Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways’ roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested . One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here,  Gene by Environment Interactions (GEIs). GEI analysis showed that regulatory pathways can play different roles in different environments, even reverse roles in specific contexts. Which pathway impacted phenotype the most changed across environments revealing decentralized control within the network. This finding led to the identification of an underappreciated pathway as a major regulator. Environmental differences caused changes in pathway interactions seen by conditional redundancy and conditional cross-pathway regulation. The environment also triggered conditional regulation of gene expression for target genes that were differentially regulated by different pathways. These insights into network plasticity may apply to signaling networks in general. Our work suggests that studying a biological process under diverse environments captures a more complete picture of its regulatory pathways. GEI analysis reduces the risk of mis-characterizing pathways studied in a single context and is a powerful method to explore the regulatory basis of complex phenotypes.Phenotypes can change during exposure to diverse environments through the regulation of signaling networks. How networks produce different phenotypes in different environments is not fully understood. We explored the regulatory network that controls a complex phenotype in yeast by analyzing  Gene (or Genotype) by Environment Interactions (GEIs)]. GEIs are found across all kingdoms of life, including humans and other animals , and media with varying amounts and/or sources of carbon, nitrogen, and phosphate, including media thought to mimic a natural environment . Media ay , where [M&M and ]. Wild-tFig 1D, Function). To define how the regulation of invasive growth differs in each environment, the PWA was performed with mutants that perturb key pathways and protein complexes required for invasive growth . Genes were chosen that ablate pathway activity based on previous tests comparing mutants that lack key pathway components . Because invasion phenotypes can be influenced by genetic background , which ys/genes . TherefoFig 2F, full PWA in S7A). All four pathways played a major role in regulating invasive growth at an early time point tested (2d) but played different roles at different times  compared to a later time point (21d) for invasive growth . Therefore, major regulatory pathways can make varying contributions during the progression of invasive growth.We became interested in understanding whether the pathways also differentially regulate invasive growth at different time points. To address this possibility, invasive growth was examined over time. In one environment (YPGAL), invasive growth increased over a three-week incubation . However, pathways played different roles over time in the two conditions, based on the levels and patterns of invasive growth (S7C). For example, when comparing 7d to 21d, RIM101 contributed similarly to invasive growth at both time points in one environment (YPGAL) but differently at both time points in another environment (SD) [Fig 2G]. Moreover, all four pathways did not equally contribute by 21d on SD like they did on YPGAL medium . Invasive growth after 21d also revealed GEIs for invasive growth patterns not seen at earlier time points between YPGAL and SD media . Thus, the temporal roles pathways play in the development of the response may change in different environments.GEI analysis was also used to explore the temporal development of invasive growth across environments. Like for YPGAL medium, on SD medium invasive growth increased over time (ras2Δ tec1Δ double mutant was less invasive than the ras2Δ and tec1Δ single mutants [S8)]. This indicates that the pathways play additive roles.When regulating a phenotype, signaling pathways can make separate (i.e. additive) contributions from one another, or they can make overlapping (i.e. redundant) contributions. To test if the major pathways played additive or redundant roles during invasive growth, double mutant combinations were generated among the major regulators. In most environments, the double mutants were less invasive than the single mutants. For example, on YPD, the ras2Δ tec1Δ double mutant had the same level of invasive growth as the ras2Δ and tec1Δ single mutants on YP (ras2Δ opi1Δ double mutant was less invasive than either single mutant in one environment (YPD), equal to one of the single mutants in multiple environments , and equal to both single mutants in yet another environment (YP) . This may suggest that a pathway in the network can make an additive contribution in one environment , a redundant or similar contribution to one pathway in another environment , and a redundant or similar contribution to more than one pathway in a third environment . In environments where double mutants invade, it can be inferred that at least one additional pathway is required. These observations are consistent with the idea that pathways that regulate invasive growth can play additive or redundant roles depending on the environment, which we refer to as conditional redundancy.In some environments, double mutants showed the same invasion as single mutants. For example, the Fig 1C, RTG, PHO85, and other pathways, red or blue in different environments). These included Retrograde (RTG), which regulates the response to mitochondrial distress . To defRE-lacZ, ,145] wasfindings , this reFig 4B, tec1Δ, green). Reporter levels in cells lacking a negative regulator of the MAPK pathway, Dig1p ) in most environments tested, based on examination of invasive growth across conditions  and over time . One exception was on YP media, where invasive growth, but not MAPK pathway activity, was closer to the upper boundary . This could be due to redundancy with another pathway that regulates invasive growth in this environment (i.e. RAS). Although an environment that triggers maximal invasive growth and MAPK pathway activity may remain to be identified, these results suggest that the MAPK pathway typically operates at less than half its maximum levels of activity, and promotes invasive growth to less than half its maximal levels, even in environments where the pathway is activated to stimulate invasive growth. Thus, the MAPK pathway functions at modest levels to induce invasive growth.The levels of the reporter in cells lacking a positive regulator of the MAPK pathway defined a lower boundary for MAPK pathway activity  exhibits plasticity. Furthermore, conditional cross-pathway regulation may account for how conditional redundancy occurs between the RAS and MAPK pathways .Signaling pathways can regulate each other’s activity –156. In RAS ,89, RIM1MAPK pathway activity might be conditionally regulated by altering the expression of the genes encoding pathway components. The sensor of the pathway, MSB2, the kinase of the pathway, KSS1, and the transcription factor, TEC1, show different expression patterns in different environments  were tested by the PWA. We found each aspect of invasive growth contributed differently from one another, in agreement with previous studies ; and adhesion of cells to plastic , another Flo11p-dependent adhesion phenotype . OPI1 aanalysis  and Ino4analysis . RAS regpression ,86, perhpression .SFG1 (like FLO11) showed changes in expression by signaling pathways across environments. In particular, when considering the mutant analysis, OPI1 showed a > 1.7-fold change between YPD and YPGAL media in regulating SFG1 gene expression . Such regulation may account for the phenotypic plasticity of invasive growth.MAPK pathway is known to regulate cell elongation by extending the G1 phase of the cell cycle S1, ENG1 –169,199]MAPK pathway is the central regulator of polarity and cell shape. Because differentiation requires alterations to the cell cycle to trigger cell elongation . Homolo G. Fink . PCR souribed in .2PO4, 2% dextrose. Natural and domesticated environment mimics were prepared as follows: SOE [synthetic oak extract [https://www.teknova.com/malt-extract-broth)]: 0.6% malt extract, 0.18% maltose, 0.6% dextrose, 0.12% yeast extract. WL: Wallerstein laboratory nutrient agar : 0.17%  extract ]: 1% sucent agar  . Invasion values were averaged across three biological replicates. To determine relative invasion, invasion was normalized to wild type, set to 1. Error bars represent standard deviation between trials. Significance was determined using the program GraphPad Prism  by one-way ANOVA analysis unless otherwise indicated. Dunnett’s multiple comparisons test was used for comparisons of mutants to wild type. Tukey’s multiple comparisons test was used to compare mutants to mutants or a mutant across environments. Confidence interval was set to 95%. Reported p-values were adjusted for multiple comparisons. Statistical analyses can be found in S3 File.Invasive growth quantitation has been described . Images Invasive growth patterns (plot profiles) were quantified as described . Subtractec1Δ mutant onto SGAL medium. Plates were incubated for 7 d at 30°C. Cells were washed off of plates in a stream of water. A razor blade and sterile forceps were used to excise the cells that had become embedded in the agar due to invasive growth. Chunks of agar containing invaded cells were placed on microscope slides to which a coverslip was added. For invasive squashes, gentle pressure was applied to squash the agar, revealing the trapped invaded cells. Cells were visualized by microscopy using the 5X, 10X, and the 100X objectives.Invasive scar cross sections were performed by spotting equal concentrations of wild-type cells and the MAPK pathway activity, the ß-galactosidase (lacZ) assay was performed. The following strains were used: wild type (PC313) and the tec1Δ (PC7675), dig1Δ (PC7676), opi1Δ (PC7674), rim101Δ (PC7673), and ras2Δ (PC6222) mutants. Cells were spotted on synthetic medium lacking uracil (SD-URA) to maintain selection for plasmids and were incubated for 21 h at 30°. Cells were then scraped into dH2O and adjusted to the same optical density. Equal amounts of cells were then inoculated in indicated liquid media for 7 h, with shaking, at 30°. Cells were harvested by centrifugation and stored at -80° for at least 30 minutes. The ß-galactosidase (lacZ) assay was then performed as described  as the SFG1, FLO11, NFG1, RGD2, RPI1, and TIP1. The following strains were used when indicated: wild type (PC538) and the opi1Δ (PC2847), ste12Δ (PC539), tec1Δ (PC569), rim101Δ (PC2953), and ras2Δ (PC562) mutants. For experiments measuring the expression of SFG1 and