DEVELOPMENT OF TOOLS FOR ATOM-LEVEL INTERPRETATION OF STABLE ISOTOPE-RESOLVED METABOLOMICS DATASETS

Metabolomics is the global study of small molecules in living systems under a given state, merging as a new ‘omics’ study in systems biology. It has shown great promise in elucidating biological mechanism in various areas. Many diseases, especially cancers, are closely linked to reprogrammed metabolism. As the end point of biological processes, metabolic profiles are more representative of the biological phenotype compared to genomic or proteomic profiles. Therefore, characterizing metabolic phenotype of various diseases will help clarify the metabolic mechanisms and promote the development of novel and effective treatment strategies. Advances in analytical technologies such as nuclear magnetic resonance and mass spectroscopy greatly contribute to the detection and characterization of global metabolites in a biological system. Furthermore, application of these analytical tools to stable isotope resolved metabolomics experiments can generate large-scale high-quality metabolomics data containing isotopic flow through cellular metabolism. However, the lack of the corresponding computational analysis tools hinders the characterization of metabolic phenotypes and the downstream applications. Both detailed metabolic modeling and quantitative analysis are required for proper interpretation of these complex metabolomics data. For metabolic modeling, currently there is no comprehensive metabolic network at an atom-resolved level that can be used for deriving context-specific metabolic models for SIRM metabolomics datasets. For quantitative analysis, most available tools conduct metabolic flux analysis based on a well-defined metabolic model, which is hard to achieve for complex biological system due to the limitations in our knowledge. Here, we developed a set of methods to address these problems. First, we developed a neighborhood-specific coloring method that can create identifier for each atom in a specific compound. With the atom identifiers, we successfully harmonized compounds and reactions across KEGG and MetaCyc databases at various levels. In addition, we evaluated the atom mappings of the harmonized metabolic reactions. These results will contribute to the construction of a comprehensive atom-resolved metabolic network. In addition, this method can be easily applied to any metabolic database that provides a molfile representation of compounds, which will greatly facilitate future expansion. In addition, we developed a moiety modeling framework to deconvolute metabolite isotopologue profiles using moiety models along with the analysis and selection of the best moiety model(s) based on the experimental data. To our knowledge, this is the first method that can analyze datasets involving multiple isotope tracers. Furthermore, instead of a single predefined metabolic model, this method allows the comparison of multiple metabolic models derived from a given metabolic profile, and we have demonstrated the robust performance of the moiety modeling framework in model selection with a 13C-labeled UDP-GlcNAc isotopologue dataset. We further explored the data quality requirements and the factors that affect model selection. Collectively, these methods and tools help interpret SIRM metabolomics datasets from metabolic modeling to quantitative analysis

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation

Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations

The EC2KEGG output for the RV1423 analysis sorted in ascending order by the FDR value. (XLSX 58 kb

BKM-react, an integrated biochemical reaction database

<p>Abstract</p> <p>Background</p> <p>The systematic, complete and correct reconstruction of genome-scale metabolic networks or metabolic pathways is one of the most challenging tasks in systems biology research. An essential requirement is the access to the complete biochemical knowledge - especially on the biochemical reactions. This knowledge is extracted from the scientific literature and collected in biological databases. Since the available databases differ in the number of biochemical reactions and the annotation of the reactions, an integrated knowledge resource would be of great value.</p> <p>Results</p> <p>We developed a comprehensive non-redundant reaction database containing known enzyme-catalyzed and spontaneous reactions. Currently, it comprises 18,172 unique biochemical reactions. As source databases the biochemical databases <it>BRENDA</it>, <it>KEGG</it>, and <it>MetaCyc </it>were used. Reactions of these databases were matched and integrated by aligning substrates and products. For the latter a two-step comparison using their structures (<it>via InChIs</it>) and names was performed. Each biochemical reaction given as a reaction equation occurring in at least one of the databases was included.</p> <p>Conclusions</p> <p>An integrated non-redundant reaction database has been developed and is made available to users. The database can significantly facilitate and accelerate the construction of accurate biochemical models.</p

Path2Models: large-scale generation of computational models from biochemical pathway maps

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized

ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii

BACKGROUND: The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. RESULTS: In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. CONCLUSION: ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de

IPAVS: Integrated Pathway Resources, Analysis and Visualization System

Integrated Pathway Resources, Analysis and Visualization System (iPAVS) is an integrated biological pathway database designed to support pathway discovery in the fields of proteomics, transcriptomics, metabolomics and systems biology. The key goal of IPAVS is to provide biologists access to expert-curated pathways from experimental data belonging to specific biological contexts related to cell types, tissues, organs and diseases. IPAVS currently integrates over 500 human pathways (consisting of 24 574 interactions) that include metabolic-, signaling- and disease-related pathways, drug–action pathways and several large process maps collated from other pathway resources. IPAVS web interface allows biologists to browse and search pathway resources and provides tools for data import, management, visualization and analysis to support the interpretation of biological data in light of cellular processes. Systems Biology Graphical Notations (SBGN) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway notations are used for the visual display of pathway information. The integrated datasets in IPAVS are made available in several standard data formats that can be downloaded. IPAVS is available at: http://ipavs.cidms.org

COMAN: a web server for comprehensive metatranscriptomics analysis

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