T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA.Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed  T2-statistic, which estimates the associations among gene products, to perform pathway analysis for quantitative proteomic data. Instead of calculating the associations directly from data, we use the confidence scores retrieved from protein-protein interaction databases. We also design an integrating procedure to reserve pathways of sufficient evidence as a regulated pathway group. We compare the proposed T2-statistic to other popular statistics using five published experimental datasets, and the T2-statistic yields more accurate descriptions in agreement with the discussion of the original papers.Pathway analysis is a common approach to quickly access the pathways being regulated in the experiments. There are numerous statistics to perform pathway analysis; most of them assume that the genes or proteins are independent of each other for statistical ease. This assumption, however, is unrealistic to the real biological system and may cause false positives in practice. A standard way to address this issue is to measure the associations among genes or proteins. Unfortunately, the estimation of associations requires sufficient sample size, which is usually not available for proteomic data produced by mass spectrometry. In this study, we propose a  Great progress has been made toward the development of high-throughput technologies and their application to biological and clinical research. In a quantitative experiment, genes or proteins with significant changes in expression are potential to have important roles in a given phenotype or phenomenon. Therefore, the analysis of quantitative experimental data generally produces a list of differentially expressed genes or proteins in order. The list may share some insights if the aim of the experiments is restricted to few targets. As regards high-throughput data, the list hardly provides biological understanding of the mechanisms being studied, since the data involve complicated regulations among biomolecules and the number of biomolecules is too large to examine all candidates individually. Systematically investigating the underlying mechanisms from a high-throughput data therefore become a new challenge.pathway analysis to identify the genes or proteins that are known to be involved in a biological process or interaction based on the existing knowledge. Many approaches of pathway analysis have been developed over years concerning different methodologies. Some general reviews of pathway analysis approaches can be found in [a competitive test suggests that the target pathway (or a predefined gene set) is differentially expressed as well as the rest of all pathways. Practically a competitive null is closely tied to  a gene sampling approach [A gene sampling model principally implies the independence assumption; the assumption presupposes that genes are expressed independently of each other so that these genes can be manipulated as the sampling subject to produce the null distribution. The null hypothesis of a self-contained test, on the other hand, suggests that the target pathway is not differentially expressed between distinct phenotypes. Using the phenotypes of the experiments as the sampling subject is the setup of a subject sampling model. In other words, a competitive null with a gene sampling approach let pathways compete with each other in order to rank these pathways and use the number of genes as the sample size in the meanwhile; whereas a self-contained null with a subject sampling approach examine each single pathway to determine if the pathway is indeed differentially expressed between phenotypes and use the number of experiments as the sample size. Intuitively, a competitive null aims to find the pathway that is most significant among all pathways; a self-contained null aims to find the pathway that is the most significantly expressed between phenotypes. The classification of null hypotheses and sampling methods is firstly suggested by Geoman et al [p-values, cause serious false positives [To confront the challenge, one idea is to apply found in –4. Theseapproach . A gene an et al . Even boositives , and resositives . Furtherositives –9, and oositives .univariate tests and multivariate tests. Univariate statistics, such as a modification or a weighted summation of the t-scores [Another issue of pathway analysis comes from the construction of test statistics. These statistics can be divided into t-scores –14, onlyt-scores –20. Somet-scores , 21–23 ht-scores  along wipathway is a group of biomolecules that participate in a particular cellular process. The members of a pathway are usually defined by the tradition  of molecular biology scientists. The structure of pathway diagrams is not standardized and therefore arises some issues to pathway analysis. First, the same pathway from different databases or other sources may have the same core members but different side members. Under different experimental conditions, the size of accessible biomolecules also changes. For example, the phosphorylation proteomic data may not provide information to the proteins not belong to phosphoproteome. Since the number of members within a pathway is not a constant, using this number as a parameter to determine if the pathway is significant or not may lead to inconsistent results. This issue arises with the assumption of a competitive null. Second, some molecules appear over pathways may play important roles as communication centers. For example, p53 appears in 38 pathways in the KEGG database. The shared members may interact or cooperate with each other and form a functional module. If this module is regulated , the subsets of this module may present in abundant pathways and make these pathways seemingly significant. To identify the regulated pathway among the significant pathways of common modules, biologists usually utilize the distinctive molecules participating in that specific pathway; whereas pathways do not contain distinctive molecules may be irrelevant to the underlying mechanism. However, most of the current approaches do not take consideration of this issue. The suggestion of irrelevant pathways due to the redundancy over the significant pathways usually causes confusion to data description. A recent study [p-values, and try to address this problem under a competitive null.The composition of pathway diagrams also become a challenge to pathway analysis. A nt study  also focT2-statistic, to perform pathway analysis for quantitative proteomic data. The most serious problem of analyzing proteomic data produced by mass spectrometry is the limited number of experiments. We usually obtain only a few replicates  per experimental condition. To manage this issue, we had two special designs in our test. First, instead of using the sample covariance matrix (which is not robust when the sample size is limited), we use the covariance matrix that is constructed of the probabilistic confidence scores provided by the STRING and HitPredict databases. The proposed T2-statistic is then built of the protein expression profile and the covariance matrix to consider the expression level of individual proteins along with the associations among them. Second, we designed a self-contained model to produce a null distribution of altering protein expression while retaining the structure of protein associations. We are not capable of applying a subject sampling model because the number of experiments is too limited. In addition to the settlement of sample size issue, we designed an integrating procedure to categorize significant pathways as well as to avoid redundancy. The performance of the proposed T2-statistic is demonstrated using five public experimental datasets with different levels of biological complexity: the T-cell activation, the cAMP/PKA signaling, the effect of dasatinib on the BCR-ABL pathway, the differentiation process of myoblast, and the protective effect of myocilin via the MAPK signaling pathway. The first four datasets are proteomic data produced by mass spectrometry; the last dataset is a gene expression data of low sample size. We compared T2 with other popular statistics: DPA [T2 yielded more accurate descriptions in agreement with the discussion of the original publication.In this study, we introduced a multivariate test, based on the Hotelling’s ics: DPA , GSEA [2ics: DPA , DAVID [ics: DPA , 28, andics: DPA . For mosWe took four proteomic datasets of different biological complexity and experimental properties to demonstrate our approach. To be comprehensive, the testing datasets include the case of pathway activation and inhibition; also the case of signaling phosphoproteome and cellular proteome. We only used the final ratios provided by the datasets since they may have different integration approaches  under different experimental designs. The summarized ratio is also the most available format for quantitative proteomic data. In this situation, the sample size of data becomes only one, calculating a covariance matrix is not even possible. Our approach provide a solution to undertake this difficulty. We also applied our approach on a gene expression dataset of three samples to demonstrate that the general idea is applicable to other quantitative data of low sample size.ϵ, to initiate the TCR signaling transduction in the human leukemia cell line Jurkat T lymphocyte. They used stable isotope labeling by amino acids in cell culture (SILAC) method to perform large-scale quantitative phosphoproteomic analyses and identified 696 TCR-responsive phosphorylation sites on 453 proteins. Phosphopeptides showing more than 1.85-fold change in abundance are qualified as “TCR-responsive” sites, suggested by the authors under their experimental conditions. We used these 696 proteins since the original publication does not provide raw data, and we aimed to use this small dataset as a positive control to evaluate the proposed statistic. The dataset contains three time points: 5, 15, and 60 min; the number of UniProt accessions are 30, 376, and 330, respectively. The authors specifically enriched pTyr-contained peptides in 5 and 15 min experiments since pTyr constitutes less than 1% of the total amino acid content; a global phosphopeptide-enrichment approach was used for 15 and 60 min experiments. The 5 min experiment is extremely small because it contained only pTyr-enriched peptides.The T-cell receptor (TCR) signaling phosphoproteomic data  aim to rThe cAMP-dependent protein kinase  signaling phosphoproteomic data  aim to pThis cellular proteomic data  monitor The pathology of chronic myeloid leukemia (CML) is commonly associated with an oncogenic tyrosine kinase BCR-ABL. Dasatinib is an inhibitor of the BCR-ABL and Src family tyrosine kinase, and it serves as a clinical drug for treatment of CML. This phosphoproteomic data  aim to iμm MEK inhibitor U0126 two hours prior to the induction of myocilin expression. The gene expression profiling was performed using Human Affymetrix Gene Chip U133 Plus version 2.0. The processed dataset includes three treatment and three control expression values. We took the mean of the triplicated data as the representative expression. Then we took the difference of