Flavonoids are the largest class of plant polyphenols, with common structure of diphenylpropanes, consisting of two aromatic rings linked through three carbons and are abundant in both daily diets and medicinal plants. Fueled by the recognition of consuming flavonoids to get better health, researchers became interested in deciphering how flavonoids alter the functions of human body. Here, systematic studies were performed on 679 flavonoid compounds and 481 corresponding targets through bioinformatics analysis. Multiple human diseases related pathways including cancers, neuro-disease, diabetes, and infectious diseases were significantly regulated by flavonoids. Specific functions of each flavonoid subclass were further analyzed in both target and pathway level. Flavones and isoflavones were significantly enriched in multi-cancer related pathways, flavan-3-ols were found focusing on cellular processing and lymphocyte regulation, flavones preferred to act on cardiovascular related activities and isoflavones were closely related with cell multisystem disorders. Relationship between chemical constitution fragment and biological effects indicated that different side chain could significantly affect the biological functions of flavonoids subclasses. Results will highlight the common and preference functions of flavonoids and their subclasses, which concerning their pharmacological and biological properties. Flavonoids are a family of phenolic substances sharing the same backbone structure of 2-pheny1-1,4-benzopyronemay, which are very abundant in nature, being accumulated in regular human diets including flowers , fruits On the basis of common core scaffold, various combinations of substituent chemical groups on different positions may lead to structure diversity of flavonoids. This diversity can be further increased with possible variations of different functional groups, such as hydroxyl, methoxyl, carbonyl, and olefinic groups . AccordiIn this study, a comprehensive bioinformatics analysis was performed based on a large-scale dataset including 679 flavonoids and 481 corresponding targets to decipher the mechanism of action (MOA) of flavonoids with a new perspective. Results illustrated the structure activity relationship of different flavonoids subclasses, which hint the protective roles of flavonoids subclasses in different human diseases. With the accumulation of flavonoids and corresponding targets, it is possible to comprehensively investigate the MOA of flavonoids in a systematic level and interpret the therapeutic mechanism to guide the drug discovery from natural flavonoid products.Figure 1A. Further, corresponding direct targets of flavonoids were selected from 5,337 targets of natural plant products in NPASS. After that, 679 flavonoids and 481 corresponding targets were selected and listed in Supplementary Table 1. Number of targets for different flavonoid subclasses were illustrated in Figure 1B.A total number of 5,006 chemical structures of natural plant products were derived from Natural Product Activity and Species Source Database (NPASS) . Among tTargets of natural flavonoid products were mapped into Kyoto Encyclopedic of Genes and Genomes (KEGGs)  and GeneTo distinguish the specific pathway of flavonoids from other natural plant products, permutation test was implemented 1,000 times to identify the specific pathway of flavonoids’ targets by setting the 4,327 other natural plant products as background.Protein–protein interaction (PPI) networks of flavonoids’ targets were generated and modularized through Metascape . FurtherIn order to analyze the structure–activity relationship, basic physicochemical properties including molecular mass (weight), lipid water distribution coefficient (LogP), hydrogen bond receptor (NumHAcceptors), hydrogen bond donor (NumHDonors), rotatable bond (NumRotatableBonds), topological molecular polarity surface area (TPSA) and Lipinski’s Rule of five were calculated for different natural flavonoid products through RDKit .Also, the core scaffold and side chains of each natural flavonoid products were derived according to their chemical structures. Since flavones, flavonols, flavanones, flavanonol, and flavan-3-ols share the same core scaffold, the structure–activity relationships of above five subclasses were analyzed. Then, according to GO , the bioFigure 2 and Supplementary Table 2). Results showed that, the targets of flavonoids were enriched in multiple essential pathways including metabolism, genetic information processing, environmental information processing, cellular process, organismal systems, and multiple pathways which were related to human diseases such as infectious diseases and cancer. For instance, in environmental information processing, flavonoids were enriched in multiple cell signaling pathways including MAPK signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway and cAMP signaling pathway. In cellular processes, flavonoids can significantly regulate pathways such as apoptosis, focal adhesion, cell cycle, and autophagy. Further, it can be found that flavonoids’ targets were significantly enriched in several organismal systems including immune system, endocrine system and nervous system. Especially for immune system related pathways, flavonoids were enriched in Th17 cell differentiations, IL-17 signaling pathway, Toll-like, and NOD-like signaling pathways. Besides, multiple flavonoids’ targets can be found in the endocrine system pathways, such as progesterone-medicated oocyte maturation, GnRH signaling, oxytocin signaling and thyroid hormone signaling pathways. Also, nervous system-related pathways such as serotonergic synapse, and neurotrophin signaling pathways were enriched by corresponding targets. Moreover, flavonoids’ targets existed in pathways of essential human diseases such as multi-cancer, insulin resistance and infectious diseases including HTLV-1 infection, Epstein–Barr virus infection and Hepatitis B.The biological function of flavonoids’ target was deciphered through pathway enrichment analysis based on the background pathway dataset -mediated gene expression and induce anti-inflammatory activities through the PI3K and MAPK signaling pathways . Also, bFigure 3 and Supplementary Table 3). Among them, isoflavones and flavones were enriched in multi-cancer related pathways, flavan-3-ols can regulate the pathway of microRNA in cancer and isoflavones significantly enriched in the pathway of breast cancer, indicating the potential anti-cancer preferences of flavonoid subclasses. It can be noticed that natural flavan-3-ol products such as EGCG could alter epigenetic processes through DNA methylation, histone modification and miRNA regulation such as miR-92, miR-93, miR-106, miR-7-1, miR-34a, and miR-99a  of targets in here. Targets mapped in the same modules were marked in the same color and network of targets in six major modules were analyzed through KEGG and GO to discover the function of flavonoid (Table 1), top 10 enriched pathways and GO terms were list in Supplementary Table 4. Entrez Gene ID and symbol in each module were listed in Supplementary Table 5.To globally view the enrichment pathways of flavonoid, the network of all enriched targets for six flavonoids’ subclass were analyzed and decomposed into eight modules. In Targets in module 1 were mainly enriched on epoxygenase P450 pathway, VEGF signaling pathway, fluid shear stress and atherosclerosis pathway, which related with cardiovascular regulations such as vascular dilatation. Meanwhile, the enrichment of pathways for FoxO signaling, mitotic cell cycle regulation, cell cycle arrest, negative regulation of cell cycle showed that cell cycle related functions can also been reflected in module 1. For module 2, targets were significantly enriched on pathways for T-cell receptor signaling, NF-kappa B signaling, inflammatory mediator regulation of TRP channels, immune response-regulating cell surface receptor signaling, immune response activating cell surface receptor signaling and immune response-activating signal transduction, which indicated the function of module 2 was related to immune inflammation. Further, it can be noticed that targets in module 3 were significantly enriched on pathways of insulin resistance, peptidyl-serine phosphorylation, peptidyl-serine modification, cellular response to nitrogen compound, cellular response to organonitrogen compound, positive regulation of kinase activity and cellular response to hormone stimulus, which illustrated the function of module 3 were closely related with functions of cell response to stimulation and hormone regulation. Moreover, the enrichment in neuroactive ligand-receptor interaction, cAMP signaling, calcium signaling, serotonergic synapse, cGMP-PKG signaling and dopaminergic synapse pathways reflected the targets in module 4 were related to neuromodulation and signal transduction. In addition, module 5 was found to relate with human diseases such as cancer and viral-related diseases since most targets were enriched in viral carcinogenesis pathway, cancer and infectious disease related pathways. For module 6, targets were enriched in flavonoids glucuronidation, glucuronate pathway, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, which meant functions of module 6 were mainly embodied in the process of glyoxylic acid metabolism.In order to understand the function differences among flavonoid subclass, targets of six flavonoid subclasses were mapped into above modules. Major nodes reflected the common targets for each flavonoid subclass.Supplementary Figure 1). For example, MAPK 14 in module 3 was found to participate in multiple cellular processes including cell proliferation, differentiation, transcriptional regulation and development . For example, nodes such as CYP1A1, CYP1A2, CYP1B1 in module 1 belonged to cytochrome P450 (CYPs) family, which could enrich in epoxygenase P450 pathway and relate with