Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in  The functions of most sequenced proteins have not been determined by experiment . They arA pathway model is represented as a graph in which the enzymes, substrates, and products are nodes and the enzymatic reactions are edges . Input i−63, suggesting that the observed level of similarity between the two predicted ligand lists is unlikely to have occurred by chance  and a list of endogenous metabolites that are candidate substrates or products of these enzymes . The paty chance . Thus, tce versa . Finallyce versa . Thus, pmanno-octulosonate 8-phosphate (CMP KDO-8P) biosynthesis (four enzymes), and serine biosynthesis (five enzymes) (The method was tested by retrospectively ordering enzymes and identifying their substrates in three well characterized pathways: glycolysis (10 enzymes) , cytidinenzymes)  and 2. Denzymes)  and 2. Tenzymes)  or when enzymes) .The accuracy of the predicted benchmark pathways is not limited by the lack of sampling , but ratSeveral caveats bear mentioning. Both thorough sampling and accurate scoring become more difficult when the number of possible pathways increases (which in turn arises from a large set of candidate enzymes and metabolites), when some enzymes or metabolites are not in the input set, when the pathway is long, or its length is unknown. Here, only linear pathways are sampled; thus, non-linear pathways, including cyclic pathways, are not modeled. The preparation of input information requires manual processing. Although docking, chemoinformatics, comparative modeling, chemical transformations, and differential scanning fluorimetry (DSF) screening information may be collected in an automated way, the quality of information often benefits from expert choices. For example, comparative model building can be especially time consuming when low sequence similarity structures are available for target building, and docking may require expert intervention when parameterization of cofactors is necessary for correctly defining the binding site. Nevertheless, we emphasize that once the input information is provided, its conversion into the predicted pathway is automated and does not require human intervention. Finally, docking against modeled structures will sometimes fail, even with the added advantages of insisting on consistency in docking hit lists. Some of these pitfalls can be detected through testing the thoroughness of sampling , statistH. influenzae  from fluenzae , using Dfluenzae . The HiGormation .E. coli)  were identified from the genome neighborhood around the TRAP-solute-binding protein (Uniprot ID P71336) . Third, E. coli) . FinallyE. coli) .HiGulD) as the first catalytic step (HiUxuB) produces D-mannonate; its dehydration by the dehydratase (HiUxuA) produces 2-keto-3-deoxy-D-gluconate. The last few steps in the pathway model are part of a conserved Entner-Dourdoroff pathway, ending with glyceraldehyde 3-phosphate and pyruvate, known members of central metabolism. Even when individual restraints are excluded from the input information, the best-scoring pathway falls within the top-scoring pathway models. For example, the same pathway has the highest score if either the starting or end point is not restrained. If information about both starting and end points is excluded, this pathway model drops to 15th in score.The sampling algorithm found 154 unique high-scoring pathways, which clustered into 12 groups . The bestic step . Next, rThe pathway model was tested experimentally in five independent ways, including by enzyme activity, X-ray crystallography, fitness growth assays of the deletion mutants, transcript analyses, and isotopic metabolic labeling .cat/KM values larger than 103 M−1s−1 for their predicted substrates . Second, the model is supported by the crystallographic structure of the complex between the TRAP SBP protein and L-gulonate  and the proteins, which are binding proteins, transporters, or enzymes converting substrates to products. In addition, we allow for the inclusion of a dummy node representing an unknown and uncharacterized protein in the pathway. The sampling space of the models is constrained by the candidate enzyme and metabolite node identities that are given as input, as well as the linearity and length of the pathway.−120, corresponding to a median sequence identity between proteins of ~60%  colocalization of genes providing operon/metabolic context for prokaryotic proteins (A sequence similarity network (SSN) and genome neighborhood network (GNN) were constructed using the EFI-EST webserver  and Pyth of ~60% . At this of ~60% . Analysiproteins , (2) coeproteins , (3) co-proteins , (4) proproteins , (5) proproteins , and (6)proteins .To obtain the smallest candidate subset of KEGG that contains all metabolites needed to predict a pathway, we considered only the metabolites with good virtual screening scores against any of the candidate proteins as well as metabolites that can be derived from the virtual screening hits by applying chemical transformations related to the known activities of enzymes in the relevant superfamilies. Therefore, the top 1000-scoring metabolites from each docking screen are added to a single list of metabolites. Chemical transformations performed by each predicted enzyme are applied on the top-scoring metabolites using OEChem Tools  excludinPathwayZ) for ranking alternative L-gulonate pathways is a sum of Z-scores for each type of restraint, including virtual screening (VSZ), chemical transformations (CTZ), SEA analysis (SEAZ), known pathway boundaries (CMZ), high-throughput screening (HTSZ), and homology to characterized enzymes (HSZ):Information about the pathway is encoded as pathway restraints that are summed into a scoring function. For example, a candidate edge between a given enzyme and metabolite is restrained by a virtual screening score for the pair. In an attempt to ‘weigh’ each piece of information optimally, each term in the scoring function is expressed as a Z-score. Our scoring function can in principle benefit from all available information, even when some information is not available for every enzyme or ligand. In such cases, the corresponding terms are simply omitted from the scoring function. A dummy node in a model contributes no score, except towards the chemical transformation term. Thus, the scoring function . Compounds were ranked by a physics-based scoring function that evaluates ligand-protein complementarity considering van der Waals and electrostatic interactions, corrected for ligand desolvation  number describes the substrate specificity of an enzyme, the generic chemical transformation typically corresponds to the third level EC classification . For exaThe chemical transformation is determined from the Pfam classification  from theN is the total number of substrate-product-enzyme triads in the pathway model.Tanimoto coefficients were converted into Z-scores, similarly to the docking Z-scores. The score for transformations is the average Z-score over the Z-scores of all substrate-product-enzyme node triads in a model:Comparison of ensembles of ligands using the Similarity Ensemble Approach (SEA) version 1.0 can predict functionally-linked proteins from the similarity of their ligands , irrespeA and B is first converted into the iS by subtracting the mean and dividing by the standard deviation obtained from the distribution of PathwayZ is the iS score averaged over all consecutive protein pairs in the tested pathway:Specifically, the SEA E-value  subunit of a TRAP transporter was identified, based on its strong sequence similarity to the TRAP SBP family. Thus, this transporter defines the start of the metabolic pathway to be modeled, with the rest of the pathway corresponding to intracellular enzymes acting on the transporter’s substrate in series.Similarly, knowledge about the endpoints of metabolic pathways can also constrain integrative pathway mapping. For catabolic pathways of sugars, we assume that the pathway produces a compound that feeds into central carbohydrate metabolism. Therefore, pathways in which the final product is a compound in central metabolism are more likely to be correct .TC is the Tanimoto coefficient between the final product in the pathway model and the most similar compound in central metabolism. SD is the standard deviation of Tanimoto coefficients for all comparisons between each candidate metabolite and all compounds in central metabolism.The final metabolite in the model is compared by the Tanimoto coefficient using RDKit Morgan Fingerprints to all metabolites in central metabolism, and the maximum Tanimoto coefficient is used. The central metabolism endpoint score is:TC is the Tanimoto coefficient between the substrate in the pathway model and the hit in the screening assay. For multiple screening hits, the maximum of the Tanimoto coefficients between the substrate and hits is used. SD is the standard deviation of Tanimoto coefficients between the substrate and all metabolites.In our predicted pathway, L-gulonate and D-mannonate were identified as hits for the TRAP solute binding protein , using screening of 189 compounds by DSF . We assuE. coli. Therefore, pathways in which the substrate of the dehydratase is similar to D-mannonate are more likely to be correct than others. The proposed dehydratase substrate in an evaluated pathway model was compared to D-mannonate by the Tanimoto coefficient, using RDKit Morgan Fingerprints:TC is the Tanimoto coefficient between the proposed substrate and the known substrate. SD is the standard deviation of Tanimoto coefficients for all comparisons between each candidate