DicerSOM and crossed it with a catalytically-dead mutant of Mili, an essential piRNA gene. Normal follicular and oocyte development in double mutants showed that RNAi does not suppress a strong ovarian piRNA knock-out phenotype. However, we observed redundant and non-redundant targeting of specific retrotransposon families illustrating stochasticity of recognition and targeting of invading retrotransposons. Intracisternal A Particle retrotransposon was mainly targeted by the piRNA pathway, MaLR and RLTR10 retrotransposons were targeted mainly by RNAi. Double mutants showed accumulations of LINE-1 retrotransposon transcripts. However, we did not find strong evidence for transcriptional activation and mobilization of retrotransposition competent LINE-1 elements suggesting that while both defense pathways are simultaneously expendable for ovarian oocyte development, yet another transcriptional silencing mechanism prevents mobilization of LINE-1 elements.Germline genome defense evolves to recognize and suppress retrotransposons. One of defensive mechanisms is the PIWI-associated RNA (piRNA) pathway, which employs small RNAs for sequence-specific repression. The loss of the piRNA pathway in mice causes male sterility while females remain fertile. Unlike spermatogenic cells, mouse oocytes posses also RNA interference (RNAi), another small RNA pathway capable of retrotransposon suppression. To examine whether RNAi compensates the loss of the piRNA pathway, we produced a new RNAi pathway mutant  Retrotransposons are mobile genomic parasites causing mutations. Germ cells need protection against retrotransposons to prevent heritable transmission of their new insertions. The piRNA pathway is an ancient germline defense system analogous to acquired immunity: once a retrotransposon jumps into a piRNA-producing locus, which provides a kind of a “genomic sensor” for actively transposing elements, it is recognized and suppressed. Remarkably, the murine piRNA pathway is essential for spermatogenesis but not oocyte development. In contrast, zebrafish lacking the piRNA pathway do not develop any germ cells. It was hypothesized that RNA interference pathway could rescue oocyte development in mice lacking the piRNA pathway. RNA interference also targets retrotransposons and is particularly enhanced in mouse oocytes. To test this hypothesis, we engineered mice lacking both pathways and observed that oocytes in these mice develop normally, which argues against the hypothesis. Furthermore, analysis of individual retrotransposon groups revealed that in specific cases the two pathways mutually compensate each other. However, this redundancy apparently evolved stochastically and is restricted to specific retrotransposon groups. Finally, our results indicate that there must be yet another layer of retrotransposon silencing in mouse oocytes, which prevents high retrotransposon activity in the absence of piRNA and RNA interference pathways. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Lin HeAssociate EditorPLOS GeneticsGregory BarshEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Taborska et al. examined redundant roles of two small RNA pathways, RNAi and piRNAs, in the silencing of retrotransposons in mouse oocytes. Specifically, this works asked if RNAi activity can explain the lack of apparent germ cell phenotypes in piRNA-deficient mouse oocytes. To address this question, the authors:1. generated an RNAi pathway mutant mice lacking Dicer protein (Dicer SOM). This is a tighter Dicer mutant allele compared to the one created in their previous study on the oocyte-specific Dicer isoform;2. characterized phenotypes of the Dicer SOM mice;3. generated double-mutant Dicer SOM; Mili and control animals;4. evaluated retrotransposon expression in single and double mutant combinations.The main finding of this work is that lack of prominent female germ cell phenotypes in the absence of piRNAs cannot be explained by the redundancy with the RNAi pathway. A minor observation is that retrotransposon silencing by the individual small RNA pathways do not overlap completely. As such, this work clarifies the individual and combined contributions of the two small RNA pathways to retrotransposon silencing in the female germline. However, the study represents an incremental advancement in our understanding of retrotransposon regulation in the female germline. Crucially, the primary mechanism(s) that ensures retrotransposon silencing in mouse oocytes remains unknown.Major comments:1. Small RNAseq data in figure one is in from Yang et al., 2016. Small RNAseq data should be acquired for single and double mutants to validate putative RNAi targets from mRNA seq, as well as show changes in global production of siRNA and piRNA with mutations.2. Aside from eliminating truncated Dicer variant, the Dicer SOM mouse doesn't seem to significantly change the overall phenotype compared to Dicer deltaMT, except from few putative RNAi targets and upregulated genes in RNA-seq. Not really significant or novel that this mouse was generated.3. What is a "genomic checkpoint" that the piRNA pathway relies on? Describe better as this is mentioned twice.4. Explain better the significance of including comparisons to Ago2, and differences in Dicer vs Ago2 oocyte phenotypes?Minor comments:Figure 2B. Missing a loading control.Figure 2C. Needs labels, what is green and blue? What is the genotype?Figure 2E. Endogenous control gene for qPCR?Figure 3B. n oocytes are from how many females?Figure 3C. Reference for initial set of genes that are sensitive to RNAi pathway?Figure 3D. Need to validate putative RNAi targets using small RNAseq. Otherwise gene expression differences could be indirect and due to oocyte phenotypes.Figure 3E. What is the difference between Ago2 mutants from Stein et al., 2015 in orange vs. pink? Triangle vs. square symbols?Figure 4A. More detailed quantification of primordial follicles vs advanced follicles, maybe there is a difference at that resolution between WT and mutants.Figure 4D. Better to use RNAseq to analyze abundance of TEs in mutants to eliminate background elements within genes that are changing as a consequence of mutation. Also, how many biological replicates per bar in qRT-PCR?Reviewer #2: Mammalian PIWI-piRNA pathways have been studied mostly in mouse. Mice express three PIWIs . All three PIWI proteins are expressed at different stages during spermatogenesis, but only MILI is expressed, albeit weakly, in female germ cells. Deficiency in MIWI, MILI or MIWI2 leads to the activation of transposable elements (TEs) including L1 and IAP elements, and spermatogenesis is arrested at specific stages of spermatogenesis. Mutations in mouse PIWI genes affect the male germline but not the female germline. The authors previously identified an oocyte-specific truncated Dicer isoform, termed Dicero. This Dicer isoform produces endogenous siRNAs and together with AGO2 targets TEs and regulates their activity. These findings have led to assumptions that PIWI-piRNA pathways do not play a role during mouse oogenesis. To address the significance of redundancy of endogenous siRNA and piRNA pathways in oocytes, the authors have generated a mouse model lacking Dicero and then crossed it with MiliDAH mice expressing catalytically inactive MILI. In this study, the authors find that the simultaneous loss of endogenous siRNA and piRNA pathways does not affect oocyte development. The authors also find that endo-siRNA and piRNA pathways suppress some but not all TEs in a redundant manner. This raises the possibility that yet another TE silencing mechanism must operate in female germ cells to maintain genome integrity. Although their findings are themselves not surprising and completely expected, studies of this sort are good references and resources for further comparisons.Minor comments:1. The quality of images used in Figure 2C is poor. Its legend lacks important information such as which panel shows what with which antibody.2. The quality of images used in Figure 3A is also poor.3. Figure 3C: The authors should add non-targeted genes in this experiment.4. Figure 4A – C: here the authors analyze ovarian weight, histology and number of fully-grown GV oocytes using mice with age between 8 and 18 weeks. The authors should describe in legends the age in weeks of mice they use for each experiment. In my opinion, the authors should use mice with same age in weeks in these experiments. In addition, the authors’ better present data with male mice in Figure 4D for the reader to understand the degree of contribution of RNAi and piRNA pathways to suppress TEs in female gonads.Reviewer #3: The manuscript by Taborska et al genetically explores the combined roles of the RNAi and piRNA pathways in the female mouse germ cell development through the use of advanced mouse genetics. The study is designed to address the question of whether the RNAi pathway masks a function for the piRNA pathway in the female mouse germline. An elegant new allele of Dicer is generated that forces the expression of somatic Dicer isoform throughout the entire animal inclusive of the germline. These Dicer-som are viable but present female-specific infertility. The combination of Dicer-som with the catalytically inactive MILI results in the same developmental phenotype but has a distinct molecular phenotype comparted to Dicer-som mice. This stringent genetic experiment and the molecular analysis undertaken substantiate the principal claims of the manuscript. I am intrigued by the data of Figure 5. Overall, I am supportive of publication but have concerns to be addressed:The upregulation of RNAi targets in figure 4D & F could also be possibly presented in cumulative frequency plots (S-curves). These have traditionally been used for miRNA effects and are quantitative and very easy to understand.The data presented in Figure 5 is not presented in the results section. This needs to be rectified.The data presented in Figure 5 is an extremely important observation. I think it should be given some mention in the abstract. I also think the key message from the low levels of L1 transcription in the female germline and minor upregulation of L1 transcripts  in the absence of the RNAi and/or piRNA pathways would be substantiated by staining for L1 ORF1 protein. The expectation would be that it should not be detected in the respective mutant genotypes.I think the discussion should give a sentence or two on why the piRNA-pathway expression in should be retained in the female germline of species that does abundantly express active TEs therein. The piRNA pathway is more than just the PIWI protein but requires the expression of many biogenesis factors (at least >10). Could the expression of the pathway be an insurance policy against a future invasion?