Here, problem formulation, an initial step in environmental risk assessment (ERA), was performed for simulated field releases of the dsxFCRISPRh transgene in West Africa.Population suppression gene drive has been proposed as a strategy for malaria vector control. A CRISPR-Cas9-based transgene homing at the Building on consultative workshops in Africa that previously identified relevant environmental and health protection goals for ERA of gene drive in malaria vector control, 8 potentially harmful effects from these simulated releases were identified. These were stratified into 46 plausible pathways describing the causal chain of events that would be required for potential harms to occur. Risk hypotheses to interrogate critical steps in each pathway, and an analysis plan involving experiments, modelling and literature review to test each of those risk hypotheses, were developed.dsxFCRISPRh homozygotes, many could also be applicable to field releases of a range of other transgenic strains of mosquito (n = 18). In addition to population suppression of target organisms being an accepted outcome for existing vector control programmes, these investigations also revealed that the efficacy of population suppression caused by the dsxFCRISPRh transgene should itself directly affect most pathways (n = 35).Most potential harms involved increased human (n = 13) or animal (n = 13) disease transmission, emphasizing the importance to subsequent stages of ERA of data on vectorial capacity comparing transgenics to non-transgenics. Although some of the pathways (n = 14) were based on known anatomical alterations in Modelling will play an essential role in subsequent stages of ERA by clarifying the dynamics of this relationship between population suppression and reduction in exposure to specific potential harms. This analysis represents a comprehensive identification of plausible pathways to potential harm using problem formulation for a specific gene drive transgene and organism, and a transparent communication tool that could inform future regulatory studies, guide subsequent stages of ERA, and stimulate further, broader engagement on the use of population suppression gene drive to control malaria vectors in West Africa. Anopheles gambiae has been proposed as one such complementary approach to vector control .–90.88–90dsxFCRISPRh transgenics by promiscuous cleavage by Cas9 at non-target genomic sequences followed by their misrepair [Off-target mutations could theoretically occur in isrepair . Retargeisrepair . Off-tarIn vitro and in vivo molecular analyses have been developed to screen CRISPR-Cas9 transgenic lines for off-target mutations [The propensity for off-target effects is dependent on the uniqueness of the guide RNA target site; the efficiency, intracellular concentration and duration of the nuclease activity; and, the chromatin state of the tissues and cells in which the nuclease is expressed . In vitrutations . Bioinfoutations  and promutations .dsxFCRISPRh transgene in West Africa, eight broad, potentially harmful effects were identified. These were stratified into 46 plausible pathways leading to potential harms to the four protection goals  would insert at the guide RNA target site (23 bp) of the genome of a valued species’ germline cells. For reference, the e 278 Mb . The secAn. gambiae.A fourth risk hypothesis postulated that the guide RNA target site would not be conserved in the genomes of valued species. Bioinformatics could be used to assess the degree of conservation of the RNA target site in valued species of insects associated with For water quality protection goals, two plausible pathways to potential harm were identified Figs.  and 12. For Pathway 9, the accumulation of transgenic product could lead to toxicity in detritivores, leading to negative effects on water quality for aquatic NTO flora and fauna Fig. . Four riFor human health protection goals, 20 plausible pathways to potential harm were identified Figs. , 31, 32.dsxFCRISPRh transgenic mosquitoes could lead to allergic responses in humans from transgenic biting beyond those that occur from non-transgenic biting , compared to 0.29, 0.05 and 0.0094% of total DALYs for LF, yellow fever and dengue, respectively [An. gambiae for the most common LF parasite, Wuchereria bancrofti, has not yet reliably been established [Pathway 21 relates to how altered anatomy in transgenics could lead to increases in the transmission of lymphatic filariasis (LF) Fig. . After mectively . Despiteablished .dsxFCRISPRh transgenic mosquitoes have reported anatomical alterations, including specifically to mouthparts, with most heterozygotes appearing to be normal in this respect [dsxFCRISPRh transgenics may increase the probing rate of homozygous transgenics, or in heterozygotes increase the biting rate or the transmission rate from a given biting rate. Moreover, the cibarial armature is implicated in transmission of filariae to mosquitoes [Homozygous  respect . This pasquitoes –105.An. gambiae. A fourth risk hypothesis proposed that population suppression from gene drive would have a greater effect on vectorial capacity than any increase in vector competence caused by altered dsxFCRISPRh transgenic anatomy. This hypothesis could be tested by calculating the net effect on vectorial capacity of any such increase in vector competence in the presence of population suppression from gene drive.Four risk hypotheses were developed to investigate this pathway. In the first, it was hypothesized that transgenics did not differ anatomically to non-transgenics in anatomy related to the success of pathogen transmission. This could be assessed in transgenics and non-transgenics in insectary anatomical studies. The second risk hypothesis posited that transgenics did not bite humans more than non-transgenics to increase disease transmission. This could be tested in the laboratory by comparing the biting and probing rate of transgenics with non-transgenics using a panel of artificial attractants, and the increase in the number of infectious bites required to increase background disease transmission could be calculated. A third risk hypothesis proposed that transgenics showed no increase in vector competence, or decrease in extrinsic incubation period, compared with non-transgenics. Both of these parameters could be assessed in the laboratory for a representative panel of pathogens vectored by Anopheles 1,000 genomes reference genome sequence [in vitro CIRCLE-Seq [in vivo evidence of their mutation [Pathway 25 describes how the accumulation in transgenics of Cas9/guide RNA off-target or retargeted mutations in genomic loci involved in insecticide resistance, fitness or vector competence could lead to an increase in vectorial capacity and human disease transmission Fig. . Three rsequence , 107, anRCLE-Seq ) of mosqmutation .Where off-target or retargeted mutations would be detected in transgenics by molecular and bioinformatic analyses, as discussed for Pathway 3 above, they could occur via two scenarios: ‘phenotypic changes due to mutation generation followed by natural selection’ or ‘phenotypic changes due to mutation pressure’. The former may not be observable in the laboratory but would, in any event, be occurring naturally in large field populations in the absence of the transgene , 93, 97.A third hypothesis proffered that population suppression from the gene drive would have a greater effect on vectorial capacity than from any increase in insecticide resistance, fitness or vector competence, the net effects of which could be calculated empirically.An. gambiae could cause reductions in the density of a predator that leads to and expansion in the density of another human disease vector  than from feeding on another species (D) which is a human disease vector, so that under such normal circumstances G and D would be in asymmetric apparent competition with each other [An. gambiae, the population density of P could also reduce. This could, in effect, release D from asymmetric apparent competition with G, leading to increases in the population density and vectorial capacity of D, resulting in increased disease transmission in human by D.Pathway 29 describes how population suppression of tor Fig. . This woch other . HoweverAn. gambiae. This could be tested in the first tier of an analysis plan by assessing the literature and field observations for evidence of predators that predominately feed on An. gambiae, such as from studies DNA barcoding the gut contents of predators [An. gambiae also feeds on another human disease vector species, would only need to be investigated where the first risk hypothesis was falsified. This could be tested in the second tier of the analysis plan by further literature investigation and field observations for evidence of shared predators between An. gambiae and other vector species.Five risk hypotheses were developed to interrogate this pathway. In the first, it was posited that no predator predominantly feeds on redators . A seconAn. gambiae, which could be investigated via ecological modelling. A fourth risk hypothesis proffered that D is a less efficient vector of diseases that are also transmitted by An. gambiae and/or the diseases transmitted by An. gambiae are more harmful than those transmitted by D. This could be tested by modelling the net effect of disease transmission of An. gambiae and D both before and after population suppression of An. gambiae, as well as assessing the literature on the burden of human disease in West Africa from mosquito species that share predators with An. gambiae.A third risk hypothesis posited that there are no other human disease vectors in asymmetric apparent competition with An. gambiae, which would be tested by assessing the literature on the entomological and epidemiological impacts of conventional vector control measures against An. gambiae, such as indoor residual spraying or insecticide-treated bed nets [The fifth, and last, risk hypothesis for this pathway stated that the disease burden in West Africa will not increase following population suppression of bed nets .For animal health protection goals, 16 plausible pathways to potential harm were identified Figs. , 47, 48.Plasmodium or O’nyong’nyong virus (ONNV) [Pathway 32 defines how decreases in transgenic mosquito defence responses via alterations to endogenous RNA, protein or microbiomic species could lead to increased livestock disease Fig. . Mosquits (ONNV) –116. It Two risk hypotheses were developed to interrogate this pathway. In the first, it was proposed that transgenics would not transmit higher levels of animal pathogens than non-transgenics, which could be tested in the analysis plan using measurement endpoints in vector competence assays involving a representative panel of pathogens. The second hypothesis stated that population suppression from gene drive would have a greater overall negative effect on livestock disease incidence than any increased levels of vector competence, the net effect of which could be calculated from empirical data.An. gambiae could release another animal disease vector species from competition, thereby increasing the incidence of livestock disease , many of these could still be applicable to field releases of other strains of transgenic mosquito (n = 18). Other pathways could be selectively applicable to population suppression gene drive strains (n = 8), CRISPR-Cas9-based strains (n = 3) or other gene drive transgenic strains (n = 2) , with sub-sets of this causes of potential harm category including changes in transgenic vector competence (n = 14) or in host biting rates (n = 5).Most causes of harm related to changes in the biology of transgenics compared to non-transgenics at the level of the individual mosquito. For example, homozygous  fertile . Homozygtal lobe . Given tdsxFCRISPRh transgene (n = 3). Increases in fitness could, for example, allow transgenics to extend their geographic range or contribute directly to increases in vectorial capacity unless the efficacy of population suppression were to outweigh the effect. Fitness costs in the transgenic were also causes of potential harms to water quality from increased larval and pupal mortality (n = 2). After fitness effects, the next most common category of cause of harm was from the transgenic containing a toxin or allergen (n = 9).The second-most common category of causes of potential harm was fitness effects in transgenics (n = 11), with either increases in transgenic fitness (n = 8) or fitness costs in individuals possessing the An. gambiae complex was considered to be an intended effect and not a harm itself, because spread of the gene drive transgene to all species of the An. gambiae complex is