the treatment group and the control group since the processed data is already normalized. We used the identifier mapping files from the official websites and the final data include 21816 unique UniProt identifiers.The MAPK signaling gene expression data  aims to KEGG and Reactome provide pathway categories served as predefined gene sets, STRING and HitPredict contributes the confidence scores to estimate the covariance between protein expressions.To provide more generalized results, we choose two pathway databases and two protein-protein interaction databases: Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database [hsa) contains 303 pathway diagrams. We excluded the overview maps since they depict very general biological concepts (e.g. one overview map is titled “Metabolic pathways”). There remain 290 pathways for the following analysis. For each pathway, we retrieved the components tagged as gene, enzyme, and group. Those components are associated with gene products so we use them to map the identifiers of experimental data onto pathways.The database  collectsReactome pathway database [9606) contains 1618 pathway diagrams of the lowest level (i.e. most specific) and we only used the lowest level pathways for the following analysis.The database  providesneighborhood, gene fusion, co-occurrence, co-expression, experiments, databases, textmining and homology [low confidence (0.150), medium confidence (0.400), high confidence (0.700), and highest confidence (0.900). The version for the following analysis is version 9.1, and we only used the interactions above medium confidence since it is the default threshold of the STRING website. The results were produced using the experiment PPI score to avoid artificial correlation due to special interest in the research community.The STRING database  scores ahomology . STRING protein-protein interactions (PPI). The database integrates five popular source database: IntAct, BioGrid, HPRD, DIP, and MINT. HitPredict also annotates the confidence scores into two levels: low and high. The version for the following analysis is version 4, and we only retrieved the interactions annotated with high quality.The HitPredict database  provides2 transformation and winsorization on the data subsequently (if needed). Extreme values beyond the threshold are replaced with a maximum permitted value within the threshold. For example, if the expression ratios are  and the threshold is set to 5, then the extreme value 6 will be replaced by the maximum permitted value 4, and -7 replaced by -4. Users can adjust the threshold based on the experimental conditions . The reason to set a threshold is to manually control the contribution of proteins with extreme values, since those proteins usually have very a low abundance (maybe beneath the threshold of quantification) in one of the experimental conditions. The default setting of the threshold is 10 and none of the testing data presented in this paper exceed the threshold. Second, we deal with the problem of multiple identifications and multiple ratios. This problem originates from the mapping of different primary identifiers to UniProt accession numbers. If there exists any ratio with multiple identifiers, we assign the same expression ratio to those identifiers. If there exists any identifier with multiple ratios, we assign the median of the ratios to the identifier. Third, we standardize the data by dividing the standard deviation. Last, we map these proteins with their expression ratios to the pathways. The identifier mapping tables are downloaded from the KEGG and the Ensembl [The required input format is a list of protein identifiers (e.g. UniProt accession number) with the expression ratios of the experimental group to the control. The list is subjected to the following data processing steps. First, we perform log Ensembl  officialT2-statistic is a multivariate generalization of Student’s t statistic. Let x1, …, xn be n independent random variables of an m-variate normal distribution, an original Hotelling’s T2-statistic for an one-sample T2 test is defined as follows:S is an m × m sample covariance matrix, and μ0 is a given vector. The null hypothesis is that the population mean vector of data μ is equal to a given vector μ0; μ.Hotelling’s T2-statistic. Let a pathway pi indicates a specific protein with a corresponding ratio xi, and q the number of mapped proteins . To ensure the biological robustness of the expression ratios {xi}, we have another threshold representing our confidence toward the expression direction of the data, the default setting is 1.5. Only the proteins showing more fold change beyond the threshold are qualified to possess their original ratios; the ratios of other proteins are recognized as disturbance and set to zero. We use the proteins with high expression ratios to represent the expression directions of the pathway. If directions of these proteins fit the covariance matrix, then the pathway is more likely to be enriched. In other words, we think the direction is not stable for those proteins of low expression ratios; we only care if the proteins of high expression ratios fit the covariance matrix. Again, users should adjust the threshold based on the experimental conditions.Here we introduce the notation to describe the design of the proposed x. The proposed T2-statistic is constructed with the vector x and the covariance matrix S of x, the former represents the significance of protein expression; the latter represents the interaction structure among the proteins. The covariance matrix S is not computed from the experimental data. Due to the limitation of sample size, calculating an informative covariance is infeasible. Our idea is to use the confidence score provided by protein-protein interaction databases to represent the strength of the covariance, and use the expression direction provided by the testing dataset to indicate the sign of the covariance. In order to apply this idea, we have an assumption below:We collect the processed ratios into a column vector i and pj have a strong interaction supported by experimental evidence, their expression ratios xi and xj will have a high covariance sij with a consistent signIf p,xi and xj are any two elements of x and sij is the corresponding covariance of S. To be more precise, we denote the PPI database as the set sij of S is determined by the following four rules:where i = j, the main diagonal of S, namely the variance of x. In this case, there is no evidence score to refer, we have to assign a constant to the diagonal elements. This constant represents our confidence toward the accuracy of the data, we use the medium confidence level suggested by STRING, which is 0.4.For the elements where i ≠ j, the confidence score between protein pi and pj exist in xi and xj are of the same sign. In this case, either xi and xj are both up-regulated or both down-regulated, the covariance between them should be positive. We use cpipj directly as the substitute of the covariance.For the elements where i ≠ j, the confidence score between protein pi and pj exist in xi and xj are of opposite signs. In this case, one of xi and xj is up-regulated and the other is down-regulated, the covariance between them should be negative. We take the negative value of cpipj to be the substitute of the covariance.For the elements where i ≠ j, and the confidence score between protein pi and pj does not exist in For the elements where sij can be summarized as follows:The possible values of For those proteins mapped to the pathway being evaluated, they are not differentially expressed between distinctive phenotypes under certain structure of protein interaction.μ is equal to zero  under the same S. We presuppose the sample size is equal to one because in data processing section we already use the median of ratios to represent the expression level for each protein. Therefore we use x as an estimator of μ. The proposed T2-statistic for a specific pathway x is the vector of expression ratios, xT is the transpose of x, S−1 is the inverse of the covariance matrix S, and q is the number of mapped proteins in S is constructed from databases, it is possible that S is degenerate. In this case, we construct a Moore-Penrose pseudoinverse of S as a substitute, and q becomes the rank of S. The p-value of the pathway p-values under a self-contained null are not for comparison among pathways. The significance only indicates if the pathway being tested, as an individual, is differentially expressed from one phenotype to another.This self-contained null suggests that the expression ratios of data delegates to categorize pathways into pathway groups. A pathway group is a set of pathways, in which the pathway being the superset of other pathways is defined as the delegate. Since other pathways do not show any distinctive proteins to support themselves, the significance of other pathways may simply originate from the regulation of the delegate. In other words, we want to avoid the situation that a pathway is enriched only because of some common proteins that are shared with other pathways.As we mentioned in Introduction, subsets of one common active module may cause a lot of pathways statistically significant. These pathways may only have slight relevance to the target mechanisms . To avoip-value. We iteratively perform the following steps until all the pathways in Our pathway integration procedure operates as follows. The set of the pathways being evaluated is denoted by We identify the pathway with the maximum number of mapped proteins We create a new set For each pathway p-value for every delegate p-value are preserved as our final result.We check if there is any pathway remaining in A demonstrative example can be found in The pathway integration procedure aims to find the pathways with the most sufficient information to represent current data. Please notice that this idea is not similar to pathway hierarchy in which higher level pathways are defined as the supersets over lower level pathways, in that case only the pathways of highest level are able to be delegates. One can only apply the integration procedure to pathways without hierarchy relationship. In addition, the procedure is only applicable to the statistical test using a self-contained null since the measure of significance is independent for each pathway.T2-statistic, we compared our results with popular pathway analysis services within the community: Ingenuity® Pathway Analysis (IPA), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Gene Set Enrichment Analysis (GSEA), and Direction Pathway Analysis (DPA). Among these tools, IPA and DAVID use a competitive null; DPA uses a self-contained null; and GSEA uses a hybrid approach by default but suggests to use a gene sampling model when the sample size is smaller than seven [T2-statistic has been evaluated in [T2-statistic generally outperforms other tools in all simulation cases in their study. The proposed T2-statistic, DPA, and GSEA were evaluated using the same version of KEGG and Reactome databases; whereas DAVID had their own versions as web services and IPA was bounded to its own curated database. The following discussion mainly focused on the results of KEGG pathways since the they are canonical to the community; the results of Reactome pathways were summarize in To demonstrate the advantages of the proposed an seven . The paruated in , which sAs we mentioned above, different approaches of pathway analysis may have different statistical assumptions; comparing the performance between these approaches becomes even harder. Since there are no accepted gold-standards to evaluate the methods of pathway analysis, we tried to match the results reported by these methods to the biological idea provided by the original publication. In other words, we took the aspect of explanatory power (i.e. the interpretation of true positive cases). Generally speaking, a good statistic should be able to determine if a pathway is significant: if a statistic reports very little significant pathways (much less than our expectation regarding the data), it might have the problem of false negatives; if a statistic reports a large number of significant pathways, it might have the problem of false positive. Practically the false positive under a competitive null may originate from high mapping rate with low expression level; the false positive under a self-contained null may originate from low mapping rate with high expression level. We supposed that the number of significant pathways is one of the attribute to evaluate these methods, at least for the significance evaluation part . Also, a good statistic should be capable of enriching the true positive pathways. Based on these assumptions we had a preliminary evaluation as below.