metabolite and the known substrate.Substrates of enzymes in the pathway model are expected to be the same or similar to substrates of homologs that share high sequence similarity. For the L-gulonate pathway, the dehydratase has 73% sequence identity to a characterized mannonate dehydratase in D is the difference between the old and new pathway scores and T is the simulated annealing ‘temperature’ parameter. The temperature drops over the course of the MC run:N is the MC step number normalized by the total number of MC steps. With these parameters, a sampling run generally converges  sampling by the Metropolis-Hastings algorithm with simulated annealing . The setonverges , terminaonverges .The resulting ensemble of good-scoring models is analyzed in terms of its precision and satisfaction of the restraints . Using hA Cytoscape  app, NetThe total computation time depends on the numbers of enzymes and ligands considered. The computationally most demanding part of integrative pathway mapping is the preparation of the input information, not the sampling of the alternative pathways. For the three benchmark and L-gulonate pathways, the computing times for various steps in the process are as follows. Parallelized virtual screening of a library of ~20,000 compounds by docking each ligand against each of ~10 enzymes takes just over an hour on a cluster of 1400 nodes. The SEA analysis takes ~15 min to screen ~20,000 compounds against ~10 target proteins on 10 computing nodes. Finally, chemical transformation calculations take ~1 hr for 10 enzymes on a single node. Once the inputs are in hand, the runtime for a single Monte Carlo sampling run is approximately 1 hr on a single computing node; a few hundred independent Monte Carlo optimizations are typically performed in parallel on a cluster of compute nodes. In conclusion, the entire process, from preparation of inputs to the sampling of the pathways, can be performed in a few hours on a cluster of a few hundred nodes.We assess our method on three known metabolic pathways, including the glycolysis pathway , CMP KDO-8P biosynthesis pathway , and serine biosynthesis  . PathwayThe enzyme composition of a pathway is not always known with certainty, so we considered other scenarios beyond the simple case . We testGC is the normalized intersection between the set of protein pairs in the gene cluster and the set of protein pairs in the pathway model. SD is the standard deviation of the normalized intersection for all comparisons between the proteins associated with the gene cluster and possible subsets of candidate proteins.The set of protein pairs associated with the gene cluster identified by genome neighborhood analysis is compared to sets of protein pairs from all possible subsets of the protein candidates. The intersection between the sets of pairs is normalized by the larger of the number of pairs in the sets. The possible subsets range in number from as few as three to the number of total candidate proteins. The gene cluster score is:For the case in which the candidate set of enzymes is incomplete, a dummy enzyme that represents an unknown pathway enzyme was used . For eacHiUxuB (Uniprot ID P44481), HiKdgK (Uniprot ID P44482), and HiKdgA (Uniprot ID P44480) were amplified from H. influenzae strain Rd KW20 (ATCC 51907) genomic DNA. PCR was performed using KOD Extreme DNA Polymerase (Novagen) according to the manufacturer’s guidelines. The conditions were: 2 min at 95°C, followed by 40 cycles of 20 s at 95°C, 20 s at 66°C, and 20 s at 72°C. Primers are listed in The genes HiUxuB-pSGC-His, HiKdgK-pSGC-His, HiKdgA-pSGC-His vectors were transformed into BL21 (DE3) E. coli containing the pRIL plasmid (Stratagene) and used to inoculate a 20 mL 2 x YT culture containing 25 μg/mL Kanamycin or 100 μg/mL Carbomycin and 34 μg/mL Chloramphenicol. The cultures were grown overnight at 37°C in a shaking incubator. The overnight culture was used to inoculate 2 L of PASM-5052 auto-induction media containing 150 mM 2–2-bipyridyl, 1 mM ZnCl2, and 1 mM MnCl2  and lysed by sonication. The lysate was clarified by centrifugation at 35,000 x g for 30 min, loaded onto a 5-mL Strep-Tactin column (IBA) on an AKTAxpress FPLC , and washed with five column volumes of lysis buffer, and eluted in StrepB buffer . The eluent was loaded onto a 1-mL HisTrap FF column , washed with 10 column volumes of lysis buffer, and eluted in buffer containing 20 mM HEPES pH 7.5, 500 mM NaCl, 500 mM Imidazole, and 10% glycerol. The purified sample was loaded onto a HiLoad S200 16/60 PR gel filtration column, which was equilibrated with SECB buffer . Peak fractions were collected, protein was analyzed by SDS-PAGE, concentrated to 2.4 g/L, 1.9 g/L, and 2.0 g/L, respectively, flash frozen in liquid nitrogen, and stored at −80°C.HiGulD (Uniprot ID Q57517) and HiUxuA (Uniprot ID P44488) were amplified from H. influenzae strain Rd KW20 (ATCC 51907) genomic DNA. PCR was performed using KOD Extreme DNA Polymerase (Novagen) according to the manufacturer’s guidelines. The conditions were: 2 min at 95°C, followed by 40 cycles of 20 s at 95°C, 20 s at 66°C, and 20 s at 72°C. Primers are listed in The genes HiGulD and HiUxuA constructs were transformed into E. coli BL21 (DE3) for expression. Both HiGulD and HiUxuA were purified from 1 L of culture using DEAE Sepharose, Q‐Sepharose, and phenyl-Sepharose columns  as previously described . The reaction was left to proceed at 37°C for 48 hr. Afterward, the enzyme was removed by filtration using 30,000 NMWL ultrafiltration membranes (Millipore). The identity of the product was verified via 1H-NMR.2-Keto-3-deoxy-D-gluconate was synthesized via an enzymatic procedure. The reaction (1.5 mL) contained 50 mM potassium HEPES, pH 7.9, 10 mM MgCl2, 120 mM ATP, 100 mM D-mannonate, 1 μM D-mannonate dehydratase (Uniprot ID B0T0B1), and 1 μM 2-keto-3-deoxy-D-gluconate kinase (Uniprot ID A4XF21). The reaction was left to proceed at 37°C for 48 hr. Afterward, the enzyme was removed by filtration using 10,000 NMWL ultrafiltration membranes (Millipore). The identity of the product was verified via 1H-NMR.2-Keto-3-deoxy-D-gluconate-6P was synthesized via an enzymatic procedure. The reaction (1.5 mL) contained 100 mM potassium HEPES, pH 7.9, 10 mM MgCl1H-NMR.The kinetic assays were run in 200 μL aliquots at 37°C and monitored using a continuous spectrophotometric assay. The identities of all products were verified via −1 cm‐1) of L‐gulonate at carbon-5 by HiGulD . The substrate concentration was varied from 100 μM to 10 mM.Oxidation was quantitated by measuring the increase in absorbance at 340 nm (ε = 6220 M−1 cm‐1) of D-mannonate at carbon-5 by HiUxuB . The substrate concentration was varied from 50 μM to 5 mM.Oxidation was quantitated by measuring the increase in absorbance at 340 nm (ε = 6220 M−1 cm‐1) of D-mannonate by HiUxuA . The substrate concentration was varied from 100 μM to 30 mM.Dehydration was quantitated by measuring the decrease in absorbance at 340 nm (ε = 6220 M−1 cm‐1) of 2-keto-3-deoxy-D-gluconate by HiKdgK in a coupled assay with lactate dehydrogenase . The substrate concentration was varied from 100 μM to 5 mM.Phosphorylation was quantitated by measuring the decrease in absorbance at 340 nm (ε = 6220 M−1 cm‐1) of 2-keto-3-deoxy-D-gluconate-6P by HiKdgA in a coupled assay with lactate dehydrogenase . The substrate concentration was varied from 100 μM to 5 mM.Cleavage was quantitated by measuring the decrease in absorbance at 340 nm (ε = 6220 MH. influenzae Rd KW20 (ATCC 51907) was grown aerobically at 37°C with shaking at 225 rpm, and was routinely cultured in Brain Heart Infusion  broth or on BHI solid medium, supplemented with nicotinamide adenine dinucleotide (NAD) and hemin at 10 µg mL−1 (sBHI). For gene expression analyses and carbon utilization studies, the defined medium of Coleman et al. (2003) was used. Glucose-free RPMI-1640 (Sigma R1383) was supplemented with the following additives: HEPES, 6 mg mL−1; NaHCO3, 2 mg mL−1; inosine, 1.75 mg mL−1; uracil, 87 µg mL−1; NAD, 10 µg mL−1; hemin, 10 µg mL−1. D-glucose, L-gulonate, or D-mannonate (10 mM) served as the source of carbon. Kanamycin was added at 10 µg mL−1 when appropriate.H. influenzae Rd KW20 were grown overnight in sBHI, washed in minimal medium lacking carbon source, and re-suspended in an equivalent volume of minimal medium lacking carbon source. Each well contained 300 µL minimal medium with D-glucose, L-gulonate, or D-mannonate (10 mM), and was inoculated to 1% with washed starter culture. Plates were incubated at 37°C with continuous shaking at medium amplitude and the optical density at 600 nanometers (OD600) was recorded every 30 min for 48 hr.Growth curves were recorded using the Bioscreen C instrument  and 100-well plates. Starter cultures of H. influenzae Rd KW20 were grown overnight in sBHI, washed in minimal medium lacking carbon source, and re-suspended in an equivalent volume of minimal medium lacking carbon source. This culture was used to inoculate 5 mL minimal medium (1% inoculum) with 10 mM glucose, and cultures were grown until OD6000.3–0.5. Cells were washed and re-suspended in 4 mL minimal medium lacking carbon source. Cultures were divided into two equal 2 mL volumes, 10 mM glucose was added to one volume and 10 mM L-gulonate or D-mannonate was added to the other, and the cultures were grown until OD6000.8–1.0. At the time of cell harvest, one volume of RNAprotect Bacteria Reagent (Qiagen) was immediately added to two volumes of each actively growing culture. Samples were mixed by vortexing for 10 s and incubated for 5 min at room temperature. Cells were pelleted, flash frozen in liquid nitrogen, and stored at −80°C.Starter cultures of 260) using the Nanodrop 2000 (Thermo) and absorption ratios A260/A280 and A260/A230 were used to assess sample integrity and purity. Isolated RNA was stored at −80°C until further use.RNA isolation was performed in an RNAse-free environment at room temperature using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. Cells were disrupted according to the ‘Enzymatic Lysis Protocol’ in the RNAprotect Bacteria Reagent Handbook (Qiagen); lysozyme (Thermo-Pierce) was used at 15 mg/mL. RNA concentrations were determined by absorption at 260 nanometers , according to the manufacturer’s instructions. Primers for quantitative real-time (qRT) PCR were designed using the Primer3 primer tool; amplicons were 150–200 bps in length. Primers were 18 to 24 nucleotides in length and had a theoretical TT method , using tH. influenzae Rd KW20 genomic DNA with primers Del_HI0052_arm1fwd and Del_HI0052_arm1rev, (b) the kanamycin resistance cassette from p34s-Km (H. influenzae Rd KW20 genomic DNA with primers Del_HI0052_arm2fwd and Del_HI0052_arm2rev. The 3’ end of fragment ‘a’ and the 5’ end of the kanamycin resistance cassette (fragment ‘b’) were engineered with 50 bps of identical overlapping sequence, as were the 3’ of the kanamycin resistance cassette and the 5’ end of fragment ‘c’. One hundred ng of each of these PCR fragments were combined in a triple overlap extension PCR with primers Del_HI0052_arm1fwd and Del_HI0052_arm2rev to generate a ~3 kb fragment with arms homologous to the genomic regions flanking HI0052, with an intervening kanamycin resistance cassette.To create a genetic deletion of the putative L-gulonate SBP (HI0052), triple overlap extension PCR was used. Briefly, using Pfu Ultra High-Fidelity DNA polymerase (Agilent), three PCR fragments were generated: (a) a fragment corresponding to the genomic region ~1000 bps upstream of HI0052 was amplified from  p34s-Km  was amplH. influenzae Rd KW20 genomic DNA. The kanamycin resistance cassette from p34s-Km was amplified with primers Kan_OL_delHI0053_fwd and Kan_OL_delHI0053_rev. One hundred ng of each of these PCR fragments were combined in a triple overlap extension PCR with primers Del_HI0053_arm1fwd and Del_HI0053_arm2rev to generate a ~3 kb fragment with arms homologous to the genomic regions flanking HI0053, with an intervening kanamycin resistance cassette. Primer sequences are provided in The same approach was used to create a genetic deletion of the putative L-gulonate dehydrogenase (HI0053). To generate the triple overlap extension product for deletion of HI0053, primers Del_HI0053_arm1fwd and Del_HI0053_arm1rev and primers Del_HI0053_arm2fwd and Del_HI0053_arm2rev were used to amplify the regions ~1000 bps upstream and downstream of HI0053, respectively, from H. influenzae Rd KW20 cells made competent by the M-IV method . Cells were harvested by centrifugation , washed twice in defined medium without added carbon source, and re-suspended in this medium. Cell density was adjusted to OD600 = 6.0, and the cell suspension was then depleted of catabolic metabolites by incubation at 37°C for 30 min before transferring to ice. A mixture of 5 mM 13C labeled L-gulonate plus 5 mM unlabeled L-gulonate, or a mixture of 5 mM 13C labeled D-glucose plus 5 mM unlabeled D-glucose, was added to the samples followed by incubation at 37°C. At time points of 1, 2, 10, and 60 min, samples were pelleted by centrifugation , supernatants were removed, and cell pellets were flash frozen in liquid nitrogen. Samples were stored at −80°C prior to extraction. Metabolites were extracted directly from cell pellets by re-suspension in 0.5 mL extraction buffer  followed by 10 min of vortexing at room temperature. Cell extracts were cleared of debris via two rounds of centrifugation at 16,000 × g for 1 min, split into two equal portions, dried, and stored at −80°C prior to analysis.GC-MS-based metabolic analysis of whole cell extracts was carried out with samples of 13C labeling . Alternatively, to determine the labeling of gulonate and other 6-carbon molecules in the proposed pathway for L-gulonate metabolism, extracts were derivatized with 30 µL methoxyamine hydrochloride  for 20 min at 80°C, followed by 30 µL BSTFA  for 60 min at 80°C. Derivatized metabolites were analyzed by GC-MS as described before , extracts were derivatized with isobutylhydroxylamine and N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (TBDMS), and analyzed by GC-MS as described before . Data wed before , using ahttp://integrativemodeling.org and https://github.com/salilab/pathway_mapping (https://github.com/elifesciences-publications/pathway_mapping). Open access metabolite docking and libraries at http://metabolite.docking.org/ and at http://blaster.docking.org/. NetIMP is available in the Cytoscape app store.The source code for the IMP program, benchmark, input scripts files, and output files for the benchmark and the gulonate pathway calculations are available at  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Michael Marletta (Senior Editor) and three reviewers, one of whom, Nir Ben-Tal (Reviewer #1), is a member of our Board of Reviewing Editors.Thank you for submitting your article "Integrative mapping of enzymatic pathways" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The manuscript introduces a novel approach to enzyme annotation based on analyzing the enzyme within the context of its metabolic pathway. Structural information is utilized to enhance systems models. In particular, predictions of the substrates and products of the enzymes, as well as structural data (from experiment and prediction) are used within the framework of Monte Carlo search to assemble putative metabolic pathways. The most highly ranked pathway is considered as hypothesis, and experiments are designed to examine it.H. influenzae.The strength of this study is that it is an intuitive, well-fleshed out method. Structural prediction data from docking, which is not so strong on its own for functional prediction, is coupled to locations within a pathway, restricting the search space of possible solutions to chemically similar compounds in the pathway proximity of each other. There is also strong experimental evidence for parts of the pathway prediction in The weakness of this study is that the initial starting points to build these pathways seems vague, pathway models are not fully experimentally verified , and application on a larger (genome) scale is not discussed, where this would be incredibly useful. An assumption is made to start with genome organization to select the pathway members which is unexplained and should be backed up.Opinion:H. influenzae  are not very convincing. It is also a bit unclear what constitutes actual results of this paper, as significant elements of L-gulonate catabolic pathway discovery in H. influenzae were already published by the same authors in 2015. The authors should either rebrand their approach as aiming at cleaning and solving apparent inconsistencies in existing annotations, or come up with more convincing examples of discovery and annotations of genuinely novel functions/pathways. Such examples would provide more realistic evaluation of the proposed approach. In summary: The main text should make it clearer as to what the immediate application is. Is this method currently limited to filling in parts of a pathway, or is it feasible to now try reconstructing entire cells from "bags" of proteins and metabolites? If the latter, suitable examples should be presented.It is an interesting approach but because of several hype statements the reader ends up somewhat disappointed. The approach is validated on three well known and one novel pathway. This is very impressive both in the scope of work and in the conceptual completeness, but potentially misleading as the computational approaches clearly depend on the amount and quality of already existing information. From experimental structures cocrystallized with correct ligands that would help in virtual screening, to reliance on well understood biochemistry and on homology to already characterized enzymes this approach seems to be aiming for semi-automated reanalysis of already well characterized pathways rather than on annotations of novel, uncharacterized ones – which seems to be the stated motivation for this work. In this context the benchmarks and the "discovery" of L-gulonate catabolic pathway in Major issues:1) Assuming that the examples will not change, many statements should be tuned down, starting in the title and Abstract, to reflect the fact that: (a) the proposed approach can, at best, resolve conflicts in existing annotations rather than suggest new annotation, and (b) that even that requires a lot of manual interventions, as opposed to fully automated method.2) The reasons for the data types chosen to be integrated over other potentially useful sources should be flashed. Reasons could include increasing the computational time, limitations of experimental data, etc.3) Methods of picking the initial proteins should be explained, since that is a nebulous first step to get around before using this useful method.4) Selecting based on proximity in genome organization is not backed up . Statistics or citations should be shown on known pathways if this is true. This is an important step to explain because it seems like it may ignore many enzymes which are not in the genome neighborhood. In eukaryotic organisms this does not seem to be nearly as applicable as to prokaryotes.5) Computational time is unknown, throughout the paper – estimated time to run all docking predictions, chemical similarity, binding site locating, MC sampling, etc. What is the bottleneck? Or main source of human time/input?6) Is docking done only to one predicted active site in an enzyme? How reliable or comprehensive the binding site estimates used here are? How much does docking add over geometric + biochemical comparisons of active sites? Would it be sufficient to do that and compute similarity analyses of that, rather than running many docking simulations?7) Discussion, first paragraph. There is additional related work of genome-scale metabolic reconstructions that include structural data:- http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000938 – structural properties used in predicting drug off target pathways.