The structural novelty of bottromycin stems from its unprecedented macrocyclic amidine and rare β‐methylated amino acid residues. The N‐terminus of a precursor peptide (BtmD) is converted into bottromycin A2 by tailoring enzymes encoded in the btm gene cluster. However, little was known about key transformations in this pathway, including the unprecedented macrocyclization. To understand the pathway in detail, an untargeted metabolomic approach that harnesses mass spectral networking was used to assess the metabolomes of a series of pathway mutants. This analysis has yielded key information on the function of a variety of previously uncharacterized biosynthetic enzymes, including a YcaO domain protein and a partner protein that together catalyze the macrocyclization.Bottromycin A Untargeted metabolomics is frequently used to assess the total metabolome of an organism,ΔbtmD or the production medium. Mature bottromycins . An analysis of the metabolomes of ΔbtmE and ΔbtmG was used to map molecules produced by these mutants onto this network. Nodes representing species that were not present in ΔbtmD were manually assessed using MS2 to identify molecules related to the btm pathway. This global metabolomic analysis showed that the bottromycin pathway contributes much more to the total metabolite profile of S. scabies than was previously understood,1–20; Figure 2 fragmentation homology, was an abundant species with m/z 406.27  produced by both ΔbtmF and the amidohydrolase mutant ΔbtmI, but not by WT S. scabies. Masses of 873.45 Da and 887.47 Da correspond to the addition of H2O to carboxylated O‐desmethylated bottromycins A2 (1) and C2,ΔbtmF and ΔbtmI. MSn revealed that these molecules are not macrocyclized but do feature the thiazoline ring .Analysis of the comparative metabolomic and MSycins A2  and C2,ΔΔbtmF, the only abundant species that could be confidently assigned as a BtmD‐derived metabolite in ΔbtmE was 17 , which is a trimethylated tripeptide that is also found in the WT, ΔbtmF, ΔbtmI, and ΔbtmJ strains . This assignment is consistent with the absence of 17 in ΔbtmD and in the RSMT mutants ΔbtmC and ΔbtmG. Unfortunately, this provided no evidence on Cys8 cyclization; the absence of cysteine‐containing peptides could reflect rapid peptide degradation when cyclization does not occur. The lack of any thiazole or thiazoline‐containing metabolites does imply that BtmE catalyzes thiazoline formation, although further in vitro characterization is required to confirm this. The absence of macrocyclized metabolites suggests that thiazoline formation is an early step in the pathway. BtmH, the only uncharacterized hydrolytic enzyme in the pathway, is proposed to remove the follower peptide, although it is possible that it could also participate in heterocyclization.In contrast to btm cluster lacks a flavin‐dependent dehydrogenase that is required for the biosynthesis of all other thiazole/oxazole‐containing RiPPs.ΔbtmJ revealed two abundant compounds with m/z 841.43 and 855.44  using MS2 .The m/z 855.44 and 841.43 . This could reflect a mixture of epimers at the aspartate residue, which has a non‐proteinogenic d‐stereocenter in bottromycin A2. Therefore, we hypothesized that aspartate epimerization occurs after thiazoline formation, when the pKa of the aspartate α‐proton is lowest owing to imine–enamine tautomerization that is disfavored once the aromatic thiazole is formed , indicating that this position can readily undergo non‐enzymic epimerization. The drop in abundance of both forms of m/z 855.44 in the WT compared to ΔbtmJ  implies that a dynamic kinetic resolution converts this mixture of epimers into stereochemically pure mature bottromycins.To further assess whether this proton is exchangeable, we carried out a deuterium labeling experiment. Here, all exchangeable protons were replaced with deuterium in DΔbtmD, which is unable to produce the precursor peptide. Any diffusible molecules produced by mutants that are genuine intermediates should be converted into 1 by the functional enzymes in ΔbtmD. Only the ΔbtmJ+ΔbtmD co‐cultivation resulted in the production of 1 , which implies that 12 and 13 are true intermediates and supports the proposed roles and substrate specificities of BtmJ and BtmB. In contrast, the failure of the ΔbtmF and ΔbtmI co‐cultivation experiments suggests that the linear compounds 10 and 11 are shunt metabolites rather than authentic intermediates, and that BtmF and BtmI require a substrate that contains a follower peptide. However, we cannot rule out the possibility that 10/11 are not exported/imported as effectively as 12/13. The lack of an O‐methyl group on the d‐aspartyl residue in any of the metabolites identified from mutant strains indicates that O‐methylation is the last step in the pathway, thereby generating an active antibiotic.4 using recombinant BtmB .To investigate whether any of the metabolites reported are authentic pathway intermediates, each mutant strain was co‐cultivated with btm pathway.S. scabies, bottromycin production is either severely reduced or entirely abolished when either RSMT gene, btmC or btmG, is deleted. BtmG methylates Val 4 and Val 5, and BtmC methylates Phe 6,ΔbtmC and ΔbtmG have highly similar metabolite profiles, and the production of macrocyclized shunt metabolites 6 and 7 indicates that C‐methylation is not a prerequisite for cyclization. However, the fully C‐methylated metabolites produced by ΔbtmF and ΔbtmI demonstrate that macrocyclization is not a prerequisite for C‐methylation either. Also, the production of methylated tripeptides by ΔbtmC and ΔbtmG indicates that the pathway can stall before cyclization when C‐methylation is disrupted. The data are consistent with incomplete C‐methylation reducing the efficiency of various downstream modification steps.Three RSMTs catalyze four C‐methylations in the 2  should be addressed to the authors.SupplementaryClick here for additional data file.


The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP , which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries , can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package ( Metabolomics is undeniably powerful for uncovering disease biomarkers ,2,3. BeyWhile numerous methods and approaches that integrate gene expression and metabolomics data have been reported ,11,12, phttps://github.com/mathelab/RaMP-DB/ and can be used in two different ways: (1) it can be downloaded as a mysql dump (https://github.com/mathelab/RaMP-DB/inst/extdata/), for integration into any other tool; (2) it can be accessed via a user-friendly R Shiny web interface that supports basic queries, enrichment analysis given a list of genes and metabolites, and network visualization of gene-metabolite relationships. Overall, RaMP provides an up-to-date, comprehensive gene and metabolite pathway annotations that can be used as a stand-alone resource or can readily be incorporated into other tools. It is our hope that this resource will improve biological interpretation of metabolomics phenotypes, will guide data-driven hypothesis generation on the modulation of these phenotypes, and will thus advance scientific knowledge of metabolic phenotypes.To address these limitations, we developed RaMP , a publicly available, comprehensive database of gene and metabolite pathways. RaMP is carefully designed to enable complex searches across genes and metabolites , and across distinct types of annotations, such as biofluid location, disease, and biological pathways . This design also allows analysis of pathway content overlap for development of improved pathway enrichment statistics. RaMP is publicly available at A multi-database integration approach has been successfully applied for gene/metabolite enrichment analysis ,19,20,21Reactome pathways were included because they are derived from published experimental evidence and are curated by expert molecular biologists. Reactome also contains relevant disease pathways. The hierarchy in Reactome is such that the lowest level pathways represent single reactions, which is important for retrieving the gene(s) that catalyze reactions involving metabolites of interest. Finally, we incorporated WikiPathways because it is one of the largest human pathway collections to date and has recently undergone considerable growth in metabolic pathway annotations ,32. Impohttps://github.com/Mathelab/RaMP-BackEnd. Importantly, the design of the database , and the associated R package are publicly accessible on our GitHub site Once installed, the application runs by simply typing “runRaMPapp (password = ”mysqlpassword”)” in the R console. The interface supports 4 basic types of queries  that canhttps://github.com/mathelab/RaMP-DB/inst/extdata/) enables advanced users to easily set up the database locally. We have improved interpretation of pathway enrichment analysis by calculating pathway overrepresentation using 3 databases  in RaMP, and by providing different groupings of enriched pathways (by database origin or pathway overlap). Furthermore, all the underlying Python code used to create the RaMP MySQL file is publicly available (https://github.com/Mathelab/RaMP-BackEnd), thereby ensuring full transparency of the database construction, and complying to reproducibility best practices. Lastly, we have wrapped RaMP into an R package that contains a user-friendly web interface for performing several queries and pathway overrepresentation analysis. The R package is publicly available on GitHub at https://github.com/mathelab/RaMP-DB/, where detailed installation instructions are provided. One of the first steps in statistical analysis of metabolomics data is to identify metabolites that are altered between disease states or conditions under study. This step however is oftentimes insufficient to fully leverage the data and understand the underlying biological mechanisms at play. To provide such further insights, one can combine metabolomics data with other data, such as gene expression and pathway annotations. To facilitate such integration at a pathway level, we have developed the relational database RaMP, which incorporates gene and metabolite pathway annotations from four large, and commonly leveraged databases: HMDB, KEGG, Reactome, and WikiPathways. RaMP was designed to allow complex and batch queries, to facilitate integration with other tools, and to provide improved pathway overrepresentation functionality. The relational structure supports complex and batch queries, and the publicly available MySQL dump  while others cannot. One existing solution to this problem is the Metabolomics Workbench Refmet resource [In addition, the background number of metabolites used to calculate pathway enrichment is based on the number of metabolites represented in each pathway database . The default number of genes used for background is set to 20,000. In the future, users will have the option to provide a list of genes or metabolites assayed to build a custom contingency table for the test. This capability is particularly relevant for analysis of metabolites, where the number of metabolites measured in a given experiment is variable. Because RaMP is continuously being developed, we anticipate expansion of the RaMP functionalities to increase utility and usability. In addition to the aforementioned pathway enrichment changes, we also plan to develop more query capabilities. Furthermore, while overrepresentation analysis can be useful for uncovering disrupted biological pathways, we recognize the existence of improved, second and third generation methods that take into account topology ,40,41, aWhile RaMP is currently focused on human pathways, we plan to expand the database to other organisms. In particular, with the increasing appreciation of the impact of microbial metabolites on human metabolism, microbial pathway databases could be integrated into RaMP to further expand its utility for integrative pathway analysis. With this in mind, it is important to note that the content of RaMP revolves around analytes (genes and metabolites) and how they are related . Therefore, when information from source databases  is included, only information that pertains to downstream pathway enrichment analysis is retained. With this mindset, we hope to retain the simplicity of our database design . In conclusion, RaMP is a standalone database and application, usable through a web interface that was developed to facilitate gene and metabolite pathway analysis. RaMP can be used independently as a MySQL database that can be readily integrated with other tools, or can be accessed through our R package and web interface. RaMP is thus a first step toward a comprehensive integration of genes and metabolites at a pathway level, and it is our hope that our transparent approach, with all code publicly available, will generate further developments and improvements toward more complete interpretation of metabolomics data.https://github.com/Mathelab/RaMP-BackEnd. Because the format of the data varies by database, individual classes and parsing procedures were created for each databaseAll metabolite and pathway data were downloaded from HMDB, KEGG, Reactome, and WikiPathways using Python scripts, including Python library urllib, based on HTTP protocol. All the code is available at The HMDB data, in Extensible Markup Language (XML) format, was parsed using the Python built-in parser from the ElementTree XML API. First, the HMDB ID is retrieved through the “metabolite” tag of the XML file. Next, for each “metabolite” tag, information for other tags are retrieved, including gene names and IDs, pathway names, and other ontologies . While parsing, dictionaries are created where the keys are HMDB IDs and the associated values are all available attributes  pertaining to that metabolite. http://rest.kegg.jp/list/pathway/hsa) was used to retrieve information on the pathways and associated genes and metabolites. For example, information on the first pathway in the complete list of human pathway IDs, “hsa00010”, is accessible through the link http://rest.kegg.jp/get/hsa00010. Parsing compound and gene IDs from this pathway entry allows us to retrieve further information on the compounds and genes related to that pathway .The KEGG data was retrieved through the REST API as “txt” files, and each file type was parsed in the following order: pathways, metabolites, metabolite synonyms, genes, and gene synonyms. To use the REST API, the complete list of human pathway IDs  format, which is a custom XML format compatible with pathway analysis tools such as Cytoscape, GeneMAPP and PathVisio. This file format retains all of characteristic of XML, so we apply the same procedure used for parsing the HMDB database.Finally, the physical entity identifier mapping files that map compound (ChEBI) IDs and gene (UniProt) IDs to Reactome pathways were downloaded from Reactome. Each file is tab-delimited and 3 columns are retrieved: (1) compound/gene identifiers; (2) Reactome pathway ID; (3) Reactome pathway name; (4) genes and species. As with the other databases, only human pathways were selected. The Python library “libChEBI” is used to retrieve the ChEBI common name from each ChEBI ID retrieved from Reactome. Similarly, the gene common names are retrieved through the UniProt REST API. Metabolite and gene names have many synonyms and sometimes, the synonyms can be the same for different molecules. Furthermore, different databases use different identifiers. To properly map identifiers from one database to the next, we (1) created dictionaries of IDs for each database source and (2) ensured that identifiers linked to common IDs had the same RaMP ID. In the first step, source IDs were used as the key in the dictionaries and the values were the other identifiers present in the source database see . In the RaMP IDs have a prefix, followed by a unique number. The prefix “RAMP_C” is used for compounds, “RAMP_G” for genes, “RAMP_P” for pathways, and “RAMP_OL” for ontologies. Prefixes are then concatenated to a number (from “000000001” to “999999999”). While RaMP IDs are created to map metabolites and genes appropriately across the different databases, these IDs are internal and are not returned to the user through the R package. https://github.com/mathelab/RaMP-DB/). Instructions are provided on how to set up MySQL and the RaMP database on this GitHub site. The RaMP R package can be installed via the install_github command from the devtools package and requires R (≥3.2.0). Questions and concerns can be raised as issues on the GitHub site. Further documentation is provided in the The R package for RaMP is available online via GitHub  and genes (Pm), independently. Of note, if pathways contain only genes or only metabolites, then Pm or Pg, respectively, cannot be computed. A combined p-value (Pcomb) is then calculated for pathways that are annotated with both genes and metabolites, using Fisher’s method [p-values are combined using Fisher’s combined probability test, where the test statistic, Tcomb is calculated as:RaMP supports pathway overrepresentation analysis of user-supplied lists of metabolites and/or genes. Fisher’s exact tests are performed to calculate pathway overrepresentation s method . Specificomb follows a χ2 distribution with 2 degrees of freedom and the associated p-value, Pcomb, is calculated using the R function pchisq and 2 degrees of freedom. When Pm is missing, Pcomb = Pg. Conversely, when Pg is missing, Pcomb = Pm. Resulting Pcombp-values are adjusted for multiple comparisons using the Benjamini and Hochberg method and the Holm method to control the false discovery rate. Similar to other approaches [Tproaches , the defCalculating analyte overlap: The degree of analyte overlap was calculated for all possible pairs of pathways. Gene overlap and metabolite overlaps were calculated separately. Given two pathways, m and n, the overlap score Omn represents the Jaccard index, which is calculated as:mn is the number of analytes (genes or metabolites) present in both pathways, and Lm and Ln are the number of total analytes in pathways m and n, respectively. When no analytes are in common between two pathways, Omn = 0. Conversely, Omn = 1 if all analytes overlap between two pathways.Identifying seeds: The overlap scores Omn are used to identify cluster seeds. Pathways with a high degree of overlap with multiple other pathways  are considered “seeds”. Thresholds for percent overlap and number of pathways to overlap with can be defined by the user. Initial pathway clustering: Once seeds are identified, pathways are clustered to the seeds based on the overlap scores. Pathways that have overlap scores with seed pathways greater than or equal to a user-defined threshold  are clustered with the corresponding seed pathway. Of note, this approach allows for a single pathway to belong to multiple clusters, as long as it is sufficiently similar to the seed pathway of those clusters. Calculate cluster overlap: Overlap scores between clusters are calculated with the same formula as Equation (2), with the following definitions for I and L: Imn is now the number of pathways in common (based on their names) between clusters m and n, and Lm and Ln are now the number of pathways in clusters m and n, respectively. All pairwise cluster similarities  are ranked, and the cluster pair with the highest overlap score is merged into a single cluster, provided that their overlap score is greater than a user-defined merge threshold .Repeat cluster overlaps: Step 4 is repeated until there are no cluster overlap scores above the merge threshold. By default, pathway enrichment analysis results are returned for each database , ordered by the database the enriched pathway was found in. To improve interpretability of pathway analysis results, enriched pathways are placed in groups according to the proportion of analytes they share in common, allowing the user to more efficiently navigate through redundant pathways. To accomplish this, we implemented an agglomerative clustering algorithm based on the heuristic fuzzy multiple-linkage partitioning algorithm, which is used by the DAVID gene functional annotation tool . The algWith this clustering approach, large and complex lists of enriched pathways are grouped into clusters of highly similar pathways. This feature is important as it allows users to more easily interpret functional implications of pathway enrichment results.p-values were adjusted using the False Discovery Rate (FDR) method. Metabolites, mappable to KEGG or HMDB IDs, that had a fold-change greater than +/− 1.5 with an FDR adjusted p-value <0.05 were then input into the RaMP web application using the “Return pathway from given analytes” tab and the “Input Multiple Metabolites (batch query)” subtab. Overrepresentation analysis was performed on the list of metabolites and pathways were retained if their Holm-adjusted p-values were <0.01. Clustering of these pathways was performed using the following parameters: overlap threshold for medoid establishment = 0.2, number of similar neighbors = 2, overlap threshold for cluster merge = 0.75. Overrepresentation analysis was repeated with a list of metabolites and genes as input . Parameters for clustering these pathways were: overlap threshold for medoid establishment = 0.2, number of similar neighbors = 2, overlap threshold for cluster merge = 0.5.Metabolite data was obtained for a previously published breast cancer study comparing tumor and adjacent non-tumor breast tissue . Metabol


Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager–Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes. Identifying pathways and transition states is critical to understanding chemical and biological reactions. Here, the authors introduce a capable computational approach using conformational space annealing to find multiple reaction pathways via global optimization of the Onsager-Machlup action. Finding multiple plausible reaction pathways between two end states is a long-standing challenge in computational sciences123456792et al.20212220Various chain-of-states methods have been suggested based on the assumption that a dominant transition pathway between two states follows the minimum energy pathway11121514151617181918SOM152627282930S is proportional to kB is the Boltzmann constant and T is a temperature. Thus the most dominant pathway corresponds to the one that minimizes SOM and the same result can be obtained by solving the Fokker–Planck equation78SOM requires the second derivatives of a potential function, which are computationally expensive, at best, and wholly unavailable for many quantum mechanical energy surfaces.For diffusive processes, the second problem can be avoided by using the Onsager-Machlup (OM) action In this work, we propose an efficient computational method, Action-CSA, that finds multiple low OM action pathways without second derivative calculations. For global search and optimization of a pathway space, we used an efficient global optimization method called conformational space annealing (CSA), which is based on a combination of genetic algorithm, simulated annealing, and Monte Carlo with minimization33343536373839414244454648Action-CSA efficiently explores the pathway space regardless of the heights of energy barriers via crossovers and mutations of pathways. Without calculating the second derivatives of a potential energy, multiple diverse pathways with low OM action were obtained by combining local optimization of pathways using classical action and selection of pathways using the OM action. From benchmark simulations using alanine dipeptide, our method finds multiple transition pathways, which are consistent with long-time Langevin dynamics (LD) simulations. The rank order statistics and transition time distributions of the multiple pathways are in good agreement with those of the LD results. For the conformational change of hexane from the all-gauche(−) to all-gauche(+) states, Action-CSA finds all possible transition pathways. Also, the lowest action folding pathway of FSD-1 is consistent with recent experiments reported after the submission of this work. These results