Using the enzymatic mechanism of phosphoesterase as a template, we were able to modify a metal–organic framework such that the hydrolysis rates were 50 times faster than previously demonstrated with UiO-66. e.g., decontamination wipes used to clean exposed clothing/skin/vehicles). We thus turned our attention to derivatives of UiO-66 in order to probe the importance of functional groups on the hydrolysis rate. Three UiO-66 derivatives were explored; UiO-66-NO2 and UiO-66-(OH)2 showed little to no change in hydrolysis rate. However, UiO-66-NH2 showed a 20 fold increase in hydrolysis rate over the parent UiO-66 MOF. Half-lives of 1 minute were observed with this MOF. In order to probe the role of the amino moiety, we turned our attention to UiO-67, UiO-67-NMe2 and UiO-67-NH2. In these MOFs, the amino moiety is in close proximity to the zirconium node. We observed that UiO-67-NH2 is a faster catalyst than UiO-67 and UiO-67-NMe2. We conclude that the role of the amino moiety is to act as a proton-transfer agent during the catalytic cycle and not to hydrogen bond or to form a phosphorane intermediate.The hydrolysis of nerve agents is of primary concern due to the severe toxicity of these agents. Using a MOF-based catalyst (UiO-66), we have previously demonstrated that the hydrolysis can occur with relatively fast half-lives of 50 minutes. However, these rates are still prohibitively slow to be efficiently utilized for some practical applications ( The cleavage and formation of P–O bonds are responsible for converting ATP to ADP and 9 and efficacy towards this family of nerve agents. Thus, given the importance of phosphate esters in biological systems, it is not surprising that there has been much work done in studying enzymes, proteins, and small molecules which are capable of performing phosphate-ester hydrolysis on nitrophenol-containing pesticides/nerve-agent simulants.Other organophosphorous containing compounds, such as methyl-paraoxon, are phosphate-based pesticides. Methyl paraoxon's toxicity is due to the ease in which the P–O group of the nitrophenol can undergo transesterification under physiological conditions. Interestingly, due to the high-use of these pesticides, enzymes have evolved as a response to these pesticides. Ultimately, this has led to enzymes, both natural and synthetic, which have a high specificity16 Taking a lesson from nature, many transition-metal complexes employ hydroxide bridged dimers as catalysts.20 For example, the family of phosphoesterase enzymes utilize a ZnII–OH–ZnII active site which is often further supported by a bridging carboxylate.21 Given that early row transition metals are often found in biological materials due to bioavailability, researchers such as Brown have utilized coordination complexes with more Lewis-acidic centres such as LaIII and ZrIV as catalysts for phosphate-ester hydrolysis.22Transition-metal, or rather Lewis-acid, hydrolysis is ubiquitous in the field of phosphate-hydrolysis.III and ZnII as catalysts for the degradation of phosphates such as methyl-paraoxon.26 We observed that AlIII–porphyrin dimers24 and tetramers26 significantly outperformed their ZnII counterparts; half-lives of 10 hours were observed for the methanolysis of methyl- and phenyl-paraoxon by these AlIII-complexes. In-line with these materials, we began looking at porous-organic polymers (POPs) as potential catalysts.25 These POPs offered the combined advantage of homogeneous catalysis  and heterogeneous catalysis .Our group has also been investigating the role of Lewis-acidic centres such AlWe observed two key design features in the aforementioned systems: (i) strong Lewis-acidic metals were key for fast turn over, and (ii) the use of simple synthesis, such as those used in the formation of POPs, are attractive heterogeneous catalysts. Thus, we turned our attention to another class of porous materials, namely, metal–organic frameworks (MOFs).e.g., Zr6O4(OH)412+) and anionic bridging ligands .27 In addition to our two design principles above, MOFs are ordered/crystalline materials which are needed for rapid screening and hypothesis driven research. Due to their high porosity28 as well as thermal and chemical stability,32 MOFs are promising materials in applications such as gas-storage,35 chemical separations,41 sensing,43 catalysis,47 and light harvesting.51 In keeping with our aforementioned design principle, we have shown that UiO-66 412+ clusters, which can be easily formed in high yield using a variety of different synthetic procedures,52 had a half-life of 50 minutes for the hydrolysis of methyl-paraoxon at room temperature.53 This half-life is among the fastest, non-enzymatic, half-lives reported to date. Hatton et al.54 have also demonstrated that the Lewis-acidic CrIII centre in Cr-MIL-101 is also capable of hydrolysing phosphate esters with a half-life of nearly 3 hours.MOFs are porous materials formed from inorganic cationic nodes 2, UiO-66-NO2, UiO-67, UiO-67-NMe2, UiO-67-NH2, and methyl-paraoxon were synthesized according to literature procedures (see the ESI2 isotherms).61 UiO-67, UiO-67-NMe2, and UiO-67-NH2 were de-solvated by supercritical CO2.52All reagents were purchased from commercial sources and used without further purification. UiO-66, UiO-66-NHN-ethyl-morpholine (0.45 M); the N-ethyl-morpholine is used as a buffer to limit pH changes. The resulting mixture was stirred for 30 minutes to finely disperse the MOF particles. To the suspension was then added methyl-paraoxon . Periodic monitoring was carried out by removing a 20 μL aliquot from the reaction mixture and diluting with an aqueous solution of N-ethyl-morpholine  prior to UV-Vis measurements (Varian Cari 5000) . All background reactions were carried out under identical reaction conditions without the MOF catalyst.Hydrolysis experiments were carried out at room temperature as described previously. Briefly, to a solid sample of UiO-66  in an Eppendorf tube was added an aqueous solution of ri 5000) . Progres62 Polynomial fits of order 3–5 were used with the lowest observed correlation coefficient of 0.98.Initial rates were determined using the method of initial rates.53 The ease in which alternative linkers can be incorporated into the framework72 makes UiO-66 an attractive platform for generating a large parameter space which can be used to easily probe a mechanism. As illustrated in 2 is 20 times greater than that of UiO-66.73 In order to compare the amino moiety in our system with the aspartate and histidine moieties in the enzymatic system, we also synthesized UiO-66-NO2 and UiO-66-(OH)2. These two MOFs are capable of hydrogen bond donating and receiving, similar to UiO-66-NH2, but they cannot act as a Brønsted base like the amino moiety. As illustrated in in 2 and UiO-66-NO2; the initial rates for the catalytic hydrolysis by UiO-66-(OH)2 and UiO-66-NO2 (0.0063–0.0070 mmol s–1 respectively) are slightly slower than that of UiO-66 (0.010 mmol s–1). Based on these observations, we concluded that the amino moiety acts as a base and not as a hydrogen-bonding moiety. Furthermore, considering there is no difference between UiO-66, UiO-66-NO2 (an electron withdrawing group) and UiO-66-(OH)2 (a steric electron donating group), there is little to no electronic effect and only a slight steric effect from the linker on the rate limiting step of the hydrolysis reaction (As we have previously demonstrated, the hydrolysis of methyl-paraoxon with UiO-66 is the fastest MOF-based catalysts reported to date.reaction .In order to further investigate the role of the amino moiety, we turned our attention to UiO-67 and its derivatives. In UiO-67 , the fun74 Nevertheless, as with UiO-66, UiO-67-NH2 and UiO-67-NMe2 are faster than UiO-67 (2. Due to the fact that UiO-67-NH2 and UiO-67-NMe2 show nearly identical kinetic traces (2 3.2 times faster than UiO-67. Furthermore, the hydrolysis rate of UiO-67-NMe2 decreased more than that of UiO-67-NH2 (2 and UiO-67-NMe2 are limited by another step in the catalytic cycle when compared to normal catalyst loadings.In comparison with UiO-66, UiO-67  is a fasn UiO-67 , Table 2c traces , we suspc traces . The hydO-67-NH2 ; these r2 hydrolysis which is slower than the UiO-67-NH2 hydrolysis likely due to the fact that the dimethylamino moiety is a stronger base and thus it is harder to deprotonate the moiety in order to regenerate the catalyst.Thus, given the results above, we propose that the role of amino moiety in these derivatives is to act as a Brønsted-base whereby a proton is transferred to and from the base during the catalytic cycle. This is supported by the UiO-67-NMe2, is the fastest methyl-paraoxon hydrolysis MOF-based catalysts reported to date. With these results in hand, we are currently testing the generality of these design rules on other MOF topologies.The hydrolysis rate of six UiO-based MOFs was measured in order to probe the importance of various functionalities on the overall hydrolysis rate. We observed that only amino moieties were able to enhance the hydrolysis rate. A rate enhancement of up to 20 times was observed for UiO-66. Due to a careful comparison between UiO-66 and UiO-67 derivatives, we propose that the role of the amino moiety is to act like a Brønsted base, and subsequently a Brønsted acid during the catalytic cycle. With half-life of about 1 minute, UiO-66-NH


It also has a function to build a regulatory network from the pathway information. Here we describe an extension of this function. The new function allows the user to build graphs of entire pathways, including regulated as well as non-regulated elements, and therefore provides a maximum of information. This function is available as part of the rBiopaxParser distribution from Bioconductor.Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. One can retrieve the information in this data model in the scripting language R using the package Several pathway databases, either curated or not, have adopted the BioPAX [RRID:SCR_009881]  language as a standard for pathway representation using the RDF (Resource Description Framework) data model2.Biological pathways represent signalling and/or metabolic events involving protein and non-protein molecules. They are increasingly used in gene and protein expression studies to provide an aggregate score for gene sets encoding for defined biological eventsThe structure of BioPAX is founded upon groupings, called classes, for physical entities and interactions with hierarchical networks of their sub-classes. Interactions between physical entities are represented such that conjoint interactions may form a specific pathway with defined, but different types of interactions between the involved physical entities. The BioPAX format is being actively developed, with BioPAX level 2 format focusing on metabolic pathways and BioPAX level 3 introducing full support for signalling pathways.3. TherBiopaxParser [RRID:SCR_002744]4 is an R package to retrieve data stored in a BioPAX RDF format. It comes with several options that are useful to probe the data and extract specific information from it, for example participants of a pathway, stoichiometric conditions to be fulfilled for an interaction, etc.SPARQL (Simple Protocol And RDF Query Language) is a query language able to retrieve and manipulate data stored in RDF. Pathway information is often combined with statistical data analysis using tools such as Rpathway2RegulatoryGraph (P2RG) function that converts a pathway into a graphical structure. This is extremely useful for visual representation and subsequent graph-based network analysis. The P2RG function returns the parts of a pathway that are regulated (activated or inhibited) by proteins or protein complexes; this is important to understand the role of regulated proteins in pathways. Here we present an adaptation of P2RG, denotedpathway2Graph (P2G) which can be used to build a graph of the entire pathway, including the regulated as well as the non-regulated elements. This new function expands P2RG and can be used to investigate all different types of processes and connections of pathways instead of only studying the regulated elements of pathways. P2RG retrieves regulatory interactions, such as inhibitions and activations  represent signalling events. They regulateBiochemicalReaction andDegradation interactions which mostly represent metabolic reactions.The classes ofControl,Catalysis andModulation interactions that are either activating or inhibiting other interactions. This method provides a graph with plenty of information on the regulatory components of the pathway. The nodes of this graph are physical entities likeProteins orSmallMolecules and the directed edges are either activation or inhibition events. An example of such a reconstruction is shown inControl interactions and could result in the loss of valuable information in the graphical representation of the pathway.To create a regulatory graph, the P2RG function starts with theThe new function P2G can start with any type of interaction in order to obtain a graph with all possible physical entities involved in the pathway. Similar to the result of the P2RG function, the P2G function gives a graph with nodes that are physical entities, but the edges are not strictly activation or inhibition events. The directed edges could represent several types of events like translocation of a protein or cleavage of DNA, these are shown as dis-continuous edges in the cartoon inThe Reactome database (v51) categorizes pathways into 27 branches. Here we worked with pathways that have more than one interaction, which resulted in 1,666 pathways. Using P2RG, graphs for 1,548 pathways were retrieved. By using the new P2G function, we were able to retrieve information on all 1,666 pathways. The highest number of pathways were obtained, using either method, in the “Disease” category . In 85% of the cases, pathways retrieved using P2G consisted of more physical entities (nodes) than those retrieved using P2RG. 19% of the P2G retrieved pathways have at least twice the number of nodes, and 60% have at least twice the number of interactions between nodes (edges) as compared to the P2RG version,As an example, we discuss here the ‘Apoptosis induced DNA fragmentation’ pathway, which is in the “Programmed Cell Death’ category . When thpathway2Graph) is currently available in therBiopaxParser package in the Bioconductor 3.4 release.P2G is a useful addition to the rBiopaxParser package because it retrieves all the components of a pathway from the database and provides complete graphical information for both signalling as well as metabolic pathways. The P2G function  issues I had with the article previously, thanks!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. The authors of this paper describe a new function provided by the rBiopaxParser library, which is a R based system for parsing BioPax documents. BioPax is coded in RDF, which is a linked data format that describes the subject matter using graph triples in the form of a Subject, Predicate and Object. Their pathway2Graph and the old gathwayRegulatoryGraph essentially attempt to do graph transformations, taking the RDF graph and converting it to a graph that is suitable for analysis.  The paper’s primary metric for demonstrating improvements in their method is measuring the number of non-zero pathways retrieved after extracting data from Reactome. In addition, I tested code and their supplied supplemental data.  ``` > library(rBiopaxParser) > biopax=readBiopax("19c8ac7b-96b2-4db4-a78a-c2defed535ae.owl") > a <- pathway2RegulatoryGraph > b <- pathway2Graph > nodes(a)  [1] "CASP3"                        "CASP3(176-277)"  [3] "DFFA(1-?)"                    "DFFB"  [5] "DFFA(118-331)"                "DFFA(1-117)"  [7] "DFFA(225-331)"                "DFFA(118-224)"  [9] "Histone H1"                   "DFF40 homodimer/homooligomer" [11] "HMGB1/HMGB2" > nodes(b)  [1] "Protein8776"       "Protein8777"       "Complex4232"  [4] "Complex4233"       "Complex4234"       "Complex4235"  [7] "Complex4169"       "Complex4238"       "Complex4236" [10] "Complex4239"       "Complex4240"       "Protein8779" [13] "Protein8784"       "Protein8785"       "Protein8783" [16] "Complex4241"       "PhysicalEntity567" "Complex2061" [19] "Complex4242"       "Protein8786"       "PhysicalEntity109" [22] "Complex4243"       "Complex4244" ``` From the old version (a) to the new extraction (b), for this particular pathway the number of nodes went from 11 to 23. Interestingly, the names of the vertices became less descriptive in the newer method, going from gene names like ‘CASP3’ to strings extracted from the RDF urls, like ‘Protein8776’. It would seem that the authors claim, that the number of extracted elements from the BioPax files is accurate. But what is slightly confusing is the significance of this change. Was the original method faulty and this is a bug fix? Or is the method being updated to deal with the BioPax standard as it changes from version 1 to 2 to 3?  This seems like a minor but necessary change to the library to make it have a better extract information from BioPax. More explanation about the nature of this change, and how the new parsing strategy improved how the library was able to deal with all of the various types of relationship classes in the BioPax format would make a big difference in helping to illustrate the improvements mentioned in this paper.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. The authors have developed a new function that allows the user to build a regulatory network in a graph format based on pathway information. In the version that the authors developed, the output graph includes regulatory and non-regulatory interactions and allows the viewer to more fully comprehend the underlying network. Nodes in the network represent classes, while the edges show the relationships among these classes. An example of the approach is provided with the Reactome database. The authors’ approach, P2G, was used to analyze data for 1,666 pathways. P2G returned more nodes than was retrieved by the earlier version in 85% of the cases. The software will be available as part of the Bioconductor 3.4 release. This will likely be a valuable addition to the Bioconductor package that will provide scientists with a means for generating graphical and intuitive networks from gene expression and metabolic data. The manuscript is clear with an appropriate title and abstract. The article is clearly written and the conclusions are based on the data.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. Thank you for your comments. rBiopaxParser package to generate figures from BioPAX formatted biological pathway data. This new function, calledpathway2Graph (P2G) replaces an older function calledpathway2RegulatoryGraph (P2RG). P2G includes more interaction terms than P2RG, and therefore generates more complete interaction graphs. That said, as written the changes are mysterious - why did the original function limit the interaction types when generating graphs and what is advantageous about including more interactions? Addition of the P2G function is a small improvement to the rBiopaxParser package that will be useful, but the discussion of P2G's advantages and disadvantages should be expanded.The authors have developed a new function in theThe language throughout is highly technical, potentially compromising its readability to end users.The edge labels in Figure 1 are very difficult to read. As these are discussed in the text, their font size and/or weight should be increased.It would increase the readability of this software tool article if the authors described a  scenario where the P2G function would be uniquely useful compared to P2RG. It’s obvious that including more interaction types will lead to more complete graphs, but in what scenario would this be useful for a user?Why were all interaction types not originally included in P2RG? The advantages and any potential disadvantages of including all interaction types should be discussed more.Currently, the difference between P2G and P2RG is rather minor  – might this difference change in the future if more edges are added through the interaction types that are unique to P2G? Specific points: The work is technically sound and presents a useful extension to the rBiopaxParser package, but the paper describing this work will be useful to a broader audience if changes similar to those above are incorporated.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. Thank you for your comments. In the new version we have expanded the Introduction with non-technical specifics that should explain the basic differences between the functions to a broader audience. Figure 2  has been changed to increase visibility of edge labels. In order to explain the differences between the outputs of the two functions we have added a section in the Methods and Results section where we describe a particular pathway (‘Apoptosis induced DNA fragmentation’) in terms of the extra information gained by using the new function. A new table (Table 2) with more biological information on this pathway has also been added. This articles describes the addition of a new function to the extant rBiopaxParser R library. This new function converts a BioPAX-formatted pathway of gene or protein interactions into a graphical structure that is human-viewable. This function supercedes an earlier function which performed the same task, but was unable to convert all nodes and egdes in the pathway. This resulted in a loss of information. This loss is now remedied, however, with the new and improved function. I installed the package and tested it and it seemed to work flawlessly, although I didn't try anything fancy. I imagine that the new function is a significant advance for people working on these pathways routinely as it must have been frustrating to have missing data. My only reservation about the paper is that I was unclear on the point of the new function until I'd read most of the paper. Perhaps the authors could extend the last paragraph of the introduction to be more clear about why the new function was necessary and how it has improved the R package. Although this request is a small change, the article is confusing as it is and making this would be a big improvement.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. Thank you for your comments. The Introduction has been expanded with an image  to emphasize the differences between the two functions.