FLO11, cells were spotted and grown on YPD or YPGAL semi-solid agar media for 2 d at 30° before harvesting. For experiments measuring the expression of NFG1, RGD2, RPI1, and TIP1, cells were spotted and grown on YPD (High Glu) semi-solid agar medium for 2 d at 30° or inoculated in 5 mL YPGAL liquid medium for 5 h, with shaking, at 30° before harvesting. Cells were stored at -80° before RNA extractions.Quantitative reverse transcription PCR (RT-qPCR) was used to measure the relative expression of the genes S4 Table. The 2Ct−Δ formula was used to calculate relative gene expression , RNA stability determination (by agarose gel electrophoresis), cDNA generation , and RT-qPCR were performed as described in . The Biopression ,225. Ct http://www.yeastract.com/index.php] was used to assess pathway-dependent changes in gene expression.The repository YEASTRACT We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Michael FreitagAssociate EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This manuscript by Vandermeulen and Cullen takes advantage of the invasive growth phenotype in budding yeast to quantify gene-environment interactions and how phenotypic plasticity can be mediated through condition-specific signaling. The authors examined mutants lacking main regulators of invasive growth across multiple environments, which allowed them to determine their relative importance under different conditions. In addition to the pathways known to play major roles in regulating invasive growth, the authors found that the transcriptional regulator Opi1 plays an even larger role under most conditions. The authors tested various double mutants under different conditions, which revealed that pathways can act additively, redundantly, or even opposingly depending on the environment. Finally, the authors measured in mutants the gene expression of target genes ultimately responsible for different aspects of invasive growth, ultimately allowing them to begin to connect different pathways to expression output.Altogether, I think this manuscript rather thoroughly describes the diverse ways that different signaling pathways can interact leading to both regulatory and organismal phenotypic plasticity. I do have some comments and suggestions, largely to clarify for the reader.1) I think it would be helpful to have the first figure show at least the abbreviated major pathways  and how they connect to major output genes and cellular phenotypes relevant to invasive growth .2) Please consider changing the color scheme for red/green heat maps for the sake of any colorblind colleagues (blue/red should work).3) While I appreciated how the authors used the ranking of mutational effects to determine which pathways played the largest roles under different environments, the authors may also consider hierarchical clustering by both mutant and environment . In particular, clustering by environment may help determine which signals are being used by which pathways (e.g. nitrogen level vs. carbon source), and it may help to determine the environmental trigger that causes certain mutants to no longer have changes in reaction norms.4) Minor point: how was the order of mutants determined in Figure 1--it’s not exactly the same as S3.5) This may be coincidental, but the opi1 mutant seems to be the phenotypic mirror image of dig1. Reynolds 2020 speculates that Opi1 is a repressor that is indirectly causing FLO11 activation. Could it be that Opi1 is a repressor for Dig1? There may be some transcriptomic datasets that could help answer that question.6) For the double mutant analysis, lack of significance in a t-test is not really the same as testing for equivalency, and it may be inherently difficult with the invasive growth assay to determine non-additivity for mutants with already major defects. I think the interpretation of interacting pathways like Ras should be toned down unless there is other evidence for cross talk. Otherwise, I think testing expression of reporter genes like FLO11 in single and double mutants may be more sensitive for identifying conditions where pathways are acting in parallel or are interacting.7) I’m misunderstanding something in Figure 4 comparing “A” and “C.” The order of pathway activity in “C” does not match the order in “A.” Likewise, not sure where the order in “D” for invasive growth is coming from.8) Figure 5H: Consider changing to have the arrows all point in the same direction like Figure 3C. For gene expression, I instinctively associated up arrows with up-regulation and down arrows with down-regulation.Reviewer #2: This is an interesting paper that uses gene-by-environment analysis of invasive growth in yeast to identify conditional gene-gene and gene-phenotype relationships within the signaling network that controls the phenotype. This work has the potential to be of interest to a wide group of researchers interested in signaling network dynamics and the plasticity of networks under different environmental conditions.I have two major concerns with the paper, one suggestion for increasing the impact of the work and a number of minor issues.First major concern: I am not convinced that the invasive growth measurements fully control for confounding effects of growth. Invasive growth is measured as scar-intensity/colony area. Unless this ratio stays the same throughout colony growth then you could imagine the following scenario: Wild-Type has an invasion value of 2 at day-2 , value of 3 at day-5 (medium colony) and value of 4 at day 7 (large colony). Deleting gene X has an effect only on growth, so that colonies follow the same trajectory as WT but with a delay, so that at day 5 they have an invasion value of 2  and at day 7 an invasion value of 3 (medium colony). Using the authors’ approach, this would suggest that deletion of gene X causes lower than WT invasion at days 5 and 7 (ratios of 2/3 and ¾), when all that was really happening was reduced growth, with no effect on invasion behavior (relative to colony size). This line of thought suggests that normalization between genotypes or conditions should be done between colonies of the same size. Can the authors comment on this concern?Second major concern: I have a major concern with the statistical analyses. The research presented is exploratory and involves a large number of statistical tests , both explicitly presented in the figures and implicitly in the fact that the examples that are illustrated are derived from a much larger number of possible comparisons, with the choice of what to present likely driven by what appeared most significant. I can find no mention in the paper of correcting p-values for multiple hypotheses. If I am correct that no correction for multiple hypotheses was undertaken, then I am skeptical that many of the fairly modest effects discussed are actually statistically significant.Increasing the impact: The paper does a thorough job of illustrating that gene-gene and gene-phenotype relationships can change between environments. There is also some illustration of how changes in gene expression of a small number of effector genes accompany these changes in environment and genotype. To make this paper of more general interest, the authors need more molecular data explaining HOW an environmental change changes gene-gene and gene-phenotype relationships, perhaps exploring expression levels and phosphorylation states of more genes in the signaling network, rather than just downstream effectors.Minor Points:The authors need to be more precise about the meaning of terms used, particularly as relating to “regulating” the signaling network. It is often unclear whether this means changing the network graph, or producing different outputs from a stable network because of different environmental inputs.P5. “Phenotypic changes based on the environment are referred to as Gene (or Genotype) by Environment Interactions (GEIs)”. This definition of GEI is incorrect (it does not include the effect of different genes/genotypes).P9. “The fact that invasive growth regulatory pathways show different roles depending on environment is surprising, because the prevailing view is that the major pathways play an equal role, in that their loss causes a complete reduction in invasive growth” – I think this is a straw man and that this view is not generally held. In the absence of a reference clearly arguing that the role of the different pathways is equal, the authors should remove this claim.P9. The ranking exercise the authors undertake is interesting, but they need to be clearer about its limitations. The set of conditions that were tested is only a very small subset of all possible environmental conditions, therefore the ranking is specific only for the set of conditions used and cannot be generalized to an absolute ranking of the pathways. In addition, describing Opi1p as the “main regulator of invasive growth” implies ‘necessary and sufficient’ (at least to me) perhaps something more cautious, like “strongest single regulator” would be better.P9. “Invasive growth was also examined to identify the roles pathways and protein complexes play in regulating changes to invasive growth that occur when transitioning from one environment to another”. The authors should rewrite this as no transitions actually occurred, instead invasion was simply compared between conditions for different mutants. Similarly, on P10 “when transitioning between certain environments”.P10. I am not sure about the utility of Figure 1E. I have concerns about normalizing to YPD for the reaction norms  or normalizing to any other condition . In terms of this study there is nothing special about YPD (or any other condition) that makes it an appropriate “base condition”, instead it is simply one of several environments tested. For deletion strains where the normalization condition is an outlier, this will distort their entire profile across the remaining environments. This issue affects the whole discussion on P10. Instead, it would be safer to compare rows directly from Figure 1C to make arguments about how the importance and direction of effect of different genes (relative to WT) changes in different conditions. This is because WT is an appropriate baseline to compare the other genotypes to.P12 The second paragraph, beginning “The invasive growth network” makes contradictory statements about the set of genes regulating invasive growth vs complex colony morphology. I think the authors are arguing that the same gene network regulates both phenotypes, but that the relative importance and role of genes/pathways in the network differs between the above-surface and below-surface environments . The authors need to clarify what they mean here.P12. I’m not sure it is possible