demonstrate that Action-CSA searches multiple reaction pathways including the most dominant one in an efficient and robust way.C7eq→C7ax transition by clustering all pathways sampled from the Action-CSA simulations  has the second lowest SOM and the lowest SOM value was observed at 0.4 ps. These are consistent with the LD results in which all 118 transitions that crossed barrier C occurred within 1.1 ps and their most probable transition time was 0.7 ps = and barrier A and B becomes the pathway with the second lowest SOM. From the LD simulations, when t>0.9 ps, 25 pathways similar to Path3 were identified, which makes them the second dominant pathway. These results demonstrate that the profile of SOM values is consistent with the distributions of transition times obtained from the LD simulations. Note that the most probable transition times observed from the LD simulations are longer than the minimum action transition times obtained from the CSA simulations. This is because high-frequency motions due to thermal fluctuations are filtered out in the minimum action pathways115In addition, it is also identified that Action-CSA simulations provide information on the transition times of various pathways. Until cis state, all possible transition pathways can be enumerated  state (g−g−g−) to the all-gauche(+) state (g+g+g+). We assessed the sampling ability of Action-CSA by investigating the diversity of sampled pathways. If it is assumed that dihedral angles do not cross the highest energy barrier around the umerated . For theSOM shows that hexane crosses six energy barriers  is determined by using the path integral approach and OM action SOM[x(t′)]26where x(t′). This relationship suggests that if the SOM values of all physically accessible pathways are obtained, one can determine the relative populations of multiple pathways. Thus, SOM is a proper target objective function of global optimization. The generalized OM action of a pathway x(t) is defined262765where V=V(xf)−V(xi). In the original formula of action derived by Onsager and Machlup, the last term of 2666SOM using analytic local minimization algorithms requires analytic third derivatives. This makes the direct global optimization of SOM hard to be applied to detect transition pathways of biomolecules with all-atom force fields due to the complexity of implementation and high computational cost. For numerical calculations based on a chain-of-states representation, the OM action should be discretized. The method uses the second-order discretization of the symmetric OM formula, which uses only gradients for SOM calculationswhere ΔP+1 is the number of replicas, Δt is a time step between successive replicas, and t=PΔt is the total transition time. This formula is more efficient than the direct implementation of V to evaluate SOM.where SOM. In general, a pathway is represented as a chain of P−1 replicas with N atoms for each replica leading to 3N(P−1) total degrees of freedom. Each replica is represented by a sequence of 3N−6 internal dihedral angles and 6 net translational/rotational degrees of freedom. An Action-CSA calculation starts with a set of random pathways on a pathway space. Subsequently, the actions of the random pathways are locally optimized.Here, we describe the application of CSA to optimize SOM using analytic gradients is computationally challenging. For a computationally feasible local action optimization, we optimized a pathway using a modified action from ADMD instead of using SOM. The discretized classical action is defined:As stated previously, direct minimization of Sclassical. Finding such pathways is a computationally difficult task because Sclassical is not bounded; Sclassical can be minimized or maximized, and the stationary points of Sclassical can be minima, maxima or saddle points. Another practical problem is that the total energies of pathways satisfying the stationary condition δSclassical=0 may not be conservedPhysically accessible pathways correspond to the stationary points of E is a targeted total energy of a system, μA and μE are the weighting parameters of the classical action, and the restraint term for energy conservation. The minimization of Θ requires only the first derivatives of V.where x(t);E] is called the first bank. The first bank remains the same throughout the optimization and is used as the reservoir of partially optimized pathways to enhance the diversity of pathway search. A copy of the first bank is generated and called a bank. The pathways in the bank are updated during a calculation while the size of the bank is kept constant. By using the pathways included in the first bank and the bank, new trial pathways are generated by crossover and mutation (random perturbation) operations. For a crossover operation, two pathways, a seed pathway from the bank and a random pathway either from the bank or the first bank, are selected and random parts of two selected pathways are swapped. For a random perturbation, a certain number of degrees of freedom of a seed pathway, up to 5% of total degrees of freedom, are randomly changed. The generated trial pathways are locally optimized by minimizing Θ to remove any possible artifacts generated by the crossover and the mutation operations. After local optimizations, the bank is updated by comparing the SOM values of the existing pathways and the new ones instead of Θ.The set of locally optimized initial random pathways using Θ[α, the pathway separation distances D between α and the existing ones in the bank are calculated. If the distance between α and its closest neighbor is less than a cutoff distance Dcut, only the better configuration in terms of the objective function, SOM in this work, is selected. If D>Dcut, α is considered a new configuration and it replaces the worst configuration in the bank if it is better. At initial stages of a calculation, Dcut is kept large for wider sampling. As the calculation proceeds, it gradually decreases for a refined search near the global minimum. The bank keeps updating until no better configuration is found. In this work, a distance between two pathways was measured by the Fréchet distance3334353739A key feature of CSA is a sophisticated bank-update procedure that prevents a search being trapped in local minima during the optimization and keeps the diversity of the bank. For a newly obtained configuration, a pathway in this work, SOM values, we applied our method to investigate the conformational transition of alanine dipeptide from C7eq to C7ax in the vacuum. Here, we used the polar hydrogen representation in the PARAM19 force fieldt in t to keep the time step between successive replicas Δt=5 fs. All simulations were performed at temperature T=350 K with a collision frequency γ=1.0 ps−1. The reference total energy E in V(xi)=−43.3 kcal mol−1 and a kinetic energy of 12.5 kcal mol−1 estimated by 3NkBT/2 with the number of atoms N=12. The weighting parameters μA and μE in μs LD simulations and counted the number of the C7eq→C7ax transitions. An Action-CSA calculation requires 10 adjustable parameters, and they are listed in To verify that Action-CSA successfully finds multiple pathways and allows one to determine the rank order of the pathways based on their optimized http://charmm.chemistry.harvard.edu/). All relevant data are available from the authors upon request.The Action-CSA code is freely available for academic, government and nonprofit use as a part of the CHARMM molecular dynamics package .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Tables and Supplementary Figure


Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data.BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, ’Pathways summary’ and ’Oncoprint’ with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis.BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. Multi-omics data such as SNV, CNV, and gene expression can be visualized simultaneously on KEGG pathway maps, together with TF-TG correlation and relationships among cancer driver genes.Users can perform comparative analysis of BRCA data, including selection of differentially expressed genes (DEGs) in arbitrary patient groups, mutual exclusivity module (MEMo) summary of genomic alterations (SNV and CNV).Data can be downloaded by REpresentational State Transfer Application Programming Interface (REST API).Transcriptome data measured at the whole genome level requires interpretation at a higher level. For this reason, biological pathway analysis of transcriptome data has become a standard approach, e.g., Pathview , PathwayBRCA-Pathway consists of three components: Database system, REST API, and Web front-end. Overall system design is described in Fig. BRCA-Pathway stores and utilizes data such as TCGA breast cancer data, breast cancer subtype by PAM50, TF-TG regulation data, Hallmark gene sets, driver gene list and KEGG pathway data. Data is accessible by web-server system or REST API. Data source and status is described in the Table TCGA breast cancer data was obtained from FIREHOSE . ClinicaBreast cancers are classified into subtypes based on gene expression data. PAM50 breast cancer subtyping is widely used method to classify breast cancer into four subtype: Luminal A, Luminal B, Basal-like, HER2-enriched. Breast cancer subtype data is generated using PAM50 predictor bioclassifier R script . The genBRCA-Pathway stored KEGG pathway data by accessing KEGG API  and KEGGKEGG pathway includes information about the gene-gene relation . However, relationships between TFs and potential target genes are not provided by the KEGG pathway. To supplement the KEGG pathway, we provide Pearson correlation coefficient between TF-TG with the KEGG pathway. TF-TG relationships were obtained from two different databases. Molecular Signatures Database provides gene sets that share a transcription factor binding site defined in TRANSFAC database . Human THallmark gene sets summarize and represent well-defined biological states or processes and display coherent expression. Fifty Hallmark gene sets were obtained from MSigDB . When reDriver gene is a gene that contains driver gene mutations or is expressed aberrantly in a fashion that confers a selective growth advantage . We obtaIn order to start with BRCA-Pathway, users need to select pathways of their interest. There are several ways to select pathways Fig. . First, BRCA data can be explored in different ways. For example, when users specify subpopulation of TCGA breast cancer patients, the system loads multi-omics data of the selected subpopulation into the web-browser. By clicking ‘Data overlap option’, users can change the type of data  mapped onto the pathway. This enables users to see the same pathway by three different points of view. BRCA-Pathway colorizes KEGG Pathway entries according to the user-controllable classification criteria. The system provides a function that highlights specific patterns of omics data such as gene expression level up, mutation free and copy number deletion Fig. . In addiFigure 33BRCA-Pathway provides visualization of user data to extend the usability of the system. After switching to ‘User data mode’, input a text file consisting of Entrez geneId and fold change value, and the gene expression level is shown in color. Adjusting color or threshold value helps to customize pathway visualization. However, unlike ‘TCGA mode’, only a single gene selected by the entry label is considered and all the other genes belonging to the entry are ignored.REST API separates data extraction from the developmental environment so that users can easily extract data without understanding the internal system . Given ptcga-brca.bhi2.snu.ac.kr/api/landscapetcga-brca.bhi2.snu.ac.kr/api/search?keyword=erbb1tcga-brca.bhi2.snu.ac.kr/api/genes/hsa00010+hsa00030tcga-brca.bhi2.snu.ac.kr/api/pathways/hsa00010/related_pathwaystcga-brca.bhi2.snu.ac.kr/api/TCGA-BRCA/hsa00010/CNV?gender=maleThe domain address is the server URL that BRCA-Pathway is configured on. After the slash(/) mark, at least one argument should be given. The 1st argument specifies the data to