Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. These organism-specific databases have undergone variable degrees of curation. The EcoCyc (Escherichia coli Encyclopedia) database is the most highly curated; its contents have been derived from 27,000 publications. The MetaCyc (Metabolic Encyclopedia) database within BioCyc is a “universal” metabolic database that describes pathways, reactions, enzymes and metabolites from all domains of life. Metabolic pathways provide an organizing framework for analyzing metabolomics data, and the BioCyc website provides computational operations for metabolomics data that include metabolite search and translation of metabolite identifiers across multiple metabolite databases. The site allows researchers to store and manipulate metabolite lists using a facility called SmartTables, which supports metabolite enrichment analysis. That analysis operation identifies metabolite sets that are statistically over-represented for the substrates of specific metabolic pathways. BioCyc also enables visualization of metabolomics data on individual pathway diagrams and on the organism-specific metabolic map diagrams that are available for every BioCyc organism. Most of these operations are available both interactively and as programmatic web services.BioCyc.org is a genome and metabolic pathway web portal covering 5500 organisms, including  The BioCyc.org website provides a number of metabolomics data analysis and visualization services in concert with the 5500 metabolic pathway databases available at the site. These operations include metabolite search, metabolite enrichment analysis, visualization of metabolomics data on pathway diagrams and metabolic map diagrams and translation of metabolite identifiers across multiple metabolite databases. These operations are provided via two access modalities: interactive website operations and programmatic web services.  that generate data and to web APIs that generate web pages.)BioCyc is a genome and metabolic pathway web portal  that conThe MetaCyc ,5,6 dataPGDB data can be accessed in a variety of forms . The BioOur metabolite translation service was inspired by previous work, such as the Chemical Translation Service (CTS) of Wohlgemuth , UniChemKEGG provides metabolomics visualization services for its pathway diagrams via the KEGG Mapper . The KEGhttp://www.kegg.jp/kegg/atlas/?01100, we clicked “eco” at the upper-left to display the diagram, then clicked the “P” button at the top-right and entered several metabolite-ID/color pairs, as per the instructions, but no metabolites were ever highlighted upon clicking “apply”). Furthermore, KEGG Atlas uses a set of seven fixed diagrams for different areas of small-molecule metabolism—fixed in the sense that the same seven diagrams are used for all of the organisms in KEGG. Thus, no one diagram contains all metabolic pathways for all organisms, and many of the reactions in any given diagram are not actually present in any specific organism (reactions not present in a selected organism are grayed out). In contrast, each organism in BioCyc has its own custom-generated Cellular Overview diagram containing only those reactions present in the PGDB for that organism. It does not support animation nor omics pop-up diagrams.KEGG Atlas  is an anKaPPA-View enables viewing of metabolomics data on individual pathway diagrams for a number of plant species . It doesMetscape  supports2.Escherichia coli. Similarly, when visualizing metabolomics data on a metabolic map diagram, each PGDB has a different metabolic map diagram, and the operation must use the diagram for a specified organism. Additional information about these operations can be found at /monoisotopicwt?wts=[MONOISOTOPICMW]&tol=[TOLERANCE]orhttp://websvc.biocyc.org/[ORGID]/monoisotopicwt?wts=[MONOISOTOPICMW]&tol=[TOLERANCE]&fmt=jsonwhere:Acidithiobacillus ferrooxidans, Strain ATCC 23270).[ORGID] is the BioCyc identifier for the organism database, e.g., ECOLI (for EcoCyc), META (MetaCyc), AFER243159  format; the default output is in a tab-delimited format.www.json.org) is particularly useful, because JSON data are structured and regular yet easy to parse. JSON is a native format within the JavaScript programming language that is often used to implement web services in a browser, thus JSON can be directly used by JavaScript without any need for parsing.The JSON format :123.009 1 123.008705 3-chloro-L-alanine CHLORALAN-CPD123.009 1 123.008705 3-chloro-D-alanine 3-CHLORO-D-ALANINE123.009 1 123.008705 2-chloro-L-alanine CPD0-1475123.009 1 123.008705 3-chloro-DL-alanine 3-CHLORO-DL-ALANINE56 0The first column contains the input monoisotopic molecular weights. The second column indicates whether the query was successful (1) or unsuccessful (0). The third column contains the monoisotopic molecular weight of the compound that is stored in the PGDB. The fourth column contains the metabolite name. The fifth column contains the BioCyc identifier of the compound.JSON format: 2.3.2.This service finds the BioCyc IDs of all metabolites that exactly match a supplied chemical formula. This web service can be used if the user is retrieving the results by either the “POST” or “GET” method. The URLs to search metabolites based on chemical formula are as follows.http://websvc.biocyc.org/[ORGID]/CF?cfs=[CHEMICAL-FORMULA]orhttp://websvc.biocyc.org/[ORGID]/CF?cfs=[CHEMICAL-FORMULA]&fmt=jsonwhere:[ORGID] is the identifier for the organism database, e.g., ECOLI, META, AFER243159.[CHEMICAL-FORMULA] is the chemical formula to be matched, in the format ([element-symbol][coefficient])+, where “+” is a regular-expression operator that means the preceding expression can be repeated one or more times. The element symbol is case sensitive. Coefficients of one may be omitted.The chemical formula input may contain one or more values, which may be separated by commas. The search will return all metabolites in the specified database that have a chemical formula(s) equal to the one provided. Note that two chemical formulas are equal if and only if they are element-wise equal. For example, C6H6 is equal to H6C6 because both have elements H6 and C6.fmt=json requests output in JSON format; the default output is in tab-delimited format.Example URLs:http://websvc.biocyc.org/META/CF?cfs=C6H6Retrieve the compound that contains a chemical formula of C6H6 in MetaCyc. The result of this search is:C6H6 1 BENZENE benzenehttp://websvc.biocyc.org/META/CF?cfs=C12H16N3O2S,C6H2N3O3Cl,H3O3Retrieve the compounds that match any of the three provided chemical formulas in MetaCyc. The result of this search is:C12H16N3O2S 1 CPD-15776 oxythiamineC6H2N3O3Cl 1 CPD0-1250 4-chloro-7-nitrobenzo-2-oxa-1,3-diazoleH3O3 0http://websvc.biocyc.org/BSUB/CF?cfs=C6H6&fmt=jsonRetrieve the compound that contains a chemical formula of C6H6 in JSON format. The result of this search is:The default output is a tab-delimited format, as shown in the second example. The first column contains the provided chemical formula. The second column indicates whether a valid compound that matches the chemical formula was found (1). The third column contains the BioCyc identifier of the compound that matches the chemical formula. The fourth column contains the common-name of the compound.2.3.3.This service finds the BioCyc ID of a metabolite given a foreign ID, that is an identifier of that object in an external database. This service depends on the foreign ID being stored in the DB-Links slot of the BioCyc metabolite, and we note that BioCyc’s links to external databases are incomplete. This search works not only for metabolites, but also for BioCyc objects, such as pathways, reactions, genes and proteins. This web service can be used if the user is retrieving the results by either the “POST” or “GET” method. URLs to search for metabolites based on the foreign identifier are as follows.http://websvc.biocyc.org/[ORGID]/foreignid?ids=[DATABASE-NAME]:[FOREIGNID]orhttp://websvc.biocyc.org/[ORGID]/foreignid?ids=[DATABASE-NAME]:[FOREIGNID]&fmt=json\where:[ORGID] is the identifier for the organism database, e.g., ECOLI, META, AFER243159.[DATABASE-NAME] is the name of external database, e.g., KEGG, PUBCHEM, UNIPROT, and is case-insensitive.[FOREIGNID] is the external database identifier. The foreign id input may contain one or more values, which may be separated by commas. Foreign IDs are case sensitive.fmt=json requests output in the JSON format; the default output is in tab-delimited format.Example:http://websvc.biocyc.org/META/foreignid?ids=Kegg:C00849Retrieve the MetaCyc metabolite that contains a KEGG ID of C00849.The default output is a tab-delimited format as follows:Kegg:C00849 1 ETHYLACETATEThe first column is the input foreign ID. The second column indicates whether a valid object that matches the foreign ID was found (1). The third column indicates the BioCyc identifier of the object that matches the foreign ID.2.4.BioCyc provides both interactive and programmatic services to translate sets of metabolites specified in many alternative ways to the BioCyc identifiers for those metabolites and to identifiers for external databases. For example, a user could supply a list of KEGG or PubChem metabolite identifiers, or a list of InChI-1S strings, or a list of chemical names, and obtain the BioCyc and ChEBI identifiers for all of the preceding items that are recognized. This service can be invoked interactively through .The input to the service is a set of lines, one line per metabolite. Each line contains one or more metabolite names, identifiers and an optional InChI string, InChI key, monoisotopic molecular weight and chemical formula. The service looks up each of the preceding fields within the specified BioCyc database. Three cases are possible for each line:(1)None of the provided fields is recognized in the specified BioCyc database, in which case “unknown” is returned, along with the unknown input fields.(2)All of the recognized names, identifiers, InChI string, InChI key, monoisotopic weight and chemical formula match a single metabolite, in which case “successful” is returned, along with the following tab-separated fields:BioCyc ID of the matching metabolite;BioCyc common name of the matching metabolite;Additional identifiers present in BioCyc from other databases for the matching metabolite ;InChI-1S string for the metabolite.In this case, unrecognized input items are ignored.