Xiao Ke Yin Shui (XKYS) formula is a traditional Chinese medicine formula treating type 2 diabetes mellitus (T2DM). XKYS formula consists of four herbs, i.e., Coptidis rhizoma, Liriopes radix, bitter melon, and Cassiae semen. Herein, the chemical profiles of four herb extracts were investigated, and further analysis of the underlying mechanism of XKYS formula treating T2DM was performed using network pharmacology. The main components were selected for our network-based research. Targets of XKYS formula were mainly collected from two databases, SwissTargetPrediction and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the text-mining method was also implemented. T2DM relating genes and therapeutic targets were collected from five databases. Subsequently, STRING and Cytoscape were employed for the analysis of protein-protein interaction (PPI) networks. Functional annotation and pathway analysis were conducted to investigate the functions and relating pathways of target genes. The content of 12 compounds in the herb extracts was determined. With the analysis of PPI networks, a total of 76 genes were found to be important nodes and could be defined as the main target genes regulated by XKYS formula in the treatment of T2DM and its complications. Components in XKYS formula mainly regulate proteins including protein kinase B (Akt), phosphatidylinositol 3-kinase (PI3K), insulin receptor substrate (IRS), and tumor necrosis factor (TNF). XKYS formula exerts therapeutic effects in a synergetic manner and exhibits antidiabetic effect mainly via reducing insulin resistance. These findings could be guidelines in the further investigation of this formula. Diabetes mellitus (DM) is now recognized as a complex metabolic disorder, and type 2 diabetes mellitus (T2DM) is the most common type of diabetes . NowadayXiao Ke Yin Shui (XKYS) formula, is recorded in Bencaogangmu (compendium of Materia Medica). XKYS formula contains four herbs, namely, Coptidis rhizoma (dried rhizomes of Coptis chinensis Franch.), Liriopes radix (the dried tuberous roots of Liriope spicata (Thunb.) Lour. var. prolifera Y. T. Ma), bitter melon (the immature fresh fruits of Momordica charantia L.), and Cassiae semen (the dried seeds of Cassia obtusifolia L.). All these herbs are widely used in clinical practice treating diabetes [Traditional Chinese medicine (TCM) is a rich resource, possessing data that can still be hints for the development of new drugs. The focus of the current study, diabetes .Several ingredients in these four herbs are generally accepted as the main bioactive components in the treatment of T2DM and its complications, i.e., alkaloids in Coptidis rhizoma, polysaccharides in Liriopes radix, triterpenoids and polysaccharides in bitter melon, and anthraquinone and naphthopyrone in Cassiae semen –11. Therγ) [γ [In XKYS formula, a single component may be responsible for the therapeutic effect through different pathways, and some components may act on the same targets as well. For example, isoquinoline alkaloids from Coptidis rhizoma like berberine are generally recognized as activators of adenosine 5′-monophosphate- (AMP-) activated protein kinase (AMPK) but could also exert therapeutic effects on T2DM through inhibition of protein tyrosine phosphatase 1B (PTP1B) and peroxisome proliferator-activated receptor gamma (PPARγ) –14. Polyγ) , 16. Triγ) [γ , 18. TheNowadays, researchers have been aware that the “one key, one lock” mode is insufficient to decipher the drug actions, especially in those complex diseases. Network pharmacology, however, analyzing drugs and drug targets in a systemic manner may provide us with novel insights into drug actions . The keyIn brief, this study provided chemical profiles for the extracts of four herbs. And, network pharmacology analysis was applied to understand the underlying mechanisms of XKYS formula in the treatment of T2DM and its complications.Coptidis rhizoma, Liriopes radix, fresh bitter melon, and Cassiae semen were authenticated by Professor Jiachun Chen . Voucher specimens of these herbs were deposited in Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, HUST.The four herb extracts were total alkaloids of Coptidis rhizoma (TACR), Liriopes radix polysaccharides (LRP), bitter melon extract (BME), and Cassiae semen extract (CSE), respectively. Detailed preparation methods for the extracts were reported in Supplementary Materials (Part 1).3CN) was purchased from Sigma Aldrich (USA). Water was deionized water. Hydrochloric acid, methanol, phosphate buffer, anthrone, sulfuric acid, fructose, glucose, and formic acid (HCOOH) were purchased from Sinopharm Chemical Reagent Co., Ltd. .Acetonitrile (CHμm) columns. HPLC analysis of LRP was performed on the Hitachi L-2130 system with an Agilent TC-C18  column. Content of total carbohydrate was determined on an ultraviolet-visible spectrophotometer, UV-1750 .HPLC analysis of TACR, BME, and CSE was performed on the Agilent 1260 system with Agilent TC-C18 . Momordicoside L, 7β,25-dihydrocucurbita-5,23(E)-dien-19-al-3-O-β-D-allopyranoside, and momordicoside F2 were self-prepared . The flow rate was 1.0 ml/min. The injection volume was 20 μl. Column temperature was held constant at 25°C. The UV detection wavelength was 250 nm.Monosaccharide composition analysis of LRP was performed using the HPLC method after derivation with 1-pheny-3-methyl-5-pyrazolone (PMP), and the detailed method was reported in Supplementary Materials (Part 3) . The mobThe total carbohydrate content of BME was examined using anthrone-sulfuric acid method according to the previous report .3CN (A) and water (B). The elution program was 0 min 20%A, 10 min 30%A, 30 min 50%A, and 50 min 70%A. The flow rate was 1.0 ml/min. Column temperature was held constant at 25°C. The detector was evaporative light scattering detector (ELSD), with the evaporator temperature at 60°C and nebulizer temperature at 40°C. The flow rate of nitrogen was 1.6 l/min. The standard  and sample (20 μl) dissolved in 70% methanol were subjected to HPLC analysis.HPLC analysis of BME was conducted with the mobile phase consisting of CHHPLC analysis of CSE was carried out according to the previous report .http://www.uniprot.org) for validation of their gene names. Hereby, a target gene list of XKYS was obtained through in silico investigation.To identify the corresponding targets of the main components of XKYS formula, several approaches combined with chemometric method, information integration, and data-mining were implemented. These components, except polysaccharides, were submitted to TCMSP and SwissTargetPrediction server to find component-target interaction information. TCMSP mainly predicts drug-target interactions with random forest (RF) and support vector machine (SVM) method, while SwissTargetPrediction is a 2D/3D similarity measurement of small molecules , 27. Allhttp://www.drugbank.ca/), Online Mendelian Inheritance in Man , Kyoto Encyclopedia of Genes and Genomes , Therapeutic Target Database , and Text-mined Hypertension, Obesity, and Diabetes Candidate Genes database . And all genes and targets were submitted to the UniProt database for validation of their gene names.Another gene list relating to T2DM was established after screening of five databases, including DrugBank database  was used to analyze the GO function and KEGG pathway of the main target genes of XKYS formula in the treatment of T2DM and its complications. The biological process, cellular component, molecular function, and the pathway involved were also described.The network model of component-target-pathway was established using Cytoscape 3.2.1. In this network, nodes represent components (C), targets (T), or pathways (P), and edges represent the interaction of C-T or T-P. Based on the results of KEGG enrichment analysis and C-T database, the C-T-P interactions were shown to provide an overview on the mechanisms of XKYS formula in the treatment of T2DM and its complications.Four isoquinoline alkaloids, three cucurbitane-type triterpenoids, two anthraquinones, and three naphthopyrones were determined using HPLC. Results of the HPLC analysis are shown in The screening of 19 components  in XKYS The new network consists of 76 genes, representing the main target genes regulated by XKYS formula in the treatment of T2DM. In addition, among these genes, some genes like AKT1, mitogen-activated protein kinase 1 (MAPK1), tumor necrosis factor (TNF) were also found to be important genes involved in the progression of diabetic complications –31. ThusThe new PPI network contains 76 nodes and 333 edges . In thisAs can be seen in P < 0.01) were selected based on −log P values , leading to the reduction of IR and amelioration of lipid metabolism [β, which was distinct from the mechanism of berberine [According to C-T-P network, alkaloids from Coptidis rhizoma are the main components in XKYS formula that exert antidiabetic effects. Results also indicated that around 64% of the content in TACR could be determined using the HPLC method. Among these isoquinoline alkaloids determined, berberine accounted for ∼35% of the fraction and was also proved to be a key node in the C-T-P network. Berberine has a wide range of biological activities, among which anti-hyperglycemic and anti-hyperlipidemia are two major benefits in the treatment of T2DM . Berberitabolism , 32. Momerberine . This coLRP are highly enriched according to its total content of carbohydrate. Our previous studies had found that LRP could upregulate the insulin signaling pathway and exert therapeutic effects in diabetic rodents through activation of PI3K/Akt signaling pathway , 23. AurκB). These pathways play important roles in the regulation of protein synthesis, cell survival, differentiation, proliferation, and apoptosis and are highly related to the proliferation and regeneration of islet β-cells [As can be seen in the C-T-P network, AKT1, PI3KCG, PI3KR1, and TNF were key genes regulated by XKYS formula in the treatment of T2DM and its complications. Proteins expressed by genes AKT1, PI3KCG, and PI3KR1 were all important members in the signaling transduction of PI3K/Akt. This signaling pathway is of great significance in the progression of T2DM due to its role in glucose metabolism . The PI3 β-cells .κB signaling pathways are stimulated by TNF and further regulates inflammatory response, oxidative stress, and apoptosis [Tumor necrosis factor (TNF) is a cytokine that could be secreted by macrophages and adipose cells. It can induce IR by downregulation of the activity of PI3K/Akt signaling pathway. It has also been reported that MAPK and NF-poptosis .P value. Other pathways on the KEGG list were adipocytokine pathway, AMPK pathway, insulin pathway, T2DM, FoxO pathway, NAFLD, etc. And, this finding was further approved by the C-T-P network. All of these signaling pathways are all highly related to IR. Thus, we could make a preliminary inference from this result that the most important mechanism of XKYS formula in the treatment of T2DM may be reducing IR.As shown in KEGG pathway analysis, 23 out of 76 targets were found to participate in IR, ranking no. 1 according to its −log β-cells and diabetic microangiopathy and then leading to the onset of T2DM and its complications [β-cells. The dysfunction of β-cells is caused by various factors, including oxidative stress and inflammatory response. The FoxO pathway is highly related to the risk factors mentioned above [IR is a condition in which the target organs are insensitive to insulin. Long-term of unhealthy diet and lacking exercises could cause overweight or even obesity. Overweight or obese patients are in metabolic disorder states with excessive fat accumulation . Some exications . Moreoveications . The Foxed above .In addition, the pathway in cancer was also an important pathway enriched in the C-T-P network . For oneIt should also be noted that the PI3K/Akt signaling pathway is also regarded as a key regulator in the development of diabetic complications . And, amThe network-based pharmacological analysis has shown that XKYS formula could regulate the glucose and lipid metabolism and alleviate IR mainly through insulin, AMPK, adipocytokine, and FoxO pathway. And, this formula could also be used in the treatment of diabetic complications due to its effect on PI3K/Akt, HIF-1, and VEGF pathways.This study provided an overview on the antidiabetic effects of XKYS formula in a holistic manner. However, in vivo and in vitro experiments are required to offer more information about the mechanisms of XKYS formula.Components in XKYS formula mainly regulate proteins including Akt, PI3K, IRS, and TNF. XKYS formula exerts therapeutic effects in a synergetic manner and exhibits antidiabetic effect mainly via reducing IR. These findings could be guidelines in the further investigation of this formula.


