These results provide an improved understanding about possible shared mechanisms and treatments strategies for MS and IS. They also provide some basis for further studies of how these two diseases are linked at the molecular level.Patients with multiple sclerosis (MS) appear to have an increased risk of ischemic stroke (IS). Although MS and IS have very different phenotypes, gene-based and pathway-based analyses of large-scale genome-wide association studies (GWAS) have increasingly enhanced our understanding of these two diseases. Whether there are common molecular mechanisms connecting MS and IS is still unclear. Here, we describe the outcome of gene-based test and pathway-based analysis of GWAS datasets that explored potential gene expression links between MS and IS. After identifying significant gene sets individually of MS and IS, we performed pathway-based analysis in four biological pathway databases  and GO categories. We discovered that there were 9 shared pathways between MS and IS in KEGG, 2 in PANTHER, 14 in REACTOME, 1 in WikiPathways, and 194 in GO annotations ( Multiple sclerosis (MS) is a neurodegenerative, demyelinating disease of the nervous system that respects few demographic boundaries. It has an autoimmune basis, which leads to widespread nervous system tissue lesions and dysfunction, resulting in communication breakdown between neurons  of MS and IS revealed the respective genetic characteristics of these two diseases. Various major histocompatibility complex (MHC) variants  and MS  GWAS datasets following a pathway-based analysis. We found that MS and IS have in common 9 shared pathways in KEGG, 2 in PANTHER and 15 in REACTOME, 1 in Wiki pathways, and 194 in GO annotations. In short, we believe that these new results may represent significant steps toward defining the genetic mechanism underlying the association of IS with MS.We used a large-scale MS-GWAS dataset from IMSGC, which was derived from the Wellcome Trust Case Control Consortium 2 (WTCCC2) project , and then are summarized to compute a gene-based test statistic that would have a χ 2 distribution with n df under the null hypothesis to define corresponding genes, if SNPs are in linkage equilibrium  online. This approach is a more flexible method to assess individual SNPs and conduct gene-based testing . The detailed results are listed in All the 9,541,572 IS SNPs, and 464,357 MS SNPs were applied in this study. We sorted through 21,913 IS gene sets and 14,811 MS gene sets using the web-based version of VEGAS2 . After fp < 0.05)  . In GO, f < 0.05 .p-value of < 0.05 (p < 0.05)  .p < 0.05): 9 KEGG pathways, 2 PANTHER pathways, 14 REACTOME pathways, 1 WikiPathways, and 194 GO pathways. In KEGG, the pathways fell into six categories. (1) Immune system (n = 3): natural killer cell mediated cytotoxicity (hsa04650), Toll-like receptor signaling pathway (hsa04620), and Th1 and Th2 cell differentiation (hsa04658). (2) Environmental information processing (n = 2): ErbB signaling pathway (hsa04012) and phospholipase D signaling pathway (hsa04072). (3) Drug resistance and endocrine (n = 1): EGFR tyrosine kinase inhibitor resistance (hsa01521). (4) Nervous system (n = 1): neurotrophin signaling pathway (hsa04722). (5) Cancers (n = 1): glioma (hsa05214). (6) Infectious diseases (n = 1): hepatitis B (hsa05161)  .In PANTHER, two pathways were shared: Cadherin signaling pathway (P00012) and Wnt signaling pathway (P00057). In REACTOME, 14 significant pathways were shared, for example, cell-cell communication (R-HSA-1500931) and interferon gamma signaling (R-HSA-877300) . In WikiIn GO annotations, of the 194 shared pathways identified, 85 were biological-process pathways, 78 were cellular component pathways, and 31 were molecular-function pathways. After analyzing these data using Cytoscape software  were suggested to be associated with IS susceptibility at a significant test threshold  cell-mediated cytotoxicity pathway (hsa04650). NK cell-mediated cytotoxicity is one of the main characteristics of NK cells. NK cells release cytotoxic granules onto the target cell's surface, causing effector proteins to penetrate the cell membrane and induce programmed cell death.dim, CD16hi, and CD56bright, the latter of which may play a key role in MS and IS immunopathology. CD56bright NK cells, which are normally weakly cytotoxic, can acquire cytotoxic properties and produce cytokines if they are stimulated ; however, only the NK cell signaling pathway was unique in that it was significantly shared by all stroke and IS subtypes . The Toll-like receptor (TLR) family is a well-known class of proteins that are prototype pattern-recognition receptors (PRRs) capable of recognizing pathogen-associated molecular patterns (PAMPs), which are signature motifs possessed by certain pathogenic microorganisms; and danger-associated molecular patterns (DAMPs), which are host molecules that initiate an inflammatory response to damaged tissues or lesions , causing them to differentiate into Th1 and Th2 cells, which are two major subtypes of effector CD4+ T cells. The differentiation of Th1 and Th2 cells depends on the signals they receive. Th1 cells are triggered by IL-12 and secrete IFN-γ and IL-2, whereas Th2 cells are triggered by IL-4 and IL-2, and secrete IL-4, IL-5, IL-9, IL-10, IL-13, and IL-25 . Through this pathway, naïve CD4Neurotrophin signaling pathway (hsa04722) is another pathway shared by MS and IS. It influences the differentiation and survival of neural cells. Nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), neurotrophin 3 (NT-3), and neurotrophin 4 (NT-4) are major members of the neurotrophin family. The Trk family of tyrosine kinase receptors and p75 neurotrophin receptors (p75NTRs) are two major neurotrophin receptors , as one of the most significant GO functional category shared by IS and MS. Adhesion molecules of lymphocytes are significantly elevated in IS patients (Tsai et al., Despite of the novel findings, there are some limitations to this research. The GWAS datasets we chose were representative but the replications of multiple datasets should be conducted to improve the validity of the results. Next, due to the absence of IS subtypes' data, we did not analyze the associations between MS and the etiological subtypes of IS. Nevertheless, risk variants of IS are gradually identified to be related to its subtypes. At last, we have used reliable statistical methods to identify significant shared pathways after filtering out significant genes, respectively, but there is a chance that the commonalities might be found out between two diseases that are unlikely to share mechanisms. In further studies, our results need more experiments to explore and validate.We report on the significant pathways and GO annotations shared by MS and IS, with the goal of understanding more about their biological functions and relationships. By analyzing the pathways of the immune system and nervous system, we can verify that links between MS and IS exist and infer gene expression level correlations. Leveraging information about where biological functions overlap, we believe that a multidisciplinary approach will advance studies on the pathophysiological mechanisms in and treatments for both MS and IS.HL and JH conceived and designed the study for MS and IS. HL administered the analyses and wrote the manuscript. LC and XM was responsible for manuscript revision. PC and WL provided analyses support. All authors gave approval for the final version for submission.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.


