Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level. High throughput technologies, such as genomic sequencing and microarrays, allow the genome-wide analysis of molecular factors associated with disease. While the technologies have advanced and have been refined to generate an increasing amount of high quality data, challenges remain in understanding the biological processes involved in disease etiology, particularly for complex disorders.As we know, individual genomic alterations may result in diseases. For example, Hemophilia A is an X-linked recessive bleeding disorder caused by a deficiency in the activity of coagulation factor VIII . HuntingIn addition to monogenic diseases, many diseases are complex, such as diabetes, schizophrenia, or cancer, and are believed to be caused by a combination of genomic alterations, epigenetic, environmental and lifestyle factors . GenomicIt is well known that the topological structure is very important for biological networks and it determines how genes interact with each other, governing how specific genes and biological pathways operate in the promotion or inhibition of human diseases . RelatedMost of the approaches for topology-based network and pathway analysis are based on different correlation-based metrics to identify differential networks between two different phenotypes. Generally, there are three main ways to compare networks for differential network analysis. The first approach handles weighted networks and uses some functions of the edge-specific weight differences as edge weights to construct differential networks . The secAlthough the above methods for differential network analysis can deal with some important biological questions, they are still limited. In general, they are based on a basic hypothesis that some connections between genes across the groups could be thought of as “passenger” events and other connections are unique to either one of groups and thus could be “driver” events that contribute to disease progression and development. Hence, they focus on the contribution of individual differential connections to disease. This results in several limitations. First, each differential connection is regarded by these methods to have an equal contribution to disease. However, it is well understood that loss of a connection between two hub genes from normal to disease is more deleterious than loss of a connection between two non-hub genes. Second, how differential connections (“driver” connections mentioned above) between pairs of genes are associated with diseases is still not very biologically intuitive, because how the dependency between genes contributes to diseases is usually little understood.To address these problems, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways associated with cancer. PageRank is an algorithm initially used by Google Search to rank websites in their search engine results . It is aOur approach embodied by PoTRA is motivated by the observation that the loss of connectivity is a common topological trait of cancer networks , as wellOur approach embodied by PoTRA is also motivated by that the topological ranks of genes within biological pathways might be altered in cancer. Based on this, we propose to detect pathways involved in cancer by testing if the distribution of PageRank scores of genes for each pathway is altered between normal and cancer samples.Therefore, the PoTRA approach computes topological ranks of genes in each pathway, and then detects pathways with significantly altered number of hub genes between normal and cancer, and, alternatively, detect pathways with significantly altered distributions of topological ranks of genes in corresponding pathways between two phenotypes. Namely, we first use the Google search PageRank algorithm to measure the relative topological ranks of genes in a biological pathway across different conditions. Then, we use Fisher’s exact test to estimate if the number of hub genes in each pathway is significantly different between normal and cancer, and, alternatively, use the Kolmogorov–Smirnov test to estimate if the distribution of PageRank scores of genes in each pathway is significantly different between normal and cancer. As an illustration, we apply PoTRA to study hepatocellular carcinoma (HCC) and its subtypes and identify disease-relevant pathways. In conclusion, PoTRA is a new approach to explore and discover cancer-associated pathways. PoTRA can be used as a complement to other existing methods to enrich our understanding of the biological mechanisms behind cancer at the systems-level.Below we detail the steps of the PoTRA method, as illustrated in 1. Datahttps://cancergenome.nih.gov/) hepatocellular carcinoma (HCC) study. We analyze and contrast 50 HCC samples and 50 tumor-adjacent normal samples . In addition, the datasets also include gene expression profiles for several HCC subtypes. We further analyze and contrast 22 hepatitis B-induced HCC samples and 22 tumor-adjacent normal samples, 34 hepatitis C-induced HCC samples and 34 tumor-adjacent normal samples, and 50 alcohol-induced HCC samples and 50 tumor-adjacent normal samples. There are 20,531 gene expression values for each sample. Pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  .The output from the PageRank algorithm is a probability distribution representing the likelihood that a person randomly clicking on links will arrive at any particular web page. A probability is a numeric value between 0 and 1. The sum of probabilities for all web pages is equal to 1. The probability of a web page is proportional with the time spent at the web page when a person surfs the web. This idea can also be intuitively extended to ranking genes in gene networks where the probability of a gene is proportional with the time a research scientist spends looking and returning at the same gene when analyzing research results. For additional details of PageRank, please refer to 4. Detect pathways with significant changes between normal and cancer scores4. 1 Detect pathways with significantly altered number of hub genes between normal and cancer using Fisher’s exact testAs mentioned above, PoTRA is motivated by the observation that the loss of connectivity is a common topological trait of cancer networks  and the Fisher’s exact test estimates the probability of obtaining any such set of values, given by the hypergeometric distribution:n = a + b + c + d, and where Formula 1 gives the exact hypergeometric probability of observing this particular arrangement of the data, assuming the given marginal totals, on the null hypothesis that the number of hub genes is the same for a specified pathway between normal and cancer. If this test statistic is significant, it indicates that there is a significantly different number of hub genes between normal and cancer, thereby this pathway might be involved in cancer. By studying many pathways from the KEGG database we generate a multiple hypothesis testing problem. We address this issue by correcting the P-values calculated for each pathway using the False Discovery Rate (FDR) approach, using the Benjamini and Hochberg procedure  is the indicator function, equal to 1 if Xi ≤ x and equal to 0 otherwise.where The two-sample Kolmogorov–Smirnov test may be used to test if two underlying one-dimensional probability distributions differ. In this case, the Kolmogorov–Smirnov statistic is: Fn1, and Fm2, are the empirical distribution functions of the first and the second sample respectively, and sup is the supremum function.where P-values calculated for each pathway using the False Discovery Rate (FDR) approach, using the Benjamini and Hochberg procedure  hepatocellular carcinoma (HCC) study.To illustrate the PoTRA method, we use a cancer-associated pathway, the “MAPK signaling pathway”, as an example in the following section.As suggested before, the “MAPK signaling pathway” might be comprised of different interactions between genes under different conditions, such as normal versus cancer conditions. First, we need to find the genes that this pathway consists of by using the KEGG database. In practice, we implement it by using an R package called graphite. Second, for the genes of this pathway, we identify the interactions between genes for normal and cancer samples, separately. There are 14,005 edges for normal and 5,170 edges for cancer. For the information of the “MAPK signaling pathway” in normal and cancer samples, see Based on the interactions identified in the previous section, we can obtain a PageRank score for each gene in the “MAPK signaling pathway” for normal and cancer, separately, which quantifies the influence of a gene on the activity of other genes in this pathway. For the results for this step, two vectors with 250 PageRank values, one for normal and one for cancer, separately, see The number of hub genes changes. While there are only 13, strongly-connected  hub genes in the normal samples, there are 40 hub genes in the cancer samples, more loosely-connected .As mentioned before, the process of the network losing connectivity might be the process of disrupting the structure of the network which includes a small number of hub nodes and a large number of non-hub nodes, namely, the number of hub genes might be altered in cancer compared to that in normal. The altered number of hub genes might be a trait of a pathway in cancer, which suggests the pathway is involved in cancer if the change in hub gene number is statistically significant.Importantly, we find increased variance  of gene expression in cancer samples compared to normal samples, which is included in We next use Fisher’s exact test to test if the number of hub genes for the “MAPK signaling pathway” is significantly different between normal and cancer. The result for the “MAPK signaling pathway” pathway is included in P-value in The low Then we apply the same approach to other pathways from KEGG to compare HCC vs. normal samples. The significant pathways are shown in We find three significant pathways with altered number of hub genes between normal and cancer. It is well known that these three pathways are strongly associated with cancer in general. The MAPK signaling pathway plays a role in the regulation of gene expression, cellular growth, and survival . Abnormap-values are larger than that when the threshold is 0.95, and we do not find any significant pathways using Fisher’s exact test. This suggests that, for this test, using a limited number of genes of higher “hubness”  is important when comparing normal and cancer.The levels for parameter thresholds can be varied. Hence, we also have tried different threshold levels for hub genes in Fisher’s exact test: 0.9, 0.85, 0.8, 0.75 and 0.7. For these threshold levels, the We next use the Kolmogorov–Smirnov test to test if the two distributions of PageRank scores of