Your Inner Engine: An Introductory Course on Human Metabolism

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Dietary iodine deficiency can result in the impaired ability to synthesize T 3 and T 4 , leading to a variety of severe disorders. As a result of this hyperstimulation, thyroglobulin accumulates in the thyroid gland follicles, increasing their deposits of colloid. The accumulation of colloid increases the overall size of the thyroid gland, a condition called a goiter Figure 3.

A goiter is only a visible indication of the deficiency. Other iodine deficiency disorders include impaired growth and development, decreased fertility, and prenatal and infant death. Moreover, iodine deficiency is the primary cause of preventable mental retardation worldwide. Neonatal hypothyroidism cretinism is characterized by cognitive deficits, short stature, and sometimes deafness and muteness in children and adults born to mothers who were iodine-deficient during pregnancy.

In areas of the world with access to iodized salt, dietary deficiency is rare. Instead, inflammation of the thyroid gland is the more common cause of low blood levels of thyroid hormones. Called hypothyroidism , the condition is characterized by a low metabolic rate, weight gain, cold extremities, constipation, reduced libido, menstrual irregularities, and reduced mental activity. In contrast, hyperthyroidism —an abnormally elevated blood level of thyroid hormones—is often caused by a pituitary or thyroid tumor. Hyperthyroidism can lead to an increased metabolic rate, excessive body heat and sweating, diarrhea, weight loss, tremors, and increased heart rate.

The person may also develop a goiter. The thyroid gland also secretes a hormone called calcitonin that is produced by the parafollicular cells also called C cells that stud the tissue between distinct follicles. Calcitonin is released in response to a rise in blood calcium levels. It appears to have a function in decreasing blood calcium concentrations by:. However, these functions are usually not significant in maintaining calcium homeostasis, so the importance of calcitonin is not entirely understood.

Pharmaceutical preparations of calcitonin are sometimes prescribed to reduce osteoclast activity in people with osteoporosis and to reduce the degradation of cartilage in people with osteoarthritis. The hormones secreted by thyroid are summarized in Table 4. Of course, calcium is critical for many other biological processes. It is a second messenger in many signaling pathways, and is essential for muscle contraction, nerve impulse transmission, and blood clotting.

Given these roles, it is not surprising that blood calcium levels are tightly regulated by the endocrine system. The organs involved in the regulation are the parathyroid glands. The thyroid gland is a butterfly-shaped organ located in the neck anterior to the trachea. Each of the models uses a set of physicochemical properties and structural features of a molecule for substrate specificity prediction.

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These fingerprints encode other pattern definitions for key functional groups and structural features relevant to CYPcatalyzed metabolism, which were obtained through data mining. Feature selection, and parameter optimization, cost-sensitive learning, and cross-validation based evaluation were performed to design highly accurate models for each CYP model. For a more detailed description about the list of fingerprint generation, training process, and resulting models, the user is referred to the CypReact paper [ 57 ].

These include inorganic compounds, and several classes of glycero- and glycerophospholipids, among others. Subsequently, it implements the constraints and biotransformation rules encoded within the reaction knowledgebase to predict the structures of the resulting metabolites. As for any other transformer module, the user can vary the parameters, including the number of transformation steps, and whether to use certain precedence rules.

Phase I reactions tend to render the lipophilic xenobiotics more reactive by adding or modifying functional groups, such as an amino-, hydroxyl-, or carboxyl group. Some examples of Phase I reactions include aliphatic hydroxylation, and epoxide hydrolysis. In Phase II, the more reactive metabolites are conjugated to cofactors, making them less toxic, more hydrophilic, and thus easier to eliminate. Some of the more common Phase II reactions include the conjugation of xenobiotics to glucuronic acid glucuronidation , sulphate sulfation , a methyl group methylation , an N-acetyl group N-acetylation , glutathione, taurine, and glycine.

