The PI3K/mTOR signalling pathway plays a central role in the governing of cell growth, survival and metabolism. As such, it must integrate and decode information from both external and internal sources to guide efficient decision-making by the cell. To facilitate this, the pathway has evolved an intricate web of complex regulatory mechanisms and elaborate crosstalk with neighbouring signalling pathways, making it a highly non-linear system. Here, we describe the mechanistic biological details that underpin these regulatory mechanisms, covering a multitude of negative and positive feedback loops, feed-forward loops, competing protein interactions, and crosstalk with major signalling pathways. Further, we highlight the non-linear and dynamic network behaviours that arise from these regulations, uncovered through computational and experimental studies. Given the pivotal role of the PI3K/mTOR network in cellular homeostasis and its frequent dysregulation in pathologies including cancer and diabetes, a coherent and systems-level understanding of the complex regulation and consequential dynamic signalling behaviours within this network is imperative for advancing biology and development of new therapeutic approaches. The PI3K/mTOR pathway is a critical signalling pathway that is involved in a diverse range of cellular functions, including cell growth, survival, metabolism, protein synthesis and autophagy . The PI3To ensure a cell makes efficient decisions, its signalling networks must be both flexible and robust . FlexibiFlexibility and robustness are an absolute necessity for cellular networks, but the more flexible and robust the network the more complex it must become. This presents a significant challenge to researchers trying to understand signalling events in a disease context such as cancer. It is becoming increasingly clear that if we want to understand a signalling network to the predictive level, we must uncover their constituent feedback loops and crosstalk, and the consequential network behaviours at the systems level. A prime example of failing to understand a network at the systems level is the underwhelming ability of current PI3K-targeted therapeutics to overcome cancer.Reflecting its physiological importance, the PI3K/mTOR pathway represents one of the most frequently deregulated pathways in cancer. Genetic aberrations of the pathway components such as PI3K, mTOR, Akt, rapamycin-insensitive companion of mammalian target of rapamycin (RICTOR) and phosphatase and tension homologue deleted on chromosome 10 (PTEN) have been reported in different cancer types . NotablyIn this review, we summarise the complexity in the regulation and dynamic behaviour of the PI3K/mTOR signalling network that emerge from an intricate web of feedback loops, competing protein interactions and pathway crosstalk. We will first provide an overview of signal transduction within the PI3K/mTOR pathway, highlighting important regulatory mechanisms and the non-trivial network behaviours arising from these. We will then describe the crosstalk between the PI3K/mTOR pathway and other signalling pathways and discuss the induction of any feedback this crosstalk might cause. Finally, we end with thoughts on challenges facing the PI3K/mTOR signalling field and provide a perspective on the role of computational modelling in overcoming these.2) into phosphatidylinositol -bisphosphate (PIP3), a second messenger, which is subsequently converted into PtdInsP2 or PtdInsP2 by phosphatases, including PTEN, SH2-domain-containing inositol phosphatase 2 (SHP2) and phosphoinositide 3-phosphatases (PI3Ps) [The activation of PI3K usually begins with the activation of an upstream receptor tyrosine kinase (RTK) upon extracellular stimuli, with the insulin receptor (IR) being a canonical activator of PI3K. The binding of insulin to the IR triggers IR dimerization and trans-auto-phosphorylation of IR’s cytosolic domain . The pho (PI3Ps) . A schem3 is able to recruit PH domain-containing proteins to the plasma membrane, and two major proteins recruited to PIP3 are mammalian target of rapamycin complex 2 (mTORC2) and phosphoinositide-dependent protein kinase 1 (PDK1). mTORC2 is recruited to PIP3 through the PH domain of the mTOR subunit stress-activated map kinase interacting protein 1 (Sin1). When Sin1 binds PIP3, this releases Sin1′s autoinhibition and activates mTOR’s catalytic functions [3 via a PH domain. The co-localisation of Akt and activated PDK1 and mTORC2 results in the dual phosphorylation of Akt, where PDK1 phosphorylates Akt at threonine 308 (Thr308), and mTORC2 phosphorylates Akt at serine 473 (Ser473), leading to complete activation of Akt [3 into the cytosol, where it phosphorylates a large range of downstream target proteins.As a membrane-bound second messenger, PIPunctions ,18. In an of Akt ,20. FullOne of the key downstream proteins governed by Akt activation is mTOR complex 1 (mTORC1). Active Akt phosphorylates tuberous sclerosis complex 2 (TSC2), inactivating it and causing its inhibitory effect on mTORC1 to be shutdown. Newly active mTORC1 can then phosphorylate and activate ribosomal protein S6 kinase (S6K) and inactivates eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1). The phosphorylation of 4E-BP1 prevents its ability to bind to eukaryotic translation initiation factor 4E (eIF4E), releasing its inhibitory effect on eIF4E and ultimately stimulating the biosynthesis of major classes of macromolecules, including proteins, lipids, and nucleic acids . Other dFeedback control is fundamental to the robust functioning of cell signalling networks . The exiActivation of the PI3K/mTOR signalling pathway initiates several mechanisms that are capable of suppressing pathway activity, generating distinct negative feedback mechanisms. Both mTORC1 and S6K can serine-phosphorylate IRS1 (serine 636 and 639) causing it to adopt an inactive form ,27,28 th3 back into PIP2 and can dephosphorylate and activate two negative regulators of mTORC1 activity, TSC1/2 and AMP kinase (AMPK) [3 and Akt. A notable implication of this feedback is that targeted inhibition of mTORC1 by pharmacological inhibitors may initially suppress PI3K/mTOR signalling, but the consequential down regulation of PTEN may lead to a rebound in pathway activity over the long-term [Recently it has been shown that mTORC1 can promote the activity of the phosphatase PTEN, a key negative PI3K signalling regulator, thereby forming another negative feedback control mechanism . PTEN coe (AMPK) ,38. The e (AMPK) . The actong-term .Both mTORC1 and AMPK are critical signalling nodes that are responsible for nutrient and energy sensing. AMPK is known to inhibit mTORC1, either directly by phosphorylating RAPTOR at S792 or indirectly by phosphorylating and activating TSC2 at T1387 ,40. On tPH domain leucine-rich repeat protein phosphatase (PHLPP) is a serine/threonine protein phosphatase with multiple substrates in the PI3K/mTOR pathway . PHLPP d3 [3 may not be necessarily required for mTORC2 membrane localisation and that IRS1/2 can potentially release Sin1 auto-inhibition and thus activate mTORC2 [In addition to negative feedback regulation, the PI3K/mTOR pathway also contains positive feedback, such as from mTORC2 to IR and insulin-like growth factor receptor (IGF-1R) . mTORC2 3 . The abie mTORC2 .Several studies have demonstrated the existence of a negative feedback loop from S6K1 to mTORC2 through its subunits Sin1 and RICTOR, however, this interaction remains controversial ,55,56. OIt is well established that mTORC2 positively regulates Akt activity by phosphorylating Akt at serine 473 ,58. In tBy combining predictive computational modelling and cell biology, our recent study reveals a new Akt-controlled negative feedback on PI3K-mediated PIP3 that is rapid and powerful . We demoAkt has a plethora of target downstream substrates, including cytosolic proteins as well as transcriptional factors such as FOXO. Phosphorylation of FOXO by Akt inhibits FOXO and creates a negative feedback loop that suppresses Akt activity . FOXO isThe kinase complexes mTORC1 and mTORC2 form the core of the PI3K/mTOR pathway. While each possesses distinct subunit proteins such as Sin1 and RICTOR for mTORC2, and regulatory-associated protein of mTOR (RAPTOR) and proline-rich AKT substrate of 40 kDa (PRAS40) for mTORC1, they also share common members, notably mTOR-associated protein LST8 homolog  and DEP domain-containing mTOR-interacting protein (DEPTOR). The existence of shared subunit proteins effectively creates competition for these subunits, and so the relationship between mTORC1 and mTORC2 is one of mutual antagonism. Furthermore, a recent study identified a switch mechanism involving reversible ubiquitination of mLST8 that dynamically regulates the relative abundance of the two mTOR complexes . MechaniIt has been known that the existence of competitive protein interactions coupled with affinity-modulating post-translational modifications can lead to highly non-linear behaviours such as switch-like and biphasic responses ,75. To sDEPTOR is another shared subunit of mTORC1 and 2, which acts to negatively regulate them both . Loss ofInterestingly, in-silico experiments show that although DEPTOR is considered a negative regulator of mTORC1/2, both low and high levels of DEPTOR have the same effect on Akt activity, Akt hyperactivity. In the case of low DEPTOR abundance, DEPTOR has a greatly reduced inhibitory effect on mTORC1/2 resulting in the complexes having high activity and strong downstream signalling. High DEPTOR expression results in the suppression of the mTOR complexes which has the ultimate effect of suppressing the activity of S6K1. As S6K1 represents a strong negative feedback (discussed above) that shuts down the pathway at the level of IRS, the suppression of S6K1 activity releases its inhibitory effect and promotes Akt activity. Consistent with these findings, both low and high DEPTOR concentrations can be observed in cancer cells .The above discussion highlights that the PI3K/mTOR signalling network possesses many feedback mechanisms, as depicted in Crosstalk between signalling pathways is a vital phenomenon that allows cells to integrate multiple sources of information about the cellular state and make efficient decisions regarding the most beneficial response. When signalling pathways converge on a biological output, such as the stimulation of growth, the topology of the network that controls how the signals are integrated will dictate the types and strengths of response. Pathway crosstalk thus enables a plethora of distinct spatiotemporal response patterns, which may help discriminate between combinations of extra- and intra-cellular cues and result in different cellular decisions ,82. ReflRetinoblastoma protein (Rb) is a tumour suppressor known for its pivotal role in the negative regulation of cell cycle progression. Rb binds to and inhibits the transcriptional factor E2F, causing the cell cycle to be arrested in the G1 phase . Active It has been demonstrated that phosphorylated Rb can become cytosolic, and recently it was shown that phosphorylated Rb can bind to Sin1 and suppress mTORC2 activity . This reCyclin A2 is regulated by E2F and is usually at its highest concentration during S phase. The cognate CDK for cyclin A2 is CDK2, and recently it was demonstrated that the cyclin A2-CDK2 complex can phosphorylate Akt at serine 477 and threonine 479 . These tThe NF-κB signalling network regulates a diverse set of cellular behaviours, including inflammation, proliferation and cell survival . IKKα isDan et al. demonstrated that the knockdown of IKKα decreased the level of Akt and S6K1 phosphorylation, however, the knockdown did not affect the phosphorylation levels of the Akt substrates TSC2 and PRAS40 . SpecifiFeedback nutrient availability is required for biosynthesis, bioenergetics, and redox balance in cells. Glutamate and glucose are two major sources of such