genes for the “MAPK signaling pathway” are significantly different between normal and cancer. The result for the “MAPK signaling pathway” pathway is included in P-value in The low Then we apply the same approach to other pathways from KEGG to compare HCC with normal samples. The significant pathways are shown in We find three significant pathways with altered distribution of PageRank scores of genes between normal and cancer. In addition to the “MAPK signaling pathway”, we find two other pathways: “RNA transport” and the “mRNA surveillance pathway”. For the “RNA transport” pathway, it is well known that the nuclear export of mRNA is intrinsically linked to the co-transcriptional processing of nascent transcripts synthesized by the RNA polymerase II. This functional coupling is very important for the survival of cells allowing for nuclear export of fully processed transcripts, which could lead to genome instability and to various forms of cancer . The “mRMany complex diseases have subtypes and/or can be classified into different categories based on diagnosis, pathology, phenotype characteristics, etc. To further assess the PoTRA method, we apply it to several subtypes of the HCC TCGA data. There are several risk factors associated with HCC, such as hepatitis B, hepatitis C and alcohol . Here, wThere is one common pathway, Pathways in cancer, between These results suggest that PoTRA can be used to identify not only the pathways associated with cancer in general, but also those pathways associated with cancer subtypes, such as hepatitis B-induced HCC specifically.Results of the PoTRA analysis from two other HCC subtypes, hepatitis C-induced HCC and alcohol-induced HCC, are included in In We find two common pathways between In Results of the PoTRA analysis from two other HCC subtypes, alcohol-induced HCC and hepatitis B-induced HCC, are included in We find three common pathways between There is no common pathway between The above results show that the Fisher’s exact test and the Kolmogorov–Smirnov test can identify cancer-related pathways based on correlation networks. In addition to correlation networks, in this section we apply PoTRA to gene networks constructed by intersecting correlation networks with pre-defined networks from the KEGG database. As performed in the previous section we investigate if Fisher’s exact test and Kolmogorov–Smirnov test are still able to robustly discover cancer-associated pathways and differential pathways between HCC and subtypes of HCC based on the combined networks.For HCC, we use Fisher’s exact test and identify no significant pathway with altered number of hub genes between normal and HCC.For hepatitis C-induced HCC, we identify two significant pathways, listed in As mentioned above, the Epstein-Barr virus infection is a pathway associated with virus infection, which is specifically related to hepatitis C induced HCC. P53 signaling pathway is a classical oncogenic pathway, and it can regulate the cell cycle, apoptosis and help prevent cancer. The major component of p53 signaling pathway, p53 protein, is most frequently altered in human cancer .For hepatitis B-induced HCC, we identify one significant pathway, listed in As such, we identify the same significant pathway “Epstein-Barr virus infection”, which is also specific for hepatitis C-induced HCC, discussed above.For alcohol-induced HCC, we identify no significant pathways.First, we can observe a large loss of connectivity for combined networks vs. correlation networks in normal and in cancer. For example, Furthermore, because combined networks have a large loss of connectivity from correlation networks to combined networks, the PageRank scores of genes are more evenly distributed in combined networks than in correlation networks. Hence, fewer significant pathways with altered number of hub genes between normal and cancer are identified for combined networks than that for correlation networks. Although Fisher’s exact test identifies fewer significant pathways, this test can still discover differential pathways specific for subtypes of HCC between normal and cancer, suggesting that the Fisher’s exact test is able to robustly discover pathways involved in cancer and subtypes of cancer.All significant pathways for HCC and each subtype of HCC  using the Kolmogorov–Smirnov test on combined networks are included in Among the results of the PoTRA analysis for HCC in Among the results for hepatitis C-induced HCC , we findAmong the results for hepatitis B-induced HCC , we alsoAmong the results for alcohol-induced HCC , we alsoAs mentioned above, the combined networks, which intersect correlation networks with curated database networks, have a large loss of gene-gene interactions when compared to correlation networks. Hence, the distribution of PageRank scores of genes might be changed more from normal to cancer than that in correlation networks. Hence, more significant pathways with altered distribution of PageRank scores of genes are identified for combined networks than that for correlation networks. Although the Kolmogorov–Smirnov test identifies a large amount of significant pathways, we can use this test for combined networks to rank the pathways associated with cancer.We propose a PageRank-based method, Pathway of Topological Rank Analysis (PoTRA), for identifying pathways involved in cancer. PoTRA is motivated by the observation that the loss of connectivity is a common topological trait of cancer networks  and the To illustrate the method, PoTRA is applied to several TCGA hepatocellular carcinoma datasets. The results in our study are in agreement with prior knowledge of HCC from literature. We find that a high proportion of statistically significant pathways play important roles in cancer, indicating that the altered number of hub genes for these pathways might indeed be a reflection of the underlying biological causes that lead to cancer. Moreover, in the comparison between normal and each subtype of HCC, most importantly, the “Hepatitis B” pathway and several pathways associated with virus infection dramatically become significant pathways in hepatitis B-induced HCC, suggesting that PoTRA is capable of detecting pathways associated with disease subtypes. We also find several pathways associated with HCC generally and subtype specifically in hepatitis C-induced HCC and in alcohol-induced HCC.In our approach, the correlation method is used to construct gene co-expression networks for normal and cancer, respectively. A gene co-expression network is an undirected graph, where each node represents a gene, and each edge is established if there is a significant co-expression relationship between two genes . Stuart Here, we construct gene networks using two ways. One approach is based on correlation networks, while the other approach is to combine (intersect) the correlation networks with pre-defined networks from pathway databases. Because the combined networks lose a large amount of gene-gene interactions from the correlation networks, the power is reduced and the PageRank scores of genes tend to more evenly distributed in combined networks than that in correlation networks. Hence, fewer significant pathways with altered number of hub genes between two phenotypes are identified for combined networks than that for correlation networks. Moreover, the distribution of topological ranks of genes might be changed more from normal to cancer than that in correlation networks. Thus, more significant pathways with altered distribution of PageRank scores of genes are identified for combined networks than that for correlation networks. Although the Kolmogorov–Smirnov test identifies a number of significant pathways for combined networks, we can use the Kolmogorov–Smirnov test to rank those pathways.We apply the Fisher’s exact test and the Kolmogorov–Smirnov test to analyze the PageRank scores for the two types of gene networks as mentioned above. The corresponding results suggest that the Fisher’s exact test and the Kolmogorov–Smirnov test can identify and rank cancer-associated pathways. This suggests that the PoTRA method is robust to choice of network building approach and to statistical analysis method for identification of cancer-related pathways.The hypothesis of our study is based on the fact that the loss of connectivity is a common topological trait of cancer networks . It is nIn this study, we apply PoTRA to pre-defined biological pathways, from the well-curated KEGG pathway database. However, the PoTRA method can also be applied to any set of genes of interest, such as functional gene subnetworks. This could be an interesting area to further explore.In this article, we focus on the details of the PoTRA methodology. For different pathway databases, such as Reactome, Biocarta, etc., the method would apply in a similar manner, while the final results might vary slightly, depending on the data being used. This direction—changes in analysis results based on different pathway databases—would be an interesting area to investigate in the future.In summary, PoTRA provides a new method for detecting cancer-associated pathways. PoTRA may be used to augment existing methods and provide a richer, more systematic understanding of cancer mechanisms.10.7717/peerj.4571/supp-1Table S1Section 1.1–Section 1.2 into one table with four columns, namely: (1) Gene index (1–250) (2) Gene symbol (3) The PageRank score of each gene in this pathway for normal (4) The PageRank score of each gene in this pathway for cancer. (5) The variance of gene expression for each gene across normal samples. (6) The variance of gene expression for each gene across cancer samples.We consolidate the information from Click here for additional data file.10.7717/peerj.4571/supp-2Table S2P-values are below 0.05. E.comb.normal represents the number of the combined network for normal samples, while E.comb.case represents that for cancer samples.FDR adjusted Click here for additional data file.10.7717/peerj.4571/supp-3Table S3P-values are below 0.05. E.comb.normal represents the number of the combined network for normal samples, while E.comb.case represents that for cancer samples.FDR adjusted Click here for additional data file.10.7717/peerj.4571/supp-4Table S4P-values are below 0.05. E.comb.normal represents the number of the combined network for normal samples, while E.comb.case represents that for cancer samples.FDR adjusted Click here for additional data file.10.7717/peerj.4571/supp-5Table S5P-values are below 0.05. E.comb.normal represents the number of the combined network for normal samples, while E.comb.case represents that for cancer samples.FDR adjusted Click here for additional data file.10.7717/peerj.4571/supp-6Supplemental Information 1Click here for additional data file.10.7717/peerj.4571/supp-7Supplemental Information 2Click here for additional data file.