ϵ, is a monoclonal antibody, whose target is specific and the influence should be limited. The case of the MAPK dataset is similar, U0126 is a highly selective inhibitor for MEK1 and MEK2. In the case of the PKA dataset, four targets of PGE2 are all subtypes of EP receptors . Most of the downstream pathways are regulated by the second messenger cyclic adenosine monophosphate (cAMP) [T2-statistics with positive coefficients between the data and the result, DPA and DAVID with some positive and some negative coefficients, and GSEA and IPA with negative coefficients all along. Having a negative coefficient between the data and the result is counter-intuitive, but a competitive null tends to behave in this fashion . Besides the null hypothesis, the construction of the statistics may also contribute. Both DPA and GSEA are based on the approach combining many gene-level statistics into a pathway-level statistics. The pathway-level statistic usually take into consideration the number of gene-level statistics it integrates, therefore the effect size for each gene-level statistic becomes important. In simple words, the average expression of the pathway dominates the enrichment result. For example, the TCR dataset is a cleansed dataset , and pathways for each dataset are listed in e (cAMP) , 42. On e (cAMP)  although dataset  showed t dataset . These ptarget pathway for each dataset according to their original publication: “TCR signaling pathway” for the TCR dataset, “cAMP signaling pathway” for the PKA dataset, “ECM-receptor interaction” for the myogenesis dataset, “chronic myeloid leukemia” for the CML dataset, and “MAPK signaling pathway” for the MAPK dataset. Generally the target pathway is directly provoked by the treatment, but in the myogenesis dataset, there does not exist such a clear target since the removal of serum takes away various kinds of growth factors. Therefore we chose one pathway that is clearly stated to be differentially expressed for both experimental conditions (24hr/0hr and 72hr/0hr). As a preliminary evaluation of performance, we tried to locate the target pathway among the significant pathways reported by each tool. The ranks of the target pathways were summarized in T2 with STRING (T2×ST) and T2 with HitPredict (T2×HP) found all the target pathways significant; and DAVID found eight out of ten; IPA found seven targets and DPA also found six. GSEA did not perform very well; it might because the design of the statistics is more suitable for the dataset of more experiments.We determined the T2×ST, T2×HP, GSEA, DAVID, and IPA in the 5 min experiment. Please note that pathways are human-defined biological concepts, the components of one pathway may appear in other pathways and these components usually interact with each other. Our presentation only depicted the main branches directed from the target pathway that are commonly described in the literature. In addition, since the pathway database of IPA is different from others, we used the most similar pathway title instead. Even the pathway is of the same title, the components of the pathway may still differ; the result is obliquely comparable.The mechanisms at pathway level behind the five datasets were depicted: ϵ activated TCR signaling pathway. The TCR signaling pathway in KEGG and IPA depicts the signals from the TCR receptors all the way to the IL-2 expression. Usually, the response of signal transduction comes rapid, we expected that the TCR signaling pathway should be enriched in early time points; the downstream from the TCR signaling pathway to the IL-2 expression should be enriched in late time points. From T2×ST, T2×HP, DAVID, and IPA enriched the TCR signaling pathway in all experiments; GSEA enriched the TCR signaling pathway in 5 and 15 min experiments only. The ranks of the TCR signaling pathway are top in T2, DAVID, and IPA. The downstream of the TCR signaling pathway was illustrated in T2×ST were well-grounded for the following reasons:We discussed the flow of signal transduction in time order since the dataset is a time-series of 5 min, 15 min, and 60 min experiments. In the beginning, the treatment anti-CD3T2×ST, T2×HP, DAVID, and IPA achieved this goal.The phosphorylation of Ras/MAPK is an early event . Most prT2×ST and IPA matched the description, but in IPA these pathways are of low ranks.Most proteins in the PI3K-Akt/mTOR are phosphorylated at serine and threonine, and the phosphorylation should happen in order. both T2×ST, T2×HP, DAVID, and IPA successfully enriched the cytoskeleton remodeling route.The regulation of actin as the response of TCR signaling is also a main subject of the original publication. T2×ST fit our expectation: T2×ST enriched the TCR signaling pathway in the early time data, the route TCR/Ras/MAPK and TCR/PI3K-Akt/mTOR in time order, and the response of actin regulation, in both pathway databases. T2×HP, DAVID, and IPA enriched most of the expected pathways in KEGG, although the ranks of these pathways are occasionally low in IPA; T2×ST, T2×HP and DAVID also enriched most of the expected pathways in Reactome  or a competitive null  in the TCR dataset.To sum up above, we found the results of Reactome ; GSEA faT2×ST, T2×HP, DPA, and IPA enriched the cAMP signaling pathway for both 1 min and 60 min experiments. The downstream of the cAMP signaling pathway was illustrated in T2×ST, T2×HP, and IPA; and the cytoskeleton remodeling route also, with an addition of DPA. The DNA repair route was enriched by T2×ST, T2×HP, and DPA, in both pathway databases. The glycogen synthesis route, on the other hand, was enriched only by IPA. The reason is that T2 takes expression ratios as an important feature, whereas the mapped proteins of the “Glycolysis / Gluconeogenesis” pathway are all of low ratios .The target of the original publication is PKA substrates. As the authors described in their results; PGE2 induced a rapid and maximal increase in phosphorylation level after 1 min, and the level remained high before the number of substrates gradually returned to near-basal conditions after 60 min . From TaT2 reasonable: T2×ST and T2×HP enriched the cAMP signaling pathway for both datasets; the PKA/Rap1/PI3K-Akt route in time order, and also the cytoskeleton remodeling route and the DNA repair route. DPA and IPA also enriched most of the expected pathways. GSEA and DAVID failed to enrich most of the expected pathways in both pathway databases. We also found that the result using a self-contained null  enriched more expected pathways than a competitive null . Since the pathways competes with each others under a competitive null, the success of some pathways will obstacle other pathways. For example, the top 1 enriched pathway provided by DAVID for the 1 min data is “Ribosome”. The pathway includes 87 proteins, and 51 of them were mapping by the data. The cAMP signaling pathway, on the other hand, includes 73 proteins, but only 8 of them were mapping by the data. The high mapping rate of “Ribosome” will makes it harder for DAVID to enrich the target cAMP signaling pathway.Briefly, we found the results of cell cycle withdrawal (72hr/0hr), cell adhesion and migration (24hr/0hr), RNA metabolism (both 24hr/0hr and 72hr/0hr), myofibril formation (72hr/0hr), and proteolysis, fusion, and ECM remodeling (both 24hr/0hr and 72hr/0hr). The corresponding KEGG pathways were illustrated in T2×ST, T2×HP and DAVID for both experiments. The original publication focused on the change of cellular phenotype accompanying myogenic differentiation and the development of myofibril. From T2×ST, T2×HP, DAVID, and IPA. T2×ST and T2×HP further enriched muscle contraction related pathways, which were associated with myotube maturation as discussed in the original publication. The cell adhesion and migration play an essential role in the fusion of mononucleated myoblasts. Pathways related to adhesion and migration were enriched by T2×ST, T2×HP, and DAVID; those related to fusion were enriched by T2×ST, T2×HP, DAVID, and IPA. The elevation of lysosomal proteins contributed in remodeling intracellular components during the course of myotube formation. All tools enriched the lysosome pathway with an exception of IPA. The RNA metabolism and the cell cycle withdrawal routes represented the termination of proliferation since the growth factors and nutrition were removed from the medium. Related pathways were enriched by T2×ST, T2×HP and IPA. Briefly, T2×ST and T2×HP enriched all the pathways discussed in the original publication; the interpretation made by T2 fit the description of the dataset pretty well. In the meantime, DAVID and IPA also enriched most of the expected pathways, asides from muscle contraction pathways. DPA enriched most of the expected pathways in Reactome, but failed in KEGG; GSEA failed in both pathway databases. We also found that there is no distinguishable difference between the result using a self-contained null  or a competitive null  in the myogenesis dataset.This study aims to characterize the changes in protein expression underlying the phenotype conversion from mononucleated muscle cells to multinucleated myotubes. According to the their analysis, five functional clusters were identified in this dataset: T2×ST, T2×HP, DAVID and IPA for both experiments. The downstream of the CML pathway was illustrated in T2 were plausible for the following reasons:This dataset, unlike the previous, is not a time series; it is a dose-comparison experiment. According to the original publication, two datasets shared nearly all identified proteins although 50nM dataset did down-regulate more phosphopeptides . ConsequT2×ST, T2×HP, DAVID, and IPA all agreed with the description. The BCR-ABL/PI3K-Akt/Apoptosis route was also enriched by T2×ST, T2×HP and IPA.In the case of the BCR-ABL/Ras/MAPK