cardiovascular-related functions such as vascular ectasia. APEX1 in module 2 was found affecting cancer RNA metabolism and triple-negative breast cancer . Similar to flavanones, nodes such as CYPs in module 1 and APEX1 in module 2 were also detected in flavanonol, which indicated the potential function of it on cardiovascular and cancer related functions. Meanwhile, MAPT in module 2 was found closely related with neurodegenerative diseases, including Parkinson’s disease  and flavonols  were distributed in all six major modules, which indicated the broad function of compounds from those two subclasses. Besides common nodes such as CYPs, APEX1, MAPT, which reflect the same function for cancer, cardiovascular and neurodegenerative as other flavonoid subclasses flavones contains other nodes such as ALDH1A1 in module 5, which reflect potential associations with cancer invasion . Previous researches indicated that BRCA1 in module 1 was associated with risk of estrogen-receptor-negative breast cancer . Results showed that the rotatable bonds (NumRotatableBonds) and molecular weight (Weight) in different subclasses are quite similar, however, difference can be detected in H-bond acceptor (HAcceptor), H-bond donor (HDonor), lipid-water partition coefficient (LogP), and Topological polarity surface area (TPSA) for different flavonoid subclasses. LogP and TPSA could affect the absorption and distribution of drug, which should contain a certain degree of dissolution and appropriate lipid water distribution to be effective. Further, to provide nervous system activity, drug with larger liposolubility may be easier to pass the blood–brain barrier (BBB) . For example, apigenin of flavones, quercetin, and genistein of isoflavones, hesperidin of flavanones and rutin, quercetin, and kaempferol of flavonols would have the ability to across the BBB . Among them, genistein and apigenin could provide stronger ability to across the BBB since their larger LogP and lower TPSA  . The noner (BBB) . Naturalwer TPSA , which iSupplementary Figure 8). It can be found that for flavones, flavonols, flavanones, flavanonol, and isoflavones, near half of the compound can pass ROF, while for flavan-3-ols the percentage of ROF-passed compounds is extremely low, which indicated different drug-likeness of flavonoid subclasses.Further, in order to evaluate the drug-likeness of flavonoids, Lipinski’s Rule of Five (ROF) of different flavonoid subclasses were analyzed . For example, in rule 01–07, side chain such as hydroxyl in position 1 on the core scaffold structure of flavanones may assist the negative regulation of PERK-mediated unfolded protein response. Also, in rule 09–13, hydroxyl side chain in position 1, 3, 10, 11, and 12 on the core scaffold structure of flavonols closely related with error-prone translesion synthesis. Among them, natural products such as myricetin, robinetin, tricetin could against hydrogen peroxide-induced DNA damage and might reduce the risk of multiple cancers . Previous studies indicated that the number and position of glycoside and hydroxyl groups in flavonoids would affects the ability of permeation (Supplementary Table 6). Analogously, flavonols meet rule 23, which contains pentose in position 1 and hydroxyl in position 3, 11 will increase the bioactivity for 32.37 times than others for the function of DNA topological change. Natural products such as kaempferol glycoside could targeting the DNA topoisomerase, which closely related with DNA replication and cell cycle . Considering the substituent groups and positions of side chain, the relationship between structure and bioactivity analyzed in here may help to enhance the understanding of flavonoids and its potential ability for new drug discovery.Since flavonoids contain the same core scaffold, the functional difference was mainly related with the substituent groups. Relationship between chemical constitution fragment and biological effects indicated that different side chain can significantly affect the activity of flavonoids on the same target. Flavonoids with structures meet the corresponding rules will enhance the bioactivity of molecules for dozens of times. For 26 rules summarized in this article, the bioactivities were increased over three times at least. Among them, seven rules could enhance the bioactivities for over 10 times, and two rules (rule 23 and 26) could increase the activities for 30 times (TQ and KT wrote the manuscript. DW and LY conceived and designed the experiments. HY and TQ analyzed and interpreted the results. TQ and QW modified the manuscript. ZC and KT supervised the project. All authors have read and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers. Cancer is a multifactorial disease which is conditioned by alterations arising from biological, chemical, radiological impacts, as well as inherited. Tumor transformation is characterized by frequent accumulation of genetic mutations . The pivRecently, high throughput studies of cancer genomes were initiated to identify mutation enrichment specific for the different cancer types. For example, the large scale projects like Wellcome Trust Sanger Institute's Cancer Genome Project, the International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas (TCGA) showed very high molecular heterogeneity of cancer, not only between different cancer types, but also among the individual tumors of the same type –8. This Identification of informative and robust genetic markers of cancer is one of the major tasks of the contemporary biomedicine. Many reports have been published featuring cancer-specific mutations and gene fusions, as well as epigenetic alterations –13. SomeDespite recent success in high throughput analysis of molecular basis for cancer transformation, traditionally the focus is being made on the roles of the individual genes , 17. Howpathway activation strength (PAS), is a cumulative value aggregating relative expression levels of the enclosed gene products in relation with their functional roles in a pathway  proportionate to the relative number of mutated genes in a pathway and developed a specific bioinformatic algorithm for quantization thereof. Using high throughput gene mutation profiles, we identified affected molecular pathways that specifically define the major human cancer types. We took cancer somatic mutation data published in the TCGA project for 5,956 patients representing 15 different cancer types. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. The robustness of mutation-based molecular pathway approach dramatically exceeded that for the individual gene biomarkers. This trend was also reproducible when only truncating mutations were considered for PI calculations, thus confirming consistency of the new method. Finally, we provide a list of 660 novel robust cancer type-specific pathway mutation biomarkers.Here, for the first time, we propose a new type of molecular biomarkers based on DNA mutation impacts on the molecular pathways. We introduced a quantitative metric termed The Cancer Genome Atlas (TCGA) project  project . We down project . The TCG project . Totally project . The datIn parallel, for the additional analysis we selected a fraction of gene truncating mutations that possibly lead to the loss of gene function. We meant truncating mutations as those having the following labels in COSMIC description: “Deletion-Frameshift,” “Insertion-Frameshift,” “Complex-frameshift,” “Substitution-Nonsense.” Totally, we selected 161,760 truncating mutations in 5,297 tumor samples corresponding to the same 15 cancer types  was performed with package made 4 in R.nMR) and pathway- (PI) specific mutation metrics.For clustering, we used Ward's criterion and Ward.d2 algorithm  for the In this study, we applied quantitative molecular pathway approach to human cancer DNA mutation data. We developed algorithm for quantization of mutational impact on molecular pathways and applied it for screening of 1,748 human pathways. The DNA mutation data was extracted from the Catalog Of Somatic Mutations In Cancer (COSMIC) database . For thePathway instability (PI) scoring for a molecular pathway depends on the mutation frequencies in the genes participating in this pathway. To assess mutation burden of the individual genes, we introduced Mutation rate (MR) value calculated according to the formula:MRn is the Mutation rate of a gene n; N mut is the total number of mutations identified for a gene n in a group of samples g; N samples (g) is the number of samples in a group g. However, the MR values strongly positively correlated with the lengths of gene coding DNA sequences (CDS)s, Spearman correlation was 0.798 for all mutations and 0.629 for truncating mutations, p < 2.2e-16 in both cases  value expressed by the formula:To avoid the bias linked with the CDS lengths, we next introduced a nMRn is the Normalized mutation rate of a gene n; MRn is the Mutation rate of a gene n; Length CDS(n) is the length of CDS of a gene n in nucleotides.where nMR did not correlate with the size of CDS for the respective genes  scores for every pathway to estimate their relative enrichments by cancer-specific mutations. Pathway instability is expressed by the formula:We next calculated PIp is Pathway instability score for a pathway p; nMRn is the Normalized mutation rate of gene n; PGp.n is pathway-gene indicator that equals to one if gene n belongs to pathway p, or equals to zero if gene n doesn't belong to pathway p; Np–total number of gene products that belong to pathway p. Unlike previous metrics for pathway activation scoring based on gene expression data, the current equation for PI calculation doesn't have coefficients defining activator or repressor molecular roles of genes participating in a molecular pathway under investigation (Pathway instability (PI)  values for all individual genes and by the Pathway instability (PI) values for 1,748 molecular pathways. As shown by the principal component