MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and other functional non-coding RNAs (ncRNAs) have emerged as pivotal regulators involved in multiple biological processes. Recently, ncRNA control of gene expression has been identified as a critical regulatory mechanism in the immune system. Despite the great efforts made to discover and characterize ncRNAs, the functional role for most remains unknown. To facilitate discoveries in ncRNA regulation of immune system-related processes, we developed the database of immunologically relevant ncRNAs and target genes (IRNdb). We integrated mouse data on predicted and experimentally supported ncRNA-target interactions, ncRNA and gene annotations, biological pathways and processes and experimental data in a uniform format with a user-friendly web interface. The current version of IRNdb documents 12 930 experimentally supported miRNA-target interactions between 724 miRNAs and 2427 immune-related mouse targets. In addition, we recorded 22 453 lncRNA-immune target and 377 PIWI-interacting RNA-immune target interactions. IRNdb is a comprehensive searchable data repository which will be of help in studying the role of ncRNAs in the immune system.Database URL:http://irndb.org Rapid development of high-throughput technologies enabled identification of non-coding RNAs (ncRNAs) as a highly abundant class of transcripts in the pervasively transcribed eukaryotic genome . Among nMiRNAs constitute a family of ∼22 nucleotides small ncRNAs that bind to target mRNAs to mediate post-transcriptional repression or degradation of the mRNA . GreaterPiRNAs constitute another class of small ncRNAs whose well-established function is to silence transposable elements in complex with PIWI proteins in germline cells . Recent LncRNAs are defined as ncRNAs longer than 200 nucleotides . SeveralThe numbers of experimental studies on ncRNA-target interactions as well as the numbers of computational prediction approaches and tools have increased drastically over the years , 16. AccIn this study, we report the development of IRNdb, the first, to our knowledge, specialized database for immune-related ncRNA in mice, a model organism for the study of the immune system. IRNdb was developed with a goal of establishing a universal resource for mouse ncRNAs in immunological research. We integrated information on mouse miRNAs, lncRNAs, piRNAs and their immunologically relevant target genes. We combined multiple sources of experimentally supported and predicted miRNA-target interactions, thus eliminating the user’s need to browse multiple websites and run different computational tools. In IRNdb, we visualize information on experimentally supported and predicted interactions separately, thus, allowing users to choose a category of interest. NcRNAs and target genes are linked to external databases to offer additional information such as sequences and tissue-specific expression. We implemented a feature that enables users to perform functional analysis and identification of significant biological pathways for any selected subset of target genes using Enrichr, a popular tool which offers a large collection of gene set libraries . In addiWe integrated public domain data from various sources . Experimhttp://www.septicshock.org/) were used as sources of mouse-specific immunological gene information. Immunologically relevant human genes were extracted from ImmPort (http://www.ncbi.nlm.nih.gov/homologene). To date, IRNdb is focused solely on mouse data.The set of genes involved in immunological processes was retrieved from the following sources: InnateDB  and Sept ImmPort , the Imm ImmPort , 40, imm ImmPort  and Inna ImmPort  and convWe retained ncRNAs in IRNdb if they could be connected to one of the immunologically relevant target genes through the earlier mentioned interaction databases. In total, we were able to extract 12 930 experimentally supported interactions between 724 miRNAs and 2427 target genes . In addihttps://www.ncbi.nlm.nih.gov/gene). Biological pathway and process information was downloaded from Kyoto Encyclopedia of Genes and Genomes (KEGG)  , WikiPats (KEGG) , Gene Ons (KEGG)  and Reacs (KEGG) .In addition, lists of up- and down-regulated genes in immunologically relevant experiments were downloaded from the Molecular Signature Database (MSigDB) . We inteTFBS information was retrieved from ENCODE  and HT-Chttp://irndb.org) is an open-access database implemented in the DJANGO web-framework (https://www.djangoproject.com/), running on an NGINX web-server (https://www.nginx.com/) with a MySQL database server (https://www.mysql.com/) for storing data and Memcached (https://memcached.org/) for object caching. The web-frontend is developed using the Bootstrap framework (http://getbootstrap.com/). The source code for IRNdb is freely available at https://github.com/sschmeier/irndb2.IRNdb (accessible at http://irndb.org), which is divided into four main sections ‘miRNAs’, ‘piRNAs’, ‘lncRNAs’ and ‘Target genes’. In addition, the ‘Documentation’ section provides instructions and examples for using the database, contact information and a statistical summary of the IRNdb interaction data. Later, we illustrate IRNdb features and functionality using miRNAs mmu-miR-125b-5p and mmu-miR-873a-5p, as well as their targets as examples.IRNdb collects information on ncRNAs targeting immunologically relevant protein-coding genes and biological annotation data . To acceIRNdb provides an interface for the convenient retrieval of ncRNA and target information. Users can browse ncRNAs in tabular form that is searchable, e.g. by identifier or names. Clicking the ncRNA name in the table leads to an individual ncRNA entry (e.g. ‘miRNA view’ in case of miRNAs).http://fantom.gsc.riken.jp/5/sstar) which, again, shows that Tnfrsf1b is expressed in mouse macrophages. Finally, we provide a link to TBvis, a resource where we implemented visualization of cap analysis gene expression (Mycobacterium tuberculosis (Mtb)  , 55. TBvIf regulation of miRNA transcription is of interest, users can browse the table in the ‘TFBS’-tab of the ‘miRNA view’. The table lists TFs for which TFBSs were found upstream of miRNA in question, along with additional information on cell type, TFBS location and significance. The ‘Cell types’-tab shows relative cloning frequencies of corresponding miRNA across a set of mouse tissues and cell types . MacrophIRNdb provides a separate search interface for immune system-related ncRNA target genes. Genes can be searched using gene symbol, gene name, Entrez Gene ID or their parts. Moreover, users can select any set of genes by clicking on the corresponding rows and perform the gene set enrichment analysis (GSEA) by Enrichr tool  with ‘RUWe continue the demonstration of how IRNdb can be used to characterize Tnfrsf1b, the predicted target of mmu-miR-125b-5p. In addition to the aforementioned ‘Expression’ field with three links to the external sources of tissue and cell type-specific gene expression, the ‘Target gene view’ provides six tabs .The ‘miRNAs’, ‘lncRNAs’ and ‘piRNAs’-tabs show tables with corresponding ncRNAs that target the gene in question. To date, for Tnfrsf1b IRNdb lists seven lncRNAs, six experimentally supported miRNAs and 37 predicted miRNAs. In the case of miRNAs, each table can be sorted by the number of sources of miRNA-target information. Thus, if one aims at testing some of the predicted Tnfrsf1b-targeting miRNAs, the mmu-let-7 family and mmu-miR-98-5p might represent miRNAs of interest as they were predicted by the largest number of tools .The ‘Pathways’- and ‘GO’-tabs provide pathways and processes associated with the target gene. They indicate that Tnfrsf1b is involved in apoptosis and oxidative damage-related processes. The ‘Experiments’-tab provides additional functional information on the target, listing immunologically relevant experiments where the target was shown to be up- or down-regulated. Searching for ‘macrophages’ reveals that Tnfrsf1b was found to be up- or down-regulated in multiple experiments on macrophages. The experiments can be searched by any key words to retrieve, for instance, disease, stimulation or cell type of interest. We integrated experiments performed for mouse and human which is indicated in the ‘Species’ field.We implemented two complementary approaches for ncRNA functional analysis via analysis of the corresponding target genes. We demonstrate how IRNdb can be used for functional analysis of ncRNAs and their targets on the example of miRNA mmu-miR-873a-5p.A list of targets of a ncRNA of interest can be subjected to the GSEA by Enrichr tool  by pressThe list of experimentally supported targets can be analysed in a similar manner. However, in the case of mmu-miR-873a-5p, only six experimentally supported targets are reported in IRNdb to date. In such case, it might be beneficial to browse all pathways associated with these targets instead of focusing on statistically significant ones, as with such a small target set, no significance can be reliably calculated. With this purpose, we created ‘Pathways’-tab of ncRNA view. It allows users to browse WikiPathways, KEGG and Reactome pathways that contain at least one target of the ncRNA under investigation. Pathways are presented as a table for each pathway source, respectively. Using this feature, we found that one of mmu-miR-873a-5p experimentally supported targets, tumour necrosis factor, alpha-induced protein 3 (Tnfaip3) is associated with the WikiPathway entry TNF-alpha NF-κB Signalling Pathway. These data combined propose that mmu-miR-873a-5p might have a strong role in Tnf signalling which is in agreement with previously published data . MoreoveSimilarly to the ‘Pathways’-tab, the ‘GO’-tab provides users with a list of GO-process and GO-function terms associated with the ncRNA targets. We believe that exploring the ‘GO’- and ‘Pathways’-tab, in addition to GSEA, enhances IRNdb capability of identification of biological pathways and processes possibly affected by the ncRNA of interest.The IRNdb repository includes information on biological pathways with immunologically relevant ncRNA target genes. Users can reach the pathway data in three different ways by selecting the pathway from the ncRNA view’s ‘Pathways’-tab, from the target view’s ‘Pathways’-tab or from the ‘by pathways’-option for a ncRNA class of interest in the navigation panel. In the latter case, the link leads to a ‘Browse pathways’-page, where IRNdb lists for each pathway the immunologically relevant experimentally supported ncRNA target genes and corresponding targeting ncRNAs.Here, we focus on ‘TNF-alpha NF-κB Signalling Pathway’ of the WikiPathways catalogue. As discussed earlier, two miRNAs mmu-miR-125b-5p and mmu-miR-873a-5p could play a role in regulating this pathway. After searching for the pathway in the table on the ‘Wikipathway’-tab, we enter a pathway-centric view by clicking on the pathway name. The ‘miRNAs’-tab of the ‘Pathway view’ lists miRNAs with experimentally supported or predicted targets in the pathway. Searching for ‘mmu-miR-873a-5p’ shows that, while Tnfaip3 is its only experimentally supported target in the pathway, mmu-miR-873a-5p is predicted to target an additional 13 genes in the pathway, including Ikbkb and Nfkbie which regulate the NF-kappa-B TF . InteresOver the past years, research into ncRNAs has increased significantly within the scientific community. For example, several miRNA databases with diverse scopes and goals have been recently introduced , 58–60. Undoubtedly, these efforts lead to a better data accessibility and understanding of ncRNA biology and function. One of the insights in ncRNA control is that it is exerted in a tissue- and condition-specific manner. Hence, without a specialized resource, an investigation of ncRNA roles under a certain condition might require time-consuming navigation between multiple data sources and gathering scattered condition-specific information from published literature. To bridge the gap in the understanding of ncRNA control of the immune system, we developed IRNdb. To our knowledge, this is the first resource to focus on immune-related mouse ncRNAs and their target genes. IRNdb is an integrative, user-friendly and easy-to-use platform with a wide range of applications . IRNdb b