to determine roles in “initiating” and “maintaining” invasive growth using the results in Figure 2F. These results look consistent with a simple reduced rate of invasive growth in all of the mutants.P13. Why does the fact that the sum of the mutant invasion scores is greater than 1 suggest redundancy?P13 Line304. I see very few instances where a pathway is required for invasion  and many situations where pathways are partially dispensible.P15. “Intriguingly, the environment where RTG played a negative role, PHO85p played a positive role and vice versa”. Deletion of either reduces invasion in YPD, so this is only true for SD vs SLPD.P16. It would be more informative to see a scatterplot of MAPK activity against (WT) invasive growth in the different environments tested rather than trying to compare Figures 4C and 4D.P16 The use of the Tec1 and Dig1 deletions to define upper and lower invasive growth boundaries in Fig 4D is not explained.P20 +21. I am not sure that expression levels of SFG1 are a good proxy for FLO11-independent adhesion mechanisms, particularly because SFG1 also regulates FLO11.P23. Describing OPI1 as a “top regulator” implies it is upstream of all the pathways, not that it has the single strongest effect on the phenotype.P26 What does “mutant phenotype analysis” refer to that is different from GEI?P26 The sentence beginning “GEI analysis…” is very opaque and the authors should clarify their meaningReviewer #3: -Overall very interesting dataset that will be of interest to this community. I believe the data merit strong consideration for publication, if care is taken to correct issues in the presentation of that data. In particular, I felt that the paper was careless in its claims of novelty for some of its findings. My concerns are further spelled out below.-Strong statements throughout about prior knowledge in the field. I don't agree that all the reported findings are new, for instance some of what they claim is new is spelled out in the review Bruckner 2012, which is cited. A few example lines are below. More broadly, statements like "How networks are regulated to produce different phenotypes in different environments is not well understood" in the abstract could use justification. Much work in molecular biology has focused on how pathways respond to environmental cues."These results indicate that invasive growth is not binary (ON/OFF) but occurs in a phenotypic spectrum based on the environment." [182]"the prevailing view is that the major pathways play an equal role, in that their loss causes a complete reduction in invasive growth" [201]"In any case, this remarkable finding demonstrates that the same phenotype can arise through the action of pathways that have opposing roles in different settings." [Line 342]-Definition of GEI in line 111 seems off. Shouldn't that be about variant genotypes interacting with the environment, not just phenotypes changing due to environment?-Further, I am not sure I came away clear on what GEI analysis is. There is not a clear definition of this as an approach. Line 156  implies that the authors are treating GEI analysis as something other than exposing mutants to different environments, which is how I would define GEI analysis.-The use of "reaction norms" starting at line 220 could use expansion. Materials and methods were insufficient in describing what this is or what it can tell you that is distinct from a standard normalization procedure.-In line 253, "invasion scar" is used for the first time without a definition. Could go in the introduction when defining the phenotype.-Work from the Ehrenreich lab (and others) has shown that mutations related to invasion phenotypes are strongly influenced by genetic background. It would be good to point this out, as some of the findings, like the dominance of the OPI pathway, may be background-specific. A few lines on this are in the figure 1 legend, but should be located somewhere in the main text instead and would benefit from more discussion.-The mix of transcription factors and upstream regulators may also impact the results regarding the ranking of pathways. When Ras2 is knocked out, its downstream regulators are still present, whereas with OPI, the actual TF is gone. Adding in some additional mutants in genes in different locations withing these signaling pathways would be great, if possible. At the least discussion of the possible impacts of these choices is essential in my view.-Many interpretive statements would benefit from being moved to the discussion, such as line 372 "Perhaps other pathways operate at low overall levels as a safeguard to curb pathway activity. It is clear at least for the ERK pathway that overactivation can lead to cancer and other diseases."-Figure 6b on round cell invasion: I'm not sure I see it like the authors. The cross section looks like a mix of elongated and round cells. Could it be that only some cells elongate to invade, and some incidental round cells hang around after washing due to expression of flocculins?-Two papers that may be of interest came to mind while reading this. I believe they may have useful information for further interpreting your results:1.1. Reynolds, T. B., Jansen, A., Peng, X. & Fink, G. R. Mat Formation in Saccharomyces cerevisiae Requires Nutrient and pH Gradients. Eukaryot Cell 7, 122–130 (2008).1.2. Váchová, L. & Palková, Z. How structured yeast multicellular communities live, age and die? FEMS Yeast Res 18, (2018).-Again, this is a very exciting dataset and I hope that these comments are constructive.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and provided the requested additional statistical analyses to address the possibility of addressing additional, alternative hypotheses (Reviewers 1 and 2). Concerns by Reviewer 2 about varying growth effects are now addressed by Fig S4. You adapted blue/red color schemes to avoid green/red in figures. You updated your citations and modified wording as needed, as was requested by Reviewer 3. You also went beyond the reviewers’ comments to improve the manuscript, as indicated in the last section of your response letter. As the reviewers did, we applaud your thorough approach to identifying gene-phenotype relationships in a complex signaling environment, and believe that your work will be interesting to a wide audience working on network dynamics and phenotypic plasticity under changing environmental conditions.Before your submission can be formally accepted and sent to production you will need to complete our formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Please note: the accept date on your published article will reflect the date of this provisional acceptance, but your manuscript will not be scheduled for publication until the required changes have been made.plosgenetics@plos.org.Once your paper is formally accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you’ve already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at https://www.editorialmanager.com/pgenetics/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production and billing process. Note that PLOS requires an ORCID iD for all corresponding authors. Therefore, please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about making your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Michael FreitagAssociate EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):----------------------------------------------------Data DepositionDryad Digital Repository. As you may recall, we ask all authors to agree to make data available; this is one way to achieve that. A full list of recommended repositories can be found on our website.If you have submitted a Research Article or Front Matter that has associated data that are not suitable for deposition in a subject-specific public repository (such as GenBank or ArrayExpress), one way to make that data available is to deposit it in the The following link will take you to the Dryad record for your article, so you won't have to re‐enter its bibliographic information, and can upload your files directly: http://datadryad.org/submit?journalID=pgenetics&manu=PGENETICS-D-21-01193R1http://www.datadryad.org/depositing. If you experience any difficulties in submitting your data, please contact help@datadryad.org for support.More information about depositing data in Dryad is available at data availability policy requires that all numerical data underlying display items are included with the submission, and you will need to provide this before we can formally accept your manuscript, if not already present.Additionally, please be aware that our ----------------------------------------------------Press Queriesplosgenetics@plos.org.If you or your institution will be preparing press materials for this manuscript, or if you need to know your paper's publication date for media purposes, please inform the journal staff as soon as possible so that your submission can be scheduled accordingly. Your manuscript will remain under a strict press embargo until the publication date and time. This means an early version of your manuscript will not be published ahead of your final version. PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  30 Dec 2021PGENETICS-D-21-01193R1 Gene by Environment Interactions Reveal New Regulatory Aspects of Signaling Network Plasticity Dear Dr Cullen, We are pleased to inform you that your manuscript entitled "Gene by Environment Interactions Reveal New Regulatory Aspects of Signaling Network Plasticity" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Soon after your final files are uploaded, unless you have opted out or your manuscript is a front-matter piece, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work! With kind regards,Zsofia FreundPLOS GeneticsOn behalf of:The PLOS Genetics TeamCarlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdomplosgenetics@plos.org | +44 (0) 1223-442823plosgenetics.org | Twitter: @PLOSGenetics