Chronic periodontitis (CP) is a multifactorial inflammatory disease. For the diagnosis of CP, it is necessary to investigate molecular biomarkers and the biological pathway of CP. Although analysis of mRNA expression profiling with microarray is useful to elucidate pathological mechanisms of multifactorial diseases, it is expensive. Therefore, we utilized pooled microarray gene expression data on the basis of data sharing to reduce hybridization costs and compensate for insufficient mRNA sampling. The aim of the present study was to identify molecular biomarker candidates and biological pathways of CP using pooled datasets in the Gene Expression Omnibus (GEO) database.Three pooled transcriptomic datasets  of gingival tissue with CP in the GEO database were analyzed for differentially expressed genes (DEGs) using GEO2R, functional analysis and biological pathways with the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction (PPI) network and hub gene with the Search Tool for the Retrieval of Interaction Genes database, and biomarker candidates for diagnosis and prognosis and upstream regulators of dominant biomarker candidates with the Ingenuity Pathway Analysis database.We shared pooled microarray datasets in the GEO database. One hundred and twenty-three common DEGs were found in gingival tissue with CP, including 81 upregulated genes and 42 downregulated genes. Upregulated genes in Gene Ontology were significantly enriched in immune responses, and those in the Kyoto Encyclopedia of Genes and Genomes pathway were significantly enriched in the cytokine-cytokine receptor interaction pathway, cell adhesion molecules, and hematopoietic cell lineage. From the PPI network, the 12 nodes with the highest degree were screened as hub genes. Additionally, six biomarker candidates for CP diagnosis and prognosis were screened.CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN) and upstream regulators of biomarker candidates for CP diagnosis (TNF and TGF2). We also confirmed key genes of CP pathogenesis such as CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA. To our knowledge, this is the first report to reveal associations of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP.We identified several potential biomarkers for CP diagnosis and prognosis  contains supplementary material, which is available to authorized users. Chronic periodontitis (CP) is a multifactorial inflammatory disease caused by genetic, immune, environmental, and microbiological factors and lifestyle habits –3. CP isDevelopment of high-throughput experimental methods in biological studies has yielded extensive omics data. Additionally, transcriptomic studies using microarray analysis have advanced our understanding of the expression landscape for biological mechanisms of multifactorial diseases. Integration of multiple microarray datasets has generated disease-associated mRNA profiles for screening. While the experimental condition of each dataset is clinically and technically different, common differentially expressed genes (DEGs) related to CP among multiple datasets may identify key genes as potential targets for CP diagnosis and prognosis.At present, data sharing and integration of omics data for investigating mechanisms of multifactorial diseases have gained attention. Registration of biological experimental data in public databases has also been recommended to help facilitate data sharing. Use of pooled microarray gene expression datasets is a method to reduce hybridization costs and compensate for insufficient amounts of mRNA sampling –9. Many The National Center for Biotechnology Information developed the Gene Expression Omnibus (GEO) database to promote pooling and sharing of publically available transcriptomic data to facilitate biomedical research –30. ArraIn this study, we focused on gene expression in gingival tissue from CP patients. We selected and analyzed three pooled microarray platform datasets in the GEO database. The aims of the present study were to identify biomarker candidates for CP diagnosis and prognosis based on functional and molecular analyses by evaluating DEGs in gingival tissue between healthy control and CP groups.Homo sapiens,” “gingival tissue,” and “microarray platform GPL570: Affymetrix Human Genome U133 plus 2.0 Array.” These three datasets were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). A summary of the individual studies is shown in Table In the present study, we selected microarray datasets of gingival tissue from CP patients in the GEO database and investigated clinical biomarker candidates for CP diagnosis and prognosis based on functional and molecular pathway analyses of DEGs. We selected three datasets of gingival tissue with CP, GSE10334, GSE16134, and GSE23586, using the following keywords: “chronic periodontitis,” “http://www.ncbi.nlm.nih.gov/geo/geo2r/). GEO2R is an interactive web tool and an R-based web application for comparing two groups of datasets in the GEO database, which we used to compare normal healthy control and CP groups. Common up- or downregulated DEGs in the three selected datasets were extracted. We set p < 0.05 and |fold change (FC)| > 2 as the cut-off criteria.Up- or downregulated DEGs in the three selected datasets were identified using GEO2R (https://david.ncifcrf.gov/). We set p < 0.05 and false discovery rate (FDR) < 5% as the cut-off criteria.Functional analysis of DEGs was carried out using the Gene Ontology (GO) database. Signaling pathways of DEGs were investigated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG). GO and KEGG analyses were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID) (http://string-db.org/), which is an online repository that imports PPI data from published literature. We used default function in STRING. We calculated degrees of each protein node, and the top 12 genes were identified as hub genes.The PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://www.ingenuity.com). Applicable biomarkers were selected based on IPA-biomarkers analysis. We set p < 0.05 and |FC| > 2 as the cut-off criteria.Common molecular biomarker candidates for CP diagnosis and prognosis among the three datasets were investigated using Biomarker Analysis in QIAGEN’s Ingenuity Pathway Analysis (IPA) software , Cellular Component (CC), and Molecular Function (MF) and pathway enrichment analyzed based on the KEGG pathway using DAVID are shown in Tables Upregulated genes were significantly enriched in BP related to immune response and cell adhesion. Downregulated genes were significantly enriched in epidermis and ectoderm development and keratinocyte, epidermal cell, and epithelial cell differentiation.Significantly enriched KEGG pathways of upregulated genes included cytokine-cytokine receptor interaction, adhesion molecules, and hematopoietic cell lineage. The pathways of downregulated genes were not significantly enriched.cluster of differentiation (CD) 19 (CD19), interleukin (IL)-8 (IL8), CD79A, Fc fragment of IgG receptor (FCGR) IIIb (FCGR3B), selectin L (SELL), colony stimulating factor 3 (CSF3), IL-1 beta (IL1B), FCGR IIb (FCGR2B), C-X-C motif chemokine ligand 12 (CXCL12), complement component 3 (C3), CD53, and IL-10 receptor subunit alpha (IL10RA)  were identified as common biomarker candidates for CP diagnosis, and CXCL12, IL1B, membrane spanning 4-domains A1 (MS4A1), and platelet and endothelial cell adhesion molecule 1 (PECAM1) were identified as candidates for CP prognosis. Molecular pathways of biomarker candidates are shown in Additional file Common molecular biomarker candidates for diagnosis, prognosis, and other processes were identified using IPA software Table . Among ttumor necrosis factor (TNF) and fibroblast growth factor 2 (FGF2) were identified as upstream regulators of dominant biomarker candidates for CP diagnosis such as CSF3, CXCL12, IL1B, and TAGLN  signaling pathway . Common hub genes were IL8, IL1B, CXCL12, CSF3, CD79A, and SELL.Guo et al reported several hub genes of periodontitis using microarray analyses . SimilarCD19, formyl peptide receptor 1 (FPR1), interferon regulatory factor 4 (IRF4), and IL1B. Common results of GO enrichment analysis in upregulated DEGs were cell activation, positive regulation of immune system process, extracellular region, extracellular region part, and antigen binding, while those in downregulated DEGs were epidermis development, keratinocyte differentiation, epidermal cell differentiation, structural molecule activity, and structural constituent of the cytoskeleton. Common KEGG pathways of upregulated DEGs were cytokine-cytokine receptor interaction, hematopoietic cell lineage, and CAMs appear to be related to inflammation and bone loss process in periodontitis.Song et al reported several DEGs and functional enrichment analysis of inflammation and bone loss process in periodontitis. With comparing the results of our present study to them , common CSF3, CXCL12, IL1B, and TAGLN as biomarker candidates for CP diagnosis and CXCL12, IL1B, MS4A1, and PECAM1 as biomarker candidates for CP prognosis. CSF3, CXCL12, IL1B, and MS4A1 are related to immune response. CXCL12 and MS4A1 are related to lymphocyte activation and cell activation. PECAM1 is related to phagocytosis and endocytosis. TAGLN is a TGF-β1-inducible gene [We also identified ble gene .TNF and FGF2 are common upstream regulators of all biomarker candidates for CP diagnosis. Mitogen-activated protein kinase 1  is a common upstream regulator of all biomarker candidates for CP prognosis. Additionally, IL1B is one of the upstream regulators of CSF3 and CXCL12. Furthermore, vascular endothelial growth factor A and prostaglandin-endoperoxide synthase 2 are upstream regulators of CSF3, CXCL12, and IL1B.Furthermore, CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP has not been previously reported. Potential reason is that biological information in databases for bioinformatics analysis is continuously updated as omics data become available and developed functions of software improves. CD53 plays a role in the regulation of growth. CD79A encodes the Ig-alpha protein of the B-cell antigen component. MS4A1 plays a role in the development and differentiation of B-cells into plasma cells. PECAM1 is a member of the immunoglobulin superfamily and involved in leukocyte migration. Furthermore, CD53, CD79A, MS4A1, and PECAM1 are associated with immune responses to infection by microorganisms. Lastly, TAGLN is a member of the calponin family and expressed in vascular