nutrients in the majority of cells . xCT is In contrast to the suggestion that mTORC2 is only sensitive to growth-factor stimulation , nutrienGlucose uptake is also affected by mTORC2 and does so through the activation of Akt ,102,103.Along with the PI3K/mTOR pathway, the mitogenic Ras/ERK  signalling pathway  is the cell’s chief regulator of externally stimulated growth, metabolism, and cell survival, and is probably the best described pathway in terms of crosstalk to PI3K/mTOR signalling. There are multiple levels of interplay between the two pathways where they influence each other both negatively and positively, and the coordination of these actions determines complex cell-fate decisions  see Fig.PI3K signalling has long been known as the main effector pathway of Ras where Ras directly interacts with and activate PI3K, forming a major point of crosstalk between the two pathways ,108. Imp3 by PI3K induces the recruitment of adaptor proteins such as IRS and Grb2-associated binding partner (GAB) to the plasma membrane via their PH domain. Once there, these proteins are phosphorylated on multiple sites by membrane-bound receptors and non-receptor tyrosine kinases, which serve as docking sites to bring additional PI3K molecules to the membrane and enhance the activation of PI3K signalling. This effectively creates a positive feedback loop between PI3K and GAB [Crosstalk also occurs at the level of the plasma membrane. Generation of PIP and GAB ,117. Imp and GAB . Interes and GAB . Moreove and GAB .3 levels and PI3K signalling. On the other hand, PDK1 can enhance ERK signalling through phosphorylation of MEK on serine 222 and 226, which is critical for the full activation of MEK [ERK also inhibits PI3K signalling via other routes in addition to GAB and TSC. Active ERK and p90RSK phosphorylate and inhibit GSK3 , a negatn of MEK . PDK1 aln of MEK  and PAK1n of MEK . Togethen of MEK .The mammalian Hippo/MST pathway regulates organ size, cell proliferation and cell death. In addition, it has been shown to play a central role in the regulation of cellular homeostasis and is often disrupted in human cancers . In mammThere are multiple crosstalk between the PI3K/mTOR and Hippo/MST pathways that enable them to mutually regulate each other . One of Consistent with the anti-apoptotic role of Akt, PI3K promotes cell survival through the positive regulation of YAP/TAZ, and this has been shown to promote breast cancer progression. The mechanics behind this connection are relatively complex, where PI3K activates PDK1 and Akt, which inhibit LATS and YAP, respectively. Active PDK1 promotes the dissociation of MST1/2 from SAV1/WW45 and reduces LATS1 kinase activity ,132. On It has been shown that MST1 is capable of forming a complex with and suppressing the activity of Akt, therefore negatively regulating PI3K/mTOR signalling . On the The PI3k/mTOR signalling pathway is a remarkably complex pathway that contains multiple feedback, feed-forward and competing protein mechanisms; and displays crosstalk with many other signalling pathways. These complex regulatory mechanisms have the potential to induce highly non-linear dynamic signalling behaviours that are too complex for intuitive prediction and reasoning. Computational modelling is thus an instrumental tool with which to investigate such network properties, but the usefulness of any models produced is predicated on accurate and comprehensive biological knowledge . The ideOne of the themes that has emerged in feedback mechanics is that the cellular context can have a strong influence on which feedback mechanisms are active and are influencing signal transduction. Here, the cellular context refers to any differences between cells and cell types that results in a difference in signal transduction. Examples of these differences include the expression profiles of the relevant proteins and post-translational modifications that alter the protein interactions strengths. Changing the expression level of even a single protein can alter the balance between network states and cause a feedback loop to significantly increase or decrease in strength . A changThe large number of opposing feedback mechanics contained within the PI3K/mTOR network is at first confusing. Why does a network possess multiple feedback mechanisms that seem to counteract each other? One possibility is that this facilitates a very fine-tuned control over signalling outputs. In this scenario, each feedback mechanism influences signalling outputs with varying strengths, giving rise to a range of output levels . AnotherThe extensive crosstalk exhibited by the PI3K/mTOR and other signalling pathways certainly endows cells with robust abilities for decoding and processing the combinatorial variety of external signals under physiological conditions. Such complex crosstalk, however, also make it hard to predict the possible network-wide effect of cancer therapeutics targeting the pathway’s nodes. For example, pharmacological inhibition of PI3K or Akt may inadvertently activate ERK signalling, allowing tumour cells to evade apoptosis and maintain growth . MoreoveGiven how frequently the PI3K/mTOR pathway is altered in cancer, it is critical that we continue to investigate and uncover all of the possible feedback mechanisms and pathway interactions. Understanding how the cellular context influences these mechanisms and interactions and how the various PI3K-interacting signalling pathways support and compensate for each other will be equally as important. As research continues, the combination of this knowledge with computational modelling will one day enable us to make incredibly specific and accurate predictions about therapeutic perturbations, down to the individual patient level.


Repair of DNA Double-Strand Breaks (DSBs) can be error-free or highly mutagenic, depending on which of multiple mechanistically distinct pathways repairs the break. Hence, DSB-repair pathway choice directly affects genome integrity, and it is therefore of interest to understand the parameters that direct repair towards a specific pathway. This has been intensively studied using genomic reporter constructs, in which repair of a site-specific DSB by the pathway of interest generates a quantifiable phenotype, generally the expression of a fluorescent protein. The current developments in genome editing with targetable nucleases like Cas9 have increased reporter usage and accelerated the generation of novel reporter constructs. Considering these recent advances, this review will discuss and compare the available DSB-repair pathway reporters, provide essential considerations to guide reporter choice, and give an outlook on potential future developments. The integrity of our genome is constantly challenged by DNA damaging lesions that arise during normal cell growth and division, and are caused by exposure to environmental mutagens and irradiation . A partiIn human cells the majority of DSBs are repaired by classical Non-Homologous End-Joining (c-NHEJ), which requires no or very little (≤4 nucleotides) homology at the DSB-ends to ligate them together . Repair In addition to HR, DSB end-resection can prime repair by either Single-Strand Annealing (SSA) or by Micro-Homology Mediated End-joining (MMEJ), which is also known as alternative End-Joining or Theta-mediated End-joining . During Which pathway is employed to repair a given DSB is dependent on many factors including sequence context, chromatin environment, and cell cycle stage . EngagemThere are numerous published methods that could be considered reporter assays because they quantitatively detect DSB-repair by a given pathway. For simplicity, this review will be limited to describing the genomic reporter constructs that are designed to detect gain or loss of expression of a marker gene, as a result of defined sequence changes associated with DSB-repair by a specific pathway. The marker gene in the reporter could be a drug resistance cassette, and we will mention a few examples of such reporters. However, the main focus of this review will be on reporters that carry a marker gene that encodes a fluorescent protein .MAT locus of Saccharomyces cerevisiae. This locus is targeted by the HO endonuclease which can result in mating-type switching if the DSB is repaired by HR-mediated gene conversion using one of two homologous HM genes , which contains a functional mCherry gene inserted into a split, and thus non-functional, GFP gene . Cas9 isFinally, whereas there are many dual-pathway reporters, only two systems can monitor three pathways simultaneously. SSA-TLR is a variant of the TLR that is flanked by truncated iRFP genes, which can be joined by SSA to form an intact gene . This syThere is substantial variation within the reporter repertoire, and one reporter might be more suited to specific research needs than the other. A first consideration is whether to use single-pathway or multi-pathway reporter systems. An advantage of single-pathway systems is their simplicity and, at least for many of them, their extensive validation by widespread use for many years. However, with similar research efforts, multi-pathway reporters generate a more comprehensive view of DSB-repair pathway activity. When studying DBS-repair factors, for instance, such reporters can immediately reveal whether a factor functions in one or multiple pathways, the latter of which can also indicate at which node in the DSB-repair network it acts.et al. used a single-stranded oligo as template in their GFP to BFP conversion reporter (A second consideration is to use either reporters that require ectopic HR repair templates, or repeat-containing reporters that carry the template embedded within the construct. Glaser reporter , which hreporter . The othreporter . Moreovereporter . TherefoA third consideration is that some reporters, in particular end-joining reporters, might not be very specific but actually measure the collective frequency of repair by multiple pathways, as explained above. More specific detection of repair by a single pathway is in most cases preferable. However, it should be taken into account that too high specificity can come at the cost of low frequency. This is, for example, the case in the RFP-SCR reporter, which can distinguish between gene conversion sub-pathways. The frequency of cells undergoing long-tract gene conversion is consistently less than 0.1%, requiring analysis of large cell populations for reliable quantification .Fourth, reporters are designed to detect DSB-repair either by loss or by induction of marker gene expression. The latter confers specificity because the expression is strictly dependent on a defined change in the reporter sequence . This coFinally, even though reporter constructs have proven extremely useful they do have limitations. First, the DSB is generated in a specific sequence context, which can impact repair pathway employment . Second,SceI nuclease target sequence. Moreover, it has also expanded the nuclease repertoire with a variety of blunt-cutting enzymes, the staggered cutting Cas12a/Cpf1, and Cas9 nickase variants that can be targeted to either strand (The advent of targetable nucleases like Cas9 has spurred the construction of novel reporter systems . It has r strand . Therefor strand . Given tIn addition to the development of new reporters, genome editing tools will facilitate targeted integration of reporter constructs. This will allow for comparison of repair pathway usage between genomic loci, for example, in hetero- and euchromatic regions, which is an active area of investigation . InteresIn conclusion, DSB-repair reporters have evolved from designated constructs to study HR, to complex multicolor tools that can measure repair by two or three pathways in one assay. This evolution is expected to continue, driven by Cas9-technology, ensuring that reporters will remain an essential element in the DSB-repair toolkit, as they have been for multiple decades.