(3)The recognized names, identifiers, InChI string, InChI key, monoisotopic weight and chemical formula match more than one BioCyc metabolite, in which case “ambiguous” is returned, along with the multiple matches that were found.Consider the following example .Input:L1: PubChem:125 Kegg:C00001L2: InChI:InChI=1S/C2H4O2/c1-2(3)4/h1H3,/p-1L3: CF:C6H6 MW:78.0469501 InChI-key:InChIKey=UHOVQNZJYSORNB-UHFFFAOYSA-NL4: TRPL5: Kegg:C00183 InChI:InChI=1S/C5H11NO2/c1-3(2)4(6)5(7)8/h3-4H,6H2,1-2H3,/t4-/m0/s1L6: Kegg:C00004L7: Kegg:C45698L8: Kegg:C00001 MW:18.01056468Output (InChI strings are omitted from the output for brevity):L1: ambiguous BioCyc:4-HYDROXY-BENZYL-ALCOHOL BioCyc:WATERL2: success BioCyc:ACET acetate HMDB:HMDB00042 DrugBank:DB03166 IAF1260:33590 ChemSpider:170 PubChem:175 ChEBI:30089 KEGG:C00033L3: success BioCyc:BENZENE benzene HMDB:HMDB01505 ChemSpider:236 PubChem:241 ChEBI:16716 KEGG:C01407L4: success BioCyc:TRP L-tryptophan HMDB:HMDB00929 IAF1260:33772 ChEBI:57912 PubChem:6923516 KEGG:C00078L5: success BioCyc:VAL L-valine MetaboLights:MTBLC57762 HMDB:HMDB00883 IAF1260:34167 ChEBI:57762 PubChem:6971018 KEGG:C00183 CAS:72-18-4L6: success BioCyc:NADH NADH HMDB:HMDB01487 IAF1260:33484 ChemSpider:10239197 ChEBI:57945 PubChem:21604869 KEGG:C00004L7: unknown Kegg:C45698L8: ambiguous BioCyc:WATER BioCyc:OH2.5.The SmartTables facility in BioCyc  allows SmartTable operations are found under the SmartTables item in the main BioCyc menu. Because SmartTables are associated with a user’s BioCyc account, it is best to create a BioCyc account before working with SmartTables.http://humancyc.org/group?id=biocyc13-61-3633296894.A typical scenario is to for a user to define a new SmartTable by importing a file of metabolites. The metabolites are specified by name, by BioCyc ID or by external database ID. To illustrate SmartTables, we have created a SmartTable containing metabolomics data from a personal omics study  see Fig. This SmOnce the SmartTable is defined, the user can display database attributes of the metabolites as SmartTable columns through the “Add Property Column” menu. In i.e., the set of pathways in Transformations are a more advanced set of SmartTable operations that compute a new column of information for a SmartTable based on a starting column. For example, in Different sets of transformations are available for different types of SmartTables. For example, a SmartTable of pathways can be transformed into the set of genes in the pathway, or the set of reactions in the pathway, or the set of metabolites in the pathway. A SmartTable of genes can be transformed into those genes that regulate the initial gene set, into those genes that are regulated by the initial gene set, into those genes that are present (as orthologs) in another PGDB, or into the metabolic pathways in which each initial gene plays a role.2.6.When analyzing a metabolomics dataset, one question that arises is whether the list of metabolites is over-represented for metabolites in particular metabolic pathways. That is, does the list of metabolites contain more compounds from certain metabolic pathways than one would expect by chance? This question can be answered using Fisher’s exact test of statistical significance. Because different pathways contain different numbers of metabolites, the probability of observing a metabolite by chance differs for different pathways.p-value indicating the probability of finding the observed metabolites from that pathway by chance; and its third column lists those metabolites from the starting SmartTable that are present in each pathway.BioCyc provides metabolite enrichment analysis via SmartTables. Given a SmartTable of metabolites e.g., , the Enr2.7.BioCyc provides both an interactive tool for painting metabolomics data onto pathway diagrams and a web-service interface to this facility. Both approaches provide several possible display formats, which depend in part on whether a single data point  is provided for each metabolite or whether multiple data points are provided:Single data point: In this case, the supplied data values are depicted as colored boxes next to each metabolite in the pathway diagram, with the colors indicating the data values.Multiple data points: In this case, the metabolite data are depicted within individual pop-up window, one window per metabolite. These windows can be formatted as vertical bar charts, heat maps or X-Y plots.In both approaches, the metabolomics data are provided in a tab-delimited data file. We next describe that file format.2.7.1.The data-file format for supplying metabolomics data is as follows. The first column of each line specifies a metabolite, and some subsequent tab-separated columns specify metabolomics data values. Those data values can represent concentrations or any other quantity and can result from any processing chosen by the user. Note that not every data column need contain metabolomics data; the user selects which columns will be used in the visualizations, and all other columns are ignored.An excerpt from a sample data file follows (lines beginning with the “#” character are ignored):## Example metabolomics data file#1-hydroxy-2-propanone -8.85 6.72 1.65 5.12BETAINE_ALDEHYDE 8.03 -6.06 5.13 1.4ChEBI:16947 1.26 5.39 -9.71 9.8PubChem:68107 2.05 2.93 4.12 -5.78glutarate semialdehyde$BioCyc:CPD-280$KEGG:C03273 8.81 4.59 6.45 9.46ChemSpider:131491$HMDB:HMDB$01164 -5.79 1.58 5.84 8.53Because of the many alternative names and database identifiers used by different metabolomics databases and software systems, we provide a flexible mechanism for specifying metabolites. The user can specify each metabolite in one or more names or identifiers, and the software will attempt to recognize the metabolite using all of the information provided. The first data line in the preceding example shows a metabolite specified by a chemical name known to the HumanCyc database. The second line shows a metabolite specified by the BioCyc identifier used for the compound . In the third and fourth lines, metabolites are specified using their ChEBI and PubChem identifiers, respectively.The fifth and sixth lines show examples of specifying a single metabolite using multiple alternatives separated by the “$” character; in the case of Line 5, a chemical name, BioCyc ID and KEGG ID are provided. When multiple alternatives are provided, the software ignores alternatives that are unknown to it. The software reports when the alternatives provided in one line identify different metabolites.2.7.2.The interactive tool for visualizing metabolomics data on pathways can be invoked by first retrieving a pathway page at BioCyc (example: ) and the2.7.3.The visualization shown in http://biocyc.org/[ORGID]/new-image?type=PATHWAY&object=[PATHWAY]&[PARAMETER]=[VALUE]where:[ORGID] is the identifier for the organism database, e.g., ECOLI, META, AFER243159;[PATHWAY] is in the BioCyc identifier for the pathway, e.g., GLYCOLYSIS, ARGSYN-PWY, PWY0-1299;Multiple parameter/value pairs can be specified see .The omics data can be supplied in two ways: as a POST request  and as a GET request, where a parameter to the GET request is itself a URL that provides the location of the omics data on a publicly accessible web server.Additional details regarding Log parameter: Applicable when expressiontype = relative. On implies the data are log ratios that use a zero-centered scale—that the numerical data within the omics file may contain positive and negative values. The value zero is considered to be the center of the numerical values provided in the data file.Off implies the data are ratios that use a one-centered scale—any negative or zero values in the data file will be ignored. For example, the value 0.1 is considered to be at the same distance to one as the value 10. Accordingly, a logarithm of base 10 is applied to the data before the linear coloring mapping is applied.omicsPopups parameter: Omics pop-up windows are the only way to show data from multiple columns in the input file. A single column of omics data can be shown either in pop-ups or (if unspecified) as color-coded squares within the diagram.Color parameter: Data values are divided into color bins. This parameter determines which color scale is used—a color scheme that ranges from orange  through gray in the center to blue ; or from red through blue in the center to green and yellow. For each of these color schemes, two options are available:–The color bins range over the entire color scale, and the cutoff values for the color bins are derived from the data itself. As a result, different experiments could be displayed using different color schemes, making it difficult to compare them directly.–maxcutoff parameter) bin. All displays that use the same maximum value cutoff will use the same color scale (assuming other settings are the same) and are therefore directly comparable. All data values greater than the maximum cutoff value will be displayed in the highest bin color.Users may specify a value for the maximum value cutoff , purple for data values that are less than the inverse of that threshold and gray for values in between.2.8.Metabolism→ Cellular Overview.Pathway-enrichment analysis and pathway visualization of metabolomics data treat metabolomics data in a localized context, that of individual metabolic pathways. In contrast, the operations described in this section enable metabolomics data to be visualized in the context of the full metabolic network of an organism. Each BioCyc PGDB contains an automatically generated diagram called the Cellular Overview. This diagram is an organism-specific view of the metabolic pathways and transporters of the organism. The diagram is zoomable and searchable, can be colored with animated experimental datasets and is generated using the command The diagram see  is boundUsers can create colored overlays of the diagram to aid understanding of experimental datasets, including gene expression, metabolomics and multi-omics datasets. Experimental data are uploaded via a tab-delimited file using the format described in Section 2.7.1. Its first column identifies the entity to be colored; its subsequent columns contain numerical values that determine the coloring. If more than one numerical column is specified for coloring, an animated view is generated in which each time-point of the animation corresponds to one data column. Each frame of the animation shows a different column of data.3.The BioCyc.org website provides a number of operations that allow scientists to analyze metabolomics data in the context of metabolic pathways. Scientists can search for metabolites by chemical formula and monoisotopic molecular weight. The site enables translation of metabolite identifiers across multiple metabolite databases. Researchers can store metabolite lists as SmartTables within their BioCyc account. SmartTables enable exploration of the relationships among metabolites via transformations and set operations, and SmartTables can be shared with selected collaborators or made publicly accessible. SmartTables also support metabolite enrichment analysis, an operation that identifies metabolite sets that are statistically over-represented for the substrates of specific metabolic pathways. BioCyc also enables visualization of metabolomics data on individual pathway diagrams and on the organism-specific metabolic map diagrams that are available for every BioCyc organism. Most of the preceding operations are available both interactively and as programmatic web services.


