Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways. Rheumatoid arthritis (RA) is a common, chronic autoimmune disease, which characterized by nonspecific inflammation of the peripheral joints. It affects 1% of the world's population, and it occurs more frequently in women than in men . In lateGenetic factors are known to affect disease susceptibility. In recent years, there are many researches to identify the genetic factors  related to RA. Genome-wide association studies (GWAS) is one of the main methods in finding genetic variants. Many GWAS have been successfully applied to RA and have identified large number of single nucleotide polymorphisms (SNPs) related to RA -8. But tBesides genetic association studies, there are still a lot of bioinformatics analysis methods to help us to understand the molecular mechanism of RA. Genome-wide gene expression analysis as a tool to investigate pathogenesis of RA has found many candidate genes for RA. In 2004, Devauchelie et al. identified differential expression genes between RA and osteoarthritis (OA) by DNA microarray analysis . In 2006It is well known that genes do not work in isolation, instead, complex biology pathways and molecular networks are usually involved in disease susceptibility and disease progression . So geneThere are many pathway analyses studies of RA based on a single data type, for example, SNP data or gene expression data, which have successfully revealed some biology pathways associated with RA  2, 18-2-218-20. P-value of a SNP by trend test and assess gene-wise association value based on SNP dataset; (2) compute the gene expression P-value of every gene based on gene expression data; (3) compute the gene association score by integrating GWAS and gene expression; (4) compute the pathway association score based on SNP dataset, gene expression data, and integrated datasets respectively; (5) compute the significance of association between the pathways and RA by permutation test; (6) identify the pathways related to RA after multiple testing corrections. By comparing the results, we found integrative analysis can provide more stable results and also revealed novel pathway associations.In this study, we integrated SNP dataset and gene expression information to identify the pathways related to RA. This multi-leave procedure consists of six computation steps: (1) compute the P-value of all tagSNPs in a gene was chosen to be the gene-wise association value.In the WTCCC SNP dataset, there were 370,227 SNPs passed quality control. After mapping SNPs to genes, we found that there were 32,591 genes that contained at least one SNP. We used 1000 genome dataset from Europe to correct the LD and obtained tagSNPs of all 32,591 genes. The smallest q-values were less than 0.05 after FDR correction were identified as RA-related pathways based on GWAS. The detailed information of these pathways is presented in Table The 234 pathway risk scores were computed by the Fisher's method. The RA-related pathways were identified by the permutation test and FDR correction. The 19 pathways whose n=8), infectious diseases (n=7), signaling molecules and interaction (n=1), transport and catabolism (n=1), endocrine and metabolic diseases (n=1) and cardiovascular diseases (n=1). All of these pathways are reported as RA-related pathway in the previous studies. It is indicated our results are consistent with previously published studies [According to the KEGG classifications, we found these pathways were mainly involved in immune system  were normalized by “affy” package in R software. Then the probes were mapped to the genes according to platform information. After data preprocessing, 12,752 genes were for following analysis. The differential expression n=17), infectious diseases (n=8), cellular processes (n=3). Among these pathways, there are 24 pathways to be found respectively as RA-related pathways in the pervious published studies [We found the majority of these 33 pathways are related to immune system , infectious diseases (n=7), signaling molecules and interaction (n=1), transport and catabolism (n=1), endocrine and metabolic diseases (n=1), development (n=1) and cardiovascular diseases (n=1). The detailed information of these pathways is presented in Table Finally, the 28 pathways (FDR< 0.05) after FDR correction were identified as RA-related pathways based on integration GWAS and gene expression data. Among these pathways, they were mainly involved in immune system  was one of the most prominent pathways [We identified 16 pathways which were related to immune system. In the present studies, there was no evidence to elucidate the relationship between Chemokine signaling pathway and RA until 2015. In 2015, zhang et al. used real-time polymerase chain reaction to determine gene expressions and found the chemokine signaling pathway was involved in CCL2 expression in RA patient peripheral blood and synovial tissues . The sampathways . Guo et pathways . The eviP = 2.0E-04) [P = 1.0E-03) [P = 1.57E-08) [We identified 7 pathways which were related to infectious disease. These seven pathways are identified respectively by GWAS, gene expression analysis and integration analysis. Among these pathways, Influenza-A pathway was not found as RA-related pathway in previously published studies until 2013. Sharma et al. performed a multistage integrative pathway analysis on RA and firstly detected a relation between the influenza-A pathway and RA (2.0E-04) . Besides1.0E-03) . Peter e1.0E-03) . In the .57E-08) . In addiIn our research, we performed a pathway analysis by integrating GWAS and gene expression analysis and detected some pathways related to RA. We found integrative analysis did not only detect RA-associated pathways which had confirmed in previous studies, but also can identify some novel RA-related pathways. However, in order to reveal the pathogenesis of RA, we are required to integrate more types of data into pathway analysis and perform genetic studies or experiments to prove the relationship between the novel pathways and RA in further research.P-value <0.001 for Hardy-Weinberg equilibrium (HWE) or a genotype percent <0.25 were excluded from the analysis [GWAS SNP data came from WTCCC RA datasets. The dataset included 459,446 autosomal SNPs from 12,468 subjects . The individuals from the dataset are Caucasian. SNPs with a minor allele frequency (MAF) <0.01 or a analysis . After qGene expression data are from three microarray datasets in Gene Expression Omnibus (GEO) database. The three microarray datasets are GSE55235, GSE55457 and GSE55584. Their platform is GPL96 (Affymetrix HG-U133A). The individuals of three datasets are Caucasian. In this study, 53 individuals are contained, including 33 RA patients and 20 healthy individuals. The detailed information of individuals is shown in Table ftp://ftp.ncbi.nlm.nih.gov/genomes/MapView/Homo_sapiens/sequence/BUILD.37.3/). There are altogether 34,402 autosomal genes for the following analysis. The detailed information of these genes was extracted, such as ‘gene ID’, ‘chromosome ID’, ‘gene start position’, ‘gene end position’ and so on. Then we map SNPs to genes according to the information.Gene data were downloaded at the National Center for Biotechnology Information (Pathway data were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) . The KEGP-values of all SNPs in the WTCCC datasets. Then we mapped a SNP to a gene if it was located within the gene or 50kb immediately upstream or downstream. All the SNPs from WTCCC RA datasets were mapped to 34,402 autosomal genes and their extended gene intervals.We used the Cochran-Armitage trend test to calculate r2>0.8. We chose the smallest P-value of all tagSNPs in this gene to assess the gene-wise association value. See part I of workflow  from GEO database. Then we mixed the three datasets together and obtained 53 individuals, including 33 RA patients and 20 controls. The data were normalized by affy package in R software. Then the probes were mapped to the genes according to platform information.t-test, analysis of variance and so on. In this study, we chose t-test method to analyze the microarray data after normalization and compute the differential expression P-values of all genes. See part II of workflow  distribution [ig into the probability of chi-square distribution. The probability represented gene association P-value by integrating GWAS and gene expression and marked as iP.Because ribution , we tranP-value. We still adopted Fisher's combination method to calculate pathway association scores for 234 human pathways. See part III of workflow  formed an estimated background distribution of this pathway. The smaller gene association P-value, the pathway association score is greater. The greater the pathway association score, the more associated the pathway is with RA. A permutationP was computed for each pathway by counting the number of permutations that have randomS more than trueS divided by the total number of permutations.In order to further determine whether a pathway was significantly associated with RA, we performed a permutation test by shuffling the pathway labels. The null hypothesis is that there is no association between the pathway and RA. We counted the number of non-redundant genes that had association q-value (FDR) was used to identify RA-realted pathways.Multiple hypothesis tests had been used to identify pathways related to RA, but they could increase type I errors and the false-positive error rate. Therefore, we adopted multiple testing corrections to solve this problem. A threshold 0.05 of The workflow of this study was shown in Fig.


Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is affected by both environmental and genetic factors. Although the genetic component of PCOS is evident, studies aiming to identify susceptibility genes have shown controversial results. This study conducted a pathway-based analysis using a dataset obtained through a genome-wide association study (GWAS) to elucidate the biological pathways that contribute to PCOS susceptibility and the associated genes.We used GWAS data on 636,797 autosomal single nucleotide polymorphisms (SNPs) from 1,221 individuals (432 PCOS patients and 789 controls) for analysis. A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA). Top-ranking pathways or gene sets associated with PCOS were identified, and significant genes within the pathways were analyzed.P-values < 0.05). In addition, INS, GNAQ, STXBP1, PLCB3, PLCB2, SMC3 and PLCZ1 were significant genes observed within the biological pathways .The pathway analysis of the GWAS dataset identified significant pathways related to oocyte meiosis and the regulation of insulin secretion by acetylcholine and free fatty acids  By applying MAGENTA pathway analysis to PCOS GWAS data, we identified significant pathways and candidate genes involved in PCOS. Our findings may provide new leads for understanding the mechanisms underlying the development of PCOS. Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is characterized by chronic oligo-anovulation, clinical and/or biochemical hyperandrogenism and polycystic ovaries . PCOS isPrevious genetic studies on PCOS have been based mainly on candidate gene identification, which has revealed many of the genes involved in insulin expression and steroidogenesis , 7. HoweTHADA), DENN/MADD domain-containing 1A (DENND1A) and luteinizing hormone/choriogonadotropin receptor (LHCGR) [KHDRBS3  associated with telomerase activity [GWAS is a powerful, unbiased method for screening susceptible genes associated with complex diseases , 9. The  (LHCGR) . In a st (LHCGR) . In Koreactivity , 13.One strength of GWAS is the ability to discover significant SNPs and novel genes associated with a disease. However, GWAS studies primarily focus on individual SNPs that meet a stringent significance criterion, neglecting the interplay of genes. Additionally, most identified SNPs lack functional relevance, explaining only a small portion of genetic heritability , 15. TheThe pathway-based approach integrates GWAS results with genes in biological pathways or gene sets from predefined human databases, ranking all genes according to their statistical significance , 17. ThiIdentifying the genetic pathways involved in PCOS may provide a more contextualized understanding of the mechanism underlying PCOS. The aim of this study was to use a pathway-based analysis of a GWAS dataset to elucidate the biological pathways involved in PCOS and the associated genes.th percentile  based on the testosterone levels recorded in 1,120 healthy, regularly cycling women [The pathway analysis was conducted using a PCOS GWAS dataset that we generated previously. The dataset included data from 1,000 patients with PCOS and 1,000 controls. This study was performed in the Endocrinology and Gynecology Clinics of Ewha Womans University Hospital from December 2008 through November 2010. PCOS was diagnosed using the National Institutes of Health (NIH) criteria, which define the disorder as the presence of chronic oligo-anovulation and clinical and/or biochemical hyperandrogenism; the NIH criteria exclude other disorders, such as Cushing syndrome, adult-onset congenital adrenal hyperplasia and androgen secreting neoplasm . Oligo-ang women . Clinicang women , 24.2). Waist circumference was measured to the nearest 0.1 cm on bare skin during mid-respiration at the narrowest indentation between the tenth rib and the iliac crest. Systolic and diastolic blood pressures were also measured. Hirsutism was assessed by a single trained nurse using the mFG scoring method.Weight and height were measured in all subjects, and body mass index (BMI) was calculated (kg/mhttp://www.issam.ch/freetestos.htm), free testosterone levels were calculated using total testosterone, SHBG and albumin levels [After an overnight fast of at least 8 hours, a venous blood sample was obtained from each subject on the third day of the follicular phase of the menstrual cycle. Standardized enzymatic methods were used to analyze lipid profiles, including serum total cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels. For evaluation of glucose tolerance, a standard 75 g oral glucose tolerance test (OGTT) was performed in all subjects after an overnight fast to determine fasting plasma glucose and 2-hour post-load glucose. Total testosterone levels were measured using the chemiluminescent immunoassay method , and sex hormone-binding globulin (SHBG) levels were measured using immunoradiometric assays . Using the formula from the International Society for the Study of the Aging Male  were available. Additive models were used for analysis.Genomic DNA was extracted from individual peripheral blood samples and genotyped in 2,000 samples using the Illumina HumanOmni1-Quad v1 BeadChip . Quality control (QC) procedures were applied using PLINK version 1.07 , excludihttp://broadinstitute.org/mpg/magenta) to identify biological pathways or gene sets associated with PCOS [http://pathguide.org) online resource.A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA)  are selected. Gene scores are then corrected for confounders, including gene size, SNP numbers and linkage disequilibrium-related properties. Gene sets enriched with highly ranked gene scores are analyzed with the selected biological pathway or gene sets, and gene-set enrichment P-values are calculated. Additional information, including the 95th and 75th percentile cutoffs, the names of genes within each pathway or gene set, and the nominal GSEA P-value and false discovery rate (FDR), is analyzed through multiple test correction. Because the 75th percentile cutoff demonstrates greater power in interpreting complex diseases with high polygenesis, we used this cutoff value for interpretation [P-values of less than 0.05 were considered to be significant genes involved in the selected pathways or gene sets.The analytical steps of MAGENTA are as follows . SNP assretation , 27. AftThe clinical and biochemical characteristics of the women with PCOS and controls included in this study are shown in P-values < 0.05), including neural tube closure (GO term), other kinases (PANTHER), the calcium signaling pathway (KEGG), acyltransferase (PANTHER), the negative regulation of osteoclast differentiation (GO term), cytoskeletal protein binding (GO term) and developmental processes (PANTHER). The FDR values for oocyte meiosis, the regulation of insulin secretion by ACh and FFAs and calcium signaling pathways were 0.078, 0.152, 0.110 and 0.222, respectively.The top ten significant biological pathways or gene sets associated with PCOS are displayed in SMC3 , CCNE2 (cyclin E2), PPP2R5D , INS (insulin), PPP2R5C , PLCZ1 , PPP2R5A , PPP1CB  and SPDYA (speedy/RINGO cell cycle regulator family member A) . The genes INS, STXBP1 (syntaxin binding protein 1), PLCB3 , GNAQ  and PLCB2  were identified in the pathways related to the regulation of insulin secretion by ACh and the regulation of insulin secretion by FFAs   and PLCB2 were significant. Detailed information on the genes identified in other biological pathways is shown in The genes involved in the biological pathways were further evaluated. The significant genes involved in the pathway of oocyte meiosis were  < 0.05) . In the In this study, a pathway-based approach was applied to a GWAS dataset of patients with PCOS. The study identified significant pathways involved in ovulation and insulin secretion, including oocyte meiosis and the regulation of insulin secretion by ACh and FFAs.P-values such as MAGENTA have been introduced; these approaches analyze the statistical power of GWAS by integrating the P-values for variant associations into gene scores [Pathway analysis is a post-GWAS analysis method that can be applied to further interpret GWAS results. Early pathway-based approaches employed raw genotype data for GSEA, which are not provided in all GWAS, and required intensive computational permutations . To simpe scores . Throughe scores , 27.In the present study, oocyte meiosis was identified as the top-ranking biological pathway associated with PCOS. Oocyte quality, maturation and fertilization are affected by factors such as hyperandrogenemia and insulin resistance, which are important phenotypes of PCOS and can lead to premature follicular luteinization and anovulation , 29. TheINS was observed in all three top ranking pathways associated with PCOS. Previous studies showed an association of this gene with insulin resistance, obesity and type 2 diabetes through variation of the VNTR (variable number of tandem repeats) locus at class III allele [INS was also associated with anovulation in PCOS, although there are conflicting studies [INS and PCOS.I allele –35. INS  studies –38. Thes studies . Severe  studies , 40. MorGNAQ, a Gq protein encoding gene, is a known candidate gene of PCOS that mediates the insulin induced translocation of GLUT4 in adipocytes and is associated with insulin resistance and obesity in PCOS [STXBP1, PLCB2 and PLCB3 have not been identified in PCOS yet. However, published studies have demonstrated abnormal expression in these genes, leading to abnormal insulin secretion and disordered glucose homeostasis [From the genes identified in the pathway of regulation of insulin secretion by acetylcholine and FFA,  in PCOS . Other geostasis , 43.LHCGR was identified in this pathway, which is a known susceptibility loci of PCOS discovered through GWAS, having an association with hyperandrogenism [EGFR and PLCZ1 were also observed in this pathway. Abnormal expression of EGFR was related with oocyte incompetence in PCOS women [LCZ1 is expressed in sperm, and variations of this gene lead to low