While the presence of adequate attachment and functional groups is required for conjugation, the lipophilicity of a molecule is also significantly influenced by its shape, mass, and functional group composition, among other parameters. Therefore, a simple structure-based chemical classification would not be enough to predict whether a candidate molecule is suitable for Phase II.

The Phase II Filter was designed as a simple machine learning model that takes physicochemical properties as well as structural features of a molecule to predict whether it is ready for Phase II metabolism. In contrast to CypReact, which combines nine independent predictors one for each CYP isozyme , the P2F consists of a single machine learning model. Selected compounds included xenobiotics e. Standardization operations e. Certain classes of compounds, such as glycerolipids, are known not to undergo conjugation by any of the Phase II enzymes.

Since these compounds could be pre-filtered using a simple structure search, they were not included in the training set. Furthermore, compounds that do not contain adequate reaction sites i. This is because such compounds could be easily filtered by structural pattern matching. After the collection and standardization of our training set, a total of 32 molecular descriptors were calculated for each of the molecules.

These included nine constitutional descriptors and molecular properties e. The molecular descriptors were all computed with the CDK library. The structural features are represented as binary features in a custom chemical fingerprint to encode their absence 0 or presence [ 1 ] in the query molecule.

A list of structural features and physicochemical parameters is available in Additional file 3 : Table S1. Feature selection was performed to select a set consisting of the features that are most significant in explaining the training data. Overall, 25 physicochemical properties and structural features were selected to build and evaluate several models evaluated by fold cross validation using several different machine learning algorithms i.

Upon comparative evaluation of the F-1 measure and ROC area, a random forest model was selected as the best predictor. The model achieved a weighted average F1-measure of 0.

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Our training was limited to compounds possessing necessary structural motifs e. A number of chemical classes, including ether lipids, glycerolipids, and glycerophospholipids, sphingolipids, and acyl-CoA conjugates were excluded from the training set, as such compounds are known either not to be transformed by any of the seven Phase II enzyme classes, or to be conjugated following a very specific metabolic pathway. For these reasons, a simple rule-based filtering module was implemented to eliminate the most trivial non-candidates, before applying the trained model.

The rule-based module excludes compounds from the five aforementioned chemical classes. Metabolite identification is one of the main tasks of untargeted metabolomics. The aim of untargeted metabolomics is to analyze biofluids e. Mass spectrometry MS is one of several analytical approaches used to perform this task. When coupled with gas or liquid chromatography, a mass spectrometer produces a set of spectra that contain features e. While spectral searching is a method commonly used to identify metabolites, the lack of reference spectra for many metabolites is a bottleneck in rapid and accurate compound identification.

Therefore, the comparison of spectral features e. The BioTransformer metabolite identification tool BMIT is an additional module within BioTransformer that is designed to assist users in metabolite identification. It relies on the BMPT to find compounds of a specific mass within a user-specified threshold or chemical formula that are generated upon single- or multistep metabolism of a given parent molecule. BMIT takes the chemical structure of the starting molecule as input, as well as a list of neutral chemical masses or molecular formulas for the metabolites to be identified.

If applicable, the BMIT returns each matching metabolite, including its structure, its chemical formula, its molecular mass, and a pathway leading to it, starting from the query compound. The results are saved in a single SDF file in which each pathway is stored as an ordered list of chemical reactions with reaction name, and a list of catalyzing enzymes. BioTransformer was implemented in the Java programming language, and can be used as a command-line tool on Linux, Mac OSX, and Windows to perform metabolism prediction and metabolite identification of small molecules.

The CDK programming library is used for several operations, including the calculation of physicochemical properties, the execution of superstructure search operations, and the handling of chemical structures, among others. For metabolite identification tasks, the BioTransformer metabolite identification tool BMIT makes use of those predictions to suggest putative metabolites of a compound that have a given neutral mass or molecular formula.