**********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 your responses to reviewer comments. If eligible, we will contact you to opt in or out. *Dear Dr Svoboda,Thank you very much for submitting your Research Article entitled 'Restricted and non-essential redundancy of RNAi and piRNA pathways in mouse oocytes' to PLOS Genetics. 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All points should be backed up by data provided with the submission.Please be aware that our Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.[LINK]Please let us know if you have any questions while making these revisions.Yours sincerely,Lin HeAssociate EditorPLOS GeneticsGregory BarshEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Overall, I am satisfied with authors' responses.I still have an issue with the genomic "checkpoint". The current draft describes piRNA clusters as "... specific genomic regions serving as "checkpoints" sensing invading mobile elements". This is not entirely correct since a genomic region senses nothing by itself. If not expressed, its resident sequences are invisible to the piRNA system and the cell. It is an unnecessary invention that will only confuse readers, and I ask authors to refrain from using this term. For example, the same idea can be expressed as follows:"The piRNA pathway relies on specific genomic regions (piRNA clusters) whose expression primes piRNA production".Reviewer #2: On the whole it has been improved and the authors have addressed some of the reviewers’ concerns. However, it is already known that the deficiency of PIWI genes results in no discernible phenotype in female mice. In contrast, the authors previously found that the deficiency of the oocyte-specific Dicer variant causes frequent meiotic spindle defects upon resumption of meiosis. Thus, it is still not clear the rationale for why the authors try to examine possible redundancy between RNAi and piRNA pathways in mouse oocytes. The authors had better focus on characterization of Dicersom/som mutant oocytes in my opinion.Other points:1. Figure 2 E & F show that expression levels of the full-length Dicer are markedly affected in Dicersom/som mutant oocytes. Why?2. Figure 3A: the authors should show the figures provided to response to Reviewer #2 comments as supplementary figures.3. The figure showing L1 ORF1 staining in the authors response to reviewer #3 appears to show that part of chromosomes (DAPI staining) is already excluded from the nucleus in Dicer mutants. This may imply that the deficiency of the Dicer activity already causes chromosome abnormality in early oocyte development and thus, the spindle defects observed are secondary.4. The figure showing expression levels of piRNA pathway genes in oocytes contains very important information in the field. Thus, the authors should show the figure in the current manuscript. Studies of this sort are good references and resources for further comparisons.Reviewer #3: All my concerns have been addressed.**********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?). 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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  13 Dec 2019PGENETICS-D-19-00984R2 Restricted and non-essential redundancy of RNAi and piRNA pathways in mouse oocytes Dear Dr Svoboda, We are pleased to inform you that your manuscript entitled "Restricted and non-essential redundancy of RNAi and piRNA pathways in mouse oocytes" 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. 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Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies. Alcohol Use Disorder (AUD) is a syndrome condition characterized by a number of symptoms and behaviors according to the Diagnostic and Statistical Manual of Mental Disorders (DSM) 5 , includiThere are numerous studies that have compared gene expression in patients with alcohol dependence with healthy controls across several brain areas implicated in addiction, such as the prefrontal cortex (PFC), basolateral amygdala (BLA), central nucleus of the amygdala (CNA), nucleus accumbens (NAC), hippocampus (HPC), and the ventral tegmental area (VTA) ,12,13,14There is great potential for genome-wide gene expression data to improve the diagnosis and treatment of complex diseases, and this has been enhanced with the development of the Broad Institute’s Connectivity Map (CMap) and the Library of Network-based Cellular Signatures (LINCS) databases ,17. Theshttps://networkmeds.shinyapps.io/rshiny_app/). This resource can be used to nominate drugs that target pathways of interest or to nominate pathways predicted to be affected by a drug for studies of potential mechanisms of action. In addition to the alcohol dependence-specific insights this study provides, we hope that this disease-agnostic tool will be useful to the wider scientific community.The current study differs from previous gene mapping approaches in an important way: instead of targeting individual genes, we sought to target pathways. With this approach, the focus is shifted from single genes to groups of genes known to act together in a biological pathway. Here, we hypothesized that individuals with alcohol dependence have modifications in expression levels of genes within important biological pathways, and that drug perturbations producing opposing effects on the same biological pathways may be beneficial in treating AUD. We defined the pathways that are perturbed in alcohol dependence across six discrete brain areas. We also used L1000 and CMap to define the pathways affected by over 3000 repurpose-able compounds, i.e., those that are currently marketed (launched), in clinical trials, or preclinical compounds with good absorption, distribution, metabolism, and excretion (ADME) properties. Additionally, we created a website where these findings are easily accessible  for each gene was estimated using the method of Benjamini and Hochberg [p values for the moderated t test statistic, and adjusted p values based on the FDR. We included 17 gene expression datasets in our analysis that assessed gene expression levels in postmortem brain tissue from patients with alcohol dependence and healthy controls . FifteenHochberg . Differehttps://clue.io/connectopedia/glossary for more information). We downloaded the differential gene expression signatures for over 20,000 compounds (L1000 level 5 data) from Gene Expression Omnibus , where each signature is the result of a different drug, dose, cell line, and time point for which differential expression was assessed. To increase the reliability of the L1000 gene expression signatures, we further limited our analysis of level 5 L1000 data to only include signatures with distil_cc_q75 ≥ 0.15, where distil_cc_q75 is a measure of the reproducibility of a signature .We downloaded the CMap amplitude data (amplitudeMatrix.txt) from To maximize the application of our findings to human studies we used information from the Drug Repurposing Hub to highlight the repurposeable compounds from L1000 and CMap, i.e., those that are currently marketed, in clinical trials, or preclinical compounds with good absorption, distribution, metabolism, and excretion (ADME) properties . We idenp value was estimated as the number of random gene sets with the same or more extreme ES value divided by the total number of generated gene sets [p values for each pathway are presented in We performed a gene set enrichment analysis using the fgsea  Bioconductor package version 1.8.0 . The preene sets ,27. The ene sets . The obsFor the human alcohol dependence gene expression datasets, the t values were treated as the gene statistic values S. Z scores from the L1000 dataset and amplitudes from the CMap dataset were treated as the gene statistic values S. We set minSize = 15, maxSize = 500, nperm = 10,000 in the fgsea function. Because the L1000 database is much larger than CMap, we had to make the following modifications: we performed the analysis on a computer server at UT Austin and ran the R session through the command line. We ran the following R commands before fgsea: setTimeLimit and setSessionTimeLimit. For the fgsea function we set nperm = 1000 and nproc = 20. For all pathway enrichment analyses, we collapsed similar pathways together using the collapsePathways function. This enabled us to find which biological pathways are perturbed in alcohol-dependent brain tissue and by repurposeable drugs, along with the direction of perturbation (increased or decreased compared with control brain).https://www.rdocumentation.org/packages/WGCNA/versions/1.25-1/topics/userListEnrichment/. The userlistEnrichment function determined the overlap between the cell type marker genes and the differentially expressed genes. We provided the up-regulated differentially expressed genes  and the down-regulated differentially expressed genes  to determine direction of change for the cell type markers. The userlistEnrichment function uses the hypergeometric distribution to assess the significance of the cell type gene enrichment and Bonferroni correction was applied for multiple testing (Bonferroni-corrected p < 0.05 was used for significance). The enrichment p values for the cell type markers are presented in We tabulated the significantly perturbed pathways for each human alcohol dataset to identify those most commonly implicated in alcohol dependence. To determine if genes known to be cell type markers were affected disproportionately, we also performed an enrichment analysis for cell type specific datasets using the userlistEnrichment function from the WGCNA package version 1.68 with useBrainLists = TRUE. The cell type marker lists include neurons , glutamaTo enable accessibility of our results, we built a user-friendly web interface using the shiny package version 1.3.2  in R. UsAltogether, there were 17 datasets spanning six brain areas: prefrontal cortex (5), hippocampus (2), nucleus accumbens (3), basolateral amygdala (3), central nucleus of the amygdala (3), and ventral tegmental area (1) . The celITGB4, VWF, AKT1 COL4A1, COL6A1, COL6A2, COL6A3, ERBB2, LAMB2, SHC1, ZYX, CAV2, COL4A2, FLNA, FN1, ITGA5, ITGA6, KDR, VASP, ACTN4, CAV1, FLNB), complement and coagulation cascade , MAPK signaling , systemic lupus erythematosus , oxidative phosphorylation .We found 58 up-regulated and 36 down-regulated pathways in at least one dataset . Out of We assessed 186 KEGG pathways, and 172 (92.5%) of these were predicted to be targeted by drugs in CMap. For the druggable pathways, 1 (0.08%) drug (for Mismatch Repair) to 1169 (89.3%) drugs (for Neuroactive Ligand Receptor Interaction) were predicted to target the