Compound Kushen injection (CKI), a medicine in widespread clinical use in China, has proven therapeutic effects on cancer. However, few molecular mechanism analyses have been carried out. To address this problem, bioinformatics approaches combining weighted gene co-expression network analysis with network pharmacology methods were undertaken to elucidate the underlying molecular mechanisms of CKI in the treatment of esophageal cancer (ESCA). First, the key gene modules related to the clinical traits of ESCA were analysed by WCGNA. Based on the results, the hub genes related to CKI treatment for ESCA were explored through network pharmacology. Molecular docking simulation was performed to recognize the binding activity of hub genes with CKI compounds. The results showed that the potential hub targets, including EGFR, ErbB2, CCND1 and IGF1R, are therapeutic targets of CKI for the treatment of ESCA. Moreover, these targets were significantly enriched in many pathways related to cancer and signalling pathways, such as the PI3K-Akt signalling pathway and ErbB signalling pathway. In conclusion, this research partially highlighted the molecular mechanism of CKI in the treatment of ESCA, offering great potential in the identification of the effective compounds in CKI and biomarkers for ESCA treatment. The 5-year survival rate of ESCA is between 12 and 20% and differs substantially by sex2. China is a high-risk area for ESCA, especially in some rural areas, where the incidence rate far exceeds that of urban areas due to lifestyle and environmental reasons3. ESCA can be divided into esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) according to histological classification. In recent decades, the incidence of EAC in Western countries has increased several times, and the proportion of ESCC has exceeded 90% throughout China5. The introduction of chemo(radio)therapy and surgical therapy led to increased survival rates and reduced the incidence of recurrence6. Because conventional methods do not adequately improve patient survival of ESCA, however, scientists are seeking more effective treatments. Recently, traditional Chinese medicine (TCM) has taken the world stage as complementary and alternative medicine7. Compound Kushen Injection (CKI) consists of two herbs, Kushen (Radix Sophorae Flavescentis) and Baituling (Rhizoma Smilacis Glabrae). CKI mainly contains various anticancer ingredients, such as matrine and oxymatrine, which can inhibit the growth of tumour cells, overcome resistance to metastasis and multidrug resistance, and protect human immunity8. CKI has been utilized in clinical practice for decades to treat various solid tumour types, including liver cancer, breast cancer, gastric cancer, and other cancer types9. The analysis of medical data on 2,550 ESCA patients from 22 large-scale hospitals in China confirmed that CKI has been relatively widely used in the clinical treatment of ESCA of different severities10. In addition, a previous study reported that CKI limited cancer pain both directly by blocking TRPV1 signalling and indirectly by reducing tumour growth11. For ESCA, it is worth noting that CKI used alone or combined with conventional radiotherapy can not only enhance antitumor efficacy but also reduce the toxicity induced by radiotherapy, thereby improving the quality of life12. Our early studies also found that combination with CKI can improve the clinical effectiveness rate and performance status of radiotherapy for ESCA. Furthermore, CKI can also provide treatment by reducing gastrointestinal reactions and radiation esophagitisEsophageal cancer (ESCA) is widespread worldwide. According to Global Cancer Statistics 2018, it ranks seventh in incidence and sixth in mortality17. To better analyse and predict the molecular mechanism of CKI in the treatment of ESCA, this study adopted weighted gene co-expression network analysis (WGCNA) integrated with the network pharmacology method. WGCNA can be used to find clusters (modules) of highly related genes, correlate modules and correlate with external sample traits and can be used to identify candidate biomarkers or therapeutic targets18. Network pharmacology not only caters to the “multi-component, multi-target” characteristics of TCM but also identifies drug-gene-disease links, explaining the therapeutic mechanism of drugs at the molecular level21.Tumorigenesis is a complex process that is driven by a combination of networks of genes and environmental factors; there is a lack of effective methods to identify functional networks that Chinese medicine interferes with tumorigenesisIn the present work, we first used WGCNA to analyse ESCA mRNA datasets from The Cancer Genome Atlas (TCGA) to predict significant gene modules. Second, gene modules were combined with predicted targets of key CKI components to form a drug-gene-disease network and further analysed. In addition, molecular docking methods were adopted to confirm the degree of binding between the hub gene and the component. This study is intended to explain the mechanism of action of CKI in the treatment of ESCA at the molecular level and to provide a better basis for the diagnosis, treatment and prognosis of ESCA. Figure R2 = 0.85) was selected as the soft-thresholding parameter to ensure a scale-free network.   Fig. . The topol) Fig. . Each mo22, the 23 active ingredients contained in CKI were selected for investigation, and the three-dimensional structure data of 16 active ingredients were obtained from the PubChem database23.Following a literature searchThe 16 active compounds of CKI are shown in Table The compound-predicted target network was combined with the blue and turquoise module genes. Thirty-two identical gene targets were considered potential targets for the CKI treatment of ESCA Fig. A. To furP < 0.01). Figure P < 0.01. Moreover, these targets were significantly enriched in many pathways related to cancer and signalling pathways, such as the PI3K-Akt signalling pathway, ErbB signalling pathway and FoxO signalling pathway , molecular function (MF), and cellular component (CC) and KEGG functional enrichment analysis of the selected PPI targets. Eventually, 559 enriched GO terms were identified, of which 477 were BPs, 54 were MFs, and 27 were CCs  were directly related to the CKI active ingredients . These 4 potential target proteins and their corresponding small-molecule ligand components were docked by AutoDock Vina. The docking results showed that the binding affinity was not greater than −6.0 kcal/mol, which proved that CKI components had good binding ability to the targets , blue-histological type (p = 2e−29), turquoise-cancer status (p = 2e−05) and turquoise-pathologic T (p = 0.008). Blue modules and turquoise modules were selected for network pharmacology analysis with the CKI predicted targets. Through network merging and PPI, a total of 16 gene targets were obtained with topological characteristics degree greater than the average. Among them, EGFR (degree = 21), ERBB2 (degree = 18), CCND1 (degree = 16) and IGF1R (degree = 15) were the predicted targets directly corresponding to the CKI components that were considered to be the hub genes related to CKI treatment of ESCA, and detailed discussions were conducted.First, this study used the WGCNA method to explore the pathogenesis of ESCA and to study the link between gene expression data and clinical traits to screen for important gene modules. According to the WGCNA results, a total of 10 related modules were obtained, among which the blue module and the turquoise module were strongly related to multiple clinical traits, such as blue-new tumour event  activity and a proto-oncogene of the human epidermal growth factor receptor family35. Overexpression of ErbB2 accelerates tumour growth, metastasis, and tumour blood vessel formation, increasing its invasion in vitro36. Equally important, ErbB2 can further improve the ability of tumour cells to migrate and adhere, promote tumour invasion and encourage local and/or distant metastasis37. Hoffmann38 proposed a diagnostic method to detect ErbB2 amplification in single disseminated cancer cells, demonstrating that ErbB2 amplification in esophageal cancer patients is significantly correlated with short-term survival. Previous studies have shown that ESCA can be treated by taking ErbB2 inhibitors such as trastuzumab and ramiximab39. The molecular docking results obtained in this study are similar to the docking results of the ErbB2 small-molecule antibody drugs erlotinib and lapatinib in the Rambabu Gundla study40. SER-783, THR-862, THR-798 and ASP-863 residues can all generate hydrogen bonds to connect with the compound. Therefore, we believe that CKI can treat ESCA by inhibiting ErbB2. CCND1 is a cell cycle regulating protein that can control the transition of the cell cycle from G1 to S phase, which is closely related to the occurrence