We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set of modern reactions catalyzed by these metaconsensus enzyme functions, we reconstruct a representative metabolic network that may reflect the core metabolism of early life forms. Our results show that ten enzyme functions, four hydrolases, three transferases, one oxidoreductase, one lyase, and one ligase, are determined by metaconsensus to be present at least as late as the last universal common ancestor. Subnetworks within central metabolic processes related to sugar and starch metabolism, amino acid biosynthesis, phospholipid metabolism, and CoA biosynthesis, have high frequencies of these enzyme functions. We demonstrate that a large metabolic network can be generated from this small number of enzyme functions. The expansion of genomic and biomolecular data has led to large repositories of genetic sequences et al.Harris et al., The work of the Caetano-Anollés group Another substantially different comparative bioinformatics analysis was performed by Srinivasan and Morowitz i.e. a consensus between these independent consensus methods). The results are used to determine a higher confidence minimal catalytic repertoire of LUCA and to extrapolate the metabolic properties produced by these catalytic functions.While the gene content of modern organisms provides strong evidence for a common ancestor et al.et al.In order to compare the three aforementioned datasets to one another, the contents of each were converted to 3-letter Enzyme Commission (EC) codes . We henceforth refer to the converted datasets as the universal sequence  is the most ancient fold associated with any metaconsensus enzyme function, but this fold catalyzes NTP hydrolysis or NDP phosphorylation that is coupled to the enzyme rather than the specific catalytic function of the enzyme. Other ancient folds associated with metaconsensus enzyme functions that do not confer specific catalysis include the DNA/RNA-binding 3-helical bundle , the NAD(P)-binding Rossmann fold , and the Oligonucleotide/oligosaccharide binding (OB) fold , to name a few. In combination with the ancestral catalytic folds, these folds probably allowed ancient enzymes to bind a range of substrates and cofactors and couple reactions to NTP hydrolysis. The combinatorial power of these ancient folds could conceivably produce a robust metabolism in LUCA. The prevalence of nucleotide and nucleic acid binding folds associated with metaconsensus enzymes suggests a strong connection to a preceding RNA world scenario.Having identified these metaconsensus EC groups, we were able to evaluate the antiquity of modern metabolic pathways based on the presence of these conserved enzyme functions. We performed a survey of enzymes across all metabolic pathways defined by KEGG The high frequency of conserved EC groups in sphingolipid metabolism is interesting because sphingolipids are found in eukaryotes and some bacteria, but are not taxonomically universal To extend our analysis beyond the constraints of modern pathways, we reconstructed a representative ancient metabolism using only the metaconsensus enzyme functions. These ten enzyme functions perform nearly three hundred reactions as defined in the KEGG reactions database This reconstructed ancient metabolism includes component subnetworks spanning the breadth of modern central metabolism, from monomer synthesis and interconversion, to the synthesis of RNA, proteins, starches, and phospholipids. Even though it is thought that LUCA had a DNA genome It is also curious that the reconstructed metabolism includes enzymatic peptide synthesis. We have previously shown that the translation system reached a modern level of sophistication during the RNA-protein stage of early evolution A substantial and general conclusion from this work is that a large metabolic network can be produced with only these ten metaconsensus enzyme functions. This observation demonstrates the strength of the patchwork model of primitive metabolism in which a small number of functionally ambiguous enzymes can generate the groundwork for a complex metabolism et al.http://www.manet.uiuc.edu/) http://www.ncbi.nlm.nih.gov/COG/) http://www.genome.jp/kegg/) The dataset of universal COGs was copied directly from In the resulting datasets, the final (fourth) term of each EC code was deleted in order to remove a layer of specificity from the conserved enzyme functions. This final term is defined by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB) as the “serial number of the enzyme in its sub-subclass“. For example the sub-subclass of EC 1.2.3.- is [Oxidoreductase. Acting on the aldehyde or oxo group of donors. With oxygen as acceptor]. The serial number 1.2.3.4 designates oxalate oxidase, while the serial number 1.2.3.5 designates glyoxylate oxidase, a very a similar reaction. Our generalization of enzyme functions is consistent with the patchwork hypothesis of early metabolic pathway evolution These sets of conserved enzyme functions were made nonredundant by removing all but one instance of repeated 3-term EC codes. The ancestry of protein fold architectures associated with these ten conserved enzymes were extracted from the MANET data file ftp://ftp.genome.jp/pub/kegg/). These pathway lists were generalized to 3-term EC codes and made nonredundant in the same manner described previously for metaconsensus enzyme datasets. Percentages of conserved enzymes were calculated with respect to the total number of conserved enzymes and the total number of enzyme groups within each pathway to produce 2O and CO2 were removed from the reactions as these would impose false connectivity between reactants and products. Similarly, the conversion of ATP to ADP was removed from reactions when its presence was only attributable to energetic coupling and did not otherwise contribute to the reaction product. Networks were generated with reactants and products as nodes and the enzyme functions that catalyze them as edges. The resulting networks were visualized using Osprey Lists of metabolic pathways and their associated enzymes were downloaded from the KEGG FTP site Click here for additional data file.Data S1A text file containing nonredundant lists of generalized enzyme functions (by Enzyme Commission code) that are predicted to be ancient by previous comparative bioinformatics studies.(TXT)Click here for additional data file.Data S2A text file containing nonredundant lists of generalized enzyme functions (by Enzyme Commission code) in metabolic pathways defined by the KEGG database.(TXT)Click here for additional data file.Data S3A text file listing percentage overlap between the metaconsensus enzyme set and metabolic pathways defined by the KEGG database.(TXT)Click here for additional data file.Data S4A text file listing all reactions stored in the KEGG database that are catalyzed by metaconsensus enzyme functions.(TXT)Click here for additional data file.


ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture. Databases that include explicit mappings between proteins and the small-molecules that interact with them as bioactivity modulators offer expanding opportunities in chemogenomics and pharmacological informatics. However, their proliferation also presents challenges. One of these is to discern incremental utility of individual resources and their combinations in various portals, for particular tasks. The interpretation of integrated results needs an understanding of each database from which they are extracted. This is The key to assessing utility is to compare databases in detail and thereby acquire an understanding of the different rules by which they have been populated. This work outlines ways of approaching this by using four well-established and high-value databases: ChEMBL, DrugBank– ChEMBL data is mainly curated from journals covering a significant fraction of global medicinal chemistry reports and structure-activity-relationship (SAR) results. Release 15 (January 2013) specifies on the website: 9570 targets, 1254575 distinct compounds, 10509572 activities and 48 735 publications .– DrugBank collates target and mechanism-of-action information. Version 3.0 (January 2011) contains 6715 drug entries including 1452 FDA-approved small molecules, 131 biologicals, 86 nutraceuticals and 5076 experimental compounds. These are mapped to 4233 protein IDs. Half the detailed information in the records is devoted to the drug, the other half to sequences, pharmacological properties, pharmacogenomic data, food-drug interactions, drug-drug interactions and experimental ADME data.– HMDB collates detailed chemical, clinical and biochemical data on human metabolites. These are linked to other databases including enzymes involved in the transformations. Version 3.0 (September 2012) contains 40437 chemical entries and 5650 protein sequence identifiers. Because they have both been developed at the same institution, linkages are provided between DrugBank and HMDB at the compound, protein and pathway levels. In May 2013, HMDB switched their version number to 3.5, but without major changes in the data.– TTD is conceptually similar to DrugBank but the compound-to-target mappings are focussed on primary targets. Another difference is the three-way split of targets and compounds into marketed, clinical trial and research phase. The latest version 4.3.02 (August 2011) includes 2025 targets, 17816 chemical structures, including 1540 approved drugs.Our analysis is divided between the two main themes of chemistry and proteins. The following section will outline the basic steps and how these were enhanced in 2013 but for a full oversight we recommend consulting our previous publication.For the extrinsic analysis of chemistry we included the sets available for download as SD file from each website in September 2010 with those available in January 2013. The questions we wanted to answer are how the set of chemical structures and the number of unique structures in each of the four databases has grown, how much the older version (2010) overlaps with the newer one (2013), and also how the structural overlap between the databases has changed. Table In comparison to 2010, ChEMBL has more than doubled in the latest release . The SID count for ChEMBL in PubChem is about 450 000 records smaller than the direct download from ChEMBL (see PubChem comparison section below). DrugBank has grown ∼30% between version 2.0 and 3.0 published in 2011. For HMDB, we used 2.5 in our 2010 study. The SD file from September 2010 contained 7888 records  but when 2.5 was re-downloaded in early 2013 we found 8553 structure records. The latest HMDB (3.0) has grown to ∼40000 records in the download file. TTD increased between 2010 and 2013 to ∼15000 structure records and has a similar count of Substances in PubChem (14771 SIDs).The content of all original database SD files were processed with the cheminformatics toolkit CACTVS. The struFor comparisons, the IUPAC International Chemical Identifier (InChI) Standard InChIKey version 1.04) were calculated for all original structure records. For this.04 were Unlike 2010, when TTD and HMDB were not yet present, all four databases are now specifically selectable as submission sources in PubChem. There are two caveats. Firstly, completion