Flavonoids are frequently used as dietary supplements in the absence of research evidence regarding health benefits or toxicity. Furthermore, ingested doses could far exceed those received from diet in the course of normal living. Some flavonoids exhibit binding to estrogen receptors (ERs) with consequential vigilance by regulatory authorities at the U.S. EPA and FDA. Regulatory authorities must consider both beneficial claims and potential adverse effects, warranting the increases in research that has spanned almost two decades. Here, we report pathway enrichment of 14 targets from the Comparative Toxicogenomics Database (CTD) and the Herbal Ingredients’ Targets (HIT) database for 22 flavonoids that bind ERs. The selected flavonoids are confirmed ER binders from our earlier studies, and were here found in mainly involved in three types of biological processes, ER regulation, estrogen metabolism and synthesis, and apoptosis. Besides cancers, we conjecture that the flavonoids may affect several diseases via apoptosis pathways. Diseases such as amyotrophic lateral sclerosis, viral myocarditis and non-alcoholic fatty liver disease could be implicated. More generally, apoptosis processes may be importantly evolved biological functions of flavonoids that bind ERs and high dose ingestion of those flavonoids could adversely disrupt the cellular apoptosis process. Flavonoids are a group of natural compounds sharing the basic parent structure of 2-phenyl-1,4-benzopyrone. They are widely distributed in modern human diets, including fruit, vegetable, tea and even wine [Flavonoids can be generally grouped into six chemical classes: flavones, flavonols, flavanones, flavanols, anthocyanidins and isoflavones . EpidemiCaveat emptor seems to be the consumer’s smartest tactic when choosing supplements.The supplement industry is a conglomeration of small to large companies comprising product pipelines. The companies are required to ensure that products are safe, but not to prove so with rigorous testing—so they do not. The Food and Drug Administration (FDA) will only take action if strong evidence appears of harm, or if specific marketing claims are deemed by the FDA to be untruthful and not misleading—see Dietary Supplement Health and Education Act of 1994 . Thus, mThe health conscious public has become increasing aware of the potential health benefits of endocrine active chemicals such as the flavonoids. The active chemicals have been conserved across many plants and especially vertebrate metazoans through co-evolution. In certain cases, plants use isoflavones as biochemical defense weapons against predation. Not surprisingly, the market responded with various flavonoids compound formulations. In some formulations, ingested dose could be considered as high. For example, the recommended daily dose of quercetin supplement is up to 1136–2272 mg , comparein silico predictive models for assessing endocrine activity in the FDA-regulated products and environmental chemicals [The endocrine system contains glands that produce and release hormones that are carried to distant target organs in the body through circulation. A wide range of tissue-dependent biological responses are resulted from hormone binding endocrine receptor complexes, such as growth and development ,23, vaschemicals ,46,47,48We retrieved 22 flavonoid’s ER binding activity data from our earlier competitive binding assays . Basic iCTD  is a comHIT  target iThe KEGG  pathway Pathway enrichment analysis is the predominant approach used for deciphering the biological functions associated with a list of genes . In thisp-value using Equation (I) and an enrichment factor (EF) using Equation (II).a is the number of common targets associated with pathway i; n is the number of all genes in pathway i; M indicates the number of all genes associated with all pathways containing the common targets; and A is the number of all common targets covered by the pathways.For each pathway i, we calculated a a ≥ 2 (the enriched pathways contain at least two common targets), EF ≥ 3 and p-value ≤ 0.05 were used to designate enriched pathways.In the enrichment analysis, the criteria i.e., two or more common targets, p-value ≤ 0.05 and EF ≥ 3. The pathways designated as enriched are listed in After mapping the 14 common targets to the 223 biological process pathways of KEGG, we found 11 common targets were associated with 35 biological process pathways. Among the 35 biological process pathways, eight were designated as enriched based our stated criteria, This category contains two pathways: prolactin signaling pathway and tryptophan metabolism pathway. Prolactin is a polypeptide hormone that binds to prolactin receptors. The expression of prolactin is modulated by estrogen . The 22 endogenous estrogens and androgens [CYP1A1, CYP1B1 and CYP19A1 are involved in the biosynthesis of estrogens. Steroid hormone biosynthesis [CYP1A1, CYP1B1 and CYP19A1. Generally, estrogens are removed from the body through transformation into estrogenically inactive metabolites. The concentration of estrogens could be regulated by the common targets (CYP1A1 and CYP1B1) in this pathway. Specifically, the estrogenically inactive metabolites of 2-hydroxyestradiol and 4-hydroxyestradiol from estrogens are catalyzed by CYP1A1 and CYP1B1, respectively [This category consists of three pathways: ovarian steroidogenesis, steroid hormones biosynthesis, and metabolism of xenobiotics by cytochrome CYP450. Ovarian steroidogenesis comprises two processes: converting cholesterol to androgens and transforming androgens into estrogens. Since the 22 flavonoids are ER binders, they may influence the concentration balances between ndrogens . In thisynthesis  is the pectively .The last category of pathways involves cellular apoptosis. Estrogen’s role in regulating apoptosis has strong evidence ,58. In fp-value ≤ 0.05 and EF ≥ 3. Interestingly, most of them were involved in five disease types: cancer, infectious diseases, neurodegenerative diseases, cardiovascular diseases, endocrine and metabolic diseases.We also investigated the potential disease pathways influenced by the flavonoids’ common targets. After mapping the 14 common targets to the 71 human disease pathways, we found nine common targets that could be involved in the disease pathways. In total, 42 disease pathways contained the common targets of the 22 flavonoids. in vitro and animal experimental data showing associations between cancer risk and flavonoid chemicals such as quercetin [Several epidemiological studies ,62,63 stuercetin  and (-)-uercetin . Howeveruercetin . In the In response to bacterial or parasitic infections, apoptosis would be initiated by the host cells. Such innate immune response could remove the external pathogens at the early stage of infection. Flavonoids could modulate apoptosis by targeting Casp3 in the infection process of pertussis, legionellosis, amoebiasis, toxoplasmosis and hepatitis B. In addition, during the subsequent inflammation process followed by infection, flavonoids could regulate inflammation through TNF-alpha. Some flavonoids also have antimicrobial effects, such as antibacterial  and antiAmyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease, causing muscle weakness and atrophy throughout the body. Astrocytes and microglias are involved the neurodegenerative process of ALS ,71. FlavEpidemiological evidence on the reduction of the CVD risk in high flavonoid diets is very consistent . In a 7-Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease associated with lipid deposition and inflammation in hepatocytes. Insulin resistance is an important factor for progressive steatohepatitis in NAFLD . Flavonoversus benefit profiles. Identifying the targets beyond ER itself is the starting place. Molecular docking [Chemicals exert their biological functions through binding or interacting with the targets. The overall target characteristics of flavonoids that bind ER may provide clues for more fully delineating their risk  docking ,81,82,83 docking ,85, text docking ,87 are aPathways are manually curated series of molecular interactions that encode diverse research results that converge and elucidate a complex biological process connecting molecular entities and events. As long as more and better data are forthcoming, pathways will increasingly encompass the vital interconnections that are the basis of life. Pathway enrichment analyses provide a means for disentangling more accurately the true biology in a system so complex that false random solutions abound. In our present work, we have applied reasoning to filter data to increase actual cause and effect and reduce random correlations, in this case to help reveal the multiple biological meanings and actions associated with flavonoids, based on knowledge about their molecular targets. According to our pathway enrichment results, CYP1A1, CYP1B1, CYP19A1, Casp3, TNF-alpha are genes associated with multiple pathways having substantial cross talk. For example, CYP1A1, CPY1A2 could participate in estrogen metabolism and biosynthesis as well as tryptophan metabolism. Casp3’s and TNF-alpha’s apoptosis suggest their affiliation with the pathological development of across several diseases. Flavonoids could regulate the cell death of astrocytes and microglias in brain, myocytes in heart, as well as cancer cells or abnormal or injured cells. Given that apoptosis is ubiquitous in biological process across cell types, risks incurred with flavonoid doses higher than what humans have evolved with should be concerning and more carefully studied. Indeed, several identified pathways based on targets in CTD and HIT had already been validated by previous studies of flavonoids compounds. For example, apigenin can induce the apoptosis process of NCI-H460 cells by up-regulating expression of Bax and Casp3 and down-regulating the expression of Bcl-2 . QuercetIn addition, co-incubation with lamivudine (3TC), entecavir (ETV), or adefovir (Ade) further enhanced the quercetin-induced inhibition of Hepatitis B virus (HBV) replication. This inhibition was partially associated with decreased heat shock proteins and HBV transcription levels. The results indicate that quercetin inhibited HBV antigen secretion and genome replication in human hepatoma cell lines, which suggests that quercetin may be a potentially effective anti-HBV agent.Here, we used pathway enrichment analysis on carefully vetted data to derive a profile of potential risk associated with 22 flavonoids that bind highly conserved estrogen receptor nuclear proteins. To improve the likelihood of enriched pathways with true positives, we restricted data to 14 targets of flavonoids identified by both CTD and HIT manually curated databases. Enriched pathways of targets were found to be mainly involved in three paramount biological process types, ER regulated processes, estrogens’ metabolism and synthesis, and as well as apoptosis. Among the findings is a cause for concern that flavonoids via apoptosis pathways could be especially important for cancer, infection, amyotrophic lateral sclerosis, viral myocarditis and non-alcoholic fatty liver disease. Further attention should be paid to potential apoptosis effects of flavonoids that bind ERs. The approach we present should provide ever improving clues to potential risk posed by the important flavonoids that have long been a component of human food, and that is now prominent in food supplements at doses far above historical norms.