WIF1, SFRP4, CD82, WNT2, WNT3, WNT5A, HOTAIR, CRNDE, and UCA1. Notably, HOTAIR is known to silence WIF1, and WIF1 inhibits the Wnt ligands to negatively regulate the pathway. The lncRNA CRNDE positively regulates WNT5A, while UCA1 positively regulates WNT2 and WNT3. We note that HOTAIR was unable to silence WIF1. CRNDE and UCA1 were found to be upregulated, which may explain the high expression of the WIF1 targets. Furthermore, 10 single-nucleotide polymorphisms (SNPs) were identified in five of the candidate genes above. A possible novel SNP in CD82, chr11:44619242T > C, was predicted to introduce a ZBTB7A binding site. These SNPs are hypothesized to contribute to aberrant and discrepant regulation of the Wnt pathway in a context of CRC pathogenesis. These findings collectively inform future research on diagnostics and therapeutics innovation in CRC.Colorectal cancer (CRC) is the third leading cause of death worldwide, comprising nearly 8% of cancer-related deaths per year. In South Korea, for example, CRC is the second most common cancer in men, and third in women. This study reports on the association of CRC with genetic variations in long noncoding RNAs, activators, and inhibitors of a cell proliferation pathway. Five normal colon mucosa tissue samples and their matched five-stage IV CRC samples were evaluated (dataset Gene Expression Omnibus accession: GSE50760). We identified more than 5000 differentially expressed genes (DEGs). The Wnt pathway had the greatest portion of DEGs, including activators, inhibitors, and associated long noncoding RNAs (lncRNAs), suggesting the importance of Wnt pathway in CRC. The following genes were aberrantly expressed:  There is a high lifetime risk of developing colorectal cancer (CRC): 4.7% for men and 4.4% for women Kweon, . The surLong noncoding RNAs (lncRNAs) are >200 bp, polyadenylated, and lack an open reading frame. lncRNAs have been reported to have major impact on regulation, epigenetic modifications, and splicing, among other effects in cell biology , urothelial carcinoma associated 1 (UCA1), and colorectal neoplasia differentially expressed (CRNDE). HOTAIR is one of the first known lncRNAs, which is located on chromosome 12 and comprises nearly 2 kb and six exons  to trimethylate histone H3 on lysine 27 (H3K27) and recruiting lysine-specific demethylase 1 (LSD1) to demethylate lysines 4 and 9 on histone H3.Some notable lncRNAs are HOX transcript antisense RNA . WIF1 is a Wnt antagonist and acts by binding to WNT ligands to prevent binding to the Fzd/LRP receptor complex , which is a member of the SFRPs and is an inhibitor of the Wnt pathway by targeting the WNT ligands. SFRPs contain a cysteine-rich binding domain, which gives SFRPs the capability to bind to Fzds. This results in a competitive binding between SFRPs and WNT ligands to bind to Fzd. In addition, SFRPs can bind directly with WNT ligands to inhibit their binding to Fzd, thus interfering with the Wnt pathway in two ways.Inhibitors of the Wnt pathway include Wnt Inhibitory Factor 1 (APC) gene and β-catenin. Mutations in APC cause a loss of function. APC is a key component in the destruction complex, which targets β-catenin. Mutations in APC can arise sporadically or originate from a hereditary illness, familial adenomatous polyposis (FAP). Mutations in β-catenin cause a loss of a phosphorylation site in the N-terminus, which is pivotal for the destruction complex to recognize, phosphorylate, and degrade β-catenin. The loss of this site, in turn, allows for constitutive signaling of the canonical Wnt pathway.There are various mutations that account for the upregulation of the Wnt pathway in CRC , initially published by Kim et al., in 2014. The original study used matched tissue samples from 18 patients with primary stage IV CRC cancer and liver metastasis. Three tissue samples from each patient were sequenced: primary CRC tissue, metastatic liver tissue, and normal colon tissue. All patients were treated in the Asan Medical Center in Seoul, South Korea, between May 2011 and February 2012 .In this study, five paired primary cancer tissue datasets and five normal tissue datasets were studied. The reads were paired-end reads, which generated two reads per sample, resulting in 10 normal tissue replicates and 10 cancer tissue replicates. These sequence reads were processed by splitting the ends and trimming and filtering poor-quality reads. The processed reads were then assessed for quality using FastQC v 0.11.2 (www.bowtie-bio.sourceforge.net/index.shtml) and TopHat v.2.1.0 (https://www.ccb.jhu.edu/software/tophat/index.html) ; the transcripts for all replicates are merged by CuffMerge v2.0.2 (www.cole-trapnell-lab.github.io/cufflinks/cuffmerge/) , and finally, the differential expression patterns were visualized using an R package, CummeRbund v3.2.3 (www.compbio.mit.edu/cummeRbund/)  from the processed RNA sequences, from paired normal and cancerous tissues. First, the processed reads were aligned to the human genome Hg38 using Bowtie v.2.2.6.0  was used. PANTHER is available online at lncRNA2Target (v 1.2) is a web tool that finds the lncRNA that targets the gene of interest or finds what genes particular lncRNA targets  (v 2.3.97) was used to visualize transcripts and sequences of the data  was found in IGV, Single Nucleotide Polymorphism Database (dbSNP) was used to determine the novelty or if the SNP was previously recorded , the set of upregulated genes, and the set of downregulated genes using the PANTHER Database.WNT9A, WNT3, WNT11, WNT5A, and WNT2 with fold-change values of −1.503, 1.503, 1.996, 2.261, and 5.722, respectively  is targeted by HOTAIR  for methylation. SFRP4  is targeted by CRNDE  for negative regulation by an unknown mechanism. WNT2  and WNT3  are targeted by CRNDE for positive regulation by an unknown mechanism. WNT5A  is targeted by UCA1  for positive regulation by an unknown mechanism.Six inhibitors, five WNT ligands, and their associated lncRNAs were found through lncRNA2Target database. The results are shown in HOTAIR is well studied and had a high fold-change value, the targets of HOTAIR were found with the PANTHER database , showed the opposite of the expected expression pattern. Under normal conditions, WIF1 and CD82 are expected to be silenced by HOTAIR; in this dataset, WIF1 and CD82 are upregulated  present in four of the sample datasets. SFRP4 had four SNPs, rs1802073 (missense) (SFRP4-M1), rs1052981 (3′UTR) (SFRP4-3), rs1132553 (synonymous) (SFRP4-S1), and rs1132552 (synonymous) (SFRP4-S2), which were present in 7, 5, 8, and 10 of the sample datasets, respectively. WNT5A had one SNP, rs669889 (3′UTR) (WNT5A-3), present in seven of the sample datasets. UCA1 had one SNP, rs7258210 (intron variant) (UCA1-IV1), present in seven of the sample datasets. CD82 had three SNPs, two previously identified rs7107335 (synonymous) (CD82-S1) and rs11541053 (missense) (CD82-M1), which were present in 4 and 10 of the sample datasets, respectively. A possible novel SNP (CD82-N) was also identified in CD82 at the location chr11:44,619,242 and was present in 10 of the sample datasets.The SNPs were arbitrarily named for function and simplicity. CD82, CD82-S1 and CD82-S2, both were found to be present in a splice site; however, CD82-M1 was found to be benign. The SNP in UCA1, UCA1-IV, was found to be in a transcription factor binding site. SFRP4-3 was found to be in a miRNA binding site. SFRP4-S1 and SFRP4-S2 were found to be in a splicing site. SFRP4-M1 was found to be possibly damaging. WNT5A-3 was found to be in a miRNA binding site. WIF1-S1 was not found to have any adverse effect.Using FuncPred prediction software, any possible adverse effect of the SNPs and the possible affected binding sites was predicted . For theBy investigating the SNP frequencies in the Asian population and comparing it with the global average, we hypothesized which SNP may play a role in cancer progression in stage IV CRC .There are intensive efforts to identify new molecular targets for CRC with an eye to diagnostics and therapeutics innovation. In the original sample set published in 2014 by Kim et al. using RNA-seq methods, they found 19 genes associated with CRC aggressiveness and developed a risk-score classifier to predict response to adjuvant chemotherapy in four independent cohorts. The present study offers several important leads in this context.The Wnt pathway can be divided into three subtypes: Wnt/β-catenin or canonical pathway, noncanonical planar cell polarity pathway, and noncanonical Wnt/Calcium pathways  pathway  and inhibitors (WIF1 and SFRP4) were upregulated. Further investigation showed that there were three lncRNAs associated with these activators and inhibitors, HOTAIR, CRNDE, and UCA1. Differentially expressed HOTAIR targets were extracted and examined whether HOTAIR effectively asserted its action. These results showed that WIF1 and CD82 expressed the opposite of the expected effect of HOTAIR. When examining genetic variations, 10 SNPs were found in WIF1, SFRP4, WNT5A, UCA1, and CD82. Nine of the 10 SNPs found have been previously recorded and 1 appears to be novel in CD82.Taken together, this study investigated the relationships between lncRNAs, activators, and inhibitors and how genetic variations in these players may affect cell proliferation pathways. It was found that the Wnt pathway was differentially expressed and within this gene set, several activators (WIF1 and CRNDE to activate WNT2 and WNT3, and between WIF1 and UCA1 to ultimately activate WNT5A. Furthermore, CRNDE is known to negatively regulate the Wnt inhibitor, SFRP4, and in turn, SFRP4 inhibits the WNT ligands. In this dataset, SFRP4 has four SNPs and is upregulated along with CRNDE. Therefore, the accumulation of the SNPs in SFRP4 may be responsible for its lack of function in inhibiting the WNT ligands and may be acted upon by CRNDE. Finally, the novel SNP may have created a ZBTB7A binding site, which may be the one of the causes for the lack of metastasis suppression by CD82. The SNPs accumulated in CD82 have indicated that they may contribute to its lack of metastasis suppressor ability.The aberrant expression of the activators and inhibitors coupled with the accumulation of these SNPs indicated a competition between WIF1, SFRP4, WNT5A, UCA1, and CD82 by DNA sequencing of South Korean male patients with stage IV CRC. Also, functional assays should be conducted to determine the efficiency of the Wnt pathway in the presence of WIF1, SFRP4, UCA1, and CRNDE. Although at the RNA level, these players are highly expressed; there may be post-transcriptional and post-translational modifications that are not shown by this analysis. Very little is known about the mechanism of action of lncRNAs UCA1 and CRNDE. Further investigation should delve into the functions of these lncRNAs to understand their precise role in the Wnt pathway. When the roles for UCA1 and CRNDE are elucidated, they may be potential therapeutic targets. Inhibiting UCA1 and CRNDE may allow WIF1 to effectively inhibit the Wnt pathways, which would decrease the cell proliferation, cell polarity, and migration.Because only transcriptomes from five South Korean males were analyzed in this study, future studies should confirm these findings with all the dataset samples available in the GEO Dataset GSE50760 and compare findings with females and males in other populations. In addition, future studies should confirm the mutation in SFRP4, DKK2, DKK4, APCDD1, and NOTUM. The full elucidation of the mechanisms and pathways of the inhibitors in the Wnt pathway would be a powerful tool for new diagnostics and therapeutics advances in the case of stage IV CRC.Finally, further research is needed on the mechanisms of the remaining differentially expressed inhibitors found in this dataset:


Using systems pharmacology analysis and molecular docking evaluation, 28 bio-active compounds and 10 potential therapeutic targets of XHP were identified. Functional analysis showed that the core therapeutic targets against TNBC were mainly involved in epidermal growth factor receptor (EGFR)-phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway to prevent cancer cell proliferation and angiogenesis, as well as to enhance cancer cell apoptosis. The in vitro proteomics analysis identified 153 differentially expressed proteins (DEPs), including HASP90AA1, AKT1, and EGFR, which were also identified as therapeutic targets against TNBC through systems pharmacology analysis. Protein function analysis showed that the DEPs were mainly involved in PI3K-AKT signaling pathway, which was consistent with the result of systems pharmacology, suggesting the reliability of systems pharmacology analysis. Taken together, these findings uncover the underlying mechanism of XHP against TNBC, and provide a scientific method for the rational development of traditional Chinese medicine.Xihuang pill (XHP), a traditional Chinese herbal formula, has been clinically used as an adjuvant therapy against triple-negative breast cancer (TNBC) via inhibiting cancer cell invasion and proliferation, as well as promoting cancer cell apoptosis. However, its anti-TNBC bio-active ingredients and possible mechanisms are still unclear. Herein, the hub bio-active compounds and underlying mechanisms of XHP against TNBC were systematically elucidated by integrating systems pharmacology approach and   The statistics from 2020 show approximately 276,480 women are diagnosed with BC and estimation of 42,170 women are expected to die of breast cancer . HoweverTrametes robiniophila Murr.) granules have long been clinically used as an adjuvant therapy in post-surgical treatment for stage I–III TNBC patients, and can effectively increase the disease-free survival and overall survival of patients furan-5-yl] acetate , cabraleone (MOL001093), morin (MOL000737), guggulsterol IV (MOL000996), myrrhanone A (MOL001027), myrrhanol C (MOL001026), (2 R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one , and quercetin (MOL000098) .Figure 33.1.5.To understand the molecular function of the hub targets, GO analysis and KEGG pathway enrichment were conducted. GO function enrichment analysis ) showed KEGG pathway enrichment analysis was performed to further investigate the underlying mechanism of XHP on the treatment of TBNC. The results showed that the hub targets were highly related to PI3K-AKT signaling pathway, Rap1 signaling pathway, HIF-1 signaling pathway, focal adhesion, cell cycle, and tumor-related pathways, such as melanoma, glioma, non-small cell lung cancer, pancreatic cancer, prostate cancer, and proteoglycans in cancer ). These 3.2.To further confirm whether the bio-active compounds of XHP will directly interact with the hub targets, the binding affinity between compounds and hub targets was evaluated by molecular docking. The results showed that the 28 bio-active compounds of XHP had a high binding affinity with the hub targets that less than −20 kJ/mol . For doc3.3.Based on systems pharmacology analysis and molecular docking evaluation, we proposed that XHP can exert anti-TNBC effects by preventing angiogenesis and cell proliferation and increasing apoptosis mainly through inhibiting the EGFR-phosphatidylinositol 3-kinase (PI3K)-AKT signaling pathway .Figure 63.4.1.Homo sapiens canonical protein database. After quality evaluation, a total of 88,727 MS/MS spectra  were harvested. Of these spectra, 18,161 peptides  and 2,801 protein groups  were identified (Table S3).To validate the reliability of systems pharmacology, the triple-negative breast epithelial cells MDA-MB231 were treated with 12 mg/ml XHP for 24 h and following analyzed by label-free LC-MS/MS. The raw MS/MS spectra data were searched using MaxQuant against UniProt-SwissProt 3.4.2.P ≤ 0.05 and a ratio ≥1.2 or ≤0.83, a total of 153 DEPs including 118 down-regulated and 35 up-regulated DEPs were obtained by comparing with normal triple-negative MDA-MB231 breast epithelial cells  between XHP treated and normal triple-negative MDA-MB231 breast epithelial cells, a T test comparing analysis was performed. Based on the criterion of al cells , Table S3.4.3.To further understand the molecular function of the DEPs, GO and KEGG pathway enrichment were performed. GO enrichment analysis showed that the DEPs were mainly involved in cell adhesion, protein phosphorylation or autophosphorylation, viral process, MAPK cascade, angiogenesis, VEGF receptor signaling pathway, glucose metabolic process, and regulation of translational initiation , Table SKEGG pathway enrichment analysis of the DEPs showed that multiple cancer-related pathways were highly enriched, including the signaling pathways of PI3K-AKT, mitogen-activated protein kinase (MAPK), Rap1, insulin, forkhead box O (FoxO), ErbB, gonadotropin-releasing hormone (GnRH), T cell receptor, hypoxia-inducible factor-1 (HIF-1), prolactin, estrogen, VEGF, and mammalian TOR (mTOR) , Table S4.The potential bio-active ingredients and anti-TNBC mechanisms of XHP were dissected by integrating systems pharmacology and proteomics. Using systems pharmacology analysis and molecular docking evaluation, multiple bio-active compounds, and potential therapeutic targets of XHP against TNBC were identified. Meanwhile, through global protein profile of triple-negative MDA-MB231 cells treated with XHP, a large number of DEPs were identified including three intersected with systems pharmacology analysis.in vivo or in vitro [Based on systems pharmacology analysis, a total of 28 bio-active compounds and 10 potential therapeutic hub targets of XHP were identified. Among these compounds, eight compounds were linked with more than three hub targets. Of these compounds, quercetin is one of the plant-derived flavonoids which is reported to possess effective anti-cancer effects on multiple molecular subtypes of breast cancer models in vitro . Many stin vitro . It is qin vitro . As a nain vitro  or supprin vitro . A recenin vitro . Naringein vitro . In addiin vitro , which cin vitro ,62, whicin vitro ,63–65. Min vitro ,10,23,30To confirm the reliability of systems pharmacology analysis, a global protein profile of TBNC MDA-MB231 cell treated with XHP was analyzed using label-free LC-MS/MS. By comparing with a normal cell, a total of 153 DEPs were obtained. KEGG function enrichment analysis showed that the DEPs were involved in several signaling pathways, including the signaling pathways of PI3K-AKT, MAPK, FoxO, VEGF, and mTOR. Of these pathways, PI3K-AKT signaling pathway was the most significantly enriched, which was consistent with the result of systems pharmacology analysis. Of note, XHP treatment can downregulate 25 different components of PI3K-AKT signaling pathway ). These In addition, several limitations exist in the present study. For example, dosage of bio-active compounds within the formula was not addressed to verify potential activity in the context of treatment as a supplement to standard therapeutic approaches, just one breast cancer cell line MDA-MB231was used as the sole method of validation of the predictions of systems pharmacology prediction, only three major DEPs were obtained through the intersection between systems pharmacology analysis and proteomics verification, and negative bio-active compounds were not selected to be subjected to systems pharmacology prediction. Therefore, it is necessary for us to address these limitations in further study.5.Collectively, the results of systems pharmacology and proteomics indicated that XHP can effectively exert anti-TNBC activities through synergistic effects of multiple compounds, targets, and signaling pathways. We proposed that XHP can function as an effective TBNC inhibitor by preventing cancer cell proliferation and angiogenesis, as well as enhancing cancer cell apoptosis mainly through inhibiting EGFR-PI3K-AKT signaling pathway . Taken tClick here for additional data file.


FFQ assess habitual dietary intake and are relatively inexpensive to process, but may take up to 60 min to complete. This article describes the validation of the Flower-FFQ, which consists of four short FFQ measuring the intake of energy and macronutrients or specific (micro)nutrients/foods that can be merged into one complete daily assessment using predefined algorithms.n 401). Urinary N (n 242) and K excretions (n 361) were measured. We evaluated: (1) group-level bias, (2) correlations and (3) cross-classification.Participants completed the Flower-FFQ and validated regular-FFQ  years.v. regular: 7953 v. 8718 kJ/d) and macronutrient intakes  were comparatively similar. Spearman correlations between Flower-FFQ and regular-FFQ ranged from 0·60 to 0·80 for macronutrients and from 0·40 to 0·80 for micronutrients and foods. For all micronutrients and foods, ≥ 78 % of the participants classified in the same/adjacent quartile. The Flower-FFQ underestimated urinary N and K excretions by 24 and 18 %; 75 and 73 % of the participants ranked in the same/adjacent quartile.Flower-FFQ1, Flower-FFQ2, Flower-FFQ3 and Flower-FFQ4 were completed in ±24, 9, 8 and 9 min , respectively. The regular-FFQ was completed in ±43 min. Mean energy (flower Completing the Flower-FFQ required 50 min with a maximum of 25 min per short FFQ. The Flower-FFQ has a moderate to good ranking ability for most nutrients and foods and performs sufficiently to study diet–disease associations. In many well-known cohort studies, the food FFQ has been the method of choice to assess dietary intake–5. FFQ capture individual habitual long-term dietary intake and are relatively easy and inexpensive to process. However, FFQ may also be time-consuming to develop and complete. To illustrate, an extensive 200-item FFQ addressing the intake of energy, macronutrients and the majority of micronutrients is usually completed in approximately 45–60 min. This completion time is considered burdensome by many respondents and often results in the return of incomplete questionnaires. Additionally, questions at the end of a questionnaire are also more likely to be affected by measurement error compared with questions in the beginning of a questionnaire. To reduce participant burden and associated measurement error, we decided to develop a new type of FFQ for the Lifelines Cohort Study, a multi-disciplinary prospective population-based cohort study including over 160 000 Dutch citizens.Prospective cohort studies provide the unique opportunity to characterise potential risk factors before disease onset,10. Based on the number of items queried in the Flower-FFQ, the estimated time needed to complete all four FFQ would be approximately 60–75 min. The additional time needed to complete the Flower-FFQ compared with a ‘regular’ FFQ relates to the fact that the four Flower-FFQ contain overlapping items, which is crucial to ensure proper linkage. Therefore, we assumed that the time needed to complete the Flower-FFQ would be about 20–25 % more than the time needed to complete a comparable regular-FFQ, but that the administration mode  would be more convenient for the participant and less sensitive to errors.This new-FFQ, called Flower-FFQ, was designed to derive a valid long-term estimate of the habitual dietary intake using an innovative approach that combines one main questionnaire (representing the heart of the flower) with three short complementary questionnaires  administered at different time points. Each questionnaire focusses on different nutrients and/or foods Fig. . Within , which provides an impression of the strength and direction of association. Ideally, these correlation coefficients are supported by other validity measures, such as cross-classification data showing whether or not participants are classified in the same category with the two methods , and t tests or group-level bias ,11. To evaluate actual validity, the use of biomarkers is essential. However, to date there are still just a few validated nutritional recovery biomarkers, which include urinary N, K, Na and doubly labelled water to estimate absolute intakes of protein, K, Na and energy, respectively.Validation of this newly developed Flower-FFQ is essential to show to what extent measurement error may interfere with diet–disease relationships observed in future studies that use this FFQ. The relative validity can be examined by comparing the FFQ with a reference method. Usually this comparison is made by exploring correlations between the new FFQ and the reference method,13. Participants of the Nutrition Questionnaires plus study completed a Flower-FFQ, a validated regular-FFQ,15 and provided urine to determine urinary N and K excretions as commonly accepted recovery markers for the intake of protein and K. This article describes the development of the Flower-FFQ for the Lifelines Cohort Study and its external validation within the Nutrition Questionnaires plus study.Within the Lifelines Cohort Study, no other dietary assessment method than the Flower-FFQ has been administered and as such no reference method is available to quantify the habitual dietary intake