- https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-016-0271-6 – structural properties used to compare lifestyles and pathway usage.8) 9) What is the author's opinion on applying this method on a genome scale (i.e. entire metabolic network)? Is it feasible in terms of speed and data curation? Is it possible for the authors to generate genome-scale network predictions and compare them to available reconstructed metabolic models? These would be interesting questions to address in the main text.10) How broadly applicable the approach is? How many new enzymes can be annotated this way?11) The statement concerning the applicability of the method also to protein-protein interaction (PPI) networks is a bit of a stretch. For now, the essence of the method is docking of the metabolites to their binding/catalytic sites. In PPI this component will have to be replaced, and it is unclear with what. Protein-protein docking, the first possibility that comes to mind, is far less accurate, especially for transient interactions. I would eliminate the statement, or tune is down significantly.12) Methods:a) The terms in the first equation should be defined.b) Using the DOCK scores. Usually, in drug discovery campaigns, the docking scores are considered an indication for possible binding, but the actual values or ranks are often inaccurate. It is surprising that here they are taken as a given and nevertheless the pipeline works fine. Major issues:1) Assuming that the examples will not change, many statements should be tuned down, starting in the title and Abstract, to reflect the fact that: (a) the proposed approach can, at best, resolve conflicts in existing annotations rather than suggest new annotation, and (b) that even that requires a lot of manual interventions, as opposed to fully automated method.We have edited the manuscript throughout to tone down overreaching statements. For example:"Prediction of enzymatic pathways by integrative pathway mapping”.The title was changed to: can identify” → “predicts”; and “Demonstrated the utility by discovering a L-gulonate pathway” → “predicted”. We also removed any mention of “discovery” of the pathway throughout the main text. For instance, “we sought to predict this putative pathway using integrative pathway mapping”.We rephrased claims that we discovered a new pathway, instead focusing on development of an approach to predict the functions of the individual enzymes. For example, in the Abstract, “A computational method that The input is indeed not prepared entirely automatically, which is true for all automated methods as at some point inputs must be prepared at least in part “by hand”. However, once all the input information is in hand, our method does convert it into the output automatically. For example, the input for the L-gulonate pathway prediction was mainly docking results, chemoinformatics analysis, chemical transformations, genomic neighbourhood, and experimental data obtained as the project evolved. This information was then fed into integrative pathway mapping that automatically produced the output .We clarified this point: “The preparation of input information requires manual processing. […] Nevertheless, we emphasize that once the input information is provided, its conversion into the predicted pathway is automated and does not require human intervention.”Also, we state: “While not all types of input for integrative pathway mapping can be obtained automatically , the mapping itself is entirely automated.”As the reviewer expected, we have indeed not eliminated the description of the L-gulonate pathway from the manuscript. As described in the summary above, this was a genuinely new pathway prediction, one tested and supported in this work with an experimental rigor still relatively rare in the field.2) The reasons for the data types chosen to be integrated over other potentially useful sources should be flashed. Reasons could include increasing the computational time, limitations of experimental data, etc.A major goal of the manuscript is to introduce an integrative approach to pathway mapping. Accordingly, we selected several accessible types of input to demonstrate this idea, while suggesting that additional sources of information could be considered in the future. As more types of information are added to the integrative approach, they might further improve its accuracy, precision, and applicability. Docking, the chemoinformatic Similarity Ensemble Approach, and chemical reactions seemed like sensible initial inputs, as they exploit established methods. The application to the gulonate pathway illustrates the use of several other types of input, including experimental data from differential scanning fluorimetry (it can be done in high-throughput) and additional theoretical considerations , thus illustrating the relative ease with which information about a pathway can be included.In Materials and methods, we now add: “First, input information has to be collected from computational and/or experimental sources. […] Moreover, additional types of information can be added, hopefully improving the accuracy, precision, and applicability of the approach, as illustrated by the gulonate pathway prediction that also depends on differential scanning fluorimetry data, pathway anchor points, and protein homology considerations.”3) Methods of picking the initial proteins should be explained, since that is a nebulous first step to get around before using this useful method.eLife, 2014), which displays conserved protein families found in proximity to the TRAP SBP gene.The candidate enzymes involved in the gulonate pathway were selected by identifying genes that are conserved in the genome neighborhood of the TRAP SBP gene (which encodes the transporter known to import gulonate into the cell) across multiple bacterial species. This analysis involved the construction of a genome neighborhood network ; the GNN approach has been demonstrated to accurately predict enzymes and transporters that function together in metabolic pathways based on conserved protein families in genome neighborhoods across different species .”H. influenzae are co-localized in the genome with the TRAP SBP gene.”Also, specific details for the gulonate pathway are given in Materials and methods: “A sequence similarity network (SSN) and genome neighborhood network (GNN) were constructed using the EFI-EST webserver  and Pythoscape v1.0 software  for an anchor protein, TRAP SBPs (Uniprot ID P71336 and Uniprot ID A7JQX0), to provide candidate pathway members […] Analysis of the GNN identified five enzyme families as candidate pathway members, including two dehydrogenases, one sugar dehydratase, one carbohydrate kinase, and one aldolase. The genes associated with these families in 4) Selecting based on proximity in genome organization is not backed up . Statistics or citations should be shown on known pathways if this is true. This is an important step to explain because it seems like it may ignore many enzymes which are not in the genome neighborhood. In eukaryotic organisms this does not seem to be nearly as applicable as to prokaryotes.We agree. Accordingly, we added a discussion of the relationship between the genome neighborhood and pathway membership for prokaryotes. We also point out that the relationship is not nearly as strong in eukaryotes, most likely invalidating this particular consideration for predicting eukaryotic pathways. We also note that the integrative framework allows for incorporating other information about pathway membership , which might reduce the size of the input set of potential protein members.In Discussion, we now write: “Moreover, no single type of input information is essential, provided sufficient information is available from other sources. […] Thus, the integrative approach is at least in principle not limited to prokaryotic pathways.”Also, in section “Stage 2: Designing pathway model representation and evaluation” in Discussion, we added: “This step can be substituted or supplemented by any other method that identifies candidate genes, including but not limited to: 1) colocalization of genes providing operon/metabolic context for prokaryotic proteins , 2) coexpression measured through chip-based and RNA-seq technologies , 3) co-regulation predicted by upstream DNA motifs , 4) protein-protein interaction studies , 5) protein fusion events , and 6) phylogenetic profiles across different genomes .”5) Computational time is unknown, throughout the paper – estimated time to run all docking predictions, chemical similarity, binding site locating, MC sampling, etc. What is the bottleneck? Or main source of human time/input?To address this point, we added a new section entitled “Computational cost”, in Methods): “The total computation time depends on the numbers of enzymes and ligands considered. […] In conclusion, the entire process, from preparation of inputs to the sampling of the pathways, can be performed in a