of the submission of all HMDB structures is still pending because of curation updates. The secoComparisons of protein content were performed using lists of UniProt protein identifiers derived from each source. For TTD these were parsed from a text dump of the records. For DrugTable Because of their different input normalisation stringencies, each of the identifiers indicates an expected reduction in the number of unique structures compared to the original counts, with bracketed numbers giving the percentage of this value. The upper part of Table The comparison of the unique structure counts obtained by Standard InChIKey vs. FICTS Table  shows siIf a stricter handling of tautomerism is performed via the FICuS identifier and the second set of calculated InChIKey (i.e. switching on the InChI tautomer flags “T13” and “KET”), the unique structure counts reduce further. However, the effect is small if compared to the numbers of FICTS and Standard InChIKey. Changes and improvements between 2010 and 2013 are very similar for all databases and their releases.The uuuuu identifier offers a diversity assessment of basic connectivity . It also estimates the number of unique chemical skeletons (including bond orders). Thus, for ChEMBL, DrugBank, and HMDB, the uuuuu counts of unique structures are ∼6 and ∼12% lower than the original structure count, with the exception of the 2010 version of TTD that dropped ∼30%.The impression given by Table Our discussion of content overlap will be restricted mainly to Standard InChIKey since this is the most established identifier. However, analysis by uuuuu also reveals interesting aspects. Figure The much smaller number of unique structures reported by the uuuuu identifier can be explained in most cases by the different chemical scopes of uuuuu vs. Standard InChIKey identifier. For instance, for the 2010 release of ChEMBL we record 2669 unique structures by uuuuu compared to 25588 uniques by Standard InChIKey . For 2013 ChEMBL covers now substantially larger parts of DrugBank (up from 37% to 55%), and TTD (up from 57% to 91%) although TTD has grown itself in absolute numbers from 2834 to 14111 structure records (unique by Standard InChIKey).Figure The set of structures in common  would be of interest we had to restrict ourselves to MW as the most comparatively informative. This highlighted distinct differences .While a series of chemical property profiling such as Logs Figure , 5.MW compared to ChEMBL. The same effect would be predicted for TTD and this is observed. However, the median is slightly higher from a larger proportion of high-MW entries compared to DrugBank.Starting with ChEMBL we can see a continuous distribution with the median ∼400. The implication is of a more “lead-like” than “drug-like” content. This wouMW 1526 (HMDB57781). As a corroborative cross-check, the search terms “triglyceride” and “cardiolipin” retrieve 13923 and 3277 results, respectively, which approximately fits the peak size ratio and supports a focus on large lipid capture for the latest update.The cumulative statistics of HMDB are confounded because of the pronounced tri-modal 2013 distribution. This is not only a major change but also indicates two large new clusters around ∼1000 and ∼1500. These are outside the envelope we might have expected for small-molecule metabolites as seen in the 2010 pattern. Inspecting selected entries under these peaks provided an explanation. For example, the first peak included a long-chain monolignoceric acid triglyceride with a MW of 1027 (HMDB47321). Under the second peak we find a cardiolipin of This chemistry comparison section concludes with the results from comparing the databases inside PubChem where they are now instantiated as separate sources. We chose 11 categories of content to display in Figure 1. Literature-linked (962666). Because this is largely ChEMBL-plus-PubMed the former is obviously recorded as 100%. The 60% coverage in DrugBank indicates lower primary literature capture but note these could be secondary sources such as review articles. It does raise the question as to which structures encompassed in the DrugBank 40% without primary literature but these may be PDB entries for which the individual reports are not cited. At 30% HMDB has the lowest literature coverage. An explanation is that the large number of lipid records can neither be cross-referenced to ChEMBL journal articles nor alternative PubMed IDs linked to a CID for the structure. In contrast to DrugBank, the proportion in TTD is up to 90%. This is corroborated by a 90% overlap at the structure level . This is a simple lead-like filter, encompassing drug-likeness at the lower MW end. While not predictive per se, this filter enriches for bioactive compounds . Note that Figure +250–800  1812 0513. Active in PubChem Bioassay (883627). For ChEMBL the figure of 55% seems unexpectedly low since this source is the major contributor to BioAssay, as mostly positive activity results of one sort or other extracted from the literature. However, ChEMBL data in PubChem does not get an active/inactive tag generated via thresholds in the same way as is done for the Molecular Libraries Screening Centre Network (MLSCN) result sets. The fact that this drops to 48% for DrugBank where one might expect positive linked assay results for all drug candidates may be related to the same active flagging issue. The 9% level of actives in HMDB is actually higher than anticipated since metabolites are not expected to be inhibitors at concentrations typically tested in assays but this is partially explained by drug content. The fact that TTD (87%) ranks significantly above ChEMBL in this filter also supports the idea that curation was specifically picking up compounds with potency data from ChEMBL.4. Patent Sources (15 039 047). The observation that ∼50% of ChEMBL structures are in patent sources, compared to 13% overall in PubChem, can be explained by structures from the medicinal chemistry literature being first exemplified in patents. This rises to 67% and 71% for DrugBank and TTD respectively reflecting higher  drug and clinical candidate content. While TTD is low (26%) we would not expect metabolites to be claimed as structures. What may contribute to this are not only the drugs but also biochemical names in the dictionaries used for manual or automated patent extraction.5. Source-Unique (25806124). This means the CID is specifically only from one submitter. At ∼20% the unique content of ChEMBL is the highest in the set, however, it would be even ∼100 K CIDs higher without the circularity arising from common chemical content between BindingDB and ChEMBL (i.e. the intersect between them represents 94% of the former). The interpretation here is that many of the structures extracted from papers by ChEMBL are (by CID rules) not hitherto represented in other PubChem sources. The caveats are not only that alternative stereo and/or other tautomeric representations may be present  but that any may be “correct” in reflecting representations derived from different extracted papers. As an example, the CID for CHEMBL1797692  has fully specified stereo but the InChIKey skeleton matches a “flat” supplier compound . The unique content of DrugBank and TTD is very low. The implication is that their structures are independently corroborated by other submissions merged in the CID records (e.g. ChEMBL as indicated above). However, this would be confounded if a proportion of curation was circular (i.e. did not involve de novo SID generation). At 38% the unique content of HMDB is high but 90% of this has a MW of above 800. Ranking these by MW and visual inspection indicates the unique content is substantially derived from complex lipids. This corroborates the MW results but note that ∼75% of the HMDB structures are not yet captured in this PubChem analysis. Corroborative information was supplied by an intersect with LipidMaps at 3576 (i.e. 35% inside PubChem).6. Disconnected structures of 2 or more components (1571436). This is a useful measurement for salts and mixtures . Only ChEMBL has a significant count (6.5%) which includes salt forms of drugs specified in the literature and USAN approvals.7. NIH Molecular Libraries (397823). This is the physical collection shared between US screening centres. These structures can accumulate extensive cross-reactivity data depending on how long they have been in the collection. On a proportional basis the sources here do not have large intersects but ChEMBL is highest on an absolute basis. DrugBank is up to 25% which may be related to the high coverage of drugs and candidates.8. INN/USAN (10388). Using this as an “and/or” query with a restriction to the PubChem Compound synonym field returns both currently approved drugs and historical advanced-stage candidates. It thus constitutes a comprehensive drug collection that is nominally independent . With a 72% intersect  ChEMBL has the largest coverage while both DrugBank and TTD are surprisingly low at 14% and 19% respectively. Possible explanations include differences in stereo and or salt forms. The databases may also have a lag time for newly approved drugs but there is no PubChem source from which these can be cleanly selected for comparison. The fact that HMDB includes a selection of drugs (229) is mentioned in their 2013 paper but note that some metabolites, vitamins and hormones also have INNs or USANs for pharmaceutical formulations.9. Pharmacological Actions (11 912.) This important subset flags up where in vivo activity is assigned to a structure via MeSH curation of one or more PubMed IDs. It therefore indicates therapeutic testing of drug action in animal models and clinical trials with useful specificity. Here again ChEMBL scores high compared to DrugBank and TTD . Contributing factors here are the much larger scale of ChEMBL and the fact that many of the research compounds captured in DrugBank and TTD do not have in vivo characterisation data. The low figure in HMDB would include the drug and hormone content.10. Protein 3D Structures (23 562). This category indicates CIDs identified within a protein structure. The inclusion of all hetero-atoms exceeds specifically pocket-bound small-molecule ligands but intersecting these with the filter above (ROF+250–800) indicates ∼7900 could be in this category. While ChEMBL ranks top in absolute numbers (5690), DrugBank is proportionally highest (51%), significantly exceeding TTD (7%). This is in accord with the historical focus on ligands by DrugBank but this includes a small proportion of false-positives. An example is Alpha-D-Mannose (DB02944) classified as an experimental drug and mapped to 76 targets. The mapping is via hetero-atom entries rather than authentic ligands, although these proteins are not flagged with pharmacological action.11. Biosystems (9757). This NCBI resource maps compounds into pathways via protein target links in BioAssay records and pathway databases.