Solanaceae family hosted at the Sol Genomics Network. Currently, SolCyc comprises six organism-specific PGDBs for tomato, potato, pepper, petunia, tobacco and one Rubiaceae, coffee. The metabolic networks of those PGDBs have been computationally predicted by the pathologic component of the pathway tools software using the manually curated multi-domain database MetaCyc (http://www.metacyc.org/) as reference. SolCyc has been recently extended by taxon-specific databases, i.e. the family-specific SolanaCyc database, containing only curated data pertinent to species of the nightshade family, and NicotianaCyc, a genus-specific database that stores all relevant metabolic data of the Nicotiana genus. Through manual curation of the published literature, new metabolic pathways have been created in those databases, which are complemented by the continuously updated, relevant species-specific pathways from MetaCyc. At present, SolanaCyc comprises 199 pathways and 29 superpathways and NicotianaCyc accounts for 72 pathways and 13 superpathways. Curator-maintained, taxon-specific databases such as SolanaCyc and NicotianaCyc are characterized by an enrichment of data specific to these taxa and free of falsely predicted pathways. Both databases have been used to update recently created Nicotiana-specific databases for Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis by propagating verifiable data into those PGDBs. In addition, in-depth curation of the pathways in N.tabacum has been carried out which resulted in the elimination of 156 pathways from the 569 pathways predicted by pathway tools. Together, in-depth curation of the predicted pathway network and the supplementation with curated data from taxon-specific databases has substantially improved the curation status of the species–specific N.tabacum PGDB. The implementation of this strategy will significantly advance the curation status of all organism-specific databases in SolCyc resulting in the improvement on database accuracy, data analysis and visualization of biochemical networks in those species.SolCyc is the entry portal to pathway/genome databases (PGDBs) for major species of the https://solgenomics.net/tools/solcyc/ The post genomic era has seen the rise of new technologies and intensified research focusing on function and regulation of genes and their encoded proteins in the cell and within complex metabolic networks . With thwww.ingenuity.com), e.g. integrate mostly animal and human pathways, while the Kyoto Encyclopedia of Genes and Genomes (KEGG)   aims to s (KEGG) , 12. Pros (KEGG) , 14, mets (KEGG)  and the s (KEGG) , 17. Spes (KEGG) .http://www.metacyc.org/) (http://biocyc.org/), but very few have been curated in a way that would corroborate the predicted metabolic network and integrate experimental information from the published scientific literature (Escherichia coli in EcoCyc (Saccharomyces cerevisiae in YeastCyc http://yeast.biocyc.org/), mammals , fungi attleCyc ] and plan AraCyc , 24, MedMedicCyc , FragarigariaCyc , Oryza s RiceCyc  and Zea MaizeCyc . While m Network .http://solcyc.solgenomics.net/), which is hosted at the Sol Genomics Network (SGN), a comparative repository for a broad range of biological information revolving around species of the Solanaceae family  as ecological expression system  to improve the completeness and accuracy for metabolic networks in important crop species such as tomato, potato and tobacco; (ii) to increase the reliability for analysis tools available in the databases for functional ‘Omics’ datasets; and (iii), as a long-term goal, to identify overlapping metabolic areas between Solanaceae host plants and pathogenic organisms as potential targets for concerted metabolic responses.In the following, we describe the manual curation of new N.tabacum accession TN90 (GCA_000715135.1), N.sylvestris (GCF_000393655.1) and N.tomentosiformis (GCF_000390325.2) were downloaded from NCBI assembly (https://www.ncbi.nlm.nih.gov/assembly)  Previous mRNA annotations for each of the genomes were downloaded from SGN database (ftp://ftp.solgenomics.net/genomes/). Additionally mRNA sequences were complemented with publicly available Sanger ESTs from NCBI GenBank and assembled 454 ESTs from SGN (ftp://ftp.solgenomics.net/transcript_sequences/by_experiment/decipher_ntab/assembly/)  of the coding protein genes with the GenBank NR, TAIR10 and SwissProt databases (downloaded on the 21 July 2014). Additionally the protein domains were annotated using InterProScan. Functional annotations were integrated using the program AHRD v2.0.2 (https://github.com/groupschoof/AHRD).The assemblies for sembly/) . De-novo Maker-P  with theSolanum and Nicotiana specific databases are assembled on the Pathway Tools curator GUI which connects the MetaCyc database via VPN to the internal MYSQL server at SRI International and the curatable Solanaceae MYSQL and FILE databases via SSH to the internal development site at SGN. Curation is done by extracting information about pathways, reactions, genes, enzymes and compounds from peer-reviewed resources, for instance, NCBI’s PubMed (https://www.ncbi.nlm.nih.gov/pubmed), Google scholar (https://scholar.google.com/), Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB) (http://www.chem.qmul.ac.uk/iubmb/enzyme/), UniProt and Swiss-Prot (http://www.uniprot.org/), GenBank (https://www.ncbi.nlm.nih.gov/genbank/), the Gene Ontology Consortium (http://www.geneontology.org/) and chemical databases such as ChEBI (http://www.ebi.ac.uk/chebi/), PubChem (https://www.ncbi.nlm.nih.gov/pccompound) and ChemSpider (http://www.chemspider.com/). The Pathway Tools predicted metabolic networks for species-specific PGDBs are evaluated, falsely predicted pathways removed and new pathways for the respective species added. Newly curated pathways are assembled in accordance with the published literature and reactions furnished with full or partial EC numbers, catalyzing enzymes, encoding genes and regulatory interactions. Hyperlinks to pathways feeding into or branching out of the pathway are provided and comments written to pathways, genes and enzymes. BLAST of UniProt enzymes allows for matching and merging with gene and protein ID’s of the individual species-specific database.The SolCyc collection of databases for Solanaceae species  and one PGDB of the Rubiaceae family, i.e. Coffee species. All SolCyc databases at the SGN have been created using the Pathway Tools software component Pathologic, which predicts the metabolic networks in those organisms based on MetaCyc as reference database (https://github.com/solgenomics/cyctools). The freely accessible SolCyc databases represent computational predictions of their metabolic maps and have not been reviewed or updated by curators. The recent introduction of the curated SolanaCyc database aims to elevate the two Solanum databases and four Nicotiana databases . Metabolic databases with a high degree of manual curation and continuous updating are rare and mostly limited to model organisms, for instance, Arabidopsis thaliana in AraCyc (Trypanosoma brucei) and the skin affecting Leishmaniasis disease (Leishmania major). Both TrypanoCyc  and a high familiarity with the database’s structure, features and performance. The extraction and editing of data from peer-reviewed publications are a central element in the multifaceted curation procedure. Reported details about catalyzing enzymes such as physico-chemical properties and kinetic parameters, cellular localization and regulation as well as encoding genes and their expression pattern is noted on the respective detail pages and summarized in enzyme and gene specific comments. These comments also point out the employed purification method and status of the characterized enzyme and reference all relevant publications. FASTA files of enzymes or genes deposited in GenBank and/or UniProt are blasted against the PGDB of the species and result in the merge of the curated enzymes and genes with the annotated genes and enzymes of the species database. The next curation steps concern the designation of the reaction and built of the pathway. If reported, reactions are associated with full or partial EC numbers, which are either obtained from the curated literature or inferred by the curator using external resources . The general process of building a pathway with a more detailed description of the pathway curation is depicted in During the curation process, biocurators face a number of challenges which have to be solved in accordance with the rules and structural setup of the database. Several decisions a biocurator has to make when adding a new pathway require intensive study of the relevant literature. MetaCyc and its derivative databases such as SolanaCyc define pathways and their boundaries in a different way than KEGG. The modular concept of Cyc pathways describes base pathways for single organisms with a defined start and stable end metabolite, the latter usually a branch-point toward other related base pathways. This kind of pathway, which would ideally have been experimentally verified for individual species, is referred to as conspecific pathway. Pathways composed of information from various species are chimeric pathways, which imply the notion that this pathway may not occur in all of those species , 44. HowMeaningful mining and analyses of the many data in PGDBs of the MetaCyc family very much depend on the quality of the data and the available bioinformatics tools and applications in the database . AlthougSolanaceae family is known to produce a number of specialized metabolites, also referred to as secondary metabolites, such as alkaloids, flavonoids and polyphenols which have been employed in varying capacities for human health and food. Tomatoes with elevated levels of healthy polyphenolic phytochemicals exerting antioxidative activities (S.tuberosum) or solasodine and α-tomatine (S.lycopersicum) are biosynthesized by and restricted to members of the nightshade family. We have curated those pathways from the literature, added them to SolanaCyc and use the biosynthesis of the glycoalkaloid α-tomatine in tomato as an example to demonstrate data processing and display.The tivities , 51 and tivities , as welltivities  and recotivities  have alrtivities . Some ofS.lycopersicum have been experimentally characterized and described in the literature , NbenthamianaCyc (Nicotiana benthamiana), NsylvestrisCyc (Nicotiana sylvestris) and NtomentosiformisCyc (Nicotiana tomentosiformis). The databases were constructed