route, T2×ST and T2×HP were the only tools that enriched the “JAK-STAT signaling pathway” in 50nM data.Even though not mentioned in the original publication, the BCR-ABL/JAK-STAT/Apoptosis route is also well-known –50. AmonT2×ST, T2×HP, DAVID, and IPA achieved to enrich the pathway “Regulation of actin cytoskeleton”; but only T2×ST and T2×HP enriched the “Focal adhesion” pathway.The initiation of focal adhesion components is also related to BCR-ABL. The STRING analyzing result of the original publication also implied the mechanisms of cytoskeleton remodeling. T2 pleasant: T2×ST and T2×HP enriched the BCR-ABL/Ras/MAPK route, the BCR-ABL/PI3K-Akt/Apoptosis route, the JAK-STAT signaling pathway, and the pathways related to actin response. Both DAVID and IPA enriched some of the expected pathways. GSEA and DPA failed to enrich most of the expected pathways in both pathway databases. Generally there is no distinguishable difference between the result using a self-contained null or a competitive null, but T2 enriched more expected pathways than other methods.In short, we found the performance of T2×ST, T2×HP, DPA, and DAVID enriched the MAPK signaling pathway; all three methods under a self-contained null successfully enriched the target pathway, whereas only DAVID is under a competitive null. The upstream and downstream of the MAPK pathway was illustrated in T2×ST, T2×HP and DAVID, DPA only enriched the GPCR/Ras/MAPK route and IPA only enriched the Notch/Ras/MAPK route. In Reactome, both upstreams were enriched by T2×ST and T2×HP, DAVID only enriched the Notch/Ras/MAPK route  and another 100 experiments with 30% and 60% purged confidence scores (i.e. we randomly remove the scores from the PPI databases), and we checked if the expected pathways are still significant under current significance level (α = 0.05). From The importance of applying the covariance matrix is to estimate accurate confidence interval. We illustrated an example in p-values is limited.In practice, most of expected pathways contain proteins of high expression ratios, so the influence toward their Cell cycle in CML 5 nM experiment and PI3K-Akt signaling pathway in MAPK 10 μM experiment. In this case, the covariance matrix for the pathway is close to an identity matrix. It might be a M3 case that we do not have enough information to construct the covariance matrix, the p-values tend to be smaller, so the interpretation of significant pathways should be careful.Some pathways are more easily to be affected by permuted scores rather than purged scores, such as PI3K-Akt signaling pathway in PKA 1 min experiment and Endocytosis in myogenesis 72 hr experiment. In this case, the covariance matrix for the pathway provides abundant information. The removal of these information will decrease the chance for the pathway to be enriched. It might be a M4 case that we use some fake information to construct the covariance matrix, the p-values tend to be larger, so the users should manually examine the pathways of borderline p-values.Some pathways are more easily to be affected by purged scores rather than permuted scores, such as T2-statistic showed T2 is heavily dependent on expression ratios. After all, the null hypothesis for T2 is to test if the mean vector equals to zero. The contribution of applying the covariance matrix is to estimate p-values in a more accurate manner: to rescue some pathways with moderate expression ratios but their regulation directions are consistent with current knowledge of protein interaction, and to discard some pathways with inconsistency.The construction of the proposed T2 approach to perform pathway analysis for quantitative proteomic data of a limited number of experiments. The proposed T2 is constructed as a multivariate statistic and the test of significance is under a self-contained null. We use the probabilistic confidence score provided by the STRING or HitPredict databases to approximate the covariance matrix of the protein profiles. The proposed T2-statistic is therefore able to reveal the influence of protein-protein interactions while performing the analysis. In addition, our pathway integration procedure is able to categorize pathways into pathway groups as well as to avoid redundancy. We performed the T2-statistic on five published quantitative proteomic dataset. In all cases, T2 was able to eliminate irrelevant pathways, as well as correctly identify relevant pathways that had been discussed in the original publication. The idea of incorporating biological evidence into conventional statistic can be widely applied to the analysis of quantitative proteomic data.In this study, we presented a knowledge-based S1 Text(PDF)Click here for additional data file.S1 FigThis diagram used a toy example to illustrate the procedure of data processing, filtering, pathway mapping, statistical testing, and finally pathway integration.(PDF)Click here for additional data file.S2 Fig(a) The TCR dataset contains three proteomic experiments. The 5 min data describe the initiation of the TCR signaling pathway. The following response interfered lots of downstream proteins, resulting the 15 min data have the largest number of proteins among this dataset. Then the signal was transmitted to the nuclear and the amount of high-ratio proteins decreased, as described in the 60 min data. (b) The PKA dataset contains two proteomic experiments. The initiation of the cAMP signaling pathway came rapid, so the 1 min data almost illustrate all the following events. The 60 min data have fewer high-ratio proteins because the response of the signal had gradually vanished. (c) The myogenesis dataset contains two proteomic experiments. The 24 hr data have more differentially expressed proteins than the 72 hr data. (d) The CML dataset contains two proteomic data. Their distributions look quite alike, despite that the 50 nM treatment did down-regulate more proteins. Most proteins are down-regulated since the experiment aims to repress the BCR-ABL signaling pathway. (e) The MAPK dataset contains one gene expression experiment. The up-regulated probes slightly outnumber the down-regulated (54% versus 46%), and there are few probes of high ratios.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Tableϵ is specific to CD3ϵ; PGE2 targets four EP receptors but the downstream is almost under control of cAMP; serum starvation may result in cell cycle arrest and turn on muscle regulatory factors to promote myogenesis; dasatinib mainly targets BCR-ABL but has about 10 other targets of different kinase families; U0126 is a highly selective inhibitor of MEK1 and MEK2.The enriched pathways are the pathways that fulfilled the significance requirements described in (PDF)Click here for additional data file.


The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the  Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) significantly associated with complex diseases , such asPathway analysis has become popular as a secondary analysis strategy for GWAS data. Pathway analysis hypothesizes that SNPs in genes in the same pathway have a joint effect on the disease. One of the advantages of pathway analysis is that the statistical power for identifying disease susceptibility genes can be increased by the joint modeling of the effects of SNPs. Another advantage is that the results can provide biologically meaningful insights into the complex disease mechanism. Furthermore, multiple testing correction burden can be reduced in pathway analysis by testing hundreds or thousands of pathways instead of testing hundreds of thousands or a million of SNPs.Current statistical methods for pathway analysis using GWAS data can be divided into two categories  based on their null hypotheses . The comTesting gene-gene interactions in GWAS is challenging because a very large number of interaction pairs needs to be examined, which is computationally expensive and results in a high multiple testing correction burden. For example, there are hundreds of billions of possible SNP pairs for a GWAS with 1 million SNPs. With the increased biological knowledge of protein-protein interactions (PPI), several public PPI databases are available, such as STRING  and BioGSeveral network-based tests have been proposed to identify gene networks associated with the disease also based on PPI networks, without using prior knowledge of pathway definitions . For exaAs multiple gene-gene interactions can occur within a pathway , combiniAA, Aa, and aa, and the other SNP has genotypes BB, Bb, and bb, as shown in ORcase and ORcontrol are the odds ratios calculated based on the 2 by 2 tables for cases and controls, respectively. Assuming that the two SNPs are in Hardy-Weinberg Equilibrium (HWE) and linkage equilibrium (LE), the statistic follows a standard normal distribution under the null hypothesis of no gene-gene interaction for the two SNPs.We first review the PLINK interaction statistic  as the PUPPI was developed based on the statistic. Two 2 by 2 allele tables, collapsed from two 3 by 3 genotype tables, are created separately for cases and controls. For example, assume we categorize all cases into a 3 by 3 table based on their genotypes at two SNPs, where one SNP has genotypes k MB apart on the same chromosome. Because the PLINK interaction statistic assumes there is no LD between SNPs tested for interaction, we consider pairs of genes that are not linked if they are on the same chromosome. The value of k was set as 1 in our real data analysis. For each pair of genes in ψ, the PLINK interaction statistics are calculated for all possible pairs of SNPs between the two genes. Then the maximum statistic M from the statistics for all pairs of SNPs between the two genes is selected.The PUPPI algorithm was previously described in our conference paper . Here, wM than a gene pair with small gene sizes because M was selected from a larger set of interaction statistics for the pair of large genes. We therefore adjust M by gene size so that large genes do not contribute more to the pathway statistic simply due to their size. The effective numbers [Seff estimated from a set of S SNPs. In Babron et al. [S was calculated as Seff (Seff -1)/2, which is the number of all pairwise combinations from the set of Seff elements. Their simulation results suggest that the number slightly overestimated the real effective number of independent SNP pairs. Similarly, assume that m and n are the effective numbers for the SNPs in the gene pair, where SNPs between the gene pair are independent, then m×n estimates the number of independent tests between the two genes. The adjusted statistic M′ for M is calculated as:x) is the cumulative distribution function for the random variable x following a chi-square distribution with 1 degree of freedom. In M, calculated based on a standard normal distribution. The adjusted statistic M′ is the statistic corresponding to the p-value with the Bonferroni correction if the adjusted p-value is less than l. The adjusted statistic M′ is set as 0 if the adjusted p-value is ≥ l.A gene pair with large gene sizes can generate larger  numbers  are usedn et al. , an effeX for pathway i is the sum of the adjusted statistics M’ for gene pairs in the pathway. A permutation procedure, which permutes the case-control affection status, was used