and development of many tumours41. Matrine is one of the active components of CKI and has been shown to have anti-inflammatory, immunosuppressive, antitumour and antifibrotic effects42. Studies have shown that matrine can mediate the expression of CCND1 in breast cancer cells and thus inhibit cancer cells43. Guo44 detected rhabdomyosarcoma cells treated with matrine at different concentrations by MTT, flow cytometry, and RT-PCR and found that matrine significantly inhibits the proliferation of rhabdomyosarcoma cells by reducing the expression of CCND1 mRNA and blocking the cell cycle of the G0/G1 phase. Genome-wide screening revealed that the amplification of cyclin D1 is one of many genetic changes in ESCC. Accordingly, CKI may be used as an inhibitor of CCND1 to treat ESCA45. Insulin-like growth factor-1 receptor (IGF1R) is a tyrosine kinase that is involved in the pathogenesis of many cancers. After binding to the ligand, IGF1R can activate PI3K/AKT/mTOR and Ras/Raf/MEK/to activate the MAPK pathway, which can regulate cell proliferation, survival, differentiation, movement, invasion and angiogenesis47. Studies have shown that IGF1R is overexpressed in cancer tissues compared to normal tissues adjacent to the cancer. In addition, a mouse xenograft model was used to test the function of IGF-1R in vitro and in vivo. IGF1R was found to have carcinogenic effects in regulating cell proliferation, colony formation, the cell cycle and apoptosis49.Epidermal growth receptor factor (EGFR) is an expression product of the proto-oncogene c-erb-B1 and is a member of subtype 1 of the receptor tyrosine kinase (RTK) family. The family also includes ErbB2, ErbB3 and ErbB450. Studies have shown that the PI3K/AKT pathway is abnormally activated in a variety of cancers, such as esophageal, gastric, and breast cancer53.The enriched GO analysis indicated that the hub genes localized mainly to the cyclin-dependent protein kinase holoenzyme complex, protein kinase complex and serine/threonine protein kinase complex, while their molecular functions were associated with the cell cycle. Similarly, KEGG pathway analysis showed enrichment in some cancer pathways and signalling pathways, such as the PI3K-Akt signalling pathway and ErbB signalling pathway. The ErbB family, after binding to its corresponding ligands , downstream related genes such as PI3K/AKT and MAPK can be activated, thereby regulating cell proliferation, differentiation, migration, and apoptosis activities54. Additionally, the deviant activation of the PI3K/AKT pathway inhibits the degradation of CCND1, increases its expression, promotes its shift to the nucleus, and interferes with the transition from G1 to S phase of the cell cycle55. The ligands IGF-1 and IGF-2 bind to IGF1R, leading to receptor autophosphorylation and the activation of various signalling pathways, including the PI3K/AKT pathway, which leads to cell proliferation and prevents apoptosis56. Abnormal activation of pathways caused by abnormal expression of these proteins is a critical factor affecting the progression of esophageal cancer. Zhang57 found that CKI can increase the ability to inhibit lung cancer cell proliferation and increase sensitivity to gefitinib by downregulating the PI3K/AKT pathway. A study has also shown that matrine derivatives, which are one of the main components of CKI, can be downregulated, CCND1, and attenuated the PI3K/Akt pathway to induce G1 cell cycle arrest and autophagy in cancer cells through immunofluorescence analysis, western blotting and murine models58. Therefore, these potential targets and pathways may be the key to the CKI treatment of ESCA.Upstream genes such as ErbB2 and various growth factors such as EGF and IGF1 can activate PI3K, resulting in aberrant activation of the PI3K/AKT pathway59. In previous studies, Li60 provided a powerful means for identifying mechanisms of Ge–Gen–Qin–Lian decoction in the treatment of type 2 diabetes through the network pharmacology strategy. Liang61 exploited drugCIPHER to incorporate the traditional network pharmacology concept to analyse the target network of the TCM traditional prescription Liu-Wei-Di-Huang pill. This study was based on the network pharmacology method combined with WGCNA analysis, aiming to accurately detect the genes related to ESCA from the aspect of close to clinical traits to analyse the mechanism of CKI treatment of ESCA. However, there are some limitations in this method. First, our data collection is based on existing database information, so it may produce deviations and incomplete results. Second, biological experiments are urgently needed to validate our results because our study was performed based on data analysis.The central idea of TCM has a lot in common with network pharmacology, which can explain the treatment process of many complex diseases in a system mannerIn summary, by combining WGCNA and a network pharmacology method, we revealed that CKI controlled the growth of ESCA by regulating potential hub genes, such as EGFR, ErbB2, CCND1 and IGF1R, as well as important related pathways. The study preliminarily verified and predicted the molecular mechanism of CKI against ESCA but still needs further experimental verification. These findings provide insights into the underlying mechanism of CKI for the treatment of ESCA and provide a reference for the study of the more complex mechanism of action of this Chinese herbal compound.https://portal.gdc.cancer.gov) in September 2019, with a total of 164 samples. The clinical metadata of the 164 samples were also downloaded and filtered for useful information. After the removal of samples containing incomplete analytical data and/or other malignancies, 161 samples were retained. Since some genes lacked significant changes in expression between samples, we chose the top 5,000 genes that were most important in terms of differential expression for the next WGCNA analysis.RNA sequencing data in fragments per kilobase million (FPKM) of ESCA were obtained from the TCGA data portal (62. The appropriate soft threshold power β was selected by using the integration function (pickSoftThresshold) in the WGCNA software package. With this soft threshold function, the co-expression similarity was improved to achieve a scale-free topology64. Then, we reconstructed the topological overlap matrix by calculating the topological overlap measure (TOM), which is a robust measure of network interconnectedness66. The dynamic tree-cut algorithm method was adopted to identify the module of gene co-expression with the maxBlockSize of 6,000, minModuleSize of 30 and mergeCutHeight of 0.2.The gene co-expression networks were constructed by the WGCNA package. We used the similarity between gene expression profiles to construct a similarity matrix based on pairwise Pearson correlation coefficient matrices. The similarity matrix was transformed into an adjacency matrix using a power adjacency function18.Module eigengene (ME) is the first principal component of each gene module, and the expression of ME is considered representative of all genes in one module. The module membership (MM) is the correlation between the ME and the gene expression profile. Gene Significance (GS) is the absolute value of the correlation between a specific gene and a clinical trait. According to ME, GS, and MM, we can associate modules with clinical traits not only to calculate the correlation between ME and clinical traits but also to analyse clinically vital modules67, SuperPred68, SwissTargetPrediction69 and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) 70 databases for retrieval. The predicted multiple target information of the compounds and the obtained information were introduced into Cytoscape 3.6.1 (https://www.cytoscape.org/) to obtain a compound-predicted target network map. Cytoscape is bioinformatics analysis software that visualizes biological pathways and intermolecular interaction networks and provides a basic set of data integration, analysis and visualization capabilities for complex network analyses71.The 3D chemical structure data of 16 active ingredients were imported into the Search Tool for Interactions of Chemicals (STITCH)https://string-db.org/) is a database of known and predicted protein interactions that contains direct and indirect protein associations72. The overlapping genes were input into the STRING 10.5 database, and the species selection "Homo sapiens" was selected as the confidence data with a scoring value greater than 0.7 and 1st shell no more than 20. Then, the data were introduced into Cytoscape to construct a protein–protein interaction (PPI) network.The compound-predicted target network and clinically important module network obtained from WGCNA analysis were merged in Cytoscape. The overlapping proteins in the two networks are likely to be potential targets for the treatment of ESCA by the active ingredients of CKI. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 10.5 (https://geneontology.org/) was used to identify the possible biological mechanisms using high-throughput genome or transcriptome data73. KEGG is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data, which is a reference for biological interpretation of genome sequences and other high-throughput data74. In addition, the R package clusterProfiler was used to perform GO and KEGG functional enrichment analysis75.The GO database  (https://www.rcsb.org/) database to find their 3D structure78. Proteins that met the following conditions were considered appropriate protein conformations: (1) The 3D protein structures were determined by X-ray crystallography. (2) Crystal resolution Protein was less than 3 Å; (3) Genotype protein analysis was reliable. Molecular docking simulation of potential targets and their corresponding components was performed using AutoDock 4.2 and AutoDock Vina software according to published methods80. A suitable grid box size with a spacing of 1.0 Å between grid points was generated to cover almost the entire favourable protein binding site. The X, Y and Z centres are adjusted according to different macromolecular forms. The results of docking are displayed by Pymol and Ligplot81.Molecular docking can reflect the binding energetics of drug molecules to protein receptors by calculating the binding affinity between ligands and receptors and the corresponding intermolecular interactions82.Survival analysis was conducted using the Kaplan–Meier method and log-rank tests. Hazard ratios (HRs) were calculated using a Cox proportional hazards model with R software and the “survival” R package. In the survival analysis, death from any cause was considered an eventEthical approval was not necessary in the current study because our study gathered data from TCGA, and this procedure did not address any patients’ personal data or harm any patient.Supplementary file1Supplementary file2Supplementary file3


Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods  and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site. An organism’s metabolism is comprised of all of the chemical reactions involved in its cells; the small-molecule intermediates and products of these reactions are known as metabolites. Untargeted metabolomics provides a profile of all detectable metabolites in a system, allowing the unique chemical fingerprint left behind by the metabolism to be investigated. Such studies are imperative in understanding changes in biological mechanisms related to environmental and genetic variations. Mass spectrometry (MS) is one of the most commonly used techniques for untargeted metabolomics and is often coupled with chromatographic separation using liquid chromatography (LC). Experimental data from a typical LC–MS experimental sample are a series of spectra, where signal intensities are generated for each detected ion. After data preprocessing, a table of chromatographic peaks is produced; each peak can be represented by its mass-to-charge ratio (m/z), retention time (RT), and intensity values. Peaks with a similar m/z and RT can then be grouped across samples into features, which leads to a matrix of peak intensities, indexed by features along the rows and samples as the columns. Identification is performed to associate features with metabolite identities, either by matching the m/z and RT of features with internal library standards or through fragmentation spectra comparisons against mass spectral databases.In a cell or organism, metabolites seldom work alone. As result, it it often useful to analyse related groups of metabolites rather than considering individual metabolites in isolation. One way to define metabolite sets is through prior knowledge of how metabolites participate in a series of related chemical reactions, or metabolic pathways. Ranking biologically relevant pathways through changes in the intensities of associated features provides high-level information that helps prioritise relevant pathways . An alteIn recent years, enrichment analysis algorithms, originally developed for analysing large collections of genes or transcripts, have been adapted to metabolomics. These methods were traditionally used to test gene expression data for functions or processes that are overrepresented  with regard to functional gene sets, pathways, and networks. Considering differentially expressed (DE) gene sets, two of the most commonly used types of gene enrichment techniques are overrepresentation analysis (ORA) and functional class scoring (FCS). ORA approaches rely on testing for the presence or absence of significantly changing genes in a pathway, using statistical significance tests. FCS methods, which include gene set enrichment analysis (GSEA)  use statThe nature of metabolomics data can make enrichment analysis for metabolite groups more challenging than for groups of genes. In particular, an untargeted metabolomics experiment contains a large diversity of chemical structures with the identity of many of the features unknown in mass spectral databases. In addition, there is uncertainty in annotating an MS feature, as a single compound can be matched to multiple features and vice versa even at a high mass accuracy of 1 ppm . Other mhttps://pals.glasgowcompbio.org/ (accessed on 5 February 2021) [In this manuscript, we introduce Pathway Activity Level Scoring (PALS), a comprehensive and modular Python tool for metabolite set enrichment. PALS is designed to look at how metabolic pathways or other groups of metabolites change between different experimental conditions. It achieves this by decomposing peak intensities into latent factors corresponding to observed metabolic changes (activities). Our adaptation of the PLAGE method, denoted as mPLAGE (PLAGE for metabolomics), was independently implemented in Python for use in metabolomics. Using mPLAGE, small correlated changes in a group of metabolites are rated as more interesting than large changes in an individual metabolite, where other metabolites in the set are not changing. The changes are increases and/or decreases in feature intensities, but must occur as part of the group. This is particularly important in situations where enzyme inhibition results in substantial increases to metabolite abundance before a lesion in a pathway, with concomitant decreases after the lesion (see  for an ery 2021) . It can To assess the robustness of mPLAGE, benchmarking experiments were performed. Initially, these experiments were performed using synthetic data, as the ground-truth of untargeted metabolomics experiments can be hard to define. In this section, pathways are described for simplicity, but this procedure could be applied to any set of metabolites. Seven synthetic pathways were constructed with significant changes (showing clear block structures between the case and control groups in their peak intensity matrix) between two experimental conditions, each consisting of four samples. To mimic the inclusion of formulae that arise due to incorrect identification/annotation, decoy features were generated using a normal distribution  and stanEach of the seven pathways contained a different number of formulae, and each pathway was named according to the number of features associated with it for clarity, i.e., pathway names for numbers of features were: Two; Four; Six; Ten; Twenty; Forty; and Eighty. A one-to-one relationship between an observed feature and a metabolite was assumed. In addition, 100 background pathways, where each pathway contains between 5 to 50 metabolites that show no significant changes between the case and control groups, were generated and added to the synthetic data set. To simulate missing features, which often occurs in real metabolomics data, features were also randomly removed from pathways with a uniform probability of 0.2. The performance of mPLAGE was compared to widely used overrepresentation (ORA) analysis and gene set enrichment analysis (GSEA) methods . Each pathway analysis method m will return p-values below The following evaluation metrics were implemented to assess the performance of the different methods. Let m, the following counts can now be evaluated: True Positives (TP) = in T; False Positives (FP) = in T; False Negatives (FN): in T but not in For a method In this experiment, an increasing number of decoy features were added to the seven pathways comprised of significantly changing features. This scenario reflects the case of attempting to find pathways, or sets of metabolites, with high activity levels when the number of significantly changing metabolites are small compared to noisy data, nonchanging or wrongly annotated metabolites. The level of decoy features are defined in an increasing order of severity from 0%, 25%, 50%, 100%, 250%, 500%, and 1000% of the original number of features