Pairwise datasets representing changes in gene expression associated with growth arrest (day 2 to 5 in culture), differentiation (day 5 to 10 in culture), and osteoblast maturation (day 10 to 28 in culture) were analyzed using Ingenuity Systems Pathways Analysis to generate predictions about signaling pathway activity based on the temporal sequence of changes in target gene expression. Our data indicate that some pathways involved in osteoblast differentiation, e.g. Wnt/β-catenin signaling, are most active early in the process, while others, e.g. TGFβ/BMP, cytokine/JAK-STAT and TNFα/RANKL signaling, increase in activity as differentiation progresses. Collectively, these pathways contribute to the sequential expression of genes involved in the synthesis and mineralization of extracellular matrix. These results provide insight into the temporal coordination and complex interplay between signaling networks controlling gene expression during osteoblast differentiation. A more complete understanding of these processes may aid the discovery of novel methods to promote osteoblast development for the treatment of conditions characterized by low bone mineral density.Bone remodeling involves the coordinated actions of osteoclasts, which resorb the calcified bony matrix, and osteoblasts, which refill erosion pits created by osteoclasts to restore skeletal integrity and adapt to changes in mechanical load. Osteoblasts are derived from pluripotent mesenchymal stem cell precursors, which undergo differentiation under the influence of a host of local and environmental cues. To characterize the autocrine/paracrine signaling networks associated with osteoblast maturation and function, we performed gene network analysis using complementary “agnostic” DNA microarray and “targeted” NanoString nCounter datasets derived from murine MC3T3-E1 cells induced to undergo synchronized osteoblastic differentiation  Bonein vitro studies offer the advantage that cellular development can be synchronized, offering a better opportunity to view differentiation as a linear process. In bone, the replication of undifferentiated osteogenic precursor cells, their recruitment to remodeling bone matrix, and their subsequent acquisition of differentiated function, results from the complex interplay of signals transmitted by mechanical load, polypeptide growth factors, steroid and thyroid hormones, and locally produced cytokines and prostaglandins [in vitro, indicating that, once triggered, the process is autonomous, i.e. independent of ongoing exposure to systemically derived factors.In contrast, glandins , 4. Whilin silico analyses, which enable gene expression profile data to be expressed as the statistical probability that a particular pathway is regulated, can “fill in the blanks”, leading to a more holistic view of process-related changes in signaling pathway activity.Because it offers a means of capturing “unbiased” data about changing patterns of transcription across the whole genome, gene array technology is a potentially powerful tool for understanding complex biological processes. But all such studies have inherent limitations. Genomic datasets are invariably incomplete since the statistical methods for determining which genes are “significantly regulated” involve a trade off between the sensitivity needed to detect real changes in mRNA abundance and the specificity needed to avoid false positive “hits”. The more stringent the statistical filter, the lower the rate of false discovery, but the more genuine significantly regulated, and potentially important, genes will be missed. In addition, important pathway components may not be regulated at the transcriptional level. A further limitation of transcriptomic approaches is that it is difficult to translate lists of significantly regulated genes into changes in biologically relevant signaling networks. Circumventing each of these limitations requires the use of bioinformatic approaches that compare changes in gene expression against databases of known protein-protein interactions to establish the probability that a given signaling or metabolic pathway is regulated under varying experimental conditions , 6. Thesin vitro. Taking advantage of their well-defined maturational sequence [To better understand the temporal regulation of osteoblast differentiation, we performed microarray analysis of gene expression followed by signal transduction pathways analysis on murine MC3T3-E1 cells undergoing osteoblastic differentiation sequence –10, we i2 atmosphere at 37 °C. Until the time of study cells were maintained in log phase growth by passage every 3–5 days using 0.001% pronase (w/v) to detach adherent cells. For studies of the temporal sequence of osteoblast differentiation, cells were plated at an initial density of 20,000 cells/well in 6-well plates or 100,000 cells/dish in 10 cm dishes, and grown for 2 to 28 days in α-MEM supplemented with 10% v/v fetal bovine serum, 5 mM β-glycerol phosphate and 50 μg/mL ascorbic acid [Stock cultures of MC3T3-E1 cells  were maintained in α-minimum essential medium (MEM) supplemented with 10% v/v fetal bovine serum, penicillin (100 units/mL) and streptomycin (100 pg/mL) in a humidified 10% COBetween days 1 and 5 in culture, cells in 6-well plates were treated with 0.001% pronase (w/v) to achieve detachment and directly counted in a hemocytometer.6 cells. For illustrative purposes, representative cultures were also stained using the Alkaline Phosphatase Detection Fast Red Stain Kit  according to the manufacturer’s instructions.Alkaline phosphatase activity was measured by para-nitrophenyl phosphate hydrolysis as previously described . Briefly1  with horseradish peroxidase-conjugated donkey anti-mouse IgG  as secondary antibody. The cell content of each sample was determined by western blotting in parallel for β-actin using mouse monoclonal anti-actin IgG1 . Immune complexes were visualized on X-ray film by enzyme-linked chemiluminescence and quantified using a Fluor-S MultiImager. Data were expressed as the ratio of type I collagen to actin in each sample.Type 1 collagen production was determined by western blotting. Monolayers of MC3T3-E1 cells were lysed directly in 1X Laemmli sample buffer, dispersed by sonication, and resolved by sodium dodecyl sulfate—polyacrylamide gel electrophoresis. Immune complexes on nitrocellulose membranes were detected using mouse monoclonal anti-type I collagen IgGMatrix mineralization was quantified by Alizarin red staining as described . MonolayMC3T3-E1 cells were cultured as described in 10 cm dishes for 2, 5, 10 or 28 days prior to isolation of RNA. Total RNA from three independent cultures was isolated at each time point. Cells were harvested by scraping and RNA was isolated with Trizol Reagent  and purified using the RNeasy kit  according to the manufacturer’s protocols . Total Rwww.genome.duke.edu/cores/microarray/). MIAME compliant microarray data files have been deposited with the NCBI GEO database (www.ncbi.nlm.nih.gov/gds) (GEO Series GSE64485). Data pre-processing and normalization were performed on GenePix scan results files (.gpr files) using the Bioconductor LimmaGUI package 1.28.0 run with R 2.13.0 software [Samples underwent RNA amplification , labeling with Cy3, and hybridization to Mouse Operon 17,000 gene feature  spotted oligonucleotide arrays in the microarray facility of the Duke University Institute for Genome Sciences and Policy . With NanoString technology fluorescent single strand RNA probes are hybridized to complimentary target strands of mRNA and quantified based on the fluorescence of each target gene within each sample [The NanoString nCounter gene expression system  was used for expression profiling of selected mRNA species isolated from MC3T3-E1 cells at Days 2, 5, 10 and 28 in culture using a custom nCounter CodeSet composed of 243 probes  includinh sample ,16. Briehttps://analysis.ingenuity.com/), yielding 976 analyzable transcripts from the microarray dataset  ≥1.3, compared with a standard murine background database were considered analyzable. The IPA output was exported as Microsoft Excel files to prepare the https://software.broadinstitute.org/morpheus/).Network analysis of genesets representing changes in mRNA abuundance between specified time points was performed using the Ingenuity Systems Pathways Analysis (IPA) tool . IPA compares Genbank Accession number/expression information with a proprietary protein-interaction database to establish the probability that a given signaling or metabolic pathway is activated under varying experimental conditions. For the microarray dataset, expression ratios for all relevant pairwise comparisons, i.e. D2 vs D5, D2 vs D10, D2 vs D28, D5 vs D10, D5 vs D28, and D10 vs D28, were calculated using the ANOVA p<0.005 set of 1005 significantly regulated genes. For the NanoString dataset, pairwise expression ratios were calculated for each of the 237 measurable genes. Expression ratio data were uploaded into the IPA Pathways Analysis system , integrin-binding sialoprotein (Ibsp), a major structural protein of the bone matrix, and parathyroid hormone receptor (Pthr1), increase steadily from day 2 to day 28. Still other mRNA species are abundant throughout development, e.g. collagen type 1A (Col1a1), and some increase between days 10 and 28 after differentiation is well underway e.g. the osteoblast-specific matrix protein periostin (Postn). Such differences are consistent with a temporally coordinated process wherein early events trigger the sequential activation of a transcriptional program driven by intracellular signaling networks.Predictably, osteoblastic differentiation of MC3T3-E1 cells is reflected in changes in the abundance of mRNA encoding bone marker proteins. As shown in DNA microarrays, because they capture information about the abundance of a large number of unselected mRNA species, provide an “agnostic” snapshot of gene expression patterns at a given point in time. Combining microarray data on changes in mRNA abundance over time with bioinformatic tools, such as Ingenuity Systems IPA, provides a means to translate microarray data into a more complete picture of metabolic activity , 6. To iTo test the hypothesis that the biological processes underlying osteoblastic differentiation of MC3T3-E1 cells are reflected in the coordinated changes in the transcriptome over time, the DNA microarray data were analyzed using Ingenuity Systems IPA software. IPA compares empirically derived “omics” datasets, e.g. DNA microarray data, with a curated database of reported gene-gene and protein-protein interactions to predict signaling pathway activity based on observed changes in upstream regulators and/or the downstream genes whose expression they control. The IPA output includes two statistical measures. The first, which is typically expressed as –log, represents the probability that the correlation between an input set of observed factors and co-regulated genesets in the IPA database did not occur by chance. Hence, a –log greater than 1.3 represents p<0.05 of a significant association. The second, termed an activation z-score, is based on the degree to which observed changes in factor levels, e.g. increases or decreases in mRNA abundance between two points in