Adjusting the capacity of metabolic pathways in response to rapidly changing environmental conditions is an important component of microbial adaptation strategies to stochastic environments. In this work, we use advanced dynamic optimization techniques combined with theoretical models to study which reactions in pathways are optimally targeted by regulatory interactions in order to minimize the regulatory effort that is required to adjust the flux through a complex metabolic network. Moreover, we analyze how constraints in the speed at which an organism can respond on a proteomic level influences these optimal targets of pathway control.We find that limitations in protein biosynthetic rates have a strong influence. With increasing protein biosynthetic rates the regulatory effort targeting the initial enzyme in a pathway is reduced while the regulatory effort in the terminal enzyme is increased. Studying the impact of allosteric regulation for different pathway topologies, we find that the presence of feedback inhibition by products of metabolic pathways allows organisms to reduce the regulatory effort that is required to control a metabolic pathway in all cases. In a linear pathway this even leads to the case where the sole transcriptional regulatory control of the terminal enzyme is sufficient to control flux through the entire pathway. We confirm the utilization of these pathway regulation strategies through the large-scale analysis of transcriptional regulation in several hundred prokaryotes.This work expands our knowledge about optimal programs of pathway control. Optimal targets of pathway control strongly depend on the speed at which proteins can be synthesized. Moreover, post-translational regulation such as allosteric regulation allows to strongly reduce the number of transcriptional regulatory interactions required to control a metabolic pathway across different pathway topologies.The online version of this article (doi:10.1186/s12859-015-0587-z) contains supplementary material, which is available to authorized users. The control of metabolic pathways has been studied in-depth in the context of metabolic control analysis MCA, ,2). This. This2])Saccharomyces cerevisiae . If not stated otherwise, 200 optimization runs with randomized parameter and dilution values were conducted for each analysis. For the consideration of limitations in protein biosynthetic rates, we additionally constrained concentrations changes of enzymes to a maximum of m: Optimizations were performed over 30 (arbitrary) time units. For randomized runs, kinetic parameters were uniformly drawn from the interval  as done previously in  and valuFor more information, see Additional file tf and sampling approach for kinetic constants on results. We found that our conclusions for the basic optimization problem  .The non-linear character of the models and the presence of algebraic constraints may induce multimodality. Therefore, global NLP solvers are required. In the context of dynamic optimization, the importance and efficiency of global optimization techniques has been discussed in depth  and ref,23.In this work we use the enhanced scatter search method, eSS , which oE. coli. Therefore, we used the length of promoter regions as an indicator for the regulatory effort that is used to control a specific gene as done previously [The number of transcription factor binding sites as well as the number of different transcription factors controlling each gene are available only for a selected number of organisms such as eviously . Thus, wPromoter lengths were determined as the length of the region upstream of the first gene of the operon to which the gene belongs  based on the annotation provided in the MicroCyc database . Operon To make promoter lengths comparable across organisms, we first made them comparable across the pathways within a single organism and subsequently across several organisms. To make promoter lengths comparable across pathways within a single organism, we subtracted the average promoter length of genes within this pathway from the promoter length of each gene. This is necessary since factors such as protein abundance have a strong influence on the regulatory effort targeted at a gene  and therSeveral lines of evidence support that promoter lengths are a good indicator of the number of transcription factors regulating a gene and thereby of the regulatory effort targeting this gene.E. coli, they make up only 12% of all known transcription factor binding sites in EcoCyc release 14.6 [First, most parts of bacterial genomes are made up of coding regions and non-functional elements of the genome are rapidly lost, in particular due to a bias towards deletions in bacterial genomes ,28. Sincase 14.6 . MoreoveE. coli and found a significant increase in the length of promoter regions with the number of known transcription factors controlling a gene . ClassiE. coli previously [Third, we find increased promoter lengths for initial and terminal enzymes in transcriptionally sparsely regulated metabolic pathways  sites of proteins were retrieved from the data base dbPTM , which cp-value =1.27·10−5 between regulatory effort for m=0.06 and m=0.15) and a concomitant increase at the terminal step of the pathway  and a significant increase at the end of pathways . We performed this analysis in more detail for two pathways amongst the transcriptionally sparsely regulated pathways in the largest number of organisms: tryptophan biosynthesis and protoheme IX biosynthesis, an intermediate of heme biosynthesis. In both cases we observed a decrease in the regulatory effort targeting the first reaction and an increase in the last reaction of the corresponding pathways for organisms with faster protein biosynthetic rates . Subsequently, we determined the average relative length of promoter regions for the initial as well as terminal enzymes in sparsely regulated metabolic pathways Figure B. In a dE. coli is stronger at the end of metabolic pathways than at the beginning [E. coli has a high copy-number of rRNAs in comparison to other prokaryotes.These results also provide an explanation why we observed that the increase in the frequency of transcriptional regulatory interactions in pathways in eginning , since Ee1 of the pathway by the product P \dockr,1 corresponding to the strength of the feedback inhibition. For increasing values of kr,1 inhibition is weaker and for decreasing values it is stronger.with σ using unit inhibitory constants  was significantly decreased across all cases. This was mostly marked for a low weight of initial enzyme concentrations  while it was not as strong for a high weight of initial enzyme concentrations . Though we observed a tendency of the initial concentrations of enzymes to increase with introduction of the feedback inhibition, this increase was not significant .We did not observe a significant change in the objective function values after introduction of the inhibition across both weights for initial enzyme concentrations Figure A. The cop-value <10−16) for a low weight of initial enzyme concentrations. In consequence, particularly for pathways with lowly abundant proteins  the introduction of a feedback inhibition appears to relieve the requirement of a control of the first enzyme. Thus, due to the presence of the feedback inhibition, the flux through the entire pathway can be controlled through transcriptionally regulating the terminal step of the pathway. These results substantiate the observation that flux through a pathway can be controlled much more precisely through a regulation of the terminal enzyme. This is in contrast to the classical biochemical picture of pathway control in which the first enzyme has been considered the most relevant [Analysing changes in the regulatory effort targeting individual enzymes Figure , we founrelevant . Howeverp-value =2.8·10−4). Thus, while protein abundance has an influence on the strength of the reduction, it is significant across all the considered cases. This is in line with previous observations that the utilization of post-translational regulation is not influenced by the abundance of proteins [For a high weight of initial enzyme concentrations the reduction in the regulatory effort was reduced but still significant .The regulatory effort in different pathway positions is displayed in Figure These results show that in the case of a metabolic pathway with two convergent branches, it is still sufficient to control both the initial and the terminal steps of the pathway and there is no regulation around the converging step. Thus, the flux through the entire pathway, also after the branch, can be controlled by the initial enzymes of the individual pathway branches.p-value =0.29).To verify the prediction that there are no differences in the regulatory effort around a convergent branch, we analyzed the average length of promoter regions of proteins catalyzing the corresponding reactions across our collection of prokaryotes. In confirmation of the optimization results, we did not observe a change in the length of promoter regions between reactions prior to a converging reaction and the converging reaction itself  were regulated . A specific example for the regulatory effort before and in a set of branching reactions in adenosine nucleotide biosynthesis is shown in Figure To test the predictions of the optimization, we analyzed the relative length of promoter regions of enzymes catalyzing reactions before and after branching reactions in the metabolic networks of our prokaryote collection. We found that in sparsely regulated metabolic pathways there was a significant increase in the length of promoter regions of enzymes after the branching reaction compared to the preceding reactions  an d Figure A, 2) an e3 and e4).In general, we observed a decrease in the regulatory effort for enzymes targeted by feedback inhibition Figure B. In theE. coli metabolism including aromatic amino acid biosynthesis, the combined metabolism of lysine, methionine as well as threonine, branched chain amino acid biosynthesis and purine biosynthesis . The relative difference in promoter lengths was largest for initial reactions. Thus, the in vivo validation confirmed that post-translational regulation reduced the amount of regulatory effort targeted at an enzyme. Beyond the consideration of optimal programs for pathway regulation this result also represents a very interesting aspect considering that it is usually assumed that post-translational and transcriptional regulation act on completely different time-scales [For transcriptionally sparsely regulated metabolic pathways, we compared how the presence of post-translational regulation influenced promoter lengths at different pathway positions Figure . We obsee-scales . Our optIn this work we used simplified models of metabolic pathways to study the influence of protein synthesis rates as well as feedback inhibition on optimal programs for the control of metabolism. Considering constraints on protein synthesis rates, we observed that a slower protein synthesis rate entails a reduction of the regulatory effort in the terminal step with a concomitant increase at the beginning of pathways. Through an analysis of the regulatory effort in pathways in a large number of prokaryotes, we could confirm these predictions. Considering individual organisms, these results also imply that environmental conditions might influence the optimal strategy to control a metabolic pathway. Since protein biosynthetic rates are strongly influenced by environmental conditions , there mE. coli metabolism. These results are of particular importance in the context of gene expression analysis. There it is often implicitly assumed that changes in the expression of enzymes are equally important across all enzymes of a pathway. As our results show and additionally to the influence of allosteric regulation, transcriptional control favours those enzymes that provide better control of the flux through a pathway. Thus, depending on the topology of the metabolic network, changes in the expression of key enzymes are likely much more relevant than those of other enzymes of the same pathway. In the extreme case, a transcriptionally sparsely regulated metabolic pathway might only show changes in the expression of the terminal enzyme which will be certainly missed by conventional gene set enrichment analyses [Analyzing the relevance of post-translational regulation with feedback inhibition as an example for pathway control, we found that post-translational regulation allowed to decrease the regulatory effort that is required to control a pathway for a wide variety of pathway topologies. We confirmed these results through the analysis of the interplay between transcriptional and post-translational regulation in our organism set. As a result, for the full control of a linear metabolic pathway it is sufficient to only regulate the terminal enzyme if the initial enzyme is inhibited by the product of the pathway. For branching pathways, we found that the optimal control points we identified resembled known patterns of feedback inhibition implemented in analyses . Howeveranalyses , there e


