Myc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitt’s lymphoma and ovarian cancer.http://bicresources.jcbose.ac.in/ssaha4/mycbase) is a collection of experimentally supported functional sites in Myc that can influence the biological cellular processes. The functional sites were compiled according to their role which includes mutation, methylation pattern, post-translational modifications, protein-protein interactions (PPIs), and DNA interactions. In addition, biochemical properties of Myc are also compiled, which includes metabolism/pathway, protein abundance, and modulators of protein-protein interactions. The OMICS data related to Myc- like gene expression, proteomics expression using mass-spectrometry and miRNAs targeting Myc were also compiled in MYCbase. The mutation and pathway data from the MYCbase were analyzed to look at the patterns and distributions across different diseases. There were few proteins/genes found common in Myc-protein interactions and Myc-DNA binding, and these can play a significant role in transcriptional feedback loops.MYCbase (In this report, we present a comprehensive integration of relevant information regarding Myc in the form of MYCbase. The data compiled in MYCbase provides a reliable data resource for functional sites at the residue level and biochemical properties of Myc in various cancers. Myc is a multifunctional protein and is believed to regulate expression of 15% of all genes through binding on Enhancer Box sequences (E-boxes) CACGTG and recruiting histone acetyltransferases (HATs) . Since iWith the advent of new technologies such as high throughput proteomics and genomics and development of epigenetics, much new information about Myc has been generated. The methylation pattern of the c-Myc gene has also been linked to colorectal cancer  which giOver the last few decades, a substantial number of publications and conferences have been dedicated to specific key essential genes including p53 and Myc. Although an extensive amount of research is being done in the field of Myc biology, a gap remains in understanding and targeting the deregulated Myc in the disease state. The primary concern with anti-Myc therapy lies in the fact that it continues to be essential for normal proliferating tissues. Myc is also involved in a vast network of protein-protein interactions (PPIs) as well as protein-DNA interactions and modulates many signalling pathways which make it even harder to inhibit without causing serious side effects. A deeper understanding of these aspects of Myc may guide researchers into effectively targeting Myc for anti-cancer therapy. The foremost challenge lies in collecting the extensive amount of information from the vast reserve of scientific literature and secondary databases. Even though existing databases such as UniProt  and GeneIn order to get an overall idea about the functional sites of Myc along with its properties, an attempt has been made to construct a database named MYCbase. MYCbase can be seen as a repository of all aspects of Myc relevant to its biochemical characterization including mutations, PPIs, small chemical drug molecule targeting, metabolic pathways, methylation pattern and others. We also focus on various analysis and conclusions that can be derived from this database which may help the scientific community in designing and troubleshooting their experiments in the field of Myc biology. To the best of our knowledge, there is no existing database, which compiles this variety and quantity of information regarding Myc, with relevance to cancer, studies under a single platform.MYCbase was developed as a relational database using the Apache HTTP 2.2.15 web server and MySQL 5.1.69. The PHP 5.3.3, HTML, JavaScript and CSS were used at the front-end of the database. The PHP-based web interfaces were designed to execute the SQL queries dynamically.MYCbase comprises eleven categories of information about Myc protein and gene, collected from different resources as shown in Fig. Mutation: Around 446 different abstracts were downloaded from PubMed till November 2015 by using the keywords 'c-myc gene' and 'c-myc gene mutations'. All relevant publications were sorted to extract information on c-myc gene mutations. The manually extracted information was placed in categories such as the location of mutation, residue mutated, type of mutation, cell line or sample used, methods, the disease affected, follow-up method, PubMed ID, and frequency of mutations. The functional sites mutated at the residue level are also linked to the PTM table so that any modifications at the site of mutations can also be seen easily.Methylation pattern: PubMed was searched for (Myc[Title]) AND (Methylation[Title]) from which 42 relevant hits were obtained till April 2016. Only 29 publications had relevant information about c-myc methylation with disease states. These findings were arranged under the region explored, the nucleotide sequence, methylation status, cell line used, an outcome of methylation and relation to any disease.Pathways/Metabolism: PubMed was searched for Myc in connection with metabolism and pathway which gave 126 till November 2015 results with information pertinent to the database. All papers were thoroughly examined and results were grouped under pathway, enzyme, regulatory action, comments, cell line and PubMed ID.Protein-Protein Interactions (PPIs): The already existing database for PPIs Biological General Repository of Interaction Datasets (BioGRID) . We. We33]. We analyzed data regarding breast cancer in MYCbase by performing a simple keyword search with “Breast Cancer” in all options on the home page. The results we received, summarized the role of Myc in breast cancer. Gene amplification, including duplication, is the most important type of mutation that has been reported in breast cancer (in 7 out of 11 cases present in MYCbase). This is consistent with the knowledge that MYC amplification plays an important role in breast cancer development, progression and also associated with poor outcome , 36. BreThe recent development in proteomic, transcriptomic and whole genome sequencing methodologies have led to the generation of extensive amounts of data and research articles related to c-Myc. MYCbase was developed by compiling the literature and existing secondary databases. MYCbase contains information of various functional sites at the residue level and biochemical aspects at a single platform, using which researchers can gain complete knowledge of the role of Myc in disease biology. MYCbase will be updated regularly to incorporate any updates in the secondary databases as well as in scientific literature.Myc is a master regulator and can regulate of over 1500 coding and non-coding genes that include approximately 48 validated transcription factors , 40. ManThe data analysis of MYCbase has enabled us in drawing some conclusions on metabolic pathways and mutations at the residue level. First, Myc plays a major role in nucleotide biosynthesis which is essential for proliferating cells involved in tumorigenesis. A possible mechanism by which Myc regulates nucleotide biosynthesis is postulated to be through regulation of phosphoribosyl-pyrophosphate synthetase 2 (PRPS2) which results in promotion of increased nucleotide biosynthesis . Second,MYCbase is a tertiary database compiling various aspects of Myc relevant to cancer biology. Information present here has been accumulated from peer-reviewed literature and secondary databases. It is an open access database and information present can be downloaded freely by the users. In addition to the development of MYCbase, analysis of compiled data provided valuable insights regarding the role of Myc in cancer. An example of a case study using breast cancer incorporating the application of MYCbase has also been described.http://bicresources.jcbose.ac.in/ssaha4/mycbase. These data are freely available without restrictions for use by academics.Database homepage:


Biological pathways are subsets of the complex biomolecular wiring that occur in living cells. They are usually rationalized and depicted in cartoon maps or charts to show them in a friendly visible way. Despite these efforts to present biological pathways, the current progress of bioinformatics indicates that translation of pathways in networks can be a very useful approach to achieve a computer-based view of the complex processes and interactions that occurr in a living system.Path2enet that provides a translation of biological pathways in protein networks integrating several layers of information about the biomolecular nodes in a multiplex view. Path2enet is an R package that reads the relations and links between proteins stored in a comprehensive database of biological pathways, KEGG , and integrates them with expression data from various resources and with data on protein-protein physical interactions. Path2enet tool uses the expression data to determine if a given protein in a network  is active (ON) or inactive (OFF) in a specific cellular context or sample type. In this way, Path2enet reduces the complexity of the networks and reveals the proteins that are active (expressed) under specific conditions. As a proof of concept, this work presents a practical “case of use” generating the pathway-expression-networks corresponding to the NOTCH Signaling Pathway in human B- and T-lymphocytes. This case is produced by the analysis and integration in Path2enet of an experimental dataset of genome-wide expression microarrays produced with these cell types .We have developed a bioinformatic tool called Path2enet is an open source and open access tool that allows the construction of pathway-expression-networks, reading and integrating the information from biological pathways, protein interactions and gene expression cell specific data. The development of this type of tools aims to provide a more integrative and global view of the links and associations that exist between the proteins working in specific cellular systems.The online version of this article (doi:10.1186/s12864-016-3066-7) contains supplementary material, which is available