to approximate the distribution of X and calculate the p-value. The null hypothesis for the PUPPI is that there are no interaction effects between genes on the disease within the pathway. As the PUPPI compares the test statistic to the same test statistics for the same genes under the null, the PUPPI is also a self-contained test. The PUPPI algorithm is summarized in the following steps:The PUPPI statistic For each gene in ψ, calculate the effective number for SNPs in the gene.The PLINK interaction statistics are calculated for all pairs of SNPs between each pair of genes in ψ.M from the statistics for all pairs of SNPs between two genes in ψ is selected, and the adjusted statistic M′ is calculated based on The maximum statistic X for pathway i is the sum of M′ for gene pairs in the pathway.The PUPPI statistic K times. For each permutation, steps 2–4 are repeated and a permuted PUPPI statistic Mp′ is calculated. The p-value for pathway i is calculated as (# of Mp′ ≥ M′)/K.Perform permutations for l in We used computer simulations to evaluate the type I error rates for the PUPPI, and to compare the power of the PUPPI with other methods under different scenarios. SeqSIMLA2  was usedFor type I error simulations, we simulated three different sample sizes, including 500 cases and 500 controls, 1,000 cases and 1,000 controls, and 2,000 cases and 3,000 controls, for the two pathways. For power studies, we simulated a scenario where there were both main effects and interaction effects for the disease SNPs (Scen1). We also simulated another scenario where there were only interaction effects  for the disease SNPs (Scen2). For Scen1, the four epistasis models (Models 1–4) used in Wan et al.  were adoWe compared the power of the PUPPI with three other self-contained tests, the PLINK set-based test, HYST , and SKAhttp://www.biocarta.com) databases from the Molecular Signatures Database (MSigDB) on the GSEA website (http://www.broadinstitute.org/gsea). We downloaded the PPI information from the STRING database [We downloaded the pathway definitions based on the KEGG , REACTOMdatabase . Each padatabase . A PPI wdatabase . We applA total of 1,078 pathways were downloaded from the GSEA website. There were 423,220 PPI pairs with scores > 0.8 in the STRING database. After QC, the WTCCC datasets consisted of 2,938 shared controls, 1,868 BD cases, 1,926 CAD cases, 1,748 CD cases, 1,952 HT cases, 1,860 RA cases, 1,963 T1D cases, and 1,924 T2D cases. There were 457,710 SNPs left for the analysis. After adjusting for multiple testing based on the familywise error rates (FWERs) or false discovery rates (FDRs) using the methods described in Wang et al. [Metal ion SLC transporters, contains the pathways which transport ions such as Cu2+, Fe2+, Fe3+, Mg2+, Mn2+, Zn2+, etc. The solute carrier (SLC) is a group of membrane transport proteins. The genes solute carrier family 30 member 3, member 6 and member 7 , and solute carrier family 39 member 6 (SLC39A6) encoding zinc transporters are expressed in the brain [For BD, the significant pathway, he brain –49. The he brain .M’ = 7.26) in the PUPPI test. Moreover, the interaction between RelA and CBP/p300 is modulated by protein kinase A (PKA), which can phosphorylate RelA. Such reaction may induce cardiac remodeling, an important process in the development of coronary artery disease [The functions for the significant pathway for CAD are acetylation and deacetylation of RelA in the nucleus. RelA (p65) is a member of the NFκB family, consisting of transcription factors regulating mainly the immune response, and having some functions in heart. RelA has been implicated in cardiac remodeling , which i disease .chaperone modulate interferon signaling pathway for CD is the most significant pathway in the overall analyses. The protein hTid-1 is a chaperone that modulates interferon signaling and can also repress NFκB [The ess NFκB . Persistess NFκB . Moreoveess NFκB . More iness NFκB . This paM’ values not equal to 0 in the chaperone pathway. The most significant gene pair, interferon gamma receptor 1 (IFNGR1) and interferon gamma receptor 2 (IFNGR2), are the receptors of interferon gamma. A cross-linking experiment has shown that IFNGR2 is associated with interferon gamma only when the IFNGR1 chain is present [in vivo screen tests [ present . If inteen tests . These ten tests . TherefoThe function of the significant pathway for HT is related to the import of mitochondrial protein. Hypertension is associated with the elevation level of reactive oxygen species (ROS) , and theComplement and coagulation cascades pathway has three stages: the complement cascade, the Kallikrein-Kinin cascade, and the coagulation cascade. For the complement cascade, the complement activation can recruit the inflammatory and immunocompetent cells to kill the pathogens. This cascade has three pathways: the alternative pathway, the lectin pathway and the classical pathway. All three pathways are related to the complement system, which helps the antibodies and phagocytic cells to remove the pathogen from the body. In the lectin pathway, the gene pair mannose-binding lectin 2 (MBL2)-mannan-binding lectin serine peptidase 1 (MASP1) has a significant interaction in the PUPPI test (M’ = 10.42). The gene MBL2 encodes mannose-binding lectin, which can recognize microorganisms. MASP1 can play a role as an enzyme to interact with MBL2 to activate the lectin pathway [The  pathway . The sec pathway . In the  pathway .The IL-7 signal transduction pathway can lead to immune response. Interleukin-7 (IL-7) is a cytokine which can trigger the immune system to develop B-cells and T-cells. In the etiology of type 1 diabetes, IL-7 is believed to be involved in the infiltration of the effector T-cells into pancreatic beta cells . Two stuSignaling by FGFR3 mutants pathway for T2D is also promising. T2D patients have dysfunctional β-cells in the islets [The e islets . Fibroble islets . FGFR3 ie islets .downregulation of TGF-β receptor signaling pathway with p-value = 0.0014) and fifth  significant pathways are both related to TGFβ signaling. In fact, the fourth significant pathway is a part of the TGF-β receptor signaling, which activates the Smad pathway. TGFβ is expressed more in patients with hypertension than in the normal controls [phosphoinositides and their downstream targets pathway is the third significant pathway, with p-value = 0.0088. This pathway shows the downstream target of phosphoinositides, which can be added to a phosphate molecule on the 3 position of inositol by phosphoinositide 3-kinase (PI3K), which is a subfamily of lipid kinase. The target downstream of PI3K can control many cell functions, such as proliferation, migration, and survival [Some pathways, while not the most significant for a given disease in our analyses for the WTCCC data, are nonetheless also functionally promising for that disease. For HT, the fourth  to parallelize the code. The program can be downloaded for free from the website: S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


AbstractAcinonyxjubatus) in the Thabazimbi area in Limpopo Province, South Africa. This study was undertaken to determine the spatial ecology of free-roaming Cheetahs that occur outside of formal protected areas on private ranchland, where they frequently come into conflict with, and are sometimes killed by, private landowners. The data were collected between September 2003 and November 2008, resulting in a total of 3165 location points (65 points from VHF collars and 3100 from GPS collars) for nine individual Cheetahs.In partnership with the University of Pretoria, the Endangered Wildlife Trust’s Carnivore Conservation Programme collared six male and three female free-roaming Cheetahs (www.GBIF.org) and provides the largest dataset on Cheetahs thus far, and, although it is spatially limited to a relatively small region on the African continent, it is the first study of its kind within South Africa. Also of significance is that the fate of 6 of the 9 collared Cheetahs is known, all except one of which died of anthropogenic causes.This dataset provides distribution information about this Vulnerable species occurring outside of protected areas within South Africa. The dataset has been published to the Global Biodiversity Information Facility ( Acinonyxjubatus) exist in South Africa: 1) a population of approximately 400 individuals occurring in the Kruger National Park  a menal Park  and 3) anal Park , as wellThis free-roaming population  is important because, in South Africa, the protected area network alone is insufficient for conserving populations of large carnivores . Only twPantheraleo) and Spotted Hyaenas (Crocutacrocuta) have been extirpated, leaving a depauperate carnivore guild dominated by Cheetahs, as well as Leopards (Pantherapardus) and Brown Hyaenas (Parahyaenabrunnea). A few studies have been done to determine the movement and range use of free-roaming populations of Cheetahs .Male Cheetah home ranges are generally larger than female ranges. Male 95UDs ranged from 121.5km2, and cheetahs typically ranged across an area approximate in size to 18 properties . .Home ranges were generally larger than the average ranch size of 18 kmIt was concluded that the large ranges of Cheetahs outside protected areas are likely driven by two key factors: 1) the search for suitable habitat in an encroached environment and 2) human avoidance (Marnewick & Somers 2015). Cheetahs in other systems  are mainly driven by alternative factors such as prey availability and competition with more dominant predators.Although what drives the large range use of Cheetahs in Thabazimbi could not be determined, this study provides useful information on the movement of Cheetahs and shows that generally, Cheetahs do not limit their movement to one property, thus preventing severe impacts on the prey base on individual properties Acinonyxjubatus) collared in the Thabazimbi area, Limpopo Province, South Africa.Tracking data from nine Cheetahs  with the key land-use being wildlife ranching or a combination of wildlife and stock ranching (2 (Hyaenabrunnea) and Leopards (Pantherapardus) with occassional reports of African Wild Dog (Lycaonpictus). Prey in the area includes livestock and wild game (Marnewick 2015).The Thabazimbi District in Limpopo was used as the core study area. The mean ranch size in the district is 18kmranching . Propertranching . The areranching . Human pching  occur in Limpopo; typically around the Thabazimbi area located in the western reaches of the province. Some of the points (< 5 %) occur in the northern areas of the North-West Province. The study comprised a component of a Doctoral thesis by Kelly Marnewick at the University of Pretoria.Capraaegagrushircus) as bait in areas where Cheetahs had been detected 2) on fence lines (standard game fences) frequently walked by cheetahs, and 3) at Cheetah scent marking posts (Cheetahs were trapped using double door traps which were placed at three various site location types: 1) using live, but protected, goats  were fitted to two individuals. Later in the study, these were replaced by GPS/GSM collars  that were utilized to obtain more robust data. The latter devices were set to take either two or four locations per day . Two collars needed to be replaced