MicroRNAs (miRNAs) are small non-coding RNA molecules whose primary function is to regulate the expression of gene products via hybridization to mRNA transcripts, resulting in suppression of translation or mRNA degradation. Although miRNAs have been implicated in complex diseases, including cancer, their impact on distinct biological pathways and phenotypes is largely unknown. Current integration approaches require sample-matched miRNA/mRNA datasets, resulting in limited applicability in practice. Since these approaches cannot integrate heterogeneous information available across independent experiments, they neither account for bias inherent in individual studies, nor do they benefit from increased sample size. Here we present a novel framework able to integrate miRNA and mRNA data  available in independent studies  allowing for a comprehensive analysis of the given phenotypes. To demonstrate the utility of our method, we conducted a meta-analysis of pancreatic and colorectal cancer, using 1,471 samples from 15 mRNA and 14 miRNA expression datasets. Our two-dimensional data integration approach greatly increases the power of statistical analysis and correctly identifies pathways known to be implicated in the phenotypes. The proposed framework is sufficiently general to integrate other types of data obtained from high-throughput assays. High-throughput technologies for gene expression profiling, such as DNA microarray or RNA-Seq, have transformed biomedical research by allowing for comprehensive monitoring of biological processes. A typical comparative analysis of expression data, e.g. patients versus healthy samples, generally yields a set of genes that are differentially expressed (DE) between the conditions. These sets of DE genes contains the genes that are likely to be involved in the biological processes responsible for the disease. However, such sets of genes are usually insufficient to reveal the underlying biological mechanisms. In addition, due to inherent bias and batch effects present in individual studies, independent experiments studying the same disease often yield completely different lists of DE genes, making interpretation extremely difficult12www.biocarta.com).In order to translate these lists of DE genes into a better understanding of biological phenomena, researchers have developed a variety of knowledge bases that map genes to functional modules. Depending on the amount of information that one wishes to include, these modules can be described as simple gene sets based on a function, process or component 9Non-coding RNAs, especially microRNAs (miRNAs) have come into the spotlight more recently. Data describing observed and predicted interactions between miRNA and mRNA is accumulating rapidly in several databases, such as miRTarBase19Two of the most widely used approaches to include miRNA expression data for the purpose of pathway analysis are MicrographiteThe first drawback of these tools for integrating miRNA and mRNA is that they need sample-matched data. In other words, these tools require both data types to be available for each individual patient. This reduces their practical availability since sample-matched data is relatively rare and difficult or expensive to obtain. Therefore, the vast amount of available expression data, both mRNA and miRNA, is not fully utilized.cancergenome.nih.gov), ArrayExpressocg.cancer.gov/programs/target) store thousands of datasets, within which there are independent experimental series with similar patient cohorts and experiment design. Expression data, mRNA as well as miRNA, are particularly prevalent in public databases, such that some disease conditions are represented by half a dozen studies or more.The second drawback is that these methods are unable to exploit heterogeneous information available across independent studies. Therefore, they are not able to address the inevitable bias inherent in individual studies. It would be tremendously beneficial if all datasets associated with a given condition could be analyzed together because of the increased power expected to be associated with the much larger number of measurements in the combined dataset. Large public repositories such as Gene Expression Omnibus23vertical integrative analysis, while that of combining multiple unmatched studies using the same data type is referred as horizontal meta-analysisorthogonal classes of data integration. For microarray data, the method proposed by Rhodes et. al.horizontal approaches to combine multiple microarray datasets, using Fisher’s method. Since then, other sophisticated approaches have been proposed for the integration of multiple gene expression datasets, on both the gene and pathway levels2829The process of combining sample-matched data of different types is referred to as horizontal and vertical data integration. Each building block or technique of our pipeline can be easily substituted for by any other similar technique to suit the purpose of future analysis.Here we propose a framework that is able to integrate unmatched miRNA and mRNA data obtained from many independent laboratories. While validated in the context of pathway analysis, the framework can be modified to adapt to other domains or applications. This framework is not meant to compete with any existing approach, but to serve as a bridge between We illustrate the new framework using 15 mRNA and 14 miRNA datasets related to two human diseases, colorectal cancer and pancreatic cancer. They were generated by independent labs, for different sets of patients. For both conditions, the new framework is able to identify pathways relevant to the phenotypes. We demonstrate that the accuracy is obtained only by integrating the data in both directions .To the best of our knowledge, this is the first article that describes an orthogonal meta-analysis. Our results suggest that orthogonal classes of integrative techniques can be further combined to unravel the underlying mechanisms of complex diseases. With vast databases of various data types being made available, this framework is expected to be widely applicable because of its relaxed restrictions on the data being integrated.The classical pathway analysis begins by considering a comparison between two conditions, e.g. disease versus healthy. Evidence for differential gene expression can be provided by any technique such as fold change, t-statistic, Kolmogorov-Smirnov statistic, or perturbation factor. These statistics are then compared against the null distribution to determine how unlikely it is for the observed differences between the two conditions to occur by chance, thereby producing a ranked list of DE genes. After this hypothesis testing is done at the gene level, the next step is hypothesis testing at the pathway level producing a ranked list of impacted pathways. In summary, the input of a classical pathway analysis method includes: (i) a pathway database, and (ii) a gene expression dataset. The output is a list of pathways ranked according to their p-values.Similarly, the input of the new approach includes: (i) a pathway database, (ii) a database of miRNA-mRNA interactions, (iii) multiple gene expression datasets, and (iv) multiple miRNA expression datasets. Each dataset is obtained from an independent study of the same disease. Here we describe a framework that transforms the new problem into the classical pathway analysis problem.Colorectal cancer, where blue nodes are genes and red nodes are miRNAs. The black arrow-headed lines represent activation while the red bar-headed lines represent inhibition. For example, hsa-miR-483-5p is known to suppress the expression of MAPK3 and therefore an inhibition relationship is added between the two nodes in the pathway. All pathways are extended to include the known miRNA-mRNA interactions. The next step is to estimate the expression changes of each node  under the effects of the disease.Pathways in public databases are typically described as graphs, where nodes are genes and edges are interactions between genes. In the first step, we extend the existing pathways with additional interactions between miRNAs and mRNAs. Panel (d) shows a part of the pathway MAPK3 gene is used as an example. In the forest plot shown in this panel, each horizontal line represents the expression change in each study. The small black box in each line shows the standardized mean difference (SMD) and the segment shows the confidence interval of SMD. We use the standardized mean difference instead of the raw difference because the independent studies measure the expression in a variety of ways . The number on the right side of each line is the p-value of the test for differential expression, using the modified t-test provided in the limma packagePanel (e) shows the expression changes and the p-values for one gene in the mRNA data, across several datasets. In this case, the As shown in the figure, the SMD and p-value of a gene vary from study to study. We use the REstricted Maximum Likelihood (REML) algorithm323334The augmented pathways, the combined p-value, together with the estimated size effect then serve as input for classical pathway analysis. In this work, we use Impact Analysis, which is a topology-aware pathway analysis method, to calculate the p-value for each augmented pathway (panel g).n1 and n2 the number of samples in each group, and Spooled the pooled standard deviation of the two groups. The pooled standard deviation and the standardized mean difference (SMD) can be estimated as:Consider a study composed of two independent groups, and suppose we wish to compare their means for a given gene. Let d36d is given as follows:The estimation of the standardized mean difference described in Spooled. The standard error of d is the square root of Vd. We note that Cohen’s d, which is based on sample averages, tends to overestimate the population effect size for small samples. Let n be the degrees of freedom used to estimate Spooled, i.e. n = n1 + n2 − 2. The corrected effect size, or Hedges’ gIn the above equation, the first term reflects uncertainty in the estimate of the mean difference, and the second term reflects uncertainty in the estimate of g as the standardized mean difference (SMD) between disease and control groups for each gene/miRNA.where Γ is the gamma function. In this work, we use Hedge’ m studies where the effect size estimates, y1, …, ym have been derived from a set of studies, each of them modeled as in 323334Consider a collection of In contrast, the random-effects model allows for variability of the true effect. For example, the effect size might be higher (or lower) in studies where the participants are older, or have a healthier lifestyle compared to others. The random-effects model assumes that each effect size estimate can be decomposed into two variance components by a two-stage hierarchical process3339μ is the central tendency of the effect size, N represents the error term by which the effect size in the ith study differs from the central tendency μ, and where The derivation and formulation of the REstricted Maximum Likelihood (REML) algorithm has been described in the literature334142overall effect sizecombined p-value of individual genes and miRNAs serve as the input for Impact Analysis.The REML estimators of We first briefly recap some classical methods for combining independent p-values. Next, we describe the additive method284445m independent significance tests, the resulting p-values P1, P2, …, Pm are independent and uniformly distributed on the interval  under the null hypothesis. The random variables Xi = −2lnPi  follow a chi-squared distribution with two degrees of freedom  → 0 regardless of P2, P3, …, Pm.Fisher’s methodm. Denoting ϕ as the standard normal cumulative distribution function, and pi (i ∈ [1..m]) the individual p-values that are independently and uniformly distributed under the null, the z-scores are calculated as zi = ϕ−1 (1 − pi). By definition, these z-scores follow the standard normal distribution. The summary statistic of Stouffer’s method Stouffer’s methodm independent significance tests, P1, P2, …, Pm. Let the sum of these p-values, X ∈ ), be the new random variable. X is known to follow the Irwin-Hall distribution45The additive method28444546m is large, some addends will be too small or too large to be stored in the memory. This leads to a totally inaccurate calculation when m passes a certain threshold, depending on the number of bits used to store numbers on the computer. For this reason, a modified version of the additive method, named add-CLT, was proposedwhen Y represent the average of p-values: Y ∈ ). Since Y can be derived using a linear transformation of X as follows:Let Y is the mean of m independent and identically distributed (i.i.d.) random variables , that follow a uniform distribution with a mean of m i.i.d. variables follows a normal distribution with mean m. The transition from the additive method to the Central Limit Theorem takes place at the m ≥ 20 threshold.The variable In this work, we use the add-CLT method described above to combine the p-values calculated from the