Arthropods interact with humans at different levels with highly beneficial roles (e.g. as pollinators), as well as with a negative impact for example as vectors of human or animal diseases, or as agricultural pests. Several arthropod genomes are available at present and many others will be sequenced in the near future in the context of the i5K initiative, offering opportunities for reconstructing, modelling and comparing their metabolic networks. In-depth analysis of these genomic data through metabolism reconstruction is expected to contribute to a better understanding of the biology of arthropods, thereby allowing the development of new strategies to control harmful species. In this context, we present here ArthropodaCyc, a dedicated BioCyc collection of databases using the Cyc annotation database system (CycADS), allowing researchers to perform reliable metabolism comparisons of fully sequenced arthropods genomes. Since the annotation quality is a key factor when performing such global genome comparisons, all proteins from the genomes included in the ArthropodaCyc database were re-annotated using several annotation tools and orthology information. All functional/domain annotation results and their sources were integrated in the databases for user access. Currently, ArthropodaCyc offers a centralized repository of metabolic pathways, protein sequence domains, Gene Ontology annotations as well as evolutionary information for 28 arthropod species. Such database collection allows metabolism analysis both with integrated tools and through extraction of data in formats suitable for systems biology studies.Database URL:http://arthropodacyc.cycadsys.org/ Drosophila melanogaster (Mus musculus (Rattus norvegicus (http://arthropodgenomes.org/wiki/i5K)  , 9.Escherichia coli biology (http://biocyc.org/biocyc-pgdb-list.shtml for an updated listing). The upcoming deluge of fully sequenced genomes, driven by NGS technology, demands the development of a novel genomic infrastructure (http://acypicyc.cycadsys.org), a database dedicated to the pea aphid Acyrthosiphon pisum metabolism that was developed during the annotation phase of the genome of this insect   constitu biology . The qua biology , MetaCyc biology , HumanCy biology , AraCyc  biology , YeastCy biology , LeishCy biology  and Tryp biology . Such extructure . To conttructure . CycADS s insect . As manyad hoc input file (a BioCyc ‘Path-o-logic file’ format) used by the Pathway Tools software , the metabolic network of 28 arthropods with sequenced genomes , 21–45, 16, the msoftware  to produsoftware . GenomesOrganisms across all kingdoms of life are associated with microbial partners, with interactions ranging from parasitism to mutualism. Arthropods are no exception and can harbour microorganisms at their external surface, or internally as endosymbionts, gut microbiota, parasites or pathogens. Despite the use of specific DNA extraction protocols, massive sequencing of arthropod genomes may generate sequences contaminated by prokaryotic DNA. Since the ArthropodaCyc databases aims at collecting high-quality functional annotations, we decided to implement a pipeline for the detection of putative contaminant bacterial sequences, to be used before the reconstruction step of the arthropod metabolic pathways.First, genomic sequences, annotation files (GFF/GBK) and protein sets of each arthropod genome project were retrieved from public repositories . GenomicWe used multiple methods to perform a functional annotation: the online KAAS-KEGG annotation pipeline  and the Drosophila melanogaster and integrated in the ArthropodaCyc databases using CycADS as previously described , KEGG Orthology, Gene Ontology and MetaPhOrs orthology that were integrated in the databases. In the note section for each gene/protein page, the information relative to the annotation results are recorded to allow the researchers to evaluate the confidence for each putative function assigned to a protein . Useful It is important to underline that, as data formats in genomics can be very disparate depending on the source file format used , data on each arthropod genome were manually checked to ensure that a unique identifier for genes and products was present and that clear relations could be established among the different features. The flexible CycADS parsers were parameterized using its detailed configuration file . Finallyhttp://arthropodgenomes.org/wiki/Main_Page). Our collection takes advantage of CycADS , the enzyme involved in the synthesis of phenylalanine from phenylpyruvate are present in the pea aphid genome with one of them (ACYPI004243) specifically regulated during embryo development in aphids reproducing by parthenogenesis   in the lysine degradation pathway as uniquely missing in the genomes of the five dipteran species available in ArthropodaCyc (rsitans) . These eWe present here ArthropodaCyc, the most comprehensive collection of BioCyc databases for arthropods, which we expect to be of great interest for a broad community of scientists. Several genomes of arthropods are being sequenced and many more will be sequenced in the future as part of the i5K initiative. The CycADS pipeline empowers both the development and the update of the PGDB in ArthropodaCyc. Our databases are an arthropod research resource that is also linked, whenever possible, to single organism community based genomic databases, thus offering to the researchers an integrated access to different sources of annotations.Supplementary DataClick here for additional data file.


Sweet potato leaf curl virus-Jiangsu (SPLCV-JS), a sweepovirus cloned from diseased sweet potato plants in East China. Ectopic expression of the C4 in Arabidopsis altered plant development drastically with phenotypic changes including leaf curling, seedling twisting, deformation of floral tissues and reduction of pollen fertility, and seed number. Using bimolecular fluorescence complementation analysis, this study demonstrated that the SPLCV-JS C4 protein interacted with brassinosteroid-insensitive 2 (AtBIN2) in the plasma membrane of Nicotiana benthamiana cells. The C4 AtBIN2 interaction was further confirmed by yeast two-hybrid assays. This interaction led to the re-localization of AtBIN2-interacting proteins AtBES1/AtBZR1 into the nucleus which altered the expression of brassinosteroid (BR)-response genes, resulting in the activation of BR-signaling pathway. The interaction of SPLCV-JS C4 and AtBIN2 also led to the down-regulated expression of key genes involved in anther and pollen development, including SPROROCYTELESS/NOZZLE, DEFECTIVE IN TAPEL DEVELOPMENT AND FUNCTION 1, and ABORTED MICROSPORES, which caused abnormal tapetal development, followed by defective exine pattern formation of microspores and pollen release. Consequently, male fertility in the C4 transgenic Arabidopsis was reduced. The present study illustrated how the sweepovirus C4 protein functioned in host cells and affected male fertility by interacting with the key components of BR-signaling pathway.Sweepoviruses have been identified globally and cause substantial yield losses and cultivar decline in sweet potato. This study aimed to investigate the interaction between sweepovirus and plant host by analyzing the function of the viral protein C4 of  Ipomoea batatas), sweepoviruses also infect other Ipomoea species worldwide, such as I. nil or I. setosa  and phylogenetically distinct from the Old and New World begomovirus groups . Recentl. setosa , and hav. setosa . As a ve. setosa . Thus, tBeet severe curly top virus (BSCTV) can up-regulate the RING finger protein to affect BSCTV infection by regulating the host cell cycle (Beet curly top virus (BCTV) were shown to interact with 7 of the 10 members of the Arabidopsis thaliana SHAGGY-like protein kinase (AtSK) family  (Tomato leaf curl virus (ToLCV) (Tomato golden mosaic virus (TGMV)   and inhi (ToLCV)  and bipas (TGMV) .A. thaliana SHAGGY-like protein kinases are homologs of the evolutionarily conserved glycogen synthase kinase 3 (GSK3) family of serine/threonine kinases in animals , was identified from diseased sweet potato in East China. Infectivity analysis showed that SPLCV-JS could cause mild symptoms in both Nicotiana benthamiana and sweet potato, and this virus could synergize with heterologous beta-satellite DNA to enhance symptom severity and viral DNA accumulation  C4 was aligned with selected geminivirus C4 protein sequences from GenBank using the ClustalX program. After multiple sequence alignments, phylogenetic analysis was performed with MEGA software version 4.0 using the neighbor-joining method and the bootstrap option (1000 replicates) .C4 gene was amplified by polymerase chain reaction (PCR) from the SPLCV-JS genome using the primer pair C4-5FPK/C4-3RPP. The PCR product was digested with KpnI and PstI, and ligated into the binary vector pCAMBIA1301 , which was digested with the same enzymes to construct pCAMBIA1301-C4. Similarly, the full-length sequence of AtBIN2 amplified from the cDNA of Arabidopsis seedlings with the primer pair AtBIN2-5FPK/AtBIN2-3RPP was cloned into the vector pCAMBIA1301 using KpnI and PstI sites to construct pCAMBIA1301-AtBIN2.All primers used for gene cloning and plasmid construction are listed in Supplementary Table C4 was amplified with the primer pair C4-5FPNde/C4-3RPP2, and cloned into the vector pGBKT7 (Clontech) to form a fusion protein with the GAL4 DNA-binding domain (pGBKT7-C4). The N-terminal C4N55 was amplified with the primer pair C4-5FPNde/C4N55-3RP and cloned into the vector pGBKT7 via double digestion with NdeI and PstI to form pGBKT7-C4N55. The C-terminal C4C31 was amplified with primers C4C31-5FPNde and C4-3RPP2 and cloned into pGBKT7 via NdeI and PstI sites to generate plasmid pGBKT7-C4C31. AtBIN2 was amplified from the cDNA of Arabidopsis seedlings with the primer pair AtBIN2-5FPNde/AtBIN2-3RPE and then ligated into pGADT7 (Clontech) through NdeI and EcoRI sites to form a fusion protein with the GAL4 activation domain (pGADT7-AtBIN2).For yeast two-hybrid assays, the C4 was PCR-amplified and digested with BamHI and SpeI and ligated into vector 35S-N1-YFPN to generate an in-frame C-terminal fusion to the