to authorized users. Path2enet.Large-scale “omic” experiments that capture the physical associations and links between genes, proteins and other molecular components within the cells are producing extensive data on biomolecular interactions which are stored in new generation databases and resources . The humPath2enet is an R package that reads the relations and links between proteins stored in the major and highly curated pathways database KEGG (Kyoto Encyclopedia of Genes and Genomes) [Path2enet tool uses the expression data to determine if a given node (protein) in a generated network is active (ON) or inactive (OFF) in a specific cellular context, cell type or condition. The transformation of pathways into comprehensive networks plus the mapping of active –i.e., expressed– nodes, can help researchers to integrate different levels of molecular information, placing it in a relational specific context. In addition, the integration of protein-protein interaction data within a pathway-network view can help to find relevant relations and critical nodes in the processes studied. As a practical example, we applied Path2enet tool to the analysis of the NOTCH Signaling Pathway in human lymphocytes in order to uncover the specific differences between B cells (CD19+) and T cells (CD4+ or CD8+).Genomes) , 4 and iGenomes) , 6. PathPath2enet is an R package that uses and integrates several databases and resources to generate pathway-derived networks in an expression specific context. These resources are the following: (A) pathways data, the tool collects the pathways information from KEGG, taking the KGML-files and generating a MySQL database from such files  [B) protein-protein interaction data, the tool also uses a dataset of human protein-protein physical interactions (PPIs) from the dataserver APID [C) gene expression data, the tool integrates four types of expression information. These are: (C1) ESTs (expressed sequence tags) from the Unigene database that includes 18,880 gene/protein entries detected in 51 human tissues (http://www.ncbi.nlm.nih.gov/unigene); (C2) Barcode gene expression from high-density oligonucleotide microarrays that store 17,268 gene/protein entries detected in 195 tissues and cell lines [C3) RNA-Seq data of the Human Body Map 2.0 (ArrayExpress Experiment E-MTAB-513) that stores FPKM expression data of 18,744 gene/protein entries in 16 human tissues  RNA-Seq data from the Human Protein Atlas which stores the FPKM expression data of 19,078 gene/protein entries of 33 human tissues (http://www.proteinatlas.org) [r human) , 4; (B) ver APID , which a Biomart ; and (C4las.org) .Path2enet uses the gene expression Barcode algorithm with the R package fRMA [Path2enet his own expression ON/OFF thresholds, for example using experimental RNA-Seq data. However, the identification of such thresholds is not trivial and the Barcode algorithm is most efficient in this task.Beside the pre-processed expression datasets provided in several of the integrated resources, age fRMA  to evaluPath2enet uses Brainarray [Gatexplorer [Ensembl gene identifiers.For the ID mapping and integration, ainarray  or Gatexexplorer  within RPath2enet uses as key identifiers (IDs) of the genes/proteins the entry IDs from UniProtKB database [Ensembl gene identifiers in the datasets are annotated to the UniProt entry IDs using the mapping tables that UniProt provides. Path2enet also uses the R package RMySQL [MySQL databases using R programming. Finally, in order to build the networks, Path2enet uses the R package igraph [To achieve a correct unification of databases and resources, database . Therefoe RMySQL  to builde igraph , which iPath2enet to analyze the NOTCH Signaling Pathway in human lymphocytes, detecting the way in which this pathway is expressed in these cells and also finding the specific differences in activated genes/proteins between “naive” B cells (B cells that have not been exposed to an antigen) and T cells. To perform this analysis we downloaded and normalized an expression dataset that included 163 human samples. These samples were genome-wide expression microarrays of platform Human Genome U133 Plus 2.0 from Affymetrix (GEO reference: GPL570). The samples corresponded to naive B cells (CD19+), 32 microarrays; T cells (CD4+), 96 microarrays; and T cells (CD8+), 35 microarrays. The specific. CEL files  that correspond to these samples are indicated in Additional file As a practical example, we applied Path2enet has been developed in R . In this way, a full operative R package has been built and it is available at http://bioinfow.dep.usal.es/path2enet. The software will be uploaded to the R CRAN package repository (CRAN.R-project.org) once this article is published. An R vignette  from different tissues and cell types. Path2enet uses these datasets to build a network view of biological pathways in an expression-specific context. The tool is capable of identifying the genes/proteins that are ON in specific samples appliying the Barcode algorithm, and allows the use of specific experimental expression data to present focused views of the human pathways map as specific biomolecular networks.Path2enet, the “nodes” correspond to the proteins included in the queried pathway plus the information about the active- or inactive-state of such proteins (derived from the expression data of the cell-types or the tissues studied in each case). The “edges” of the network correspond to the links or associations between the biomolecular entities (derived from the information included in the pathways). These links can be activation, inhibition, expression, phosphorylation, etc. In order to facilitate further analysis of the networks, the edges generated by Path2enet are taken as undirected.In the networks built using Path2enet can generate two different types of networks. The first is the “local” network which strictly includes the nodes of the canonical pathway selected from KEGG. For example, in the case of the NOTCH Signaling Pathway (KEGG ID: hsa04330) . Thus, Path2enet generates a network where each node is a protein and the edges are colored according to the type of association reported in the pathway . In this way, Path2enet is not restricted to predefined pathways since it can create large networks blending multiple layers of biological information.Considering the coverage over the map of human pathways, 30) Fig.  the “locway Fig. . The secPath2enet, calculations of the network topological parameters  can be performed, because the tool generates igraph objects [Path2enet provides ways to identify hubs and clusters in the network.Once a network is built with  objects , that caPath2enet to generate expression networks of the NOTCH signaling pathway in three types of human cells: B cells (CD 19+) and T cells (CD 4+ and CD8+)  provides KGML files for each biological pathway on its website. For example, in the case of the human NOTCH signaling pathway (KEGG ID reference: hsa04330) the KGML file can be downloaded freely as “hsa04330.xml”. The link for this file is: http://www.kegg.jp/kegg-bin/download?entry=hsa04330&format=kgml. In this way, any specific pathway is accessible via its KGML file in the KEGG website and Path2enet R package provides functions to download these files and create a MySQL database derived from the KGMLs (as explained in the R vignette included with Path2enet). Moreover, to facilitate the use of the pathway KMGL files within the application Path2enet, we also provided an SQL dump file (“Path2enet_KeggSQL.sql”) generated with all the KMGL files of Homo sapiens . This allows the creation of the necessary SQL database within the user’s computer to query for specific pathways and to use the other functions of Path2enet. This database resource is not just a compendium of KMGL files from KEGG given that it provides some important added values: (i) it includes a mapping of all the gene and protein identifiers (IDs) from KEGG to the IDs of UniProtKB (used as the reference protein database in Path2enet); (ii) it includes a relational SQL structure, based on the extracted data from the pathways, that allocates such information in two principal indexed tables: one describing the pair-wise links or relations between protein pairs, and another one describing the characteristics of each singular protein.KEGG pathways database  to SBML, BioPAX, SIF, SBGN, SBML-qual, GML, GraphML and LaTeX. Moreover, in Bioconductor (https://www.bioconductor.org/) there are packages to parse, modify and visualize BioPAX data, like rBiopaxParser [PaxtoolsR [With respect to the use of other formats, other than XML and KGML, anslator , an easyaxParser  or PaxtoaxtoolsR . At the Path2enet produces pathway-expression-networks reading and integrating high quality pathway data, protein interaction data and expression cell specific data. The development of this type of tools can be very useful to achieve a more integrative and global view of the links and association between the proteins working in specific cellular systems. The tool is not restricted to predefined pathways since it can create large networks blending multiple layers of biological information. Moreover, the tool can use either pre-processed expression data from selected repositories or experimental expression data from RNA-Seq or microarrays.Path2enet to the analysis of the NOTCH signaling pathway in B cells and T cells. We showed that the expression networks based on a large microarray data set of these samples are different for each cell type, modulating the original general view of the canonical pathway provided by KEGG. Moreover, the observed differences have clear biological meaning, as demonstrated, for example, when only 2 out of the 4 NOTCH paralog proteins  were expressed in B cells and T cells. Thus, a clear signal in all lymphocytes was observed for NOTCH2; while NOTCH1 was also detected in B cells CD19+ and in T cells CD4+. We also found that key regulators like DTX1 and HES1 are strongly expressed in B cells and less expressed, or not present, in T cells. All these results give support to the the value of the networks that Path2enet generates that are cell-type and context specific. In conclusion, users have the possibility to combine several pathways and include protein-protein interaction data to find key players in a specific biological context either for normal or for pathological samples.In this study we applied