retrieve and the argument can be landscape, search, genes, pathways, TCGA-BRCA. In case of tcga-brca.bhi2.snu.ac.kr/api/landscape, ‘landscape’ represents the current status of TCGA data and KEGG pathway. ‘search’ means that the pathway list will be provided by searching gene names or pathway names, and ‘genes’ provides the gene list in pathways specified by argument 2. Furthermore, ‘pathways’ returns pathway information specified by argument 3’s endpoint filtered by argument 2. REST API examples are listed below. The last example means that it will provide the result of aggregating CNV data from TCGA-BRCA data given the patients option is male and the genes filtered by the pathway ‘hsa00010’. For more customized use, reference Table Basal|Her2 |LumA|LumB|Normalsubtype_BHI_RNASeq_Log2 : all |years_to_birth_from : integer & years_to_birth_to : integerindeterminate|negative|positiveer_status : all |indeterminate|negative|positivepr_status : all |indeterminate|negative|positive|equivocalher2_status : all |vital_status : all |0|1 *0: alive, 1:deadpathologic_stage : all |stage_i |stage_ii |stage_iii |stage_iv |stage_iv |stage_tis |stage_xpathologic_T_stage : all |t1 |t2 |t3 |t4 |txpathologic_N_stage : all |n0 |n1 |n2 |n3 |nxpathologic_M_stage : all |cm0_ |m0 |m1 |mxfemale|malegender : all |no|yesradiation_therapy : all |histological_type : all |infiltrating_carcinoma_nos |infiltrating_ductal_carcinoma |in-filtrating_lobular_carcinoma |medullary_carcinoma |metaplastic_carcinoma |mixed_history(please_specify) |mucinous_carcinoma |other__specifynumber_of_lymph_nodes : all |0|1|2 *1: #of node less than or equal to 10, 2: greater thane 10asian|black_or_african_american |whiterace : all |american_indian_or_alaska_native |ethnicity : all |hispanic_or_latino |not_hispanic_or_latinoindeterminate|peri|post|premenopause_status : all |Patient_options are listed below: P-value by the logrank test is provided for the comparison of two groups, and all breast cancer patients are depicted as blue line for the convenience. Figure 5p-value of 0.05, which indicates a significant difference between the survival curves. Figure 5p-value of 0.02, the survival curve of ‘Her2’ patients having mutation in ‘Oocyte meiosis’ and ‘Her2’ patients having no mutation in ‘Oocyte meiosis’ pathway.BRCA-Pathway provides survival analysis of selected patients. It divides the selected patients into two groups according to the presence of mutation in genes belong to a particular pathway and provides survival analysis for the patient population. For example, if user selected ‘Basal’ subtype and ‘Cell cycle’ pathway then patients of ‘Basal’ subtype are divided into two groups, mutation group and mutation free group. Patients having at least one mutation in the genes involved in the ‘Cell cycle’ pathway belong to the mutation group. On the other hand, patients without a mutation in ‘Cell cycle’ pathway belong to the mutation free group. Two hundred twenty-eight patients of ‘Basal’ subtype are divided into mutation group (168 patients) and mutation free group (60 patients). The green and red line represent the survival curves of mutation group and mutation free group, respectively. p-value is over 0.05, correlation coefficient in ‘Basal’ subtype samples (-0.07) is shaded. Although we could not identify the responsible genes that promoted transcription of CCNA1 and CCNA2, we found that positive correlation between FOXP3 as TF and CCNA2 as TG is disrupted in tumor samples.If the user wants to prioritize genes that is significant in ‘Basal’ subtype breast cancer, the exploration can start from DEGs in ‘Basal’ subtype. By selecting patient subpopulation as ‘Basal’ and comparison condition as normal pool, then DEGs and related pathways are shown. Since the ‘Cell cycle’ pathway is listed at the top rank, it is natural to select and load ‘Cell cycle’ pathway for the next step. After loading a pathway, the user can explore multi-omics data on the ‘Cell cycle’ pathway of ‘Basal’ subtype patients. Figure 33BRCA-Pathway helps researchers navigate multi-omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. In the future, BRCA-Pathway could include other omics data sets such as miRNA expression and DNA promoter methylation profiles to support more extensive research. And besides breast cancer data of TCGA, other cancer dataset availability is also needed.


The various pathophysiological processes occurring in living systems are known to be orchestrated by delicate interplays and cross-talks between different genes and their regulators. Among the various regulators of genes, there is a class of small non-coding RNA molecules known as microRNAs. Although, the relative simplicity of miRNAs and their ability to modulate cellular processes make them attractive therapeutic candidates, their presence in large numbers make it challenging for experimental researchers to interpret the intricacies of the molecular processes they regulate. Most of the existing bioinformatic tools fail to address these challenges. Here, we present a new web resource ‘miRnalyze’ that has been specifically designed to directly identify the putative regulation of cell signaling pathways by miRNAs. The tool integrates miRNA-target predictions with signaling cascade members by utilizing TargetScanHuman 7.1 miRNA-target prediction tool and the KEGG pathway database, and thus provides researchers with in-depth insights into modulation of signal transduction pathways by miRNAs. miRnalyze is capable of identifying common miRNAs targeting more than one gene in the same signaling pathway—a feature that further increases the probability of modulating the pathway and downstream reactions when using miRNA modulators. Additionally, miRnalyze can sort miRNAs according to the seed-match types and TargetScan Context ++ score, thus providing a hierarchical list of most valuable miRNAs. Furthermore, in order to provide users with comprehensive information regarding miRNAs, genes and pathways, miRnalyze also links to expression data of miRNAs (miRmine) and genes (TiGER) and proteome abundance (PaxDb) data. To validate the capability of the tool, we have documented the correlation of miRnalyze’s prediction with experimental confirmation studies.Database URL:http://www.mirnalyze.in Signal transduction pathways are known to be the fundamental regulators of cellular development and pathophysiological processes. The progressive demands of survival, growth and development in living systems require achieving different biological outcomes. Such milestones are in turn manifestations of the differential regulation of various cellular signaling cascades. These modulations in expression of cellular signaling pathway members (genes and proteins) are mediated by various genetic and epigenetic mechanisms. Small non-coding RNA molecules known as microRNAs (miRNAs) are beginning to gain importance as one of the robust instruments for regulating gene expression patterns, and hence cellular signaling cascades . miRNAs The mechanism of action of miRNAs [partial or total complementarity matching to 3′-untranslated regions (UTRs) of mRNAs] have led computational biologists to devise several target prediction tools like TargetScan, miRanda, PicTar, PITA, that provides us with the putative interactions of miRNAs with mRNAs, with each tool having its own advantages and disadvantages. Researchers have also attempted to analyse interactions among miRNAs, target genes and associated drugs using in-silico tools like PharmacomiR . AlthougThe ever increasing number of miRNA-related publications evidence the fact that the interest in these non-coding RNA molecules and diseases is increasing rapidly. Researchers are being constantly challenged by the ever increasing pool of newly discovered miRNAs. miRbase is the most prominent database which systematically provides miRNA data to miRNA research community . ConsequKEGG is an elaborated collection of information of genes/proteins involved in pathways across species , 17 thatMicroRNAs are believed to contain a ‘seed matching’ region that extends from second to seventh nucleotide (from 5′ end) of the miRNA. MicroRNA targets are predicted by the presence of complementary seed matching region(s) on the gene of interest. These seed match sites may include 6mer, 7mer-A1, 7mer-m8 and 8mer sites as described in The microRNA expression regulates the expression of specific genes in a signaling pathway. Hence, integration of both microRNA and target expression data is important for understanding overall regulation of signaling pathways. miRmine is a publicly available database which provides human microRNA expression data in a variety of human tissue. We have linked expression data for each microRNA in different human tissue by utilizing miRmine database . TiGER is another database for tissue specific gene expression and regulation. We have also linked expression data for each target member in various human tissue by using TiGER database . AdditioThe frontend of the tool has been developed with Angular JS, a Model-View-Controller (MVC) JavaScript framework. The server side of the tool is developed with Laravel, a PHP framework. The Laravel backend acts as a RESTful web API that sends data to the frontend in JavaScript Object Notation (JSON) format. After its first load, the tool acts as a Single Page Application (SPA), which consumes data from the backend. The SPA behaviour allows for easy navigation between pages without reloading.miRnalyze is a dynamic web-based tool for enlisting miRNAs associated with particular cell signaling pathways and target genes. It utilizes two freely available massive database resources: Kyoto Encyclopedia of Genes and Genomes Database and TargetScanHuman (Release 7.1). The logistics based on which miRnalyze has been designed is shown schematically in Salient features of miRnalyze along with specific example of each of the feature are represented in miRnalyze can be used for experimental design and hypothesis construction. Results of miRnalyze are based on computational prediction only. It is obvious that computational prediction do not replace experimental confirmation but rather provide a weighty emphasis for such experiments. miRnalyze predicts microRNA targets in cellular signaling pathways directly through identifying involved genes in those pathways. We showed that miRnalyze prediction includes experimentally confirmed dysregulated miRNAs which cause disease through altering the pathway . miRnalyRecent empirical evidence from across the globe strongly emphasize the implications of differential miRNA expression patterns in health and disease. Dysregulated miRNA expression patterns are known to be associated with development of several diseases . These sWith rapid progress of molecular biology technologies, more than thousands of miRNAs have been identified . This laThus, miRnalyze is designed to identify the interaction among miRNAs, genes and cell signaling pathways integrating KEGG pathway database and TargetScanHuman 7.1 target prediction tool. KEGG is one of the earliest pathway databases developed  repositiThe interface of this web tool is very simple to use and produces nested data for easy interpretation. Therefore, miRnalyze is a useful tool for developing hypotheses in miRNA-mediated cell signaling pathways. We believe that miRnalyze has immense value for aiding researchers in predicting miRNA-related regulation of cell signaling pathway in the future.In the future, we will expand the miRnalyze by integrating more data from freely available resources such as KEGG pathway database and TargetScan target prediction tool.This work has been supported by the Institute Scheme for Innovative Research and Development (ISIRD), funded by Sponsored Research & Industrial Consultancy (SRIC), Indian Institute of Technology, Kharagpur, India [grant number IIT/SRIC/SMST/BHA/2015-16/122]. S.S.D. is supported by a fellowship funded by Council of Scientific and Industrial Research (CSIR), India [CSIR sanction number 09/081(1267)/2015-EMR-I].Conflict of interest. None declared.


Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure.Here, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs.Overall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition.We believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.The online version of this article (10.1186/s12967-018-1535-2) contains supplementary material, which is available to authorized users. Breast cancer (BC), the most invasive cancer in women worldwide, is a heterogeneous disease, characterized by different subtypes that lead to different clinical prognosis and responses to treatments .The advent of genome-wide technologies has made possible the generation of new hypotheses about the role of genomics in the efficiency of drugs developed for cancer and the event of adverse responses to cancer therapy.In this context, several studies examined the effects of drugs considering protein network approaches . In partHence, two critical points for drug discovery tools are: (i) to inhibit not single but multiple targets at the same time, and (ii) to prevent the formation of new interactions that could lead to phenomena of resistance or inefficacy of the drug.Moreover, these two critical conditions can be further complicated by the interactions between pathways  that can modify the effect of drugs.For instance, in some cancer cells, rapamycin-like drugs inhibit mTORC1 complex but at the same time indirectly activate phosphatidylinositol 3-kinase (PI3-kinase) and AKT, mitigating the inhibitor effect of drugs . MoreoveTherefore, understanding how and where the pathway cross-talk can be inhibited by drug treatments during a disease process ideally could lead to more effective therapies, reducing the problem of drug resistance.Once a map of pathway cross-talk specific of a disease is known, a potential solution hindering the formation of alternative signaling pathways created in response to therapy could be the administration of drugs able to act on both direct and indirect targets (generated by the inhibition of direct targets).However, on this potential solution, the current opinions are conflicting. Indeed, multi-target drugs show lower affinity than one-target-drugs , 3. One Understanding the effect of individual or combined drugs is critical in clinical studies. An important issue is the low reliability of the cell lines to predict the efficacy of drugs since cell lines did not show to be good models . Thus, cA recent work by Jaeger et al.  has propIn our recent studies on BC , 8, we gIn the present study we propose a computational method based on the work of Jaeger et al.  and optiWe applied the computational approach on four BC subtypes with different diagnostic classification and prognosis : “luminaWe considered gene expression data from tissue samples studied by IlluminaHiSeq RNASeqV2 and derived from TCGA dataset: 233 BC luminal A samples, 103 BC luminal B samples, 43 BC HER2-overexpressing samples, 74 BC basal samples and 113 normal samples (NS).We tested the approach with respect to its performance in classifying different subtypes by using an independent testing dataset from Gene Expression Omnibus (GEO) database (GSE58212): 121 luminal A, 69 luminal B, 36 basal and 32 HER2-overexpressing samples.We validated the approach with respect to its performance in identifying drug target pathways, by using the Matador database  that proThe computational approach consists of seven steps .All the steps were applied to each of the four BC subtypes. The first four steps were applied 50 times in order to obtain a solid network of de-regulated cross-talking pathways for each BC subtype. More specifically, in order to perform a bootstrapping, we implemented a Monte Carlo cross-validation by randomly selecting some portions of the dataset (60%) to build the training dataset of the classifier and the rest of data (40%) as testing dataset. Step 1, 2, and 3 were applied on the training data set. Step 4 was applied on both training and testing dataset. To avoid problems of unbalanced classes, we randomly generated the same number of samples for each class of BC subtypes and NS.Then, for each BC subtype, we generated a network-based model of BC subtype pathways (5. Pathway network), and we simulated in silico the drug-induced inhibition of pathways  focusing on the change of network efficiency.At the end of the sixth steps, we found potential drug target pathways for each BC subtype, which, if inhibited, can change significantly the network efficiency. These drug targets could be considered for future applications in drug discovery (7. Drug target pathway network).Figure We validated our network-based models of BC subtype pathways and BC drug target pathways.For the first validation task, we assessed the accuracy of using our subtype pathways in classifying four different BC subtypes using the GEO dataset, as data set independent from the TCGA dataset used for the identification of the subtype-derived pathways.For the second validation task, we evaluated the mechanism of action of 13 FDA approved drugs for BC on our BC subtype drug target pathway network (DTPN). We compared the decrease of pathway cross-talk within the DTCN followed by inhibition of drug target pathways.For each BC subtype, differential expression analysis (DEA) was applied with respect to NS using TCGAbiolinks . In partFor each BC subtype, we identified a group of pathways significantly enriched with the list of subtype-derived DEGs. The original list of pathways (589) was obtained from IPA. Pathway Enrichment Analysis (PEA) was performed with a Fisher’s test between DEGs and genes within IPA pathways . We defix, and y.Pathway cross-talk between pathways enriched with subtype-derived DEGs was quantified using a discriminating score (DS) , 8. ThisFrom this step we created a matrix for each sample (BC subtypes and NS) containing a DS value for each pair of pathways enriched with subtype DEGs.In order to select the best discriminating pairs of cross-talking pathways, we implemented a Random Forest Classification using the R-package , 17 to cFor each combination of pathways we estimated the Area Under Curve (AUC) values by cross-validation method . We used the following parameters: mtry (number of variables randomly sampled as candidates at each split) = sqrt(p), p being the number of variables in the matrix of data; ntree (number of trees grown) = 500.The classification was performed on the training dataset 50 times for each matrix obtained from the previous three steps. Every time we obtained the top-10 pairs of subtype cross-talking pathways with the best AUC value. Then we validated these pathways on the testing dataset.In conclusion, for each BC subtype and for all 50 bootstraps we obtained 10 × 50 couples of pathways with the best AUC values validated on the testing dataset.For each BC subtype, by univocally selecting the pairs of cross-talking pathways better discriminating each subtype, we then generated a network-based model of BC subtype pathways, where the nodes of each subtype network represent pathways and the links that connect nodes the pathway cross-talks.In a network-based model, the network efficiency is defined as the sum of the reciprocals of the shortest direct path lengths between all pairs of network elements.i,j is the shortest direct path lengths of two elements i and j,If N is the number of network elements and dentclass1pt{minimai, j in N).For each BC subtype, the network efficiency (NE) of the disease was calculated starting from the subtype pathway network, the number of the pathways (nodes) and the shortest direct path lengths between two pathways  we considered the gene expression levels belonging to our BC subtype pathway networks. We tested their ability to classify each subtype. We then investigated the role of the top-10 genes that obtained the best classification performance in the perspective of DTPN.We validated DTPN by assessing if FDA-approved BC drugs have the target pathways within DTPN, and evaluating how these drugs could reduce effectively the NE of the DTPN.For each BC subtype, we evaluated the association of 13 FDA-approved BC drugs (from Matador database) and DTPN. For this purpose, we applied PEA with a Fisher’s test between gene targets of the drug and genes within IPA pathways. We defined pathways enriched with drug target genes if p-value of Fisher’s test was < 0.01.For each BC subtype, we measured the effects of the considered drugs, when administered individually and in combination on the DTPN. We quantified this effect by PCI.For the Luminal A we found a pathway network composed of 73 individual pathways and 157 pathway cross-talks  was 0.3218. nNE, calculated after the drug-induced in silico inhibition of each single pathway (among the 73) or each