YFP N-terminal fragment downstream of the CaMV 35S promoter (C4-YFPN). AtBIN2 was PCR-amplified and digested with BamHI and EcoRI and ligated into vector 35S-N1-YFPC to generate an in-frame C-terminal fusion to the YFP C-terminal fragment (AtBIN2-YFPC).For the bimolecular fluorescence complementation (BiFC) assay, the AtBES1 and AtBZR1 were PCR-amplified without the stop codon, digested with KpnI and NotI and cloned into an intermediate cloning vector that was tagged with eGFP at the C-terminal. Then, the AtBES1-eGFP and the AtBZR1-eGFP fragments were amplified by AtBES1-5FPK/eGFP-3RPB or AtBZR1-5FPK/eGFP-3RPB, respectively, and digested with KpnI and BglII to ligate into the KpnI- and BamHI-digested expression vector pCAMBIA1301.For subcellular localization, Arabidopsis thaliana ecotype Columbia (Col0) was transformed following standard protocols .The total genomic DNA was extracted from the transgenic ern blot . DigesteLacZ reporter gene was initially monitored using X-α-Gal as the β-galactosidase substrate.For SPLCV-JS C4 self-activation assay, yeast strain AH109 (Clontech) was transformed with pGBKT7-C4, pGBKT7-C4C31, pGBKT7-C4N55, and the pGBKT7 control. For the C4 and AtBIN2 interaction assay, plasmids pGBKT7-C4, pGBKT7-C4C31, and pGBKT7-C4N55 were co-transformed with plasmid pGADT7-AtBIN2 into the yeast strain AH109. A yeast colony co-transformed with pGBKT7-53 and pGADT7-T plasmids was used as a positive control in the experiment. A yeast strain co-transformed with pGBKT7-lam and pGADT7-T was used as a negative control. All transformants were grown on a dropout medium lacking tryptophan, leucine, histidine, and adenine but containing 5 mM 3-amino-1,2,4-triazole (3-AT) to inhibit residual HIS3 expression. The expression of Nicotiana benthamiana plants were grown in pots at 23°C with a 16 h light/8 h dark photoperiod and 60% humidity. Before agroinfiltration, the solutions of Agrobacterium tumefaciens harboring binary C4-YFPN or AtBIN2-YFPC were mixed equally. N. benthamiana plants grown to the four to five leaf stages were agroinfiltrated with the bacterial suspension using a syringe. N. benthamiana plants agroinfiltrated with A. tumefaciens harboring C4-YFPN and YFPC or YFPN and AtBIN-YFPC were used as negative controls. The agroinfiltrated leaves were observed under a laser confocal microscope  at 36 h post- infiltration (hpi).N. benthamiana leaves were agroinfiltrated with the A. tumefaciens harboring the expression vectors AtBES1-eGFP and AtBZR1-eGFP, separately. The suspensions of A. tumefaciens harboring the expression vector AtBES1-eGFP/AtBZR1-eGFP and pCAMBIA1301-AtBIN2 were mixed equally and used for agroinfiltration to illustrate the effects of AtBIN2 and C4 on the subcellular localization of AtBES1/AtBZR1. In addition, the suspensions of A. tumefaciens harboring the expression vector AtBES1-eGFP/AtBZR1-eGFP, pCAMBIA1301-AtBIN2, and pCAMBIA1301-C4 were mixed equally for the agroinfiltration experiment. All agroinfiltrated leaves were observed under a laser confocal microscope (36 hpi).Arabidopsis seeds were surface-sterilized and sown on Murashige and Skoog (MS) agar plates with 30 g/L sucrose. The plates were cold-treated for 2 days at 4°C. For root growth inhibition assays, MS plates were placed in a vertical orientation for 4 days in a growth chamber at 23°C with a 16 h light/8 h dark photoperiod and 60% humidity, and the seedlings were then transferred to MS plates amended with 1 μM epibrassinolide (BL) for 5 days. The root length was measured before and after BL treatment, and the hormone sensitivity was measured as root growth inhibition.Arabidopsis seedlings or floral buds containing approximately stages 7–9 anthers using the Tiangen Plant RNAprep Pure Kit . The first-strand cDNA was synthesized using the Takara PrimeScript First-Strand cDNA Synthesis Kit  and then used for real-time quantitative reverse transcription-PCR (qRT-PCR) analysis. Primer sequences for each gene are listed in Supplementary Table Actin or a U-BOX gene At5g15400  staining buffer on the slide. After staining for 20 min, the slide was observed under an optical microscope in blue light (wavelength = 495 nm), and viable pollen grains emitted fluorescence. FDA staining was performed as described by Arabidopsis flowers that were just open were picked and the pollen grains were dusted on the solid pollen germination medium  fixing solution , dehydrated in a graded ethanol series , embedded in epoxy resin and sectioned. Anther transverse sections were stained with 1% toluidine blue at 42°C for 1–2 h .For scanning electron microscopic (SEM) analysis, wild-type and mutant inflorescences were fixed overnight in FAA, dehydrated in a graded ethanol series as described earlier, and samples were critical-point dried in liquid CO4 overnight at 4°C. The samples were then dehydrated in a graded ethanol series, infiltrated with a graded series of epoxy resin in epoxy propane, and embedded in Epon 812 resin. Ultrathin sections were stained with 1% uranyl acetate, followed by lead citrate solution and viewed under a TEM .For transmission electron microscopic (TEM) analysis, the buds approximately containing anthers at stage 9 were collected and fixed in 2.5% glutaraldehyde in phosphate buffer (pH 7.2) for 4 h at 4°C. After fixation, the tissue was washed and post-fixed with 1% OsOt-test was used for analyzing the significance of the differences between the wild-type and transgenic Arabidopsis lines. A value of P ≤ 0.05 or P ≤ 0.01 was indicated by an asterisk (∗) or double asterisks (∗∗), respectively.All data were presented as the mean ± standard deviation (SD) of at least three independent experiments. Statistical analysis was performed using the SPSS statistical package, version 15.0 . Student’s The 258-bp long open-reading frame of SPLCV-JS C4 encoded a 9.2-kDa protein consisting of 85 amino acids. The amino acids BLAST analysis showed that C4/AC4 proteins were diverse among different strains/isolates. The most similar ones were chosen to perform phylogenetic analysis. Most branches of the phylogenetic tree were less than 70 bootstrap values and the SPLCV-JS C4 protein was grouped together with other sweepoviruses  . The N-terminal G2 site in a conserved consensus N-myristoylation motif was required for membrane binding of both the bipartite begomoviruses EACMCV AC4 .Further sequence alignment of SPLCV-JS C4 was performed with three well-studied C4 proteins , including C4 of BCTV, AC4 of TGMV, and AC4 of CMCV AC4  and the CMCV AC4 . Besidesgenicity . Mills-LA. thaliana lines expressing the C4 protein were produced to better characterize the developmental defects induced by the C4 in planta in a system more amenable to genetic studies. The C4 gene was expressed under the control of the CaMV 35S promoter in order to have constitutive expression. After transformation, none of the transgenic Arabidopsis T1 generation showed a segregation ratio of 3:1, indicating that the transgene integration was not a single insertion. Indeed, Southern blot analysis showed that all the transgenic lines were multi-copy insertion , C4-transgenic seedlings showed elongated and twisted petioles and severely curled leaves . During growth, the whole plantlet was distorted and deformed, as illustrated by the C4-11 plant line . Different from Col0 in flowering status , the flower buds of the C4 transgenic plants were not completely enclosed by the sepals and the early flowers were poorly organized . The anthers were shrunk , and the siliques of the transgenic lines were distorted and had fewer seeds compared with the wild type after destaining . The phenotype severity was in accordance with the C4 expression level in C4-11, C4-36, and C4-37 seedlings . The yeast strain co-transformed with the C4 and AtBIN2 could grow well in the dropout medium (SD/-Trp/-His/-Ade) supplemented with 15 mM 3-AT and X-α-Gal, and was blue, in both combinations pGBKT7-C4 + pGADT7-AtBIN2 and pGADT7-C4 + pGBKT7-AtBIN2 . When C4 was truncated into two domains, N-terminal C4N55 and C-terminal C4C31, the C4C31 could interact with AtBIN2 weakly, and the C4N55 did not interact with AtBIN2 . Using the BiFC assay, it was further investigated whether the SPLCV-JS C4 protein interacted with the AtBIN2 in plant cells. The C4 alone was localized in both the plasma membrane and the nucleus of N. benthamiana protoplasts and epidermal cells as described in a previous study . No detectable signal was noted in the negative controls . The result indicated that SPLCV-JS C4 protein interacted with AtBIN2 in vivo, and the interaction mainly occurred at the periphery of the cell.The interaction between SPLCV-JS C4 protein and AtSK21 (AtBIN2) was confirmed by the yeast two-hybrid assay , different from the eGFP control where fluorescence was reported in the cytoplasm and nucleus . When co-infiltrated with C4, the patterns of AtBES1-eGFP and AtBZR1-eGFP in the N. benthamiana epidermal cells were unchanged 36 hpi  in comparison with the eGFP control , indicating that C4 protein did not directly affect the subcellular localization of AtBES1/AtBZR1. When AtBES1-eGFP or AtBZR1-eGFP were co-infiltrated with AtBIN2, both AtBES1-eGFP and AtBZR1-eGFP were mainly localized at the cell periphery and in the cytoplasm , suggesting that the proteins were phosphorylated and relocalized from the nucleus to the cytoplasm. This finding was in accordance with previous reports that AtBIN2 could phosphorylate AtBES1/AtBZR1 and target them for degradation in the proteasome , suggesting that the C4 protein reversed the effects of AtBIN2 on the subcellular localization of AtBES1/AtBZR1 and maintained the accumulation of these two transcription factors in the nucleus.Both AtBES1 and AtBZR1 tagged with eGFP protein were localized in the nucleus in the oteasome . In contCPD and DWF4, which were regulated by AtBES1/AtBZR1 , similar to the C4-expressing lines . Real-time qRT-PCR indicated that before BL treatment, the expression of CPD and DWF4 was much lower in the C4-expressing lines than the wild type. After BL treatment, the gene expression of both CPD and DWF4 was repressed to a lower level in all the C4-expressing lines and the wild type, compared with mock-treated samples . These data showed that the phenotypes with curled leaves, elongated and twisted petioles, and repressed expression of CPD and DWF4 of C4-expressing lines were similar to those of the wild-type plants treated with BL. These results indicated that, just like the case of BL-treated plants, the BR-signaling pathway was activated in the C4-expressing Arabidopsis lines.The effects of C4 on the phenotype and the expression of two BR biosynthetic genes 1/AtBZR1 , were fuC4 transgenic lines were studied to further characterize the impact of SPLCV-JS C4 on Arabidopsis development. With FDA staining, the pollen viability of C4-11, C4-36, and C4-37 was 15.9, 24.5, and 22.0%, respectively, which was significantly lower than that of wild type .The microsporogenesis and pollen morphology of Figure 7A). A considerable number of pollen grains remained in the inner wall of C4-11, C4-36, and C4-37 anthers , indicating that these C4-expressing lines were deficient in pollen release after anther dehiscence, similar to the previously reported