several amino acids highlighted the contribution of pre-existing pools that need to be accounted for in 13C-flux analysis studies. This study provided the first qualitative insights into central carbon metabolic pathway activities in Xoo.

scholarly journals DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

2020 ◽  
Author(s):  
Pablo Rodríguez-Mier ◽  
Nathalie Poupin ◽  
Carlo de Blasio ◽  
Laurent Le Cam ◽  
Fabien Jourdan
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Relevant Information ◽  
Experimental Information ◽  
Correct Identification ◽  
Good Representation ◽  
Metabolic State ◽  
Whole Space ◽  
Context Specific ◽  
Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

scholarly journals idCOV: a pipeline for quick clade identification of SARS-CoV-2 isolates

2020 ◽  
Author(s):  
Xun Zhu ◽  
Ti-Cheng Chang ◽  
Richard Webby ◽  
Gang Wu
Keyword(s):  
Personal Computer ◽  
Source Code ◽  
Command Line ◽  
Sequencing Data ◽  
Link Type ◽  
Public Dataset ◽  
Virus Isolates

AbstractidCOV is a phylogenetic pipeline for quickly identifying the clades of SARS-CoV-2 virus isolates from raw sequencing data based on a selected clade-defining marker list. Using a public dataset, we show that idCOV can make equivalent calls as annotated by Nextstrain.org on all three common clade systems using user uploaded FastQ files directly. Web and equivalent command-line interfaces are available. It can be deployed on any Linux environment, including personal computer, HPC and the cloud. The source code is available at https://github.com/xz-stjude/idcov. A documentation for installation can be found at https://github.com/xz-stjude/idcov/blob/master/README.md.

Sign in / Sign up

Export Citation Format

Share Document