We developed gene signatures that reflect the level of expression of phosphorylated-Serine473-AKT (pAKT) and phosphorylated-Serine2448-mTOR (p-mTOR) separately, capturing their corresponding level of pathway activation. Our analysis revealed that the pAKT pathway activation was associated with luminal A BC while the p-mTOR pathway activation was more associated with luminal B BC (Kruskal–Wallis test  One of the key signaling pathways involved in regulating cancer cell growth and survival is not linearly connected in luminal breast cancers, as researchers had long assumed. A team led by Amir Sonnenblick from Tel Aviv University, Israel, and Christos Sotiriou from Institut Jules Bordet, Belgium, studied tumors with elevated expression of two different proteins in the PI3K/AKT/mTOR pathway. They showed that tumors with activated AKT had distinct gene expression profiles from tumors with activated mTOR. What’s more, patients with AKT activation had better outcomes on average and were more likely to carry mutations in the oncogene PIK3CA, whereas patients with mTOR activation tended to relapse sooner and were more likely to carry mutations in the tumor suppressor p53. The findings suggest that doctors should look closely at PI3K/AKT/mTOR signaling activation when personalizing treatment decisions. AKT can also activate mTORC1 by PRAS40 phosphorylation, thereby relieving the PRAS40-mediated inhibition of mTORC1.9While AKT is activated by phospholipid binding and activation loop phosphorylation at Threonine308 by PDK1 and by phosphorylation within the carboxy terminus at Serine473, mTOR is phosphorylated at Serine2448 via the PI3K-signaling pathway.PIK3CA mutations were associated with relatively low mTORC1 functional output and with good outcomes in patients who received adjuvant tamoxifen monotherapy.4 Therefore, to gain better insight into the relative contribution of each of the signaling pathways which lie downstream to PI3K (namely AKT and mTOR) to BC outcomes, we have developed a novel in silico approach which assessed the activation of each of these signaling pathways separately, by integrating reverse phase protein array (RPPA) and matched gene expression.The PI3K/AKT/mTOR pathway is usually considered as a linear signal transduction pathway in BC, however in the ER-positive disease, we have previously shown that 10 This showed that the pAKT signature was significantly enriched in genes up-regulated in less aggressive invasive BC tumors . In contrast, the p-mTOR signature was enriched in genes expressed in mammary stem cells and more aggressive luminal B cancers12 . A network clustering analysis using the pAKT and p-mTOR signatures, as well as other RPPA-derived signatures that demonstrated a significant intersection with them, identified two main sub-networks according mainly to their proliferation status, namely pAKT-high/p-mTOR-low and pAKT-low/p-mTOR high characterized with low and high proliferation levels, respectively  and p-mTOR proteins , using ER-positive tumors from the TCGA repository. It is important to note that the two signatures did not share any common genes Fig. . We nextely Fig. . These oely Fig.  and S1. ely Fig.  and S2. Altogether, these results demonstrate that the pAKT and p-mTOR pathways, assessed through these RPPA-based gene expression signatures, have exclusive distribution according to luminal molecular subtypes and are not necessarily linearly connected.p < 10−10) whereas p-mTOR pathway activation was associated with luminal B cancers (p < 10−10) . Similar results were obtained with a dataset consisting of patients treated with endocrine therapy only . Indeed, patients with pAKT pathway activation had better outcomes irrespective of their specific subtype . In contrast, patients with p-mTOR pathway activation had significantly worse outcomes in all luminal patients  and this remained true when tested in the dataset consisting of patients treated with endocrine therapy only   Fig. . We nextPIK3CA and P53 mutation status and pAKT and p-mTOR pathway activation in an independent set, namely the TCGA BC patients with RNA-sequence gene expression data that were not used to design the signatures. While the pAKT signature was associated with PIK3CA mutations (p = 0.0001), the p-mTOR signature was not (p = 0.22) , and negatively correlated with pAKT (p = 0.0003)  Fig. . The opp03) Fig. . Analysilts Fig. , althoug13 Analysis of the correlations between the effectiveness of this treatment and the developed signatures suggests as expected that the pAKT signature is associated with less response to Everolimus , have distinctly different impacts on disease biology and consequently on outcomes in early disease.PIK3CA mutations whereas the opposite was observed with p-mTOR pathway activation. In contrast, pAKT pathway activation was associated with good clinical outcome despite its known tumorigenic effects. Between 30% and 40% of BCs, especially ER-positive tumors, have mutations in PIK3CA.14 The vast majority of the PIK3CA mutations are missense mutations which are positioned in the helical domain  and the kinase domain  in hotspot clusters.15 These mutations have direct effect on AKT phosphorylation. The effect of PIK3CA mutations/pAKT on prognosis is mixed in early BC.16 We found that exons 9 and 20 mutations in PIK3CA were more associated with pAKT than mutations in other exons.The goal of the present study was to better understand the distinct contribution to disease biology and clinical outcomes of signaling through the AKT and mTOR downstream pathways, which typically occur as part of PI3K pathway activation in luminal BCs. We found that pAKT and p-mTOR were differentially expressed according to luminal subtypes, implying different degrees of pathway activation, and that, more importantly, the pathways were not linearly connected. Additionally, we found that pAKT pathway activation was positively associated with PIK3CA mutations were associated with improved outcome and low levels of signaling through the mTOR pathway in BC.17 Several possible hypotheses were raised regarding the reasons for this. Some data available on PP2A and PML, both known to have an inhibitory effect on both AKT and mTOR,19 have suggested that they may be upregulated in PIK3CA-activated tumors. Negative feedback regulation in PI3K-mediated cells through the insulin receptor substrate20 and relatively weak pathway activation in PIK3CA-mutated cancers have also been suggested as possible explanations for low levels of signaling through mTOR in ER-positive BC.We previously reported that PIK3CA wildtype versus mutant, whereas p-mTOR pathway activation was not significant for both. The inconclusive and relative activation of p-mTOR by the mutant PIK3CA may be also attributed to the different roles and activators of mTOR and the fact that mTOR is at the cross section of multiple signaling pathways. Several studies have clearly demonstrated that mTOR is a direct substrate for the AKT kinase and identified Serine2448 as the AKT target site in mTOR.21 However, additional studies have demonstrated that rapamycin, an inhibitor of mTOR function, blocks serum-stimulated Serine2448 phosphorylation of mTOR in an AKT-independent manner and identified S6 kinase as a major effector of mTOR phosphorylation at Serine2448.22 Indeed, our analysis of the TCGA data shows that the S6 kinase (downstream of mTOR) is associated with luminal B and P53 mutations suggesting that while mTOR itself is at the cross section of conflicting pathways its downstream targets are not PIK3CA dependent. In addition, there are alternative kinases that can activate the mTOR pathway independently of AKT, such as RSK which leads to phosphorylation of TSC resulting in increased mTOR signaling and the PDK1–SGK1 axis that can sustain mTOR activity upon AKT suppression.25According to our findings only pAKT pathway activation was found to be significantly different between the luminal subtypes (A and B) and 26 confirmed a high frequency of PIK3CA mutations in luminal BC. Multiple platforms, which examined the relationship between PIK3CA mutation and protein expression, have demonstrated that pAKT and pS6 were not elevated in PIK3CA-mutated luminal cancers; instead, they were highly expressed in basal-like and HER2 subtypes. Our dataset, which is restricted to luminal cancers, demonstrated that pAKT pathway activation is associated with luminal A/PIK3CA mutations and good prognosis, while p-mTOR/pS6 is not, suggesting that the presence of HER2 and basal subtype in the primary analysis of the TCGA masked these observations.The primary TCGA report, which investigated all BC subtypes,PIK3CA mutations, luminal A and good prognosis, while p-mTOR pathway activation is associated with luminal B, P53 mutations, and bad prognosis. These results may have important clinical implications considering that in low p-mTOR BCs, treatment with mTOR inhibitors, such as everolimus, which is highly toxic, will possibly be of lower value since the pathway is not activated. Additionally, pAKT pathway activation, as measured through our gene signature, can add to presently used outcome prediction tools in both luminal A and luminal B tumors.In conclusion our data suggest that the AKT and mTOR pathways are not linearly connected in luminal BCs. pAKT pathway activation is associated with 26 . To identify the genes that were differentially expressed between the low and high expression groups, and to find the genes that would optimize the predictive power of our signatures, we used a machine learning approach as previously described.28 After this process, we were left with 69 signatures (Supplementary Data) presenting a relevant AUC for proteomic status prediction. Among others, p-mTOR achieved an AUC of 0.71 (p ~ 10−6) and pAKT an AUC of 0.77 (p ~ 10−11) in both luminal A and B cancers.We downloaded clinic-pathological, normalized gene expression and RPPA data from the publicly available TCGA repository using its online bioinformatics tools28 In brief, we evaluated using a nested 10-fold cross validation the maximal Benjamini–Hochberg false discovery rate and the minimal gene fold change that would optimize the ability of the differentially expressed genes to predict the high/low status of the RPPA in luminal A and B patients together and separately. While the parameters were selected in a 10-fold cross validation, the procedure itself was assessed using a nested cross validation. All analyses were performed using the genefu package of the R (v3.2)/bioconductor (v1.18) statistical suite.The expression levels of the signatures in the gene expression datasets were computed as previously described.28 The MCL graph clustering algorithm29 was applied. Visualization of the network has been rendered using the yED software.Network analysis and clustering was performed as previously described.PIK3CA mutation status and AKT and p-mTOR gene pathway activation, as represented by the gene signature scores, we analyzed the TCGA cohort of RNA sequenced data that was not used for the computation of the signatures (309 samples), and for which both mutational and gene expression data were available. Each sample was considered as mutated or not (so a sample with four mutations was considered just like a sample with one mutation). All mutations were taken into account. PIK3CA mutations were also analyzed by specific exons .We analyzed 38 gene expression datasets totaling more than 7000 tumors (detailed in Table Further information on experimental design is available in the SUPPLEMENTAL MATERIALReporting SummarySupplementary Data