time, correlate with the expected changes associated with pathway activation or inhibition. An activation z-score >2 or <-2 predicts pathway activation or inhibition, respectively, with p <0.05.The biochemical characterization of differentiating MC3T3-E1 cells  demonstrTo resolve the temporal changes in signaling networks associated with osteoblastic differentiation of MC3T3-E1 cells we performed IPA Upstream Regulator and Canonical Pathways Analysis using the 976 significantly regulated genes identified by ANOVA. The IPA Upstream Regulator Analysis predicts which transcriptional regulators are activated or inhibited based on observed changes in expression of downstream genes. Predicted upstream regulators with activation Z-scores >2 or <-2 during at least one phase of differentiation are shown in To gain insight into how predicted changes in the activity of individual upstream regulators were integrated into signaling networks, we next performed IPA Canonical Pathways Analysis, which compares observed changes in mRNA abundance to the expected direction of change associated with pathway activation or inhibition. The list of signaling pathways represented in the dataset along with –log and activation z-score are shown in hybridization reaction [To validate our “agnostic” microarray data on signaling pathway activation, we performed a “focused” analysis of MC3T3-E1 cell gene expression using NanoString nCounter. The NanoString nCounter system uses color-coded molecular “barcodes” attached target-specific probes to count up to several hundred unique transcripts in a single reaction ,16. The To determine how the changing levels of pathway components translated into changes in pathway activity during differentiation, we performed IPA Upstream Regulator and Canonical Pathways Analysis using expression ratios derived from comparisons of the NanoString data for D2 vs D5, D5 vs D10, and D10 vs D28. Predicted upstream regulators with activation z-scores >2 or <-2 during at least one phase of differentiation are shown in We next performed IPA Canonical Pathways Analysis using the NanoString dataset. The list of signaling pathways represented along with –log and activation z-score are shown in To test the overall similarity between the NanoString and microarray datasets we compared the pathway activity predictions generated from each using the IPA Canonical Pathways molecular activity predictor tool, which graphically depicts the predicted change in pathway activity based on observed changes in upstream and downstream gene expression. To illustrate the temporal evolution of signaling network interaction during MC3T3-E1 cell differentiation, we generated pathway activity predictions for the IPA osteoarthritis canonical pathway, which integrates multiple signal inputs controlling expression of bone-related genes. As the first transcription factor required for osteoblastic differentiation, control of Runx2-dependent transcription is central to the process ,18. Runxin vivo is a continuous process wherein osteoblasts at all stages of development are present, from mesenchymal stem cell precursors to osteocytes, along with cells of the osteoclast lineage and other cell types [Complex biological processes like osteoblast development involve the coordinated regulation of multiple intracellular signaling pathways controlling gene expression. Thus, studies focusing on the contribution of any individual growth factor or pathway are invariably incomplete. Developing a more complete picture requires the use of “omics” approaches that capture as much information as possible about changes in intracellular signaling networks in as unbiased a manner as possible. Further complicating matters, bone remodeling ll types ,2. As a ll types –10. We ein vitro microarray studies of osteoblastic differentiation performed using various cell types, e.g. mesodermal progenitor cells, calvarial osteoblasts, osteocytes, periodontal ligament cells, and embryonic stem cells [Our data complement other em cells ,26–30, oem cells –34. Our et al. [et al. [et al. [et al. [et al. [in vitro.Qi et al.  reported [et al.  focused  [et al.  examined [et al.  compared [et al.  compared [et al.  examinedin vivo occurs under the additional influence of circulating hormonal factors, e.g. sex steroids and parathyroid hormone, and paracrine factors produced by neighboring cells, e.g. sclerostin from osteocytes. Thus, any in vitro study of osteoblast differentiation, regardless of the cell model, will not capture the process as it occurs in the native environment. Since our study was intended to examine the temporal coordination of intrinsic signaling networks during osteoblast differentiation, we chose MC3T3-E1 cells as a homogenous cell population studied during synchronized differentiation.MC3T3-E1 cells are widely used in studies of osteoblast function and the effects of drug or hormone treatment. While alternative models, e.g. primary calvarial osteoblasts, might be considered more physiologic, individual isolates of calvarial osteoblasts are heterogenous cell populations, introducing more variability between biological replicates. Moreover, osteoblast differentiation In silico pathways analysis using bioinformatic tools, such as IPA, that infer changes in upstream pathway activity based on observed changes in network components, provide a means to translate such incomplete transcriptomic datasets into a more complete picture of metabolic activity. By basing conclusions on the number and magnitude of expression changes across gene clusters, rather than individual genes, it decreases the probability of false discovery, while simultaneously providing a quantitative measure of the probability of change in a given signaling network.While gene array technology is a powerful tool for determining the transcriptional basis of changing developmental or pharmacological processes, the resulting datasets are both too complex and too error prone to reliably base conclusions on casual inspection ,6. Transin vitro, and find evidence of pathway cross talk at the transcriptional level.In our study, we compared the results of signaling pathways analysis performed on two independently generated transcriptomic datasets, an “agonistic” microarray dataset and a “targeted” NanoString nCounter dataset. For the former, we performed the analyses using the list of significantly regulated genes identified by traditional ANOVA. For the latter, we used the mRNA abundance of all measureable transcripts regardless of whether they were observed to change with differentiation. Despite these different approaches, the IPA pathways analysis results from the two datasets were quite similar, particularly with respect to the sequence of activation of the important Wnt/β-catenin, TGFβ/BMP, cytokine/JAK-STAT and TNFα/RANKL signaling pathways. The most notable differences were that the NanoString nCounter dataset gave more coverage of the pathways it was designed to examine, i.e. generally larger activation z-scores, and that the samples used in the NanoString experiment showed changes in pathway activity that were somewhat later/slower than those in the microarray experiment. Since the microarray and NanoString datasets were from independent experiments, some of the difference in timing may arise from variation in the rate, but not process, of differentiation between biological replicates. Close inspection of the data  Tables sin vivo is subject to regulation by numerous circulating factors [in vitro [Although osteoblast differentiation  factors , our res factors ,18, whic factors . Runx2 e factors ,36. Our  factors ,38, appe factors –22. More factors ,23. Of tin vitro ,40. FGF2in vitro . TNFα plin vitro ,43, promin vitro , while din vitro ,46. Convin vitro . Thus ouS1 FigA. Pathway activity prediction based on the microarray dataset. B. Pathway activity based on the NanoString dataset. Observed increases (red) and decreases (green) in mRNA abundance are indicated, as are predicted activation (orange) and inhibition (blue) of downstream targets.Observed Day 2 to Day 5 changes in expression ratios were used to predict WNT/β-catenin pathway activity using the IPA molecular activity predictor tool. (TIFF)Click here for additional data file.S2 FigA. Pathway activity prediction based on the microarray dataset. B. Pathway activity based on the NanoString dataset. Observed increases (red) and decreases (green) in mRNA abundance are indicated, as are predicted activation (orange) and inhibition (blue) of downstream targets.Observed Day 10 to Day 28 changes in expression ratios were used to predict TGFβ/BMP pathway activity using the IPA molecular activity predictor tool. (TIFF)Click here for additional data file.S3 FigA. Pathway activity prediction based on the microarray dataset. B. Pathway activity based on the NanoString dataset. Observed increases (red) and decreases (green) in mRNA abundance are indicated, as are predicted activation (orange) and inhibition (blue) of downstream targets.Observed Day 10 to Day 28 changes in expression ratios were used to predict NFκB pathway activity using the IPA molecular activity predictor tool. (TIFF)Click here for additional data file.S4 FigA. Pathway activity prediction based on the microarray dataset. B. Pathway activity based on the NanoString dataset. Observed increases (red) and decreases (green) in mRNA abundance are indicated, as are predicted activation (orange) and inhibition (blue) of downstream targets.Observed Day 10 to Day 28 changes in expression ratios were used to predict STAT3 pathway activity using the IPA molecular activity predictor tool. (TIFF)Click here for additional data file.S1 TableGene symbol, accession number, annotation, NanoString probe ID, and mRNA abundance data are shown for triplicate determinations at each of four time points.(XLSX)Click here for additional data file.S2 TableGene symbol, accession number, gene name, mRNA abundance data, and z-standardized expression values are shown for triplicate determinations at each of four time points.(XLSX)Click here for additional data file.S3 TableDisease or function annotation, -log, activation z-score, number and name of pathway molecules are shown for all functions with activation z-score >1 or <-1 in the D2 vs D5, D5 vs D10, and D10 vs D28 pairwise comparisons.(XLS)Click here for additional data file.S4 TableGene symbol and activation z-score are shown for all upstream regulators with activation z-score >2 or <-2 in the D2 vs D5, D5 vs D10, and D10 vs D28 pairwise comparisons.(XLSX)Click here for additional data file.S5 TableCanonical Pathway name, -log, activation z-score, and observed pathway molecules are shown for predicted regulated pathways in the D2 vs D5, D5 vs D10, and D10 vs D28 pairwise comparisons.(XLSX)Click here for additional data file.S6 TableGene symbol and activation z-score are shown for all upstream regulators with activation z-score >2 or <-2 in the D2 vs D5, D5 vs D10, and D10 vs D28 pairwise comparisons.(XLSX)Click here for additional data file.S7 TableCanonical Pathway name, -log, activation z-score, and observed pathway molecules are shown for predicted regulated pathways in the D2 vs D5, D5 vs D10, and D10 vs D28 pairwise comparisons.(XLSX)Click here for additional data file.