An important challenge in cancer biology is to computationally screen mutations in cancer cells, separating those that might drive cancer initiation and progression, from the much larger number of bystanders. Since mutations are large in number and diverse in type, the frequency of any particular mutation pattern across a set of samples is low. This makes statistical distinctions and reproducibility across different populations difficult to establish.In this paper we develop a novel method that promises to partially ameliorate these problems. The basic idea is although mutations are highly heterogeneous and vary from one sample to another, the processes that are disrupted when cells undergo transformation tend to be invariant across a population for a particular cancer or cancer subtype. Specifically, we focus on finding mutated pathway-groups that are invariant across samples of breast cancer subtypes. The identification of informative pathway-groups consists of two steps. The first is identification of pathways significantly enriched in genes containing non-synonymous mutations; the second uses pathways so identified to find groups that are functionally related in the largest number of samples. An application to 4 subtypes of breast cancer identified pathway-groups that can highly explicate a particular subtype and rich in processes associated with transformation.http://www.visantnet.org/misi/MUDPAC.zip.In contrast to previous methods that identify pathways across a set of samples without any further validation, we show that mutated pathway-groups can be found in each breast cancer subtype and that such groups are invariant across the majority of samples. The algorithm is available at The online version of this article (doi:10.1186/1471-2164-15-605) contains supplementary material, which is available to authorized users. Large collaborative efforts, including the Cancer Genome Atlas (TCGA)  and the Common approaches hypothesize that mutations conferring a selective advantage on tumor initiation and progression will occur at high frequency across a wide range of tumor samples. The useful implementation of this concept, however, faces a number of well know challenges. First, the vast majority of drivers occur only rarely, making them difficult to detect statistically . Second,One way to proceed is to identify known pathways that are enriched in genes carrying somatic mutations , 7. MoreHowever, very few methods involve a principled procedure for taking account of pathway interactions, i.e. pathways that are mutated in the same sample, and that are mutated together across a large subset of samples. We develop a two-step procedure - referred to as Mutational Driver Pathway Collaboration (MUDPAC) - for identifying groups of interactive pathways, and apply it to the analysis of 4 breast cancer subtypes.MF) and a gene Interaction Factor (IF). The MF scores a gene by a weighted difference between its non-synonymous and synonymous mutation rates. The IF takes account of mutational patterns, i.e., we allow the score of a mutated gene g* in the list to depend on the number and type of mutated genes  in its functional neighborhood, as explained in Methods. The ranked list of genes is analyzed in the usual way [The first step identifies candidate driver pathways using a mutational pathway enrichment analysis, based on a modification of Pathway Enrichment Analysis (PWEA) developed previously in our Lab . Genes asual way  to identThe second step searches the identified pathways for combinations that are most informative about a particular cancer subtype, assuming that pathways involved in producing a distinct cancer phenotype will tend to be cooperative, i.e., they will be simultaneously aberrant in the majority population of samples. The main idea here is that patients with the same cancer subtype will have some common set of perturbed cellular functions.We applied the method to 29,900 somatic mutations identified in 11,897 genes from 498 breast cancer (BRCA) patients distributed over four subtypes annotated in the Cancer Genome Atlas (TCGA) project : Basal-lDetails of the algorithm are described in Methods, while a brief introduction is summarized here, with a schematic representation shown in Figure MF), i.e., the difference between the impact of non-synonymous and synonymous mutations, and an Interaction Factor (IF), which takes account of the mutational landscape in the functional vicinity  of a particular gene. Pathways whose genes are significantly overrepresented toward the top of the ranked list are then identified as candidate driver pathways.In the first step of MUDPAC we proceed in analogy to Gene Set Enrichment Analysis (GSEA) , which iP < 0.01) in comparison to the case where mutations of the genes in the new pathway are distributed randomly (see Methods for details) across the samples; (ii) the new MCR after the addition of the new pathway is at least 5%  the largest number of samples, and then iteratively adds new pathways to form a collaborative group that achieves a Maximal Coverage Rate (MCR), which is the percentage of samples covered by current set of driver pathways. This is done subject to the restrictions that (i) the collaboration (mutation co-occurrence) between a new pathway and all previously selected pathways is statistically significant . PI3k-Akt is therefore selected as the first pathway of the collaborative set because the MCR of the pathway is much higher than the single gene mutation rate (threshold = 5%). We then searched for the next pathway that has the largest coverage rate in the samples covered by the collaborative pathway set. That turns out to be the focal adhesion pathway. The MCR of these two pathways is 70%, and the gene with the highest mutation rate in the focal adhesion pathway is still PIK3CA, therefore the MCR is again much higher than the mutation rate of any single gene in the focal adhesion pathway. In addition, the collaboration between two pathways with an MCR 70% is statistically significant based on 5000 permutation test by randomly distributing the mutations of the genes in focal adhesion pathway across the different samples (P < 0.0002). The focal adhesion pathway is therefore selected as part of the set. The search procedure continues interactively until no qualified pathways can be found.As shown in Figure We started with the Top 60 pathways obtained in Step 1, and identified the pathway that has maximum coverage rate, which turns out to be PI3k-Akt. The MCR of PI3k-Akt pathway is 78% and P < 0.000. The focTP53 (mutation rate 82%), is the most homogeneous subtype in breast cancer, so there are only 2 collaborative pathways selected with MCR = 88%. The luminal subtypes, on the other hand, are reported as heterogeneous, with the largest number of mutated genes but low overall mutation rates [PIK3CA is the highest mutated gene in both Luminal-A and Luminal-B with mutation rates of 44% and 31% respectively, and Luminal-B includes 15 collaborative pathways with MCR as low as 36%. Luminal-A, however, is not as obviously heterogeneous as Luminal-B, with only 6 pathways identified and MCR equal to 49%. This might explain its better prognosis and lower relapse rate in comparison with Luminal-B.The Basal-like subtype, which is dominated by TP53 (mutation rate 72%) related pathways and PIK3CA (mutation rate 40%) related pathways.HER2+ is closely related to Luminal-B. Previous studies found that about 50% of clinically HER2+ tumors are observed predominantly in luminal mRNA subtypes . Our resA list of driver pathways identified in each of the subtypes can be seen in Table TP53, HER2+ subtype is a series function of TP53, ITGAV, PTEN, TLR4, COL5A3, CDKN1B and PIK3R1, in descending order of the gene score. In Luminal-A subtype, PTEN and PIK3CA are the most important genes while CDKN1B and KIT also play roles in perturbing this pathway. Luminal-B is mainly affected by PTEN and TP53, but TLR4, PIK3CA, KIT and LAMA5 also contribute to functional disruption.Although the PI3K-Akt signaling pathway is common to all subtypes, the significantly mutated genes of this pathway in different subtypes are quite different, as shown in Figure TP53, PTEN, PIK3R1, PIK3CA, KIT are all oncogenes or tumor suppressing genes that have records in the Catalogue of Somatic Mutations In Cancer (COSMIC) (http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/) or the Online Mendelian Inheritance in Man (OMIM) (http://www.omim.org/), and many of the genes have been reported to be associated with cancer or other diseases. For example, it is proposed that protein encoded by ITGAV interacts with several extracellular matrix proteins to mediate cell adhesion and may play a role in cell migration. This protein may also regulate angiogenesis and cancer progression [CDKN1B is found to bind to and prevent the activation of cyclin E-CDK2 or cyclin D-CDK4 complexes, therefore controls the cell cycle progression at G1 and its polymorphism appears to be an important predictive factor for breast cancer risk [For the genes mentioned above, gression . The procer risk .The KEGG Color Pathway 2D diagrams for every driver pathway of all the 4 subtypes can be seen in Additional File TP53 mutations, suggesting the loss of TP53 function occurs within most of basal-like cancers [The driver pathway set in the basal-like subtype includes pathways of “PI3K-Akt” and “MAPK signaling”, with a MCR of 88%. As shown in Figure  cancers , 30. AltHER2 gene; while the enrichment of TP53 associated pathways, including “PI3K-Akt signaling” [TP53. They are shared with basal-like subtype.A pathway collaboration of size 16 was identified with a MCR of 46% in this subtype. Most of these pathways are associated with two genes: the mutational enrichment of pathways such as “ErbB signaling”  and “Focgnaling”  and “MAPgnaling” , probablERBB2 amplification into Luminal B and HER2+ [The HER2+ subtype shares most of its driver pathways with luminal subtype, especially Luminal-B: not only the number and pattern of MCR of driver pathway collaborations, but also the involved pathways. This agrees with the clinical-pathological view that in general it may not be necessary to divide samples with nd HER2+ .RANK and immune receptors.The exclusive pathways in this subtype are “TNF signaling”, “Osteoclast differentiation” and “Aldosterone-regulated sodium reabsorption”. The TNF signaling pathway induces a wide range of intracellular signaling pathways including apoptosis and cell survival as well as inflammation and immunity. TNFα, which is expressed by nearly all cells and is the major receptor for TNF, can cross-talk with the EGFR/HER2 pathway at various points and affect the sensitivity to EGFR/HER2 inhibitors . And oveFrom a pathway category perspective, we found that besides categories of “Signal transduction” and “Cellular Processes” that are common among all subtypes, HER2+ has functional disruptions in “Organismal systems”, including “Immune system” , “Nervous system” (“Cholinergic synapse”), “Endocrine system” , “Development” (“Osteoclast differentiation”), “Excretory system” (“Aldosterone-regulated sodium reabsorption”), and “Digestive system” (“Carbohydrate digestion and absorption”). A direct and complete relation of HER2+ with organismal systems has not been found, but some of the pathways above were indeed highlighted with their considerable connections, like “Insulin signaling”  and “CheSome of the pathway collaborations could be supported by the known drug resistance. Trastuzumab (Herceptin) is a clinically approved antibody for HER2-overexpression of breast cancer , but mul6 driver pathways have been identified with an MCR of 49%. There are no unique pathways in this