BR-related mutants that were defective in pollen release . Under higher magnification, the wild-type exine had a network-like structure with many lacunae and three narrow apertures . In contrast, the lacunae of C4-11, C4-36, and C4-37 lacked the regularity exhibited by the wild type, and some of the apertures of the transgenic lines were sunken . Further examination of pollen development using TEM analysis showed that the microspores of C4-11, C4-36, and C4-37 at stage 9 were poorly organized compared with that of the wild type , and the exine was irregular . The complete bacula/tectum structure in the wild type was deficient in the transgenic lines . Moreover, the bacula/tectum structure had almost disappeared in some transgenic plants . The abnormal structure of the exine in the C4 transgenic lines could explain the deficiency of pollen grains release from the inner surface of the anther wall, much like the bri1-116 mutant of Arabidopsis , and their tapetal cells were larger and more vacuolated also . At stage 8, a number of vesicles containing fibrous materials could be found in the tapetum of the wild type , while the tapetum of the transgenic lines was still significantly vacuolated . Since tapetum is responsible for the synthesis and secretion of exine material, the abnormal development of tapetum may be an important reason for the reduced male fertility in the C4 transgenic plants.To investigate the morphological changes that lead to pollen grain defects in the transgenic lines, the anther and microspore morphology of the C4-11, C4-36, and C4-37 lines was examined using TEM. At stage 5, the cell arrangements of tapetum and microspore mother cells of C4-11, C4-36, and C4-37 were irregular compared with those of the wild type , DYSFUNCTIONAL TAPETUM 1 (DYT1), DEFECTIVE IN TAPEL DEVELOPMENT AND FUNCTION 1 (TDF1), ABORTED MICROSPORES (AMS), MYB domain protein 103 (MYB103), MALE STERILITY 1 (MS1), and MALE STERILITY 2 (MS2) . Furthermore, the expressions of several MS1 downstream genes were measured . This was in accordance with the case of the BR mutant cpd reported previously   were sigmeasured . It was eviously . Since Apression , C4-actiArabidopsis caused severe phenotypic changes and reduced male fertility. The BiFC assay showed that the C4 could interact with AtBIN2 mainly in the plasma membrane to activate AtBES1/AtBZR1 components, so that they could enter the nucleus and activate transcription of BR-response genes.Geminivirus C4 proteins are small proteins with diverse functions, and play an important role in virus–plant interaction . The C4 Arabidopsis plant lines that showed severe diseased-like phenotypes were the multi-copy insertion of transgene , which was verified by BR-responsive target genes such as CPD and DWF4 . Previous studies have reported that the S49 residue of BCTV C4 was critical for its interaction with AtSKs and phenotype induction . Instead, the C4C31 could induce slight phenotype changes in the transgenic Arabidopsis plants . Further experiments are needed to reveal whether and which sites of SPLCV-JS C4 protein are phosphorylated by AtBIN2. Besides AtBIN2, SPLCV-JS C4 may also interact with other AtSKs as the case of BCTV C4 protein . Histological, SEM, and TEM analyses of microspores and pollens in SPLCV-JS C4 transgenic Arabidopsis plants also revealed that the expression of C4 caused abnormal tapetum development and pollen maturation, leading to defective exine pattern formation of microspores and pollen release , a scenario similar to the BR-related mutants  genes. These BRBT genes are related to a wide range of cellular activities and biological processes, including responses to other hormones and environmental stress , for exae WRKY17 . The expBrassinosteroid has already been found to induce disease resistance in plants . The intOutbreaks of sweepovirus disease have been reported worldwide and caused substantial yield losses and cultivar decline in sweet potato. Nevertheless, sweepoviruses have not drawn much attention of virologists. Only recently several strains or isolates were identified and their infection assays established , 2014. AHB performed most of the experiments and drafted the manuscript. WF conducted part of yeast two-hybrid assay. PZ coordinated and designed the study and revised the article.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.


The assumption of additivity applied in the risk assessment of environmental mixtures containing carcinogenic polycyclic aromatic hydrocarbons (PAHs) was investigated using transcriptomics. MutaTMMouse were gavaged for 28 days with three doses of eight individual PAHs, two defined mixtures of PAHs, or coal tar, an environmentally ubiquitous complex mixture of PAHs. Microarrays were used to identify differentially expressed genes (DEGs) in lung tissue collected 3 days post-exposure. Cancer-related pathways perturbed by the individual or mixtures of PAHs were identified, and dose–response modeling of the DEGs was conducted to calculate gene/pathway benchmark doses (BMDs). Individual PAH-induced pathway perturbations  and pathway BMDs were applied to models of additivity  to generate predicted pathway-specific dose–response curves for each PAH mixture. The predicted and observed pathway dose–response curves were compared to assess the sensitivity of different additivity models. Transcriptomics-based additivity calculation showed that IA accurately predicted the pathway perturbations induced by all mixtures of PAHs. CA did not support the additivity assumption for the defined mixtures; however, GCA improved the CA predictions. Moreover, pathway BMDs derived for coal tar were comparable to BMDs derived from previously published coal tar-induced mouse lung tumor incidence data. These results suggest that in the absence of tumor incidence data, individual chemical-induced transcriptomics changes associated with cancer can be used to investigate the assumption of additivity and to predict the carcinogenic potential of a mixture.The online version of this article (doi:10.1007/s00204-016-1891-8) contains supplementary material, which is available to authorized users. Human exposures to environmental chemicals occur via contact with complex mixtures rather than single chemicals in isolation. Currently, quantitative hazard and risk assessment of chemical mixtures is conducted using two approaches: (a) a whole mixture approach or (b) a component-based approach that only examines prioritized mixture components. The whole mixture approach is used when data are available for the mixture in question or for a surrogate mixture that is sufficiently similar to the mixture under investigation. The whole mixture approach is preferred by several regulatory agencies such as Health Canada and the US Environment Protection Agency CCME ; howeverThe component-based approach relies on a series of established reference models centered on the assumption that individual components of the mixture do not interact, do not influence each other’s toxicity, and that their toxicological activity is additive. Accordingly, the toxicity of mixtures composed of similarly acting chemicals  is calculated as the sum of the concentrations/doses of selected chemicals, each adjusted using a relative potency scalar to convert amounts to equivalents of a potent reference substance . For mixtures of dissimilarly acting chemicals, the total toxic response of the mixture is calculated by summing the toxic responses of each chemical component . Although pragmatic, the CA approach is limited in scope since it can only estimate the combinatorial toxicity of the mixture up to the maximal toxic response of the weakest mixture component. Consequently, recent advances in mixtures toxicology have led to the development of alternative models, such as generalized concentration addition model (GCA)  are a class of structurally similar chemicals, many of which have been characterized as genotoxic carcinogens. They are formed during the incomplete combustion of organic materials and are ubiquitously found in many environmentally relevant complex mixtures to which humans are regularly exposed. Human health risk assessments of PAH mixtures commonly employ the CA approach that incorporates quantitative contributions from the 16 PAHs that were previously selected by the USEPA as priorities for concern and control . In a recent study, we selected 8 of the 16 priority PAHs that are genotoxic carcinogens and applied global transcriptomic profiling, in combination with other apical endpoint analyses, to determine if these 8 PAHs share a similar mode of action . Since the lung transcriptome was the most perturbed of the three tissues studied in our previous work in terms of transcriptomic responses and DNA damage induction, in this study the pulmonary transcriptomes following exposure to the mixtures were compared to the pulmonary transcriptomes from our previous studies of eight PAHs individually anthracene (BaA), benzo(b)fluoranthene (BbF), benzo(ghi)perylene (BghiP), benzo(k)fluoranthene (BkF), dibenz(ah)anthracene (DBahA), and indenopyrene (IP) were purchased from Cambridge Isotopes Laboratories (USA). All compounds had purity ≥98.8%.BaP and chrysene (CHR) were purchased from Sigma-Aldrich Canada Ltd. (Canada). Benz was prepared as described previously  or only eight PAHs (8PAH-Mix). The PAHs selected for the 4PAH-Mix and 8PAH-Mix constitute the panel of four and eight PAHs recommended for routine examination of food samples by the European Food Safety Authority because they are the genotoxic PAHs that were measured in coal tar mixtures used in oral carcinogenicity studies EFSA . ExposurThe animal care, exposures, and tissue collection procedures have been previously described  and purified using RNeasy Mini Kit  as described previously  under the accession number GSE87691.Details of the statistical normalization methods for the transcriptomic data were published previously  from each PAH congener  models were used to fit the gene expression dose–response data. For each gene, the best fitting model was selected based on (1) a nested Chi-square test (cutoff of 0.05) to choose between linear and polynomial models, (2) the lowest Akaike information criterion (AIC) values for the nested, Hill, and power models, and (3) curve goodness-of-fit P > 0.1. Genes with BMD values higher than the highest dose were excluded. The resulting gene BMD datasets were mapped to all pathways/processes described in Labib et al.  were used to generate predicted dose–response curves for the perturbed pathways for each PAH-containing mixture using three models of additivity . Thus, when we refer to predicted pathway perturbation or predicted dose–response curves, we are referring to those generated using the CA, GCA, and IA models. The complete strategy is described in detail in Fig. Step 1: Analysis of pulmonary transcriptional data for mice exposed to individual PAHs. In the first step, lung-specific transcriptional changes for eight individual PAHs were analyzed at each dose to identify significantly altered pathways associated with pulmonary carcinogenesis ; Step 2: Pathway selection for quantitative prediction of mixture responses. For the selection of pathways to be used