Each prediction must be run in the single module mode, where the user selects one of the five transformer modules CYP, EC-based, phase II, gut microbial, or environmental microbial. The Biotransformer options used to specify the modules are cyp CYP metabolism module , ecbased EC-based metabolism module , phaseII Phase II metabolism module , hgut Human gut microbial degradation module , and envmicro Environment microbial degradation module.

This super transformer integrates the CYP, EC-based, phase II, gut microbial transformers and covers a number of different reaction types, including hydrolysis, oxidation and reduction, and conjugation. After the metabolite prediction step is completed, the structures and biotransformations are annotated Fig. Based on the information from the predicted biotransformation s , BioTransformer builds a metabolic tree by associating each metabolite with its parent s.

Moreover, each predicted metabolite is annotated with additional information that provides structural identification, reports its physicochemical properties, and an explanation of its origin or provenance. The results are returned in a SDF or CSV file that contains the structure and annotation of the predicted metabolites.

The returned information can be used separately to analyze metabolic pathways. It can also be used to compute neutral losses for MS-based analyses that can be used to experimentally detect each biotransformation. Given a starting molecule, a set of molecular masses and a mass tolerance threshold in Da or simply a set of molecular formulas, BMIT identifies potential metabolites for each valid mass or molecular formula, via single or multi-step metabolism, depending on the user input. For mass-based searches, the default number of steps, and mass tolerance are set to one, and 0.

A metabolic pathway linking the starting structure and each of the metabolites is returned, based on the metabolic tree obtained upon metabolism prediction. Metadata include the structures, identifiers, reaction types, and enzymes. The BioTransformer software package can be used as a command line tool or as a Java library. The BioTransformer web service is freely available at www.

The web service allows users to manually or programmatically submit queries, and retrieve the corresponding results using the workflow described in the previous section. Moreover, the web server provides information about each previously predicted single-step metabolic transformation of the compound, including the corresponding biosystem, reaction type, metabolizing enzymes, and transformation products. The web application offers several advantages compared to the command-line tool, namely: 1 it is easier to use than the stand-alone program; 2 users need not be programmers or need to install a local program to run the web service; 3 several queries can be processed simultaneously; 4 the computation is faster, as previous prediction results are saved in a database to facilitate more rapid retrieval; and 5 metabolite prediction and identification data can be accessed manually or programmatically and downloaded in several formats.

While the command-line executable does not benefit from the database of computed metabolites, it also does provide some advantages, namely: [ 1 ] it allows users to submit large sets of compounds; [ 2 ] it does not rely on an Internet connection, and; [ 3 ] queries are executed immediately and not put in a queue. In order to evaluate the performance of BioTransformer, we performed a comparative analysis with two popular in silico metabolism prediction tools, namely Meteor Nexus [ 26 ], and ADMET Predictor [ 29 ].

For each of the tests, BioTransformer was run on a 2. The procedures and results are presented in the Results section. The first test involved a comparative assessment of the performance of BioTransformer and Meteor Nexus v. In contrast to BioTransformer, Meteor Nexus clearly defines several levels of reasoning that express different levels of confidence. The assessment was performed by comparing the precision i. For details about the evaluation, see Additional files 2 and 4.

Examples of predicted metabolites: a hydroxyethinylestradiol, a reported metabolite of Ethinylestradiol was predicted by BioTransformer only. Examples of human non gut microbial metabolites predicted by BioTransformer. Comparative assessment of BioTransformer and ADMET predictor Simulations Plus in predicting single-step human CYP metabolism for 60 drugs, pesticides, phytochemicals, and other xenobiotics, as well as endobiotics e.

The respective structures were retrieved from ContaminantDB [ 41 ].

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This was designed to simulate a real case involving the MS-based experimental analysis of epicatechin metabolites produced by rats upon a five-day treatment with epicatechin, as done by two of the co-authors of this manuscript CM and JF. Epicatechin is an important compound from the chemical class of flavanols, and is known to exhibit cardiovascular health benefits [ 85 — 87 ].