Exercise-induced physiological cardiac hypertrophy is generally considered to be a type of adaptive change after exercise training and is beneficial for cardiovascular diseases. This study aims at investigating exercise-regulated microRNAs (miRNAs) and their potential biological pathways. Here, we collected 23 miRNAs from 8 published studies. MirPath v.3 from the DIANA tools website was used to execute the analysis, and TargetScan was used to predict the target genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to identify potential pathways and functional annotations associated with exercise-induced physiological cardiac hypertrophy. Various miRNA targets and molecular pathways, such as Fatty acid elongation, Arrhythmogenic right ventricular cardiomyopathy (ARVC), and ECM-receptor interaction, were identified. This study could prompt the understanding of the regulatory mechanisms underlying exercise-induced physiological cardiac hypertrophy. Cardiac hypertrophy, including physiological hypertrophy and pathological hypertrophy, is one of the most important adaptive mechanisms for the heart in various situations of stress . PatholoThe biological mechanism underlying the physiological cardiac hypertrophy induced by exercise is complex and still incompletely elucidated . In receWe searched the PubMed website using the keywords “microRNA,” “exercise,” and “hypertrophy” to identify all of the published microRNAs associated with exercise-induced cardiac hypertrophy that were eligible for further bioinformatics analysis. We collected all the appropriate published articles and extracted all the data needed for further analysis.P values less than 0.05 (P < 0.05) were considered to be statistically significant.The software MirPath v.3 from the DIANA tools website was used to identify potential miRNA target genes and pathways in our study. TargetScan was used in this study to predict the potential target genes and demonstrate the possible relationships among the databases. Pathway analysis was performed to determine the involvement of coexpressed genes in different biological pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene Ontology (GO) analysis was used to investigate the pathways associated with biological processes, cellular components, and specific molecular functions corresponding to the target genes of miRNAs identified by the software TargetScan. In this study, As P < 0.05) pathways corresponding to downregulated miRNAs were as follows: Fatty acid elongation, Arrhythmogenic right ventricular cardiomyopathy (ARVC), Other types of O-glycan biosynthesis, Thyroid hormone synthesis, Tyrosine metabolism, Mucin type O-glycan biosynthesis, Other glycan degradation, Glycosphingolipid biosynthesis-lacto and neolacto series, Tryptophan metabolism, Gap junction, Proteoglycans in cancer, and signalling pathways regulating pluripotency of stem cells. The significant pathways corresponding to upregulated microRNAs were as follows: ECM-receptor interaction, Fatty acid degradation, Fatty acid metabolism, Amoebiasis, Fatty acid elongation, Protein digestion and absorption, PI3 K-Akt signalling, Tyrosine metabolism, Other types of O-glycan biosynthesis, Glycosphingolipid biosynthesis-lacto and neolacto series, Valine, leucine and isoleucine degradation, and Focal adhesion  analysis was used to investigate the pathways associated with biological processes, cellular components, and specific molecular functions corresponding to the target genes of miRNAs identified by the software TargetScan. The pathways corresponding to the target genes of downregulated miRNAs included cell, intracellular biological process, anatomical structure development, molecular function, cellular component, organelle, cell differentiation, embryo development, chromosome organization, anatomical structure formation involved in morphogenesis, ion binding, and cell morphogenesis . SimilarExercise is widely known as a safe and well-accepted nonpharmacological strategy to improve cardiac function and protect the heart from cardiovascular disorders . It is qThe analysis of downregulated miRNAs regulated by exercise revealed that Fatty acid elongation pathway and Arrhythmogenic right ventricular cardiomyopathy (ARVC) pathway could be associated with the hypertrophy process. In the Fatty acid elongation pathway, ELOVL family member 6 (Elovl6) is a microsomal enzyme, which has been shown to regulate the monounsaturated Fatty acids and elongation of C12–16 saturated Fatty acids . Elovl6 The analysis of upregulated miRNAs induced by exercise revealed that the ECM-receptor interaction pathway is the most valuable one. It was recently identified that in the mouse model of angiotensin II-induced cardiac remodelling, ECM-receptor interaction pathway may be involved in the process of cardiac remodelling , 22. HerIn conclusion, we predicted the most likely biological pathways associated with physiological cardiac hypertrophy induced by exercise in this study through extracting published miRNA data and performing analysis using bioinformatics methods, which may help us better understand the regulatory networks of exercise-induced physiological cardiac hypertrophy. However, more in-depth studies and clinical investigations are still needed in the future.


The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.http://biohealth.snu.ac.kr/software/contextTRAP.Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at The online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users. The advancement of gene profiling techniques has expanded the genomics research from a single gene analysis to the analysis of genome-wide gene expression data , 2. The A lot of efforts have been made to define sets of genes that perform key roles for common mechanisms. As a result, a number of databases have been developed to curate sets of genes as pathways . KEGG isWith gene expression profiling techniques and well curated pathway databases, gene expression data is now routinely analyzed in terms of biological pathways. Over the years, a number of tools for pathway analysis have been developed and they can be categorized as i) over-representation analysis (ORA), ii) functional class scoring (FCS), and iii) pathway topology (PT) based approach . ORA metRecently, time-series data has been considered as important key resources to understand the dynamics of biological mechanism over time and the number of datasets or research projects producing time-series gene expression data has increased dramatically . Thus, sMAPK1, is involved in 85 pathways. These overlapped genes among multiple pathways make some pathways significant regardless of the relation with the context, concurrently. Thus, the result of pathway analysis can produce pathways that are not related to the context being investigated.Pathway analysis from gene expression data using these tools identifies which biological pathways are important to understand the context of data or research being investigated (e.g. phenotype). However, there is no guarantee that all pathways selected by pathway analysis are relevant to the context . One of One effective way to verify whether a significant pathway is actually related to the context or not is to search the literature information. If some literatures support specific relationship between the pathway and the context of data, we can be more confident to choose the pathway as one significantly expressed and truly related to the context. Thus, our goal in this study is to come up with a computational framework to combine pathway analysis of gene expression data and the literature information to select pathways relevant to the the context of the experimental condition, typically control vs. treated. To describe our research clearly throughout the paper, we introduce two concepts as below.Significance: It is to measure the correlation between a pathway and the context from gene expression data[significance and it will be used throughout the manuscript.sion data. CurrentRelevance: It means how a pathway is truly associated to the context [literature information reporting the observations or evidences of association between the pathway and the context. It is stronger than significance since significance is simply to measure how much correlation exists between the pathway and the context through expression values, while relevance requires direct observations or evidences that the pathway is actually related to the context. In this paper, this concept is defined as relevance and it will be used throughout the manuscript. context  and one significance and relevance. In particular, the integration of these two concepts is to combine analysis of gene expression data (significance) and the literature information based on the contextual information provided by the user (relevance). Figure significance of pathways is calculated using existing pathway-based gene expression data analysis tools. The relevance of pathways is obtained from the literature search upon keywords that are provided by the user to specify the context of the experiment. Scores for significance and relevance are combined into a single score by summing up the two scores as a weighted sum.The goal of this study is to come up with a computational method to combine both For the implementation of the proposed concept, we used an existing pathway analysis method and a literature search tool. TRAP  is selecsignificance and relevance can be easily done with these two tools. Integrating TRAP and BEST, we implemented ContextTRAP as in Fig. significance of pathways. Using keywords, BEST is used to obtain the relevance between a pathway and the context as an entity score.The integration of significance and relevance, we introduce two score terms. Using the concept of posterior probability, Context Score (CS) measures the relevance of pathway from the entity score of BEST. Meanwhile, Impact Score (IS) measures the significance of pathway from p-value result of TRAP analysis for time-series gene expression data. Then, CS and IS are integrated via an automatically assigned weight called Discovery rate.To quantify and integrate CS, how to transform the result of TRAP to IS, and how to calculate Discovery rate that will be used as weight to integrate of CS and IS. In addition, we introduce the method of Pathway Set Enrichment Analysis (PSEA) to evaluate the result of ContextTRAP in Section “In this section, we introduce how to transform the result of BEST to π={π1,…,πn}. π has n pathways and i-th pathway is denoted as πi. A keyword is needed to be specified as an input for BEST which is the contextual information related to the context like the experimental condition or the phenotype which the research wants to investigate . This keyword is denoted as k.The set of pathways to be analyzed is denoted as Context Score ) measures how many research or articles reported some association of the pathway πi and the keyword k. CS can be computed as a form of posterior probability like Eq.  is calculated easily from the entity score of πi by finding πi from the retrieved list of entities. like Eq. . By enteP(πi|k) to Eq. (P(k|πi) instead of P(πi|k), meaning that retrieves the entity score of k entering πi as a search word into BEST. Considering this concept, if user select k from the entity list of BEST in advance, CS of any pathway can be obtained from the entity score of k. However, some pathways are not included as entities in BEST, probably because the current literatures, though increasing rapidly, are yet to be complete enough to cover all biological pathways. Thus, it is difficult to get the entity score for whole pathways by Eq. . To hand) to Eq.  using thπi that is used as a search word for BEST, the entity score of k is transformed to P(k|πi) as Eq. (si+1) to base b. si is the entity score of k derived from using πi as search word and b is smax+2, where smax is the maximum of all si. Logarithm is a monotonically increasing function, so a higher entity score is mapped to a higher probability. Because the base of logarithm is smax+2, the highest entity score is mapped to a probability approximated to 1. Thus, Eq. . In addition, this function is concave down, which means the rate of increase is larger for smaller entity scores but it decreases and converges gradually. Thus, use of the logarithm function effectively makes densely distributed small entity scores widely scattered while big entity scores remain distinguishable