within that cohort. Therefore, an external validation study on the Flower-FFQ was conducted within the Nutrition Questionnaires plus study and the Nutrition Questionnaires plus study. Participants were recruited in the surroundings of Wageningen, the Netherlands. Participants were eligible for participation in the study when they were between 20 and 70 years of age at the time of recruitment, competent to make own decisions and provided a written informed consent. Participants were not eligible when they were unable or unwilling to comply with the study procedures, enrolled in another study in same period or not able to read and speak Dutch. All participants gave written informed consent before commencement of the study.Between June 2011 and February 2013, 2048 Dutch men and women aged 20–70 years were enrolled in the National Dietary Assessment Reference Databasen 404). Participants with unreliable or incomplete Flower-FFQ and/or regular-FFQ data  or >17 573 kJ , women <2092 kJ  or >14 644 kJ ) were excluded (n 3) and as such 401 participants were included in the analyses. All participants gave written informed consent. Analyses on protein intake and urinary N excretion could be performed in a subsample of 242 participants; 361 participants provided data on K intake and urinary K excretion. The study was approved by the ethical committee and was conducted according to the declaration of Helsinki.The current analyses were conducted using data of participants with complete dietary data – including data obtained with the Flower-FFQ and regular-FFQ  (FFQ3) and the vitamin ACE FFQ providing information on retinol equivalents, vitamin C, vitamin E and dietary fibre , and fats and oils) (FFQ4). Figure . Specifically, for each food the contribution to the absolute intake and the between-person variability of the selected nutrient was calculated. Foods contributing to at least 80 % cumulative contribution of absolute intake or explaining at least 80 % of the between-person variability were included in the Flower-FFQ. Combined, the four FFQ cover 212 items and ≥92 % of the absolute level of intake and ≥90 % of the between-person variability of each nutrient as assessed by 2-d food records in the DNFCS 1998. Questions pertaining to frequency were completed by selecting answers ranging from ‘never’ to ‘6–7 d per week’. Portion sizes were estimated using natural portions and commonly used household measures. Average daily energy and nutrient intakes were calculated by multiplying consumption frequency by portion size and nutrient content per gram, as indicated in the Dutch food composition table of 2011. In general, the main FFQ assessed the frequency and number of servings of all major food groups according to the Dutch food composition table, and the three complementary FFQ assessed the frequency of the food subgroups. The intake of specific nutrients or food subgroups as assessed by the complementary FFQ was calculated by combining the number of servings of the major food reported in the main FFQ with the specific type reported in the complementary FFQ. For instance, the main FFQ assessed the consumption frequency and number of servings of rice, and the complementary FFQ identified the type of rice, that is, white or brown. Prior to data collection, we decided that in case of inconsistencies between the main FFQ and complementary FFQ, the data of the main FFQ would be considered superior, which were amongst others based on the theory that question ordering can impact retrieval when asking about a series of events that occur over time, for example, remembering one event may help to remember the next (or previous) event in the sequence. As the main FFQ registered the overall habitual diet, without many details, we felt that this FFQ was the most efficient one to help participants remember their food intake. To illustrate, if the main FFQ indicated the consumption of a food while it was not reported in the complementary FFQ, the particular food subgroups received a weighted consumption average. If the main FFQ indicated that a food was not consumed while it was consumed according to a complementary FFQ, the food was recorded as not consumed. However, data checks eventually showed that inconsistencies between the main FFQ and petal FFQ appeared to be negligible. To illustrate, in the Flower-FFQ vegetables were covered by nineteen items, and as such we assumed that this food group was at a relatively high risk of being affected by inconsistencies between the main and petal FFQ. Despite that, we only identified five participants reporting a ‘zero-intake’ in the main FFQ, while they did report vegetables in the petal FFQ. However, in the petal FFQ the reported vegetables were raw vegetables, which were intentionally not asked in the main FFQ. As such, the item on raw vegetables was completely calculated based on the petal data (including the information on grams), and thus no inconsistencies were observed for vegetable intake. Subsequently, we did a similar analysis for the food group bread, which was covered by eleven items. Data of the main FFQ showed three ‘zero-intake’ reporters, of whom none reported bread consumption in the petal FFQ. Finally, we explored potential inconsistencies for rice and pasta; these foods are usually consumed on 1–2 d per week as part of the Dutch diet. Rice and pasta were both covered by two items that distinguished between whole wheat and plain types. For pasta, twenty-six ‘zero-intake’ reporters were identified and for rice there were sixty-eight ‘zero-intake’ reporters; none of them reported either pasta or rice in the petal FFQ. The Flower-FFQ was administered online, randomly distributed over the week, via the open-source survey tool LimeSurveyTM (LimeSurvey Project Team/Carsten Schmitz.) within a period of 2 years, assuming stable food consumption patterns over time. This assumption is supported by stable BMI measures over the course of the current study  25·6± (3·7) (n 401), 25·4± (3·6) (n 399) and 25·6± (7·5) (n 301), respectively). The main FFQ was completed ±5 months following baseline, for example, participants included in June 2011 completed the main FFQ in November 2011. About 10 months later (Augusts 2012), these participants completed petal 1, followed by petal 2 another year later (September 2013). Finally, petal 3 was completed 1 month after the completion of petal 2 (October 2013). A sample of the Flower-FFQ (in Dutch) can be obtained by contacting the authors.The name Flower-FFQ is derived from its design. The FFQ consists of one main FFQ FFQ1), which symbolises the heart of the flower and measures the intake of energy and macronutrients. The three complementary FFQ symbolise the flower petals and focus on specific (micro)nutrients and food components, that is, the fatty acids FFQ providing information on SFA, MUFA, PUFA, EPA and DHA  FFQ2); the B-vitamins FFQ providing information on vitamin B, which s; the B-vr 0·65 with phone-based 24-h dietary recalls), fats (r ranges between 0·24 and 0·33 for adipose tissue), dietary fibre and a selected number of vitamins and food groups ,15,20. This FFQ covered ≥ 96 % of the absolute level of intake and ≥ 95 % of the between-person variability of each nutrient as assessed in the DNFCS of 2011. Questions relating to consumption frequency were followed by answer categories ranging from ‘never’ to ‘6–7 d per week’. Portion sizes were estimated using natural portion sizes and commonly used household measures. Subsequently, energy and nutrient intakes were calculated through multiplying the consumption frequency by portion size and nutrient content (grams) as indicated in the Dutch food composition table of 2011. The FFQ was administered online, randomly distributed over the week, via the open-source survey tool LimeSurveyTM; the first participants completed this regular in December of 2011, about 1 month following the main FFQ.Habitual dietary intake was also assessed by a validated semi-quantitative regular-FFQ including 183 items, where the reference period of the FFQ was the previous month. Previous validation studies of this FFQ showed acceptable to good correlation for the intake of energy . Urinary protein was calculated with the following formula: 6·25 × (urinary N/0·81), accounting for approximately 19 % faecal and skin losses. Urinary K was measured with an ion-selective electrode on a Roche 917 analyser, assuming a urinary excretion of 81 % for K. As the Observing Protein and Energy Nutrition study did not observe an effect of the exclusion of participants with incomplete urines on correlations and attenuation factors, our primary analyses on protein and K were conducted using the data from all urine samples. Secondary analyses confirmed that also within our sample excluding those with a para-aminobenzoic acid recovery < 85 % did not substantially alter the results.In this validation study, data of a single 24-h urine collection were used in order to determine urinary N and K excretions as commonly accepted recovery markers for the intake of protein and KTM. Questionnaires included items on demographics, educational attainment and smoking habits,13. Physical examinations were also conducted at baseline at the study centre according to a standardised protocol by a well-trained staff. Height was measured with a stadiometer (SECA) to the nearest 0·1 cm, without shoes. Weight was measured on a digital scale (SECA) to the nearest 0·1 kg, without shoes and sweaters and empty pockets. BMI was calculated as weight/heigth2.Health and lifestyle questionnaires were completed at baseline via the online open-source survey tool LimeSurveysd) or n with percentages (n (%)). Means with sd are also provided for intakes of energy, macronutrients and food groups. Macronutrients and ethanol were additionally expressed in energy densities to adjust for energy. Although the main focus of this validation study was on the ranking ability of the Flower-FFQ, absolute intake differences between the Flower-FFQ and regular-FFQ were expressed as group-level bias : (mean intake Flower-FFQ/mean intake reference method) × 100 – 100. For the intake of protein and K, the level of bias was evaluated by plotting the distribution of the self-reported intake against the distribution of the intake based on urinary excretion. The ranking ability of the Flower-FFQ was assessed by dividing the intake of nutrients and foods as assessed by Flower-FFQ and regular-FFQ over quartiles after which we examined whether persons were ranked into the same, adjacent or extreme quartile. If ≥ 50 % of the participants were classified in the same quartile, this was considered a good outcome. Additionally, Pearson and Spearman rank correlations were calculated and classified according to the cut-offs as suggested by Lombard and colleagues, that is, good in case of r ≥ 0·50, acceptable in case of r 0·20–0·49 and poor in case of r < 0·20. Nevertheless, given the high probability of correlated errors between the Flower-FFQ and the reference FFQ, we feel that these cut-offs should be interpreted with caution and that correlations should be at least in the upper regions of the acceptable range. All statistical analyses were performed using SAS 9.3.Participant characteristics are reported as mean with SD (mean ± sd age of 54± (11) years, 56 % were ≥ 55 years, 48 % were men and 51 % had a BMI ≥ 25 kg/m2. Levels of educational attainment were predominantly medium (31 %) or high (61 %). Participants with a history of myocardial infarction (2 %), stroke (1 %), diabetes (2 %) or cancer (6 %) were rare. Participants completed the Flower-FFQ1, Flower-FFQ2, Flower-FFQ3 and Flower-FFQ4 in ±24, 9, 8 and 9 min , respectively. The regular-FFQ was completed in ±43 min.Population characteristics of 401 men and women are shown in Table For the Flower-FFQ, the covered level of intake ranged between 93 and 95 % for energy and macronutrients and 93 and 97 % for micronutrients; the covered variance of nutrient intake ranged between 93–97 % and 95–100 %, respectively Table . The covv. 0·09 g/d), DHA (0·16 v. 0·11 g/d) and ethanol (8 v. 12 g/d). Although group-level bias was modest for most nutrients, bias generally pointed towards lower nutrient intake estimates as assessed by the Flower-FFQ. Spearman correlations were r 0·6–0·8 for all macronutrients and macronutrient-fractions (g/d) and r 0·4–0·8 for micronutrients. Moreover, the Flower-FFQ classified ≥ 80 % of the participants in the same or adjacent quartile as the regular-FFQ for all nutrients under study except retinol (78 %). Misclassification ≥ 5 % in the extreme quartile did not occur for any of the nutrients under study.The Flower-FFQ and regular-FFQ showed relatively similar mean intakes for energy and most macronutrients (group-level bias ≤ 10 %) Table . Percentv. 