HIV-associated encephalitis (HIVE) is one of the common complications of HIV infection, and the pathogenesis of HIVE remains unclear, while lncRNA might be involved it. In this study, we made re-annotation on the expression profiling from the HIVE study in the public database, identified the lncRNA that might be involved in HIVE, and explored the possible mechanism.GSE35864) of three regions of brain tissues  was chosen, updated annotation was performed to construct the non-cording-RNA (ncRNA) microarray data. Morpheus was used to identify the differential expressed ncRNA, and Genbank of NCBI was used to identify lncRNAs. The StarBase, PITA and miRDB databases were used to predict the target miRNAs of lncRNA. The TargetScan, PicTar and MiRanda databases were used to predict the target genes of miRNAs. The GO and KEGG pathway analysis were used to make function analysis on the targets genes.In the GEO public database, the microarray expression profile (Seventeen differentially expressed lncRNAs were observed in the white matter of brain tissues, for which 352 target miRNAs and 6,659 target genes were predicted. The GO function analysis indicated that the lncRNAs were mainly involved in the nuclear transcription and translation processes. The KEGG pathway analysis showed that the target genes were significantly enriched in 33 signal pathways, of which 11 were clearly related to the nervous system function.The brand-new and different microarray results can be obtained through the updated annotation of the chips, and it is feasible to identify lncRNAs from ordinary chips. The results suggest that lncRNA may be involved in the occurrence and development of HIVE, which provides a new direction for further research on the diagnosis and treatment of HIVE. Cognitive impairment is one of the challenges that HIV patients may face . HIV-1 eIn the human genome, more than 70% of the genes are transcribed into RNAs, but less than 2% of them are protein coding genes, and most of them are noncoding RNAs . Non-codIn recent years, expression microarray technology has played an important role in the research on the disease occurrence and development, and many research results can be reviewed and downloaded in the public database. Because updated annotation of microarray results has always been continuing, new results and novel revelation can be accomplished through the re-annotation and analysis of published studies on microarray results. In this study, we retrieved the HIVE study related microarray data from the GEO database, re-annotated and analyzed these data, identified the lncRNAs that might be involved in the pathogenesis of HIVE, and performed the correlation analysis (the work flow was shown in http://www.ncbi.nlm.nih.gov/geo), curated by the National Center for Biotechnology Information (NCBI), is a public functional genomics database, and the data could be downloaded for free. In the GEO database, the microarray expression profile (GSE35864) in three regions of the brain, basal ganglia, white matter, and frontal cortex, in normal, HIV infected, HIV infected with neurocognitive impairment, and HIV infected with both neurocognitive impairment and encephalitis patients was chosen. Twenty-four human subjects in four groups were examined: Group A (n = 6) HIV-1 uninfected with no neuropathological abnormalities at autopsy; Group B (n = 6) HIV-1-infected (HIV+) neuropsychologically normal with no neuropathology; Group C (n = 7) HIV+ with substantial HIV-associated neurocognitive impairment (HAND) as defined below, with no encephalitis (HIVE) or substantial neuropathological defect; Group D (n = 5) HIV+ with HAND and HIVE. RNA from neocortex, white matter, and neostriatum was processed with the Affymetrix Human Genome U133 Plus 2.0 Array platform.The Gene Expression Omnibus . Morpheus (https://software.broadinstitute.org/morpheus/) was used to analyze online and identify the differential expressed “NR_” between the groups  of each region tissue . This analysis was based on the t-test and adjusted according to the characteristic that the noise of microarray data was correlated with the peak value of expression data. We considered that p-value <0.01 was statistically significant. The number of upregulation and downregulation was 100). The Wayne map was drawn based on the intersection of the above results to select differential “NR_” participating in the occurrence and development of HIVE. The pseudogenes, rRNAs, microRNAs and other short RNAs  were filtered out through Genbank from NCBI database. The final remainder was the differential expressed lncRNAs.First, the latest annotation files of HG-U133_Plus_2 Annotations, CSV format, Release 36 (7/12/16) of the Affymetrix Human Genome U133 Plus 2.0 Array were downloaded from the website https://www.ncbi.nlm.nih.gov/nucleotide), which were then input into StarBase (http://starbase.sysu.edu.cn/), PITA (https://genie.weizmann.ac.il/pubs/mir07/mir07_data.html) and miRDB (http://www.mirdb.org/), to predict the target miRNAs of the lncRNAs. The miRNAs were input into TargetScan (http://www.targetscan.org/vert_71/), PicTar (http://pictar.mdc-berlin.de/) and MiRanda (http://www.microrna.org/microrna/home.do) to predict the corresponding target genes.The sequences of lncRNAs were retrieved through NBCI Nucleotide (http://david.abcc.ncifcrf.gov/) is an online program that provides a comprehensive set of functional annotation tools for researchers to understand biological meaning behind many genes. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the identified DEGs using the DAVID database. After GO functional enrichment analysis, we considered the biology process terms with p-value <0.05 was statistically significant. For KEGG analysis, we considered the subpathway with p-value <0.05 was statistically significant.The Database for Annotation, Visualization and Integrated Discovery  of each region tissue . As shown in We re-annotated the The target micRNAs of the lncRNAs were identified from the database, and a total of 352 target miRNAs were predicted in 17 differentially expressed lncRNAs . The 6,6The 6,659 target genes were uploaded to the DAVID software, and the significant GO classification and KEGG pathway were selected. GO cell component (CC) analysis showed that the target genes were obviously clustered in the nucleus, cytoplasm, Golgi apparatus, lysosome, plasma membrane, etc. , Table 2p < 0.05), which were obtained through KEGG analysis, including mucin type O-Glycan biosynthesis, proteoglycans in cancer, pathways in cancer, glutamatergic synapse, long-term depression and other relevant pathways (pathways . Eleven There are two main highlights in the research. (1) The differentially expressed lncRNAs were identified through the re-annotation of published microarray results. (2) The target miRNAs of the lncRNAs and target genes were predicted using a bioinformatics method, and GO function and KEGG pathway analyses were performed to learn about the possible mechanisms of lncRNA involved in the occurrence and development of HIVE.β signaling pathway was also involved in the pathophysiology of the nervous system, which can limit inflammation and reduce neurological damage in the nervous system infection process (β signaling pathway was also related with the tolerance of dendritic cells (β signaling pathway can inhibit the progression of autoimMune encephalopathy (In recent years, hundreds of lncRNAs have been discovered, and the changes in the expression of lncRNAs have been associated with the occurrence and development of many diseases. Plenty of evidence has shown that lncRNA is involved in the replication process of the virus  and that process . The TGFic cells . Togethealopathy . For thealopathy . The actalopathy , and thealopathy . Moreovealopathy . TherefoThe brand-new microarray results from a different perspective can be constructed through the updated annotation of the precious and published microarray data. In this study, the lncRNA results were obtained through the re-annotation of microarray data, which provides the foundation for further research on the role of lncRNA in the occurrence and development of HIVE. From the point of view of the lncRNA-miRNA-target genes, cluster analysis was performed by various bioinformatic methods to explore the role of lncRNA. GO analysis showed that lncRNA may be involved in the occurrence and development of HIVE via its participation in the nuclear transcription and translation. The KEGG pathway analysis showed that most of the KEGG pathways with statistical significance were associated with the function of the nervous system. Therefore, we can speculate that lncRNA is indeed involved in the occurrence and development of HIVE, which is of great significance for future research on lncRNA on HIVE. It is also proved that it is feasible to identify lncRNAs from public database.10.7717/peerj.5721/supp-1Data S1Click here for additional data file.


Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes.The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of “Renin-angiotensin system” pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.The online version of this article (10.1186/s12859-019-2635-6) contains supplementary material, which is available to authorized users. Analysis of gene expression experiments comparing two groups of samples , typically results in long lists of differentially expressed genes (DEGs). These long lists of genes are often hard to be interpreted by researchers. As a result, some methods have been developed to transform the gene expression data into meaningful sets. An example is to identify the set of genes that function in the same pathway which is commonly referred to as pathway analysis. This analysis is appealing to researchers for two reasons: first, grouping thousands of genes by the pathways in which they exist and involve, reduces the complexity to some hundred pathways; second, it facilitates identifying gene signaling networks relevant to a given condition which can help in understanding the mechanisms of diseases , 2, deveThe pathway analysis methods usually use two types of data as inputs: the experimental data, like gene expressions obtained when comparing two conditions and the pathway knowledge, which was previously known and stored in pathway databases. There exist several pathway databases providing collections of pathways for various organisms, most of which are drawn manually and updated regularly. Examples of these databases include KEGG , BioCartThe pathway analysis methods are classified into two groups according to their strategy for incorporating the pathway data into their analysis: The first group considers pathways as simple gene lists –18 and tMost PT-based methods , 24 modeDue to the similarity between biological systems and distributed systems studied in computer science, modeling techniques developed in formal methods can be applied to biological systems as well . Formal In this section, first, some related basic concepts and primary definitions are briefly explained and then proposed approach is described in more details.Probabilistic model checking is a variant of model checking used for analyzing systems that exhibit probabilistic behavior. A probabilistic model checker requires (a) a formal description of the system , and (b) the specifications of one or more desired properties of that system in temporal logic . A model is typically a state-transition structure in which each state represents a configuration, and the transitions represent the evolution of the system from one configuration to another. In probabilistic model checking, the models are probabilistic , in the sense that they are augmented with a probability of making a transition between states. As an endpoint, a probabilistic model checker returns “yes” or “no” indicating whether or not each property is satisfied, or the probability of some properties of the model, based on a systematic and exhaustive exploration of the model.PRISM , 37 is aA model described in the PRISM language consist of a set of modules that can interact with each other. The state of each module is being represented by the value of a set of finite-ranging variables. The global state of the whole model is determined by the local state of all modules. The behavior of each module is described by a set of commands of the form:a is an action label used for synchronization. If a transition does not have to synchronize with other transitions, then no action label needs to be provided for that. The guard is a predicate over all the variables in the model. When the guard is true, the model is updated according to the transitions and their probabilities described in the updates. The transitions are specified by giving the new values of the variables in the module, possibly as a function of other variables. The primed variable is used to represent the new values for the variables . pathprop is a formula that evaluates to either true or false for a single path in a model that describes the desired behavior  when the null hypothesis is true [p-values are not uniformly distributed, the result of the pathway analysis method may be biased. For example, pathways that have p-values biased towards zero may often be falsely identified as significant.The  is true . If the In  an approCurrently, available pathway databases are not complete and may be noisy. To inspect how incompleteness and noise affect FoPA’s performance, they are mimicked by randomly removing or rewiring a portion  of interactions in a pathway.By interaction removal, an interaction between genes is removed and by interaction rewiring the endpoint of an interaction is set uniformly to a new gene from the pathway. The edge removal and edge rewriting will not remove genes from the pathways but will make changes in the gene-gene interactions.At each portion of edge removal or rewriting for each pathway, the procedure is repeated 100 times. The similarity measure for each pathway is computed as the number of generated pathways identified related or unrelated as the main pathway divided by the number of genrated pathways.p-values and the median of the ranks of the target pathways over benchmark datasets are defines as sensitivity and prioritization of the methods. The results considering both the rankings and P-values of the target pathways associated with each dataset, Figs. The median of The summary of results for the four different methods based on the panel of 36 datasets is tabulated in Table p-values of FoPA and PADOG. Thus, it can be concluded that PADOG and FOPA are able to prioritize target pathways with high sensitivity when compared with CePa and SPIA.The mean and mean reciprocal ranks of the target pathways are almost equal in PADOG, and FoPA suggested that on average FoPA ranks the target pathways as well as PADOG ranks them. This Wilcoxon signed rank test which is done on the rank and p-value of PADOG and FoPA also confirms this outcome and shows that there is no statistically significant difference between the distribution of ranks and Two experiments are used to measure the false positive rate of the methods. In the first, 50 trials of the original datasets with randomly permutated class labels  are used. The percentage mean of the significant pathway subject to these random datasets is considered as the false positive rate of the methods. In the second, the simulated decoy pathways generated by connecting random genes using the same network structure as the KEGG pathways, are chosen as a set of negative controls. Compared methods are run on both KEGG real and decoy pathways to check their ability to distinguish decoy from real pathways.The false-positive rate of all methods for different significance threshold produced by analyzing 50 permuted versions of original datasets is shown in Fig. The results of three methods FoPA, CePa and PADOG in ranking decoy and real pathways are shown as ROC curves in Fig. An analysis is performed for five independent colorectal cancer datasets . The results of methods for these datasets are assessed at multiple rank thresholds. At a given rank threshold  the overlapping pathways between the results of each pair of datasets are counted which is shown in Fig. ‘colorectal cancer’ and ‘Renal cell carcinoma’ are overlapped respectively among the significant pathways found by PADOG in four and three of the colorectal cancer datasets. However, the null distribution analysis performed in [p-values produced by PADOG for the ‘colorectal cancer’ and the ‘Renal cell carcinoma’ pathways are biased towards zero, that makes these pathways detected as significant regardless of input dataset.In Table ormed in  has show‘Vasopressin-regulated water reabsorption’ and the ‘Aldosterone-regulated sodium reabsorption’ pathways overlapped between the significant pathways in four colorectal datasets.FoPA found the ‘Vasopressin-regulated water reabsorption’ pathway as an overlapped pathway in colorectal cancer datasets is in line with other findings.The presence of vasopressin receptors was reported in transformed epithelial cells, as well as in a wide panel of human tumor cell lines . In addi‘Aldosterone-regulated sodium reabsorption’, is also consistent with the other findings. In this research, some colorectal datasets are integrated to elucidate the potential key candidate genes and pathways in CRC. The ‘Aldosterone-regulated sodium reabsorption pathway’ is among the candidate pathways that have been identified as common in CRC.We refer to the research done by Guo et al.  to indicFrom the five colorectal cancer dataset above, GSE8671 is chosen, since it has more samples than the others. This dataset is randomly resampled to obtain sub-datasets of size 8, 16, and 32. This procedure is repeated 50 times to create different groups of samples. The result of the four methods for these datasets is assessed for consistency of results. At each given rank threshold the average number of overlapped pathways are shown in Fig. KIA1199 gene is reported as the most overexpressed gene in this study. There is an increasing body of evidence that suggests the involvement of this gene in cancer progression, metastasis and poor prognosis of patients with colorectal cancer [KIAA1199 and ‘Wnt-signaling pathway’ genes are correlated [Wnt signaling pathway’ is likely to be relevant to the condition under study in this dataset. Four pathway analysis methods are compared regarding their ability to identify the ‘Wnt signaling pathway’ as relevant to the present dataset. The results are tabulated in Table Wnt signaling pathway as significant’  and with a lower rank than the other compared methods.As an example, the results of FoPA are analyzed on one of the datasets used in the target pathway technique and shown it is consistent with other available studies. This dataset compares 32 pairs of samples collected from colorectal adenomas with those of normal mucosa from the same individuals  using Afl cancer . Cancer rrelated . Thus, ‘non-small cell lung cancer signaling pathway’. This is consistent with a recent study [According to the analysis accomplished by FoPA, six pathways  are recognized as relevant to colorectal adenoma (GSE8671). The first one is the ‘nt study  shown thIntestinal immune network for IgA production’. The differentially expressed genes of the present dataset show that the expression of the majority of genes in the ‘intestinal immune network for IgA production’ pathway is lower than that in the normal mucosa. These include a series of human leukocyte antigen (HLA) class II genes . These genes encode major histocompatibility complex class II molecules in antigen presenting cells , which are essential for the proliferation and differentiation of B cells [The second identified one is the ‘ B cells . Since I B cells , 60.Aldosterone-regulated sodium reabsorption’. In this pathway, Aldosterone binds MR , which translocate into the nucleus and regulates gene transcription. A recent study [The third identified pathway is ‘nt study  have demTight Junction’(TJ) pathway. Claudin family proteins consisting of at least 24 members are essential for the formation of TJs and have a significant effect on the biological behavior of tumor progression. Previous studies have demonstrated the several claudin . However, five high ranked pathways worth to be considered as the likely related pathways to autism. Through reviews of literature, pieces of evidence are provided showing these pathways may be related to autism.The results of applying FoPA to autism-related genes are shown in Table It has been reported that atypical processing of odor and taste stimuli is presented in autism spectrum disorders (ASD) , 68. A sAsthma is another identified KEGG pathway. Recently, researchers at Sydney University’s Brain and Mind Centre have published a study that shows a relationship between a mother’s active immune response during pregnancy to allergies and asthma and severe social impairment symptoms in children with autism .GABAergic synapse may be one of the important relevant pathways to ASD. Several lines of evidence suggest that an impairment of GABAergic transmission contributes to the development of ASDs. GABAergic signaling dysfunction early in development leads to a severe excitatory/inhibitory unbalance in neuronal circuits, a condition that may account for some of the behavioral deficits observed in ASD patients .The last pathway in the list is the Renin-angiotensin system (RAS). This pathway has been hypothesized to have a pivotal role in some neurodegenerative diseases, such as Parkinson, Alzheimer, Huntington and Multiple Sclerosis (MS) .Angiotensin-converting enzyme (ACE) is the essential enzyme in this pathway which plays a major role in the degeneration of a family of neurotransmitters in the central nervous system (CNS). The implication of neurotransmitters in psychiatric disorders is supported by their function in the regulation of emotions, cognition, behavior, and memory which are disrupted in autism .Renin-angiotensin