The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions.https://pathme.scai.fraunhofer.de/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies.Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.The online version of this article (10.1186/s12859-019-2863-9) contains supplementary material, which is available to authorized users. Thus, parallel to the development of novel data-driven approaches, pathway databases emerged as comprehensive resources that could be used to complement analyses with prior knowledge. These resources have embraced standard file formats and schemata in order to facilitate the exchange of pathway knowledge. However, each resource has chosen a different one and though these formats possess overlapping capabilities to produce computational models of biology, their intended purposes and applications are somewhat distinct. For instance, Systems Biology Markup Language (SBML) is a standard for the representation of computational models of systems biology, Systems Biology Graphical Notation (SBGN) facilitates the storage and exchange of signaling pathways, metabolic networks and gene regulatory network information, and Biological Pathway Exchange (BioPAX) has been designed with the purpose of establishing a common exchange format for biological pathway data , 25, 31.graphite  and Omnigraphite  have beegraphite , RaMP [4graphite , and Comgraphite , have fographite effectively account for and harmonize metabolites, genes, and proteins when integrating pathways from multiple databases, but exclude biological types at higher order scales, such as biological processes, and other entities, such as miRNAs. On the other hand, Pathway Commons can incorporate multiple scales of biology by retaining original entity identifiers; however, it does not directly harmonize biological entities.The representation of pathway knowledge can span several scales including molecular events, cellular processes and/or phenotypes, which are captured in varying degrees by integrative resources. For example, ConsensusPathDB and An ongoing challenge in harmonizing pathway resources is the use of distinct nomenclatures by individual databases. For example, for gene and gene products there exist several standard terminologies such as ENTREZ , UniProtJust as pathway databases should be regularly updated to incorporate continual changes in pathway definitions, pathway metadatabases should also be updated in parallel to reflect such changes; it has been shown that by using outdated resources, results of studies are strongly influenced, and follow-up studies are negatively impacted . CorrespHere, we introduce PathMe, an extensible package that harmonizes multiple databases using Biological Expression Language (BEL) as a common interoperable schema and enables pathway knowledge evaluation and exploration powered by a stand-alone web application with a special focus on highlighting pathway crosstalks and consensus..PathMe framework is comprised of two parts: the open-source Python package that converts the different database formats into BEL and the web application that allows for the exploration of the resulting networks Fig. .Fig. 1DeIntegrating pathway knowledge from multiple databases first requires transforming the content of each database into a common underlying schema. While multiple triple-based formats can be used to formalize pathways in system biology, we adopted BEL as the pivotal unifying schema since it provides a reasonable trade-off between expressivity and standardized organization. Until now, we have implemented parsers for three major databases In order to harmonize entities, we prioritized standard nomenclatures for each of the modalities  included in the three studied databases  could not be translated into BEL due to the lack of correspondingly equivalent edges in the BEL syntax. However, these four relationships represent non-causal interactions between biological entities and are also minimally utilized by KEGG curators. Mappings between edges from the source databases to BEL are reported in Additional file https://github.com/bio2bel)  and the xml Python package for Extensible Markup Language (XML)). Moreover, the parsers are structured into their own packages inside the main Python module to facilitate the inclusion of additional database parsers in the future. During the entity normalization process, mappings across identifiers are facilitated through the numerous packages included in the Bio2BEL framework , we also implemented a novel tool called PathMe Viewer to fulfill these unmet needs and complement the PathMe package. Since the target audience for this application are pathway curators and researchers, we opted to implement the viewer in the form of a user-friendly web application compatible with any device. The front-end extends the visualizations from BEL Commons  and provhttps://jquery.com), D3.js (https://d3js.org), and Bootstrap (https://getbootstrap.com). The source code is available at https://github.com/PathwayMerger/PathMe-Viewer so that all visualizations and components can be reused or extended by future applications. Furthermore, the web application is distributed through PyPI and can also be deployed with Docker which facilitates the reproducibility of this work since Docker’s automated deployment process ensures that every single instance runs with the exact same settings, regardless of the host machine. Documentation is included in the GitHub repository and it is also accessible through Read the Docs (https://pathme-viewer.readthedocs.io/en/latest/). Finally, we provide access to a public deployment of the PathMe Viewer at https://pathme.scai.fraunhofer.de.PathMe Viewer follows a model-view-controller (MVC) software architecture. While the back-end is implemented in Python using the Flask microframework, the front-end is implemented in JavaScript using libraries such as jQuery (Case scenario II). We used a variation of the Szymkiewicz–Simpson/Overlap coefficient  the KEGG and Reactome representation, ii) the KEGG and WikiPathways representations, and iii) the Reactome and WikiPathways representations. Therefore, the pathway similarity index (S) lies between 0 ≤ S ≤ 1 .As an application of the software, we conducted the following protocol to evaluate the degree of overlap between the three representations of each equivalent pathway . Moreover, database specific features include functionalities to flatten all group nodes  in KEGG and exclusively parse canonical pathways from WikiPathways and Reactome. In conclusion, these functionalities combined with the ones already offered by the PyBEL ecosystem assist bioinformaticians in transforming, exploring, and analyzing the generated pathway networks.The PathMe package offers a set of functionalities for the set of databases incorporated thus far: i) download the raw pathway files, ii) generate BEL networks and export them as binary data, iii) summarize the transformed content, and iv) calculate detailed network statistics  Table . MoreoveBeyond the software concerned with the integration of pathway knowledge, a novel web application (i.e. PathMe Viewer) was implemented for intuitive querying, browsing, and navigating of the normalized BEL networks. Queries can be submitted for a single or a set of pathways on the main page of the viewer, as illustrated in Fig. The PathMe Viewer is powered by multiple, built-in functionalities enabling users to navigate through the pathway(s). Although the initial network layout is defined by the D3 force algorithm which enables users to get a comprehensive overview of the relevant parts of the network, the network arrangement can also be customized by dragging and moving nodes around the viewer. Furthermore, node and edge meta-information can be accessed via double click. For nodes, this includes specifications on their name, function and namespace, while for edges, the pathway name, identifier and source database are provided. When multiple pathways are queried, marked boundaries delineate the topological landscape of each of the networks which synergistically contribute to the consolidated one to facilitate the exploration of pathway crosstalks (i.e. the interaction of pathways through their sharing of common entities) Fig. b. Furthehttps://git-scm.com) for version control on GitHub (https://github.com), flake8 (https://github.com/PyCQA/flake8) to enforce code quality, setuptools (https://github.com/pypa/setuptools) to build distributions, pyroma (https://github.com/regebro/pyroma) to enforce package metadata standards, sphinx (https://github.com/sphinx-doc/sphinx) to build documentation, Read the Docs (https://readthedocs.org) to host documentation, pytest (https://github.com/pytest-dev/pytest) as a unit and integration testing harness, and Travis-CI as a continuous integration server to run each of these with each commit (https://travis-ci.com/PathwayMerger/PathMe and https://travis-ci.com/PathwayMerger/PathMe-Viewer). Each package is distributed publicly through PyPI such that they can be included in other Python projects with requirements.txt or included in the setup.py using the install_requires setting without the need for complicated build steps or any other user configuration.Successful contributions to the bioinformatics domain are predicated by their ability to be replicated and reused. In line with community standards designed to foster these attributes, the PathMe and PathMe-Viewer packages use git (https://github.com/PathwayMerger/PathMe-Resources) that illustrate useful commands that might serve to assist similar future analyses.Because PathMe works on frequently updated external pathway data from multiple sources, it must be re-run frequently to incorporate those updates. Following the recommendation from Kim et al.  for buil. We would like to note that in order to ensure the quality of the comparison presented in this case scenario, this analysis exclusively uses a highly cited and peer-reviewed pathway set provided by WikiPathways (approximately 510) that has been approved and tagged for usability in data analysis. While we attempted to maximize the retention of biological entities, we found severe differences in the level of overlap across resources which demonstrates the importance of database integration to gain a holistic picture of pathway knowledge.As a first application, we conducted a global comparison of biological entities across major modalities