an expected outcome.As well as potential harms caused by changes to the biology of transgenic mosquitoes at the individual level, some potential harms were identified that would occur at population level as a result of a population suppression gene drive (n = 7). Unlike those potential harms reported in Roberts et al.  and TeemAn. gambiae would, of itself, be the primary cause of potential harm by leading to the release of other species from competition  or indirect n = 16) , in which cases dsxFCRISPRh transgenics. A minority of pathways (n = 4) could occur via allergic or immune system responses in individual humans exposed to potentially single transgenic mosquitoes or their body parts, and these could occur without any gene drive efficacy. Only 11 potential harms would be unrelated to the presence of the transgene in the environment and three, caused by off-target or retargeted mutations, could occur either due to the continued presence or subsequent absence of the transgene. However, for most potential harms to occur (n = 42), they would depend of the successful efficacy of the gene drive  would also be dependent on the absolute numbers of transgenics in the environment, where the greater the efficacy of population suppression, the less exposure there should be leading to potential harms. However, exposure levels leading to a minority of potential harms n = 8) could increase with successful population suppression gene drive, such as in Pathways 6, 7, 27, 29, 30, 43, 45, and 46 shown in Figs.  could inIt is also important to recognize that any potential investigational release of a gene drive is first and foremost into a local environment with specific conditions which will vary from place to place and season to season . So it cAnalysis of risk hypotheses and allied analysis plans revealed the experimental outputs that should provide the widest impact on testing of pathways and potential harms with the least levels of residual uncertainty. The most common theme amongst all risk hypotheses , that the transgenic would not transmit pathogens better than non-transgenics, can be informed by testing in the laboratory by assessments of vector competence and extrinsic incubation periods for a number specific representative pathogens. Data from such experimental evaluations can then further evaluated in the context of additional parameters relevant to vectorial capacity, to inform risk conclusions in subsequent stages of ERA. The second most common theme amongst risk hypotheses was that the transgenic was not toxic or allergenic , which can be tested initially from analysing bioinformatic and literature databases, supplemented by feeding studies in indicator species. The third most frequent theme in risk hypotheses involved fitness being unaffected in transgenics , which can be tested in small and large cage laboratory studies. The next most common theme in risk hypotheses was that transgenics did not bite more than non-transgenics . Rates of biting and probing can be assessed in the laboratory using a panel of artificial attractants. Finally, another common theme in risk hypotheses was that transgenic saliva did not differ from non-transgenic saliva , which can be tested via a variety of laboratory experiments that will assess levels of guide RNA, endogenous allergens, or RNA and proteins implicated in host defence responses.The exercise which led to the development of the current set of plausible pathways to potential harm took place between July 2019 and October 2020. Over that period, several provisional pathways were developed that were ultimately excluded from this analysis as invalid or inconsequential . These iloss of efficacy of population suppression gene drive from the suite of initial pathways considered, as it was agreed they did not directly result in potential harms to protection goals and therefore should not be included amongst the plausible pathways to potential harm;n = 5 pathways to transboundary movement as it was subsequently recognized that ERA does not formally include socio-economic or legal issues, such as the potential for transboundary movement of transgenics, despite these warranting further exploration in the context of gene drive organisms that are anticipated to cross national borders;n = 3 pathways related to vertical gene transfer (via hybridization) of the population suppression gene drive from An. gambiae to species outside the complex, as it was subsequently recognized that the most closely related species to those within the An. gambiae complex is An. christyi, but which is separated by circa 9 million years of evolution. The absence of observed gene flow between species of An. gambiae and An. christyi supports the lack of any significant hybridization between these species so that, for even less closely related species of Anopheles, hybridization is considered implausible. Moreover, in species of Anopheles more distantly related to An. gambiae than An. christyi, the guide RNA target DNA sequence of the dsxFCRISPRh transgene diverges from that found in An. gambiae;n = 1 pathway to potential harm that would be caused by increased transgenic larval mortality because it was based on the false assumption that population suppression would automatically lead to increased larval mortality and because it was ultimately considered biologically implausible as a potential route to increased disease transmission;n = 2 pathways leading to potential increases in disease transmission caused by transmission of a novel pathogen caused by altered host-seeking behaviour in, or off-target mutations in, or extension in geographic range of, the transgenic. By contrast, pathways involving altered transgenic anatomy or physiology leading to the transmission of a novel pathogen were retained as plausible;n = 6 pathways to potential harm to either human or animal health that would result in n = 6 pathways, which were eliminated when n = 12 pathways were combined together into n = 6 pathways at the request of peer reviewers to help identify more efficiently common risk hypotheses and key experiments that could interrogate the viability of a number of pathways to a low level of remaining uncertainty.dsxFCRISPRh transgene in West Africa. Although based on the gene drive strain containing the dsxFCRISPRh transgene as described in Kyrou et al. [doublesex, for example those exploiting multiplexed guide RNAs  potential harms and exposure routes; (b) areas of uncertainty in our scientific understanding of plausible pathways to potential harms; and, (c) analysis plans to credibly assess pathways. Although superficially similar to fault tree analyses in some probabilistic risk assessment , the purdsxFCRISPRh transgenics on NTOs could increase disease transmission in livestock, toxicology tests could be conducted on indicator species and the toxicity of transgenic proteins investigated in the literature and bioinformatically, mostly based on evidence from humans. Nonetheless, evidence from investigation of these evidence requirements should be sufficient to allowed informed decision-making about the potential for such a harm. Additionally, for any given risk hypothesis analysis plans should be tiered so that components likely to contribute most to informing the ERA and allied decision-making can be prioritized [Depending on the nature of the individual pathway, there are four possible outcomes in subsequent stages of ERA: (i) all hypotheses for a given pathway could be rejected in which case the pathway to harm would be considered valid; (ii) one or more hypotheses that nullify one or more individual steps in a pathway would cause the entire pathway to be rejected; (iii) evidence from a series of hypotheses may together produce sufficient ‘weight of evidence’ to indicate rejection of that pathway; or, (iv) evidence from a series of hypotheses may together produce sufficient weight of evidence to indicate acceptance of that pathway. For example, in Pathway 50, Potential toxicological effects of oritized , 80. WhedsxFCRISPRh transgene in West Africa were identified. These were stratified into 46 plausible pathways that would be required for potential harms to occur. Most identified potential harms involved increased disease transmission, emphasizing the importance to subsequent stages of ERA of data assessing vectorial capacity in transgenics compared to non-transgenics. Although some of the pathways were based on considerations arising from anatomical alterations typically found in dsxFCRISPRh homozygotes, many pathways could be relevant to field releases of a range of other transgenic strains of mosquito. This analaysis revealed that the efficacy of population suppression caused by the dsxFCRISPRh transgene should itself have a direct impact on the majority of pathways. Mathematical modelling will therefore play an essential role in subsequent stages of ERA by clarifying the dynamics of this relationship between population suppression and reduction in exposure to specific potential harms [Building on a series of consultative workshops held in Africa that previously identified relevant environmental and health protection goals, eight potentially harmful effects of simulated field releases of the al harms –35, 139.al harms –35, and


Ulcerative colitis (UC) is a chronic inflammatory bowel disease, and Gegen Qinlian Decoction (GQD), a Chinese botanical formula, has exhibited beneficial efficacy against UC. However, the mechanisms underlying the effect of GQD still remain to be elucidated. In this study, network pharmacology approach and molecular docking in silico were applied to uncover the potential multicomponent synergetic effect and molecular mechanisms. The targets of ingredients in GQD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) database, while the UC targets were retrieved from Genecards, therapeutic target database (TTD) and Online Mendelian Inheritance in Man (OMIM) database. The topological parameters of Protein-Protein Interaction (PPI) data were used to screen the hub targets in the network. The possible mechanisms were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to verify the binding affinity between the active compounds and hub targets. Network pharmacology analysis successfully identified 77 candidate compounds and 56 potential targets. The targets were further mapped to 20 related pathways to construct a compound-target-pathway network and an integrated network of GQD treating UC. Among these pathways, PI3K-AKT, HIF-1, VEGF, Ras, and TNF signaling pathways may exert important effects in the treatment of UC via inflammation suppression and anti-carcinogenesis. In the animal experiment, treatment with GQD and sulfasalazine (SASP) both ameliorated inflammation in UC. The proinflammatory cytokines  induced by UC were significantly decreased by GQD and SASP. Moreover, the protein expression of EGFR, PI3K, and phosphorylation of AKT were reduced after GQD and SASP treatment, and there was no significance between the GQD group and SASP group. Our study systematically dissected the molecular mechanisms of GQD on the treatment of UC using network pharmacology, as well as uncovered the therapeutic effects of GQD against UC through ameliorating inflammation via downregulating EGFR/PI3K/AKT signaling pathway and the pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6. Ulcerative colitis (UC), a subcategory of inflammatory bowel disease (IBD), is an idiopathic chronic inflammatory disorder of intestinal mucosa or lamina propria . The carChinese pharmacopoeia, GQD consists of four botanical drugs, namely Pueraria lobata (Willd.) Ohwi , Scutellaria baicalensis Georgi , Coptis chinensis Franch , and Glycyrrhiza uralensis Fisch  at the ration of 8:3:3:2. Our previous study and clinical studies have revealed that GQD possessed amazing curative effects in the treatment of UC  (http://bionet.ncpsb.org/batman-tcm)   and Bioiman-tcm) . Then weTo verify the pharmacokinetic characteristics of drugs, a compound screening model provided by the TCMSP data platform, including the evaluation of oral bioavailability (OB), Caco-2 cell permeability (Caco-2) and drug-likeness (DL) were employed. And the three parameters above were clarified as following. OB refers to the rate of an orally administered drugs of unmodified drug that delivers to circulatory system, which is considered predictive of bioactive molecule indicators as therapeutic agents . Accordihttp://www.swisstargetprediction.ch/), which can be mined automatically to retrieve specific information for a large number of molecules (https://pubchem.ncbi.nlm.nih.gov/), which is a pivotal chemistry resource served as an archive of chemical substance description and structure