system and GABAergic synapse pathways in ASD. It can be concluded that there may exist relations between GABAergic synapse pathway and RAS in the development of autism. Further genetic studies can support this finding.This analysis highlights the role of p-values of KEGG signaling pathways for each of the datasets are calculated using FoPA. The results, Fig. To analysis, the possible bias of the FoPA, a total of 1000 resampled datasets from 41 control samples of 4 Alzheimer’s datasets  is generated. Some of them (18) are randomly labeled as disease and the remaining marked as normal samples. This procedure is repeated 500 times to create different groups of 18 disease and 17 control samples. To eliminate the effect of the group size 100 datasets consisting of 10 control and 10 diseases, 200 datasets consisting of 10 control and 20 diseases and 200 datasets consisting of 20 control and 10 diseases samples are also generated. The Pathway database incompleteness and noise are mimicked by randomly removing or rewiring a portion  of the interactions in a pathway. At each portion of edge removal or rewriting for each pathway, the experiment is repeated 100 times. The similarity measure for each pathway is computed as the number of the generated pathways identified the same as the main pathway (both relevant or both not relevant) to the condition under study. The average of these measures for each pathway at each portion is illustrated in Fig. In this study, a new pathway analyzing approach is introduced, that uses formal methods to rank pathways according to their relevance to a given clinical condition . To the best of the authors’ knowledge, it is the first attempt in using the formal methods in solving such a problem. Formal modeling has many advantages over the modeling by graphs. It helps researchers to express any relations among biological components involved in an interaction. This helps to create a reliable model of signaling pathways that can be effective in reducing the false results in pathway analysis studies. The proposed tool named FoPA is constructed based on this new approach. We have compared FoPA with three other analysis methods, two topology-based  and one gene set-based (PADOG). These methods are chosen regarding their performance in previous comparisons.P-values of the target pathways. The results indicate that PADOG and FoPA are able to prioritize target pathways with high sensitivity when compared with CePa and SPIA. However, considering the target pathway technique performance is not enough. Thus, the simulated false inputs (permuted class labels and decoy pathways) are created as a set of negative controls to measure the false-positive rate of the methods. The number of significant pathways identified by giving permuted class labels to FoPA is less than the other three methods; that is, FoPA differentiates significantly between actual and random clinical data. Moreover, the area under the curve ROC is greater in FoPA compared with PADOG and CePa, which indicates that FoPA excludes more decoy pathways from real ones.Some techniques are used to evaluate the FoPA’s performance and compare it with other methods. We have assessed the results considering both rankings and The results of the methods on independent colorectal cancer datasets indicate that, FoPA identifies more overlaps than PADOG and SPIA at almost all rank thresholds and its finding is in line with other researches. Although CePa has found more overlaps in some rank thresholds, these overlaps appear to have false pathways. The consistency analysis is also performed on a group of dependent datasets (datasets made by resampling). The average overlapped pathways found by FoPA on these datasets is more than that of other methods.We also have demonstrated that there is no systematic bias in FoPA that makes some pathways detected as significant regardless of the input differentially expressed genes.Renin-angiotensin system and GABAergic synapse pathways in ASD.As an application of FoPA, we apply it to a list of autism-related genes and show that FoPA can discover pathways relevant to autism. This analysis highlights the role of These lines of evidence well demonstrate FoPA’s advantage over the other methods. One of the disadvantages of FoPA may be its high running time compared with available statistical methods. Though this running time is tolerable, it will be decreased through the improvement made in our modeling and also in formal verification techniques.The other disadvantage of FoPA is that it treats pathways as independent entities, and gives more focus on pathway-specific genes rather than overlapped genes among pathways. While some of these overlapped genes will lead to “crosstalk” phenomenon that could influence other pathways. As a result, considering cross-talk genes and inter-pathway relations may lead to better performance of FoPA.It is worth mentioning that FoPA is the first attempt of using formal methods in pathway analysis. So, by adding other details in model specification and considering some other aspects such as pathway’s cross-talk, the result of the experiments would be improved significantly.Additional file1:The file contains the additional details on the following: i) formal definition of Markov chains ii) probability measure of Markov chains iii) reachability probabilities iv) a toy example showing how the model checking based approach works. IV) Results of FoPA on different disease datasets. (DOCX 3053 kb)Additional file 2:The 36 datasets used as a benchmark in the target pathway technique. (XLSX 11 kb)Additional file 3:P-values of the target pathways associated with each benchmark dataset) for each of four methods . (XLSX 15 kb)The results of the target pathway technique  on four colorectal cancer datasets . (XLSX 120 kb)Additional file 5:The results of four methods  on GSE8671 dataset. (XLSX 37 kb)Additional file 6:List of the differentially expressed genes identified for GSE8671 dataset. (XLSX 1282 kb)Additional file 7:The results of FoPA on autism genes. (XLSX 416 kb)


This study aims to determine differentially expressed genes (DEGs) and long noncoding RNAs (lncRNAs) associated with Parkinson's disease (PD) using a microarray.  We downloaded the microarray data GSE6613 from the Gene Expression Omnibus, which included 105 samples. We selected 72 samples comprising 22 healthy control blood samples and 50 PD blood samples for further analysis. Later, we used Limma to screen DEGs and differentially expressed lncRNAs (DElncRNAs) and estimated their functions by the Gene Ontology (GO). Besides, the competing endogenous RNA (ceRNA) network, including microRNAs, lncRNAs, and mRNAs, was constructed to elucidate the regulatory mechanism. Furthermore, we performed the KEGG pathway enrichment with mRNAs in the ceRNA regulatory network and constructed a final network, including pathways, mRNAs, microRNAs, and lncRNAs.  Overall, we obtained 394 DEGs, including 207 upregulated DEGs and 187 downregulated DEGs, and 7 DElncRNAs, including 2 upregulated DElncRNAs and 5 downregulated DElncRNAs. Insulin-like growth factor-1 receptor (IGF1R) was considerably enriched in the endocytosis pathway. In the ceRNA regulation network, IGF1R was the target of hsa-miR-133b and lncRNAs of XIST, and PART1 could also be the target of hsa-miR-133b. While the upregulated DEGs were enriched in the GO terms of the cytoskeleton, cytoskeletal part, and microtubule cytoskeleton, the downregulated DEGs were enriched in the immune response. PRKACA was markedly enriched in numerous pathways, including the MAPK and insulin signaling pathways.  IGF1R, PRKACA, and lncRNA-XIST could be potentially involved in PD, and these diverse molecular mechanisms could support the development of the similar treatment for PD. Parkinson's disease (PD), a chronic and progressive neurodegenerative disease, accounts for the progressive degeneration of dopaminergic (DA) neurons in the substantia nigra . TypicalLong noncoding RNAs (lncRNAs), a major subclass of ncRNAs, are larger than 200 nucleotides in length  and haveScherzer et al.  used the2 fold-change (FC)| > 0.5) and collect the specific genes for PD. In addition, the lncRNAs and mRNAs coexpression network, as well as the ceRNA regulatory network, was further constructed to unlock the key mechanisms underlying PD comprehensively. Reportedly, analyses based on differential statistical tests could result in different outcomes [Using the same microarray data , this stoutcomes ; thus, whttp://www.ncbi.nlm.nih.gov/geo/), which included 105 samples. We selected 72 samples comprising 22 healthy control blood samples and 50 PD blood samples for further analysis.We downloaded the transcription profile of GSE6613 (Affymetrix Human Genome U133 Plus 2.0 Array)  from thehttp://www.bioconductor.org/packages/release/bioc/html/oligo.html) from the downloaded GEL profile. We identified mRNAs and lncRNAs in all samples and annotated using the database of the HUGO Gene Nomenclature Committee (HGNC). Then, we used the Limma package in R language to screen DEGs and DElncRNAs between PD and healthy controls [P value into the FDR [2 FC| > 0.5 as the cutoff criteria for DEGs and DElncRNAs.The gene expression values were called and quantile-normalized using Oligo  was used to conduct the Gene Ontology (GO) function annotation, including Biology Process, Molecular Function, and Cellular Component. Furthermore, we screened the significantly relevant GO functions using Fisher's exact test with the criterion of P < 0.05.We extracted the gene expression values of considerable differentially expressed genes and lncRNAs, with which the pheatmap package in R language (version 1.0.8) was used to perform the two-way clustering based on the Euclidean distance. In addition, the online tool DAVID  for the PD-relevant miRNAs using the keyword “Parkinson's disease.”We searched the miR2Disease database (http://starbase.sysu.edu.cn/index.php).The interactions between lncRNAs and miRNAs were analyzed by using the starBase database (http://www.microrna.org/microrna/home.do) and mapped the mRNAs that were coexpressed with lncRNAs to the targets to construct the regulatory network between miRNAs and mRNAs. Finally, we constructed the ceRNA regulatory network, which was subsequently visualized by Cytoscape 3.4.We estimated the targets of miRNAs by using the miRanda database (http://ctd.mdibl.org/) for the PD-relevant KEGG pathways with the keyword “Parkinson's disease.” In addition, we conducted the considerably enriched KEGG pathways of the mRNAs in the ceRNA regulatory network. Finally, the overlapping pathways, mRNAs, lncRNAs, and miRNAs were used to construct the network.We searched the Comparative Toxicogenomics Database 2017 update (P=0.045116), positive regulation of the macromolecule metabolic process , and positive regulation of the cellular biosynthetic process , cytoskeletal part , and microtubule cytoskeleton  and cell proliferation  and 459 coexpression pairs  and two lncRNAs (XIST and PART1) according to starBase. In addition, 76 pairs of miRNAs-mRNAs were detected. Finally, the ceRNA regulatory network, including 65 nodes  and 173 regulatory pairs, was constructed . Furtherα-syn) is a presynaptic protein expressed throughout the central nervous system, and it is the primary component of Lewy bodies, one of the histopathological features of PD. Furthermore, the α-syn could be taken by neurons through endocytosis, the evaluation of which might account for PD [PD is the second leading age-related neurodegenerative disease. However, lncRNAs and genes involved in PD await elucidation. In this study, we determined that IGF1R was considerably upregulated in PD. Reportedly, PD results from the loss of DA neurons in the substantia nigra with autophagy dysfunction , and IGFt for PD .For DElncRNAs between PD and controls, XIST was markedly downregulated in PD. Reportedly, lncRNA-XIST is the most vital regulator of X chromosome in mammals . In non-α-syn could induce mitochondrial dysfunction, which was an essential factor for PD through the spectrin and actin cytoskeleton [α-syn and its modified forms could initiate the innate and adaptive immune responses, and growing evidence has revealed an essential role of the central and peripheral immune response in driving the PD initiation and progression [According to the GO terms enrichment with DEGs, we determined that upregulated DEGs were enriched in the GO terms of the cytoskeleton, cytoskeletal part, and microtubule cytoskeleton. A previous study  reportedskeleton . In addigression . ConsequFor the KEGG pathway enrichment with DEGs in the ceRNA regulatory network, PRKACA (markedly upregulated in PD) was considerably enriched in several pathways, such as the MAPK and insulin signaling pathways. Reportedly, the P38 MAPK signaling pathway could regulate nuclear factor-κB and inducible nitric oxide synthase expressions in the substantia nigra in a mouse model of PD . PRKACA In conclusion, PRKACA, IGF1R, and lncRNA-XIST could be involved in PD. However, further research is warranted to elucidate their action mechanisms in PD.