Fig. . We woul. The proportion of genes present in all databases was lower than the results obtained by Stobbe et al. (15%), though they exclusively focused their work on a set of metabolic pathways present in five major databases which included KEGG and Reactome. Total consensus of miRNAs in all three databases was unsurprisingly low due to a disproportionate representation of miRNA species across the databases. Specifically, as few as 13 miRNAs were derived from Reactome while 149 were present in KEGG. Similarly, the total consensus for metabolites was grossly deficient at less than 2%.The degree of consensus of biological entities across all three databases was found to be relatively low, albeit variable across the assessed modalities Fig. . The proThe number of entities per modality across databases is highly variable (Table ). Per definition, sets with significant variations in cardinality  limit the likelihood of consensus since only a portion of the larger sets can intersect with the smaller ones. For instance, KEGG contains 4048 metabolites while WikiPathways only contains 655. Accordingly, the maximum number of metabolites that can be common among them is limited to the number of metabolites contained in WikiPathways . In this case, the maximum overlap would be the total number of metabolites contained in WikiPathways divided by the total number contained in KEGG, or 16.18%. Thus, the maximum degree of consensus between databases can be constrained by databases which contain fewer entities.le Table . Per defThe scope of the pathways comprised in each database varies. Each database places a distinct emphasis on discrete aspects or regions of biological pathways which tend to be defined subjectively in the absence of standard nomenclatures, as outlined by [lined by  who repoHighly specific entity identifiers impede entity mappings with major standard nomenclatures. Some entity identifiers have no discernible mapping to major standard nomenclatures because they exhibit a high degree of specificity. This is particularly evident for genes where curators have also captured entities such as specific protein events , protein mutations , protein families , protein fragments , or splicing events ”). A way to account for these high granular cases would be to standardize protein family names with resources such as Pfam [proteinModification, fragment, variant, etc.). as Pfam  or FamPl as Pfam . For casBiological modalities can be broadly defined. We characterize modalities to correspond to BEL node classes (Table ). For instance, the genes modality comprises gene, protein, and RNA BEL nodes. While this modality is clearly defined, there is a higher degree of variability in the entity types that can be that can be classified with the metabolites modality since the latter comprises a broad range of abundance BEL nodes . WithouFor partial overlap, we found that results varied across the three modalities, with a higher degree of overlap between miRNA species at approximately 30%, followed by genes with nearly 20%, and metabolites with approximately 11%. Accordingly, we found the proportion of distinct entities to be substantially higher than those present in any two or all three databases. The particularly low levels of overlap observed in all modalities can be largely attributed to several factors:(Table ). While conducting a cross-database pathway comparison previously required either extensive manual curation or harmonization of both entity identifiers and data formats on a case by case basis, this example illustrates how PathMe can be exploited to enable a systematic comparison of equivalent pathways.Merging pathway knowledge enables analyzing the crosstalks for any set of pathways through the PathMe Viewer. As a case scenario, we used PathMe in conjunction with the viewer to explore the knowledge consolidated from 21 equivalent pathways across the three databases previously curated by Domingo-Fernández et al. Table . While cTo evaluate the degree of overlap between the three representations of each equivalent pathway, we used a variation of the Szymkiewicz–Simpson coefficient calculated for the common molecular nodes between the networks Eq. .Each of the 21 equivalent pathways showed partial overlap, except ‘Non-homologous end-joining’ which did not contain the pathway information required to convert the pathway into BEL in two of its original files. Among the equivalent pathways with the highest degree of similarity, we found well-studied pathways such as ‘Cell cycle’, ‘Toll-like receptor signaling’, ‘mTOR signaling’, Hedgehog signaling’, and ‘Apoptosis’. Although the three databases represent the most widely studied molecular players in each of these pathways, merging their knowledge assists in filling the gaps between the complex interactions occurring in these pathways. Pathways with low similarity, such as ‘TCA Cycle’ and ‘Sphingolipid Metabolism’, indicate the resources captured distinct aspects of the biology within the pathway. Unsurprisingly, this is in concordance with the findings reported by Stobbe et al. who conducted a comparison of the ‘TCA Cycle’ across five metabolic pathway databases. We would like to note that while previous approaches to characterize pathway similarity were purely gene-centric, our approach includes not only gene sets, but a range of modalities represented in pathways. Finally, beyond harmonizing entities and concepts, PathMe also harmonizes relationships, thus facilitating further analyses where pathway topology is included, as shown in the next case scenario. signaling pathway to demonstrate its utility in enriching pathway knowledge. In Fig. Fig. a.Fig. 4N5a and sourced from KEGG, has already been well-described in the literature [. The inhibition of mTOR has been noted to be a potent repressor of protein translation while mTOR activation can stimulate mRNA translation through EIF4EBP1 [Similarly, by superimposing the mTOR signaling pathways from KEGG and WikiPathways, downstream interactions between mTOR and mRNA translation via EIF4EBP1 are evident Fig. a and b. EIF4EBP1 , 19. ThoParallel developments of pathway databases during recent decades have resulted in different formalization schemas, hampering the interoperability between these resources and creating data silos. Overcoming this obstacle is instrumental to better understand the mechanisms underlying pathway knowledge. Additionally, while our approach can accommodate multi-scale pathway information from divergent database formats into a singular and standardized schema, a minority of entities and interactions have no discernible equivalencies in BEL and, as such, had to be omitted. For instance, so far PathMe parsers can extract information from both humans and other species; however, despite the capacity of PathMe to harmonize human identifiers, additional work is required for the harmonization of identifiers belonging to other species as integration can help in identifying evolutionarily conserved genes and processes.Here, we have presented a framework through which content across multiple pathway databases can be integrated and transformed into a unified schema. Although PathMe currently only incorporates content from three major pathway databases, its flexibility allows for future inclusion of additional pathway databases. Moreover, it holds the capacity to update its content and track developments in pathway knowledge, an issue earlier outlined by Wadi et al.. Finally, the three case scenarios presented illustrate how the framework can be used to assist researchers in addressing biological questions at varying degrees of specificity such as: i) integrating the pathway landscape at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and studying consensus at the molecular level.Ultimately, we have shown how integrating pathway databases and making them interoperable enables global pathway representations that can contribute to a more holistic overview of pathway knowledge than the knowledge contained in any single one of the databases. In the future, these global representations could be used to conduct more comprehensive pathway-centric analyses. Furthermore, the reproducibility of previous pathway enrichment analyses could also be evaluated by replicating them using any database combination. In other words, what would happen if, instead of KEGG, an identical analysis were to be performed using the Reactome or Wikipathways databases, or any combination of the three?Project name: PathMeProject home page:https://github.com/PathwayMergerOperating system(s): Platform independentProgramming language: Python and JavaScriptOther Requirements: Python 3License: Apache License 2.0Any restrictions to use by non-academics: KEGG Commercial LicenseAdditional file 1:Supplementary Text. (PDF 442 kb)


Gc and Rgn play important roles connecting VC and VD pathways in mice. The r values of expression levels between Gc and Rgn in mouse spleen, liver, lung, and kidney are 0.937, 0.558, 0.901, and 0.617, respectively. The expression QTLs of Gc and Rgn are mapped onto the same locations, i.e., 68–76 MB in chromosome 7 and 26–36 MB in chromosome 9. In humans, there are positive correlations between CYP27B1 and SLC23A1 expression levels in kidney (r = 0.733) and spleen (r = 0.424). SLC23A2 and RXRA are minimally associated in both mouse and human. These data indicate that pathways of VC and VD are not independent but affect each other, and this effect is different between mice and humans during VC and VD synthesis and transportation.Vitamin C (VC) and vitamin D (VD) have been widely used as the dietary supplements and in treatment of diseases both independently and in combination. Whether there is a connection between their pathways is critical for their therapeutic applications. Using whole-genome expression profiles, we performed multiple measures of associations, networks, eQTL mappings and expressions of key genes of interest in VC and VD functions. Several key genes in their pathways were found to be associated.  