standardization (http://www.genecards.org), therapeutic target database (TTD) (http://db.idrblab.net/ttd/) and Online Mendelian Inheritance in Man (OMIM) database (http://omim.org/).The protein targets of the candidate substances in GQD were retrieved from The Swiss Target Prediction database  with the species limited to “Homo sapiens” and a confidence score > 0.7 (The protein-protein interaction (PPI) of each target were generated from STRING database (re > 0.7 . Becausehttp://david.abcc.ncifcrf.gov). GO terms and KEGG pathways with p value less than 0.05 according to Fisher’s exact test were considered statistically significant and subjected to further analyses.To systematically explore the concerned biological processes of GQD as a therapy against UC, we performed GO and KEGG enrichment analysis by the functional annotation tool DAVID , a web used for predicting the binding sites and affinity with a popular docking program, Autodock Viana : control group, model group, GQD group and SASP group. All animals were maintained under a 12 h light/dark cycle environment. The room temperature was controlled at (22 ± 1)°C, with (60 ± 5) % humidity, and adequate food and water were provided. The animal experiments were approved by the Laboratory Animal Ethical Committee of Three Gorges University, China .A total of 60 male C57BL/6 mice weighing about 18–22 g were purchased from the Laboratory Animal Center of China Tree Gorges University . All mice were randomly assigned to four groups (Pueraria lobata (Willd.) Ohwi , Scutellaria baicalensis Georgi , Coptis chinensis Franch , and Glycyrrhiza uralensis Fisch . All the botanical drugs were purchased from Yichang Hospital of Traditional Chinese Medicine, Hubei province, China. Dextran sulfate sodium  was obtained from MP Biomedicals . EGFR antibody, and PI3K antibody were obtained from Proteintech Group ; p-AKT antibody was purchased from Affinity Biologicals , and β-actin antibody was obtained from Boster Biological Technology .The botanical formula GQD is comprised of four medicinal botanical drugs: Pueraria lobata (Willd.) Ohwi (24 g), Scutellaria baicalensis Georgi (9 g), Coptis chinensis Franch (9 g), and Glycyrrhiza uralensis Fisch (6 g) were soaked in 75% ethanol at eightfold volume overnight and then boiled for 1.5 h, the first decoction was thus obtained. Then the residue was boiled a second time with an addition of sixfold volume of 75% ethanol for 1 h to obtain the second decoction. Finally, the first and second decoction were mixed, filtered through gauze, and evaporated to dryness under reduced pressure, and then the extractum was freeze drying to powder.For the extraction of GQD, The analytical standards were purchased from Push Bio-technology Co., Ltd. , and the analysis of GQD was performed on Agilent 1,260 Infinity Ⅱ HPLC system  equipped with a Diode Array Detector (DAD). A reversed phase Welch Ultimate XB C18  was used and kept at 30°C. The mobile phase consisted of aqueous solution of phosphoric acid and methanol with a gradient elution at a flow rate of 1.0 ml/min. And the detection wavelength was set at 250, 350, 280, and 250 nm.n = 15/group): control group, model group, GQD group, and SASP group. All mice except the control group were induced by administration of 3% DSS dissolved in drinking water continuously for 7 days. And the mice in normal group received distilled water without DSS throughout the entire experimental period. According to clinical administration dose, the mice in GQD group and SASP group were administered with GQD enema (0.68 g/kg per day) or SASP enema (0.5 g/kg per day), respectively. Meanwhile, the mice in control group and model group were administrated with saline by enemata. The animals were monitored once a day for body weight, stool consistency, and presence of blood in the feces and the anus. At day 7, mice were anesthetized and sacrificed by cervical dislocation, and the colon samples were collected immediately. Then the colon length was measured and stored for subsequently experiments.Mice were randomly allocated to four groups  was calculated as the average of body weight loss score, diarrhea score, and fecal blood score. DAI evaluation was calculated under the guidance of previously established scoring system , and theColon tissues were collected and fixed in 4% paraformaldehyde overnight, then dehydrated at gradient concentrations of ethanol, and embedded in paraffin. Tissues were sectioned at 4 μm thickness with a paraffin and stained with hematoxylin and eosin (HE). The images of the tissues were obtained using Image-Pro Plus 5.0 system.To test the anti-inflammatory effects of GQD on ulcerative colitis, colon homogenate supernatants of all the experimental groups were collected, and the expression of TNF-α, IL-1β, and IL-6 was measured by ELISA kits  according to the manufacturer’s instructions.p-AKT , and β-actin (1:500).The total protein of colon tissue was extracted using RIPA lysis buffer with the protease inhibitor cocktail , and protein concentration was measured using the BCA protein assay kit (Beyotime Biotechnology) according to the manufacturer’s instructions. Equivalent amounts of protein (20 μg) were separated by SDS-PAGE and then transferred onto 0.45 μm PVDF membranes . The membranes were then blocked in 5% nonfat milk in TBST buffer for 1 h at room temperature, followed by incubation with primary antibodies at 4°C overnight. Then membranes were washed with TBST and incubated with secondary antibody for 1 h at room temperature, proteins were detected using ECL reagent . The antibodies used were as follows: EGFR , PI3K , p value < 0.05 was considered statistically significant.All the experimental data were analyzed using SPSS 20.0 statistical software . GraphPad Prism 6.0 software  was used for the calculations. All results are expressed as means ± standard error of the mean (SEM). Multigroup comparisons were performed using one-way analysis of variance (ANOVA) and post hoc Statistical least significant difference (LSD) test. After the absorption, distribution, metabolism, elimination (ADME) parameters screening , 75 potential components of the four botanical medicines were collected from database and relate literatures. Among them, 3 ingredients belong to GG, 9 belong to HL, 20 belong to HQ, and 47 belong to GC. It is worth mentioning that formononetin  as an important component existed in both GG and GC. Additionally, beta-sitosterol , exhibited good ADME properties, can be collected from GG and HQ. Both coptisine  and epiberberine  are common ingredients found in HQ and HL. Additionally, two compounds, puerarin and daidzein, did not meet the three criteria were taken into consideration as their obvious bioactivities . ConsequBased on The Swiss Target Prediction database, a total of 740 targets related to the 77 compounds were obtained in GQD. In order to explore the relevance and underlying mechanism of these targets specific to GQD in the development of UC, A total of 3785 UC-related genes were retrieved from the GeneCards, OMIM, and TTD databases by employing a keyword-based search. After eliminating the overlaps, 287 targets were shared between 740 compound-related targets and 3785 UC-related targets. PPI network of these 287 targets were established in STRING database and visualized by Cytoscape software. There were 287 nodes and 4,490 edges in this interaction network. According to previous study, a node would be defined as a hub target when the degree was more than twofold the median degree of all nodes in the network . Eventuap-value and gene count. These targets were obviously enriched in negative regulation of apoptotic process, protein phosphorylation, cell proliferation, MAPK cascade and inflammatory response, which have a strongly correlation with the pathogenesis of UC. These results indicated the multiple synergies of GQD on biological processes. To further reveal the potential mechanisms of the therapeutic effects of GQD, we conducted KEGG pathway enrichment analysis on 59 targets, and screened out 20 pathways based on the threshold of p-value < 0.05. The top 20 significantly enriched pathways contained pathways in cancer, HIF-1 signaling pathway, PI3K-AKT signaling pathway, TNF signaling pathway, VEGF signaling pathway and Ras signaling pathway , while this elevation was relieved by GQD and SASP treatment . However, the administration of GQD and SASP inhibited the content of TNF-α, IL-1β, and IL-6 significantly than that of the model group (p < 0.01). These data demonstrated that GQD and SASP could inhibit the inflammatory reaction induced by DSS in UC.As pro-inflammatory factors play important roles in the pathogenesis and progress of UC, we investigated whether GQD possess the anti-inflammatory effect in UC. ELISA essay was performed in colonic samples of all the experimental groups. As shown in p-AKT. As shown in p-AKT in colon tissue, which indicates the activation of EGFR/PI3K/AKT signaling pathway in UC. Notably, the elevation of these protein targets was blocked by GQD treatment. Similarly, administration of SASP downregulated the protein expression of EGFR, PI3K, and p-AKT as well. These results demonstrated that GQD protects against UC partially through inhibiting the activation of EGFR/PI3K/AKT signaling pathway.Based on numerous studies and the network pharmacology analysis, inflammation plays an essential role in the pathogenesis and process of UC, and the inflammatory response may partially mediate by EGFR/PI3K/AKT signaling pathway . TherebyUC is a common intestinal inflammatory disease. Presently, numerous studies have demonstrated that GQD, as a classical complementary and alternative TCM prescription, exerted acclaimed therapeutic effects against UC . Whereasin vivo (Pueraria lobata (Willd.) Ohwi, has been known to possess anti-inflammatory and antioxidant effects in UC via down-regulating the expression of nuclear factor-κB and the secretion of pro-inflammatory mediators , the major bioactive compounds isolated from HQ, had a better absorption effect. It has been reported that baicalein possesses potent anti-inflammatory and anti-colitis effect via suppressing the activity of TNF-α and interleukin IL-1β , as wellin vivo . Accordiin vivo . Epiberbin vivo . Moreoveediators . Overallediators . And in Among the main putative target mapped to compounds in the C-T network, many targets  with higher degree to compound might play a crucial role in the process of UC treatment. ESR1, an estrogen receptor, has been identified as a potential marker for identifying individuals at increased risk of neoplasia among those with long-standing and extensive UC . EGFR, tDysregulated inflammatory response has been demonstrated to play a crucial role in the initiation, development, and progression of UC . Inflammp-AKT in the colon tissues was significantly decreased in the GQD and SASP groups compared to model group, suggesting the protective effects of GQD and SASP in the treatment of UC. From the integrated drug target prediction, pathway enrichment analysis and in vivo experiment, the results demonstrated that the effective downregulation of the expression of EGFR, PI3K, and p-AKT by the administration of GQD and SASP may contribute to ameliorating UC.To examine the effect of GQD on the inflammatory response in UC, as well as the role of EGFR-regulated PI3K/AKT signaling pathway in DSS-induced UC, we evaluated colon inflammation and injury in mice administrated with DSS. DSS-treated mice in model group exhibited more severe injury and acute colitis, while these abnormalities were ameliorated by GQD or SASP administration. We found that GQD and SASP could alleviate the shortening of the colon length, the DAI parameters, the pathological changes and the secretion of inflammatory cytokines. Moreover, the results indicated that EGFR/PI3K/AKT signaling pathway was activated abnormally in model group and played an important role in inflammation, consequently, a series of inflammatory responses and mucosal damage occurs, resulting in the development of UC. However, the expression of EGFR, PI3K and Chinese Pharmacopoeia is the cornerstone of national standards for TCM (The limitation of this study is lacking standardization of active compounds in GQD. However, it has been demonstrated that the governing authorities’ rules on market entry of TCM products is primarily focused on the quality, safety and efficacy of TCM . Compare for TCM . The con for TCM . Develop for TCM . TCM staNetwork Pharmacology Evaluation Method Guidance was proposed, which addressed the data collection, network analysis, experiment verification principles, procedures, and evaluation indicators in network pharmacology research (Most recently, the draft of research . Accordiin vivo experiment indicated that GQD could effectively ameliorate DSS-induced colitis, and the therapeutic effect of GQD was associated with its synergic modulation on inflammation suppression via EGFR/PI3K/AKT signaling pathway. Therefore, we suggest that GQD could be developed as a promising therapy for UC, and it provided novel indications for further mechanistic studies of GQD on UC treatment. Our study also suggested that the network pharmacology prediction may exert as a useful tool to characterize the pharmacological mechanisms of TCM in detail.In summary, the combination of network pharmacological analysis in silico and


Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with structural annotations of protein-protein interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.The online version contains supplementary material available at 10.1186/s13059-021-02538-1. Alternative splicing (AS) boosts transcript diversity in human cells  and thusA major challenge in AS analysis is the functional interpretation of a set of events, including isoform switching events and differentially spliced exons. The usual approach is to perform gene set enrichment or overrepresentation analysis –10. ThisThis motivated the creation of databases and tools that predict the consequences of individual AS events or isoform switches. IsoformSwitchAnalyzeR , tappAS Most existing tools allow investigating AS-driven changes in an explorative fashion but tools for systematic analysis of functional effects of AS are lacking. Exon Ontology performs statistical tests to identify enriched features within a set of skipped exons. One example is domain families affected by AS across proteins more frequently than expected. However, none of the existing tools offer a systems biology view to specifically highlight functional consequences of AS events.To tackle these limitations, we developed the first tool for functional enrichment of AS events. NEASE (Network-based Enrichment method for AS Events) first detects protein domains affected by AS and then uses protein-protein interactions (PPI) integrated with domain-domain interactions (DDI) , residueNEASE uses a hybrid approach that combines biological pathways with PPIs and DDIs to perform functional enrichment of AS. First, we use the structural annotation of known isoforms by mapping protein domains from the Pfam database  to the cWe then perform statistical tests to find enriched pathways and most likely responsible genes Fig. B. FollowThe Python package provides an interactive analysis. Using a list of exons or events, users can run a general enrichment on 12 different pathway databases (collected from the ConsensusPathDB resource ), followTo check if the structurally annotated PPI is more biased to hubs than the standard PPI network, we computed the node degree distribution of the network before and after filtering for the structure evidence. As shown in Additional file z score standardized PSI values , which we refer to as gene-level enrichment, to detect enriched pathways see the “” sectionp values of the hypergeometric test). For a more realistic scenario, our background set of exons considers only exon skipping events that can actually be found in these tissues . These results suggest that the formation of this complex is co-regulated by AS. A similar finding about the role of the Clathrin light chain in neurons was also described in [https://vastdb.crg.eu/). As shown in the examples in Additional file The exons upregulated in neural tissues showed enrichment in the synaptic vesicle cycle pathway responsible for the communication between neurons Fig. D. Gene-lribed in . NEASE hribed in . VastDB AS does not only drive tissue-specific regulation but also plays a major role in cell differentiation and maturation. To illustrate an example of the utility of NEASE in such studies, we used the RNA-seq data set from  which coSince AS has been described to occur in platelets , we wantp value: 0.061, Additional file p value: 0.004, Additional file We observed that the enrichment at the gene-level using the Reactome  databasep value ranking. The top gene is GNAQ , which is known to be involved in signal transduction in platelets leading to platelet activation [We also looked at the individual AS events driving this enrichment. For each affected feature, NEASE tests if it significantly interacts with the GPCR downstream signaling pathway  is a chronic inflammatory demyelinating disease of the central nervous system. Early in the disease course, MS is characterized by focal lesions in the brain induced by an influx of systemic inflammatory cells. These active lesions infiltrated by immune cells and activated microglia are characterized by inflammatory demyelination and axonal loss . The surWe used RNA-Seq of macrodissected areas from postmortem white matter tissue of patients with progressive MS . We compGene-level enrichment ranks high pathways likely irrelevant that are involved in muscle contraction, cardiac conduction, and membrane trafficking, with the exception of Ca2+ ion flow across membranes  overactivity of MAPK pathways in microglia (the resident immune cell of the brain) during neurodegeneration [(ii) increased phosphorylation of MAPK kinases in the systemic immune cells of MS patients [Two of the most significant genes in the “Neurotransmitter receptors” pathway were GRIN1 and GRIA1  . DCM is In contrast to gene-level enrichment analysis, NEASE is able to score the contribution of alternatively spliced genes that are interacting with but are not part of the DCM pathway, allowing us to highlight putative biomarkers  by consG=, where V is the set of genes and E is the set of edges, to a subgraph G′= containing only structurally annotated interactions E′ and their nodes V′. An interaction is considered structurally annotated if it is supported by at least one of these resources: domain-domain interactions, motif-domain interactions, or residue-level interactions. For a submitted query list of exons, NEASE first identifies affected domains, linear motifs, and residues that overlap with the exons and their interactions. Let N be two times the number of edges in G′ (the degree of the network) and n be the number of affected edges from the query. These edges are then considered using a test modified from [P with degree K, let k be the number of affected edges that are connected to P. We model X whose outcome is k as a random variable following a hypergeometric distribution:For NEASE enrichment, we filtered the PPI graph ied from . For evek is considered as the number of observed successes out of n draws, from a population of size N containing K success. Subsequently, NEASE tests if the number k is significant using a one-sided hypergeometric test (over-representation). In contrast to the test proposed in [n=7, and K=11 is the total degree of pathway A (11 possible success), the number of affected edges that are linked to the pathway is k=4. The enrichment p value of pathway A corresponds then to the significance of this last number. After testing for multiple pathways, the obtained p values for the edge-level enrichment are corrected, using the Benjamini-Hochberg method [where posed in , our tesg method . The detn is the number of all affected interactions (edges) of a spliced gene and k is the number of affected interactions (edges) across genes that are linked to the pathway of interest. In the example of Fig. A just by chance due to its high number of affected interactions. For this reason, it is ranked lower than the genes G3 and G4.For a pathway of interest, a similar test can be applied to determine if a splicing event significantly affects interactions of a specific gene with this pathway  can be computed by NEASE to scale the natural logarithm of the p value with the total number of significant genes using a cutoff from the user :As a result, for every pathway, NEASE provides an overall g is the total number of significantly connected genes obtained after testing individual spliced genes. Thus, the NEASE Score prioritizes pathways that are affected by a larger number of spliced genes rather than pathways that have a larger number of affected interactions (edges). The user can choose to rank enrichment based on the adjusted p value or by the NEASE score.where p values. The empirical p values were then obtained by asking how likely it is to obtain a p value as low or more extreme than the one reported by NEASE in the original set of neural and muscles upregulated exons.For permutation tests, the set of highly confident 2831 exon skipping events obtained after the initial quality filtering is considered as the background set of exons. We compared the enrichment obtained in the pathways “Muscle Contraction” and “Synaptic vesicle cycle” from the set of exons upregulated in muscles/heart and neural tissues respectively, with 10,000 random sets of exons of the same size and then derived distribution of z scores). For every exon, we calculated the mean of PSI values from the samples of the same tissues. To extract muscles/heart and neural-specific exons and to ensure that we only consider functional events, we applied two filters: namely that the exon PSI value in the relevant tissue is higher than 20 and that the z score is higher than 2.PSI values of the exon skipping events from VastDB were quantified by the developers using vast tools , 112. InRaw RNA-Seq reads for two types of platelets and multiple sclerosis patients were downloaded from the GEO repository (access numbers: GSE126448 and GSE138614 ). The number of samples and sequencing depth are reported in Additional file NEASE’s Python package relies on NumPy , pandas https://github.com/louadi/NEASE). Step-by-step tutorials for running NEASE are available at .The package provides the option to automatically filter exons that are likely to disturb the open reading frame of the transcript based on the prediction in . In the Additional file 1:Tables S1-S3.Table S1. Enrichment of the pathway “Muscle contraction” from Reactome for the exons upregulated in the muscles generated by the NEASE package. Table S2. Enrichment of the pathway “Synaptic vesicle cycle” from KEGG for the exons upregulated in the neural tissues generated by the NEASE package. Table S3. RNA-Seq samples used in the study. Figs.S1-S6.Fig. S1. Node degree distribution of the classic PPI and structurally annotated PPI, the latter contains only interactions with evidence from DDIs and DMIs or residue-level evidence from the co-resolved structure. Fig S2. Network Enrichment Analysis using EviNet webtool for exons upregulated in muscles and neural tissues. Fig. S3. NEASE visualization highlights the interactions of differentially spliced genes with the DCM pathway. Fig. S4. The PSI values of two exon skipping events in the genes TPM1 and DST from the GTEx dataset confirm that both the exons are upregulated in muscles and heart tissues. Fig. S5. The PSI values of two exon skipping events in the genes CLTA and CLTB from the GTEx dataset confirm that both the exons are upregulated in neural tissues. Fig. S6. Pseudocode of NEASE algorithm.Additional file 2: Tables S4-S7. The analysis of the upregulated exons in muscles. Table S4: List of spliced domains. Table S5: List of affected edges. Table S6: Gene-level enrichment. Table S7: NEASE enrichment.Additional file 3: Tables S8-S11. The analysis of the upregulated exons in neural. Table S8. List of spliced domains. Table S9. List of affected edges. Table S10. Gene-level enrichment. Table S11. NEASE enrichment.Additional file 4: Tables S12-S16. Differential splicing analysis between reticulated platelets and mature platelets. Table S12. List of spliced domains. Table S13. List of affected edges. Table S14. Gene-level enrichment. Table S15. NEASE enrichment. Table S16. Enrichment of the pathway “GPCR downstream signal”.Additional file 5: Tables S17-S21. Differential splicing analysis between normal-appearing white matter and acute lesion from multiple sclerosis patients. Table S17. List of spliced domains. Table S18. List of affected edges. Table S19. Gene-level enrichment. Table S20. NEASE enrichment. Table S21. Enrichment of the pathway “Neurotransmitter receptors and postsynaptic signal transmission”.Additional file 6: Tables S22-S24. Differential splicing analysis between Dilated Cardiomyopathy patients and controls. Table S22. List of spliced domains. Table S23. List of affected edges. Table S24. Gene-level enrichment. Table S25. NEASE enrichment. Table S26. Enrichment of the pathway “Dilated cardiomyopathy”.Additional file 7: Table S27. The PSI values for VastDB exons in different tissues.Additional file 8. Review history.