Drynariae Rhizoma (Chinese name: Gusuibu), has been proved to be an antioxidant flavonoid on promoting osteoporotic fracture (OF) healing, but relevant research is scanty on the underlying mechanisms. We adopted target prediction, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and molecular docking to establish a system pharmacology database of NG against OF. Totally 105 targets of naringin were obtained, including 26 common targets with OF. A total of 415 entries were obtained through GO Biological Process enrichment analysis (P < 0.05), and 37 entries were obtained through KEGG pathway enrichment analysis with seven signaling pathways included (P < 0.05), which were primarily concerned with p53, IL-17, TNF, estrogen, and PPAR signaling pathways. According to the results of molecular docking, naringin is all bound in the active pockets of the core targets with 3–9 hydrogen bonds through some connections such as hydrophobic interactions, Pi-Pi stacked interactions, and salt bridge, demonstrating that naringin binds tightly to the core targets. In general, naringin may treat OF through multiple targets and multiple pathways via regulating oxidative stress, etc. Notably, it is first reported that NG may regulate osteoclast differentiation and oxidative stress through the expression of the core targets so as to treat OF.Naringin (NG), as the most abundant component of  Osteoporosis (OP) is a bone disease that often results in severe consequences such as fracture , 2. OsteDrynariae Rhizoma (Chinese name: Gusuibu) is widely used to prevent and treat OF and OF-related bone diseases, whose isolated active constituents are composed of flavonoids, phenolic acids, triterpenes, and their glycosides [Drynariae Rhizoma. It has been reported that total flavonoids of Drynariae Rhizoma can reduce the production of reactive oxygen species (ROS) to alleviate osteoporosis [Drynariae Rhizoma, has the curative effect of treating osteoporosis and promoting fracture healing, so it has a good application prospect in clinics [ycosides . Among toporosis . Naringi clinics . Current clinics . Moreove clinics . HoweverOxidative stress (OS) is considered to be one of the most critical pathogenic factors of age-related bone loss, which is a primary factor in OF; oxidative stress and bone loss increase with aging, thus leading to OF . StudiesFor better understanding the potential mechanism of NG treatment on OF, we adopted an integrative strategy of network pharmacology and molecular docking , which whttps://tcmsp-e.com/) [https://pubchem.ncbi.nlm.nih.gov/), which was input into the SwissTargetPrediction database (http://new.swisstargetprediction.ch/) [http://www.uniprot.org/uniprot/), and the “popular organisms” search field was set to humans, so that target proteins of naringin were obtained, which were referred to as gene symbols.Naringin-related structure and targets were obtained in 3 steps. Step 1: Data retrieval was performed with the TCMSP database (-e.com/) . NG was ion.ch/)  to obtaihttps://www.genecards.org/) [https://omim.org/) [Homo sapiens.” The proteins corresponding to OF-related genes were standardized using the UniProt database for subsequent analysis.Genes of OF were obtained from 2 databases (GeneCards and Online Mendelian Inheritance in Man). GeneCards, a human gene database (ds.org/) , includeim.org/)  containsWe used R (v3.6.1) software  to take the intersection between OF-related proteins and the target proteins of NG to obtain ITPs.https://string-db.org/) [http://www.cytoscape.org/) [To explore the interaction between ITPs, we adopted the STRING database (db.org/)  to obtaipe.org/) . BrieflyP < 0.05 were extracted.GO and KEGG analyses of the intersection targets were performed using clusterProfiler package (R3.6.1). The enrichment results with https://pubchem.ncbi.nlm.nih.gov/). AutoDockTools  was used to combine nonpolar hydrogen and distribute charge for naringin, the results of which were converted into a PDBQT file. The crystal structures of proteins were obtained from the RCSB PDB website (http://www.rcsb.org/), and the selection of proteins should satisfy the following three principles: (i) it should be a human protein; (ii) the protein owns one or more eutectic ligands, and the eutectic ligand having higher structural similarity with naringin is preferred; and (iii) the crystal structure with smaller “resolution” value is selected. Molecular docking simulations were not conducted, when the eutectic ligand structure of the protein could not be found in the PDB database or its specific active sites could not be obtained through literature search. We used AutoDockTools to separate the target protein from its ligand, add polar hydrogen and distribute charge, and then output the results to a PDBQT file. AutoDockTools was also used to determine the size and center of the docking box. The specific process of docking is as follows: First, the eutectic ligand of target protein was used to obtain the affinity of the docked protein as the comparison of the docking results of naringin. Then, naringin was successively docked with the target proteins and every affinity was calculated. Finally, the docking results of naringin were plotted and analyzed using PyMOL software .AutoDock Vina (v1.1.2) software   was utilFinally, 105 targets of naringin were obtained from TCMSP and SwissTargetPrediction databases. After input into the UniProt database, target proteins of naringin were obtained, which were referred to as gene symbols. The structure and target information of naringin are shown in Supplementary Tables Totally 840 target proteins of OF were retrieved in GeneCards and OMIM databases. After they were mapped with the target proteins of naringin, ITPS were obtained including 26 intersection targets, which are shown in We input ITPs into the STRING database, hiding the targets with no interactive relationship with others. And then, the data of protein-protein interaction (PPI) were obtained, which was imported into Cytoscape (v 3.7.2) to plot the PPI network in We obtained 415 items of biological process (BP). The KEGG pathway analysis of 26 target genes was performed using R software. A total of 37 items were obtained, and Among 11 core targets, a total of 8 proteins suitable for molecular docking were obtained after screening, including ESR1, CASP3, ACE, TNF, PPARG, SERPINE1, CYP19A1, and MMP1. To verify how naringin binds to core targets as previously referred, molecular docking using AutoDock Vina was developed in this section. We predicted whether naringin could enter the active pocket of target proteins successfully or not and calculated the affinities between them. The relevant information of the eight proteins and the docking results of eutectic ligands are shown in Supplementary As presented in As presented in As presented in As presented in As presented in As presented in As presented in As presented in Drynariae Rhizoma has been widely applied in treating osteoporosis and osteoporotic fracture clinically for many years. Naringin is one of the most abundant flavonoids in Drynariae Rhizoma, which is also found in citrus fruits and many other Chinese medicines [Chinese traditional medicine edicines . Studiesedicines , 22.In this study, there were 26 common targets obtained between NG and OF, among which 11 core targets were identified, namely CASP3, TNF, ESR1, MMP2, MMP1, SERPINE1, IGFBP3, CYP19A1, PPARG, CDKN1A, and ACE. PPI network topology analysis suggested that the targets were mainly characterized by oxidative stress, inflammation, cell cycle, and lipid and hormone metabolism-related proteins. The top three targets in terms of degree are TNF, CASP3, and ESR1, indicating that they may be the key targets of NG in the treatment of OF.α, which is the earliest inflammatory medium produced in oxidative stress response, can promote the generation of inflammatory mediators and induce macrophage colony-stimulating factor (M-CSF) expression [α can promote oxidative stress and regulate bone homeostasis and bone reconstruction [α expression [TNF (tumor necrosis factor), known as TNF-pression . TNF canpression . TNF-α ctruction , 26. Morpression . TherefoCASP3 (caspase-3) affects the apoptosis of osteoclasts . Some stDrynariae Rhizoma revealed a double directional adjusting function of estrogenic and antiestrogenic activities. Pang et al. [ESR1 (estrogen receptor 1) has a close relationship with bone formation . When ESg et al.  also demMMP2  and MMP1  are all members of the family of matrix metalloproteinases (MMPs). MMP2 is expressed in the cytoplasm of osteoblasts and some osteoclasts, which can regulate the dissolution of bone matrix, inhibit bone resorption, and contribute to bone reconstruction , which pγ2 signaling pathway [γ2 and activation of the Notch signaling pathway in a rabbit model.PPARG (peroxisome proliferator-activated receptor gamma), which is related to the regulation of cell differentiation, inflammatory response, and oxidative stress, mainly affects the catabolism of lipids and plays a key role in the process of adipocyte differentiation . It can  pathway , 40. A s pathway  reportedG1 phase is the most important [D1 and CDK4 (cyclin-dependent kinase 4) forms the CDK4-cyclin complex to promote the G1 phase [G1 phase regulatory protein, inactivates the CDK4-cyclin complex by binding to it, leading to cell cycle stagnation in the G1 phase [P < 0.01). It is also reported that the downregulation of p21 protein expression can promote the proliferation of osteoblasts [CDKN1A (cyclin-dependent kinase inhibitor 1), also known as p21, is a negative regulator of cell cycle that regulates cell proliferation, differentiation, and senescence . The nummportant . In thisG1 phase . CDKN1A G1 phase . An animG1 phase  has proveoblasts . Thus, pIn addition, CASP2, CASP3, CASP7, and CASP8 in The results of GO enrichment analysis are similar to those of PPI network analysis. Notably, GO.BP enrichment analysis reveals that the regulation of oxidative stress and osteoclast differentiation play a critical role in NG treating OF, as shown in KEGG pathway enrichment analysis demonstrated that p53, IL-17, TNF, estrogen, and PPAR signaling pathways may play a key role in naringin treating OF. Similarly, the results of KEGG analysis are consistent with those of PPI and GO analysis.Some studies have verified that the activation of p53 signaling pathway can destroy the maturation of osteoblasts and inhibit chondrocyte differentiation . Studiesα, IL-6, and NF-кB, thus promoting osteoclast differentiation induced by RANKL [The IL-17 signaling pathway can stimulate the synthesis of TNF-by RANKL . Moreoveby RANKL . So, IL-by RANKL  and TNF by RANKL  have cloThe estrogen signaling pathway can regulate the proliferation, differentiation, and apoptosis of osteoblasts and osteoclasts . It has Collectively, the results in our study predicted some pathways and targets that may be potentially therapeutic targets and provide reference for future research on NG treating OF. Nevertheless, a limitation of this study is that further experiments are necessary to demonstrate our findings.In summary, for the first time, our results revealed that naringin may treat OF possibly by regulating numerous signaling pathways and targets related to oxidative stress and osteoclast differentiation. These results will provide a theoretical basis for the treatment of OF. However, these predicted altered signaling pathways or target genes still need to be further verified in the future study.