pathway .We downloaded gene expression signatures for 1309 unique compounds assayed by CMap. We determined that 861 of these compounds were repurposeable using annotation provided by the Drug Repurposing Hub . We inclWe explored 186 KEGG pathways, and 167 (89.8%) of these were predicted to be targeted by drugs in L1000. For the druggable pathways, 214 (8%) drugs (for Sphingolipid Metabolism) to 2599 (97.7%) drugs (for Neuroactive Ligand Receptor Interaction) were predicted to target them .We downloaded gene expression signatures for 20,629 unique compounds assayed by L1000. We determined that 2661 of these compounds were repurposeable using annotation provided by the Drug Repurposing Hub . Out of Most gene mapping studies that nominate treatments for a disease operate under the reversal hypothesis, which posits that reversing the disease-induced gene expression signature will treat disease phenotypes. More specifically, these approaches look for drugs that up-regulate the genes that are down-regulated in the disease state, and down-regulate genes that are up-regulated in the disease state. We took a similar approach here, but instead of looking for reversal of individual genes, we looked for reversal of disrupted pathways. Following this premise, we searched for compounds that down-regulated the following pathways: 04610: Complement and coagulation cascades, 04510: Focal adhesion, 04010: MAPK signaling pathway, and 05322: Systemic lupus erythematosus, and up-regulated the 00190: Oxidative phosphorylation pathway. There were 244 L1000 compounds predicted to target the full combination of pathways in the desired direction across numerous drug classes, including EGFR inhibitors, topoisomerase inhibitors, MEK inhibitors, tyrosine kinase inhibitors, HDAC inhibitor, glucocorticoid receptor agonists, dopamine receptor antagonists, PARP inhibitors, and phosphodiesterase inhibitors . Many haThere were no CMap compounds predicted to target the full combination of pathways in the desired direction. There were 32 compounds predicted to target one of the pathways in the desired direction . Of theshttps://networkmeds.shinyapps.io/rshiny_app/) is disease-agnostic and could apply to any biological state of interest. Therefore, it may appeal to a broad range of researchers including biologists, pharmacologists, chemists and clinical scientists.The development of R Shiny environments  enable RWe previously used the top differentially expressed genes between HDID and control HS mice to capture molecular changes important for the HDID phenotype of intense binge-like drinking . An alteOur study highlights the pathways and cell types implicated in alcohol dependence across multiple datasets. Half of the up-regulated pathways and one third of the down-regulated pathways occurred in multiple datasets. The cell type results were strikingly consistent across studies. There was a general up-regulation of glial transcripts (microglia ≫ astrocytes). The direction of change for astrocyte- and oligodendrocyte-specific genes were somewhat variable (sometimes up-regulated and sometimes down-regulated) which could indicate that subtypes of these cells are being differentially regulated in alcohol dependence. Notably, many of the up-regulated pathways were immune-related: complement and coagulation cascade, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, natural killer cell mediated cytotoxicity, antigen processing and presentation, the JAK STAT signaling pathway, and the toll-like receptor signaling pathway. The up-regulation of these pathways and microglial genes across brain areas and datasets reinforces the hypothesis that neuroinflammation plays an important role in AUD ,67,68,69Pathways related to the extracellular matrix  pathways) were also prominent in the up-regulated pathways for all brain areas. The importance of the extracellular matrix in learning and memory processes and substance use disorders including AUD has become an active area of research in the last decade ,71. PeriThe majority of tissue samples used in the original studies are independent and were obtained from the New South Wales Tissue Resource Centre at the University of Sydney with the exception of the Zhou 2011  samples We identified over 240 drugs that target the disrupted pathways in a direction proposed to treat AUD. More than 20 of these compounds are known to alter alcohol drinking, which increases confidence in the ability of this approach to identify useful drug candidates. The candidates we prioritize in L1000 identified more drugs targeting AUD pathways than CMap. It could be that the additional drug and cell line coverage in L1000 provides a more comprehensive catalog of drug responses. Another interpretation is that L1000 findings are spurious , which deserves consideration because most of the gene levels are imputed from the 978 measured genes in L1000. One reasonable approach to increase the chance of finding a successful AUD treatment is to select the most robust drugs to test  as suggested and implemented by us and others ,63,90,91http://www.cuilab.cn/drugpath) [http://gene2drug.tigem.it/) [There are existing databases that catalog biological pathways up- and down-regulated by compounds in the CMap database: Drug-Path (rugpath)  and Genegem.it/) . These sIn conclusion, we highlighted cell types and biological pathways that are consistently different between alcohol-dependent and control brain tissue. We mined publicly available databases to identify drugs that target these pathways in an opposing manner and highlighted eight compounds that warrant further testing. If preclinical testing of the prioritized compounds yields positive results, these compounds are already approved for other indications or are in clinical trials, and therefore, could be relatively rapidly tested in patients with AUD.


Network pharmacology revealed that fucosterol targets signaling molecules, receptors, enzymes, transporters, transcription factors, cytoskeletal, and various other proteins of cellular pathways, including tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), neurotrophin, and toll-like receptor (TLR) signaling, which are intimately associated with neuronal survival, immune response, and inflammation. Moreover, the molecular simulation study further verified that fucosterol exhibited a significant binding affinity to some of the vital targets, including liver X-receptor-beta (LXR-β), glucocorticoid receptor (GR), tropomyosin receptor kinase B (TrkB), toll-like receptor 2/4 (TLR2/4), and β-secretase (BACE1), which are the crucial regulators of molecular and cellular processes associated with NDD. Together, the present system pharmacology and in silico findings demonstrate that fucosterol might play a significant role in modulating NDD-pathobiology, supporting its therapeutic application for the prevention and treatment of NDD.Fucosterol is an algae-derived unique phytosterol having several medicinal properties, including antioxidant, anti-inflammatory, anticholinesterase, neuroprotective, and so on. Accumulated evidence suggests a therapeutic promise of fucosterol in neurodegeneration; however, the in-depth pharmacological mechanism of its neuroprotection is poorly understood. Here, we employed system pharmacology and  Neurodegenerative disorders (NDD), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD) are the major causes of dementia worldwide. Although the exact mechanism is unclear, inflammation and oxidative stress might play pivotal roles in the pathogenesis of NDD . The celPhytosterols are important molecules not only for their structural and signaling functions in the host plants  but alsoin silico analysis offers an efficient computation tool that can better explain how a bioactive molecule interacts with the signal molecules of various cellular pathways and contribute in the therapy of multifactorial disease like NDD. Here, we employed integrated system pharmacology and in silico analysis to explain the pharmacological mechanism of fucosterol against NDD. First, through multiple bioinformatics tools, we demonstrate that fucosterol highly interacted with signaling pathways related to the development and survival of neuron, inflammation and immune response. Next, we employed in silico analysis to verify the biophysical interaction of fucosterol with some potential targets. The present study elucidates the underlying mechanism of molecular neuropharmacology of fucosterol and establishes a basis in favor of its therapeutic application against neurodegeneration.The system biology approach combined with A diagrammatic overview displaying the different steps of network pharmacology was illustrated in A total of 72 potential targets of fucosterol were retrieved from Pathway Assembly from Literature Mining-an Information Search Tool (PALM-IST) database  and valiBDNF, APP, APOE, AKT1, PPARG, NFKB1, NFE2L2 (Nrf2), CYSC, MMP9, MAPK1, SOD1, JUN, FOS, CAT, TNF, IL6, IL1B, NOS2, CASP3, CASP9, and HMOX1 are the dominant hub genes that participate in the major cellular pathways.F–T–T network demonstrated the interaction between fucosterol and its targets as well among the targets and identified most interacted protein targets in the network A. This nBDNF, BCL2, BAX, ATF2, APP, TNF, APOE, TGFB1, AKT1, SOD1, AChE, ACE, PPARG (PPAR-γ), PIK3CA, ABCA1, NFKB1, NFE2L2 (Nrf2), MMP9, MAPK1, IL6, IL1B, HMOX1, GRIN2A (GluN2A), CAT, CASP3, and CASP9, each was connected with NDD, indicating the involvement of these targets or others to their upstream signaling as a potential drug-target for fucosterol in the NDD treatment. Moreover, there are some other potential targets, including NTRK2 (TrkB), NR1H2 (LXR-β), NR3C1 glucocorticoid receptor, (GR), and β-secretase, that are closely associated with pathways and cellular processes in NDD pathology.F–T–NDD network presented the interaction of fucosterol with those targets that are associated with NDD. A total of 56, 41, and 34 targets, respectively, were associated with AD, PD, and HD B. Among Top 10 highly enriched GO terms under cellular component (CC), biological process (BP), and molecular function (MF) were displayed A–C. The p-value < 0.01) in the “signal transduction” module  pathway annotation. A total of 15 signaling pathways were enriched , synaptic plasticity , inflammation , and apoptosis  (COX2) and apoptosis-related proteins  were targeted by fucosterol (SOD1 and GPX1), neuronal growth and survival , inflammation , and apoptosis  were the targets of fucosterol , cholesterol homeostasis and Aβ-clearance , neuronal growth and survival  A. Among costerol B. On thecosterol C.We employed molecular docking analysis to validate the interaction patterns and efficiency