analysis (PCA), the complete sets of 19,872 nMR biomarkers and 1,748 PI biomarkers could not distinguish between the tumor localization types  values. The AUC value is the universal biomarker robustness characteristics depending on its sensitivity and specificity(i) for comparing every separately taken tumor type  vs. all other tumors, and (ii) for all possible pairwise comparisons among the tumor types.However, many molecular pathways had characteristic PI scores that were clearly distinctive of the different tumor types, as shown by the high ecificity. It variecificity. This stecificity–46. We pnMR characteristics of every sample. The tests were performed for all mutations and in parallel—for only truncating mutations (Supplementary Tables nMR) with the aggregated pathway-based mutation characteristics (PI).In parallel, the same AUC tests were performed also for every gene PI) compared to gene based (nMR) approach in finding good quality biomarkers in all types of the comparisons made. For example, for the analyzes when one tumor localization was compared against fourteen others, the total number of good quality (AUC>0.75) biomarkers was 660 for all mutations and 21 for truncating mutations for the pathways (PI), compared to only six for all mutations and one for truncating mutations for the individual genes (nMR). Similarly, for the pairwise comparisons we identified totally 32,594 good quality PI biomarkers vs. only 226 nMR biomarkers for all mutations  for the maximum number of cancer types. Top 25 most informative biomarker pathways are shown on Table ROC AUC biomarkers, a distance matrix can be created for all the tumor localizations under comparison, and a clustering dendrogram can be built. In this study, we used pairwise comparisons to analyze common features and clustering of 15 tumor types. The distance matrix was built separately for the gene (nMR) and the pathway (PI) mutation biomarkers , for example, all fourteen comparisons having no good biomarkers according to PI values, also had no nMR biomarkers there , but they were identified in a smaller group of cancer types . Second, distances between the cancer localizations were more degenerated on the nMR tree . Pathway instability (PI) scores are then calculated showing relative mutation burden of each pathway in every biosample. These findings can be valuable atabases –40.Pathway instability (PI) mutation data can be used as the additional criteria for differential diagnostics in oncology. Tumor relationship based on the pathway specific genetic signatures such as those shown on Figure Taken together, our data suggest that in addition to better understanding of fundamental tissue-specific mechanisms of carcinogenesis, molecular pathway approach can be beneficial in finding reliable tumor type-specific biomarkers for identification of tumor origin in the low- or non-differentiated tumor histotypes. Although a major focus of this study was made on the specific deviations in PI between the cancer types, we could also identify molecular pathways that were strongly mutated in all the cancer types, as previously predicted in the literature , 21.Finally, we provide the list of 660 marker molecular pathways that distinguish between the major human cancer types. This list can be helpful for better understanding molecular grounds of carcinogenesis and for further investigations in molecular oncology and drug development.https://cancer.sanger.ac.uk/cosmic/download).The datasets analyzed in this study are available in COSMIC public repository (COSMICv76; CosmicGenomeScreensMutantExport.tsv.gz, MZ developed algorithms, did mutation/Pathway instability analyses, planned the research, and wrote the manuscript. SR planned the research, extracted, and filtered cancer mutation data. MS completed the molecular pathway database. NB developed algorithms, did statistical analyses, and planned the research. AB developed algorithms, planned the research, and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.Pathway-centric approaches are widely used to interpret and contextualize - PathwaySeveral integrative resources have been developed, including meta-databases [e.g., Pathway Commons , MSigDB via statistical enrichment analysis, also known as pathway enrichment. The task of navigating through the continuously developing variants of enrichment methods has been undertaken by several recent studies which benchmarked the performance of these techniques   are combined. This approach exploits the pathway mappings and harmonized pathway representations described in our previous work  to facilitate reproducing the results with other databases or datasets in the future.The aim of this work is to systematically investigate the influence of alternative representations of the same biological pathway  on the results of statistical enrichment analysis s (SPIA)  using fis (SPIA) . In addi ssGSEA;  can be ious work . We demoIn the first two subsections, we describe the pathway resources and the clinical and genomic datasets we used in benchmarking. The following sections then outline the statistical enrichment analysis and predictive modeling conducted in this study. Finally, in the last two subsections, we describe the statistical methods and the software implemented to conduct the benchmarking.Numerous viable pathway databases have been made available to infer biologically relevant pathway activity . In thisIn order to systematically compare results yielded by different databases, we retrieved the contents of KEGG, Reactome, and WikiPathways using ComPath  and convTo test the potential utility of an integrative pathway resource, we used equivalent pathways across the three databases that were manually curated in our previous work . Detailed statistics on the number of pathways from each resource are presented in We next compared the latest versions of pathway gene sets from KEGG, Reactome, WikiPathways, and MPath with pathway gene sets from MSigDB, a highly cited integrative pathway database containing older versions of the KEGG and Reactome gene sets . We downhttps://gdc.cancer.gov) portal and cBioportal  on 14-03-2019. RNA-seq gene expression data subjected to an mRNA quantification analysis pipeline for BRCA, KIRC, LIHC, OV, and PRAD TCGA datasets were queried, downloaded, and prepared from the GDC through the R/Bioconductor package, TCGAbiolinks   . The dat symbols , where pIn this subsection, we describe three different classes of pathway enrichment methods that we tested: 1) statistical overrepresentation analysis (ORA); 2) functional class scoring (FCS); and 3) pathway topology (PT)-based enrichment Figure 2q value <0.05 using a one-sided Fisher’s exact test , using normalized RNA-seq expression quantifications (FPKM-UQ) obtained for the BRCA, KIRC, LIHC, and PRAD datasets containing both normal and tumor samples  of pathway analogs across each possible pair of databases  and in each statistical enrichment analysis  with the Wilcoxon signed-rank test. It assessed the average rank difference of the pathway analogs and reported how significantly different the results were for each database pair. Importantly, we only tested statistical enrichment of the analogous pathways in order to avoid statistical biases due to differences in the size of pathway databases.To better understand the impact of database choice, we compared the raw ssGSEA was conducted to summarize the gene expression profile mapping to a particular pathway of interest within a given patient sample, hence resulting in a pathway activity profile for each patient. We then evaluated the different pathway resources with respect to three machine learning tasks:Prediction of tumor vs. normalPrediction of known tumor subtypePrediction of overall survivalvia a 10 times repeated 10-fold stratified cross-validation. Importantly, tuning of elastic net hyper-parameters  was conducted within the cross-validation loop to avoid over-optimism , while OV, which only contains tumor samples, was omitted. We performed this classification using a commonly used elastic net penalized logistic regression model . Predictoptimism .via a grid search. We assessed the multi-class classifier performance in terms of accuracy, precision, and recall.The second task was to train and evaluate multi-label classifiers to predict tumor subtypes using sample-wise pathway activity scores generated from ssGSEA. This task was only conducted for the BRCA and PRAD datasets, similar to the work done by The third task was to train and evaluate machine learning models to predict overall survival of cancer patients. For this purpose, a Cox proportional hazards model with elastic net penalty was used . PredictTo understand the degree to which the observed variability of area under the ROC curve (AUC) values, accuracies, and c-indices could be explained by the actually used pathway resource, we conducted a two-way analysis of variance (ANOVA). The ANOVA model had the following form:via an F test. In addition, we performed Wilcoxon tests analysis to understand specific differences between databases in a dataset-dependent manner.We then tested the significance of the database factor The workflow presented in this article consists of three major components: 1) the acquisition and preprocessing of gene set and pathway databases; 2) the acquisition and preprocessing of experimental datasets; and 3) the re-implementation or adaptation of existing analytical pipelines for benchmarking. We implemented these components in the pathway_forte Python package to facilitate the reproducibility of this work, the inclusion of additional gene set and pathway databases, and to include additional experimental datasets.https://github.com/bio2bel) in order to provide uniform access to the underlying databases and to enable the reproduction of this work as they are updated. Each Bio2BEL package uses Python’s entry points to integrate in the previously mentioned ComPath