by using genomic annotation information of the respective Nicotiana species , which in turn is used to predict the pathways in the organism’s metabolic network  and 5.9 reactions per pathway (N.sylvestris). The second input factor of importance for Pathologic is the quality of the genome assembly and annotation, which primarily defines the number of genes, enzymes and enzymatic reactions. Assessing the genome completeness of these four Nicotiana species using BUSCO  outside the common intersection, followed by N.tomentosiformis (0.6%) and N.sylvestris (0.4%). The comparison of predicted pathways between each two individual Nicotiana species (results not shown) revealed the highest divergence between N.tabacum (23%) and N.sylvestris (1.3%). This is expected considering that pathologic predicted 120 fewer pathways for N.sylvestris than for N.tabacum, whereas N.tomentosiformis was only 28 pathways short of the pathway count predicted in N.tabacum. Out of the 67 unique N.tabacum pathways, the main part, i.e. 27 (∼40%) pathways, was classified under the specialized metabolite category. The divergence in some metabolic divisions in cultivated and wild tobacco species is certainly also due to the observed high molecular diversity in the genus Nicotiana, where the high degree of genetic polymorphism results in gaining or losing the ability to biosynthesize a range of metabolites (Despite the good agreement for the composition of the metabolic networks each organism  63). Th. ThNicotabolites .N.tabacum in various external databases such as the EC nomenclature, GenBank and UniProt. In addition, NCBI’s PubMed and Google scholar were used to find relevant literature either validating or rejecting the occurrence of pathways or components thereof in N.tabacum. Of the 156 invalid pathways 112 pathways were found to be specific for bacteria, fungi or metazoa with no evidence to occur in tobacco. Furthermore, 41 pathways of specialized metabolites were identified which have not been reported in this species. The constant curation and updating of pathways in MetaCyc often results in deleting obsolete or redundant pathways from the database  allowing for the visualization of the pathway and related metabolic processes on the metabolic map of m annuum . The devm annuum , stress-m annuum  and evenm annuum .Solanum and Nicotiana has elucidated the complex interrelationships between the plant and its environment. The Solanaceae produce a great variety of specialized compounds, amongst them a number of alkaloids used by the defense system of the plant to repel pathogens and harmful insect attacks (Solanaceae. Similar attempts on single pests have already been undertaken to determine the common metabolic denominator and to identify metabolic pathways used as gateway by the attacker to gain access to the host. The analytical power of MetaCyc derived databases has been used in RiceCyc for the identification of metabolic differences in rice plants susceptible or resistant against a common pest in rice, the small brown planthopper (The deep exploration of primary and specialized metabolism in plant genera such as  attacks , 70. Thenthopper . Anothernthopper .Solanaceae family was chosen to use synergies in the current database infrastructure, which will guarantee the most intensive and complete curation of Nicotiana and Solanum specific PGDBs, the upgrade of the databases to tier 1 and the creation of a database environment that allows species comparison and identification of common intersections of metabolism between Solanaceae species and pathogenic organisms.The approach of extending the central role of SolCyc as organizational hub for SGN metabolic databases and SolanaCyc as the curator database for the Solanaceae family comprises a number of important crop plants, which brought this plant family into the focus of breeders, geneticists, biochemists and bioinformaticians. High quality annotated and constantly updated genomes like that of Solanum lycopersicum have provided insights in many aspects of metabolism including neofunctionalization of genes for qualitative traits such as color and flavor (Aspergillus westerdijkiae allowed for the identification of genes encoding for enzymes involved in host invasion and pathogenicity (It is well documented that the accuracy of computationally predicted PGDBs is directly correlated with the quality of the genome annotation of a species . The Sold flavor . The used flavor , which id flavor . Other ggenicity . Manuallgenicity  and the genicity .N.tabacum is next to S.lycopersicum, the most highly curated plant species in SolanaCyc contributing about one third of all pathways in the database (N.tabacum is a valuable crop and model organism and has been the subject of numerous biochemical, genetic, molecular biological and bioinformatics research projects. However, although its genome has been closely studied and improved (Nicotiana species genomes and the enrichment of curated metabolic data in the associated databases will contribute to a more accurate representation of their metabolic networks.With its 72 manually curated pathways , N.tabacdatabase . N.tabacimproved , 59 a fiDatabase Online .This work was supported by PMI.Conflict of interest. None declared.Supplementary DataClick here for additional data file.


Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients’ survival in an independent cohort than using the canonical human metabolic pathways . Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism. Cancer proliferating cells adapt their metabolism to support the conversion of available nutrients into biomass, which often involves an increased rate of specific metabolic pathways, such as glycolysis. Surprisingly, however, we observe that aggregating individual gene expression using canonical human metabolic pathways frequently fails to enhance the classification of noncancerous vs. cancerous tissues and in the task of predicting cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we introduce a novel algorithm (MCF) that aims to identify these cancer-mediated ‘composite’ metabolic pathways by identifying those that best differentiate between cancerous vs. non-cancerous tissues gene expression. Remarkably, MCF successfully builds robust classifiers integrating different datasets of the same cancer type. We further show that the data-driven pathways identified by MCF, in contrast to the canonical literature-based pathways, successfully generate clinically relevant features that are predictive of breast cancer patients’ survival in an independent dataset. Our findings thus suggest that cancer metabolism may be rewired via non-standard composite pathways. Metabherently . Undoubtherently . In cancherently  showed therently  recentlyherently ,12 and aHere we turn to study whether the expression of metabolic pathways as a whole is predictive of cancer’s outcome and prognosis. One may expect that pooling information across genes in a pathway would be an effective method for feature generation by mitigating experimental noise in the measurement of individual genes. There has been therefore considerable interest in cancer classifiers that utilize network- and pathway-based meta-features –16 HowevWe first tested if the use of canonical pathways enhances the accuracy of cancer classification. We overlaid gene expression data derived from 3611 samples across ten datasets of five cancer types  onto canonical metabolic pathways defined in the RECON1 human metabolic model  and quanMetabolic classifier and feature generator (MCF): (1) We first define a differentially expressed reaction as a reaction whose ranked expression level within a sample is significantly different in noncancerous vs. cancerous samples  predictive of the cancer vs. non-cancer states. (5) We then build a support vector machine (SVM-MCF) ensemble classifier of cancer vs. noncancerous tissue based on the gene expression of the new composite pathways as classification features. We apply a five-fold cross validation procedure to test the classification rate (accuracy) and area under the cover (AUC) for each dataset studied  from each seed metabolite to all other metabolites in the network. For the purpose of identifying the new composite paths, the metabolic network hypergraph is transformed to a regular graph representation having metabolite nodes and (directed) edge connecting any two metabolites that participate in a given reaction as a substrate and a product, respectively (if the reaction is directed).Identify the most differentially expressed (‘heaviest’) pathways: For each source metabolite s we find the L = 10 target metabolites Ts such that the heaviest distance from s leading to each of the targets in Ts differs most between the noncancerous and cancer training sets.Building an SVM classifier: For each of the N source metabolites s we train an independent SVM classifier to distinguish cancerous from noncancerous samples using the weight of the L selected paths from s to Ts as features. This results in an ensemble of N SVMs. A test sample is then classified by a majority vote over the N classifiers.We compared the accuracy of the MCF to MGE-SVMs classifiers that are based on individual metabolic gene expression by comparing their AUC and mean accuracy scores in a five-fold cross validation on various cancerous vs noncancerous classification tasks. We find that MCF performs as well as MGE-SVM in all 10 datasets studied spanning five different cancer types, and significantly outperforms MGE-SVM in five of these datasets .As MCF aggregates transcriptional information in network-based manner, we hypothesized that it will be more robust than MGE-SVM when trained on data of the same cancer type but aggregated from multiple studies. To test this we merged the available tumor/tissue samples expression . These target metabolites are specific for cancer type . This sSeveral of the target metabolites are known to be associated with their respective cancers: Oxalate has been studied as a survival marker in lung cancer ; sperminindependent datasets. For training we used the combined GEO and TCGA breast cancer data. For testing, we used an independent dataset  that inETABRIC,  . This finding explains their predictive power across these different tasks and datasets, and further testifies to their clinical significance.To evaluate the aggregate predictive power of the set of pathways selected by MCF as a whole, we compared patients predicted by MCF to have the best and worst prognosis  to