The results showed that SBs could be well separated on UiO-66-NH2 column but not on UiO-67 column. Nonetheless, PAHs could be well separated on UiO-67 column. The separation mechanisms of SBs and PAHs on UiO-66 analogues may be involved in the pore size and functional group in the frameworks of UiO-66 analogues. Introduction of the–NH2 into UiO-66 significantly reduced its adsorption capacity for SB congeners, which resulted in less separation of SBs on UiO-66-NH2. As for the separation of PAHs on UiO-67 column, the π-π stacking effect was supposed to play a vital role.UiO-66 analogues are good candidates as stationary phase in HPLC because of their chemical/thermal stability, large surface area, and two cage structures. Here, two UiO-66 analogues, UiO-66-NH Metal-organic frameworks (MOFs) are a category of complexes constructed by organic ligands and inorganic nodes . Most MO6O4(OH)4 nodes and organic linkers. Due to the combination of strong Zr-O bonds, the UiO-66 possesses high thermal stability and broad solvent resistance [2 and UiO-67, were used as stationary phase in HPLC to separate SBs and PAH, and the structure-performance relationship of UiO-66 analogues was assessed.UiO-66 is Zr-cluster-based MOF consisting of cationic Zrsistance . Becausesistance , 17. In 2 and the linker-lengthened metal-organic framework UiO-67 are isoreticular MOFs both derived from UiO-66. The UiO-66-NH2 was obtained by replacing the UiO-66 organic linker H2BDC with amino-functionalized linker H2BDC-NH2, and the UiO-67 was obtained by replacing H2BDC with longer linker H2BPDC [2 in UiO-66-NH2 endows it with some special properties and further results in some potential application, such as photocatalysis in oxidation/reduction processes and adsorption of nitrogen-containing compounds [2, CH4, and H2 [2 and UiO-67 have similar topological framework, which is also similar to that of UiO-66. For example, the UiO-66, UiO-66-NH2 and UiO-67 have the same metal coordination, similar secondary building unit (SBU), and similar interlinking mode of SBUs. The three MOFs have a certain solvent stability property, which is essential for the potential application in HPLC separation. However, the UiO-66, UiO-67 and UiO-66-NH2 has different pore size from each other, which enables an investigation of the effects of pore size on the performance of MOFs in chromatographic separation. In the present study, UiO-66-NH2 was applied for NP- and RP-HPLC separation, whereas UiO-67 was used only for NP-HPLC separation due to its lower solvent stability. What’s more, the structure-performance relationship of UiO-66 analogues in the same topology structures was demonstrated.The amino-functionalized metal-organic framework UiO-66-NHr H2BPDC ,19. The ompounds –26. Whil, and H2 –29. Both4), terephthalic acid (H2BDC), 2-amino-terephthalic acid (H2BDC-NH2), 4,4ˊ-biphenyldicarboxylic acid (H2PDC), N,N-dimethylformamide (DMF), concentrated hydrochloric acid (HCL), ethanol, n-hexane, and benzoic acid were all analytical grade and purchased from Sinopharm Chemical Reagent Co., Ltd. . O-xylene, m-xylene, p-xylene, ethylbenzene, styrene, naphthalene, fluorene, acenaphthene, anthracene, phenanthrene, pyrene, chrysene, phenol and cresol were all analytical grade and obtained from TCI Development Co., Ltd. . Methanol (MeOH), acetonitrile (CH3CN), n-hexane, and methylene chloride (DCM) were chromatographic grade and purchased from J&K Scientific Ltd. . Ultrapure water was obtained from Milli-Q Water Purification System .All chemicals and reagents were of high purity . Zirconium chloride (ZrClλ = 1.54178 nm) at room temperature. Thermogravimetric analysis (TGA) was performed on a TGA-50 (SHIMADZU) thermogravimetric analyzer with a heating rate of 2°C·min–1 under N2 atmosphere. The N2 adsorption isotherms were measured on a Micromeritics ASAP2020 surface area and pore analyzer. The scanning electron microscopy (SEM) images were recorded on a Shimadzu SS-550 scanning electron microscope at 15.0 kV.The powder X-ray diffraction (PXRD) patterns of UiO-66 analogues were obtained on a BRUKER D8-Focus Bragg-Brentano X-ray Powder Diffractometer equipped with a Cu sealed tube . HPLC separations were performed on a LC-20AT chromatograph equipped with a tunable UV-Vis detector, an auto-sampler, a binary pump, a column oven, and a LC-solution chromatography data system .4, 1.32 g of terephthalic acid, 0.665 mL of concentrated hydrochloric acid, and 24 mL of DMF were mixed in a Teflon-lined bomb. After ultrasonic treating for 5 min, the bomb was sealed and then heated at 120°C for 16 h. The white solid product was obtained after cooling down and centrifuging at 8000 rpm for 10 min. The product was washed three times sequentially with DMF and acetone, and then dried at 60°C.UiO-66 was synthesized and activated following to the methods described in our previous study . Briefly2 was synthesized in the same way as that of UiO-66 but replacing H2BDC with H2BDC-NH2, according to the method described by Zhang et al. [2 was activated using solvent exchange method (washed with DMF and acetone in turn). UiO-67 was synthesized by adding 0.12 g of ZrCl4, 0.125 g of H2PDC, and 1.83 g of H2BDC into 20 mL of DMF in a glass vial. The vial was sealed and then heated at 100°C for two days. The resultant white powder was collected and washed with DMF and acetone in sequence for three times, and then dried at 80°C.UiO-66-NHg et al. . The pro2 packed column, 1.20 g of UiO-66-NH2 was dispersed in 50 mL n-hexane under ultrasonication for 5 min. The resultant suspension was then downward packed into a stainless steel column  under 5000 psi for 15 min. The suspension was kept intact by the stainless steel frit with a pore size of 2 μm. The packed column used for NP-HPLC was conditioned with n-hexane at a flow of 1.0 mL·min−1 for 1 h. The packed column used for RP-HPLC was conditioned with methanol at a flow of 0.5 mL·min−1 for 1 h. The UiO-67 packed column was prepared as follows: the well-dispersed suspension of 1.30 g of UiO-67 in 50 mL n-hexane was downward packed into a stainless steel column  under 5000 psi for 15 min. The UiO-67 packed column was assembled in a similar way to that of the preparation of UiO-66-NH2 packed column except that1.3 g of UiO-67 rather than 1.2 g of UiO-66-NH2 was used.The UiO-66 packed column for RP- and NP-HPLC separation was prepared following the method as described in our previous study . For the2 as the stationary phase were performed using MeOH/H2O as mobile phase at a flow rate of 0.5 mL·min−1, with UV detection at 254 nm and column temperature at 30°C. The NP-HPLC separation experiments were conducted using UiO-66-NH2 and UiO-67 packed columns, with n-hexane/DCM as mobile phase at a flow rate 1.0 mL·min−1, UV detection at 210 nm. Data acquisition and processing were carried out using a LC-solution chromatography data system.The prepared packed column was connected to the HPLC system to start the HPLC separation experiments under the indicated chromatographic conditions. The RP-HPLC separations using UiO-66-NHSeveral parameters calculated by a van’t Hoff model, Gibbs free energy change (ΔG), enthalpy change (ΔH) and entropy change (ΔS), were used to evaluate the thermodynamic properties of the HPLC separation .2 is a product of amino-functionalized UiO-66 with–NH2 pointing into the pores. The UiO-67 is a product of linker-lengthened UiO-66 and has larger pore sizes (12 and 16 Å) compared to UiO-66 (8 and 11 Å). Physico-chemical properties of UiO-66 have been reported in our previous study [2 adsorption-desorption and SEM experiments were further conducted to characterize the properties of UiO-66-NH2 and UiO-67. The experimental PXRD patterns of the UiO-66-NH2 and UiO-67 matched well with respectively simulated curves , indicating a successful preparation of UiO-66-NH2 and UiO-67 , UiO-66-NH2 possesses smaller surface area due to the presence of -NH2, While UiO-67 has nearly twice surface area due to the longer linker ligand 4,4’-biphenyl. In addition, the activated UiO-66-NH2 and UiO-67 have a relatively broad particle size distribution ranging from 150 to 450 nm , styrene and xylenes  was smaller than that of UiO-66 (1200 m2∙g-1), which led to weaker van der Waals force between xylenes and UiO-66-NH2 and resulted in faster elution  of xylenes on UiO-66-NH2 column than on UiO-66 column.Compared to the performance of SBs on UiO-66 column , SBs on 6 column . Howeverls force . Because2 packed column, o-xylene could be separated from p- and m-xylene, while the separation of m- and p-xylene was failed. Similar to UiO-66, UiO-66-NH2 has a preferential affinity toward o-xylene. This might be explained in two aspects [O-xylene which has the largest molecular size among SBs had the strongest Van der Waals interaction with the UiO-66-NH2 framework [2 packed column than the other SBs did. Secondly, the two methyl groups in o-xylene had a strong interaction with carboxylate groups in UiO-66-NH2, which might also result in a longer retention of o-xylene on UiO-66-NH2 packed column [On UiO-66-NH aspects . Firstlyramework , and cond column .o-xylene on UiO-66 column, the baseline separation of m-xylene and p-xylene failed on UiO-66-NH2 column [2 could be partially blocked by–NH2. As a result, the surface area decreased and the interaction between xylenes and UiO-66-NH2 reduced. In addition, only one methyl group was available in m- and p-xylene and the methyl group could similarly interact with the carboxylate groups in UiO-66-NH2, which made m- and p-xylene be co-eluted from UiO-66-NH2 [Unlike the performance of 2 column . This miO-66-NH2 .2 packed column. And the retention time of styrene was shorter than EB. The retention order of EB and styrene on UiO-66-NH2 is opposite to that on UiO-66 [2 might be ascribed to its geometry configuration [2. Therefore, it was expected that the retention time of styrene on UiO-66-NH2 column was shorter than that of EB.EB and styrene could be separated on UiO-66-NHn UiO-66  Fig 3)..2 packedn UiO-66 . The