framework in order to support uniform preprocessing and enable the integration of further pathway databases in the future, without changing any underlying code in the pathway_forte package. The network preprocessing defers to PathMe (https://github.com/pathwaymerger). Because it is based on PyBEL (https://github.com/pybel), it is extensible to the growing ecosystem of BEL-aware software.The acquisition of KEGG, MSigDB, Reactome, and WikiPathways was mediated by their corresponding Bio2BEL Python packages (https://github.com/pandas-dev/pandas). Unlike the pathway databases, which were amenable to standardization, the preprocessing of each new dataset must be bespoke.While the acquisition and preprocessing of experimental datasets is currently limited to a subset of TCGA, it is extensible to further cancer-specific and other condition-specific datasets. We implemented independent preprocessing pipelines for several previously mentioned datasets using extensive manual curation, preparation, and processing with the pandas Python package  such that the results presented in each section of this work can be generated with a corresponding command following the guidelines described by https://github.com/PathwayForte/pathway-forte, its latest documentation can be found at https://pathwayforte.readthedocs.io, and its distributions can be found on PyPI at https://pypi.org/project/pathway-forte.The source code of the pathway_forte Python package is available at https://github.com/pytest-dev/pytest) as a testing framework, coverage (https://github.com/nedbat/coveragepy) to assess testing coverage, sphinx (https://github.com/sphinx-doc/sphinx) to build documentation, flake8 (https://github.com/PyCQA/flake8) to enforce code and documentation quality, setuptools (https://github.com/pypa/setuptools) to build distributions, pyroma (https://github.com/regebro/pyroma) to enforce package metadata standards, and tox (https://github.com/tox-dev/tox) as a build tool to facilitate the usage of each of these tools in a reproducible way. It leverages community and open-source resources to improve its usability by using Travis-CI (https://travis-ci.com) as a continuous integration service, monitoring testing coverage with Codecov (https://codecov.io), and hosting its documentation on Read the Docs (https://readthedocs.org).The pathway_forte Python package has a tool chain consisting of pytest (Computations for each of the tasks were performed on a symmetric multiprocessing (SMP) node with four Intel Xeon Platinum 8160 processors per node with 24 cores/48 threads each  and 2.1-GHz base/3.7-GHz Turbo Frequency with 1,536-GB/1.5-TB RAM (DDR4 ECC Reg). The network was 100 GBit/s Intel OmniPath, storage was 2× Intel P4600 1.6-TB U.2 PCIe NVMe for local intermediate data and BeeGFS parallel file system for Home directories. The results of the benchmarking study have been divided into two subsections for each of the pathway methods described above. We first compared the effects of database selection on the results of functional pathway enrichment methods. In the following subsection, we benchmarked the performance of the pathway resources on the various machine learning classification tasks conducted.q value <0.01) in the LIHC dataset for Reactome, its analogs in WikiPathways  Production”) and KEGG  were not. However, the pathway was found to be significantly enriched in MPath. Such differences were similarly observed for the “Notch signaling” pathway in the PRAD dataset, in which the pathway was highly statistically significant  for Reactome and MPath, but showed no statistical significance for KEGG and WikiPathways. Similar cases were systematically observed for additional pathway analogs and super pathways, demonstrating that marked differences in rankings can arise depending on the database used.As illustrated by our results, pathway analogs from different pathway databases in several cases showed clearly significant rank differences Figure 3q < 0.05) in MPath.Similar to ORA, GSEA showed significant differences between pathway analogs across databases in several cases Figure 3The final of the three statistical enrichment analyses conducted revealed further differences between pathway analogs across databases. As expected, differences in the results of analogous pathways were exacerbated on topology-based methods compared with ORA and GSEA, as these latter methods do not consider pathway topology  Figure 3via the AUC and precision-recall curve . The AUC indicated no overall significant effect of the choice of pathway database on model prediction performance . We used Wilcoxon tests to investigate if each pair of distributions of the accuracies based on each database were different, but did not achieve statistical significance (q < 0.01) after Benjamini–Hochberg correction for multiple hypothesis testing. While the lack of significance is probably due to the limited amount of datasets (only two contained subtype information) and measurements, we would like to note that MPath showed the best classification metrics (similar to the previous classification task).We next compared the prediction performances of a multi-class classifier predicting known tumor subtypes of BRCA and PRAD using ssGSEA-based pathway activity profiles. p = 0.28, ANOVA F test). A limiting factor of this analysis is the fact that overall survival can generally only be predicted slightly above chance level (c-indices range between 55% and 60%) based on gene expression alone, which is in agreement with the literature  led to more biologically consistent results and, in some cases, improved prediction performance.Generating a merged dataset such as MPath is non-trivial. We purposely restricted this study to three major pathway databases because of the availability of inter-database pathway mappings and pathway networks from our previous work which enabled conducting objective database comparisons. The incorporation of additional pathway databases into MPath would first require the curation of pathway mappings prior to conducting the benchmarking study, which can be labor-intensive. Furthermore, performing the tasks described in this work comes with a high computational cost Table 1.Our strategy to build MPath is one of many possible approaches to integrate pathway knowledge from multiple databases. Although alternative meta-databases such as Pathway Commons and MSigDB do exist, the novelty of this work lies in the usage of mappings and harmonized pathway representations for generating a merged dataset. While we have presented MPath as one possible integrative approach, alternative meta-databases may be used, but would require that researchers ensure that the meta-databases’ contents are continuously updated .Our developed mapping strategy between different graph representations of analogous pathways enabled us to objectively compare pathway enrichment results that otherwise would have been conducted manually and subjectively. Furthermore, they allowed us to generate super pathways inspired by previous approaches that have shown the benefit of merging similar pathway representations . In thisOne of the limitations of this work is that we restricted the analysis to five cancer datasets from TCGA and we did not expand it to other conditions besides cancer. The use of this disease area was mainly driven by the availability of data and the corresponding possibilities to draw statistically valid conclusions. However, we acknowledge the fact that data from other disease areas may result in different findings. More specifically, we believe that a similar benchmarking study based on data from disease conditions with an unknown pathophysiology  may yield even more pronounced differences between pathway resources. Additionally, further techniques for gene expression-based pathway activity scoring could be incorporated, such as Pathifier or SAS .All datasets generated/analyzed for this study are included in the article/DD-F conceived and designed the study. SM and DD-F conducted the main analysis and implemented the Python package. HF supervised methodological aspects of the analysis. CH and AG assisted technically in the analysis of the results. MH-A acquired the funding. SM, HF, CH, MH-A, and DD-F wrote the paper.This work was supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking under AETIONOMY (grant number 115568), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution.HF received salaries from UCB Biosciences GmbH. UCB Biosciences GmbH had no influence on the content of this work.