inform the analysis of cancer vs. noncancerous gene expression. It detects key hubs of metabolic alterations and infers the composition of non-standard pathways altered in a specific cancer type. Applied across five different cancer types, we find that MCF outperforms standard methods in the basic task of cancer vs. noncancerous classification. Remarkably, MCF derived pathways successfully predict patients’ survival in an independent dataset while standard metabolic pathways fail to do so, testifying on the robustness and utility of the metabolic features learned by MCF.Meta-learning is of great relevance to cancer classification as it can potentially exploit one of the hallmarks of cancer, deregulation of pathways and cellular processes, by taking knowledge on relations between genes and pathways into account in the classifier ,37,38. HWhile metabolic reprogramming is a substantial part of cancer biology, the methodological insights obtained from developing MCF are general, and could potentially be built into path-centric approaches that would involve other cellular networks. This could lead to stronger predictors based on reliable models of signaling and regulatory networks on a genome scale. Second, finding the most separating paths in differently weighted graphs is an NP-complete problem. Here, we only offer a heuristic solution that is obviously sub-optimal. This could be improved upon by employing more exhaustive and/or efficient weighted path searching methods. We can expect that follow-up work will advance the identification of top separating pathways in differentially weighted metabolic graphs, potentially improving the power of MCF further.In summary, we show that integrating gene expression measurements within a genome-scale map of human metabolism via MCF results in the identification of clinically relevant features capable of predicting survival, while enhancing cancer classification power from gene expression data. We believe that future applications of MCF may help identify cancer specific onco-metabolites and advance our understanding of metabolic alterations in cancer.We focused on five cancer types, and for each one utilized datasets from TCGA  and GEO In addition, we used the METABRIC breast cancer database by Curtis et al.  to test Throughout this study, we evaluate classifier performance by computing the AUC and average accuracy in a five-fold cross-validation procedure. We repeated 100 times the following:Down-sample either the cancerous or normal groups: Assume that the data has N normal samples and C cancerous samples and |N|>|C|. We randomly chose |C| samples out of the normal group and excluded the rest. Similarly, if the data had more cancerous samples than normal ones, we down-sampled the cancerous group to the size of the normal group. This ensures that the accuracy statistic is not biased due to an over-representation of one of the groups, which occurs in many of the datasets studied here.5-fold cross validation: We split the chosen samples into 5 folds, each time training on 4/5 of them and testing by computing the AUC or accuracy on the remaining 1/5.The AUC and accuracy shown here is the average of the 100 repetitions, and the paired t-test p-values are from the resulting vector of 100 AUC or accuracy values for each such random selection.To classify cancer vs. normal samples according to metabolic gene expression, we trained a support vector machine (SVM) using the expression of 1,496 metabolic genes as features. We denote these machines MGE-SVMs. Metabolic genes are defined in this study as the set of 1,496 genes annotated in Recon1  a well-cWe observed that SVMs trained on this reduced set of gene expression features consistently outperformed SVMs trained on the expression of all genes. This is not surprising seeing that the metabolic subset has roughly one-order of magnitude smaller dimensionality, and yet remains highly informative because of the key role of metabolic adaptations in cancer ,50,51. ARecon1 defines a mechanistic genotype-phenotype relationship through Boolean rules that encode gene-protein-reaction (GPR) associations. To convert ranked gene expression to biochemical reaction expression, we evaluated the Boolean GPR rule of that reaction while replacing the “AND” and “OR” operators with “min” and “max”, respectively as described in . DiffereClassification based on metabolic pathways relied on the pathway definitions embedded in Recon1, which associates every reaction with a single pathway out of a total of 99 pathways defined based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) LIGAND database. To compute a pathway expression, we first converted the ranked gene expression to ranked reaction expression as described above, and then summed the ranked expression of all the reactions associated with the pathway. An alternative methods of computing pathway in which for each pathway we use the sum the ranks of all its associated genes showed inferior performance comparing to the method presented here, as well as using the mean of ranked reaction expression instead of the sum.MCF builds metabolic pathways that have highly differential expression between the two target states . However, identifying the most differentially expressed pathways between two groups of weighted networks is a NP-hard problem by reduction from the problem of finding the longest-path   from s to the all other metabolites in the network in all of the train samples. For the purpose of computing paths, we followed the common approach [s of L target metabolites for which the paths from s were most differentially expressed. I.e., for every target metabolite t we compute the Wilcoxon rank sum p-value when comparing the heaviest distance from s to t in the normal vs. the cancer samples, and we finally choose the Ts with L metabolites that obtained the smallest p-values out of all possible targets. The distances from s to the chosen L metabolites (denoted Ts) are used as features for an SVM.To build a classifier based on the differential expression of the pathway from source metabolite approach ,55 transs, and for each s a distinct SVM is trained. This results in an ensemble of N SVMs. A test sample is then classified by the majority vote of the N individual classifiers (no ties ever occurred in the present study).Let N be the number of source metabolites detected. MCF repeats the procedure described above for each of the source metabolites x is the sum of N scores assigned to x by the N SVMs. Therefore:fi (x) is the predicted response of x for the trained classification function fi (trained on the features selected for source metabolite i)αi,1…αi,n, bi) are the estimated parameters, G is the dot product in the predictor space between X and the support vectors and the sum indicates training set observations.The MCF classifier is an ensemble of N SVMs (for each detected source metabolite). The MCF classification score for classifying observation To train the model and select the features we use the combined GEO and TCGA breast cancer datasets and train it on the original classification task of separating noncancerous from cancer tissues . This results in 80 composite pathways that are generated and selected by MCF . We then use the METABRIC dataset and calculate the weights of the 80 selected pathways for this dataset  as well as the weight of the 99 human metabolic network pathways. In the two pathways sets, we define the weight of each patient for every pathway by the sum of ranks of the reactions associated with the pathway. For every pathway we evaluated the KM log-rank p-value taking top 10% and bottom 10% weighted pathways.weighti(p) is the weight of pathway p for patient i. Pc is the set of pathways  in which high expression levels were associated with cancer state, and Pn is the set of pathways in which high expression levels were associated with noncancerous healthy state. Both Pc and Pn are determined by analyzing the two breast cancer datasets from TCGA and GEO . These Pc and Pn set of pathways were then used to predict the patients survival an independent METABRIC breast cancer dataset, by assessing weighti(p) for every sample based on its transcriptomics and computing score(patienti) accordingly. A KM analysis is then employed to examine the survival difference of high score versus low score patients’ samples.To calculate an aggregated pathway score using either the 80 MCF selected pathways or the 99 canonical model pathways we calculate the weights of these pathways using the METABRIC gene expression data. We compute for each patient’s tumor two aggregate scores (one over the MCF pathways and over the model pathways) as follows:To test MCF’s robustness, we introduced noise into every sample’s gene expression vector by adding random Gaussian noise with distributions N, N and N. We then trained MCF classifiers based on the perturbed data and evaluated the source and target metabolites MCF selected.S1 FigMetabolic pathway expression is defined based on the mean of the expression of all genes associated with a pathway . Accurac(DOCX)Click here for additional data file.S2 Fig(A) A bar plot describing the accuracy over a five-fold cross validation procedure for MGE-SVM classifiers trained on metabolic gene expression levels (red bars) and for MCF classifier (blue bars). Accuracy is the percentage of correctly classified samples. (G) Stands for datasets from GEO and (T) for datasets from TCGA (as in the legend of (DOCX)Click here for additional data file.S3 Figα = 0.05 yields threshold of p < 0.0039.FDR correction with (DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Netrin-1, a multifunctional axon guidance cue, elicits axon outgrowth via one of its receptors deleted in colorectal cancer (DCC) in several types of neurons, including cerebral cortical neurons of embryonic mice. However, we and others have observed de novo formation of axon branches without axon outgrowth induced by netrin-1 in cortical culture of neonatal hamsters. These previous reports suggested the possibility that netrin-1 function might alter during development, which we here investigated using dissociated culture prepared from cerebral cortices of embryonic mice.Imaging analysis revealed netrin-1-induced outgrowth in embryonic day (E) 14 axons and netrin-1-induced branching in E16 axons. Netrin-1-evoked filopodial protrusions, which sprouted on the shafts of E16 axons preceding branch formation, were visualized by a novel method called atmospheric scanning electron