lesguration . Styrene2 packed column for the NP-HPLC separation of SBs were evaluated using two blends: benzene and toluene, and EB, styrene and o-xylene (R), peak area, peak height, and half peak width (W1/2) for five replicates ranged from 0.06 to 0.08%, 0.15 to 0.98%, 0.20 to 0.88%, and 0.17 to 0.40%, respectively. Therefore, it was concluded that the UiO-66-NH2 packed column gave good reproducibility for NP-HPLC separation of SBs. Also, it was found that there was a linear increase of the chromatographic peak area and peak height with the increase of mass of analytes  as mobile phases were investigated , S4 Fig.In order to further explore the roles of pore size of UiO-66 analogues in chromatographic separation, NP-HPLC separation of large molecule PAHs on UiO-67 column were studied. Several PAHs with different numbers of rings, including naphthalene (two rings), fluorene, acenaphthene, anthracene and phenanthrene (three rings), pyrene and chrysene (four rings) were used. Also, different ratios of n-hexane/DCM were used as mobile phases. Based on the one-component injected experiments, two PAH mixtures: (1) naphthalene, anthracene and chrysene; and (2) naphthalene, phenanthrene and chrysene were used for NP-HPLC separation on the UiO-67 column.The ratio of n-hexane/DCM showed significant effect on NP-HPLC separation of PAHs on UiO-67 column . When n-As shown in 2 and UiO-67 were investigated under different temperatures. The mixture of EB, styrene and o-xylene and the mixture of benzene and toluene were separated on UiO-66-NH2 column with temperatures ranged from 20°C to 60°C Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point  of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC . On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis. Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related deaths around the world . HepatocIn recent years, the concept of multi-step human hepatocarcinogenesis has been well documented ,5,6. TheRecently, a novel, model-free approach based on nonlinear dynamic theory, termed dynamic network biomarkers (DNB), was developed to detect critical transitions or tipping points during the progression of complex diseases ,13. GeneGiven the difficulty to diagnose early HCC, it is a key problem to identify when and how the tipping point or the critical transition happens at a molecular level. In this work, from stage-wise gene expression profiles of HCC initiation , we identified the tipping point or pre-HCC state of HCV-induced HCC by DNB model. The obtained DNB formed a specific module with 59 genes at the LGDNs stage to signal the tipping point just before the drastic deterioration in HCC progression. We also partially revealed molecular mechanism on the HCC initiation by functional analysis of DNB, which both provides biological insights into the dynamic regulations of the critical transitions and opens a new way for the identification of therapeutic targets. We further identified biological pathways responsible for the critical transition, including several pathways in immune system reactions, cell growth and death, and cell adhesion. And furthermore, DNB was validated as an effective predictor of prognosis for HCC patients by survival analysis on independent data.https://www.ncbi.nlm.nih.gov/geo/) under accession ID GSE6764. And the normalized expression values processed by marker aided selection (MAS) method were downloaded. The dataset includes the expression profiles of 75 tissue samples  from 48 patients with HCV infection representing the stepwise carcinogenic process from preneoplastic lesions to HCC. The earlier five stages were selected to study pathogenesis of HCC. In this dataset, probe sets without corresponding gene symbols were excluded during our analysis, while multiple probe sets mapping to the same gene were averaged as the expression values. And for the probe sets mapping to more than one gene, we took the first annotation.The gene expression profiles for DNB analysis were obtained from the Gene Expression Omnibus database  provided by RIKEN (project code: LIRI-JP) including 260 donors [As an independent dataset for validation, a cohort of HCC patients was subjected to survival analysis. The dataset was deposited in International Cancer Genome Consortium (ICGC) database (0 donors . Both thCondition 1:inPCC) in an absolute value becomes very high.The DNB members are closely correlated to each other, i.e., their average Pearson correlation coefficient (Condition 2:outPCC) between DNB members and non-DNB members becomes very low.The DNB members lose correlations with other non-DNB members, i.e., the average PCC (Condition 3:inSD) becomes very high.The DNB members are highly fluctuated, i.e., their average standard deviation  is not always smooth but there is an abrupt change after a system state passes over a critical state or pre-disease state, resulting in the drastic transition or serious deterioration to a disease state. However, in contrast to the significant difference between normal state and disease state in terms of molecular concentrations , there is generally no significant difference between normal state and pre-disease state. Hence, traditional molecular biomarkers or methods may fail to diagnose the pre-disease state. To overcome this problem, based on nonlinear dynamical theory, the DNB method was proposed to detect the pre-disease state or critical state by exploring fluctuation information of the measured omics data, rather than the information of traditional differential expressions . In brieBased on nonlinear dynamical theory, whenever DNB satisfying all above three criteria appears, the system is at the tipping point. Therefore, the three conditions are considered as the generic properties to detect early-warning signals of the pre-disease state or critical state. Note that DNB is a functional module, which signals the imminent transition or deterioration from the normal state to the disease state, and, therefore, is considered to be causally related to the initiation and progression of the disease ,13,17,18CI) to quantitatively detect the DNB as follows:inSD and inPCC are the average standard deviation and average absolute PCC of all molecules in DNB, corresponding to Conditions 3 and 1, respectively. The outPCC is the average absolute PCC between molecules inside and outside of DNB, corresponding to Condition 2.The three conditions can be combined into a single composite index . The detailed flow of the computational algorithm for detecting DNB as well as the tipping point as follows:t, calculate PCCs for all pairs of genes or molecules with respect to the samples. The PCC at the 0.05 quantile of the descending order PCCs among all pairs was regarded as the threshold for a high PCC at each stage.For each stage t, select the genes with their standard deviations above 50% percentile based on gene expression profiles. Then we get a module of molecules at each stage as Mt.For each stage t to get smaller modules whose molecules have high correlations at each stage. The distance used in clustering is defined as 1−|PCC|, and the cutoff is set based on the significance test for PCC in step one. Denote the resulting modules at each stage t as tC = {tc}.Hierarchically cluster the Mtc∈tC, calculate its CI and find the module with the maximum index at each stage as the candidate DNB.For any module CI value among all T stages with T candidate DNBs. This module is our DNB and the corresponding stage is the tipping point, at which the system is considered at the pre-disease or critical state.Find the module with the maximum Assume that we collect molecular profiles of several samples  in each stage p < 0.01) [http://www.R-project.org/). Principal component analysis (PCA) was performed using the prcomp function in the stats package from R software.Unsupervised hierarchical clustering of all samples along development of HCC was performed by the PCC distance based on differentially expressed genes (DEGs) assessed using one-way analysis of variance (ANOVA) ( < 0.01) . Visualihttps://david.ncifcrf.gov/), clusterProfiler package and signaling pathway impact analysis (SPIA) package of R were utilized to perform Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis [p-values under 0.05 after correction for multiple hypotheses testing by false discovery rate (FDR) were considered as significantly enriched. Ingenuity Pathway Analysis (IPA) software 01-10  was also used for pathways enrichment analysis. Those genes with known gene symbols were uploaded into the software and each gene symbol was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Significance threshold of Student’s t-test was set at 0.05. To further analyze the functions of the DNB, STRING resource (https://string-db.org/) was utilized for protein–protein interactions (PPIs) network analysis [http://www.cytoscape.org/). In the network, each gene was corresponded to a node and the nodes connected by edges (lines). The degree of a gene was defined as the number of the first neighbor genes linked to it within a network, which can assess the relative significance of a gene in the network. Thus, in a network, the more the first neighbor genes a DNB member connects, the higher degree it has and the more important it is.During functional enrichment analysis of genes, the online software Database for Annotation, Visualization, and Integrated Discovery  of obtained DNB were satisfied with typical features of the critical state characterized by DNB . Then, we got five candidate DNBs at five different stages and the scores of the candidate DNBs were shown in d by DNB . The DNBd by DNB  and someBased on DNB theory, the DNB subnetwork as the early-warning signal was strongly related to disease development. For analyzing the mechanism of DNB at a network level during hepatocarcinogenesis, we firstly performed the GO enrichment analysis and KEGG pathway enrichment analysis on 59 DNB members .Type I interferon signaling pathway, response to virus, and negative regulation