71 g/d), bread (130 v. 129 g/d), eggs (13 v. 14 g/d), fruit (177 v. 190 g/d), cereals (8 v. 8 g/d), legumes (13 v. 14 g/d), vegetables (172 v. 167 g/d), sweets (26 v. 29 g/d), tea (284 v. 275 g/d), meat (67 v. 68 g/d) and fruit juice (47 v. 50 g/d) showed the most comparable mean absolute intake estimates for the two FFQ (Table v. 167 g/d), soft drinks (27 v. 20 g/d), savoury snacks (25 v. 35 g/d), nuts/seeds (14 v. 20 g/d) and fish (30 v. 24 g/d). Spearman correlations ranged from r 0·4 to 0·6  to r ≥ 0·8 (tea). The Flower-FFQ classified ≥ 80 % of the participants in the same or adjacent quartile as the regular-FFQ for all food groups, except vegetables (79 %).For food groups, potatoes (71 se 1·2) with the mean urinary N excretion (98 se 1·6) showed a ˜24 % underestimation of protein intake, which is visually displayed in Fig. n 242). In addition, 75 % of the participants were classified in the same or adjacent quartile when comparing the FFQ and urine data; corresponding Pearson and Spearman correlations were 0·41 and 0·40. The mean self-reported K intake was 3169 mg (se 39), whereas the urinary K excretion was quantified at 3878 (se 64) mg, indicating an 18 % underestimation by the Flower-FFQ (n 361). The Flower-FFQ and urinary data classified 73 % of the participants in the same or adjacent quartile and 3 % in the extreme quartiles; Pearson and Spearman correlations between the two methods were r 0·33 and r 0·37 , the ranking ability of the Flower-FFQ was promising for most nutrients and foods.,25. Nevertheless, the regular-FFQ has been shown to have a good ranking ability (r 0·82) with respect to the estimated energy intake as compared with the actual energy intake  among 516 men and women participating in controlled dietary intervention studies. Moreover, an acceptable to good ranking ability has been observed for a broad variety of nutrients and food groups using multiple 24-h recalls (n 128). Finally, Feunekes and colleagues showed strong Pearson correlations for total fat (r 0·78) and saturated fat intakes (r 0·75) as measured by the regular-FFQ and dietary history; correlations between adipose tissue fatty acids and regular-FFQ were r 0·57 for linoleic acid and r 0·52 for PUFA in participants with a stable body weight. Given these previous validation results of the regular-FFQ as well as the fact that we did assess actual validity by means of urinary N and K, we feel that the current validation study provides solid background data on the performance of the Flower-FFQ. The second methodological issue that needs to be mentioned is that the Flower-FFQ was administrated over a 2-year period. Although partial correlations between dietary variables adjusted for assessment date of the FFQ did not differ from unadjusted correlations, we can merely speculate about potential time effects of the order of administration of the different FFQ in relation to the collected dietary data. However, given the fact that the inclusion of participants was spread between June 2011 and February 2013, where the FFQ were more or less randomly distributed over the year, we do not assume major time effects. Third, our analyses were conducted using data of a subsample (n 401) of the total study population (n 2048), which comprises slightly older participants (54 v. 51 years) and a lower proportion of men (48 % v. 52 %). Fourth, one must also bear in mind that our population is higher educated than the general Dutch population (12) and that previous analyses using the National Dietary Assessment Reference Database database have shown higher attenuation factors among those with a higher educational attainment. Thus, validity measures of the Flower-FFQ may be lower in populations with a lower educational attainment. Although our sample was mainly high educated  or medium  educated, comparing Spearman correlations for these two groups indeed showed substantial differences  for some of the key nutrients and food groups under study, particularly for total fat, cheese, fish and meat. Further analyses within a more diverse population with respect to educational attainment are needed to draw more definite conclusions on this aspect. Finally, a strength of the current study is that we used multiple statistical approaches to assess the validity of the FFQ, which has been suggested to be most optimal to assess the robustness of the validation process,11.Before elaborating on the results of this validation study, several methodological issues warrant attention. First, a validated regular-FFQ was used as the reference method to evaluate the Flower-FFQ. As both FFQ rely on memory, same food composition tables and similar measures to estimate portion sizes, the true performance of the Flower-FFQ may represent an overestimation due to correlated errors. Repeated measures of biomarkers, 24-h dietary recalls or diet records share less correlated errors with the questionnaire under study and would have been more suitable reference methods. The difference in EPA and DHA intake between the two FFQ was also reflected by a difference in fish intake. As both FFQ included eleven highly comparable food items to assess the intakes of EPA and DHA, absolute intake differences are unlikely to be explained by differences in design between FFQ. However, the timing of the two methods did not fully overlap and may therefore account for some of the difference between the methods , that is, the Flower-FFQ assessing fish intake (FFQ2) was administered between August 2012 and March 2015, whereas the regular-FFQ was completed between December 2011 and August 2014. For ethanol, the Flower-FFQ yielded lower intakes than the regular-FFQ; this difference was also reflected in a lower intake of alcoholic beverages. DNFCS data showed median ethanol intakes of 16·1 and 3·7 g/d for men and women, which was 8·9 and 4·6 g/d in our sample. Again, the number of items assessing ethanol intake of the regular-FFQ and Flower-FFQ was rather similar. However, the timing of both methods did not fully overlap, the regular-FFQ had a slightly higher covered (variance in) nutrient intake than the Flower-FFQ and the question structure of the FFQ somewhat differed. With respect to the latter, the regular-FFQ quantified the consumption frequency of each type of alcoholic beverage separately, whereas the Flower-FFQ first quantified the consumption frequency of the total number of alcoholic beverages consumed and thereafter identified the type of alcoholic beverage consumed. Particularly this questionnaire structure may account for some of the observed differences between the two questionnaires, which has been illustrated by a previous review on alcohol intake assessment. Specifically, directly assessing consumption frequency of specific alcoholic beverages resulted in 19 % higher alcohol intake estimates compared with a situation in which first the total number of alcoholic beverages was assessed followed by a more detailed assessment of the specific types of beverages consumed. Nevertheless, despite these differences in – rather low – absolute EPA, DHA and ethanol intake levels, these data still provide valuable information for epidemiological purposes. Namely, in nutritional epidemiology, the ranking of participants according to their intake levels is usually more relevant than absolute intakes. Relating to this ranking ability, correlations and cross-classification of the Flower-FFQ with the regular-FFQ showed good results. Correlations for most macronutrients and macronutrient-fractions in g/d were r 0·6–0·8 and 85 % up to 94 % of the population ranked in the same or adjacent quartile as compared with the regular-FFQ. Moreover, for the specific nutrients with a relatively high rate of misclassification in absolute intakes, correlations and cross-classification results were r 0·6–0·8  as well. Thus, despite absolute misclassification, such variables can be confidently used for epidemiological purposes. This is further accentuated by the fact that the validity measures of the Flower-FFQ are comparable to the results of previous studies exploring the validity of Dutch FFQ,26,29,30. To illustrate, compared with the FFQ-NL1.0, the Flower-FFQ showed comparable or higher correlations for energy (r 0·68 v. r 0·43), macronutrients , ethanol (r 0·79 v. r 0·77) as well as EPA (r 0·63 v. r 0·33) and DHA (r 0·65 v. r 0·28). Cross-classification results for both FFQ were rather comparable as well. However, we do need to indicate that the FFQ-NL1.0 was validated against multiple 24-h recalls, whereas we used an FFQ as the reference method. Due to more correlated errors with the reference method, our validity measures are therefore probably inflated.Our results for macronutrient(-fractions) (g/d) showed high group-level bias for EPA, DHA and ethanol. DNFCS data show median EPA/DHA intakes of Dutch men and women around 0·10 and 0·09 g/d, which is in line with the estimates resulting from the regular-FFQ12, showing lower Flower-FFQ estimates for retinol, vitamin B2, vitamin B6 and folic acid and vitamin E, and higher estimates for vitamin B12 and vitamin C. However, with correlations and cross-classification results for micronutrients ranging from r 0·47 (78 % in same or adjacent quartile) for retinol to r 0·65 for vitamin C (87 % in same or adjacent quartile) and r 0·62 (86 % in same or adjacent quartile) for Ca, results are still well within the range as suggested by Willet and colleagues (r 0·4–0·7). Moreover, also for the micronutrients our validity measures are generally in line with previous validation studies of Dutch FFQ, for instance when comparing the Flower-FFQ with the FFQ-NL1·0, vitamin B6 showed correlations of r 0·46 v. r 0·28, folic acid of r 0·58 v. r 0·30, vitamin B12 of r 0·56 v. r 0·28 and Ca of r 0·62 v. r 0·42.As can be expected, the absolute intake differences for micronutrients were larger and more diverse than for macronutrients. Group-level bias percentages ranged from –17·6 % for retinol equivalents to 15·9 % for vitamin B,26. The most notable results are those for water and soft drinks. The explanation for the extreme misclassification rate of water is clear. Whereas the regular-FFQ only assesses bottled water, the Flower-FFQ assesses both tap and bottled water. The discrepancy for soft drinks may be explained by the fact that the Flower-FFQ queries for both regular soft drinks and energy drinks, whereas the regular-FFQ only queries for regular soft drink. As for nutrients, cross-classification and correlations of all other food groups were generally very acceptable with about half of the food groups showing moderately strong associations, and about half of the food groups showing moderate correlations. In line, cross-classification results were generally good where only vegetables showed a comparability below 80 % in the same or adjacent quartile .Our results on food groups are also fairly comparable to preceding FFQ validation studies. For K, the Flower-FFQ showed a substantially higher underestimation than the 5–6 % observed in a pooled analyses by Freedman and colleagues. The timing of the urine sampling v. the assessment period of the FFQ did not fully overlap and we only collected a single urine sample, which may explain the higher rates of underestimation as compared with the previous validation studies. Moreover, it needs to be mentioned that the Flower-FFQ was not specifically developed to assess K. Nevertheless, for both nutrients the ranking ability was acceptable, that is, 75 % (r 0·41) and 74 % (r 0·33) of the participants were classified in the same or adjacent quartile.In this validation study, we also had the opportunity to compare the intakes of protein and K to their level of urinary excretion, which indicated a ˜24 and 18 % underestimation in protein and K intake. For protein, our results are within the range of results as observed in a pooled analyses of five studies by Freedman and colleagues showing a 10–29 % underestimation for proteinIn conclusion, although group-level bias was relatively high for some nutrients, all nutrients and foods showed a good ranking ability, which suggests that the Flower-FFQ is a suitable tool to study a wide variety of diet–disease associations.