More recently many researchers have focused on the functions of VD and VC in different diseases and/or in different tissues7. Converging evidence suggests that VC and VD may exert similar effects in some circumstances. Wei et al. reported that there may be a moderate inverse association between dietary VC intake and non-alcoholic fatty liver disease (NAFLD) in middle-aged and older adults3. A different group found that timely initiation of VD supplementation is a critical determinant of treatment outcome in NAFLD4. VC and VD are each important in modulating bronchial asthma5. In addition, VD and VC both have pleiotropic effects on the immune, cardiovascular, and neurological systems and also share antineoplastic activity7. In spite of similarities between VD and VC functions in some diseases and human health, their applications in therapeutics and diet supplementation have largely been assessed independently, with little attention devoted to their potential interaction.Vitamin C (VC) and vitamin D (VD) are indispensable for optimal health in humans. More than half a century ago, researchers found that VD deficiency could lead to rickets, and VC deficiency could lead to scurvyDhcr7) is one of the regulatory genes in DHC production. Humans also obtain Vitamin D3 through diet. In the body, vitamin D3 is first converted to 25-hydroxyvitamin D (25(OH)D) and then to 1α,25-dihydroxyvitamin DD3)8. Gc is the key gene to producing VD binding protein (DBP), which binds 25(OH)D and 1,25(OH)D3 in plasma. Vitamin D receptor (Vdr) in target cells binds specifically to 1,25(OH)D3 with high affinity9. A large number of genes are involved in VD molecular pathways in target cells. The most important ones are Cyp24a1, Trpv6, Cyp27b1, Cyp2r1, and Rxra which are studied in our lab10. Among them, Rxra is an obligatory co-receptor for Vdr mediating transcription11.With the help of ultraviolet B (UVB), both mouse and human body can synthesize Vitamin D3 from dehydrocholesterol (DHC). 7-dehydrocholesterol reductase  is essential for the conversion of L-gulonate to L-gulono-γ-lactone. SMP30 is produced by the regucalcin (Rgn) gene12. Two sodium-dependent transporters Slc23a1 and Slc23a2 , are important regulators in VC transport as well13. When Rgn or Slc23a2 are knocked out, mice cannot synthesize VC14. In this research we chose to study the role of Gulo, Rgn, slc23a1, and Slc23a2 in VC function and the role of Vdr, Gc, Cyp24a1, Trpv6, Cyp27b1, Cyp2r1, Dhcr7, and Rxra in VD function.Mice can synthesize vitamin C 15. Theoretically, after more than 20 generations of inbreeding, most genetic sites of inbred animals should be homozygous. The BXD database can be downloaded from GeneNetwork, a public data source website that is a large whole genome expression resource platform. Using GeneNetwork, we worked to find potential overlap in synthesis and transport pathways of VC and VD.For over half a century, recombinant inbred (RI) strains of mice have been well-studied as models in biomedical research. In this research, we chose BXD RI strains, which represent the best developed animal model and have made significant contributions to the study of human diseases. The BXD families of RI strains were derived by crossing C57BL/6 J (B6) and DBA/2 J (D2) mice. Multiple generations of individuals from the F2 group were producedGulo, Rgn, Slc23a1, and Slc23a2) and VD key genes  from whole genome mRNA expression profiles in four different mouse and human organ tissues . Next, network graphs of these key genes were created using a threshold of absolute values greater than 0.35 as we previously reported16.From the GeneNetwork platform, we first obtained the expression levels of VC key genes  are also connected. Thus, in post-synthesizing pathways there are both similarities and differences between humans and mice. Of note, in the human literature and tissue correlation analyses, connections between SLC23A1 and CYP27B1 are lacking.For the genes involved in transport of VC and VD, it seems that 17.Since initial analysis of correlations among key genes in the VD and VC pathways showed both similarities and differences between mice and humans, we further examined in detail the correlation matrix among these genes in mouse spleen (N = 81), liver (N = 32), lung (N = 49), and kidney (N = 55) using the GeneNetwork platform with the Pearson coefficientGulo and Rgn, Gc and Gulo, and Gc and Rgn in mouse and GC and RGN in human. Figure Gulo and Rgn in mouse spleen, liver, lung, and kidney were 0.876, 0.678, 0.848, and 0.794, respectively. Correlations between Gc and Gulo in these four tissues in mice were 0.848, 0.550, 0.777, and 0.542, respectively. The R values between Gc and Rgn in these tissues of mice are 0.937, 0.558, 0.901, and 0.617, respectively. All of these correlations are statistically significant at less than 0.001. Rgn, Gc, and Gulo are highly expressed in mouse liver, and their correlations are more than 0.5 in this tissue. These detailed analyses in the four tissues confirm the positive correlation among these three genes. At the same time, the correlations of GC and RGN in humans are 0.386, 0.110, 0.088, and 0.301 . Further, we have found a similarly strong correlation between Gc and Rgn in rat tissues  and sorted the different expression results in the key genes of VC and VD. As can be seen in Fig. Cyp24a1, Gc, and Cyp27b1. In addition, the expression levels of Slc23a1 and Gulo also showed significant differences when compared between the Vdr knockout group and the wildtype group (P < 0.05). The expression levels of those key genes are not affected in arthritic mice models with the non-mutated Gulo gene when compared to healthy control mice D) also varies21. Similarly, recommended optimal VC and VD levels have also generally been independent, without considering possible interactions.A critical issue is that different individuals may utilize VD or VC with different efficiency, as the pathways are not independent. Our data may partially clarify why results of studies on the effects of VD and VC vary, i.e., because one element is affected by the other25. According to our data, the optimal amount of VC or VD when used in treatment should be based on the balanced levels of both VC and VD. It is understandable that results of different combinations among drugs and VC or VD may also differ due to particular interactions between the drug and VC or VD. It is necessary to investigate whether it is optimal to add other vitamins to work synergistically to produce a better therapeutic effect.Equally important, VC and VD have been used in combination with other agents in the treatment of disease27. When these genes are utilized, their association may influence each other, which in turn, may affect the VC and VD pathway. Thus, a much broader effect of these genes should be considered when they are used for other purposes.The key genes in the connection between VC and VD may be used as alternative targets in disease treatment, nutrition, and supplementation. In addition to their function in VC and VD pathways, these genes have other important functionsIn this article, we focus on the mRNA expression level or genomic levels, not on protein levels. However, with multiple analyses with different methodologies and at different levels, we have found evidence to demonstrate that some overlap exists between VC and VD synthesis and transport pathways. It is clear that additional studies such as at protein levels should be conducted in the future. Our study has several limitations. First of all, most of our analyses are based on mouse models, or gene expressions profiles at one time point. The human sample is from the Genotype-Tissue Expression (GTEx) project, with a sample of around 500 that was 85% Caucasian. Secondly, we did not test for potential sex differences. Thirdly, the mouse data are mainly from BXD RI strains, which were derived from two parental strains, (C57B6/J and DBA/2 J), and thus may not represent all mouse strains. Furthermore, our analysis is only based on the association of the expression levels between genes. Influence by polymorphism and other epidermal factors was not been considered. Further studies on verification of these results and further analysis of potential age and sex differences will advance research into mechanisms of VD and VC effects.Gc-Rgn and Gc-Gulo. In humans, the connection is mainly through SLC23A1 and CYP27B1. The significant differences raise the question of whether it is suitable to conduct research using VC endogenous production rodent models. The association between VD and VC pathways indicates a need for both to be considered in their nutritional and therapeutic applications.In summary, our study suggests that in mice, the VC and VD functional pathways are connected mainly through http://www.GeneNetwork.org. We separately analyzed BXD strains’ whole genome expression profile in spleen (UTHSC Affy MoGene 1.0 ST Spleen (Dec10) RMA), liver (GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA)28, lung (HZI Lung M430v2 (Apr08) RMA), and kidney  RMA). These four sets of data using the GeneNetwork platform were the best resource for us to explore whether VC and VD have relationships. For humans, we chose whole genome expression data at GTEx V5 group in GeneNetwork and chose the same tissues as we have used in mice30.All the relevant data were derived from GeneNetwork which is publicly accessible via 32. RNA extract and data analysis followed our previous procedures31. These experiments were designed to compare the expression level in Sfx mice with/without VC treatment and in Balb/c mice, and the tissues included liver, femur, and muscle. Real-time PCR was applied to validate the microarray results.Sfx mice were used to validate the connection of key genes in the VC and VD pathways. The key gene in VC synthesis, Gulo, was mutated in the Sfx mice. Because Sfx mice are under Balb/c background, Balb/c mice were used as the control group. The microarray data has been reported in previous studieshttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61583). Mice were divided into three groups: Vdr knockout mice group, Vdr heterozygous mice group, and wildtype mice group34.The data from a public database in NCBI GEO DataSets (GSE61583) was used to confirm that VC genes are affected when Vdr was knocked out. The study is an analysis of mouse placenta retrieved at day 18.5 pc with Vdr gene knockout.  for all graphic subjects. Different correlations were drawn in different colors which are the same as in our previous publication16.We used the standard resolution provided by GeneNetwork to identify eQTL position. Interval mapping tools mapped each chromosome with 1000 permutation tests to compute eQTLs. Expression values from different strains are considered to be phenotypes. A molecular marker along the chromosome was used as the genotype of the position on the chromosome. Then, the expression value of the probability of a particular genotype at the test position between the two flanking markers was compared. Statistical probability was used to assess the significance of the eQTL at a location to ultimately produce an eQTLGc or Rgn and any other traits in the sample database selected. Pearson’s Rank was chosen when the sample size is large. Spearman’s Rank was used when the sample size was small (<20) or when there are influential outliers. We separately computed the top 100 probes for Gc and Rgn in each tissue.The sample correlation is computed between expression levels of 35.R values and P values were obtained from GeneNetwork automatically. We used standard criteria to categorize the strength of the correlation. Thus, when the R value was equal to or more than 0.7 or −0.7, we regarded the correlation as a strong positive or negative index. An R value of between 0.35 and 0.69 or between −0.35 and −0.69 was considered a weak correlation. We treated R values between 0 and 0.35 or between 0 and −0.35 as no correlationSupplementary materials