of viral genome replication were the most relevant biological processes, validating the significant correlation between the disease evolution and the DNB ,27. Excet-test statistics with p < 0.05 after FDR correction. The largest amount of DEGs between different disease stages , highlighted the stark differences before and after the tipping point (www.cytoscape.org/)  A. Three ric test A, implyiThe degree of each DNB member was defined as the number of the first neighbor genes linked to it within the network, which could assess the relative significance of the DNB member in the network. The top ten DNB members with the highest degrees are listed in At a network level, DEGs were significantly enriched in DNB-associated network A which ip < 0.05) based on DNB and DEGs were similar. Four overlaps and close crosstalk between DNB and DEGs could be detected  project in the ICGC database.Dynamic network biomarkers played a key functional role during HCC onset and we found that 43 of the 59 DNB members were differently expressed with p-values < 0.05. According to the expressions of genes in DNB, the HCV-induced HCC patients were separated into two groups by k-means clustering, and group 2 displayed obviously poor survival, as shown in In this study, we identified a critical stage (or pre-HCC) just before the crucial transition from preneoplastic lesions to HCC based on multi-stage gene expression profiles during HCC progression by DNB theory. Unlike traditional molecular biomarkers which usually distinguish disease state from normal state, DNB can detect the critical state just before the disease state to realize early. Although the DNB method is a model-free approach, it generally requires multiple samples in each sampling period so as to detect the tipping point. Based on the three conditions of DNB, LGDNs was revealed as the tipping point during multistep hepatocarcinogenesis. It was consistent with reports in the literature that found that after the LGDNs stage, the disease drastically deteriorated to HGDNs, which is the closest manifestation to HCC, based on histopathological features and clinical follow-up studies with a high risk of transformation ,10,40. AIt should be noted that the DNB subnetwork is not necessarily a set of driving factors , but proIn DNB, only two genes presented significantly differential expressions between cirrhosis and HGDNs. However, 43 DNB members were differently expressed between normal tissues and HCC tissues, implying the effectiveness of DNB members in HCC progression. The effect of DNB at HCC onset depended not on their differential expressions, but on collective fluctuations according to DNB theory. Thus, we detected the relation between DNB and DEGs during critical transition at a network level. The DEGs before and after the tipping point were significantly enriched in the DNB-associated network, further implying that DNB plays core functional roles in coordinating the critical transition from preneoplastic lesions to HCC, which results in drastic deterioration phenomena after LGDNs. We also found that in the HCC related pathways which contained genes from both DNB and DEGs, several DNB members stayed at relatively important places, such as receptors, upstream regulators and transcription factors. Hence, we hypothesized that the emergence of DNB modules made the expression of those closely related genes change considerably and caused several vital biological processes to become abnormal, ultimately leading to the drastic deterioration to the irreversible HCC state. Although only two DNB members (RBX1 and ISG20) belonged to DEGs, the pathways significantly regulated by DNB and DEGs were similar. During the critical transition, pathways in immune response and cell adhesion were down-regulated, such as the JAK–STAT signaling pathway, NF-κB signaling pathway, and RIG-I-like receptor signaling pathway. Meanwhile, pathways related to cell growth and death, such as cell cycle, the Notch signaling pathway, and Hedgehog signaling pathway were up-regulated. Moreover, it had been reported that immune response and cell adhesion were upregulated in cirrhosis and downregulated on HCC initiation, and cell proliferation was upregulated on HCC initiation . It coulFurthermore, we performed survival analysis based on 122 HCV-induced patients for detecting whether or not DNB has clinical application value. The results show that DNB is effective as a clinical predictor of prognosis for HCV-induced HCC patients. In conclusion, DNB during hepatocarcinogenesis can be used as early-warning signals of HCC, and this work also opens a new way to understand the underlying mechanisms responsible for HCC initiation and provides a new method to facilitate the identification of molecular targets. This method can also be applied to the analysis of other diseases ,43,44 in


Topical Betamethasone (BM) and Pimecrolimus (PC) are widely used drugs in the treatment of atopic dermatitis (AD). Though the biomolecules and biological pathways affected by the drugs are known, the causal inter-relationships among these pathways in the context of skin is not available. We aim to derive this insight by using transcriptomic data of AD skin samples treated with BM and PC using systems biology approach.www.persistent.com/eskin), to perform pathway enrichment analysis for the given datasets. eSkIN consists of 35 skin specific pathways, thus allowing skin-centric analysis of transcriptomic data. Fisher’s exact test was used to compute the significance of the pathway enrichment. The enriched pathways were further analyzed to gain mechanistic insights into the action of these drugs.Transcriptomic datasets of 10 AD patients treated with Betamethasone and Pimecrolimus were obtained from GEO datasets. We used a novel computational platform, eSkIN  contains supplementary material, which is available to authorized users. Atopic dermatitis (AD) is one of the most common disorders of skin that affects approximately 20% of children and 3% adults worldwide . The patBM, a corticosteroid, is known to suppress the inflammation, but fails to adequately restore the damaged skin barrier which subsequently leads to secondary skin infections. BM binds to its corticosteroid receptor in skin and perturbs various biomolecules in keratinocytes involved in processes like inflammation, keratinocyte differentiation, proliferation and cellular adhesion . On the US FDA issues TCI drugs with a boxed warning owing to a potential risk of malignancy, in spite of various studies disproving any such association . This faDAVID [A central piece for gaining mechanistic insight into drug action is to understand the biomolecular interactions and pathways that are impacted by the drug, which in turn, determine the therapeutic efficiency and adverse effects. In this study, we have used eSkIN, a novel systems biology based computational platform specially designed to aid skin omics research. eSkIN contains a comprehensive model of skin with 35 manually-curated skin-specific pathways and 2600+ genes. This allows skin-centric analysis and interpretation of omics data, which to the best of our knowledge, is not available in other commonly used software applications (e.g. DAVID ).We present the detailed mechanistic analysis of BM and PC highlighting the biomolecular interactions and pathways involved in their mechanism of action and adverse effects. Publicly available transcriptomic data from patients treated with BM and PC  were useGSE32473.Transcriptomic data from lesional AD skin samples of 10 patients, before and after topical treatment of BM and PC twice daily for three weeks, were used in this study . The datth percentile (median) of that sample. To ensure quality of the input data, only probe sets with present or marginal calls in at least 70% of samples per analysis group were considered. Median expression values of probes were assigned to gene.The datasets of BM and PC were analyzed separately. All the samples were normalized as per Jensen et al., . Brieflywww.persistent.com/eskin) was used to perform skin-centric analysis of the transcriptomic data owing to the availability of 35 manually-curated skin-specific pathways  samples of respective drug. Two fold change was used as a threshold to identify differentially expressed genes i.e. up-regulated genes ≥ + 1 Log2 fold change and down-regulated genes ≤ − 1 Log2 fold change. Sensitivity analysis of fold change cutoff was performed by increasing and decreasing one fold change of the default value to understand its effect on our analysis .We computed the LogPathway enrichment analysis is based on the assumption that behavior of the genes involved in the same biological pathways is correlated. Using statistical methods, it helps to identify the most perturbed pathways based on an input set of genes . Such anp-value.We used the skin-centric knowledge-base of eSkIN as the backend database for performing pathway enrichment analysis. This facilitates the identification of skin-specific pathways that are perturbed due to the treatment and thus, helps in understanding the skin-centric effects of the treatment. eSkIN uses Fisher’s exact test for computing the significance of the enrichment of pathways. Fisher’s exact test with following parameters is used for computing p-value from Fisher’s exact test is a measure of the chance of random association between differentially expressed genes and a pathway. Smaller the p-value, lower is the random chance, and thus, higher is the likelihood that a pathway is significantly enriched.In Eq. , a = nump-value < 0.05) in BM and PC treated samples were further analyzed by overlaying the transcriptomic data on these pathways using Gene Expression Overlay feature of eSkIN. This feature of eSkIN allows pathway enrichment analysis and visualization of transcriptomic data in the context of skin related pathways. The genes are colored based on their expression levels, and thus, helps in exploration of the enriched pathways in the context of their molecular