The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways.A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient—a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis.A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.The online version of this article (doi:10.1186/s12859-016-1285-1) contains supplementary material, which is available to authorized users. To gain more mechanistic insights into the underlying biology of the condition being studied, analyzing high-throughput molecular measurements at the functional level has become more and more appealing . EspeciaIn the last decade, the pathway analysis has experienced the over-representation approach (ORA) –11 and a= [Proportion of DEG in the pathway] × [average log2 fold change of the DEG] × [average of –log P-value of the DEG]” and “Impact direction = Impact of up-regulated DEG-Impact of down-regulated DEG” [For time-course experiments and multiple treatments experiments, the Dynamic Impact Approach (DIA) had been validated to be an effective functional analysis method in real study based on a priori biological knowledge , 30. In ted DEG” . In factIn this study, we attempted to develop a decision analysis model to select the most important pathways from the same KEGG category or subcategory pathways, and to demonstrate the regulation mechanisms among the pathways belonging to the same KEGG category or subcategory. In this method, a decision coefficient (DC) index was conceived by considering the mutual regulation between pathways and was used to identify the most impacted pathways. Besides, the subdivision of DC included not only the direct determination factor of each pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the mutual regulation mechanisms among pathways can be demonstrated by the subdivision of DC. In addition, the impact direction of each pathway can be preliminarily estimated by using the sign of DC. Moreover, the decision percentage can be obtained by the ratio of DC absolute value of the given subcategory pathway divided by the sum of DC absolute values of all subcategories in the same category. According to the decision percentage, a decision tree can be constructed to visualize the decision results. We tested the utility of the method using the DIA impact value dataset from a functional analysis of the bovine mammary transcriptome during the lactation cycle.To introduce the decision analysis model, we take the KEGG pathways for an example to define the following notations.X = T, Xi = T, , Xij = T,  are the sets of KEGG pathway categories, subcategories and the secondary pathways, respectively. Let yi be the impact values of the i-th KEGG pathway category and x = T be the impact values of its corresponding subcategory. The vector x is assumed to follow a normal distribution x ~ N, where Rx is the correlation matrix of x. Based on the path analysis model, the total effect can be subdivided into the direct effect and indirect effect through the equation Rx, and x and yi, b* = T is the solved path coefficient indicating the direct effect of subcategory pathways [R2 ≤ 1) is the proportion of total variation of outcomes explained by the model, which provides a measure of how well observed outcomes are replicated by the model [SSres is the residual sum of squares, indicating the discrepancy between the data and an estimation model; SStot is the total sum of squares, indicating the total “variability” of data set.We assumed that pathways . In facthe model . In otheR2) can be subdivided into the direct CD , the decision coefficient (DC) Rj)2) is the direct determination factor that demonstrates the direct decision-making capacity of the given pathway. The bj* value corresponds to the direct effect in the path analysis model. The second term , and n is the sample size, q is the number of independent variables. The tp(n − q − 1) is the upper (p/2) ‐ quantile of t-test statistics with degrees of freedom n − q − 1 under the given p probability threshold. This quantiles will increase when the probability threshold decreases due to p = P{|t| > tp(n − q − 1)}. The quantiles tp(n − q − 1) can be obtained by consulting the ‘Quantiles  for Student’s t-Distribution table’. For example, let p = 0.05, t0.05(n − q − 1) can be consulted, then Rj)cut − offcut − off have statistical significance at the significant level of 0.05. Obviously, the two processes of p = P{|Robs| ≥ Rj)cut − off(} ≤ 0.05 and {|Robs| ≥ Rj)cut − off is calculated as follows:The decision coefficient (DC) is a more scientific and comprehensive index conceived in reflecting the decision-making capability of the given pathway, and it truly reflects the variable decision of each independent variable to dependent variable. In this way, the most impacted pathway can be chosen. The larger the absolute value of DC of given pathway is, the larger the decision-making ability of given pathway to its upper level pathway. The value of DC can be positive or negative due to the positivity and negativity of indirect determination factor. Therefore, the sign of DC can characterize the impact direction of given pathway to a certain degree. Additionally, the decision tree was constructed below Fig.  to displTo explain how the proposed decision analysis to identify the most significant pathways and to demonstrate the regulation among pathways, we performed computer simulations. The random data satisfied the assumption of normal distribution under the regression analysis. The detailed analysis results of simulated data were listed in Table p-value) was set at 0.01, the two most significant subcategory pathways (x3 and x4) were identified. But when the significance level was set at 0.05, the three most significant subcategory pathways  were all selected. This result demonstrated that more significant pathways can be identified with the significance level increasing. Second, the direct and indirect determination factors from the DC can clearly display the correlated regulation among the pathways . For illustrative purpose, we selected the most significant subcategory pathways x3 and x4 (p ≤ 0.01) to probe into the regulation mechanisms among pathways. As Table x1 and x2 to x3 (2b3∗r31b1∗ = 2.046 and 2b3∗r32b2∗ = 0.4336) were all positive, but the regulation of x4 to x3 (2b3∗r34b4∗ = − 12.7715) was negative and far exceeded the positive regulation from x1 and x2. Thus, the total indirect determination factor of x3 (x3 (R(3) = − 3.6966) was negative because the total negative indirect determination from x1, x2 and x4 exceeded the direct determination of x3 ((b3∗)2 = 6.7252). However, the indirect regulations of x1, x2 and x3 to x4 were all negative. The larger negative indirect regulation led to the negative DC value. The detailed comparison of indirect regulation determination of x1, x2 and x3 to x4 showed that the negative regulation of pathway x3 was the largest and that of pathway x2 was the smallest. Third, the sign of DC value can be used to predict the impact direction of pathways. The calculated results of simulated data demonstrated that the impact direction of pathways x1 and x2 were up-regulated and that of the pathways x3 and x4 were down-regulated.By fully incorporating the correlated structure of KEGG pathways, the decision analysis model shows distinctive advantages as below. First, the significant pathways can be identified through the DC cut-off calculated by formula (3) with the given significance level. To the given simulated data, when the significance level ( Rj) of decision analysis and the total effect (rjy) of path analysis [rjy) and its subdivision. In fact, the regulations between pathways were mutual and non-equivalent. Take x3 and x4, for example, the regulation of x4 to x3 was positive (r34b4∗ = 2.4624), conversely, the regulation of x3 to x4 was negative (r43b3∗ = − 2.5847). The total effect of path analysis included the direct effect (bj∗) and indirect effect  according to the total effect of path analysis. In contrast, pathway x2 had a higher rank (first) according to the DC value of decision analysis due to the retro-regulation of pathway x2 to x1, x3 and x4. The strategy of borrowing information from retro-regulation allows the decision analysis to identify the most significant and mainly contributed pathways. It is true that there is no gold standard to compare the methods in real studies because the biological truth is unknown. Therefore, the analysis results based on the simulated data only help to illustrate the distinctive characteristics of decision analysis.We have also compared the DC value  of the selected KEGG pathway categories and subcategories had been calculated. The CDs (R2) of subcategories ‘Energy Metabolism’ and ‘Metabolism of Other Amino Acids’ were 0.8613 and 0.9972, respectively. The CDs (R2) of the other KEGG pathway categories and subcategories were almost up to 1. These results showed that the observed outcomes were replicated by the model very well.According to the path analysis approach, the total CD . The results showed that the different DC cut-offs were identified for different category and subcategory pathways. After integration, three DC cut-offs 0.3, 0.4, and 0.5) were chosen to compare  and their impact directions. The details of compare results are listed in Additional file In addition, we roughly compared the results of the decision analysis with those of KEGG-PATH approach. The results demonstrated that the concordance rate of pathway impact direction under decision analysis was significantly higher than that under KEGG-PATH approach when DIA pathway impact directions were used as standard. For example, for pathway categories ‘Metabolism’ and ‘Environmental Information Processing’, the concordance rates of pathway impact direction under decision analysis were 54.5 % (6/11) and 100 % (3/3), respectively. However, the corresponding concordance rates under KEGG-PATH were 45.5 % (5/11) and 33.3 % (1/3). From the view of all the secondary pathways belonging to the same category, the concordance rates of pathway impact direction were also obviously improved from 49.3 % (34/69) and 57.1 % (8/14) under KEGG-PATH to 58 % (40/69) and 64.3 % (9/14) under decision analysis for categories ‘Metabolism’ and ‘Environmental Information Processing’, respectively. For the most impacted pathways comparison, the concordance rate compared with KEGG-PATH seemed still higher than that compared with DIA  of the DC. Still further, the sign of the DC also gave consideration to the dependences among the pathways. In this study, the result of the decision analysis showed that the subcategory pathway “Lipid Metabolism” had the largest positive decision-making ability; however, the impact direction of its secondary pathway ‘Fatty acid metabolism’ was negative. These results match our expectations because the lipid metabolism had a lot to do with the synthesis of the lactose and the reduction of fatty acid metabolism was considered towards the synthesis of milk fat through taking up the fatty acids by the mammary tissue. Besides, the fact that the impact direction of the TGF-beta pathway was negative based on the decision analysis was in accordance with the fact that this pathway appeared to have a negative role on mammary cell proliferation .In this study, a decision analysis model is first proposed to identify the most impacted pathways. The decision analysis model borrows the decision coefficient to judge the importance of the pathways, which not only considers the direct determination factor of pathway itself, but also adds the correlation indirect determination factor with the other related pathways. Compared with DIA approach, the decision analysis method overcomes the deficiency of analyzing each pathway independently. Compared with KEGG-PATH approach, the decision analysis method constructs a DC index based on the coefficient of determination of regression analysis, rather than correlation coefficient. Importantly, the retro-regulation among pathways was considered in decision analysis. Therefore, the decision analysis model is a statistical data mining at a deeper level. For the estimation of impact direction, the DIA method averages the impact direction values of the pathway during different time course. The KEGG-PATH approach needs to use the gradient analysis from principal component analysis (PCA) to estimate the impact directions of pathways. However, the decision analysis can judge the impact direction directly through the sign of decision coefficient. More importantly, the sign of decision coefficient was caused by the correlated regulation from the other related pathways. Thus, the identification of pathway impact direction (up-regulating or down-regulating) through the decision coefficient also gave consideration to the dependences among the pathways. Hence, it is a major bright spot of the decision analysis model that the identification of the most impacted pathways and their impact directions through the decision coefficient took account of the correlation among pathways from the angle of ‘variation determination’.−15 and it can be neglected.In addition, the regulation mechanisms among pathways can be demonstrated through the subdivision of decision coefficient. This numerical expression of the correlation regulation among pathways is another major highlight of the decision analysis model. The construction of decision tree can visually display the results of decision analysis. We have developed a program in Matlab  to implement the decision analysis  based on coefficient of determination (CD) of regression analysis gives consideration to the inter-pathway dependence in identifying the most impacted pathways and their impact directions. Meanwhile, the regulation mechanisms among pathways were demonstrated from the angle of ‘variation determination’. The decision analysis model is an initial attempt of optimizing pathway analysis methodology.


Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) is a gram-positive intracellular bacterial pathogen and it is observed in many habitats [habitats . In humahabitats , 3. In thabitats . LM utilhabitats . Much ofhabitats  and by ehabitats . HoweverIn the recent years, with the accumulation of large amount of “omics” data in public databases, gene expression profiles have been widely utilized to detect signatures. Frequently, many computational approaches have been created to identify differentially expressed genes (DEGs) between disease and normal conditions , 8. NeveHerein, in the current study, we sought to extract dysregulated pathways based on the PIN. Specifically, gene expression profile of human peripheral monocytes infected by LM (accession number E-MEXP-1613) was recruited from the public database of EMBL-EBI. Cellular pathways and human PPIs were, respectively, obtained from the Reactome and String databases for further analysis. Then, after the genes were aligned to the pathways, principal component analysis (PCA) method  was usedn = 10), Staphylococcus aureus (n = 10), Streptococcus pneumoniae (n = 10), and control samples without infection by bacteria (n = 10). In our study, with the goal of investigating the response in human monocytes to infection with LM, we only selected peripheral blood monocytes infected by LM and controls to do further analysis.The gene expression profile under the series number of E-MEXP-1613  was recrgmn represented the expression value of gene m in sample n and mean(gm) and std(gm), respectively, stood for mean and standard deviation of the expression vector for gene m across all samples.Before analysis, data was log-2 transformed and normalized using quantile approach . After tAll human PPIs were recruited from the String database . String Moreover, the predefined cellular pathways (1675 pathways) were downloaded from the Reactome database . After tk in sample n, the activity score Pkn was calculated based on the following formula:zmnk stood for the standardized expression value of gene n from pathway k in sample m and wmnk represented the weight for zmnk. That was to say, the activity of each pathway was considered as the linear combination of the expressions of all genes in this pathway, and each pathway was believed as a meta-gene.After the genes were aligned to cellular pathways, an activity score for each pathway was defined as the summary of the expression values of all genes of this biological pathway. Specifically, PCA method  was utilThe first principal component from PCA especially was utilized as the activity score for the corresponding pathway herein. The pathways which had different activities between disease and control conditions were possibly associated with the disease. That was to say, the activity score for a given pathway among LM-infected samples and control subjects was different, and the difference demonstrated the correlation to LM infection. The greater the difference was, the more close the relevance of this pathway to the LM infection was. In our study, the pathway with maximum changes in activity scores between disease and control conditions was selected as the seed pathway.t-test was employed to determine which genes were differentially expressed between the two groups using the criteria of P < 0.05. Moreover, we also computed Pearson's correlation coefficient (PCC) and the absolute value of PCC for the PPI interactions in these two groups.In the present study, Student's A PIN was built with each node denoting a pathway, where one edge was laid between two pathways if they shared at least one gene or there were interactions between genes from the two pathways based on PPIs. Due to the condition specificity of gene expression and pathway activity, we further needed that at least one of the common genes between two pathways was differentially expressed in the two conditions, or the two genes that coded a pair of interacting proteins employed to lay an edge between two pathways were highly coexpressed . If not, the edges between two pathways were discarded. As we all know, PCC, as a common measure, was used to measure the strength of the association between two variables . In our Specifically, a single pathway that could best discriminate between disease and control was firstly extracted as the seed pathway (the first pathway biosignature), and the second pathway that could be added to the first pathway to obtain better classification performance was selected from those pathways that interacted with the first pathway in the PIN. This process was repeated to add new pathways to detect pathway biomarkers till no more pathways could be added to improve classification accuracy, and the final selected pathway sets were regarded as potential dysregulated pathways in diseases.In the selection procedure, support vector machines (SVMs) were utilized to formulate the detection of dysregulated pathways. The classification performance was evaluated using fivefold cross-validation, and Area Under The Curve (AUC) score was adopted as classification performance index. In an attempt to obtain robust results, fivefold cross-validation was repeated for 100 times and the mean value of classification accuracy was used as the final result.Centrality measures are broadly utilized for analyzing the properties of network, which cover degree, closeness, betweenness, and eigenvector centrality . Among tP < 0.05, a total of 1682 DEGs were selected. To construct the PIN, we conducted the selection for the edges between every two pathways based on the criteria of that at least one of the common genes between two pathways was differentially expressed in the two conditions, or the two genes that coded a pair of interacting proteins employed to lay an edge between two pathways were highly coexpressed . Finally, an original PIN including 96270 interactions among the pathways was constructed. After we computed the summation of the absolute values of PCC for the PPIs in every two pathways, the top 5% pathway interactions were selected to build a new PIN for the detection of dysregulated pathways. Overall, a total of 4814 interactions were extracted to construct the new PIN, as shown in The schematic diagram of detecting dysregulated pathways is shown in https://doi.org/10.1155/2017/3195348.Since there were differences for pathways in the new PIN, thereby how to assess the importance of each pathway and choose a significant one of the PIN became a challenge. An activity score was assigned to each pathway according to PCA method with the goal of evaluating its significance. The pathway of activity score with maximum changes between LM-infected samples and control conditions was selected to be seed pathway. In the current study, the seed pathway was RNA polymerase II pretranscription events (ID = 501). Taking this seed pathway as start, we conducted the identification of dysregulated pathways based on the classification accuracy increase. Overall, we obtained one pathway set (including 9 dysregulated pathways) with AUC score of 1.00. The good performance of this method demonstrated that the identified dysregulated pathways could be served as robust biomarkers. The detailed results are shown in With the goal of extracting the hub pathways in the original PIN, all nodes in the original PIN were ranked in a descending order based on the degree distribution of all nodes. The specific information of degree distribution is shown in In order to evaluate whether the PIN method was feasible or not, we compared the introduced method with traditional topological analysis. We found that 4 hub pathways of DNA replication (ID = 144), RNA polymerase II transcription (ID = 503), mRNA splicing (ID = 340), and mRNA splicing-major pathway (ID = 341) were the common pathway obtained from PIN approach and topology method. Hence, we demonstrated that this PIN method can provide a flexible tool to extract pathway biomarkers for disease. Moreover, these identified dysregulated pathways will serve to benefit novel vaccine design and improve the therapeutic strategies in the infection procedure mediated by LM.LM is the etiology of listeriosis, which causes a severe human infection with 30% mortality . AdaptivIn the current study, to obtain information towards identifying LM transcripts that correlate with infection of human monocytes, we used PCA method to calculate the activity value for each pathway, and the pathway of RNA polymerase II pretranscription events was selected as seed pathway. Eventually, we extracted 1 pathway set with AUC of 1.00 (9 dysregulated pathways), such as RNA polymerase II pretranscription events, DNA replication, and RNA polymerase II transcription. Among these 9 dysregulated pathways, the pathway of DNA replication and RNA polymerase II transcription had a higher number of pathway genes and DEGs. Moreover, the classification performance demonstrated the availability of this method to select dysregulated pathways in LM-infected samples and indicated that these dysregulated pathways were useful to be as biomarkers to diagnose disease.Pathogen recognition and inducing immune reactions are crucial for efficiently counteracting infection. However, pathogen LM can use some strategies to avoid or modulate the immune detection. Several researches have demonstrated that LM manipulates the expression of host gene via modifying histones of immune genes which are activated by innate receptors during infection , 31. MorIn our study, the pathway of RNA polymerase II pretranscription events was regarded as the seed pathway. Moreover, RNA polymerase II transcription had the most number of DEGs with 24 in the pathway set. Type I interferons (IFNs) secreted by infected cells influence the development of innate and adaptive immune responses . IFNs ex Staphylococcus aureus and Streptococcus pneumoniae infections. We did not compare the “pathway dysfunction” upon challenge with these other pathogens, and we would compare the specificity of the results obtained in this study in the future. Additionally, our analysis was implemented based on existing data using bioinformatics method; yet the findings have not been proved by animal experiments or patient tissue; this was the main weak point of this study. Therefore, further investigations are needed to uncover the changes of these pathways in the understanding of the infection procedure mediated by LM based on the animal experiments and patient tissues. Despite these limitations, this study provided some preliminary evidence to uncover alterative candidate therapeutic strategies for LM infection. Our analysis implies that this 1 pathway set (9 dysregulated pathways) worked together to influence the progression of LM infection, and making use of specific blockage-related pathways in LM infection will shed new insights for therapeutic and preventive methods in clinic.Taken together, we successfully detected 1 pathway set with AUC of 1.00 (9 dysregulated pathways) in LM-infected human monocytes on the basis of the PIN which presented the functional dependency between pathways. The dysregulated pathways had cross-talks with each other. The functional relationship between pathways shed light on the molecular mechanisms of LM infection. Our data indicate that this created method is helpful to predict new biosignatures and even drug targets in robust way. Significantly, our identified dysregulated pathways will be available to be as biomarkers to diagnose LM infection, serve to benefit novel vaccine design, and improve the therapeutic strategies. However, several limitations must be taken into consideration. To begin with, there is a very high number of gene expression profiles concerning infection of diverse human cell lines with LM, but we only used E-MEXP-1613 dataset in our study. Thus, we will use other datasets about infection of diverse human cell lines with LM to verify our findings. Moreover, in the dataset of E-MEXP-1613, there was information about other bacterial infections includingSupplemental Table 1: DEGs enriched in the final dys-regulated pathways.