combination of pair of pathways (among the 2628) is shown in Fig. Additional file DTPN of Luminal A was built considering only the 34 pathways of the network that if inhibited, reduced the NE (nNE < NE).Starting from the 13 FDA approved drugs for BC  we obtained 8 drugs that interact with the DTPN of Luminal A   that have a significant number of target genes belonging to this pathway.The ‘FXR/RXR Activation Pathway’ if inhibited, reduces the NE of 0.75%, and it is target of three drugs . The ‘RAR Activation Pathway’, if inhibited, reduces the NE of 1.5% and it is target of two drugs . The ‘Regulation of Cellular Mechanics by Calpain Protease’ pathway, if inhibited, reduces the NE of 0.6% and is target of two drugs (fulvestrant and gemcitabine).Among the drug target pathways found in our DTPN, we found 5/34 pathways that were ranked as part of the top 10 pathways, playing the major role in the reduction of NE. These pathways are ‘Estrogen Receptor’ targeted by tamoxifen and raloxifene; ‘Human Embryonic Stem Cell Pluripotency’ targeted by tamoxifen; ‘RAR Activation Pathway’ targeted by raloxifene and tamoxifen; ‘Bladder Cancer Signaling’ targeted by fulvestrant; ‘Factors Promoting Cardiogenesis in Vertebrates’ targeted by tamoxifen. In particular, tamoxifen acting on 4 pathways reduces the NE of 8.59%, thus it is very effective.FDA-approved drugs do not seem to act on ‘LXR/RXR Activation’, ‘Extrinsic Prothrombin Activation’; ‘Ethanol Degradation IV’, ‘Fatty Acid oxidation’ and ‘Glioma Invasiveness Signaling’. In particular the first two pathways, according to our findings, could be potential dug targets for anticancer drug treatment.Furthermore, considering FDA-approved drugs, our study confirms that tamoxifen is the drug with the best efficacy on the pathway network since it is able alone to inhibit four pathways .In luminal B we found a pathway network composed of 73 individual pathways and 129 pathway cross-talks Fig. a. In theThe efficiency of the pathway network was 0.3272. Figure The inhibition of 27/73 individual pathways reduces the NE of the pathway network with values that range from 0.3123 to 0.3270 Fig. c. The inAdditional file DTPN of luminal B was built considering the 27 pathways that inhibited reduced the NE. We obtained 4 BC drugs (among the 13 FDA approved)  that interact with the DTPN of luminal B . The ‘Epithelial Adherens Junction Signaling Pathway’, if inhibited, reduces the NE of 2.3% and it is target of docetaxel. The ‘Germ Cell-Sertoli Cell Junction Signaling Pathway’, if inhibited, reduces the NE of 0.4% and it is target of docetaxel. The ‘Regulation of the Epithelial-Mesenchymal Transition Pathway’, if inhibited, reduces the NE of 2.1% and it is target of tamoxifen. The ‘Wnt/catenin Signaling Pathway’, if inhibited, reduces the NE of 0.5% and it is target of tamoxifen.Among the drug target pathways found in luminal B DTPN, we found 2/27 pathways that were ranked as to be part of the top 5 pathways playing the major role in the reduction of NE. These pathways are ‘Epithelial Adherens Junction Signaling’ and ‘Regulation of the Epithelial-Mesenchymal Transition Pathway’ targeted by docetaxel and tamoxifen, respectively.FDA-approved drugs do not seem to act on ‘Cell Cycle Control of Chromosomal Replication’, ‘P2Y Purigenic Receptor Signaling Pathway’ and ‘Growth Hormone Signaling’; pathways that according our findings were the best potential drug targets. In particular, tamoxifen acting on three pathways reduces the NE of 3%.Furthermore, considering FDA-approved drugs our study demonstrates that docetaxel is the drug with the best action on pathway network since is able to inhibit ‘Epithelial Adherens Junction Signaling’.We found a pathway network in HER2 BC composed of 100 individual pathways and 222 pathway cross-talks Fig. a. Mean AThe efficiency of the pathway network in HER2 was 0.3492. Figure Additional file DTPN was built considering 39 pathways whose inhibition reduces the efficiency of the network. Starting from the 13 BC drugs, we obtained five drugs  that interact with the HER 2 DTPN . The ‘Bladder Cancer Signaling Pathway’, if inhibited, reduces the NE of 0.5% and it is target of fulvestrant. The ‘Epithelial Adherens Junction Signaling Pathway’, if inhibited, reduces the NE of 0.2%, and it is target of docetaxel. The ‘Human Embryonic Stem Cell Pluripotency pathway’, if inhibited, reduces the NE of 0.8% and it is target of tamoxifen. The ‘Regulation of the Epithelial-Mesenchymal Transition Pathway’ if inhibited, reduces the NE of 0.1% and it is target of tamoxifen. The ‘Regulation of the Salvage Pathways of Pyrimidine Deoxyribonucleotides’, if inhibited, reduces the NE of 0.2% and it is target of fluoracil, and methotrexate.Among the drug target pathways found in our DTPN we found 1 pathway that was ranked as to be part of the top 10 pathways playing the major role in the reduction of NE (‘Human Embryonic Stem Cell Pluripotency’).FDA-approved drugs do not seem to act on ‘Role of BRCA1 in DNA Damage Response Pathway’ and ‘NAD biosynthesis II (from tryptophan)’, pathways that, according to our findings, were the best potential drug targets.Furthermore, considering FDA-approved drugs, our study confirms that tamoxifen is the drug with the best action on pathway network since it is able to inhibit ‘Human Embryonic Stem Cell Pluripotency’.We found a pathway network in basal BC composed of 43 individual pathways and 74 pathway cross-talks Fig. a. In theThe efficiency of the pathway network in basal BC was 0.3445. nNE in case of the single inhibition of the 43 individual pathways or each combination of pairs of pathways are shown in Fig. Additional file DTPN of basal BC was built considering 19 pathways that reduced NE. We obtained 6 drugs  that interact with the DTPN in basal BC Table .Table 4DIn particular, the ‘Salvage Pathways of Pyrimidine Deoxyribonucleotides’ is target of two FDA-approved drugs  and ‘Aryl Hydrocarbon Receptor Signaling’ of 4 FDA-approved drugs . The ‘Aryl Hydrocarbon Receptor Signaling’, if inhibited, reduces the NE of 5%. The ‘Salvage Pathways of Pyrimidine Deoxyribonucleotides’, if inhibited, reduces the NE of 0.5%. The ‘Triacylglycerol Degradation Pathway’, if inhibited, reduces the NE of 0.4% and it is target of capecitabine.Among drug target pathways found in our DTPN we found 1 pathway of the top 5 pathways playing the major role in the reduction of NE . In particular, fluorouracil acting on 2 pathways reduces the NE of 6.86%, thus it is very effective.The considered FDA approved drugs for BC do not seem to act on ‘EIF2 Signaling’ and ‘Mismatch Repair in Eukaryotes’, pathways that, according to our findings, could be interesting potential drug targets.To classify luminal A, we considered the expression levels of 2693 genes from the independent GEO dataset GSE58212 and belonging to our luminal A pathway network. We found PRC1 (AUC = 0.862), CCNB2 (AUC = 0.847), BIRC5 (AUC = 0.838), PTTG1 (AUC = 0.831), CCNA2 (AUC = 0.827), E2F1 (AUC = 0.826), KIF11 (AUC = 0.826), CDC25A (AUC = 0.823), UBE2C (AUC = 0.82), and CDK1 (AUC = 0.817) as the top 10 genes obtaining the best classification performance. Among them, 7/10 genes play a crucial role in luminal A DTPC. PRC1, CCNB2, PTTG1, KIF11, and CDC25A belong to ‘Mitotic Roles of Polo-Like Kinase’ pathway. The inhibition of this pathway can reduce the NE of luminal A of 1% (PCI). CCNA2 and CDK1 belong to ‘Regulation of Cellular Mechanics by Calpain Protease’ pathway. Its inhibition can reduce the NE of luminal A of 0.6% (PCI).In luminal B BC we considered the expression levels of 2158 genes from the GEO dataset belonging to our luminal B pathway network. We found CX3CL1 (AUC = 0.779), KIF11 (AUC = 0.777), PTTG1 (AUC = 0.766), PRC1 (AUC = 0.761), CCNA2 (AUC = 0.757), ACTG2 (AUC = 0.756), CCNB1 (AUC = 0.755), RND3 (AUC = 0.752), KIF23 (AUC = 0.751), and CLDN8 (AUC = 0.749) as the top 10 genes. Among them, 6/10 genes play a crucial role in luminal B DTPN. KIF11, CCNB1, PTTG1, PRC1, and KIF23 belong to ‘Mitotic Roles of Polo-Like Kinase’ pathway, whose inhibition can reduce the NE of luminal B of 2% (PCI). ACTG2 belongs to ‘Tight Junction Signaling’ pathway. Its inhibition can reduce the NE of 1.7% (PCI).In HER2 BC we considered the expression levels of 1172 genes from the GEO dataset belonging to 100 pathways of our HER2 pathway network. We found ESR1 (AUC = 0.811), E2F1 (AUC = 0.78), RARG (AUC = 0.769), PNMT (AUC = 0.758), PLCB3 (AUC = 0.757), ERBB2 (AUC = 0.741), MYL5 (AUC = 0.729), PPP1R10 (AUC = 0.729), BCL2 (AUC = 0.727), and WNT3 (AUC = 0.724), as the top 10 genes.Among them 6/10 genes play a crucial role in DTPN in HER2. E2F1 belongs to ‘Role of BRCA1 in DNA Damage Response’ and ‘Growth Hormone Signaling’ pathways. These pathways are involved