in the pathway. For example, adding 50% decoy features to pathway Forty results in the 40 significantly changing features and the addition of 20 new decoy (nonchanging) features in the pathway. The resulting synthetic data matrix with the addition of decoy features is then used as input to the methods being compared. This procedure is repeated 500 times for each level of decoy features.p-values of the true changing pathways with higher number of decoy features reveals that increasing the number of decoy features generally produces higher p-values , GSEA appears most sensitive to the perturbation, while mPLAGE performs best amongst the methods compared.Assessing changes to the p-values . This sup-values returned by all methods generally decreases with an increasing number of changing features in a pathway. This suggests that the greater the number of metabolites that are changing together, the higher ranked that pathway would be. As a result of the small number of metabolites in the pathway, all methods struggle to correctly identify pathways Two and Four as significantly changing at any level of added decoy features and generally perform better on the larger pathways Twenty, Forty, and Eighty. This reveals that the larger the number of changing features identified in a pathway (or metabolite set), the more tolerant the method is to nonchanging (random) features.Inspection of individual significant pathways of varying sizes  revealedp-values compared to ORA and GSEA, which means that more significantly changing metabolite sets can be detected. This is similar to findings reported by Evangelou et al. [For all pathways across all levels of added decoy features, mPLAGE returns lower u et al.  that comfeatures . At highp, and this value was varied from 20%, 40%, 60%, and 80% of the full data set. The different pathway ranking methods were run 500 times for each data set with each having a different value of p. The results in p-values with smaller variances than ORA or GSEA. This suggests that mPLAGE is generally more robust to missing features than the alternative methods tested. These results were also supported by the Additionally, the effect of introducing an increasing number of missing features from the pathways was explored. For this experiment, the level of decoy features was fixed to 100% in order to allow for an equal number of changing and nonchanging features. The percentage of features randomly missing from the data was indicated by formance , where mTrypanosoma brucei, which is transmitted to a human/mammalian host by the bite of the tsetse fly. Two data sets of samples collected from human blood plasma and cerebrospinal fluid (CSF) are available from the study, denoted as the Plasma and CSF  data sets, respectively. The control group consists of parasite-free patients, while the two case groups are those with stage 1  or stage 2  trypanosomiasis.To evaluate the performance of the proposed method on actual complex biological data, PALS was used to analyse metabolomics data obtained from a study on human African trypanosomiasis (HAT) introduced in . The caup-value < 0.05) were found between the disease stages for the CSF than Plasma data . In all of the comparisons, CSF samples have a greater number of changing pathways than those seen in the Plasma data. In particular, there is a notable difference in the changing pathways between the S1:C and S2:C/S1:S2 in the CSF. This can be explained by the fact that the parasites are only present in the CSF in S2 parasitemia and consequently more metabolic changes are expected in the CSF from S2 patients.Using the HAT data with PALS allows for complex clinical data to be explored and demonstrates how PALS can be used to draw relevant biological conclusions. The Plasma and CSF data sets from the HAT study, comprising of 20/17 control (C) samples and 20 samples from both S1 and S2, respectively, were uploaded and processed through the Polyomics Integrated Metabolomics pipeline (PiMP) . PALS waT. brucei is predicted to be auxotrophic for the amino acids arginine, glycine, histidine, isoleucine, leucine, lysine, phenylalanine, tryptophan, tyrosine, and valine [T. brucei (listed above)  were found to be decreasing in the CSF of patients with S2 of the disease . Anothephic for ), and cop was set from 20% to 80%.The HAT case study results demonstrate how the mPLAGE method in PALS identified significantly changing pathways that can be explained and interpreted as biologically relevant. In this section, the robustness of the different pathway ranking methods on this data is assessed. In a manner similar to the synthetic experiment, a range of missing feature proportions p times the total number of features were randomly removed, and the rest were used as input for pathway analysis. Assuming that a particular pathway ranking m on the complete data will be better than the ones obtained from the reduced data (with missing features), results from the complete data could be used as the true answers for evaluation. Here, true answers T are restricted to be the set of significant pathways above a threshold of 0.05 for their mPLAGE and ORA p-values, and a larger threshold of 0.25 for GSEA  with the Plasma data, mPLAGE achieves a mean ectively . SimilarTo demonstrate the analysis of other types of metabolite sets, PALS was used to assess the activity levels of potentially unknown metabolites grouped into molecular families (MFs) through their fragmentation spectral similarities, and into Mass2Motifs based on shared fragment and neutral loss features.p-value ≤ 0.05) containing 10 or more molecules were found between the case (eating >30 plant-based foods a week) and control (<10 plant-based foods a week) groups. Of particular interest is one significantly changing MF  that was identified by PALS, consisting of 23 molecules that have steroid-related GNPS library hits. The intensities of the associated MS peaks in this steroid-related MF were found to be higher in the control group  example data were taken from the American Gut Project . From thTo anaylse Mass2Motifs, a data set from the GNPS-MS2LDA workflow, consisting of 70 Rhamnacea species from two clades and various genera, was used . In the Rhamnus and Ziziphus genera with features generally overrepresented in Rhamnus .Consistent with , PALS reFinally, looking at Mass2Motifs in the American Gut Project data, 11 significant Mass2Motifs were prioritised using PALS, with one DE Mass2Motif being more abundant in “plant-eaters” and annotated as related to ferulic acid (12 members)—a molecule that is typically found in plants and could be linked to plant-based foods in this data set , and if only some of the sample values in a factor are zero, then these are replaced by the mean value of the nonzero samples in that factor. As an option for data normalisation, the intensity matrix is subsequently transformed to log space and standardised using the preprocessing module in Scipy [p-values of significantly changing metabolites [As input to activity level decomposition using mPLAGE see , a peak in Scipy  such thaabolites .https://reactome.org/ (accessed on 5 February 2021) [Metabolites, pathways, and the mapping of metabolites to pathways are obtained in PALS by querying from a local copy of the KEGG database  or by qury 2021) .The presence of isomeric and isobaric compounds  in a sample means that multiple features with close m/z values can be annotated with the same molecular formula, although their associated peaks are actually produced by different compounds in the MS. Resolving the actual molecular identities of annotated features is a challenging problem, even at a high resolution . To ensuX (features vs. samples), can be used for for activity level decomposition. Here, two other approaches for producing metabolite sets by grouping features in a data-dependent fashion, in contrast to pathways that rely on prior knowledge, are described. The first method uses molecular networking  to clusg  to geneg , which X can be used to determine an AL score for a compound in a pathway or set of metabolites in mPLAGE. This is calculated as the level of the first metacompound from the SVD of the peak intensity matrix (X for a group of metabolites (where rows are the features annotated with formulae and columns are the samples in the data set), the decomposition of X can be written as U are the orthonormal left singular vectors representing “metacompounds’’, V are the right singular vectors representing the contribution of the corresponding metacompounds to each sample. The AL score in a sample j is given by v is the first row in V, which is a vector of coefficients representing the activity levels of the first metacompound in U  across samples. The singular values X can be constructed—in fact, the square of each i. For more details, see [The initial activity level (AL) score for a set of