microscopy. Treatment with an anti-DCC function-blocking antibody affected both axon outgrowth and branching.Morphological analyses suggested a possibility of a shift in the function of netrin-1 in cortical axons during development, from promotion of outgrowth to promotion of branch formation starting with filopodial protrusion. Function-blocking experiments suggested that DCC may contribute not only to axon outgrowth but branching. Mesocricetus auratus [Netrin-1 is known as a diffusible axon guidance cue that plays various important roles in the correct wiring of the nervous system during development. Netrin-1 induces axon outgrowth (which is closely related to growth cone attraction) via one of its receptors DCC  in several types of neurons , includi auratus –11. More auratus  and E15. auratus  mice. Ev auratus , 14. Thi auratus , 10, 15.Although less understood than axon outgrowth, two distinct modes are known for axon branching: “growth cone bifurcation” and “axon collateral branching” . It has Neonates of the hamster are thought to be less mature than those of other rodents, reflecting its shorter (16-day) gestation period , 18, 19.Collectively, these previous reports raised the possibility of a shift in the function of netrin-1 in rodent cortical neurons during development, from promotion of axon outgrowth to promotion of axon collateral branching. In the present study we investigated this possibility by morphometric imaging analysis employing dissociated culture of cerebral cortical neurons prepared from mouse embryos.Corticofugal tracts are comprised of subcerebral axonal tracts  and the corticothalamic projection. Embryonic and early postnatal development of those in mice has been studied in detail , 22–24, We also performed visualization of netrin-1-dependent filopodial protrusions on the axon shafts which were thought to be induced transiently in advance of branch formation, by a novel method called atmospheric scanning electron microscopy (ASEM) ; this teFurthermore, in this study we investigated whether the receptor DCC would be involved in the netrin-1 function of promoting branch formation in cortical axons, as other receptors such as UNC5 and DSCAM (Down syndrome cell adhesion molecule) are also known to mediate netrin signaling –28. ContFirst, we carried out immunocytochemical analysis of DCC in cortical neurons prepared from E14 and E16 mice and grown for 5 days. Expression of DCC protein was observed both in E14 and E16 cortical axons , E14 Fig.  and E16 Contrary to the results obtained from E14 neurons, netrin-1 induced no significant change in the length of primary axons in E16 neurons . A significant negative correlation was found in E14 axons under basal conditions , as well as in E14 axons with 4 h netrin-1 treatment in the absence of anti-DCC antibody , indicating that longer axons had lower density of branches in these two experimental groups. No significant correlation was found in the other four groups, that is, E14 axons with netrin-1 treatment in the presence of the antibody , as well as E16 axons under basal conditions  and with netrin-1 treatment in the absence  or presence  of the antibody, with p values larger than 0.05.We then sought filopodial protrusions sprouted from the axon shafts of E16 neurons prior to axon collateral branching in response to netrin-1 employing ASEM Figs. , 6, as wASEM images of E16 neurons are presented in Figs. Figure As it was not known whether netrin-1 would promote sprouting of filopodial protrusions on the axon shafts concomitant with induction of axon outgrowth, we also examined protrusions in E14 axons by means of ASEM Fig. g, h as wA variety of physiological roles are known for an axon guidance cue netrin-1. In this study, netrin-1-induced axon outgrowth was observed in cerebral cortical neurons dissociated from E14 mice, while netrin-1-induced axon branching was observed in E16 cortical neurons. Netrin-1-induced outgrowth of mouse cerebral cortical axons was reported in vivo, which accounts for a few steps in the development of corticofugal tracts including their extension toward, and then into, the internal capsule in embryonic mice starting from E12.5 , 22, 24.On the other hand, we and others previously reported netrin-1-induced axon branching without outgrowth of primary axons in neuronal culture dissociated from sensorimotor cortices of neonatal Syrian golden hamsters, of which gestation period is as short as 16 days –11. At tThere remained an issue whether netrin-1 would stimulate sprouting of filopodial protrusions from the shafts when it promotes axon outgrowth, as was the case in promoting collateral branching. What observed in E14 axons, however, was an abundance of protrusions irrespective of netrin-1 treatment: both phase contrast microscopy and ASEM unexpectedly showed that a substantial portion of E14 cortical axon shafts had lots of protrusions even in the absence of netrin-1 Figs. b, 6g; moAs compared to E16 axons, E14 axons exhibited not only high density of netrin-1-independent pre-existing filopodial protrusions Figs.  but alsoOur results presented here provide an explanation as to why both axon outgrowth and branching have been reported in rodent cortical axons in response to netrin-1. The results, while each of the E14 and E16 culture was maintained for an additional 5-day period in vitro, suggested a switch in netrin-1 function in cortical neurons during development, from promotion of axon outgrowth to promotion of axon branching. Since the neurons in different cortical layers are born and differentiate at different times, the culture employed here should be highly heterogeneous containing multiple populations of neurons originated from different layers of each area in the cerebral cortex . PreviouWhile sprouting of actin filament-based filopodial protrusions is thought to be an initial step of axon collateral branching, invasion of axonal microtubules into the protrusions is required for further development and stabilization of nascent axon branches, without being retracted back into the axons , 17. As Expression of DCC protein was observed both in E14 and E16 cortical axons of mice  was reported in retinal ganglion cells of elopment . More reelopment  and E15 elopment ; the forIt is believed that axon branching is modulated independently of axon outgrowth/attraction , 14, 15.We showed two distinct functions of netrin-1—promotion of axon outgrowth, and promotion of axon collateral branching that involves filopodial protrusion from the shafts—in mouse cerebral cortical neurons in vitro. As netrin-1 promoted outgrowth in E14 axons and branching in E16 axons, and thus exhibited each function depending on the stage of embryos utilized for cortical culture, our study raised the possibility of a shift in netrin-1 function during cortical development. Furthermore, our results suggested that a netrin-1 receptor DCC may contribute to both cortical axon outgrowth and branching.All reagents were purchased from Sigma  unless otherwise specified. A mouse monoclonal antibody raised against the extracellular domain of human DCC protein (clone AF5) was purchased from Millipore  and utilized not only for detecting DCC protein but also as a function-blocking antibody against DCC . A rat mAll animal experiments were approved by the Institutional Animal Care and Use Committee of Saitama Medical University.2+/Mg2+ (Gibco) for 15 min at 37 °C, and dissociated by trituration in plating medium , 5% fetal bovine serum , B27 supplement, 0.3% glucose, 1 mM l-glutamine (Gibco), and 37.5 mM NaCl). Cell suspension was filtrated with a cell strainer of 70 μm mesh size  and centrifuged at 500 rpm (46×g) for 7 min.Timed-pregnant C57BL/6 N mice were purchased from Japan SLC, Inc. . Dissociated cultures were prepared from cerebral cortex of mice on E14 and E16, essentially by the method of Dent and Kalil  with mind-lysine  for 1 h; laminin-1 was excluded from the substrate in this study, since Höpker et al. reported that it converts netrin-1-mediated attraction into repulsion in Xenopus retinal growth cones [2; after 1 h, medium was changed to a serum-free formulation ; cultures were then maintained at 37 °C in 5% CO2 for 5 DIV until treated as described in the next subsection. To perform ASEM, specialized dishes were employed for culture and cells were treated as described later.Cells obtained were resuspended in plating medium and utilized for 5 d culture on coverslips or dishes treated in advance with poly-th cones . To perfFor immunocytochemical analysis of DCC, cells on coverslips were fixed with 4% paraformaldehyde  in Krebs’ buffer with 0.4 M sucrose for 20 min at room temperature . Fixed cFor morphometric analysis, cortical cultures were treated with 250 ng/mL netrin-1  for 30 min or 4 h by bath application , 15 in tDIC images were analyzed morphometrically employing MetaMorph 7.6 software . E14 and E16 neurons after 4 h treatment with or without netrin-1 were subjected to the analysis of axon branching/outgrowth : the len2 wide) in its base. During image acquisition employing the ClairScope system , the electron beam of inverted SEM scans the sample in solution through the window which separates vacuum (below) and atmosphere (above), from underneath. Backscattered electrons are captured by the backscattered electron imaging (BEI) detector located just beneath the window.ASEM, a recently developed technique , was perd-lysine coating was applied to the SiN film window using 40 μL of 1 mg/mL solution. Twenty microliters of cell suspension containing 15,000 cells in plating medium was used for inoculation on the coated SiN film window. After 1 h incubation at 37 °C in 5% CO2, 2 mL of serum free medium was added to the culture. Cultures were maintained for 5 DIV and then treated with 250 ng/mL netrin-1 for 10 min, 30 min and 4 h, with or without an anti-DCC function-blocking antibody (1 μg/mL) treatment starting 30 min earlier than netrin-1 application. Cultures were then fixed with 2.5% glutaraldehyde  in 0.1 M phosphate buffer, pH 7.4 for 30 min and washed three times with double distilled water (DDW). Fixed cells were stained with 2% phosphotungstic acid [In this study, cortical neurons were cultured on the ASEM dish. In advance of plating cells, poly-tic acid  for 30 mtic acid  or propytic acid , employiIn order to compare with ASEM images, phase contrast images of specimens on the ASEM dishes were acquired employing the BIOREVO all-in-one inverted microscope system  equipped with a 40×/0.60 NA objective.