smooth muscle [Among biomarker candidates and hub genes, the association of h muscle .CSF3, CXCL12, IL1B, MS4A1, and PECAM1, upstream regulators such as TNF and FGF2, and hub genes such as CD53, CD79A, MS4A1 and PECAM1 are related to immune response and inflammation.Biomarker candidates such as CSF3, CXCL12, IL1B, TAGLN, CD19, IL8, and CD79A and upstream genes of biomarker candidates such as TNF and FGF2, which could provide potential targets for CP diagnosis. For clinical application, a combination of biomarkers would likely be necessary for CP diagnosis or prognosis. Bioinformatics analysis of pooled microarray datasets is useful for screening to investigate biomarker candidates of CP. Further validation of these predicted molecular biomarkers obtained from bioinformatics analysis using experimental research approaches such as qRT-PCR is necessary.In summary, our study, which analyzed pooled omics datasets with distinct clinical and experimental baselines, provided new clues for elucidating common genetic factors of multifactorial diseases such as CP. Data mining and integration with sharing and using pooled omics data could be useful tools to investigate biomarker candidates for diagnosis and prognosis of diseases in clinical practice and to understand complicated underlying molecular mechanisms. We also identified key genes related to CP pathogenesis such as Additional file 1:Figure S1. Most relevant genetic network related to common biomarker candidate gene CSF3 analyzed by IPA. (PDF 354 kb)Additional file 2:Figure S2. Most relevant genetic network related to common biomarker candidate gene CXCL12 analyzed by IPA. (PDF 503 kb)Additional file 3:Figure S3. Most relevant genetic network related to common biomarker candidate gene IL1B analyzed by IPA. (PDF 420 kb)Additional file 4:Figure S4. Most relevant genetic network related to common biomarker candidate gene MS4A1 analyzed by IPA. (PDF 407 kb)Additional file 5:Figure S5. Most relevant genetic network related to common biomarker candidate gene PECAM1 analyzed by IPA. (PDF 442 kb)Additional file 6:Figure S6. Most relevant genetic network related to common biomarker candidate gene TAGLN analyzed by IPA. (PDF 398 kb)Additional file 7:Table S1. Functional and pathway enrichment analyses of upstream regulators of CSF3. (XLSX 41 kb)Additional file 8:Table S2. Functional and pathway enrichment analyses of upstream regulators of CXCL12. (XLSX 45 kb)Additional file 9:Table S3. Functional and pathway enrichment analyses of upstream regulators of IL1B. (XLSX 98 kb)Additional file 10:Table S4. Functional and pathway enrichment analyses of upstream regulators of MS4A1. (XLSX 11 kb)Additional file 11:Table S5. Functional and pathway enrichment analyses of upstream regulators of PECAM1. (XLSX 37 kb)Additional file 12:Table S6. Functional and pathway enrichment analyses of upstream regulators of TAGLN. (XLSX 41 kb)


Background. Hyperuricemia (HUA) is a kind of a metabolic disease that seriously threatens human health worldwide. Chicory, a natural herbal medicine, has an obvious effect of reducing uric acid. The aim of this study is to explore the potential components and pharmacological pathways that may play a role in hypouricemia activity of chicory. Bioinformatics and metabonomics were applied to this research. Firstly, component-target network was used to identify possible components related to the pharmacological properties and their corresponding mechanisms pathway of chicory. Afterwards, animal pharmacodynamic experiments were performed. Blood and stool samples were collected for untargeted metabolomic analysis by dint of UHPLC-Q-TOF/MS methods, and principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed for the pattern recognition and characteristic metabolites identification. Significant enriched function pathways were used in bioinformatics suggesting that chicory might have the effect of regulation of lipolysis in adipocytes. PLS-DA analysis was applied to discover differentiating metabolites, and pathway enrichment analysis indicated that chicory had powerful effects of glycosylphosphatidylinositol- (GPI-) anchor biosynthesis, inositol phosphate metabolism, glycerophospholipid metabolism, and steroid hormone biosynthesis. Combining bioinformatics and metabolomics results, we consider that chicory may develop on lowering uric acid by adjusting lipid metabolism. In addition, we chose quail as animal model innovatively and discussed the treatment of hyperuricemia with chicory in multiple methods, which may render reference for the research of HUA. Uric acid as the end-product of purine metabolism is due to the lack of uricase in human being and avian which causes the elevated uric acid level in serum . Normal Cichorium intybus L., as we know as chicory, is a traditional herb from Asteraceae family. Chicory is used as a traditional Uighur folk medicine to treat icterohepatitis, with the function of clearing liver and cholagogue, strengthening stomach and promoting digestion. Literature has shown that the pharmacological actions of chicory include antioxidant, anti-inflammatory, antihypoglycemic, and antihyperlipidemic effects [ effects . In prev effects , 10. How effects , 12. In Effective components of chicory were searched through Traditional Chinese Medicine Systems Pharmacology (TCMSP http://lsp.nwu.edu.cn/tcmsp.php) database. Alternative components needed to meet both oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 conditions. Moreover, since TCMSP database did not include all chemical compositions, we also searched for latent components outside the database based on literatures from the past 10 years and then looked for homologous targets from the database. We took these chemical components and targets found in TCMSP database and references as a database in our research.All target proteins have undergone gene names conversion in Uniprot (http://www.uniprot.org/) and were organized in Microsoft Excel 2016 spreadsheets. Component-target (C-T) network was used to identify possible components and their corresponding mechanisms pathway was visualized using Cytoscape 3.1.2. p value <0.05 and enrichment count ≥2 were deemed as thresholds. Cluster Profiler R 3.4.3 was used to clarify functional and pathway enrichment analyses of massive genes acquired from previous database [Gene Ontology (GO) analysis and pathway analysis have become the main methods for functional studies. In present research, GO analysis involved three areas: the biological process (BP), cellular component (CC), and molecular function (MF) of the founded genes based on the gene ontology project. In the aspect of pathway analysis, the KEGG database contains information about interactions between molecules or genes . In our database .Chicory applied in our study was identified by Professor Yonghong Yan . Chicory was grinded into powder and weighed. The extracts were prepared with boiling water for 1 hour and extracted twice. Then a rotary evaporator was used to concentrate the solution after filtering and was deliquated to different volume with purified water .3; degree of polymerization range 2 to 30; and free glucose less than 5%. Fenofibrate tablets (0.1g) were acquired from Beijing Yimin Pharmaceutical Co., Ltd. The molding agent yeast extract was purchased from OXOID Co., Ltd, and mixed into the commercial feed formulation according to the weight ratio of 15 g.kg-1. Acetonitrile (HPLC grade) was acquired from Merck , formic acid and methanol (HPLC grade) were obtained from Fisher Chemicals , and water was purified by a Millipore's ultrapure water system. The reagents for the detection of serum uric acid (UA), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) were purchased from Jiancheng Biological Technology, Co., Ltd .Chemical characteristics obtained from this merchandise tested in our laboratory were the following: average molecular weight 1.38×103 cages; temperature, humidity, and ventilating device are all in the standard environment . All the quails were freely provided feed formulation and water during the experimental period. Feed formulation was obtained from Beijing Deling Quail Farm.All operations on quails were in conformity with the relevant provisions of the Animal Care and Ethics Committee in Beijing University of Chinese Medicine. The molding agent yeast extract is rich in purine ; thus, tFeedstuff formulation was added twice per day and the surplus feedstuff would be weighed in each group next morning. Based on this, the average intake of food in each group was calculated at weekly intervals. The whole period of the experiment lasted 35 days.Body weight was monitored weekly. Blood samples were gathered from the jugular veins after 12-hour fasting. These blood samples need to clot for 60 minutes at room temperature then centrifuge at 3500 rpm for 10 min. On the 30th day of the experiment, the metabolic cages were used to collect the stool samples . Meantime, these quails were normal water supplied and fasted for 12 hours. Each stool was collected into the polypropylene tube; after adding 5 ml pure water vortex mixing was underway. After centrifugation, the supernatant was taken for use. The serum and stool were divided into two parts, respectively; one part was stored at -20°C for the detection of serum biochemical markers and the other part was stored at -80°C for UHPLC-Q-TOF/MS.