in the additivity modeling, we first assessed if these eight pathways were also significantly perturbed following exposure to the three PAH-containing mixtures . Since alteration in expression of individual genes can be associated with several redundant biological functions and processes, the ability of a PAH or a PAH mixture to perturb biological pathways  was selected as the metric for prediction calculations , GCA, and IA models were then employed to predict pathway perturbations associated with mixture exposures. The formulas and methodologies employed for CA, GCA, and IA modeling are described below.The CA model is used to predict the concentrations of the mixture that would elicit a predetermined effect. In principle, concentrations of individual mixture components that are capable of eliciting the same effect on their own are added, after being multiplied by a scaling factor that accounts for potency differences. CA predictions were modeled according to Eq. BaP CCME    for 28 consecutive days by gavage. For the defined mixtures, the PAHs were mixed according to their proportional amounts in the complex coal tar extract. The exposure regime did not cause any overt signs of toxicity and there was no significant body weight loss in any of the exposed mice compared to vehicle treated controls. Lung tissue was collected three days after the final exposure, and global changes in the lung transcriptome were assessed. PAH mixture-specific transcriptomic profiles were established, and the results were quantitatively compared with transcriptomic profiles from pulmonary tissue of mice similarly exposed to eight individual PAH congeners that are constituents of the mixtures examined herein , KEGG pathways (EASE P ≤ 0.05), and IPA canonical pathways (P ≤ 0.05), as described in the Methods. Six main functional categories of pathways and processes were perturbed: DNA damage response, altered cell signaling, altered metabolism, immune/inflammatory response, cytoskeletal reorganization, and cellular homeostasis.Figure Clustering analysis of DEGs induced by the mixtures and individual PAH exposures  for each pathway were derived for each treatment. Online Resource 4 presents the median and mean gene expression changes for each PAH for all doses and pathways, as well as the fold change of the most affected gene for each pathway. Differences in maximal effects at the doses tested ranged from 48% for CHR and BghiP to 108% for BbF for AHR Signaling, from 56% for BkF to 71% for IP for Apoptosis Signaling, from 59% for BghiP, BkF, and CHR to 75% for BaP for Cell Cycle Signaling, from 58% for DBahA to 167% for IP for Circadian Rhythm Signaling, from 62% for BaA to 174% for CHR for the P53 Signaling, and from 48% for BaA to 106% for IP for Xenobiotic Metabolism Signaling.Curve fitting and BMD analysis for both probes and pathways altered following exposure to individual PAHs was conducted using BMDExpress. BMD analysis was conducted for a range of BMRs based on the maximum effect levels for the individual PAHs, and median pathway BMD values were calculated for each BMR. Median BMD values at BMR 10% are shown in Fig. Step 3: Mixture prediction using mathematical models of additivity. In order to determine if pathway perturbations elicited by exposures to individual PAHs, and an assumption of additivity, can be used to realistically predict pathway perturbations elicited by PAH mixtures, we compared predicted mixture pathway perturbations obtained using CA, GCA, and IA component-based models to pathway perturbations induced by the three tested mixtures. Next, the best predictive model was identified by comparing the BMD values for the predicted and observed dose–response functions. The model  that yielded a predicted BMD that is closest to the observed BMD was denoted the best model. The results obtained using each of the component-based models are outlined below.As expected, the CA model Eq.  predicteNext we used the GCA model Eq.  to prediFinally, we predicted the mixture-induced pathway perturbations using the IA model Eq.  generallPlease note that in order to assess the impact of filtering the transcriptome data by P value and fold change Steps 2 and 3 were repeated without the application of any filters. More specifically, as in the analysis described above, pathway perturbations were measured as the change in gene expression for all genes annotated to a pathway that passed the BMDExpress filter relative to controls; however, only genes that had a “present” call in at least one dose group were modeled. The results of this analysis were comparable with the results detailed above (Online Resource 5). Briefly, the CA model-predicted dose–response curves were positioned to the left of the observed dose–response curves for all pathways and mixtures tested, the GCA model predictions corresponded with the observed pathway perturbations for several pathways for the defined mixtures but did not correspond with the observed pathway perturbations for the CT-Mix, and the IA model yielded the most reasonable predictions that corresponded to the observed pathway perturbations for all pathways and mixtures tested.For the CA model, the predicted BMD50 values for the 4PAH-Mix ranged from a factor of 2.7-  to 9.3-fold  less than the observed values. For the 8PAH-Mix, the predicted BMD50 values ranged from a factor of 14.5-  to 30.2-fold  less than the observed values. For the CT-Mix, the predicted BMD50 values ranged from a factor of 1.3-  to 3.5-fold  less than the observed values  to 1.4-fold  less than the observed values to a factor of 3.2-  to 5.6-fold  greater than observed. For the 8PAH-Mix, the BMD50 values ranged from a factor of 1.1-  to 4.1-fold  less than observed to 1.1-fold  greater than the observed values. For the CT-Mix, the predicted BMD50 values ranged from a factor of 2.1-  to 5.2-fold  greater than observed.For the IA model, the predicted BMD50 values for the 4PAH-Mix, 8PAH-Mix, and CT-Mix were close to the observed BMD50 values. Specifically, for the 4PAH-Mix BMD50 values ranged from a factor of 1.2-  to 3.5-fold  greater. For the 8PAH-Mix, the predicted BMD50 values were the same as observed for Circadian Rhythm Signaling and 1.1-  to 2.4-fold  greater than the observed values. Similarly, for the CT-Mix, the predicted BMD50 values ranged from a factor of 1.3-fold  less than observed to 1.2-  to 1.7-fold  greater than observed.BMD modeling was conducted using the coal tar-induced lung tumor incidence data from the Culp et al.  study. BIn this study, we analyzed the pulmonary transcriptome of mice administered defined- and complex PAH mixtures by gavage daily for 28 days and compared these results with pulmonary transcriptomes of mice similarly exposed to individual PAHs  differentially expressed in livers of C57BL/6 mice treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB153), or a binary mixture of the two. The authors applied statistical modeling of the gene expression data and noted non-additive gene expression responses for several of the individual genes. Similarly, Zucchi et al.  of a PAH mixture relative to the actual toxicological response elicited by the mixture itself. For example, Lemieux et al.  noted thThe GCA model is an extension of the original CA model that takes into consideration the inability of CA model to account for differences in maximal effects between mixture components , at the molecular level they induced similar perturbations of pathways related to carcinogenesis. This suggests that the mechanisms underlying the carcinogenicity of PAH-containing mixtures are concordant. Previously, we showed that the pathways perturbed by the individual PAHs varied across PAHs and tissues  is the main contributor to a PAH-containing mixture’s carcinogenicity is of particular interest. BaP has traditionally been used as a point of reference in the estimation of excess lifetime cancer risk posed by PAH-containing mixtures CCME . This isThis work scrutinized the utility of transcriptomic data for evaluating different models of additivity to assess the toxicological properties of PAHs and PAH mixtures. Since the analyses are based on pathway perturbations related to carcinogenesis, the results have implications for the quantitative risk assessment of PAH-containing mixtures. To enhance the utility of transcriptomic profiling for quantitative risk assessment, we also compared the transcriptional BMD values with those for a relevant apical endpoint . The CT-Mix transcriptional BMDs for the cancer-related pathways Table  observedThis study used transcriptomics-derived pathway perturbations induced by individual PAHs to evaluate the applicability of such data in deriving predictions of pathway perturbations for synthetic and complex mixtures of PAHs. We demonstrate a strategy that used pathway-level transcriptional data and conventional mathematical models to evaluate several approaches to predict the effects of PAH-containing mixtures. In contrast to the assumption of dose additivity that is commonly employed for PAH-containing mixtures , our results imply that perturbations of toxicity pathways, some of which have been implicated in carcinogenesis, are best predicted by a model that assumes dissimilar modes of action for mixture components . The median change in expression value for DEGs associated with perturbed pathways and dose–response analyses of the gene expression data for perturbed pathways were successfully incorporated into existing predictive models of additivity. The transcriptional BMDs generated using the mathematical models of additivity resulted in values consistent with lung tumor development induced by coal tar . Since the pathway perturbations noted in our study are biologically linked to PAH-induced carcinogenesis, it is biologically plausible that the doses at which these pathways are affected might approximate the dose at which adverse outcome would be expected. As the public repository of genomics datasets for individual chemicals is populated, and the underlying mechanisms of toxicity are revealed, these types of data can be used for prioritizing mixtures for further toxicity testing.Our comparisons of observed and predicted responses under different additive models indicate that CA, which is traditionally used for risk assessment of PAH mixtures, over-estimates the responses compared to what is actually observed. This suggests that the traditional additive paradigm, which employs CA and PEFs to determine the effective amount of a reference compound , may yield conservative risk estimates. Additional work examining other PAH mixtures would be required to validate this conclusion.Online Resource 1Analytical assessment of purified coal tar (PDF 296 kb)Online Resource 2Gene lists for PAH-containing mixtures in lung tissues (PDF 6214 kb)Online Resource 3Hierarchical cluster analyses (PDF 330 kb)Online Resource 4List of median and mean gene expression changes for each pathway in lungs from mice exposed to the 4PAH-Mix, 8PAH-Mix, CT-Mix, and the individual PAHs (PDF 274 kb)Online Resource 5Impact of no statistical filtering on mixture predictions using mathematical models of additivity (PDF 185 kb)Below is the link to the electronic supplementary material.


Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs.Bugula neritina with and without its symbiotic bacterium “Candidatus Endobugula sertula”. We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR).XPathway tools have been applied to RNA-Seq data from the marine bryozoan http://alan.cs.gsu.edu/NGS/?q=content/xpathway.Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at The online version of this article (doi:10.1186/s12864-016-2823-y) contains supplementary material, which is available to authorized users. For the past several years, RNA-Seq has revolutionized biological research through the many advantages it provides. Because of RNA-Seq, it is easier to characterize transcripts and their isoforms, to detect genes without need of prior information in the form of probes or primers, and estimate expression levels of transcripts with good precision.MEGAN4 [MetaPathways [MinPath [In contrast to microarray data, RNA-Seq data allows frequency of expression of all transcripts without a priori knowledge of the gene sequence. RNA-Seq data can also account for all RNA transcripts producing enzymes for a given pathway. When applied to metatranscriptome data, the first challenge of pathway analysis is to decide which metabolic pathways are active in the sampled community . Recent software tools  conducted using a housekeeping gene also identified in the data. The housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), was chosen to normalize the qPCR data, as is standard practice. Based on our results, we applied qPCR experiments to quantify transcripts in the fatty acid elongation pathway [Our experimental study was performed with RNA-Seq data from the marine bryozoan, Bugula neritina data in the “The rest of the paper is organized as follows. Our formal models to analyze pathways and to infer pathway activity are presented in the next section entitled “In this paper we introduce a graph-based and an expectation maximization approach to identify specific differences between biological systems on the level of ortholog groups and pathways.p-value using parameters extracted from the network to answer two different statistical questions: (1) When and based on what parameter can we say that a set of proteins significantly map to a pathway? (2) What is the probability of finding such a mapping by chance given the data (transcripts/reads/proteins) and a pathway topology? Finally, significant metabolic pathways are selected by comparing the p-value of the original pathway with the ones from different bootstrapped samples. The expectation maximization method on the other hand uses the interaction among identified ortholog groups to infer pathway activity. The last part of the flow consists of validating both branches. First, we conduct differential expression analysis on all contigs extracted from pathways output by both branches. Secondly, a qPCR experiment is carried out on the contigs which have a fold change of 1.2 or more.Figure w be a pathway that is considered to be a set of enzymes represented by their ortholog groups w={p1,…,pk}. Since an ortholog group can have multiple functions and participate in multiple pathways, the pathways can be viewed as a family of subsets W of the set of all ortholog groups P. Below we start by introducing a uniform binary pathway activity model based on a discrete ortholog group expressionl model.In this section we present an EM-based algorithm for inferring pathway activity levels based on metatranscriptome sequence data. Let uniformity, namely that each molecule from an ortholog group participates in each active pathway with the same probability  and of binary activity, which postulates that a pathway is active if the level of ortholog group activity exceeds a certain (possibly pathway dependent) threshold. Formally, let δ(w) be a binary variable indicating the activity status of w, i.e., δ(w)=1 if w is active and δ(w)=0, otherwise. Also, let the activity level of pathway w be the summation over constituent ortholog groups g of their participation gw in w. Since we assume that each ortholog group g is equally likely to participate in each pathway containing it, it follows that fw of pathway w is given by The uniform binary pathway activity model is based on the assumptions of w is computed from its activity level fw and the threshold Tw as follows The binary activity status of δ(w)=1 to each pathway w∈W, i.e., Δ0(W)={δ0(w)|w∈W}←1 and then repeatedly updates the activity level according to (Δ0(W)=1,Δ1(W),Δ2(W),… starts to oscillate Δn+k(W)=Δn(W) or converges. In all our preliminary experiments, an oscillation with period k=2 is achieved in at most 10 iterations. Also the threshold Tw does not significantly change the order of pathways sorted with respect to their activity levels estimated as the mean fw after convergence. The model is represented in Fig. The uniform binary model described by Eqs. –2) can  can 2) cAlthough the uniform binary model allows the computation of pathway activity by assigning ortholog groups to pathways, it does have some limitations hindering it for capturing specific attributes of the metabolic network. For example, the binary uniform model assigns only value 1 or 0, if the ortholog group belongs to a pathway or not, respectively. This yes or no assumption is not always true since there may be a fractional part of an ortholog group belonging to different pathways. Moreover, the uniformity model is not easily applicable to natural processes because all assignments are never equally likely. Finally the model is not completely stable but rather periodic with some subsets of ortholog groups fluctuating between pathways.Ideally, a comprehensive pathway analysis method would take into consideration the position and role of each gene in a pathway, the efficiency with which a certain reaction is carried out, and some limiting factors . With genome data, it is possible to consider pathways size, gene length and overlap in gene content among pathways  to compuHenceforth, in our second approach, each pathway is viewed as a network of enzymes also called EC numbers (Enzyme Commission numbers) in order to compute their statistical significance. Significance of pathway activity in a sample is measured by the randomness of the positions of matched enzymes in the corresponding KEGG pathway graph. The randomness is measured using a permutation model for finding significant pathway alignments and motifs .Number of nodes. A node represents a protein that got mapped during BLAST. KEGG usually assigns a green color to those proteins in the graph.Density = (Number of edges)/(Number of nodes − 1)x. x is defined by:Fraction of 0 in and out-degree nodes. Let call this number x = ((number of nodes with out-degree = 0) + (number of nodes with in-degree = 0)) / 2 * (number of nodes)We also consider other criteria such as (1) number of green connected components, (2) Largest number of nodes in a connected component and (3) Largest number of edges in a connected component.This model assumes that the subset of expressed enzymes in an active annotated pathway should be connected. The enzyme permutation model finds the average vertex degree in the subgraph induced by expressed enzymes. Then the same parameter is computed for sufficiently many random permutations of enzyme labels. The statistically significant match should have density higher than 95 % of permutations. Specific characteristics of the graph taken into account in our analysis are: Using these metrics, we compute the density of the induced graph composed of only mapped proteins. We obtain the names of those proteins through EC numbers on the graph. Below, we present two graph-based models, the vertex label swapping and the edge swapping for random graph generation, to analyze pathways. This model is explained by the left side of Fig. In this model, we keep the same topology but we allow swapping of labels between two vertices  and then by the number of nodes having 0 in-degree or 0 out-degree . A pathway is significant if its p-value is less than 5 %, very significant if its p-value is less than 1 % and the most significant if its p-value is less or equal to 0.5 %.Differential analysis of pathway significance is based on the p1 be the p-value for pathway X in sample 1 and let p2 be the p-value for pathway X in sample 2. We say that pathway X is differentially significant between the two samples if the probability computed by the equation of probDiff(X) below is greater than 50 %. Let m=200 randomly generated graphs and the vertex label swapping model. In Fig. p-value p1=0.5 % (most significant pathway given the 200 graphs). In Sample 2, the mapped enzymes (filled rectangles) form a sub-graph with density = 1.375, number of 0 in/out degree = 0.22 for that sub-graph and its position after sorting is 148. This results in a p-value p2=74.5 % (not a significant mapping).For example, let us consider probDiff(ko00062)=.74 which is greater than 50 %. We conclude that ko00062 is differentially significant in the two samples.Based on the value of p1 and p2, Bugula neritina is a colonial marine invertebrate found in temperate waters around the world [B. neritina associates with an uncultured microbial symbiont, “Candidatus Endobugula sertula” [he world . B. nerisertula”  that hassertula” , 14. Becsertula” , 16, we B. neritina growing on floating docks were collected from four locations on the Eastern coast of the USA: Radio Island Marina and Yacht Basin Marina, Morehead City (North Carolina) in March 2012, Oyster public docks, Oyster (Virginia) in June 2012, and Indian River Inlet, Delaware City (Delaware) in June 2012. The colonies were rinsed in filtered sea water and preserved in TRIzol reagent  at −80 degree celsius prior to RNA extraction. Total RNA was then extracted from the preserved samples . The RNA was purified and treated with RNase-free DNaseI to remove any contaminating DNA. The purified total RNA was processed according to standard operating procedure for preparation of mRNA library for sequencing . The adapter-ligated cDNA library was hybridized to the surface of Illumina flow cell and sequenced on an Illumina HiSeq 2500 sequencing platform. The paired-end reads were assembled de novo using Trinity software package [Adult colonies of  package  and the B. neritina.The samples used for analysis include: Lane 1: symbiotic; Lane 2: non symbiotic (aposymbiotic) ; Lane 3:de novo co-assembly of RNA-Seq data and BLAST-ing resulting contigs against protein databases, with a certain confidence, we can infer the ortholog groups expressed in the sample. This is an important attribute of KEGG. We use KEGG to generate pathways from Trinity contigs and proteins. From the pathway databases we can easily extract the enzyme information associated with each pathway. We actually extracted all pathways along with all mapped proteins.By p-values.KEGG represents proteins as KO numbers and we also follow this representation. It uses KGML, an exchange format of KEGG pathway maps, to interact with external applications. The next step was to download all KGML files associated with the pathways using the API provided by KEGG. To convert KGML files to graph of node and vertices, we implemented and ran a novel tool called KGMLPathway2Graph. Mapping the output of KGMLPathway2Graph with KO numbers from KEGG analysis of our data, allowed us to compute patways with significant Bugula neritina (Lanes 1 and 2) are shown in Table m=200 graphs each generated after n=1000000 permutations of labels or edges. This high number of permutations is necessary to introduce sufficient