interactions. This provides insight into the various signaling events triggered by the drugs that are discussed in the Results and Discussion sections.Furthermore, eSkIN eliminates the need to average out sample level information as it allows analysis of multiple samples simultaneously. The enriched pathways (i.e. eSkIN https://david.ncifcrf.gov/) [For comparing our results with DAVID (rf.gov/) , we assip-value < 0.05), were considered for comparison.We performed DAVID enrichment analysis using GO biological processes (GO_BP_FAT) and KEGG pathways as the annotation datasets. Significantly enriched processes based on similar criterion to that of eSkIN . It is well-known that BM is more effective in curbing inflammatory effects of AD but also known to cause more adverse effects especially on skin barrier formation as compared to PC . Our anaAs evident from Fig. Our pathway enrichment analysis shows that the pathways associated with inflammation  and skin barrier  are enriched see Fig. , thus, cAdditionally, pathways such as Basal Layer Formation and cellular pathways like Cell Migration, Cell Adhesion, Proteasomal Degradation, Autophagy, DNA Damage and Repair, Lipid Synthesis and ROS (Reactive Oxygen Species) Generation were also enriched in certain BM and PC samples.BM, a corticosteroid, is known to activate glucocorticoid receptors NR3C1) on skin, which in turn, inhibit the activity or transcription of various triggers of inflammation like IL1-β, IL4, IL11, TNF-α, TGF-β, MMPs , IFN-γ and VEGF [C1 on skiOur analysis of Inflammation pathway shows that NR3C1 can inhibit TNF pathway, albeit with an unclear mechanism . Its invNF-κB is one of the important factors in the transcription of inflammatory cytokines, and its deactivation affects inflammation by downregulating the expression of following genes: (i) inflammatory markers: CXCL1, CXCL9, CXCL10, IL18, CCL2, CCL5, CCL13 and CD86; (ii) adhesion molecules: ICAM1 and VCAM1; (iii) matrix metalloproteinases responsible for degradation of collagen: MMP9; and (iv) factors causing accumulation of leukocytes at the site of inflammation: SELE , 13. AlteSkIN also shows that TLRs (TLR1 and TLR2), which are known to promote atopic dermatitis , are doweSkIN also depicts a route for NR3C1 mediated inhibition of IL4 pathway and IFN-γ (IFNG) pathway. Although, the transcriptomic data indicated downregulation of IL4 and its receptors in BM samples, IFN-γ pathway seems to be unaffected  . NFAT alIt is known that JAK-STAT cascade helps in the transcription of genes responsible for leukocyte, eosinophil and T-cell migration , 21. OurChemokine Signaling is known to affect processes like adhesion, migration, phagocytosis, immune response and anaphylaxis. Our analysis shows that most of the signaling cascades like RAS-RAF pathway, MAPK cascade and JAK-STAT pathway, triggered by various chemokines and growth factors like HBEGF and AREG , 23, shoKeratinocyte differentiation is central to the formation of healthy skin barrier. BM is known to efficiently inhibit the primary cause of AD i.e. inflammation, but it also affects the skin barrier leading to secondary complications like skin atrophy and infection.Our analysis corroborates the fact that BM and PC upregulate several key skin barrier formation genes such as Loricrin (LOR), Filaggrin (FLG), TGM5 and CDSN , which regulates the transcription of barrier proteins. Our analysis depicts another, calcium independent route, to activate PLC-γ through AKT1-PKN2 complex which is activated by CD44  Fig. 4a4a. This Other transcriptional regulators involved in the activation of skin barrier proteins like JUND and FOSL1, are also found to be downregulated. Furthermore, CEBP-α (CEBPA), an important transcription factor involved in the transcription of IVL and desmocollins , is alsoKeratinocyte Differentiation pathway also indicates that the anti-microbial peptides S100A7, S100A8 and S100A9 are under expressed in BM samples , an important trigger for cellular processes like cell migration, vascular permeability, angiogenesis, cell invasion and coagulation, is significantly more under expressed in BM than in PC  [To evaluate the performance of eSkIN pathway enrichment analysis, we compared the results with the most widely used functional enrichment tool, DAVID (rf.gov/) . BM and The enriched processes for BM dataset yielded 781 GO Biological Processes (GO BP) terms and 24 KEGG pathways  to be enriched. The pathways related to skin physiology, which are enriched in DAVID analysis are also obtained using eSkIN analysis  to derive mechanistic insights into the action of the drugs. On the other hand, eSkIN allows further exploration of the enriched pathways in terms of the molecular interactions in them, and thus, helps in deriving mechanistic insights into drug action , which is positively correlated with AD clinical severity , 45, is Furthermore, it is interesting to note that the suppressors of cytokine family (SOCS), SOCS1 and SOCS3 are downregulated even after the treatment with BM and PC. SOCS proteins are known to contribute towards disease manifestation in psoriatic skin . Our anaOur study suggested the causal biomolecular inter-relationships involved in the action of BM and PC on human skin, apart from highlighting the existing evidence on these drugs in the context of skin-associated functional networks. It is evident that BM downregulates molecules involved in inflammation, T-cell & eosinophil chemotaxis, adhesion, DNA repair and cell cycle, while PC targets a smaller section of inflammatory genes. Also, both BM and PC upregulate few important barrier proteins to restore the skin barrier functions. However, BM also downregulates many other barrier components which is largely unaffected by PC, thus, accounting for the impaired skin barrier due to the treatment with BM. It should be noted that the results reported in this study are based on 10 AD patients’ data that were available in NCBI GEO database, and needs further validation in a larger cohort.Additional file 1:Table S1. List of 35 pathways of eSkIN and their categorization. (DOCX 13 kb)Additional file 2:Sensitivity analysis to evaluate the effect of fold change cutoff on pathway enrichment analysis. (DOCX 12 kb)Additional file 3:Pathway enrichment analysis for different fold change cutoffs. Table and charts of pathway enrichment analysis for different fold change cutoffs. (XLSX 30 kb)Additional file 4:Figure S1. Comparison of enriched pathways at three different fold change cutoffs (Log2 fold change (FC) = 0.5, 1 and 1.5). (TIFF 3928 kb)Additional file 5:Figure S2. Expression profile of genes involved in TNF pathway in BM samples. (TIFF 4333 kb)Additional file 6:Figure S3. Expression profile of genes regulated by NF-κB in BM samples. (TIFF 12640 kb)Additional file 7:Figure S4. Section of eSkIN Inflammation pathway showing TLR mediated activation of NF-κB and the expression profile of genes involved in this pathway. (TIFF 12017 kb)Additional file 8:Figure S5. Section of eSkIN Inflammation pathway showing NR3C1 mediated inhibition of NF-κB through IκB (highlighted in pink) and the expression profile of IκB. (TIFF 7299 kb)Additional file 9:Figure S6. Sections of eSkIN Inflammation pathway showing: (a) activation of JAK-STAT pathway by IL4 and IFN-γ and their expression profiles (b) genes regulated by STAT6. (TIFF 9845 kb)Additional file 10:Figure S7. Expression profile of genes activated by IL4 via JAK-STAT pathway. (TIFF 5004 kb)Additional file 11:Figure S8. Expression profile of inflammatory genes that show downregulation in PC samples. (TIFF 10440 kb)Additional file 12:Figure S9. Section of eSkIN Immune Response pathway showing various activators of JAK-STAT pathway and their expression profile in BM and PC samples. (TIFF 10437 kb)Additional file 13:Figure S10. Expression profile of skin barrier proteins in BM and PC samples. (a) Expression profile of genes that are upregulated in both BM and PC in order to restore barrier functions; (b) Expression profile of skin barrier genes that show treatment specific difference in their expressions. (TIFF 8378 kb)Additional file 14:Figure S11. Expression profile of important transcription factors of barrier proteins in BM. (TIFF 5538 kb)Additional file 15:Figure S12. Expression profile of anti-microbial peptides in BM samples and a section of eSkIN pathway showing their transcriptional regulation by IL4. (TIFF 5747 kb)Additional file 16:Figure S13. Expression profile of junction proteins that show treatment specific difference in their expressions. (TIFF 6474 kb)Additional file 17:Figure S14. Section of eSkIN Wound Healing pathway showing VEGF mediated activation of cellular functions like focal adhesion, actin remodeling, cell migration, vascular permeability, angiogenesis, degradation of collagen, cell invasion and blood coagulation, and expression profile of VEGF in BM and PC samples. (TIFF 12210 kb)Additional file 18:Figure S15. Expression profile of basal layer genes that show treatment specific difference in their expressions. (TIFF 12463 kb)Additional file 19:Figure S16. Sections of eSkIN Lipid Synthesis pathway showing: (a) lipid transporters and their expression profiles in BM samples (b) enzymes involved in fatty acid conversion and their expression profiles in BM samples. (TIFF 5067 kb)Additional file 20:Figure S17. Sections of eSkIN Lipid Synthesis pathway showing: (a) the genes involved in the synthesis of lipids and fatty acids and their expression profiles in BM samples (b) genes involved in lipid metabolism and their expression profiles in BM samples. (TIFF 7877 kb)Additional file 21:Figure S18. Section of eSkIN DNA Damage and Repair pathway showing the genes involved in DNA repair mechanisms and their expression profiles in BM samples. (TIFF 10956 kb)Additional file 22:p-value < 0.05) using GO biological process (GO_BP_FAT) and KEGG pathways as annotation datasets in DAVID. BM and PC datasets were analyzed separately. (XLSX 168 kb)Pathway enrichment analysis using DAVID. Tables of enriched pathways (


Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D. T2D is characterized by a decrease in the number of functional insulin-producing β-cells2. Much effort has been devoted to identifying T2D susceptibility genes including linkage analysis, candidate gene study, and especially the genome-wide association studies (GWAS)3. However, the identified genetic variants or susceptibility genes just explain less than 20% of the overall estimated genetic contribution to T2D4. It is apparent that additional risk variants remain to be discovered.Type 2 diabetes (T2D) is a common human complex disease caused by a combination of genetic and environmental factors15. Pathway-based methods have also been applied into T2D GWAS datasets to investigate the biological mechanisms. Perry et al. analyzed three T2D datasets from Diabetes Genetics Initiative (DGI), Wellcome Trust Case Control Consortium (WTCCC) and Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics (FUSION)16. Using a modified Gene Set Enrichment Algorithm (GSEA) algorithm, they reported 26 significant pathways including 6 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 BioCarta pathways, and 17 GO (Gene Ontology) pathways in the WTCCC dataset. The WNT signaling pathway was most strongly associated with T2D. However, no pathways were associated with T2D after correcting for multiple testing (P < 0.05). Zhong et al. developed a novel approach by integrating the pathway analysis and gene expression into T2D WTCCC GWAS dataset. They analyzed 110 KEGG pathways and identified 22 significant pathways (P < 0.05)17. However, only the olfactory transduction pathway and TGF-signaling pathway were shared in both studies17.Fortunately, several studies have demonstrated importance of pathway-based approacheshttp://diagram-consortium.org/downloads.html)18. The meta-analysis consists of 12,171 T2D cases and 56,862 controls from 12 T2D GWAS from European descent populations18.To investigate more genetic signals in T2D, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS from the DIAGRAM Consortium  and VEGAS n = 1514) methods. We found that 570 T2D risk genes were shared in these two methods. We also compared the pathway analysis results using T2D risk genes from PLINK and VEGAS. We identified 8 KEGG pathways to be shared in both methods with a Bonferroni correction test with 514 methoet al. identified a total of 26 pathways with a nominal P < 0.05 in WTCCC dataset including 6 KEGG pathways including WNT signaling pathway, Olfactory transduction, Galactose metabolism, Pyruvate metabolism, Type II diabetes, TGF-signaling pathway. Interestingly, we successfully replicated Type II diabetes as described in supplementary Table 16.We further compared pathway analysis results with previous findings. Perry et al. integrated the pathway analysis and gene expression into T2D WTCCC GWAS dataset17. They highlighted the involvement of 22 KEGG pathways in T2D risk17. Interestingly, we successfully replicated 9 of these 22 pathways including Tight junction, Neuroactive ligand-receptor interaction, Cell cycle, and Antigen processing and presentation, as described in supplementary Table P < 0.05/220 = 2.27E-04.Zhong 17. Here, we performed a pathway analysis of large-scale T2D GWAS meta-analysis dataset, and identified 9 significant pathways shared in both methods. We confirm previous findings, such as Tight junction and Cell cycle17. Here, we also highlight some new T2D risk pathways, such as Melanogenesis, Vibrio cholerae infection, Gastric acid secretion, Phagosome, Ubiquitin mediated proteolysis, and Protein processing in endoplasmic reticulum.Until now, pathway analysis of T2D GWAS dataset has been performed21. However, some pathway analysis methods may have limitations and the analysis results may be unstable21. The gene set size and gene length, LD patterns and the presence of overlapping genes could inflate the gene-score p-values and further may cause biases in pathway analysis22. It is suggested that multiple methods should be used to evaluate the reliability of the results21. Here, we selected PLNK19 and VEGAS20 to perform the gene-based test. Both PLNK and VEGAS could adjust the gene length and LD patterns.It is reported that pathway analysis of GWAS datasets may be more successful for some complex traitsP values across all the SNPs in genes20. In VEGAS, all the P values across all the SNPs in genes are converted to uppertail chi-squared statistics with one degree of freedom20. VEGAS applied the summarized chi-squared 1 degree of freedom statistics within a specific gene20. PLINK uses the average association test statistic across a given set of SNPs as the “set-based” test statistic20. VEGAS software uses the sum rather than average20. Second, there are different methods to map the SNPs to their corresponding genes in PLINK and VEGAS. In PLINK, a given set of SNPs were mapped to genes if these SNPs were located within the genomic sequence corresponding to the start of the first exon and the end of the last exon of any transcript corresponding to that gene23. In VEGAS, a given set of SNPs were mapped to ±50 kb of the 5′ and 3′ UTR of the corresponding genes20. Third, there are different methods to account for the LD, gene size, and the P value in PLINK and VEGAS. PLINK uses permutation test and VEGAS uses the simulation test20. Fourth, VEGAS method is much faster than the PLINK set-based test20. Here, we focus on the overlapping pathways resulting from the genes from PLINK and VEGAS methods based on these differences above.There are some differences in PLINK and VEGAS. First, there are different statistic methods in PLINK and VEGAS. PLINK applied an approximate Fisher’s test to combine the 17. Perry et al. identified a total of 26 pathways16. Perry et al. analyzed 439 pathways from the Gene Ontology, BioCarta, and KEGG databases16. Meanwhile, Perry et al. selected the Bonferroni-adjusted P value to define the significant pathways, which further limits the number of significant pathways. Zhong et al. integrated the pathway analysis and gene expression into T2D WTCCC GWAS dataset17. They highlighted the involvement of 22 KEGG pathways in T2D risk17. We think that the integration of pathways and gene expression may be more powerful compared with the single pathway analysis, which may further reduce the number of significant pathways.Until now, two pathway analyses have been performedIn summary, we analyzed the large-scale T2D GWAS meta-analysis dataset using PLNK and VEGAS. We not only confirm previous findings, but also highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D. We will further replicate our findings using other available pathway analysis methods in future. Further replication studies are also required to evaluate our findings.http://diagram-consortium.org/downloads.html)18. Here, we give a brief description about the GWAS meta-analysis dataset18. More detailed information is provided in the original studies18. The meta-analysis consists of 12,171 T2D cases and 56,862 controls from 12 T2D GWAS from European descent populations18. All these samples were genotyped using kinds of genotyping platforms18. Each SNP was tested for association with T2D under an additive model after adjustment for study-specific covariates including indicators of population structure18. Full details of genotyping, quality control and imputation in each study are described in Supplementary Table 18. We got the association summary statistics from the original study18.The GWAS dataset came from a meta-analysis of T2D GWAS datasets from DIAGRAM Consortium  information from the HapMap CEU population23. PLINK applied an approximate Fisher’s test to combine P values across all the SNPs in genes to get the overall significance23. Meanwhile, PLINK supports larger genes (genes with more SNPs), and uses permutation testing to account for the correlation between SNPs (LD), gene size, and the P value from gene based test23.PLINK (SET SCREEN TEST) was used to perform the gene-based test of the T2D GWAS dataset in the gene level20. The software utilizes all SNPs within a gene and adjusts the gene sizes, SNP density, and LD relation in SNPs20. VEGAS first assigns SNPs within ±50 kb from the 5′ and 3′ UTR to the corresponding genes according to the position information20. In a given gene, all the SNPs association P values are converted to uppertail chi-squared statistics with one degree of freedom20. The gene-based test statistic is the summarized chi-squared 1 degree of freedom statistics within a specific gene20. Meanwhile, simulations are used to adjust the LD relation in SNPs within a specific gene using the HapMap2 CEU genotype dataset20.VEGAS was also applied to conduct a gene-based test of the T2D GWAS dataset in the gene level24. In a specific KEGG pathway, a hypergeometric test was used to detect an overrepresentation of the T2D-related genes among all the genes in the pathway24. The entire Entrez gene set is selected to be the reference gene list. In a specific pathway, the minimum number of genes was 5. Meanwhile, a Bonferroni correction method was used to adjust for multiple tests with P < 0.05/220 = 2.27E-04. 220 is the number of KEGG pathways. A specific pathway with a P < 0.05/220 = 2.27E-04 is considered to be a significant pathway.We used the online database WebGestalt to conduct a pathway analysisG1and G2 are the gene sets of pathway p1and p2, respectively. |.| is the number of genes in the specified set. We then calculated the weights of all the pair-wise pathways and constructed a pathway network, where a node represents as a pathway and an edge as the weight of the pair-wise pathways more than zero.The weight of pair-wise pathways based on their related genes is defined on account of Jaccard Index as following.Dataset 1


Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that tends to co-occur with other diseases, including asthma, inflammatory bowel disease, infections, cerebral palsy, dilated cardiomyopathy, muscular dystrophy, and schizophrenia. However, the molecular basis of this co-occurrence, and whether it is due to a shared component that influences both pathophysiology and environmental triggering of illness, has not been elucidated. To address this, we deploy a three-tiered transcriptomic meta-analysis that functions at the gene, pathway, and disease levels across ASD and its co-morbidities.Our analysis reveals a novel shared innate immune component between ASD and all but three of its co-morbidities that were examined. In particular, we find that the Toll-like receptor signaling and the chemokine signaling pathways, which are key pathways in the innate immune response, have the highest shared statistical significance. Moreover, the disease genes that overlap these two innate immunity pathways can be used to classify the cases of ASD and its co-morbidities vs. controls with at least 70 % accuracy.This finding suggests that a neuropsychiatric condition and the majority of its non-brain-related co-morbidities share a dysregulated signal that serves as not only a common genetic basis for the diseases but also as a link to environmental triggers. It also raises the possibility that treatment and/or prophylaxis used for disorders of innate immunity may be successfully used for ASD patients with immune-related phenotypes.The online version of this article (doi:10.1186/s13059-016-1084-z) contains supplementary material, which is available to authorized users. While at an organismal level, two or more diseases may appear unrelated, at the molecular level, it is unlikely that they arise entirely independently of one another. Studies of the human interactome—the molecular network of physical interactions  between biological entities in cells—demonstrate that gene function and regulation are integrated at the level of an organism. Extensive patterns of shared co-occurrences also evidence molecular commonalities between seemingly disparate conditions .Indeed, different disorders may share molecular components so that perturbations causing disease in one organ system can affect another . Yet, sip11.2 CNV encompassing around 600 kb  has been implicated in multiple psychiatric disorders with the deletions being associated with ASD, developmental delay, and intellectual disability, and duplications being associated with ASD, schizophrenia, bipolar disorder, and intellectual disability . We also considered the ‘no correction’ case for completeness. We selected the most informative one, based on the level of accuracy we could achieve in classifying cases of a particular disease, vs. controls, using the genes selected under a specific test with a significance cutoff of p<0.05. We tested the accuracy of the case–control classification for each of the 53 disease data sets using four different classification methods, namely, naive Bayes method, Fisher’s linear discriminant analysis, k nearest neighbor, and SVM. The set of significant genes selected under different FDR corrections was considered as a feature of the classification methods. We performed threefold cross validation and calculated the average accuracy. We selected the FDR correction test that produced the best average accuracy in each disease. See Additional file To adjust the combined k or more significant disease genes, out of n total genes, appearing in a specific pathway gene set. It helps identify whether or not the specific disease gene set is over-represented in a certain pathway, by providing a p value per pathway per disease.From the disease-level gene-centric expression analysis, we obtained a list of significant genes per disease. For each disease, we then performed a hypergeometric enrichment test for each pathway. This test uses the hypergeometric distribution to calculate the statistical significance of p values for the pathways per disease, first we calculated the pairwise Pearson correlation of pathway p values across diseases , we safely assumed the distributions to be independent. Next, we calculated combined p values for each pathway across all the diseases using Fisher’s combined probability test. We corrected for multiple comparisons using Bonferroni correction. We defined a significance threshold of adjusted p value <0.05 and called any pathway that passed this threshold, significant. We restricted our results to the pathways that appeared significant in ASD.Once we obtained the p values (<0.05), because after BY correction none of the genes remained significant. On the significant gene list, we performed hypergeometric enrichment analysis to obtain a pathway p value distribution. We repeated this process 100 times to obtain 100 null p value distributions. We calculated the prior for each pathway by looking at how many times the pathway appeared significant  during these 100 runs. We took an average of the 100 distributions to obtain the null p value distribution.To estimate the prior probability of pathways, we selected a publicly available GEO study of 109 gene expression profiles of blood drawn from healthy individuals enrolled at a single site (GEO accession: GSE16028). We assigned case–control labels randomly to the samples and performed differential expression analysis using R package limma. We selected differentially expressed genes using uncorrected p values is that p values are uniformly distributed and the alternative hypothesis is that smaller p values are more likely than larger p values. Following the approach of Sellke, Bayarri, and Berger [e is Euler’s constant.The null hypothesis for pathway d Berger , we estiχ2-distributed test statistics, we used Johnson’s formula [x is the chi-square statistic that gave rise to the observed p value and v is the degrees of freedom.For calculating minimum BFs for  formula : \documq is the prior probability.Following Goodman’s approach , we usedThe null distributions and priors for all KEGG pathways and the minimum BFs, and minimum posterior probabilities of null hypotheses for KEGG pathways are given in Additional file We performed threefold classification and measured the average accuracy of the case–control classification for each disease with the SVM classifier using the union set of the genes from KEGG Toll-like receptor signaling and chemokine signaling pathways shared across ASD and its co-morbidities to see how well the overlapping genes could distinguish the disease state from controls and compared it with the classification accuracy using randomly selected genes that do not overlap with these two pathways Fig. . We repeWe selected 11 disease conditions that co-occur most commonly in ASD patients. Each of these diseases has at least 5 % prevalence in ASD patients . The prep values only.To identify publicly available studies relevant to these co-morbidities, we performed an extensive literature search of the GEO of the National Center for Biotechnology Information (NCBI) , 90. UsiWe collected 1320 curated pathway gene sets, including those from the KEGG pathways , 92, Rea


Alzheimer's disease (AD) affects about 10% of the population over 65 years old  across multiple molecular pathways is a non-trivial endeavor. One approach to identify whether crosstalk exists between two pathways is to determine if both pathways work together to perform a biological function such as the Toll-like receptor and complement pathways interacting to reinforce innate immunity  iden Figure , and (C)p-value for crosstalk likelihood between a pathway pair.Traditional statistical approaches for characterizing interactions have only a limited ability to capture the heterogeneity and complexity of pathway crosstalk and consequently a number of novel informatics approaches have been proposed  and gene expression (transcriptomics) data collected in large naturalistic studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Dominant Inherited Alzheimer's Network (DIAN) or large clinical trials such as the Alzheimer's Disease Genetic Consortium (ADGC). Using SNP-data as an illustrative example, characterization of patient-specific pathway crosstalks could be broken down into four steps schematically as shown in Figure Obtain a mapping of SNPs to pathways using genetic information.Identify the list of SNPs that are present in a patient.Use the mapping obtained in Step 1 and the patient-specific SNP list in Step 2 to obtain the pathways that are “SNP-enriched” in the patient.Use the “SNP-enriched” pathways from Step 3 to obtain patient-specific pathway crosstalks.To determine which of the pathway crosstalks in the generic reference map may be utilized as a biomarker for AD, one could then go further to characterize If a different “-omics” data source is used such as proteomics or metabolomics, then a simple substitution of SNPs for the desired data source in the above schema is needed to obtain “patient specific data-enriched” pathways.A number of other informatics approaches that attempt to predict crosstalk are possible and have been reviewed . We further hypothesize that building dynamic network biomarkers and pathway crosstalk reference maps using the combined power of several protein/gene-level knowledge priors could accelerate discovery of disease-specific mechanisms and novel drug targets by enrichment with patient-specific genetic information. Application of this methodology to large public AD datasets is needed to test our hypotheses and refine the methods. Subsequent replication in independent datasets and population studies as well as functional validation of mechanisms in laboratory models will be the next steps. Ultimately, it is our hope that such novel methods may yield further insights into both disease mechanisms as well as novel targets for biomarker development and drug discovery.PD and NS designed and provided conceptual guidance for all aspects of this work. KP, KS, and GB executed the work, conducted a thorough review of literature, and collaborated with PD and NS to draft, critically revise, and approve the final opinion piece.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.