fertilization and male infertility [Calcium signaling pathway might have an association with androgen excess. Calcium is crucial in gonadotropin secretion, and studies have shown that calcium signaling is affected by androgen levels , 45. LHCrogenism , 46, 47.OS women . PLCZ1 iertility , 50. VarSMC3 is related with Cornelia de Lange syndrome, characterized by features such as growth and mental retardation with abnormal limb formation, and associated with the development of atopic asthma and myeloid neoplasms [PPP1CB caused chronic lymphocytic leukemia, whereas CCNE2 was related to the development of non-small cell lung cancer and breast cancer [PP2A regulatory subunit B family of genes resulted in features associated with overgrowth, and because it is an important gene in the phosphorylation of tau protein, which is crucial in neurofibrillary tangle formation, it could lead to Alzheimer’s disease [Although there is a lack of studies on the genes identified in the biological pathways of oocyte meiosis and PCOS, many genes have been studied in other human diseases. Mutation of eoplasms –55. Inact cancer –58. Muta disease –61.We used a pathway-based approach to identify multiple biological pathways or gene sets that are involved in the pathogenesis of PCOS. To our knowledge, this was the first GWAS dataset-based pathway analysis study to be conducted for PCOS. One of the strengths of this study is that the subjects were accurately selected, and homogenous PCOS groups were recruited using well-defined diagnostic criteria. Although the identified pathways did not show an FDR of less than 0.05, significant pathways associated with ovulation and insulin secretion were discovered at an FDR of less than 0.2. Because ovulatory dysfunction and abnormal insulin secretion are major features of PCOS, the biological pathways identified in this study may be important. However, validation of these pathways using other pathway approaches will be necessary.LHCGR have been identified as susceptibility loci in Han Chinese, Hui Chinese and Egyptian populations [There are some limitations of this study. First, the number of women with PCOS included in the GWAS dataset is relatively small. Second, the pathway analysis tools applied in the study are biased toward detecting well-defined pathways. However, the majority of the genes in the genome are relatively unknown, and their biological function still needs to be established. Third, our study is confined to Korean women only. Because different phenotypes of PCOS are seen in women with different ethnic backgrounds, our results may not be generalizable to other ethnic groups. However, genes such as ulations , 57, 62.In conclusion, by applying pathway analysis to a GWAS dataset for PCOS, significant biological pathways and genes associated with ovulation and insulin secretion were identified. Our results may contribute to understanding the mechanisms underlying PCOS.S1 Table(DOC)Click here for additional data file.


Escherichia coli (E. coli) and Saccharomyces cerevisiae (S. cerevisiae) were extensively studied to biosynthesize terpenoids, because they are both fully amenable to genetic modifications and have vast molecular resources. On the other hand, our literature survey (20 years) revealed that terpenoids are naturally more widespread in Bacillales. In the mid-1990s, an inherent methylerythritol phosphate (MEP) pathway was discovered in Bacillus subtilis (B. subtilis). Since B. subtilis is a generally recognized as safe (GRAS) organism and has long been used for the industrial production of proteins, attempts to biosynthesize terpenoids in this bacterium have aroused much interest in the scientific community. This review discusses metabolic engineering of B. subtilis for terpenoid production, and encountered challenges will be discussed. We will summarize some major advances and outline future directions for exploiting the potential of B. subtilis as a desired “cell factory” to produce terpenoids.Terpenoids are the largest group of small-molecule natural products, with more than 60,000 compounds made from isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). As the most diverse group of small-molecule natural products, terpenoids play an important role in the pharmaceutical, food, and cosmetic industries. For decades,  However, uncertainty still looms around some aspects such as genetic engineering, characterization, reliability, quantitative strategy, and independence of biological modules  naturally possess more genes and proteins related to terpenoid biosynthesis pathways   is 7 to 13 nmol per gram cells per hour  FDA , has inhRAS) FDA . The intB. subtilis has 15 inherent enzymes, belonging to five terpenoid biosynthesis pathways: two terpenoid backbone biosynthesis upstream pathways , the terpenoid backbone biosynthesis downstream pathway, carotenoid biosynthesis pathway, and ubiquinone and other terpenoid-quinone biosynthesis pathway  units. In terpenoid biosynthetic pathways, IPP and DMAPP  are the basic terpenoid building blocks, generated by the Mevalonate and MEP pathways (the terpenoid backbone biosynthesis upstream pathways). The terpenoid backbone downstream pathway is responsible for biosynthesis of geranyl diphosphate (GPP), farsenyl diphosphate (FPP), and geranylgeranyl diphosphate (GGPP), which are the precursors of monoterpenoids C10), sesquiterpenoids (C15), and diterpenoids C20), respectively. 0, sesqui0, respecB. subtilis MEP pathway. Most of the MEP pathway studies are based on E. coli. Withers and Keasling have described the MEP pathway of E. coli briefly , which forms 1-deoxy-d-xylulose 5-phosphate (DXP) by the condensation of d-glyceraldehyde 3-phosphate (GAP) and pyruvate. This enzyme is not only specific for the MEP pathway but also plays a role in thiamine metabolism , 4-diphosphocytidyl-2-C-methyl-d-erythritol kinase (ispE), and 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase (ispF) are required to convert MEP to 2-C-methyl-d-erythritol 2,4-cyclodiphosphate (MECDP)  and isopentenyl-diphosphate delta-isomerase (idi). Some researchers consider them as key enzymes in the MEP pathway  and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (ispH), but their catalytic mechanisms are still unclear  producing an approximately 5:1 mixture of IPP and DMAPP in return , resulting in a significant increase in downstream terpenoid production -4-hydroxy-3-methylbut-2-enyl-diphosphate synthase , salt (0.3 M), and H2O2 (0.005 %) can induce the production of isoprene. In addition, they found that these effects are independent of SigB, which is the general stress-responsive alternative sigma factor of B. subtilis  crtP and crtQ genes into B. subtilis, together with crtM and crtN  in B. subtilis and got the highest yield of amorphadiene (∼20 mg/L) at shake-flask scale. They thought that the lack of genetic tools for fine-tuning the expression of multiple genes is the bottleneck in production of terpenoids in B. subtilis. So they modified B. subtilis genes by using a two-promoter system to independently control the expression levels of two gene cassettes  have been validated as the most productive part of the MEP pathway genes in this study.In 2009, Yoshida et al. first successfully transcribed and transfected tN Maeda . Later, 35 terpenoids and their enzymes, which were found in B. subtilis, like heterodimeric enzyme, heptaprenyl diphosphate synthase (HepS and HepT), and tetraprenyl-β-curcumene synthase (YtpB), which are responsible for forming long prenyl diphosphate chains (C35)  and liquid chromatography-MS (LC-MS). Other techniques such as nuclear magnetic resonance (NMR)  to measure isoprene and MEP pathway metabolites . Cho’s untargeted metabolomics study  extraction methods that maintain the original structure of intermediates and subsequently allow the identification of those compounds and their accurate quantification; (3) extraction coupled quantification methods that can be used to quantify minor components from small-scale bacterial cultures to reduce the workload; and (4) data pre-processing, biostatistics, and bioinformatics methods for big data analysis, integration, and modeling that can reflect the cell bio-net, narrow the research scope, target the key products, genes, and enzymes, and finally lead us to further improvements.As is the case for biosynthesis of different chemical compounds, genetic modification often leads to dead ends. The difficulties in metabolic engineering of bacteria for terpenoid production normally are not terpenoid detection but problems in the complex metabolic net  its fast growth rate and ability to survive under harsh conditions, (2) its GRAS status, (3) its wide substrate range and inherent MEP pathway genes, (4) the fact that it is a naturally high isoprene producer, (5) its clear genetic background, abundant genetic tools, and (6) its innate cellulases, which can digest lignocellulosic materials and use the breakdown products as its carbon source, which would decrease large-scale production costs. Still, B. subtilis share some of the features of other gram-positive bacteria like plasmid instability. Also, there are some B. subtilis-specific engineering challenges that need to be explored. The catalytic mechanisms of two MEP pathway enzymes  in B. subtilis are unclear yet. The importance of DXR and IDI in the MEP pathway is controversial. DXS has been generally regarded as the essential rate-limiting enzyme, but even the functional parameters of DXS in B. subtilis have not yet been reported. Many questions regarding the mechanism of the MEP pathway, the interactions of related enzymes and metabolites, and the kinetic parameters of MEP pathway enzymes in B. subtilis remain unanswered. Obviously, the organism is promising and the questions are fascinating. There is thus significant reason for detailed investigations of terpenoid biosynthesis via the B. subtilis MEP pathway, particularly in metabolic engineering where there is not yet sufficient knowledge about the precise mechanisms or the effects of co-regulation of the enzymes.


Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users.KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module.We introduce an online web application, named KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments.In conclusion,  Obtaining a comprehensive list of genes is routinely performed today, given the high-throughput biological experiments and the plethora of methods for further statistical analysis of the data obtained. The subsequent association of a list of genes with particular cellular functionalities, through the identification of specific molecular pathways, highlights the modular character of the cellular response to the change of conditions, for instance the chemical environment, disease states, or drug-induced effects Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway database Molecular Signatures Database (MSigDB) Onto-expressGoMinerGeneTrail for example Pathway-ExpressScorePAGEThe most common approaches for pathway analysis are based on the statistical enrichment scores of various annotations KEGG Enriched Network Visualizer (KENeV), which exploits the widely used KEGG pathway database, in order to automatically derive and visualize the enriched molecular networks. KEGG is a library of molecular networks that has been widely used as a reference point for biological interpretation of large-scale datasets. The application takes as input a list of significant genes, from an expression analysis experiment, possibly accompanied by corresponding fold change measurements. The workflow [StRAnGER algorithm In this work, we have developed an online web application based on the ORA approach, named workflow  starts w22.1StRAnGER algorithm, described in detail in StRAnGER avoids the utilization of multiple test correction approaches  that are very restrictive and problematic in regard to the finite nature of annotation vocabularies. The main problem of these methodologies is that they tend to promote enrichments yielding very low p-values (close to 0), but holding a very low biological content (e.g. enrichments such as 2/2 and 1/2). Bootstrapping is a non-parametric, empirical alternative to multiple test correction, which provides a corrected measure for the statistical significance of the enrichments based on their frequencies of observation. Instead of adjusting the p-values, the algorithm reorders the initial distribution and prioritizes the less frequently observed enrichments. The enrichments are derived as statistically significant if they satisfy both the hypergeometric and the bootstrap thresholds, but they are ranked according to their bootstrap p-value. Hence, the algorithm prioritizes pathways with less frequent enrichments which tend to represent broader pathways or functions and, thus, are of stronger biological content. The final output of significant KEGG IDs is spanned by the KEGG pathways that are mapped to the significant elements.The pathway enrichment analysis is performed by the 2.2ElementTree Python XML parser) and their fields are stored as respective objects. Different types of compounds  and relations  are detected using the KEGG annotation fields and are stored in the database. Each compound is then mapped to a unique node and to all reactions in which it participates. Finally, two merged networks are constructed using the libsbml library The statistically important pathways derived from the enrichment analysis are automatically downloaded from the REST interface of the KEGG database as KGML format files, which is the XML format used by KEGG. Subsequently, the KGML files are parsed , was used for the storage of KEGG data . The application was programmed to automatically download the relevant files on a weekly basis, using the KEGG REST API (freely accessible for academic use). The Network Visualization Tool was implemented using a custom made JavaScript algorithm complemented by jQuery (http://jquery.com/) and “sigma.js” (http://sigmajs.org/) JavaScript libraries.The application was implemented in the 33.1http://www.grissom.gr/kenev. The form accepts as input a list of differentially expressed genes, derived from a genomic experiment, optionally accompanied by their expression fold change values  in tab-delimited format. The user is able to select the examined organism species from the complete list of KEGG organisms (currently including 3677 species). Additionally, the user is able to choose the cut-off values of the two aforementioned statistical tests. On form submission, the pathway enrichment analysis is performed, resulting to a final ranked list of KEGG pathways. The user is then able to click on a link that performs the pathway merging step and yields the signaling and metabolic super-pathways. Remark that it is possible that the pathway list does not include both metabolic and signaling pathways. In this case, only one type of SBML file is returned. The SBML files are available for downloading, in order to be processed or visualized by external software, for instance by the popular programs CytoscapeCellDesignerThe online application is available under the url 3.2For the case of the metabolic/signaling graphs using the generated SBML files see , the netFor the case of genes–pathways mapping see , visuali3.3KENeV is showcased here using as input a well defined list of genes that has been related to Type I Diabetes Mellitus and was found deposited in the Autworks database (http://autworks.hms.harvard.edu/) Autworks began as a cross-disease biology web portal in order to store genes lists that have been found associated with autism or related disorders. Currently, it contains ~ 2300 additional disorders that can be compared with autism in terms of common gene networks beneath disorders.KENeV and the workflow was executed up to the visualization module . KENeV inorder of statistical significance, based on the bootstrap extracted p-value (see last column). Some interesting pathways are related to autoimmune disorders and inflammation, while the pathway for the disease itself appears first in the output. KENeV, focusing on the cross-talk between the NF-kB/RelA and PI3K pathways, is shown in CellDesignerThe list of 407 genes associated to Type I Diabetes Mellitus was submitted to 44.1KENeV in a fully automated integrative online application, namely pathway enrichment analysis, automatic pathway retrieval, handling of metabolic and signaling pathways, handling of KGML inconsistencies, SBML conversion, SBML merging and visualization of the super-pathways, including highlighting of the differentially expressed genes.There are several software tools that allow the retrieval, visualization and/or the analysis of KEGG pathways. However they all have different features and orientations and none combines all key features of KEGG2SBML and KGML2BioPAXFor instance, two available KEGG pathway converters, PathviewR/Bioconductor package, which provides KEGG pathway visualization, with very useful features, for instance automatic pathway retrieval, biochemical data modification and enhancement and integration of biological data provided by the user . However, Pathview does not include functions for merging multiple pathways into super-networks.KEGGgraphR and Bioconductor, which enables KGML data retrieval, parsing and visualization. Although this package supports merging of multiple graphs , all functionalities are performed manually through specific R commands, and thus the toolbox is not intended to be used automatically by non programming experts.KEGGParserMATLAB functions, and on Bioinformatics and Image Processing toolboxes. KEGGParser's advantage is the automatic correction of inconsistencies between KGML files and static pathway maps.KEGGTranslatorKEGGTranslator performs multi layered correction/augmentation of KGMLs biological data, such as the removal of examined-organism unrelated nodes, the completion of reactions, the annotation of each entry with multiple identifiers and the addition of stoichiometric information. Additionally, it provides a functional graphical user interface and translates KGML files into various file formats . These output files can be used for simulation analysis due to the integrated stoichiometric information.A more thoroughly developed KEGG pathway illustrator is Pathway ProjectorPathway Projector is not able to visualize signaling and gene regulation networks. Furthermore, Pathway Projector does not extract pathway data in an exchangeable format such as SBML and BioPAX.Another web application is KEGG ConverterPathwayLinkerKENeV in that it does not perform KEGG pathway enrichment calculations and pathways prioritization, and does not provide visualizations for the metabolic network.DAVIDGeneTrailWebgedstaltKENeV to other gene-enrichment tools given the results/lists of KEGG pathways is out of the scope of this manuscript and could not be adequately informative for a thorough comparison. Different tools may provide different or re-arranged lists of pathways due to the different timing in updating the KEGG database (KENeV updates on a weekly basis the KEGG database). In terms of functionality, we note here that the following derived from comparing KENeV to DAVID, Webgestalt and GeneTrail: KENEV does not require a least number