in HER2 DTPN since their inhibition can reduce the NE of 5.3 and 1.1% (PCI), respectively.PLCB3, ERBB2, MYL5, and WNT3 belong to ‘Axonal Guidance Signaling’ pathway, whose inhibition can reduce the NE of HER2 of 0.8% (PCI). PPP1R10 belongs to ‘Protein Kinase A Signaling’. Its inhibition can reduce the NE of 2.8% (PCI).Furthermore, ESR1 is targeted by fulvestrant, toremifene, raloxifene, and tamoxifen; ERBB2 is targeted by tamoxifen, and BCL2 is targeted by fulvestrant, gemcitabine, docetaxel and tamoxifen.In basal BC we considered expression levels of 1196 genes from the GEO dataset belonging to 43 pathways of basal pathway network. We found RHOB (AUC = 0.946), FBP1 (AUC = 0.942), RARA (AUC = 0.911), PPP1R14C (AUC = 0.909), E2F3 (AUC = 0.905), F7 (AUC = 0.904), RND1 (AUC = 0.903), ESR1 (AUC = 0.902), CDC20 (AUC = 0.892), and CCNE1 (AUC = 0.892), as the top 10 genes.RARA, ESR1, and CCNE1 belong to ‘Aryl Hydrocarbon Receptor Signaling’ pathway whose inhibition can reduce the NE of 6% (PCI). E2F3 belongs to ‘Role of BRCA1 in DNA Damage Response’ pathway. This pathway is involved in basal DTPN since its inhibition can reduce the NE of 4% (PCI).We have constructed a model of BC heterogeneity based on different subtype pathway networks. Table We can observe in particular that: (i) ‘LXR/RXR Activation Pathway’, that in our analyses emerged as a new potential drug target for luminal A, is specific for this subtype; (ii) ‘NAD biosynthesis II (from tryptophan)’ pathway, that according to our findings was found the best potential drug target in HER2, is specific for HER2 subtype; and (iii) ‘Aryl Hydrocarbon Receptor Signaling’, that is targeted by 4 FDA-approved drugs  is specific of basal subtype.The formation of new alternative signaling pathways upon drug treatment is one of the main causes of inefficacy or development of drug resistance in cancer. The study of alternative signalling through pathway cross-talk can help drug-development strategies. Therefore, in this work, we optimized a computational method to investigate the role of pathways target of FDA approved drugs or new drugs that specifically addresses this issue. Our in silico method is based on the identification and quantification of the pathway cross-talk for distinct cancer subtypes. The effects of drugs inhibiting individual or combined pathways can be simulated and measured with the purpose to find new potential drug-targets or synergistic combination drugs.Most works that study drug-development approaches use network models. Usually the most used methods to examine the effects of drugs involve protein–protein interactions, metabolic control analysis or neural network –21. Nevevs normal tissues. In line with the last scenario, the computational method of Jaeger et al. is suitable to model static pathway cross-talks, while our method could be used to represent dynamic disease progression.Although based by the computational method of Jaeger et al. , our appUsing Monte-Carlo cross validation and classification, we built networks of cross-talking pathways able to classify with high performance cancer subtypes versus NS. We inhibited pathway cross-talks in such subtype-derived networks and identified a number of pathways that could be potential targets of drugs by measuring drug ability in reducing the network efficiency.We applied our network-based model of cross-talking pathways to the BC subtype gene expression level of an independent GEO dataset, to test independently the ability of the pathway networks to classify BC subtypes. We found that the top 10 genes achieving the best classification performance belong to the expected cross-talking pathways.We then associated some drugs already approved by FDA for BC with the networks of cross-talking pathways. The association was done considering the number of known drug-target proteins mapped on pathways and selecting potential synergistic combination of drugs. In general, the inhibition of pairs of pathways of the pathway network reduces the network efficiency more than the inhibition of individual pathways. The considered FDA approved drugs for BC act on several pathways included in the networks of cross-talking pathways. However, they do not act on other pathways that, according to our findings, could be interesting potential drug targets in BC.In luminal A, we found that the individual inhibition of ‘LXR/RXR Activation’ pathway reduces the efficiency of the network of 5.6%. LXRs are nuclear receptors (NRs) involved in cholesterol, glucose, fatty acid metabolism and inflammatory responses. NRs are a family of transcription factors that bind to respond to lipophilic signaling molecules (ligand) regulating downstream effectors. They also represent one of the most important drug-development targets, since the designing of synthetic compounds that mimic the functions of ligands can selectively modulate the activity of NRs . SelectiIn luminal B subtype we found that the individual inhibition of ‘Cell Cycle Control of Chromosomal Replication’ reduces the efficiency of the network of 4.5% while, in our pathway network, synergistic drug combinations acting on: (1) ‘Growth Hormone Signaling’ and ‘Regulation of the Epithelial-Mesenchymal Transition’ Pathways; (2) ‘Mitotic Roles of Polo-Like Kinase’ and ‘Cell Cycle Control of Chromosomal Replication’; (3) ‘Cell Cycle Control of Chromosomal Replication’ and ‘Growth Hormone Signaling’ reduce of almost 10% the efficiency of the network. To date, synergistic drug combinations targeting on these pathways are not presented. Regarding FDA approved drugs, we confirm docetaxel and tamoxifen as promising candidates for synergistic drug combinations since they can modulate ‘Epithelial Adherens Junction Signaling’ and ‘Regulation of the Epithelial-Mesenchymal Transition Pathway’, respectively. Previous studies showed that a combination therapy of these drugs in BC cell lines increases the anti-proliferative effects of single agents , 30.In HER2 subtype we found that the inhibition of BRCA1 in ‘DNA Damage Response’ reduces the efficiency of 5.2% while, in our pathway network, synergistic drug combinations acting on ‘NAD biosynthesis II (from tryptophan)’ and ‘Role of BRCA1 in DNA Damage Response’ reduce the efficiency of 8%.In basal subtype we found that the inhibition of EIF2 Signaling reduces the efficiency of 8.5%. While, in our diagnostic pathway cloud, synergistic drug combinations acting on ‘Mismatch Repair in Eukaryotes’ and ‘Cell Cycle:G2/M DNA Damage Checkpoint Regulation’ reduce the efficiency of the network of 37%. Regarding FDA-approved drugs, we suggest fulvestrant, or fluorouracil and/or capecitabine as promising candidates for synergistic drug combinations since they can modulate ‘Epithelial Salvage Pathways of Pyrimidine Deoxyribonucleotides’, ‘Aryl Hydrocarbon Receptor Signaling’ and ‘Triacylglycerol Degradation’.Our approach presents some limitations. One limit is the dependence on expression data analysis to identify affected pathway, mainly based on the use of changes in gene expression that are largest in size or level. However, some studies  show thaAs overall result, our approach have generated promising hypotheses for identifying altered pathways as potential therapeutic targets, either by using synergistic drug combinations and new/approved drugs. Despite the number of drugs is increasing, the specific molecular mechanisms underlying drug combinations with therapeutic effect remain often unclear. We believe that the current efforts based on pathway cross-talk drug strategies will provide key information that are required to decipher molecular mechanism contributing to resistance or inefficacy of drugs.Overall, our computational method has identified a set of new potential drug targets that have a large impact on cross-talk inhibition. We consider that further experiments are required to enable the translation of new drug target pathways into therapeutic strategies, however our results show that methods focusing on the Monte Carlo cross-validation and PCI could offer valuable approaches to discover synergistic drugs.Moreover, we focused our study on BC, but we suggest the application of our approach also to other complex and heterogeneous diseases, in which pathway cross-talk is likely to cooperate important functions.Additional file 1. Shows the network efficiency after inhibition of individual and combined pathway(s) in luminal A BC.Additional file 2. Shows the network efficiency after inhibition of individual and combined pathway(s) in luminal B BC.Additional file 3. Shows the network efficiency after inhibition of individual and combined pathway(s) in HER2 BC.Additional file 4. Shows the network efficiency after inhibition of individual and combined pathway(s) in basal BC.