of fucosterol to some of the vital target proteins, which are crucial regulators of several NDD-related molecular and cellular processes. Here we selected LXR-β, GR, TrkB, TLR2/4, BACE1, and AChE depending on their crucial roles in the inflammation, oxidative stress, apoptosis, and other risk factors associated with the pathobiology of NDD. Molecular docking followed by binding energy analysis showed that fucosterol showed the highest binding affinity to LXR-β, followed by GR and TrkB with binding energies of −80.37, −49.53, and −34.06 kcal/mol , respectAs an initial consideration, binding and interaction patterns revealed from molecular docking simulation were assessed. The result demonstrated that fucosterol formed significant interactions with these receptors following hydrophobic interactions. As shown in The findings from molecular docking studies were further validated by 50 ns additional molecular dynamics simulations. Initially, the root means standard deviation (RMSD) of both protein and ligand from individual complexes was calculated by comparing with their initial position and represented in Interestingly, fucosterol maintained contacts with the active site residues, including Ile353, Leu345, Leu442, Phe268, Trp457, Ala275, Phe340, and Phe243 residues over the 80% of total simulation time, which were consistent with molecular docking simulation. In addition, fucosterol also showed maximum interactions with Arg319 and Glu281 residues during the simulation. Consequently, Met604, Leu608, and Phe623 residues in GR showed maximum contacts with fucosterol, while TrkB maintained hydrophobic interactions through the Ile323, Leu324, and Tyr329 residues. Furthermore, hydrogen bond occupancy showed that the hydroxyl group of fucosterol maintained hydrogen bond with Glu281 of LXR-β during the 10.74% of total simulation time but not with Asn239, which concluded that the conformational drift that exerted by fucosterol at the initial stage of simulation, changed the binding orientations, which might be more favorable and stronger than the complex predicted from docking simulation.Physiologically, the activation of human LXR by either sterol and nonsterol agonists is usually exerted by a perpendicular histidine-tryptophan switch in the ligand binding domain, which turns the receptor into its active conformation, where the sterol guided activation is mediated by Trp457 . Having The sifting of interaction pattern from Arg319 in molecular docking to Glu281 in molecular dynamics simulation suggests that fucosterol might induce conformational changes and thus activates LXR-β. In the case of GR, fucosterol showed a similar hydrogen bonding profile, where the compound maintained 8.94% of hydrogen bonding occupancy with Gln570; however, the hydrogen bonding pattern remained stable for TrkB, where Ile323 made 9.55% of H-bonding during the simulation. Together, it can be concluded that hydrophobic interactions play a significant role in the binding of fucosterol to LXR-β, GR and TrkB.Several earlier reports highlighted that the fifth subdomain at the extracellular part of the TrkB receptor plays major role in the BDNF mediated TrkB interactions ,40,41,42Here, we unraveled the molecular mechanism of neuropharmacological action of fucosterol against neurodegenerative disorders using integrated system pharmacology and molecular simulation approach. Initially, we characterized the pharmacokinetic behaviors of fucosterol, which supports its drug-like properties and accessibility to the brain tissue. Moreover, referring to its cholesterol-like chemistry, fucosterol could pass through the cell membrane to reach directly to various intracellular targets. The network pharmacology data indicated that fucosterol showed a close association with the target proteins of many crucial pathways at the molecular and cellular levels. Gene ontology-based bioinformatics analysis identified several enriched biological processes, including inflammatory response, apoptotic process, transcription regulation, aging, NF-κB activity, and cellular response to ROS, which are highly linked with the pathobiology of NDD. KEGG pathway analysis further demonstrates that fucosterol targeted proteins are the critical components of these highly enriched molecular and cellular pathways.in silico data showing a significant binding affinity of fucosterol to LXR-β suggest the involvement of a similar mechanism. Moreover, the glucocorticoid receptor (GR), upon ligand binding, exerts its potent anti-inflammatory actions through repressing NF-κB signaling [In the pathway analysis, inflammation-related pathways, including TNF, HIF-1, NF-κB, and VEGF signaling, are particularly enriched, indicating that the anti-inflammatory action of fucosterol could play a significant role in the treatment of NDD. Being a ubiquitous transcription factor that plays a crucial role in amplifying inflammatory and immune responses , NF-κB pignaling . The preApoE, ABCA1, ABCG1, and SREBF1, the key genes in reverse cholesterol transport [LXR target genes, such as ABCA1, ABCG1, and ApoE [Given the key role of cholesterol in brain physiology and function, disturbances in cholesterol homeostasis provoked inflammation and oxidative stress, and thus, have been associated with the onset of major NDDs . LXRs, pransport . The actransport ,52. Hoanand ApoE . A similand ApoE . These fAnother enriched signaling pathway is the PI3K/Akt pathway, which, along with MAPK signaling pathway, regulates cellular growth and survival. This pathway has multiple downstream effectors, including those involved in cell survival , which suggests that this compound might function as BDNF-mimetic. Moreover, PI3K/Akt signaling pathway downstream to the neurotrophin signaling pathway was one of the enriched pathways in our study, indicating that these pathways might be employed in the pharmacological actions of fucosterol against NDD. In addition, Oh and colleagues reported that fucosterol protects against Aβ1-42-induced cytotoxicity through activating TrkB-mediated ERK1/2 signaling in primary hippocampal neurons, a TrkB-dependent neuroprotective effect, which was reversed by a selective TrkB inhibitor, cyclotraxin B, indicating its physiological interaction with TrkB in neuronal cells . These iathology ,55. A prathology  and our 2+ signaling, and thus plays a significant role in synaptic plasticity, which constitutes the biochemical basis of memory and cognition. The agonistic behavior of fucosterol to GluN2A could, therefore, help improve cognition deficits in AD patients. On the other hand, AChE catalyzes the breakdown reaction of acetylcholine at the cholinergic synapse, regulating synaptic transmission. However, AD pathology is accompanied by the cholinergic deficit, which could be compensated by the AChE inhibitor. Previous studies showing the inhibitory activity of fucosterol against AChE [Moreover, some vital synaptic proteins, including GluN2A and AChE were connected to fucosterol. GluN2A at the glutamatergic synapse mediates Canst AChE ,28 and oin silico analysis with this protein target. Molecular docking analysis demonstrates that fucosterol interacted with both TLR2 and TLR4, suggesting that this compound could improve inflammation-induced NDD pathology through TLR-mediated immune response. In addition to their ability to activate the expression of genes linked to lipid metabolism, LXRs also antagonize inflammatory gene expression triggered by TLR activation [In the KEGG pathway analysis, there was an enrichment of several immune-related pathways that are deregulated and compromised in NDD. In particular, several protein molecules of the TLR signaling pathway were targeted by fucosterol. TLRs play a critical role in innate immunity by recognizing pathogen-associated molecular patterns . Deregultivation .in silico analysis also revealed that fucosterol interacted with β-secretase, suggesting its potent anti-amyloidogenic activity.β-Secretase cleaves amyloid precursor protein leading to the production of Aβ in the brain. Aberrant β-secretase activity is believed to be implicated in the pathogenesis of AD. Thus, targeting β-secretase is a viable therapeutic approach for AD. However, evidence suggests that the complete abolishment of β-secretase may be associated with specific behavioral and physiological alterations . TherefoPharmacokinetic parameters including absorption, distribution, metabolism, and excretion/transport (ADME/T) of fucosterol were analyzed through QikProp . QikProp is an efficient ADME/T prediction tool that forecasts whether the selected compound would exhibit satisfactory ADME/T performances during clinical trials.http://www.uniprot.org/) [The information on fucosterol targets was retrieved primarily from PALM-IST database. We verified every target protein through the PubMed link. We excluded those targets that have no direct interaction with fucosterol and included additional targets while scanning PubMed database. The protein information, including protein name, gene ID, and host organism were verified through UniProt (ot.org/) .A protein–protein interaction (PPI) network among the targets was built using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), an online tool for functional protein association networks. The fucosterol-target network and PPI network were merged through Cytoscape v3.7.1  . This neThe lists of AD, PD, and HD-related genes were retrieved from DisGeNET database v6.0  , a databhttps://www.networkanalyst.ca/). GO terms with a p-value of < 0.05 were considered significant. The functional enrichment analysis of GO for the biological process, molecular function, and cellular components was performed and presented in bar graphs using Network Analyst  . Pathways highlighting fucosterol targets were retrieved through the KEGG pathway mapper [https://www.genome.jp/kegg/tool/map_pathway2.html).Gene functional annotation for KEGG pathways was retrieved from the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. First, gene counts for each pathway in the given gene set were calculated. Then, significantly enriched pathways in the given gene set compared to the genome background are defined by a hypergeometric test. Taking database  , GR (PDB ID: 6DXK), TrkB (PDB ID: 1HCF), TLR2 (PDB ID: 6NIG), TLR4 (PDB ID: 5IJC), BACE1 (PDB ID: 5HDZ), and AChE (PDB ID: 3QT0) were retrieved from the protein data bank  and prepAfter that, the docked complexes were subjected to molecular dynamics simulations using Yet Another Scientific Artificial Reality Application (YASARA) v.16.9.23 (YASARA Biosciences GmbH) Dynamics software. Before the simulation, all structures were cleaned and subjected to the optimization of hydrogen bonding network. For each simulation system, a cubic simulation cell with periodic boundary condition was generated and then all atoms were parameterized with the Assisted Model Building with Energy Refinement (AMBER14)  force fiin silico findings demonstrate that fucosterol interacted with the proteins of major molecular and cellular pathways that are involved in neuronal growth and survival, neuroinflammation, and immune response, and thus could help modulate and improve pathobiology of neurodegeneration. Our system biology approach to unravel the neuropharmacological action mechanism of fucosterol further offers a platform to explain the complex pharmacological mechanism of a bioactive molecule against NDDs in a multitarget approach. However, further experimental validation by cellular and animal studies is warranted before its recommendation for clinical application.The network pharmacology and


Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 (miRNA), GSE68316 (lncRNA/mRNA) and GSE32453 (mRNA) expression profiles downloaded from the Gene Expression Omnibus datasets. Bioinformatics tools were employed to perform function and pathway enrichment analysis, protein-protein interaction, lncRNA-miRNA-mRNA and hub gene networks. Subsequently, DEGs were used as targets to predict drugs. The results indicated that a total of 2,234 DElncs , 5 DEmiRs (2 upregulated and 3 downregulated) and 42 DEGs (35 upregulated and 7 downregulated) were identified in 4 microarray profiles. Gene ontology analysis revealed that DEGs were mainly involved in actin filament and stress fiber formation and in calcium ion binding, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the hypoxia inducible factor-1, transforming growth factor-β and tumor necrosis factor signaling pathways as the main pathways involved in these processes. The hub genes were screened using cytoHubba. A total of 1,086 lncRNA-miRNA-mRNA interactions including 67 lncRNAs, 5 miRNAs and 25 mRNAs were mined in the present study based on prediction websites. Drug prediction indicated that the targeted drugs mainly included angiotensin converting enzyme inhibitors or β-blockers. A comprehensive bioinformatics analysis of the molecular regulatory lncRNA-miRNA-mRNA network was performed and potential therapeutic applications of drugs were predicted in HCM patients. The data may unravel the future molecular mechanism of HCM. Hypertrophic cardiomyopathy (HCM) is a heterogeneous monogenic myocardial disorder that is characterized by myocardial hypertrophy, asymmetric hypertrophy of the ventricular septum, decreased ventricular cavity and abnormal hypertrophy of cardiac muscle cells. The prevalence of HCM is >1 in 500 0.2%) (% 2–4). . 4). It et al (TSC1) that was significantly upregulated in the myocardial tissue of HCM patients  are a novel class of small non-coding RNAs, which can negatively regulate gene expression at the post-transcriptional level by directly binding to mRNAs . Long noet al  performepatients . TSC1 ispatients , which cAt present, big data mining and precision medicine have gained considerable attention. Digitization and informatics technologies facilitate the flow of information between various fields, laying the foundation for the conduct of interdisciplinary research. With the achievements of transcriptomics and genomics, the correlation between disease phenotypes and potential genotypes has attracted great scientific interest. The present study combined bioinformatics, transcriptomics and epidemiology in order to examine differentially expressed lncRNAs (DElncs), differentially expressed miRNAs (DEmiRs) and differentially expressed genes (DEGs). In addition to their expression, their associated regulatory network was investigated. The data can elucidate the underlying etiology of HCM and further provide reliable molecular targets for drug therapy.http://www.ncbi.nlm.nih.gov/geo/) using the keywords ‘hypertrophic cardiomyopathy’ and ‘lncRNA’ or ‘miRNA’ or ‘mRNA’. Eligible datasets had to meet the following inclusion criteria: i) Microarray profiling studies on human patients with HCM; ii) HCM and non-HCM control/healthy samples for comparison; iii) reports of sample sizes; iv) a group label for each sample size; v) a corresponding annotation (gene symbol) or GeneBank ID in the platform file for each probe of the microarray and vi) availability of raw data. The exclusion criteria included: i) Animal samples or cell lines; ii) non-HCM samples; iii) non-microarray profiles; iv) samples without controls and v) probes without gene symbol or GeneBank ID annotation. Finally, 4 profiles of GSE36961, GSE36946 and GSE68316 were downloaded. The flow chart of the screening datasets is presented in LncRNA, miRNA and gene microarray expression profiles between HCM and healthy controls were investigated by the Gene Expression Omnibus database . The Limma package was employed to identify DElncs, DEmiRs and DEGs, respectively using a linear model and the empirical Bayes model (http://www.ncbi.nlm.nih.gov/geo/geo2r/), which is useful for comparing 2 or more groups of samples, and can characterize differentially expressed RNA molecules based on t-test or analysis of variance  following batch normalization. Ultimately, the mixed model of variance analysis with a false discovery rate   were selected to identify novel DEGs; however, the DEGs in each dataset were not combined due to the variation of distinct platforms, manufacturers and temperature . The datst, FDR)  was usedwww.microrna.gr/LncBase) (http://www.lncipedia.org) (http://starbase.sysu.edu.cn/) (http://www.targetscan.org/), miRTarBase 7.0 (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) (http://www.mirbase.org)  , LNCipeddia.org)  and staredu.cn/) . At leasedu.cn/) , whereasdex.php)  and miRBase.org) . The putase.org) ,20,21.http://david.ncifcrf.gov/) was used for the Gene Ontology (GO)  ,23 and Kogy (GO) ,25 enricogy (GO) .http://www.malacards.org/) was selected for the HCM-related GO and KEGG pathway items in order to ensure the accuracy of the results. Malacards is an integrated compendium of human maladies and their annotations are mined from several well-known data sources. Malacards contain 14 annotation topics, including Summaries, Symptoms, Related diseases, Drugs and therapeutics, Gene and Expression, GO terms and Pathways .The human disease database Malacards  is an online functional protein association network. The associations in STRING include direct  and indirect  interactions, as long as they are specific and biologically meaningful (http://www.cytoscape.org/) plug-in used to identify hub genes more accurately by 12 topological analysis methods  contains more than 7,000 drug entries and 4,000 non-redundant proteins , ‘cellular components’ (CC) and ‘molecular functions’ (MF) . GO/KEGGThe PPI network analysis aimed to study the molecular mechanism of diseases and identify novel drug targets from a systematic perspective. The STRING database is designed to depict functional interactions between the expressed proteins by integrating known and predicted protein-protein association data among various species . The PPIFurthermore, 1,086 pairs of lncRNA-miRNA-mRNA biomolecular interactions were integrated in a single network constructed by Cytoscape. The lncRNAs exhibited high tendency of aggregation with the miRNAs. miR-30c-5p and miR-541 exhibited the highest number of lncRNA binding sites, suggesting that these 2 miRNAs could bind to lncRNAs when competing with other miRNAs. It is worth mentioning that lncRNA ENSG00000269821 and ENSG00000224078 have binding sites for all the five miRNAs, which are thought to be of substantial significance and may provide clues to future researchers .In the PPI network, some hub genes/proteins are highly connected with other proteins, suggesting a central regulatory role. CytoHubba provides a user-friendly interface to examine the interactions among hub nodes in the biological network by topological analysis , which cHCM can be divided into hypertrophic obstructive cardiomyopathy (HOCM) and hypertrophic nonobstructive cardiomyopathy according to the presence of obstruction in the left ventricular outflow tract. Medical treatment should be the first choice for the majority of patients. Therefore, it is vital to select the most suitable drug candidate. At present, the main therapeutic drugs for HOCM are β-blockers, calcium channel blockers and amiodarone .The DrugBank database is a unique, comprehensive drug repository covering detailed drug function, formulation, basic structure and drug target information . The DEGHCM is a primary cardiovascular disease, which has been regarded as the most common risk factor of sudden death among young people; it is now well accepted that multiple mutations in gene encoding regions are responsible for the development of this disease . TherefoIn recent years, several studies ,42 have ALOX5AP is a crucial regulatory factor used in the biosynthesis of inflammatory leukotrienes. Previous studies have reported that genetic variations in ALOX5AP exhibit significant associations with ischemic stroke and myocardial infarction  lncRNA was reported as cardiac-specific and abundant in adult hearts . Mhrt pret al  performeAt present, β-blockers (propranolol and penbutolol) and calcium antagonists  are mainly prescribed for pharmacological treatment of HCM in routine clinical practice ,52. PropData mining can extract the relevant biological information from high-dimension data. The integration of multiple histological techniques will become a new trend of disease diagnosis in the era of precision medicine. These molecules have the potential to be used as disease biomarkers in personalized medicine for patients and can improve the diagnosis, treatment and prevention of several diseases.The present study contains certain limitations: i) The lncRNA-miRNA-mRNA large-scale crosstalk network did not indicate whether these RNAs were co-expressed in the same tissues. ii) The present study is a preliminary analysis, which requires validation in a large population by PCR or western blot analyses. In addition, the biological mechanism of specific non-coding RNAs requires verification by animal or cellular studies. iii) The specificity of the identified DElncs, DEmiRs and DEGs for HCM requires investigation in future studies.In conclusion, the present study generated a holistic view of a candidate lncRNA-miRNA-mRNA network for HCM and proposed potential predicted drugs that could be used for this disease by integrating several microarray data. The authors hope that the present study will be beneficial for discovering new biomarkers or therapeutic drugs for the reduction of the risk of this life-threatening disease.