Bioinformatic tools for the enrichment of ‘omics’ datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard’s distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases . The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites.We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.The online version of this article (10.1186/s12859-017-2006-0) contains supplementary material, which is available to authorized users. Enrichment techniques for ‘omics’ data are key tools for understanding complex biological systems. These tools reduce the complexity of the data, improve interpretation and understanding of biological systems, and help to generate hypotheses. Although the number of tools for ‘omics’ is rapidly growing, suitable tools for metabolomics are still scarce. Most of the available tools for metabolomics data have been previously developed for other ‘omics’ technologies. These tools have been described in detail elsewhere –6.Enrichment tools denote any analytic technique that benefits from molecular pathway or network information to gain insight into a biological system . The mosTo the best of our knowledge studies evaluating the performance of enrichment tools for metabolite sets do not exist yet. The aim of the present work will be to dissect, for the first time, these techniques. First of all, we have carried out an exploratory multivariate analysis of the state-of-the-art of bioinformatic tools for metabolomics sets to visualize their diversity. Then, we have examined the completeness of metabolite databases, the performance of ORA methods and accuracy of disease-based analyses. For these purposes, we have used datasets from metabolomic repositories, whose results have been already published in peer-reviewed journals. In addition, we have enriched selected metabolic pathways and then compared the outputs of these tools when using real datasets or enriched data. Thus the present study provides a global insight of the current status of bioinformatic tools for the analysis and interpretation of metabolite sets from metabolomic studies.p-values were obtained from the original papers [p-value < 0.05 were used for tools comparison. The list of metabolites is shown as  from a metabolite database. Although Kyoto Encyclopaedia of Genes and Genomes (KEGG Compound) identifiers  are the We analysed the current completeness of the following metabolite libraries: Human Metabolome Database (HMDB) , KEGG, PThe list of significant metabolites from –15 was uMost of the bioinformatic tools for the enrichment of metabolomics datasets accept a list of identifiers as output, while a lower number require quantitative data, e.g. concentration, fold change or peak intensity. Therefore we decided to work with a list of metabolites (name or identifier) from real datasets and enriched data to compare these tools. Although this approach do not allow us to assess some of the available tools, i.e. 3omics or PAPi, the use of simulated o synthetic data would have allowed us to examine a lower number of tools.n = 42, obtained from [For data enrichment, the dataset with the most metabolites was selected . The list of significant metabolites of this dataset (ned from ) was ananed from , were chn = 113) with their corresponding KEGG pathways. One was assigned if the metabolite was part of that particular pathway, or 0 if not. Then five metabolites of each pathway were randomly sampled. Enriched data are shown in Additional file The R package KEGGREST (v.1.17.0)  was emplThe most commonly used tools for metabolomics data enrichment were chosen for similarity analysis. This selection was formed by 3omics , BioCyc/The main features of these tools were summarized on a binary matrix (Yes/No responses) including whether they 1) perform ORA, integration with other ‘omics’ or other enrichment analyses; 2) visualization of pathways, networks or other types of visualization; 3) use KEGG, BioCyc, Reactome, Wikipathways, SMPDB or other pathway databases; 4) are databases, programmable, open-source or online tools  . First, n = 42) [The performance of the tools that perform ORA in metabolomics datasets was assessed with the list of significant metabolites of the colorectal cancer dataset ST000284) (n = 42)  and of e84  were recorded from each output.The output of these tools was examined for the following metabolic pathways: 1) KEGG: the three aforementioned pathways; 2) Reactome: Metabolism of amino acids and derivates pathway; and 3) HumanCyc: tRNA charging pathway. Ranking (position in the list of pathways sorted by significance), total number of metabolites/pathway, number of hits/pathway, p- and adjusted p-value), total number of metabolites/disease, number of hits/disease, p- and adjusted p-values were recorded from each output.Disease-based enrichment analyses were performed by using the list of significant metabolites of the five datasets on: 1) MetaboAnalyst (SMPDB disease pathway database) ; 2) MBRoFigure Metabolites of the five datasets were used to assess the completeness of the metabolite and pathway databases. Almost all the metabolites presented PubChem (97%), ChEBI 91%), METLIN (91%), KEGG (88%), ChemSpider (87%) and HMDB (86%) identifiers, and the 97% of the lipid subset had LipidMAPS identifiers. In some cases, KEGG, HumanCyc and Recon2 provided chemical class identifiers instead of a single identifier to certain metabolites, especially to the lipid subset  than other tools, or not all the tools mapped the same metabolites of the dataset onto the queried pathways (not shown).Minor differences in the total number of metabolites/pathway and number hits/pathway were found. For instance, MPEA Table  and MBRop > 0.05  were not successfully identified by these tools, as they appeared in a low position in the list of potential diseases and most of the times they presented a Interpretation of metabolomic data is much less straightforward than that with genomic and proteomic datasets . In the Metabolomics is a developing field, thus bioinformatic tools designed to perform enrichment of metabolomics datasets are being developed and released by various groups using diverse statistical tests . Our expTo date about 30,000 endogenous metabolites have already been identified, but this number is rapidly increasing due to advances in high-throughput technologies . CurrentMissing, ambiguous or redundant entries have been commonly found in public repositories . Indeed KEGG and HumanCyc are the most used pathway libraries in metabolomics , 17 and The number of total metabolites and hits per pathway varied according to the tool used and those tools that employ the newer database versions Table  presentep-value adjustment or pathway database used), despite the limitations and small differences found between outputs. The most discordant result was obtained with MPEA, probably due to the fact that it employs a different method to handle many-to-many relationships that may occur between the query compounds and metabolite annotations [We can conclude that current ORA methods, despite their differences, provide consistent, robust and reproducible results regardless of their analytic approach  base their searches of literature dated between 1975 and 2008, as stated in the outputs of these tools. Since 2008, advances in high-throughput techniques have remarkably improved metabolomics analyses and, consequently, more knowledge about these diseases is available. As previously discussed, the use of not updated annotation sets strongly affect the quality of the analyses . On the Although extensive work in developing bioinformatic tools for metabolite sets has been carried out in recent years, more effort in improving metabolite/pathway databases and tools is still needed. On one hand, metabolite databases have to rapidly absorb new information from unstoppable advances in high-throughput technologies. On the other hand, enrichment methods should include a wider range of metabolite identifiers  and metabolite pathway databases in order to increase the metabolome coverage. For instance, the LipidMAPS Structure Database contains about 30,000 human endogenous lipids and 12,000 plant lipids, but also databases based on lipid metabolism and signalling pathways, MS/MS spectra and protein-related data , 56. CheWe have extensively reviewed, for the first time, the state-of-the-art of bioinformatic tools for the enrichment of metabolite sets from metabolomics studies, visualized their diversity, and examined their performance. The redundancy of identifiers, the use of chemical class identifiers and the incompleteness of metabolite databases and disease metabolite sets limit the extent of the analyses and reduce their accuracy. In general, ORA tools provided consistent results among tools revealing that these analyses are robust and reproducible regardless of their analytic approach. However, more work in the completeness of metabolite/pathway databases is required to get more accurate and global insights of the metabolome.Additional file 1: Table S1.P-value < 0.05). Dataset A refers to dataset ST000091, B to ST000383, C to MTBLS364, D to MTBLS424 and E to ST000284. (XLSX 28 kb)Full list of significant metabolites of the five datasets used in the present study Additional file 3: Table S3.List of features of the tools analysed by multiple correspondence analysis. Abbreviations: N, no; Y, yes. (XLSX 12 kb)Additional file 4: Table S4.n = 67), while the rest of identifiers were considered in all the metabolites of the datasets (n = 147). (DOCX 16 kb)Number of metabolites with identifiers of the following metabolite databases. Metabolite databases are sorted by the number of identifiers found. *LipidMAPS identifiers were only searched in lipids (


In the interaction network, ACC1, SOD2, COX2, PKC-B, IR, and ROCK1 proteins had the most frequent interactions with the six compounds. We performed visual molecular docking in silico and experimentally confirmed competitive component-protein binding by SPR and an enzyme activity test, which highlighted the relationships of wogonin to COX2 and SOD2, astragaloside IV to ACC1, and morroniside to ACC1. We concluded that the potential synergistic mechanism of the Gandi capsule resulted from high affinities with multiple proteins and intervention in multiple pathways in combination therapy of diabetic nephropathy.Gandi capsule, a traditional Chinese herbal medicinal formulation that consists of eight herbs, is used as a clinical therapy for diabetic nephropathy. To clarify the potential synergistic mechanism, this study adopted a network pharmacology strategy to screen the action targets that corresponded to the active components in the Gandi capsule. We first constructed a compound database of 315 components in the Gandi capsule and a target database of diabetic nephropathy, which included 155 target proteins. Six representative compounds were selected to dock with 99 proteins found in the UniProtKB database with their PDB code, and interaction networks between the active ingredients of the Gandi capsule and their targets were mapped out. Results revealed 47 proteins with a high affinity with at least one compound molecule in the Gandi capsule. The main action pathways closely related to the development of diabetic nephropathy were the TGF- Diabetic nephropathy is a severe complication of diabetes mellitus, especially type 2 diabetes. One-third of patients with type 2 diabetes will develop diabetic nephropathy, which ultimately leads to end-stage renal disease (ESRD) . Moreove Astragali Radix, Corni Fructus, Radix Rehmanniae, Leonuri Herba, Sophorae Flos, Scutellariae Radix, Bombyx Batryticatus, and Phyllanthi Fruit  (SOD2).Moreover, every molecule could dock with proteins with a high affinity for at least one protein in the different diseases, except astragaloside IV and rutin in DM1. Meanwhile, only RXRa could dock with high affinity to all six molecules. 