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.This integrative genomics study identified both well-characterized  and novel biological processes  involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.The online version of this article (10.1186/s12918-018-0671-x) contains supplementary material, which is available to authorized users. It is mainly characterized by vascular remodeling, epidermal hyper-proliferation, and inflammation . The devtability , 5 and lConventional GWAS and EWAS examine individual genetic and epigenetic markers one at a time and typically only reveal a small number of top signals due to severe multiple testing penalty. As such, they are not adequately powered to identify genes and loci with moderate to subtle effect sizes that are part of the missing heritability , nor areWe and others have recently demonstrated that integration of multidimensional genomic resources into tissue-specific network models can improve our understanding of human complex diseases –12. In tIn this study, a comprehensive integration of tens of diverse datasets related to psoriasis using Mergeomics allowed us to unravel the gene regulatory networks that capture the full range of genetic/epigenetic perturbations (from strong to moderate and subtle), elucidate the relationships among the disease susceptibility genes/pathways informed by genetic and/or epigenetic associations, illustrate the key commonalities and differences between genetic and epigenetic mechanisms, and pinpoint key regulators for psoriasis. These findings built on multi-omics big data integration provide systems-level insights into the etiology of psoriasis and potential treatment avenues.We present the essential methods in the main text and the detailed descriptions can be found in Additional file www.ncbi.nlm.nih.gov/gap) with accession phs000019.v1.p1  database for studies involving gene expression profiling for psoriasis. To avoid systematic bias by different experimental platforms and small sample size, we obtained the studies using the following criteria: a) the microarray studies were performed on Affymetrix Human Genome U133 platforms (GPL570/GPL571), which are the most commonly used in the psoriasis studies; b) the sample size of both the control and psoriasis groups should be over three. Applying these criteria yielded 696 gene expression profiles , a web-based program that employs the Bioconductor packages GEOQuery [We detected differentially expressed genes and differentially methylated CpG sites between psoriasis and normal skin using GEO2R  package  was applThe knowledge-based biological pathways included 1827 canonical pathways from the Reactome, Biocarta, and KEGG databases , 24 and p values of the corresponding markers are extracted to test for enrichment of association signals based on a chi-squared-like test statistic, followed by FDR estimation  affected by molecular markers (genetic loci or methylation sites) associated with psoriasis, we used MSEA in the Mergeomics package, which has been demonstrated with superior performance compared to other gene set enrichment analysis methods . For a gAB × rBA)0.5, where rAB is the proportion of genes in A that are also in B while rBA is the proportion of genes in B that are also in A. We set the overlap ratio r cutoff to > = 0.2 and also required Fisher’s exact tests for the number of shared genes among pathways to be statistically significant (FDR < 0.05). If a resulting superset had more than 500 genes, we trimmed it down to the core genes that were shared across overlapping gene sets.We merged the common pathways associated with psoriasis with FDR < 5% in both GWAS and EWAS using a merging algorithm in Mergeomics . BrieflyThe Bayesian gene regulatory networks of skin and blood tissues were retrieved from GIANT . GIANT ghttp://www.coremine.com/medical).To investigate the relevance of the identified KDs to psoriasis based on previous literature evidence, we assessed relevant information from different resources: 1) Mouse Phenotypes from the Mouse Genome Database (MGD) , 2) the Besides literature mining, we utilized RNA-seq transcriptomes (GEO accession: GSE54456) from 92 psoriatic and 82 normal punch biopsies , as indeWe first analyzed potential changes in gene-gene relationships involved in psoriasis based on gene co-expression patterns. We retrieved skin transcriptomic datasets from 696 samples in eight transcriptome studies .The above transcriptome-based analyses are correlative in nature and cannot differentiate the upstream, disease-causing processes from those that are downstream, reactive to the disease condition. Genetic signals from GWAS, due to their inheritable nature, precede disease development and therefore have the power to infer causality. To this end, we integrated the 68 differential coexpression modules and the DEG set from the above skin transcriptome analyses with the full statistics of a large psoriasis GWAS from the Collaborative Association Study of Psoriasis  using MSWe found 14  differential coexpression modules and 105 (out of 1827) canonical pathways to be significantly enriched for genetic risk variants of psoriasis , suggesting that both genetic and epigenetic risks could converge on similar sets of genes and pathways  into supersets (8) while keeping the non-overlapping ones (14) intact  or EWAS 100) into relatively independent supersets separately, resulting in 46 supersets from GWAS and 57 from EWAS. As these supersets may still share gene members and can be highly overlapping, we then applied Fisher’s exact test with FDR adjustment to estimate the overlaps among GWAS, EWAS, and Common supersets. If a superset from one list does not meet FDR < 0.05 threshold in enrichment analysis with any supersets from the other lists, we define them as a “unique” superset. We identified 16 and 13 unique supersets for GWAS and EWAS respectively  common to GWAS and EWAS and to detect important hub genes (termed key drivers or KDs herein), we used a weighted key driver analysis (wKDA) implemented in Mergeomics [97 genes ICAM1, IL15, STAT1, TNFAIP3, and GRB2 to be the network hubs connecting numerous genes in the psoriasis-associated supersets  and EWAS (p < 1e-5) were mostly peripheral nodes in the networks  and potential drug targets  [TNFAIP3, STAT1, NFKB2, MCL1, LCK, IL15, IKZF1, and ICAM1) and are involved in immune system and cell migration. Besides, genes in the IL23/IL17 immune positive control pathway are over-represented in the networks  , 41 wereBPTF, RASA2, MFF, and ALG8) tend to be surrounded by partner genes with moderate to strong GWAS signals . The top KD subnetworks for the EWAS-unique supersets in the skin tissue are enriched with strong EWAS signals  or EWAS signals (p < 1.0E-5). Our search revealed that the 47 and 83% of the KDs for the common supersets are associated with psoriasis or relevant conditions in > = 2 or at least one of the above criteria, respectively; five KDs  for the GWAS- or EWAS-unique supersets were associated with psoriasis or relevant conditions in at least one of the criteria  the 36 KDs shared between psoriasis supersets and tissue networks for the common supersets between GWAS and EWAS, 2) 19 KDs for GWAS-unique supersets, and 3) 7 KDs for EWAS-unique supersets (see Methods for details). Briefly, we queried the KDs for evidence of psoriasis association based on the following criteria: gene knockout and mouse mutation models, psoriasis gene signatures [HLA-G, STAT1, TNFAIP3, BIRC3, TNFSF10, HLA-E, HLA-A, IL1B, LCK, and IL15 , which are completely independent of the other datasets used in the previous analyses. Among the 62 KD subnetworks (36 KDs from the shared supersets and 26 KDs from the GWAS/EWAS-unique supersets),10 subnetworks showed significant up-regulation patterns in psoriasis vs control at FDR < 5%), including High-throughput genomic studies have revealed a plethora of genomic and epigenomic changes contributing to psoriasis via GWAS, EWAS, and transcriptome profiling studies. However, an integrative systems analysis fully utilizing the complementarity of diverse omics data has not been conducted to capture a comprehensive view of disease regulation. To address this challenge, we integrated psoriasis GWAS, EWAS, functional genomics information (eQTLs and ENCODE), knowledge-driven pathways, transcriptome, and data-driven networks to uncover biological processes and key regulators mediating the actions of psoriasis genetic and epigenetic signals. This systematic multi-omics integration unraveled both shared and unique biological processes and gene networks associated with psoriasis between GWAS and EWAS, uncovered interactions between psoriasis genes and processes, and prioritized potential central regulators of disease pathogenesis. The comprehensive insights obtained would not have been possible without a thorough utilization and integration of the diverse existing datasets.Many of the identified pathways replicated previous findings. For example, the IL17 pathway, one of the most well-known immune processes underlying psoriasis pathogenesis, exhibited strong enrichment for psoriasis associated genetic and epigenetic variants. Other pathways replicated based on genetic evidence include “NO2-dependent IL12 pathway”, “Th1/Th2 pathway”, and “Natural killer T cell” , 42. In As our multi-omics integration leveraged the full spectrum of disease association  as well as functional information such as eQTLs, ENCODE, pathways, and gene networks, we observed numerous novel processes for psoriasis, such as the BCAA, ER phagosome, and proteosome pathways in GWAS and the platelet and coagulation, lipid metabolism, insulin signaling, adipokine signaling, collagen formation, and cell-cell communication pathways in EWAS Fig. . The ideBy investigating both GWAS and EWAS in the same study, we found converging pathways both genetically and epigenetically associated with psoriasis, making these a robust and prioritized set of pathways for future mechanistic and therapeutic investigations. These common pathways in GWAS and EWAS can be partitioned into 22 categories, including many previously implicated processes such as “Cytokine signaling”, “JAK/STAT signaling”, and “PPARA pathway”, and novel pathways such as “ABC transporters” and “Endocytosis”. Most identified drug transporters belong to the ATP-binding cassette family expressed in the skin and might be associated with drug-induced psoriasis , 52.HLA-A, ICAM1, IL15, STAT1, and TNFAIP3) and novel genes which may regulate immune processes and cell cycle . Among the KDs that are known psoriasis genes, STAT1 from IL23/IL17 pathway and cytokine-cytokine receptor interaction appears to be a KD in both the blood and skin networks. Among the novel KDs, the protein encoded by CTSH is a lysosomal cysteine proteinase important in the overall degradation of lysosomal proteins. Other cathepsin family members, such as CTSS [CTSK [CTSD [CTSH was surrounded by CTSS, CTSD and other critical factors involved in complement and adhesion  in the psoriasis network  encodes an interferon-induced transmembrane protein that contributes to the control of cell growth through a multimeric complex involved in the transduction of anti-proliferative and homotypic adhesion signals. It is induced by IFN-γ in primary keratinocytes and plays a role in keratinocyte apoptosis in