[atabases. Because We recoded protein content changes in all four databases. We improved the resolution of identifiers in 2013 by accessing UniProt IDs directly, rather than use the Protein Identifier Cross-Reference Service (PICR) to BLAST-map downloaded FASTA sequences as we had utilised in 2010. In additThe results fall into two groups that we can term replacement and expansion. Thus, ChEMBL and HMDB have predominantly expanded whereas the other two have undergone removals as well as additions. The major change for DrugBank is due to the recent option of being able to download just those proteins that are flagged “Pharmacological action: yes” (highlighted in green in the Target records). This approximates to what could be termed a primary target mapping for the 716 proteins. While we could have chosen to compare different download sets from the same year, this would have made the analysis overly complex. Where a new choice is available, our default is to take the set with highest mapping specificity, even if this confounds retrospective comparisons. In Figure Before moving on to the current proteins we performed a three-way comparison between just the three drug discovery databases between 2010 and 2013 because HMDB is “odd-man-out” in not sharing a similar target mapping concept Figure . The comWe initially addressed some basic questions. The first was to examine correlations between the statistics reported by the databases and our ID downloads. For ChEMBL the targets to UniProt ID ratio was 9570:5797 because not all targets can be protein-mapped). For instance, whole-cell screening is widely used to measure the growth inhibition effects for anti-infectives of all types in the primary literature. Consequently, searching ChEMBL with “plasmodium” gives 14 results and “mycobacterium” gives 38 results. Over 20000 compounds are thus mapped to these organisms as species “targets”, a relationship type not typically captured by the two drug databases. The latest DrugBank reported statistics are 4167 targets, 221 enzymes, 11 carriers  and 120 transporters. While we recorded 4023 UniProt IDs from the data extractor download we selected the new primary target subset of 715. Currently, there are no reported protein content statistics for HMDB that we can compare with our download results but the mappings are under revision. The subsets from TTD specify 2025 “targets”, including 364 successful, 286 clinical trials, 44 discontinued and 1331 research. Our analysis found 1768 de-duplicated UniProt IDs Thus, for TTD “target” does also not always equate to a UniProt ID. For example, TDC00233 specifies “Gamma secretase” as a clinical trial target for two compounds. However, there is evidence of mixed curatorial rules because while this target entry was mapped to a UniProt ID, in TTDR00444 and TTDR00445 the (same) 11 compounds are mapped to the gamma secretase presenilin 1 and 2 subunits, respectively .Two other important questions are the species split between human vs. non-human and the Swiss-Prot-to-TrEMBL ratio (SP:TR). Results from the UniProt query interface are showCoffea Arabica (CHEMBL5217) with 94 compounds aligned against it. It turns out this is a mechanistic exemplar enzyme for cross-screening human alpha-mannosidase inhibitors as potential antitumor agents. DrugBank has a distinct human vs. anti-infectives split but is rodent-free because orthologous substitution has been their chosen curatorial practice. This means human protein IDs replace rodent or other mammalian proteins specified in the references for the drug entries.[E.Coli orthologue has been added or substituted for the Staph. or Strep. target. Many species are also included in TTD but TTDR00218 (O23733), a cysteine synthase from Brassica juncea is an error. The viral strain polymerases in HMDB will be removed during a current revision of protein mappings.[The details cannot be expanded here but some features can be interpreted. In the case of ChEMBL, as might be expected from the wide range of primary medicinal chemistry literature extracted, the zoo of targets includes 45% human and 15% rodent (698 mice plus 199 rat). Some of the other 400 species seem counter-intuitive as drug targets, such as Q42656 from  entries. Analogoumappings.The topic of TrEMBL entries generated by automated annotation is also too detailed to go into here. However, while the number of human TrEMBL entries is low Table  their prThe data underline the marked differences in species capture between the four databases. We chose human-only comparisons since they have a number of advantages compared to using total UniProt IDs. Firstly, this normalises the comparison (i.e. apples vs. apples). Secondly, we can compare the three drug-centric databases as a separate set and, thirdly, comparisons of protein function distribution are more valid for single-species. Only salient features can be highlighted, but note that Venny can be used to reproduce any of these via the supplementary data protein lists. Given the caveats mentioned for 2010 vs. 2013 three points are clear from Figure Having normalised the databases to human protein IDs there are many options for property and annotation comparison that could provide insights into coverage selectivity. We have chosen the Genome Ontology (GO) molecular function because this is conveniently generated via the Panther web site. This useFrom left to right, both ChEMBL and HMDB have the highest proportions of enzymes. It is also clear that HMBD has a capture scope that extends beyond metabolic enzymes. DrugBank is similar to TTD but the latter has proportionally less ion channels and receptors. DrugBank looks most similar to the consensus proteins in the high proportion of receptors, enzymes and ion channels. This is unsurprising, since Figure The target proteins of approved small-molecule drugs are of intense interest but listings that have appeared in the literature have not typically been compared. We have used this work as an opportunity to make such comparisons by using three human-only sets. The first of these is the 3-way consensus between the 2013 versions of ChEMBL, DrugBank and TTD . Analogously, the overlap sets of size 53, 58 and 76 represent a two-way consensus. The set with at least two intersects (407) would thus be a good approximation to a human primary target set (up to 2011). The extensive unique content from the SOU set is expected because this includes a “long tail” of research targets, 473 of which had a chemistry-to-protein relationship curated from only one document. We can utilise GO classification charts Figure  to compaTaking the 2-or-3-way set first, not unexpectedly, because it contains only 56 more proteins, this looks similar to the 351 protein set from the 3-way drug database consensus and DrugBank primary targets. Also not unexpected is that the large number of proteins in the SOU-only set show the highest proportion of enzymes because these constitute a substantial part of the long tail of research targets. Given that the other two unique sets are small, the distributions need to be interpreted with caution, but they do indicate differences. While the selectivity that might have caused this would need a detailed analysis, we can take one example. The dark purple sector in Figure Our final protein content comparison looked at mappings associated with just one drug, atorvastatin (Lipitor), an inhibitor of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, HMGCR (P04035). The Venn diagram Figure  shows priK of 58 µM) is not captured in the record. DrugBank cites the same reference but orthologously substitutes pig for human and tags the relationship as having no pharmacology. Given the high iK this seems an appropriate curatorial judgment (this transitive assignment has been incorporated into the PubChem record for atorvastatin). However, further mapping complexity is introduced via CHEMBL393220. This maps the atorvastatin calcium salt as a separate entity to a series of antimalarial whole-parasite screens plus rat HMGCR (P51639). For HMDB three of the 17 proteins have reaction schema related to HmgCoA metabolism. However, the reasons for mapping the other 14 proteins into this entry are unclear.Beyond one other protein in common  the remaining unique assignments indicate marked differences in relationship capture between the databases. As can be seen in the Venn diagram, TTD has P04035 mapped to atorvastatin as single primary target. On the other hand, the DrugBank record, (DB01076) is mapped (in 2010 and 2013) to two additional targets, Dipeptidyl peptidase 4  and the Aryl hydrocarbon receptor (P35869). Only P04035 is captured in the 2013 download because this is tagged as known pharmacology. It turns out that DPPIV is also linked to atorvastatin in ChEMBL but to pig not human (P22411). Inspection of the record shows this to be the correct link via PMID 18068977 but the data quoted in the abstract . Despite the impact of these on exploitation we have had to limit ourselves here to what we consider the two most important features of chemistry and protein content. The approaches outlined are generic and we thus encourage others to perform such studies, especially where broader adoption of InChIKey and UniProtIDs make comparisons more straightforward than hitherto. The databases in this study provided useful download options but differ in the extent of post-download processing necessary for standardised comparisons. Thus, broader intra-database harmonisation would be welcome, for example, if each of the chemical and target sets could be downloaded as SD files, protein ID lists in Excel, UniProt cross-references, have an API  as well as complete and selectable PubChem sets.Our chemistry results show that all the sources have improved. This is likely to be a combination of enhanced structure handling rules and manual curation. While this has increased overlap there is also divergence and expansion. This enhances their complementarity and aggregate coverage. Nevertheless, questions as to “how they became the way they are” are important for exploitation. In this respect the ChEMBL publications, regular release notes, and explanations of their medicinal chemistry journal triage, go a long way to answering these questions. While the other databases are also well described in their publications, analysis is necessary to discern selectivity that is not made explicit. Examples include the fact that DrugBank is still over 50% PDB derived, TTD has used ChEMBL for their expansion, HMDB contains many drugs and that lipids are now a major proportion of HMBD content.A limitation of the current study is the restriction to exact matches between the chemistry collections. Apparent increases in divergence by this parameter may not necessarily translate into wider chemical coverage. In this respect, the impact of these databases is not only on accessing data, but using it to develop models. Thus, updates will have relatively little impact on QSAR and similarity models if similarity remains high. Further work would be needed to see if distribution of pairwise similarities (2D and/or 3D) had shifted significantly between 2010 and 2013.The aggregate protein content for the four databases in 2013 shows a major expansion encompassing not only binding events in the thermodynamic sense but also biochemical and pharmacological activity. However, the high numbers we have recorded, along with some of the individual examples, indicate divergent curatorial rules and stringencies for each source. For example, the three drug-centric databases Figure  cover 30