modified t-test (limma package).P =  be the graphical representation of the pathway we want to extend with miRNA-mRNA interactions. V is the set of vertices (genes) while the directed edges in E represent the interactions between genes in the pathway. Each interaction consists of an ordered pair of vertices and the type of interaction between the pair, i.e. E = {, ri} where xi, yi ∈ G (gene set) and ri is the type of relation between xi and yi, such as activation, repression, phosphorylation, etc. Topology-based pathway analysis methods, such as Impact Analysis, use interaction types to weigh the edges or to set the strength of signal propagation along the paths in a pathway.Here we give the formal description of the pathway augmentation process. Let Z as the set of known miRNAs, ζ ∈ Z is one miRNA, and t(ζ) is the set of known targets for the miRNA ζ. The augmented pathway of P =  is denoted as P* =  and is constructed as follows:From the miRNA database, we get a set of miRNAs and their targets. Let us denote ζ targets a gene g that belongs to the pathway, we add ζ to the pathway and then connect ζ with its targets in the pathway. By default, the interaction type of new edges is repression, which represents the translation blockage of miRNAs to mRNA. The interaction type can be changed to suit the interaction between the miRNA molecule and its targets. We extend all pathways in the pathway database using the formulation described in www.bioconductor.org).In other words, if a miRNA PNDE and PPERT. Here we review the Impact Analysis formulation.The Impact Analysis method149PNDE, is obtained using the hypergeometric model9PPERT, depends on the identity of the specific genes that are differentially expressed as well as on the interactions described by the pathway. It is calculated based on the perturbation factor in each pathway. The perturbation factor of a gene, PF(g), is calculated as follows:The first p-value, g, i.e. log standardized mean difference as shown in panels  of βug quantifies the strength of interaction between u and g. Here, βug = 1 for activation and βug = −1 for repression.The first term represents the signed normalized expression change of the gene PFg. The net perturbation accumulation at the level of each gene, Acc(g), is calculated by subtracting the observed expression change from the perturbation factor.The above equation essentially describes the perturbation factor PF for a gene as a linear function of the perturbation factors of all genes in a given pathway. In the stable state of the system, all relationships must hold, so the set of all equations defining the impact factors for all genes form a system of simultaneous equations whose solution will provide the values for the gene perturbation factors The total accumulated perturbation in the pathway is then computed as:Acc(Pi) is built by permutation of expression change. The p-value, PPERT, is then calculated by the probability of having values more extreme than the actually observed Acc(Pi).The null distribution of PNDE and PPERT, the following input is required: the graphical representation of the pathway, the combined p-value of each node of the graph, and the estimated overall standardized mean difference. In short, the graphical representation of the augmented pathways is provided in E(g), is estimated by iteratively maximizing the log-likelihood function in PNDE and PPERT, are then combined to get a single p-value that represents how likely the pathway is impacted under the effect of the disease.To compute We analyzed a total of 1,471 samples from 29 public datasets for two human diseases, colorectal and pancreatic cancer. The datasets were generated in independent laboratories, from different individual tissue samples, and were run on different high-throughput platforms. The diseases were selected based on two criteria: (i) there are many publicly available miRNA and mRNA datasets, and (ii) there is a pathway specific to the disease (target pathway). The colorectal data consists of 7 mRNA and 8 miRNA datasets while the pancreatic data consists of 8 mRNA and 6 miRNA datasets. The processed data sets were downloaded directly from the Gene Expression Omnibus using the GEOquery packagemodified t-test, available in the limma packageadd-CLTHedges’ gREML methodThe databases used in our data analysis are KEGG for pathways, and miRTarBase for miRNAs. We downloaded 182 signaling pathways from KEGG version 76 (Dec-04-2015) by means of the R package ROntoToolsFor both diseases, we compare the orthogonal approach  with 5 other approaches: pathway-level meta-analysis , gene-level meta-analysis , plus the 3 meta-analysis approaches available in MetaPath package29For pathway-level meta-analysis , we perform Impact Analysis on each mRNA expression dataset and then combine the independent p-values for each pathway. For example, if we have 7 mRNA datasets, we have 7 nominal p-values per pathway–one for each study. These 7 p-values are independent and thus can be combined using the add-CLT method to get one combined p-value. The final result is a list of 182 p-values for 182 signaling pathways. We then adjust the combined p-values for multiple comparisons using FDR.For gene-level meta-analysis , we perform the modified t-testThe integrative approach  is similar to ImpactAnalysis_G, with the exception that ImpactAnalysis_I uses both mRNA and miRNA data. The meta-analysis is done on the mRNA/miRNA level and then the combined p-values and estimated effect sizes of mRNA/miRNAs serve as the input to the ImpactAnalysis.MetaPath29target KEGG pathway, which is the pathway created to describe the main phenomena involved in the respective disease. The augmented pathway for Colorectal cancer is displayed in β (in the far left of the figure) has 9 miRNAs that are known to target the gene but only two miRNAs (hsa:miR-375 and hsa:miR-633) were included in the miRNA data. Similarly, the augmented pathway for Pancreatic cancer is displayed in target pathways are heavily regulated by miRNA molecules.For each of the two diseases, we have a In this experimental study, we expect that a good pathway analysis approach would be able to identify the very pathway that describes the disease phenomena as the most significant in each particular disease. Hence, we will compare the various methods based on this criterion.We obtained 8 miRNA  and 7 mRNA  datasets from the Gene Expression Omnibus (GEO), as shown in FDR = 0.01. The pathway highlighted in green is the target pathway Colorectal cancer. MetaPath_P  identifies no significant pathway at the 1% cutoff, and ranks the target pathway at position 16th. Similarly, MetaPath_G  and MetaPath_I (combination of gene- and pathway-level) identify no significant pathways. They rank the target pathway at positions 9th and 15th, respectively.Colorectal cancer is not significant and is ranked 61st with adjusted p = 0.99. The gene-level meta-analysis  offers some improvement over ImpactAnalysis_P by improving the ranking (10th) and adjusted p-value (p = 0.1) of the target pathway Colorectal cancer. However, the target pathway is still not significant with the given threshold. The orthogonal meta-analysis, ImpactAnalysis_I, is able to further boost the power of the gene-level meta-analysis. It identifies 5 significant pathways, with the target pathway Colorectal cancer ranked at the very top. This is very likely due to the additional information provided by miRNA expression and prior knowledge accumulated in miRTarBase.The ImpactAnalysis_P approach identifies 12 pathways, among which there are many pathways that are related to cancer. However, the target pathway Cell Cycle and Ribosome Biogenesis pathways are implicated in the proliferation aspect of cancer tissue. PPAR signaling has a role in colorectal cancer, although it is not fully understoodProgesterone-mediated oocyte maturation is clearly a false positive which may have appeared due to the presence of several cell cycle genes in that pathway.Three of the other 4 pathways that are identified by ImpactAnalysis_I appear to be true positives. The We obtained 8 mRNA  and 6 miRNA datasets  from Gene Expression Omnibus (GEO), as shown in 30Graft-versus-host disease is ranked on top with adjusted p-value 0.4782. The target pathway Pancreatic cancer is ranked 17th with adjusted p = 0.89. MetaPath_G identifies 7 significant pathways. The target pathway is not significant (adjusted p = 0.22) and is ranked 91st. In consequence, the combination of these two methods, MetaPath_I, also fails to identify the target pathway as significant (adjusted p = 0.34 with ranking 91st).MetaPath_P identifies no significant pathway and PI3K-Akt signaling pathway and MicroRNAs in cancer as significant. The significance of MicroRNAs in cancer may indicate the importance of miRNA in pancreatic cancer, and PI3K-Akt signaling alteration is known to be involved in many cancers. However, the target pathway is not significant (adjusted p = 0.95 with ranking 32nd). The gene-level meta-analysis  improves the ranking of the target pathway (8th) but the p-value of the target pathway is still not significant. The orthogonal approach, ImpactAnalysis_I, identifies 7 pathways as significant. The target pathway Pancreatic cancer is ranked on top with FDR-corrected p-value 0.0017.The pathway-level meta-analysis  identifies the Small cell lung cancer, Pathways in cancer, Proteoglycans in cancer). The breakdown of cell matrix adhesions, such as Focal Adhesion is an important property of metastasis - most pancreatic cancers are discovered when they are already high grade.Of the 6 significant non-target pathways found by ImpactAnalysis_I, three are cancer-related by name , and correctly report the target pathway as the most significantly impacted pathway in both meta-analysis studies. The results demonstrate that the correct pathways are identified only when the data are integrated both horizontally (combining multiple studies using the same data type) and vertically (combining miRNA with mRNA expression). This orthogonal meta-analysis uses three different kinds of data integration: integration of mRNA and miRNA, combining p-values and combining SMDs for genes and miRNA molecules.The data analysis was done on a personal MacBook Pro that has 8 GB 1600 MHz DDR3 RAM, 2.9 GHz Intel Core i7. Since MetaPath cannot exploit multiple processors, we run all the analysis using a single core. The time needed to run MetaPath was 39 minutes for Colorectal cancer and 47 minutes for Pancreatic cancer.For ImpactAnalysis_I, we first calculate the p-value for each gene/miRNA in each dataset using the limma packagehorizontal integration is to combine individual p-values provided by each study. In this way, one can apply any pathway analysis approach  under the studied condition. This list of DE genes/miRNAs is obtained from a large number of studies and therefore it is expected to be more reliable than any individual study taken alone. From the list of DE genes/miRNAs and the computed statistics (effect sizes and variances), we can identify new putative targets of miRNAs using casual inference techniques616466In this article, we present a two-dimensional data integration that is able to combine mRNA and miRNA expression data obtained from many independent experiments. The framework first augments pathway knowledge available in pathway databases with miRNA-mRNA interactions from miRNA knowledge bases. It then computes the statistics that are essential for pathway analysis, i.e. the standardized mean difference (SMD) and p-value for differential expression. For each entity, these p-values and the SMDs are computed by combining multiple studies using robust horizontal meta-analysis techniques. Finally, the framework performs a topology-based pathway analysis to identify pathways that are likely to be impacted under the given condition.To evaluate the framework, we examine 1,471 samples from 15 mRNA and 14 miRNA expression datasets related to two human cancers, using 6 different meta-analysis approaches . We demonstrate that the correct pathways are identified only when the data are integrated both horizontally (combining multiple studies using the same data type) and vertically (combining miRNA with mRNA expression).This work serves as a bridge between the two orthogonal types of data integration. The result is to unblock the sample-matched data bottleneck, by successfully integrating mRNA and miRNA datasets measured from independent laboratories for different sets of patients. Furthermore, it increases the power of statistical approaches since it allows many studies to be analyzed together. With vast databases of various data types being made available, this framework is expected to be widely applicable because of its relaxed restrictions on the data being integrated. The framework is flexible enough to integrate data types other than mRNA and miRNA. It can also be modified to suit other purposes besides pathway analysis.How to cite this article: Nguyen, T. et al. Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data. Sci. Rep.6, 29251; doi: 10.1038/srep29251 (2016).