Six subjects were anovulatory PCOS women diagnosed by Rotterdam criteria and six ovulatory non-PCOS women matched for age and body mass index. UCB was collected at delivery of the placenta; the DNA was extracted and submitted to methylation analysis. A differential methylation picture of prevalent hypomethylation affecting 918 genes was detected. Of these, 595 genes (64.8%) carried single or multiple hypomethylated CpG dinucleotides and 323 genes (35.2%) single or multiple hypermethylated CpG dinucleotides. The Ingenuity Pathway Analysis (IPA) online platform enlisted 908 of the 918 input genes and clustered 794 of them into 21 gene networks. Key features of the primary networks scored by IPA included carbohydrate and lipid metabolism, neurotransmitter signaling, cardiovascular system development and function, glycosaminoglycan signaling regulation and control of amino acid biosynthesis. Central to the network activities were genes controlling hormonal regulation (ESR1), mitochondrial activity , and glucose metabolism (INS). Regulatory pathways such as G-protein coupled receptor signaling, inositol metabolism, and inflammatory response were also highlighted. These data suggested the existence of a putative “PCOS epigenomic superpathway” with three main components: glucotoxic, lipotoxic, and inflammatory. If our results are confirmed, they hint at an epigenetic at risk PCOS “signature” may thus exist that may be identifiable at birth. Additional studies are needed to confirm the results of this pilot study.Polycystic ovary syndrome (PCOS) affects 5–15% of women. PCOS is a heterogeneous disorder displaying endocrine, metabolic, and reproductive dysfunction and cardiovascular risk manifestations. Evidence of heritability exists, but only a portion of the genetic transmission has been identified by genome-wide association studies and linkage studies, suggesting epigenetic phenomena may play a role. Evidence implicates intrauterine influences in the genesis of PCOS. This was a pilot study that aimed at identifying an epigenetic PCOS reprogramming signature by profiling the methylation of the DNA extracted from umbilical cord blood (UCB) from 12 subjects undergoing  The Polycystic Ovary Syndrome (PCOS) is one of the most common human endocrine/reproductive/metabolic disorders; depending on the definition applied –3, PCOS The strong heritability of PCOS is supported by twin  and genevia an adverse intrauterine environment  the pre-pregnancy metabolic, endocrine, and vascular changes of the PCOS mother; compounded by (b) the prevailing physiologic changes of pregnancy; and (c) the acute pathologic dysfunctions  associated with pregnancy in PCOS patients. To test this hypothesis, we compared the global methylation patterns in umbilical cord blood (UCB) of neonates delivered from PCOS and non-PCOS women. Although the results of this pilot study must be considered preliminary, our data suggest the existence of a “PCOS epigenomic superpathway”—a PCOS epigenetic signature—involving 10 differentially methylated gene networks governing lipid, carbohydrate metabolism, and inflammation/immunologic systems.in vitro fertilization. Patients with irregular menstrual cycles and/or signs of hyperandrogenism had measurement of androgen levels and clinical evaluation of hirsutism through the Ferriman–Gallwey score. Twelve patients were recruited; six with PCOS and six without PCOS. The diagnosis of PCOS in study patients was based on the 2003 Rotterdam consensus criteria. Patients were diagnosed with PCOS by meeting two of the following three criteria: oligo/anovulation, hyperandrogenism , and sonographic evidence of polycystic ovaries. Clinical hyperandrogenism was defined clinically by the presence of hirsutism, alopecia, and/or acne, or subclinically by increased levels of serum testosterone and/or dehydroepiandrosterone sulfate. Polycystic ovaries were diagnosed by pelvic sonography according to the Rotterdam conference criteria. Patients who met Rotterdam criteria for PCOS were identified through a natural language processing query of the electronic medical record data base. They were approached in accordance with approved IRB protocols, and all signed informed consent for participation in the study. There were no exclusion criteria. The control group included ovulatory women matched for age and BMI without PCOS features and delivered a full-term singleton over the same time period  . Consenting subjects received a full infertility work-up including measurement of serum anti-Müllerian hormone (AMH), FSH, and LH concentrations and transvaginal ultrasound (performed on day 3 of menstruation), and were deemed appropriate candidates for Umbilical cord blood was collected at delivery of the placenta in Purple Top BD Vacutainers , thoroughly mixed and aliquoted in 2 ml cryovials , snap frozen in liquid nitrogen and stored at −80°C.The CXT 350 frozen sample aliquotter  was used to retrieve tissue subaliquots for downstream applications to prevent the thawing of the full UCB aliquots. DNA was extracted using the Maxwell 16 automated DNA/RNA extraction equipment  and quantified with Nanodrop spectrophotometer .DNA samples were subjected to methylation analysis using the Illumina Infinium HumanMethylation450 BeadChip Kit  at the University of Cincinnati Genomics, Epigenomics and Sequencing Core (GESC). Native DNA was sent from our labs to GESC. GESC personnel were blinded about the samples’ diagnosis. Samples were treated following the HumanMethylation450 BeadChip consolidated protocol. Briefly, native DNA was bisulfite treated using the EZ-96 DNA Methylation-Gold Kit according to the manufacturer’s protocol . Bisulfite-treated DNA was then hybridized to HumanMethylation450 BeadChips that were later scanned. The intensities of the images were extracted using the GenomeStudio (v.2011.1), “Methylation Module” (1.9.0) software , which normalizes within-sample data using different internal controls that are present on the HumanMethylation 450 BeadChip and internal background probes.p > 0.05) across all samples were excluded from analysis. For each CpG dinucleotide, we calculated a Beta-value representing the ratio of the methylated probe intensity and the overall intensity (sum of methylated and unmethylated probe intensities). Beta-values spans between 0 and 1, or 0 and 100%. Following a consolidated procedure, we transformed the Beta-values into M values using the following equation:Processed data were sent back to our lab for analysis. Probes with a low signal .For all genes carrying multiple differentially methylated CpG dinucleotides, the Additionally, because of the asymmetric distribution of sexes of the babies in the control and PCOS groups Table , in our http://www.ingenuity.com). The standard setup for network analysis provided by the IPA core analysis was employed with one exception; because of the confidence provided by the large amount of input genes, the molecules per network parameter was set at 140.Network and pathway analysis was conducted using the Ingenuity Pathway Analysis (IPA) engine for the analysis of “omics data” . Genes were fed to IPA complete of their differential methylation Only significant genes and pathways identified by employing the built-in right-tailed Fisher’s exact test were used for the network-specific canonical pathway analysis and constructing the PCOS “superpathway.”p-value < 0.001—hypermethylated CpG dinucleotides = 0.739; p-value < 0.001) .A total of 614 hypermethylated and 1,066 hypomethylated CpG dinucleotides were identified , and 786 of 1,066 hypomethylated CpG dinucleotides mapped in or near 606 genomic tracks banked in the same database .The remaining 405 of 614 hypermethylated CpG dinucleotides mapped in or near 335 genomic tracks banked in the UCSC genome browser . Because the vast majority of unique genes  resided in networks 1–10, we focused our analysis on these 10 networks .ESR1), metabolic rate control through the control of the mitochondrial activity , and glucose metabolism .Table ESR1 which interfaces with the Wnt pathway  Signaling and Mitochondrial Dysfunction  family.Network 2  was dominated by the G-Protein Coupled Receptor Signaling pathway that directly interfaces with the Insulin Receptor Signaling pathway and other signaling pathways involved in inflammatory and regulatory metabolic functions . These functions intersect with the activity of d-myo-inositol Biosynthesis, key pathways for phosphatidylinositol metabolism and neurotransmitter signaling .Network 3  highlights strong influence on lipid metabolism and neurotransmitter signaling. Network 3 included different forms of the SHH, a key gene for the embryo development programing that is tightly linked to cholesterol metabolism.Networks 4 and 5  feature nodes involved in cardiovascular system development and function. Specifically, Network 4 includes pathways related to inflammatory responses  with upstream analysis showing modification of activity of interferon alpha, a key pro-inflammatory cytokine. Network 5 is populated by pathways with important links to cardiovascular system development . The canonical pathway webs of both network 4 and 5 were linked to Type I and II diabetes pathways, and network 5 linked with hepatic functions. Network 5 canonical pathways also included the Androgen Signaling pathway. The IPA upstream regulator analysis also pointed at differential effects on the regulation of the peptidase TGFB1 as highlighted by the IPA upstream regulator analysis. Additionally, differentially regulated pathways showed a role for control of amino acid biosynthesis centered on glutamate signaling and operated by the Urea Cycle, Arginine Biosynthesis and Citrulline biosynthesis pathways . Of relevance is the significant scoring for the Thyroid Hormone Metabolism and the Estrogen Receptor Signaling pathways . Among other relevant