Streptomyces coelicolor A3 (2) strains: the wild type M145 and its derivative mutant M1146, in which gene clusters encoding the four main antibiotic biosynthetic pathways were deleted. Metabolic Flux Analysis and 13C-labeling allowed us to reconstruct a flux map under steady-state conditions for both strains. The mutant strain M1146 showed a higher growth rate, a higher flux through the pentose phosphate pathway and a higher flux through the anaplerotic phosphoenolpyruvate carboxylase. In that strain, glucose uptake and the flux through the Krebs cycle were lower than in M145. The enhanced flux through the pentose phosphate pathway in M1146 is thought to generate NADPH enough to face higher needs for biomass biosynthesis and other processes. In both strains, the production of NADPH was higher than NADPH needs, suggesting a key role for nicotinamide nucleotide transhydrogenase for redox homeostasis. ATP production is also likely to exceed metabolic ATP needs, indicating that ATP consumption for maintenance is substantial.Our results further suggest a possible competition between actinorhodin and triacylglycerol biosynthetic pathways for their common precursor, acetyl-CoA. These findings may be instrumental in developing new strategies exploiting S. coelicolor as a platform for the production of bio-based products of industrial interest.Metabolic Flux Analysis is now viewed as essential to elucidate the metabolic pattern of cells and to design appropriate genetic engineering strategies to improve strain performance and production processes. Here, we investigated carbon flux distribution in two  Since the late 1960s, considerable effort has been devoted to discover new antibiotics. However, the discovery of biologically active molecules has proved progressively more difficult due to biochemically and technically long and costly processes. Furthermore, the prevalence of multi-resistant bacteria has now increased quite substantially Streptomyces, are significant sources of new bio-active molecules Streptomyces species Streptomyces species, the genome comprises 20 to 40 gene clusters presumably involved in the production of many secondary metabolites, while three to five only are currently produced under ordinary laboratory culture conditions.In this regard, the soil-inhabiting, Gram-positive bacteria, belonging to the genus The genetic organization of genes encoding proteins involved in secondary metabolite production has been studied extensively. Several gene clusters encoding antibiotic biosynthetic pathways, as well as specific or pleiotropic regulators, have now been described Streptomyces strains in order to define conditions under which the accumulation of precursors, preferential expression of the above-mentioned gene clusters, or antibiotic synthesis are optimal.However, although primary metabolism provides precursors, reductive power and energy used for biomass and secondary metabolites biosyntheses, an integrated picture of relationships between primary and secondary metabolism is still missing. A metabolic flux analysis describing carbon flux distribution within the metabolic network, that would complete previous transcriptomic Streptomyces coelicolor A3 (2) M145 is a model strain often used to investigate the regulation of antibiotic production and its genome was the first one to be sequenced and annotated amongst Streptomyces species S. coelicolor M145, which presumably indicates an efficient synthesis of the polyketide precursor acetyl-CoA, S. coelicolor M145 has been chosen as a ‘genetic platform’ to create a super-host for the heterologous expression of gene clusters associated with secondary metabolism from other Streptomyces species as well as organisms from other genera S. coelicolor M145 were deleted, yielding strain M1146 Considering the production yield of S. coelicolor M145, production of the Calcium-Dependent Antibiotic (CDA) coincides with growth In S. coelicolor metabolic fluxes under steady-state conditions. N-limitation was chosen as a typical condition promoting actinorhodin production by S. coelicolor M145, as previously described μ∼0.04 h−1), M145 producing only actinorhodin during its growth phase.In the present paper, we developed a N-limited minimal medium to study S. coelicolor using a combination of stoichiometric inventory described by Holms 13C isotopic methods . Data from genome We took advantage of metabolic steady conditions to compute a carbon flux distribution in S. coelicolor M145 and M1146 strains and compare them so as to gain insights on effects of the impairment of secondary metabolism on metabolic homeostasis.We elucidate the flux distribution in primary carbon metabolism in both S. coelicolor A3 (2) strain M145 and S. coelicolor A3 (2) strain M1146 . S. coelicolor strain A3 (2) M1146 cannot produce actinorhodin (ACT), undecylprodigiosin (RED) and calcium dependent antibiotics (CDA) and no cryptic type I polyketide synthase (Cpk) was expressed. Both strains were a generous gift from M.J. Bibb and J.P. Gomez-Escribano from the John Innes Centre .The present study was carried out with −1) or HCl (0.5 mol L−1). CO2 and O2 concentrations were monitored with a Bluesens BCpreFerm gas analyzer. Aeration was adjusted to 0.4 volume air (volume culture)−1 min−1 (vvm) and agitation was 800 rpm so as to maintain dissolved oxygen near 70% saturation (aerobic conditions).Batch cultures were grown in a bioreactor (Infors Labfors 3) with a 2 litre working volume, 2 Rushton-type impellers and 3 baffles. The bioreactor was equipped with a cooled condenser to avoid medium evaporation. Temperature and pH were controlled at 28°C and 7.0, respectively, with automatic addition of NaOH  L−1 and to grow for more than 5 generations in the exponential growth phase. Note that exponential growth during at least 5 generations was a prerequisite for steady-state isotope labeling in metabolites 13C] glucose (CortecNet). For fermentation experiments, 100 mL of medium no. 1 , 3.0 g L−1 NaCl, 1.0 g L−1 CaCO3, pH 7.2) in a baffled polycarbonate 500-mL Erlenmeyer flask were inoculated with 109 spores from a 30% glycerol stock. The culture was incubated at 28°C and agitated with a magnet  for 48 h. Eight mL were then used to inoculate 100 mL of medium no. 2 , pH 7.2). The second culture was incubated for 24 h. Cells from 40 mL were collected by centrifugation , suspended in 20 mL of TES solution (270.5 mmol L−1), and immediately used to inoculate the bioreactor. An additional sample of 10 mL was used for cell dry weight quantification, after filtration through a cellulose acetate filter (0.22 µm) and distilled water washing before drying at 95°C for 24 h.A defined minimal medium  was deve2 Exhaust Rate (CER) and the O2 Uptake Rate (OUR) could be calculated at each time with online measurements: In bioreactors, liquid and gas phases were well mixed and gas diffusion was rapid such that both the CO2 and N2 in the liquid phase was negligible. Flows (ginf) as well as mole fractions  were measured with dehydrated air to avoid any mole fraction imbalance due to water vapour. CER and OUR were plotted versus time and summed in order to quantify total CO2 evolution and O2 consumption. The latter values are used in section Materials and methods 12.1. to determine CO2/SY and O2/SY respectively. All the abbreviations and symbols used in this study are listed in In these equations, it is assumed that the build-up of dissolved COet al. et al.et al.et al. S. coelicolor was determined with an elemental analyser  on cells harvested during the exponential phase of growth. Elemental analyses were carried out at the Institut de Chimie des Substances Naturelles, UPR 2301, CNRS, Gif sur Yvette, France.Nucleic acids were quantified after Norris and Ribbons 4 35% (0.1 mL (mL supernatant) −1) and then centrifuged 2 min at 6,000 g at room temperature. Supernatants were neutralized with cold KOH (7 mol L−1), centrifuged 2 min at 6,000 g at room temperature and filtered through a 0.22 µm polyether sulfone membrane.Supernatants were de-proteinated by acid precipitation with HClO2SO4 (5 mmol L−1) as the eluent (0.5 mL min−1). Metabolites were detected either with an Agilent SPD UV detector (210 nm) or an Agilent RID detector (40°C).Glucose, α-ketoglutarate (2-oxoglutarate), pyruvate, L-lactate, acetate and ethanol were analysed from de-proteinated supernatants on an Agilent 1200 HPLC instrument with an Aminex HPX-87H column at 35°C using H−1) pH 8.0, NADH (0.2 mmol L−1), ADP (0.6 mmol L−1), α-ketoglutarate (10 mmol L−1), and 5 units of glutamate dehydrogenase from bovine liver (Sigma #G2626). The A340 was monitored until the end of the reaction (∼8 min).The ammonium content was measured enzymatically in 20 µL of de-proteinated samples with glutamate dehydrogenase in 1 mL of an assay mixture made of Tris/HCl . The −1) buffer pH 7.8, MgCl2 (10 mmol L−1), 10% glycerol. RNase A (1 mg mL−1) and DNase I (1 mg mL−1) were added when needed. The sample was sonicated 8 times with a Branson Sonifier 250 sonicator set at a power level  =  2, during 20 seconds at 30 seconds intervals. Cell debris was removed by centrifuging at 10,000 g for 15 min at 4°C. The supernatant was used as the crude extract. The protein concentrations were measured by the Bradford method using BSA as a standard et al. et al. 40 mL of culture were harvested and bacterial cells were suspended in 10 mL Tris-Tricarballylic acid  trifluoroacetamide and injected on a GC-MS . Peak integration was performed using LECO Pegasus software. Because automated peak integrations have been occasionally unreliable, manual controls and/or corrections were systematically performed for each metabolite.GC-MS analyses of amino acids to determine the μ, Sq, O2q, CO2γ, X/SY, ACT/SY, CO2/SY and O2/SY values were constant and at their maximal values. MID values for Glu, Thr, Asp, Ala and Val were identical, with less than 1% RDS. We therefore conclude that both metabolic and the isotopic steady-states were ensured in our experiment.Five independent samples were collected along a 1.5-generation period and analysed in triplicate. During the sampling period, S. coelicolor was deduced primarily from genome annotation The central metabolic network of S. coelicolor were well described Glucose entry and phosphorylation through the Glk-GlcP permease complex in S. coelicolorAll genes and enzymes involved in the Embden-Meyerhof-Parnas pathway (EMP), the tricarboxylic acid cycle (TCA), and the pentose phosphate pathway (PPP) were detected in Streptomyces species (i) there was little growth with two carbon substrates and (ii) both isocitrate lyase and malate synthase were not detected in the S. coelicolor proteome The Entner-Doudoroff pathway (ED) has been shown to be non-functional in several CO2γ values, and analysis of 13C-labeling in valine, alanine, glutamate, glutamine and proline provided further evidence of these assumptions for S. coelicolor under our experimental conditions (see 3.4.).In the present paper, comparison of the experimental and calculated Streptomyces species PEP carboxylase was shown to be the sole anaplerotic enzyme in Streptomyces species are available S. coelicolor could be branched, with a type I NADH dehydrogenase excreting 4 