MetaCyc.org) is a freely accessible comprehensive database describing metabolic pathways and enzymes from all domains of life. The majority of MetaCyc pathways are small-molecule metabolic pathways that have been experimentally determined. MetaCyc contains more than 2400 pathways derived from >46 000 publications, and is the largest curated collection of metabolic pathways. BioCyc (BioCyc.org) is a collection of 5700 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems, and pathway-hole fillers. The BioCyc website offers a variety of tools for querying and analyzing PGDBs, including Omics Viewers and tools for comparative analysis. This article provides an update of new developments in MetaCyc and BioCyc during the last two years, including addition of Gibbs free energy values for compounds and reactions; redesign of the primary gene/protein page; addition of a tool for creating diagrams containing multiple linked pathways; several new search capabilities, including searching for genes based on sequence patterns, searching for databases based on an organism's phenotypes, and a cross-organism search; and a metabolite identifier translation service.The MetaCyc database ( MetaCyc.org) is a highly curated reference database of small-molecule metabolism from all domains of life. It contains data about enzymes and metabolic pathways that have been experimentally validated and reported in the scientific literature (MetaCyc (terature . MetaCycBioCyc.org) website. In addition, many groups outside SRI have generated many thousands of additional PGDBs. Interested scientists may adopt any of the SRI PGDBs through the BioCyc website for further curation (biocyc.org/intro.shtml#adoption).In addition to its role as a general reference on metabolism, MetaCyc can be used by the PathoLogic component of the Pathway Tools software  as a refNucleic Acids Research publication (2 years ago) (Since the last ars ago) , we addeFurther, the number of enzymes, reactions, chemical compounds, and citations in the database grew by 15%, 11%, 13%, and 23%, respectively; the number of referenced organisms increased by 9.5% (currently at 2694). See Table Since the last Nucleic Acids Research publication we enhanced the reaction-balance-checking algorithm, which verifies the balance of each reaction in the database, to check not only for elemental composition but also for electric charge. As of August 2015, MetaCyc contains 12 560 balanced reactions. The database also contains 1465 reactions that cannot be balanced for assorted reasons .G′°) values have been added to most MetaCyc compounds, and standard change in Gibbs free energy (ΔrG′°) values have been added to most MetaCyc reactions. While a small number of values were obtained from the experimental literature, most values were computed.Standard Gibbs free energy of formation (ΔfGº), using a technique based on the decomposition of the compounds into chemical groups with known energy contributions to the larger compound  and ionic strength of 0.25 (ΔfG′º), using a technique developed by Robert A. Alberty .Compound duplicates: A program scans for potential duplicate compounds by comparing all possible pairs of chemical structures.EC numbers: A program checks that every EC number has at least one reaction associated with it, and that no reaction refers to a deleted or transferred EC number.Inverse links: These are links between two objects in the database that refer to each other. For example, if gene x refers to protein X as its product, then protein X should refer to gene x as the gene encoding it. The software ensures that all such links are reciprocal.GO terms: A program checks whether all GO terms are up-to-date, and enables the removal or replacement of all terms that have become obsolete.Internal hyperlinks and HTML formatting: A program checks that all internal hyperlinks point at valid targets, and that all HTML tags are balanced (e.g. every <i> tag is followed by an </i> tag).Citations: A program checks whether all PubMed citations in the database refer to valid PubMed IDs and that all such references have been properly imported.Non-specific enzyme names: A program compares all enzymatic activities in MetaCyc to a list of nonspecific names to ensure that the names describe a specific activity and to prevent meaningless matching between MetaCyc reactions and enzymes during the generation of a new PGDB .Before each release of MetaCyc, we conduct a series of quality-assurance tests that expose errors in the data. A combination of automated and manual procedures is employed to find and correct problems, and to improve data accuracy and consistency. Although the full list of these procedures is too long to describe here, we would like to mention a few of them.Tier 1 PGDBs have received at least one year of manual curation. Although some Tier 1 PGDBs (e.g. MetaCyc and EcoCyc) have received decades of manual curation and are updated continuously, others are less well curated.Tier 2 PGDBs have received moderate (less than a year) amounts of review, and are usually not updated on an ongoing basis.Tier 3 PGDBs were created computationally and received no subsequent manual review or updating.The BioCyc databases are organized into three tiers.During the past two years, the number of BioCyc PGDBs increased from 2988 in version 17.1 to more than 5700 in version 19.0.As of version 19.0, Tier 1 includes 7 PGDBs, Tier 2 includes 39 PGDBs, and Tier 3 includes more than 5700 PGDBs. Some Tier 2 PGDBs were provided by groups outside SRI. The database authors are identified on the database summary page .Most of the genomes contained in BioCyc have been retrieved from the NCBI Reference Sequence Database (RefSeq) ,7. Genomhttp://www.hmpdacc.org/catalog/grid.php?dataset=genomic&project_status=Complete).Of the more than 5700 PGDBs found in BioCyc, >900 genomes are from the Human Microbiome Project (The following sections describe significant enhancements to Pathway Tools (the software that powers the BioCyc website) during the past 2 years.The gene/protein page has been redesigned with a new tabbed layout to enhance the ability of users to find information, and to modernize its appearance (this feature will be released in mid-November 2015.) Some key information has been moved to the top of the page. Tabs enable the user to conveniently switch between different types of data without the need to scroll. The number of tabs is dynamic and depends on the available information. For example, ‘Essentiality’ and ‘GO terms’ tabs appear only if this type of data is available for the protein  is a spreadsheet-like structure that can contain both PGDB objects and other data such as numbers or text. Like a spreadsheet, it is organized by rows and columns that the user can add to or delete. A typical SmartTable contains a set of PGDB objects in the first column (e.g. a set of genes generated by a search). The other columns contain properties of the object , or the result of a transformation .Creating ‘frozen’ copies: This feature allows a user to publish a set of PGDB objects, such as a gene or metabolite list, within a publically readable, non-modifiable SmartTable. Because frozen SmartTables cannot be changed, they can be referred to in scientific publications.Database links: A new type of property column contains identifiers in an external database for SmartTable objects. For example, the user can select a column that contains BioCyc compounds and then add a new column that lists the ChEBI identifiers of these compounds.Multiple databases: A single SmartTable can hold objects from multiple BioCyc databases.Sequence variant data analysis: This new tool aids in analyzing how nucleotide-sequence variation can affect the protein product of a gene. The tool acts on SmartTables of replicon regions and associated sequence variants, which can be created easily via a file import operation. A new transformation ‘Sequence - nearest gene to DNA region’ adds additional columns to the SmartTable showing the nearest gene to each altered region, and the amino-acid change caused by each sequence substitution, insertion, or deletion.Since the last Nucleic Acids Research publication we have added several important capabilities to the SmartTables, including:Pathway visualization of transcriptomics, metabolomics, and reaction-flux data facilitates the interpretation of such data by putting it within a biological context.The following are among the enhancements to the Omics viewers made during the last two years.A new function enables the painting of omics data with multiple time points on individual pathway diagrams. The ‘Customize or Overlay Omics Data on Pathway Diagram’ operation, which is available from the right sidebar of every pathway page, lets the user upload an omics dataset with one or more time points. Datasets containing multiple time points are presented in the form of omics popups .The omics-data loading software has