from the small entity scores. Given a pathway ) as Eq. . It is ahus, Eq.  convertsP(πi) is calculated using Eq. (P(πi) represents the prevalence of πi in all possible keywords, and this prevalence can be acquired by ti derived from summing up all scores of entities related to πi in BEST. Because obtaining scores of all possible entities is a time-consuming work and scores of low-ranked entities have very small values, ti is approximated by entering πi into BEST and adding top ten entity scores in three categories , respectively. Then, b is calculated as tmax+2 to make P(πi) range from 0 to 1, where tmax is the maximum of all ti.Prior probability sing Eq. , which ising Eq. . P(πi) CS of multiple keywords is obtained by calculating the average of CS derived by each keyword.In some cases, multiple keywords are needed to cover the context of data. Then, final Impact Score (IS(πi)) measures how significant pathway πi is as a result of gene expression data analysis. To handle time-series data, ContextTRAP uses TRAP for analyzing gene expression data. Before deriving IS(πi), we define IS∗(πi) like Eq.  to the maximum (or minimum) of CS like Eq. (IS(πi) can have an equal influence as CS when integrated into a single combined score. After applying the logarithm-based transformation, a min-max normalization adjusts the maximum (or minimum) of like Eq. . Then, IIS(πi) and CS are normalized in the same scale so that the integration of the two scores can be easily done. Two scores are integrated by a weighted sum as in Eq. (IS(πi) and CS. Discovery rate (wθ) is a dynamically assigned weight as in Eq.  is bigger than zero. It represents the ratio of pathways that are not selected as significant by TRAP but discovered as meaningful in BEST. In other words, Discovery rate shows how many pathways are missed by the gene expression analysis but have been investigated and reported in the literature v.s. all the pathways. n is the total number of pathways and θ means a threshold of p-value for selecting insignificant pathways from TRAP and 0.05 is used in this paper, since p-value of 0.05 is a widely used cutoff value for indicating statistical significance. Discovery rate reflects some characteristics about data. 1) If the user-defined context is well supported by the literature and BEST can cover many pathways related to the context, Discovery rate will be bigger to increase the effect of CS and vice versa. 2) Although BEST finds many pathways with the context, if TRAP covers most of the pathways detected by BEST, the importance of CS decreases. s in Eq. , so it is in Eq.  to refleπi is derived by combining CS and IS(πi) with Discovery rate as in Eq. ) at rank i like Eq. (RS(i) is increased or decreased from RS(i−1), depending on whether πi) is initialized to 0. Then, if πi) is increased by a proportion of ri which means the impact of rank i, where ri is n−i+1. On the other hand, if πi) is decreased by a reciprocal of the number of π−. Enrichment Score (ES) is the maximum of RS, which measures how significant the pathway list is. The higher the ranks of overall π∗, the higher the ES score is. To evaluate the pathway list determined by ContextTRAP in Section “like Eq. . In the by DAVID , 28 analrelevance, using various keywords, each of which has different strength of representing the context of data. Secondly, Pathway Set Enrichment Analysis (PSEA) introduced in Section “Discovery rate estimates a proper weight or not for integration. Next, Kolmogorov Smirnov (KS) test was used to compare the performances of ContextTRAP and the original TRAP in terms of prioritization. Finally, we compared ContextTRAP with original TRAP and also with three existing pathway analysis methods, one from each of major pathway analysis categories of ORA, FCS and PT-based methods, in terms of F1 score. Additionally, we investigated the biological importance of the prioritized pathways in ContextTRAP.In this section, we evaluated the performance of ContextTRAP by analyzing two public datasets. Two datasets have time-series gene expression data measured in control vs. treatment experiments. Using these datasets, we evaluated the performance of ContextTRAP in various ways. Firstly, we tested whether the literature information truly supports the H5N1 identified signaling networks affected by highly pathogenic avian influenza H5N1 infection [Th17 identified regulatory networks controlling the TH17 cell differentiation triggered by TGF- β1 and IL-6 [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28166 for H5N1 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43955 for Th17.To evaluate the performance of ContextTRAP, two public datasets are selected. i) nfection . ii) Th1and IL-6 . Raw datCS. It should be one of the entities of BEST, thus we selected the simplest and the most relevant keywords from the entity list in BEST. In Table Influenzas is used as a keyword for H5N1, while TGFB1 and IL6 are used as keywords for Th17. In the web version of ContextTRAP, user can search and select keywords from the entities of BEST to specify the experimental condition of the input dataset.Firstly, keywords representing the context of data are necessary to obtain H5N1 and 29 pathways from Th17 are selected as relevant pathways of each dataset. In this paper, these relevant pathways are represented as π∗ and the rests are represented as π−. Then, the purpose of our research is prioritizing π∗ from pathway analysis result. The list of π∗ for each dataset is provided in additional file [see Additional file To define the pathways which are relevant to the context of each dataset, DAVID , 28 analFor running TRAP algorithm, we selected time-lag factor, parameter to adjust the ratio of influence from the previous time point, as 1. Also, cutoff value, threshold to find DEGs by fold-change as logarithm, was selected as 1, according to the default values.Discovery rate was calculated automatically: 0.30 for H5N1 and 0.21 for Th17. The result of data processing is shown in Table H5N1 is time-series gene expression data of Homo sapiens and has 295 KEGG pathways in total referred as |π|. Th17 is for Mus musculus and has 291 KEGG pathways. The |π∗| means the number of π∗ which are context-relevant pathways retrieved from DAVID.Using the result of BEST and TRAP, relevance, we tested whether the distribution of π∗ in pathway list returned from BEST is different in accordance with the degree of association between keyword and context of data. Figure To show whether the literature information can represent the H5N1 using Alcohol dependence, Infectious diseases and Influenzas as keywords. Influenzas is the best keyword well representing the context of H5N1 and Infectious diseases that is more comprehensive term than Influenzas was selected as secondly relevant keyword. Alcohol dependence was selected to show the result of keyword having little relevance with the context. In the bottom of the figure, each color marker shows the position of π∗ in 295 pathways ordered by BEST score derived from each keyword. Boxplot above shows the distribution of rank of π∗ in pathway list. In Influenzas which is most relevant to H5N1, π∗ are mainly positioned at the front of pathway list. It means Influenzas well represents the context of H5N1. As a secondly relevant keyword, Infectious diseases shows similar result with Influenzas, but π∗ are mainly positioned at lower rank than Influenzas. In Alcohol dependence, most π∗ are distributed in backward of pathway list. It suggests that the result of literature search with a keyword that well reflects the context of data makes reasonable pathway scores representing the relevance between pathway and the context.Figure Th17 using Alcohol dependence, TGFB1, IL6 and combination of TGFB1 and IL6 as keywords. Like Fig. π∗ are marked in color. The result of Alcohol dependence which is a irrelevant keyword shows that π∗ are uniformly distributed on the pathway list without any tendency. Two mainly relevant keywords, TGFB1 and IL6, show good performance where π∗ are focused on top of list. When two relevant keywords are used together like a combination of IL6 and TGFB1, however, the result is improved than the result of single keyword. It seems that combined keywords mutually supplement search results and make synergy from each keyword.Figure Discovery rate, the weight in Eq.  Th17. In Fig. π∗ in the sorted pathway list determined by ContextTRAP are indicated in x-axis at 7 different weights, π− in gray and π∗ as bars in color other than gray. The line graph shows a change of RS(i) through x-axis from the first rank to the last rank.To show how well t in Eq. , is set H5N1 and  Th17. Inπ∗ generally tend to be focused on the front of pathway list, which indicates that TRAP performed well. However, considering the literature information, ContextTRAP prioritized π∗ more from the result of TRAP. Figure ES which is the maximum RS at weights of 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 and also Discovery rate. With Discovery rate, ContextTRAP improved ES compared to original TRAP, from 0.761 to 0.787 for H5N1, and from 0.726 to 0.763 for Th17. It is the highest of the results from each weight. This experiment shows that our strategy of combining relevance and significance of pathways is quite good without requiring the user to set the weight value. Note that our system determines Discovery rate automatically and it reduces the bias which can be caused by a fixed weight.With the weight of 0.0 in Fig. D statistic that represents the maximum distance between two empirical distribution functions of two samples. Applying the KS test, we compared distributions of π∗ and π− in sorted pathway list returned from ContextTRAP and original TRAP. If π∗ are positioned at the head of list and π− are positioned at the tail of list, D between them becomes higher. If π∗ and π− are mixed in list, on the other hand, D will be a lower value.We measured how much improvement was achieved by comparing performance of ContextTRAP that incorporated BEST and the original version of TRAP that does not utilize literature information. Using Kolmogorov Smirnov (KS) test, we can compare two distributions effectively. KS test measures the H5N1 and Fig. Th17. The pathway list is sorted by score of each analysis and is present in x-axis of each plot. Red markers indicate the position of π∗ in the pathway list. In each plot, points in red color represent the empirical distribution function of π∗ and blue points indicate that of π− in the pathway list returned from pathway analysis.Figure π∗ are distributed at the head of pathway list and empirical distribution function of π∗ increases more rapidly in ContextTRAP compared with original TRAP. In addition, D is higher in ContextTRAP than original TRAP. It means π∗ are prioritized well in ContextTRAP by using literature information.For two datasets, plots show that 1 score of a pathway list determined by each of the pathway analysis tools. F1 score is a harmonic mean of precision and recall and it is widely used to evaluate the performance of binary classification tests. In this analysis, we consider π∗ set as a positive condition set and π− set as a negative condition set in terms of true condition set. Then, pathways are predicted as positive or negative, depending on whether a pathway is classified as significant or insignificant by each of pathway analysis tools. Using the result of predicted condition set and the pre-defined true condition set, F1 score is calculated.We compared the performances of ContextTRAP with three other pathway analysis methods included in the graphite web server , also wiCS and IS of all pathways. Then, pathways having p-value under 0.05 are selected as significant for ContextTRAP. For other pathway analysis tools, we selected pathways having p-value below 0.05 as significant.To split the pathway list of ContextTRAP into significant and insignificant, p-value of each pathway is calculated by permutation. P-value is derived from a distribution of permuted scores by generating all possible combinations of 1 scores for five methods—ContextTRAP, original TRAP, Fisher’s exact test, GSEA and CliPPER. Figure H5N1 and Fig. Th17. For two datasets, original TRAP shows higher F1 score than other three methods—Fisher’s test, GSEA and CliPPER. It is probably because other methods except TRAP do not consider the effect of time factor in a proper manner, handling the time-series samples as multiple