Cinnamomum cassia Presl  and the Aconitum carmichaelii Debx  can be effective in CCVDs, which is recorded in the ancient classic book Shennong Bencao Jing, Mingyibielu and Thousand Golden Prescriptions. However, the active ingredients and the molecular mechanisms of rougui-fuzi in treatment of CCVDs are still unclear. This study was designed to apply a system pharmacology approach to reveal the molecular mechanisms of the rougui-fuzi anti-CCVDs. The 163 candidate compounds were retrieved from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP). And 84 potential active compounds and the corresponding 42 targets were obtained from systematic model. The underlying mechanisms of the therapeutic effect for rougui-fuzi were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, component-target-disease (C-T-D) and target-pathway (T-P) networks were constructed to further dissect the core pathways, potential targets, and active compounds in treatment of CCVDs for rougui-fuzi. We also constituted protein-protein in interaction (PPI) network by the reflect target protein of the crucial pathways against CCVDs. As a result, 21 key compounds, 8 key targets, and 3 key pathways were obtained for rougui-fuzi. Afterwards, molecular docking was performed to validate the reliability of the interactions between some compounds and their corresponding targets. Finally, UPLC-Q-Exactive-MSE and GC-MS/MS were analyzed to detect the active ingredients of rougui-fuzi. Our results may provide a new approach to clarify the molecular mechanisms of Chinese herb pair in treatment with CCVDs at a systematic level.Cardiocerebral vascular diseases (CCVDs) are the main reasons for high morbidity and mortality all over the world, including atherosclerosis, hypertension, myocardial infarction, stroke, and so on. Chinese herbs pair of the  Cardiocerebral vascular diseases (CCVDs), including thrombosis, atherosclerosis, hypertension, cardiac hypertrophy, myocardial infarction, heart failure, stroke, and cerebral ischemia, are the major health problem all over the world, claiming more than 17.3 million lives annually , 5. WestCinnamomum cassia Presl  and the root of Aconitum carmichaelii Debx  that can be effective in the treatment of CCVDs, and usually in a proportion of 1 : 1, according to the classic ancient book Shennong Bencao Jing, Mingyibielu and Thousand Golden prescriptions [Cinnamomum cassia Presl can increase coronary blood flow, improve coronary circulation and myocardial nutritional status, and was often used in the prevention and treatment of coronary heart, arrhythmia, rheumatic heart disease, cardiogenic hypertension, and cardiovascular diseases [Aconitum carmichaelii Debx has the functions of protecting cardiac muscle cell, fight arrhythmia, decrease inflammatory reaction, and inhibit tumor. Moreover, several active constituents of these two herbs are documented to exhibit various biological activities, which contribute to the CCVDs. For example, cinnamaldehyde, an index component of the Cinnamomum cassia Presl, can dilate peripheral blood vessels, improve blood circulation at the end of blood vessels, and improve myocardial blood supply. The aconite polysaccharide could increase the synthesis of metallothionein, reduce the production of malondialdehyde (MDA) and the release of lactate dehydrogenase (LDH), and inhibit cardiomyopathy apoptosis [Herb pairs, the simplest form and the centralized representative of Chinese herbal compatibility, intrinsically convey the basic idea of traditional Chinese medicines' prescriptions that has attracted comprehensive attention for their ability to treat complex and chronic diseases due to their moderate treatment effect and lower side effect –11. The riptions . Among tdiseases –16. Aconpoptosis . As is dSystems pharmacology has become a powerful tool to systematically reveal the function and behavior of a complex biological system, and it has become a novel and efficient way to explore the pharmacological mechanism of traditional Chinese medicine , 19. It E and GC-MS/MS analyses were performed  and target-pathway (T-P) networks were established. Meanwhile, protein-protein interaction (PPI) was mapped using important pathway-related targets for screening core target proteins based on (STRING) database. After that, molecular docking was carried out to verify the reliability of compound-target binding interactions. Finally, to further determine rougui-fuzi components, the UPLC-Q-Exactive-MSAconitum carmichaelii Debx and Cinnamomum cassia Presl were extracted from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform  [The constituents of medicinal herbs msp.php) , 25. TCMmsp.php) .https://www.uniprot.org/) database, the target gene name is entered to define the species as “Homo sapiens,” and all protein names are corrected to their official name . The human CCVD-related diseases associated with genes were secured from the curative targets database such as the Online Mendelian Inheritance in Man  and Pharmacogenomics Knowledgebase  [Drug target indentifying is momentous to elucidating the biological basis of traditional Chinese medicine. Thus, in this work, we applied a systematic model that efficiently enriched chemical, genomic, and pharmacological information for drug target, based on random forest (RF) and support vector machine (SVM) methods. The robust model showed optimal performance of predicting the drug-target interactions, with 82.83% concordance, 81.33% sensitivity, and 93.62% specificity, which calculated the possibility of interactions between each ingredient and its target from TCMSP , 28. Thegkb.org) –32. The gkb.org) .Based on the above, the previous compound targets are mapped with the genes of human CCVD-correlative disease to obtain their common targets, for better deciphering the role of rougui-fuzi against CCVDs.http://www.genome.jp/kegg/pathway.html) and gene ontology (GO) enrichment analyses were performed based on the ClusterProfiler software package on R platform, and compared with Visualization [R platform [P value was calculated in these two enrichment analyses, and P < 0.05 suggested the enrichment degree of statistically significant [To better comprehend the potential biological processes and pathways of rougui-fuzi in treatment of CCVDs, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway . The version 11.0 of STRING was employed to seek for the PPI data, with the species limited to “Homo sapiens” and a confidence score >0.4 [For better dissecting the potential molecular mechanism of rougui-fuzi, we established three corresponding networks: (1) compound-target-disease (C-T-D) network. Active constituents of rougui-fuzi, corresponding targets, and CCVD-related diseases were employed to generate the C-T-D network in which an ingredient and a target are connected with each other if this protein is a known or validated either target or disease of this molecule. (2) Target-pathway (T-P) network. We extracted the precise pathway information of targets from the database of KEGG, and then constructed a target-pathway bipartite graph that comprises targets and their corresponding normative pathways. The previous two were generated in Cytoscape 3.7.2 software that is a standard tool for biological network visualization and data integration to further analyze the mutual relations in the network , 39. (3)ore >0.4 . Meanwhiore >0.4 .http://www.pdb.org), so that proteins and ligands could be prepared in the AutoDock tools before performing the docking progress [To verify the reliability of compound-target binding interactions, molecular docking program was in progress using AutoDock software (version 4.2), which employs the Lamarckian genetic algorithm (LGA) for each progress . All theprogress . The auxprogress .E analysis was applied. Some reagents were also used: rougui (batch no. 19061412) was provided by Shaanxi Kangyu Pharmaceutical Co., Ltd , and fuzi (batch no. 150816) was provided by Shaanxi Kangyu Pharmaceutical Co., Ltd . LC-MS-grade acetonitrile was obtained from Thermo Fisher Scientific (China) co., ltd. , MS-grade formic acid was obtained from Thermo Fisher Scientific (China) co., ltd. . The rougui-fuzi extracts were analyzed on a Thermo Fisher Scientific quadrupole-electrostatic field orbital well high-resolution system coupled with H-Class UPLC system. Separations were accomplished on an Accucore AQ C18 column . The mobile phase included 0.1% formic acid aqueous solution (Solvent A) and acetonitrile (Solvent B) and a gradient elution program was performed: 0 min, 98% solvent A; 17 min, 50% solvent A; 20 min, 98% solvent A. The flow rate was set at 0.3 mL/min. Column temperature was kept at 30°C and the total run time was 20 min. The autosampler was conditioned at 4°C and the injection volume was 1 μL. The ion source of thermoelectric spray (HESI) mass spectrometry was used to collect data in full switch ion mode. The sheath gas flow rate was set 30 L/min, spray voltage was set 3.50 kV, and capillary temperature and aux gas heater temperature were 300°C. S-lens RF level was set 55 V. The scan mode was Full MS and dd-MS2, Full MS resolution of the detection mode was 7000, and the dd-MS2 resolution was 175000. The scan range was from 100 to 1200 Da. Leucine-enkephalin was used as the lock mass for generating reference ions in the positive and negative modes.To verify the potential ingredients of rougui-fuzi, the UPLC-Q-Exactive-MSμm) capillary column. The inlet temperature was set at 260°C; the carrier gas  was maintained at a constant flow of 1.0 mL·min−1; the mode of inlet was split less; the injection volume was 1 μL; the temperature of column oven was programmed from an initial value of 60°C (hold for 10 min), and then raised at 4°C·min−1 up to 220°C (hold for 20 min); the split ratio was set 20 : 1.The rougui-fuzi extracts were analyzed on Shimadzu TQ8040 coupled with Shimadzu 2010Plus GC system. Separation was performed on a 19091s-433HP-5MS , cell component (CC), and molecular function (MF) terms. In the tion see . The enrtion see . The enrtion see .P < 0.05) through the bubble diagram , betweenness centrality (BC), and closeness centrality (CC). The DC is the sum of the number of direct connections between a node and other nodes. It is the most direct metric to characterize the importance of nodes in network analysis. CC is the sum of the distances of a node to all other nodes, reflecting the extent of proximity of a node to other nodes. Betweenness centrality is the number of shortest paths passing through a node, reflecting the extent of cohesion of the nodes in the network. The greater the number of the shortest paths passing through a node, the higher its value .In this work, C-T-D network was constructed including 131 nodes  and 293 edges (the interactions between different nodes) see . The yelIn this study, the target-pathway (T-P) network was constructed including 36 nodes  and 41 edges see . The pinAs a