protons per NADH, an uncoupled type II NADH dehydrogenase and two terminal oxidases, the bd complex that excretes 2 protons per NADH and the 1/aa3bc supercomplex that excretes 6 protons per NADH Escherichia coli1/aa3bc was shown to have a lower affinity for oxygen and to be the predominant complex in aerobic conditions 1/aa3bc supercomplex were identified in S. coelicolor proteome 1F0-ATP synthase. Under the assumption that the synthesis of one ATP by F1F0-ATP synthase requires three to four protons, NADH re-oxidation via the bc1/aa3 branch yields 1.50–3.33 ATP. FADH re-oxidation was assumed to be due to succinate dehydrogenase Few experimental data on the respiratory chain efficiency in 2 consumed) of 2.0 could be reasonably considered for S. coelicolor. Nevertheless, our results on carbon flux distribution are not sensitive to the P/O value used.Considering all these data, a P/O value (moles of ATP produced per 0.5 mole of OS. coelicolor (SCO7622 and SCO7623) putatively encode a PntAB-type nicotinamide nucleotide transhydrogenase that interconverts NADP + NADH to NADPH + NAD in E. coliTwo genes of S. coelicolor appeared to be identical to those present in other bacteria Amino acids, nucleic acids, small molecules, peptidoglycan, triacylglycerol and lipids biosynthetic pathways in S. coelicolor were reconstructed using S. coelicolor genome and published data The biosynthetic pathways for the 3 known antibiotics produced by S. coelicolor cells had to be determined. The respective amounts of the main polymers involved in the S. coelicolor biomass composition, namely proteins, DNA, RNAs, triacylglycerol, lipids and peptidoglycan were taken from Borodina et al. To calculate carbon fluxes according to the stoichiometric approach of Holms E. coliS. coelicolor was calculated. We also calculated specific precursor, energy and reducing power requirements to synthesize 1 mole of antibiotics.Using the biomass composition, reactions associated with building-block biosynthesis (primary metabolism) and reactions for secondary metabolism, we calculated precursor, cofactor and energy requirements to synthesise 1 g of dry cells. On the basis of Ingraham's calculations for ln(X(t)) versus t, X(t) versus S(t), ACT(t) versus S(t), RED(t) versus S(t), KET(t) versus S(t), PYR(t) versus S(t), 2(t)CO versus S(t), 2(t)O versus S(t), where X denotes biomass and S denotes source glucose. During exponential growth, each curve was linear, and the slope allowed us to determine growth rate constant μ (h−1) and the following yields: X/SY, ACT/SY, RED/SY , CDA/SY, KET/SY, PYR/SY, CO2/SY, O2/SY. For example, X/SY is the yield of biomass production with respect to glucose, etc. Note that yields are expressed in carbon equivalents, X/Se.g. Y is in moles of biomass carbon per mole of glucose carbon. Using these values, the glucose consumption and productivity of metabolites (μmol (g cell dry weight)−1 h−1) were computed as :For each experiment, we calculated the following graphs: Carbon balance (in %) was calculated as follows:enolpyruvate, pyruvate, acetyl-CoA, α-ketoglutarate and oxaloacetate are the 11 precursors from central metabolism. These precursors are sufficient to synthesize any macromolecule in the bacteria. That is, carbon flux to anabolic reactions can be represented by 11 individual fluxes from precursors to biomass and/or secreted metabolites . Known molar requirements of biomass or antibiotic for precursors −1 and μmol (μmol antibiotic)−1, respectively) and productivity  allow one to calculate anabolic requirement fluxes iΦ and the sum iv for any precursor i, as follows: Glucose-6-phosphate, fructose-6-phosphate, ribose-5-phosphate, erythrose-4-phosphate, glyceraldehyde-3-phosphate, 3-phosphoglycerate, phosphoiΦ were calculated as described in Materials and Methods 12.2, catabolic fluxes in central metabolism calculations remained to be determined to obtain a full carbon-flux distribution. For this purpose, metabolic reactions were represented as a stoichiometric matrix (S) of size {m, n}. Each row of this matrix represented one unique compound amongst the m = 48 compounds and each column represented one reaction amongst the n = 46 lumped metabolic reactions. Note that the 46 lumped reactions correspond to 265 enzymatic reactions. S was a sparse matrix, with 0 as the most represented value. The flux through all reactions in the metabolic network was represented by the vector v (dimension n). The set of concentrations in metabolites was represented by the vector x (length m). The system dynamics is described by dx/dt = Sv. In the steady state, dx/dt = 0, that is, Sv = 0. In the present study, this equation is true as metabolism was studied at the steady state, that is, in the exponential growth phase in which turn-over fluxes are constant and therefore there is no build-up in intracellular metabolite concentration. Glucose specific consumption (Sq) and the 11 anabolic fluxes (iΦ) are used as external constraints. However, the system still has one degree of freedom. Data from labeling experiments were used to solve the system. O2 consumption, CO2 evolution and the respiratory quotient (QR) were not used as constraint here and were rather compared to the computed O2 and CO2 fluxes to check the reliability of our system, as discussed in section Results 2.Although anabolic fluxes 13C-labeling data. The EMU framework used here for the simulation is provided in 13C-1] glucose was used here as the labeling substrate and allowed us to determine the flux-ratio between the pentose phosphate pathway and glycolysis (PPP/Glyc ratio). In fact, when [13C-1] glucose was metabolized by the PPP, the 13C label was lost by decarboxylation, while the 13C label was incorporated into amino acids when it enters glycolysis. We recognize that reversible triose phosphate isomerase and aldolase activities could lead to a 13C-labeling in the C-6 atom position in fructose-1, 6-bisphosphate and then possibly in hexose phosphates 13C-labeling in tryptophan and histidine should provide evidence of the labeling in C-6 because their -COOH group comes from this very atom position. While tryptophan was absent from the spectra since it is degraded to a considerable extent by acid hydrolysis 13C-labeling pattern in histidine  framework istidine  showed tMID data from mass-spectrometer were normalized to total mass-signals as follows, giving relative isotopic abundance:Sq, the 11 anabolic fluxes iΦ and the normalized MID in glutamate (m0/z = 348), threonine (m0/z = 320), aspartic acid (m0/z = 334), alanine (m0/z = 218) and valine (m0/z = 246) were combined to provide a unique solution for all carbon fluxes, thereby solving vector v.Normalized MID were also corrected for natural isotope  abundance during flux calculations. The glucose specific consumption et al. For sensitivity analyses, carbon fluxes were estimated by minimizing the variance-weighted sum of squared residuals (SSR) between the simulated and model-predicted MIDs using non-linear least-squares régression −1 h−1) or relative fluxes with respect to glucose uptake (μmol (μmol glucose) −1). Relative fluxes allowed the comparison between strains, as M145 and M1146 did have different glucose uptake rates.Metabolic fluxes were expressed as absolute (μmol (g cell dry weight)2, CO2). The total CO2 evolution rate was computed from catabolic CO2 production  and anabolic production as: With the formula used for precursors, the stoichiometry of reactions, including those for antibiotics syntheses , and the2 production rate to verify the validity of the carbon-flux distribution (see section Results 4). In other words, experimental CO2 evolution was an independent data point not used as a constraint to calculate carbon-flux distribution. We assumed that NADPH requirement and production should have been equal since the metabolism was in steady state. If the production of NADPH was more important than the need, excess NADPH might have been converted to NADP+, thereby reducing NAD+ into NADH, by nicotinamide nucleotide transhydrogenase.Then, this value was compared with our experimental CO2 consumption, the anabolic demand was completed by the extra consumption required for excess NADH re-oxidation with the following stoichiometry: 1 mole of re-oxidized NADH consumed 0.5 mole of O2. For ATP as well, anabolic and catabolic fluxes were completed by the production of ATP associated with excess NADH re-oxidation. At this stage, knowledge of the actual P/O value (moles of ATP generated per mole of oxidized NADH) would be ideal. As stated in section Materials and Methods 11.1, we assumed that under 70% dissolved O2, the 1/aa3bc cytochrome pathway probably prevailed, and so a P/O value of 2 could be used to estimate ATP production from oxidative phosphorylation. Under this assumption, as for NADH, ATP production would be higher than ATP consumption. Excess ATP production would be likely compensated for by the consumption required for cell maintenance. Such a consumption is hereafter denoted as mATP, that is, energy requirements for homeostasis and turn-over of structural molecules as well as others processes that could not be quantified precisely (e.g. energy needed to excrete antibiotic).Similarly, the metabolic requirement for NADH should match NADH production; but here and quite generally, NADH production was larger than NADH oxidation. In order to satisfy our steady state condition, we assumed that excess NADH was re-oxidized by the respiratory chain. For Oet al. −1) in cell wall composition As mentioned in section Materials and Methods 11.4, we mainly used here metabolite contents reported by Borodina The difference in phospholipid content was considered to be acceptable  since it was previously shown that such a low difference in biomass composition had a negligible impact on carbon flux determination −1). As the mass of the S. coelicolor genome is 1.86-fold that of E. coli, which has a well-accepted DNA content of 3.1% S. coelicolorThe experimental DNA content obtained here was 6.0% (0.06 g (g cell dry weight)2O excess 1.62 O0.45 N0.25 , with 5.7% of ash. This is close to those found in other microorganisms The chemical elemental formula calculated from the data in −1 for M145 and 0.03 g (g cell dry weight)−1 for M1146 .A difference in the triacylglycerol content was found in the biomass composition of M145 and M1146, with a triacylglycerol content of 0.02 g (g cell dry weight)i) the biomass composition (ii) the stoichiometry of reactions describing the biosynthesis of metabolites (iii) the energy requirements re-calculated for S. coelicolor on the basis of published energy requirements for the polymerisation of macromolecules in E. coliUsing −1 and no detectable activity with NAD. Furthermore, we found that 6-phosphogluconate dehydrogenase was strictly NADP-dependent, with a specific activity of 236.4±6.4 µmol min−1 (mg protein)−1 and no detectable activity with NAD. We also verified that isocitrate dehydrogenase was strictly NADP-dependent, with a specific activity of 132.7±4.3 µmol min−1 (mg protein)−1 and no activity at all with NAD.In S. coelicolor genome contains two genes (SCO7622 and SCO7623) encoding a PntAB-like nicotinamide nucleotide transhydrogenase, but no UdhA-encoding gene. In E. coli, PntAB