of genes per KEGG pathway since bootstrapping excludes such pathways from being reported in the final list of pathways. KENeV provides the bootstrapping method instead for providing the user the choice for a series of p-value correction methods (user could be unfamiliar to such methods). Importantly, KENEV merges pathways into super-pathways and provides all visualization features described. However, we provide in an .xls file (provided as KENeV as described in the DAVID, Webgestalt and GeneTrail tools. It can be observed that KENeV provides a shorter list of pathways, which is however similar in the firstly prioritized KEGG pathways to the longer ones obtained by the other tools.With respect to the pathway over-representation analysis, there are tools that include the KEGG pathways as canonical pathway backgrounds, such as KEGGgraph, KEGG Converter and PathwayLinker, cannot perform multiple pathway integration into a merged network. Another drawback, specifically concerning users without any experience in programming, is that some tools are available as programming packages or simple scripts, instead of automated web applications, in contrast to KENeV, which enables the automated reconstruction of both signaling and metabolic enriched networks, using as only input a list of gene identifiers with their respective fold changes.Overall, the majority of the aforementioned tools, except from 4.2KEGG Reaction database and it will be used for a future implementation of the application that will derive a simulation-enabled metabolic SMBL file, in addition to the current ones that are enabling visualization. Important steps towards this direction are the effective handling of inconsistencies in matter of pathway gaps, reaction mass balance and missing co-factor information.In the current version, the application is oriented towards visualization and therefore does not deliver stoichiometric-enabled SBML files for simulation purposes. Nevertheless, the KEGG maps are themselves designed for visualization purposes and hence they are not consistent for simulation. Notably, they contain gaps whereas they do not contain neither the necessary stoichiometry, nor the reactions' cofactors, which are essential for the oxidoreduction and energy balance. This information is however separately available in the MSigDBReactomeAdditionally, a merged metabolic/signaling hyper-network can be derived from the integration of metabolic and signaling sub-networks, provided the overcoming of a number of challenges, with respect to the expected high network complexity, especially the handling of complex protein–protein interactions presenting high connectivity. Finally, more interaction databases, for instance 5KENeV web application presented here enables the automatic execution of a workflow that combines pathway enrichment analysis, reconstruction of the enriched metabolic and signaling networks, and finally instant interactive visualization of the obtained networks. In conclusion, the application provides an effective tool for the interpretation of the underlying biological mechanisms, which are detected by genomic experiments that derive a subset of genes with differential expression among various conditions.Pathway enrichment analysis and visualization approaches provide valuable tools for the interpretation of genomics experimental results, typically characterized by increased dimensionality and complex connectivity. From this perspective, the


Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.WikiPathways ( The human pathway collection is the largest and most active collection by species, having increased 6-fold to include 640 pathways. In terms of coverage of unique human genes, WikiPathways is comparable to KEGG (http://www.kegg.jp/kegg/docs/upd_map.html). The quantity, quality and diversity of content in WikiPathways is due to hundreds of individuals contributing their time and domain knowledge each year.Launched in 2008 as an experiment to see if crowdsourcing could work for a pathway archive, WikiPathways started out with 500 pathways across six species maintained by four individuals . Today,  to KEGG . Our advhttp://www.wikipathways.org/index.php/WikiPathways:License_Terms). This extends to individual and bulk downloads of all available formats, as well as all means of programmatic access. Furthermore, as an open source collaboration from the start, the technical development of the WikiPathways platform itself is also open to community participation (https://github.com/wikipathways).It is not enough, however, to only amass a large collection of pathways; it must also be actively distributed and made maximally accessible. WikiPathways guarantees free and open access to its full collection under a Creative Commons (CC BY 3.0) license and liberal terms of use ensuring that content contributed by the community will always be available to the community  and extracellular RNA . The exRNA portal at WikiPathways has accumulated over 45 pathways in the last year that highlight exRNA and miRNA roles in everything from differentiation and inflammation to ovarian cancer and Alzheimer's . We coordinate with the exRNA community to make sure every consortium publication that includes a pathway figure is also captured as a properly modeled pathway.Building upon the canonical set of pathways found at most pathway archives, we have made a concerted effort to also capture more specialized models of biology. Our most successful approach has been to engage established research communities already focused on a model organism, class of pathway or particular cell type. For example, the curators at WormBase launched a dedicated portal at WikiPathways for diverse pathways related to organism . The Worhttp://www.sbgn.org .Since our last update, we have established a collaboration with Reactome to boost the human pathway collection at WikiPathways. Reactome shares our goal of providing a free, open access collection of peer-reviewed pathways and associated software tools . With a sbgn.org  and httpsbml.org . This cohttp://www.wikipathways.org/index.php/Help:Curation_Protocol). The protocol steps include screening all new edits, orienting new users, fixing poorly annotated pathways and molecules, as well as assessing the competence of content for common downstream data analysis and visualization workflows (see the next section). Part of this process is computer-assisted by validation reports from regular run bots and tests. Anyone with a demonstrated interest in WikiPathways can apply to join the administrative curation roster.In addition to crowdsourced curation, watchlist activity and curation tag procedures, we have instituted another level of quality control at WikiPathways in the form of a roster-based curation protocol. Year-round, on any given week, there is a designated administrative curator who executes an 8-step quality control protocol encompassing the entire pathway archive (http://www.wikipathways.tumblr.com), and Google Group (wikipathways-discuss).With a WikiPathways user account, you can manage your own ‘Watchlist’ of pathways to be notified of changes. However, you can also track general updates to the project and content via Twitter (@WikiPathways), Tumblr . The plugin also allows users to create and upload new pathways, or edit and curate existing pathways. Within PathVisio, researchers have access to an array of core and plugin features to support the analysis of omics data and customized data visualization.PathVisio is a commonly used pathway editor, visualization and analysis tool, and additional features are provided via the plugin repository. The WikiPathways plugin was developed to support browsing and searching the database directly from within PathVisio . The ‘Editing Tour’ video provides an overview of various means of editing a pathway, including the new Quick Edit option, the full-featured Applet option, and the offline-capable PathVisio options that leverages the new WikiPathways plugin (see Supplementary Data: Editing Tour).http://www.wikipathways.org/index.php/Special:TissueAnalyzer). The goal of this feature is to combine the pathway knowledge from WikiPathways with baseline expression data for 27 different tissues from Expression Atlas , a JavaScript solution that works immediately in all modern browsers, eliminating the need to install additional software. The first set of Quick Edit tabs support the editing of DataNode annotations, labels, fonts and colors.We have developed a number of new tools to make pathway editing as easy as possible, so everyone can contribute their specialized knowledge. The most radical approach we introduced this past year is the w Figure . These fFor full-featured editing capability, including offline support, we developed the WikiPathways plugin for PathVisio. The plugin allows you to use your WikiPathways username and password to query, import, and upload pathways to and from the site. Together with other curation-focused plugins, the WikiPathways plugin facilitates the curation of WikiPathways pathways. The ‘Editing Tour’ video includes the latest updates to the WikiPathways plugin for PathVisio and the Quick Edit feature .A key aspect of modeling pathways is assigning standardized