differences in the generated graphs. Table Bugula neritina conditions.Pathway expression differences in symbiotic and aposymbiotic na Lanes  and 2 arIn Additional file From our statistical analysis, we identified some pathways that were differentially expressed (DE) by all methods. The next step was to experimentally validate these results. The first validation step is done through IsoDE, a software to analyze differentially expressed genes. Through KEGG, we are able to get all the proteins (contigs) participating in a pathway. IsoDE then indicates which of those contigs are also DE. From those DE contigs, we extracted the genes to be tested via quantitative PCR (qPCR), the next validation step.B. neritina. It is used to validate the gene expression given by IsoDE. Primers were designed using Primer 3 Plus [B. neritina colonies was converted to cDNA using Superscript III  using random hexamers according to the manufacturer’s instructions. cDNA was subjected to qPCR analysis after and expression in the samples was compared using the ΔΔCt method [B. neritina transcriptome (contig m.4423) was used for normalization [The goal of qPCR is to quantify the level of expression in the symbiotic and aposymbiotic r 3 Plus . Total Rt method . The glylization , 23. TheUsing IsoDE, nine gene pathways were chosen from KEGG and 2485 top differentially expressed contigs were taken from the list of all contigs. Within the selected pathways, there was a total of 637 contigs extracted. Each gene in these contigs was checked for fold change 1.2 or higher. Next, the number of genes that had significant fold change was compared to the total number of genes in the pathway. The fatty acid elongation pathway (ko00062) was chosen for further investigation by qPCR [B. neritina [ng/μL to 0.1 pg/μL. In order to use △△Ct method, every primer had to have the same efficiency and efficiency around 100 % [The fatty acid elongation pathway contains fourteen (14) KEGG mapped contigs and three (3) of those were found significantly differentially expressed. They are very-long-chain 3-oxoacyl-CoA reductase (DHB12), 3-ketoacyl-CoA thiolase (fadA) and 3-ketoacyl-CoA thiolase B, peroxisomal (THIKB). Once all the contigs in the pathway were checked, additional information was compiled: KEGG pathway and protein numbers, contig number, UniProt accession number and predicted fold change between symbiotic and aposymbiotic neritina . qPCR prneritina . The prind 100 % .B. neritina colonies. Following the Direct-zol RNA MiniPrep protocol , 50 mg of B. neritina tissue was homogenized and RNA was extracted. Then the RNA was further purified using the OneStep PCR Inhibitor Removal Kit . To eliminate any contaminating genomic DNA, a DNase I treatment was performed according to the manufacturer’s protocol . Finally, the RNA was further purified with the RNA Clean-up and Concentrator Kit . The concentration of RNA was quantified in triplicate using a Nanodrop spectrophotometer.RNA was extracted from symbiotic and aposymbiotic Both symbiotic and aposymbiotic cDNA were synthesized using the Superscript III protocol  with random hexamers. qPCR primer efficiencies were determined using qPCR. All qPCR reactions were performed using 7500 Fast Real-Time PCR system (Applied Biosystems) with hot-start Taq polymerase, SYBR Green fluorescent dye and ROX passive reference dye (Maxima SYBR Green/ROX qPCR Master Mix (2X), Life Technologies, Carlsbad, California, USA). The efficiencies for each primer pair were calculated using the slope of amplification curve in the equation E = 10(-1/slope) .fadA, DHB12, and THIKB were measured in symbiotic and aposymbiotic B. neritina with G3P acting as an endogenous control. The reactions were run in triplicate for symbiotic and aposymbiotic cDNA along with a negative template control. The Ct averages and standard deviations were calculated to find the Ct differences between the target gene and the control (△Ct) and △Ct standard deviation. △△Ct was calculated by subtracting the symbiotic or aposymbiotic △Ct by the symbiotic △Ct. This resulted in symbiotic △△Ct equal to 0 to compared the fold change between symbiotic and aposymbiotic expression levels.The expression levels of three genes, fadA had a predicted fold change of 2.91, while DHB12 had a value of 1.90 (non-significant difference), and THIKB equaled 2.84. qPCR analysis showed that when aposymbiotic gene expression was compared to symbiotic gene expression, fadA had 6.88 higher expression in aposymbiotic B. neritina. DHB12 had 0.66 times lower expression and THIKB had 2.52 higher expression, indicating that computational method closely predicted expression in independent biological samples.As presented in Table Although the EM and the graph-based methods worked on the same data generated by KEGG, the input to each approach were very different. For example, the Trinity output of sample1 on KEGG generates about 306 pathways. All of these pathways were considered for EM methods while only a small subset of 80 was used as input to each of the graph-based model. Different factors contributed to this reduced number of pathways analyzed in the edge/vertex swapping model: (1) We were not able to extract the KGML of all pathways from from KEGG; (2) We were not able to convert all KGML to actual graphs and (3) Some graphs did not carry enough mapping to be significant (we excluded pathways with less than 3 ortholog groups mapped).Consequently, the graph-based approaches yield considerable fewer differentially expressed pathways than EM methods although results from both models in the graph-based approaches were very consistent. Also, the graph-based analysis appears to be more stringent selecting only the pathways which are the farthest apart according to our statistic criteria.B. neritina with or without the symbiotic relationship still exhibits very similar metabolic reactions. As shown in Table Looking at the overall small number of differential pathways, we can say that B. neritina, is more challenging due to the lack of tools for genetic manipulation, for future work, we plan on the following. First, run XPathways tools in other organisms, including model organisms to further verify their efficacy and second, extend the model to handle not only metabolic pathways put also signaling pathways.Because working with non-model organisms, such as Bugula neritina RNA-Seq data with or without the symbiotic bacteria enabled the identification of metabolic pathways with differential activity. The qPCR experiment successfully validated our findings.XPathway tools are able to efficiently infer pathway activity as well as pathway significance, using an expectation maximization and a graph-based approach, respectively. Rather than trying directly to identify differentially expressed genes from RNA-Seq data for a non-model organism, XPathway tools allows to more accurately predict differential expression of genes using the wealth of information collected in the KEGG database for related organisms. Our experimental comparisons on


In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states. Knowledge base-driven pathway analysis  has beco“Omics” technologies are capable of identifying differentially expressed genes and metabolites associated with specific diseases. On the other hand, biological pathway databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) ,3,4 or PSeveral applications have been implemented to visualize and analyze “Omics” data in the context of known biological pathways . To thisPathway-based analysis techniques can be grouped into three generations of approaches : (i) OveFirst-generation methods statistically evaluate the number of altered genes in a pathway with respect to the set of all analyzed genes ,10,11,12Second-generation methods tried to compensate some disadvantages of ORA approaches ,15. TheyThe third generation class, Pathway Topology-based methods, can be divided into two subgroups of tools. The first one examines the effect of a phenotype at the global level. The second, and more recent approach, extracts subpathways specifically related to the phenotype under study.p-value is computed by means of exponential distribution.In Draghici et al. , an analp-value computed in ORA style to make a full assessment of the statistical significance of the perturbation of each pathway.The analysis presented by Draghici et al.  has beenTo reduce the number of false positives, Vaske et al.  presenteHowever, both SPIA and PARADIGM completely ignore post-transcriptional regulatory interactions involving miRNAs. To overcome this limitation, Calura et al.  developep-values), as well as a predicted degree of alteration for each endpoint .Recently, Alaimo et al.  proposedThe second subgroup of pathway perturbation methods focus on subpathways , which have the potential to represent the underlying biological phenomena more accurately. The central idea in shifting the efforts toward subpathways is that when a subpathway responds to a specific condition , its corresponding pathway should also respond. The high redundancy of pathways together with their high degree of crosstalk suggests that subpathways respond more effectively and sensitively than the whole pathway. Indeed, key subpathway regions are more relevant for interpreting the associated biological phenomena than whole-pathway approaches. In addition, the cross-talk among subpathways could allow going beyond existing pathway knowledge. Furthermore, perturbations in subpathway regions of metabolic pathways could allow shedding light on understanding the causes of diseases ,23.R-based tool. Once the user provides genes and metabolites of interest, the system identifies metabolic subpathways by: (i) mapping genes and metabolites of interest to graphs of pathways after graph-based reconstruction of metabolic pathways; (ii) locating subpathways within pathways according to signature nodes; (iii) evaluating the statistical significance of each subpathways using an hypergeometric test. Subpathway-GM improves the identifications of subpathways with respect to previous methodologies, by taking into account that, from a biological perspective, dysfunctional genes should be closely related to dysfunctional metabolites in pathways.In Li et al. , authorsIn Judeh et al. , authorsR package. The tool enables users to perform customized analysis on the problem under investigation through several operation modes to uncover local perturbations within networks.In , authorsR package. It aims to identify abnormal metabolic subpathways mediated by miRNAs. The pipeline contains three main components: (i) creation of reconstructed KEGG metabolic pathway graphs (RMPGs) that integrate miRNA-target interactions (supported by low-throughput experiments), by converting KEGG metabolic pathways into graphs with genes as nodes; (ii) mapping of condition-specific genes and miRNAs into RMPGs and identification of miRNA-mediated metabolic subpathways; (iii) evaluation of the significance of candidate subpathways using the hypergeometric test. Subpathway-GMir provides a platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles of miRNAs in diseases. The system has been tested also with The Cancer Genome Atlas (TCGA) data.Subpathway-GMir  uses a lMore recently,  proposedAlthough the literature is rich with useful tools capable of extracting subpathways from expression data, a simple interface, connected with updated sources, is needed. Moreover, the addition of miRNAs in the subpathway extraction technique is essential since many alterations of these post-trascriptional regulators have been found