metabolites is computed using singular value decomposition (SVD) following the method described for PLAGE , and aday matrix D as follils, see .t-statistic for each metabolite set across the varying experimental factors  distribution [t-statistic of a pathway or set of metabolites is then evaluated against the fitted density in order to produce the final mPLAGE p-values of the metabolite sets.To perform activity level analysis, two experimental factors are compared—for example, a control versus a condition. This is achieved by using the AL scores obtained from the decomposition step to calculate the  factors D. As in ribution , with pap-values. Expert users can also incorporate the PALS library directly in their Python application or use it in an interactive data analysis environment such as Jupyter Notebook [To facilitate the use of the activity analysis described in this project, PALS (Pathway Activity Level Scoring) software was developed. PALS can be run either as a standalone tool, imported as a Python library, or used via a Web interface allowing users with a wide range of expertise and requirements to use it . From PALS, users obtain the ranking of changing pathways, or user-defined sets of metabolites, between experimental factors. Database queries that map compounds to pathways are integrated into the system using either KEGG or Reactome databases, while code is also available to automatically retrieve measurement data and metabolite sets of molecular families and Mass2Motifs from GNPS and perform activity measurements. Manuscripts comparing pathway ranking methods for metabolomics are still relatively rare. One such manuscript [This work describes PALS, a comprehensive system that can be used to prioritise metabolite sets, that is freely available at nuscript  comparesflymet.org (accessed on 5 February 2021) [webomics.glasgowcompbio.org (accessed on 5 February 2021) [It is also worth emphasising that unlike many tools surveyed here that specifically deal with pathways, PALS could be easily used to prioritise metabolite sets derived from many sources, whether based on knowledge (pathways) or driven by fragmentation data (molecular families and Mass2Motifs). To our knowledge, this is a unique capability not present in other tools. PALS also presents a comprehensive Python-based library that can be easily run in several modes: as a standalone tool, an interactive Web application (PALS Viewer), or a Python library. This allows PALS to be embedded in many different contexts: as part of a larger Web application, or a custom Python-based workflow and scripts, or to be imported directly into interactive computing environments like Jupyter. As a demonstration of its utility and versatility, the PALS library has been integrated and actively used in several projects such as PiMP , FlyMet ry 2021) ), and Wery 2021) ). To sup


Artemisia annua (AA) on the treatment of hepatocellular carcinoma (HCC) based on network pharmacology.  To investigate the potential active ingredients and underlying mechanisms of  In the present study, we used a network pharmacological method to predict its underlying complex mechanism of treating HCC. First, we obtained relative compounds of AA based on the traditional Chinese medicine systems pharmacology (TCMSP) database and collected potential targets of these compounds by target fishing. Then, we built HCC-related targets target by the oncogenomic database of hepatocellular carcinoma (OncoDB.HCC) and biopharmacological network (PharmDB-K) database. Based on the matching results between AA potential targets and HCC targets, we built a protein-protein interaction (PPI) network to analyze the interactions among these targets and screen the hub targets by topology. Furthermore, the function annotation and signaling pathways of key targets were performed by Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking.  A total of 19 main active ingredients of AA were screened as target prediction; then, 25 HCC-related common targets were seeked out via multiple HCC databases. The areas of nodes and corresponding degree values of EGFR, ESR1, CCND1, MYC, EGF, and PTGS2 were larger and could be easily found in the PPI network. Furthermore, GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in tumor cell proliferation, apoptosis, angiogenesis, tumor invasion, and metastasis to accomplish the anti-HCC activity. The molecular docking analysis showed that quercetin could stably bind to the active pocket of EGFR protein 4RJ5 via LibDock.  The anticancer effects of AA on HCC were predicted to be associated with regulating tumor cell proliferation, apoptosis, angiogenesis, tumor invasion, and metastasis via various pathways such as the EGFR signaling pathway, ESR1 signaling pathway, and CCND1 signaling pathway. It is suggested that AA might be developed as a broad-spectrum antitumor drug based on its characteristics of multicomponent, multipath, and multitarget. Hepatocellular carcinoma (HCC) is the second most lethal cancer worldwide with persistently increasing mortality in the Asia-Pacific region , 2. HCC Artemisia annua (AA) is a Chinese medicinal plant, which is used throughout Asia and Africa as tea or press juice to treat malaria. Since the 2015 Nobel Prize in Physiology or Medicine conferred to Chinese scientist, Youyou Tu, AA drew attention worldwide amino]-3-phenylpropyl] acetate, 6,8-di-c-glucosylapigenin-qt, and artemisinin identified as the potential active ingredients of AA, of which the biological activities against HCC were reported previously. For example, several studies reported antiproliferative and proapoptotic effects of quercetin in HCC, and the beneficial activities of quercetin were also demonstrated in hepatoprotective effects with antioxidant and anti-inflammatory properties , 20. DihThe results of AA potential target-HCC target network analysis acquired 25 key targets of AA acting on HCC. These key targets were mainly involved in biological processes such as tumor cell proliferation, apoptosis, cell cycle regulation, epithelial stromal transformation, angiogenesis, tumor invasion and metastasis, tumor signal transduction, immune regulation, drug resistance, and other important biological processes . The preAs observed in the results of KEGG pathway enrichment analysis, the key targets of AA acting on HCC were mainly related to the EGFR signaling pathway, ESR1 signaling pathway, CCND1 signaling pathway, MYC signaling pathway, PTGS2 signaling pathway, and so on. HCC is a heterogeneous disease that the underlying mechanisms have not been clearly elucidated. It is recognized that the pathogenesis of HCC is associated with various biological processes such as tumor cell proliferation, apoptosis, cell cycle regulation, epithelial stromal transformation, angiogenesis, and tumor signal transduction. It was reported that the EGF-EGFR pathway was involved in the development of inflammatory microenvironment in HCC; EGF might facilitate DNA synthesis, regeneration, tumor growth, and progression of HCC cells and bind with EGFR as the potential connection between inflammation and HCC . MYC wasMolecular docking is a useful method of predicting binding strength between TCM ingredients and targets based on the spatial structure of ligands and receptors . The molIt is usually difficult to reveal the effect of the active ingredients of AA on the targets and its activity of treating HCC alone because of the complex ingredients of AA and limited experimental research methods at present. However, this study showed that multiple targets and pathways might involve in the occurrence of HCC, and the possible molecular mechanism of AA active ingredients was revealed for the first time based on the combined methods of network pharmacology and molecular docking technology, which indicated that it might be an effective method to reveal the holistic characteristics of AA in the treatment of HCC by using combined methods of network pharmacology and molecular docking technology. Furthermore, quercetin has many targets, which may lead to off-target effects. But we only focused on the key targets that linked the active compounds and HCC. And the therapeutic effect of TCM not only depends on the unique target.In conclusion, our study predicted the targets of the ingredients of the AA and explored the underlying mechanism of the potential anti-HCC effects via the method of network pharmacology. The anticancer effects of AA on HCC were predicted to be associated with regulating tumor cell proliferation, apoptosis, angiogenesis, tumor invasion, and metastasis via various pathways such as EGFR signaling pathway, ESR1 signaling pathway, and CCND1 signaling pathway. It is suggested that AA might be developed as a broad-spectrum antitumor drug based on its characteristics of multicomponent, multipath, and multitarget and has a broad application prospect.