Purpose: The external preparation of the Tibetan medicine formula, Baimai ointment (BMO), has great therapeutic effects on osteoarthritis (OA). However, its molecular mechanism remains almost elusive. Here, a comprehensive strategy combining network pharmacology and molecular docking with pharmacological experiments was adopted to reveal the molecular mechanism of BMO against OA.Methods: The traditional Chinese medicine for systems pharmacology (TCMSP) database and analysis platform, traditional Chinese medicine integrated database (TCMID), GeneCards database, and DisGeNET database were used to screen the active components and targets of BMO in treating OA. A component–target (C-T) network was built with the help of Cytoscape, and the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment through STRING. Autodock Tools which was used to dock the key components and key target proteins was analyzed. Animal experiments were performed to verify the key targets of BMO. Hematoxylin–eosin and toluidine blue staining were used to observe the pathology of joints. Protein expression was determined using enzyme-linked immunosorbent assay.Results: Bioactive compounds and targets of BMO and OA were screened. The network analysis revealed that 17-β-estradiol, curcumin, licochalone A, quercetin, and glycyrrhizic acid were the candidate key components, and IL6, tumor necrosis factor (TNF), MAPK1, VEGFA, CXCL8, and IL1B were the candidate key targets in treating OA. The KEGG indicated that the TNF signaling pathway, NF-κB signaling pathway, and HIF-1 signaling pathway were the potential pathways. Molecular docking implied a strong combination between key components and key targets. The pathology and animal experiments showed BMO had great effects on OA via regulating IL6, TNF, MAPK1, VEGFA, CXCL8, and IL1B targets. These findings were consistent with the results obtained from the network pharmacology approach.Conclusion: This study preliminarily illustrated the candidate key components, key targets, and potential pathways of BMO against OA. It also provided a promising method to study the Tibetan medicine formula or external preparations. Osteoarthritis (OA) is a common chronic disease with cartilage degradation in the clinic, characterized by arthritis, synovial inflammation, joint edge, and underlying subchondral bone lesions . ClinicaClinical Notes of Tibetan Medicine in the 16th century with a long history, and has its origins in Four Medical Classics and other traditional Tibetan medicine classics . Diclofenac diethylamine Emulgel  was purchased from Novartis Pharma Corporation . The hematoxylin–eosin (HE) staining kit (C0105) was purchased from Beyotime Biotechnology . A toluidine blue staining kit (DA0059) was purchased from Leigen Biotechnology . The enzyme-linked immunosorbent assay (ELISA) kits including CXCL8, IL-1β, IL-6, MAPK1, tumor necrosis factor (TNF), and VEGFA were obtained from Meimian Biotechnology . All other chemicals used in this study were of analytical grade.Curcumae longae Rhizoma, Zanthoxyli Pericarpium, Moschus, and Glycyrrhizae Radix et Rhizoma, the main ingredients of BMO, were collected from the traditional Chinese medicines for systems pharmacology database and analysis platform (TCMSP) (http://tcmspw.com/) and traditional Chinese medicine integrated database (TCMID) (http://www.megabionet.org/tcmid/)  parameters were added as follows: half-life (HL) ≥ 4 h (https://www.genecards.org/) (http://targetnet.scbdd.com/) with an area under the curve ≥7 and prediction probability = 1. Finally, the UniProt database (https://www.uniprot.org/) was used to correct the targets obtained to official names with selected species “Homo sapiens.”Firstly, the components of /tcmid/) . Drug li/tcmid/) . Since tL) ≥ 4 h , hydrogeL) ≥ 4 h . Secondlds.org/) , and thehttp://www.disgenet.org/web/DisGeNET/menu/home/). After removing the repeated targets, they were corrected to official names with selected species “Homo sapiens” using the UniProt database.The targets associated with OA disease were collected through the TCMSP database, GeneCards database, and DisGeNET database (https://cytoscape.org/). In the network, the nodes with diverse colors and shapes were symbolled for different components and targets. According to the degree of nodes, the key compounds were screened via the C-T network.The network of components and related targets of BMO was constructed for visual analysis through Cytoscape 3.6.1 (http://bioinfogp.cnb.csic.es/tools/venny/). The candidate targets were inputted to STRING 11.0 (https://string-db.org/) which includes a great number of protein–protein interaction (PPI) relationships .Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted with the overlapped targets using the STRING database. According to the biological process (BP), cell composition (CC), molecular function (MF), and the actual roles of the pathways with https://pubchem.ncbi.nlm.nih.gov/) in the sdf format, then Chem3D software (https://www.chemdraw.com.cn/) was used to convert them to pdb formats. The 3D structures of core target proteins were downloaded from the Protein Data Bank database (http://www.rcsb.org/) in pdb formats. Secondly, ligands and proteins were prepared through PyMOL (https://pymol.org/) involving charge addition, atomic type assignment, water removing, and extraction of original ligands and ions. Then, the ligands and proteins were saved in the pdbqt format using AutoDock Tools 1.5.6 (http://autodock.scripps.edu/).Thirdly, AutoDock Tools was used for docking after setting grid box parameters. For each ligand, the lowest binding energy was selected as the result of molecular docking. Lastly, the docking conformation analysis and mapping were carried out by PyMOL software, and PLIP (https://projects.biotec.tu-dresden.de/plip-web/plip) was used to analyze the force between ligands and proteins (The key components and key targets were obtained from the component–target (C-T) network and PPI network, respectively, for molecular docking. Firstly, the three-dimensional (3D) molecular structures of the core components and chemical drugs used for treating OA were downloaded from the PubChem database (proteins .The Sprague Dawley (SD) rats used here were obtained from Vital River Laboratory Animal Technology Co., Ltd., . All animal experimental procedures were approved by the Animal Ethics Committee of Beijing University of Chinese Medicine (BUCM-4-2019101701-4001). 24 SD rats were randomly divided into four groups, namely, sham-operated group, model group, positive control group, and BMO group. We used a modified Hulth method to establish the rat OA model . FirstlyAfter operation, BMO and Votalin Emulgel were applied to the right knee joint of the rats in the BMO group and positive control group, respectively, followed by massage for 5 min. The dosage of the ointment was 1 g per time, twice a day. The period of administration was 4 weeks.After 4-week administration, the right knees of all rats were cut and put into 4% paraformaldehyde for fixation, then routinely decalcified with ethylenediaminetetraacetic acid. After being washed, the knee joints were dehydrated by step gradient ethanol and hyalinized by dimethylbenzene. Next, the joints were paraffin-embedded and sliced into 5-μm sections. Neutral gum sealing was performed after conventional HE and toluidine blue staining, and light microscopy used to observe the slices. The modified Mankin score  was usedAfter 4-week administration, the rats in all groups were anesthetized and fixed, and their skin and subcutaneous tissues cut to expose the joint capsule. Then, 400 μl sterile phosphate-buffered saline was injected into the joint cavity. After repeatedly moving the joint, 300 μl joint fluid was carefully extracted for ELISA, which was diluted and added according to the ELISA kit operating instructions. Firstly, it was incubated at 37°C for 30 min, and the concentrated solution was washed thoroughly after dilution. It was then treated to enzymes, warm bath, washing, and color rendering and the termination liquid added to terminate the reaction. CXCL8, IL-1β, IL-6, MAPK1, TNF, and VEGFA were measured within 15 min after the addition of the stop solution. Absorbance was measured immediately at 450 nm using an enzyme marker , and a standard curve was derived to read the contents of IL-1β, TNF, MAPK1, IL-6, CXCL8, and VEGFA in the joint fluid in different groups.p < 0.05 was considered statistically significant.Data were presented as the mean ± standard deviation with at least six independent experiments. SPSS 20.0 software was used to perform statistical analyses by one-way analysis of variance, and Curcumae longae Rhizoma, 8 in Zanthoxyli Pericarpium, 7 in Moschus, and 22 in Glycyrrhizae Radix et Rhizoma , which included 1804 of BP, 183 of MF, and 95 of CC. Most of the BP terms were related to response to inflammatory, regulation of cytokines, cell proliferation, apoptosis, etc., and the main terms of MF were associated with protein binding, receptor binding, cytokine binding, etc. CC enrichment was mainly involved in the cytoplasm, endoplasmic reticulum, organelles, etc.STRING 11.0 was used to analyze the BP, CC, and MF annotations of the 169 potential targets. There were 2082 GO terms  were enriched, mainly covering diseases, inflammation, immunity, metabolism, and cell regulation. Ranked according to the p-value, the top five pathways were pathways in