atopic dermatitis patients [Beside retrieving the overlapping molecular processes informed by both GWAS and EWAS in conjunction with other functional evidence, our network modeling demonstrated that the psoriasis-associated pathways interconnect via network hub genes Fig. , includi as CTSS , CTSK [5SS [CTSK , and CTSSK [CTSD , have beignaling . GRB2 wasiveness . IFITM2 patients . These pCompared to previous genomics studies of psoriasis, our study is the most comprehensive in terms of the diversity of data types included , the number of data sets, and the variety of analytical strategies. Importantly, our study utilizes the full spectrum of genetic and epigenetic association signals instead of only the top genome-wide significant hits, which offers unique power to capture the missing heritability and mechanisms. We also incorporated function-guided mapping of genetic signals to target genes using eQTLs and ENCODE data and included tissue-specific gene expression patterns. As such, in this single study we were able to uncover numerous known pathways and processes revealed through decades of psoriasis research, in addition to a number of novel processes. Additionally, our study is the first to compare GWAS and EWAS to map the convergence and divergence in the genetically and epigenetically perturbed disease processes. Moreover, our network modeling enables a bird’s eye view of the pathogenic networks and offers a prioritized list of novel regulators as potential therapeutic targets.We acknowledge the following limitations in our study. First, we could only access one full GWAS dataset out of more than 10 published GWAS studies, highlighting the challenges in data access . EncouraOur comprehensive integrative genomic approach helps unveil the molecular mechanisms underlying pathogenesis of psoriasis from genetic and epigenetic aspects. In addition, we identified potential central regulators of psoriasis gene networks, which opens opportunities for future experimental testing and may aid the clinical diagnosis and treatment of psoriasis.Additional file 1:Table S1. Transcriptome, DNA methylome, and GWAS studies included in the study. Table S2. Knowledge-based artificial pathways from published GWAS studies and large-scale meta-profiling of diverse collections of gene expression data sets. Table S3. Differential coexpression modules in psoriasis. Table S4. Gene signatures in the psoriasis transcriptomic study. Table S5. MSEA results in GWAS. Table S6. MSEA results in EWAS. Table S7. Common Pathways between GWAS and EWAS. Table S8. GWAS/EWAS-unique supersets and key driver analysis. Table S9. Key driver analysis of Common supersets in psoriasis skin and blood networks. Table S10. In silico mining of the key driver genes using bioinformatics tools. Table S11. Gene expression perturbations of key driver subnetwork in lesional skin tissue in psoriasis patients. (ZIP 358 kb)


Increasing age negatively affects different phases of bone fracture healing. The present study aimed to explore underlying mechanisms related to bone fracture repair in the elderly. GSE17825 public transcriptome data from the Gene Expression Omnibus database were used for analysis. First, raw data were normalized and differentially expressed genes (DEGs) were identified. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were implemented to evaluate pathways and DEGs. A protein–protein interaction (PPI) network was then constructed. A total of 726, 861, and 432 DEGs were identified between the young and elderly individuals at 1, 3, and 5 days after fracture, respectively. The results of GO, KEGG, and PPI network analyses suggested that the inflammatory response, Wnt signaling pathway, vascularization-associated processes, and synaptic-related functions of the identified DEGs are markedly enriched, which may account for delayed fracture healing in the elderly. These findings provide valuable clues for investigating the effects of aging on fracture healing but should be validated through further experiments. The eldFracture healing is a complex but well-orchestrated process that can be temporally divided into three partially overlapping processes, namely, inflammation, repair, and remodeling –9. In thHowever, changes in the inflammatory system occur with age. In older animal models, prolonged inflammation results in delayed chondrogenesis and smaller callus size , 13. AgeIn this study, transcriptional analysis of GSE17825 was used to illustrate molecular mechanisms by which age affects fracture healing. Specifically, differentially expressed genes (DEGs) at three different time-points, namely, 1, 3, and 5 days after fracture, were evaluated by pathway and functional enrichment analysis. A protein–protein interaction (PPI) network was then constructed using these DEGs. These analyses revealed several molecular mechanisms that may contribute to age-related changes in fracture healing.Transcriptome profiles of GSE17825, which was submitted by Liang et al., were obtained from the National Centre of Biotechnology Information (NCBI) Gene Expression Omnibus database . GSE17825, which comprises a total of 18 chips, including nine old group (8-month-old mice) and nine young group (6-week-old mice) samples, was based on the platform of the PL1261 Affymetrix Mouse Genome 430 2.0 Array. For each group, fracture calluses were dissected and total RNA was isolated at each of the three time-points (n = 3 per time-point).The robust multichip average algorithm from the Oligo package  was used to preprocess non-normalized raw data by background correction normalization, probe summary, and log2 transformation . Probe iIn this study, several paired groups were compared as follows: (i) old vs. young group at 1 day; (ii) old vs. young group at 3 days; and (iii) old vs. young group at 5 days. Student's t-test was used for comparisons. Genes with fold changes > 2.0 and P < 0.05 were considered DEGs.The Database for Annotation, Visualization, and Integrated Discovery  can provide functional annotation of enormous quantities of genes derived from different genomic resources. In this study, DAVID was used to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses to determine significant DEGs . GO termThe Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (http://www.string-db.org) was used to evaluate potential protein–protein interactions. Cytoscape is an open-source tool for network visualization of genes, proteins, and other types of BPs . The defBox plots of GSE17825 value distribution before and after data normalization were generated . Data fr−4), taurine and hypotaurine metabolism (P = 5.02 × 10−3), fat digestion and absorption (P = 7.08 × 10−3), glycerolipid metabolism (P = 2.13 × 10−2), and dorsoventral axis formation (P = 2.69 × 10−2). At 3 days, the upregulated DEGs were primarily involved in ECM–receptor interaction (P = 4.38 × 10−5), focal adhesion (P = 3.76 × 10−4), regulation of actin cytoskeleton (P = 5.50 × 10−4), PI3K-Akt signaling pathway (P = 1.23 × 10−3), and phagosome (P = 2.13 × 10−3). Finally, at 5 days after fracture, these were involved in complement and coagulation cascades (P = 4.25 × 10−3), neuroactive ligand–receptor interaction (P = 2.19 × 10−2), cell adhesion molecules (P = 2.86 × 10−2), rheumatoid arthritis (P = 4.11 × 10−2), and leukocyte transendothelial migration (P = 7.69 × 10−2).The five most enriched KEGG pathways of up- and downregulated DEGs at each time-point  are presented in Figures −8), ECM–receptor interaction (P = 1.33 × 10−5), focal adhesion (P = 9.59 × 10−5), platelet activation (P = 1.13 × 10−3), and PI3K-Akt signaling pathway (P = 1.76 × 10−3). At 3 days, these were primarily enriched in pathways related to ABC transporters (P = 1.12 × 10−2), morphine addiction (P = 1.45 × 10−2), Wnt signaling pathway (P = 1.61 × 10−2), phototransduction (P = 2.82 × 10−2), and glycosphingolipid biosynthesis: lacto and neolacto series (P = 2.82 × 10−2). Finally, at 5 days after fracture, these were enriched in vascularization-associated pathways including platelet activation (P = 1.43 × 10−5), hematopoietic cell lineage (P = 3.86 × 10−5), and VEGF signaling pathway (P = 2.89 × 10−2).At 1 day, the downregulated DEGs were involved in protein digestion and absorption (P = 3.28 × 10−6), tissue development (P = 5.61 × 10−6), negative regulation of multicellular organismal process (P = 6.69 × 10−6), negative regulation of cell development (P = 3.32 × 10−6), and trabecular formation (P = 1.04 × 10−4). At 3 days after fracture, these were the most enriched in antigen processing and peptide or polysaccharide antigen presentation via MHC class II (P = 5.58 × 10−7). At 5 days after fracture, these were predominantly associated with synaptic-related functions, such as anterograde trans-synaptic signaling (P = 5.80 × 10−7), trans-synaptic signaling (P = 5.98 × 10−7), synaptic signaling (P = 7.67 × 10−7), neuron–neuron synaptic transmission (P = 1.00 × 10−5), and chemical synaptic transmission (P = 4.16 × 10−5).The 10 most significantly enriched GO terms (BPs) for the identified DEGs are listed in PPI network analysis was performed using Cytoscape software. Results revealed that, at 1 day after fracture, DEGs were enriched in platelet-derived growth factors binding, extracellular matrix structural constituent, and the Wnt signaling pathway . At 3 daFracture healing involves multiple biological phases that are characterized by both anabolic and catabolic processes . While aBecause inflammation is a critical step in fracture healing, any disruption of this process can negatively affect overall healing . Our GO β-catenin signaling must be precisely regulated to facilitate the differentiation of osteoblasts; dysregulation of this pathway alters the normal bone healing response [MSC differentiation is another key process in fracture healing, and understanding the mechanisms leading to delayed cell differentiation is important for understanding the cause of reduced skeletal regeneration with age , 31. Herresponse , 36. In KEGG pathway enrichment and PPI network analysis revealed that the downregulated DEGs were primarily related to vascularization-associated functions  at 5 days after fracture. Vascularization is a complex process that is essential for successful bone repair . DecreasNotably, DEGs at day 5 after fracture were primarily enriched in synaptic-related functions. The exact role of sensory nerves in fracture healing remains elusive –39 becauOne limitation of the present study is that the analyzed raw dataset did not include transcriptome data from 2, 3, 4, or 5 weeks following fracture, limiting conclusions about changes in molecular processes after bone fracture over time. Taken together, our findings demonstrate that elevated inflammatory status, a dysregulated Wnt signaling pathway, and the downregulation of vascularization-associated functions and synaptic-related processes play essential roles in fracture healing in the elderly. Further studies are required to determine the mechanisms by which these molecular events affect fracture healing.