subtype. In addition to the common pathway “PI3K-Akt signaling” that is present in all subtypes, there are 2 pathways shared with HER2+, another 2 pathways shared with Luminal-B and 1 pathway shared by both HER2+ and Luminal-B. The 2 pathways that only appear in luminal subtypes are “Estrogen signaling” and “Jak-STAT signaling”. The estrogen signaling pathway is a new pathway released by KEGG in 2013. It is not surprising that this pathway only appears in the luminal subtypes, as the luminal subtypes are characterized by the expression of genes activated by estrogen receptor transcription factor that are typically expressed in the luminal epithelium lining the mammary ducts . The rolThe number of driver pathways in Luminal-B is 15 and they cover a MCR of 36% tumor samples. Compared with Luminal-A, Luminal-B always comes with a more difficult prognosis and a higher recurrence rate. A lot of efforts have been made to distinguish these two subtypes, here we focus on the differences at a driver pathway level. We found that both of these two subtypes are active in signal transduction, like “PI3K-Akt signaling”, “Jak-STAT signaling”, but Luminal-B tends to be more active with more mutated pathways in this category, including “HIF-1 signaling”, “mTOR signaling”, “ErbB signaling” and “Phosphatidylinositol signaling system”. “HIF-1 signaling” and “mTOR signaling” pathways are the unique pathways in Luminal-B. The hypoxia inducible factor-1 (HIF-1) is discovered to be interchangeable with estrogen as both similarly modulate epithelial-endothelial cell interaction , HIF-1α Although both Luminal-A and Luminal-B are estrogen positive, Luminal-B seems to be more enriched in endocrine system pathways. Besides “Estrogen signaling”, there are two other endocrine related pathways in Luminal-B, “Progesterone-mediated oocyte maturation” and “Insulin signaling”. The “Progesterone-mediated oocyte maturation” pathway is exclusive to Luminal-B. The transition is accompanied by an increase in maturation promoting factor MPF or Cdc2/cyclin B, which is the main biological difference between two luminal subtypes found so far , 54. InsCross-talk between estrogen receptor and growth factor has been discussed broadly to act as a molecular target for tamoxifen resistance , 58. TheTP53*, NF1*, LAMA5, ITGAV, NFKB2, PRKAA2, CACNA1B, LAMA1, IL2RB, PTEN), HER2+ , Luminal-A , Luminal-B . There are 24 unique genes in the 4 different groups, 8 of them have overlap with genes in COSMIC or OMIM, marked with *, the remaining 16 of them also reflect potential relations to cancer, like LAMA5 is implicated in a wide variety of biological processes including cell adhesion, differentiation, migration, signaling, neurite outgrowth and metastasis; overexpression of ILK is implicated in tumor growth and metastasis, which makes it an attractive target for cancer therapeutics [ERBB3 and/or overexpression of its protein have been reported in numerous cancers, including prostate, bladder, and breast [Finally, our results identify the key genes in all driver collaborative pathways for each subtype. In theory, we assume all the genes in driver pathway collaborations are with very high probability to be driver genes as they cooperate with each other to make a unique mechanism, but we are more interested at those genes with higher mutation scores. We ranked all the genes collected from driver pathway collaboration in each subtype according to their ranking score, and output Top 10 of them in ranking score descending order: Basal-like  , and alsAlthough a number of methods have been developed to identify driver pathways in cancer, including Mutual Exclusivity Modules (MEMo) , MutatioMulti-Dendrix recovers pathways from mutual exclusivity pattern of genes without any prior information about their interactions, while MUDPAC focuses on KEGG pathways to take account of the topological influence on our discoveries. The main drawback of Multi-Dendrix is that it can only find optimal solutions with limited number (2–4) of very small pathways (3–5 genes only) . MUDPAC TP53. For example, both p53 signaling and cell cycle are in the Top 60 significantly enriched pathways in the Basal-like subtype, but the MCRs of these two pathways are 80% and 81% respectively, comparable to and slightly less than the single gene mutation rate of TP53 (82%). Cell cycle pathway is also enriched in HER2+ subtype whose MCR (72%) can’t meet the required 5% difference to the single gene mutation rate of TP53 (72%).For the 4 pathways that were found in BRCA by Multi-Dendrix, 2 of them also showed up in our results , the other two  were selected as significantly mutated pathways in our Step 1 but filtered out in Step 2, as we found that the mutational contributions of these two pathways are dominated by We also compared the sample coverage of driver pathways for both methods. The highest and lowest sample coverage for Multi-Dendrix are 61% and 36% respectively, but 88% and 36% respectively for MUDPAC.Finally, Multi-Dendrix is not designed to discover subtype-specific pathways while MUDPAC is aimed to identify subtype-specific collaborative set of pathways because it is built based on the hypothesis that different cancers are resulted from the different systematic failure of cellular functions. As clearly shown in Figure TP53; ERBB2 related pathways and some TP53 related pathways tend to be active in HER2+ tumors. In addition, HER2+ shares most of its characters with Luminal-B, not only the number and pattern of MCR of driver pathway collaborations, but also the involved pathways. Furthermore, HER2+ is the subtype that has the largest number of enriched pathways in organismal systems . PIK3CA related pathways seem to play more important roles to drive the disease in luminal subtypes because most of these pathways appear in the corresponding driver pathway set. The distinction between Luminal-A and Luminal-B is that Luminal-B seems involving more pathways in the category of signal transduction, endocrine system, as well as immune system.The distributions of mutated genes within driver pathways, as well as their corresponding mutational frequencies, are also interesting. Figure MUDPAC identifies representative driver pathways for all 4 subtypes of breast cancer using TCGA data sets. The number of driver pathways in Basal-like, HER2+, Luminal-A and Luminal-B are 2, 16, 6 and 15 respectively. The common pathway for all subtypes is PI3K-Akt signaling. Some subtype-specific pathways are also found such as estrogen signaling pathway in both luminal subtypes and ErbB signaling pathway in both HER2+ and Luminal-B. Driver collaborative pathways in Basal-like are all associated with Several factors may impact our results, including single gene dominance, high mutation frequency and our limited knowledge of pathways. Some of them have been addressed in the current methods while others may need further development.TP53 and PIK3CA in our study. We take two steps to address this problem. First, the enrichment analysis filters out those pathways where only one or two genes have mutations; second, we require the MCR must be higher than the single gene mutation rate in any driver pathway by some threshold. By doing this, we still find that there are influences of single genes penetrating through collaborative pathways in each subtype, like pathways in Basal-like are all TP53 related, pathways in Luminal-A and Luminal-B are all PI3K related, while pathways in HER2+ are a mixture of these two genes. Although the influences of these two genes in breast cancer have been well established [PIK3CA and Luminal-A subtype as examples, there are totally 30 pathways including PIK3CA as input of our algorithm, 13 of them were selected as Top 60 in the first step of our method, which filtered out some pathways that are not highly enriched in mutations, and only 6 out of these 13 survived after Step 2, which adopted a more strict criteria to unearth the correlations among selected pathways. This indicates that our 2-step framework works efficiently to locate sets of distinguishable pathways for each subtype that are highly correlated with each other in the maximum number of samples. The collaborative pathway patterns for each subtype without considering single gene effects can be seen in Additional file The single gene dominance points to the case where one gene has high mutation frequency in several different pathways, such as TP53 in KEGG pathway database (excluding disease related pathways) as our input, 5 of them showed up in the Top 60 of Step 1 for this subtype, only 2 of them were considered as collaborative pathways in Step 2. The other 3  are also important and related to cancer; and the reason they are not selected is that they are dominated by single gene effects with MCRs equal or lower than the mutation rate of TP53. In addition, they are not significantly correlated with 2 identified pathways.Our method may fail if there are individual genes whose mutational rates are very high for a given cancer , because the statistical significance of pathway collaboration in our current method will be difficult to achieve. As a result, very few pathways may be identified as the set of driver pathways. The Basal-like subtype, the subtype of breast cancer that is most similar to ovarian cancer, may be impacted by both single gene dominance and high mutation frequency. There are a total of 7 pathways that consist of Another limitation of MUDPAC is that present analysis is based on prior knowledge of known pathways in KEGG only. This is far from accurate if taking into account the incompleteness of human pathways or missing topology information. We need to make the definitions of this method feasible to a more general network. It could be possible to expand the pathway data set using other data sources, like Functional Linkage Network (FLN)  or HumanBRCA1/BRCA2 for example, increase the risk of breast and ovarian cancer [One potential improvement of MUDPAC is to include inherited mutations when performing mutational pathway enrichment analysis. It is well known that for most sporadic cancers, somatic mutations are accumulated and restricted to an individual cell of the body through a human being’s lifespan. However, cancer also has heritable components. Inherited mutations in n cancer . If heriIt will also be useful to meliorate MUDPAC by integrating a larger panel of driving alterations beside the mutations, both genetic and epigenetic, such as somatic copy number alteration (SCNA), methylation and transcription factor (TF) etc. Identifying the oncogene and tumor suppressor gene targets of driver SCNAs and elucidate the functional roles of SCNAs have been considered important in cancer diagnostics and therapeutics. It is well recognized that the known oncogenes as well as novel ones involved in the significantly altered regions would enable researchers to identify new causes and molecular pathways that may one day be targeted to treat cancer . It is aMUDPAC identifies collaborative driver pathways in cancer using a two-step approach: mutational pathway enrichment analysis followed by greedy search for the collaborative driver pathways. The enrichment analysis examines whether a given pathway shows statistically significant differences between non-synonymous mutation group and synonymous mutation group while the greedy search identifies the pathway group from the top enriched pathways that achieves a Maximal Coverage Rate (MCR) in a sufficient number of tumor samples. In comparison to the previous approaches, MUDPAC discovers driver pathways not only by their enrichment of mutated genes but also their invariant presence among majority of samples that has been ignored previously. Using four subtypes of breast cancers as examples, our results reasserted that collaborative pathways rather than individual pathway are much more effective in discovering and interpreting the biological correlations for the complicated diseases , 76. TheMUDPAC requires two input data sets: mutation data in mutation annotation format (.maf), and pathway data with topology information. The .maf file was downloaded from TCGA on March, 2013. The sample subtype information was obtained from supplementary table of , where Pn genes G = {g1, g2, …, gn}, m samples S = {s1, s2, …, sm}, MuSiC [gi having available alignment data. MuSiC is a comprehensive mutational analysis pipeline that establishes correlations among mutation sites, genes and pathways. It uses Broad Institute’s analysis infrastructure Firehose to count bases with sufficient coverage of each gene from the given wiggle tract format file. Wiggle files contain dense, continuous data such as GC percent, probability scores, and transcriptome data, and were downloaded from http://gdac.broadinstitute.org on Aug 2012. The thresholds for sufficient coverage are at least 8 fold read depth in normal tissue, and at least 14 fold read depth in cancer tissue.Given }, MuSiC  is used MAk is calculated using MutationAssessor [k in a TCGA data set. A simple scoring rule similar to [The functional impact score Assessor  for eachmilar to  is develM of size 2n*m is formed with a pair of rows for each gene. The elements of alternating rows contain mutation scores based on synonymous and non-synonymous mutations, which we define Mij_* ) for gene i in sample j as the number of mutated bases, weighted by the functional impact score of this mutation, divided by the total number of bases that have sufficient read depth.A matrix MFi for gene i is defined as difference between the synonymous and non-synonymous mutation impacts, averaged across all samples, multiplied by a gene-specific weighting factor. The weighting factor is the ratio of the number of non-synonymous (Ni_NSY) to synonymous (Ni_SY) mutations for gene i.The mutation factor IF) for a gene gi, which is to be ranked, is designed to take account of the mutation landscape in its functional neighborhood [The interaction factor  The shortest distance een them  under thCij measures the covered samples by gi and gj, which is defined as the fraction of samples in which only one of the genes, either gi or gj, is non-synonymously mutated.