few hours on a cluster of a few hundred nodes.”6) Is docking done only to one predicted active site in an enzyme? How reliable or comprehensive the binding site estimates used here are? How much does docking add over geometric + biochemical comparisons of active sites? Would it be sufficient to do that and compute similarity analyses of that, rather than running many docking simulations?For each enzyme, as we now describe in the section “Molecular docking screens”, the docking was run against a single predicted binding site. Identical predictions were obtained by two independent methods: (1) similarity to a protein structure with a known site and (2) identifying the largest cavity in the structure using the program PocketPicker .As the reviewer points out, one could take known ligands of proteins homologous to a potential pathway member and rank the corresponding protein ligand pairs higher than those with other potential ligands, perhaps considering the degree of similarity of active sites and/or ligands. We explored this idea, but did not incorporate it in the current version of the method because we do not always have information about homologous proteins and their ligands. Perhaps more importantly, benchmarking showed that virtual screening can potentially identify ligands more accurately than a simple homology-based ligand transfer  (now pointed out in the first paragraph of the Discussion). Nevertheless, homology-based transfer may be one of the scoring function terms to add in the future, to further improve our integrative approach.7) Discussion, first paragraph. There is additional related work of genome-scale metabolic reconstructions that include structural data:- http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000938 – structural properties used in predicting drug off target pathways.- https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-016-0271-6 – structural properties used to compare lifestyles and pathway usage.We added these citations in the first paragraph of the Discussion.8) The relative natural abundances of the two carbon sources may impact the upregulation of genes involved for D-mannonate conversion. For example, D-mannonate is a part of the catabolic pathway of D-glucuronate, which is commonly found in nature. L-gulonate metabolism intersects with D-glucuronate metabolism at fructuronate, but we are unaware of any previous reports of the use of L-gulonate.GulPQM on a D-mannotate source is that the GulPQM transporter could have a lower binding efficiency for D-mannonate vs. L-gulonate  What is the author's opinion on applying this method on a genome scale (i.e. entire metabolic network)? Is it feasible in terms of speed and data curation? Is it possible for the authors to generate genome-scale network predictions and compare them to available reconstructed metabolic models? These would be interesting questions to address in the main text.The framework of the approach can, in principle, take any information that can be used to rank alternative pathways, and then samples alternative pathways to find those that are highly ranked. Thus, it could take information used for constructing entire metabolic networks by others (or indeed other networks) and, given a sufficiently large computing facility and perhaps improved sampling algorithm, in principle predict entire metabolic networks. The quality of the output is limited by the amount of information and exhaustiveness of sampling. In practice, however, reconstructing entire metabolic networks is out of scope for this first paper and for the current implementation of the method. We do believe, as alluded to above, that the method can, even now, complement the genome reconstruction methods in the realm of “dark” metabolic space. Still, not wishing to mislead on this point and succumb to hype, we prefer to leave such possible future applications uncommented on in the manuscript. We beg your indulgence here.We now end Discussion with an appropriately cautious statement: “With further development, the framework may be applicable on a larger scale, approaching complete genomes, but mapping topologies of networks will be more demanding as it will require more input information and larger computation.”10) How broadly applicable the approach is? How many new enzymes can be annotated this way?To answer this question to a degree, we consider the fraction of enzymes that have known X-ray structures or sufficiently high quality comparative protein structure models – the latter of which we use in this manuscript – for application of virtual screening: domains in about 70% of proteins have known or modelable structures ; not every enzyme in the pathway needs to be analyzed by virtual screening  The statement concerning the applicability of the method also to protein-protein interaction (PPI) networks is a bit of a stretch. For now, the essence of the method is docking of the metabolites to their binding/catalytic sites. In PPI this component will have to be replaced, and it is unclear with what. Protein-protein docking, the first possibility that comes to mind, is far less accurate, especially for transient interactions. I would eliminate the statement, or tune is down significantly.We agree, and have eliminated the statement.12) Methods:a) The terms in the first equation should be defined.We agree, and now define each of the terms in the equation.b) Using the DOCK scores. Usually, in drug discovery campaigns, the docking scores are considered an indication for possible binding, but the actual values or ranks are often inaccurate. It is surprising that here they are taken as a given and nevertheless the pipeline works fine.The reviewer correctly points out that the scores and the ranks in a docking campaigns are often inaccurate, which is why the integrative mapping approach relies on several inputs – and not only on one source of information such as docking – to reach a consensus ranking. However, docking can enrich for real ligands at the top of the docking list. Therefore, in the L-gulonate pathway example we limit the input to the top 500 docking-ranked metabolites. This is illustrated directly in


Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways  can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention. Ankylosing spondylitis (AS) is a chronic inflammation disorder that attacks sacroiliac joints and spine  with a 0RPL17, MRPL22, PSMA6 and PSMA4. In 2015, also using the same data, Shi et al. , and detected the pathway for immune response regulation. Nevertheless, so far, most studies that assessed the genetics of AS focused on a single gene or a single pathway. However, pathway cross-talk is frequently neglected.In recent years, genetic-associated research has detected several new genes related to AS. Of note, some of these genes seem specific for AS, but others have pleiotropic associations ,6. Moreoi et al.  identifiIn general, different pathways interact with each other, and an abnormity in one pathway may influence the activities of many other related pathways. Understanding pathway interactions might be beneficial to explore the pathogenesis of AS. No reliable method is used to quantify the cross-talks for pathway pairs . HoweverThus, in the current study, to explore the pathogenesis of AS, we undertook the microarray data analysis of AS to identify the significant pathways considering the functional dependency among pathways using Monte Carlo cross-validation. We believe that our results may contribute to a better understanding of the molecular processes driving AS progression.Using microarray profile as well as biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathways. In this process, all steps were iterated 50 times. For each run, we implemented differential expression analysis (DEA), pathways enrichment analysis, calculation of a discriminating score (DS), and random forest (RF) classification. After 50 runs, the top 10 pathway pairs with the best area under the curve (AUC) values were identified (significant paired pathways). The flow chart of the analysis is shown in http://www.ncbi.nlm.nih.gov/geo/), based on the GPL6947 platform of Illumina Human HT-12 V3.0 expression BeadChips. In the GSE25101, there were 16 active AS patients and 16 normal controls with matched gender and age. Included AS patients had Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) scores >4 and Bath Ankylosing Spondylitis Functional Index (BASFI) scores >4. The raw probe annotation files were obtained for subsequent analysis.The expression profile of GSE25101 deposited by Pimentel-Santos et al.  was downThe raw probe sets were pre-processed by means of robust multiarray averaging method. Then, the probes were mapped to the genomics, and the human gene symbols were obtained. Afterwards, Bayesian approach  was utilhttp://www.ingenuity.com/)  software (ty.com/) . Then, wty.com/) . In the The DS is often used to compare the expression levels in the samples with amplification and the samples without amplification . The DS RF is a powerful classifier utilized to handle two issues of variable selection, and it has become a standard analysis tool in bioinformatics . Thus, iAs reported, during the validation analysis, the ratio of 6 to 4 is a commonly used distribution proportion . Thus, tAfter robust microarray analysis, 11,586 genes were identified for differential expression analysis. According to |log2FC| >1 and FDR < 0.05, 19 genes were found to be differentially expressed between AS and control samples, as listed in With the goal of assessing the classification ability of this approach, we used 10-fold cross-validation to calculate the AUC