Narcissus tazetta flowers can be anticipated due to their biochemical origin, as well as their similar biological role. Despite the obvious aesthetic and ecological significance of these colorful and fragrant components of the flowers and the molecular profiles of their pigments, fragrant formation has addressed in some cases. However, the regulatory mechanism of the correlation of fragrant components and color patterns is less clear. We simultaneously used one way to address how floral color and fragrant formation in different tissues are generated during the development of an individual plant by transcriptome-based weighted gene co-expression network analysis (WGCNA). A spatiotemporal pattern variation of flavonols/carotenoids/chlorophyll pigmentation and benzenoid/phenylpropanoid/ monoterpene fragrant components between the tepal and corona in the flower tissues of Narcissus tazetta, was exhibited. Several candidate transcription factors: MYB12, MYB1, AP2-ERF, bZIP, NAC, MYB, C2C2, C2H2 and GRAS are shown to be associated with metabolite flux, the phenylpropanoid pathway to the production of flavonols/anthocyanin, as well as related to one branch of the phenylpropanoid pathway to the benzenoid/phenylpropanoid component in the tepal and the metabolite flux between the monoterpene and carotenoids biosynthesis pathway in coronas. It indicates that potential competition exists between floral pigment and floral fragrance during Narcissus tazetta individual plant development and evolutionary development.A link between the scent and color of  Narcissus tazetta var. chinensis belongs to the Amaryllidaceous family and is a perennial bulbous plant widely cultivated in East Asia and China, and is one of the ten most famous traditional flowers in China, with its beautiful flower and rich fragrance serving high ornamental value [The cultivated variety “Jinzhanyintai” of al value ,2.Flower color and scent are not only commercially important, in which they influence the yield and quality of many crops, but they are also important in their commercial appeal. In nature, the color and fragrance of flowers are two of the main characteristics adopted for plants to attract pollinators to ensure the reproductive success of the plant ,4,5,6,7.Petunia [Arabidopsis into Petunia can cause the simultaneous enhancement of both branches of the phenylpropanoid pathway, leading to the production of color and scent in flowers [Petunia have revealed a conserved mechanism for the formation of benzenoid/phenylpropanoid components and flavonol/anthocyanin pigmentation in petals. Pigments of anthocyanin are only produced in the overlapping expression domains of the the R2R3-MYB and bHLH coregulators of anthocyanin biosynthetic genes [PhMYB12 locus encodes an R2R3-MYB transcription factor that regulates the expression of CINNAMIC ACID 4-HYDROXYLASE (C4H), 4-COUMARATE COA LIGASE (4CL) and, FLAVONOL SYNTHASE (FLS), which presumably redirects metabolite flux from anthocyanin biosynthesis to the production of colorless flavonoids, indicating that a low level of FLS expression in the petal lobe and a consequent absence of the spatial patterning in Petunia flowers [A link between pigment and fragrance can be anticipated due to their biochemical origin, as well as their similar biological role. The diversion of metabolic flux from one branch of phenylpropanoid pathway to another has been reported in Petunia . The tra flowers . Studiesic genes ,29. Furt flowers . DespiteNarcissus tazetta by transcriptome-based weighted gene co-expression network analysis (WGCNA). Several candidate transcription factors are shown to associate the metabolite flux of the phenylpropanoid pathway with the production of colorless flavonols, from one branch of the phenylpropanoid pathway to the benzenoid/phenylpropanoid component in the tepal, and the metabolite flux between monoterpene and carotenoids biosynthesis pathway in the corona. It indicates that the potential competition exists between floral pigment and floral fragrance during the individual plant development of Narcissus tazetta and evolutionary development.In this study, we simultaneously use one way to address that the floral pigmentation and fragrance formation are generated during the development of an individual plant, and that their patterns, which are diversified among tissues, are generated from different gene expressions during development. Using this approach, we analyzed a spatiotemporal pattern variation of flavonols/carotenoids pigmentation and fragrant component between the tepal and corona tissues of Narcissus tazetta cultivated variety “Jinzhanyintai”, flower-development processes including the tepal and corona of Narcissus were observed during a full growing period. Flower buds sprout in bulbs after summer dormancy; the newly formed flower buds are nearly colorless. After five days of planting, bulbs grow shoots with sheathed leaves and after 20-day spathes are completely dehiscent; the color of tepals gradually fades, turning from deep-green to white [In order to characterize the floral pigment and fragrant component of the Narcissus tepal and corona were carotenoids and flavonols only and anthocyanin was not present [Narcissus tepals and coronas during flower development, we used a powerful analytical tool, HPLC, according to the method [E)-3-hexenyl acetate significantly increased in the coronas from C2 to C3 and then decreased in C4 (E)-ocimene, benzyl acetate, heptanal, and y-n-heptyl butyrolactone were increasingly emitted both in the tepals and coronas -oc coronas F. Phenyl coronas F.Narcissus , and the transcriptome data of the tepals at the T1, T2, T3 and T4 stages were retrieved from our previously published data [p < 0.05 was defined as the sample-specific module, thus, 11 tissue-specific modules were obtained in 9 samples, as shown in To discover the structure of the pigment and fragrance metabolic pathways and the relationship between flower color and scent formation during flower development, a transcriptome-profiling analysis of the tepals and coronas of hed data . The modIn order to gain insight into the metabolic pathways that play important roles during the development of the tepal and corona, KEGG enrichment analysis was carried out in the genes on the above specific modules, and a significant enrichment pathway was screened (Q < 0.05). The results showed that in tepals: a “plant hormone signal transduction” pathway was significantly enriched at the T0 stage; the “photosynthesis, metabolism of purine and chlorophyll, and biosynthesis of flavonoids” pathway was enriched at the T1 stage; the “phenylpropanoid biosynthesis and aromatic compounds degradation” pathways were enriched at the T3 stage; and the “carotenoid biosynthesis” pathway was enriched at the T4 stage B. In corNarcissus flower, transcriptome datasets from our previous work [PHENYLALANINE AMMONIA LYASE (PAL) and 4CL (c19257) were upregulated in both the tepal and corona during flower development, while C4H (c33958), CHALCONE SYNTHASE (CHS), and FAVANONE 3-HYDROXYLASE (F3H) were downregulated in both the tepal and corona from T1 to T4. However, DIHYDROFLAVONOL 4-REDUCTASE (DFR) and FLS were upregulated in corona, although FPKM values were low, one of C4H  stably maintained a high level in coronas during flower development (AMINE OXIDASE (AOC3), PHENYLACETALDEHYDE REDUCTASE (PAR), and 3-KETOACYL-COA THIOLASE (KAT1) genes were up-regulated both in tepals and coronas.Both branches of the phenylpropanoid pathway, leading to the production of color and scent in flowers, have been demonstrated in petunias . In ordeous work  were reaelopment . Simultaelopment , and ind4CL and decreasingly expressed C4H led to metabolite flux from the anthocyanin/flavonols biosynthesis pathway to the formation of the benzenoid/phenylpropanoid component pathway. While one of C4H’s expression levels remained unchanged in coronas, DFR and FLS expression levels slightly increased in coronas. Compared with the above changing profiles of pigment and fragrance component in tepal and corona during flower development, it suggests that C4H and 4CL transcript levels affected metabolite flux from the anthocyanin/flavonols biosynthesis pathway to the formation of the benzenoid/phenylpropanoid component pathway, leading to an increasing amount of products of benzenoid/phenylpropanoid in the tepals and coronas, and a decreasing amount products of flavonols. Additionally, there was no anthocyanin in tepals from T1 to T4. It is similar with diversified branches of the phenylpropanoid pathway, leading to the production of color and scent in petunias [Narcissus flower, to flavonols/anthocyanin (color), and two pathways to scent   .Narcissus, orange color (a high amount of carotenoids and flavonols) and a high amount of monoterpenoids and indoles were accumulated, and a dwarfism corona phenotype was compared to the phenotype of the tepal , supposing the competition of the carotenoid and terpene backbone biosynthetic branches for GGPP existed in the corona compared to in the tepal. The branches of the MEP pathway in the Narcissus flower were established by transcriptome datasets analysis, and gene expression profiles of structure genes were exhibited in the pathway (ENT-COPALYL DIPHOSPHATE SYNTHASE (CPS) is an entry enzyme that directs metabolite flux into terpene backbone biosynthesis (gibberellin biosynthesis) [Narcissus, with the time course of flower development, the GERANYLGERANYL PYROPHOSPHATE SYNTHASE (GPPS) and GGPPS transcript level was upregulated in the tepal and corona. Transcript levels of PSY was downregulated in both tepal and corona, GERANYLGERANYLREDUCTASE (GGR) was downregulated in the tepal but maintained a high level in the corona, even upregulated from the C1 to C3 stage of the corona, while the transcript levels of CPS, TERPENE SYNTHASE (TPS), and PAL -β-ocimene, and benzyl acetate scent in the corona of narcissus.Three branches of the MEP pathway, leading to carotenoids biosynthesis, the biosynthesis of the phytol side chain of chlorophylls, and of diterpenoids such as gibberellins, were demonstrated in many plants ,40,41,42 pathway . GGPP is and PAL  in diterelopment . It is cNarcissus flower development, a total of 3193 transcription factors were identified from our transcriptome dataset tepal  were identified (In order to identify transcription factors (TFs) for regulating the metabolite flux to the anthocyanin/flavonols biosynthesis pathway or to the formation of the benzenoid/phenylpropanoid component pathway during et tepal . WGCNA aet tepal A, and 12entified . The majentified C.The result of Gene Ontology (GO) enrichment analysis of the transcription factor genes of sample-specific modules was exhibited in In order to avoid losing multiple functional TFs, we performed a second-round alterative screen. To this end, WGCNA analysis of DEGs was performed to find the co-expression modules of pigment and fragrance metabolism and to identify the TFs among them. According to the role that the highly connected hub gene is usually a regulatory factor (an upstream position in a regulatory network), generally, the KME (eigengene connectivity) value is calculated and the hub gene is selected by using | KME | > = threshold (0.8), Top100 or 30%; the selected hub genes possess high connectivity in modules . The resnarcissus flower development, we made KEGG enrichment analysis of TFs of sample-specific modules exhibited in In order to identify more specific TFs related to floral pigment and floral fragrance formation during MYB, NAC, and AP2-ERF coexpressed closely with the top five genes, 4CL, C4H, and FLS, that were involved in metabolite flux from