The study of the rolling tyre is a problem framed in the general context of nonlinear elasticity. The dynamics of the related phenomena is still an open topic, even though few examples and models of tyres can be found in the technical literature. The interest in the dissipation effects associated with the rolling motion is justified by their importance in fuel-saving and in the context of an eco-friendly design. However, a general lack of knowledge characterizes the phenomenon, since not even direct experience on the rolling tyre can reveal the insights of the correlated different dissipation effects, as the friction between the rubber and the road, the contact kinematics and dynamics, the tyre hysteretic behaviour and the grip. A new technology, based on fibre Bragg grating strain sensors and conceived within the OPTYRE project, is illustrated for the specific investigation of the tyre dissipation related phenomena. The remarkable power of this wireless optical system stands in the chance of directly accessing the behaviour of the inner tyre in terms of stresses when a real-condition-rolling is experimentally observed. The ad hoc developed tyre model has allowed the identification of the instant grip conditions, of the area of the contact patch and allows the estimation of the instant dissipated power, which is the focus of this paper. The automotive industry is one of the major actors in the stage of greenhouse emission and electricity production  and the Analogous to a drag force, the resistance occurring during rolling is the expended energy in a unit of travelled distance. Tyres are made of a viscoelastic material, i.e., by reinforced rubber, and undergo phenomena of hysteresis. During loading and unloading phases, the stiffness curves of the tyre do not exactly match, producing energy loss: the more a tyre deforms, the higher the amount of generated heat. In addition, hysteretic effects depend on the mechanical characteristics of the tyre; for instance, as a counterintuitive phenomenon, the dissipated energy decreases as the temperature increases. This aspect explains the reason why the design of an intelligent tyre ,4,5,6 fiThe development of an embedded system of sensors ,9 for moThere are several other methods, other than the indirect techniques, that take advantage of direct observations with higher accuracy. Among them, Micro electro-mechanical systems (MEMS) are the most commonly used as pressure sensors  and for Spatial and time accuracy are not the most remarkable features of the data acquired through the above-mentioned methods and rapidly varying dynamic parameters are hard to be sensed and transmitted . FurtherIt becomes clear how sensing systems, provided with simple architecture, and accurate identification methods ,25,26 reThe OPTYRE grip sensor is part of the Sapienza University of Rome’s autonomous vehicle project, "Auto Sapiens", developed by the Vehicle Dynamics and Mechatronics Lab team. The OPTYRE provides in real-time the state of the grip of the tyre to control the vehicle during braking, acceleration and during cornering manoeuvres and allows excellent safe driving performance using suitable controllers to be achieved ,29,30,31The paper is organized into four main sections. This section describes the measurement scheme and the experimental setup, starting from the fibre Bragg grating (FBG) sensors embedded inside the tyre. The FBG based sensors, composed of a single-mode optical filter, present a spatially recurring glass refraction index. Because of this grating, the fibre becomes an optical filter: when infrared light is sent into the fibre, only a specific narrow band is reflected back towards a spectrum analyser. The remaining light is transmitted undisturbed along the optical waveguide.B, the refractive index n of the fibre and the period of the refractive index modulation Λ: λB = 2nΛ. The FBG turns into a strain sensor able to detect local axial deformation of an optical fibre. The previous expression clearly states how the reflected wavelength λB is sensitive to any modification of the grating sensors properties. Among them, the temperature variations change n as a consequence of the thermo-optic effects; Λ as well varies with the thermal modification if the fibre is unconstrained; in the same way, deformation and strain of the fibre cause alteration of Λ and n. This behaviour is completely described by Equation (1) in which the two terms on the right-hand side are related to the effects on λB due to variations of the strain and the temperature, respectively.This makes the FBG a distributed Bragg reflector , shown iAccurate monitoring of the variations of the wavelength of the FBG sensors allows the estimation of these perturbations. The simultaneous presence of strain and temperature excursion demands two FBG sensors for the acquisition: one must be in-built to the structure to acquire the strain, the other, dedicated to temperature recording, positioned close the first one, must be kept detached from the structure, to avoid the influence of the strain. FBG sensors present several advantages, reasons for the increasing interest they enjoy and the number of applications that see them involved, especially on structural health monitoring ,34. DataIn this particular circumstance, the idea was to place a number of optical fibres, equipped with several FBG sensors, along the inner circumference of the tyre. The case here investigated saw a single FBG sensor, An optical led source produced the light beam, whose wavelength belonged to the far-infrared range; the path of the light beam was the optical fibre that, equipped with several FBG sensors, was attached to the circumference of the tyre.A spectrum analyser acquired the variations of the reflected light frequency bandwidth, caused by deformations of the tyre.The computer received and processed the signal sent by the spectrum analyser, which has the function to suitably sample the incoming analogue data.ε is evaluated through:j-th FBG sensor to the strain. In Equation (1), Once the instant wavelength of each FBG sensor is detected by the spectrum analyser, the local strain Since the FBG interrogator was integral to the car chassis, a fibre optic rotary coupler, inserted between the interrogator and the tyre as in This setup went beyond the current limits of the available technology, in terms of power supply and transmission data systems. The FBG sensors were supplied by a generator mounted externally to the tyre and integral to the car body. The generator produced the light source: the beam was sent to the optical fibre, embedded in the tyre carcass, through the rotary optical coupler. The optical signal sent on-board the tyre was modulated passing through the FBG sensor and was reflected back outside the tyre, thanks to the rotary optical coupler, which replaced any data transmission device. One of the main advantages of the solution was that the same physical light beam carried both the power to sensors and the information to the interrogator. Further advantages of the present setup arose from the material of these sensors, which had low rigidity and were capable of long-term acquisitions, even under the severe working conditions experienced by the tyres, and were very compatible with the rubber of the tyre. The size of the tyre adopted for the present was 215/45 R17, the tyre was installed on a prototype vehicle shown in The design of a tyre, which results from the compromise between comfort and driving performances ,20, demaThe theoretical investigation was the essential background to the following experimental campaign, it recalled the classical elasticity theory, focusing on the strain along the circumferences of the tyre, and pursued closed-form solutions. Accordingly, a model to characterize the strain was introduced as a model to estimate the rolling resistance of the tyre.The model here discussed found its inspiration in Reference , which, w is:x the axial coordinate along the beam, as shown in the t is time variable, respectively, EJ represents the flexural stiffness, µ is the mass per unitary length, k is the Winkler foundation coefficient, P is the constant punctual vertical force, moving at constant speed c with respect to the points of the rolling tyre, and, eventually, δ(x) is the Dirac’s function. It is worth mentioning that E and E and E and The characterization of the analytical model requires the introduction of some hypothesis: i) the shape of the tyre, usually assumed to be a toroid a, is stre OPTYRE  also useIt is also worth mentioning that the model here considered was linear elastic, i.e., constant Young’s modulus in Equation (2), in spite of the fact that tyre rubber is hyperelastic. This simplifying assumption was made to have a reasonable trade-off between two antithetic needs, such as the accuracy of predictions and the analytical/numerical efforts involved with the solution of the equations that govern the phenomenon. In fact, the stress–strain relationship remains linear also for the tyre rubber in the case of small and moderate deformations ,37, diveThe wave solution to the Equation (2) is obtained by the change of variable The solution to Equation (3) is achieved by introducing the integral Fourier transformationsAt last, the constants s.It is apparent that, in case of a conservative tyre, for The evaluation of the damped response is not straightforward to obtain and needs the expression as in Equation (6) to be expanded as a Taylor series around Since the terms on the right-hand side in the square brackets are all even functions multiplied by the odd function This implies that the damped deformation x direction and y the distance from the neutral axis. According to Equation (13), also the circumferential strain is expressed in terms of an even function, related to the undamped response, and a small perturbation, that assumes the form of an odd function:The stress–strain constitutive relationship provides the deformation of the beam:It has already been mentioned how the contact patch undergoes a vertical load that is not perfectly centred, and it suffers an offset since the normal pressure is not uniformly distributed over the surface of the footprint; indeed, the trailing half is more unloaded with respect to the leading half. This phenomenon produces a torque, which operates in opposition to the forward motion, and it is mainly due to the hysteresis of the tyre, intrinsically associated with the deformation of the rubber during the rolling motion. This eventually generates the rolling resistance, and actually almost 90% of it is produced by the energy dissipated because of the deformation . This imWith these premises, the present section was aimed at defining a measure of the energy loss because of the rolling resistance, based on few parameters that must be controllable and easily measurable, as the geometric features of the tyre and the strain along the circumferences of the tyre.P done on the deformed solid of volume V, confined by the surface