values for pathway pairs using the RF model. Each pathway pair was then ranked based on its corresponding AUC value. Of note, there were 35 pairs of pathways with AUC not less than 0.800. It is known through the literature that AUC >0.7 is determined as good, and an AUC of 1.0 suggests a perfect classification . GreaterAfter this, the top 10 pairs of pathways were extracted based on the occurrence frequency in the 50 bootstraps ≥3. According to this procedure, the pair ‘SAPK/JNK signaling’ and ‘mitochondrial dysfunction’ were involved in 5 bootstraps, the pair ‘mitochondrial dysfunction’ and ‘G beta gamma signaling’ appeared in 4 runs, and the pair ‘mitochondrial dysfunction’ and ‘G protein signaling mediated by Tubby’ also appeared in 4 runs. Importantly, among these top 10 pathway pairs, the ‘mitochondrial dysfunction’ interacted with 6 different pathways. Specific information is shown in AS, as a common rheumatic disorder, leads to inflammatory back pain, thereby reducing the quality of life . The potThe pathway pair ‘antigen presentation pathway’ and ‘fMLP signaling in neutrophils’ got the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from the normal subjects. As reported, exogenous antigens are presented by major histocompatibility complex (MHC) class I molecules . Signifiα, as a key pro-inflammatory cytokine, is responsible for the inflammation-related bone loss, and TNF-α can suppress BMP-mediated osteoblastogenesis through activating the SAPK/JNK pathway  is a common feature of AS . Further pathway . Further pathway . Further pathway . As docu pathway . Additio pathway . Therefoin silico analysis without validation in animal models or patient tissues. Thus, these pathway pairs should be further investigated using western blotting or PCR-based experiments to reveal the pathway changes in AS.Nevertheless, several study limitations must be noted. First, the sample size was rather small. Second, this was a preliminary study of mechanisms underlying AS and results were achieved based on In conclusion, our analysis provided new knowledge for AS and identified several bio-signatures for this disease. Based on our results, the detected pathway cross-talks might be helpful to identify patients with AS for early intervention. However, these paired pathways call for future functional studies.pdf].Click here to view [


Objectives. We aimed to find the key pathways associated with the development of osteoporosis. Methods. We downloaded expression profile data of GSE35959 and analyzed the differentially expressed genes (DEGs) in 3 comparison groups . KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were carried out. Besides, Venn diagram analysis and gene functional interaction (FI) network analysis were performed. Results. Totally 520 DEGs, 966 DEGs, and 709 DEGs were obtained in old_op versus middle, old_op versus old, and old_op versus senescent groups, respectively. Lysosome pathway was the significantly enriched pathways enriched by intersection genes. The pathways enriched by subnetwork modules suggested that mitotic metaphase and anaphase and signaling by Rho GTPases in module 1 had more proteins from module. Conclusions. Lysosome pathway, mitotic metaphase and anaphase, and signaling by Rho GTPases may be involved in the development of osteoporosis. Furthermore, Rho GTPases may regulate the balance of bone resorption and bone formation via controlling osteoclast and osteoblast. These 3 pathways may be regarded as the treatment targets for osteoporosis. Primary osteoporosis is a polygenetic disease characterized by an imbalance of bone homeostasis including microarchitectural deteriorations and low bone mineral density . It is rκB ligand)/RANK (receptor activator of NF-κB)/OPG (osteoprotegerin) signaling system was essential for skeletal homeostasis, and disruption of it led to inhibition of bone resorption in vitro [β-catenin signaling, a central pathway of skeletal homeostasis, and LRP5 deficiency results in OPPG (osteoporosis-pseudoglioma), whereas SOST deficiency induces lifelong bone gain in mice and humans [ P2X7  receptor gene are related to osteoporosis [ CNR2 (Cannabinoid Receptor 2) gene may result in postmenopausal osteoporosis in Han Chinese women [ Mettl21c (methyltransferase-like 21C) may play bone-muscle pleiotropic roles through the regulation of the NF-κB signaling pathway [It has been reported that strontium results in increased bone formation and decreased bone resorption by the modulation of several pathways including CaSR, ERK1/2-MAPK, and NFATc/Wnt signaling pathways . One stuin vitro . The bond humans . Azuma ed humans . Pineda d humans . Furtheroporosis . Two sinse women . Mettl21 pathway . HoweverBone marrow mesenchymal cells (BMSCs) are the major source of osteoprogenitor cells resulting in remodeling of bone in adults . The forhttps://www.ncbi.nlm.nih.gov/geo/) database [The expression profile data of GSE35959 deposited by Benisch et al. was downloaded from the GEO . Background correction, normalization between arrays, and calculated expression were included in the process of preprocessing. The probe ID was transformed into gene symbol combined with annotation files of the platform.The raw data were preprocessed by R package affy (version: 3.24.15)  in Biocop value for DEGs in primary osteoporosis elderly MSC versus middle aged MSC (old_op versus middle), primary osteoporosis elderly MSC versus old elderly MSC (old_op versus old), and primary osteoporosis elderly MSC versus senescent MSC (old_op versus senescent) were analyzed with t-test in limma (version: 3.24.15) [p value was adjusted as FDR  values by BH (Benjamini-Hochberg) [Significant 3.24.15) . The p vochberg) . FDR < 0p values for enriched DEGs were calculated by hypergeometric distribution, and p < 0.05 was set as significantly enriched pathway.KEGG is a database used for putting associated genes into the corresponding pathways . R packahttp://www.irp.nia.nih.gov/bioinformatics/vennplex.html) [VennPlex (ex.html)  can be uex.html)  can findDEGs and log2FC of 3 comparison groups were input into VennPlex, and the similarities and differences in 3 comparison groups were observed. Furthermore, KEGG pathways enriched by intersection DEGs in 3 groups were obtained. The correlations among intersection DEGs were analyzed by Cytoscape (version: 3.2.1)  app-GeneThe ReactomeFIViz app  can consGene functional interaction networks were analyzed with Cytoscape app-ReactomeFIViz. The input dataset was expression matrix of all DEGs, and pathway enrichment analysis for every function module was performed to find biological pathway involved by every module genes (FDR < 0.05). Other ReactomeFI parameters were set as defaults.A total of 520 DEGs, 966 DEGs, and 709 DEGs were obtained in old_op versus middle, old_op versus old, and old_op versus senescent groups, respectively .The significantly enriched KEGG pathways for 3 comparison groups were shown in Venn diagram analysis for DEGs was shown in The functional interaction network for DEGs obtained by ReactomeFI was shown in In the current study, with the expression profile data of GSE35959, totally 520 DEGs, 966 DEGs, and 709 DEGs were obtained in old_op versus middle, old_op versus old, and old_op versus senescent groups, respectively. Lysosome pathway was the significantly enriched pathways enriched by intersection genes. Furthermore, the pathways enriched by subnetwork modules suggested that mitotic metaphase and anaphase and signaling by Rho GTPases in module 1 had more proteins from module.Lysosome pathway was the significantly enriched pathways enriched by intersection genes in our present study. RANKL/RANK/OPG signaling system was essential for skeletal homeostasis, and one study showed that RANKL was found to be localized to secretory lysosomes in osteoblastic cells . Yoneshi Runx2 mRNA is dependent on mitosis and the translation of it after mitosis in early osteoprogenitors to control the gene expression required for reinforcement of cell fate decisions in committed preosteoblasts [Furthermore, mitotic metaphase and anaphase in module 1 had more proteins from module in this study. The transcription ofeoblasts . One stueoblasts . Thus, m Wrch1 (Ras Homolog Family Member U) regulated precursor adhesion and migration of osteoclast [β-catenin in osteoblasts under flow [In addition, our study also showed that signaling by Rho GTPases in module 1 had more proteins from module. Rho GTPases may control podosome arrangements in osteoclasts . Brazierteoclast . Touaitateoclast . Besidesder flow . Thus, RHowever, there are several limitations in this study. First, only 19 samples including 5 middle aged MSCs, 4 old elderly MSCs, 5 primary osteoporosis elderly MSCs, and 5 senescent MSCs were included in this study. Second, in Results, the interaction network among intersection genes includes 79 intersection genes and 367 interaction pairs, but there is no significant difference among the weight of these intersection genes. Third, our study is concluded from the bioinformatics analysis of the expression profile data downloaded from the GEO database, and further experiments are needed to verify our results.In conclusion, lysosome pathway, mitotic metaphase and anaphase, and signaling by Rho GTPases may be involved in the development of osteoporosis. Furthermore, Rho GTPases may regulate the balance of bone resorption and bone formation via controlling osteoclast and osteoblast. Lysosome pathway, mitotic metaphase and anaphase, and signaling by Rho GTPases may be regarded as the treatment targets for osteoporosis. However, further studies with large samples and verification experiments are needed.