Bigelowiella natans, the cryptophyte Guillardia theta, the “green” dinoflagellate Lepidodinium chlorophorum, and three dinoflagellates with diatom endosymbionts (“dinotoms”): Durinskia baltica, Glenodinium foliaceum and Kryptoperidinium foliaceum. Bigelowiella natans appears to contain two separate heme pathways analogous to those found in Euglena gracilis; one is predicted to be mitochondrial-cytosolic, while the second is predicted to be plastid-located. In the remaining algae, only plastid-type tetrapyrrole synthesis is present, with a single remnant of the mitochondrial-cytosolic pathway, a ferrochelatase of G. theta putatively located in the mitochondrion. The green dinoflagellate contains a single pathway composed of mostly rhodophyte-origin enzymes, and the dinotoms hold two heme pathways of apparently plastidal origin. We suggest that heme pathway enzymes in B. natans and L. chlorophorum share a predominantly rhodophytic origin. This implies the ancient presence of a rhodophyte-derived plastid in the chlorarachniophyte alga, analogous to the green dinoflagellate, or an exceptionally massive horizontal gene transfer.Tetrapyrroles such as chlorophyll and heme are indispensable for life because they are involved in energy fixation and consumption, i.e. photosynthesis and oxidative phosphorylation. In eukaryotes, the tetrapyrrole biosynthetic pathway is shaped by past endosymbioses. We investigated the origins and predicted locations of the enzymes of the heme pathway in the chlorarachniophyte  While there are only two documented primary plastid acquisitions ,2, involsymbiont –14. Howesymbiont ,15–18, ssymbiont . For inssymbiont ,19–23. Trophorum ,25; whild diatom ,27. Serid diatom –33 and td diatom ,35. In cd diatom ,36,37.The process of endosymbiosis involves endosymbiont genome reduction via gene transfer to the host nucleus ,39, alloPhytomonas serpens has been shown to be able to live in the absence of heme, it is an extremely rare metabolic deviation [Euglena gracilis two independent tetrapyrrole biosynthesis pathways are present [E. gracilis, the two tetrapyrrole pathways do not overlap, and thus produce tetrapyrroles separately for the cytosol and mitochondrion and for the plastid [Perkinsus marinus [Chromera velia, on the other hand, employs a hybrid tetrapyrrole pathway. Synthesis is initiated in the mitochondrion via the C4 pathway and is predicted to continue in the plastid. This hybrid synthesis qualifies Chromera as the only known phototroph able to synthesize tetrapyrroles from glycine [One of the essential biochemical pathways carried out in plastids is tetrapyrrole synthesis. Tetrapyrroles are cyclic porphyrins coordinated by iron (heme) or magnesium (chlorophyll). They are essential for life, since heme is a substantial component of the respiratory chain and chlorophyll is an indispensable compound in the conversion of light energy to chemical bonds in carbohydrates through photosynthesis. Although the kinetoplastid flagellate eviation . In phot present –55, like present ,37. Thes present . The oth plastid ,54. The  plastid  and it i marinus , for exa marinus ). The al glycine , similar glycine .Guillardia theta, the chlorarachniophyte Bigelowiella natans, the green dinoflagellate Lepidodinium chlorophorum and the dinotoms Durinskia baltica, Glenodinium foliaceum and Kryptoperidinium foliaceum.The organization of heme synthesis is currently uncharacterized in most algae with complex plastids. In order to map the level of pathway conservation, reduction or replacement in further phototrophic lineages, we investigated phylogenetic relationships and predicted the cellular locations of enzymes involved in tetrapyrrole biosynthesis in the cryptophyte B. natans, we identified thirteen and nine sequences, respectively, of enzymes belonging to the algal endosymbiont heme pathway (autotrophic pathway) and to the heterotrophic  pathway. The chlorarachniophyte host pathway is typical for eukaryotic heterotrophs, with ALA being synthesized by the mitochondrial C4 pathway; the enzymes involved are predicted to localize to the mitochondrion  and cytosol  (see E. gracilis and oomycetes (non-photosynthetic stramenopiles). This clade, composed of animal, fungal, (phototrophic) excavate, heterotrophic stramenopile and chlorarachniophyte sequences, very likely represents the only PBGD enzymes originating in the eukaryotic nucleus because all phototrophic eukaryotes utilize PBGD of α-proteobacterial origin (In  -3) see ; a predil origin  [50,56,5B. natans protein is dual-targeted to the cytosol and the plastid.In spite of the presence of two tetrapyrrole pathways in the chlorarachniophyte, only a single gene coding for a putatively plastid-targeted uroporphyrinogen-III synthase (UROS) was found in the genome database. The enzyme possesses a bipartite targeting sequence at the N-terminus, necessary for delivering the protein into the secondary plastid . NotablyB. natans, with the exception of UROD. All six genes coding for UROD display eukaryotic origin in B. natans; however, four of the UROD sequences  are obvious multiple paralogs likely originating from the secondary host nucleus, while two paralogs originate from the endosymbiont  nucleus (BnUROD3 and -6)  . Accordi and -6) .B. natans; the former appeared within the clade composed of red-derived secondary algae, and the latter has a nuclear origin with an unsupported sister position to ciliate sequences , the only representatives of the SAR group in this particular clade  predicted to function within the heterotrophic pathway in equences . A eukarlytomies , with B.ar clade .B. natans, similar to that in other eukaryotic phototrophs. It is mostly composed of cyanobacterial-derived enzymes , but also of enzymes likely originating from the endosymbiont (primary host) nucleus , one enzyme displaying an α-proteobacterial  origin (BnPBGD1), and an additional CPOX (BnCPOX4) of uncertain origin  of UROD and CPOX. In summary, the level of metabolic reduction is comparable in Bigelowiella and Euglena, which suggests that they acquired their green plastids at approximately the same time (assuming similar rates of evolution) or a constraint imposed on cellular metabolism that prevents a loss of redundancy in chlorarachniophytes.ic group  and the ic group . In spitic group , they diic group . Both alartments . Similarmediates . FurtherGuillardia theta , only the set of enzymes originating from the algal endosymbiont and putatively targeted to the plastid compartment was found in ia theta . We founia theta ; both muia theta –4 and S1ia theta . This isSH group  may be tCyanophora paradoxa  taminant . The oriOxyrrhis marina, numerous cyanobacteria, a glaucophyte, the rhizarian Paulinella chromatophora, chlorophytes, plants, heterokonts, eustigmatophytes, a haptophyte, Euglena gracilis and chromerids. The other clade is sister to apicomplexans, chromerids, and E. gracilis, and contains rhodophytes, peridinin-pigmented dinoflagellates, and a heterotrophic dinoflagellate (Cryptheconidium cohni). Its origin is unclear but might be proteobacterial , not included in previously published analyses . Ferrochacterial . The treGlenodinium orthologs of KfUROD1, KfUROD4 and the KfCPOX3+4+5 cluster and the Kryptoperidinium ortholog of the GfCPOX3+4 cluster , and proteobacteria. Together these factors make the evolution of heme pathway enzymes difficult to follow, particularly in eukaryotic phototrophs. However, the phylogenetic placement of ene loss , similarene loss  and L. cont Figs , S1 and