Gene duplication and co-option also appear to be responsible for the evolution of the C4-associated PEP transporter, PPT1. Lyu et al. identify differences in coding and non-coding regions between C3 and C4 orthologs of PPT1 associated with increased expression of the transporter in C4 mesophyll cells (MC). They find that gene duplication and neo-functionalization led to recruitment of a PPT1 paralog found in roots to a role in C4 function.Gene duplication has been proposed as one of the early steps in the recruitment of genes during evolution of a C Monson, . Troncon4 species operate a carbon pump with the help of Kranz anatomy, wherein MC surround highly specialized bundle sheath cells (BSC) that are concentrically arranged around the vasculature , which catalyzes the first step in the C4 pathway, a housekeeping isoform, and a bacterial-type PEPC.Most C4 syndromes, identification of regulatory components controlling the spatial expression of C4-associated enzymes is important for understanding C4 function. Here, Afamefule and Raines use C3 and C4 grasses to screen upstream regions of genes encoding four enzymes in the Calvin-Benson-Bassham (CBB) cycle for conserved nucleotide sequences that might enable cell-preferential expression. They identify cis-regulatory elements putatively involved in BSC-enriched expression of genes encoding CBB enzymes as well as candidate transcription factors potentially binding to those sites. In addition, Górska et al. identify three trans-acting factors that bind the upstream region of the C4-associated PEPC homolog of maize. Characterization of these factors highlights the complexity of cell-preferential expression in a C4 leaf and the role of repression in establishing some C4-type expression patterns.Given the apparent flexibility of gene recruitment during evolution of CMoody et al. in a study on PEPCs from older and younger C4 lineages, optimization of the enzyme continues after a C4 syndrome is realized. Similar comparative studies of other enzymes in the C4 acid cycle may also contribute to our understanding of how a C4 syndrome evolves at the molecular level.Of course, evolution is ongoing. As suggested by the results of 4 syndrome could enable a C3 plant to be engineered for C4 features. This is highly desirable because C4 crops have higher yields and increased nitrogen and water use efficiency relative to C3 crops. Replicating the C4 process in C3 crops such as rice would therefore help feed a growing world population. Support for introducing a C4 pathway into rice is provided by Lin et al. Genes encoding four of the major enzymes in the maize NADP-ME-type C4 pathway, PEPC, NADP-malate dehydrogenase (NADP-MDH), NADP-ME and pyruvate phosphate dikinase (PPDK), were inserted into the rice genome. Subsequent measurements with 13CO2 demonstrate that production of 13C-labeled malate was high in the transformants, suggesting that a partial C4 pathway is functioning in these plants.A better understanding of the molecular events underpinning evolution of a C4 physiology and metabolism are also important to improve breeding programs of C4 crops. In particular, light harvesting and nutrient availability and uptake are key determinants for crop productivity. Collison et al. explore relationships between leaf age and light availability with the phenomenon of shade maladaptation exhibited by the NADP-ME-type C4 crops maize, sorghum and sugarcane. Leaf age had little influence on the quantum yield of CO2 assimilation. Instead, optimization of the leaf light environment mitigates the negative effects on productivity associated with this maladaptive response. These results can inform breeding strategies related to canopy structure and agricultural practices such as planting densities to increase crop yield.Studies on CJobe et al. highlight the need to consider the nutritional value of C4 crops in addition to yield. They review nutrient assimilation pathways in C4 plants and how they differ from C3 plants as well as discuss gaps in our knowledge of how nutrient uptake and levels are controlled in C4 plants. They also consider the effects of increasing atmospheric CO2 on C3 and C4 crop micronutrient assimilation and content in light of micronutrient-related malnutrition . Such considerations are important for producing future C4 crops that will effectively address global food needs.4 evolution and function. This new knowledge will inform future work in evolutionary biology, C4 metabolism, and crop improvement strategies.In summary, this collection of articles expands our understanding of CAll authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.  •Integrating protein interaction data with metabolic models expands multiomic mapping.•Proteomic profiling of tumors and cell lines reveals altered metabolic-related signatures.•Metabolite measurements validate pathway alterations in cancer cell lines and tumors. Metabolism is recognized as an important driver of complex diseases, but global metabolite profiling remains a challenge. Protein expression is a poor proxy because pathway enrichment models provide an incomplete mapping between the proteome and metabolism. We developed MOMENTA, a multiomic network approach for interrogating metabolic pathways from proteomics data. Analysis of data from cancer cell lines and human tumors reveals metabolic network rewiring and oncogene connections. The metabolic networks altered in cancer are linked to clinical outcomes. Mass spectrometry (MS) is a technology of choice for studying changes in the abundance and interactions of proteins, PTMs, and small molecules in response to physiological cues and pathological stresses , 11. Howe.g., ), includta types , 19, 20.e.g., connecting edges, network propagation) enable the study of dynamic biomolecular systems that respond to perturbations. By representing metabolites, enzymes, and their other cellular interaction partners  as nodes, and reactions and interactions as linking edges, networks provide a unifying mechanistic framework to systematically explore and discover unexpected molecular relationships and functional dependencies  proteomic surveys utilize some form of enrichment analysis to find functional trends among a set of observed differential molecular features. Such approaches fail to fully leverage metabolic models, which currently have annotated associations between metabolites, enzymes, regulators/cofactors, transporters and their respective small-molecule ligands that encompass only about 1200 to 1500 distinct gene products  using proteomic (or transcriptomic) data types. This network principle provides a unifying foundation to interpret changes in proteomic and metabolic activity at a biochemical pathway level since the functional relationships are centered on metabolic pathways, derived from the same metabolic model for both the proteomic and metabolomic analysis. Statistical enrichment analysis with MOMENTA on input differential protein (or gene) derived measurements using a functional model that extends metabolic pathways with reliable and scored PPI information elucidates biochemical pathway activity beyond mere correlations or primary pathway annotations.Traditionally, comparative . For all studies, pathway-level findings are validated with orthogonal data sets.For cell culture proteomic and metabolomic analysis, cells were grown and analyzed in replicates of 5 and 6, respectively, for each of two conditions (with and without glucose). Differential analysis and comparison between groups are performed using a 2. Cells were then either continued to be cultured in the same medium or switched to a no (0%) glucose condition (DMEM containing L-glutamine but without glucose or pyruvate) for 48 h before harvesting.MDA-MB-231, a human breast cancer–derived cell line, was obtained from the American Type Culture Collection (ATCC). Cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) without pyruvate containing 4.5 g/l of glucose and L-glutamine (Gibco), supplemented with 10% heat inactivated fetal bovine serum  and 100 units/ml penicillin-100 μg/ml streptomycin (Hyclone) in a humidified incubator at 37 °C in 5% COAfter pelleting, cells were resuspended in 100 μl of 8 M urea, containing protease inhibitors  and phosphatase inhibitors . After brief sonication on ice, the lysates were reduced with addition of dithiothreitol to a final concentration of 5 mM for 60 min at room temperature, followed by alkylation with the addition of iodoacetamide (5 mM) and incubation at room temperature for 30 min in the dark. The samples were then diluted with 50 mM ammonium bicarbonate to bring the urea concentration below 1 M. Proteins were digested overnight with sequence-grade (Thermo Scientific) trypsin (1:50 enzyme to protein ratio) at 37 °C followed by the addition of formic acid to 1% (v/v). The resulting peptides were desalted using a C18 cartridge as per the manufacturer’s instructions (Thermo Scientific).Peptide quantification was determined by Pierce quantitative colorimetric assay (Thermo Scientific). For each sample, 100 μg of peptide was resuspended in 0.1 M triethylammonium bicarbonate (TEAB) and incubated with a tandem mass tag (TMT) 10-plex isobaric labeling reagent (0.8 mg Thermo Scientific). The ratio of TMT to substrate was 0.4 mg reagent to 0.1 mg peptide. The reaction was carried out for 1 h at room temperature and quenched using 5% (v/v) hydroxylamine for 15 min. Equal amounts of each sample were combined in a new tube and desalted using a C18 Tip.The labeled peptide mixtures (1 mg) were fractionated using a Waters XBridge BEH C18 column  on an Agilent 1100 HPLC system operating at a flow rate of 0.45 ml/min with two buffer lines: buffer A (consisting of 0.1% ammonium hydroxide-2% acetonitrile-water) and buffer B . Peptides were separated by a gradient from 0% to 10% B in 5 min, followed by a linear increase to 30% B in 23 min, 60% B in 7 min, and then 100% in 8 min and continued for 5 min. The 48 fractions collected were combined into 12 and evaporated to dryness in a vacuum concentrator. Two micrograms of peptide from each fraction was reconstituted in 1% formic acid and kept