reduces NADH using NADP as a reductant in vivo while UdhA catalyses the reverse reaction −1 (mg protein)−1, and no activity at all with thio-NADP and NADH. Since no evident NADPH phosphatase-encoding gene was found in the S. coelicolor genome, transhydrogenase was then introduced in our model as the enzyme that balanced NADPH overproduction.The −1 and 9 g (cell dry weight) L−1, respectively. The stationary growth phase began when the medium turned to be nitrogen-depleted, glucose and phosphate being limiting approximately 9 h after nitrogen depletion for both strains.Batch-cultivation of the strains M145 and M1146 yielded a bacterial density of 8 g (cell dry weight) Lμ) of M1146 was 13±0.1% higher compared to M145 while the glucose specific uptake rate was nearly 13±0.5% lower (2 consumption and CO2 production) was 20±2% lower in M1146 compared to M145 and furthermore, strain M145 produced 2.03±0.08 µmol (g cell dry weight)−1 h−1 actinorhodin (up to 400 mg L−1 at the end of the culture) while no antibiotic production was detected at all in M1146.During the exponential phase in bioreactors, the growth rate constant  and slightly lower than 100%, suggesting that a certain carbon amount was not recovered. HPLC, LC-MS analyses and bioassays on culture supernatants did not allow the identification of extracellular metabolites that could account for this apparent loss of carbon. Nevertheless, these carbon balances are similar to the 94±9% value found by Borodina −1 h−1 in M145 and 162 µmol±6 (g cell dry weight)−1 h−1 in M1146) and pyruvate (21 µmol±1 (g cell dry weight)−1 h−1 in M145 and 167 µmol±11 (g cell dry weight)−1 h−1 in M1146). There was, therefore, a clear difference in carbon allocation between the two strains, with M1146 liberating more pyruvate and α-ketoglutarate in the medium, likely as a result of carbon re-direction in the absence of growth and antibiotic production, as discussed in section 4.4.During the stationary phase, both strains produced α-ketoglutarate (82 µmol±4 (g cell dry weight)13C-labeling ([13C-1] glucose) and 13C Metabolic Flux Analysis in the isotopic steady-state to estimate the relative commitment to PPP as opposed to glycolysis. Isotopic patterns obtained by GC-MS in amino acids from protein hydrolysates are shown in 13C-labeling  in the strain M1146, clearly showing a lower commitment of the 13C-label to glycolysis and the TCA (from which those amino acids are synthesized) and thus a larger commitment to PPP.The use of stoichiometric coefficients and biomass composition to infer metabolic fluxes  could not be carried out properly since an uncertain flux value remained at the PPP/glycolysis branching-point. We thus used 13C-label patterns in alanine and valine clearly showed the absence of 13C label in the C-1 atom position in both amino acids  with a 95% confidence level in all cases. According to Wiechert et al. (1997), our flux maps are representative descriptions of the metabolic processes in S. coelicolorWe analyzed the statistical significance and confidence of estimated fluxes in In M145 and M1146, the flux through the PPP was found to be 22±0.3% and 38±0.2% of available glucose-6-phosphate, respectively.−1 h−1 or normalized to glucose uptake (μmol (μmol glucose)−1). Consistently, the committed step of PPP (glucose-6-phosphate dehydrogenase) was found to be associated with a flux of 0.372±0.002 µmol (μmol glucose)−1 in M1146, which is 1.7-fold higher than in M145 (0.214±0.003 µmol (μmol glucose)−1). In contrast, the commitment to the TCA was 1.019±0.02 µmol (μmol glucose)−1 in M145, that is, 1.4-fold higher than in M1146 (0.730±0.018 µmol (μmol glucose)−1). Precursor abstraction from primary carbon metabolism to biomass synthesis was larger in M1146 −1, giving 60±1% (molC mol−1C) of carbon uptake allocated to biosyntheses) compared to M145 (0.813±0.02 µmol (μmol glucose)−1, 47±1%). There was a much larger commitment of phosphoenolpyruvate to carboxylation by the PEP carboxylase in M1146 and therefore, a larger anaplerotic oxaloacetate production to sustain the TCA.Our results from Metabolic Flux Analysis and isotopic labeling were combined to draw a carbon flux map in M145 and M1146 during the exponential phase of growth , Fig. 2.2 gave the total calculated CO2 evolution rate . Experimental and computed CO2 and O2 exchange rates gave respiratory quotients (CO2γ/O2q) of 0.982±0.002 for both strains, as would be expected during aerobic growth.Summing anabolic and catabolic fluxes for CONADPH production was similar in both strains , allowing to quantify reliably the 13C-commitment to glycolysis and the TCA versus the PPP.Nevertheless, Metabolic Flux Analysis is usually viewed as unlikely to give a completely faithful representation of the actual flux distribution, and consideration of areas in which thecarbon flux-balance model diverges from known metabolic behaviour may be potentially more informative. For example, the oxidative reactions of the oxidative pentose phosphate pathway typically carry no flux in flux-balance solutions of heterotrophic metabolism in plants S. coelicolor M145  was near 0.04 h−1Using this approach, we determined a contribution of PPP to glucose catabolism in 22±0.3%,  close toμ near 0.04 h−1) in S. lividans (35%) during actinorhodin production Streptomyces noursei (33%) during nystatin production Streptomyces C5 (30%) during ε-rhodomycinone production In M1146, our estimate of the PPP contribution is 38±0.2% , similarS. coelicolorWe postulate that the higher flux through the PPP in M1146 is the consequence of a higher NADPH demand to sustain both a higher growth rate .−1) would cover ATP needs for anabolism (3.927±0.052 µmol ATP (μmol glucose)−1). To the contrary, an apparent ATP deficit (1.118±0.013 µmol ATP (μmol glucose)−1) would exist in strain M1146.In strain M145, ATP generated by catabolism (4.000±0.043 µmol ATP (μmol glucose)−1 and 4.453±0.089 µmol NADH (μmol glucose)−1 were generated in excess by metabolism and from putative NADPH transhydrogenation, respectively.For M145 and M1146, 5.643±0.113 µmol NADH (μmol glucose)As the carbon balance do not close, we are aware that the calculation of ATP needs for anabolism may be affected if ATP is required for biosynthesis of the unidentified metabolites. This should not be the case for NADH, as NADH needs for anabolism are negligible .The amount of ATP generated by NADH reoxidation by the respiratory chain obviously depends upon the P/O ratio. Here, even a P/O ratio as low as 0.25 would have been sufficient to cover ATP needs in M1146.etc.) would have produced an apparent ATP excess in both strains.If P/O ratios larger than 0.25 were taken into account, both the oxidation of total NADH to NAD by the respiratory chain and intrinsic metabolic phosphorylation −1 h−1) of those found in various bacteria S. coelicolorHere, taking into account a P/O value of 2, mATP values (on a biomass basis) would be 7.545±0.3 and 4.482±0.2 mmol ATP (g cell dry weight)−1 h−1) as a function of the dilution rate D (h−1) in a chemostat y intercept being the maintenance energy value Sq values in Streptomyces and a value of 0.425±0.05 mmol glucose (g cell dry weight)−1 h−1 is found. Using our own experimental data, maintenance energy should correspond to 0.498±0.019 mmol glucose (g cell dry weight)−1 h−1 in M145 and 0.341±0.015 mmol glucose (g cell dry weight)−1 h−1 in M1146. Even though such values agree reasonably well with the average value from literature, an experimental determination of energy maintenance under our conditions remains to be determined.A standard way to estimate maintenance energy is to plot specific glucose uptake rate (mmol glucose (g cell dry weight)Since M145 produced solely actinorhodin from acetyl-CoA during its growth under our conditions, the genetic deletion of the cluster for actinorhodin biosynthesis in M1146 expectedly resulted in visible metabolic changes , Fig. 2.−1 h−1) than in M145 (21±1 µmol (g cell dry weight)−1 h−1) during the stationary phase. In the absence of growth, acetyl-CoA could not be redirected to biomass in M1146 and accumulation was simply avoided by excreting its direct, freely diffusible precursor pyruvate.Other evidences for the absence of acetyl-CoA accumulation came from the excretion of larger amounts of pyruvate in M1146 (167±11 µmol (g cell dry weight)−1 h−1 are dedicated to biomass production and 32.5±1 µmol acetyl-CoA (g cell dry weight)−1 h−1 to actinorhodin production. In M1146, with no other modification in glucose uptake and carbon flux distribution, a complete redirection of 32.5±1 µmol acetyl-CoA to biomass would result in a 40% increase of the specific growth rate (0.0552 h−1), a 40% increase of the anaplerotic flux and a decrease of O2q −1 h−1) and CO2γ −1 h−1) values in M1146.During the exponential growth of M145, 80.4±1 µmol acetyl-CoA (g cell dry weight)O2q −1 h−1) and CO2γ −1 h−1) values were also lower.However, the specific growth rate of M1146 was only 13±0.1% higher than that of M145 and the rate of glucose consumption was 13±0.5% lower. In M1146, there was therefore a slightly lower flux to acetyl-CoA synthesis due to the lower glucose consumption rate and as a result, the flux through the TCA and ATP production were lower . AccordiS. coelicolor could grow with μ values up to 0.16 h−1, a high Sq value (2 mmol (g cell dry weight)−1 h−1) and a low actinorhodin production rate (ACTγ = 6 nmol (g cell dry weight)−1 h−1) The reasons of the limited increase in M1146 growth rate are not straightforward since Streptomyces peuceticus var. caesius and S. coelicolor, a transcriptional regulation of glucose uptake rate has been demonstrated First, in et al. S. coelicolor. This regulon encodes proteins involved in the transport or the turn-over of small redox-active toxic compounds E. coli by Fridovitch Second, the absence of actinorhodin synthesis in M1146 might also cause growth limitation as a side effect. Indeed, quite recently, Shin μ) and antibiotic synthesis by accumulating neutral lipidic compounds . In a similar molecular context, that is, in the bldA mutant of S. coelicolor unable to produce actinorhodin and undecylprodigiosin, Plaskitt and Chater (i) a higher actinorhodin production has been observed in the sco0958 mutant impaired in triacylglycerol synthesis (ii) the simultaneous deletion of the gene clusters encoding 5-polyketide synthase and addition of a plasmid comprising the gene cluster encoding actinorhodin biosynthesis, resulted in a dramatic increase of actinorhodin production in S. coelicolorThird, the carbon metabolism in M1146 partly compensated for the reduced growth rate , noviose (derived from glucose-1-phosphate) and 3- or 5-methyl-pyrrole-2-carboxylic acids. These pyrrole groups originate from threonine  or proline (precursor α-ketoglutarate). Flinspach et al. In the case of clorobiocin and coumermycin A1, precursors are, in that order of incorporation, prephenate Click here for additional data file.Table S2Additional GC-MS analyses.(DOC)Click here for additional data file.File S1Supplementary Material and Methods.(DOC)Click here for additional data file.File S2Abbreviations and symbols.(DOC)Click here for additional data file.File S3S. coelicolor.The metabolic network of (DOC)Click here for additional data file.


Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output.Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions, including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell. Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes.CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processes crucial for the accomplishment of an adequate cardiac output. Cardiovascular diseases are the main cause of death worldwide. The myoMore insights into the metabolic changes and efficiency of cardiomyocytes under conditions of heart failure and myocardial hypertrophy may improve treatments of these diseases. A prerequisite for such an approach is the reconstruction of the metabolic network of the human cardiomyocyte. Previous genome-scale metabolic network reconstructions-4 have sDiabetes and obesity have been especially shown to be associated with oxidative stress and damaIn addition to permanent utilization of substrates, cardiomyocytes are able to store energy-rich substrates, which prevents myocardial injury in hypoxic or ischemic conditions. An important short-term store is glycogen. The formation is strongly dependent upon nutritional composition of the blood. In particular, a reduction of free fatty acids in plasma by nicotinic acid treatment was found to lower levels of cardiac glycogen in rats. This reThe aim of the present investigation was to reconstruct a genome-scale metabolic network of the human cardiomyocyte. Using methods of constraint-based optimization, we demonstrate how changes in oxygen and substrate supply influence the efficiency of selected metabolic functions of cardiomyocytes and provide valuable suggestions for substrate compositions allowing optimal accomplishment of metabolic functions to provide cellular integrity and maintain cardiac work.First, we reconstructed the metabolic network of the human cardiomyocyte (CardioNet) and tested physiological functions of the cardiomyocyte to ensure full functionality and consistency of the network. Furthermore, we compared our network to two previously reported mitochondrial networks,24 and oSecond, we determined substrate and oxygen requirement in varied availability of four different substrates while satisfying a baseline ATP consumption rate. We further analysed the efficiency of these varied substrate compositions and compared our findings to experimental results.Finally, we performed an extensive simulation of varied availability for nine different substrates while satisfying a complex target function of the cardiomyocyte and analysed the efficiency.We developed a fully compartmentalized network of the human cardiomyocyte, which accomplishes various sets of physiological functions of the human heart. The network totals 1793 reactions, including 560 transport reactions and 728 metabolites assigned to 6 different compartments: extracellular, cytosol, mitochondrion, microsome, lysosome and peroxisome  which dMoreover, we included the metabolism of distinct fatty acids, glycerolipids, glycerophospholipids and sphingolipids, as far as we could find biochemical evidence for their occurrence in the human heart  (see Methods). The required functions included phospholipid synthesis (e.g. cardiolipin), conversion of amino acids into citric acid cycle intermediates by transamination or oxidative deamination as well as nucleotide synthesis -3-hydroxybutanoate), essential amino and fatty acids as well as vitamins, while the release of intermediates were restricted to metabolic end products, e.g. lactate and glutamine  were revealed, which required further manual literature review to complete the network functionality. We revised reactions and eventually included new reactions into the metabolic network based on additional evidence for occurrence in the human cardiomyocyte from e.g. experimental studies  of 21.6 mmol·min−1·(l cell)−1[We analysed the efficiency of cardiac metabolism in altered substrate supply by applying the metabolic network. The dephosphorylation of ATP through myosin light chain kinase is an important step in the cross-bridge cycle to generate cardiac contraction,57. Thisl cell)−1 to incluvt) for these simulations reads as following: The target function (vATPase). We chose glucose, oleate, acetoacetate and lactate as alternative energy-delivering substrates, that can be taken up and oxidized by cardiomyocytes to generate ATP. The external uptake rate for each substrate is expressed by vm  and were described as following: Oxidation of available substrates m should provide sufficient ATP synthesis to enable this ATP consumption rate (β (0 ≤ βm ≤ 1) denotes the relative share of the respective substrate m in the total substrate uptake flux vs (see Methods). We modified the coefficient β on a fine grid of values between 0 and 1 and performed in total 176851 flux minimization computations, while minimizing the sum of external uptake fluxes without restricting the oxygen supply −1 and only reached in exclusive utilization of glucose, as can be seen in Table−1·(l cell)−1, while only a minimal total substrate uptake rate was required (vs = 0.162 mmol·min−1·(l cell)−1). To identify optimal substrate compositions, we used the oxygen and total substrate uptake rate as criteria. As illustrated in FigureThe demanded ATP consumption rate could be achieved with each simulated substrate composition for glucose, oleate, acetoacetate and lactate. Nonetheless oxygen and substrate demands differed clearly between substrate combinations as presented in Figures = 1.2701 mmol·min−1·(l cell)−1, thus 7.84-fold higher than in case of oleate utilization. We determined the lowest efficiency for a total substrate combination of 13% acetoacetate and 87% lactate . The optimization problem was repeated for substrate combinations with the highest and lowest calculated efficiency  < 2e-16, p ≤ 0.001) and 74.41% unique flux solutions (s = 0). The largest variance (s = 0.24) was found for flux rates of creatine and phosphocreatine transport into the mitochondrion as well as cytosolic and mitochondrial creatine kinase. However, we found all fluxes representing the external substrate and oxygen uptake with unique solutions  of 21.6 mmol·min−1·(l cell)−1[To validate our results, we simulated substrate compositions as determined in a recent experimental study, which il cell)−1. The resMoreover, the total substrate uptake rate is increased in simulations for sole utilization of acetoacetate and in combined utilization of acetate and glucose. Here, the ratio of calculated oxygen demand to total substrate uptake rate shows the greatest deviance to experimentally obtained values. To further compare our simulations with the experiment, we determined for each substrate composition efficiency measures as described above. As depicted in FigureThe comparison of experimental results in oxidation of acetoacetate with our simulations are limited, due to reduced cardiac work during the perfusion experiment and altered substrate application. Our simulations consider direct presence of acetoacetate and glucose, while in the experiment glucose was added at a later time in the perfusion. The data, summarized in Tablevt) by including the production of NADPH besides ATP and important membrane lipids: ceramide (cer), cardiolipin (cl), phosphatidylcholine (pc), phosphatidylethanolamine (pe) and sphingomyelin (sm). Cardiomyocytes have to maintain an adequate ATP synthesis together with a multitude of metabolic functions including abundance of contractile proteins, membrane integrity and protection against reactive oxygen species. To reflect these metabolic functions, we extended the metabolic target function −1 and 3.05e-03 mmol·min−1·(l cell)−1for the cytosolic and microsomal glucose-6-phosphate dehydrogenase, while no flux was found for the NADPH producing isocitrate dehydrogenase. The rate of cytosolic glucose-6-phosphate dehydrogenase corresponded to 0.08% of the hexokinase flux rate distribution. We found an overall NADPH production rate of 1.42e-05 mmol·min−1·(l cell)−1 which was included into the simulations as a minimal required rate of NADPH synthesis in addition to the metabolic target function.The myocardial defending mechanisms against hypoxia are mostly represented by NADPH to maintain reduced glutathione. To consider this aspect, we performed an initial simulation to determine the basal NADPH formation in case of unrestricted substrate supply. We determined a rate of 2.13e-05 mmol·minIn total we performed 218618 simulations of the altered supply of 9 different substrates (ns=9). As expected, no solutions were found in case of absent alpha-linoleate and docosahexaenoate supply, thus these fatty acids are required as a precursor for the biosynthesis of certain phospholipid species in the metabolic network and cannot be replaced by any other substrate.vs) and (iii) endogenous glucose derived from glycogen turnover (vGL). We identified high efficiency indices for substrate combinations with a major share of fatty acids and glucose, as presented in Tablevs = 4.8859 mmol·min−1·(l cell)−1 ;vs = 10.2332 mmol·min−1·(l cell)−1, see Additional fileTo identify optimal substrate combinations, we calculated for each simulation the efficiency index  in substrate combinations with the highest and lowest calculated efficiency  < 2e-16, p ≤ 0.001). The largest variance (s = 29.83) was again found for flux rates of creatine and phosphocreatine transport into the mitochondrion as well as cytosolic and mitochondrial creatine kinase. In addition we found variability for beta-oxidation of fatty acids and ATP:nucleoside-diphoshate phosphotransferase. Each flux representing the glycogenolysis, external substrate and oxygen uptake was found unique (s=0) in all simulations −1. Rates of exogenous glucose entering glycolysis were significantly greater than those from endogenous glycogen degradation −1; vendo = 0.091 ± 0.189 mmol·min−1·(l cell)−1; p ≤ 0.05). In fact oxidative phosphorylation contributed the most to cellular ATP production from exogenous and endogenous glucose utilization  supplemented by glucose (21%) showed to maintain most efficiently the required ATP production. We found that in sole oleate utilization total substrate requirement and oxygen consumption are more favourable compared to glucose, lactate and acetoacetate. Furthermore, predominant utilization of lactate and acetoacetate was least optimal to maintain ATP production. These findings are supported by previous studies,83 documTo further validate our results, we simulated substrate proportions as present in a previous study of the isolated working rat heart and compOn the basis of these results, we expanded the metabolic target function by demanding besides ATP the production of NADPH and the important membrane lipids: ceramide, cardiolipin, phosphatidylcholine, phosphatidylethanolamine and sphingomyelin. For this purpose, we further modified the fraction of fatty acids in the set of importable substrates by saturated , monounsaturated (oleate), long chain poly-unsaturated omega-6  and omega-3  fatty acids. Although long chain poly-unsaturated fatty acids (PUFA) predominately serve as membrane lipids,15,16, t−1·(l cell)−1) was demanded as minimal requirement in order to maintain cellular protection against ROS.Demanding metabolic flux rates as reported in previous experimental studies of membrane lipids,72 and i−1·(l cell)−1. The vast majority of utilized glucose during simulations derived from exogenous uptake, indicating that endogenous glycogenolysis was only utilized in a decreased glucose supply. Both external and endogenous glucose essentially contributed to ATP production by oxidative phosphorylation.In addition, we considered cardiac short-term storage of energy in particular glycogen, which could act as a potential precursor for sn-glycerol, a known intermediate for phospholipid biosynthesis, and ATP production. During simulations we allowed the synthesis of glycogen while including the limited amount of glycogen storage by restricting glycogenolysis to a maximal rate as determined in previous investigations. As showPreviously, Henning et. al. demonstrIn order to test the robustness of our solutions we analysed the uniqueness of calculated flux solutions. Depending on the complexity of the target function we found up to 90.70% fluxes with unique solutions which included flux rates for glycogenolysis and external substrate and oxygen uptake for every tested substrate combination. Although a certain amount of flux solutions showed variability none of the alternative distributions showed significant difference from the original distribution. The efficency analysis of substrate combinations is not compromised by this small varibility and based on fluxes with unique solutions.To our knowledge this is the first study investigating the efficiency of a large set of