μL thawed serum and 300 μL precooled methanol were transferred to a 1.5 mL polypropylene tube, respectively, and whirled more than 30s then placed in refrigerator at 4°C for more than 20 minutes to precipitate protein and other solids. After that, the sample was centrifuged at 12,000 rpm for 10 min at 4°C. Supernatants were gathered to filter through a syringe filter (0.22 μm). The same method is for preparing stool samples.100 μm, Agilent Technologies, USA), and the column temperature was kept at 30°C and was served to chromatographic separation. The sample sequence was parallelism and 4 μL of each sample was injected into the column. The optimal mobile phase was made up of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The flow rate referenced to the literature was 0.3 mL/min with a gradient performed as below [The Agilent 6550 iFunnel Q-TOF/MS system  was applied for chromatographic analysis. We had A ZORBOX RRHD C18 analytical column  in both positive and negative mode. The optimal conditions of analysis referenced to the literature were set as follows : dry gas >1 and |p (corr)| ≥ 0.5 were chosen and served to further data analysis.The original data were preprocessed with Profinder software  for retention time correction and feature peak extraction then we obtained two-dimensional data sets including characteristic peak mass charge ratio, retention time, and peak intensity. MetaboAnalyst3.0 is used to filter and normalize the above data . Then thp value < 0.05 and folder change > 2). They were identified by METLIN (https://metlin.scripps.edu/index.php) as well as KEGG (http://www.genome.jp/kegg/) database. To further appraise the potential metabolic pathways, the pathway analysis of these biomarkers was enforced by MetaboAnalyst 3.0 software  based on the pathway data library. Similarly, significant differences were considered when the p values < 0.05.Components were selected as latent biomarkers with obvious changes among groups  of 69 alternative targets was 1, and by more than 2 times the mean degree value, we further screened potential targets and 25 targets were obtained. Simultaneously, the mean degree value of filtered components and targets was 1, and all the 12 components had a degree value >2, indicating that components play multiple therapeutic roles by regulating various targets. Specifically, 2 components, luteolin and beta-sitosterol, acted on 37 and 32 targets, respectively, meaning they are crucial active components of chicory because of their important positions in this network. Additionally, the results denoted that many targets were hit by multiple components in the C-T network .The comprehensive GO analysis was used to acquire a deeper understanding of the picked genes that were closely related to chicory. We used a total of 25 genes, which screened through the mean degree value, to enrich 573 GO terms . Among the GO terms, the most remarkable biological process was intracellular receptor signaling pathway, in which 7 vitally enriched function clusters associated with this signaling pathway. The most prominent cellular component was apical part of cell, having 4 importantly enriched function clusters related to apical part of cell. And the most significant molecular function was steroid hormone receptor activity, which had 5 momentously enriched function clusters in connection with steroid hormone receptor activity .Using ClueGO to reveal biological processes about enrichment analysis of filtered targets, obvious differences could be found, among them IL-17 signaling pathway, regulation of lipolysis in adipocytes, estrogen signaling pathway, prolactin signaling pathway, regulation of fibroblast proliferation were particularly observed . CombineThe levels of serum UA, TC, TG, HDL-C, and LDL-C in each group were shown in The PCA score plot visually displayed the changes in the metabolic patterns of organisms among CON, MOD, FEN, and CHI groups. PCA analysis of serum and stool was shown in Figures + analysis about the serum and stool samples were shown in Figures + analysis suggesting that metabolic profiles significantly changed in 3 groups. Commonly, the R2X and Q2X provide an estimate of predictive ability in OPLS-DA model. In the ESI+ model, the parameters about serum for classification from the software were R2X =0.388 and Q2X= 0.288, and for stool samples, in the ESI+ model, R2X= 0.627 and Q2X= 0.269, respectively, indicating that the OPLS-DA model was well established. VIP>1 or |p (corr)| ≥ 0.5 were selected difference variables as potential candidate markers, which might be the key metabolites of inulin acted on hyperlipidemia quail.OPLS-DA was served to investigate potential biomarkers in different groups. The results of OPLS-DA model originated from data of ESIBased on the above research, in the metabolomic of serum and stool samples, 22 components were identified as endogenous biomarkers via combining all the markers of positive ions, and by comparing the exact mass ratio of KEGG to the METLIN database, 9 and 94 biomarkers were further screened through KEGG pathway analysis, respectively, which were screened into the MetaboAnalyst software for enrichment pathway analysis, then 4 and 15 metabolic pathways were screened, respectively, in the serum and stool samples. According to the results of metabolomic, we concluded that the 4 potential mutual pathways where chicory could act on the HUA quails model were finally obtained. As shown in Hyperuricemia is positively correlated with hyperlipidemia, diabetes, atherosclerosis, obesity, and other metabolic disorders. Related studies have indicated that hyperlipidemia and obesity are often accompanied by HUA. Chicory, which is a cholagogic and diuretic agent in Uighur folk medicine, is widely used as the forage, leafy vegetable, crude materials for sugar and coffee substitute materials. The literature showed that the pharmacological functions of chicory include antioxidant, anti-inflammatory, antihyperlipidemia, and hypoglycemic effects , 23. In β-sitosterol had the effect of inhibiting lipid peroxidation, regulating lipid metabolism, and preventing atherosclerosis [In recent years, network pharmacology has provided a new research method for scientific research in the field of traditional Chinese medicine, which can offer the prediction for drug component and its corresponding target research through network construction based on big data. It helps us to find the possible ways of action of the drug through enrichment analysis, bringing hints to subsequent research to make later study more purposeful. In this study, we constructed a total of 12 effective components and 25 potential targets by constructing a C-T prediction network. Flavonoid and terpenoids were the main components, and the prediction results were consistent with experiments [clerosis . Luteoliclerosis . In addiclerosis , and delclerosis . These pclerosis –32. ThroBased on the previous research on chicory in our laboratory , 21, in In multivariate statistical analysis, CHI was far away from MOD and even away from CON; we consider that it may be related to the dosage of chicory, or there were other potential pharmacological mechanisms of chicory; further studies are still necessary to explore. Subsequently, in the analysis of serum and stool samples metabolomics, combined with the above results, we focus on discussion of glycerophospholipid metabolism.In the pathway of glycerophospholipid metabolism, stearic acid, 1-acyl-sn-glycerol 3-phosphate, 1-acylglycerophosphoinositol, and 1-acyl-sn-glycero-3-phosphoglycerol were upregulated in MOD more than CON, and hexadecanoic acid, l-palmitoylcarnitine, and nonanedioic acid were downregulated in MOD. Chicory adjusts glycerol phospholipid metabolism by reversing these metabolites. Combined with the results of this study, it is suggested that abnormal metabolism of glycerophospholipid may be one of the metabolic pathways involved in the pathogenesis of HUA. As we all know, the state of HUA has the effect of promoting oxidation as well as inducing translocation of the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunit in mitochondrion. This results in increased mitochondrial oxidative stress and mitochondrial dysfunction along with release of citrate to the cytoplasm for lipid and triglyceride synthesis . In addiFatty acids such as stearic acid, and 1-acyl-sn-glycerol 3-phosphate are mainly involved in the synthesis of fatty acids, the extension of fatty acids in mitochondria, fatty acid metabolism, and the synthesis of unsaturated fatty acids , 42. TheIn summary, chicory is beneficial to regulate the disorder of lipid metabolism for hyperuricemia. In this study, we chose quail as animal model innovatively and explored the treatment of hyperuricemia with chicory in multiple methods to provide reference for the study of hyperuricemia. In addition, we have to point out that we need to further build the target-disease (T-D) network to discuss the prediction about disease in the treatment of chicory. Moreover, only LC/MS was used to analyze samples and detected potential biological markers with biological significance. It could be seen that our detection method was simple and still needed further comprehensive research to combine with GC-MS, NMR, etc. Meanwhile, more researches needed to combine with molecular biology experiments to elucidate the pharmacological mechanisms of chicory lowering uric acid and antihyperlipidemia effects systematically.In conclusion, firstly, the present study predicted the effective components and targets of chicory via bioinformatics analysis, which acquired 12 related effective components along with 25 corresponding targets, and these genes were enriched in regulation of lipolysis in adipocytes. Secondly, animal pharmacodynamics experiment combined with metabolomic profiling with UHPLC-Q-TOF/MS methods, and 9 and 94 potential biomarkers were screened in the serum and stool samples, respectively, and enriched in the corresponding metabolic pathways. Finally, under our experimental conditions, it is suggested that chicory may play a role in lowering uric acid and antihyperlipidemia by adjusting lipid metabolism. Our research not only supplies new method for the search for potential active components but also provides comprehensive analysis to interpret the underlying therapeutic effects of chicory in diet-induced HUA. However, it is important to note that bioinformatic and metabolomic analyses do not provide any conclusive evidence, but rather provide hypothesis and references for future studies, and we need to confirm these conclusions.