C4 photosynthesis in the wheat seed provides an adaptation to heat and moisture stress and is an efficient mechanism for minimizing carbon loss due to respiration during grain filling through re-fixation of carbonAnalysis of the transcriptome revealed the expression of a complete set of enzymes specific for a C4 photosynthetic genes in the genome (Rangan et al., 4 photosynthetic genes being optimal during early to mid-grain filling. This illustrates the power of genomics in providing novel perspectives in plant biology (Abberton et al., 3 plant. Careful analysis of the literature over a long period of time reveals extensive but highly fragmented evidence for C4 photosynthesis in developing wheat grains. Elaborate explanations have often been offered to explain away the results due to the clear evidence for C3 photosynthesis in the leaves and the dogma of a requirement for Kranz anatomy for C4 photosynthesis to be accomplished. Different pathways may be found in different parts of the same plants or in different environments (Hibberd and Quick, New technologies often provide novel perspectives that modify long standing assumptions. The widespread application of modern sequencing (Rossetto and Henry, 4 photosynthesis in the pericarp of cereals has been controversial because of uncertainties due to the difficulty of demonstrating flux of carbon through the pathway and the carbon isotope discrimination (Farquhar et al., 4 leaves. The complex pathway of carbon to the pericarp via photosynthetic fixation in C3 photosynthesis in the leaves and respiration in the endosperm before arriving at the pericarp explains these issues. Isolated pericarp tissues have been used to demonstrate the flux of carbon into malate and then sugars in barley (Nutbeam and Duffus, Evidence for C4 photosynthesis was discovered around 50 years ago and has been associated with adaptation to low carbon environments within the plant often due to high temperatures and the associated management of water loss in dry environments. Plants have been categorized as C3 or C4 based upon leaf anatomy. Wheat is clearly a C3 plant on this basis. However, different photosynthetic pathways have been reported in different organs of plants; and the possibility of C4 photosynthesis without Kranz anatomy supports the possibility of grain specific photosynthetic pathways.C4 leaf displays Kranz anatomy allowing the different parts of the C4 pathway to be compartmentalized in different cells within the leaf. The inner pericarp of the developing wheat grain has two distinct cell layers, cross cells, and tube cells, that correspond anatomically with the mesophyll and bundle sheath cells of the C4 leaf. We propose here that this seed anatomy be described as “Bose” anatomy in honor of the early photosynthesis researcher Jagadish Chandra Bose who reported that C4 acids are involved in carbon fixation in Hydrilla verticillata during summer (Bose, 3 pathway of carbon fixation was described in 1957 (Calvin, The classical Cer Bose,  and not 4 pathway have been detected in the wheat genome and are expressed in the developing wheat grain at the relative levels required to support C4 photosynthesis with appropriate subcellular compartmentalization (Rangan et al., Genes encoding a complete NAD-dependent C2 release. This suggests a role for seed photosynthesis in recapture of CO2 generated in the endosperm. Early work by Kriedemann (Seed photosynthesis is necessarily unique as the seed is a major carbon sink while most photosynthetic tissues are a net source of carbon. This complicates the interpretation of macromolecule assembly may involve significant COiedemann  showed tGenetic improvement of wheat is critical to the food demands of a growing human population. Targeting selection for enhanced seed photosynthesis may contribute significantly to improving wheat yields. This may be especially important under harsh environmental conditions late in the growth of the crop. Stress late in crop development is often encountered in wheat production and may become increasingly important as a result of climate change (Henry et al., All authors conceived, wrote, and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of  Metabolomics is the science that studies the chemical reactions taking place in a living organism by measuring their lightweight reactants and products, also called metabolites. Metabolomics is used in the study of human disease, biomarker identification, drug evaluation and treatment prognosis . MetabolCurrent methods to measure metabolites mainly rely on Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) technologies , the latData interpretation, known as secondary analysis, benefits from the identification of metabolic pathways to draw conclusions, encouraging the use of so-called pathway enrichment techniques. Their purpose is to provide the metabolites with their biological context, drawing from comprehensive databases like Kyoto Encyclopedia of Genes and Genomes, KEGG , ReactomThe first generation of enrichment techniques is based on Over Representation Analysis (ORA), a statistical test that assesses whether the occurrence of a label within a subset is greater than expected by chance in the background population. Applied to metabolomics, it takes as input the identifiers of affected metabolites  and assesses a p-value for each pathway. ORA is available through the web tools IMPaLA , MetaboAA second generation of enrichment methods, Functional Class Scoring (FCS), avoids the cutoff choice in generating the affected metabolite list and claims the capability of capturing subtle but consistent changes in concentration , 19. ThiThe third generation of enrichment techniques attempts to incorporate topological data on the underlying biological networks. This concept was applied early to genetic data through ScorePAGE  and is aPathway enrichment techniques face challenges, such as dealing with pathway crosstalk and overlap  or genergs to node gi, by introducing a flow on gs and allowing it to leave through all the nodes. The diffusion score of node gi, i. A new undirected graph is built using the weights w = min, in which sub-networks encompassing a large number of gene mutations are sought. TieDIE , see After the curation step, our knowledge base graph contains 10,183 nodes and 31,539 edges. The nodes are stratified in 288 pathways, 178 modules, 1,149 enzymes, 4,699 reactions and 3,869 compounds. The degree distribution of its vertices follow a scale-free network model, where On the other hand, MS led to 168 quantified metabolites from KEGG. Two identifiers that each appeared twice have been dropped, as well as a KEGG drug, excluded from the KEGG compound category. The remaining 163 metabolites have been tested between both conditions, leading to 38 significant metabolites , of which 33 have been mapped to our KEGG graph.HD) and PageRank (PR) are followed by norm  or sim (Monte Carlo permutations). Normalised scores have been computed through the null models with nin = 33, followed with subgraph selection with a desired number of nodes k = 250. For simulated methods, a consensus subgraph using nvote = 9 runs of nperm = 10,000 permutations each has been derived by majority vote on each node.The 33 MS-derived compounds served as input for each of the proposed enrichment algorithms. Heat Diffusion  the absence of signal, the mean rank of a pathway is expected to be uniform on  and have a mean value of 0.5. If the mean value is closer to 0, the pathway might be systematically favoured in any analysis and could become a recurrent false positive. HD shows small deviations from 0.5 in the mean rank of the 288 pathways in the KEGG graph while PR and Fisher’s exact test show more dispersion. This may be due to the discrete nature of Fisher’s exact test, which is partly inherited by PR as it only allows upwards propagation. In (b) the presence of signal, a target pathway generates the signal and is ranked in the prioritisation of each method. In the first sampling scheme, the target pathway is actually a decoy and is expected to be ranked uniformly on . This is the case for HD and PR, but Fisher’s exact test shows an asymmetrical distribution, probably a consequence of pathways tied at 0 hits. If the sampling strategy is affine to Fisher’s exact test alternative hypothesis, this test has an edge over HD and PR in terms of discovering the true positive. Conversely, if the sampling is network-based, HD and PR perform better, as the binary nature of Fisher’s exact test cannot account for metabolites close to, but not inside of, a target pathway. This sampling generates signals that are harder to recover because of the network topology: crosstalk effects are present and unspecific metabolites divide their contribution over all the pathways to which they belong. This implies that, focusing on the pathway ranking problem, the optimal choice between Fisher’s exact test and HR or PR depends on the network influence in the generative model of the data.An added value of our approach is in providing further details about the reported pathways, together with more specificity due to the presence of KEGG modules. Our results offer sub-pathway resolution and, unlike other sub-pathway focused tools, details at several molecular levels between the metabolites and the pathways. Entities like enzymes or metabolites that appear relevant and shared among pathways can give insights of pathway overlap and crosstalk that is specific to the condition under study. Our pathway hits are consistent with the current techniques, both using list format and abundance data . The samOur prior studies  suggest The lack of a gold standard procedure and a reference benchmark dataset with known biology for pathway enrichment , 21 encoWe propose a secondary analysis methodology for summary metabolomics data that combines pathway enrichment and sub-network analysis. Instead of reporting a list of pathways, we build meaningful sub-pathway representations of the biology at several molecular levels, derived through a null diffusive process on a curated graph object built from the KEGG database. This approach accounts for pathway over-representation, topology and crosstalk. Nodes reported as relevant are drawn in a comprehensive heterogeneous network that contains not only pathways and compounds, but also enzymes, reactions and KEGG modules. This richer biological context adds value to the top pathway hits by suggesting possible paths through which affected compounds translate into dysregulated pathways.The proposed methodology has been tested and assessed in a case-control study, where the suggested pathways are consistent with alternative pathway enrichment techniques and the reported reactions have been partially validated through NMR-based tracking of glucose carbon. Our analysis suggests that the Glutathione metabolism is one of the most affected pathways. Glutathione is critical for the suppression of reactive oxygen species and this result is consistent with our preliminary observations that these cells exhibit higher levels of mitochondrial reactive oxygen species. Tests on simulated data suggest that our methodology can benefit from pathway signals whose generative model is network-based. These results support the potential of our novel methods for aiding in the interpretation of complex metabolomics datasets.S1 TableReported subgraphs and pathway analysis using IMPaLA and MetaboAnalyst on the experimental dataset.(XLSX)Click here for additional data file.S2 TableReported pathways for the IMPaLA example data using top 250 z-scores in heat diffusion, IMPaLA and MetaboAnalyst.(XLSX)Click here for additional data file.S3 TableReported pathways for the MetaboAnalyst example data using top 250 z-scores in heat diffusion, IMPaLA and MetaboAnalyst.(XLSX)Click here for additional data file.S1 AppendixDetails on how to generate and curate the KEGG graph.(PDF)Click here for additional data file.S2 AppendixFormulation of the heat diffusion scoring method.(PDF)Click here for additional data file.S3 AppendixFormulation of the PageRank web ranking algorithm.(PDF)Click here for additional data file.S4 AppendixDefinition of the null models and visualisation of the pathway correlation matrix.(PDF)Click here for additional data file.S5 AppendixSolution stratification, CC evolution, computational cost of Monte Carlo permutations and damping factor influence.(PDF)Click here for additional data file.