Therefore, it appears that most of the enzymes considered here originate from a rhodophyte source, in spite of the dinoflagellate’s chlorophyte-derived plastid. The chlorophyte-derived plastid is thought to have replaced the original peridinin pigmented dinoflagellate plastid through serial secondary endosymbiosis in this species . RegardlL. chlorophorum likely represent a set from the previously acquired peridinin plastid, but the “purchased” old genes were put into a newer, better shopping bag. The presence of red-derived heme pathway enzymes in B. natans plastids could be a result of non-endosymbiotic HGT from (red) algal prey according to the “you are what you eat” hypothesis [B. natans, including photosynthesis-related genes, has already been reported [B. natans sequences often cluster with the CASH taxa. If these genes were horizontally transferred from one of the CASH lineages, they already coded for some plastid targeting signal as heme synthesis is presumably plastid-located in these lineages. Bigelowiella natans does not use the same protein import complex (SELMA) as algae from the CASH taxa [Lotharella amoeboformis and B. natans [L. chlorophorum, we can speculate that the green dinoflagellate “heritage” scenario also applies to B. natans. The rhodophyte origin of the tetrapyrrole pathway in B. natans may therefore reflect the previous presence of a hypothetical red-derived plastid in the ancestor of chlorarachniophytes , present in all investigated CASH taxa, is strong molecular evidence for the monophyletic origin of the CASH plastid [In order to determine how endosymbiotic events occurred, a robust reconstruction of the gene repertoire of photosynthetic algae is needed. Not all genes diverged at the same time and gene multiplication and lineage-specific losses may hinder phylogenetic signal resolution. Indeed, genes with conserved or ancient evolutionary histories display different topologies than those acquired more recently via HGT  or those plastid ,6,14. Du plastid . ConversG. theta, the pathway is located almost entirely in the plastid, with the exception of a eukaryotic ferrochelatase apparently localized to the mitochondrion, indicating either a slow evolutionary rate or an evolutionary constraint. Furthermore, we observed that the majority of the pathway is evolutionarily conserved and related to the red lineage even in organisms that currently possess a plastid of green algal provenance, i.e. the dinoflagellate Lepidodinium chlorophorum and the chlorarachniophyte B. natans. Hence, if the protein targeting machinery is compatible with the new plastid compartment, the tetrapyrrole synthesis pathway can be relocated “as is”, which is illustrated in the case of L. chlorophorum. Intriguingly, such a scenario may imply the existence of a cryptic red-derived plastid earlier in the history of chlorarachniophytes. While the evolution of eukaryotes is becoming clearer with increasing data from deeper lineages, the history of plastid acquisitions resists revealing an unequivocal scenario due to massive gene transfer and phylogenetic bias. We suggest that a targeted approach directed at conserved processes could result in new, relevant hypotheses even in the genomic era.The tetrapyrrole biosynthesis pathway in phototrophic eukaryotes is an evolutionary mosaic originating in proteobacteria, cyanobacteria and eukaryotes. It represents a shopping bag of enzymes collected during the history of plastid endosymbiosis retained, due to its essential role in metabolism, even after photosynthetic capabilities have been lost. Here we confirm that the tertiary plastids of dinotoms represent largely independent compartments with tetrapyrrole biosynthesis occurring parallel to biosynthesis in the peridinin plastid. The enzymes putatively localized to the former plastid branch sister to dinoflagellate enzymes, while the tertiary plastid contains enzymes branching sister to those of diatoms, mirroring the origin of the respective organelles. In Guillardia theta (http://genome.jgi.doe.gov/Guith1/Guith1.home.html) and the chlorarachniophyte Bigelowiella natans (http://genome.jgi.doe.gov/Bigna1/Bigna1.home.html) were searched using BLAST [et al. [The complete genomic sequences of the cryptophyte alga ng BLAST  for gene [et al. ; newly a [et al.  and ambi [et al. . Phyloge [et al. ), Bayesi [et al. ) and a m [et al. ). ML treG. theta and B. natans were inspected for the presence of N-terminal leader sequences by SignalP 3.0 [Karenia brevis  was generated using the NEBNext Ultra Directional RNA Library kit  according to the manufacturer`s instructions. Quality assessment and sequencing were performed in a specialized facility, using the Illumina MiSeq (2×250 bp) platform. The generated reads were quality-trimmed using the FASTQ Toolkit (v1.0) of the Illumina BaseSpace platform and then assembled using Trinity v2.1.1 [The transcriptome library of y v2.1.1  and SOAPy v2.1.1  and clusy v2.1.1 . Gene asy v2.1.1  on proteS1 FigLepidodinium chlorophorum, ochre for Bigelowiella natans and red for Guillardia theta. The tree demonstrates the mitochondrial origin of ALAS. Numbers near branches indicate Bayesian posterior probabilities followed by the bootstrap of respective clades from the likelihood analysis. Only support values greater than 0.85 (Bayesian) and 50 (likelihood) are shown. dt—different topology in the likelihood tree, see A) ALAS, B) GTR, C) PBGD, D) UROS, E) UROD, F) CPOX and G) PPOX. Taxa of interest of this study are highlighted by colored bars: blue for dinotoms, green for (PDF)Click here for additional data file.S2 FigNumbers near branches indicate bootstrap values; only support values greater than 50 are shown. A, ALAS—delta-aminolevulinic acid synthase; B, GTR—glutamate-tRNA reductase; C, GSA—glutamate-1-semialdehyde aminotransferase; D, ALAD—aminolevulinic acid dehydratase; E, PBGD—porphobilinogen deaminase; F, UROS—uroporphyrinogen synthase; G, UROD—uroporphyrinogen decarboxylase; H, CPOX—coproporphyrinogen oxidase; I, PPOX—protoporphyrinogen oxidase; J, FeCH—ferrochelatase. LcPPOXa, -b, -c; LcUROSa, -b = non-overlapping protein models, putatively fragments of LcPPOX1 and LcUROS.(PDF)Click here for additional data file.S1 TableTargeting probabilities were determined using SignalP and TargetP as described in Materials and Methods. Respective targeting peptide sequences are listed. The presence of a signal peptide followed by a chloroplast targeting peptide (cTP) implies localization to the plastid; mitochondrial enzymes encode mitochondrial targeting peptide presequences, while cytoplasmic enzymes lack presequences. If available, models with longer N-termini  were included in pre-sequence analysis and are listed in the table.(PDF)Click here for additional data file.S2 TableNumber of reads and bases of two libraries are listed after quality trimming Click here for additional data file.S3 Tableet al. 2015 Plant J, 10.1111/tpj.12734; jgi = DOE Joint Genome Institute; MMETSP = Marine Microbial Eukaryote Transcriptome Sequencing Project; Nori = NoriBLAST, Porphyra Genome Project; psb = bioinformatics.psb.ugent.be; VH = Courtesy of Vladimír Hampl, unpublished; Cmb = combined samples.Gene copy designation for species of interest in this study is shown in brackets according to their designation in respective trees (an asterisk marks putative contaminant sequences). Databases: CGP = Cyanophora Genome Project; CryptoDB = Cryptosporidium Genomic Resource; gb = GenBank; Gruber ea. = Gruber (PDF)Click here for additional data file.