It is a major component from cocoa extracts, and is also abundant in apples, grapes, berries, and tea. Identification of predicted metabolites of Epicatechin in humans which are assumed to be nearly identical for rats. The figure illustrates: a metabolites correctly identified by BMIT, and corresponding to masses in Da observed in our experimental study; b metabolites correctly identified by BMIT, and corresponding to masses observed exclusively in previous studies, and; c a previously reported metabolite of epicatechin not identified by BMIT.

We also tested whether BMIT could identify any of the remaining 38 known metabolites corresponding to 26 unique masses previously reported, but not observed in our study, or not selected by our data treatment parameters. The 26 unique masses were provided to BioTransformer as input, and the identification was performed using the same mass tolerance as before 0. BMIT was able to suggest 28 molecules for 19 unique masses. Among those, 21 compounds corresponding to 18 unique monoisotopic masses had previously been reported as epicatechin metabolites Additional file 7 : Table S2.

Overall, BMIT was able to suggest 39 epicatechin metabolites that were previously reported in the literature, 18 of which were observed in our study. Moreover, BMIT suggested 28 epicatechin metabolites that had not been reported in previous studies 17 corresponding to masses that do not match previously reported ones, and 11 extra structures matching previously known masses.

BioTransformer is a software tool that combines both a knowledge-based approach and a machine learning approach to predict the metabolism of small molecules, and to assist in metabolite identification. The knowledge-based system consists of a biotransformation database MetXBioDB , a knowledgebase the reaction knowledgebase , and a reasoning engine. MetXBioDB is a unique resource that is freely available, and covers a wide range of enzymatic reactions that take place in human tissues, the human gut and the environment soil and water microflora.

For each biotransformation, at least one scientific source or reference is provided. One potential application of MetXBioDB is in the design of biotransformation rules with narrow specificity, which can be used for in silico metabolism prediction. Although it covers a large number of enzymatic reactions, it is clear that more data is needed in order to cover an even larger set of reactions e.

Moreover, users could benefit from data about the different sites of metabolism for each specific biotransformation, as it would serve as a training set for the development of models for the prediction of sites of metabolism SoMs. For the current version of MetXBioDB, the intent was simply to provide an easily readable and comprehensible data set. However, providing MetXBioDB in a database format that can be parsed and queried in a more sophisticated way e. SQL would make the database much more useful to a broader number of users.

We welcome and encourage contributions in regard to the curation, improvement, and expansion of this resource. In our first test, BioTransformer was evaluated against Meteor Nexus v. Meteor Nexus is a commercially available software tool that is considered to be the gold standard for predicting biotransformations of xenobiotics. It is worth noting that BioTransformer heavily relies on the selective nature of the biotransformation rules and other structural constraints, in addition to its implementation of relative reasoning.

On the other hand, Meteor Nexus combines the continuous absolute scoring of biotransformations with relative reasoning, providing binned data for different levels of reasoning through a more dynamic scoring system. Overall, the performance of BioTransformer suggests that the freely accessible BioTransformer tool could be used to assist scientists in various drug discovery and environmental safety studies. The better performance, compared to the first test, can be partly explained by the fact that some endobiotics, such as sphingo- and glycerophospholipids, follow very classical and well-known metabolic pathways Additional file 2 : Fig.

S3 , which were encoded in the reaction knowledgebase.

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Therefore, these results still show that BioTransformer was also able to accurately predict the metabolism of compounds with a more complex metabolism Fig. In fact, BioTransformer was able to correctly predict the human and human gut metabolism of polyphenols e. Epicatechin , and pharmaceuticals e. This is very promising, as little is known about gut microbial metabolism of those classes of compounds. Even for the well-studied, and biologically relevant class of polyphenols, a lot of experimental work is needed to validate the metabolic pathways for hundreds of known compounds.