at −80 °C until analyzed by the nLC-MS/MS system.4OH with 25% ACN and dried by speed vac before nLC-MS analysis.TiO2-coated magnetic beads  were used to enrich phosphopeptides obtained from combined HPLC fractions. Beads (5 μl/mg) were preconditioned with DHB buffer  for 15 min, then incubated with the peptide mixtures, resuspended in 500 μl of DHB buffer , for 30 min with shaking. Beads were washed in steps: 1% TFA-80% ACN, followed by 1% TFA-50% ACN, and 1% TFA-10% ACN twice, then the supernatant was discarded to remove abundant contaminants. The phosphopeptides were eluted by 5% NHTotal peptides and bead-enriched phosphopeptides from each HPLC fraction were individually loaded onto a C18 trap column , connected in-line to a C18 analytical EasySpray (Thermo Scientific) column  using the EasyLC 1200 system (Thermo Scientific) in a column oven at 55 °C. The nanoflow gradient consisted of buffer A (composed of 2% (v/v) ACN with 0.1% formic acid) and buffer B (consisting of 80% (v/v) ACN with 0.1% formic acid). For protein analysis, nLC was performed for 180 min at a flow rate of 250 nl/min, with a gradient of 2% to 8% B for 5 min, followed by 8% to 20% B for 96 min, then 20% to 35% for 56 min, 35% to 98% B for 3 min, 98% buffer B for 3 min, followed by column recycling with 100% to 0% B for 3 min, and finishing with 5% B for 14 min. Peptides were directly ionized from a nanospray ion source into an online Q-Exactive HF (QE-HF) mass spectrometer (Thermo Scientific) operated in a data-dependent data acquisition mode.6 or maximum injection time of 60 ms and a scan range of 350 to 1650 m/z. MS2 scans were performed at 45,000 resolution, with an ion-packet setting of 2e4 for AGC, maximum injection time of 90 ms and using 33% total normalized collision energy. Source ionization parameters were optimized with the spray voltage at 2.1 kV, transfer temperature at 275 °C. Dynamic exclusion was enabled for 40 s.The Q-Exactive HF was run using a ddMS2 top ten scans acquired per single profile mode full-scan mass spectrum using HCD fragmentation. Full MS spectra were collected at a resolution of 120,000 with an AGC of 3eFor the phosphopeptide analysis, the metal-bead enriched peptide fractions were loaded onto a 50 cm C18 column. nLC was performed for 90 min at a flow rate of 250 nl/min, with a gradient of 2% to 6% B for 5 min, followed by a 6% to 20% B for 39 min, a 20% to 35% gradient for 23 min, and a 35% to 98% B gradient for 3 min, 98% buffer B for 3 min, 100% to 0% gradient of B for 3 min, and finishing with 5% B for 14 min. The QE-HF was operated using a top six scan ddMS2 acquisition mode, with a maximum ms2 injection time of 400 ms.in vitro cancer cell line data analysis, raw files from five  were incubated with 1 ml cold MeOH:ACN:H2O  solvent with vortexing for 30 s, followed by incubation in liquid nitrogen for 1 min. The samples were then allowed to thaw on ice followed by sonication (10% setting) for 10 min. This freeze/thaw procedure was performed three times. Afterward, the lysate was incubated for 1 h at −20 °C, followed by 15 min centrifugation at 12,000The dry metabolites were reconstituted in 200 μl PBS, transferred to a 96-well plate, and subject to a solid-phase microextraction (SPME) using resin-coated blades preconditioned for 30 min in methanol:water  . Samples6 ions, and maximum ion injection time of 25 ms. Source ionization parameters were optimized with the spray voltage at 2.1 kV and −1.8 kV for positive and negative mode, respectively, with a transfer temperature of 300 °C. MS2 scans were performed at 15,000 resolution, with a maximum injection time of 64 ms, using NCE steps of 10, 20, and 40. The dynamic exclusion was set to 10 s.Metabolite analysis was performed using a nLC-QE HF Hybrid Quadrupole-Orbitrap (Thermo Scientific) using a EasySpray microcolumn  in an oven set to 40 °C. The mobile phase A was 2% ACN, and mobile phase B was to prevent ion suppression in negative mode, formic acid and TFA were eliminated from the mobile phase. The flow rate was 300 nl/min. The gradient started from 2% mobile phase B to 60% mobile phase B for 20 min and then reaching to 95% mobile phase B in 10 min and then lasted for 15 min. The Q Exactive HF mass spectrometer was operated under a dual (positive/negative) ESI switching mode in real time during data acquirement, requiring a longer gradient compared with standard analytical flow rate methods. Full mass scan (m/z 67–1000) was performed at a resolution of 60,000, with an AGC target set at 3ee.g., [M+Be]+/−) were defined as decoys during the database search along with native adducts. A stringent confidence cutoff score (>50) was used to improve the confidence of annotated metabolite matches using an empirically determined FDR of <0.1 probability (relative to decoy matches). Quality control data (QC) were specified as a reference file for alignment between samples. Data matrices were exported as tab delimited text files for subsequent enrichment analysis.Raw data files were converted to mzML format and split into positive and negative spectra using msConvert prior to analysis using MS-DIAL4 . “LinearA language and environment for Statistical Computing  using an in-house pipeline . Where available, E.C. codes were converted into gene/protein names based on mappings from Uniprot  based on scoring from the PPI databases. Similarly sized control gene sets were also created by expanding with (1) the same number of random proteins drawn from the PPI network, or (2) the same number of random gene products from the PPI network that were second-degree neighbors to each other (random subnetworks). “Base” gene sets consisting of enzymes alone were also generated directly from the metabolic models for comparison.Metabolic models were downloaded from MetaboAnalyst ( Uniprot . For eac Uniprot  (human)  Uniprot   that were not detected in any dataset were removed to declutter the final image. Nodes were colored based on changes between conditions if they were detected, while shapes were specified to distinguish metabolites, reactions, genes, and PPI associated proteins, as described in figure legends.For visualizing pathway-based protein–metabolite subnetworks, network files were generated incorporating PPIs and metabolite reaction pathways. Since reaction information was not included with the metabolic models downloaded from MetaboAnalyst, specific pathway information was retrieved from MetScape (human). Files were integrated manually to visualize interaction networks encompassing metabolites, reactions, enzymes, and associated proteins (human gene products). Then, for a specified pathway, the Metabolic models based on curated pathway annotations, such as KEGG , offer aExpanded”) of annotated enzymes (A), which markedly increased the number of associated gene products , but created a range of gene set sizes  that impacts enrichment statistics (Neighborhood”) , which substantially increases the number of unique genes/proteins for all pathway models  while creating a uniform distribution of gene set sizes for standardized enrichment  scoring . Extending the metabolic pathways through the PPI network, as opposed to metabolic network, maintains the metabolic pathway at the geometric center of the subnetworks as much as possible, which supports the intended use of proteomic data to study metabolic pathways.To examine this systematically, we started with metabolic models supported by MetaboAnalyst , a leadi enzymes A, which atistics ; or (ii)atistics  gene setExpanded and Neighborhood gene set definitions, which we can apply in a boosted enrichment analysis pipeline to examine differences in metabolic pathways starting from experimental proteomic profiling data. Benchmarks for the obtained enrichment results include both standard “Base” gene sets, representing traditional annotated metabolic pathways , and against two control gene sets, based on globally randomized sets of proteins (“Control 1”) or randomized interaction subnetworks (“Control 2”).Our method of network integration results in these A). For replicate cultures, we measured proteins and phosphopeptides by isobaric tandem mass tag (TMT) multiplexing and nLC/MS, quantifying 7214 proteins (A) and 5785 phosphosites (A), respectively after normalization . Differential analysis identified 3055 high-confidence  proteins and 1528 phosphosites.As a demonstration of utility, we first applied MOMENTA to examine metabolic constraint and pathway adaptation in a cancer cell line model system . Tumors Fig. 2proteins A and 578phosites A, respecB and supplemental Fig. S2A), including upregulation of TXNIP, a downstream target of IGF1 (insulin) signaling that is linked to cellular redox signaling  and encompassed only a small fraction of the number of total gene products as compared with other major functional categories .The proteomic analysis showed consistent differential expression , including downregulation of the metabolism master regulator, RPTOR, consistent with reduced nutrient levels  and mapped to a sparse subset of the differential phosphoproteins deemed differential in the data .Differential analysis of the phosphoproteomic data likewise showed a robust signature . To compare global enrichment results, we primarily use these distributions of pathway significance to show effects on the statistical tests across all pathways. This effect was eliminated when the underlying protein and phosphoprotein data matrixes were randomized, effectively removing the signature derived from the curated molecular interaction network , demonstrating that the signal is due to underlying biologically functional connections between proteins.In order to more thoroughly interrogate metabolic signatures based on the proteomic data, we performed GSEA using the MOMENTA-derived gene sets. In this analysis of the proteomic and phosphoproteomic data, we compared the I and supplemental Fig. S4B), which provided increased gene and pathway coverage over the proteomic data alone. Using the MFN model, the number of significant  pathways increased 25-fold, from only three with the Base gene sets to 82 and 72 in the Expanded and Neighborhood