as a result of deteriorating batteries. The two male (AM196 - GeorgeJoss) and three male (AS68 - CBU) coalitions were initially monitored using VHF collars resulting in 56  and 12  data points being obtained respectively. Locations for animals wearing VHF collars were recorded by tracking the individuals from a microlight aircraft with one pilot and one researcher on board. For GPS collars, all GPS fixes were recorded directly from the device and transmitted through the GSM cell phone network. The data were then accessed and downloaded through an online platform. See Table 1The dataset has gone through a cleaning and georeference verification process to ensure GPS points and location information is accurate. Fourteen records were removed from the dataset as they were determined to be outliers due to their large distance from the study area in general, and also from other consecutive GPS locations. Terms in the dataset are in accordance with those set by the Darwin Core (DwC) Standard . The dataset was published to the Global Biodiversity Information Facility (GBIF) in September 2016, and is thus freely available for download and use (see Usage Rights and Data Resources sections).The geographic range of the bulk of the dataset covers two South African provinces: 1) the north-west reaches of Limpopo Province and 2) the northern areas of the North West Province Subfamily: FelinaeCommon Names: CheetahGenus: AcinonyxSpecies: A.jubatusGeneral taxonomic coverage description: This dataset focuses exclusively on Cheetah (Acinonyxjubatus), which is categorized as Vulnerable in the IUCN Red List : 147-151.Marnewick, K. & Cilliers, D. 2006. Range use of two coalitions of male cheetahs Acinonyxjubatus outside protected areas in South Africa. African Journal of Wildlife Research 45 (2): 223–232.Marnewick, K. & Somers, M.J. 2015. Home range size of cheetahs Acinonyxjubatus  and African wild dogs Lycaonpictus  in South Africa. Phd. University of Pretoria.Marnewick, K. 2015. Conservation biology of cheetahs


The metabolic syndrome is becoming increasingly prevalent in the general population that is at simultaneous risk for both type 2 diabetes and cardiovascular disease. The critical pathogenic mechanisms underlying these diseases are obesity-driven insulin resistance and atherosclerosis, respectively. To obtain a better understanding of molecular mechanisms involved in pathogenesis of the metabolic syndrome as a basis for future treatment strategies, studies considering both inherent risks, namely metabolic and cardiovascular, are needed. Hence, the aim of this study was to identify pathways commonly dysregulated in obese adipose tissue and atherosclerotic plaques.We carried out a gene set enrichment analysis utilizing data from two microarray experiments with obese white adipose tissue and atherosclerotic aortae as well as respective controls using a combined insulin resistance-atherosclerosis mouse model.Il7r, C3ar1, Tlr1, Rgs1 and Semad4d being the highest ranked genes for the inflammatory response pathway and Maob, Bckdha, Aldh6a1, Echs1 and Cox8a for the oxidative phosphorylation pathway.We identified 22 dysregulated pathways common to both tissues with p values below 0.05, and selected inflammatory response and oxidative phosphorylation pathways from the Hallmark gene set to conduct a deeper evaluation at the single gene level. This analysis provided evidence of a vast overlap in gene expression alterations in obese adipose tissue and atherosclerosis with In conclusion, this study provides extensive evidence for common pathogenic pathways underlying obesity-driven insulin resistance and atherogenesis which could provide a basis for the development of novel strategies to simultaneously prevent type 2 diabetes and cardiovascular disease in patients with metabolic syndrome.The online version of this article (doi:10.1186/s12933-016-0441-2) contains supplementary material, which is available to authorized users. The metabolic syndrome is a worldwide public health challenge with a prevalence above 20 % within adults in Western societies . This diDespite a number of investigations focusing on alterations leading to the development of either insulin resistance or atherosclerosis, there is no record in the literature systematically looking for dysregulated pathways common to insulin resistance and atherosclerosis in the same individual. Due to the concurring risk of type 2 diabetes and cardiovascular disease, the elucidation of dysregulated pathways in adipose tissue and atherosclerotic plaques should be based on an animal model that mirrors human disease by simultaneously developing adipose tissue inflammation/insulin resistance and atherosclerosis. Therefore, the aim of this study was the identification and analysis of common dysregulated pathways in obesity-induced adipose tissue inflammation and atherosclerotic plaque formation to elucidate interrelations in the concurrent development of type 2 diabetes and cardiovascular disease. Increasing our understanding on simultaneous dysregulation may indicate common molecular mechanisms that underlie type 2 diabetes and cardiovascular disease to facilitate novel preventive and therapeutic strategies in patients with metabolic syndrome. In this study we performed a pathway analysis using GSEA software with data from an own microarray experiment carried out with gonadal white adipose tissue (AT) and aortae (AO) samples from a combined insulin resistance/atherosclerosis mouse model established in our lab . With thLdlr/−−) were placed for 20 weeks either on normal chow  or diabetogenic diet   and processed based on manufacturer’s instructions for the RNA isolation. Total RNA (1 μg) was used for GeneChip analysis, for AT preparation six individual samples were used, whereas in the case of AO each of the three individual samples were pooled in three groups due to a limited quantity of material. Terminal labeled cDNA, hybridization to genome-wide Mouse Gene 2.0 ST Gene Chips and scanning of the arrays were carried out according to the manufacture’s indications (Affymetrix). Robust Multiarray Average (RMA) signal extraction, normalization and filtering were performed as described (http://www.bioconductor.org) [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76812).The frozen tissue samples were homogenized in TRIzoltor.org) , 17. TheFor the GSEA analysis, output primary raw data from the AT or AO microarray experiments was set up as the expression data set in accordance with GSEA indications and uploaded to the software collectively with the phenotype labels, chip annotations and either Biocarta, KEGG, Reactome or Hallmark gene sets downloaded from the Molecular Signatures Database (MSigDB) from the GSEA website. Subsequently the program was run with 1000 permutations and gene set as a permutation type, obtaining all dysregulated pathways, their respective normalized enrichment score (NES) and the enrichment plot for each microarray experiment. Statistical significances were set at a nominal p < 0.05 and false discovery rate q < 25 %. According to the GSEA directions, the pathways upregulated by NC were taken as pathways downregulated by DDC.Afterwards, the leading edge analysis function was executed to determine all those genes that significantly contribute to the dysregulation of the pathway of interest, also called leading genes. GSEA software was also used for perform the leading genes heat maps, where higher expression values are represented with red and lower expression values with dark blue. All Venn diagrams were made with the free access Venn Diagram Plotter from the Pacific Northwest National Laboratory.t test.Data are given as mean ± SEM. Dietary treatment differences were estimated by unpaired two-tailed Student Ldlr/−− mice were fed DDC or NC following the combined cardiometabolic mouse model previously established and characterized in detail in our lab [Ldlr/−− mice fed DDC for 16 weeks compared to those on NC animals. Atherosclerotic plaque formation as analyzed by en-face staining revealed markedly enhanced atherosclerotic lesions in Ldlr−/− mice fed with DDC  and possible overlaps in terms of dysregulated pathways between both tissues. The analyses were compared with Biocarta, KEGG, Reactome or Hallmark as gene sets, obtaining upregulated pathways by DDC or NC for either AT or AO Fig. . To idenHallmark gene set involves many well characterized biological processes combining numerous pathways. The dysregulated pathways from the GSEA with the Hallmark gene set are shown in Table To investigate changes at the single gene level involved in the inflammatory response and oxidative phosphorylation pathways, we first proceeded with a leading edge analysis, which allowed us to identify those genes that are significantly affecting the dysregulation of each pathway, called leading genes. The leading genes are represented in the heatmaps Figs. , 5 and iIl7r, C3ar1, Tlr1, Rgs1 and Semad4d were the highest ranked genes. Among the most highly ranked in the oxidative phosphorylation pathway were Maob, Bckdha, Aldh6a1, Echs1 and Cox8a. Non-overlapping genes regulated solely in AT or AO for inflammatory response and oxidative phosphorylation pathways are listed in the Additional file We next tested for a possible overlap between the leading genes of each pathway in AT and AO. The percentage of matched leading genes from AT and AO in the inflammatory response pathway was 36 % Fig.  or oxidaType 2 diabetes and cardiovascular disease are common risks inherent with presence of metabolic syndrome and are triggered by insulin resistance and atherosclerosis, respectively. Several immunological and metabolic changes in serum levels from factors such as TNF-α , IL-6 1, plasminInitially, we took advantage of a recently established combined insulin resistance/atherosclerosis inbred mouse model and analyzed microarray raw data from AT and AO with the GSEA software. Using four different gene sets, Biocarta, KEGG, Reactome and Hallmark Fig.  we obtaiInterestingly, the Hallmark analysis results included novel as well as already described common dysregulated pathways related with insulin resistance and atherosclerosis such as inflammatory response, IL6 JAK STAT3 signaling, TNFA signaling via NFKB, interferon gamma response, fatty acid metabolism, oxidative phosphorylation and adipogenesis Table . This liAfter evaluation of the common pathways altered in both metabolic processes, we selected the inflammatory and oxidative phosphorylation pathways as representatives of up- and down-regulated pathways, respectively, due to the importance of the involved genes in the pathogenesis of type 2 diabetes and atherosclerosis –41. We cA large amount of studies have been performed to identify potential treatment strategies for multifactorial diseases which are caused by a collective dysregulation of many genes. Recently, identification of dysregulated pathways was proposed as possible biomarkers for such disorders , 44. ThiIn conclusion, we describe analogies at the pathway as well as individual gene level between obese adipose tissue and atherosclerotic aortae potentially to be considered as a basis to achieve novel therapeutic approaches for the simultaneous prevention of type 2 diabetes and atherosclerotic cardiovascular disease.