BioTransformer could be used to provide accurate suggestions about the identity of their metabolites and propose metabolic pathways, which could then in turn be validated experimentally. Overall, the first three tests demonstrate BioTransformer ability to accurately predict human and human gut microbial metabolism for a very diverse set of metabolites, covering endogenous metabolites, pharmaceuticals and personal care products, food compounds, as well as other exogenous compounds. In particular, BioTransformer is open access, and it covers a much wider range of chemical substrates and metabolic biotransformations.

These results suggest that BioTransformer could also be used to accurately predict environmental microbial metabolism. This task tacitly relies on the metabolism prediction task, and BioTransformer was able to suggest 37 metabolites matching 20 masses from a list of monoisotopic masses extracted from the MS analysis of urine samples collected after exposure to epicatechin Additional file 7 : Table S1. Of those, 18 metabolites were identified as previously known metabolites. Twenty-six monoisotopic masses matching to 36 reported epicatechin metabolites were not observed in our experimental study.

This variation in the observed metabolites may be caused by different experimental settings and analytical conditions e. For example, rats are expected to perform less sulfonation of epicatechin than humans [ 87 ]. In a second run, BMIT was used to search metabolites corresponding to monoisotopic masses that were observed in previous studies but not in our experimental dataset.

In this test it was able to correctly identify another 21 known epicatechin metabolites. Overall, BMIT was able to predict 39 out of 56 previously reported compounds. The discrepancy between the number of metabolites suggested by BMIT and the number of previously reported metabolites could be explained by several factors.

Furthermore, to have a functional hepatocyte model for a study of non-alcoholic fatty liver disease, an extensive description of lipid metabolism from HMR2. Several recent studies have shown that the metabolism of the gut microbiome has influence on overall human metabolism, and we therefore also started to collect GEMs for human gut microorganisms.

In this version of the HMA, there are GEMs for five key species that are representatives of the human gut microbiome These GEMs provide significant information for understanding the gut microbiome ecosystem as well as its effect on human metabolism. Hreed is a comprehensive information ecosystem that can support model reconstruction and data curation, and can also serve as a context-aware data query system.

Due to the limitation of data models in the previous database, a new database system, which is designed based on an object-oriented graph data model, was implemented to manage gene, transcript, protein, small molecule and reaction data and to provide high accuracy of human metabolism information to the research community. Information in Hreed can be accessed using the graphical data query interface or the database application programming interface. The graphical data query interface, as shown in Figure 1 , is suitable for users without programming knowledge.

The data objects can be queried using keywords from their properties such as names, id, cross-references or other object-specific properties including InChI and InChIKey. Query response is in the form of a web page in tablar format, with links to additional details to be pulled from the Hreed itself or from external databases.

The query system also provides options for downloading data as a text file in table, XML or JSON format, which is more convenient for further downstream computational analysis. Web-based data query system. Users provide keywords into the filter input box. For benchmarking Hreed, compound and reaction information from the global reconstruction human metabolic model, Recon version 2. Compound data were compared based on InChI, which are provided by both resources. To visualize and compare h-tGEMs among different cell types, a visualization system was implemented providing a better view of tissue-specific metabolic models.

A web-based visualization system, called Atlas, was developed with an interactive interface representing information from the latest release of INIT normal models on KEGG metabolic maps The Atlas is designed as a web application with an intuitive, clean graphical user interface. A new map can also be opened by clicking on the corresponding pathway name in a map. To overlay maps with selected tissue information, selecting or deselecting the checkboxes can change the tissue list filter. Only the selected tissue information will be overlaid on maps. Summary of Atlas map viewer functions. A The main view of Atlas with control panel comprises a pathway input box for selecting specific sub metabolic map to be opened.

The pathway input box provides an autocompleting search by pathway names. Atlas starts with a global metabolic pathway map by default. B Sub metabolic map with data overlaid and bar plot representing the number of genes that are present in this pathway map for each cell type can be opened by the control panel, clicking on pathway name in every map and clicking on pathway id in the information window.