(ii) MEij measures the mutual exclusivity of gi and gj, which is defined as the number of samples in which only one of the genes is non-synonymously mutated, divided by the number of samples in which at least one of the genes is non-synonymously mutated [(iii)  mutated .IF of a gene i as the average mutational influence that this gene imposes on the rest of the genes in the pathway [IFi means the pairwise mutational relation dominated by gene i exhibits a relatively neutral effect to the pathway [IFi is high, gene i could aggregately perturb the pathway by a strong connection to others. IFi only counts genes that with interaction to gene i higher than a threshold of α, which is used to control the sensitivity and selectivity of the IF and is set as 0.005 in our following experiment.We define the  pathway . Smaller pathway . ConversIFi for gene i in pathway k is defined as the interaction i and gene j, averaged over all genes in the pathway.The log of the interaction factor WhereΘ is a function that compares if fij satisfy a pre-defined threshold α, which is used to control the contribution of gi to fij.The ranking score for each gene is computed by combining the mutation and interaction factors for that gene.MF can be either positive or negative and 0 < IF < 1, exponential function is applied to make sure the ranking score increases as MF and IF increases.Since For a given pathway, the scores of those genes outside the pathway are imputed using information from genes inside the pathway since their topology information is not available. In practice, the imputations are performed after the scores of all genes from all pathways are computed, i.e., a background distribution is generated by parameterizing from the mean and standard deviation of all gene scores from all pathways instead of individual pathways. Scores for the genes outside a pathway are then calculated by drawing random samples from the background distribution.Θ, MUDPAC measures the possibility of passing event for genes inside the pathway and applies this possibility to the genes outside the pathway while performing imputation, i.e., imputation will be only carried out when a passing event happens. The aim of this is to keep the same percentage of genes inside a pathway and outside a pathway in order to maintain the distribution of their IF scores. Imputations of IF scores for genes outside a pathway are important for fair ranking to avoid artificial bias toward genes inside a pathway.Under the consideration that not all genes within a pathway can pass Pk and that are not in Pk at position i in the rank can be written as:The statistical significance of each pathway is calculated by Weighted Kolmogorov–Smirnov (WKS) test. The cumulative distribution function (CDFs) of genes that are in a pathway andNNot Pk is the number of all genes not belonging to Pk. The maximum deviation (MD) of CDF and CDF is calculated after n permutations of mutation status shuffling and the P-value of pathway Pk is the percentage of iterations that have higher maximum deviations than the original data. In our experiments, n is set to 5000.where P-values for all pathways are computed, FDR is calculated to correct for multiple testing with FDR = P*m/k, where m is the total number of pathways and k is the rank of the pathway under consideration.After To access the biological relevance of the identified candidate driver pathways, a greedy algorithm is developed to identify sets of cooperative pathways by examining if the co-occurrence patterns of pathways are conserved in the majority of cancer samples, with the requirement that the Maximal Coverage Rate (MCR) of driver pathways is higher than the maximum mutation rate of any single genes in a given pathway. We use the Top 60 pathways identified in Step 1, and rank each by its coverage rate, i.e. by the number of samples in which it has at least one non-synonymous mutation. We then walk down the ranking list, starting with the pathway of highest coverage rate, add one more pathway in each step to achieve the MCR with existing collaborative pathways and satisfy the two criteria detailed below. The algorithm terminates when there are no pathways satisfying above constrains can be added.P-value is computed as the percentage of times when the background MCRs are greater than or equal to the observed MCR of the selected pathway. Pathway can be selected only if it satisfies a P-value threshold of 0.01. If the pathway with the highest MCR does not satisfy these two criteria, the pathway with second highest MCR will be examined, so on and so forth.Two more criteria are considered when selecting a new pathway into the collaborative set. First, the newly selected pathway along with all pathways already in collaborative pathway set, should have a MCR higher than the highest mutation rate of genes in this particular pathway by a given threshold, which is set to be 5% in this study  for mutational pathway enrichment analysis. It also contains supplementary figures: KEGG 2D color plots of all driver pathways for each subtype. (DOC 1 MB)Additional file 1:Supplementary tables. This .xls file contains details of selected pathway and pathway collaboration for each subtype. (XLSX 65 KB)Additional file 2:


The model classes translate into restrictions of the pathways in the network topology. Wnt dependent models are more flexible than Wnt independent models. Bayes factors are the standard Bayesian tool to compare different models fairly on the data evidence. In our analysis, the Bayes factors depend on the number of potential Wnt signalling target genes included in the models. Stability analysis with respect to this number showed that the data strongly favours Wnt ligands dependent models for all realistic numbers of target genes.Cellular signalling pathways consolidate multiple molecular interactions into working models of signal propagation, amplification, and modulation. They are described and visualized as networks. Adjusting network topologies to experimental data is a key goal of systems biology. While network reconstruction algorithms like nested effects models are well established tools of computational biology, their data requirements can be prohibitive for their practical use. In this paper we suggest focussing on well defined aspects of a pathway and develop the computational tools to do so. We adapt the framework of nested effect models to focus on a specific aspect of activated Wnt signalling in HCT116 colon cancer cells: Does the activation of Wnt target genes depend on the secretion of Wnt ligands or do mutations in the signalling molecule  Cellular signalling pathways like the Wnt pathway can be represented as networks modelling signal propagation, amplification and modulation of multiple molecular interactions. An important objective of systems biology is learning and adjusting the network topologies of pathways from experimental data.During signal propagation, upstream components of the pathway control the activation of downstream components and, indirectly, the expression of target genes. Perturbations of signalling components affect the regulation of target genes and these gene expression changes reflect the topology of the pathway: Blocking an upstream component like a molecule of a receptor complex automatically blocks the activation of downstream components like signalling mediators or transcription factors. In fact, we use this concept as a definition of upstream/downstream relationships in pathways. With this definition, upstream components change the expression of more target genes then downstream components. Nested effects models (NEMs) implement this concept into an algorithm for inferring pathway topologies –3. They A new perturbation data set can support the current working model of a pathway or it can falsify it. NEMs are statistical tools designed to analyze such data. They predict a topology that can be compared to the current model. To apply the basic NEM algorithm, we must specify all signalling components in a pathway, silence or inhibit all of them, monitor all target genes, and provide this data as input to the algorithm, which returns the best scoring pathway topology. There can be many obstacles to this enterprise: Lists of a pathway’s signalling components and target genes are incomplete, or they differ between references. It might be too expensive or even impossible to perform all perturbation experiments. Even if we accomplish all experiments, the pathway with all its fine details can be so large that computing its topology becomes prohibitive. We argue that in these cases resolving the full pathway is too ambitious.yes or a no. The basic NEM algorithm will provide a complex topology. A topology that with limited input data is very questionable. However, if we combine nested effects models with statistical principles like model comparison and stability analysis we can address the questions directly focussing on the single bit of information desired. In a statistical case study on Wnt signalling in HCT116 colon cancer cells, we show how properties of the pathway’s topology can be studied using only a few perturbation assays and easily feasible computations.A working model is falsified, if a specific aspect of the topology like an edge is in clear conflict to data. For instance, a pathway model that does not include cross-talk between two parallel branches of a pathway, while the data cannot be explained without such cross-talk. The question whether there is cross talk reduces to a single bit of information: a APC) or the CTNNB1/β-catenin gene. Upon activation of the pathway, the destruction complex dissociates, β-catenin accumulates and translocates to the nucleus. There β-catenin acts as a transcriptional coactivator for TCF transcription factors (including TCF7L2) leading to the regulation of target genes such as AXIN2. Many cells produce Wnt ligands that signal in an autocrine or paracrine manner. Wnt ligands secretion depends on the Wnt-specific secretion factor Evi/Wls. The cell line HCT116 has a mutated CTNNB1 gene, which encodes the signalling protein β-catenin. We focus on the following question: Does the activation of Wnt target genes still depend on the secretion of Wnt ligands (a) or do the CTNNB1 mutations make this activation independent from them (b)?More than 85% [We framed the binary question into two competing classes of pathway models: Models that depend on Wnt ligand secretion versus those that do not. The model classes translate into restrictions of the pathway topology space. Since Wnt dependent models are more flexible than Wnt independent models, we need to account for the extra complexity in our comparison of the two models. Bayes factors do so and fairly