been enhanced to recognize a wider variety of gene and metabolite names and identifiers in the first column of the file. Genes and metabolites can now be specified using multiple alternative names and identifiers, including identifiers from other databases that are present in BioCyc, such as Chemical Entities of Biological Interest (ChEBI)  and KyotThis new tool enables the user to create a diagram containing multiple pathway diagrams, called a pathway collageThis new tool, available from the Search menu, enables name-based searches across any specified set of organisms in BioCyc. For example, it is possible to search all cyanobacterial PGDBs for the text string ‘sulfolipid’. The user can restrict the search to a particular type of object, including proteins, enzymatic reactions, compounds, reactions, pathways, genes, and RNAs.Pathway Tools now supports the inclusion of MIGS [Minimal Information about a (Meta)Genome Sequence] data, which is a standard that defines the minimal essential information required to provide an accurate description of a genome and the organism or collection of organisms it was derived from . The MIGThis new tool, available from the Search menu, enables searching a genome for matches to short (<20) patterns of nucleotides or amino acids sequences. The tool scans a selected genome for matches to exact or degenerate sequences, returning a table with hyperlinks to all results.This new analysis tool generates Functionally Linked Gene Clusters (FunGCs) using genome-context methods, which are computational comparative-genomics methods that search for patterns of gene occurrence across 1800 reference genomes in BioCyc ,12. Theshttp://biocyc.org/metabolite-translation-service.shtml). The input is a series of lines, one per metabolite, where each line contains one or more identifying attributes, such as name; identifier from some metabolite database (including BioCyc); molecular formula; InChI key or string; or monoisotopic weight. If a unique match is found in BioCyc, the output will include a line listing the name for that metabolite, its BioCyc identifier, and the identifiers for that metabolite found in all other databases that BioCyc has data for. Ambiguous matches are also indicated.This new tool, available from the Metabolism menu, translates metabolite names; identifiers; InChI strings; InChI keys; monoisotopic molecular weights; and molecular formulas, between metabolite databases. The metabolite data can be submitted to the metabolite translation service via a file or by pasting the data into a form  module of Pathway Tools, has been enhanced to enable creating models of organism communities in which several organisms can compete and cooperate in a user-specified growth environment. MetaFlux achieves this by applying dynamic FBA (dFBA)  to a co  . Once imThe PGDB Info Editor has been extended to support entry and updating of organism phenotype data, such as the temperature range and aerobicity of the organism via the MIGS [Minimal Information about a (Meta)Genome Sequence] standard.http://biocyc.org/samples.shtml); a guide to the BioCyc database collection (http://biocyc.org/BioCycUserGuide.shtml); a guide for MetaCyc (http://www.metacyc.org/MetaCycUserGuide.shtml); a guide for EcoCyc (http://biocyc.org/ecocyc/EcoCycUserGuide.shtml); a guide to the concepts and science behind Pathway/Genome Databases (http://biocyc.org/PGDBConceptsGuide.shtml); and instructional webinar videos that describe the usage of BioCyc and Pathway Tools (http://biocyc.org/webinar.shtml). We routinely host workshops and tutorials (on site and at conferences) that provide training and in-depth discussion of our software for both beginning and advanced users. To stay informed about the most recent changes and enhancements to our software, please join the BioCyc mailing list at http://biocyc.org/subscribe.shtml. A list of our publications is available online at http://biocyc.org/publications.shtml.The BioCyc.org and MetaCyc.org websites provide several informational resources, including an online BioCyc guided tour (http://biocyc.org/download.shtml for download information. New versions of the downloadable data files and of the BioCyc and MetaCyc websites are released three times per year. Access to the website is free; users are required to register for a free account after viewing more than 30 pages in a given month.The MetaCyc and BioCyc databases are freely and openly available to all. See


However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at:  Such circuits can include bi- or multi-furcations and typically consist of nodes that activate other nodes but they can also contain nodes that inhibit the activity of other nodes. Nodes can be composed of one or more proteins. In the Boolean model, gene expression is taken as a proxy of the presence of a protein in the pathway ,12,13,18Additional gene list box, which allows specifying values for a list of genes that overwrite the values reported in the database. In addition, a complete list of user-defined gene expression values can be provided through the button Custom for conditions not represented in the tissue databases used.The models here cover an ample range of tissues compiles in the Human Protein Atlas  and the http://www.ensembl.org/info/genome/variation/predicted_data.html) describe a primary classification of possible variant effects. Only a subset of consequence types is compatible with variation that affects protein coding genes. These are: exon_variant, intron_variant, synonymous_codon, coding_sequence_variant, non_synonymous_codon, splice_region_variant, splice_acceptor_variant, splice_donor_variant, stop_gained, stop_lost, 5_prime_UTR_variant, 3_prime_UTR_variant, 5KB_upstream_variant, 5KB_downstream_variant.Consequence types are taken from CellBase . The default choice is: non_synonymous_codon, stop_gained and stop_lost consequence types, with SIFT and PolyPhen thresholds of 0.05 and 0.95, respectively, which corresponds to the more obvious scenario of deleterious, loss-of-function variants.Typically, stop loss, stop gain and splicing disrupting variants are considered damaging mutations. The impact and damaging effect of non-synonymous variants depend on the type of amino acid change and can be predicted by computing SIFT  and Polysignalome has been modeled and the unperturbed signaling pattern in any tissue has been derived, the prediction of the effect of deleterious variants affecting to pathway proteins is straightforward. PATHiVAR removes from the model those proteins harboring deleterious mutations (as defined by the user) that impairs its proper functionality and recalculates the probability of signal transmission across all the stimulus-response circuits of the pathways studied in the selected tissue again, obtaining thus the perturbed signaling pattern.Once the Run item in the main menu), and therefore all the uploaded data and the results obtained  will be lost at the end of the session. The program can also be used in ‘registered user’ mode. Registration is free and the options are the same, the only difference is that registered users can maintain their data and results in the PATHiVAR workspace with a limit of 10 GB.PATHiVAR can be directly used in anonymous mode (Select VCF file option) of the form brings about the STUDIES workspace, where VCF data files reside, or where they can be uploaded from the local computer. If the file selected is a multisample VCF, the specific sample to be analyzed can be selected with the Sample name from VCF option  or provides a complete list of user-defined gene expression through the button Custom that will be used for the model to derive the unperturbed system . Once the job is launched, it appears in the job list in running state. When the job is finished and the result is available, the job appears in the finished status with a check symbol.Once the analysis parameters have been defined, a name can be assigned to the job and then the algorithm can be run , which provides a graphical output in which aspect, colors and shapes of the components of the pathway can easily be reconfigured to produce camera-ready figures. In addition to the graphical output, each pathway displays a table which lists the affected circuits with relevant information, including receptor and effector proteins, the activation status and the particular cell functionality triggered by the circuit. A table summarizing the results obtained for all the selected pathways can also be downloaded.The graphical output Figure  represenConventional analysis of genomic data seeks to relate gene expression  or mutatThe systems biology perspective, provided by the models of signaling circuits presented here, offers an optimal framework to locate recurrent functional failures that can be better related to diseases or complex traits than the original raw genomic measurement (gene expression or mutation) from which the models were derived.