cancer (p = 5.42E−32), AGE-RAGE signaling pathway in diabetic complications (p = 2.65E−29), TNF signaling pathway (p = 5.85E−18), IL-17 signaling pathway (p = 1.04E−17), and hepatitis B (p = 1.04E−17).The KEGG pathway annotation indicated that 159 pathways  between the sham-operated group and model group. After administration, the scores decreased significantly (p < 0.001). There was no significant difference between the positive control group and BMO group (p > 0.05).The knee joints of rats in each group were observed by HE and toluidine blue staining . In the p < 0.001) in the model group, indicating the successful establishment of the OA model. After administration, the levels of those cytokines decreased significantly (p < 0.001) in both the BMO group and positive control group, which suggested BMO had effects on treating OA to a certain extent by regulating CXCL8, IL-1β, IL-6, MAPK1, TNF, and VEGFA in the joint fluid.The levels of CXCL8, IL-1β, IL-6, MAPK1, TNF, and VEGFA in joint fluid were detected by ELISA. As shown in wear and tear arthritis as well. The lesions of the disease that occur most frequently are in the knees, hips, and hands. OA is usually treated with combined therapies including medications, surgery, and exercise . Lastly, although molecular docking technology has been widely used in various drug research fields, it still has some limitations just as a computer virtual screening method. For example, quercetin and cholesteryl ferulate, the main components of BMO screened in network pharmacology, had high binding energy with targets, which meant difficulty in combining. It may be because the target is not fixed and may change with the environment In this study, it was found that BMO had multicomponents and multi-targets in treating OA. The possible molecular mechanism of action is that the candidate key components of glycyrrhizic acid, 17-β-estradiol, curcumin, licochalcone A, and quercetin act on IL6, TNF, MAPK1, VEGFA, CXCL8, and IL1B and these key targets regulate the TNF signaling pathway, NF-κB signaling pathway, and HIF-1 signaling pathway, so as to play a therapeutic role . The verTraditional medicine is a valuable source for disease treatment, such as Tibetan medicine has abundant applications in the clinic. Hence, to study its mechanism for treatment is necessary. However, its molecular mechanism is difficult to reveal because of complicated components. This study combined network pharmacology and molecular docking methods with pharmacological experiments to uncover the molecular mechanism of Tibetan medicine BMO in treating OA from three aspects of components, targets, and pathways. The strategy we adopted was both reliable and efficient to study the molecular mechanism of external preparation with a combination of theoretical pharmacology and experimental pharmacology.The significance of this study is preliminarily clarifying the molecular mechanism of BMO in treating OA, thus providing theoretical foundation for clinical application and further research. The obtained candidate key components may be designed for new drugs due to their great effects. Also, the key targets and potential pathways selected can provide new ideas and directions for targeted intervention therapy in treating OA in future development. With the progress of bioinformatic technologies such as molecular fishing, molecular dynamics simulation, and so on, the study of molecular mechanism and drug design has been efficient. Later, binding with advanced bioscience techniques such as DNA chips, single-cell sequence, and surface plasmon resonance can be an effective verification. The theoretical–experimental research pattern may serve as a universal method in drug study.


Silybum marianum (S. marianum) and determine how they protect against nonalcoholic fatty liver disease (NAFLD). TCMSP, DisGeNET, UniProt databases, and Venny 2.1 software were used to identify 11 primary active components, 92 candidate gene targets, and 30 core hepatoprotective gene targets in this investigation, respectively. The PPI network was built using a string database and Cytoscape 3.7.2. The KEGG pathway and GO biological process enrichment, biological annotation, as well as the identified hepatoprotective core gene targets were analyzed using the Metascape database. The effect of silymarin on NAFLD was determined using H&E on pathological alterations in liver tissues. The levels of liver function were assessed using biochemical tests. Western blot experiments were used to observe the proteins that were expressed in the associated signaling pathways on the hepatoprotective effect, which the previous network pharmacology predicted. According to the KEGG enrichment study, there are 35 hepatoprotective signaling pathways. GO enrichment analysis revealed that 61 biological processes related to the hepatoprotective effect of S. marianum were identified, which mainly involved in response to regulation of biological process and immune system process. Silymarin was the major ingredient derived from S. marianum, which exhibited the hepatoprotective effect by reducing the levels of ALT, AST, TC, TG, HDL-C, LDL-C, decreasing protein expressions of IL-6, MAPK1, Caspase 3, p53, VEGFA, increasing protein expression of AKT1. The present study provided new sights and a possible explanation for the molecular mechanisms of S. marianum against NAFLD.The study aimed to identify the key active components in   NAFLDBecause there are no identifiable symptoms or indications, most individuals are suspected of having NAFLD based on the inadvertent discovery of elevated serum alanine aminotransferase (ALT) and γ-glutamyl transpeptidase (GGT) levels or imaging data demonstrating diffuse fatty liver . NAFLD iSilybum marianum L.) is a herbaceous plant of the genus Silybum in the Compositae family that originates in Southern Europe and North Africa [Silybum marianum L. (S. marianum) was used to treat liver and gallbladder diseases in the Western world as early as the fourth century BC. It was also revealed that S. marianum exhibits anti-inflammatory, immunomodulating, antifibrotic, antioxidant, and liver-regenerating properties in patients with alcoholic liver disease, nonalcoholic fatty liver disease, viral hepatitis, and drug-induced liver injury [S. marianum, which mainly includes isosilychristin, isosilybin, silybin, silychristin, silydianin, and toxifolin [For a long time, herbs and herbal therapies have been used to treat relevant liver illnesses, including NAFLD . Milk thh Africa . It has r injury . Silymaroxifolin . It was oxifolin . In addioxifolin .S. marianum’s pharmacological properties have been investigated for many years, and it is widely used in clinical applications due to its good curative impact, few side effects, easy oral administration, and low cost [S. marianum, its primary material foundation (including significant active components/ingredients) and molecular mechanism for treating NAFLD have yet to be completely investigated. Furthermore, no relevant network pharmacology was used to investigate the effects of S. marianum active components on the mechanism of NAFLD treatment. As a result, the network pharmacology technique was utilized in conjunction with a relevant database to screen the primary active components in S. marianum and extensively investigate its protective effect on NAFLD targets, related signaling cascades, and biological processes. Furthermore, experimental validation based on network pharmacology analysis was carried out to elaborate and confirm the targets and related signaling pathways in NAFLD. The key active components in S. marianum and hepatoprotective mechanism against NAFLD are still unclear. The present study aimed to figure out the active hepatoprotective ingredient of S. marianum and study the specific mechanism of NAFLD.The concept of ‘multi-target and multi-component treatments’ is emphasized in network pharmacology, which has the benefits of total treatment and may overcome the shortcomings of ‘single target.’ low cost . However2.2.1.https://www.uniprot.org/) [https://www.disgenet.org/) [https://string-db.org/) [https://metascape.org/gp/index.html#/main/step1) [https://tcmspw.com/tcmsp.php/) [UniProt Protein database (ot.org/) , DisGeNEet.org/) , String db.org/) , Metascan/step1) , TCM phasp.php/) , and sofsp.php/)  were uti2.2.S. marianum were obtained by searching ‘Silybum marianum’ on the TCMSP database platform. According to the standard of oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.18, and molecular weight (MW) < 500, the main active ingredients/components and targets of S. marianum were ascertainedy  of nodes in large complex pharmacological networks is investigated.From ‘Section 2.4.S. marianum’s active ingredient targets were mapped using Venny 2.1, as well as the common target was chosen as the relevant target of S. marianum’s active ingredient against NAFLD. The target network of ‘active compound-hepatoprotective on NAFLD of S. marianum’ was then constructed in Cytoscape 3.7.2 software, with its topological properties were studied using the ‘network analyzer’ function.‘Nonalcoholic fatty liver disease or NAFLD,’ was utilized as a keyword to search the DisGeNET database for target information relating to NAFLD’s protective effect. The NAFLD target and 2.5.Using a string database, the target protein–protein interaction (PPI) network of related target proteins was built. The species (protein species) was set to ‘Homo sapiens,’ and the minimal interaction threshold (0.7) was set to ‘medium confidence.’ The remaining parameters were left at their default values. The network analyzer function in Cytoscape 3.7.2 software is then used to investigate the topological properties of the PPI network. After the process, the PPI network diagram was created.2.6.2.5. All of the core genes were tested, with the critical value of significant functions and pathways set at the threshold P < 0.05. The primary routes and biological processes via which the main active components of S. marianum exert considerable hepatoprotective pharmacological effects in NAFLD were identified. Furthermore, the effects of S. marianum on NAFLD have been established. To validate the liver protection mechanism of S. marianum against NAFLD across numerous targets and multiple pathways, the KEGG mapper function was utilized to mark the target of the selected active ingredient on the pathway associated with NAFLD.To enable full biological function annotation information, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene function annotation (GO) were used in the study. The Metascape database performed a KEGG pathway and GO biological process enrichment analysis on core genes obtained in Section 2.7.2.7.1.SPF male C57BL/6 J mice aged 7–8 weeks and weighing 20–22 g were obtained from the Chongqing Academy of Traditional Chinese Medicine. Nanjing Jiancheng Bioengineering Institute  provided diagnostic kits for the determination of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C). Proteintech Co., Ltd  provided rabbit anti-serine/threonine protein kinase 1 (AKT 1), interleukin 6 (IL-6), mitogen-activated protein kinase 1 (MAPK1), cysteinyl aspartate specific proteinase (Caspase 3), p53, TNF-polyclonal primary antibodies from mice, an anti-mouseIgG-HRP-conjugated secondary antibody from goat, and anti-rabbit IgGHRP-conjugated secondary antibody goat. Sangon Biotech Co. Ltd  provided the total protein extraction kit. Chongqing Yonghui Supermarket  supplied sucrose, lard, and eggs. Dalian Beier Pharmaceutical Co., Ltd  provided cholesterol, sodium cholate, and propylthiouracil.2.7.2.Control group: mice were given intragastric PBS for consecutive 16 weeks.Model group: mice were given an intragastric nutritional solution  for consecutive 16 weeks.Low-dose silymarin group (20 mg/kg silymarin group): For consecutive 16 weeks, mice were given silymarin at a level of 20 mg/kg body weight per day along with a nutritional solution.High-dose silymarin group (40 mg/kg silymarin group): For consecutive 16 weeks, mice were treated with silymarin at a level of 40 mg/kg body weight/day and nutritional solution.After the experimental mice had been adaptively fed for 7 days in a row, they were randomly separated into four groups: control, model, low-dose silymarin, and high-dose silymarin, with 20 mice in each group. All mice were housed in a specific compartment with controlled air conditions and were free to eat and drink during the experiment.2.7.3.Molecular docking of known key targets with silymarin, the docking affinity reflects its stability. Using PDB database  and PubC2.7.4.After consecutive 16 weeks of feeding, all of the mice were anesthetized with an intraperitoneal injection of 1% sodium pentobarbiturate and sacrificed. For histopathological examination, liver tissues of mice were obtained and fixed in a solution, which contained 10% neutral buffered formalin. For routine pathological examination, a 4 μm thick section was cut and stained with hematoxylin–eosin (H&E). A microscope was used to examine the pathological changes in liver tissues, and photographs were taken with a 200-fold magnifying glass.2.7.5.Mice were subjected to fasting with water for 12 h, and were anesthetized by intraperitoneal injection of 1% sodium pentobarbiturate. Mice eyeballs were removed to collect the blood. The blood was centrifuged at 5,000 r/min for 15 min, at 4°C for 2 h,  centrifuged the serum into a 1.5 mL centrifuge tube. The levels of ALT, AST, TG, TC, HDL-C, and LDL-C were measured, respectively, by HITACHI 7600–020 automatic biochemistry analyzer .2.7.6.β-actin serving as an internal standard.Western blot assays were used to survey the protein expression of the related signaling pathways predicted by the previous network pharmacology in mice livers. The mice liver tissue lysate was separated using 12.5% SDS-PAGE and transferred to a PVDF membrane before being washed three times with TBST. The blots were then blocked in 5% skim milk for 2 h at 37°C before being incubated overnight at 4°C with the following primary antibodies: AKT1 (1:1000), IL-6 (1:1000), MAPK1 (1:2500), Caspase 3 (1:2500), p53 (1:2500), and VEGFA (1:2500). Secondary IgG-HRP-conjugated antibodies were added and incubated at 37°C for 2 h. Image J software (version 1.8.0) was used to quantify and visualize target proteins, with 2.8.All of the relative animal studies were carried out in accordance with the rules for the care and use of laboratory animals, which were authorized by the Southwest University’s Office of Experimental Animal Management Committee .2.9.P < 0.05 or extremely significant at P < 0.01, respectively. All statistical analyses were performed using GraphPad Prism 8.0 .Using SPSS 19.0 statistical software  for statistical analysis. The differences between the two groups were compared using independent samples t-tests. Differences were considered significant at 3.S. marianum and study the specific mechanism of NAFLD . We applied network pharmacology and experimental validation to explore the pharmacological mechanism of S. marianum against NAFLD. The active hepatoprotective ingredient of S. marianum against NAFLD was silymarin. It could exhibit the hepatoprotective effect on NAFLD by improving the levels of ALT, AST, TC,TG, HDL-C, LDL-C, decreasing protein expressions of IL-6, MAPK1, Caspase 3, p53, VEGFA, and increasing protein expression of AKT1.The purpose of the current study was to find out the active hepatoprotective ingredient of 3.1.S. marianum, which including arachidonic acid, (-)-taxifolin, stigmasterol, Vitamin E, 24-methylidenelophenol, silybin, silydianin, silymonin, silandrin, quercetin, and CLR, respectively.TCMSP database screening parameters of OB ≥ 30%, DL ≥ 0.18, and MW < 500 resulted in the discovery of 11 active components of 3.2.S. marianum were finally identified. Among them, arachidonic acid has 36 gene targets, (-)-taxifolin has 3 gene targets, stigmasterol has 28 gene targets, Vitamin E has 1 gene target, 24-methylidenelophenol has 3 gene targets, silybin has 2 gene targets, silydianin has 1 gene target, silymonin has 0 gene target, silandrin has 8 gene targets, quercetin has 145 gene targets, and CLR has 3 gene targets. Creating a ‘compound-gene target network’ with Cytoscape 3.7.2 software . Then, for visual analysis, import the PPI network data into Cytoscape 3.7.2 software , set thsoftware . AKT1, Isoftware ). Finall3.5.P < 0.05 principle was used to filter the pathways, and the top 20 KEGG pathways were listed in order of P-value . It is  P-value . These t3.6.The molecular docking of silymarin with the above key targets has an average docking affinity of −6.77 kcal/mol . The det3.7.inP < 0.01). The pathological score provided visual evidence related to the effect of silymarin on NAFLD. It was shown that silymarin exhibit the obviously hepatoprotective effect on NAFLD (Effects of silymarin on mice’s body weight are shown on NAFLD . The effon NAFLD . Compareon NAFLD . Howeveron NAFLD  and 40 mon NAFLD . All the3.8.P < 0.01). In addition, the content of TC (P < 0.01), as well as the content of HDL-C (P < 0.05) in the model group. It was revealed that the content of blood lipid in the model group increased, liver function was damaged to a certain extent, and obvious inflammatory lesions appeared. On the contrary, mice pretreated with silymarin showed a significant reduction in the level of these parameters  and ASTnt of TC ) and TG nt of TC ) were alof HDL-C ) and LDLof HDL-C ) signifirameters .Figure 3.9.To explore the effect of silymarin on NAFLD, relevant signaling pathways proteins  predicted by network pharmacology were investigated by Western blot . In the 4.S. marianum on NAFLD were preliminarily analyzed by network pharmacology. The study is the first to propose a comprehensive strategy combining pharmacological experiments and network pharmacology methods to explore the material basis of S. marianum pharmacodynamics and its possible liver protection mechanism against NAFLD.In recent years, more and more related studies and researches are focused on conventional pharmacological strategies. Despite the pharmacology and mechanism of the drug was revealed by traditional pharmacological methods, it is difficult to explain the complicated interaction and molecular process that exists between medications and the human body . FurtherS. marianum extract affected hepatic steatosis and oxidative stress in mice fed a high-fat diet. The results demonstrated that S. marianum extract effectively lowered mice body weight, fat mass, serum triglyceride, free fatty acid, glucose, insulin, and other biochemical indicators [S. marianum, with antioxidant, anti-inflammatory, and anti-apoptotic properties in the treatment and prevention of NAFLD and NASH. Perumpail et al. have found that the usage of Vitamin E reduced liver enzymes while improving