replicates. However, ContextTRAP shows the highest F1 score, even higher than the score of original TRAP. It indicates that literature search realizes the prioritization of context-relevant pathways and improves the quality of significant pathway set from original TRAP.Figure Table H5N1, the rank of the pathways that are related to the immune system and affected by infection of H5N1 influenza moved up significantly. Invading to host, H5N1 viruses activate PI3K-Akt signaling pathway to increase their replication efficiency [β, a regulatory subunit consisting in PI3K, and induces activation of Akt [In ficiency . The NS1n of Akt . It leadn of Akt . In addin of Akt . The hemn of Akt .Th17, pathways related to cytokines that induce differentiation of TH17 cell moved upwards. Differentiation of TH17 cell is induced by IL-6 and TGF- β1 together [β and IL-21 play a role in amplifying the differentiation IL-6 and TGF- β1 [β [TH17 cell [TH17 cell differentiation and proliferation [In together . In addi TGF- β1 . MitogenF- β1 [β , 40. TolH17 cell . TLR2 haferation .Impact Score and Context Score, to measure significance from the result of pathway analysis and to specify relevance from the result of literature search, respectively.Many gene expression analysis methods are available for identifying significant pathways from transcriptome data. However, these methods are often misled by many genes that are involved in a number of pathways. To address the challenge, we developed a new computational framework to combine analysis of gene expression data and the literature information based on the contextual information provided by the user as keyword. We defined two scores, H5N1 and Th17, in comparison with the state of the art pathway analysis tools in each of the three categories of pathway analysis tools: Fisher’s exact test representing ORA methods, GSEA [1 scores, ContextTRAP achieved better performances than existing methods and than the original TRAP. One notable feature is that ContextTRAP automatically combines Impact Score and Context Score into a single score by setting Discovery rate dynamically. In extensive experiments with various weights, Discovery rate showed the maximum performance, which demonstrates the ability of ContextTRAP to combine analysis of transcriptome data and the literature information dynamically, depending on the level of literature knowledge related to the experiment.Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. We evaluated the performance of ContextTRAP with two data sets, ds, GSEA  represenWe believe that ContextTRAP will be a very useful resource for the pathway based analysis of gene expression data for the time-series, since the user can specify the context of the biological experiment in a set of keywords.


Whereas genome-wide association study (GWAS) has proven to be an important tool for discovery of variants influencing many human diseases and traits, unfortunately its performance has not been much of all-around success for some complex conditions, for example, hypertension. Because some of the existing effective pharmacotherapeutic agents act by targeting known biological pathways, pathway-based analytical approaches could lead to more success in discovery of disease-associated variants. The objective of the present study was to identify functional variants associated with blood pressure in the aldosterone-regulated sodium reabsorption pathway using the simulated and real blood pressure phenotypes provided for Genetic Analysis Workshop 19.The present analysis included 1942 samples with exome sequencing data and for whom blood pressure phenotypes were available. Because only odd-numbered autosomes were available, we restricted analysis to 127 quality-controlled single-nucleotide polymorphisms from the aldosterone-regulated sodium reabsorption pathway. We performed pathway-based association analysis using appropriate regression models for single variant, haplotype and epistasis association analyses. To account for multiple comparisons, statistical significance was empirically derived by permutation procedure and Bonferroni correction.PRKCA gene for diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP) in both real and simulated data. The associations remained significant (P <0.05) after multiple testing corrections for the number of genes. Similarly, the pathway-based 2-locus epistasis analysis indicated significant interactions between INSR and PRKCG for SBP and MAP; INS and PIK3R2 for DBP; PIK3CD and ATP1B2 for hypertension in the real data set. We also observed significant within-gene interactions in INSR for SBP, DBP, and hypertension in the simulated data set.The topmost pathway-based association signals were observed in The findings from this study show that pathway-based analytical approach targeting known biological pathways can be useful in identification of disease-associated variants that are otherwise undetectable by GWAS. The approach takes advantage of the assumption of nonindependence of variants within and across pathway genes which leads to reduced penalty of multiple testing and thus less-stringent statistical significance threshold. Genome-wide association study (GWAS) has proved to be a useful tool in the discovery of genetic variants associated with many complex diseases and traits . UnfortuGiven what is known about the biology of these diseases and traits, it is suspected that important variants with moderate to large effect sizes remain to be identified; this is commonly referred to as the “missing heritability” , 3, 6, 7Consequently, the main objective of the present study was to perform pathway-based association analysis to identify blood pressure phenotypes–associated functional variants in the aldosterone-regulated sodium reabsorption pathway using whole exome sequence data provided for Genetic Analysis Workshop 19 (GAW19). The aldosterone pathway was chosen because it is one of the known target biological pathways for pharmacological control of hypertension. We hypothesize that functional genetic variant in the pathway influences susceptibility to blood pressure elevation.Analyses were based on the unrelated data set of human whole exome sequence data plus the simulated and real phenotypes data as provided for GAW19 and described by Almasy et al .The study samples included 1943 adult Hispanic subjects, that is, 1021 type 2 diabetes cases and 922 controls from the San Antonio Family Heart Study, San Antonio Family Diabetes/Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study family component (HA) –29; and PP = SBP − DBP), mean arterial pressure (MAP) (defined as MAP = DBP+[PP/3]), and hypertension (defined as blood pressure ≥140/90 mm Hg or use of antihypertensive medication). Sex, age, and age-squared were treated as covariates in the analysis.We analyzed both the simulated blood pressure phenotypes in the “SIMPHEN.1” data set and the real blood pressure phenotypes in the “T2D-GENES_P1_Hispanic_phenotypes” data set. Outcome variables included in the analysis are DBP, SBP, pulse pressure (PP) (defined as http://samtools.github.io/bcftools/bcftools.html) to extract data on biallelic (single nucleotide and deletion/insertion) variants and then recoded the genotypes from 0/1/2 to ACGT using the information on both the reference and alternate alleles for each variant. The quality control (QC) of the genotype data was carried out using the software PLINK [p <0.001 were excluded. One sample with missing genotypes of greater than 10 % was excluded. The final quality-controlled genotype data set was made up of 1942 samples and 45,055 biallelic variants. Principal component analysis was performed using all 45,055 variants and the first of 10 components was extracted and included in association analysis to control for population stratification. Only the 1850 subjects with complete data on blood pressure phenotypes were included in association analysis.Whole exome sequence data were provided on 11 odd-numbered autosomes. The genotypes used in the present analysis were based on NALTT  as provided in the variant call format (VCF) files. We used the software BCFtools (re PLINK . Of the http://www.genome.jp/kegg/pathway.html). The pathway comprises of 39 genes located across 14 autosomes and the X chromosome. Twenty-two genes were on the 11 odd-numbered autosomes available for the present analysis (Table http://snp.gs.washington.edu/SeattleSeqAnnotation138/index.jsp). Of the 45,055 biallelic variants that passed QC, a total of 127 were in the aldosterone-regulated sodium reabsorption pathway. With the exception of the SFN gene, each of the 22 genes on the odd-numbered autosomes had at least 1 variant available for analysis.The aldosterone-regulated sodium reabsorption pathway was defined using KEGG PATHWAY Database  performed single-variant association analysis and selected up to N variants with p values below P, starting with the most significant one; (c) from the subset of variants, we calculated set-statistic as the mean of the single-variant statistics; (d) permuted the data set 5000 times and repeated steps (b) and (c) for each permuted data set; (e) calculated empirical p value as the number of times the permuted set–statistic exceeded the original data set–statistic. Software default values of 0.5, 0.05, and 10 were used for the parameters R2, P, and N, respectively. For each outcome variable, haplotype and epistasis association analyses were also done. The epistasis association involved all pairwise combinations of the 127 variants and their interaction. We also performed Bonferroni correction for multiple testing using number of testing as equal to the number of genes in the pathway. This was based on the assumption of nonindependence of variants in the pathway genes.Using the simulated and real phenotypes, we fitted additive linear  and logistic (for hypertension status) regression models for each outcome variable with the variant as explanatory variable coded as dosage of the minor allele. Sex, age, age-squared, and first principal component were included as covariates. The software PLINK  was usedPRKCA gene with DBP, SBP, and MAP remained significant in both real and simulated data. None of the empirical p values reached significant level. Figure INS vs. PIK3R2 for DBP, whereas for simulated phenotypes there were significant within-genes interactions such as in INSR gene for SBP, MAP, and hypertension (Table Figure In this study, we explored pathway-based analytical approach for the discovery of functional variants influencing blood phenotypes as additional method that could lead to identification of additional variants for complex human conditions. We focussed on a known biological pathway rather than pathways constructed from none proven biological systems. Our hypothesis was that because existing effective pharmacotherapeutic agents for blood pressure control act by targeting specific biological pathways, appropriate analytical methods that focus on such pathways could lead to identification of additional variants linked to complex human conditions than currently discovered by GWAS and candidate gene approaches. Results from this analysis indicate that, indeed, the use of known biological pathways for genetic association analysis can be a useful approach in the presence of true association since it takes advantage of the nonindependence of variants across pathway genes for setting threshold for statistical significance. We do note that because our analysis included genes from only the odd-numbered autosomes provided for GAW19, these results and their interpretations cannot be taken as fully representative of the aldosterone pathway. We are of the opinion that pathway-based analysis of variants from all genes in the pathway with those from the regulatory regions would lead to identification of important associations that can be interpreted with less limitation than in the present study. The use of known biological pathways in this study represents useful extension of genetic association analysis for complex human diseases.The findings from this study show that pathway-based analytical approaches can be useful in identification of important disease-associated variants that are otherwise undetectable by GWAS because of the assumption of nonindependence of variants within and across pathway genes which leads to reduced penalty of multiple testing and thus less stringent statistical significance threshold.