pathways are those that link the activity of networks 6–10 with those of networks 1–5 , FXR/RXR (RBP4) and LXR/RXR (RBP4); amino acids, fatty acids and glycogen metabolism ; estrogen receptor activity (ESR1); mitochondrial dysfunction ; G-Protein Coupled Receptors; Retinol pathways; and the PI3K family . These pathways highlight involvement of networks 6–10 in embryonic growth regulation suggested by co-existence with developmental pathways such as NANOG, NOTCH, eNOS, nNOS and OCT4 .Networks 6–10  highlight the role of the glycosaminoglycan family of signaling carbohydrates regulating the activity of the transcription factor Due to the gender imbalance between the PCOS and control offspring Table  initial As in the females, several, albeit significantly fewer, inflammation related pathways were also hypomethylated in the male PCOS offspring , these data suggest a more pronounced effect on lipid metabolism and inflammation in female than in male PCOS offspring.Polycystic ovary syndrome is a heterogeneous syndrome characterized by progressive development of reproductive, metabolic, endocrine, and cardiovascular risk factor dysfunctions –7. AlthoHere, we showed differential DNA methylation in UCB from offspring of infertile women with PCOS compared to offspring of women without PCOS. These reveal unique epigenetic signatures which may play a role in the underlying systemic dysfunctions of PCOS, namely glucotoxicity, lipotoxicity and chronic systemic inflammation. Moreover, the data pinpoint the role of key non-reproductive organs that participate in the pathophysiology of the syndrome including among others the brain, liver, kidney, muscle, and pancreas.Ingenuity Pathway Analysis identified 10 networks which taken together suggest the existence of a putative PCOS “superpathway” linking key gene networks with major canonical biologic pathways Figure A. The puESR1 (network 1)  Figure . The upres PI3Ks , 19  (APP) gene (network 1). Elevated APP also activates the Jnk domain impairing mitochondrial metabolism and increasing oxidative stress rates   and the twork 1)  in the btwork 1)  and promtwork 1) . In addiss rates , 25, whiss rates . In the PAX6 gene (network 1). PAX6 is a transcription factor crucial for pancreatic beta-cell differentiation and survival. Elimination of PAX6 activity, an epigenetic result of uterine artery occlusion induces diabetes and a dramatic reduction in the number of beta cells producing insulin in the intrauterine growth restricted rodent model (Phosphatidylinositol phosphate lipids (PIPs) are second messengers regulated by PI3Ks and controlled by myo-inositol metabolism (network 3). In diabetics, depletion of intracellular myo-inositol , 28 has nt model . AlteratESR1, the main signatory initiating the glucotoxic PCOS signature, by altering PIPs, DHA and inositol biosynthesis, also leads to the second major metabolic feature of PCOS, lipotoxicity , FXR/RXR, and LXR/RXR (network 1) pathways that control bile acid turnover, regulate lipid and cholesterol metabolism leading to dyslipidemia. Specifically, estrogens, via ESR1, promote dyslipidemia by inhibiting bile acid secretion and interfere with hepatocyte uptake (MOV10 (network 2) is also highly relevant since the downregulation of this gene is associated with altered lipid metabolism . Downregulation of the fibroblast growth factor family results in increased hepatic steatosis, worsening of the serum lipid profiles, and insulin resistance (SHH) peptidase (network 5) a gene that plays a critical role in brain cholesterol metabolism  (network 6) suggests a role in the PCOS phenotype. TGFB1 participates in the linkage of lipotoxicity, glucotoxicity, and chronic systemic inflammation .–43.37–43n = 6) and the imbalance of progeny gender between cases and controls . However, even though our dataset was small, it was a carefully selected homogenous and well characterized cohort of PCOS patients, all showing anovulatory infertility, polycystic ovarian morphology, and increased AMH profile of the Rotterdam diagnostic category. Most importantly, our cohort did not have hyperandrogenism eliminating the possibility of epigenetic changes driven by high androgen levels.The main limitations of this preliminary, exploratory study are the limited number of patients . The Materials and Methods section of the paper provides details about the protocol number. About the consent procedure, written consent was obtained from every study subject. Patients were recruited during visits to RMA (Reproductive Medicine Associates) of New York, or at initial prenatal visits at Mount Sinai Hospital. Providers notified the appropriate member of the study team of the potential subject. The provider asked the potential subject if they were interested in participating in the study. If they were, the provider gave them the study team’s contact information, or asked the potential subject if they would like to speak with the study team at that time. Once the subject was identified and permission obtained to contact the subject, a member of the study team authorized to obtain informed consent was called on-site to approach the potential subject about the research study. Potential subjects were provided both verbally and in writing information regarding the study. Potential subjects were given the opportunity to ask questions regarding the study and/or enrollment, and were provided with a copy of the consent to read.No massive sequencing methodologies have been used for the completion of the aims of this study. The microarray analysis of CpG dinucleotides differs from whole-genome or whole-exome sequencing as the results  would not show any incidental genetic findings. Our IRB reviewed this issue and approved the study.Wrote the manuscript: LL and SS. Carried out genetic data analysis: LL. Contributed to the acquisition of the clinical data and samples: AC. Contributed to the writing of the manuscript: SH. Carried out the DNA methylation data analysis: ZY and WZ. Designed the study: YT and NK. Agreed with manuscript results and conclusions: LL, SS, AC, SH, ZY, WZ, YT, and NK. Made critical revisions and approved final version: LL. All the authors revised and approved the final manuscript and agreed to be accountable for the content of the work.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.


To increase the power of GWAS, gene-based and pathway-based tests are commonly employed to detect more risk factors. However, the gene- and pathway-based association tests may be biased towards genes or pathways containing a large number of single-nucleotide polymorphisms (SNPs) with small P-values caused by high linkage disequilibrium (LD) correlations. To address such bias, numerous pathway-based methods have been developed. Here we propose a novel method, DGAT-path, to divide all SNPs assigned to genes in each pathway into LD blocks, and to sum the chi-square statistics of LD blocks for assessing the significance of the pathway by permutation tests. The method was proven robust with the type I error rate >1.6 times lower than other methods. Meanwhile, the method displays a higher power and is not biased by the pathway size. The applications to the GWAS summary statistics for schizophrenia and breast cancer indicate that the detected top pathways contain more genes close to associated SNPs than other methods. As a result, the method identified 17 and 12 significant pathways containing 20 and 21 novel associated genes, respectively for two diseases. The method is available online by  Consequently, GWAS require enormous sample sizes to identify the majority of risk variants, which remains a considerable challenge for many traits.Genome-wide association studies (GWAS) have successfully identified multiple single nucleotide polymorphisms (SNPs) associated with many diseases. However, most common risk variants can’t be detected for polygenic traits due to weak or moderate effect sizes to account for only a small fraction of the overall phenotypic variation4 or a cluster of genes5. A natural way to define a cluster of genes is the biological pathway that includes a series of actions to produce certain products or changes in a cell. By integrating prior knowledge on genes, pathway analysis is becoming an effective approach to reveal underlying genetic structures of diseases. For example, pathway analysis has been used to detect mechanisms of diseases using both gene expression6 and GWAS8 data.Instead of increasing sample sizes, an alternative way is to detect genetic risk factors by aggregating variants over a gene9 and GSEA-SNP11. Their applications are limited because raw data is not always available . In contrast, the other class requires only the association P-value for each GWAS SNP 14. With the relatively high speed and low data requirements, methods using GWAS summary statistics offer the greatest potential. Usually, these methods assess pathway associations through gene associations that are often estimated from P-values of SNPs assigned to the genes via gene-based tests. For example, GSA-SNP represents each gene by the P-value of its k-th best SNP, and evaluates a pathway by calculating a summed Z-score on the P-values of its genes relative to the random chance13. KGG14 selects the most significant genes according to gene association tests from GATES2, and uses the genes to assess the association of pathways through the hyper-geometric test15.Currently, the methods for pathway analysis on GWAS data are mainly divided into two classes according to the type of input data. The first class works with an input of raw genotype data, such as FPCAOne major issue with these approaches is their incomplete adjustment for linkage disequilibrium (LD) correlations between SNPs, where because of the non-random association of alleles at multiple loci due to LD, the statistical associations between alleles at different loci will be different from the expected independent alleles. Therefore, the associations of pathways may bias genes containing a group of significant SNPs with strong LD.16 preselects a list of significantly associated SNPs to avoid LD between SNPs, while it