The endomembrane system, known as secretory pathway, is responsible for the synthesis and transport of protein molecules in cells. Therefore, genes involved in the secretory pathway are essential for the cellular development and function. Recent scientific investigations show that ER and Golgi apparatus may provide a convenient drug target for cancer therapy. On the other hand, it is known that abundantly expressed genes in different cellular organelles share interconnected pathways and co-regulate each other activities. The cross-talks among these genes play an important role in signaling pathways, associated to the regulation of intracellular protein transport.In the present study, we device an integrated approach to understand these complex interactions. We analyze gene perturbation expression profiles, reconstruct a directed gene interaction network and decipher the regulatory interactions among genes involved in protein transport signaling. In particular, we focus on expression signatures of genes involved in the secretory pathway of MCF7 breast cancer cell line. Furthermore, network biology analysis delineates these gene-centric cross-talks at the level of specific modules/sub-networks, corresponding to different signaling pathways.We elucidate the regulatory connections between genes constituting signaling pathways such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling. Interestingly, we determine some key regulatory cross-talks between signaling pathways  and intracellular protein transport.The online version of this article (10.1186/s12859-018-2036-2) contains supplementary material, which is available to authorized users. The secretory pathway is composed of different organelles suspended in the cytoplasm. It includes rough endoplasmic reticulum (rough ER), ER exit sites (ERESs), the ER-to-Golgi intermediate compartment (ERGIC) and the Golgi complex cellular organelles, which have distinct functions in the transport of proteins to their final destination in the cell. Not only does the secretory pathway play an important role in proteins synthesis and delivery, but it also facilitates the proper folding and post-translational modifications of protein . At preshttp://lincs.hms.harvard.edu). In more detail, it consists of 1328098 expression profiles of 22268 genes  trained on GEO data. Perturbation experiments were executed on different cell lines, at different time points, silencing or over-expressing genes, or treating cells with chemical compounds. For our work, we studied 3647 experiments in which genes have been knocked-down/over-expressed in MCF7 breast cancer cell line. Among the 2552 genes of the secretory pathway, only 591 were perturbed in L1000 dataset. For each perturbation experiment, we collect data for all the biological and technical replicates at two different time points . We used the so called level 3 dataset, which includes standardized gene expression profiles of directly measured landmark transcripts plus imputed genes . This signaling network dataset contains more than 6000 proteins and 63,000 relations. These relations represent activation, inhibition and physical interactions, which in turn describe complexes that play crucial roles in cell signaling.To reconstruct the regulatory networks of secretory pathway we use the library of integrated network-based cellular signatures (LINCS) L1000, which contains more than a million gene expression profiles of perturbed human cancer cell lines , which are repeated measurements of biologically distinct samples and capture random biological variation [qk technical replicates, which are repeated measurements of the same sample . A given perturbation experiment is represented as: To obtain gene regulatory interactions and reconstruct gene network Fig. c from geariation . Each bimj is the number of biological replicates in j-th profile, and qk is the number of technical replicates for the k-th biological sample in the perturbation experiment. The mean biological sample qk technical replicates is calculated as: where In the second step, we create a matrix with all biological replicates (averages), for which we calculate the first principal component, that is the linear combination of the biological replicates pointing in the direction of maximum variance. Before performing the Principal Component Analysis, we pre-processed the data to normalize their mean as follow: σj is the standard deviation of where j, we select one biological replicate Zj. Each column of the matrix For each perturbation In this work we consider only gene interaction networks in which directed edges connecting two genes represent a biochemical process such as a reaction, transformation, interaction, activation or inhibition. We have not considered gene co-expression networks (GCN) in which the direction and type of co-expression relationships are not determined, because they are an undirected graphs where each node corresponds to a gene, and a pair of nodes is connected with an edge if they show a similar expression pattern. Therefore with the help of Cytoscape network visualization tool , we visusearch strategy to obtain high-scoring modules using local and greedy search.In Cytoscape, the j-Active Modules plugin identifies expression activated sub-networks Fig. e from prTo study the pathways enrichment and network patterns in the sub-network with respect to signaling and intracellular protein transport, we use ReactomeFI-plugin  in CytosTo understand the functional and regulatory relationship among genes in expression activated sub-networks, we use GeneMania plugin  Fig. 1gg1g. It eBy analyzing manually curated human signaling network data, we obtain further signaling interactions and introduce them to extend the regulatory interaction network already obtained from ReactomeFI network analysis. The signaling interaction data contain activation, inhibition and physical interactions. The physical relations represent complexes that play a role in cell signaling. Furthermore, we also map the transcription factors, gene ontology, and gene specific enriched reactome and KEGG pathways information on the reconstructed direct sub-network to infer direct physical and genetic interactions between perturbed genes and their effected components Fig. f. We priWe carried out Gene Ontology and pathway enrichment analysis of the complete list of perturbed genes, for which corresponding expression profiles are utilized in this study. It includes 576 unique perturbed genes which have regulatory effects on the other genes. DAVID and Transcriptator functional annotation tools are used to carry out enrichment analysis taking a multiple correction p-value cutoff ≤ 0.05. The complete list of perturbed genes is provided as Additional file Using our pipeline we reconstructed a network based on regulatory interactions consisting of 4467 nodes and 12871 edges  and cut-off for constituent nodes ≥ 12 in each module, we observed high intensity of cross-talks between signaling involved in protein transport  and cancer progression (PI3K-Akt). Genes associated with PI3K-Akt signaling interacts with calcium signaling pathway. Considering module wise study, we notice that PI3K-Akt signaling is the most prominent and enriched signaling pathway, forming the core of each cluster  kinase-Ras-Raf-MEK-ERK- pathway downstream initiated by receptor tyrosine kinase and integrins, leading to several cellular processes such proliferation, differentiation of cell, membrane genesis, protein synthesis and secretion and also has an intermediate effect on gene expression [ERBB2, EGFR and ERBB4. In the last module/cluster 4, enrichment of PI3K-Akt signaling, JAK-STAT and calcium signaling suggests activation of PI3K-Akt signaling through both calcium and stress-activated protein Jun kinases and vice versa. In the past, researchers portrays the role of intracellular Ca2+ and disturbances in its cellular concentration, with respect to tumor initiation, angiogenesis, progression and metastasis in the normal cells [CDH1 leads to the 4 fold decrease in the expression of YWHAZ gene, which is a member of the 14-3-3 protein family and a central hub protein for many signal transduction pathways. YWHAZ gene regulates apoptotic pathways critical to cell survival and plays a key role in a number of cancers and neuro-degenerative diseases [CDH1 could be a potential gene as a molecular target for cancer therapy.In cluster/module1, cluster/module2 and cluster/module3, we observe the interactions between PI3K-Akt and kinase signaling such as al cells . To delvdiseases . This geTSC2 gene. Both the genes play an important role in PI3K-Akt signaling. PTEN gene is a tumor suppressor [TSC2 mutations lead to tuberous sclerosis, and its gene products is supposedly a tumor suppressor [TSC2, LAMA2 and PTEN interactions could be useful to study a potential drug therapy for cancer as well as muscular dystrophy. Similarly CCND1 amplification and its protein expression is strongly correlated with breast cancer [CCND1 expression and controls its amplification with respect to cancer.All these genetic regulatory interactions might provide a viable target for cancer drug therapy. From our results, we observed that PTEN is heavily down-regulated by the perturbation of ppressor , and mutppressor . From thppressor . TSC2, Lt cancer , the perp-value ≤ 0.05. Furthermore, functional enrichment analysis of the nodes with respect to out-degree, in-degree and betweenness centrality, helps us to understand the underlying cross-talk between protein transport, localization on the signal transduction and transcriptional rewiring and their mutual effects on protein folding. In our regulatory network, we find some interesting and significantly enriched signaling pathways, such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling. In recent years, researchers observed the role of PI3K-Akt signaling in cancer progression, which is basically a disturbance in the balance of cell division and growth with respect to programmed cell death. This particular signaling pathway is disturbed in many human cancer and not only does it play a major role in tumor development, but also in its potential response to the treatment [From the regulatory interaction network analysis, we infer that genetic perturbations involved in protein transport have profound effects on the signal transduction, and transcriptional regulation activities of the cell. We also carried out functional analysis of the nodes with high value of betweenness centrality, as these nodes do play an important role in bridging between the sub-networks and hub nodes. The results show several enriched pathway: ER-nucleus signaling pathway (GO:0006984), cellular response to topologically incorrect protein (GO:0035967), response to topologically incorrect protein (GO:0035966), response to unfolded protein (GO:0006986) with significant reatment . In our reatment . This kireatment . Most ofretrograde protein transport. There is also an endocytic pathway, through which cargo is internalized from the cell milieu. The best characterized endocytic pathway proceeds from clathrin coated vesicles through early and late endosomes to lysosomes. The lysosomes is a major degradation site for internalized cargo and cellular membrane proteins [In addition to study the cross-talks between different signaling pathways in intracellular protein transport, we also infer the regulatory effects of signaling pathways on intracellular protein trafficking mechanism related to exocytic and endocytic pathways . Throughproteins . In our proteins , 34. Theproteins . The resproteins . It is aproteins , 37, 38.proteins . In summSimilarly, we also observe the cross-talks between Ras signaling pathways and intracellular protein transport mechanism. The cross regulatory interactions between the components of Ras and intracellular protein transport pathways are depicted in resultant Fig. . We obseIn this work, we develop a computational integrated pipeline to analyze genes perturbation experimental data and uncover the regulatory interactions among genes. We use functional genomics and network biology approach to create a directed network, where nodes represent the perturbed and impacted genes, while direct edges represents the positive and negative regulatory effects of perturbation on its neighboring elements. We implemented this approach to infer regulatory cross-talk between signaling pathways and intracellular protein transport in MCF7 cell line. Our aim is to elucidate the regulatory connection between genes constituting signaling pathways such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling and intracellular protein transport mechanism in MCF7 cell line. We focus on PI3k-Akt signaling and Ras pathway, to highlight some of their mutual key regulatory features. In our results, we find some interesting regulatory components of PI3k-AKT signaling with respect to Ras pathway as well as intracellular protein transport mechanism. From the literature, it is known that development of resistance to cancer therapy is an important clinical problem . InactivAdditional file 1Perturbed gene list. Complete list of perturbed genes. (ZIP 11 kb)Additional file 2DAVID Enrichment analysis 591 genes. Gene ontology and pathway enrichment analysis of the complete list of perturbed genes. (ZIP 203 kb)Additional file 3Node statistics table. Topological parameters of each nodes in the network. (ZIP 50 kb)Additional file 4Network enrichment. Gene Ontology and Pathways enrichment analysis results for the complete regulatory interaction network from 591 gene perturbation expression data. (ZIP 41 kb)Additional file 5Network functional modules enrichment. Module wise functional and pathways enrichment analysis of the network. (ZIP 131 kb)Additional file 6Enrichment terms for gene cross-talks. All regulatory interactions and enriched pathways for genes involved in cross-talk between intracellular protein transport and signaling pathway. (ZIP 100 kb)