A at the time t is:ρ is the mass density in the considered configuration, b the body force per unit mass distributed over the volume V, T is the contact force per unit area or traction, v is the particle velocity at the point x. All the parameters in Equation (16) are functions of the time t and the configuration x, but the dependency has been concealed for the sake of notation. For this purpose, the work–energy relation between the amount of external work, the internal work and the kinetic energy of a deformable solid are presented. The amount of external work T can be assumed to be the projection of the Cauchy stress tensor σ on the outgoing normal versor n to the surface A, namely, in Einstein notation, As long as it is reasonable to consider motions as infinitesimal, the traction The linear momentum equilibrium equation, a function of the Cauchy stress, is:Introducing Equation (18) into Equation (17) and employing the symmetry properties of the stress tensor, one obtains:A is the cross-section of the beam. Equation (20) states that the rate of external work on any part of the body equals the rate of increase of kinetic energy, e.g., the first right-hand side term, and the rate of internal work within that part, e.g., the second right-hand side term and which is often called the stress power. Generally, the stress power accounts for both stored and dissipated energy.Eventually, the work-energy relation is achieved considering Since the dissipated power wants to be expressed as a function of the circumferential strain, the explicit definition of the strain tensor is required. As discussed in x and z axis, respectively, and w represent the displacements along the longitudinal and vertical directions and θ is the rotation of the cross-section of the beam around the y axis, conform to the convention proposed in x axis, due to the longitudinal stress (first term on the right-hand side) and due to the flexural contribution (second term still on the right-hand side). Finally, the second equation represents the shear deformation purely caused by the flexural displacement. When E and G are the Young’s and shear moduli and η the viscoelastic damping coefficient, the viscoelastic constitutive relationships appear to be:In this set of equations, the indexes 1 and 3 correspond to The beam is modelled according to the Euler–Bernoulli theory, for which x is replaced with the curvilinear abscissa Rφ, and for a stationary motion, the new independent variable s becomes ω. According to the convention in x-axis.Transforming the coordinate system, i.e., the variable While the system embedded in the tyre, as presented in s is omitted.Thanks to these assumptions, it is now possible to write the longitudinal deformation in Equation (21) as a function of the inner tyre strain surface at The aim is to express the internal energy of the tyre as a function of the acquired strain and of the derivatives of the vertical displacement. This is obtained by substitution of Equation (23) into Equation (20), namely:With the same approach, the term related to the stress power in Equation (20) becomes: w and the deformation of the circumference can be expressed as even functions affected by weak perturbations, in the shape of odd functions. To obtain the final expression of the dissipated power, one should evaluate each term Equation (24) and Equation (25) are composed of. Hence, starting from the first term on the right-hand side in Equation (24) it can be developed as follows: Applying the assumptions of the models of the previous section, i.e., in the case of moderate dissipation, both the vertical displacement Since the integration domain is symmetric, the odd integrands result in a null contribution; instead, the even terms, being multiplied by the small parameter The result is the internal dissipated power becomes a function of the second power of the linear velocity of the tyre only. This implies that, given the circumferential strain, acquired through the embedded FBG sensors, the dissipated power can be easily evaluated through numerical integration, as discussed in the following section.As a final comment, in the previous sections the importance of real-time identification methods has been underlined. According to this philosophy, the presented model becomes the answer to balance the need for a rapid algorithm, for real-time applications, and accurate mathematical characterization of the dissipated energy in a rolling tyre. Even though the achievement of these tasks is already challenging, there are a few more aspects that are worth further investigation. In particular we can mention the following points that deserve additional investigation: i) the contact mechanism between rough surfaces, based on the theory developed in Reference , ii) theThe experimental campaign is the focus of the present section. The experiments were carried out operating the car on urban and extra-urban roads. The acquisitions were made through two synchronized acquisition systems. The first was the optical interrogator connected to the FBG sensor while the second system captured data such as, the vertical acceleration of the wheel, the wheel encoder see a,b and tnd order centred differencing formula through a suitable MatlabTM algorithm, namely the find peaks function, which uses the strain signal as input. The estimation is based on the fact that the maximum points of the strain are located about the centre of the footprint and that the relative distance between consecutive peaks is 2The following figures a,b reporposition , which hposition , insteadIn The model used in Equation (1), even though intrinsically related to the effects of the temperature, did not consider the mentioned temperature variation. Indeed, the model was applied to a time acquisition range in which the temperature reaches a stationary condition, identified through the stationary mean deformation trend ε. To enhance the used model, introducing a more accurate estimation of the tyre deformation due to thermal expansion at the inner line, it was advisable to equip the tyre with a further FBG sensor, detached from the inner line but close to the FBG sensor devoted to inner line deformation measuring. The specific power N samples of input at a time and takes the average of those N-samples and produces a single output point. It is a very simple low pass filter that comes in handy to filter unwanted noisy components from the original acquired data, while retaining a sharp step response. As the filter length N increases the smoothness of the output also increases, whereas the sharp transitions in the data are made increasingly less sharp. This filter has indeed excellent time domain response but a poor frequency response: this property makes it one of the best suited filters for time domain encoded signals, as the one here analysed in The time evolution of The peed see c and theEventually, the data, cleaned and smoothed, are shown in Finally, it is worth mentioning how the presented results can be related to the standard approach in the technical literature , which mThe Equation (32) is the link between the usual approach to friction losses in tyre dynamics to the one proposed in this article.Framed in the context of the tyre rolling phenomenon, this work presented an innovative technology for power dissipated identification through an ad hoc algorithm and experimental setup, conceived within the OPTYRE project.The experimental apparatus was proven to be reliable and robust, being able to obtain long-lasting acquisitions, even in unfavourable conditions. The optical setup ensured high resolution and high accuracy strain signal measurements, without requiring a complicated power supply and data transmission systems. A semi-analytical method was here proposed for real-time sensing as it guaranteed the high speed required for real-time data investigation and the identification process: the proposed model struck indeed a good balance between a high computational velocity and an accurate analysis of the strain–grip relationship. The proposed model well characterized the interaction between tyre and road, starting from the definition of the dissipated power as a function of the tyre circumferential strain. It was also found how the dissipated power depended on the second power of the speed of the tyre. These results made the dissipated power easily evaluable, in each tyre revolution, given the measured circumferential strain acquired by the embedded FBG sensors. Both experimental and theoretical results confirmed that the OPTYRE technology allowed the real time identification of the tyre rolling resistance and, more generally, of the tyre–road grip conditions. Currently, a new multi-sensing setup , made of


Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants.Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method.http://statgen.snu.ac.kr/software/pharaoh-gee/.In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at  The history of Genome-Wide Association Studies (GWAS) now has reached two decades, and those GWAS have identified almost 60,000 unique associations of over 3000 traits . HoweverFor the analysis of rare variants, the low statistical power caused by the sparseness of rare variants is one of the major issues. The use of biological information such as genes or pathways has been proven to escalate the statistical power and improve the biological interpretation, for identifying statistically significant genes and pathways associated with complex traits such as high-density lipoprotein levels, obesity, schizophrenia, and multiple cancers –8. TakinAnother approach to improving the statistical power is a simultaneous analysis of clustered phenotypes. For example, the analysis of repeatedly measured phenotypes outperforms the analysis of cross-sectionally observed phenotypes, since the information on the temporal differences within a subject improves the power . Many reThe Generalized Estimating Equations (GEE) approach is one of the most commonly used methods for the analysis of clustered and correlated phenotypes . The majHowever, unlike the gene-level analyses, to the best of our knowledge, only one method based on GEE has employed the pathway-level analysis of the correlated phenotypes  with theIn order to address this problem, we propose a novel pathway-level association test for clustered and correlated phenotypes such as repeated measurements, Pathway-based approach using HierArchical component of collapsed RAre variants Of High-throughput sequencing data using Generalized Estimating Equations (PHARAOH-GEE). While the existing GEE based pathway-level method GEEaSPU implements the individual “pathway-wise” test assuming all tests are independent, the proposed PHARAOH-GEE method implements a “global test” that considers the correlation among the pathways into account by putting all pathways simultaneously into a single model. Moreover, PHARAOH-GEE can handle various types of phenotypes , and it also retains the advantages of