identifiers to its components. This allows molecules, for example, to not only be generally recognizable, but also to be associated and visually overlaid with data. In addition to molecules and pathways, we recently added support for labeling interactions and complexes with standard identifiers as well.http://www.wikipathways.org/index.php/How_to_cite_WikiPathways). In turn, each pathway has its own bibliography. Pathways from WikiPathways can thus be used both as figures in papers and as references, like collaboratively written review articles. Every article that properly cites WikiPathways is automatically added to our weekly Tumblr feed (http://www.wikipathways.tumblr.com). We also established automatic distribution channels to diverse third-party resources where researchers might find pathways associated with Entrez Gene records  and all subsequent authors in order of the number of edits made. For every edit, a version of the pathway is stored indefinitely as a uniquely citable reference (osystems ), other osystems ), microRosystems ), and drosystems ).http://www.wikipathways.org/index.php/Template:User_ORCID). The template adds an ORCID badge and category to their WikiPathways profile page, enabling programmatic access to their contributions as well.Authors now have the option to link their WikiPathways account with their Open Researcher and Contributor ID (ORCID) , using aWikiPathways is a community curated database and we work closely with different research communities to support them in modeling their pathways of interest. Additionally, we provide the capability for communities to host their own portal sites to highlight any subset of pathways relevant to their community. Newer portals at WikiPathways include ones for WormBase, exRNA research, human disease models and plant biology . We continfobox feature. We will also be working on an interactive export option with preset graphics, or skins, to produce professional and compelling figures for publications and presentations. In parallel with these enhancements, we will be working with publishers to provide interactive views of pathway diagrams in addition to static figures, as supplemental material embedded into their online article platforms (see Supplementary Data: Interactive PathwayWidget).In the coming months, we will be adding new tabs to the Quick Edit feature to support all common editing activities. In parallel, we will build additional crowdsourced—and even gamified—options for compiling new pathway information, e.g. by parsing pathway figures from articles. In an effort to enhance the use of WikiPathways as a publishing tool, we will add sharing and citation links to the coming WikiPathways content is most effective when utilized as a collection. We provide multiple standardized formats and methods to programmatically query and model our pathway content for downstream analysis and visualization. With the latest updates, content can be pulled into various scripting environments, pipelines, standalone applications, and a myriad of web-centric workflows and interactive displays.http://www.wikipathways.org/index.php/Download_Pathways. We also routinely convert our content into the community-driven pathway exchange format, BioPAX , providing linked data support, while maintaining the lightweight and flexible qualities of the JSON format.WikiPathways maintains its own web service for programmatic access, promoting the integration of biological pathways in existing pipelines and applications . The webhttp://sparql.wikipathways.org or the Open PHACTS API (http://www.wikipathways.org/index.php/Help:WikiPathways_RDF).We also publish WikiPathways content on the semantic web, where it can be queried through SPARQL at ACTS API . IntegraACTS API . On the Pathways contain a lot of information about biomolecules and their interactions that provides valuable context and fodder for data analysis and visualization. We provide several different ways to programmatically access and use WikiPathways content. The WikiPathways plugin for PathVisio and the WikiPathways app for Cytoscape, as previously described, are examples of powerful standalone applications—each with their own extensible and scriptable potential—that have direct access to WikiPathways and perform a wide variety of analytical and visualization functions. The next two examples are targeted to programmers seeking to integrate pathway visualizations into their web projects or to integrate pathway analysis into their automated workflows.http://www.wikipathways.org/index.php/PathwayWidget). By embedding a short HTML code snippet, a specified pathway will be displayed in an interactive pathway viewer from WikiPathways, allowing pan and zoom, search and annotation panel popups. The PathwayWidget syntax also supports specifying a list of pathway elements and a corresponding list of colors to highlight, for example, specific genes and color them based on data values or any other criteria relevant to the site (see Supplementary Data: Interactive PathwayWidget).Custom, interactive pathway visualizations can be integrated into websites by means of our PathwayWidget (A new programmatic interface for PathVisio called PathVisioRPC was recently introduced . It provWe will continue to develop new features for the standalone PathVisio and Cytoscape applications to take advantage of the expanding content at WikiPathways. However, we are also dedicated to providing web-centric resources, widgets and tools for other developers to leverage and incorporate in their own projects. We plan to couple the PathwayWidget with our web services to provide simplified, single-purpose versions of the widget to make it even easier, for example, to deploy interactive pathway views starting with arbitrary gene names, ontology terms or keywords. Our linked data formats introduced here will also see further development as we identify new and meaningful semantics to associate with our biological pathway models.The WikiPathways project is essentially comprised of an open source development team, an open access, collaborative platform and, most importantly, a diverse community of contributors. The updates presented here demonstrate the success of our approach so far and the active support for researchers, curators and tool developers. We welcome all potential collaborators interested in collecting, curating and using pathway content, as well as in the development of new software tools and services. Overall, we report tremendous growth in content and features since the last update , and a chttp://www.wikipathways.orgWikiPathways: http://www.wikipathways.org/index.php/Download_PathwaysDownload: Supplementary Data are available at NAR online.


The identification of genes responsible for causing cancers from gene expression data has had varied success. Often the genes identified depend on the methods used for detecting expression patterns, or on the ways that the data had been normalized and filtered. The use of gene set enrichment analysis is one way to introduce biological information in order to improve the detection of differentially expressed genes and pathways. In this paper we show that the use of network models while still subject to the problems of normalization is a more robust method for detecting pathways that are differentially overrepresented in lung cancer data. Such differences may provide opportunities for novel therapeutics. In addition, we present evidence that non-small cell lung carcinoma is not a series of homogeneous diseases; rather that there is a heterogeny within the genotype which defies phenotype classification. This diversity helps to explain the lack of progress in developing therapies against non-small cell carcinoma and suggests that drug development may consider multiple pathways as treatment targets. In comparison to other cancers, over the last 30 years there has been little progress in the median survival time of lung cancer patients . The medMicroarrays can contribute to the treatment and therapy of NSCLC in two important ways. The first is by allowing earlier detection of the tumors through the use of biomarkers . The esset al. [One of the past difficulties in identifying genes that are important in determining cancer progression (“oncogenes”) has been the large number of differentially expressed genes. Microarray data is often noisy, and there is almost always a lack of technical replicates ,9,10 Oneet al. . The datet al. . This iset al. ,14. The et al. ,16. It wet al. . An alteIn this paper seven publicly available datasets for NSCLC are reanalyzed and a network based analysis is carried out using the pathways from the Reactome database .A search for NSCLC and organism Homo sapiens, in the ArrayExpress database yielded 223 datasets . The seaAll of the analysis was carried out using R version 3.1.0 and Bioconductor version 2.14, using R‑studio version 0.98 as a graphical user interface ,26. Qualp-value for the differential expression shows a long almost horizontal region before a sharp rise and then a steady linear increase  of larger datasets, and the much smaller E‑GEOD-6044 has a particularly small number of variable genes. However, the ribution  also sugp-value for the probability that