Circular RNA (circRNA), a kind of special endogenous RNA, has been shown to be implicated in crucial biological processes of multiple cancers as a gene regulator. However, the functional roles of circRNAs in breast cancer (BC) remain to be poorly explored, and relatively incomplete knowledge of circRNAs handles the identification and prediction of BC-related circRNAs. Towards this end, we developed a systematic approach to identify circRNA modules in the BC context through integrating circRNA, mRNA, miRNA, and pathway data based on a non-negative matrix factorization (NMF) algorithm. Thirteen circRNA modules were uncovered by our approach, containing 4164 nodes  and 67,959 edges in total. GO (Gene Ontology) function screening identified nine circRNA functional modules with 44 circRNAs. Within them, 31 circRNAs in eight modules having direct relationships with known BC-related genes, miRNAs or disease-related pathways were selected as BC candidate circRNAs. Functional enrichment results showed that they were closely related with BC-associated pathways, such as ‘KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAYS IN CANCER’, ‘REACTOME IMMUNE SYSTEM’ and ‘KEGG MAPK SIGNALING PATHWAY’, ‘KEGG P53 SIGNALING PATHWAY’ or ‘KEGG WNT SIGNALING PATHWAY’, and could sever as potential circRNA biomarkers in BC. Comparison results showed that our approach could identify more BC-related functional circRNA modules in performance. In summary, we proposed a novel systematic approach dependent on the known disease information of mRNA, miRNA and pathway to identify BC-related circRNA modules, which could help identify BC-related circRNAs and benefits treatment and prognosis for BC patients. Breast cancer (BC) is the most frequent malignancy in women, affecting more than 10% of women in western countries ,3. ThereWith the enormous development in the field of high-throughput RNA sequencing technology, a novel class of endogenous RNA, circular RNA (circRNA), has been extensively studied ,11. SomeAlthough some advance in biological protocols has been made, it is time-consuming and expensive to identify BC-circRNAs only by using experimental technologies. Thus, some systematical approaches have been developed and proposed to identify BC-related circRNAs. Lu et al. identified 1155 differentially expressed circRNAs in BC tissues through analysis of a genome-wide circRNA profile data and found the expression levels of six circRNAs were related to BC which participated in cancer-related pathways . Chen etThus, we integrated circRNA-mRNA, miRNA-mRNA, and pathway-mRNA data to identify BC-related circRNA modules based on a non-negative matrix factorization (NMF) algorithm , and dechttp://www.circbase.org/) were identified from the RNA-seq data of BC patients by using the UROBORUS tool (p < 0.005) in at least 50% of BC patient samples were retained for the following analysis.In total, there are 8953 human circRNAs recorded in the circBase database  were obtained. Further, 80 circRNAs and 13,251 mRNAs with degrees more than three  were selected and were used to characterize the circRNA-mRNA binary matrix. MiRNA and mRNA relationships were integrated by the miRNA-target gene data, which were collected from starBase, miRTarBase, and PITA. 63 miRNAs and 8385 mRNAs with more than three partners were retained and were used to construct the miRNA-mRNA binary matrix. The pathway-mRNA relations were integrated from pathway data obtained from the Molecular Signatures Database (http://software.broadinstitute.org/gsea/msigdb), which contained a large number of functional annotation information that was curated from BioCart, Kyoto Encyclopedia of Genes and Genomes (KEGG), the NCI Pathway Interaction Database (PID), and Reactome. Finally, 1329 pathways and relevant 8904 mRNAs were used to characterize mRNA-pathway binary matrix. Three characterized binary matrices in total contained 2703 common mRNAs, 63 miRNAs, 80 circRNAs and 1318 pathways  values B. In totpathways .K (the default parameter ranges from 5 to 20) equals to 13, the value of objective function F reached the minimum Euclidean error and the corresponding 13 circRNA modules were generated, including 4164 nodes  and 67,959 edges. Subsequently, 9 circRNA modules , and 1069, 3299, 864, 1375, 2708, 665, 827, 1237 and 2054 edges, respectively . Within To better characterize the relationships between circRNAs and pathways or miRNAs in the nine modules, the normalized term overlap (NTO) scores  were calTo further identify candidate circRNAs that may be potentially associated with BC patients, circRNAs having more than four direct interaction partners  were extracted (https://www.proteinatlas.org/), which also suggested that these circRNAs may be potentially served as disease biomarkers for disease diagnosis or therapy during disease development or progression.Interestingly, several circRNAs’ corresponding parental genes, such as has_circ_0007766, has_circ_0017242, has_circ_0037130, has_circ_0003759, has_circ_0007843, has_circ_0086375 and has_circ_0003614, were respectively recorded as ERBB2, AKT3, NPRL3, LPP, ARHGAP32, NFIB and ASPH in the circBase database. All these genes were remarked as ‘Cancer-related genes’ or ‘Disease related genes’ in the Human Protein Atlas . This dataset included 15 disease samples and 18 normal samples (http://starbase.sysu.edu.cn/), miRTarBase [http://mirtarbase.mbc.nctu.edu.tw/php/index.php) and PITA (https://genie.weizmann.ac.il/pubs/mir07/mir07_data.html). Pathway data were downloaded from the Molecular Signatures Database [http://software.broadinstitute.org/gsea/msigdb) database, which contained a large number of annotated functional genes collected from existing public databases, such as BioCart, KEGG, PID, and Reactome. We selected pathway data from the curated gene sets (c2) in MsigDB V6.1, which contained a total of 1329 metabolic and signaling pathways.Paired-end RNA-seq data of SRP062132, which detected by “Illumina Genome Analyzer II” were downloaded from the NCBI SRA database  by the p < 0.005 were considered as differentially expressed miRNAs. The PCC was then used to measure the co-expression relationships between differentially expressed circRNAs and mRNAs. CircRNA-mRNA pairs with PCC > 0.4 and p-value < 0.05 were used to construct the binary matrix. As for the miRNAs and mRNAs, the corresponding miRNA target mRNAs relations were used to build the miRNA-mRNA binary matrix. If a pair of miRNA and mRNA was recorded in any one of the three database starBase, miRTarBase and PITA, the miRNA and mRNA was denoted as “1” in the miRNA-mRNA binary matrix. The pathway-mRNA binary matrix was similarly constructed based on pathway information from the Molecular Signatures Database.After recognized by the UROBORUS, circRNAs recorded in the circBase database and expressed in more than 50% patient samples were retained A. The diK of BC-related functional modules from these three matrices by using non-negative matrix factorization (NMF). The objective function F for NMF was defined as:W and H were both non-negative matrices. W was an M × K (M was the number of common mRNAs in three matrices) matrix representing the basis vector. K × N  matrix, representing the coefficient vector in the dimension reduction process (K (from 5 to 20) numbers and calculated the Euclidean errors between the input matrices, and the model reconstructed data. The Euclidean error measured the distance between the input matrices and the model reconstructed data. By comparing the Euclidean errors, we selected the smallest one to build the functional modules. W and H were updated at each iteration step by using the generalized multiplicative update rules as follows:We identified different numbers proaches , and the process C. We selW and IH , we separately retrieved the top 1% to top 10% ranked mRNAs according to the Z-score values to perform the GO BP enrichment analysis, by using R-package ‘clusterProfiler’ . Then, when the top k% mRNAs enriched the most BP GO terms, we assigned the top k% genes to this module. Similarly, we assigned the top k% miRNAs, circRNAs or pathways in the corresponding row of matrix IH (Ihttps://www.genecards.org/) and HMDD (http://www.cuilab.cn/hmdd) respectively.It was worth noting that when we used the randomly generated initial matrices d method . For eace module C. Subseqe module D. Those In addition, we calculated the normalized term overlap (NTO) score to further determine the relationships between circRNAs and pathways or miRNAs. The NTO score was calculated by the formula as follows:


Plant polyamines (PAs) have been assigned a large number of physiological functions with unknown molecular mechanisms in many cases. Among the most abundant and studied polyamines, two of them, namely spermidine (Spd) and thermospermine (Tspm), share some molecular functions related to quality control pathways for tightly regulated mRNAs at the level of translation. In this review, we focus on the roles of Tspm and Spd to facilitate the translation of mRNAs containing upstream ORFs (uORFs), premature stop codons, and ribosome stalling sequences that may block translation, thus preventing their degradation by quality control mechanisms such as the nonsense-mediated decay pathway and possible interactions with other mRNA quality surveillance pathways. The eukaryotic cell has developed sophisticated mechanisms to cope with alterations that may occur during the complex process of gene expression, which could lead to detrimental consequences. Until the final gene product is released as a functional protein, numerous sequential steps such as transcription, mRNA maturation, export from the nucleus, translation, and folding demand checkpoint controls to avoid the production of defective proteins that could compromise cell viability. The translational process is usually monitored by ribosome-associated quality control pathways able to recognize faulty events caused by halted or stalled ribosomes during the process of mRNA translation . These sAs the ribosome becomes the hub for the activity of the quality control pathways, any of the cellular metabolites or proteins linked to the ribosomal activity may impinge on the activity of the clearing pathways. In this review, we focus on the role of PAs as metabolites derived from amino acid catabolism universally present in eukaryotic cells, with essential functions linked to ribosomal activity. In particular, we compile recent information of two PAs, Tspm and Spd, whose activities in translation preventing the occurrence of ribosome stalling qualify them as quality keepers, indirectly protecting mRNAs from their degradation by preventing the activation of ribosome-associated mRNA quality control pathways. S-adenosylmethionine (dcSAM) to yield higher-molecular-weight polyamines  bound to macromolecules revealed that most of these two PAs in rat liver remained as a complex with RNA and to a minor extent was bound to DNA and phospholipid . PreviouE. coli ,9. RefinE. coli , in agreE. coli ,12,13. AE. coli . In addiE. coli . Recent information on the function of two plant PAs, Tspm and Spd, has uncovered unexpected roles for PAs related to faulty translational events. These novel functions for PAs related to ribosomal activity are connected to specialized mRNA surveillance mechanisms that can detect mRNAs with defective translation and initiate their selective degradation. There are at least three known mRNA anomalies that lead to activation of the specific quality control pathways. One