PHARAOH, such as the hierarchical model that mimics the natural biological processes. By providing PHARAOH-GEE program using a powerful and fast C/C++ based framework WISARD , it suppWe used a workstation system consists of two Intel Xeon E5–2640 CPUs and 256GiB of RAM. Due to the limitation of the compared method, the R version 3.4.0 and R package ‘GEEaSPU’ were used with default settings.w = 0.1, 0.2, 0.5 and 1.0), three pathway-level effects , two correlations of phenotypes (ρ =0.25 and 0.5). For all test results, we applied the BH step-up procedure to control the False Discovery Rate (FDR) at 5% level [For our simulation study, we generated 300 replicates from the simulated data pool. Each replicate consisted of 10 pathways in which the first pathway was causal and the other nine were non-causal . For each replicate, the proposed PHARAOH-GEE method was applied to the 10 pathways simultaneously, whereas GEEaSPU was applied to each pathway individually. Here we assumed that the first pathway is causal and the others are non-causal. For the causal pathway, we considered three different parameter settings: four gene-level effects  and two (H1 = 2) out of ten simulated genes, respectively. As shown in Fig. Second, we evaluated statistical power of the methods where power was computed as a proportion of the causal pathway being statistically significant at the FDR < 0.05 over 300 replicates. In addition to three parameter settings for the causal pathway, we consider two cases when the numbers of significant genes within the causal pathway are only one . Metabolic syndrome is diagnosed as the presence of three or more of the following criteria: (1) WAIST ≥90 cm in males and ≥ 80 cm in females; (2) elevated TG ≥ 150 mg/dL or taking medication; (3) HDL-cholesterol < 40 mg/dL in males and < 50 mg/dL in females or taking lipid-lowering agents; (4) systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or taking antihypertensive medications; and (5) elevated FASTGLU ≥100 mg/dL or oral hypoglycemic agents use. From these six metabolic syndrome related phenotypes, we derived five clustered binary traits. Especially, we combined two blood pressure phenotypes (SBP & DBP) into a single phenotype, named BP, by setting 1 if either SBP or DBP satisfied the diagnosis criteria of metabolic syndrome and 0 otherwise. All other phenotypes were binarized if the diagnosis criteria of metabolic syndrome was satisfied and 0 otherwise.To demonstrate the usefulness of PHARAOH-GEE, we analyzed a large-scale sequencing dataset with six phenotypes related to the metabolic syndrome: systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), fasting glucose (FASTGLU), waist circumference (WAIST), and high-density lipoprotein (HDL). Before the analysis, we binarized these phenotypes according to the metabolic syndrome criteria of International Diabetes Federation (IDF) consensus worldwide definition of the metabolic syndrome , through their role in abdominal obesity . In addiAn analysis of the clustered phenotypes provides more information than the cross-sectional studies. Recent large cohort studies keep producing repeatedly measured phenotypes. We introduced a novel statistical method for the pathway analysis of the large-scale genetic dataset with clustered phenotypes. While our previous PHARAOH-multi method can handle only continuous phenotypes, the proposed PHARAOH-GEE can handle various phenotypes such as clustered binary and count phenotypes under the various correlation structures. Through the comparison study using the simulated datasets, we demonstrated that the proposed PHARAOH-GEE method outperforms an existing pathway method. Furthermore, our application to the large-scale WES dataset successfully identified one pathway that has not been discovered in the analyses of individual phenotype with the multiple testing adjustments.Compared to GEEaSPU the only currently available method for pathway-level test of clustered phenotypes, the proposed method has many advantages. First, PHARAOH-GEE effectively controls the complex correlations among the pathways by constructing a unified hierarchical, doubly-penalized statistical model. Second, it successfully reflects the nature of biological process from GSCA framework and takes clustered phenotypes into account from GEE framework. In conclusion, we hope that PHARAOH-GEE can serve as a main tool for the pathway-level analysis of clustered phenotypes in genetic studies.Currently, we have a number of considerations for our future research. Although we considered many possible combinations of parameters in the simulation setting, a further extensive simulation study is required for more comprehensive comparison with existing pathway-based methods. In addition, we will perform a replication study using other independent datasets with the metabolic syndrome phenotypes. Finally, we will employ other penalization methods such as lasso and elastic-net.Q clustered phenotypes and K pathways, each of which is linked to Tk genes . An example of the PHARAOH-GEE model is depicted in Fig. Technically, the proposed method is an extension of the doubly-regularized Generalized Structured Component Analysis into the GEE framework  that impyiq be the value of the qth phenotype measured on the ith individual  and Q × 1 vector of the clustered phenotypes of the ith individual. Similar to the previous description of the PHARAOH model [yiq follows an exponential family distribution with a mean μiq. Let Σi be the Q × Q covariance matrix of Ri(α) is a so-called “working correlation matrix”, α is a parameter vector that fully characterizes Ri(α), and Q × Q diagonal matrix with the marginal variance of responses. Liang and Zeger [Ri(α), e.g., the independence covariance structure, Ri(α) = IQ, where IQ is the identity matrix of order Q.Let nd Zeger  suggesteT + 1) × 1 vector consisting of all gene-level collapsed variables for the ith individual across K pathways, where T = X be an N × (T + 1) matrix of the gene-level collapsed variables for N observations, as expressed in (2).Let X to satisfy the conventional scaling constraint diag(X′X) = NI. Each element of X, xikt, denotes a gene-level summary of the ith sample for the tth gene  in the kth pathway and is generated by the weighted sum of rare variants that is same as the previous work [W denote a (T + 1) × (K + 1) matrix consisting of component weights wtk, which are assigned to xikt. This matrix can be generally expressed asAs in the previous methods , we stanous work , 10. Letηiq and g(·) denote the ith linear predictors of the qth phenotype and a link function, respectively. We define the proposed PHARAOH-GEE model asLet ith individual for the kth pathway K pathways to the qth phenotype. We can statistically examine the joint effects of the kth pathway on Q phenotypes by testing the null hypothesis H0: βk1 = ... = βkQ = 0. Moreover, it is possible to evaluate the effect of one gene on a single phenotype mediated by its corresponding pathway.where L2 penalty terms to control for potential adverse influences of high correlations between genes and/or pathways. Specifically, to estimate the parameters W and B, we seek to minimize the following penalized estimating equations.U is the estimating equation for the parameters, B is a matrix consisting of all regression coefficients tr(·) denotes the trace of matrix, and λG and λP denote ridge parameters on the L2 penalty terms for the weights and regression coefficients, respectively. A more detail on the estimating equation and solving process can be found on elsewhere [For simplicity, we describe the propose method, assuming that the phenotype ϕα, B, W, we use an iterative algorithm that repeats the following steps until no substantial changes in parameter estimates occur.To minimize Step 1: We update B for fixed W and Ri(α). Let b = vec(B) denote a vector formed by stacking all columns of B one below another. This is equivalent to minimizing the following estimating equationsb can be estimated by Step 2: We update W for fixed B and Ri(α). Let w = vec(W). Similar to step 1, it is equivalent to minimizingw* is the vector formed by eliminating all zero elements of w, and Mi is the matrix formed by removing the columns of w∗ can be estimated by W is reconstructed from Step 3: We update Ri(α) from the updated B and W using Pearson residuals with the variance function of the distribution ν,φ is estimated consistently byk-fold cross-validation (CV) to estimate the values of λG and λP, which compares the quasi-deviance values [λG and λP.We apply e values  of a twop-values. By permuting the phenotypes, the method first generates the empirical null distributions of both pathways and gene coefficients. By computing the quantile of the estimated pathway and gene coefficients from the non-permuted dataset with the corresponding null distribution, we can obtain an empirical p-value for any specific pathway and gene.Resampling methods can be used to test the statistical significance of the estimated effects of all pathways on a given set of clustered phenotypes. In the proposed method, we utilize a permutation test to obtain βk1, ..., βkQ simultaneously and suggested the Wald-type statistics [Q coefficients. Here, we define a Wald-type statistic T as.In our study, we want to test the joint effects of pathways on clustered phenotypes. In our previous study, we introduced two approaches to test atistics . SimilarUnder penalized GEE, the estimated covariance g et al.  as followhere p-values, we implemented two types of multiple testing procedure as we discussed earlier [p-values, and the Benjamini-Hochberg (BH) step-up procedure [q-values by False Discovery Rate (FDR) adjustment.For the calculated  earlier . In shor earlier  that effrocedure  that comWe conducted a simple simulation study to investigate the performance of PHARAOH-GEE and to compare the proposed method with the existing methods. We first simulated a large pool of rare genetic variants using SimRare . All simH1 and Mt1 denote the number of causal genes in the causal pathway and the number of causal rare variants in the tth causal gene, respectively. Note that Mt1 was the number of rare variants in the simulated gene varies and was used as an input variable in our simulation study. We set γtj1 to |log10MAFtj|, which represents the effect of the jth genetic variant of the tth gene. For the simplicity, we generated the phenotypes from the simulated linear predictor ηiq, by using it as a binarization threshold from the randomly generated variables from the multivariate normal distribution MVN. For each replicate, all rare variants were collapsed into genes.where In order to illustrate PHARAOH-GEE for investigating associations between multiple pathways and the clustered phenotypes, we analyzed a large-scale WES dataset from a Korean population cohort. Our WES dataset consists of next-generation sequencing data of 1087 individuals’ genomes, using the Illumina HiSeq2000 platform , as a part of the T2D-GENES consortium . All ind