25 proteins could dock with all six molecules with valid affinity, such as angiotensin-converting enzyme (ACE), integrin beta-7, and interstitial collagenase (MMP-1); 47 proteins could dock with at least one drug molecule with high affinity, such as acetyl-CoA carboxylase 1 (ACC1), glucagon receptor, protein kinase C, beta type (PKC-B), purinoceptor, tumor necrosis factor-alpha  and the adenosine 5′-monophosphate- (AMP-) activated protein kinase (AMPK) signaling pathway (degree = 9) were linked with nine proteins, and the insulin-related signaling pathway (degree = 8) and lipid metabolism pathway (degree = 8) were linked with eight proteins and the TNF-related pathway (degree = 7) was linked with seven proteins; other pathways corresponded to at least one protein. Meanwhile, for the proteins, PKC-B was involved in the most pathways (15), IR was involved in 12, COX2 was involved in 10, and ROCK1, transforming growth factor receptor 1 (TGFR-1), and mTOR were involved in 9 each. Other proteins had a lower number of related pathways; for example, ACC1 was related to five pathways and SOD2 was related to four pathways.Kd values of 92.5 μM and 13.63 μM, respectively. In contrast, wogonin has a broader affinity with COX2 and SOD2, with Kd values of 44.67 μM and 5.08 μM, respectively. Three protein-molecule pairs, PKC-B with morroniside, ROCK1 with morroniside, and mTOR with astragaloside IV, were examined in the enzyme activity test. However, all compound-protein interactions were negative. The IC50 values of these protein-molecule pairs were more than 10000 μM.Seven protein-molecule pairs were selected to take part in the SPR test: ACC1 with astragaloside IV and morroniside, COX2 with astragaloside IV, morroniside and wogonin, and SOD2 with morroniside and wogonin. The results showed that astragaloside IV and morroniside had a high response and affinity with ACC1, with The information and results of proteins and their respective molecules are listed in TCM formulations are composed of multiple components with mechanisms of action that are always connected with multiple target proteins and pathways. GDC, a TCM formulation administered only in hospital, has been used to ameliorate microvascular complications in DM, such as diabetic nephropathy and diabetic retinopathy (DR), for some years. In the pharmaceutical analysis of GDC with HPLC-ESI-TOF/MS, 52 different components were identified, including morroniside, rutin, astragaloside IV, and wogonin. The biopharmaceutical analysis of drug molecules from GDC showed that 14 components, including morroniside, rutin, astragaloside IV, and wogonin, could be absorbed in blood after the oral administration of GDC, which means that they might act through blood circulation . Given iα-glucosidase, might be a therapeutic target for diabetic nephropathy [The construction of the compound and target protein databases was the first step in our study. For the compound database, we collected compounds according to the herbs of GDC formula, obtained from Chemistry Database developed by Shanghai Organic Institute. To identify the main substances with pharmacological action in GDC, we used the following three rules for selection: (1) the rules of composition theory of the prescription in traditional Chinese medicine, (2) the quantity of substance in GDC, and 3) the hypoglycemic effect reported about diabetic nephropathy. Finally, the six representative compounds, astragaloside IV, morroniside, ferulic acid, rutin, wogonin, and kaempferol, were highlighted. To extend the database as much as possible, we chose the classification of diseases related to diabetic nephropathy as a criterion of acquiring target proteins; there were eight keywords for searching target proteins. The exact classifications were DM, DM1, DM2, special DM, DM complication, DN, MD, and diabetic microangiopathy. Finally, there are 155 proteins in our target protein database. Some studies have shown that many target proteins, such as angiotensin-converting enzyme (ACE), protein kinase C (PKC), and  the hypoKd, IC, and action mechanism with small molecules, supported their reliable crystal structure for docking. For molecule docking, a high-throughput virtual screening approach was used. Then there were 47 proteins within our network analysis, and the degree of centrality was chosen to be the standard for influence of proteins or pathways in our study system. Finally, five representative pathways were defined, and 10 compound-protein pairs were examined in the validity assay. The methods above displayed a powerful capacity for the acquisition, processing, and integration of information on compounds in a TCM formulation; furthermore, it was specific to the complexity of the GDC capsule.After the construction of the target protein database, 155 proteins were searched in UniProtKB; if they were identified in UniProtKB, the bias of the protein structure was compared with reality. After this procedure, 99 individual proteins were also found in the RCSB Protein Data Bank. The pharmacological parameters in RCSB Protein Data Bank, such as the In this study, 99 proteins were evaluated for the molecular docking of six representative compounds. The results showed that morroniside was the most affiliative drug of the six molecules, with a high binding affinity with numerous proteins; furthermore, the action of morroniside may be clearer than that for other components. Ferulic acid could dock the most target molecules, but it also had the lowest number of high affinity results, indicating that the action of ferulic acid in the treatment of diabetic nephropathy occurs only because of the sum of various weak interactions. Other compounds also show affinities to different proteins, which may be the basis of their action as diabetic nephropathy therapeutics.In this study, according to the result of molecular docking and network analysis, all protein-pathway pairs were distributed among metabolism, immune system, apoptosis, cardiovascular system, and multiple pathways. Moreover, the most frequent pathways were the TGF-related pathway and AMPK signaling pathway, which were linked with nine proteins, followed by the insulin-related signaling pathway, lipid metabolism, and the TNF-related pathway.β is a key mediator. TGF-β is active in human mesangial cells and causes oxidative stress and apoptosis in DN [β, can regulate the AGE-RAGE pathway, which activates multiple signaling pathways implicated in inflammation, cancer, and other diseases [The TGF-related pathway mainly includes TGF pathway and TGFR pathway, in which TGF-is in DN . It alsois in DN . AMPK isis in DN . Some prdiseases . That isThe insulin-related signaling pathway, which has an array of mediators, including IR and GLPR-1, is active in the process of DN. The pathway of lipid metabolism covers eight proteins, such as ACC1, ACC2, IR, and mTOR, which can regulate the generation, decomposition, or actions of lipid. A study indicated that dyslipidemia was a risk factor in the process of kidney disease in type 2 diabetes mellitus and that the accumulation of lipid causes podocyte dysfunction and apoptosis in DN .α is one of the most active proteins in the TNF-related pathway, which may be activated in inflammation development. The research by Guo et al. showed that the presence of infiltrating macrophages in DN promotes podocyte apoptosis via TNF-α-related pathway [α signaling pathway, and TGF signaling pathway.TNF- pathway . Present pathway , 21, 22.β with rutin. Moreover, protein metabolism may be another approach for the treatment of GDC. However, only a small number of proteins are involved in this way, such as DPP-IV and NEP. Meanwhile, as for RAS, some proteins are involved with compounds, such as V2R, ALD, and NEP. ALD has been defined as a regulatory activator of sodium and water, which is unbalanced in DN [In addition to these five main pathways above, some pathways related to apoptosis were also found in our investigation, such as the Ras signaling pathway, the Rap1 signaling pathway, and the Wnt signaling pathway. It appears that many pairs of protein and molecules can influence these pathways, such as PKC-B with morroniside, FGF with astragaloside IV and morroniside, and GSK-3As for proteins, the centrality degree of proteins in the network analysis and their evidence of influence for the process of DN have been considered. Ultimately, PKC-B, COX2, ROCK1, mTOR, SOD2, and ACC1 were chosen to be the representative compounds. In combination with the information extracted from the five pathways above, we found that ACC1 might influence the AMPK signaling pathway, the insulin-related signaling pathway, and lipid metabolism; mTOR could influence the AMPK signaling pathway, the insulin-related signaling pathway, and lipid metabolism; COX2 could alter lipid metabolism and the TNF-related pathway; PKC-B could participate in the insulin-related signaling pathway and the TNF-related pathway. SOD2 influenced the TGF-related and TNF-related pathways and ROCK1 influenced the TGF-related pathway. According to the pathway information, the action of ACC1 and mTOR focused on the metabolism of glucose and lipids, inflammation, and apoptosis in DN, whereas PKC-B and COX2 were concentrated on metabolism and inflammation. SOD2 altered oxidative stress and apoptosis and ROCK1 mainly influenced apoptosis. Collectively, the actions of these proteins are concentrated on metabolism, inflammation, apoptosis, and oxidative stress in DN.Moreover, the results of the validity assay indicated that the action of ACC1 can be influenced by astragaloside IV and morroniside, whereas COX2 and SOD2 can be altered by wogonin.Astragaloside IV can regulate the AMPK signaling pathway, the insulin-related signaling pathway, and the metabolism of lipids through an influence on the activity of ACC1. The research of Zhang showed that astragaloside IV altered the NF-kappa B pathway to counteract the expression of E-selectin and VCAM-1 in response to inflammatory mediators, which meant that it has credible anti-inflammatory ability . CombineκB transcription [β [While morroniside can take part in the TGF-related pathway, the insulin-related signaling pathway and the TNF-related pathway may be altered by ROCK1 and PKC-B. An early study pointed out that morroniside alleviated oxidative stress under diabetic pathological conditions through a reduction in ROS level and the downregulation of the expression of iNOS and COX2, followed by the inhibition of NF-cription . Some reption [β . Thus, mWogonin can affect the TGF-related pathway, lipid metabolism, and TNF-related pathway through the regulation of COX2 and SOD2. Clearly, anti-inflammation may be a method of action of wogonin, and a study demonstrated that wogonin inhibited high-glucose-induced vascular inflammation, which was attributed to antioxidant ability and the inhibition of LPS-induced nitric oxide (NO) production and the expression of the nitric oxide synthase (iNOS) gene . AccordiIn conclusion, our research demonstrated that these six representative compounds in GDC exert a potential influence on the pathways that are disordered in diabetic nephropathy through their constituent proteins. However, the results are only based on virtual methods; based on the results of SPR and the enzyme activity test, further research is required to reveal the actual action of GDC in diabetic nephropathy.β1 pathway, AMPK signaling pathway, insulin-related signaling pathway, lipid metabolism, and TNF-α pathway were the major pathways of treatment of GDC for diabetic nephropathy. These five pathways may be the primary aspects of the treatment for GDC in diabetic nephropathy. Moreover, astragaloside IV and morroniside were shown to alter ACC1 directly (Kd = 92.5 μM and 13.63 μM), whereas wogonin could bind with COX2 and SOD2 (Kd = 44.67 μM and 5.08 μM).In this study, six representative compounds in GDC were chosen to dock with 155 proteins related to diabetic nephropathy. As a result, 47 proteins were identified with a high affinity with at least one compound molecule. These 47 proteins were analyzed in the protein-molecule-pathway network. It was found that the TGF-In summary, the action of GDC in the treatment of diabetic nephropathy was concentrated on metabolism regulation, anti-inflammation, the inhibition of apoptosis, and exerted pharmaceutical actions based on the regulation of the medial proteins in these pathways.