abundant alteration among mRNA sequences is the presence of premature termination codons (PTCs) that may lead to the production of defective proteins with dominant negative effects, in this case the surveillance machinery engaged is the nonsense-mediated decay pathway (NMD). Another type of activated system is the no-go decay pathway (NGD) that acts when ribosomes halt during elongation due to the presence of secondary mRNA structure, of rare codon repeats, or because of structural features of the nascent polypeptide that block elongation. A third route that takes care of translational failures is the non-stop decay pathway (NSD), operating on mRNAs lacking natural stop codons. In addition to the surveillance and degradation of mRNAs halted during translation, specialized complexes take care of the removal of non-functional proteins, a process described in detail elsewhere . In the A. thaliana revealed that around 1% of coding mRNAs and up to 20% of mRNA-like non-coding RNAs are targets of the NMD pathway [trans-acting factors involved in the NMD, named up-frameshift proteins (UPFs), were initially described in S. cerevisiae [A. thaliana upf mutants defective in the NMD pathway have uncovered important functions for this pathway in development and stress responses [The NMD pathway targets mRNAs carrying PTCs whose presence may generate truncated dominant-negative proteins with deleterious consequences for the cell ,18,19. T pathway . Moreove pathway ,25. The revisiae , and therevisiae  with onlrevisiae . A. thalesponses ,30,31,32esponses ,34. Althesponses . This surveillance pathway detects ribosomes that have been stalled during translation elongation because of specific features of nascent polypeptides, secondary mRNA structures that block ribosome progression, or rare codon repeats that lead to prolonged vacancy of the A-site of the ribosome. It counts with specialized protein factors described initially in yeast and named Dom34 and Hsb1 (Dom34:Hsb1) and later in mammals and known as Pelota and HSb1-like (PELO:HSB1L) . Both prThe NSD pathway degrades mRNAs that lack in-frame stop codons, either because of premature polyadenylation leading to non-stop mRNAs with poly(A) or by endonucleolytic cleavage within the open reading frame producing non-stop mRNAs lacking poly(A) ,44. In tA. thaliana after the isolation of a mutant deficient in Tspm named acaulis 5 (acl5) [ACL5 as a spermine synthase since the analytical procedures used were unable to distinguish between Spm and Tspm [A. thaliana [acl5 mutant displaying stunted growth allowed for the genetic isolation of mutant suppressors, named sac, able to restore stem growth in the absence of Tspm to illuminate on its molecular functions [SAC genes, SAC51 and its family of bHLH-like transcription factors (SACL1-3 genes) have been key to elucidating downstream functions of Tspm in higher plants [Tspm was first discovered in thermophilic bacteria , and it 5 (acl5) . The firand Tspm , but it thaliana . Differethaliana . As an ethaliana ,56. The thaliana ,58,59. Punctions ,61,62. Ir plants . Althougr plants . SACL genes is the presence of uORF in their 5’-leader sequence, with the peculiarity that several suppressor mutations map precisely in this region [sacl mutants that no longer require Tspm indicates that Tspm is required to bypass the uORF-dependent translational inhibition of the SACL main ORF (mORF). The presence of uORFs, in particular those encoding conserved peptide sequences (CPuORFs), as is the case of the SACL genes, is often a feature to define substrates of the NMD pathway [upf mutants seem to corroborate this idea, as gene expression studies have shown the upregulation of the SACL genes in these mutants, suggesting that UPF proteins are required for the suppression of the SACL mRNAs [SACL translation helps to evade the premature termination on their uORFs, thus protecting these mRNAs from degradation by the NMD pathway  has been catalogued as one of the few protein families potentially present in the last-universal-common-ancestor (LUCA) . More emThe way eIF5A/EFP resolves problematic poly-proline ribosome pauses is among the topics of excellent reviews ,105 thatEIF5A1 gene, showing elongated half-lives of NMD transcripts [Although no evidence has been reported to date for the interaction of eIF5A with the NGD and NSD pathways, many studies have linked the functions of eIF5A to the NMD pathway. Pioneering searches for yeast mutants with increased stability of mRNA targets of the NMD pathway have uncovered eIF5A as an essential protein involved in mRNA decay . This obnscripts . More renscripts . One posnscripts . AltogetPAs are well-conserved metabolites with essential functions in eukaryotes. Among the different roles and locations within the cell, a considerable number of studies have linked their activities to the ribosome. This highlights vital functions for PAs, such as the essential nature of Spd for eukaryotes and the absolute requirement of Tspm for plant vascular development. The mode of action for Spd within the ribosome is to provide the aminobutyl moiety to activate the translation factor eIF5A, whereas for Tspm the available information suggests a direct and probably intimate role with the ribosome machinery. In both cases, a reduction in their levels correlates with stabilization of mRNAs belonging to the NMD pathway, thus indicating that both PAs may act as watchers for potential ribosome conflicts. In the case of Tspm, the known NMD targets belong to the group of genes with uORF sequences containing PTCs that act as translation inhibitors for the mORF. Unfortunately, the mechanistic molecular details of Tspm activity in the ribosome still need to be deciphered. On the other hand, the suppression of Spd-mediated hypusination of eIF5A leads to an accumulation of unstable transcripts, including some of the AS-NMD type, thus suggesting unknown connections of the eIF5A translation factor with the splicing machinery. Still, how hypusinated eIF5A connects to the NMD pathway awaits further studies. We cannot forget that the inactivation of eIF5A could lead to the activation of either NGD or the NSD pathways. For instance, if poly-proline induced ribosomal blocks are not rapidly resolved and lead to ribosomal collisions, this could activate NGD machinery. On the other hand, alterations in the termination process by inactive eIF5A may lead to non-stop ribosomal activity, which could activate the NSD pathway. What has been well documented is that both Tspm and Spd (through the activity of eIF5A) seem to act as molecular sentinels to prevent translation anomalies that may lead to activation of the NMD machinery and subsequent mRNA degradation. In this review, we have focused on two PAs that share similar proofreading functions on translation, but it would not be a surprise to find out that other PAs may fulfill related functions on this or related cellular processes. We anticipate that in the near future the roles of PAs in the cell in relation to ribosomal activity will be fully elucidated, thus helping to solve one of the remaining mysteries of molecular biology .


Ocular adnexal mucosa-associated lymphoid tissue (MALT) lymphoma is uncommon in the pediatric population. Initial misdiagnosis is common and there is lacking consensus regarding the optimal approach to treatment. Herein, we report an atypical presentation of pediatric conjunctival MALT lymphoma and review the presentation and management of this rare condition. Whilst ocular involvement occurs in only one to two percent of extranodal non-Hodgkin Lymphomas (NHLs), mucosa-associated lymphoid tissue (MALT) lymphoma is the most common type of primary ocular adnexal lymphoma . First d Chlamydia psittaci has been suggested but no definitive infectious etiology or association has been identified [ Helicobacter pylori has been shown to be the causative agent in the majority of cases [There are no accepted prognostic factors for MALT lymphoma of the ocular adnexal region. Several chromosomal abnormalities have been demonstrated in MALT lymphoma more generally. A potential association between ocular MALT lymphoma andentified . This isof cases .Conjunctival MALT lymphoma characteristically manifests as a painless fleshy-coloured “salmon-patch” lesion arising from the fornix . Bulbar Conjunctival MALT lymphoma is rare in children, with the first case reported by Tiemann and colleagues (2004) just over a decade ago . To dateA 15-year-old female presented with a one-year history of intermittent bilateral ocular erythema, irritation, and discomfort, most severe in the right eye. She was otherwise well, with no significant past medical history or family history. Visual acuity was 6/6 in both eyes. On slit-lamp examination, giant papillae were identified bilaterally in the inferior conjunctival forniceal regions, notably larger and more widespread in the right eye . BaselinA biopsy of the right palpebral conjunctival lesion showed expansion of the subepithelial connective tissue by coalescent nodules of small lymphocytes. These lymphocytes had a centrocyte-like morphology; the immunophenotype is CD20+/CD10-/CD5-/CD43-. The cell markers on flow cytometry showed a monoclonal population of mature B cells with lambda light chain restriction. The morphology and immunophenotype, including immunoglobulin light chain restriction, were diagnostic of an extranodal marginal zone lymphoma of the mucosa-associated lymphoid tissue (MALT lymphoma) . Notablyst December 2017, key words “conjunctival” OR “ocular” and “MALT lymphoma” and “paediatric/pediatric” or “child”) identified 10-13 pediatric cases of conjunctival MALT lymphoma overall  are largely credited with reporting the first definitive case of conjunctival MALT lymphoma in a ten-year-old girl who was successfully managed with surgical excision of the lesion and adjuvant local cryotherapy . Since tAn atypical case of pediatric MALT lymphoma involving the palpebral conjunctiva is presented, different from the characteristic “salmon-patch”, which typically affects the bulbar conjunctiva.Initial misdiagnosis suggests caution should be taken in pediatric patients presenting with atypical persistent conjunctivitis.Although the risk of systemic involvement is low, long-term follow-up in children is important and was significantly greater in this case than previously reported.This case report and review also demonstrate the long-term benefits of topical interferon treatment as a monotherapy.