The Protein Ontology (PRO) provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps. Diverse electronic databases now play central roles in storing, integrating, and analyzing information relevant to human biology. UniProt maintains definitive catalogs of the properties of human proteins and those of model organisms widely used in biomedical research . Model oReactome and other pathway resources are rich sources of complex information curated by experts and stored in data structures developed to meet the needs of their core user communities. This richness and specialization, however, is also a limitation. The unique organization of each resource makes attempts to integrate and analyze data across resources difficult. Biomedical ontologies provide tools that can address these problems. These ontologies provide rigorous, unambiguous descriptions of biological objects and of the relationships among them using standardized and well-understood formats. Ontology structures enable the development of powerful computational tools that can reliably integrate and through both rational and statistical methods analyze the large, diverse sets of experimental data curated by independent groups of experts and stored in independent electronic databases. Within the OBO Foundry model, ontologies have been developed to describe orthogonal features of biology, but to a common standard to ensure interoperability . Such onGO, the Gene Ontology , 6, provIn this work, we propose that PRO can aid the integration of disparate data and enable biologically sound inferences. As a proof-of-concept, we analyzed innate immune signaling data from different organisms  and sources . We studied whether Reactome’s annotations for human and chicken proteins and complexes involved in innate immune signaling  can be iThe innate immune systems of humans and mice have both been extensively characterized so this exercise has allowed us to test the reliability of annotations in one species for predicting complex formation, subcellular location, and function in the other, and to identify true differences in the signaling processes between the two species. Where experimental data exist only for one species, we have asked whether the PRO evolutionary family framework supports plausible inferences to fill gaps.Drosophila and shown to play a role in embryonic dorsal-ventral patterning , and Toll-like receptor (PR:000001096) has_part TIR domain (PF01582) [protein:domain].The PRO terms and annotations related to this paper have been collected in a separate set of TLR-specific files, available via FTP . All terThe organization and content of the PRO annotation file (PAF) have been described previously . BrieflyHere we describe strategies to integrate PRO annotations for complexes  with funTLR3 and TLR4 together represent key signaling strategies used by Toll receptors to initiate reactions of innate immunity. TLR4 is unique in that upon activation it recruits adaptor molecules for both MyD88-dependent and MyD88-independent signaling. TLR3 specifically uses the TRIF-signaling pathway but without the use of TRAM  34]. Al. Al34]. Experimental studies of chicken, mouse and human systems have established that in all three species the TLR3-mediated signaling pathway is triggered by recognition of viral dsRNA and the TLR4-mediated signaling pathway is triggered by recognition of bacteria-derived LPS , 36. TheIn vertebrates the sensing of LPS involves transfer of LPS monomers to CD14 mediated by the LPS-binding protein (LBP). CD14 in turn commonly delivers the LPS to a complex of myeloid differentiation protein-2 (MD2) and TLR4 which transduces the signal through the recruitment of adaptor proteins to the TIR domain of TLR4 , 37. TheDownstream signaling complexes such as the MD2:TLR4 complex show another interesting difference between taxa. Whereas the mammalian version participates in both the MYD88-dependent and independent signaling pathways, the chicken version may only be able to participate in the MYD88-dependent pathway , 39. ThiComparison of the PRO annotations for mouse and human CD14 complexes identified a potentially significant gap in our understanding of CD14 function ; Fig 1. Although ligand binding and transfer by CD14 has been extensively studied by mutagenesis and epitope mapping of blocking antibodies in both human and animal models , 46–49, Further we found that mouse complexes containing MyD88 protein are represented in two forms, containing alternatively spliced long and short isoforms of MyD88, MyD88l and MyD88s Tables  and 2. Tannotation of specific instances of physical entities: the collections of molecules in particular cells occupy a subcellular location or exhibit a function. Work now underway on development of a formal ontology for these classes and relationships will enable us to use these annotations as the basis for assertions to support automated reasoning. While the expert manual annotation process does not scale well, it does provide a large body of validated data that will provide a rigorous test of automated reasoning tools.A key feature of the work described in this paper is that it involves the We have described an annotation process that integrates PRO ontology terms for protein complexes with GO terms for molecular function, biological process, and cellular component. The resulting annotations are explicitly tagged to indicate their basis in experimental data or in manually verified inferences based on sequence similarity among proteins. The results highlight similarities and differences between signaling processes mediated by two members of the TLR family, TLR3 and TLR4, and among three vertebrate species, human, mouse, and chicken. This annotation strategy is readily extended to the large data sets in pathway databases like Reactome and with the continued development of ontologies and reasoning tools should allow these resources to be mined efficiently and reliably, to discover putative novel functional relationships among proteins and protein complexes and to critically assess their plausibility.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


The overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.Validation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.With novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users .A web implementation of Vikodak can be publicly accessed at:  The field of metagenomics has significantly improved our overall understanding of the microbial world within and around us. Characterizing microbial communities using the metagenomics approach involves either (a) performing a whole genome (shotgun) sequencing (referred to as WGS) of the entire genomic content of a given environmental sample, or (b) performing amplicon sequencing of specific marker genes (e.g. 16S rRNA) from any microbial community. The focus/end-objectives of the research problem (as well as associated sequencing costs) typically drive the choice between adopting a WGS and a 16S amplicon sequencing approach. The 16S approach specifically helps in deciphering the taxonomic/microbial composition of a given environmental sample, thereby paving the way for performing specific diversity analysis and subsequent identification of environment-specific marker microbes. The WGS approach, on the other hand, offers a two-fold advantage. Besides helping in obtaining a taxonomic profile of the environmental sample under study, computational analysis of WGS data provides information at a functional level (i.e. types and relative abundances of genes encoded by various microbes in a given environment). Given the relatively higher costs associated with WGS sequencing , a majority of metagenomic initiatives employ 16S sequencing for obtaining a quick comparative snap-shot of microbial diversity. Subsequently, a WGS follow-up experiment is sometimes performed , typically when 16S experiments indicate significant aberrations  in the analysed sample classes .Metagenomic data analysis of microbial communities is typically aimed at (a) identifying the resident microbes and their relative proportions , (b) profiling the functions encoded by these microbes , and (c) comparing/ co-relating the identified microbes and their functions with available sampling (phenotypic) metadata (i.e. comparative metagenomics). Several stand-alone tools and web-services are available for performing the above mentioned analyses –5. CurreThe idea of inferring the functional potential using 16S-derived microbial abundance data has been attempted by a handful of research groups. For example, approaches like procrustes analysis  have beeFunctional characterization of any metagenomic sample should ideally enable insights into (i) functional units (genes/ enzymes) expressed by resident microbes, (ii) relative abundance and expression levels of various metabolic pathways, (iii) relationships between various resident microbes based upon their individual functional characteristics, (iv) core functions of an environment, (v) differences between distinct environments, and (vi) genes/ enzymes associated with individual functions (viz. metabolic pathways). In addition, the functional profile should be devoid of functions specific to eukaryotes. While the first two objectives of an ideal functional characterisation are fulfilled by the conventional WGS-based functional annotation tools ,5 and thdecoder in Sanskrit)—a modular functional annotation tool that extends the functionality/ utility of the '16S-data-inferred-function-prediction' paradigm. A detailed description of (a) various modules of Vikodak , and (b) additional features incorporated in Vikodak that address the unmet objectives of existing 16S-based functional prediction algorithms is provided. In addition to the validation of results obtained with greater than 1400 metagenomic samples, a case study (performed using publicly available Periodontitis metagenomics datasets) that highlights the functional utility of various modules incorporated in Vikodak is also presented.In this study, we describe Vikodak  quantifying the contribution of each of the microbes (in that sample) to the predicted functions , and Local Mapper, each catering to specific end-user requirements. Briefly, given a microbial abundance data profile of an environmental sample, the Global Mapper module enables (a) an  of KEGG  hierarchThe overall functional characteristics of any microbial community are defined by the metabolic potential of the resident microbes. This, in turn, is dependent on the presence and abundance pattern of various genes/ enzymes encoded by these microbes. Given this, a pre-compiled database containing all available information about various enzymes and their copy numbers (an index of the magnitude of expression) in various microbes is expected to indirectly enable prediction of the functional traits/ metabolic potential of a given community of microbes constituting an environmental niche.In line with the above principle, Vikodak's back-end database was constructed in the following manner. The EC profiles of 12,260 prokaryotic genomes from the PATRIC database  and 'EnzA set of microbes in an environment can either exist as a consortium (i.e. with co-metabolic associations) or they might thrive independently. Considering these two possibilities , Global Mapper provides to end-users the choice of two distinct algorithmic work-flows viz. 