subnetworks, respectively . While the majority of significant pathways for Expanded and Neighborhood are shared, we can examine the individual pathway results to explain differences. For example, Purine Metabolism is significant in the Expanded results . In this case, when we examine the gene set sizes, we see that the Purine Metabolism pathway comprises 3449 genes in the Expanded case compared with 250 in Neighborhood  and visualize the overall enrichment results, we used the Enrichment Map Cytoscape plugin to graph the metabolic pathway terms returned by the A). Global analysis of untargeted metabolomics showed samples clustering based on glucose withdrawal (B) and using an empirically controlled (decoy adduct) metabolite identification pipeline (C).The use of computationally randomized controls and data goes a long way in establishing confidence in the enrichment analysis. However, ultimately biological validation requires testing results using orthogonal methods. In order to validate the results from the MOMENTA metabolic pathway interrogation, we performed global metabolomic profiling on the same glucose-starved cancer cell samples in parallel with our phospho/proteomic analysis A. Globalthdrawal B and usipipeline , we founpipeline C.Fig. 3ENeighborhood and Expanded subnetworks, followed distantly by Base and Control gene sets (D). We are able to compare these distributions between the metabolomic data and the proteomic data because in both cases, enrichments are based on the same list of pathways, derived from the metabolic models, as described previously. Additionally, the Expanded and Neighborhood enrichment results produced a comparable number and significantly overlapping  of pathways as reported by the Metabolomic experiments, relative to the sparse Base enrichments (E). Consistent with a high degree of agreement, the significant pathways found with the Expanded and Neighborhood subnetworks overlapped with the majority of the enriched pathways detected by direct metabolomic profiling (F); the mean absolute difference in significance returned for each pathway was greatest between the Base and Metabolomic enrichments, reflecting the least agreement (G). While there is a great deal of overlap between the proteomic and metabolomic analysis, examination of individual pathway results reveals technical reasons for cases where there is not agreement. Vitamin B12 metabolism is significantly altered in the metabolomic enrichment results, however, not in any of the proteomic results. Examination of the gene set composition for this pathway reveals that in this metabolic model it is only annotated with two genes  composed of metabolite molecules that are unlikely to be detected in our metabolite profiling method. Despite picking a method to be as global as possible with metabolite measurements, this illustrates the impact metabolite chemical diversity has on the feasibility of comprehensive metabolite profiling.To systematically compare the metabolomic pathway enrichment results produced by MOMENTA and direct experimental detection of differential metabolites, we integrated the MetaboAnalyst metabolic enrichment results based on the metabolomic data with the phospho/proteomic enrichment analysis using the same gene set (pathway) definitions . Notablyene sets D. We areichments E. Consisrofiling F; the megreement G. While wo genes , resultit signal . SimilarBase gene set analysis showed some signal in relevant pathways such as Fructose and Mannose Metabolism, it produced hardly any signal in Hexose Phosphorylation, Nitrogen, or Pyruvate Metabolism, all of which were deemed significantly altered in the direct metabolomic survey, as well as the Expanded and Neighborhood analyses (H). Consistent with a wider range of gene set sizes (C), the Expanded correlation with the metabolic results was less pronounced than for the Neighborhood approach, suggesting that gene set size distribution contributes some degree of noise, as expected, with uniform subnetwork sizes producing more robust findings.Notably, looking at representative pathways, while the analyses H. Consiset sizes C, the Exe.g., Breast, Pancreas), revealed broad variation across the large number of blood and solid tumor cell types surveyed (A), confirming the original report, which mentions metabolic pathways in a cursory discussion of broad pathways .A recent in-depth proteomic profiling of hundreds of cancer cell lines  offers asurveyed A, confirNeighborhood enrichment analysis based on the protein (gene) rankings according to their loadings on the first principal component (PC1)  varying pathways from the Neighborhood subnetwork model mapped to over 20 pathways related to the metabolism of extracellular glycans, diverse lipids, and oxidation related processes, among others (B), which have previously been implicated in cancer signaling , consistent with the original analysis. This also is consistent with the findings of Heparan Sulfate and Glycan pathways, which are expected to vary substantially between blood and solid organ cell types. To confirm these patterns, we downloaded and clustered the metabolomic data available for 225 metabolites in 928 cell lines  and found 39 (out of 43 detected) were significantly altered across the subtypes used for comparison  providing validation of variation across cell types in our top pathways  between the tumor and adjacent normal tissue  differential metabolic pathways  in certain pathways. One notable pathway predicted to show differential activity mapped to Glutamate metabolism, where the associated metabolites alanine, GABA, glutamate, and glutathione were found to significantly different between both breast and pancreatic cancer–derived cell lines, which show metabolite abundances in the cell lines relative to one another (D), and between tumors and normal adjacent tissue primarily in Breast relative to Pancreas cancers (E), though note these data do not adjust for differing normal levels of metabolite abundance in different tissues. This is potentially clinically significant since recent research has pointed to these metabolites as potential cotargets for immuneoncology therapeutics , we repeated the MOMENTA pathways . Again, pathways . Discord another D, and be cancers E, thoughapeutics . Our anaA) . When compared with the nutrient stressed cancer cells studied previously, we see strong agreement in the metabolic pathway perturbations in the Neighborhood enrichment results  and focused on the Urea Cycle/Amino Group together with the Lysine pathway, due to overlapping features  breast cancer study A 49). C. CA)  increased in breast cancer as compared with adjacent normal tissue. Moreover, among all cancer types, 11 of 18 metabolites associated with Lysine and Urea Cycle/Amino Group metabolism exhibited significantly  altered levels in tumors  differentially altered, showing substantial biochemical rewiring of these metabolic pathways and functionally associated proteins. While metabolic rewiring in nitrogen utilization toward pyrimidine synthesis is a well-established biochemical signature of cancer .Approximately half (130 of 266) of the proteins in the f cancer , this ex samples D.E). To investigate the functional basis of the Urea/Amino and Lysine pathway groupings, we overlaid the CPTAC tumor protein expression enrichment results together with the results of our in vitro MDA-MD-231 cell line analysis. Highly connected components in the combined subnetwork (E) included the Histone-lysine N-methyltransferase KMT2D, which was elevated in both datasets, suggesting metabolic modeling is associated with epigenetic feedback. KMT2D has been reported to physically interact with multiple proteins associated with cancer and tumor progression, including BCL9L  proteins contributing to the enrichment signature E. To invbnetwork E includeng BCL9L , MEN1  selection. We then classified the patients into high-risk and the low-risk groups based on a subset of the Urea Cycle/Amino Group and Lysine gene features. Finally, we generated a Kaplan–Meier plot to examine the survival difference between the high-risk and low-risk groups and found high expression of a subset of genes in both the Urea Cycle/Amino Group (F) and Lysine (G) Neighborhood subnetworks were significantly  associated with decreased overall survival probability. Taken together, these results establish the ability of MOMENTA to infer reproducible and clinical meaningful changes in cancer cell metabolic activity that are associated with disease progression.To independently validate the significance of these findings using orthogonal data, we downloaded the breast cancer mRNA expression and associated clinical outcomes data (TCGA-BRCA) from the cBioPortal. After data processing, we explored the mRNA expression profiles of 1045 patients to test prognosis significance of our MOMENTA no Group F and Lysd Lysine G NeighboBy leveraging existing knowledge, MOMENTA can provide unexpected mechanistic insights into complex biological processes associated with cellular metabolism. While recent progress continues to be made on correlative relationships between the metabolome and genome , we havede novo pathways have been reported  for integration into existing enrichment workflows, as well as a stand-alone R script to enable automated enrichment analysis from MOMENTA inferred subnetworks, along with efficient visualization of the combined results to examine metabolic pathway enrichment in one or more datasets. Since high-throughput identification of metabolites remains challenging , future https://doi.org/10.25345/C57X00). We also uploaded our proteomic data to MS-Viewer (search key zsl8gjg385 and fmmj3pysq4). Code for our in-house analysis scripts for proteomic data normalization, processing, plotting, and pathway enrichment is available on github (github.com/cnsb-boston/Omics_Notebook). Gene sets and pathway network files have also been uploaded to github and are freely available (github.com/cnsb-boston/MOMENTA).Data for this project have been deposited to the MassIVE archive (accession code MSV000084811; This article contains The authors declare no competing interests.