the observed distribution of expression occurred by chance. The cut-off value for the pathway p-values was chosen at 0.05. An example of the effect of normalization on the pathway analysis is given in A pathway enrichment analysis of the differentially expressed genes was carried out against the Reactome pathway database using the ReactomePA module. Like gene set enrichment analysis this produces a list of key terms, in this case pathways, and a In this case there are a large number of additional pathways after farms normalization, but the pathways identified by the rma normalized analysis are conserved in the farms normalized results. It is possible that in this case farms normalization produces a dataset that is more sensitive to changes in pathway regulation, but it is also possible that a number of these pathways will be false positives.Two of the datasets E-GEOD-18842 and E-GEOD-19188 compare normal lung tissue to NSCLC or tumor tissue, respectively. Of these E-GEOD-18842 has a much better defined list of differentially expressed genes. Again this is going to partly be a result of the larger size of the dataset as this results in a much lower standard error in the expression levels of the comparison groups and an increase in power of the experiment, but noise is also a factor as the final list contains almost all of the genes on the array. A cut-off had to be used for E-GEOD-19188 and only the top 2000 genes were used for pathway analysis.From the E-GEOD-18842 the three different normalization methods produce a very similar pathway analysis. The rma and farms normalized data give identical pathway analysis and identify 16 significant pathways . The res2/CO2 exchange in erythrocytes, hemostasis, erythrocytes take up oxygen and release carbon dioxide, erythrocytes take up carbon dioxide and release oxygen). Associations between hemostasis, platelets and cancer were proposed in the 1980s, but these associations have only recently become the focus of renewed attention for pathway analysis [The results from E-GEOD-19188 are less definitive . From thanalysis ,30,31.The overlapping pathways between the E-GEOD-18842 and E-GEOD-19188 contain many of the usual suspects, such as the control of mitosis and control of the cell cycle including checkpoints. The E2F mediated regulation of DNA replication has also been identified as important in a number of different cancers, as this transcription factor regulates cyclin E . Two othThree datasets have a comparison between normal lung tissue and adenocarcinoma: E-GEOD-6044, E-GEOD-40725 and E-GEOD-43458. Of these the E-GEOD-43458 dataset is the largest and it is the only one that was specifically targeted to adenocarcinoma.+; L1CAM interactions; phase 1-functionalisation of compounds; membrane trafficking and extracellular matrix organization. It is noteworthy that none of these pathways were amongst those identified as differences between the normal and tumor tissues and that they are not involved in cell cycle or DNA regulation. Phase 1-functionalisation is the processing and export of proteins from the endoplasmic reticulum and the reactions involve cytochrome P-450. Sugar transporters have previously been identified as having a role in cancer [The smallest of these datasets is E-GEOD-6044. This array also used the hgfocus gene array and not the more complete hgu133a array. From the rma and farms normalized data less than 10 pathways were identified and these were: biological oxidation; axon guidance; hemostasis; platelet activation, signaling and aggregation; platelet degranulation; translation of GLUT4 to the plasma membrane; response to elevated platelet cytosolic Ca2n cancer . The L1 n cancer ,39. AfteThe E-GEOD-40275 dataset uses an exon array and so it can only be normalized using rma. The results of pathway analysis are very different from those for the E-GEOD-6044 dataset and the normal‑tumor analysis. These include a number of cancer specific pathways involving mutants and also a group of pathways involved in mRNA regulation and processing .The E-GEOD-43458 dataset could only be normalized with rma and farms as gcrma is incompatible with the array type used. The results from the pathway analysis have been shown in Two datasets are available to compare the transcriptomes of normal lung tissue and squamous cell carcinoma, E-GEOD-6044 and E-GEOD-40275. The E-GEOD-6044 data shows a similar profile to the normal-adenocarcinoma pathway profile for E-GEOD-40275 including the TCF dependent signaling in response to Wnt . There aThe E-GEOD-40275 normal-squamous cell carcinoma comparison identifies platelet associated pathways as well as RNA processing as the most important groups of differentiated pathways . These oFrom the previous analysis it is apparent that there is considerable overlap in the pathways that are differentially expressed in adenocarcinoma and squamous cell carcinoma. There are three datasets that allow direct comparison between the transcriptomes of the two NSCLC sub-types, E-GEOD-6044, E-GEOD-40275 and E-GEOD-50081 . The E-GEOD-6044 dataset has many fewer differentially expressed genes . This produces a much more extensive list of pathways, with novel pathways involved in extra-cellular processes, such as extracellular matrix organization, cell-cell communication, cell-cell junction organization and immune system. The data however is very noisy and there is poor overlap between the results from pathway analysis after the three different normalization methods and so these results should be considered with some caution.Finally the E-GEOD-40275 also shows a small number of differentially expressed pathways. These agree with the extra-cellular pathways found in the E-GEOD-6044 results lending support to those findings, and also include new pathways involved in fatty acid triacylglycerol, and ketone body metabolism, as well as the metabolism of lipids and lipoproteins. There are also two pathways highlighted that are involved in the regulation of the peroxisome proliferator-activated receptor alpha (PPARA). There is some evidence that PPARA is associated with breast cancer but this is the first time it has been identified as involved in lung cancer although the gamma receptor has previously been identified and playing a role in inhibiting lung cancer cell growth ,48.The analysis shows that there is a clear genetic demarcation between healthy lung tissue and NSCLC which survives different datasets and methods of normalization. The results for the different NSCLC phenotypes (adenocarcinoma and squamous cell carcinoma) are much less clear. A comparison of the pathways which are overrepresented between the NSCLC phenotypes with normal lung tissue provides a clear core of altered cellular functions. However, when comparing the NSCLC phenotypes themselves, it is not possible to draw a clear and consistent delineation between their transcriptomes across all of the datasets. Most of the common pathway differences between the NSCLC phenotypes concern extra-cellular processes, and these are more likely to be associated with metastasis than phenotype differentiation. A possible distinguishing feature is the lipid biosynthetic pathways identified in the E-GEOD-40275 dataset, but these pathways were not reproduced in the other datasets. The small numbers of pathways that seem to distinguish adenocarcinoma and squamous cell carcinoma suggests that the histological differences might not be as strongly represented at the genetic level. This result agrees with a previous study that has shown that there are distinct sub-groups within adenocarcinoma and a second study that suggested that there might be overlap in gene expression between the adenocarcinoma and squamous cell carcinoma groups, such that alternative sub-classes to those identified by histology might exist at the genetic level ,49.Many of the pathways that have been identified as targets in this study have previously been associated with cancer, either in lung cancer or in other tissues. These include the regulation of cell cycle and mitosis. Regulation of rRNA expression by SIRT1 and of histone methylation by PRC2 as well as the WNT signaling pathways are specific targets that distinguish healthy and tumor cells. The Amyloid pathway is an interesting additional target, because of its association with other age related diseases.Future experiments need to focus on including a larger number of participants in order to sample disease diversity more effectively. Current studies have also not considered experimental repeats to reduce the effects of between sample variations. These are important when between subject variability is high because a component of this might be due to problems with experimental precision. In addition to new experimental results a complete meta-analysis of all of the available NSCLC data would improve the statistical power and reliability in predicting differentially expressed genes and pathways. This analysis would include the large datasets that are available from the Cancer Genome Atlas, which contain data for another 1000 cases. However, a serious challenge in incorporating these data is that they come from a number of alternative platforms and so combining them is difficult.