compare the data evidence for competing models. In our analysis, the Bayes factors depended on the number of potential Wnt signalling target genes included in the models. Stability analysis showed that the data strongly favours Wnt ligand dependent models for all  numbers of Wnt target genes.EVI/WLS, APC, CTNNB1/β-catenin, and TCF7L2. We monitored changes in target gene expression by RNAseq. Details about the experimental protocols are given in the appendix. This previously unpublished dataset is available at the ArrayExpress database with accession number E-MTAB-651. Supplementary material for full reproducibility of the analysis is available on github at https://github.com/annlia/featureNEM.For this analysis, we performed RNA interference experiments in HCT116 cells where we silenced just four genes: f be an arbitrary feature of a topology, we denote by f and by D provides in favour of a model B10 of model θm are integrated out rather than maximisedLet  factors  are the f and its complement class If the models consist of topology classes: a class P(D|Cf), which we turn to next.The models in each class will be sets of NEMs. To evaluate the Bayes factors we therefore need the mathematical treatment of their marginal likelihoods S, if and only if it is connected to S or to a descendent of S. NEMs are probabilistic models for reverse engineering signalling pathways . They reD, while integrating out the parameters which describe how E-genes attach to S-genes. Topologies with the same transitive closure cannot be distinguished on the basis of data because they are score equivalent [ϕij) be the adjacency matrix of a transitively closed network, θ = (θl) be the attachment parameters, such that θl = k if El is attached to Sk, and D = (dlk) a data matrix whose elements quantify the effect observed for El when ‘blocking’ Sk. The network posterior probability isP(D|Φ) is obtained by summing over θP(θ|Φ) is a prior distribution for θ and is assumed to be independent of the network structure Φ, such that it can be written as P(θ).In the Bayesian framework of  full netuivalent . Let Φ =Dl as the l-th row of the data matrix, or in other words the vector of observations corresponding to E-gene El, the likelihood decomposes asP exist for discrete [plk, where l refers to an E-gene and k to a perturbation. We used the Bayesian linear modelling implemented in the limma package [Defining discrete . Express package –13 to caf. Let N be the total number of admitted topologies on the set of S-genes and Nf and n the marginal likelihood of the data D is independent of the class Cm to which the topology belongsn ∈ {1, …, N} and for I the indicator function. The Bayes Factor then reduces tof in the working model of a pathway, otherwise f should not be part the model. More refined inference comes from including prior beliefs on the two model classes and considering the ratio of posterior probabilities.Which of the two classes δSERer45 [β-catenin protein [β-catenin upstream components (like Evi/Wls), Lrp5/6 and Dvls regulate the canonical Wnt pathway [We here describe a case study of a focused pathway analysis based on NEMs. To this end, we investigate canonical Wnt signalling in colon cancer HCT116 cells. These cells carry a heterozygous one amino acid  del pathway .β-catenin, and TCF7L2 by RNAi. Changes in the expression of Wnt target genes were monitored using RNAseq. In addition HCT116 cells were treated with a non-targeting siRNA as a control. All experiments were replicated twice, with the exception of TCF7L2, for which only one biological replicate was available. In addition, two samples of HCT116 cells were treated with a nonsense siRNA and profiled as control. Silencing efficiency of the genes was confirmed by qPCR (data not shown) and showed efficiencies above 80% . Specifically, we silenced EVI/WLS, APC, CTNNB1/bove 80% . On averbove 80% . Similaraffected . The RNAe allele . Indeed,voom function of the limma package [limma package [limma. For the direct targets of the siRNAs the posterior probabilities of change were all estimated as effectively 1. A probability of nearly 1 was also obtained for AXIN2 under depletion of CTNNB1/β-catenin, while a probability well below.01 was estimated for the non Wnt target ACTB under the same intervention. Fold changes and corresponding posterior probabilities for a number of well known Wnt targets are shown in We preprocessed the count data using the  package  and calc package . From thCTNNB1/β-catenin and EVI/WLS knock-downs. If we considered them all as bona fide Wnt target genes, the heat map would already give compelling evidence that the activation of Wnt target genes in fact depends on Evi/Wls and thus on Wnts secretion. Interestingly, most of these genes also responded to blocking TCF7L2 but only few of them to APC. To the right of the green line are some genes that are blue in the EVI/WLS row but red in the CTNNB1/β-catenin row. They respond to CTNNB1/β-catenin but not EVI/WLS. Nevertheless, they are a small minority.Transcriptome wide downstream effects are outlined in EVI/WLS, APC, CTNNB1/β-catenin, and TCF7L2. The mutated gene CTNNB1 encodes for CTNNB1/β-catenin. Hence, the first two proteins  operate upstream of the mutated protein, the third protein is mutated, and the last protein (TCF7L2) operates downstream of the mutated protein. All four proteins together constitute only a tiny fragment of the Wnt pathway.We now examine the descriptive arguments of the previous section with sound statistical inference. Our leading question is: Does the activation of Wnt target genes in HCT116 cells depend on the secretion of Wnt ligands (a) or do the CTNNB1 mutations make this activation independent from them (b)? And to answer this question we rely on perturbation data for four proteins EVI/WLS and APC) with (CTNNB1/β-catenin and TCF7L2). In contrast, for the Wnt dependent topologies (a) we requested that there must be at least one edge between the two groups of proteins. There were more topologies in the Wnt dependent class making the dependent model more complex and more flexible. A Wnt dependent topology fitted the data best. But was this simply because there are more of them, or did the data justify this additional model complexity? We had translated our initial biological question into a statistical model comparison problem. We chose Bayes factors to address it. The computational implications were already addressed in the method section.We represented the two competing models (a) and (b) by classes of topologies. For the Wnt independent topologies (b) we requested that there are no edges connecting Click here for additional data file.S1 ProtocolsDetails about the experimental protocols are given in the supplementary material.(PDF)Click here for additional data file.S1 Figβ-catenin alleles (mutated and wild-type) in HCT116 cells are reported in the supplementary material.Examples about the distribution of the sequence reads, with a quantification of the expression of both (PDF)Click here for additional data file.S2 FigResults from Western blot analysis are reported in the supplementary material.(PDF)Click here for additional data file.S1 Alternative AnalysisDetails about the No-CONAN approach.(PDF)Click here for additional data file.


Important crops such as wheat and rice are C3 plants resulting in efforts to engineer them to use the C4 pathway. Here we show the presence of a C4 photosynthetic pathway in the developing wheat grain that is absent in the leaves. Genes specific for C4 photosynthesis were identified in the wheat genome and found to be preferentially expressed in the photosynthetic pericarp tissue (cross- and tube-cell layers) of the wheat caryopsis. The chloroplasts exhibit dimorphism that corresponds to chloroplasts of mesophyll- and bundle sheath-cells in leaves of classical C4 plants. Breeding to optimize the relative contributions of C3 and C4 photosynthesis may adapt wheat to climate change, contributing to wheat food security.The C These photosynthetic pathways, able to use CO2 as a carbon source, evolved in cyanobacteria around 3.5 billion years ago3 photosynthesis, ribulose diphosphate carboxylase (RuBisCO), was reported to have evolved around the same time as cyanobacteria4 pathway originated approximately 30 Mya (million years ago)10One of the key biological innovations was development of the ability of an organism to use light as the source of energy to generate chemical energy (ATP and NAD(P)H) for metabolic activities4 photosynthesis subtypes, NADP-ME , NAD-ME  and PEPCK (phosphoenolpyruvate carboxykinase) have been defined based upon the decarboxylation reactions involved4 plants such as maize. Anatomical, biochemical, and molecular evidence has been commonly used to distinguish C4-(sub)types from C3-types4 photosynthesis3 plants (tobacco and celery) was reported to accomplish NAD-ME type C4 photosynthesis in cells surrounding vascular bundlesThree classical C4 pathway are known to contribute 25% of total photosynthesis although they represent just 3% of species3 crops to C4 provides the possibility of improving yield by 30% through improved water- and nitrogen- use efficiency3 food crops like wheat and rice to use the C4 pathway has long been explored to enhance global food security4 photosynthesis in the developing wheat grain. Genes identified as transcripts were located in the genome and their sequences analysed to determine likely specificity. This allowed an evaluation of substantial new evidence for C4 photosynthesis in wheat grains.Wheat is a major food crop critical to global food security. The current increase in wheat production of around 1% per year is not keeping pace with the rate of yield growth required to achieve the target of doubling crop production by 20504-photosynthesis. When added to earlier evidence dispersed in the literature, the present discoveries suggest the functioning of a form of C4- photosynthesis specifically in the developing wheat grain. The transcriptome of the developing caryopsis from 35 diverse wheat genotypes (31 and 32 genotypes respectively from 14 and 30 days-post-anthesis stage with 28 genotypes in common) was analyzed by RNA-Seq. Annotation of the differentially expressed genes in the wheat grain transcriptome between 14 and 30 dpa (days-post-anthesis) indicated the presence of NAD-ME type C4 photosynthesis during wheat grain development. This was an unexpected finding with wheat being a well-known C3 crop. Wheat genes involved in C4 photosynthesis, the number of copies expressed in developing wheat grains and their C4 specificity  are listed in Remarkably, transcriptome analysis and functional annotation of genes expressed in developing wheat grains revealed the presence and expression of all genes specific to NAD-ME type Cenolpyruvate carboxylase (ppc) genes were localized in wheat on the long arms of chromosomes 3 and 5. The mean expression value (in RPKM) for ppc across 31 genotypes at 14 dpa (chromosome 3) was 36.2 (ppc from chromosome 5 was upregulated in leaves (4 plants have less RubisCO protein (reflecting transcript abundance) than C3 plantsrbcS gene expression value was 512.3 and 39166 for the wheat caryopsis at 14 dpa and leaves respectively indicating a 76 fold down-regulation in the developing wheat caryopsis. This shows an enormous, 9500 fold, difference between developing wheat caryopsis and leaves for the relative expression of ppc and rbcS genes.Phosphowas 36.2 , indicatn leaves . It is waat; also known as got) is the most up-regulated among six C4 pathway genes in the developing wheat caryopsis. This is also the most up-regulated gene in the leaf tissues between C3 and C4 plantsaat gene in wheat, only two copies were the C4 type . RNA-Seq analysis indicated that these genes were differentially up-regulated at 14 dpa in the developing caryopsis (Aspartate aminotransferase (aryopsis  when comaryopsis 25.mdh) gene were localized on the long and short arm of chromosome 1 (cytoplasmic – mdh1) and chromosome 5  respectively across the three sub-genomes. The gene copy from chromosome 1 was differently expressed  with one each targeted to chloroplast and mitochondria were localized on chromosomes 1 and 2 respectively. The mitochondrial targeted gene (chromosome 2) copy supports C4 photosynthesis, converting malate into pyruvate with release of CO2 for further fixation through the C3 cycleTwo copies of the NAD-dependent malic enzyme coding gene (gpt) genes were