Delftia, was significantly more abundant in conjunctival MALT lymphoma patients, and the genera Bacteroides and Clostridium were less abundant in the MALT lymphoma patients. A specific microbiota on the ocular surface in conjunctival MALT lymphoma patients was detected, and dysbiosis may play an important role in the pathophysiology of conjunctival MALT lymphoma.To investigate the conjunctival microbiota and the association between the development of conjunctival mucosa-associated lymphoid tissue (MALT) lymphoma and dysbiosis, DNA samples were collected from 25 conjunctival MALT lymphoma patients and 25 healthy controls. To compare the microbiota, samples were collected from the following four body locations: conjunctiva, meibomian gland, periocular skin and hand. Extracted DNA was analyzed by 16S rRNA sequences, and libraries were sequenced on an Illumina MiSeq sequencer. The differences in bacteria were characterized by using principal coordinate analysis of metagenomics data, and the differences in bacterial compositions were evaluated by linear discriminant analysis effect size. The conjunctival microbiota of MALT lymphoma patients was compositionally different from that of healthy controls. For the conjunctival MALT lymphoma patients, alterations in the microbial composition were detected, and a remarkable change was detected at the conjunctiva. Detailed analysis showed that a specific population of the microbiota, the genus  The frequency of ocular adnexal lymphoma is estimated to be approximately 8% of extranodal lymphomas3. For patients with primary lymphoma, the prognosis of primary conjunctival lymphomas is reported to be good, resulting in long-term survival3. Histologically, conjunctival MALT lymphoma has similar characteristics to gastric MALT lymphoma in the stomach, and it is thought to be caused by a chronic inflammatory response6. Several studies have reported the detection of Helicobacter pylori (H. pylori) DNA in some cases of conjunctival MALT lymphoma and the association of C. psittaci in its development, and therefore, these microorganisms were thought to be causative pathogens8. However, the pathophysiology of conjunctival MALT lymphoma has not been fully investigated.Conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas are known as localized, low-grade tumors, and extranodal marginal zone B-cell MALT lymphoma is the most common histological subtype9. The microbiota in the human body, including oral, skin, conjunctival, vaginal and respiratory tract microbes, plays an important role in the maintenance of health and disease development10. Dysbiosis, or an abnormality of homeostasis in the microbiota, causes several systemic disorders, such as inflammatory bowel diseases, obesity and cardiovascular diseases13. Previous reports have shown the relationships between the immune system and the commensal microbiota in host defense and tissue repair14. Pathological changes in the microbiota, such as Staphylococcus epidermidis, a commensal skin bacterium, has been demonstrated to cause opportunistic infections and result in the development of catheter infection, prosthetic valve endocarditis and endophthalmitis18. The association of dysbiosis with central nervous disorders, such as autism, multiple sclerosis, anxiety-depressive behaviors and functional gastrointestinal disorders, is reported in clinical practice, suggesting the possibility of microbe-based therapies to treat symptoms18. Thus, alterations of the residual microbiota lead to pathogenic infections or inflammation in the host and even result in fatal conditions18.In the human body, a great number and variety of commensal bacteria populate and regulate the homeostatic balance of the host19. This stress has been implicated in the development of pterygium, dry eye, corneal dystrophy and Fuch’s endothelial dystrophy20. It is likely that changes in the microenvironment at the ocular surface lead to alteration of the microbiota, resulting in disease development. We hypothesized that mucosal microbial dysbiosis could contribute to immunological changes in the conjunctival mucosa and might be associated with the development of conjunctival MALT lymphoma. To verify this hypothesis, we investigated the microbial diversity in conjunctival MALT lymphoma and healthy controls and compared four body locations to detect their specific microbiota.The ocular surface, a part of the conjunctiva-associated lymphoid tissue (CALT), is continuously exposed to the external environment, such as temperature changes, ultraviolet light and oxidative stressIn the current study, we detected differences in the microbiota of the conjunctiva between conjunctival MALT lymphoma patients and healthy controls and discussed the role of the microbiota in the pathogenesis of conjunctival MALT lymphoma.A total of 50 persons  were enrolled in this study, and samples were collected at four locations  and at two time points (baseline and 1 month later). After DNA extraction, the DNA was analyzed by 16S rRNA sequencing, and libraries were sequenced on an Illumina MiSeq sequencer. The relative abundances of bacteria in the four locations were compared using JMP and R software. The detailed microbacterial differences were shown using the LefSe program.The clinical data and background for each patient are shown in Table A total of 18,851,375 raw 16S rRNA gene sequences were obtained, which, after quality filtering, resulted in a total of 13,094,927 paired end sequences with an average of 53,231 sequences per sample. To determine the stability of the microbiota among the four locations, DNA sampling was performed one month after the baseline for healthy controls  between healthy controls and conjunctival MALT lymphoma patients. The conjunctival and meibomian samples showed lower α diversities than the periocular skin and hand samples (P < 0.01).Microbacterial diversity among the four body locations in patients with conjunctival MALT lymphoma and healthy controls was investigated at the family level Fig. . The datThe main microorganisms composing the conjunctival microbiota of healthy and MALT lymphoma subjects are shown in Fig. In addition, we investigated the microbacterial fluctuations provoked by treatments with the permutation analysis of variance (PERMANOVA). However, we could not find any significant differences between before, under, or after treatment.21 with the data classified by genus level. These linear discriminant analysis (LDA) scores are plotted in Fig. Delftia, Xylophilus, Simplicispira, Rothia and Xanthomonas and a lower abundance of Bacteroides, Clostridium, Deinococcus, Williamsia, Parabacteroides, Chryseobacterium and Herbaspirillum were detected at the conjunctiva in the conjunctival MALT lymphoma group compared to healthy controls  or decreased (red) in the conjunctival MALT lymphoma subjects were found among the four body locations by an algorism called LefSe ols Fig. . On the ted Fig. . At the ted Fig. . On the ols Fig. .Figure 3Delftia was detected in many healthy controls and conjunctival MALT lymphoma patients, and the presence of Xylophilus, Simplicispira, Rothia and Xanthomonas was very rare in both groups  involvements  (P = 0.022) and for Bacteroides according to chemotherapy (P = 0.018). There were no correlations observed in the three microorganisms at the conjunctiva of MALT lymphoma patients and healthy controls.For ts Table . There wBacteroides and Clostridium maintain ocular surface homeostasis through the production of IgA, which raises a possibility that a decrease of these microorganism could result in lower IgA concentration23. To this end, we measured the tear IgA levels of MALT lymphoma patients and the healthy controls. The IgA concentration in tears of conjunctival MALT lymphoma patients (153.4 ± 166.9 μg/mL) showed statistically significant difference compared to that of the control group (252.9 ± 87.1 μg/mL)  26. The tear pH levels of conjunctival MALT lymphoma patients (7.15 ± 0.23) were statistically lower than those of age-matched healthy controls (7.46 ± 0.14)   Fig. . On the 01) Fig. .Figure 513. In the current study, we showed that the commensal conjunctival microbiota existed against the invasion of foreign microbes, and the conjunctival microbiota of MALT lymphoma patients was compositionally different from that of healthy controls, as highlighted by PCA. A detailed analysis showed that in conjunctival MALT lymphoma patients, the genus Delftia was significantly more abundant and the genera Bacteroides and Clostridium were less abundant than in healthy controls. Delftia might have a pathophysiological role in the development of conjunctival MALT lymphoma, and Bacteroides and Clostridium may have defensive properties in conjunctival MALT lymphoma.Several studies have shown that the commensal microbiota