C The information window represents information of reactions from the KEGG database and provides link to external databases for further information. Atlas user interface allows users to interactively explore the map, component details and overlaid data by using simple mouse controls. By mouse scrolling, the user can zoom in and out. Details of map components including id, name, link and graphs of overlaid data will be shown in a balloon popup by a left mouse clicking on the component.

By clicking on reactions, usually represented as boxes, a dialog box is displayed, with information of the reaction, related genes, proteins and pathways taken from KEGG and related tissue from the Hreed. Clicking on some text in the dialog box will link directly to major cross-referenced databases, Ensembl 23 , UniProt 24 , KEGG 17 and Hreed, for further information. Boxes and lines are shaded in blue. Darker blue means that reaction exists in more tissue-specific model, than ones in lighter blue. Yellow means that reaction does not exist in any tissue-specific model. No color means that reaction does not exist in humans, as per information in KEGG map.

Bluish green means that gene exists in corresponded tissue-specific model. The HMA website has been developed as a comprehensive web resource to i provide draft h-tGEMs generated by the automatic algorithm INIT, ii provide simulation ready functional h-tGEMs which can be used as predictive models and scaffolds for generating personalized GEMs, iii provide tools and an environment for further community driven expansion and iv provide visualization for comparative analysis of differences in metabolism of specific cell types on a metabolic map.

The direct use of GEMs as a flat file database as well as relational database to provide information of human metabolism is not efficient 2 , The Hreed and the Dactyls API library, developed using object-oriented graph data model, can be considered as an innovative tool set for organizing information about human metabolism and advanced application development. The database was specifically designed to support data exchange and enhancement, both by automatic and manual processes. With this structure, the database forms a scaffold for driving further annotation of HMRs, in particular in the area of lipid metabolism where many compounds are ill defined and could therefore not be included in Hreed.

Hreed was first propagated from HMR by an automatic script. To achieve the requirements of the database system, propagated information was standardized; especially, small molecules and gene annotation. Also, gene annotations were converted to Ensemble gene id. After a new cycle of model reconstruction, the curated information from GEMs can be integrated back to the database.

Due to the flexible data structure, the database can be expanded further for other data categories such as complex molecule, protein—protein interaction and genetic interaction without changing the physical data layer. Besides data structure expansion ability, the database APIs also allow developers to implement new functions, especially data integration and analysis, to the database system in the future.

The Atlas is currently based on KEGG metabolism map, which is easy to automatically render in interactive graphical format in a web browser. To extend the capability of the Atlas, other custom maps from other popular reaction databases: Reactome 34 , HumanCyc 9 , WikiPathway 35 and probably Panther 36 can be added and incorporated into Hreed in the future, to provide different types of model information.

Compared with the first release of the website containing only a GEM repository, the HMA has been updated with several improvements and new features, and we believe that the HMA web resource will be a valuable data exchange hub for the research community and facilitate new knowledge creation in the field of human metabolism.

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The authors thank Avlant Nilsson for web-based interface data query system development, Shaghayegh Hosseini for id conversion and Manesh B. Shah for proof reading of the article.

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Funding for open access charge: Chalmers Library. National Center for Biotechnology Information , U. Journal List Database Oxford v. Database Oxford. Published online Jul Author information Article notes Copyright and License information Disclaimer. Corresponding author. Citation details: Pornputtapong,N. Human metabolic atlas: an online resource for human metabolism. Database Vol. Published by Oxford University Press.


This article has been cited by other articles in PMC. Abstract Human tissue-specific genome-scale metabolic models GEMs provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. Introduction Metabolism comprises a large number of biochemical reactions, which are mostly catalyzed by enzymes.

Materials and methods Web site and repository development The HMA website was developed using the Ruby on Rails platform providing basic web application building blocks for the Repository, Hreed and Atlas development. Data standardization.