Serratia marcescens. The recombinant OM-PLA1 profile of appropriately 33 KDa was expressed by the engineered Pichia pastoris GS115. The OM-PLA1 activity was 21.2 U/mL with the induction of 1 mM methanol for 72 h. The expression efficiencies of OM-PLA1 were 0.29 U/mL/h and 1.06 U/mL/OD600. A complex of magnetic graphene oxide (MGO) and OM-PLA1 (MGO-OM-PLA1) was prepared by immobilizing OM-PLA1 with graphene oxide-based Fe3O4 nanoparticles by cross-linking with glutaraldehyde. The content of phosphorus decreased to 5.1 mg/kg rapeseed oil from 55.6 mg/kg rapeseed oil with 0.02% MGO-OM-PLA1 (w/w) at 50°C for 4 h. MGO-OM-PLA1 retained 51.7% of the initial activity after 13 times of continuous recycling for the enzymatic degumming of rapeseed oil. This study provided an effective approach for the enzymatic degumming of crude vegetable oil by developing a novel phospholipase and improving the degumming technology.Enzymatic degumming is an effective approach to produce nutritional, safe, and healthy refined oil. However, the high cost and low efficiency of phospholipase limit the application of enzymatic degumming. In this study, an 879 bp outer membrane phospholipase A (A1) (OM-PLA1) gene encoding 292 amino acid residues was isolated from the genome of  High-quality edible oils should meet the requirements of good stability, long shelf life, bland odor and taste, good nutritional quality, abundant vitamins, and no contaminants . Most tyOM-PLA1 from S. marcescens was applied to crude rapeseed oil degumming.Outer-membrane phospholipase A (A1) (OM-PLA1) is an enzyme present in the outer membrane of Gram-negative bacteria . OM-PLA13O4 nanocomposite is adhering to magnetic nanoiron with superparamagnetism, which makes it have good magnetic separation characteristics and strong adsorption capacity. In addition, graphene oxide Fe3O4 nanocomposite also has advantages of high specific surface area, super strength, high activity recovery, and immobilization efficiency of enzyme compared to other immobilization approaches (The protein loading of magnetic graphene oxide (MGO) is much higher than that of granular activated carbon, diatomite and powdered activated carbon . Graphenproaches .P. pastoris can effectively express exogenous protein  was preserved in College of Food and Biological Engineering, Hefei University of Technology. The smelly mandarin fish is a processed food product sold in market using traditional fermentation technology , primer pair Us-OM-PLA1 and Ds-OM-PLA1 was synthesized to amplify the S. marcescens genome for OM-PLA1 cloning using PCR amplification instrument   . A phylond AvrII . The recporation . The eleP. pastoris GS115 was investigated under different methanol concentrations and induction times (-5 % (w/v), and glycerol 1% (w/v). The engineered P. pastoris colony was cultured in fermentation media at a shaking speed of 200 rpm at 30°C for 36 h. The collected P. pastoris cells were inoculated into the fermentation media added with 1 mM methanol. The final cell concentration at OD600 of 20 was cultured with a shaking speed of 200 rpm at 30°C. A Ni2+-chelating affinity chromatography column  was pre-equilibrated by a pH 8 buffer prepared by 20 mM imidazole, 50 mM NaH2PO4, and 300 mM NaCl. After the recombinant OM-PLA1 was purified by the column  was prepared by mixing 1.5 g of freeze-dried graphene oxide, 0.3 g of FeSO4⋅7H2O, and 0.4 g of FeCl3⋅6H2O in 50 mL of deionized water  . The actS. marcescens OM-PLA1 is 879 bp after sequencing confirmation  Ultra for oil degumming . In addiholipase . In thisholipase . As a scproperty , which f3O4 nanoparticles were used to immobilize S. marcescens OM-PLA1 for the enzymatic degumming of rapeseed oil. The contents of phosphorus decreased to 8.9 mg/kg rapeseed oil from 55.6 mg/kg rapeseed oil with 2.5 h of degumming. Therefore, this study provided an effective approach for the enzymatic degumming of crude vegetable oil. However, some problems remain to be solved. First, the immobilization efficiencies of OM-PLA1 and graphene oxide-based Fe3O4 nanoparticles can still be increased. Second, the effect of the crosslinking agent glutaraldehyde on the activity of OM-PLA1 need further analysis. Third, the degumming effect of MGO-OM-PLA1 on other crude vegetable oils should be further explored.In this study, graphene oxide-based FeS. marcescens OM-PLA1 gene with a size of 879 bp was isolated and expressed in the recombinant P. pastoris GS 115. The size of OM-PLA1 was appropriately 33 KDa. After 72 h of inducible time, the highest activity of OM-PLA1 was reached (21.2 U/mL). The production efficiencies of treatment time and cell concentration of OM-PLA1 were 0.29 U/mL/h and 1.06 U/mL/OD600, respectively. After 4 h of degumming, the phosphorus contents reached 5.1 mg/kg from 55.6 mg/kg using 0.02% (w/w) MGO-OM-PLA1 prepared by graphene oxide-based Fe3O4 nanoparticles and OM-PLA1. The half life of MGO-OM-PLA1 activity was 13 times for the enzymatic degumming of rapeseed oils with the initial phosphorus content of 55.6 mg/kg. Therefore, S. marcescens OM-PLA1 gene could effectively express in the recombinant P. pastoris. In addition, the cross-linked copolymer MGO-OM-PLA1 possessed good stability and effective degumming of vegetable oils. This study has an important application value in the enzymatic degumming of crude vegetable oil.In this study, PY designed the experiment scheme. SuJ wrote the manuscript. YW performed the experiments. ZH analyzed the data. ZZ drew the figures. LC measured the enzyme activity. MD cloned the OMPLA1 gene. ShJ proposed the idea.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.