In tumor samples, our analysis detected major conserved mitogenic and survival signaling pathways strongly associated with HNSCC, suggesting that some of the pathways identified by our algorithm, but not yet validated as HNSCC related, may be attractive targets for future research. While pathways activation landscape in the majority of leukoplakias was different from that seen in OSCC, a subset of pre-neoplastic lesions has demonstrated some degree of similarity to the signaling profile seen in tumors, including dysregulation of the cancer-driving pathways related to survival and apoptosis. These results suggest that dysregulation of these signaling networks may be the driving force behind the early stages of OSCC tumorigenesis. While future studies with larger leukoplakia data sets are warranted to further estimate the values of this approach for capturing signaling features that characterize relevant lesions that actually progress to cancers, our platform proposes a promising new approach for detecting cancer-promoting pathways and tailoring the right therapy to prevent tumorigenesis.A subset of patients with oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSCC), harbor dysplastic lesions  prior to cancer diagnosis. Although evidence suggest that leukoplakia represents an initial step in the progression to cancer, signaling networks driving this progression are poorly understood. Here, we applied  Despite improvements in molecular diagnosis, oral squamous cell carcinoma (OSCC), the most common subtype of HNSCC, has a poor prognosis with a 5-year overall survival of ~50%, and no targeted curative treatments for late-stage OSCC are currently available. As such, there is a significant unmet clinical need for development of novel targeted approaches against OSCC.Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer in the world, As some of these lesions evolve to malignant neoplasms, they represent intermediate steps in OSCC progression. It has been postulated, that this multistep process from normal epithelium to early pre-malignant change to fully invasive OSCC, involves the accumulation of molecular and cellular changes. Indeed, several signaling pathways have been shown to be dysregulated in OSCC through genetic and epigenetic alterations such as those involving TP53, PIK3CA, NOTCH1, SMAD4, CDKN2A, CCND1, FBXW7, HRAS, NRAS and FAT1. Although these signaling axes have been implicated in tumorigenesis, and are correlated with poor patients prognosis, relatively few molecular changes critical to the progression of oral leukoplakia to OSCC are currently recognized, and our understanding of the complex signaling networks that both promote OSCC pathogenesis and can be targeted therapeutically is still limited.Nearly 20% of patients with OSCC harbor multiple pre-malignant lesions showing signs of dysplasia, often visually identified as leukoplakia. analyses that rely solely on gene enrichment statistics fail to capture subtle differences between samples that arise from dynamic interactions between genes at the level of signaling networks. The major advantage of pathway-based methods is their capability to perform biologically relevant dimensionality reduction as a result of the analysis. However, despite the significant advancements in large-scale analytical methodologies that infer complex transcriptomic changes into the network of biologically relevant signaling axes, a systematic comprehensive analysis of the signaling pathways activation in OSCC and oral pre-neoplastic lesions has never been done.The Gene Expression Omnibus (GEO), the largest public repository for high-throughput gene expression data, provides unprecedented opportunities to reveal molecular mechanisms of cancer. Although multiple efforts taken to identify differentially expressed genes between OSCC and normal samples have led to the identification of prospective genetic biomarkers and expression signature patterns,in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for quantitative and qualitative large-scale transcriptomic data analysis. The iPANDA method combines pre-calculated gene co-expression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. It has been demonstrated that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. In this study, we applied iPANDA on the transcriptomic data from hundreds of OSCC and leukoplakia samples to identify the differentially dysregulated signaling pathways between these neoplasms and normal oral mucosa tissues. Our work further contributes to the current understanding of the complex signaling networks underlying HNSCC, and may aid in the development of novel means of prevention, diagnosis and treatment.We have recently introduced the in silico pathway activation analysis of 359 OSCC samples obtained from the publicly available data sets deposited at NCBI GEO database   , and used for the computational algorithm as described previously. Using the normal samples cohort as a reference, we determined a quantitative measure of the signaling pathway activation scores  for each one of the signaling axes analyzed. The iPANDA values represent the signed scores showing the intensity and direction of the pathway activation . A total of 248 of 374 pathways analyzed (including the 64 main pathways) were significantly dysregulated (P<0.05) in OSCC tumors when these samples were compared with non-tumorous oral mucosa  . All theata set) . A collel mucosa . Focusinl mucosa . Upregulcontrols . Our patβ expression and spontaneous HNSCC formation. Whereas activation of PI3K/AKT via loss of PTEN or copy-number gain/activating mutations in PIK3CA also occurs in 40–60% of HNSCCs, leading to increased IL-6 production, STAT3 activation and tumor-promoting inflammation in HNSCC stroma. In concordance with this evidence, PTEN and SMAD signaling, as well as pathways associated with apoptosis were significantly downregulated in the cohort of 359 OSCC tumors analyzed  in leukoplakia samples compared to healthy controls (P<0.0005) in both OSCC (red bars) and leukoplakia (blue bars) samples  . Given tcontrols . The hea samples . Unlike l cohort . Since il cohort . Notably and potentially unravel the most critical branches associated with cancer progression. A list of pathway branches predicted to be significantly dysregulated (P<0.0005) in both OSCC and leukoplakia samples is shown in Many well-established cancer-driving genes simultaneously modulate activation or repression of multifunctional signaling networks that control cell growth, differentiation and migration. Since ‘main’ pathways aggregate branches that may be inversely activated, analysis of the pathway branches (which represent linearized fragments of the main pathway networks with multiple downstream targets) may help to scale down the complexity of common non-linear topology To adjust for the possible batch effect and other variations that may arise during the microarray processing, we have employed cross-platform normalization and filtering procedures as previously described . While most of the leukoplakia samples clustered together according to their pathway activation landscape, a subset of 20 pre-neoplastic lesions has clustered toward the center of the heatmap, demonstrating some degree of similarity to the pathway activation profile seen in OSCC , indicating that predisposition for leukoplakia progression may not have significant manifestation on the functional (pathway) level. One possible explanation is that the most differentially expressed genes in a given signature may not be part of the pathways that actually drive tumor behavior. Alternatively, expression of some genes within the cancer-driving pathways is not always predictive of the overall pathway activation. While iPANDA may be a useful tool when used as input for machine learning algorithms to make better prediction models, we were unable to use modern machine learning methods, since currently available data to system dimensionality ratio precludes the development of a strong classifier at the pathway level. As comprehensive analysis of the tumor pathway activation profile may provide broader clinical utility than raw gene expression evaluation, it would be of paramount importance to reanalyze the data upon accumulation of additional leukoplakia data sets, and further estimate the values of this approach for capturing signaling features that characterize relevant lesions that actually progress to cancers.Using the transcriptomic data from the same 86 leukoplakia samples and multiple independent OSCC data sets, Mao and colleagues proposed a 29-trancript prediction model of oral cancer progression (with 8% predicting error rate), indicating that gene expression levels can be effectively used for phenotype prediction. often fail to provide stable pathway signatures of a specific phenotype. iPANDA, a recently reported method for biologically relevant dimensionality reduction in transcriptomic data, demonstrates better performance for the noise reduction test in comparison to other pathway analysis approaches, and produces highly consistent sets of biologically relevant pathway perturbation signatures on multiple transcriptomic data sets. Here, we applied this novel bioinformatics prediction algorithm on transcriptomic data from 359 OSCC and 86 oral pre-neoplastic samples.An understanding of the protein signaling dynamics during the HNSCC progression is of critical importance for the development of new therapeutic approaches and identification of novel prognostic biomarkers. Signaling pathway activation analysis is a powerful tool for extracting biologically relevant features from large-scale transcriptomic data. While systematic application of such approach may contribute to better understanding of complex signaling networks in various types of cancer, many popular pathway-based methodsβ-catenin and TGFβ/SMAD signaling) in almost every OSCC tumor analyzed. Confirming the emerging view that while the specific molecules altered in each individual tumor may be distinct, they all participate in a handful of dysregulated molecular pathways that are likely shared among most HNSCC lesions. Although the association of the above signaling pathways with HNSCC is not novel, the ability of the gene aggregation algorithm to identify highly robust sets of pathway signatures relevant to the HNSCC tumorigenesis, suggests that our platform provides a rational biomathematical framework for studying signaling alterations driving the tumorigenic processes, and further supports the credibility of the pathway activation analysis as a promising analytical approach. It also suggests that some of the most dysregulated pathway branches identified by our algorithm but not yet wet-lab validated as HNSCC related, may provide an attractive target for future research.By utilizing our approach, we were able to detect dysregulation of the major molecular processes involved in HNSCC tumorigenesis  relies solely on gene enrichment statistics, treating pathways as unstructured sets of genes. Whereas our method estimates the topological coefficients for each gene module as a whole, rather than for individual genes inside the module, and therefore outperforms ssGSEA in respect to ranking of the putatively relevant pathways (described in details here).Interestingly, while gene set enrichment (GSEA) analysis of the same microarray data set with GSEA algorithm detected that gene sets related with proteasome machinery, MYC upregulation and ribosomes components were associated with a high risk of oral cancer development,Although this study focused on fundamental signaling processes involved in the development of OSCC, branched axes radiating from these main tumor-driving pathways may provide safer and more efficient targets for therapeutic intervention than a pan-pathway blockade. Since most of the existing pathways collections are being developed and cataloged within individual research groups, both in industry and in academia, inconsistency in