substrates, including long-chain fatty acids, through simulation. We found utilization of substrate combinations with a mixture of all investigated substrate more efficient compared to sole utilization of single substrates. Here predominant utilization of fatty acids  to study metabolic and physiological functions of the cardiomyocyte.The evaluation of metabolic efficiency in substrate supply and utilization necessitates consideration of oxygen and substrate demand, as well as endogenous glucose deriving from glycogenolysis. In aerobic conditions predominant utilization of saturated and long-chain unsaturated fatty acids supplemented by glucose proved to be more favourable for efficient cardiac metabolism than utilization of acetoacetate or lactate. Furthermore, we showed, that glycogenolysis and glycogen synthesis occurred simultaneously. In conclusion, CardioNet can serve as a reliable basis to study cardiomyocyte metabolism.To identify a tissue specific set of metabolic reactions we applied the algorithm by Shlomi et al., which iBy using the Ensembl Homo sapiens database and KEGGReactions were evaluated for tissue specificity and subcellular localisation according to database knowledge and reported experimental evidence from scientific literature. We included reactions into the network if evidence were found for occurrence in human cardiomyocytes or heart tissue in general . In case no evidence were found for humans, we relied on other mammalian species and human orthologous genes allowing inference of the reaction. Information on transport reactions was obtained from the Transport Classification database, ReactomEach reaction in the network was assigned to one of the following sub-cellular localisation: external, cytosol, mitochondrion, lysosome, peroxisome and microsome. The compartments endoplasmic reticulum (ER), Golgi apparatus and microsome are represented in the metabolic network as one combined compartment, microsome. Communication among endoplasmic reticulum, Golgi apparatus and microsome is mediated by vesicular transport processes which can be only inadequately included into the FBA methodology. In addition, recent studies demonstrated the experimental difficulty of proteomic profiling of the microsomes,97. The For the integration of different level of information we used the METANNOGEN software. The comThe network was subjected to further flux-balance simulations with different metabolic objectives to test functionality , which have to be accomplished by the network in order to maintain cellular integrity and cardiac contractility: We used the metabolic network to assess the influence of nutritional blood supply on the metabolic efficiency of the cardiomyocyte to accomplish various metabolic objectives. For this purpose, we defined the metabolic target vvr were obtained from experimentally determined synthesis rates which were taken from previous investigations[vs) as a linear combination of the external uptake rates vm  for each oxidized substrate n. The substrate availability in the external space is reflected by the coefficient βm with The metabolic flux rates igations,71,72. Wigations,74. The vs). This assumption is justified by experiments carried out with the perfused isolated heart[The higher the share of substrate n in the external medium, the higher its share in the total substrate uptake space (ed heart showing vt) while minimizing the sum of the total substrate uptake rate (vs) and oxygen uptake rate  for a single flux of the original flux distribution which was set to either 1.01-fold (v0). We repeated this additional constraining for one flux after the other and resolved the optimization problem. Presumed the original problem is feasible and a value for the objective can be calculated, the solution for v (see equation (8)) is not necessarily unique. Multiple solutions might occur to solve the problem and cause degeneration of the flux distribution. In order to identify alternate flux solutions that can equally satisfy the problem, i.e. yield the same optimal solution, we performed additional simulations. The MILP was re-solved after adding a constraint  a baseline ATP consumption rate and (2) a target function of the cardiomyocyte : the oxygen demand (q1), total substrate uptake rate  and endogenous glucose derived from glycogenolysis . Optimal substrate combinations should not only meet a minimal distance to the best achieved solution, but also a maximal distance to the worst achieved solution for each criterion. This takes into account, that one solution could show minimal requirement of substrates while the oxygen demand and glycogenlysis increases. Euclidean distances were calculated for every simulated substrate composition i  by, first, determine the best-case (qji) to the best-case  was calculated as follows:We referred all flux calculations to the volume of one single cardiomyocyte to integrate experimental flux rates from different studies  and FASIMU.Comparison of mean values between groups was evaluated with unpaired t-test. A value of p < 0.05 was considered significant. Statistical significance between flux solutions for the analysis of alternate flux solutions was determined by use of 1-way ANOVA. Vertical lines in the histograms indicate means ± SE.FBA: Flux balance analysis; CHF: Congestive heart failure; ROS: Reactive oxygen species; EPA: Eicosapentaenoate; DHA: Docosahexaenoate; GL: Glycogenolysis; GS: Glycogen synthesis; SE: Standard error; MILP: Mixed Integer Linear Problem.The authors declare that they have no competing interests.AK developed the original idea, carried out all computational analyses and drafted the manuscript. HGH, DF, HSR, GK and VRZ participated in the design and evaluation of the analyses. All authors contributed to and approved the final manuscript.Metabolites of the metabolic network. Metabolites listed in this table occur in the metabolic network. For each entry a unique network identifier is given and provided with information of metabolite title, title synonym, metabolite sum formula and assigned compartment. Additionally cross-references to other databases are given and refer to the following databases: UniProtKB (UniProtKB entry), KEGG (Compound ID), Lipid Maps (LM ID), Pub Chem (CID) and Human Metabolome Database (HMDB ID). Abbreviations used in the table for compartments are as following, ext: external, cyto: cytosol, mito: mitochondrion, lyso: lysosome, peroxy: peroxisome and micro: microsome.Click here for fileReferences. During the network reconstruction additional evidence for occurrence of metabolic reactions in the cardiomyocyte were obtained from previously reported studies. This table gives a full list of cross-references to PubMed identifier (PMID) providing evidence for included reactions of the metabolic network. Furthermore, directionality of reactions was set according to Gibbs energy  and is provided with this table.Click here for fileDefinition and overview of objectives and constraints for simulation of metabolic and physiological functions of the cardiomyocyte. To ensure consistency and full functionality of the metabolic network, we performed a critical testing of physiological functions based on knowledge of the cardiac metabolism by using flux balance analysis. The table lists all objectives and applied constraints as used in the optimization problem. Furthermore, constraints as used in functional pruning of the network are given. Abbreviations for constraints as used in simulations with FASIMU software are as follows: (+), secretion of the metabolite is allowed or the metabolite is product; (-), uptake of the metabolite is allowed or metabolite is substrate and (=), secretion and uptake of the metabolite is allowed or metabolites is either product or substrate.Click here for fileFlux distributions of metabolic and physiological functions of cardiomyocyte. To ensure consistency and full functionality of the metabolic network, we performed a critical testing of physiological functions based on knowledge of the cardiac metabolism by using flux balance analysis. Flux distributions listed in this table have been predicted for each metabolic objective as defined in Additional fileClick here for fileMetabolic network of the human cardiomyocyte in SBML format.Click here for fileTesting functionality of Human heart model. A comparison of the metabolic network to a previously reported genome-scale reconstruction of the human heart[an heart was perfClick here for fileDefinition and overview of constraints for simulations as used in the optimization problems for varied substrate availability. Constraints listed in this table were applied in simulations for varied substrate availability. For all three simulation settings the corresponding target function and applied constraints are given. The simulation settings include, first, simulations of substrate uptake rates for four different substrates and oxygen demands while satisfying a baseline ATP consumption rate. Second, simulations of substrate uptake rates for four different substrates and oxygen demands as under experimental conditions while satisfying the same baseline ATP consumption rate. Finally, simulations of substrate uptake rates for nine different substrates and oxygen demands while satisfying a predefined metabolic target function.Click here for filePredicted metabolic fluxes of substrate uptake and oxygen demand for ATP expenditure in varied substrate availability,. We simulated a varied substrate availability for four selected substrates, including glucose, oleate, acetoacetate and lactate while demanding a baseline ATP consumption rate (vATPase) of 21.6 mmol·min−1·(l cell)−1. This table lists uptake rates for oxygen, glucose, oleate, acetoacetate, lactate and the resulting total substrate uptake rate for each simulated substrate composition. Efficiency indices  for a single flux of the original flux distribution which was set to either 1.01-fold  of 21.6 mmol·min−1·(l cell)−1 and metabolic target flux, as specified in Additional fileClick here for filePredicted metabolic fluxes of substrate uptake and oxygen demand for fulfilling the metabolic target function in varied substrate availability,. See caption of Additional fileClick here for fileGene expression annotation. The identification of human heart tissue specific reactions requires a tissue specific gene expression profile. We obtained gene expression samples from different gene expression data available from Gene Expression Omnibus, including GDS181 and GSE1145. This table provides gene expression information annotated to metabolic reactions of the cardiomyocyte network. Each reaction identifier refers to a compartment localisation of the respective metabolic reaction. Furthermore, each entry in the table provides information about annotated Ensemble Gene ID, Geo Dataset ID, Geo Sample ID, Probeset ID, Gene ID, gene expression value and detection call. The information of gene expression status can be obtained from the column “Detection call”. Each expression is either categorized as present (P), absent (A) or M . We further considered genes as expressed for gene expression values with a cut-off greater than 100.Click here for file


Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis. The transcriptomic data of MDA-MB-231 were extracted and curated from up-regulated genes under hypoxic condition . The aff1H-NMR spectroscopy experiments show the metabolic differences between hypoxia- and normoxia-treated MDA-MB-231. Through the PCA analysis, not only the discrimination of different condition groups is clear, but also the regions of NMR spectra which make the two groups different were discovered and verified by statistical analysis. Seventeen metabolites were quantified, which could be seen as potential hypoxic biomarkers to determine tumor hypoxic conditions.Our Our time-dependent metabolic profile analysis showed that the discrimination between hypoxia and normoxia was ambiguous over 24 h but became clear over 48 h. This result suggests that the time point shown the change of metabolism might be between 24 and 48 h. In a previous transcriptomic study, the major changes were observed before 12 h . As metaNot only we can discover the metabolites associated with hypoxia and time-dependent biomarkers but also with the approach of a combined transcriptomics and metabolomics model, we constructed the metabolic pathway network with combining the metabolome and transcriptome for comparison of the mapped pathways of the two omics. The results indicated that there was some concordance in metabolism between the transcriptome and metabolome, but using a single platform would lead to only partial understanding of the hypoxia results. This integrated omics analysis could improve our understanding on the mechanisms of adaptation to hypoxia in breast cancer.