'Co-metabolism' and 'Independent Contributions'. The principle behind these two work-flows is as follows. The co-metabolism algorithm is based on the underlying assumption that the genes/ enzymes expressed by various microbes residing in an environment may pool together and contribute to the functioning of specific metabolic pathway(s). Therefore, the effective abundance of a metabolic pathway (in an environment with co-metabolising microbes) is expected to be a function of the total enzyme pool contributed by the co-metabolising microbes. In contrast, the 'independent contributions' algorithm assumes the independent existence of microbes in the environment. Under this assumption, the effective abundance of a metabolic pathway is the sum total of the pathway abundances computed from individual microbes residing in that environment. In addition to computing 'results' pertaining to the relative abundance of various metabolic pathways and identifying 'core' metabolic functions, the Global Mapper module incorporates two output refinement mechanisms that improve the overall confidence of the predicted/ inferred functions  for reporting a pathway to be present/ functional. This threshold is referred to as 'PEC value'  in Vikodak. Both sub-modules of Global Mapper incorporate this parameter and provide to end-users multiple results that report 'pathway abundance profiles'  for 7 values of PEC . The utility of this refinement parameter is further highlighted in results .Considering that functional analysis is being performed with an objective of studying functions encoded by the constituent 'microbes' (of an environment), it is logical to exclude pathways that are specific to and are associated with eukaryotic genomes from the final predictions. For this purpose, a list of Eukaryote-associated pathways was compiled by mining literature from public resources as well as from information available in the KEGG database. The mined list includes functions/ KEGG pathways like MAPK signalling pathway, Oocyte meiosis, Plant hormone signal transduction, Rheumatoid arthritis, Vascular smooth muscle contraction, etc. Vikodak provides this refinement procedure as an additional option to end-users.This module automates the statistical comparison of  functional profiles of two environmental samples. The input data for ISFA is a union of functional profiles (viz. KEGG pathway abundance data or pathway class/ super-class abundance data) of two different environments (sought to be compared). The module employs a 'bootstrapped' approach for statistical comparison wherein a random subset of samples (from both environments) are drawn multiple times and Wilcoxon rank sum test is (performed on sample sets drawn in each iteration) used for identifying functions (pathways/ pathway classes/ pathway super-classes) with significantly different abundances  in each iteration. The ISFA module reports a function as significantly different between two environments only when it is identified as significantly different in a minimum percentage of iterations (the 'boot-strap' threshold/ percentage being specified by the end-user). The end-user is also given the option to determine the number of iterations as well as the size of the random subset (to be drawn in each iteration). This module is a logical extension of the co-metabolism sub-module  and the ISFA module of Vikodak. Local Mapper module enables end-users to compute the relative abundance of enzymes constituting a specific pathway of interest . Except for the usage of back-end hash-maps specific only to the end-user specified pathway, the overall strategy adopted by Local Mapper (for computing enzyme abundances) is identical to that used in Co-metabolism sub-module  and those generated from Whole Genome Sequencing (WGS) datasets (generated from the same environment)?Can Vikodak distinguish between datasets taken from environments that are known/ expected to be functionally diverse? How does Vikodak compare with PICRUSt  in this Evaluating the utility of Vikodak's 'Pathway Exclusion Cut-off (PEC)' parameter.A case study highlighting the biological relevance of Vikodak's functional predictions.For estimating concurrence between functional profiles inferred from 16S data (using Vikodak) and WGS-derived functions, 16S taxonomic profiles and WGS-derived functional profiles corresponding to 103 gut metagenomes were downloaded from the MG-RAST  databaseLitoditis marina nematode species (SRP064694), and Amazonian soil (ERA411828). In the second experiment, taxonomic abundance data corresponding to 1291 samples, pertaining to various human body sites , were analysed in a similar manner. et al., 2011 [In order to evaluate the efficiency/ accuracy of Vikodak in distinguishing between datasets taken from functionally diverse environments, two experiments were performed. In the first experiment, taxonomic abundance data corresponding to 360 (publicly available) metagenomic datasets from three functionally distinct environments was analysed using (a) Global Mapper module of Vikodak (using pathway level data) and (b) the PICRUSt tool (using L3 level data). These three environments included human gut (HMP), l., 2011 . For theoil ERA4128. In thl., 2011  and geogl., 2011 ,15. ThesVikodak's 'Pathway Exclusion Cut-off (PEC)' parameter is expected to have utility in removing the subset of functions/ pathways  which were included based on the mere presence of a small proportion of genes/ enzymes corresponding to that function/ pathway. To evaluate the efficiency of this parameter, publicly available oral and skin metagenomic datasets ,17 were 91 publicly available 16S metagenomic datasets  correspoResults of PCA-based ordination analysis (using STAMP v.2.0.8 ) carriedThe Pathway Abundance Profiles  of datasets corresponding to PD  and Periodontally healthy control (PH) datasets were provided as input to the ISFA module . The objective was to highlight the utility of Vikodak in enabling automated identification of the subset of functional features that are significantly different between the compared datasets. 'beta-lactam resistance', 'photosynthesis (antenna proteins)' and 'synthesis and degradation of ketone bodies', functions known to play key roles in progression of Periodontitis –22, are Similarly, ketoacidosis (i.e. presence of ketone bodies in breath) has been recently suggested as a causative factor for halitosis  in diseases like periodontitis . ProbingIndividual contributions of various microbes towards different functions  obtained using Vikodak can, in principle, be used for generating a microbial correlation network. Analysis of such a network  is expected to facilitate the identification of (1) microbes that play key functional roles in a given community, and (2) microbes (or group of microbes) that are functionally synchronised/ antagonistic with respect to each other. Furthermore, comparing such networks generated for two communities  enables not only identification of changes in key regulatory members, but also helps in tracking the transition in overall functional interactions between the resident microbes. It is pertinent to note here that the networks discussed above are unique with respect to characterising functional interactions at a community level (as opposed to a taxonomic abundance derived network). Results obtained from the 'independent contributions' sub-module of Global Mapper (in Vikodak) enable generation of such function-based microbial networks, the latter utility being unavailable in any of the existing function prediction tools. In addition to providing information about the presence/ abundance of various genes/ proteins/ metabolic-pathways encoded by various microbes inhabiting a given environment, an ideal function characterization tool should be able to provide insights pertaining to the functional interactions between the resident bacteria. A comparison of such information generated from two or more environments is expected to provide insights relevant from a biological standpoint. Vikodak—the tool presented in this study attempts to automate the overall process of obtaining and/ or comparing the functional profiles(s) of given environment(s). In comparison to existing functional annotation tools, the functional profiles obtained using the 'Global Mapper' module of Vikodak contain detailed information with respect to the core set of functions as well as the contribution of individual microbes to various functions identified in the studied environment(s). This facilitates end-users to decipher functional relationships between various resident microbes. Furthermore, the 'ISFA module' of Vikodak automates the functional comparison between a pair of environments. Given that the comparison work-flow employs a boot-strap approach , Vikodak represents a reliable one-stop package that infers as well as compares  the functional profiles of the studied environment(s). The 'Local Mapper' module further complements the functionalities of the other two modules by enabling an in-depth (enzyme-level) analysis of specific metabolic pathways that are of interest to the end-user.In ideal circumstances, the process of inferring the presence of a pathway in an environmental sample should necessarily take into consideration the occurrence of a minimum quorum of genes/ enzymes encoding the pathway. For instance, it is inappropriate to report the 'presence' of a pathway (constituted of 20 enzymes) based on finding just 1 or 2 enzymes in the studied environment. The PEC value parameter in Vikodak duly takes care of this requirement, thereby enabling end-users to focus only on results which are bereft of possible false positive predictions. Comparison of microbial communities from two environments (using Vikodak's ISFA module) and identifying functions that are reported as significantly different at most PEC thresholds  greatly On a conceptual note, Vikodak considers enzymes as the basic functional units. This in turn has enabled the development of a relatively simplistic (yet effective) modules catering to various aspects of function prediction. The back-end data in Vikodak is therefore based on a compilation of EC copy numbers  sourced from two major data repositories viz. PATRIC and IMG , 9. GiveS1 FigDough nut plots depicting the proportions of various EC copy number z-score ranges observed at various taxonomic levels.(TIFF)Click here for additional data file.S2 FigA schematic representation of the three levels of KEGG pathway hierarchy and corresponding hash-maps used in the back-end datasbase of Vikodak.(TIFF)Click here for additional data file.S3 Figi refers to the Enzyme pool contributed by the ith bacterium (and so on). Pi1 refers to the abundance of Pathway (P1) contributed by the ith bacterium using its enzyme pool (Epi).A schematic description of the assumptions underlying the two sub-modules/algorithms of Global Mapper module of Vikodak. Ep(TIFF)Click here for additional data file.S4 FigA schematic representation of the work-flow employed in Global Mapper module of Vikodak and the format of various outputs generated using this module. Various types of analyses that can be performed using the generated outputs are also indicated.(TIFF)Click here for additional data file.S5 FigVisual depiction of the relative adbundance levels of various enzymes belonging to ‘Glycine, Serine and Threonine Metabolism’ KEGG pathway obtained using the ‘user data mapping’ file generated by Local Mapper module of Vikodak (please refer (TIFF)Click here for additional data file.S6 FigBar graphs representing the comparison of the quantum of negative interactions between the microbes residing in healthy and diseased environments. While PH and PD represent datasets pertaining to Periodontally healthy controls (PH) and subjects with Periodontitis (PD), PsC and PsL represent samples pertaining to Psoriasis Control and Psoriasis Lesional datasets.(TIFF)Click here for additional data file.S1 FileDetailed algorithms of Global Mapper module of Vikodak, with description of the methodology followed in implementing various noise and error reduction provisions.(PDF)Click here for additional data file.S2 FileThis file contains the snapshots of sample input files that can be used for various modules of Vikodak. The snapshots of various types of outputs/results generated by Vikodak are also included in this file.(PDF)Click here for additional data file.S1 TableA tabulated summary of various datasets employed to validate the utility of various modules of Vikodak.(DOCX)Click here for additional data file.