MYCN. Here, we performed cross-platform biomarker detection by comparing gene expression and pathway activation patterns from the two literature reports and from our experimental dataset, combining profiles for the 761 neuroblastoma patients with known MYCN amplification status. We identified 109 / 25 gene expression / pathway activation biomarkers strongly linked with the MYCN amplification. The marker genes/pathways are involved in the processes of purine nucleotide biosynthesis, ATP-binding, tetrahydrofolate metabolism, building mitochondrial matrix, biosynthesis of amino acids, tRNA aminoacylation and NADP-linked oxidation-reduction processes, as well as in the tyrosine phosphatase activity, p53 signaling, cell cycle progression and the G1/S and G2/M checkpoints. To connect molecular functions of the genes involved in MYCN-amplified phenotype, we built a new molecular pathway using known intracellular protein interaction networks. The activation of this pathway was highly selective in discriminating MYCN-amplified neuroblastomas in all three datasets. Our data also suggest that the phosphoinositide 3-kinase (PI3K) inhibitors may provide new opportunities for the treatment of the MYCN-amplified neuroblastoma subtype.Neuroblastoma is a pediatric cancer arising from sympathetic nervous system. Remarkable heterogeneity in outcomes is one of its widely known features. One of the traits strongly associated with the unfavorable subtype is the amplification of oncogene  MYCN-amplification are used to assess the risk group and prognosis. In many lower-stage or low-risk cases, only surgery is deemed sufficient. However, in high-risk cases intense chemo- and radio therapies followed by surgery are used [Neuroblastoma arises from sympathetic nervous system embryonal crest cells localized in sympathetic ganglia and adrenal medulla. Approximately 60% of the cases occur in infants less than 2 years old. Neuroblastoma constitutes ~ 6% of all pediatric cancers , and cauare used , stronglare used . MYCN-amare used . Hence, MYCN amplification arises in 20% of all neuroblastoma cases [MYCN gene is a member of the MYC family of proto-oncogenic transcription factors. It encodes a protein with a basic helix-loop-helix (bHLH) domain. To execute its molecular function, N-Myc protein must dimerize with another bHLH protein in order to bind DNA. Normally, N-Myc protein is expressed in the fetal brain and regulates its development [MYCN amplification and/or overexpression have been reported previously, including building molecular networks for few involved proteins [MYCN-amplified neuroblastoma patients with respect to poor or optimistic prognosis [MYCN amplification in neuroblastoma.The ma cases  and in ~ma cases . The MYCelopment . The attproteins , 16. In rognosis . HoweverMYCN amplification. We found 109 high quality (AUC>0.8) gene expression and 25 molecular pathway activation biomarkers of the MYCN amplification. We generated and validated a new molecular pathway crosslinking the identified gene expression biomarkers, termed “MYCN amplification pathway” including 41 marker gene products and 23 additional members. Among the marker pathways, the phosphatidylinositol 3’ kinase (PI3K) family members were highly enriched. We propose that in the future the selective inhibitors of PI3K can be used to supplement the current therapies for the MYCN-amplified neuroblastomas.In this report, a multi-level multi-platform transcriptomic data analysis was performed in order to capture a set of molecular characteristics associated with MYCN amplification in the three different studies, using the three alternative microarray platforms. For the first two studies . We took the pathway activation strength (PAS) as the MYCN pathway biomarker calculated according to the OncoFinder method [MYCN pathway and of all other biomarkers was tested by calculating the “area-under-curve” (AUC) values [Here, we performed a multi-level multi-platform transcriptomic data analysis in order to capture a set of molecular characteristics associated with the MYCN amplification. We compared the mRNA expression data obtained for the neuroblastoma samples with known status of  n = 243  and MAQC n = 243 ), the exr method . The qua) values . The AUC) values .MYCN amplification, for four additional samples the amplification status was unknown [MYCN amplification status. We used two-level analysis at the gene expression and the pathway activation levels. Molecular pathways regulate all major biological processes in the cell [We extracted gene expression data obtained during the TARGET research project for the 247 neuroblastoma tissue samples, among them 68 had and 175 did not have  unknown . The datthe cell –25. Chanthe cell , 27. To the cell . To calcthe cell  because the cell .MYCN –amplified biosamples compared to the clustering based the total gene expression levels  levels  enrichment analysis showed that the double marker genes formed six completely statistically significant functional clusters and many distinct significantly enriched GO terms , featuriMYCN pathway” including 41 double marker gene products and 23 intermediary members _via_STAT3 and MAPK_Signaling_Pathway_Gene_Expression_Apoptosis_Inflammation_Tumorigenesis_via_MYC_HSF1_STAT2. The first pathway deals with the IL10 influence on C-MYC expression, whereas the second, in turn, represents C-MYC influence on the expression of its responsive genes. The other marker pathways were linked with the activities of ATM kinase (3 pathways) and BRCA1-dependent DNA repair (2 pathways), with apoptosis and p53 signaling (4 pathways), PPAR signaling (1 pathway), chemokine and GPCR signaling (2 pathways), branches of NGF pathway regulating cytoskeleton (2 pathways), one Ras signaling pathway regulating intercellular interactions, four cell survival pathways via cAMP, Estrogen, IL2 and Wnt signaling, and four cell growth-promoting pathways via cAMP, IP3 and growth hormone signaling  the normal adrenal, (ii) normal neural crest tissue samples, and (iii) with the mean gene expression levels calculated for all the neuroblastoma samples. Similar to the TARGET and MAQC II datasets, the MYCN-amplified biosamples clustered together on the dendrograms for both the healthy adrenal  according to the OncoFinder method [MYCN amplification. For the (i-iii) normalizations, theMYCN amplification pathway showed a good performance with the AUC scores of 0.778, 0.767 and 0.711, respectively , the expression features of the MYCN amplification pathway nodes were usually regulated in the same direction in both the MYCN-amplified and wild-type neuroblastomas , Figure MYCN amplification in neuroblastoma is linked with the up- or down regulation of many genes. The extent of their transcription levels can distinguish between the MYCN amplified and non-amplified neuroblastomas  identified for these two datasets, and identified double biomarkers at the gene expression (n=109) and the pathway activation (n=25) levels. For the gene expression markers, we performed a search of the enriched Gene Ontology (GO) terms and found statistically significant enrichment in the processes of purine nucleotide biosynthesis, ATP-binding, tetrahydrofolate metabolism, building mitochondrial matrix, biosynthesis of amino acids, tRNA aminoacylation for protein translation, and NADP-linked oxidation-reduction processes. To combine the identified marker gene products to a single regulatory network, we created a new molecular pathway termed “MYCN amplification pathway” including 41/109 double marker gene products and 23 intermediary members. We validated this pathway on the additional sampling of 5 MYCN-amplified and 36 non-amplified neuroblastoma samples, experimentally profiled here using an alternative microarray platform  and BRCA1-dependent DNA repair (2 pathways), with apoptosis and p53 signaling (4 pathways), PPAR signaling (1 pathway), chemokine and GPCR signaling (2 pathways), branches of NGF pathway regulating cytoskeleton (2 pathways), one Ras signaling pathway regulating intercellular interactions, four cell survival pathways via cAMP, estrogen, IL2 and Wnt signaling, and four cell growth-promoting pathways via cAMP, IP3–dependent signal transduction mechanisms [Many of those pathways were previously published in relation with the severity of neuroblastoma, such as AKT signaling , cAMP- achanisms , 33, andchanisms , growth chanisms , MAPK [3chanisms , PPAR [3chanisms , NGF [38chanisms  signalinMYCN-amplified tumors, we found that some genes were highly enriched. For example, twelve phosphoinositide 3-kinase (PI3K) family members simultaneously occurred in 9/25 (36%) of the marker pathways. The TP53 gene occurred in the seven featured pathways, and four MAPK family members, ATM and AKT1 genes each took part in five marker pathways. This may suggest that the activity of phosphoinositide 3-kinases may be central in translating the molecular effects of MYCN amplifications to cell cycle progression. Indeed, the PI3K proteins promote cell growth and survival by activating Akt signaling. In turn, Akt activation has been previously reported a strong prognostic indicator of decreased survival in neuroblastoma [MYCN amplification, advanced stage and unfavorable histological features. The Akt activation in neuroblastoma proceeds in a PI3K-dependent manner, because the PI3K inhibitor LY294002 completely reversed the effects of the insulin-like growth factor–mediated activation of Akt and its protection of neuroblastoma cells from the apoptosis [MYCN-amplified neuroblastomas. These data shed light on a variety of molecular processes orchestrated by the amplification of MYCN gene and may help developing better anti-cancer molecular therapies in the future.Among the members of the twenty-five double marker pathways specifically regulated in the blastoma . In addipoptosis . AnotherFor this study, we used forty-one experimental formalin-fixed, paraffin-embedded (FFPE) neuroblastoma tissue samples obtained from 41 patients treated at the D. Rogachev Center of Pediatric Hematology, Oncology and Immunology (CPHOI), Moscow. 