pathway names annotations and number of pathway components (branches) poses a major challenge associated with prediction modeling of subnetworks within a large cancer driver superpathway. Therefore, systematic standardization and optimization of existing signaling pathways databases  will eventually contribute to better resolution of the signaling landscape, and enhance the development of novel prognostic biomarkers for risk assessment and treatment interventions at various stages of cancer progression.In conclusion, our work poses pathway-based expression data analysis as a promising hypothesis-generating approach for detecting cancer-promoting pathways at different stages of tumorigenesis. However, due to the limited number of currently available leukoplakia data sets, we could not dissect the molecular pathways predictive of progression from pre-neoplasia to carcinoma. As more data sets will become available, transcriptomic analysis of longitudinally collected leukoplakias and tumors that developed in the same patient over time may allow better detection of early drivers of OSCC carcinogenesis on the pathway level.For this study, we utilized microarray gene expression data obtained from NCBI GEO database. OSCC samples and tissue-specific normal controls used as a reference are listed in www.bioconductor.org). Microarray raw data were background-adjusted and quantile-normalized using the corresponding R packages . Obtained gene expression values were averaged across all replicates. Heatmap generation and hierarchical clustering were performed using R package gplots. Statistical tests and correlation analysis were done with the MS Excel software .OSCC and leukoplakia data with corresponding normal samples were processed using AFFY (version 1.46.1) and FRMA (version 1.20.0) algorithms from Bioconductor ) contains a set of 374 signaling pathways that cover a total of 2294 unique genes. Main pathways represent complex signaling networks with multiple downstream targets. Whereas branching axes represent dissected linear signal transduction units of a large multifunctional pathway. Each pathway contains an explicitly defined topology represented as a directed graph. Each node corresponds to a gene or a set of genes, while edges describe biochemical interactions between genes in nodes and/or their products. All interactions are classified as activation or inhibition of downstream nodes. The pathway size ranges from about 20 to over 600 genes in a single pathway. The algorithm accounts for the gene grouping into modules based on the precalculated gene co-expression data (using information from COEXPRESSdb database). Each gene module represents a set of genes that experience significant coordination in their expression levels and/or are regulated by the same expression factors. As described, it enables calculation of the iPANDA pathway activation scores, a value that serves as a quantitative measure of differential pathway activation between the two states. Pathway scoring is based on the expression level and the role of a particular gene in proprietary maps of 300 of signaling pathways. For estimation of pathway activation, we apply the following rule pathways with positive iPANDA values that are considered upregulated, while negative iPANDA values correspond to downregulated pathways.Pre-processed gene expression data were loaded into pathway analysis software, a proprietary suite developed by Insilico Medicine, Inc. that represents an implementation of the iPANDA algorithm,45 .To perform cross-platform normalization between Affymetrix U133 Plus 2.0 Array and Affymetrix 1.0 ST Array, we utilized XPN algorithm


Faecalibacterium, Roseburia, Butyrivibrio, and commensal species of Clostridia etc) ferment pyruvate for butyrate production. On the contrary, the butyrogenic gut pathogen Fusobacterium utilizes different amino acid metabolism pathways like those for Glutamate (4-aminobutyrate and Glutarate) and Lysine for butyrogenesis which leads to a concomitant release of harmful by-products like ammonia in the process. The findings in this study indicate that commensals and pathogens in gut have divergently evolved to produce butyrate using distinct pathways. No such evolutionary selection was observed in oral pathogens (Porphyromonas and Filifactor) which showed presence of pyruvate as well as amino acid fermenting pathways which might be because the final product butyrate is itself known to be cytotoxic in oral diseases. This differential utilization of butyrogenic pathways in gut pathogens and commensals has an enormous ecological impact taking into consideration the immense influence of butyrate on different disorders in humans. The results of this study can potentially guide bioengineering experiments to design therapeutics/probiotics by manipulation of butyrate biosynthesis gene clusters in bacteria.Biosynthesis of butyrate by commensal bacteria plays a crucial role in maintenance of human gut health while dysbiosis in gut microbiome has been linked to several enteric disorders. Contrastingly, butyrate shows cytotoxic effects in patients with oral diseases like periodontal infections and oral cancer. In addition to these host associations, few syntrophic bacteria couple butyrate degradation with sulfate reduction and methane production. Thus, it becomes imperative to understand the distribution of butyrate metabolism pathways and delineate differences in substrate utilization between pathogens and commensals. The bacteria utilize four pathways for butyrate production with different initial substrates  which follow a polyphyletic distribution. A comprehensive mining of complete/draft bacterial genomes indicated conserved juxtaposed genomic arrangement in all these pathways. This gene context information was utilized for an accurate annotation of butyrate production pathways in bacterial genomes. Interestingly, our analysis showed that inspite of a beneficial impact of butyrate in gut, not only commensals, but a few gut pathogens also possess butyrogenic pathways. The results further illustrated that all the gut commensal bacteria ( Humans harbor a plethora of micro-organisms comprising of around 1000 species inhabiting different body sites. Some of them are beneficial bacteria which help not only in metabolism and absorption of nutrients by the human host but also in regulation of our immune system . These bFaecalibacterium, Coprococcus, and Roseburia in the guts of healthy individuals . These pathways utilize one of the four substrates namely, pyruvate, glutarate, 4-aminobutyrate and lysine. Each of these four pathways use butyryl-CoA dehydrogenase electron-transferring flavoprotein complex (Bcd-Etfαβ) to catalyze conversion of crotonyl-CoA to butyryl CoA . It should also be noted that Glutamate is used by anaerobic bacteria for production of the substrates 4-aminobutyrate and 2-oxoglutarate while these substrates can be produced as intermediates of citric acid cycle in aerobic bacteria. Further, arginine catabolism can also be used in certain bacteria to biosynthesize 4-aminobutyrate.Four major butyrate production pathways exist in bacteria  as markers for predicting butyrate production capability by bacteria  based genomic analysis was performed to understand distribution of butyrate production pathways in commensals and pathogens inhabiting different environments. Firstly, the genomic arrangement of these pathways was confirmed and this context information was utilized for identifying domains within the cognate butyrate production pathway in 8027 bacterial genomes. The utilization of juxtaposed genome organization of domains comprising a pathway enabled elimination of homologous domains which might have different functions in other metabolic pathways also. This study, to the best knowledge, is the first to shed light on the distribution of butyrate production pathways in pathogens versus commensals across different environmental conditions like gut and oral cavity in humans. Further, an attempt was made to understand similarities/differences in butyrate production capabilities as well as pathways utilized for biosynthesis by gut pathogens and commensals. In order to further verify our findings, publicly available 16S rRNA amplicon datasets, corresponding to gut samples from 443 healthy and 567 diseased individuals, were analyzed. The results, based on the gene context information  in combination with the taxonomic composition, provided valuable insights into the differences in evolution of butyrate production pathways in commensals and pathogens in gut. The genome-wide study further gave insights into the distribution of butyrate production pathways in bacteria inhabiting the oral cavity in periodontitis patients as the butyrogenic pathways differed in strains forming the oral microbiome of these individuals.Figure 1) were studied. A preliminary analysis using HMM based approach  was performed on known butyrate producers  to confirm the evolutionary conservation of clustered gene arrangement for all four butyrogenic pathways. The identified juxtaposed arrangement of genes  constituting butyrate production pathways is in agreement with previously reported studies . Interestingly, while this gene was found to occur in context with genes constituting the pyruvate pathway, it was found to function in trans in other three pathways. The Bcd protein is known to be composed of three subunits, namely, α-subunit which acts as butyryl-CoA dehydrogenase (Bcd) and the β- and γ-subunits which function as electron-transferring flavoproteins (ETF)  enzyme is known to be a common component utilized by all butyrate producers (ns (ETF) . While ans (ETF)  and S2. dontitis . Apart f studies . Many sueduction . Furthereduction . Thus, tFigure 2) ensured accurate annotation of all butyrogenic pathways. The predicted butyrate producers , indicated higher presence of  studies . Other gcrobiota .Streptococcus, Anaerococcus, Veillonella, Escherichia, Rothia, Campylobacter, Leptotrichia, and Fusobacterium  and IBD (101 samples). Also, although butyrogenic genus Megasphaera was not observed in the guts of the subjects considered in this study, it has been reported to have higher abundance in many diseased conditions .The genome mining for four different butyrate production pathways was performed on all sequenced bacterial genomes (as described earlier). On mapping the genera showing differential abundance in healthy versus diseased datasets to the catalog of butyrate production pathways within each genome , all other strains of Lachnospiraceae found in gut belong to a different clade. This was also in agreement with previous study indicating lack of butyrate pathways in many strains of Lachnospiracea inhabiting the gut , Faecalibacterium , Butyrivibrio (Butyrivibrio fibrisolvens), Odoribacter (Odoribacter splanchnicus), which were observed as commensals in healthy gut by earlier studies . In addition, Figure 3A shows that known gut commensals like Odoribacter and Oscillibacter  also show the presence of pyruvate pathway.Pyruvate is known to be biosynthesized by the glucose-metabolizing glycolytic pathway in all bacteria. However, the results of the present analyses suggest that only butyrogenic commensals flux some of this pyruvate to bring about butyrate production. Also, it was observed that gut commensals like Enterococcus, Streptococcus, Escherichia, Campylobacter and Leptotrichia. On the other hand, the three remaining pathways  were found in only 0.2% of differentially abundant genera present in healthy subjects. Interestingly, while the 4Ab pathway was seen in Fusobacteria and Megasphaera, glutarate and lysine pathways were identified only in Fusobacteria. Although a few strains of Fusobacterium nucleatum have been reported as oral pathogens, a number of invasive strains have been associated with gastrointestinal diseases also  lack Pyruvate pathway for butyrate production. In addition studies have indicated Fusobacterium varium to be present