Artemisiae argyi folium (VOAAF) is reported with hepatotoxicity, but the underlying mechanism is still unclear.Volatile oils from In the present study this molecular mechanism was explored with the Ingenuity Pathway Analysis (IPA). The chemical components of the VOAAF were searched in the database, and their target proteins were all identified in the PubChem, while drug-induced liver injury (DILI) genes were searched in the PubMed gene databases. The molecular network of protein targets for VOAAF and DILI genes was built with the IPA. The canonical pathways between the 2 networks were compared to decipher the molecular mechanisms of the liver injury induced by VOAAF.There were 159 target proteins for VOAAF and 338 genes related to DILI identified, which were further analyzed in the IPA. The canonical pathway comparison showed that VOAAF and DILI both worked on aryl hydrocarbon receptor (AHR), lipopolysaccharide (LPS)/interleukin 1 (IL-1) mediated inhibition of retinoid X receptor (RXR) function, pregnane X receptor (PXR)/RXR activation, xenobiotic metabolism, peroxisome proliferator-activated receptor (PPAR), hepatic cholestasis, farnesoid X receptor (FXR)/RXR activation, and glucocorticoid receptor.VOAAF-induced liver injury may be involved in many pathways in which the AHR signaling and LPS/IL-1 mediated inhibition of RXR function pathways could be the most vital.The online version of this article (doi: 10.1186/s12906-017-1997-4) contains supplementary material, which is available to authorized users. The concept of bioinformatics was for the first time introduced at the forum of Information Theory in Biology in 1956 in Gatlinburg, Tennessee, USA . BioinfoArtemisia annua was studied by using on bioinformatics, and the results showed that the anti-tumor effect of Artemisia annua may be associated with regulating lipid metabolism of tumor cells, releasing energy, reducing the rate of tumor cell division and accelerating tumor cell apoptosis [For example, anti-tumor mechanism of poptosis . Also bipoptosis . Besidespoptosis .Artemisiae argyi folium, the dry leaf of Artemisia Argyi Lévl. et Vant, is one of the commonly-used Chinese herbal drugs. According to theory of traditional Chinese medicine (TCM), Artemisiae argyi folium enters liver, spleen and kidney meridians, with warm property, pungent and bitter flavors and of slightly toxicity [Artemisiae argyi folium mainly include volatile oils, flavonoid, tannins, triterpenes and polysaccharides. Volatile oils from Artemisiae argyi folium (VOAAF) have strong pharmacological activities and toxicity. The pharmacological activities of VOAAF include anti-inflammatory, antimicrobial, antiasthma, analgesic, antivirus, antioxidant, antitumor and immunomodulatory effects [Artemisiae argyi folium is mainly from its volatile oils [toxicity . Reportetoxicity . The che effects –14. Alth effects , 16, theile oils , 18, whiIn the present study, drug-induced liver injury (DILI) and VOAAF were investigated through bioinformatics by searching public databases such as GenBank, PubChem, and China National Knowledge Infrastructure (CNKI).http://www.ncbi.nlm.nih.gov/gene), which integrates information from a wide variety of species. A record may include nomenclature, reference sequences, maps, pathways, variations, phenotypes, and links to genome-, phenotype-, and locus- specific resources worldwide. We searched the Gene database  with “drug induced liver injury” or “DILI” as keywords, DILI genes were targeted for Homo sapiens.  , which is a public database on biological activities of small molecules and small interfering RNAs (siRNAs), hosted by the National Institutes of Health of American, consisting of three databases: PubChem Compound, PubChem Bioassay, and PubChem Substance. All the targeted proteins related to the active compounds in Chinese herbal drugs can be obtained from PubChem. After searching in the Grand Dictionary of Chinese Medicine [Homo sapiens , to build the molecular gene network . The molecules input into IPA software were termed as “focus molecules”. IPA generated focus molecules protein-protein interaction (PPI) as a set of networks based on different bio-functions. Molecules were represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All the edges were supported by at least 1 reference from the literature, textbooks, or from canonical information stored in the Ingenuity Knowledge Base. Nodes were displayed using various shapes that represent the gene product. The networks were evaluated by the scores, which were generated through calculation in the IPA software and represented the significance of the molecules in the network. To investigate the mechanism of DILI induced by the VOAAF, a canonical pathway analysis was performed using the IPA compare module. Through Fisher’s exact test, the significance of the association between focus molecules and the canonical pathways were determined in the IPA software.Related DILI genes were input into the IPA software online  and were associated with toxic functions and/or diseases in the Ingenuity Pathway Analysis Knowledge Database were analyzed. From the IPA analysis  a ratio of the number of proteins from the data set that map to the pathway divided by the total number of proteins that map to the canonical pathway and (2) a nes Fig. . The corWith the comparison analysis Fig. , it was According to network biology, life is a complex network which is interacted with various molecules. The occurrence and development of disease relates to a series of interacting genes or proteins, and the disease phenotype reflects the different pathological biological processes in a complex network of interactions . ChineseThe Liver Disease Group of the Chinese Medical Association defined the liver injury caused by drug hepatotoxicity as DILI , which iThe core analysis and the comparison analysis with the IPA software to the canonical pathway indicated that the target proteins of VOAAF and DILI genes both were involved with pathways of AHR signaling and LPS/IL-1 mediated inhibition of RXR function.2O2 in the primary hepatocytes of rats and mice. TCDD could down-regulate the superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) in the primary rats hepatocytes and decrease the activity of the mitochondria complex as well [+-K+-ATPase, Ca2+-ATPase and Ca2+-Mg2+-ATPase [The AHR signaling is a nuclear receptor signaling, and it is mostly related with apoptosis, cell cycle regulation and xenobiotic metabolism. AHR is a ligand-activated transcription factor which locates in the cell cytoplasm, and it belongs to the superfamily of basic helix-loop-helix per-arnt-sim homology domain protein subunits. AHR is activated by its related ligand after binding with it, and thereupon it translocates into cell nucleus to formulate the dimer complex which could bind with the corresponding deoxyribonucleic acid (DNA) sequence and enhances the expression of the cytochrome P450 (CYP450) with the AHR nuclear translocator . Cytochr as well , 36. AHR as well . In our  as well , 32. The+-ATPase . Accordi+/taurocholate cotransporting polypeptide (NTCP)), metabolizing enzymes , UDP-glycosyltransferase (UGT), SOD) and biosynthesis enzymes (CYP7A1), leads to impaired metabolism, transport and/or biosynthesis of lipid, cholesterol, bile acid and xenobiotics. A research indicated that the JNK pathway was closely related to the hepatic cells death [LPS/IL-1 mediated inhibition of RXR function is a nuclear receptor signaling. The top functions and diseases of it are lipid metabolism; molecular transport; small molecule biochemistry. Lipopolysaccharide (LPS), a major component of the outer membrane of gram-negative bacteria, potently stimulates host innate immune response. LPS binds the cysteine desulfhydrase 14 (CD14)/trithorax-like 4 (TRL4)/myeloid differentiation protein-2 (MD2) receptor complex, which promotes the secretion of pro-inflammatory cytokines like IL-1, tumor necrosis factor-α (TNF-α) in various cells, especially in macrophages. Infection, inflammation and injury down-regulate the expression of hepatic genes involved in a variety of physiological processes, collectively known as the negative hepatic acute phase response (APR). Many of the repressed genes during APR are regulated by the nuclear hormone receptor retinoid X receptor α (RXRα). In response to pro-inflammatory cytokines signaling, RXRα undergoes a c-Jun N-terminal Kinase (JNK) mediated, chromosome region maintenance 1 (CRM-1)-dependent nuclear export, leading to decreased nuclear RXRα levels and reduced nuclear DNA binding and transcriptional activity. Reduction in the expression of hepatic transport proteins (ATP-binding cassette (ABC), organic anion transporting polypeptide (OATP), multidrug resistance gene 1 (MDR1), Nals death . Usuallyls death , which lIn this work, 2 molecular networks and their functions were analyzed through IPA. One network identified genes related to DILI, and the other identified VOAAF target proteins. Although the VOAAF could affect various DILI-related functions and pathways in various ways, the major affected functions and pathways were analyzed in this paper, and the effects on AHR signaling and LPS/IL-1 mediated inhibition of RXR function might be crucial for elucidating the molecular mechanism of VOAAF. In future work, experiments in vitro and in vivo will be performed to verify the results by IPA.Additional file 1: Table S1.DILI-related genes searched in PubChem. In the GenBank database 338 genes were identified involved in DILI. (DOCX 53 kb)Additional file 2: Table S2.Target proteins of VOAAF searched in PubChem. Searched in the PubChem database, information on the drug targets of the VOAAF was listed. (DOCX 33 kb)