The hypothalamus plays a central role in the integrated regulation of feeding and energy homeostasis. The hypothalamic arcuate nucleus (ARC) contains a population of neurons that express orexigenic and anorexigenic factors and is thought to control feeding behavior via several neuronal circuits. In this study, a comparative proteomic analysis of low-fat control diet- (LFD-) and high-fat diet- (HFD-) induced hypothalamic ARC was performed to identify differentially expressed proteins (DEPs) related to changes in body weight. In the ARC in the hypothalamus, 6621 proteins (FDR < 0.01) were detected, and 178 proteins were categorized as DEPs (89 upregulated and 89 downregulated in the HFD group). Among the Gene Ontology molecular function terms associated with the DEPs, protein binding was the most significant. Fibroblast growth factor receptor substrate 2 (Frs2) and SHC adaptor protein 3 (Shc3) were related to protein binding and involved in the neurotrophin signaling pathway according to Kyoto Encyclopedia of Genes and Genomes analysis. Furthermore, high-precision quantitative proteomic analysis revealed that the protein profile of the ARC in mice with HFD-induced obesity differed from that in LFD mice, thereby offering insight into the molecular basis of feeding regulation and suggesting Frs2 and Shc3 as novel treatment targets for central anorexigenic signal induction. Obesity is a common issue worldwide and a cause of various disorders, including hypertension, diabetes, and cardiovascular disorders . Severalα-MSH derivatives that affect nuclei, including the paraventricular nucleus (PVN) of the brain /NPY) or proopiomelanocortin (POMC) derivatives , 7. Orexhe brain , 9. AsidIn this study, we characterized the ARC proteome in order to identify protein alterations in mice exposed to an HFD and provide new insights into the molecular basis of appetite regulation and energy metabolism.n = 10) were purchased from Orient Bio  and maintained at 23 ± 1°C under a 12 h light/dark cycle starting at 8:00 A.M., with ad libitum access to the designated type of food and water in a breeding room. Body weights were measured weekly.Male C57BL/6 mice .n = 5/group) for the 8-week experimental period. One group was fed a low-fat control diet , and the other was fed an HFD  ad libitum for 8 weeks . Mean body weights were measured weekly in both groups to evaluate the reliability of the DIO model.All mice underwent a week of adjustment to the housing environment and were fed a chow diet. The mice were then randomly assigned to two groups  and intracardially perfused with sterilized cold saline and 4% paraformaldehyde. Brains were extracted, snap-frozen over liquid nitrogen, and stored at −80°C. A punch biopsy was performed, as described previously, to excise ARC tissue samples from the brain , 11, andEach ARC sample was collected, as described previously , 11, andhttp://string-db.org) to generate a protein-protein interaction network (ELIXIR Core Data Resource) [http://www.kegg.jp/kegg/) and Gene Ontology (GO) (http://geneontology.org/) databases to evaluate functional enrichment. The overall characteristics of DEPs were examined by GO enrichment analysis [The list of DEPs was uploaded via the STRING 11.0 tool to the STRING database (esource) , and DEPanalysis  using thanalysis . Biochemanalysis .Bodyweight data are shown as means ± standard error of the mean and analyzed with two-way repeated measures analysis of variance implemented in SPSS .2 values, and missing values were imputed based on the Gaussian distribution. We then performed width adjustment  for normalization. Student's t-tests were used for comparisons between the normalized iBAQ intensities of the HFD and LFD groups, and fold change values  were then calculated. A DEP was defined as a protein with a p < 0.05 and either a fold change > 1.5 (upregulated protein) or <1.0/1.5 (downregulated protein). A volcano plot was generated for the DEPs based on p values and fold changes.For proteomic analysis for each protein, iBAQ intensities were transformed to logF = 12.501) to 8 weeks  after the adjustment period, the mean weight in the HFD group was significantly higher than that in the LFD group (p < 0.0001)  .10p values for the mean normalized iBAQ intensity). In total, 336 proteins were identified at a threshold p value of 0.05. After filtering with the fold change threshold, 178 proteins were identified as DEPs, including 89 upregulated DEPs (fold change ≥ 1.5) and 89 downregulated DEPs (fold change ≤ 0.66)  . Hierarc ≤ 0.66) .We evaluated a candidate pathway  based on previous results demonstrating its important roles in neurogenesis, axogenesis, and synaptic plasticity in the hypothalamus and feeding and energy imbalances due to obesity , 19. Amon = 16) when the threshold was set to two DEPs, which increased the risk of false-positive results. The five selected KEGG pathways and their corresponding genes were as follows: mitogen-activated protein kinase (MAPK) signaling pathway , thermogenesis , proteoglycans in cancer , Salmonella infection , and neurotrophin signaling pathway . The roles of the MAPK signaling pathway and the thermogenesis pathway are already clearly established; therefore, these pathways were excluded from further analyses. In addition, proteoglycans in cancer and Salmonella infection pathways were excluded, because they were highly unlikely to have therapeutic value in obesity. Accordingly, the neurotrophin signaling pathway  was selected as a key pathway for further analyses  and catalytic activity   was the most highly significant and included the most DEPs. The DEPs related to “protein binding” were further evaluated for involvement in KEGG pathways, revealing one pathway related to the hypothalamic ARC   . Comparepathway) .This study identified DEPs in the ARC between mice with HFD-induced obesity and mice fed an LFD. Glutamate transporters  are among the exceptionally abundant and important proteins in the central nervous system (~>2.5% of the brain protein mass exists in the form of glutamate transporters) and critical for the function of glutamatergic signaling . Although proteomic changes in the hypothalamus of an obesity model have been studied , our comα and interleukin 1β, especially in AgRP-producing neurons, might contribute to inflammation-induced anorexia during acute inflammatory conditions [Recent studies suggest that tumor necrosis factor nditions . Additionditions , 22. Thenditions .GO enrichment analysis identified binding and catalytic activity as key molecular functions. Notably, the DEPs related to protein binding were involved in the neurotrophin signaling pathway. These results were consistent with those of previous reports indicating that the neurotrophin signaling pathway contributes to neurogenesis, axogenesis, and synaptic plasticity in the hypothalamus and dysfunctions in feeding and energy balance due to obesity , 19. “BiTrkBARC neurons regulate food intake by projecting to BDNF-expressing neurons in the PVN [These results showed that Frs2 and Shc3 levels and the related neurotrophin pathway were altered in the ARC of a mouse model of HFD-induced obesity. Neurotrophins, which mediate receptors and signaling molecules, play multiple roles in survival and development . FRS2 fu the PVN .TrkBARC to PVN, subsequently inducing hyperphagia [A previous study reported that BDNF deficiency signaled a reduced number of axonal projections of erphagia . SHC molerphagia , 29. In We identified 178 DEPs in the ARC of a reliable mouse model of HFD-induced obesity. These DEPs play key roles in protein binding and the neurotrophin signaling pathway and include Frs2 and Shc3 as novel candidates involved in appetite and energy metabolism.


Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods’ capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization. https://cran.r-project.org/web/packages/netgsa/index.html and the development version is available on GitHub at https://github.com/mikehellstern/netgsa.With the increase in publicly available pathway topology information, topology-based pathway enrichment methods have become effective tools to analyze omics data. While many different methods are available, none are uniformly best. This paper focused on overhauling an existing topology-based method, NetGSA. The three key improvements included dramatically reduced computation time so pathway enrichment can be performed within minutes on a personal computer, integration of publicly available pathway topology databases so users can easily leverage the entire capabilities of the NetGSA method, and facilitating interactive visualization of results through an interface with Cytoscape, a popular network visualization tool. The improved NetGSA was compared to the previous version as well as other similar pathway topology-based methods and achieves competitive statistical power. With these improvements and NetGSA’s flexibility to address a diverse set of problems and data types, we believe that the new NetGSA can be a useful tool for practitioners. The updated NetGSA is available on CRAN at  PLOS Computational Biology Software paper.This is a Pathway enrichment analysis methods have become standard tools for analyzing omics data . While eMany topology-based methods have been proposed in the literature, but there is no consistent best choice for a given problem. For example, computationally efficient methods, such as SPIA  and PRS netgsa R package, an implementation of the NetGSA methodology. In addition to desirable statistical power, NetGSA has a number of appealing features. Most notably, it provides a flexible framework for testing pathway enrichment in complex experiments netgsa soology to , and CePology to . Three oology to , PathNetology to , and CePology to  were excology to ; and CePology to , all metnetgsa comparison, netgsa was also timed using REML on the entire network to estimate the variance components (NetGSA.REML.All). This is different than the power results presented in prepareAdjMat) and pathway enrichment (NetGSA) are shown in The power analyses performed in the previous section were run in parallel on a cluster with 4 nodes each with two 10-core CPUs and 128GB of memory. For the netgsa is compared with other existing methods in netgsa, the new software is not as fast as the other methods tested, reflecting a potential area for future improvement. It is worth noting that even though netgsa is still relatively slow, it is now much closer in absolute time to the other methods tested. A chief portion of netgsa’s computational time is driven by the estimation of the weighted adjacency matrix. While inherently time consuming, this step offers robustness to noise or incompleteness of externally-obtained network information. It also allows netgsa to assess changes in both mean expression levels as well as network connectivities [netgsa now comes with additional features from visualization to user interface improvements. Finally, netgsa offers additional flexibility for multi-condition studies , and can directly incorporate additional covariates.The computational timing of tivities , 13. Thunetgsa package offers important computational and user interface improvements. It obtains external pathway information from a variety of databases automatically, solving the problem common to many pathway topology-based methods; it also offers intuitive visualizations using the capabilities of Cytoscape. Furthermore, by using network clustering and REHE for estimating the variance components, netgsa is no longer prohibitively slow. Pathway enrichment analysis with ∼ 2, 500 genes can be performed in minutes on a personal laptop with no loss in statistical power or control of type-I error. With these improvements, we believe netgsa can now be a useful tool for practitioners, especially when available network information may be noisy or incomplete, or when performing more complex, multi-condition pathway enrichment analysis. The updated NetGSA is available on CRAN at https://cran.r-project.org/web/packages/netgsa/index.html and the development version is available on GitHub at https://github.com/mikehellstern/netgsa.The updated S1 Appendix(PDF)Click here for additional data file.