O-β-d-glucoside and cyanidin-3-O-malonylhexoside were detected only in peel and flesh of ‘XJ4’ but not in ‘DH’, which might contribute to the purple peel and dark-red flesh color of ‘XJ4’. Significantly decreased upstream metabolites in the early biosynthetic genes regulated domain were found only in ‘XJ4’ peel but not in the flesh. This might explain why the anthocyanins content in ‘XJ4’ peel was decreased largely at the mature stage. Taken together, our findings will give some insight into the metabolites study in flavonoid biosynthetic pathway of red-fleshed apple.In this research, we compared the phenotypical characters, total anthocyanins content, total phenols content, and antioxidant activity of red-fleshed apple cultivars ‘XJ4’, ‘QN-5’, ‘DH’ and ‘HX1’ at three fruit developmental stages. A further flavonoids metabolites study was conducted in ‘XJ4’ and ‘DH’. We found broader variation of total anthocyanins content in the peel of the four cultivars, which might result in larger differences of free radicals scavenging rate. The most significant difference in fruit phenotype, anthocyanins content, and DPPH scavenging rate was observed between ‘XJ4’ and ‘DH’ at mature stage. Therefore, the flavonoids metabolites of ‘XJ4’ and ‘DH’ at mature stage were compared to unveil the details of anthocyanins compounds. The unique compounds pelargonidin 3- Flavonoids are a big class of polyphenolic plant secondary metabolites, including flavonols, flavones, isoflavones, anthocyanins and so on . Most of2·- radicals scavenging ability and the related studies have been intensively reported in many horticultural crops [Malussieversii f. Neidzwetzkyana (Dieck) Langenf) are valuable resources attracting much more attention because they contain extremely high levels of anthocyanins compared to white and off-white fleshed apples [2·- radicals scavenging when compared to vitamin C [As strong antioxidants, anthocyanins have a strong DPPH, ·OH, and Oal crops . Previoual crops . Lyophilal crops . Purple al crops . Red-fled apples . The enritamin C . A recenitamin C . In addiitamin C . Interesitamin C .Metabolomics is the quantitative study of the metabolite components of integrated living systems, reflecting both endogenous  and exogenous  dynamic changes . PreviouO-β-d-glucoside and cyanidin-3-O-malonylhexoside significantly increased in ‘XJ4’ but not in ‘DH’, which might contribute to the purple peel and dark-red flesh of ‘XJ4’.Over the past decade, studies related to red-fleshed apple were mainly focused on the structural and regulatory genes which play important roles in anthocyanins biosynthesis ,27,28,29At young stage, the flesh color of ‘XJ4’ and ‘QN-5’ was red while only very light red flesh was observed in ‘DH’ and ‘HX1’ A. There −1 FW, followed by ‘QN-5’, whereas the peel of ‘HX1’ (109.1 mg·kg−1 FW) and ‘DH’ (170.6 mg·kg−1 FW) showed the lowest amount. In flesh, the anthocyanins content of ‘XJ4’ was approximately one third of the amount in peel. At developmental stage, total anthocyanins content in peel and flesh of ‘XJ4’ and ‘HX1’ was similar to the levels of young stage. The peel of ‘QN-5’ and ‘DH’ anthocyanins content was significantly lower than the content of young stage. At mature fruit stage, total anthocyanins content in peel of ‘XJ4’ was 441.0 mg·kg−1 FW, which was similar to the content in flesh of ‘XJ4’. The average value of anthocyanins content in peel and flesh of ‘XJ4’ at mature stage was comparable to our previous report of anthocyanins content in the mixture extract of peel and flesh of ‘XJ4’ [−1 FW and 24.3 mg·kg−1 FW respectively.The anthocyanins content in both peel and flesh of red-fleshed apple cultivars ‘XJ4’, ‘QN-5’, and ‘DH’ had decreased gradually with the development of fruit, but the anthocyanins content of ‘HX1’ remained similar level . When coof ‘XJ4’ . The low−1 FW, respectively, whereas the flesh of ‘QN-5’ at young stage showed the lowest amount (3233.6 mg·kg−1 FW). In addition, ‘HX1’ also contained the highest content of total phenols in flesh, with 5346.1 mg·kg−1 FW. At the developmental stage, total phenols content in peel of ‘XJ4’ was the significantly higher than others, whereas the flesh of ‘DH’ and ‘QN-5’ showed the lowest amount. Total phenols content in both peel and flesh of ‘XJ4’ and ‘HX1’ at mature stage was significantly higher than the other two cultivars. ‘QN-5’ peel and flesh showed the lowest amount, only 1239.6 and 327.6 mg·kg−1 FW respectively. Among the four red-fleshed apple cultivars, the anthocyanins differences between ‘XJ4’ and ‘DH’ were the most significant.The differences of total phenols content in both peel and flesh among the four apple cultivars was not as significant as total anthocyanins content. We found the total phenols content in peel was always higher than that in flesh at all three stages, which confirmed the results in previous study using 12 apple cultivars to compare the phenolic compounds contents in peel and flesh . Total p−1 FW). The dilution ratio of ‘XJ4’ was the highest due to high anthocyanins content, while the dilution ratio of ‘DH’ was the lowest. The anthocyanins extract of all four different red-fleshed apples had a stronger ability to scavenge DPPH radical, ·OH, and O2·- when compared with vitamin C (VC) at all three stages. Further, among the three types of free radicals, higher ability to scavenge DPPH radical was detected than to remove ·OH, and O2·-  was observed between ‘XJ4’ and ‘DH’ at mature stage. Therefore, ‘XJ4’ and ‘DH’ at mature stage were used as materials for metabolomics analysis.O-malonylhexoside, cyanidin 3-O-glucoside (kuromanin), and cyanidin 3-galactoside  was the largest, with 3,490,000-fold increase in XJ4PM compared to DHPM, whereas pelargonidin 3-O-malonylhexoside was the lowest, with 1820-fold high in XJ4PM  and chrysoeriol 7-O-rutinoside had extremely significantly high contents only in the DHPM. In the flesh comprision, 6-C-hexosyl-hesperetin O-hexosideluteolin 3′,7-di-O-glucoside, and selgin O-hexosyl-O-hexoside were the three largest metabolites in XJ4FM. Chrysoeriol 7-O-rutinoside, C-hexosyl-luteolin O-sinapic acid, luteolin C-hexoside, and so on were only detected extremely significantly high in the DHFM.For the flavones in the peel, selgin O-rutinoside (nocotiflorin) was the specific metabolite of DHPM. In the flesh, isorhamnetin 3-O-neohesperidoside, kaempferol 3-O-robinobioside (biorobin), and syringetin demonstrated extremely significantly high contents