We aimed to find the possible key targets of Yougui pill and Buzhong Yiqi decoction for the treatment of sexual dysfunction.  The composition of Yougui pill combined with Buzhong Yiqi decoction was obtained, and its effective components of medicine were screened using ADME; the component target proteins were predicted and screened based on the TCMSP and BATMAN databases. Target proteins were cross-validated using the CTD database. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for target proteins using the Cytoscape plugin ClueGO + CluePedia and the R package clusterProfiler, respectively. Subsequently, protein-protein interaction (PPI) analyses were conducted using the STRING database. Finally, a pharmacological network was constructed. Eucommia, Aconite, Astragalus, Chinese wolfberry, yam, dodder seed, ginseng, Cornus officinalis, Rehmannia, and Angelica), 9 for chemical components , 62 for target proteins , and 6 for pathways .  The pharmacological network contained 89 nodes and 176 relation pairs. Among these nodes, there were 12 for herbal medicines (orange peel, licorice,  NR3C1, ESR1, PTGS2, CAT, TNF, INS, and TP53 may be important targets for the key active elements in the decoction combining Yougui pill and Buzhong Yiqi. Furthermore, these target proteins are relevant to the treatment of sexual dysfunction, probably via pathways associated with cancer and signal transduction. Sexual dysfunction is difficulty experienced by a couple or an individual during any stage of a normal sexual activity . It is rRehmannia root, dogwood fruit, yam rhizome, Chinese wolfberry, antler glue, dodder seed, Eucommia bark, Angelica, cinnamon bark, and monkshood root. Buzhong Yiqi decoction is a tonic for the middle Jiao and replenishes and raises qi [Astragalus membranaceus, Zhigancao, Codonopsis pilosula, largehead atractylodes rhizome, Angelica sinensis, rattletop, Radix bupleuri, and dried tangerine or orange peel. Previous studies reveal that the combination decoction of Yougui pill and Buzhong Yiqi can be used to effectively treat sexual dysfunction [Yougui pill and Buzhong Yiqi are important traditional Chinese medicines (TCMs). Yougui pill decoction consists of aises qi , 10. It function –13. HoweNetwork pharmacology is an integrated multidisciplinary concept, and it provides a novel network mode encompassing “multiple targets, multiple effects, complex diseases” based on polypharmacology and systems biology . Gui-BiaRehmannia, 24 g; yam, 30 g; Cornus officinalis, 15 g; Chinese wolfberry, 9 g; dodder, 12 g; deer horn gum, 12 g; Eucommia, 12 g; Cinnamomum cassia, 6 g; Angelica, 9 g; Aconite, 6 g; Astragalus membranaceus, 18 g; licorice, 9 g; ginseng, 6 g; orange peel, 6 g; Morinda, 15 g; Atractylodes, 9 g; and Xianling spleen, 15 g.The composition of the combined Yougui pill and Buzhong Yiqi decoction was as follows: http://lsp.nwu.edu.cn/browse.php?qc=herbs) [The TCMSP database (c=herbs)  was usedhttp://183.129.215.33/tcmid/search/) was used to search relevant information of that was not included in the TCMSP database.In addition, TCMID database  parameters , 20 fromScreening of medicine components was based on conventional parameters, and the threshold values were OB ≥ 40% and DL ≥ 0.2.When there was no small molecule information of herbal chemistry in the TCMSP database, the information in the TCMID database was applied.http://bionet.ncpsb.org/batman-tcm/) [The TCMSP and BATMAN (an-tcm/)  databasehttp://ctdbase.org/, updated 2018) [The Mount Desert Island Biological Laboratory publishes its Comparative Toxicogenomics Database (CTD) (ed 2018)  to promoIn order to search the genes related to sexual dysfunction in the CTD database, the “sexual dysfunction” was used as key words, and ranking was conducted based on the inference score , 24. TarGO functional analysis was performed using the Cytoscape plugins ClueGO + CluePedia . The relP ≤ 0.01.KEGG pathway enrichment analysis for target proteins was conducted based on the R package clusterProfiler, with a threshold value of http://www.string-db.org/) [PPI analysis was performed using the STRING database  . A requiBased on the herbal medicine-component-target proteins-pathway data generated by previous steps, a pharmacological network was constructed using Cytoscape. The network showed the pathway regulation mechanisms of related target proteins found for important components in the decoction.Rehmannia, 76; yam, 71; Cornus officinalis, 226; Chinese wolfberry, 118; dodder, 29; deer horn gum, 2; Eucommia, 147; Cinnamomum cassia, 100; Angelica, 125; Aconite, 5; Astragalus, 87; licorice, 17; ginseng, 190; orange peel, 109; Morinda, 174; Atractylodes, 55; and Xianling spleen, 4. After screening, herbal components of significance were identified  were enriched , and thaAfter pathway analysis using clusterProfiler, a total of 66 pathways were obtained. The top 20 significantly enriched pathways, such as the cAMP signaling pathway, neuroactive ligand-receptor interaction, and proteoglycans in cancer, are illustrated in As shown in Eucommia, Aconite, Astragalus, Chinese wolfberry, yam, dodder seed, ginseng, Cornus officinalis, Rehmannia, and Angelica), 9 chemical component nodes , 62 target protein nodes , and 6 pathway nodes .Data were refined by retaining only small molecules with a degree ≥10 and pathways with enriched targets ≥10 and by removing nodes without corresponding herbal medicine-component-target proteins-pathways. The processed data were used to construct a pharmacological network. As shown in Eucommia, Aconite, Astragalus, Chinese wolfberry, yam, dodder seed, ginseng, Cornus officinalis, Rehmannia, and Angelica) which corresponded to 9 key components . These 9 key components corresponded in turn to 62 target protein nodes, among which 7 nodes  belonged to hub proteins of the PPI network. Furthermore, the network included 6 pathways: the MAPK signaling pathway, proteoglycans in cancer, dopaminergic synapse activity, the thyroid hormone signaling pathway, the cAMP signaling pathway, and neuroactive ligand-receptor interaction.TCM does not match the chemical drug paradigm of a single component with a single target. It may have a weak effect on a single target, but it can inhibit an entire pathological process through network interaction, playing a unique therapeutic role in maintaining the balance of the body . In thisThe literature was reviewed for relevant activity of the 9 key components. In streptozotocin-induced diabetic rats, quercetin can ameliorate erectile dysfunction . In the α levels are inversely related to sexual performance [In the current study, NR3C1, ESR1, PTGS2, CAT, TNF, INS, and TP53 were important target protein nodes for our 9 key components. NR3C1 has been reported as a significant downregulator of inflammation . Severalformance . Insulinformance . In the formance . TherefoIn addition, our data revealed that the 6 pathways  may be candidate key pathways involved in the progression of sexual dysfunction. The MAPK signaling pathway plays a role in some human disorders including neurodegenerative diseases and cancers . Sexual In conclusion, NR3C1, ESR1, PTGS2, CAT, TNF, INS, and TP53 may be important target proteins for the key components of the decoction containing Yougui pill and Buzhong Yiqi. Furthermore, the nine key chemical components identified in this study targeted proteins including NR3C1, ESR1, PTGS2, CAT, TNF, INS, and TP53, affecting the MAPK signaling pathway, proteoglycans in cancer, dopaminergic synapse activity, the thyroid hormone signaling pathway, the cAMP signaling pathway, and neuroactive ligand-receptor interaction. These pathways are potentially highly relevant to sexual dysfunction. However, these relationships will need to be substantiated by further research.


Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanismspFET < 1 × 10− 3 (pFET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future.We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits.The online version of this article (10.1186/s12864-018-5373-7) contains supplementary material, which is available to authorized users. Genetic variants that affect multiple traits are often called pleiotropy . It has GWAS data provide systematic, unbiased measurement of genome-wide variants. However, the marginal effect of each single nucleotide polymorphism (SNP) is often small to moderate, making it challenge to study cross-trait associations at the SNP level. Furthermore, SNP level analyses are typically less informative for biological interpretation. For example, > 80% of disease-associated variants identified from GWAS are located in non-coding regions and for the few variants that are located in coding regions, the biological mechanisms often remain unclear , 10. TheIn this work, we collected GWAS summary statistics data for 25 traits and developed an analysis framework to investigate the cross-trait associations using two complementary pathway-based enrichment analysis methods. We utilized a comprehensive annotation of pathways while controlling for pathway redundancy. Our results reported molecular pathways that were implied by genetic variants underlying multiple traits and provided insights for the understanding of cross-trait association and future clinical applications.Figure p-value < 0.01 for the following analysis. A comprehensive collection of pathways was included, consisting of 1077 pathways from BioCarta, KEGG, and Reactome databases. To reduce the redundancy among pathways, we conducted pairwise comparison and chose those pathways with size within the range of 10–200 genes and sharing no more than 30% genes with other pathways. This step resulted in 355 pathways considered as nearly non-redundant, including 79 BioCarta, 67 KEGG, and 209 Reactome pathways. Pathways that were identified as significantly associated with a trait by both methods were considered as with robust evidence. We calculated a combined p-value for each pathway using Fisher’s combined probability test [p-values per pathway per trait, we then applied Fisher’s Exact Test (FET) to estimate whether any two traits shared a significant number of pathways that were significantly associated with each of them. We used Benjamini and Hochberg’s method [Our ultimate goal is to investigate the shared genetic components of multiple traits at the pathway level. To this end, we employed two methods to conduct the pathway-based analysis, PASCAL (PAthway SCoring ALgorithm)  and INRIity test . With ths method  to correTo explore the pathways that were respectively identified as significantly associated with each trait by PASCAL and INRICH, we conducted a multi-dimensional scaling (MDS) analysis . As showspearman correlation of 0.621 , indicating the robustness of our results. For example, the immune group tended to have more pathways than other disease groups did and this trend was detected by both methods.Although PASCAL and INRICH differ in the selection of gene signals and the association tests, the results from the two methods are overall consistent and the main patterns were reproducible. The number of trait-associated pathways identified by PASCAL and INRICH showed a pcombined < 0.01, we found an average of 54 pathways per trait (ranged between 18 and 175). Among them, only HDL and BD had < 25 disease-associated pathways. LS-BMD, CD and UC each had more than 100 disease-associated pathways. Interestingly, the most significant pathways of traits that belong to the same category tended to be the same or similar. For example, in neurological/neuropsychiatric traits, the KEGG pathway “arrhythmogenic right ventricular cardiomyopathy (ARVC)” was the most significantly associated with ADHD (pcombined = 1.52 × 10− 5), ASD (pcombined = 8.76 × 10− 5), and MDD (pcombined = 4.25 × 10− 6). The ARVC pathway describes several processes that are related to heart failure, arrhythmia, and sudden death. Although the pathway itself did not show a clear relationship with neurological/neuropsychiatric traits, several genes in this pathway are frequently observed to be associated with neuronal and brain functions, such as