in several cancer types.In this paper, we propose SPECifIC (SubPathway ExtraCtor and enrICher), a technique for the extraction, visualization, and enrichment of substructures obtained from a meta-pathway built by combining KEGG pathways. The methodology extends MITHRIL  to allowComparing subpathway extraction and enrichment methods is a complex process. Therefore, we focused on a case study based on two cancer types from TCGA: BRCA (Breast invasive carcinoma) and COAD (Colon adenocarcinoma).Breast cancer is the most common malignancy in women in several countries . It is aAccordingly, three basic therapeutic groups  have beeColon and rectal cancer, which we refer to as colorectal carcinoma (CRC), are major causes of cancer-associated morbidity and mortality all over the world . Althougp-values, and compared. Since DEsubs returns only the set of significant terms enriching a subpathway, without reporting its p-value, we decided to rank each term by the number of subpathways in which such a term is significant.To assess the performances of our methodology, we performed a comparison with Subpathway-GM , SubpathWe opted to run a comparison using only the KEGG pathways database, since the correct identification of the involved pathophysiological process in a subpathway is crucial for subsequent analysis steps. To this end, pathway representing pathological states were removed from enrichment results. All methodologies have been tested using the same set of pathways, enrichment terms, and expression data, although SubPathway-GMir experienced issues when ranking non-metabolic pathways.p-value in order to analyze non-significant results). If a term enriched more substructures, the minimum p-value among all subpathways was used. The resulting set of terms, ranked by p-value, was used, and the top-20 pathways were further analyzed to determine the performances of our methodology . Then, for each statistically significant subpathway, functional enrichment was performed, and all pathway terms were collected , we performed an enrichment using disease terms taken from DisGeNET ,38, in oDisease-specific subpathways extraction is a fundamental process to accurately study processes at the basis of pathological phenotypes. From these substructures, the identification of significantly involved pathways is necessary to determine if the identified changes are at the origin of the disease or a secondary consequence. Furthermore, determining redundant pathways that lead to the same outcome is of great interest especially for precision medicine to identify and, if necessary, prevent drug resistance phenomena. In the following, we will provide a description of the results obtained from SPECifIC in comparisons with the other methods: Subpathway-GM , Subpathch lists . In Figup < 0.01). The list is reported in SPECifIC identified 163 potential pathways associated with BRCA, of which 74 are statistically significant  scarce or no evidence was found in literature.SubPathway-GM was able to identify 25 potential pathway terms, of which 14 were shared with SPECifIC . Cell cyDEsubs identified 22 terms, of which 13 were in common with our approach . Most ofSubPathway-GMir was able to identify 53 potential pathways, of which only six were shared with our technique . This mep < 0.01). The list is reported in estrone into estradiol, highly expressed in the colonic epithelium, may contribute to sex steroid-mediated effects on CRC development [SPECifIC identified 165 potential pathways associated with CRC, of which 31 are statistically significant  automatic selection of NoIs (if no user-specified ones available), extraction of subpathways, and (iv) enrichment analysis. This is available through a web interface that allows a user to select one disease, identify a set of NoIs, and visualize the results.Our algorithm performs the extraction and enrichment of disease-specific subpathways starting from a set of Nodes of Interest (NoIs). A user may specify his own NoIs or employ an automated selection procedure. The methodology performs computation in four main steps: (i) construction of the meta-pathway; (ii) execution of the MITHrIL algorithm to compute nodes perturbations and To analyze pathways and determine the impact of differentially expressed genes (miRNAs), we employed the MITHrIL algorithm  for its Meta-pathway construction. In order to extract human-specific subpathways, all KEGG pathways were merged to build a single directed network using a two step procedure. First, all disease-specific pathways were removed to form a reference set of pathways. Therefore, all nodes and edges present in the reference set were put together, removing duplicates, building a meta-pathway, which represents the human organism.Subsequently, the meta-pathway was annotated with experimentally validated miRNA-target interactions using the procedure described in Alaimo et al. , and a pSuch an analysis is computationally intensive. Therefore, to reduce computational resources, all nodes of the meta-pathway were sorted using a DFS-like (Depth-First Search) algorithm  to find p-values.Computing nodes’  The extraction of subpathway is based on the search of statistically significant substructures within the meta-pathway. This computation is based on the statistical significance of each pathway node, which is the probability of observing a perturbation factor lower than the one computed for the null model.n be a node in the meta-pathway and p-value for n is estimated as:p-values are therefore adjusted on multiple hypothesis, and the results are stored for subsequent computations.Therefore, let o et al. , we randp-values computed by means of the extended MITHrIL algorithm. Our methodology is, therefore, able to extract five types of substructures starting from a set of NoIs: (i) paths; (ii) tree; (iii) neighborhoods; (iv) induced sub-graphs; and (v) communities of induced sub-graphs. The computation of trees, path and neighborhoods is obtained by running a modified version of the Breadth-first search (BFS) algorithm [The disease-specific subpathway extraction process starts from the previously built meta-pathway annotated with perturbations and lgorithm  applied p-value of the nodes: each path from the source is extended with a new node if its p-value is greater than a user-specified threshold. Paths identified by running a BFS search make up a tree whose root is the source node.The BFS algorithm is a search method in a graph, which, starting from a node (source), obtains all the paths to the other reachable nodes of the graph. The algorithm has been modified to take into account the p-value can be computed by aggregating its nodes p-values employing the Empirical Brown’s method [k dependent p-values, Brown [i-th p-value, p-value for a k-nodes subpathway.For each subpathway identified by the algorithm, a s method . Given as, Brown  estimateThe extraction algorithm also returns subgraphs induced from the BFS visit tree, and the community obtained by merging all induced sub-graphs.p-values obtained using Empirical Brown’s method are adjusted on multiple hypotheses using the Holm–Bonferroni [All results are then filtered by removing those subpathways that have a number of nodes below a minimum user-specified threshold, and nferroni  correctip-value are discarded. Finally, all duplicates are removed, and a preliminary list of NoIs is generated. The sub-pathway extraction procedure is then applied to such a list, and each resulting path is analyzed to identify overlapping results. The latter are removed in order to keep only unique results, removing duplicates.The selection of NoIs is a fundamental process for the correct extraction of disease-specific sub-pathways. For this purpose, a semi-automated procedure has been devised for the unbiased selection of NoIs. The user specifies a significance threshold for pathways. Then, all significant pathways are selected using such threshold, and all nodes of these pathways are analyzed. The nodes that exhibit a non-significant S of the meta-pathway, we can compute a p-value t as:k is the number of nodes in S annotated with t, M is the number of nodes in the meta-pathway annotated with t, N is the number of annotated nodes in the meta-pathway, and n is the number of annotated nodes in S. Finally, all p-values are adjusted on multiple hypotheses using the Benjamini–Hochberg method [p-value lower than an user-specified threshold are kept by the analysis.All extracted subpathways are examined by means of enrichment analysis in various biological and pharmacological features. We are able to estimate associations with (i) pathways; (ii) gene ontology (GO) terms; (iii) diseases; and (iv) drugs. The enrichment analysis is performed by employing the methodology of Li et al. . Given ag method  to contrSince some terms might annotate few nodes with respect to the total, they could appear significant for a subpathway even if they annotate a single node. Therefore, to avoid this shortcoming, we chose to exclude annotations found in less than a third of a subpathway nodes.R statistical environment [In order to ensure broad accessibility and usability, our algorithm has been implemented in a web application available at . The serironment . The webironment  for dispOur platform has been designed to perform disease-specific analysis on expression data provided by The Cancer Genome Atlas. We collected all patients’ expression profiles of genes  and miRNAs  using the TCGA dump provided in Alaimo et al. . TherefoOn the aforementioned dataset, we applied the pipeline described in Alaimo et al.  in orderp-value of each NoI. If the p-value for a node is greater than the maximum threshold, it will be discarded from the analysis. The user can also vary p-value thresholds and the minimum number of nodes in substructures  and the substructure (XGMML format). The list of terms is displayed in a table together with their identifier, number of annotated nodes, p-value, adjusted p-value by applying the Benjamini–Hochberg method [Functional enrichment results are displayed in a page together  with a Cg method , and theAll tables in the web interface give the user the ability to filter results in order to facilitate subsequent analysis.In this paper, we proposed a novel methodology, SPECifIC (SubPathway ExtraCtor and enrICher), for extracting, visualizing, and enriching substructures obtained from a meta-pathway built by combining KEGG pathways. The methodology extends MITHrIL  to allowThe technique has been compared with three other methodologies, Subpathway-GM , SubpathOur findings show that SPECifIC is able to produce results in agreement with the current knowledge of the pathophysiological processes of BRCA and COAD, and with the results produced by the three competitors. In addition, the pathways, which the method discards, do not show any significant correlation with the two pathological phenotypes, according to literature. However, our results are larger than other competitors, leading to the need of a better filters in order to benefit more from them.On the other hand, our analysis clearly highlights that, although all the compared methods give in general biologically sound results, their consensus is certainly small.Future developments should also integrate information coming from expressions of other classes of non-coding RNAs, whose alteration has been correlated in different pathological phenotypes, and the introduction of gene mutations and copy number variation in order to detect more disease-specific subpathways based on the pattern of altered genes rather than their expression.