exists in different body locations29. At the meibomian gland, bacteria-related cytotoxicity or inflammation may interfere with the pathological process of meibomian gland dysfunction30. Therefore, this altered microbiota might be associated with the development of ocular surface diseases. Previous reports also showed, using PCA, that the skin microbiota is distinct31. In each location, the number of conjunctival bacteria was lower than that on the hand and equal to that of the meibomian gland. The conjunctival microbiota differed in composition between conjunctival MALT lymphoma patients and healthy controls. High-diversity skin sites were the palm, finger and foot. On the other hand, the conjunctival microbiota had significantly lower phylogenetic diversity than that of the hand31. Some factors, such as humidity, the existence of sebaceous sites, and the use of systemic treatments, have been reported to interfere with conditions31. For conjunctival MALT lymphoma, several treatments could be performed to improve the disease prognosis. In this study, no significant difference in the microbial composition among the previous therapies was observed because the dysbiosis of the conjunctival microbiota might already exist during the development of MALT lymphoma, even if some interventions were performed. Environmental characteristics may play an important role in forming the conjunctival bacterial microbiota.Previous reports showed that a lower abundance of ocular surface microbiota has a great influence on the severity of Sjögren syndrome and dry eye, and the continuous administration of eye drops for glaucoma treatment might affect the microbiota33. In the stomach, a similar phenomenon could occur in the presence of H. pylori, which changes the microenvironment of the stomach by changing the urease activity34. In our study, the frequency of Delftia was significantly higher in the conjunctiva than in the three other body locations for conjunctival MALT lymphoma patients and healthy controls. Previous reports showed that Delftia was an aerobic, gram-negative motile bacillus with polar or bipolar flagella25. Delftia does not produce urease and is catalase and oxidase positive25. Characteristically, Delftia has the ability to degrade and utilize glucose oxidatively, which may change the conjunctival environment25. Delftia was isolated from the soil and is known only as a rare pathogen that can affect immunocompromised patients35. Clinically, Delftia has resistance to antibiotics, such as β-lactams and aminoglycosides, and can adhere to contact lens cases and form biofilms, resulting in the development of both microbial and infiltrative keratitis at the ocular surface37. The glucose levels in the tears are known to be correlated with the levels in the blood, and several corneal abnormalities, such as healing of the corneal epithelium, are caused by changes in glucose levels39. Delftia, known as rare pathogens, may predispose individuals to the development of conjunctival MALT lymphoma by changing conjunctival conditions to interfere with CALT35.In the human body, mucosal organs, including the conjunctiva, are equipped with characteristic immune defense mechanisms, and the same anatomic mechanism, CALT (which contains MALT), exists at the ocular surfaceBacteroides and Clostridium, which are known as commensals and an anti-allergic microbiota22. Previous reports have shown that normal gut bacteria, such as Bacteroides, Bifidobacterium and Lactobacillus, play a protective role in preventing the proliferation of pathogens and form gut-associated lymphoid tissue (GALT) to maintain homeostasis40. Similarly, if disruptions of the ocular surface barrier, such as disruptions of CALT and MALT, occur, these changes could trigger ocular inflammation41. The development of immune-mediated diseases such as allergies has been hypothesized to arise as a result of deficiencies in exposure to microbial organisms and their products42. It is known that Bacteroides produces a bacterial polysaccharide, and it directs the cellular and physical maturation of the developing immune system43. In the small intestine, Bacteroides interacts with dendritic cells at intestinal Peyer’s patches and induces the production and maturation of immunoglobulin A as a protection mechanism in the GALT23. Therefore, the existence of Bacteroides from birth is essential to maintain the homeostasis of gut immunity, and likewise, bacteria are helpful to develop focal defensive mechanisms at the ocular surface44. Several reports suggest that mucosa-associated Clostridium populations play an important role in the induction of Tregs and IgA and the suppression of inflammatory and allergic responses45. The lowering of tear IgA concentration and pH in conjunctival MALT lymphoma patients suggest that the change in the microbiota profiles impacts the physiological response of the eye. We have not clarified whether the physiological change indeed resulted from specific bacterium or their compositions in this study because of insufficient data size, but there may be a tendency due to treatment especially in the case of Delftia. Hence, Delftia is thought to play a pathophysiological role in the development of conjunctival MALT lymphoma, and Bacteroides and Clostridium may play protective roles. The current study raises an intriguing hypothesis that the fluctuation of bacterial compositions causes disturbance of innate immunity in ocular MALT, while further validation is needed to determine whether Delftia or other bacteria identified here are responsible for MALT lymphoma.In our study, in the conjunctiva of MALT lymphoma, there were two decreased microorganisms, We studied 25 consecutive patients (50 eyes)  who were diagnosed by biopsy with conjunctival MALT lymphoma and were followed at our hospital between 2015 and 2017. The subtype was extranodal marginal zone B-cell lymphoma of MALT-type lymphoma in all cases. At the same time, 25 age-matched healthy volunteers (50 eyes)  participated in this study as healthy controls. Exclusion criteria included obvious ocular surface disease, history of recent contact lens usage, use of systemic/topical antibiotics or prescription eye medications in the past 12 months, ocular surgery in the last 12 months, active ocular infection, dry eye, systemic diseases such as diabetes, or smoking. The current study adhered to the tenets of the Declaration of Helsinki, the local ethics committee of the Osaka University Medical Hospital approved the study, and written informed consent was obtained from all subjects.Sample collections were performed in a clean ophthalmic treatment room. After instillation of sterile, topical proparacaine, DNA swabs  were used to collect samples from the superior and inferior fornixes of the conjunctiva in both eyes. To compare the conjunctiva with other parts of the skin, swabs were taken from the hand, meibomian glands and skin around the eyes. Samples were carefully transferred into DNA LoBind tubes , and all samples were promptly frozen at −80 °C until the time of DNA extraction. DNA was extracted from each sample using a PowerSoil DNA Isolation Kit  according to the manufacturer’s instructions. The extracted genomic DNA was eluted in 100 µl of the kit elution buffer and stored at −20 °C until analysis.To investigate the tear immunoglobulin A (IgA) levels, tear samples were collected from 34 eyes of MALT lymphoma patients and 40 eyes of healthy controls without anesthesia. Schirmer test strips  were placed at the outer one-third of the temporal lower conjunctival fornix for 5 minutes to collect tears. The strips were stored at −80 °C in glass vials until further analysis. IgA levels were assayed by enzyme-linked immunosorbent assay . Additionally, the serum from 10 out of 25 patients were collected to measure the IgA levels.To investigate the tear pH, tears were collected from 30 eyes of MALT lymphoma patients and 26 eyes of healthy controls. Tear fluid were collected from the inferior meniscus of unanesthetized eyes after instillation of 30 μl sterile water by micropipette onto the ocular surface, followed by movement of the eyes to mix the tear fluid content. All the collected tear volumes were over 10 μl and were carefully transferred into DNA LoBind tubes . For the measurement, all samples were measured immediately after sample collection by an ion-selective electrode handheld meter .http://sco.h-its.org/exelixis/web/software/pear/). Merged reads were quality-trimmed with BBtrim (bbmap.sourceforge.net). Twenty thousand reads per sample were randomly selected using random_sequence_sample.pl  for further analysis. The processed sequences were clustered into OTUs defined by a 97% similarity cutoff using UCLUST version 1.2.22q. Representative sequences for each OTU were then classified taxonomically by using RDP Classifier version 2.2 and the Greengenes 13_8 database. The bioinformatics pipeline QIIME, version 1.9.1, was used as the informatics environment for all relevant processing of raw sequencing data.Each library was prepared according to the “Illumina 16S Metagenomic Sequencing Library Preparation Guide” with a primer set (27Fmod: 5′-AGR GTT TGA TCM TGG CTC AG-3′ and 338R: 5ʹ-TGC TGC CTC CCG TAG GAG T-3ʹ) targeting the V1–V2 region of the 1S rRNA gene. Then, 251 bp paired end sequencing of the amplicon was performed on a MiSeq (Illumina) using a MiSeq v2 500 cycle kit. Paired end sequences were merged using PEAR (http://cran.r-project.org/), version 3.1.3. To discover different features of the microbiota in healthy and MALT subjects, the classified data were analyzed by linear discriminant analysis effect size (LefSe). A P < 0.05 was considered statistically significant.Data are shown as the mean ± SE. Statistical analyses were carried out using JMP software version 9.0  and the R software environment (in the public domain, Stability of bacterial compositions in four locations of healthy controls.