Hiccups are the sudden involuntary contractions of the diaphragm and intercostal muscles. They are generally benign and self-limited, however, in some cases they are chronic and debilitating. There are approximately 4000 admissions for hiccups each year in the United States. The hiccup reflex arc is composed of three components: (1) an afferent limb including the phrenic, vagus, and sympathetic nerves, (2) the central processing unit in the midbrain, and (3) the efferent limb carrying motor fibers to the diaphragm and intercostal muscles. Hiccups may be idiopathic, organic, psychogenic, or medication-induced. Data obtained largely from case studies of hiccups either induced by or treated with medications have led to hypotheses on the neurotransmitters involved. The central neurotransmitters implicated in hiccups include GABA, dopamine, and serotonin, while the peripheral neurotransmitters are epinephrine, norepinephrine, acetylcholine, and histamine. Further studies are needed to characterize the nature of neurotransmitters at each anatomical level of the reflex arc to better target hiccups pharmacologically. The term “singultus” (hiccup) comes from singult; a Latin word that means ‘sob’ or ‘gasp’. It refers to the sounds that are produced by the sudden involuntary contractions of the diaphragm and intercostal muscles followed by an abrupt contraction of the glottis. The air strikes the closed glottis and results in the characteristic “hiccup” sound. Hiccups are usually benign and self-limiting. They generally start without any specific reason and disappear in a few minutes. Brief episodes of hiccups are common in healthy individuals after a large meal, intake of alcoholic beverages, or sudden excitement.This article provides a review of different neurotransmitters that are related in the mechanism of action of the most commonly used drugs to treat hiccups, and the medications that induced hiccups. At the end, this paper draws a conclusion about the neurotransmitters involved in the pathophysiology of hiccups.The classification of hiccups is based on their duration. An acute attack lasts less than forty-eight hours. Persistent hiccups last more than 2 days. Intractable hiccups are present if the attack lasts more than 1 month. Persistent hiccups are most likely to be associated with an underlying pathological, anatomic or organic disease process Cymet . IntractThe afferent limb including phrenic, vagus, and sympathetic nerves to pass on somatic and visceral sensory signals;The central processing unit in the midbrain; andThe efferent limb travelling in motor fibers of phrenic nerve to diaphragm and accessory nerves to the intercostal muscles, respectively.The pathophysiological mechanism of hiccups is related to lesions in its reflex arc as shown in Fig. The central component is located in the periaqueductal grey, subthalamic nuclei (Hansen and Rosenberg The release of 5-hydroxyl-tryptamine (5HT) from the gut enterochromaffin cells and enteric vagal afferents may also lead to hiccups as seen in a case report following administration of cisplatinum, a chemotherapeutic agent Jatoi . The menThe most commonly used pharmacological treatments include metoclopramide which reduces the intensity of esophageal contraction, chlorpromazine  induced have been reported. Table The exact etiology of hiccups is unclear, and it is unknown why diverse drugs like dopamine blocking agents (DBA), baclofen, clonazepam, and phenytoin, which have widely varying mechanisms of action, can be effective in the treatment of hiccups  by altering trans-membrane potential. Glutamic acid is decarboxylated to produce GABA by the enzyme  Rodwell . The inh Rodwell . Valproi Rodwell  and is o Rodwell .the niagrostriatal pathway which is important in Parkinson’s disease;the mesolimbic pathway which plays a role in psychiatric disorders; andthe tuberoinfundibular pathway, which is related to the regulation of the endocrine system.There are many studies to support the convincing role of dopamine as the central neurotransmitter involved in hiccup pathogenesis. Dopamine is found in the brain and peripherally in the adrenal medulla, plexuses of the GI tract, and the enteric nervous system. Dopamine is an adrenergic agonist that is non-selective  as the neurotransmitter of hiccups. Serotonin is an important central nervous system neurotransmitter and a local hormone. It is also found in high concentrations in enterochromaffin cells throughout the GIT that are the site of synthesis and storage of 5 HT from tryptophan. It regulates the smooth muscles in the GIT to increase the tone and facilitate the peristalsis via 5HT2 receptors Katzung . It is aHistamine is produced by decarboxylation of histadine, an amino acid, by the enzyme histadine decarboxylases  is the neurotransmitter of the post ganglionic autonomic nervous system that is involved in the reduction of gastrointestinal motility. NE is synthesized from tyrosine that is produced from phenyle alanine. Dopamine is converted to NE by dopamine beta hyroxylases. Norepinephrine is converted to epinephrine by the enzyme phenyle ethanolamine-N-methyltransferase. The action of neurotransmitter catecholamines is terminated by the re-uptake and metabolism by the enzymes monoamine oxidases (MOA) and catecolaminotransferases (COMT). The inhibitors of these enzymes are used in the treatment of Parkinson’s disease. Adreno receptors have four subtypes, alpha1, alpha2, and beta1 for NE mainly and B2 is for epinephrine mainly. There are studies about the successful treatment of hiccups with methylphenidate (a psycho-stimulant) generally used in attention deficit disorders  and peripheral neurotransmitters . Lesions across any of these neuroanatomical structures, for instance, from the brainstem down to the diaphragm, may be responsible for the development of hiccups. Further research is needed to characterize the neurotransmitters involved in hiccups for potential new therapeutic targets.


Echinostoma caproni is an intestinal trematode extensively used as experimental model for the study of factors that determine the course of intestinal helminth infections, since this markedly depends on the host species. Although the host-dependent mechanisms for either chronic establishment or early parasite rejection have been broadly studied, little is known regarding the parasite response against different host environments.E. caproni adults, isolated from hosts displaying different compatibility with this trematode, was performed.To identify host-dependent differentially expressed proteins, a comparative proteomic analysis of the excretory/secretory products released from A total of 19 differential protein spots were identified (14 overexpressed in mice and 5 overexpressed in rats). The establishment of chronic infections in mice is mainly associated with the overexpression by adult worms of antioxidant and detoxifying enzymes  and metabolic enzymes like enolase, leucine aminopeptidase or malate dehydrogenase. However, the overexpression of cathepsin L and the structural protein actin observed in worms isolated from rats seems not to be effective for the colonization of the intestinal mucosa of this host.The observed differences suggest that protein expression and/or release is modulated by the local environment generated inside the host and provide useful insights in regards to the resistance mechanisms developed by parasites to ensure their long-term survival.The online version of this article (doi:10.1186/s13071-016-1465-x) contains supplementary material, which is available to authorized users. Although intestinal helminth infections are highly prevalent around the world, they are still amongst the most neglected tropical diseases  causing Echinostoma caproni, have been used for decades as experimental models to study the relationships between food-borne trematodes and their vertebrate hosts  were identified as aldo-keto reductases. Other antioxidant and detoxifying enzymes such as hydroxyacylglutathione hydrolase F: 1.8), dihydrolipoamide dehydrogenase (F: 5.3) and glutathione S-transferase  were overexpressed in mice  classical, signal peptide-driven secretion; 2) non-classical secretion; and 3) through secretory vesicles like exosomes. Independently of the way for release, every protein present in the extracellular milieu is susceptible of playing a role at the host-parasite interface, as they are crucial for the interaction between parasites and their hosts during infection and pathology. Moreover, it has been demonstrated that not only parasite-derived proteins can modulate the host immune response , but alste hosts . A numbete hosts , 12–17. E. caproni (i.e. genome and transcriptome assemblies), we aimed at increasing the number and accuracy of our identifications. Sequences for dihydrolipoamide dehydrogenase (spot 6) and malate dehydrogenase (spot 7) were not found in the genome assembly of E. caproni when homologous sequences from other trematodes were used for blasting in this database. This suggests that these genes may have been mis-annotated, and reinforce the idea that the combination of several databases may aid to increase the number of protein identifications in the case of non-model organisms. However, despite the combination of DNA- and RNA-based data, an accurate identification of the protein of interest is not always possible due to the lack of specific proteomic data [E. caproni infections.In this study, host-dependent ESP profiles are investigated using a quantitative 2D-proteomic approach. By combining the available 'omic data on mic data . This haE. caproni [E. caproni enolase has been suggested of great importance for the attachment to the host intestinal mucosa, due to its ability to bind plasminogen in vitro [Leishmania donovani has been demonstrated to be Th1-stimulatory, with the recombinant protein displaying a strong ability to proliferate lymphocytes along with significant IL-12, IFN-γ and nitric oxide production [E.caproni enolase, favoring the development of type 1 responses that are important both for the chronic establishment of the parasite [Several enzymes involved both in energy and non-energy metabolism were highly abundant among the proteins overexpressed in mice. Although these are typically intracellular proteins, they are usually present extracellularly, either secreted or attached to parasite surface, where they can carry out atypical functions and participate in host-parasite interactions . Enolase caproni . Furtherin vitro . More reoduction . The oveparasite  and the parasite .E. caproni.Similarly, LAP is a cytosolic metalloprotease involved in digestion and, possibly, invasion and migration through the host tissues , being aAntioxidant and detoxifying enzymes were predominant among the identified proteins. Three differential spots, 2 of them overexpressed in rats and the other one overexpressed in mice, were identified as aldo-keto reductases Table , a superE. caproni [E. caproni chronic infections in mice coincides with the development of local Th1-type responses, with high mRNA expression of IFN-γ and iNOS [E. caproni from the rat intestine, in contrast, has been associated with the development of local Th2/Th17 biased responses, in the absence of increased IFN-γ nor iNOS mRNA expression [E. caproni adults in the intestine of rats may increase their susceptibility to the granulocyte-mediated immunity, thereby impairing their establishment in the intestine of rats. Furthermore, hydroxyacylglutathione hydrolase and dihydrolipoamide dehydrogenase are detoxifying enzymes previously described in the ESPs of other parasitic trematodes [Parasites causing long-lasting infections are exposed to high amounts of reactive oxygen and nitrogen species generated as a result of the host immune response, which ultimately lead to parasite death . GST is  caproni , 42, 43,and iNOS , and theand iNOS . Hence, pression , 13. In pression . Eosinoppression . Thus, aematodes , 47, andE. caproni adults isolated from rats seem to over-release proteins related to worm invasiveness, such as cathepsin L and actin. Cathepsins are papain-like endopeptidases, used by helminth parasites in essential aspects of their relationship with the host [F. hepatica or Schistosoma spp. [E. caproni in rats [E. caproni [Echinostoma friedi [Instead of self-protective proteins such as antioxidant and detoxifying enzymes, the host . Much avoma spp. , 50. Howoma spp. , 52. Moroma spp. . The ove in rats . Overexp caproni  and the a friedi , and hasa friedi .E. caproni infections in terms of worm survival and development. However, the parasite response against each particular environment is still poorly understood. In the present paper, we have shown the proteome plasticity of E. caproni adult worms through the identification of host-dependent differentially expressed and/or released proteins. Both parasites and hosts are able to influence each other. Our results seem to indicate that, additionally to immune-mediated mechanisms, a poor adaptation of E. caproni to the microhabitat generated in the rat intestine may facilitate its rapid rejection from this host. In contrast, the ESP profile of mice-grown worms suggests a better adaptation of the parasite to the Th1-type environment developed in the intestine of this host, thereby enabling its chronic establishment. The identification of precise host-dependent mechanisms that govern parasite adaptation appears as a suitable approach to develop new strategies for the control of helminthic infections. Moreover, the identification of proteins implicated in parasite resistance may help to recognize useful targets for drug and vaccine development.Host species has been shown to be determining for the course of The present study has been approved by the Ethical Committee of Animal Welfare and Experimentation of the University of Valencia (Ref#A18348501775), with protocols adhered to Spanish  and European (2010/63/UE) regulations.The datasets supporting the conclusions of this article are included within the article and its additional files.