ignores other SNPs. Genomicper17 considers the LD relationship within pathways by a special “circular genomic permutation” approach. These methods do not directly use the LD relationship of SNPs in estimating pathway association, and may not completely remove the influence of LD. In contrast, another class of methods explicitly separates SNPs into LD blocks and employs LD blocks for assessment. For example, ProxyGeneLD18 divides SNPs in a gene into LD blocks, and adjusts the lowest P-values by the number of LD blocks in the gene. This approach adjusts for LD between SNPs within a gene but ignores the LD between genes in the same pathway. PARIS12 separates SNPs in a pathway into LD blocks and counts the number of associated blocks containing at least one significant SNP. The significance of the pathway is then measured by the chance to have a greater number of significant blocks through permutation testing. However, the PARIS uses an arbitrary threshold to define significant SNPs and considers significant blocks equally. Given that an extremely significant SNP should be interpreted differently from a marginally significant SNP, the consideration of all detailed P-values is preferable.To adjust for LD, many methods have been proposed. For example, ALIGATORIn this study, we developed a new method, DGAT-path, to assess pathways based on detailed P-values of all LD blocks. By selecting the most significant SNP  for each LD block, the p-values are converted into chi-square statistics and summed over all blocks. The summed chi-square score is then compared to those by randomly sampling pathways (permutation), and the chance to have greater scores by the random represents the significance of the pathway. In this way, we avoid the arbitrary threshold used in PARIS, and could consider all LD blocks differently based on detailed P-values. The method, DGAT-path, was found to have lower type I error rate while consistently higher power than other methods. Application to GWAS summary statistics for schizophrenia (SCZ) and breast cancer indicated that DGAT-path detected a higher percentage of associated genes according to the recent GWAS Catalog.http://www.med.unc.edu/pgc/downloads) on 2 June 2014, which includes 1,237,819 SNPs obtained from a schizophrenia GWAS of 9379 cases and 7736 controls19. The breast cancer summary statistics were downloaded from the dbGAP with accession number “phs000147.v1.pl”, including 483,123 SNPs obtained from a breast cancer GWAS of 1142 controls and 1145 cases20.In this study, we used meta-analysis summary statistics from genome-wide association studies (GWAS) on the schizophrenia (SCZ) and breast cancer. The SCZ summary statistics were downloaded from the Psychiatric Genomics Consortium (PGC) webpage  or too few (<5) genes. The pathways were evenly separated into four ranges by the number of involved genes: , ,  and , where 10, 17, 37 are 25th, 50th, and 75th percentiles of all pathway sizes. Finally, the four ranges include 1747, 1457, 1382, and 1178 pathways, respectively.A comprehensive pathway dataset was built by collecting the data from KEGGhttp://grass.cgs.hku.hk/limx/kgg/, GSA-SNP from https://sourceforge.net/projects/gsa-snp/, MAGMA  from https://ctg.cncr.nl/software/magma, PARIS from https://ritchielab.psu.edu/software/paris-download, and Genomicper from https://cran.r-project.org/web/packages/genomicper/index.html. For all methods, the default parameters were utilized in the analysis. We didn’t compare ALIGATOR because it is developed specifically for only the Gene Ontology pathways.We downloaded KGG from Briefly, as shown in Fig. 26. We employed the package GEC27 to divide the SNPs into LD blocks so that the SNPs mapped to a pathway with LD correlations (r2) higher than 0.1 were grouped in the same LD block. The pairwise LD correlations were calculated by PLINK28 based on 1,000 Genomes Project genotype data (CEU).Gene sets were obtained from the UCSC genome browser RefSeq (hg19) . Overlapping isoforms of a gene were combined to form a single full-length version of the gene. For isoforms that didn’t overlap, the isoform containing the most significant SNP  was kept. This led to 23,438 unique genes. We assigned SNPs to a gene if they mapped to between 15 kb 5′ to the 5′ transcription start site (TSS) and 15 kb 3′ to the 3′ transcription end site (TES). The 15 kb gene boundary extension was chosen because 90% of SNPs affecting expression quantitative trait loci (eQTLs) were observed within this proximitySobs) for each pathway. To calculate a pathway’s significance that is not biased by pathway sizes, we assigned SNPs to pathways, and separated them into LD blocks using the same procedure, from which the same numbers of LD blocks were randomly sampled to calculate permuted scores (Sperm). The probability of producing a permuted score larger than the observed pathway score was defined as the association P-value of the pathway. A lower association P-value indicated a more significantly associated pathway. The random sampling was repeated for 1,000 to 1,000,000 times. To speed up the calculations, the sampling will stop when a permuted P-value is >0.05.The lowest nominal P-value of SNPs in each block was converted to upper-tail chi-square statistics with one degree of freedom, and summed up to provide the observed score  that only divided SNPs into LD blocks if the SNPs mapped to the same gene (rather than the same pathway). This method is similar to the KGG method except that DGAT-path (gene) uses a permutation test, whereas KGG uses the hyper-geometric test to assess pathway significance.Simulation studies for the type I error rate. Simulated pathways were generated by randomly assigning genes to pathways. Through this way, biological relationships were broken between genes while the LD correlations between SNPs were kept. Importantly, this approach needs only GWAS summary statistics, which is different from previous approaches that require original genotype and/or phenotype data to permute phenotype labels or perform genomic randomization. The process was repeated for 1,000 times to generate 1,000 copies of random pathways. By testing each method on the dataset, the percentage of pathways with P-values below 0.05 was calculated as the type I error rate. Additionally, the type I error rates were separately calculated on pathways of four size ranges to measure the impact of the pathway size.The power was defined as the proportion of pathways with association P-value below 0.05. For each tested pathway, we multiplied P-values of all SNPs by one factor so that there are a certain percentage of significant SNPs  for the pathway. This is based on a hypothesis that pathways are equally significant if they have exactly the same percentage of significant SNPs. Here, we tested four different percentages of significant SNPs at 30%, 50%, 70%, and 80%. In order to measure the influence of pathway sizes, we have calculated power separately for pathways with different sizes.As shown in Fig. To inspect the impact of pathway size, we calculated type I error rates for four different ranges of pathway size. As shown in Fig. As shown in Fig. Fig. suggestive association (P < 1 × 10−5) within the GWAS catalog database29, last updated in year 2016.The methods cannot be assessed by directly comparing the detected pathways because there is no gold standard to determine truly associated pathways. Such comparison is further complicated given the potential impact of incomplete adjustment for LD correlation between genes mapping to the same pathway. Here, we compared all methods according to the percentage of included associated genes within top detected pathways. A higher percentage of associated genes indicate that the method is more likely to select truly associated pathways. The associated genes were selected as genes closest to SNPs with at least genome-wide nd point below the curve of DGAT-path. The sparse points of KGG in the beginning resulted from its bias to pathways of big sizes. Although KGG implicated a higher percentage compared to GSA-SNP for the number of genes below 500, the two methods produce very similar results for larger pathways. MAGMA shows similar, but consistently lower percentages compared to DGAT-path. Genomicper and PARIS generally have the lowest percentages of associated genes.Because pathways contain varying numbers of genes, we show the percentage of associated genes versus the total number of genes involved in different pathways. As shown in Fig. When applied to the breast cancer data, as shown in Fig. −5 . Under this threshold, 17 pathways were identified by our method as significantly associated with SCZ, detailed in Table 31. Interestingly, “long-term depression” and “long-term potentiation” were identified significantly associated with SCZ. Indeed, a recent investigation indicated the relationship between SCZ with these two pathways32. Moreover, “postsynaptic density”, “ion channel binding pathway”, and “postsynaptic membrane pathway” have been found enriched with genes containing SCZ-related mutations34. The “Wnt signaling pathway” has been investigated to detect mutations on genes in SCZ and/or bipolar disorder patients36, and the role of this pathway in SCZ has been examined to detect biomarkers37. Additionally, 986 genes were included in the significant pathways. Among them, five genes were identified as significantly associated with SCZ by the original study38 and 20 novel genes were identified as significantly associated by the recent GWAS study with an expanded sample size39.By considering multiple test burdens of ~5,000 pathways, the genome-wide threshold for pathway analysis was chosen as 1.1 × 10−5)  as known breast cancer genes . Another seven genes  were reported as associated with the breast cancer by recent GWA studies47.For the breast cancer GWAS summary statistics, DGAT-path identified 12 pathways with P-value below the threshold (1.1 × 10n degrees of freedom under the null hypothesis (n is the number of LD blocks in a pathway). Thus, the summation of the observed independent chi-square statistics reflects the combination of effects of