Triple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype.Gene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model.p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes.Among 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (We first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity.The online version of this article (10.1186/s12885-017-3939-4) contains supplementary material, which is available to authorized users. Breast cancer is the most commonly diagnosed cancer and leading cause of cancer-related deaths in women. In the US, it is the second-most common cause for cancer-related death in women, just behind lung cancer, with the expectation that 231,840 new cases will be diagnosed with 40,290 deaths in 2015 , 7. Of tDespite the widespread use of standard chemotherapy such as Paclitaxel (Taxol) or the combination of taxanes and genotoxic drugs, TNBCs lack the appropriate targets for the commonly used targeted breast cancer therapies, conferring an aggressive phenotype and poorer survival rate to the disease –12. For Currently, many researchers are analyzing the dysfunctional pathways unique to TNBC in order to identify possible gene targets and develop drug therapies –17. AlthThe Cancer Genome Atlas (TCGA) uses genome analysis technologies, such as large-scale genome sequencing, to aid in the understanding of the molecular basis of cancer . The mRNFor classifier comparison, we downloaded gene expression raw data files (.cel) of seven data sets from NCBI GEO database . The expression values were summarized and normalized by Robust multiarray analysis (RMA) . The Mol+/PR+/HER2− , ER−/PR−/HER2+ (HER2 enriched), ER+/NODE− (early ER+), ER+/NODE+ (late ER+), ER+/PR+/HER2−/NODE− , and ER+/PR+/HER2−/NODE+ . The gene expression values for the tumor and normal breast samples were then put through Gene Set Enrichment Analysis (GSEA) [p-values or FDRs of pathways was used to compared the pathway differences among all seven breast cancer sub-groups.The TCGA patient data were grouped into seven sub-groups based on three commonly used markers: Triple Negative (TN), ERs (GSEA)  to generp < 0.05)  and 71 Yang pathways  signature –22 was tp-value is less than 0.05 in at least one group  we were able to classify the METABRIC TNBC into six subgroups based on the level three cluster branch  and 71 Yang pathways  and the top 8 Yang pathways to build the pathway classifier. We applied this to the METABRIC TNBC cohort and as shown in Fig. 3aP-value of 2.8 × 10−2) though the group size is small  can be used to build signatures for TNBC. One hundred and fourteen genes from the Yin (133) pathways and 66 genes from the list of Yang (71) pathways were then used in the YMR signature  and testp-value , is listed as the most significant with a FDR of 0  are nuclear receptors that form heterodimers with retinoic acid receptors (RARs), which are ligand-regulated transcription factors, to control cell growth and survival. Retinoic acid binds to RARs to regulate processes such as development and cell proliferation, differentiation and apoptosis [When we input all Yang pathway genes into Ingenuity Pathway Analysis system (IPA), again the top one is the PPARα/RXRα pathway with a poptosis . In the poptosis . Recentlpoptosis . SelectiEGFR, VEGFR and FGFR gene products were particularly amplified in TNBCs and serve as putative targets for drug therapies [MUC1) for LAR, growth factor receptors such as platelet-derived growth factor (PDGF) receptor A for MES, immunosuppressing molecule (VTCN1) for BLIS and stat signal transduction molecules and cytokines for BLIA [Gene expression profiling has been used to separate TNBC into six subtypes with unique gene expression and ontologies: basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR) . It was herapies . Althougherapies  determinherapies  analyzedfor BLIA . In thisfor BLIA . We founA different YMR signature model has demonstrated significance in stratifying TNBC into high- and low-risk groups though the cohort size is small. Due to the high level of molecular and clinical heterogeneity of TNBC, this range of significance suggested that the YMR built from the Yin Yang pathway genes or FOXM1, PPARα pathway genes has potential significance in some subgroups of TNBC. However, currently TNBC data are mostly collected from patients who underwent chemotherapy, which may disturb the prognosis detection we encountered in this study.The limitation of this study is the validation of prognostic model of FOXM1 and PPARα pathways. In contrast to previous studies that purposely selected prognostic genes or pathways; we identified important pathways in TNBC tumor compared to normal and then tested their prognostic significance. We validated the 2-pathway prognostic model using the METABRIC data set. We attempted to validate our 2-pathway YMR model in other data sets , however although a similar pattern was found it did not achieve statistical significance. Therefore this is a limitation of our study. The reasons for this are unclear, although different treatments and the frequency of treated versus untreated cases in the cohorts may underlie the different results obtained. We must cautiously interpret the data where patients underwent therapy because therapy can alter prognosis or we were testing the treatment benefit. There is also a limitation in finding large sample size of TNBC without therapy treatment for our validation.Through the use of GSEA we explored the regulatory signaling pathways in TNBCs. The upregulated FOXM1 pathway and downregulated PPARα pathways were found to be the most significant in TNBC. Therefore, simultaneously targeting these two opposing pathways potentially could provide novel treatments options for some TNBC patients. The pathways can also be used as classifiers to subtype TNBC further for prognosis. The resulting TNBC subtypes exhibit different clinical outcomes, which supports the utility of our approach. This is a primary study using contrasting pathways for TNBC subtyping. Further study will focus on prognosis and treatment prediction signatures for each of these subgroups using more data sets.Additional file 1:FDRs of 191 Yin . 133 Yin pathways were selected with PDF < 0.05 (XLS 73 kb)Additional file 2:P-values of 176 Yang pathways among BC subtypes. 71 Yang pathways were selected with p < 0.05 (XLS 69 kb)Additional file 3:Figure S1. Yin pathway significant score profiling among LumA, LumB, Her2, TNBC breast cancer subtype using TCGA data. Figure S2. Yang pathway significant score profiling among LumA, LumB, Her2, TNBC breast cancer subtype using TCGA data. Figure S3. Yin Yang pathway classifier for TNBCs. Figure S4. Pathway classifier comparison. Figure S5. YMR scores of FOXM1 and PPARa pathway among Breast caner cell lines. Figure S6. FOXM1 and PPARa YMR model for GSE58812 data set. Figure S7. FOXM1 and PPARa YMR model for GSE25066 data set. (PDF 1224 kb)Other results:


Methods for the analysis of the flavonoid composition in plants are well established for different purposes. However, they typically compromise either on speed or on depth of analysis.Arabidopsis thaliana seeds and seedlings. We demonstrate that one Col-0 seed/seedling is sufficient to obtain a fingerprint of the core substances of the flavonoid biosynthesis pathway. For comparative analysis of different genotypes, we suggest the use of 10 seed(lings). The analysis of Arabidopsis thaliana mutants affecting steps in the pathway revealed foreseen and unexpected alterations of the pathway. For example, HY5 was found to differentially regulate kaempferol in seeds vs. seedlings. Furthermore, our results suggest that COP1 is a master regulator of flavonoid biosynthesis in seedlings but not of flavonoid deposition in seeds.In this work we combined and optimized different protocols to enable the analysis of the flavonoid biosynthesis pathway with as little as possible biological material. We chose core substances of this metabolic pathway that serve as a fingerprint to recognize alterations in the main branches of the pathway. We used a simplified sample preparation, two deuterated internal standards, a short and efficient LC separation, highly sensitive detection with tandem MS in multiple reaction monitoring (MRM) mode and hydrolytic release of the core substances to reduce complexity. The method was optimized for When sample numbers are high and the plant material is limited, this method effectively facilitates metabolic fingerprinting with one seed(ling), revealing shifts and differences in the pathway. Moreover the combination of extracted non-hydrolysed, extracted hydrolysed and non-extracted hydrolysed samples proved useful to deduce the class of derivative