critical signaling pathway, complement and coagulation cascades involved in 7 differentially expressed genes, including F10, F3, F2, SERPINE1, F7, PLG, and PLAU. The complement and coagulation systems are two related protein cascades in plasma that serve important roles in host defense and hemostasis, respectively . RecentlUsing pathway enrichment analysis, we found 5 differentially expressed genes involved in arachidonic acid metabolism, such as PTGS2, PTGS1, PLA2G2A, LTA4H, and ALOX5. The arachidonic acid (ARA) metabolic network produces a large family of inflammatory mediators, including leukotrienes (LTs) and prostaglandins (PGs), which contribute to numerous inflammatory-related diseases such as asthma and atherosclerosis. Cyclooxygenase enzymes work in one arm of the pathway to produce prostaglandins (PGs), while the actions of 5-lipoxygenase (ALOX5) and its associated protein work in the other arm of the metabolic pathway to produce leukotrienes (LTs) . After cIn addition, PI3K-Akt signaling pathway referred to 6 differentially expressed genes, including IFNB1, IL4, INS, PTEN, RXRA, and TLR4, and the PI3K-Akt signaling pathway is an important signaling pathway involved in the neuroprotection against ischemic brain damage . Some stConsequently, it was concluded that rougui-fuzi may mainly fight CCVDs through complement and coagulation cascades, arachidonic acid metabolism, and PI3K-Akt signaling pathway.Homo sapiens,” thereby obtaining protein interactions. Subsequently, the PPI network was further visualized and topology analyzed using Cytoscape 3.7.1 -alpha-terpineol, pyruvophenone, cinnamaldehyde, and 3-methoxycinnamaldehyde are contained in the 21 key compounds that are obtained based on the C-T-D network for rougui-fuzi in treatment with CCVDs. As a result, 10 of 21 key compounds have been verified by UPLC-Q-Exactive-MSE and GC-MS/MS.The total ion chromatography (TIC) of essential oil of rougui-fuzi is shown in E and GC-MS to analyze the potential ingredients of rougui-fuzi in the treatment of CCVDs. Collectively, herbal medicines always contain huge numbers of ingredients, diversified bioactive compounds of TCM with their related multiple targets, and multiple pathways, making it difficult to uncover the mechanisms of their interactions employing only traditional experimental approaches [In summary, we used system pharmacology method to analyze the multicomponent, multitarget, multichannel mechanism for rougui-fuzi in treatment of CCVDs, including compound database construction, target fishing, disease mapping, network construction, and analysis. As a result, 21 key compounds, 8 key targets, and 3 key pathways were obtained for rougui-fuzi. Moreover, we performed molecular docking, which validated the reliability of the interactions of chemical compounds and targets based on systems pharmacology. Finally, we used UPLC-Q-Exactive-MSproaches . Systemsproaches .


However, the isoform and posttranslational modification states of proteins are lost when converting input and pathways into gene-centric lists.Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers  to query this network. PathwayMatcher supports multiple types of omics data as input and outputs the possibly affected biochemical reactions, subnetworks, and pathways.PathwayMatcher enables refining the network representation of pathways by including proteoforms defined as protein isoforms with posttranslational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity, and it becomes possible to distinguish interactions between different forms of the same protein. In biomedicine, molecular pathways are used to infer the mechanisms underlying disease conditions and identify potential drug targets. Pathways are composed of series of biochemical reactions, of which the main participants are proteins, that together form a complex biological network. Proteins can be found in various forms, referred to as proteoforms . The difHowever, when analyzing omics data, both input and pathways are summarized in a gene- or protein-centric manner, meaning that the different proteoforms and their reactions are grouped by gene name or protein accession number, and the fine-grained structure of the pathways is lost. One can therefore anticipate that proteoform-centric networks provide a rich new paradigm to study biological systems. But while gene networks have proven their ability to identify genes associated with diseases , networkHere, we present PathwayMatcher, an open-source standalone application that considers the isoform and PTM status when building protein networks and mapping omics data to pathways from the Reactome database. Reactome  is an opSignaling by NOTCH2 from Reactome , comprising 32 and 54 reactions, yielding 28 and 141 edges, respectively. The 31 participants of the Signaling by NOTCH2 pathway are also involved in reactions in other pathways, between themselves and with 2,055 other proteins, resulting in 6,525 external edges. Note that in this pathway, Cyclic AMP-responsive element-binding protein 1 (coded by CREB1) is phosphorylated at position 46  is found in 3 forms .As an example of the complexity of hierarchical pathway information, we provide a graph representation of ome Fig. . This paproteoform-centric, where protein isoforms with different sets of PTMs are represented with different nodes, in contrast to gene-centric networks, where one node is used per gene name or protein accession. In this representation, two proteoforms are connected if they participate in the same reaction. Note that proteoforms can participate in reactions both individually and as part of a set or complex. Furthermore, they can have four different roles: input, output, catalyst, or regulator.The amount of information available on reactions involving modified proteins has dramatically increased during the past two decades Fig.   , with 3,cellular tumor antigen p53 (P04637) from the TP53 gene. In a gene-centric paradigm  gene names, (ii) protein accession numbers, or (iii) proteoforms and supports the mapping of multiple types of omics data: (i) genetic variants, (ii) genes, (iii) proteins, (iv) peptides, and (v) proteoforms. Genetic variants are mapped to proteins using the Ensembl Variant Effect Predictor , gene nasteriori . If pepti.e., a P-value for each pathway in the reference database is calculated using a binomial distribution followed by Benjamini-Hochberg correction , where X is a random variable following the binomial distribution, as detailed in Equation 1.This is done using the cumulative distribution function for the binomial distribution, which calculates the probability of getting at most p is set to the ratio between the number of total proteins or proteoforms in the pathway and the total possible entities in the database, n is the number of proteins or proteoforms in the input sample, k is the number of proteins successfully mapped in the pathway, andX is the number of entities found in the current pathway after the search.For each pathway, P-value requires the calculation of the probability of an equal or more extreme result, we use the complement of Equation k successful trials out of n, as stated in Equation 2.Finally, given that the The calculations for proteins or proteoforms are similar but are performed separately depending on the input. If the input consists of protein accessions, the number of participants is calculated by only considering proteins. On the other hand, for the proteoform input, the number of entities in the pathways and the database are the participant proteoforms.Proteins: human complement of the UniProtKB/Swiss-Prot database (release 2017_10)Peptides: ProteomeTools  as availGenetic variants: variants from the human assembly GRCh37.p13Proteoforms: annotated proteoforms in Reactome Graph database version 62The performance of PathwayMatcher was evaluated using data sets of different sizes obtained from sampling publicly available resources:Performance testing was done using a standard desktop computer (Intel® Core™ i7–6600U CPU @ 2.60 GHz with 2 cores using 64-bit Windows 10 with Java SE 1.8.0_144 on SSD). Details and code are available at github.com/PathwayAnalysisPlatform/PathwayMatcher/wiki/Performance.The metrics presented in this article were obtained by querying the Reactome graph database directly . The queThe figures in this article were built in R version 3.4.1 (2017–06-30)—“Single Candle” (r-project.org) using the following packages: ggplot2, ggrepel, igraph, scico, grid, purr, dplyr, graphlayouts, and gtable. The R scripts used to build the figures are available in the tool repository at github.com/PathwayAnalysisPlatform/PathwayMatcher_Publication/tree/master/R.Project name: PathwayMatcherProject home page: github.com/PathwayAnalysisPlatform/PathwayMatcherOperating system(s): Platform independentProgramming language: JavaOther requirements:License: Apache 2.0RRID: SCR_01 6759GigaScience repository, GigaDB [Snapshots of our code and other supporting data are available in the , GigaDB .PTM: posttranslational modification.Not applicable.Not applicable.The authors declare that they have no competing interests.LFHS, SJ, PRN, and MV are supported by the European Research Council and the Research Council of Norway. BB, CH, and HB are supported by the Bergen Research Foundation. HB is also supported by the Research Council of Norway. LFHS, SJ, PRN, and MV are supported by the European Research Council and by the Research Council of Norway. This work has been supported by National Institutes of Health BD2K grant (U54 GM114833) and National Human Genome Research Institute at the National Institutes of Health Reactome grant (U41 HG003751).LFHS did most of the programming, testing, and documentation and wrote the manuscript. BB and CH contributed with programming, testing, documentation, ideas, and manuscript writing. AF, SJ, and PRN contributed with ideas and manuscript writing. HB contributed with ideas, supervised the work, and wrote the manuscript. HH contributed with project design, manuscript writing, and supervised the work. MV contributed with project design, programming, documentation, and testing; supervised the work; and wrote the manuscript. All authors participated in the preparation of the manuscript.giz088_GIGA-D-18-00514_Original_SubmissionClick here for additional data file.giz088_GIGA-D-18-00514_Revision_1Click here for additional data file.giz088_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giz088_Reviewer_1_Report_Original_SubmissionDavid Lahnemann -- 1/10/2019 ReviewedClick here for additional data file.giz088_Reviewer_1_Report_Revision_1David Lahnemann -- 6/14/2019 ReviewedClick here for additional data file.giz088_Reviewer_2_Report_Original_SubmissionMarcella Nunes Melo-Braga, PhD -- 2/21/2019 ReviewedClick here for additional data file.giz088_Reviewer_2_Report_Revision_1Marcella Nunes Melo-Braga, PhD -- 6/11/2019 ReviewedClick here for additional data file.