A bioinformatic analysis was performed to refine pathways' signatures and to create chemical activation capacity scores to classify chemicals by their potency and selectivity of activation of each pathway. With some refinement such an approach may improve chemical safety classification and allow biological read across on a pathway level.Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects  obtained in various experimental conditions (in human and rat  Many transcriptionally activated pathways are intimately involved in responses to chemical induced perturbations and toxicological outcomes   , the following three search strategies, from the works of  and NRK-52E (rat). The study no. is DIXA-003. Differentiated cell cultures were exposed to a single bolus of non or low cytotoxic (<IC10) concentration of chemical for 6, 24, or 72 h before lysis in TRIZOL, RNA purification and transcriptomic analysis on Affymetrix microarrays as described , primary human hepatocytes, and rat hepatocytes . The study no. on the diXa database is DIXA-095. Differentiated cell cultures were exposed daily to a high (≤10% cell death) or low concentration of chemical for 1, 3 or 14 days, as described , even though they may be more reflective of background levels in case of slow absorption of the chemical administered.The heterogeneity of the sources of information of our database widens its coverage and strengthens its capacity to represent multiple conditions. However, this richness makes the database's structure complex. To simplify the analysis without losing potentially important information, we focused on conditions providing the best background to study the three pathways individually. The effects observed following exposure to a chemical could vary greatly depending on exposure duration. Exposures lasting more than 24 h tend to cause mixed stress responses that make it difficult to delineate the activation of specific molecular pathways and the initial mechanisms of toxicity of chemicals. These conditions could be a potential source of noise for the analysis and were thus excluded. Excluding all data obtained after 24 h reduced the dataset from 7,042 to 4,685 testing conditions. We chose not to eliminate the early kidney in vivo” category, no Nrf2 specific chemicals were found, and for all kidney data no ATF4 specific chemical were found either.In order to distribute the genes to pathways and pathway overlapping zones, log2 genes fold changes (FC) were ranked in decreasing order and examined on reduced datasets containing conditions relative to pathway specific activators. We define a pathway specific activator as a chemical where the mode of action is known, that the mode of action activates the specific pathways and that this mode of action is not expected to activate the other pathways under investigation. Thus, at relatively short exposures, to relatively low concentrations these chemicals will only act on their specific target. It is however possible at higher concentrations or longer time exposure, other targets will be affected due to increasing toxicity. As shown in Table For each of the pathway specific chemicals, all testing conditions were selected. For every gene, the mean of log2(FC) throughout all those conditions was calculated, to form the average activation value of each gene by each of the pathway specific activator. For AhR and Nrf2, the two average activation values obtained (one for each of the pathway specific activator) were themselves averaged. Genes were then sorted in decreasing order of average activation values per pathway. It is important to note that, since the expression of some genes can be inhibited (down regulated) by some chemicals or in certain conditions, some of the average activation values were negative. In order to select the most sensitive genes for each pathway, we computed the mean (μ) and the standard deviation (σ) of the genes' average activation values in each list. A pathways signature was formed by the genes whose average activation values were greater than μ + 2σ or smaller than μ – 2σ for this pathway. Genes appearing in the signature of more than one pathway were set apart in “overlapping signatures.”in vitro,” “Rat liver cells in vivo,” “Human liver cells in vitro” etc.) (which correspond to primary cells), to check if there would be any species-specific or in vitro/in vivo differences among signatures. We chose to work only with liver data since more data were available for liver  Rat liver cells in vivo, and (c) Human liver cells in vitro.Furthermore, we stratified signatures by original databases' categories  of the pathway signature genes over experimental conditions. Therefore, for each of the three pathways, we obtained a “chemical activation capacity” (CAC) value per chemical. This value reflects how strongly a chemical can activate a given toxicity pathway. Those CACs can be negative for chemicals inhibiting the majority of the genes of a pathway. We used CACs to estimate the pathway's selectivity of chemicals as well as the importance of their impact.Among the three liver categories where signatures were stratified, we chose to focus on the “Human liver cells AhR, K, CACNrf2, K), see Figure Each chemical can be considered as a point having three CACs as coordinates in a 3-dimensional space which axes correspond to a given pathway. Let us consider a chemical K that has a point in a bi-dimensional graph where the X-axis corresponds to AhR and the Y-axis to Nrf2. In this graph, K's coordinates would be: (CACcos(α). In theory, in a 3-dimensional space, a point is closer to an axis than to the two others when its cos(α) with this axis is greater than cos(α). On the other hand, the activation potency of a chemical proportionally increases with the module of the vector The specificity of a chemical for a given pathway can be measured by the proximity of its point K to the axis representing that pathway. Proximity can be mathematically evaluated by the absolute value of the cosine of the angle (α) between the pathway's axis and Similarly, even though C seems to have a greater activation potency than A and B (greater module), it is equidistant to both axes and therefore is not specific of any of the two pathways. The same logic applies for a 3-dimensional space, adding one extra axis for the ATF4 pathway.cos(α)) and potent In our signature-based classification of chemicals, for each pathway, after applying the chosen cut-off points, we sorted chemicals by the result of the product A visual depiction of the workflow is provided in Figure a priori pathway is the one for which the gene has come up in PubMed searches; Table a priori suspected to belong to the target pathway  while this wasn't the case of the “Inhibited genes” part of the lists. Figure NQO1, DLGAP5, CFTR, RAB39B and GSTA1), only NQO1 is a mainly activated gene while this was the case of most seven genes of the Nrf2-ATF4 overlapping zone  with two exceptions (CCL2 has negative averages for both pathways and KCNT2 for Nrf2). CYP1B1 is the only mutual gene for AhR (strong activation) and ATF4 (inhibition) and TPX2 is the only mutual gene for all three pathways (inhibition). Figure Pathway's signatures defined on the basis of the whole data set are listed in Table Table CYP1A1 is clearly, by far the most activated gene in this pathway. Three other genes appear in the AhR signature in more than one column: CYP1A2 everywhere except “Rat liver in vitro,” TIPARP everywhere except “Rat liver in vivo” and ABCC4 shows up in these two categories only. “Rat liver in vitro” AhR signature is completed by five additional genes, “Rat liver in vivo” by one more and “Human liver in vitro” by three.Table in vitro” and 15 for each of “Rat Liver in vivo” and “Human Liver in vitro”. Around two third of those genes are “Activated genes” and the rest have negative log2(FC) averages. MAFF, SLC3A2, OSGIN2 are among the “Activated genes” that appear in three out of the four categories we are studying. Other important genes show up in two columns  and some, in only one (SRXN1 in “Rat Liver in vitro” and HMOX1 in “Rat Liver in vivo”). The values of the “Rat liver in vivo” are also higher than the “Rat liver in vitro” and “Human liver in vitro” categories.Nrf2 signatures are bigger: 22 genes in the all liver data signature, 28 for “Rat Liver in vitro” and 14 for each of “Rat liver in vivo” and 19 for “Human liver in vitro.” HERPUD1 is an important gene in this pathway; it is part of the signature of every single category we are examining and exhibits values as high as 2.39 in “Human Liver in vitro” (among the highest in ATF4 signatures). Other genes also are present in the majority of the categories: IL23A, GTPBP2, and PDIA4. It is noteworthy that the ATF4 signature of “Rat Liver in vivo” results don't have a lot in common with the other three categories and its log2(FC) averages are lower than the rest .ATF4 signatures size is similar to Nrf2's signatures with a comparable proportion of activated genes: 23 genes in the all liver data signature, 28 for “Rat liver in vivo” and two genes in the two other categories). The number of genes in the AhR-Nrf2 overlapping signatures ranges from four to eight, with many typical key Nrf2 genes  appearing in more than one category. The Nrf2-ATF4 overlapping signatures contain six to eleven genes . Finally, TRIB3, FGF21, GDF15, SLC7A11, and TPX2 are in the signature of the zone mutual to all three pathways for at least two of the four categories studied.Figure cos(α), which represents the pathway activation scores of chemicals that activate each pathway both selectively and strongly. Chemicals are represented by a number that corresponds to their rank in the alphabetically ordered list. The blue dashed lines mark the vertical Figures Nrf2, ATF4, and AhR are important TFs in toxicological contexts and have well described downstream gene targets . Although some genes were now reallocated to different pathways. The overlap with “Inhibited genes” , was much poorer. This is too be expected as TF activated gene down regulation is much more complex and is often due to competition for auxiliary transcription facilitating proteins. Cytochrome P450 1A was the central element of the AhR pathway: CYP1A1 is the most prominent gene of this pathway, regardless of the experimental category, followed by CYP1A2. These findings are similar to previous investigations and have been implemented in a systems biology model  appear in the Nrf2 signature of all datasets, but also in the AhR-Nrf2 overlapping signature for most liver categories. This may reflect the fact that several AhR agonists are themselves metabolized to reactive chemicals via AhR dependent CYP expression. For example benzo(a)pyrene is a substrate of the CYP1 sub family of cytochrome P450 enzymes, and it promotes its own metabolism to reactive epoxide and quinone products pyrene and Omeprazole for AhR, Potassium Bromate and Phorone for Nrf2 and Tunicamycin A for ATF4. All conditions up to and including 24 h were pooled to generate a list of genes allocated to the three pathways ) and the activation potency We have used the exclusive pathway genes to create pathway chemical activation capacity (CAC) scores. The CAC reflects both specificity for the pathway (in vitro and in vivo, different organs are both a blessing and a curse. On the one hand, it is generally an advantage to have as broad as data set as possible, but the different sizes and focuses of the individual data sets/studies meant we needed to reduce the data to the lowest denomination. Another major issue was the low abundance of well described pathway activators. Despite these issues we have made some interesting observations and have developed a method to quantify a chemical's capacity to activate one three pathways.The size of the data set, its multiple sources, abundancy of compounds, concentrations and time of exposures, in vivo vs. in vitro. Some of these alterations are likely to be linked to pharmacokinetics, including distribution and metabolism, others may be linked to tissue specific regulation of these pathways. While some genes were very variable across experimental conditions, some were extremely robust, for example CYP1A1 in the AhR pathway and HERPUD1 in the ATF4 pathway. Some genes swing between a pathway's specific signature and overlapping zones for example GCLC between Nrf2 and AhR-Nrf2. Others are regularly on overlapping signatures for example TPX2 and TRIB3. However, it is not possible with this type of analysis to delineate whether these overlaps are solely on a gene level or also on the pathway level.We uncovered variations in AhR, ATF4 and Nrf2 signatures across tissues, compounds, species and The CAC score system developed, based on The dataset used for this analysis can be found in an Excel document in the FB and PJ conceived of the original idea and supervised the findings of this work. EZ, FB, PJ, AL, and AW planned the simulations. FB and PJ developed the theoretical formalism and verified the analytical methods. Supervised by PJ and FB, EZ performed the analytic computations and the numerical simulations. SW and BvdW generated target gene lists. XJ and AK-S designed the figures. EZ wrote the manuscript with support from FB, PJ, and AL and then all authors reviewed and commented the final manuscript. All authors provided critical feedback and helped shape the research, analysis and 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.