7 tissue samples for adrenal non-cancer controls were collected at the Department of Pathology at the Faculty of Medicine, Moscow State University, from autopsies taken from 3 independent adult healthy donors killed in road accidents. The 4 embryonal normal neural crest biosamples were taken from the post-mortal prenatal unrelated human donors. For all the biosamples, informed written consents to participate in this study were collected from the patient's representatives. The consent procedure and the design of the study were approved by the ethical committees of the CPHOI, of the First Oncology and Advisory Center, Moscow, and of the Engelhardt Institute of Molecular Biology. Both the tumors and normal tissues were evaluated by a pathologist to confirm the diagnosis and estimate the tumor cell numbers.B3 microarray synthesizer  was used for forty nucleotides-long oligonucleotide probe synthesis on CustomArray ECD 4×2K/12K slides. Synthesis was performed according to the manufacturer's recommendations. Three replicates of total 6020 unique oligonucleotide probes specific to 3706 human gene transcripts were placed on each chip. Chip design was performed using Layout Designer software . For the custom microchip, we used original oligonucleotide probe sequences of the Illumina HT 12 v4 platform.Complete Whole Transcriptome Amplification WTA2 Kit (Sigma) was used for reverse transcription and library amplification. Manufacturers protocol was modified by adding to amplification reaction dNTP mix containing biotinylated dUTP, resulting to final proportion dTTP/biotin-dUTP as 5/1. Microarray hybridization was performed according to the CustomArray ElectraSense™ Hybridization and Detection protocol. Hybridization mix contained 2.5 ug of labeled DNA library, 6X SSPE, 0.05% Tween-20, 20mM EDTA, 5x Denhardt solution, 100 ng/ul sonicated calf thymus gDNA, 0,05% SDS. Hybridization mix was incubated with chip overnight at 50°C. Hybridization efficiency was detected electrochemically using CustomArray ElectraSense™ Detection Kit and ElectraSense™ 4×2K/12K Reader.Probe signals were geometrically averaged, thus obtaining expression value for each specific type of the probe. Then quantile normalization  was perfOpen access microarray data containing gene expression profiles for the high-risk neuroblastoma samples, and the available clinical information, were obtained from the TARGET  project website. The data on mutation rate and chromosomal abnormalities for 48 microarray-profiled TARGET patients were obtained from .http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-179/protocols/), and we performed no pre-processing except for omitting probes with the negative expression values to counter the effect of inaccurate measurements of strongly underexpressed genes.The MAQCII (ArrayExpress) gene expression data were extracted from the dataset tagged with the ID ‘E-MTAB-179’. The data preparation and quantile normalization were made by the authors according to their protocol , a simulation was performed, randomly selecting gene / pathway subsets and intersecting them. The histograms for the resulting distributions for genes and pathways were drawn using ‘ggplot2’ package . The resTo perform the Gene Ontology (GO) database investigations for the gene sets associated with the discovered biomarkers, we performed enrichment analysis using DAVID functional annotation clustering software . We usedhttps://cgap.nci.nih.gov/Pathways/BioCarta_Pathways, http://www.genome.jp/kegg/pathway.html, http://www.ndexbio.org/#/user/301a91c6-a37b-11e4-bda0-000c29202374, https://www.qiagen.com/at/shop/genes-and-pathways/pathway-central/?akamai-feo=off&f=po%3a2, http://www.reactome.org/pages/download-data/). We first used Dijkstra algorithm to detect shortest pairwise distances between the gene products on the graph constructed by merging all the databases, and then cut loosely-connected branches.A MYCN amplification pathway was reconstructed using the established pairwise molecular interactions signatures extracted from the Biocarta, KEGG, NCI, Quiagen and Reactome databases (


Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC) based on the functional dependency among pathways. Protein-protein interaction (PPI) information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA) method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN), where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC. Colorectal cancer (CRC) affects millions of people in the world, and is the second most common cause of cancer-induced deaths in males and the third in females . It is cRecently, high throughput microarray technology exerted significant advances in the understanding of pathological mechanism of various diseases. In recent years, studies have created many microarray data associated with CRC; for example, the GSE4183 pathway, published by Galamb and colleagues , who evaAs reported, signaling pathways rather than individual genes govern the process of tumorigenesis and progression . With thMicroarray data. Gene expression profile for CRC with the accession NO. GSE4183 were retrieved from GEO database (https://www.ncbi.nlm.nih.gov/geo/) (ov/geo/) . This dagmn stands for the expression level of gene m in sample n, ave(gm) and std(gm) are the average and standard deviation (SD) of the expression value of gene m in all samples, respectively.In this equation, PPI network and pathway data. All PPI information, about 16,730 proteins and 787,896 interactions, was retrieved from the STRING database (http://string-db.org) (-db.org) . STRING http://www.reactome.org), which is an online curated resource for human pathway data  were extracted from Reactome database  (k in sample n was determined by the following formula:is (PCA)  was emplwmnk is the weight for zmnk, and zmnk and denotes the standardized expression level of gene m from pathway k in sample n. The first principal component obtained from PCA was defined as the activity score for the appropriate pathway. Thus, the pathways with different activity scores in disease and normal samples were possibly connected with disease progression. Thus, if the activity score for a defined pathway is different between the CRC and control samples, it indicates the relationship of CRC with this pathway. The greater this difference, the more relevant the pathway is to CRC progression. In the current study, the pathway with the maximum change in activity score between disease and control groups was defined as the seed pathway.in which, Construction of PIN. In order to determine whether a gene was expressed in a differential way, Student's t-test was employed to compare gene expression between the two conditions. We considered gene expressions significantly different between disease and control conditions if the P value was less than 0.05. Moreover, we calculated the Pearson correlation coefficient (PCC) for all PPI interactions between the two conditions, thereby having a distribution of the PCC. We also computed the difference in absolute values of the PCC for the PPI interactions in CRC and normal groups.Based on microarray profile, pathway information and PPI data, we then established a PIN, where each node denoted a pathway, and an edge was connected between pathways if they met one of the two criteria. Otherwise, the edges would be abandoned. One criterion was that two pathways shared one or more common genes, and one or more of these genes between pathways were differentially expressed in CRC and control groups. The other criterion was that two genes that coded interacting proteins employed to connect an edge between pathways were highly co-expressed . Otherwise, the edges would be discarded. If a network is too large, a certain number of significant genes and interactions can be neglected . Thus, wIdentifying dysregulated pathways from the targeted PIN. After computing the activity score for every pathway, we extracted the dysregulated pathways using a machine-learning framework, in which the minimum pathways that could best distinguish between diseases and controls were regarded to be potentially dysregulated pathways. For this selection process, support vector machines (SVMs) were utilized. An individual pathway, which best distinguished between disease and control, was first selected as seed pathway. Then, other pathways that had crosstalks with the seed pathway were combined with the seed pathway to obtain better classification accuracy. New pathways were selected when no pathways could be combined for better classification results. The ultimate extracted pathway set was kept as possibly dysregulated pathways of disease. We adopted the area under the curve (AUC) score as a measure of classification performance, and used five-fold cross validation to test the performance ability. In the cross validation, four of the five samples were employed as training set and the other was used as test set to assess the classification ability. With the goal of getting robust results, five-fold cross-validation was repeated 100 times, and then the mean level was used as the eventual result.With the P values set at 0.05, we obtained 6,201 differentially expressed genes (DEGs) between CRC and control groups. The top 20 DEGs are shown in In this analysis, with the goal of evaluating the significance of pathways, the activity score for the 1189 pathways was calculated using the PCA method. The pathway with the maximum change of activity score between CRC and normal samples was defined as seed pathway. Of note, we found that the DNA replication pathway  showed the maximum change in the activity score between CRC and normal groups, and was selected as the seed pathway. Beginning with this seed pathway, the selection of dysregulated pathways was implemented according to the classification accuracy. This procedure stopped when classification accuracy did not increase. In our study, with "DNA replication" as initial, one pathway set containing 30 dysregulated pathways was obtained with AUC score of 0.8598, which indicated that these selected dysregulated pathways can be utilized as robust bio-signatures. The specific data is shown in In order to better present the relationship among these 30 identified dysregulated pathways in the PIN, we further explored their relationship and found that these pathways were assembled into a PIN network based on interactions. Currently, pathway analysis has become the first choice to elaborate the biological functions of genes, since it increases explanatory power . TraditiCRC is the third main cause of cancer-related-deaths in developed countries. Thus, it is understandable that major efforts have been done for dissecting the potential mechanisms underlying this disease. In our study, in order to expound the molecular mechanisms of CRC, we extracted the dysregulated pathways able to distinguish CRC from normal control samples, based on the constructed PIN that presented the functional dependency among pathways. We observed 1 pathway set with AUC of 0.8598 between CRC and normal samples, which demonstrated a good performance and the ability of this method to select dysregulated pathways in CRC. Of note, in this pathway set, there were 30 dysregulated pathways, such as mRNA splicing, mRNA splicing-major, RNA polymerase I, and DNA replication pathways, that owned a high number of pathway genes, and these pathways had crosstalks with each other. These results suggest that these dysregulated pathways might be useful as biomarkers to diagnose CRC.In our study, maximum change of the activity score between CRC and normal groups occurred in the pathway of DNA replication, which was selected as the seed pathway. In the eukaryotic cell, DNA is replicated only once every cell cycle. The regulation of DNA replication is very important since loss of DNA replication control could threaten genome stability , a featuRNA polymerase I pathway is denoted uniquely to transcribe the copies of genes coding the pre-rRNA precursor, which is processed into 5.8S, 18S, and 28S rRNAs. Williamson et al.  reportedIn conclusion, this integration-based analysis has several advantages. Unlike previous publications, we focused on the functional dependency between pathways by constructing a PIN, thereby indicating the robustness of our extracted pathway bio-signatures. Our findings suggest that dysregulated pathways, especially DNA replication and mRNA splicing, are important in CRC initiation, development and progression. However, several limitations must be taken into consideration. First, our sample size was very small. Second, the current study was analyzed based on existing data through bioinformatics methods, and the findings lacked experimental verifications. Thus, further investigations are warranted to verify the alterations of these pathways in animal experiments or patient tissues. Lastly, comparison between this novel method and other methods on multiple cancer datasets was not implemented to further demonstrate the effectiveness of our method. Thus, we expect our research to provoke further investigation into the potential roles of these dysregulated pathways in CRC. Despite these limitations, our results provided some preliminary evidence to uncover alterative candidate therapeutic strategies for CRC.