SNPs and provides a valid measure of pathway association. To adjust for pathway size in estimating pathway association, we perform permutations. The method is different from PARIS because PARIS uses a fixed P-value threshold  to determine significant LD blocks. Additionally, PARIS counts the number of significant LD blocks, thus it considers all significant LD blocks equally, and totally ignores less significant LD blocks. This may explain why PARIS has significantly lower performance than DGAT-path. In addition, PARIS requires setting a parameter to define the significant LD blocks, which may cause low power and high type I error rate when the threshold is not optimized.In this study, we developed a new method, DGAT-path to assess pathway association. Through dividing SNPs in a pathway into LD blocks, a score is calculated by summing chi-square statistics converted from P-values of the most significant SNPs in each LD block. Because the summed chi-square statistics for each pathway are independent, they follow a chi-square distribution with 2The results indicated that the DGAT-path is less affected by pathway sizes with a stable type I error rate ranging from 2.7% to 3.7%. In contrast, the type I error rate of KGG significantly enlarged with an increase in pathway size, most likely because larger pathways have a higher probability to include more correlations between gene association signals. This explanation was supported by the fact that DGAT-path (gene) shows increased type I error rates with an increase in the pathway sizes. Hence, these results support the necessity to adjust for LD correlation among genes in pathway analyses. GSA-SNP appears to appropriately adjust for such potential bias of large pathways via its normalized Z-score by pathway size approach (showing no obvious relationship between the type I error rate and pathway size); however, GSA-SNP did not implicate as many genes according to GWAS catalog data as DGAT-path. This is most likely because GSA-SNP employs the average P-values of a gene set, which increase at the same time. Lastly, although MAGMA produced a lower type I error rate, it detected fewer pathways (had lower power) compared to GSA-SNP. Given that MAGMA was mainly developed for analysis of GWAS raw data, it might not be well optimized for the analysis of GWAS summary statistics.−6 (0.05/5764), the power of DGAT-path remained the highest among the tested methods, having an average power of 1.4 times higher than KGG and 4 times higher than MAGMA. While GSA-SNP detected very few associated pathways. Genomicper was utilized here by permutation at the SNP level. Genomicper requires a threshold to define the significant SNPs in the permutation test. In this study, we used 0.05 as the threshold. The low performance of Genomicper may result from non-optimized threshold. More details are included in Table S1.For the power estimations, the traditional nominal P-value cutoff of 0.05 was initially selected. When a more stringent threshold was utilized to account for testing multiple pathways, i.e., a P-value cutoff of 8.7 × 10The running times of four methods were tested for pathways of three sizes on an intel i5 CPU of 2.6 GHz, and their runtimes are comparable. As shown in Table S2, GSA-SNP is the fastest and needs eight and fourteen seconds for pathway containing six and 280 genes, respectively. DGAT-path ranks the second, and requires 12 and 34 seconds, respectively. These two methods show an increase of running time with increase of pathway size. By comparison, MAGMA and PARIS have a closely constant running time for three pathway sizes, requiring 50 and 40 seconds, respectively. Since KGG used GUI, its runtimes are not listed. Genomicper  took 185, 197, and 2239 seconds for three investigated pathway sizes, respectively.DGAT-path requires a simple file including chromosome, SNP name, p-value, and location, and a web server has been provided. DGAT-path can be applied to any pre-defined pathway databases, and GWAS summary statistics.


Production of chemicals via fermentation has been evolving over the past 30 years in search of economically viable systems. Thus far, there have been few industrially relevant chemicals that have seen commercialization, examples being lactic acid and ethanol. Currently, many of these fermentation processes still compete with food sources. In order to reduce this competition fermentation of alternative feedstocks, such as lignocellulosic biomass must to be utilized. Hemicellulosic sugars can be employed effectively for the production of chemicals by incorporating nonphosphorylative metabolism. This review covers nonphosphorylative metabolism, the pathways and enzymes involved, as well as the products that have been produced using nonphosphorylative metabolism. Over the past 30 years the quest for bio-derived chemicals, both pharmaceutical and industrial products, has driven metabolic engineering and synthetic biology to identify and develop new pathways that are economically viable  cycle are 1,4-butanediol, glutamate, and succinic acid  in less than six steps. Similarly, the Dahms pathway branches off from the Weimberg pathway at 2-keto-3-deoxy-D-xylonate and 2-keto-3-deoxy-L-arabonate to form pyruvate and glycoladehyde . The third method is nonphosphorylative metabolism.P. fragi, Burkholderia xenovorans, and Caulobacter crescentus  isomerase pathway; (2) reductive pathway; and (3) oxidative pathway. The oxidative pathway resembles that of the xylose and arabinose nonphosphorylative pathways with the first step being the conversion of D-galacturonate into D-galactarolactone using uronate dehydrogenase (UDH)  cycle, nonphosphorylative metabolism ensures high maximum theoretical yields, because xylose or arabinose flows to C5 compounds without excretion of COClostridium tetanomorphum was introduced to convert glutamate into mesaconate , and deletion of sucA, ensure an even higher yield (0.85 mol/mol xylose/arabinose)  cycle were applied to produce glutamate, and glutamate catabolic pathway from Corynebacterium glutamicum  (Wang et al., Utilization of D-xylose via nonphosphorylative metabolism has also been incorporated into Nonphosphorylative metabolism while known since the mid-twentieth century has not seen widespread usage. The development of a selection platform for identification of new nonphosphorylative gene clusters provides the opportunity to expand the number of pathways available for utilization. Furthermore, many of these enzymes have not been subjected to protein engineering efforts or selective evolution leaving much to be expanded on in the future. The ability to use one of several of the hemicellulosic sugars is not only powerful because it expands feedstock options, but it also allows for the development of a system where glucose is predominantly used for cell growth while xylose, arabinose, and/or galacturonate are directly used for product production. The incorporation of nonphosphorylative metabolism is one way to increase the efficiency of sugar utilization, but these attributes need to be combined with strain engineering for increased resiliency to inhibitors present in lignocellulosic biomass hydrolysates. These changes will help to hasten the commercialization of fermentation products for the chemical industry.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK–STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders.Using  The worldwide use of mobile phones has aroused concern about possible health effects of radiofrequency electromagnetic fields  . Even thMany studies have been performed on identifying genes being involved in biological effects caused by ELF and RF EMF using a variety of mammalian cell lines and primary cells. Most of these studies were hypothesis-driven and demonstrated changes in the expression of a limited number of genes, especially those involved in stress response –18, and The importance of cellular context was demonstrated through comparison of the effects of EMF in different cell lines .In contrast to the approach investigating EMF on a limited number of genes, studies evaluating changes in transcription profiling have been performed to speed up the identification of genes responding to EMF. Several transcriptomics studies have been performed, and the outcome has been reviewed . New metThere has been significant research on the use of gene expression data to identify people with diseases (disease biomarkers), to monitor exposure to chemicals (exposure biomarkers), and to predict effects from exposure to chemical agents (effects biomarkers). Much of the recent work on effects biomarkers has focused on the classification of genes into ontology groups that can then be used to predict a biological effect –30. ThesThe enrichment analysis approach can be used on gene sets that consist solely of lists of genes. One approach widely used to define gene sets is to base them upon the proteins in signaling or metabolic pathways already described by many years of research. There are numerous collections of pathways that could be used, such as the Kyoto Encyclopedia of Genes and Genomes Pathways (KEGG pathways) . The secThis paper uses a similar approach to identify plausible linkages between exposure to ELF and RF EMF and human diseases. Several authors have looked at changes in gene expression in cellular systems following exposure to EMF using microarrays. We will use these data to find linkages between alterations in gene expression and the KEGG pathways. We will then update the analysis done by Gohlke et al. linking KEGG pathways to human diseases, although using broader disease categories. Given the linkages between EMF and pathways and pathways and human diseases, we will predict plausible linkages between EMF and human diseases. These linkages form hypotheses that can be pursued in other research efforts to study the potential health effects of EMF.Microarray data on changes in gene expression in human cells were obtained in three separate ways. In late 2011, we searched the Gene Expression Omnibus using the following search terms: “electromagnetic,” “magnetic,” “electric,” “RF,” “radio,” and “ELF.” This located data from two studies , 38. We p-values for the significance of change in gene expression between controls and exposed cells; (1 − p) was used as the measure of gene expression.A: Human SK-N-SH neuroblastoma cells were exposed to 900 MHz GSM signals with a specific absorption rate (SAR) of 0.2 W/kg for 2 h . Duplica2 ratios of control/exposed expression values were used as the measure of gene expression.B, C: Human MCF-7 breast cancer cells were exposed to 1800 Hz RF EMF . There w2 of the gene expression ratio for four samples (two samples from two hybridizations) was used as the measure of gene expression.D: Human neuroblastoma NB69 cells were exposed to 1800 MHz RF EMF for 24 h  at an SAR of 2 W/kg , 50. ExpE–G: Human endothelial EA.hy926 cells were exposed to 900 MHz (experiment F) or 1800 MHz (experiments G and H) RF EMF for 1 h at an SAR of 1.8–2.5 W/kg , 50. ExpH: Human quiescent T lymphocyte cells were exposed to GSM-modulated RF EMF at 1800 MHz, 10 min on and 20 min off, for 44 h at an SAR of 1.4 W/kg , 50. TriI: Human U937 monocytic lymphoma (lymphoblastoma) cells were exposed to 900 MHz RF EMF for 1 h at an SAR of 2 W/kg , 50. ExpJ: Human CHME5 microglial cells were exposed to 900 MHz RF EMF for 1 h at an SAR of 2 W/kg , 50. ExpK–M: Human HL60 leukemia cells were exposed to 1800 MHz RF EMF using GSM DTX modulation for 24 h  at an SAR of 1 W/kg (experiment L) or for 24 h (continuous) at an SAR of 1.3 W/kg (experiments M and N) , 50. Exp2 ratios of control/exposed expression values were used as the measure of gene expression.N: Human keratinocytes in cell culture dishes were wounded and exposed to an electric field with a strength of 2.5 mV/cm and a frequency of 2080 Hz for 1 h . Triplic2 of the ratio was used as the measure of gene expression.O: Monocytes from human umbilical cord blood were exposed to 50 Hz, 1.0 mT ELF EMF for 45 min . The avaP, Q: Human diploid fibroblast cells (ES-1) were exposed to 50 Hz ELF EMF at 1 mT for 5 min on/10 min off for 15 h (P) or 15 h (Q) , 50. ExpR: Human neuroblastoma cells (SH-SY5Y) were exposed to 50 Hz ELF EMF at 2 mT for 5 min on/5 min off for 15 h , 50. ExpS: This pooled dataset combines all significant genes from five RF EMF experiments , 46, 47 T: This pooled dataset combines all significant genes from three ELF EMF experiments , 45, 48 Because the effects of external influences on the body are mediated through changes in cellular function that are themselves controlled by various signaling pathways, it is helpful to analyze data on genetic transcription effects using methods taking those pathways into account. One such method is the SEPEA algorithm , which eThe GAD  was downp < 0.01. This is similar to the earlier analysis  were analyzed against the 162 KEGG pathways. The version of the SEPEA algorithm used in this analysis was SEPEA NT3, which requires only one data point, a measure of strength of expression, for each gene. Where full data on gene expression levels were available, we used log2 of the exposed/control expression ratio. When full data were not available, we used different measures of expression strength, as noted in the data descriptions above.The significance of associations between experimental results and KEGG pathways were derived by using the SEPEA algorithm. For this analysis, pathways were assumed to be associated with EMF exposure if Several additional pooled analyses were done to evaluate linkages to pathways and diseases across a broad array of exposures and cell lines. We pooled all complete RF EMF microarray datasets (A–M) into one dataset for analysis, all complete ELF EMF microarray datasets (O–R), all RF EMF experiments , all ELF EMF experiments , and all experiments (A–T).p-value for significant relationship between the dataset and disease was calculated. If there are N pathways for the disease and K for the dataset, with M total pathways, and there were X pathways in common, then the probability of at least X in common was calculated [1 − H], where H is the hypergeometric CDF at X − 1 for drawing K items in N drawings without replacement from a set of M objects.Significant association between the gene expression changes from a single dataset using EMF exposure and each disease was calculated using the hypergeometric function. Under the null hypothesis that the pathways significant in the dataset are unrelated to those significant in the disease, the This research used microarray data from the literature to predict linkages between RF EMF or ELF EMF with human disease categories. The linkage was done in three steps: (1) linkage of human disease categories to KEGG pathways using genetic polymorphisms, (2) linkage of EMF exposure with KEGG pathways using gene array data, and (3) comparison of the disease-linked pathways with EMF-linked pathways to predict significant linkages between EMF and human disease.Table Cancer was linked with 21 pathways, predominantly relating to metabolism (7 pathways), hormone control (2 pathways), DNA repair 3 pathways), and cellular replication .Chemical dependency was significantly linked to 11 pathways; 6 metabolic pathways, 4 linked pathways relating to cellular homeostasis and control , and NOD-like receptor signaling involved in immune response.There were 10 pathways linked to neurological disorders. Psychological disorders had 11 linked pathways, 4 of which were shared with neurological disorders, all linked to basic cellular control and homeostasis . Both disorders had additional metabolic pathway linkage, with psychological disorders additionally linked to metabolism of linoleic acid and arachidonic acid , while neurological disorders were linked to cytochrome P450 metabolism. Neurological disorders were linked to PPAR and Notch signaling pathways, both important in neurological development. Psychological disorders were also linked to circadian rhythm control and long-term depression, a signaling network thought to be a molecular and cellular basis for cerebellar learning with multiple signal-transduction pathways involved in this process.Metabolic disorders were associated with 22 pathways, 13 of which deal with metabolism of drugs, xenobiotics, lipid, fatty acids, and steroids. The remaining pathways included the ATP-based transporters, basic cellular control and homeostasis pathways, PPAR, insulin signaling, and adipocytokine signaling, many of which have already been associated with metabolic disorders.The 12 pathways associated with reproductive disorders covered a variety of systems, some of which were related to basic cellular functions and others dealing with a variety of pathways not usually associated with reproductions. Because of the diversity of this response, it will not be further discussed.In total, 60 of the 162 pathways were associated with at least 1 human disease or disorder. Many of these were linked to five or more diseases and dealt with basic cellular functions like JAK–STAT signaling, cytokine–cytokine receptor interaction, and neuroactive ligand–receptor interaction. Many of the other multi-disease pathways involve metabolic functions. However, there were several specific to certain diseases; DNA repair pathways and cancer, circadian rhythm and psychological diseases, and insulin signaling and several metabolic pathways specific to metabolic disorders. All of the evaluations for all pathways and diseases are presented in the Spreadsheet S1 in Supplementary Material.In this study, 13 RF EMF (A–M) microarray, 1 LF EMF (N) microarray, and 4 ELF EMF (O–R) microarray datasets with human cells for which we had the original data were analyzed. In addition, two datasets were constructed by pooling five RF EMF (dataset S) and three ELF EMF (dataset T) microarray studies from the literature in multiple cell lines with multiple field strengths and exposure durations. These literature studies identified all significantly altered genes.p < 0.05 in more than one study. When all of the RF EMF studies are combined in one analysis, there were 25 significant pathways linked to the exposure.As a general rule, the individual datasets were linked to biologically diverse pathways. The total number of linked pathways was also quite diverse ranging from 3 for dataset P to 26 for dataset N. The separate analyses of the individual RF EMF studies showed 31 pathways being significant at The LF study (N) was significant for every disease/disorder category. The ELF EMF studies did not demonstrate a response as robust as the LF and RF EMF studies. The four individual studies, for which we had complete data (O–R), showed 3 to 14 linkages each. Only two pathways were significantly linked in more than one study, both being linked in two studies. These two pathways, inositol phosphate metabolism and FC gamma R-mediated phagocytosis are not related. The analysis of the LF and ELF EMF data combined  resulted in 15 significantly linked pathways. No obvious pattern emerged from these linked pathways.All of the linkages between the experiments and the KEGG pathways are provided in Spreadsheet S2 in Supplementary Material.Ten  of the 13 RF EMF studies demonstrated no significant linkages to disease based on common pathways. Dataset B demonstrated the strongest pathway linkages to disease with significant linkages to three disease categories; cardiovascular disease, psychological disorders, and reproduction. The identical study using a higher power exposure (C) was significantly linked to chemical dependency, but to none of the same linkages seen for B. Dataset G was significantly linked to reproductive disorders resulting in a total of 2 of the 13 datasets linked to this disease category.Cardiovascular disease was linked to the significant pathways from only dataset B (seven pathways) and did not show any additional linkage, even in the combined datasets. Similarly, psychological and reproductive disorders were significantly linked to study B  but did not demonstrate significant linkage in the combined analyses.Reproductive disorders show significant linkages to two pathways for dataset G with only one pathway occuring in dataset B (long-term depression). However, when