In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at  Omics technologies are revolutionizing biological research by enabling genome-scale analysis of complex biological systems and processes . FunctioNetwork analysis has become an increasingly popular tool to deal with the complexity of large datasets of all sorts. The importance of using networks lies in their ability to reveal relationships between factors, rather than seeing them as isolated entities . InterseHere we present NeVOmics , a bioinformatic tool that facilitates the functional characterization of data from OMICs technologies such as transcriptomics and proteomics. NeVOmics has been developed in programming language Python, it integrates ORA methodology and network-based visualization with R packages, allowing the generation of four different types of graphical visualization to show the enrichment results. NeVOmics applies a hypergeometric statistical test to identify significantly enriched GO terms and pathways in a list of genes or proteins. The tool supports all organisms deposited in UniProt Knowledgebase (UniProtKB) and KEGG databases, and incorporates a functionality to assign pathways to organisms with no annotated genome information available from orthologous gene pathways deposited in the KEGG database.NeVOmics has been developed in programming language Python. It integrates ORA methodology to obtain significantly over-represented GO terms and pathways in a list of genes/proteins. In addition, it uses R packages to provide network-based visualization to show the enrichment results. The code is configured to use updated information from UniProt-Gene Ontology Annotation (UniProt-GOA) ,11, UniPhttps://github.com/bioinfproject/bioinfo/). It includes NeVOmics Python script, instructions for use, system requirements, and some examples.The flowchart for data processing is depicted in NeVOmics can perform three different enrichment analyses using updated databases . The firNeVOmics uses an input file in plain text containing a list of genes (KEGG gene ID) or proteins (UniProt Entry ID) obtained by any OMICs approach, such as comparative transcriptomics, proteomics, or proteome-specific methodologies . The file can contain up to three columns (in Tabular format) depending on the results obtained in the “OMICs” experiment. The first column corresponds to the list of genes or proteins to be analyzed, for example a set of proteins showing abundance changes in a particular condition with respect to the control. The second column contains numerical values of expression/abundance or any other quantifiable value related to the study. The third column corresponds to a background list  used as reference for statistics in the enrichment analysis; if the third column is absent in the input file the program automatically uses the entire proteome (UniProt-GOA) or KEGG pathways of the corresponding organism. In order to avoid ambiguities or inconsistencies in identifiers, NeVOmics allows only identifiers compatible with UniProtKB.http://purl.obolibrary.org/obo/go.obo. It also uses the GOA association files, which are downloaded from File Transfer Protocol (FTP) in tab-delimited format (ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/), and GO annotation in UniProtKB, which is provided as flat files that are downloaded from: http://www.ebi.ac.uk/uniprot/. In Swiss-Prot entries, only the manually annotated information is displayed. To view the complete GO annotation for a Swiss-Prot entry, the master copy of the data in the GOA association files is downloaded. GOA updates its annotation information weekly, while UniProtKB does it every four weeks. KEGG database covers information at different molecular levels. The KEGG Pathways database (https://www.genome.jp/kegg/pathway.html) is a collection of manually curated pathways, including information on molecular interactions, reactions, and network relationships. The KEGG database contains more than 24 million annotated genes and 530 pathways with more than six million pathway-linked genes.The Gene Ontology Annotation (GOA) database provides high quality electronic (mapping and automatic transfer of annotation to orthologous gene products) and manual (based on the literature) annotations to the UniProtKB  using the standardized vocabulary of Gene Ontology. The GOA database contains information of nearly 60,000 species and more than 160,000 taxa, with more than 32 million annotations. NeVOmics uses the Gene Ontology of the Gene Ontology Consortium (GOC) , which iftp://ftp.ebi.ac.uk/pub/databases/GO/goa/proteomes/) from which it extracts all GO terms. For KEGG Pathways analysis NeVOmics builds a background file with all genes of a specific organism for mapping the query gene list.A background list of genes or proteins is essential for performing an adequate enrichment analysis and must be carefully chosen; a list with all the genes/proteins detected in any condition of the OMICs experiment is usually a valid background list. If the column of the background list (third column in the input file) is absent the program automatically uses the entire proteome in the case of UniProt-GOA or the entire KEGG database. For Gene Ontology analysis, NeVOmics builds background lists organized by category  of a specific organism for mapping the query protein list. NeVOmics downloads a GOA association file (x (term or pathway) in a sample (gene/protein input list) of size k drawn from a background of size N (entire proteome by default or a background list provided by the user in the third column of the input file), where m corresponds to the total number of genes/proteins associated with a term or pathway  or in the KEGG pathways, with the genes/proteins classified by term or pathway. The tool adopts GeneMerge statistical algorithm to obtain the over-represented functions or categories in the input list . A hyper pathway . An FDR p-value and additional information for building plots. (III) Finally, an .xlsx file with enrichment analysis results for plotting in other network-based tools like Cytoscape [NeVOmics generates three output files, stored in specific directories, containing lists of particular functions or KEGG pathways over-represented with statistical significance : (I) An ytoscape  and Gephytoscape . NeVOmicytoscape , and UpSytoscape . These ghttp://www.geneontology.org/) for GO terms and as annotation source (http://geneontology.org/page/go-consortium-contributors-list); the update of 20 October 2018 was used in our analyses. g:Profiler uses also GOC for GO terms, and the annotation sources are Ensembl v93 (http://jul2018.archive.ensembl.org/index.html) and Ensembl Genomes v40 (http://ensemblgenomes.org/).Gene Ontology and annotation sources for the analyses with NeVOmics were indicated in It is recommended that NeVOmics users make reference to the date of the analysis when publicizing their results, so that other researchers have information on the version of the UniProt-GOA release used for the analysis.Arabidopsis thaliana, a model system for research in plant biology [NeVOmics is an integral tool with two major features: it allows enrichment analysis from a given list with data from some ‘OMICs’ experiment, and it builds different graphical representations in network form from the enrichment results. To test the functionality of NeVOmics we performed two case studies, using datasets from two different experimental OMICs procedures recently published. The first dataset comes from a platelet proteome of patients with early-stage cancer , and the biology .p < 0.05) in early-stage cancer as compared to the control. Samples from 12 cancer patients (eight with lung cancer and four with pancreatic cancer) and 11 controls were used in the study. We analyzed with NeVOmics an input list with 61/24 platelet proteins which were ≥1.5-fold more abundant/≥1.5-fold less abundant, respectively, in platelets from individuals with early-stage cancer as compared to platelets from healthy individuals. As background we used a list with the total 4384 platelet-expressed proteins identified in this study. We carried out an enrichment analysis for Gene Ontology (with an FDR of 0.02) and KEGG Pathways (FDR 0.1). Nineteen biological processes were identified as enriched  by analyzing the transcriptome of mutants in each of the subunit-encoding genes. We used NeVOmics to perform an enrichment analysis of GO terms and KEGG pathways in four gene datasets corresponding each to the up- and downregulated genes found in a tail subunit mutant: med2 (341 genes), med5ab (283), med16 (723), and med23 (35). The total number of expressed genes identified in the Whitney and Clint’s study [The mediator complex is a central component of transcriptional regulation in nd Clint  studied ’s study  was 18,8To compare our results with those obtained by Whitney and Clint , we usedRegarding the Pathway analysis, Whitney and Clint  identifiNeVOmics is designed to perform enrichment analysis using updated information of the two main annotation databases (GO and KEGG) automatically, making it unnecessary to have to update the information by other means. NeVOmics has the versatility to use UniProt-GOA and UniProtKB separately as databases, to obtain complete  and only manually-curated information, respectively, in order to get as much information as possible. There exist other available tools that can perform enrichment analysis on gene or protein datasets, however, these tools update their information on Gene Ontology less frequently and thus are less reliable for performing function enrichment analysis . Some ofhttps://www.uniprot.org/proteomes/) and KEGG  databases, and actually any organism can be used, even those lacking annotated genome information, due to NeVOmics functionality to assign pathways searching orthologous gene pathways deposited in the KEGG database , Gorilla (version not available), GOEAST (version 1.20), and WebGestalt (version 2019 beta). Unlike NeVOmics and WebGestalt, Gorilla, and g:Profiler present flexibility limitations in the adjustment of some parameters during the analyses, for instance they control the NeVOmics is an enrichment analysis tool executable in the command line, able to analyze from five to thousands of identifiers in gene/protein datasets. It can perform three different types of analyses  and proteins participating in these processes than any of the other tools. Nevertheless, we encourage researchers to use more than one tool when performing Gene Ontology and pathway enrichment analysis, in order to make comparisons and verification of results and thus be able to make more accurate conclusions.As proof of concept, we performed different tests with datasets from distantly related organisms such as humans and https://reactome.org/) and NCG (http://ncg.kcl.ac.uk/) databases (only for Homo sapiens). We will also work to include new features such as enrichment of protein domains and building of networks based on protein-protein interactions.In addition to its capabilities for functional annotation by enrichment analysis, NeVOmics builds and provides different types of network-based graphical visualizations to get a better comprehension of experimental results from OMICs technologies, and to illustrate and communicate the information in an integral form. In future versions, we aim to include the Reactome (