MECP2) gene. This gene is responsible for many steps in neuronal development and function. Although the genetic mutation and the clinical phenotype are well described, the molecular pathways linking them are not yet fully elucidated. In this study we demonstrate a workflow for the analysis of transcriptomics data to identify biological pathways and processes which are changed in a Mecp2-/y mouse model.The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this together with the experimental data to enable visualization and understanding of the underlying processes. Rett syndrome is a rare disease, but still one of the most abundant causes of intellectual disability in females. Cause of this neurological disorder is mutation of one single gene, the methyl-CpG-binding protein 2 (The online version of this article (doi: 10.1007/s10354-016-0488-4) contains supplementary material, which is available to authorized users. Gene annotations, as defined in gene ontology [Progress in biomedical methods today enables generation of vast amounts of molecular data, namely transcriptomics data with up to 100,000 transcripts which occur in a human cell. The current challenge is to identify the needle of changed processes in the haystack of data, and to combine different types of data. Pathway analysis combines these experimental transcriptome data with structured existing knowledge. Bioinformatics analysis and visualization methods and tools allow explanation and proof of whether and how a certain biological pathway is altered as a consequence of the experiment, which may be a mutation, drug application, nutrition, or disease. One of the strengths of this approach is that it provides a general picture of how the biological network is affected without being biased by hypothesis. Instead of hypothesis-driven experiments and data analysis, we are speaking of data-driven approaches , as wellontology , give maIf a group of genes which have the same or linked gene ontology annotation are up- or downregulated together, we speak of term enrichment or overrepresentation, which is measurable and gives information about the functions, processes, and cellular components that are changed. This approach is especially useful for investigation of rare diseases and disorders like Rett syndrome, where the genetic cause and the clinical phenotypes are well known, but there are still gaps in understanding how the biological pathways are modified and lead to these phenotypes.MECP2), which is located on the X chromosome [Rett syndrome is a rare disorder which was first described by Andreas Rett  and afferomosome . Rett feromosome —or the tMecp2-/y mouse model originally published by Sugino et al. [The objective of this study is to present a bioinformatics workflow to give an overview of changed biological pathways in Rett syndrome using transcriptomics data. In this study we investigated the differentially expressed genes and affected pathways in four different primary neuronal cell types using published (but not investigated to this extent) data from a o et al. .Mecp2-/y) or wildtype controls. Brain tissue slices of the desired area were cut out and cell sorting was performed: 1. layer 5 thick tufted pyramidal neurons (TTL5) in the motor cortex labeled in YFPH line, 2. fast-spiking parvalbumin-positive interneurons in the motor cortex labeled in the g42 line, 3. noradrenergic locus coeruleus neurons labeled in the TH line, and 4. cerebellar Purkinje cells labeled in the G42 line. Three samples, each from a different animal, were taken for microarray analysis. After extraction and preparation, the RNA was hybridized to Affymetrix Mouse Genome 430 2.0 oligonucleotide arrays (MOE430v2)  [In the current study a previously published study from Sugino et al.  was usedra, USA) . This dara, USA) .t-test. Finally, a list of significantly changed genes for each cell type  was obtained using a cutoff of a ±0.5 log2-fold change and p ≤ 0.05.In ArrayAnalysis.org, namely the AffyQC and statistics tool , the quaMus musculus-specific pathways (28.4.2016). Data integration for the variety of identifiers requires use of BridgeDb identifier mapping service [p-values, the following logic was used: ((LogFC ≤ -0.5 OR LogFC ≥ 0.5) AND p ≤ 0.05). The pathways which had z‑score >1.96, minimum number of changed genes >3, and p ≤ 0.05 were considered as changed.For pathway analysis we used PathVisio version 3.2.0) . The pat.2.0 . Th service . To calcp ≤ 0.05, and minimum number of changed genes ≥3. Only Ensembl annotations were taken into the analysis, which means not considering 64 Affymetrix IDs out of 17,417 identifiers in the list of all genes investigated. Results of this analysis were visualized using Cytoscape (version 3.4.0) [Gene ontology term enrichment analysis was performed using GO-Elite version 1.2.5) . We compn 3.4.0)  and the .2.5 . WeMecp2-/y mouse model expresses a different set of genes in comparison to wildtype, and they found especially cell adhesion genes and long genes (>20 kbp) to be overrepresented among the changed genes. In the present study, the raw transcriptomics data were reanalyzed using ArrayAnalysis.org . Finally, lists of differentially expressed genes were extracted (supplementary table 1) and pathway and gene ontology term enrichment analysis was performed to reveal the biological pathways changed in this model system. 258 genes are differentially expressed in Purkinje cells, 850 in locus coeruleus neurons, 463 in TTL5, and 301 in fast-spiking interneurons.The original study by Sugino et al. already indicated that each of the four investigated subpopulations of neuronal cells of a  Mecp2-/y mice compared to wildtype mice.In each cell type there are different sets of pathways affected (supplementary table 2). Fatty acid metabolism  are the predominantly changed pathways in fast-spiking interneurons of the motor cortex Fig. . The expGene ontology in general gives a triplet of annotation (information) for each gene: cell compartment , molecular function, and biological process in which this gene is involved. Term enrichment analysis gives information about whether these terms are over- or underrepresented for a certain dataset, and, therefore, whether these biological processes, molecular functions, or cellular compartments are especially affected. The connections between these single changed processes were linked and visualized using Cytoscape. The visualization of this network allows determination of which differentially expressed genes are interfering with different processes—and whether they are linking several processes with one another.The affected cellular compartment was mainly the nucleus and nucleus-related processes like the DNA-bending complex or regulation of histone modification, except for the TTL5 (supplementary table 3). Cell adhesion- and cytoskeleton-related processes  are changed in all cell types except for the fast-spiking interneurons. Neurospecific functions  were found to be changed in all four cell types investigated.Investigating molecular function, glutamate binding and glutamate receptor activity was changed in Purkinje cells and TTL5; while ion flux-related receptors, channels, and binding proteins changed in all cell types (supplementary table 3). The specific ions were mostly calcium and potassium, indicating neuronal function and signal transduction. Receptor activities for IGF, androgen, semaphorin, ephrin, β‑tubulin, actin, clathrin, and steroid hormones were also affected. Locus coeruleus neurons revealed a lot of changed enzyme activity.There are between 46 and 165 biological processes changed for each cell type (supplementary table 3). For Purkinje cells, the most changed processes  are semicircular canal morphogenesis, regulation of cholesterol metabolic process, and behavioral defense response. The network analysis revealed the affected biological processes that are most connected with other processes, which are metal ion binding and intracellular signal transduction. Fig. Locus coeruleus neurons: With the highest z‑scores are the processes sympathetic ganglion development, self-proteolysis, and detection of temperature stimulus. Network analysis showed negative regulation of cell proliferation and regulation of neuron projection to be the most connected processes.TTL5: Behavioral defense response, labyrinthine layer blood vessel development, pyrimidine-containing compound catabolic process, and negative regulation of BMP signaling pathway are the biological processes changed with the highest z‑scores. The most connected processes are regulation of ion transport and regulation of neuron differentiation.Fast-spiking interneurons: The highest z‑scores are glutamate binding, myelin sheath, regulation of cholesterol metabolic process, and axon development. The most connected processes are regulation of neuron differentiation and regulation of cell projection organization.Mecp2-/y mouse model. We reanalyzed the raw transcriptomics data and applied pathway and gene ontology analysis. The analysis confirmed the change of biological processes and functions belonging to neuro-specific metabolism: especially synapsis function [Mecp2-/y mice, and a mismatch between excitatory and inhibitory neuronal activities is observed [Purkinje cells are GABAergic neurons. They are inhibitory, highly connected (about 500 synapses each), and contribute to motoric coordination. Changes in ion transport and membrane trafficking highly affects their function. For Rett syndrome, the mismatch between inhibitory and excitatory neuronal pathways are known contribute to disorder development .Locus coeruleus neurons: the locus coeruleus is located in the brainstem and is one of the main producers of norepinephrine. It is furthermore related to several neuronal functions like sleep/wake cycle, posture and balance, and neuroplasticity, which are indeed impaired in Rett females. It is also the center which is affected in several other neurological disorders like Parkinson’s disease. An impaired regulation of cell proliferation (which may be a cause or a consequence of changed amino acid pathway) and affected neuronal differentiation affects its proper function.TTL5: TTL5 are excitatory neurons and most important for information processing. They are the neurons with the most extensive arborization and connect the different cortical layers. Also here is ion transport most relevant for neurological signal transduction, and proper regulation of excitatory and inhibitory signals is crucial for function.Fast-spiking interneurons: interneurons are involved in positive and negative feedback mechanisms, they are able to transduce excitatory to inhibitory signals, and can create (or contribute) to electric oscillations in neuronal networks. Fatty acid metabolism was found to be affected especially here Fig. . The metThe present study is a data-driven approach to investigate the changes in gene expression and combine the results with prior knowledge. The transcriptomics data were obtained from a previously published study by Sugino et al.  using fofunction  and glutfunction –23 are aobserved , 24. TheThe present study shows how pathway and gene ontology analysis tools can be used to find biological processes of interest based on changes in gene expression. A more detailed investigation of the data is necessary, especially to separate between up- and downregulated processes, and to link them to their specific function. Knowing the changed processes, and the single genes and proteins behind these processes, furthermore allows searching for specific drugs which bind and modify these targets. We expect that the application of these tools and methods is going to improve not only research on Rett syndrome or other neurological disorders, but will contribute to a better understanding of neurological physiology in general.Table 1-3