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 Learning graph representations of biochemical networks and its application to enzymatic link prediction

2020 ◽  
Author(s):  
Julie Jiang ◽  
Li-Ping Liu ◽  
Soha Hassoun
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Complete Characterization ◽  
Graph Connectivity ◽  
Biochemical Networks ◽  
Support Vector ◽  
Enzymatic Reactions ◽  
Biological Engineering ◽  
Graph Representations ◽  
Enzymatic Transformation

Abstract Motivation The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions cataloged in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity. Results We explore transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding improves link prediction by 30% in area under curve over fingerprint-based similarity approaches and by 8% over support vector machines. We compare ELP against rule-based methods. We also evaluate ELP for predicting links in pathway maps and for reconstruction of edges in reaction networks of four common gut microbiota phyla: actinobacteria, bacteroidetes, firmicutes and proteobacteria. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can guide visualization. Availability and implementation The code and datasets are available through https://github.com/HassounLab/ELP. Contact [email protected] or [email protected]

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 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 General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 336
Author(s):  
Boštjan Murovec ◽  
Leon Deutsch ◽  
Blaž Stres
Keyword(s):  
16S Rrna ◽  
Metabolic Pathways ◽  
Large Scale ◽  
Enzymatic Reactions ◽  
Operational Taxonomic Units ◽  
Biochemical Pathways ◽  
Meaningful Information ◽  
Novel Biomarkers ◽  
Reproducible Analysis ◽  
Microbiome Profiling

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

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 Cardiometabolism as an Interlocking Puzzle between the Healthy and Diseased Heart: New Frontiers in Therapeutic Applications

2021 ◽  
Vol 10 (4) ◽  
pp. 721
Author(s):  
Teresa Pasqua ◽  
Carmine Rocca ◽  
Anita Giglio ◽  
Tommaso Angelone
Keyword(s):  
Cardiac Muscle ◽  
Metabolic Networks ◽  
Mechanical Energy ◽  
Cardiac Metabolism ◽  
Protective Mechanism ◽  
Enzymatic Reactions ◽  
Carbohydrate Utilization ◽  
Pharmacological Agents ◽  
Physiological Structure ◽  
And Function

Cardiac metabolism represents a crucial and essential connecting bridge between the healthy and diseased heart. The cardiac muscle, which may be considered an omnivore organ with regard to the energy substrate utilization, under physiological conditions mainly draws energy by fatty acids oxidation. Within cardiomyocytes and their mitochondria, through well-concerted enzymatic reactions, substrates converge on the production of ATP, the basic chemical energy that cardiac muscle converts into mechanical energy, i.e., contraction. When a perturbation of homeostasis occurs, such as an ischemic event, the heart is forced to switch its fatty acid-based metabolism to the carbohydrate utilization as a protective mechanism that allows the maintenance of its key role within the whole organism. Consequently, the flexibility of the cardiac metabolic networks deeply influences the ability of the heart to respond, by adapting to pathophysiological changes. The aim of the present review is to summarize the main metabolic changes detectable in the heart under acute and chronic cardiac pathologies, analyzing possible therapeutic targets to be used. On this basis, cardiometabolism can be described as a crucial mechanism in keeping the physiological structure and function of the heart; furthermore, it can be considered a promising goal for future pharmacological agents able to appropriately modulate the rate-limiting steps of heart metabolic pathways.

Optimal Robotic Assembly Planning Using Dijkstra’s Algorithm

1994 ◽  
Author(s):  
Sam Anand ◽  
Mohamed Sabri
Keyword(s):  
Configuration Space ◽  
Material Handling ◽  
Search Algorithm ◽  
Robotic Manipulators ◽  
Robotic Assembly ◽  
Graph Search ◽  
Assembly Planning ◽  
Robotic Welding ◽  
Fast Collision ◽  
Space Approach

Abstract Robots play an important role in the modern factory and are used in a manufacturing cell for several functions such as assembly, material handling, robotic welding, etc. One of the principal problems faced by robots while performing their tasks is the presence of obstacles such as fixtures, tools, and objects in the robot workspace. Such objects could result in a collision with one of the arms of the robots. Fast collision-free motion planning algorithms are therefore necessary for robotic manipulators to operate in a wide variety of changing environments. The configuration space approach is one of the widely used methods for collision-free robotic path planning. This paper presents a novel graph-based method of searching the configuration space for a collision-free path in a robotic assembly operation. Dijkstra’s graph search algorithm is used for optimizing the joint displacements of the robot while performing the assembly task. The methodology is illustrated using a simple robotic assembly planning task.

scholarly journals Unlocking Elementary Conversion Modes: ecmtool unveils all capabilities of metabolic networks

2020 ◽  
Author(s):  
Tom J. Clement ◽  
Erik B. Baalhuis ◽  
Bas Teusink ◽  
Frank J. Bruggeman ◽  
Robert Planqué ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Full Range ◽  
Biotechnological Potential ◽  
Metabolic Network Reconstruction ◽  
And Function ◽  
Multicellular Organisms ◽  
Genome Scale ◽  
Threat Levels ◽  
Experimental Characterisation

AbstractThe metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterisation is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organism’s annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible, until now. We extend and explain the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.

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