calcium voltage-gated channel subunit genes , catenin genes  and SLC8A1. The ARVC pathway has also been previously observed in SCZ [pcombined = 2.76 × 10− 7), RA (pcombined = 1.62 × 10− 5), and UC (pcombined = 2.48 × 10− 9). This pathway has been previously reported to be involved in the adaptive and mucosal immunity as well as epithelial repair and regeneration [pcombined = 2.93 × 10− 6) and TC (pcombined = 7.72 × 10− 5), while another much related pathway from Reactome, “chylomicron mediated lipid transport”, was mostly associated with TG (pcombined = 1.31 × 10− 6).To further explore what pathways were associated with each trait, we identified the most significantly associated pathway in each trait. Figure d in SCZ  and MDD neration –28 and ip-value as calculated by PASCAL (denoted by pgene) less than 1 × 10− 4. For the most significantly associated pathways in each of the 25 traits, an average of 13 genes had pgene < 1 × 10− 4, ranged between 2 (SCZ and LDL) to 69 (HEIGHT). The ratio of genes with pgene < 1 × 10− 4 in each pathway ranged between 3.8% (AAM) and 60% (HDL), with an average 19% , there were only two genes that were significantly associated with the trait at the gene level: SPR (pgene = 5.02 × 10− 5) and GGH (pgene = 8.06 × 10− 5). In contrast, in the Reactome pathway “HDL mediated lipid transport”, which was most significantly associated with HDL , nine out of 15 genes (60%) in this pathway showed gene-based significance (pgene < 1 × 10− 4).We next examined the individual genes in each of the most significantly associated pathways. We chose those genes that had a gene-based p-value: pALZ = 4.35 × 10− 2, pADHD = 1.52 × 10− 5, pASD = 8.76 × 10− 5, pBD = 6.44 × 10− 4, pMDD = 4.25 × 10− 6, pSCZ = 7.49 × 10− 4). There were a total of 76 genes in this pathway. As shown in Fig. pgene < 1 × 10− 4) in at least one neuropsychiatric trait. Seven genes were significant in two traits. Each trait has one or two private genes that were uniquely associated with the trait itself but not with other traits. Similarly, we chose the JAK-STAT signaling pathway to examine shared genes for the three immune-related traits , IL2 (4q27), IL21 (4q27), IL23R (1p31.3), IL2RA (10p15.1), LIF (22q12.2), and TYK2 (19p13.2). Among them, IL12RB2 was previously reported to be associated with an autoimmune disease Systemic Lupus Erythematosus in Chinese population [IL21 plays key roles in humoral immunity and autoimmune diseases [IL23R is located in a genomic region that is associated with RA [TYK2 had been considered as a potential drug target for certain common autoimmune disorders [One of the advantages of pathway-based analysis is that the combined effect of multiple genes, instead of the effects of each individual gene, was examined for disease association. We thus asked whether the same genes contributed to the disease association at the pathway level. For the neurological/neuropsychiatric traits, we chose the ARVC pathway as an example since this pathway was most significantly associated with 3 out of the 6 traits  between any pair of traits ranged between 12 (FG and FI) to 99 (UC and CD). These shared pathways proved that trait-associated genetic variants indeed converged on functional pathways that potentially contributed to the traits. Because these pathways were nearly non-redundant , they represent assessment of independent functional processes.We next explored how multiple traits were related at the pathway level. We first conducted a preliminary screen using the full-spectrum of Using hierarchical clustering, the 25 traits could be visually distinguishable through forming sub-groups that were quite consistent with their pre-assigned group labels , the KEGG “axon guidance” pathway , the “alanine aspartate and glutamate metabolism” , and “the voltage gated potassium channels”  were nominally significantly associated with multiple traits and were all previously implied in neuropsychiatric disorders [pHDL = 3.54 × 10− 4, pLDL = 1.09 × 10− 3, pTC = 1.09 × 10− 4, pTG = 1.32 × 10− 6), “HDL mediated lipid transport” , “alpha Q signaling events” , and “ubiquitin mediated proteolysis” .To further explore which pathways contributed to the trait associations, we next examined the 20 most significantly associated pathways within each group of traits  and adjusted pFET < 0.01 (referred as moderately significantly connected network). After applying the adjusted pFET < 1 × 10− 3 . In addition, we observed crosstalk between bone mineral density (LS-BMD and FN-BMD) and immune-related traits (UC and CD) [pFET = 2.29 × 10− 6) and FG and FI shared 12 pathways (adjusted pFET = 7.36 × 10− 4). Interestingly, at adjusted pFET < 1 × 10− 3, we did not observe any significant association among neurological/neuropsychiatric traits, although association between ALZ, ADHD, and MDD with traits of other groups were observed .To better represent the relationship among the multi-traits, we built a trait-trait association network based on the FET result using two thresholds: adjusted − 3 Fig. a, we obs− 3 Fig. c. UC and and CD) . FN-BMD  and CD) . We obse and CD) . For othpFET < 0.01), we observed more associations  and MDD and ADHD shared 13 pathways (adjusted pFE = 5.61 × 10− 3). Interestingly, SCZ and UC shared 25 associated pathways (adjusted pFET = 1.52 × 10− 3). This observation is consistent with increased rate of autoimmune diseases in schizophrenia patients in Denmark [pFET = 4.59 × 10− 3), where BD patients were found with higher BMI and increased metabolic comorbidity in previous studies [pFET = 5.94 × 10− 3), consistent with the previous finding that many cardiovascular risk factors are shown to increase the risk of dementia and ALZ [.When we used a relaxed threshold  to select genetic variants for further investigation [During the past decade, large-scale GWAS data for complex diseases and traits have been growing at an unprecedented rate . While ctigation , pathwaytigation , 35, 46.Our primary goal was to use individual variants found to be associated with multiple traits to identify shared biological pathways between traits, as we hypothesized that similar or related pathways are likely underlie the pathogenicity of related traits. In our results, we found the immune-related traits shared many trait-associated pathways. This finding was consistent with previous studies that immune-related traits tended to have many genetic markers with common function in immunity and inflammation . In addiOur work has several limitations. First, the current pathway resources that are publically available and widely used in the field such as KEGG unavoidably have limitations such as low resolution in defining biological pathways, relatively few genes compared to genome-wide datasets, overrepresentation of well-known pathways versus underrepresentation of pathways attracting less common interests, and lack of tissue and cell specificity, among others. All our analyses were confined with these limitations. Second, we used 3 pathway databases to increase the use of annotated pathways. However, many pathways may be redundant when more pathways are included. We evaluated and removed those highly redundant pathways (30% gene). This may exclude lots informative pathways in our analyses. However, Fisher’s Exact Test used in this study still with limitation as all associated pathway are not completely independent. Therefore, it is still a difficult task to balance the pathway independence and information. Third, pathway-based analysis can only evaluate genetic variants located in gene regions but ignore the majority of variants located in the non-coding regions. As a result, there is information lost for variants located at non-coding regions or genes without any pathway information. Fourth, our work focused on common variants based on genotyping arrays and imputation, with MAF > 0.05. Inferences from rare variants may also provide insights into cross-trait association but were not investigated in our work . DespitWe presented an integrative framework to systematically investigate cross-trait associations using GWAS summary statistics for 25 traits using pathway-based analysis. Our results revealed trait-associated pathways individually and in common, highlighting associations both within and across trait groups. We observed a significant proportion of pathways shared by the immune-related traits and anthropometric and social traits, and a number of moderately significant correlations within neurological/neuropsychiatric disorder and metabolic phenotypes. Interestingly, more than half of the associations were cross trait groups. We also constructed a novel trait-trait association network to better examine the genetic association at the pathway level. These results collectively shed lights on the molecular pathways underlying complex traits, both related or unrelated traits. The results warrant further investigation of function of critical nodes (genes) or validated when more data become available.http://web.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php), Genetic Factors of Osteoporosis  consortium, The Genetic Investigation of ANthropometric Traits , International Inflammatory Bowel Disease Genetics Consortium , Meta-Analyses of Glucose and Insulin-related traits Consortium , Psychiatric Genomics Consortium , Reproductive Genetics Consortium . These traits span a wide range of phenotype measurements categorized into four groups. We used the samples of European ancestry. For genotyped SNPs, we removed those with minor allele frequency (MAF) < 0.05. Wherever applicable, we mapped GWAS summary data to the hg19 human assembly using liftOver software [GWAS summary statistics were downloaded for 25 different traits from International Genomics of Alzheimer’s Project  and Socsoftware .p-value < 0.01). This is a commonly used strategy to find SNPs with weak or moderate association signals before they can be integrated for further analysis at the pathway level [p-value < 1 × 10− 4  and generates independent genomic intervals for the enrichment test. The Plink clumping function was employed, taking the 1000 Genomes Project European panel as the reference and the following parameters: r2 = 0.2, maximum distance between a pair of SNPs is 1 Mb. The aim to generate LD-independent intervals was to ensure that SNPs located adjacent in the human genome are analyzed as one independent unit as they might tag the same causal variants. INRICH overlaps the independent intervals with the pre-defined pathways and calculate an enrichment statistic E for each pathway, where E is the number of intervals that overlap at least one target gene in the pathway. To estimate the significance of E, a permutation test is conducted to generate a null set of intervals which match the interval size, overlapping gene, and SNP density to the original input intervals [p-value for each pathway.In our analysis using PASCAL, we chose SNPs with nominal significance of association , we restricted downstream analyses to 1032 pathways of size 10–200 genes. We further removed pathways that showed substantial redundancy, i.e., any two pathways sharing more than 30% genes  were identified and the larger pathway would be retained. Finally, a total of 355 pathways assumed as non-redundant were obtained and used for follow up analysis, including 67 KEGG, 79 BioCarta and 209 Reactome pathways.pcombined < 0.01), followed by Fisher’s Exact Test. Specifically, for two traits a and b, we build a dichotomous 2 × 2 contingency table as follows:To assess whether a pair of traits share pathways higher than chance expectation, we first determined the pathways that were significantly associated with each trait Additional file 2:Table S2. Significantly associated genes in the most significant pathway for each trait. (XLSX 14 kb)Additional file 3:Figure S1. Hierarchical clustering analysis for all associated pathways between 25 traits. (PDF 85 kb)Additional file 4:Table S3. Results of Fisher’s Exact Test for 25 traits. (XLSX 46 kb)Additional file 5:Figure S2. Heatmap showing the 20 most significant pathways for each trait group. (PDF 539 kb)