Age-related macular degeneration (AMD) is a major cause of blindness in older people and is caused by loss of the central region of the retinal pigment epithelium (RPE). Conventional methods of gene expression analysis have yielded important insights into AMD pathogenesis, but the precise molecular pathway alterations are still poorly understood. Therefore we developed a new software program, “AMD Medicine”, and discovered differential pathway activation profiles in samples of human RPE/choroid from AMD patients and controls. We identified 29 pathways in RPE-choroid AMD phenotypes: 27 pathways were activated in AMD compared to controls, and 2 pathways were activated in controls compared to AMD. In AMD, we identified a graded activation of pathways related to wound response, complement cascade, and cell survival. Also, there was downregulation of two pathways responsible for apoptosis. Furthermore, significant activation of pro-mitotic pathways is consistent with dedifferentiation and cell proliferation events, which occur early in the pathogenesis of AMD. Significantly, we discovered new global pathway activation signatures of AMD involved in the cell-based inflammatory response: IL-2, STAT3, and ERK. The ultimate aim of our research is to achieve a better understanding of signaling pathways involved in AMD pathology, which will eventually lead to better treatments. Age-related macular degeneration (AMD) is a major cause of blindness in older people in developed countries, where increases in average age and falling death rates are contributing to the prevalence of the disease . As withBoth types of AMD pathology start with the formation of insoluble aggregates, drusen, which forms in the extracellular matrix between Brush's membrane and RPE. Drusen consists of complement components, lipo-proteins, cell debris, oxysterols, oxidized phospholipids and RNA. Accumulation and extension of this drusen leads to nutrient and oxygen starvation of retinal and RPE cells. As a result, retinal cells degenerate in the “dry” form of AMD; but sometimes the RPE cells stimulate angiogenic factors  which act on the choriocapillary network above the Bruch's membrane and stimulate proliferation of new blood vessels in the areas where blood vessels are not normally present. These newly-formed blood vessels cause disruption of RPE cell integrity, causing the “wet” form of AMD.LIPC) (rs493258) is linked with AMD [Conventional methods of genetic analysis have yielded important insights into AMD , 10. Spewith AMD . An explwith AMD . Polymorwith AMD , and 7-kwith AMD .Besides lipids, it has been discovered that the dys-regulation of complement pathways is further responsible for AMD pathology . IntriguLipid metabolic intermediates can also directly affect expression level of genes involved in the inflammatory response. For example recent studies demonstrated that oxidized phospholipids can change the expression of CCR-2, IL-6, IL-8, CD-36 and VEGF, and induce endoplasmic reticulum stress via various NFkB pathways . MoreoveTGF-beta receptors and mediate TGF-beta signaling. It has also been shown that activated complement components with oxidized sterols and 7-Ketocholesterol , can directly influence the expression of angiogenic and proangiogenic molecules [Choroidal neovascularization (CNV) also contributes to the severity of AMD pathology in the “wet” form, and is usually caused by VEGF and other pro-angiogenic molecules like TGFB and pro-inflammatory cytokines. CNV might be a way to compensate for a lack of oxygen and nutrients with new vessel formation . Plus tholecules . Depositolecules  and induolecules .To conclude, AMD originates from at least 3 groups of signaling pathways: lipid metabolism  – dysregulation of these pathways leads to accumulation of insoluble deposits; complement pathways  – aberrant activation of these pathways leads to drusen formation, their extension and subsequent photoreceptor degeneration; and angiogenesis pathways  – abnormal activation of these pathways leads to neovascularization, and new blood vessel outgrowth into the RPE area.much deeper and integrated understanding to fully decipher its true pathogenesis. To this aim we developed a new bioinformatic program called OncoFinder [Improved understanding of these signaling pathways in AMD has already paved the way for new therapeutic solutions -26, but coFinder , 28. BascoFinder , which acoFinder , 28.Signaling pathways regulate all major cellular events in health and disease -33, and As stated, pathway activation analysis was performed using “AMD Medicine” developed by Vision Genomics LLC using signaling pathway activation analysis algorithms (SPAS) ; we pickWe identified 29 differently activated pathways in RPE-choroid AMD phenotypes. We found a strong correlation between transcriptional profiles established in two independent experiments. 27 pathways were activated in the AMD state compared to the normal state, and 2 pathways were activated in the normal state compared to the AMD state. In AMD, we identified a graded activation of pathways related to wound response, complement cascade, and cell survival. Also, we noticed down-regulation of two pathways responsible for apoptosis. Significant activation of pro-mitotic pathways is consistent with dedifferentiation and cell proliferation events which occur early in the pathogenesis of AMD. Significantly, we discovered new global pathway activation signatures of AMD. These pathways could be considered to be involved in AMD pathogenesis and are involved in the cell-based inflammatory response, which is a hallmark of AMD etiology . Our data are in agreement with previously published reports on AMD pathology and provide new insights into molecular etiology of AMD and its future treatment strategies.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50195, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29801). For the first time, we applied the analysis of signaling pathway activation strengths to the transition from a normal state to the AMD state of RPE-Choroid tissues.Even though our understanding of the genetics of AMD has progressed continuously in the last decade, many basic questions still need to be answered. Therefore, in this study, we evaluated changes in functional pathway networks using changes in gene expression of single genes between AMD and normal eyes, as building blocks to reconstruct changes at the pathway level. We also attempt to integrate the pathways generated by our pathway activation analysis into coherent interacting subgroups, and also propose a hypothetical mechanism for how such interactions may occur. In our research, we analyzed in-depth previously published gene expression data publicly available through the NCBI GEO bioinformatics repository website , but not in GSE50195. However, according to the authors of the GSE29801 data set, there is detectable upregulation of apoptosis-related transcripts in the geographic atrophy (GA) and CNV types of AMD . This diIn addition to pro–angiogenic, pro-inflammatory and apoptotic pathways which were previously known to play important roles in AMD, we found several new pathways activated: Wnt, mTor, Glucocorticoid, cAMP, estrogen and androgen receptor signaling pathways. While biological roles of activation of these pathways needs to be further clarified, we can consider them to belong to a newAMD-specific pathway activation signature.Since AMD has several well-known genetic variations directly associated with high risk of AMD development, we investigated the genetic background available in one of the datasets (GSE50195). We used complement factor H genotype (rs1061170 SNP)  informatIn order to validate our results, we analyzed two independent data sets obtained in two independent experiments. High variability of PAS values could be attributed to postmortem RNA degradation; despite all quality control measures it is very difficult to preserve all samples in the same way. An additional source of variability is different or misinterpreted AMD stage. There is still an open question regarding AMD disease etiology: is it a single multi symptom disease, or several diseases under the AMD manifestation umbrella? Such discrepancies could introduce a huge source of variability. Mainly to address these issues, we used data from two independent microarray experiments. Despite high variability of PAS values for almost all pathways for each data set, box plots perfectly illustrate the strong similarity of PAS values between both data sets Figure . TranscrIn this study, we discovered new pathways activated during AMD disease: Wnt, mTor, glucocorticoid, cAMP, estrogen and androgen receptor signaling pathways. Further studies will be required to figure out which cell populations are responsible for which individual pathway activation changes. Better understanding of the intimate interplay of signaling pathway activation in normal aging and AMD may uncover new diagnostic and treatment options -55 and sOur results represent an advance toward better understanding of systems-level changes in pathway activation responsible for transition of normal tissues to the AMD phenotype Figure . Also weTaking together already established facts and our new findings, we demonstrated that AMD is a single origin disease with multiple phenotypes, and which has an aging related lipid metabolism dysregulation as a starting point, followed by simultaneous aberrant activation of immune and inflammatory signaling pathways leading to a state of chronic local inflammation, followed by degeneration and neovascularization of affected tissues. The key closely-related questions are: what causes the first pathological changes in AMD, and does activation of signaling pathways observed in the current research represent a consequence or cause of AMD?Our approach of transforming gene expression data into signaling pathway activation strength profiles reveals many attractive targets for AMD therapeutics and diagnostics Figure . Along whttp://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50195, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29801) database in order to examine pathways that are affected by AMD. First dataset GSE50195 contains the information on 16 samples from RPE-Choroid tissues profiled using Affymetrix Human Exon ST 1.0 arrays: 9 samples from AMD affected tissues and 7 from normal tissues [In this study, we utilized microarray gene expression data taken from two data sets found in NCBI GEO  was used to determine structures of intracellular pathways, which were used for OncoFinder as described previously [case-to-normal ratio, CNRn, is the ratio of expression levels for a gene n in the sample under investigation to the same average value for the control group of samples. The Boolean flag of BTIF  equals to zero when the CNR value has passed simultaneously the two criteria that demark the significantly perturbed expression level from essentially normal: first, the hybridization signals for the sample lie within the tolerance interval, where p-value>0.05, and second, the value of CNR differs from 1considerably, CNR 0.66 or CNR 1.5. The discrete value of ARR (activator / repressor role) equals to thefollowing numbers: −1, when the gene/protein n is a repressor of pathway excitation; 1, if the gene/protein n is anactivator of pathway excitation; 0, when the gene/protein n can be both an activator and a repressor of thepathway; 0.5 and −0.5, respectively, if the gene/protein n is rather an activator or repressor of the signalingpathway p, respectively. Results for the 273 pathways were obtained for each sample (listed in For the functional annotation of the primary microarray gene expression data, we applied our original algorithm termed OncoFinder . It enabeviously , 28. Usieviously . Values eviously , optimiz