in the gut of diseased individuals . Similarly, Megasphaera elsdenii, another butyrogenic organism, was found to utilize only 4Ab pathway. The abundance of this genus has been reported to increase in case of diseased conditions like obesity, hepatic encephalopathy and bacterial vaginosis  were present in diseased datasets; their abundances were less than half as compared to those in healthy datasets. Another observation from this analysis emphasizes that although 4Ab, Lysine and Glutarate pathway might be present in commensal bacteria, they were found to always occur in addition to Pyruvate pathway. Therefore, specific presence of pyruvate pathway in commensals emphasizes its importance in differentiating commensals from pathogens. Figure 3B shows clustering for human microbiome associated butyrogenic bacteria on the basis of pathway distribution. The results showed that commensal bacteria formed a separate cluster distinct from pathogenic ones. Cluster 1 constituted gut commensals (for which genome sequences are available) which possessed 100% pyruvate pathway. Interestingly, the oral pathogens Filifactor and Porphyromonas also clustered with these gut commensals. On the other hand, Cluster 2 comprised of Megasphaera and was observed to have 100% of its strains utilizing 4Ab pathway for butyrogenesis. While Lysine and Glutarate pathways for butyrate production were observed to occur in 100% of pathogenic Fusobacterium strains in Clusters 3 and 4, only 11% of Fusobacterium strains of Clusters 3 and 4 were found to possess 4Ab pathway. Cluster 5 contained 100% of sequenced gut commensal Flavonifractor and oral pathogen Porphyromonas strains possessing 4Ab, Lysine and Pyruvate pathways. The presence of oral pathogens in both Clusters 1 and 5 indicated no specific preference for pathway utilization in these genera. It should be pointed out here that these percentages are limited to the sequenced strains of these bacteria in NCBI.Contrary to the commensal bacteria, butyrate biosynthesis pathways were not identified in majority of pathogenic genera that were highly abundant in the gut of diseased cohorts. These genera (described earlier) include all strains of ses also . All theividuals . Based oaginosis . Thus, tClostridia were found to be the exception to the above mentioned observation regarding lack of pyruvate pathway for butyrate production in pathogenic genera. These species were found to possess all four genes of this pathway  in majority of Fusobacteria strains. The only exception was found to be Fusobacterium_nucleatum_ATCC_25586, a strain known to be a common pathogen in periodontal infections  and 2-oxoglutarate as starting substrates respectively. Since anaerobic bacteria like Fusobacteria lack citrate acid cycle which produces these substrates as intermediates, they are likely to utilize Glutamate to form 2-oxoglutarate and 4-aminobutyrate. Thus, the production of 2-oxoglutarate can only is brought about by dehydrogenation of Glutamate by Glutamate dehydrogenase in Fusobacteria, with release of ammonia in the process. Earlier studies have shown that metabolism of amino acids in the gut might lead to an increase in pathogenic bacteria like Escherichia, Enterococcus etc.  are prevalent in gut pathogens and lead to ammonia production that is harmful for gut health.Apart from the above mentioned three pathways, previous studies have also reported involvement of methylaspartate pathway in butyrate production in m varium , a pathom varium , involvebacteria . FurtherFigure 4A. Butyrate also stimulates plasma cells to secrete serum IgA which limits the proliferation of pathogenic bacteria in gut lumen. The pathogenic bacteria which possess butyrogenic pathways lead to a concomitant ammonia production which increases inflammation in gut as explained above . Thus, this study indicates that pathogens retain butyrogenic pathways which bring about release of harmful ammonia which might cause damage to gut integrity leading to higher probability of pathogen invasion.The immunomodulation effects of butyrate toward anti inflammatory response have been studied. It stimulates regulatory T cells (Treg) to increase the production of IL-10 which is an anti-inflammatory cytokine as depicted in Fusobacterium nucleatum (discussed earlier in this manuscript) which primarily utilizes amino acid metabolism for butyrate production , was the only pathogenic genus observed in gut to possess butyrate producing capability. Several strains of Fusobacterium  utilize 4Ab, Glutarate and Lysine pathways for butyrate production . It is interesting to note that this pathogen lacks Citrate or Glyoxylate pathways whose intermediates  act as substrates for the first two pathways (4Ab and Glutarate). Similarly, this pathogen also lacks lysine biosynthesis genes for producing substrate for the third pathway (Lysine). Thus, this pathogenic bacteria probably utilizes either its amino acid metabolic pathways to produce the substrates or obtain them from the host by different transporters. The utilization of amino acid metabolizing pathways ultimately leads to formation of ammonia, a product known to be harmful for gut cells. On the other hand, obtaining essential amino acids (like Lysine) from the host is expected to create a competitive environment in the gut. Thus, the study emphasizes that it is not only important to know the butyrate producing capabilities of gut bacteria, but also the biosynthetic pathways used by them as well as the by-products formed in the process. In addition to gut pathogens, oral pathogens like Porphyromonas, Filifactor, and Tannerella also show presence of butyrate production pathways. These bacteria have evolved to utilize Pyruvate as well as amino acid fermentation for butyrate production. As explained earlier, the fact that butyrate itself is pathogenic in oral environment probably does not lead to an evolutionary selection of specific butyrogenic pathways for these pathogens. This observation is contrary to what is observed in case of gut pathogens as they have acquired harmful ammonia releasing amino acid metabolizing pathways for butyrate production.The present study indicates not only increased abundance of butyrogenic commensal bacteria in healthy cohorts, but also reveals that majority of them utilize pyruvate as initial substrate for butyrate production. On the contrary, most of the pathogenic bacteria do not possess butyrate production pathways. Interestingly, The present study thus illustrates that commensals and pathogens might have evolved to harbor different pathways for biosynthesizing the same product. In addition, the different roles of a metabolite in specific body sites within a host might also influence distribution and evolution of butyrogenic pathways in bacteria. The findings from the current analyses can potentially be applied in future for manipulating butyrate production in order to improve enteric as well as oral health.Figure 2) were obtained from the PFAM database  within a pathway were not only present, but occurred consecutively (within five genes from each other) on the genomes. In addition, the order of occurrence of these domains was noted for each genome.Firstly, it was important to confirm that butyrate biosynthesizing genes occur in context with each other on the genome in a conserved arrangement. A HMM based analysis  was perfdatabase . The gendatabase  with an 1. The HMM protocol described above was applied to all genomes and a table cataloging the genes in each of the butyrate pathways present in these genomes was obtained  corresponding to bacterial genomes  were downloaded from the NCBI2.In order to identify butyrogenic bacteria in human gut, publicly available datasets containing PCR amplicons for 16S rRNA corresponding to each of the following studies were downloaded from NCBI-SRA•http://www.mothur.org/MicrobiomeBiomarkerCRC).Gut microbiome of 30 healthy individuals and 30 Colorectal Cancer patients from Toronto (Canada), Boston (USA), Houston (USA), Ann Arbor (USA)       administration of a Chinese decoction .•Gut microbiome of 50 obese and 50 non-obese individuals from Amish population (NCBI SRA ID SRX021087).•Oral microbiome of 10 healthy individuals and 10 subgingival periodontitis patients from Chinese population    executedt-test was performed with 1000 iterations of bootstrapping. In other words, 80% of the samples were randomly selected in each iteration from healthy as well as diseased datasets and a t-test was carried out on these samples. The genera that appeared as significantly different  in more than 50% of the iterations (500) were selected. The differential genera so obtained were analyzed further to see the differences in distribution of butyrogenic pathways between commensals and pathogens.In order to identify differentially abundant taxa in healthy and diseased cohorts, the obtained normalized taxa abundances in each of the datasets were subjected to multivariate data analysis. To remove sample outliers , SA, HK, and SSM conceived and designed the experiments; SA and HK conducted the experiments; SA, HK, and SM analyzed the data; SA, HK, and SM wrote the manuscript; All authors discussed the results, commented on the manuscript and approved the final version. SA and HK have equally contributed to the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way. Cytoscape1 is the most popular biological network visualization and analysis platform, widely used in the research community to perform pathway and network analysis and visualization. The release of the CyREST app2 enables Cytoscape as an integrative and indisposable tool to build automatic software pipeline and workflow in programming languages widely used by the bioinformatics and computational biology community, includingPython andR, via a RESTful API. The standalone Java-based Cytoscape application thereby functions as a microservice servlet exposing the major features of Cytoscape.Pathway- and network-based computational approaches are now routinely used in large-scale data analysis to uncover hidden patterns that are otherwise impossible to discover. These approaches project significant genes, proteins, metabolites, and other kinds of biological entities collected from other approaches onto the context of pathways and networks, knowledge produced by many years’ experimental studies.Reactome3 is the most comprehensive open source biological pathway knowledgebase, widely used in the research community, with its web site accessed by roughly 60,000 unique IP addresses per month. To perform genome-scale network-based data analysis and visualization, we have also constructed a highly reliable Reactome functional interaction (FI) network by extracting FIs from manually curated pathways from Reactome and other popular large-scale pathway databases and predicting FIs based on a machine learning approach4. Based on this FI network and the high quality Reactome pathways, we have developed a Cytoscape app, called “ReactomeFIViz”5, which is one of most popular Cytoscape apps, downloaded over 30,000 times since it was released in September, 2013 into Cytoscape app store.ReactomeFIViz wiki page).ReactomeFIViz provides a suite of features to help users to perform pathway- and network-based data analysis and visualization for cancer and other complicated diseases. Users can construct a FI subnetwork based on a set of genes, perform network clustering, annotate found network modules using Reactome pathways and Gene Ontology terms, and perform survival analysis if clinical data is available to search for gene signatures related to patient overall survival. Users can also explore Reactome pathways directly inside Cytoscape, perform pathway enrichment analysis for a set of genes, and conduct pathway modeling using multiple types of omics data based on factor graphs converted automatically from Reactome pathways. Recently we added new features for users to visualize FDA approved cancer drugs and their targets interactions in the