from which the individual flavonoids have been released.The online version of this article (doi:10.1186/s12870-016-0880-7) contains supplementary material, which is available to authorized users. Flavonoids play a role in a wide variety of biological phenomena. In plants, their bright colors attract pollinators while their antioxidant properties offer protection from harmful UV-radiation. For humans, proanthocyanidins from red wine have been discussed to explain the “French paradox” – the co-occurrence of low coronary heart disease deaths and a diet rich in saturated fat –3. In paArabidopsis thaliana  [TRANSPARENT TESTA (TT) [As most structural genes of the pathway are monogenic in haliana) , this moSTA (TT) , 8.in planta [CHALCONE SYNTHASE (CHS/TT4) catalyses the first committed step of the pathway Fig. : the synn planta .Fig. 1FlA. thaliana mutants has revealed a multi-layered regulation for the key enzymes of the flavonoid biosynthesis pathway [cop1 mutants and transcriptionally activate multiple enzymes of the pathway [In particular the analysis of  pathway –15. Embe pathway –21. The  pathway , 23. It  pathway .Most knowledge of the regulation of anthocyanidin biosynthesis as part of photomorphogenesis by COP1 and HY5 has been derived from studies on protein stability, genetic analysis of mutants, studies on changes in gene expression in mutants and photometric anthocyanidin measurements.cop1 mutants is similar to light grown wild-type seedlings explaining the constitutive photomorphogenetic phenotype of these mutants. In cop1 mutant seedlings, e.g. increased levels of CHS, CHI, FLS1 and F3H were reported [CHS expression serves as one of the markers for COP1-dependent photomorphogenesis. Although COP1 and HY5 act in an antagonistic manner, many HY5-regulated genes overlap with the group of COP1-regulated genes [CHS, CHI, F3H, F3’H, DFR and LDOX) by directly binding to the promoters of these genes in seedlings [The expression profile of dark-grown reported , 25. CHSed genes . HY5 acteedlings .DFR expression can also be activated by PAP1 and PAP2 [and PAP2 . PAP2 caand PAP2 . The cenand PAP2  such thaand PAP2 –31.A. thaliana seeds, mainly epicatechin, proanthocyanidins and quercetin-based glycosides are detected whereas in leaves kaempferol-based glycosides and anthocyanidins dominate [Not only the amount of flavonoids is subjected to regulation but also tissue-specific composition. In dominate –34. Widedominate .A central aspect in all studies is the chosen methodology for the analysis of the flavonoid composition. Methods compromise in many respects: the extraction method determines efficiency of substance recovery and modification. The analysis typically compromises on speed, sensitivity and depth of detail for the substances. Due to chemodiversity, the extraction efficiency of substances with differing polarities depends on the solvent . For pol3 following chromatographic separation [Analysis of natural products has been highly facilitated by improvements of LC-MS detection techniques. Multiple reaction monitoring (MRM), a mode in tandem mass spectrometry, provides high selectivity and sensitivity to lower thresholds of detection. Time-of-flight detectors allow the identification of single metabolites due to precise ion traces . The ideparation –46. For paration . Meanwhiparation , 47–49.Here we describe a method that aims to facilitate high-throughput studies analysing the flavonoid biosynthesis pathway in a reasonable time frame with sufficient precision and sensitivity to obtain a fingerprint of core components. Towards this end we: 1) provide a robust simple extraction and analysis protocol, 2) established external and deuterated internal standards enabling the unambiguous identification of selected core compounds, 3) reduced the number of biological material (i.e. seeds), and 4) revealed shifts and differences of the pathway in a set of mutants as a proof of principle.For optimizing this method for high throughput analysis we considered four aspects: 1. minimizing the time for LC-MS runs and data management, 2. covering a maximum of selected substances at quantifiable levels, 3. minimizing experimental error and 4. reducing the amount of plant material.The first point is achieved by using the highly selective and sensitive MRM mode in tandem mass spectrometry, which produces data files of small size in combination with a short LC gradient. For quality control purposes two MRMs were selected per reference substance  and non-extracted (e.g. condensed tannins) substances  of its response over time in the presence of acid (1 %FA) relative to non-acidified MeOH (92 % +/- 15 %) when left for 24h at 5°C in the sample taker  and 95 % (+/- 15 %) with and without 1 %FA.In order to analyse extracted and non-extracted hydrolysates, two internal standards were required withstanding the (two-step) extraction procedure. Because we aimed to develop a method adaptable to a wide range of species, we synthesized two substances that are normally not found in plant extracts: D3-sakuranetin , while extracted, hydrolysed samples contained mainly the released aglycons. In non-extracted hydrolysates, released anthocyanidins represent the content of the non-extracted condensed tannins from which they are released . We subj3) analysis  releases epicatechin and cyanidin at quantifiable levels. In hydrolysed samples, no procyanidin B2 is detectable. The release of cyanidin from procyanidin under hydrolytic conditions has been widely used before to estimate procyanidin levels . Despitett4-11 + epicatechin). Catechin is not detectable in non-hydrolysed Col-0 seed samples but found in hydrolysed samples indicating that epiconversion occurs during hydrolysis. Therefore, no conclusions on the presence of catechin can be drawn from hydrolysed samples. Surprisingly, myricetin, pelargonidin and delphinidin released quantifiable amounts of catechin but not epicatechin under hydrolytic conditions for extracted samples. One possible explanation for this observation could be impurities in the standard.Furthermore, we found that epicatechin can be converted into catechin  which represent the most abundant released flavonoids from seeds , mutants in transcriptional regulators of the pathway including TTG1, TT8 and TT2 (that block the same branch of the pathway as mutants of DFR/TT3) and light signalling mutants of COP1, HY5, a PAP1 overexpressor line and a RNAi line repressing PAP1 through 4 , MYB114 (PAP4) [Two types of mutants with different effects on the pathway were analysed: First, defective or absent enzymes completely block whole branches or the entire pathway. Second, defective transcriptional regulators affect one or multiple enzymes of the pathway. We selected mutants defective in the enzymes CHS, F3’H and DFR (4 (PAP4) , 55.A. thaliana mutants affecting enzymes  . The blpap1-D led to an accumulation of extracted hydrolysable cyanidin derivatives at the expense of quercetin, epicatechin and their respective derivatives . This view is supported by the previous finding indicating that PAP4 overexpression in A. thaliana mesophyll protoplasts activates ANR [pap1-D and the mybRNAi line suggest the  redundant regulation of the flavonoid pathway.The flavonoid composition of seeds from the ates ANR . Non-norhy5-215 seeds revealed various changes of the flavonoid composition In hydrolysed samples, kaempferol was significantly increased and most other flavonoids are reduced  enzymes. Third, in contrast to an expected enhanced deposition of non-extracted condensed tannins or the extracted non-hydrolysed procyanidin B2 in cop1-6 seeds, we detected a significant decrease of substances released from the non-extracted fraction .First, we revealed a role of Many studies analysing flavonoids have been conducted using seedlings. Protocols have been developed for specific needs ranging from photometric analysis to different types of metabolomics and other specific applications , 59, 60.Initially, a hydrolysis time of 60 min was employed as used in other studies , 52, 53.Detected responses for several substances approached a maximum at ten seedlings which turned out to be sufficient to detect most substances Fig. . Kaempfettg1-1 seedlings and seedlings of light signalling mutants.Unexpected changes in the pathway which were revealed by fingerprinting of mutant seeds prompted us to analyse ttg1-1 to tt3-1 mutants, we again found evidence for a role of TTG1 in regulating TT7 since taxifolin levels were reduced in ttg1-1 when compared to tt3-1. Relative to tt3-1, isorhamnetin levels were drastically reduced and quercetin levels were slightly reduced in ttg1-1 mutants. This indicates that TTG1 regulates the accumulation of