biochemical properties. Furthermore, the histological investigation revealed that Vitamin E administration improved lobular inflammation and hepatic steatosis [The liver is a dynamic organ that is involved in a variety of physiological activities, including the regulation of glucose and lipid metabolism throughout the body . During dicators . Quercetdicators . It was dicators . Vitaminteatosis .S. marianum. The major targets of the ‘S. marianum-NAFLD’ PPI network have been linked to cancer, hepatitis B and C, endocrine resistance, insulin resistance, type 2 diabetes, inflammatory bowel disease, and other disorders, according to KEGG pathway enrichment data. NAFLD refers to a group of progressive liver illnesses that include simple steatosis, NASH, fibrosis, and the possibility of evolving into cirrhosis and hepatocellular carcinoma. The majority of NAFLD patients develop liver cirrhosis, and the risk of liver cancer in NAFLD patients is higher than in the general clinical population [S. marianum is linked to the IL6 pathway, the HIF-1 route, and the AP-1 pathway, all of which are enriched in the KEGG pathway. Fang et al. investigated whether IL-6/STAT3-mediated hepatic autophagosome activation and hepatocyte oxygen consumption contribute to NAFLD hepatoprotection. Caffeine’s inability to ameliorate IL-6 and hepatocyte-specific STAT3 gene deletion NAFLD in mice suggests that the IL6/STAT3 pathway is required for caffeine’s hepatoprotective action in NAFLD [KEGG pathway enrichment and GO biological process analysis were done on the proteins appearing in the PPI network to further investigate the biological pathways of NAFLD that are regulated by the active components of pulation . It has pulation . It has pulation . It was pulation . Furtherin NAFLD . Chen etin NAFLD . Hasenfuin NAFLD .The experimental verification was carried out for expanded relative indicators in silymarin treatment of NAFLD based on network pharmacology analysis. The biochemical parameter evidence examined in this study is supported by histopathological studies. Histological study of mice liver treated with gavage nutritional solution revealed apparent characteristics such as inflammatory liver tissue and neutrophil infiltration. However, silymarin therapy greatly reduced inflammatory liver tissue and neutrophil infiltration. ALT is an enzyme found in abundance in the cytoplasm of liver cells. ALT levels in healthy people’s serum are typically low. When liver cells experience apoptosis and injury, the ALT level in the serum rises dramatically . AST levNAFLD mainly includes nonspecific symptoms and signs such as fatigue, indigestion, liver pain, hepatosplenomegaly, and may have metabolic syndrome-related symptoms such as overweight and/or visceral obesity, increased fasting blood glucose, dyslipidemia, and hypertension ,58. Thro5.S. marianum’s pharmacodynamics and its possible hepatoprotective mechanism for NAFLD. Through active ingredient screening, target prediction, PPI network construction, KEGG pathway, GO biological process analysis, and experimental verification, S. marianum’s hepatoprotective mechanism for NAFLD was clarified. It has been demonstrated that the active hepatoprotective ingredient of S. marianum was silymarin on NAFLD. It could significantly improve the levels of ALT, AST, TC,TG, HDL-C, LDL-C, decrease protein expressions of IL-6, MAPK1, Caspase 3, p53, VEGFA, and increase protein expression of AKT1. It provides data support for the development of active ingredients of S. marianum and study on the hepatoprotective mechanism on NAFLD, and propose new ideas for the systematic and comprehensive study of NAFLD at the cellular and molecular levels, respectively.Taken together, the present study puts forward for the first time a comprehensive strategy that combines pharmacological experiments and network pharmacology methods to explore the material basis of Click here for additional data file.


Toxocara canis and Toxocara cati are globally occurring zoonotic roundworms of dogs and cats. Migration and persistence of Toxocara larvae in the central nervous system of paratenic hosts including humans may cause clinical signs of neurotoxocarosis (NT). As pathomechanisms of NT and host responses against Toxocara larvae are mostly unknown, whole-genome microarray transcription analysis was performed in cerebra and cerebella of experimentally infected C57Bl/6J mice as paratenic host model at days 14, 28, 70, 98, and 120 post-infection. Neuroinvasion of T. cati evoked 220 cerebral and 215 cerebellar differentially transcribed genes (DTGs), but no particular PANTHER  pathway was affected. In T. canis-infected mice, 1039 cerebral and 2073 cerebellar DTGs were identified. Statistically significant dysregulations occurred in various pathways, including cholesterol biosynthesis, apoptosis signaling, and the Slit/Robo mediated axon guidance as well as different pathways associated with the immune and defense response. Observed dysregulations of the cholesterol biosynthesis, as well as the Alzheimer disease-amyloid secretase pathway in conjunction with previous histopathological neurodegenerative findings, may promote the discussion of T. canis as a causative agent for dementia and/or Alzheimer’s disease. Furthermore, results contribute to a deeper understanding of the largely unknown pathogenesis and host-parasite interactions during NT, and may provide the basis for prospective investigations evaluating pathogenic mechanisms or designing novel diagnostic and therapeutic approaches.  Toxocara canis and Toxocara cati are globally occurring intestinal parasites with canids and felids as definitive hosts. Due to the transpacental and/or lactogenic transmission to the offspring, they are of particular veterinary importance. Infections in dogs and cats have been associated with a variety of pathologies, induced either by the tracheal or somatic migration of larvae or adult nematodes in the gastrointestinal tract ) and Mapk10 (mitogen-activated protein kinase 10 [Mapk10]). Stimulation of microglia with Aβ increased the kinase activity of PKC-δ [Despite the progressive course of NT, most pathways identified as overrepresented in the day-wise PANTHER classification were dysregulated in both the cerebra and cerebella of ase (AD) ,20,24. Aase (AD) . During of PKC-δ , and PKCof PKC-δ . Furtherof PKC-δ .A further dysregulated pathway with decreased transcriptional levels compared to the uninfected control mice was the gonadotropin-releasing hormone (GnRH) receptor pathway. The peptide GnRH is primarily known to participate in the regulation of reproduction via the hypothalamic-pituitary-gonadal axis, but may also serve as a neurotrophic factor and modulator of hippocampal synaptic activities, impacting central nervous system physiology as well as pathophysiology ,54,55,56T. canis resulted in a higher number of DTGs in the cerebellum than in the cerebrum, but PANTHER Classification analysis resulted in a higher number of affected pathways in the cerebrum, which may reflect the described species-specific brain part tropism of T. canis larvae in paratenic hosts [T. cati larvae rather migrate to the cerebellum than to the cerebrum, but larvae mainly accumulate in muscle tissue [T. cati-infected mice and no particular pathway was overrepresented during the PANTHER Classification analysis. This mirrors previous observations in T. cati-infected C57BL/6J mice, which showed delayed onset and less severe progression of neurobehavioral alterations as well as less severe histopathological alterations compared to T. canis-infected mice [The infection with ic hosts . In conte tissue . Consequted mice ,21,30. T. cati larvae, and may have reduced observed effects of T. canis larvae on both brain parts. Finally, an important point to mention is that NT is characterized by focally distributed lesions, so healthy brain tissue was most likely overrepresented in the transcriptional analysis compared to damaged tissue. This may have masked local effects induced by migrating or persisting T. canis- and T. cati-induced neurotoxocarosis in the paratenic mouse model was associated with the comprehensive transcriptional alteration. Nevertheless, the known differences between the pathogens were reflected by substantially higher numbers of DTGs in brain tissues of T. canis- compared to T. cati-infected mice. The prominent pathogenesis of T. canis-induced NT was also evident in the affected pathways. While the PANTHER analysis of DTGs in brains of T. cati-infected mice did not result in the overrepresentation of specific pathways, neuroinvasion of T. canis larvae evoke dysregulation of various pathways associated with the immune response. Furthermore, cerebral transcription of genes associated with cholesterol biosynthesis was constantly decreased during the course of infection, suggesting a fundamental impact of neuroinvasive T. canis larvae on cholesterol homeostasis. Dysregulation of the cholesterol biosynthesis pathway may provoke neurodegeneration, a typical hallmark of NT. Thus, axonal damage in conjunction with the dysregulated Alzheimer disease-amyloid secretase pathway promotes the discussion of T. canis as a causative agent for Alzheimer’s disease. Further research investigating these and other topics is required to characterize the still largely unknown pathogenesis of T. canis- and T. cati-induced NT and to develop new diagnostic and therapeutic approaches.