context of Reactome pathways and the FI network, and perform fuzzy logic based modeling to study the effects of drug application on the pathway activities , somatic mutations, and mRNA gene expression. To demonstrate use of the ReactomeFIViz CyREST API, we perform a network- and pathway-based comparison analysis between genes having somatic mutations in TCGA ovarian cancer and breast cancer. Our analysis is focused on showing the utility of our API. Therefore, we use all TCGA breast cancer samples without subtyping to simplify our workflow.Previous studyBroad firehose web site in the mutation annotation file (MAF) format using its RESTful API, and stored in two local files, one for each cancer type. The MAF file was then loaded into Cytoscape via the CyREST API, buildFISubNetwork, to construct a FI sub-network after choosing a sample cutoff to select genes forming a network composed of about 500 genes. The FI-network was then subject to network clustering analysis using the cluster call. Two sets of network modules from network clustering were compared to find modules shared and not shared between these two cancers in the Python notebook. Pathway enrichment analysis was performed using ReactomePathwayEnrichment to collect pathways not shared between them. These results suggest common and cancer-specific network and pathway patterns, facilitating researchers to understand shared and specific oncogenesis mechanisms in these two cancers. Finally, pathway diagrams were exported via exportPathwayDiagram.We performed a network comparison study for the two FI subnetworks constructed separately for TCGA BRCA and OV mutated genes. Network clustering yielded 23 and 38 modules with the TCGA BRCA and OV FI subnetwork, respectively. Overlapping analysis for modules having no less than 3 genes showed 16 out of 18 BRCA modules significantly overlapped with 15 out of 22 OV modules (FDR < 0.05. Results not shown here. See the output in the notebook for details), implying that almost all of BRCA modules can be found in the OV subnetwork and some of BRCA modules are shared with more than one OV module. For example, BRCA Module 1 is significantly overlapped with OV Modules 2 and 3.DAG and IP3 signaling,Signaling by GPCR, andGlucagon signaling in metabolic regulation. There are 7 modules in the OV subnetwork that are not significantly shared with the BRCA subnetwork. Pathway enrichment analysis showed that genes in these modules are significantly involved inIon channel transport,O-linked glycosylation,C-type lectin receptors, and several others. For detailed results, see the output from the notebook.For the pathway comparison study, we focused on pathways enriched for network modules not shared between BRCA and OV, searching for possible different oncogenic mechanisms for these two cancers. The module overlapping analysis showed that BRCA modules 13 and 17, which contains 6 and 4 genes, respectively, are not significantly shared with any OV module using FDR cutoff = 0.05, though detailed analysis showed that Module 13 has one gene, ADCY9, shared with OV Module 1, and Module 17 has another gene, FLG, shared with OV Module 7. Pathway annotation for these two modules revealed that genes in there are significantly enriched for some signaling pathways, includingTo visualize mutated genes in the context of Reactome pathways, the notebook also generated two pathway diagrams, one for each cancer, and saved into the working directory as PDF files. Entities in pathway diagrams composed of mutated genes are highlighted in purple.Reactome provides a large set of high-quality manually curated pathways. The Reactome FI network provides a genome-scale highly reliable functional interaction network covering over 60% of total human genes. The ReactomeFIViz CyREST API delivers language neutral REST-based functions for third-party software developers to leverage high-valued resources provided by the Reactome project in their own software tools.The current set of functions implemented in this version of ReactomeFIViz CyREST API focuses on some major features implemented in ReactomeFIVz, related to FI network construction, clustering, and Reactome pathway enrichment analysis. As shown in the above use case, it is very easy to integrate with other CyREST APIs and integrated into a Python or R programming language environment to perform Reactome-related pathway and network analysis and visualization.We will expose other ReactomeFIViz features in the CyREST API, including gene expression data analysis, network module-based survival analysis, pathway modeling based on Boolean network and probabilistic graph model, and cancer drug visualization and simulation. We will also develop a Python package for easy third-party tool integration.https://reactome.org/tools/reactome-fivizHome page for user guide:http://apps.cytoscape.org/apps/reactomefipluginCytoscape app store:https://github.com/reactome-fi/CytoscapePlugIn/tree/path-xLatest source code:https://github.com/reactome-fi/workflowsUse case Jupyter notebook:https://github.com/reactome-fi/CytoscapePlugIn/releases/tag/f1000_auto_paper for ReactomeFIViz,https://github.com/reactome-fi/workflows/releases/tag/f1000_auto_paper for the Python use case notebookSource code at the time of publication:http://doi.org/10.5281/zenodo.12264337 for ReactomeFIViz,http://doi.org/10.5281/zenodo.12264308 for the Python use case notebookArchived source code at the time of publication:http://www.reactome.org/?page_id=362).License: The Creative Commons Attribution 3.0 Unported License (The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2018 Loney F and Wu GData associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).http://doi.org/10.5281/zenodo.12264308Output from the network comparison are available in the Python use case notebook I want to thank the authors for their changes in response to my review.  I believe the article now reads well and users will now be able to follow the specific steps and API calls in the workflow.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I don't have any new comments.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this article, the authors have described their extensions to the popular ReactomeFIViz Cytoscape App to support automation via CyREST. They do an excellent job providing the motivation for their extension. The manuscript was clear and well-written, and I believe that the new REST API's will be quite useful. I do have a problem with the manuscript, however, in that in the Workflow section, they state: "The MAF was then loaded into Cytoscape via the CyREST API to construct a FI sub-network after choosing a sample cutoff to select genes forming a network composed of about 500 genes. The FI-network was then subject to network clustering analysis. Two sets of network modules from network clustering were compared to find modules shared and not shared between these two cancers. Pathway enrichment analysis was performed to collect pathways not shared between them." My problem with this is that they don't provide us with enough detail on the actual CyREST calls and parameters they used to allow us to understand how to reproduce it. Which part of their analysis was done in Python vs. which was done in Cytoscape through the GUI. It is true that all of my answers exist in the Jupyter notebook they provided, but that's a lot of work to figure out that they used the "buildFISubNetwork" method with the "MAF" format the fiVersion "2016".  My suggestion is to add at least enough information either in the figure or the text to help others understand the REST flow for their App without referring to the notebook.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Dear Dr. Morris, Thanks a lot for reviewing our paper and your comment. In version 2 of our paper, we have added a new table listing detailed information about actions performed in the use case workflow (Table 2), and added new words in the main text (the second paragraph in the Workflow section) to help readers understand how to reproduce the workflow without going to the Jupyter notebook. We hope our revisions have addressed your concern. Best regards, Guanming Wu, Ph.D. The authors describe the development of CyREST API for ReactomeFIViz, a Cytoscape app, which exposes some Reactome functions as REST APIs for external software components, to process pathway and network data in automatic and reproducible workflows built using almost any programming languages. The source code provided for ReactomeFiViz and for the Python use case notebook, make the use of CyREST API easier and simpler to understand. The manuscript is well-organized and the described app will be highly valuable for users working with big data related to enrichment analysis and visualization of networks.In the Introduction section (pag.2): the sentence “These approaches project significant genes, proteins, metabolites, and other kinds of biological entities collected from pre-analysis onto pathways and networks, knowledge produced by many years’ experimental studies.”, is not quite understandable, I think it should be rephrased.The Methods section (pag.2) is clearly explained and sufficient details are provided to allow replication of the method development and its use by others.The section Operation (pag.2) I think is unnecessary, the authors can add it as the subsection of Methods because here they are explaining the methods available in the CyREST API developed.In the Analysis results subsection (pag.3): I think it's possible to add a figure (such as a Venn Diagram) to better explain the results obtained.In the same subsection  more details should be provided about the results. More references to supporting literature must be provided in the manuscript in order to further substantiate the claims.  I have only some minor comments: I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Dear Dr. Piccirillo, Thanks a lot for reviewing our paper and your comments. We have made changes to address some of your comments. Please see details below: 1). In the Introduction section (pag.2): the sentence “These approaches project significant genes, proteins, metabolites, and other kinds of biological entities collected from pre-analysis onto pathways and networks, knowledge produced by many years’ experimental studies.”, is not quite understandable, I think it should be rephrased.We have rephrased this sentence and hope the meaning is clear now. 2). The Methods section (pag.2) is clearly explained and sufficient details are provided to allow replication of the method development and its use by others.Thanks!  3). The section Operation (pag.2) I think is unnecessary, the authors can add it as the subsection of Methods because here they are explaining the methods available in the CyREST API developed.This section is suggested by the article template provided by the journal editor.  4). In the Analysis results subsection (pag.3): I think it's possible to add a figure (such as a Venn Diagram) to better explain the results obtained.Thanks for the suggestion. However, we found that it is difficult to show this type of overlapping result in a Venn diagram. We hope that our text description is clear. 5). In the same subsection  more details should be provided about the results. This is a great question. However, this short software article is written to showcase how to use the new ReactomeFIViz CyREST API. We have refrained ourselves to discuss the biological implications of our analysis results. However, the exported pathway diagrams may answer questions like this, which will be addressed in our future projects. 6). More references to supporting literature must be provided in the manuscript in order to further substantiate the claims. Thanks for the comment. Along with listed references, we have also linked to external resources using hyper-links in the main text. We feel that we have cited enough supporting references for this on-line short software article. Best regards, Guanming Wu, Ph.D.