the datasets were combined , the significance could not be maintained because the combined dataset only linked to one pathway, long-term depression.Cardiovascular disease was significantly linked in dataset B to five linked pathways . In contrast, study C was not significantly linked with cardiovascular disease because of only two pathways , both also linked in dataset B. The combined analysis using all of the RF EMF microarray data had no linkage to cardiovascular disease.Chemical dependency was significantly linked to only one dataset (C) with three pathways . No linkage was seen in the combined analysis.When the RF data are combined , there are no significant linkages to diseases or disorders.Study N (LF) was linked to all seven diseases and disorders in Table Dataset N linked with seven pathways for cardiovascular disease, three of which matched what was seen for dataset B . Although dataset B linked with seven pathways for chemical dependency, only two matched dataset C . Dataset N was linked to five pathways for psychological disorders, four of which matched dataset B . Finally, dataset N linked to six pathways for reproductive disorders, matching both datasets B and G for long-term depression pathway and matching dataset B for neuroactive ligand–receptor interaction.None of the ELF EMF datasets (O–R) were significantly linked to any disease. Surprisingly, the combined ELF EMF literature dataset (T) was linked to cancer, chemical dependency, metabolic disorders, and neurological disorders, predominantly through linoleic acid metabolism, retinol metabolism, and drug metabolism by cytochrome P450. The combined LF/ELF EMF datasets  were significantly linked to chemical dependency, metabolic disorders, and neurological disorders, predominantly by datasets N and T.Combining all of the data from RF, LF, and ELF resulted in significant linkages to cardiovascular disease, chemical dependency, metabolic disorders, and neurological disorders.The strongest linkage between RF EMF and the KEGG pathways relates to cellular structure and cytoskeleton maintenance. Extra cellular matrix (ECM)–receptor interaction relates to the complex mixture of structural and functional macromolecules that help to regulate cellular structure and plays an important role in tissue and organ morphogenesis. Although this pathway was unrelated to any disease based upon our linkage analysis, it was linked to six of the RF EMF datasets. This pathway links to other KEGG pathways, including the focal adhesion pathway (four datasets linked). The focal adhesion pathway represents the signaling associated with cell membrane to extracellular matrix contact points through transmembrane receptors of the integrin family. Focal adhesion was significantly linked in four of the same datasets as ECM–receptor interaction. The findings cross multiple cell lines, although there is insufficient data to isolate any cell line as responsive or not. Both pathways were significant in both pooled datasets .Previous studies found evidence for linkage between cytoskeleton development and RF EMF. Changes in two vimentins, intermediate filament proteins that make up part of the cytoskeleton, were observed in a proteomics analysis of endothelial cells following RF EMF exposure . Gene exChanges in the ECM–receptor interaction and focal adhesion are linked to a variety of higher level cellular functions, such as apoptosis and cell cycle control. Thus, if one were seeing changes in these pathways, it would be likely that changes would be seen in related pathways that are part of the apoptotic process and/or cell cycle control. Of the eight pathways with significant linkages in three or more datasets, three are related to apoptosis. In addition to ECM–receptor interaction and focal adhesion, TGF-β signaling , apoptosThese findings are expected from the literature. MAPK signaling (two studies) plays an important role in all of these processes and appears extensively in these pathways. The ERK 1 and 2 are MAPKs that function in cellular proliferation, differentiation, and survival, and their inappropriate activation is a common occurrence in human cancers. Several authors , 46, 59 Additional support for these findings comes from other studies in the literature, including studies evaluating changes in p53 , 20 and The other strong linkage identified in our transcriptomics analyses of human cells exposed to RF EMF relates to metabolic pathways linked to some of the datasets. Unlike apoptosis, cell cycle control, and cellular structure, the linkages to metabolic processes generally involved only one or two databases and hence represent a weaker association. One pathway related to carbohydrate metabolism  and two pathways related to lipid metabolism (fatty acid elongation and primary bile acid biosynthesis) were each linked to two datasets. Many of the original publications using gene ontology analyses noted significant linkages to metabolism , 65. LipThe lack of a strong linkage of RF EMF to cancer and metabolism disorders is surprising. The pathways and processes linked to the RF EMF datasets would suggest a strong linkage to both of these disease classes.Cancer has been the major focus of studies in humans concerning RF EMF exposure. A scientific panel convened by the International Agency for Research on Cancer concluded it is a possible human carcinogen . In thisAs for cancer, there was very little linkage between RF EMF and metabolic disorders despite linkage between RF EMF and metabolic pathways. In the GAD, most of the linkages between metabolism genes and diseases are with cancer. These genes were predominantly identified in occupational cohorts where there are gene–environment interactions . More reRadiofrequency electromagnetic fields alterations in metabolism are of growing interest due to a few studies showing changes in brain metabolism following RF EMF exposure –72. NoteA complete loss of pyramidal cells in the CA1 area of the hippocampus in mouse brain after 5-day exposure to RF EMF was reported recently . Calciumvia cytochrome P450, linoleic acid metabolism, and retinol metabolism (N and T). There is insufficient consistency in these findings to draw any strong conclusions.The LF EMF exposure study (N) demonstrated numerous linkages to pathways, predominantly metabolic pathways. The individual ELF EMF studies O–R demonstrated sporadic linkages to multiple pathways with only three common linkage seen in exactly two studies including cell cycle pathway (Q and R), inositol phosphate metabolism (O and P), and FC gamma R-mediated phagocytosis (O and P). When all of the LF and ELF datasets are combined (N–R and T), there are three additional pathways that were common to two of the studies; drug metabolism The LF EMF exposure study (N) was linked to all of the disease classes listed in Table There are two aspects of potential bias to consider in this analysis: (1) cell type bias and (2) dividing cell bias in using cell lines. There are a preponderance of neurological cell lines, such as SK-N-SH, NB69, CHME5, and SH-SY5Y, which may favor detection of changes in neurological pathways and disease outcomes. This could bias the conclusion that RF EMF data suggest further study into neurological disorders. Also, the use of cell lines might favor detecting changes in cell division, extracellular matrix, and activation of many signaling pathways compared to more quiescent, non-dividing, differentiated cells. These changes in dividing cells with an active cell cycle and correspondingly engaged signaling and metabolic pathways may favor changes leading to conclusions of outcome diseases like cancer, any type of metabolic disorder, immunological effects, or damage and repair responses to disease. However, just as likely, active cell cycle and correspondingly engaged signaling and metabolic pathways may hide effects on subtle changes in cell cycle regulation that would be detectable and significant in quiescent cells like neurons.Our analysis supports a linkage between RF EMF exposure to human cells and changes in the pathways associated with apoptosis, cellular regulation, and cytoskeleton maintenance. There is weaker support for linkage to metabolic pathways and neurological pathways. Based on these linkages alone, there is reason to believe that RF EMF could play a role in carcinogenesis, metabolic disorders, and neurological development and function. The strength of the evidence linking pathways for disease to the RF EMF-linked pathways is weaker. There is little support for a direct linkage between cancer pathways and RF EMF pathways, probably due to the quality of the GAD database. Similarly, there is very little linkage to metabolic disease.Our analysis supports a linkage between ELF EMF and cancer, chemical dependency, metabolic dysfunction, and neurological disorders; however, these findings are largely driven by a single study and should be considered weak.The greatest strength of this analysis is that it is fully objective in its approach. Data were identified by literature review; all data were handled equally depending on the type of data available for the analysis. The results are all tied to objective statistical methods that demonstrate the strength of the linkage between various pathways and EMF exposure and disease. The major weakness of this analysis is the inability of these small EMF microarray datasets to provide the depth and complexity to support a more thorough analysis. Also weakening this analysis are the limitations to the GAD as discussed above. Thus, at best, this analysis generates hypotheses that may be followed up. Changes in gene expression do not always correlate to changes in the proteins, enzymes, and transcription factors that govern cellular signaling and cellular metabolism. In following up with further research, both gene expression studies and studies of protein changes should be considered.All authors contributed to the analysis and interpretation of the data in this manuscript. The original idea and work plan for this research was the work of MM and CP.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.