anthocyanin/flavonols biosynthesis to the formation of the benzenoid/phenylpropanoid component  gene which encoded HemL in the chlorophyll metabolism pathway . Among 20 candidate TFs from regulatory networks  and 4CL were upregulated in the coronas of Narcissus tazetta, hinting that there was a different metabolic flux direction between the color and scent component formation in Narcissus pseudonarcissus and Narcissus tazetta. It suggested that the potential competition of a regulatory network exists between floral pigment and floral fragrance during the individual plant development of Narcissus and evolutionary development.In order to genetically evaluate potential regulation, we compared the expression trend of candidate regulatory TFs between the tepals of networks B–E, seve tazetta , especiaPetunia hybrida) have revealed a conserved mechanism for the formation of the benzenoid/phenylpropanoid component and flavonol/anthocyanin pigmentation in the petals. In this study, we used an individual plant and two similar tissues . To create an isogenic line, we established a regulatory network of three branches of the phenylpropanoid pathway and three branches of the MEP pathway of Narcissus tazetta by using transcriptome-based WGCNA analysis. Our results highlight the potential metabolic flux of substrate competition in generating spatial patterns with color and scent contrast, and provide several candidate transcription factors: MYB12, MYB1, AP2-ERF, bZIP, NAC, MYB, C2C2, C2H2, and GRAS, which are associated with metabolite flux. It indicates that competition between benzenoid/phenylpropanoid and flavonol/anthocyanin biosynthesis produces white and aromatic tepals of narcissus, and metabolic flux among monoterpenoids and chlorophyll. The carotene biosynthesis pathway produces orange and protective aromatic coronas of Narcissus tazetta.From a genetic and metabolic viewpoint, the most extensively studied flower color and flower scent is that of the venation. Studies in petunia , and floral fragrance (benzenoid/phenylpropanoid) was provided [Narcissus tazetta var. chineses, with its variation of floral scent and floral color  might simply be explained by floral color change, but may also be influenced by multiple and/or conflicting selection pressures acting on the pleiotropic effects of flower traits. Whether pollinator-imposed selection may have a direct impact on floral scent emissions, independent of biochemical constraints due to floral color, remains unanswered. Increasing research on the effects of floral color and scent on pollinator attraction have been investigated in different species [Narcissus tepal organs possess a rich scent or a colorful Narcissus corona organ is scentless  and EOBI, which also activated the promoter of the biosynthetic gene, isoeugenol synthase [Narcissus, which belongs to the AtMYB4-like clade, was involved in the regulation of flavonoid biosynthesis in narcissus by repressing the biosynthesis of flavonols, and this led to proanthocyanin accumulation in the basal plate of narcissus. However, it seemed to be more similar to a flower-specific MYB homologous to Arabidopsis MYB21 that regulated stamen development and pollen phenylpropanoid and was involved in elevating the NtFLS transcript levels [Arabidopsis and Petunia [Arabidopsis and tomato plants [Arabidopsis NAC Transcription Factor, ANAC078, which regulated flavonoid biosynthesis via PAP1 under high-light [BENZOIC ACID/SALICYLIC ACID CARBOXYL METHYLTRANSFERASE (PhBSMT1 and 2), where mRNA is temporally and spatially down-regulated in floral organs in a manner consistent with reported models for post-pollination ethylene synthesis in petunia corollas [Arabidopsis. The bZIP is involved in the regulation of the phenylpropane metabolism pathway [Narcissus tazetta and Narcissus pseudonarcissus. The opposite expression trends of NF (c105468) and HSF (c91958) in the chlorophyll metabolism pathway of yellow tepals, compared with the coronas of Narcissus tazetta, may have resulted from their multiple functions during development. Most of the candidate TFs, including MYB12, bZIP, MYB1, MYB, C2C2, C2H2, GRAS, NAC, and AP2-ERF had the same changing trends confirmed in the tepal of Narcissus pseudonarcissus (TFs controlling the metabolic flux among the phenylpropanoid/benzenoid network was reported in petunia flowers. ODORANT1 (ODO1), a R2R3-type MYB TF, was exclusively expressed in petunia petal tissue and regulated the transcription of a major portion of the shikimate pathway, as well as entry points into both the Phe  and phenylpropanoid  branch ways . The gensynthase ,47,48. Isynthase ,30. In tt levels ,50. Ther Petunia . The NtMo plants . Althougo plants , in whico plants ,54,55. Tgh-light ,57. The corollas ,59. In a pathway ,59. In tarcissus . Therefoarcissus . It remaNarcissus tazetta cultivated variety “Jinzhanyintai” was purchased from a local farmer  in October 2015 and 2020. Narcissus pseudonarcissus cultivated variety “European yellow narcissus” was purchased from Holland in October 2015. After removing old bulb scale and roots, cleaned bulbs were sand-cultured in a greenhouse set at 23 °C with constant illumination (16 h light/8 h dark)  were homogenized and to the homogenate 1 mL of MeOH/DMSO mix  was added for 30 min at 40 °C with agitation (190 rpm) in darkness. Then samples were centrifuged at 40 °C, 9000 rpm for 15 min, the supernatants were collected and the pellets were reextracted for another 4 times until the color changed into white. All the supernatants were combined and filtered through a 0.22 μm membrane filter. The HPLC was carried out according to a previous method on an Agilent 1260 HPLC system [Tepals and cornas from S1 to S4 stages of v/v) was added. The resultant suspension was sonicated for 10 min and then mixed for 10 min using a thermomixer at 1000 rpm at 4 °C. Thereafter for phase separation, 0.4 mL of 1 M sodium chloride solution was added and contents were mixed by inversion. After centrifugation at 5000 rpm for 10 min the lower organic phase was collected. The aqueous phase was re-extracted with 1 mL of chloroform: dichloromethane  and 0.2 mL of 1 M sodium chloride solution. Both organic phases were combined, dried by centrifugal evaporation, re-dissolved in 0.5 mL of methyl tert-butyl ether  and filtered through a 0.22 μm membrane filter. The HPLC was performed according to YMC separation technology-Carotene and xanthophylls protocol with minor modifications on the Agilent 1260 HPLC system . A final volume of 8 μL was used for injection into HPLC.For extraction of carotenoids, the method described by Ren et al.  was refev/v) and MTBE (containing 0.01% BHT), respectively, and stored at −20 °C before use.The flavonoids and carotenoids standards including naringenin, dihydroquercetin, kaempferol, quercetin, lutein, and β-carotene were purchased from Sigma-Aldrich , rutin and zeaxanthin were purchased from TCI  and Sangon , respectively. All flavonoids and carotenoids standards were prepared in MeOH/DMSO  and kept at 4 °C in dark for 48 h until the tissues became white. After centrifugation at 15,000 rpm for 5 min, the supernatant was collected and was used to measure the absorbance at 470, 649, and 665 nm in 96-holes ELISA plate by using a Flexstation 3 Microplate Reader . Each hole was filled with 200 μL extract and pure 95% ethanol was used as a control. Chlorophyll a, b, the total chlorophyll and the total carotenoids contents were calculated according to the method described by Ren et al. (2017) [50 to 100 mg fresh weight of tepals and coronas from S1 to S4 stages were harvested. Pigments were extracted by immersing sample in 1 mL of 95% ethanol (Sampling was performed during the winter of 2020 and 2021. The extraction and detection of VOCs was performed according to  with minIdentification and Quantification: The retention indices (RIs) were calculated by the retention time (RT) of n-alkane standards (C7–C30) under the same conditions. Floral VOCs were identified by the RIs and mass spectra compared with the reference standards in the NIST 2011 mass spectra library implemented in the Clarus SQ 8T platform. The VOCs content was calculated by normalizing the peak areas relative to the internal standard.Narcissus tazetta were collected for PacBio full-length sequencing, of which all of the raw data, including Pacbio full-length-seq and former 4 DGE-seq library pools , were used to assemble the transcriptome. All of the sequencing services were provided by Bi-omarker Technologies Co., Ltd. .The samples of flower equally mixed from S0-S4 stage of https://github.com/ben-lerch/IsoSeq-3.0/) was used for Iso-Seq data analysis.The sequencing library was prepared according to the Iso-Seq protocol as described by Pacific Biosciences (P/N100-377-100-05 and P/N100-377-100-04). The SMARTer PCR cDNA Synthesis Kit was used to synthesize cDNA. After 23 cycles of PCR amplification, products were size-selected using the BluePippin Size Selection System with the following bins for each sample: 1–2 kb, 2-3 kb, and 3–6 kb. The amplified cDNA products were used to generate SMRTbell Template libraries according to the Iso-Seq protocol. Libraries were prepared for sequencing by annealing a sequencing primer and adding polymerase to the primer-annealed template. The polymerase-bound template was bound to MagBeads and sequencing was performed on a PacBio RSII instrument. SMRT-Analysis software (https://submit.ncbi.nlm.nih.gov/subs/SUB10083597). Clean data were obtained by removing reads containing adapters, reads containing poly-N, and low-quality reads. These clean reads were then mapped to the reference transcriptome sequence using software [https://submit.ncbi.nlm.nih.gov/subs/SUB10083597).Total RNA was extracted from frozen coronas and tepals at different stages and mRNA libraries were constructed for each sample. The corona transcriptomes at stages C1, C2, C3, and C4, and the tepal transcriptome at T0, were sequenced using the Illumina HiSeqTM-2500 platform with PE150 bp model. The tepal transcriptome data of Chinese narcissus at stage T1, T2, T3, and T4, were retrieved from our published data [software . Only resoftware  and fragsoftware . The ressoftware . Genes iNarcissus tazetta was sequenced by using the corresponding transcription group of ko number. The ID of the Narcissus tazetta gene annotated to the enzyme gene was found in the metabolic pathway, and the names of the genes of the related enzymes and the ID of the Narcissus tazetta gene were sorted. Combined with the metabolic pattern map and the expression of the enzyme gene, the heat map of the genes related to the metabolism pathway of the narcissus flower and color was drawn by TBtools after all the genes with less than 1 expression values were removed.A gene coexpression network was built using the WGCNA package (Version 4.0.2). Parameters were set up as power = 12, minModuleSize = 30, and MEDissThres = 0.25. The networks were visualized using Cytoscape v.3.6.048. For high-throughput display of the expression levels of the assigned genes, heat maps were created. Related gene expression values were retrieved from databases [Expression of representative unigenes in each metabolic pathway was confirmed by semi-qRT-PCR (semi-quantitative reverse transcription and polymerase chain reaction) with specific primers designed by Beacon DesignerTM  . The prop < 0.05 were conducted by data processing system (DPS) version 7.05 software . Means indicate at least three biological replicates. The heatmap is drawn by TBtools software . A one-way analysis of variance (ANOVA) in combination with Duncan’s multiple range test or Kruskal–Wallis test with a significance of differences of