O-linked N-acetylglucosamine, better known as O-GlcNAc, is a sugar post-translational modification participating in a diverse range of cell functions. Disruptions in the cycling of O-GlcNAc mediated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively, is a driving force for aberrant cell signaling in disease pathologies, such as diabetes, obesity, Alzheimer's disease, and cancer. Production of UDP-GlcNAc, the metabolic substrate for OGT, by the Hexosamine Biosynthetic Pathway (HBP) is controlled by the input of amino acids, fats, and nucleic acids, making O-GlcNAc a key nutrient-sensor for fluctuations in these macromolecules. The mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) pathways also participate in nutrient-sensing as a means of controlling cell activity and are significant factors in a variety of pathologies. Research into the individual nutrient-sensitivities of the HBP, AMPK, and mTOR pathways has revealed a complex regulatory dynamic, where their unique responses to macromolecule levels coordinate cell behavior. Importantly, cross-talk between these pathways fine-tunes the cellular response to nutrients. Strong evidence demonstrates that AMPK negatively regulates the mTOR pathway, but O-GlcNAcylation of AMPK lowers enzymatic activity and promotes growth. On the other hand, AMPK can phosphorylate OGT leading to changes in OGT function. Complex sets of interactions between the HBP, AMPK, and mTOR pathways integrate nutritional signals to respond to changes in the environment. In particular, examining these relationships using systems biology approaches might prove a useful method of exploring the complex nature of cell signaling. Overall, understanding the complex interactions of these nutrient pathways will provide novel mechanistic information into how nutrients influence health and disease. At its core, pathology is largely a matter of cell signaling gone awry. Think of it as a game of “Rumors,” where a group of players are passing a message down the line by whispering it to each other, but when it reaches the last person and they announce it, the message is entirely different from the original. Now, imagine the people as proteins, and when each one receives the message, they perform some function that will then signal the next protein in the pathway to perform a function until the endgame target is reached. If one of the components in a signaling pathway deviates from typical behavior or conditions, the resulting “message” can be exceedingly different from the original signal, altering cell behavior. Diseases like cancer, diabetes, and Alzheimer's are often rooted in aberrant signal transduction and abnormal pathway regulation. Nutrient-sensitivity is one of several key factors that impact a pathway's activity. Over- or under-nutrition can heighten or inhibit activity through alterations in post-translational modifications (PTM); any miscommunication in these modifications can push a cell toward pathology. A PTM can propagate a signal cascade to change the function of a final target, act as a sensor for regulators of a pathway, or alter the interactions of specific proteins in the pathway. To understand why these changes are occurring, a greater understanding of the complexity and fine-tuning of a pathway by a PTM is needed.Along with phosphorylation, acetylation, and methylation; glycosylation is one of the most important protein modifications. O-linked N-Acetylglucosamine (O-GlcNAc) is one of the many kinds of glycosidic PTMs found in eukaryotic cells, but what makes it different from other sugar additions is that it exists purely as an intracellular molecule and does form oligomers . The addThe Hexosamine Biosynthetic Pathway (HBP) utilizes approximately 3-5% of cellular glucose as well as glutamine, acetyl-Coenzyme A (CoA), and uridine, to generate the OGT substrate, UDP-GlcNAc , 17. ThiFluctuations in the levels of the macromolecules that feed into the HBP alter the output of this pathway, making it a diverse and responsive nutrient sensor . For insin vivo can alter the output of the HBP, OGT activity also plays a role in nutrient-sensing. Transgenic mice with skeletal muscle overexpression of OGT demonstrated increased levels of serum insulin, indicating hyperinsulinemia, which is characteristic in type II diabetes  binds GTP to activate mTORC1 –32. Amin2) to PIP3; the increase in PIP3 signals AKT to localize to the plasma membrane for activation  , which ced cells . Becauseed cells , mTORC1 AMP-activated protein kinase (AMPK), another nutrient sensing pathway, senses shifts in the AMP: ATP dynamic and responds inversely to energy levels in relation to mTORC1 , 40. WhiMAX  showed a slight increase in phosphorylated mTOR, OGT, and O-GlcNAcylation and a significant amplification in phosphorylated p70S6K  is a phosphorylation target of p70S6K, but is also one of many ribosomal components that can be O-GlcNAcylated. Of note, the O-GlcNAc modification on S6 does not appear to be influenced by glucose starvation . While tWhile there is not a large amount of literature detailing the interactions between mTORC1 and the HBP, current documentation does reveal an intricate, fine-tuned dynamic between the two pathways. With diabetes in particular, this relationship appears to play a substantial role, which makes it a prime focal point for future research into these diseases. However, when considering the web of interactions occurring between the HBP and the mTOR pathway, as well as their relationships with the AMPK pathway, it is important to understand the complexity of cell behavior and sheer amount of variables required for signaling events. Therefore, exploring the dynamics between these pathways using a systems biology approach, alongside conventional laboratory techniques might be the best approach for future investigations.While the evidence discussed provides a possible framework of interactions between the HBP, mTOR, and AMPK pathways, the intrinsic complexity of cellular function must be considered when formulating our understanding of these relationships. Ultimately, the true story of nutrient sensing and cellular response cannot be reduced to simple interactions, such as the repression of mTORC1 by AMPK. The maintenance of cellular homeostasis is a vital process, which must be finely tuned. Therefore, a more accurate picture would be like the mixing board used in a recording studio or a concert, rather than the volume knob on the radio in a car. There are many inputs, which interact in complex and dynamic ways. Nutritional sensing pathways, such as AMPK, mTORC1, and O-GlcNAc are replete with feedback loops , 60, whiAn additional challenge in understanding the complexities of nutrient sensing is biological “noise” in a cell. Biological pathways are frequently represented as an orderly set of protein-protein interactions (PPIs), frequently in the form of a directed graph . NaturalNevertheless, a cell must have some method for fine-tuning signals to maintain homeostasis in a noisy environment, which is where we see PTMs come into play . One sucSaccharomyces cerevisiae (Amd1, which may be regulated by TORC1. Further work was built on this approach, demonstrating that TORC1 target Sch9 may be the kinase responsible phosphorylating Amd1 (Sch9 functions similarly to S6K1 (The complex and dynamic nature of maintaining glucose homeostasis suggests the need for the more holistic view that can be provided by genomic or even multi-omic methods. However, due to the reactive nature of these pathways, this view requires more than a simple “snapshot” of the cell. Rather, approaches that can paint a picture of cellular dynamics are called for. Such studies are not only expensive, but they require new methods to understand the collected data. Nevertheless, early attempts to tackle this problem show promise , 74. A rrevisiae  in ordering Amd1 , pointin to S6K1 , which iThe HBP, AMPK, and mTOR pathways all present a unique niche in cellular function, with respect to nutrient-sensing. However, it is the crosstalk between these pathways that fosters a particularly interesting but unexplored spotlight in cell signaling. While reduced energy levels trigger AMPK inhibition of mTORC1, these conditions can also trigger conservation of energy via increased global O-GlcNAcylation. However, increased glucose and glucosamine uptake bolsters cytosolic ATP production and triggers deviations in cellular behavior, such as insulin resistance, while repressing AMPK and allowing the mTOR pathway to encourage proliferation. Amino acid uptake facilitates mTORC1 activation and can up-regulate the HBP in a fine-tuned manner, while diminishing AMPK activity. Cross-talk between OGT and AMPK presents a mutually inhibitory relationship between the two enzymes based on nutrient availability and stimulation, while the mTOR pathway and O-GlcNAc coordinate with one another to direct autophagy.Overall, there appears to be an intricate dynamic between the three pathways, where deviations in their communication lend to various pathologies. Future research should elucidate more connections between these pathways using both computational methods and traditional bench work, with the hopes that we'll understand this “real talk” and how to counteract it when it leads to disease-related miscommunication.All 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.