Traditional Chinese Medicine (TCM) is increasingly getting clinical application worldwide. But its theory like QI-Blood is still abstract. Actually, Qi deficiency and blood deficiency, which were treated by Si-Jun-Zi-Tang (SJZT) and Si-Wu-Tang (SWT) respectively, have characteristic clinical manifestations. Here, we analyzed targets of the ingredients in SJZT and SWT to unveil potential biologic mechanisms between Qi deficiency and blood deficiency through biomedical approaches. First, ingredients in SWT and SJZT were retrieved from TCMID database. The genes targeted by these ingredients were chosen from STITCH. After enrichment analysis by Gene Ontology (GO) and DAVID, enriched GO terms with p-value less than 0.01 were collected and interpreted through DAVID and KEGG. Then a visualized network was constructed with ClueGO. Finally, a total of 243 genes targeted by 195 ingredients of SWT formula and 209 genes targeted by 61 ingredients of SJZT were obtained. Six metabolism pathways and two environmental information processing pathways enriched by targets were correlated with 2 or more herbs in SWT and SJZT formula, respectively. Traditional Chinese Medicine (TCM), being an effective treatment system, is increasingly getting clinical application worldwide. After more than 5000 years of clinical practice, nearly 100,000 classical and effective TCM formulae have been developed, but the effective mechanisms of most formulae remain unclearhttp://www.kegg.jp)http://www.megabionet.org/tcmid/) contains 3,791 diseases, 47,000 prescriptions, 8,159 herbs, 6,828 drugs, 25,210 compounds and 17,521 related targets, which facilitates the study of interactions between formula, ingredient, gene and disease to uncover the molecular biological mechanisms of TCM. Meanwhile, there are several network analysis tools for biological functionality of TCM-related network analysis, such as Cytoscape18Since the TCM formulae are normally composed of several medicinal herbs, and each herb normally has many ingredients, and each ingredient has a lot of targets, a formula is a complex biologic active network. Fortunately, along with the rapid development of life science and computer science, a variety of computational tools and bioinformatic database have been developed to facilitate the analysis of a large number of genes associated with complex ingredients of TCM formulaeIncreasing TCM researchers successfully analyzed pharmacological mechanism of TCM formulae by using TCM-related databases and data analysis tools232425As shown in Retrieving from TCMID, we obtained 152 genes targeted by 162 ingredients of Radix Angelicae Sinensis, 107 genes targeted by 28 ingredients of Ligusticum chuanxiong, 21 genes targeted by three ingredients of Rehmannia glutinosa, 42 genes targeted by eight ingredients of Paeonia albiflora , and 156Interestingly, there were obviously three main grouped clusters independent from each other in the network , two cluAfter analysis of pathway associated genes, we found that there were six metabolism pathways, including beta-Alanine metabolism, histidine metabolism, phenylalanine metabolism, tyrosine metabolism, ascorbate and aldarate metabolism, glycolysis/gluconeogenesis pathways, were correlated with 2 or more herbs in SWT formula and two environmental information processing pathways, calcium signaling pathway and neuroactive ligand-receptor interaction pathway were correlated with 2 or more herbs in SJZT formula .We also found that there were many disease associated pathways were enriched. According the differentiating criterion that if more than 66% of the genes targeted by ingredients of one formula associate with a term (pathway), the term (pathway) is considered specific for the formula, the disease associated pathways were differentiated into two categories, i.e. common pathway and different pathway. The common pathway, including viral carcinogenesis, thyroid cancer, chemical carcinogenesis, legionellosis and small cell lung cancer pathways are unspecific for SWT or SJZT. The different pathway were all specific for SWT, including amphetamine addiction, colorectal cancer, choline metabolism in cancer, bladder cancer, hepatitis B, pancreatic cancer, non-small cell lung cancer, prostate cancer, chagas disease (American trypanosomiasis), cocaine addiction, chronic myeloid leukemia, HTLV-I infection, p53 signaling pathways and pathways in cancer .As mentioned above, there were many cancer and other diseases associated pathways in the network , which mIn the non-disease associated network, pathways enriched with genes targeted by ingredients of SWT were mainly involved in material metabolism , including beta-Alanine metabolism, histidine metabolism, phenylalanine metabolism, tyrosine metabolism, tryptophan metabolism, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, glycolysis/gluconeogenesis, retinol metabolism pathways . RetinolWhile pathways enriched by genes targeted by ingredients of SJZT mainly belonged to organismal system function, environmental information processing, metabolism and cellular processes pathways. The organismal system function pathways included thyroid hormone signaling pathway, ovarian steroidogenesis, prolactin signaling pathway and oxytocin signaling pathway (endocrine system), bile secretion pathway (digestive system), vascular smooth muscle contraction pathway (circulatory system) and serotonergic synapse (nervous system). The environmental information processing pathways included neuroactive ligand-receptor interaction pathway  and calcium signaling pathway . The metabolism pathways of SJZT were different to SWT, which mainly belong to lipid metabolism including linoleic acid metabolism and regulation of lipolysis in adipocytes pathways. The cellular processes pathway included Gap junction (cellular community), .p value less than 0.01 unspecific for SWT or SJZT. We considered them as the common pathways of SWT and SJZT. These pathways also belonged to organismal systems, metabolism, environmental information processing and cellular processes, including estrogen signaling pathway (endocrine system), GnRH signaling pathway (endocrine system), PPAR signaling pathway (endocrine system), GABAergic synapse (nervous system), Arginine and proline metabolism (amino acid metabolism), Steroid hormone biosynthesis (lipid metabolism), Metabolism of xenobiotics by cytochrome P450 (Xenobiotics biodegradation and metabolism), cAMP signaling pathway , TNF signaling pathway , Apoptosis (Cell growth and death), , .In order to clarify the difference between SWT and SJZT at gene level, we also screened different genes from differentiated pathways between SWT and SJZT .Combination therapy is the major feature of TCM, which is increasingly recognized by modern western medicine, such as cocktail therapy for HIVBlood deficiency normally manifests anaemia, vertigo, heart palpitations and menstrual discomfort. The SWT formula has effects on stimulating hematopoiesis in bone marrow, anti-coagulant, vasodilatation and sedative293335Applying syndrome differentiation through formula effect assessment, we could predict the pathways associated with blood deficiency and Qi deficiency syndromes according to the pathways enriched by SWT and SJZT, and then indirectly predict the molecular mechanism of blood and Qi.Interestingly, when using targets by ingredients of SWT and SJZT to enrich and construct the Network , there wExcept for disease associated pathways, SWT significantly influence amino acid and carbohydrate metabolism , which are closely related with nutrient substance. Abnormal glycolysis/gluconeogenesis was also found by another study about urine metabonomic in blood-deficient mouse modelDifferent from blood of TCM, Qi has functions of promoting substance metabolism and energy conversion, stimulating activity of organs, keeping blood circulating in vasculature, promoting human growth and development, maintaining normal temperature of human body, strengthening the ability of anti-infection, maintaining normal development of fetus in uterus and controlling the secretion and excretion of bile, sweat, urine, saliva, gastric and intestinal digestive juice.et al.et al.2+ to have activity. And, the hormone can influence the activity of calmodulin by regulating the concentration of intracellular Ca2+. Thyroid hormone has function of promoting human growth and development, calorigenic effect on maintaining normal temperature of body and function of stimulating substance metabolism and energy conversion, which can stimulate activity of organs. Ovarian steroidogenesis, prolactin signaling pathway, oxytocin signaling pathway can help to maintain normal development of fetus in uterus. All the functions of pathways enriched by SJZT are consistent with the actions of Qi of TCM.This study found that SJZT could influence several syntrophic pathways. Among them, bile secretion, linoleic acid metabolism, regulation of lipolysis in adipocytes pathways can affect bile secretion and lipid metabolism and then influencing lipid hormones synthesis; neuroactive ligand-receptor interaction, serotonergic synapse, gap junction pathway can influence the function of neuroendocrine system; thyroid hormone signaling pathway, ovarian steroidogenesis, prolactin signaling pathway, oxytocin signaling pathway can regulate corrective hormones secretion; vascular smooth muscle contraction can regulate the blood flow and pressure and keep blood circulating in vasculature; while calcium signaling pathway involve in several pathways above. Tian, R. In order to clarify the difference between SWT and SJZT at gene level, we also screened different genes from differentiated pathways between SWT and SJZT . These gIn TCM theories, blood and Qi also have close relationship, i.e. Qi can promote the formation and circulation of blood. Meanwhile blood can nourish Qi. In this study, we also found that there were many common pathways between SWT and SJZT, which reflects the close relationship between Qi and blood.As shown in the network , there w49Above all, the common pathways are mainly associated with the effects of SWT and SJZT on strengthening the ability of anti-infection, promoting human growth and development and increasing blood flow and promoting hematopoiesis, maintaining normal development of fetus in uterus, rectifying dysmenorrhea and other estrogen-related diseases, which are consistent with the actions of blood and Qi of TCM.In conclusion, SWT with the functions of influencing amino acid and carbohydrate metabolism is significant different from SJZT with the actions of influencing neuroendocrine system by affecting excitatory synapses (serotonergic synapse) to regulate thyroid hormone, ovarian steroidogenesis, prolactin and oxytocin secrction and can regulate corrective hormones secretion and promoting vascular smooth muscle contraction. The common effects of SWT and SJZT are regulating the functional activities of the hypothalamic pituitary adrenal and gonadal axis by affect inhibitory synapses (GABAergic synapse) to stimulate estrogen and steroid hormones secretion and strengthening the ability of anti-infection. All the differences and common pathways also reflect the characteristics of blood deficiency and Qi deficiency, and the molecular mechanism of blood and Qi of TCM.Ingredients of eight herbs from SWT  and SJZT  were retrieved from TCMID database, 449 ingredients and 454 targets of SWT and 545 ingredients and 568 targets of SJZT were collected and organized. In this study, Only human genes with STITCH defined high confident (combined-score more than 0.7) were chosenhttp://david.abcc.ncifcrf.gov/)p-value less than 0.01(corrected with Bonferroni step down) were collected and analyzed to interpret the biological meanings of these targeted genes datasets with comprehensive set of functional annotation tools of DAVID and KEGG.454 and 568 genes targeted by ingredients of SWT and SJZT were performed Gene Ontology (GO) and pathway enrichment analysis with DAVID Bioinformatics Resources 6.7 How to cite this article: Sun, J. et al. To Unveil the Molecular Mechanisms of Qi and Blood through Systems Biology-Based Investigation into Si-Jun-Zi-Tang and Si-Wu-Tang formulae. Sci. Rep.6, 34328; doi: 10.1038/srep34328 (2016).