isorhamnetin in both, seeds and seedlings.When comparing mybRNAi lines. In hydrolysed extracts from mybRNAi seedlings, naringenin, taxifolin and cyanidin were significantly reduced while the flavonols were not affected. This suggests that PAP genes regulate steps upstream of naringenin synthesis (e.g. CHS and CHI). An additional regulation of DFR and/or ANS is suggested by the finding that cyanidin levels were reduced, whereas quercetin levels remained unchanged. This is consistent with the previous finding that DFR expression can be activated by PAP1 and PAP2 [Differences in the flavonoid composition between seeds and seedlings were also found in and PAP2 . An altehy5-215, we found reduced levels of all substances downstream of naringenin. As kaempferol levels were increased in seeds, it is conceivable that HY5 acts at different steps in the flavonoid pathway in seeds and seedlings.In pap1-D and cop1-6 had to be diluted 20-fold to reach the linear range of the set of flavonoids. All D3-sakuranetin-normalized responses for cop1-6 in the dilutions were beyond the undiluted, normalized wild-type response. For pap1-D, similar as in seeds, cyanidin responses showed the strongest increase compared to the other detected substances and even reached similar levels as in cop1-6 mutants.The extracts of pap1-D compared to those in cop1-6. We therefore conclude that COP1 is a master regulator of the flavonoid biosynthesis pathway in seedlings but not of flavonoid deposition in seeds.The levels of all analysed substances upstream of cyanidin were lower in 3 [The qualitative and quantitative analysis of flavonoids in plants is well established. Photometric methods enable a quick estimation of anthocyanidin contents , 60 or, 3 –46. DepeDFR is regulated by TTG1, the bHLH protein TT8 and the R2R3MYB protein TT2 possibly in a TTG1-MBW complex [ttg1-1 mutants show a similar flavonoid composition and levels as tt3-1, tt8-3 and tt2-1 mutants. This is clearly not the case. On the one hand, ttg1-1 mutants do not exhibit the expected increase in isorhamnetin levels but rather a reduction. On the other hand, tt8-3 and tt2-1 mutants differ from ttg1-1 as they show wild-type levels of isorhamnetin. Moreover, ttg1-1, tt8-3 and tt2-1 exhibit decreased quercetin levels which is not seen in tt3-1. These data suggest two regulatory features of TTG1. First, the decreased quercetin levels suggest that TTG1, TT8 and TT2 are involved in earlier steps of the pathway or quercetin modification. One likely explanation is its regulation of TT7 as reported before [tt8-3 and tt2-1 mutants indicate that TTG1 also exerts this function independently of TT8 and TT2. Possible targets are OMT or isorhamnetin modifying enzymes.Our analysis of the flavonoid pathway in mutants revealed several interesting new findings. It is well accepted that  complex , 57. Oned before . The difTTG1 in regulating isorhamnetin is also found in seedlings suggesting that TTG1 regulates OMT or the isorhamnetin modifying enzyme in both tissues in the same manner. By contrast we found a tissue-specific regulation of the flavonoid pathway in light signalling mutants, in particular by COP1.The role of COP1 has little impact on the flavonoid pathway in seeds, it is the master regulator in seedlings. This is indicated by the findings that for all detected substances in seedlings, cop1-6 mutants released the highest levels.While cop1-6 mutants, pap1-D seedlings exhibited highly increased cyanidin levels relative to the other flavonoids. In mybRNAi lines naringenin, taxifolin and cyanidin levels are reduced in seedlings but not in seeds. Interestingly, HY5 is equally important in seeds and seedlings but acts at different levels of the pathway. In seeds, kaempferol accumulates in the hy5-215 mutant but is reduced in seedlings, which suggests that HY5 regulates steps upstream of TT7 in seedlings, whereas in seeds, TT7 or downstream enzymes are controlled. Together, our data support a dependence on the developmental stage for COP1 function: while the flavonoid composition of seeds is only moderately dependent on the COP1-dependent light signalling pathway, COP1 is a master regulator of the pathway in seedlings.Differential regulation of the flavonoid pathway in seeds and seedlings were also observed in mutants of transcription factors downstream of COP1. Similar to In this work, we developed a method for the analysis of the flavonoid biosynthesis pathway optimized for purposes for which quantitative comparison with limited plant material is important. In proof of principle experiments the method revealed a differential regulation of kaempferol by HY5 in seedlings versus seeds and provided evidence for the role of COP1 as a master regulator of the pathway in seedlings but not for flavonoid deposition in seeds. This method will be helpful in genetic studies and in particular for the analysis of natural variation in different ecotypes or genetic mutants.tt2-1 (N83), tt3-1 (N84), tt4-11 (N2105573), tt7-1 (N88), tt8-3 (N891), pap1-D (N3884), cop1-6 (N69041). Mutants and primers for genotyping are listed in Additional file All mutants used in this study have been described before , 62–73.  tt8-3 N81, pap1-DA. thaliana ripened seeds originated from plants grown at 21°C in 16/8 h light . For seedling analysis, seeds were sown on solid MS plates (1 % sucrose), kept in darkness at 4°C for 2.5 days prior to transfer to white light .All http://www.sigmaaldrich.com), formic acid (FA) and HCl conc.  were used at LC-MS grade. The external standards , Roth (https://www.carlroth.com)) are listed in Additional file 3-quercetin and 5 nM D3-sakuranetin. . D3-sakuranetin was synthesised by methylation of naringenin as shown before [To synthesize D BF3 gas , 76. Then before  using deProtocols are described as derived after optimizing the MeOH concentration for seeds, seed(ling) number and hydrolysis time for seed(ling)s. In all experiments, quintuplicates are analysed if not stated otherwise.2O:FA (50:49:1), spiked with 10 μl D3-quercetin (100 μM) using a TISSUELYSER  with three glass beads  for 3 min at 30 Hz. Upon centrifugation , the pellet was re-extracted in 200 μl of extraction buffer (4°C over-night) followed by centrifugation. 60 μl of combined supernatants were mixed with 440 μl MeOH (extracted non-hydrolysed sample). 120 μl of the supernatants and 380 μl MeOH:HClconc. (95:5) were hydrolysed  and diluted 1:1 with D3-sakuranetin(200 nM):MeOH (1:20) (extracted hydrolysed sample). D3-sakuranetin served in this sample to monitor fluctuation of the machine. For non-extracted proanthocyanidins, the pellet was hydrolysed  with 210μl of D3-sakuranetin(200nM):MeOH:HClconc (1:19:1). Supernatants were diluted 1:1 with MeOH following two rounds of centrifugation (non-extracted hydrolysed sample).Ten seeds were homogenized in 100 μl MeOH:H2liq-frozen seedlings with 210μl of D3-sakuranetin(210nM):MeOH (1:20) were homogenized as above but with 22Hz, incubated in darkness (4°C overnight) and centrifuged . 100 μl of the supernatant were hydrolysed , centrifuged and 60 μl supernatant were diluted 1:1 with MeOH. For 10 Col-0 and mutant seedlings, these extracts were further diluted 1:6 with MeOH to analyse kaempferol in the linear range  was used for all samples. For quantification via LC-MS/MS in MRM mode (=LC-ESI-MS-QTRAP(MRM)) an Agilent 1260 HPLC (http://www.agilent.com/home) coupled to a QTRAP 5500 mass spectrometer  were used; for identification (AutoMSMS and pseudoMS3 experiments), a DIONEX 3000 RSL UPLC  system was coupled to a MAXIS 4G  (=LC-ESI-MS-QTOF(AutoMSMS or pseudoMS3)). For details on LC-MS see Additional file A KINETEX 2.6 μm C18 100 Å (4.6 mm x 50 mm) C18 column . The Auto MSMS run revealed glycosylated flavonoids, the pseudoMS3 run identified the specific flavonoid via MS/MS fragmentation pattern as compared to the respective external standards. Both contribute through neutral loss analysis to identify the type of glycosylation  and once to LC-ESI-MS-QTOF(pseudoMShttp://sciex.com). Integrated peak areas from the inbuilt Analyst Quantitation Wizard were manually corrected. LC-ESI-MS-QTOF raw data were mass corrected with sodium formate clusters using HPC with Compass AutomationEngine 4.1. Subsequent analysis was performed with Compass DataAnalysis Version 4.1 SP5.Using non-smoothed data, the limit of detection (LOD) was defined at a signal-to-noise ratio (S/N) of 3 and the limit of quantification (LOQ) at a S/N of 10. LOD or LOQ was assigned for a mean when more than half of the replicates passed the respective threshold. All raw LC-ESI-MS-QTRAP data were analysed using Analyst 1.6  and changes to greyscale.Statistic analysis was done using R 3.0.2  –81. HeatorBrewer , 83. All