localized to the short arm of chromosomes 2 and 5 of hexaploid wheat. This cytoplasmic enzyme converts pyruvate to alanine and vice-versa in bundle sheath and mesophyll cells respectively in a classical NAD-ME type C4 pathwayTwo copies of alanine transaminase (ppdk) gene was localized to the long arm of chromosome 1 in hexaploid wheat. All four gene copies  were used to assess the RPKM expression levels in the developing wheat caryopsis at 14 dpa  carboxylase (ppc), aspartate aminotransferase , malate dehydrogenase (mdh), NAD- dependent malic enzyme (me2), alanine aminotransferase (gpt), and pyruvate, orthophosphate dikinase (ppdk)4 photosynthesis viz., ppc, aat, mdh, me2, gpt, and ppdk; in all three  sub-genomes (4 pathway is restricted to the wheat pericarp.Six genes (excluding carbonic anhydrase) were involved in the NAD-ME type C-genomes  indicate4 specific genes in the genome confirms that natural selection may have already explored the options being considered by plant breeders4 pathway varied across 31 genotypes  had sub-cellular targeting that suggests C4-type specificityppc and gpt) require sequence information to distinguish between the copies specific for C3- or C4- pathways. Analysis of gpt genes in wheat suggested both C3 and C4 forms were expressed at similar levels  in C4-isoforms rather than R884 (Arginine) as found in C3-isoforms. The translated sequence of the ppc gene from chromosome 3 (S885) and 5 (R891) of wheat cDNA  indicates the gene copy from chromosome 5 is C3-type; while the chromosome 3 copy is non-C3 type. The gene sequences from wheat and related speciesppc gene (IWGSC cDNA database release-23) from chromosome 3. Results indicated that most of the Triticeae tribe members have five copies of ppc gene  across all the Triticeae members studied  or Q  or G  or I (Amaranthus) depending on the species or taxonomic group  of the Poaceae being uniquely seen to lack C4 photosynthesis. We have also shown for the first time that all the required genes are expressed in the required compartmentalization, specifically in the pericarp, a tissue with an anatomy that is suitable for supporting a C4 pathway. The possibility of photosynthesis in the pericarp of wheat grains was predicted in the early 1960senolpyruvate carboxylase (PPC) from the wheat or barley pericarp tissues of developing grain was reported to be 50-100 times as active in carbon fixation as ribulose diphosphate carboxylase (RuBisCO)4 photosynthesis. A little later, Wirth, et al.2 released through respiration or photorespiration. Assimilation of 14CO2 to malate and 3-phosphoglyceric acid in wheat ears and flag leaf respectively; along with higher enzyme activities for enzymes of C4 and C3 metabolic pathways in ears and flag leaf respectively suggested the possibility of C4 photosynthesis in ears3- and C4-type3 plant, Δ values were used to study the plants’ water-use- or transpiration efficiency374 photosynthesis. This might be due to either inefficient less advanced C4 type photosynthesis or the fact that grain photosynthesis accounts for only 33-42% of ear photosynthesis3-type photosynthesis thereby diminishing the difference in Δ values between flag leaf and grain to a marginal level. Similarly but in reverse, in a maize plant with C4-type, maize husk leaves were reported to be C3-type and their Δ values were marginally higher than leavesWheat is widely known as a classical C14CO2 in malate, Δ values), earlier researchers failed to explore C4 photosynthesis in wheat grains due to the view that Kranz anatomy was required for C4 photosynthesis164 photosynthesis without Kranz anatomy was reported in single cells and in the petioles of C3 plants respectively17In spite of this evidence  of cotyledons and leaves exhibiting C3 and C4 type photosynthesis respectively404 pathway in different environments like terrestrial or submerged situations4223 pathway was reported in the husk leaves (hypsophylls) of the maize plant4 plant. Evidence of 4-carbon compounds specifically in wheat leaf bases4 pathways in C3 plants4 and C3 enzymatic activity has recently been reported in wheat ears under water stress3 status of wheat. This evidence suggests the presence of a diversified range of regulatory patterns of C4 pathway in plants with ontogeny and varied environmental cues. Operation of different pathways (C3 or C4) at different growth stages allows wheat to have a lifecycle that extends across seasons with varying environments .In the late 1990s, there were reports of the C4-specific gene copies  photosynthesis3 cycle (presence of RuBisCO) in both mesophyll and bundle sheath cells along with the C4 pathway in some species might be due to the fact that compartmentalization of RuBisCO is the final step in the evolution of C4 from C3 photosynthesis4-like type (less advanced) photosynthesis in developing wheat grains through the cross- and tube-cell layers of pericarp paralleling the mesophyll and bundle sheath cells of classical C4 photosynthesis  of the BOP clade4. The Aristidoideae (PACMAD) subfamily has been reported to have at least two independent evolutions of the C4 pathwayG in C4 and R in C3) in the ppc gene product required for efficient carbon fixation by the C4 pathwayHordeum and Brachypodium) had five ppc copies in their genome with four of them having the amino acid R indicating their C3 nature while one copy  has an S – a non-C3 type in place of R except Brachypodium (Around 41% of grasses are known to fix carbon through the Chypodium .3 specific amino acid position (R) was apparently conserved (4 specificity appears to have evolved at least four times in the last 30Mya (origin of C4) with either S (Panicum and Triticeae tribe) or Q  or G  or I (Amaranthus). This suggest that various amino acid substitutions at that site might result in differing efficiency of carbon fixation through the C4 pathway by altering tolerance to feedback inhibition by malate4-specific amino acids individually might help to rank their photosynthetic efficiency. However, the weakest form among the four will probably be much more efficient in carbon fixation than the C3 type (R). The presence in wheat of an amino acid specific to a known C4-type (S in Panicum laetum and P. miliaceum) is strong evidence when taken together with the grain specific pattern of expression of the C4 specific ppc gene (Brachypodium distachyon) has amino acids corresponding to the C3-type for all the five copies; while members from tribe Triticeae  have one copy of the C4-type and four copies of the C3-type (4 photosynthesis around 30Mya3-type genes (Brachypodium). Unfortunately, there are no diversification points between Brachypodium (35 Mya) and Hordeum (11.6 Mya)4 evolution in the Pooideae tribe. Derived traits like those associated with C4 photosynthesis appear at later evolutionary stages and are expressed later in plant development4 photosynthesis in wheat and its relatives specifically in the grain.The Conserved  from Bryppc gene . The tri C3-type . This fi4-like type photosynthesis specifically in developing wheat grains. Recognition of both C3 and C4-like type photosynthetic pathways in wheat provides a basis for interpretation of wheat performance as a crop adapted to maturation in hot dry environments, suggesting that the plant may rely more on C4 photosynthesis under conditions of water stress during the grain filling stage. Photosynthates from pericarp, glumes and awns are critical4 photosynthesis specifically in these tissues provides an adaptive advantage to the wheat plant while C3 photosynthesis is adequate during early vegetative growth under more temperate conditions. The potential for genetic manipulation to extend C4 photosynthesis throughout the wheat plant seems much more realistic given the existing expression of the entire pathway in the grain. This supports the view that plant species have evolved specific photosynthetic pathways in different organs, at specific developmental stages and in different environments suggesting that the classification of plants as C3 or C4 or CAM in a broad fashion cannot simply be based upon leaf anatomy. Research to establish the variation in flux through this pathway in wheat and its progenitors will shed much light on the share of carbon fixation through the C3 and C4 pathway under varying environmental conditions. This has the potential to suggest new options for the development of higher yielding wheat genotypes.Based on this molecular, cytological, taxonomical and evolutionary evidence, we propose the occurrence of Cviz., Amurskaja, Arnhem, Banks, Bativa, Beyrouth-3, Bobwihte-26, Bowerbird, Des-367, Dollarbird, Ellison, Garbo, Giza-139, Gregory, Huandoy, India-37, India-211, India-259, Iraq-46, JingHong-1, Kite, LermaRojo, Martonvasari-13T, Punjab-7, Qalbis, Saturno, Sunco, Sphaerococcum, Tunis-24, Greece-25, NW-25A, NW-51A, NW-93A, NW-108A, Pelada, and Vega were used for transcriptome analysis. Seeds for these genotypes were procured from the Australian Winter Cereals Collection. Seeds were germinated and plants grown under controlled conditions as described in Furtado, et al.Thirty-five wheat genotypes et al.et al.viz., NW-93A, NW-108A, Pelada, and Vega at 14 dpa, and three cultivars viz., Greece-25, NW-25A, and NW-51A at the 30 dpa stage due to lack of sufficient starting material.RNA isolation, cDNA synthesis, library preparation and next generation sequencing was carried out and described by Furtado, et al.Triticum aestivum Gene Indices, The Computational Biology and Functional Genomics Laboratory, Dana Farber Cancer Institute and Harvard School of Public Health) cDNA database as reference, containing 221,925 sequences (release 12.0)Sequencing data obtained was imported into CLC genomics workbench ver. 7.0.4  and further processing and analysis were done within this environment unless otherwise stated. Quality checking, trimming, and RNA-Seq analysis were performed as described in Furtado, p-value using false discovery rate (FDR) corrected least the significant difference set at 0.01 level.Transcripts that were differentially expressed between 14 and 30 dpa were analyzed using the RNA-Seq experimentation tool with default parameters. Statistically significantly differentially expressed transcripts were identified using both Gaussian (mean based) and Empirical analysis of Differential Gene Expression  statistics facilitated through CLC workbench  with 4 photosynthetic pathway existing in developing wheat caryopsis.In total, 26,477 transcripts that are common for both Gaussian and EDGE statistics were significant at FDR corrected value 0.01. Among them, 319 and 181 transcripts were unique to 14 dpa and 30 dpa respectively; while 16237 and 9740 transcripts were differentially up-regulated at 14 dpa and 30 dpa respectively. Transcript sequences for these four groups  were extracted from the reference database (TaGI) and subjected to blastx analysis against the non-redundant protein database. Blast results obtained were converted to a BLAST2GO project file and exported in “.dat” format files using the plug-in version within CLC workbench . Functional annotation for these four groups was performed independently using BLAST2GO Pro ver 3.0.10 with default parameters4 photosynthetic pathway from the differential 14 dpa group were retrieved using CLC workbench . A total of 62 transcripts for the six genes  were retrieved. Blast searches for the 62 transcripts from the TaGI database were performed against the IWGSC cDNA database containing 100,717 sequences (release-23)Based on enzyme code mapping, TaGI transcript IDs pertaining to those enzyme code (EC numbers) for the genes involved in the C4 photosynthesis. In order to construct a modified reference, Based on blast analyses using the 62 transcripts of TaGI as referenceRaw reads (100 bp paired-end sequencing on Illumina HiSeq2000) of different tissues  at three different growth stages for hexaploid wheat (‘Chinese Spring’) were available online3 and C4-specificity were reported recentlySpecific amino-acid positions for PPC (PEPCase) that are functionally related to C4 species was not available, ppc gene sequences in public databases was used to study the evolutionary pattern at specific amino acid positions  including taxa from bryophytes to angiosperms3164ositions  that areHow to cite this article: Rangan, P. et al. New evidence for grain specific C4 photosynthesis in wheat. Sci. Rep.6, 31721; doi: 10.1038/srep31721 (2016).


The 7th column in