in the DHFM. For the flavanones, only hesperetin O-malonylhexoside and naringenin were detected 13,500- and 21,300-fold in ‘XJ4’ peel and flesh compared to ‘DH’. In the XJ4PM, genistein 7-O-glucoside (genistin) was found with 20,800-fold increment. The content of gallocatechin-gallocatechin was 3,230,000-fold higher in XJ4FM compared to DHFM.The contents of quercetin and laricitrin were extremely higher in XJ4PM while kaempferol 3-In order to better understand which categories the differential metabolites fell into, the metabolites were mapped to KEGG metabolic pathways. KEGG enrichment analysis suggested that the significantly differential metabolites were mainly involved in three biosynthesis pathways, namely isoflavonoid biosynthesis pathway, flavone and flavonol biosynthesis pathway, and anthocyanin biosynthesis pathway . The larO-methylapigenin, 3′-O-methylluteolin, and isoflavone genistein 7-O-glucoside were all significantly increased in the peel of ‘XJ4’ compared to ‘DH’, especially genistein 7-O-glucoside . The downstream compounds quercetin and laricitrin are the main flavonol metabolites, which were also significantly increased in the peel of ‘XJ4’ compared to ‘DH’. Interestingly, the anthocyanins components cyanidin 3-O-glucoside, cyanin 3-O-rutinoside, and cyanidin 3,5-O-diglucoside were significantly increased in ‘XJ4’ peel compared to ‘DH’. On the contrary, only two metabolites, neohesperidin and rutin, were detected significantly increased in ‘DH’ peel. Based on the previous reports, two types of genes, regulatory genes and structural genes, are involved in anthocyanins biosynthetic pathway, and structural genes can be further classified into early biosynthetic genes and late biosynthetic genes [The annotated differential metabolites in flavonoid biosynthetic pathway in DHPM vs. XJ4PM were presented in ic genes ,39. The O-methylluteolin, and genistein 7-O-glucoside were significantly increased in ‘XJ4’ flesh. The anthocyanins components cyanidin 3-O-glucoside, and cyanidin 3,5-O-diglucoside were significantly increased in ‘XJ4’ flesh compared to ‘DH’. Naringenin was detected significantly increased in ‘XJ4’ flesh compared to ‘DH’ but not in peel. As mentioned, quercetin and laricitrin that were found significantly higher in the peel of ‘XJ4’ compared to ‘DH’, but not in the flesh. In addition, syringetin was the only metabolite that had significantly higher level in ‘DH’ flesh. The detected differential metabolites in flavonoid biosynthetic pathway in DHFM vs. XJ4FM were different from DHPM vs. XJ4PM . NaringeHere, it was obvious that significantly higher levels of metabolites in ‘XJ4’ flesh compared to ‘DH’ flesh were found in both early and late biosynthetic genes regulated domain. This was different from the comparison between ‘DH’ peel and ‘XJ4’ peel, which showed significantly higher levels of metabolites in ‘XJ4’ peel compared to ‘DH’ peel only existed in late biosynthetic genes-regulated domain. As a result, in ‘XJ4’ peel the decrease of upstream metabolites in the early biosynthetic genes regulated domain might cause the final anthocyanins content decrease. This might explain why the total anthocyanins content in ‘XJ4’ peel was decreased largely from young and developmental stages to mature stage, while the flesh anthocyanins were more stable through all three stages .O-β-d-glucoside and cyanidin 3-O-malonylhexoside were detected in ‘XJ4’ compared to ‘DH’ (O-diglucoside (cyanin), cyanidin 3-O-rutinoside (keracyanin), and cyanidin 3-O-glucoside were detected in ‘XJ4’ and at the same time annotated by KEGG into the anthocyanins biosynthesis pathway. What’s more, the accumulation of apigenin, naringenin and genistein 7-O-glucoside were detected in ‘XJ4’ and annotated by KEGG into the isoflavonoid biosynthesis pathway. Therefore, the accumulation of these six significantly differential metabolites in ‘XJ4’ resulted in the changes of anthocyanins biosynthesis pathway and isoflavonoid biosynthesis pathway. These significantly differential metabolites worked together and further regulated the peel and flesh of ‘XJ4’ to remain purple red, highest total phenols content, highest anthocyanins content, and higher free radicals scavenging ability. In addition, it’s necessary to note that the huge accumulations of anthocyanins in peel and flesh such as pelargonidin 3- to ‘DH’ , but theO-β-d-glucoside and cyanidin-3-O-malonylhexoside were the most abundant metabolites in XJ4PM and XJ4FM when compared to DHPM and DHFM. When ‘XJ4’ flesh compared to ‘DH’ flesh, the significantly increased metabolites were detected in both early biosynthetic genes regulated domain  and late biosynthetic genes regulated domain . However, in the peel comparison, the significantly higher levels of metabolites in ‘XJ4’ peel only existed in late biosynthetic genes regulated domain. The flesh anthocyanins were more stable through all three stages, which might because the significantly higher levels of metabolites in ‘XJ4’ flesh existed in both early and late biosynthetic genes regulated domain.In this study we found generally the DPPH radical scavenging rate was positively related to anthocyanins content in four phenotypically different red-fleshed apple cultivars ‘XJ4’, ‘QN-5’, ‘DH’ and ‘HX1’. Among these four cultivars, ‘XJ4’ and ‘DH’ had the biggest difference in the aspects of fruit phenotype, anthocyanins content, and antioxidant activity (mainly DPPH) at mature stage. Therefore, in order to unveil the specific details and mechanism of anthocyanins accumulation in peel and flesh of these two cultivars, the differential flavonoids metabolites in peel and flesh of ‘XJ4’ and ‘DH’ were compared. We found pelargonidin 3-Malus Robusta for 8 years at the experimental farm of Qingdao Agricultural University . Fruits were harvested on May 18 , July 12 , and August 30 , respectively. The peel and flesh of fruits were sampled separately and freeze-dried in liquid nitrogen, lyophilized and then transferred into −80 °C freezer for further use.The four different phenotypic red-fleshed apple cultivars ‘XJ4’, ‘QN-5’, ‘DH’ and ‘HX1’ were grafted on O-glucoside, DF means the dilution factor, ε  means the molar absorptivity of cyanidin 3-O-glucoside, and W means the fresh weight of each sample.The total anthocyanins from red-fleshed apple were extracted with methanol-HCl  at the ratio of 1:10  under dark for 15 h. The supernatant was subsequently filtered with a 0.45 um membrane and stored at −4 °C. Total anthocyanins content was determined by pH differential method . The amoThe total phenol content was determined by Folin-Ciocalteu method, as modified by Cai . The FolThe method of detecting scavenging capacity of DPPH radical was slightly modified from He . The sam2O2 solution was added into the injured group and sample group after 1 mL distilled water was put into uninjured group. The absorbance of three groups was detected at 536 nm after incubation at 37 °C for 1 h. The scavenging rate of ·OH was calculated with the equation:The assay of ·OH-scavenging was conducted following the method modified from Ma . The amo2·- [2 was calculated by the equation:The modified method of pyrogallol autoxidation was used to detect scavenging rate of O2·- . The amog for 10 min and filtrated before LC-MS analysis. The samples were divided into two comparison groups, which were ‘DH’ Flesh Metabolites vs. ‘XJ4’ Flesh Metabolites (DHFM vs. XJ4FM), ‘DH’ Peel Metabolites vs. ‘XJ4’ Peel Metabolites (DHPM vs. XJ4PM). DHFM and DHPM were the control group in DHFM vs. XJ4FM, DHPM vs. XJ4PM respectively. The extract of peel and flesh from red-fleshed apple was absorbed  and filtered  before LC-MS analysis.The freeze-dried peel and flesh of ‘XJ4’ and ‘DH’ at mature stage were crushed into powder using a mixer mill  with a zirconia bead for 1.5 min at 30 Hz. A total of 100 mg powder of samples was weighted and extracted overnight at 4 °C with 1.0 mL 70% aqueous methanol. The extract was centrifuged at 10,000× http://www.shimadzu.com.cn/) and a tandem mass spectrometry (MS/MS) instrument . In total 2 µL of sample were injected into the HPLC system equipped with a C18 column . Ultra-pure water with 0.04% acetic acid was used as mobile phase A and acetonitrile with 0.04% acetic acid was used as mobile phase B. The gradient program was 100:0 v/v at 0 min, 5:95 v/v at 11.0 min, 5:95 v/v at 12.0 min, 95:5 v/v at 12.1 min, 95:5 v/v at 15.0 min. The flow rate was 0.40 mL/min and the column temperature was 40 °C. The effluent was connected to electrospray ionization (ESI)-triple quadrupole-linear ion trap-MS/MS system (6500 Q TRAP). The ion source was turbo spray and source temperature was maintained at 500 °C. The ion spray voltage (IS) was 5500 V and the collision gas (CAD) was high. In addition, the ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set at 55.0, 60.0, and 25.0 psi, respectively. Triple quadrupole (QQQ) scans were acquired as MRM experiments with collision gas (nitrogen) set at 5 psi. The high performance liquid chromatography (HPLC) effluent was connected to the Applied Biosystems 6500 Q TRAP electrospray ionization (ESI)-triple quadrupole-linear ion trap-MS/MS system. A specific set of multiple-reaction monitoring (MRM) transitions was monitored for each period. Metabolite identification was based on MWDB (metware database), MassBank (http://www.massbank.jp/), KNAPSAcK (http://kanaya.naist.jp/KNApSAcK/), HMDB (http://www.hmdb.ca/), METLIN (http://metlin.scripps.edu/index.php), MoTo DB (http://www.ab.wur.nl/moto/). The thresholds of variable importance in projection (VIP) ≥1 and fold change ≥2 or ≤0.5 were set to filter metabolites with significant differences in content.The sample analysis instruments system mainly consists of an Ultra Performance Liquid Chromatography system  analysis software and Origin 9.0 software  were used to test multiple comparisons and plot charts.


Supplementation with nicotinamide and butyrate sodium can improve the meat quality of broilers. However, the potential molecular mechanism is not clear yet. This study was designed to investigate the effects of supplementation with a combination of nicotinamide and butyrate sodium on breast muscle transcriptome of broilers under high stocking density. A total of 300 21-d-old Cobb broilers were randomly allocated into 3 groups based on stocking density: low stocking density control group . The transcriptome results showed that key genes involved in glycolysis, proteolysis and immune stress were up-regulated whereas those relating to muscle development, cell adhesion, cell matrix and collagen were down-regulated in the H group as compared to the L group. In contrast, genes related to muscle development, hyaluronic acid, mitochondrial function, and redox pathways were up-regulated while those associated with inflammatory response, acid metabolism, lipid metabolism, and glycolysis pathway were down-regulated in the COMB group when compared with the H group.The H group significantly increased cooking losses, pH decline and activity of lactate dehydrogenase in breast muscle when compared with the L group. COMB showed a significant decrease in these indices by comparison with the H group (The combination of nicotinamide and butyrate sodium may improve muscle quality by enhancing mitochondrial function and antioxidant capacity, inhibiting inflammatory response and glycolysis, and promoting muscle development and hyaluronic acid synthesis. Intensive stocking in the rapidly developing poultry industry worldwide has become a norm. However, high stocking density causes oxidative stress in broilers  and reduCaenorhabditis elegans , are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 2: Figure S2. Chemokine signaling pathway analysis. Differential expressed genes that are involved in the chemokine signaling pathway [map 04062], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 3: Figure S3. Calcium signaling pathway analysis. Differential expressed genes that are involved in the calcium signaling pathway [map 04020], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 4: Figure S4. ECM-receptor interaction pathway analysis. Differential expressed genes that are involved in the inflammatory mediator regulation of RTP channels [map 04512], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 5: Figure S5. Adherens junction pathway analysis. Differential expressed genes that are involved in the adherens junction [map 04520], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 6: Figure S6. Focal adhesion pathway analysis. Differential expressed genes that are involved in the focal adhesion [map 04510], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 7: Fig. S7. Oxidative phosphorylation pathway analysis. Differential expressed genes that are involved in the oxidative phosphorylation [map 00190], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 8: Figure S8. Regulation of lipolysis in adipocytes pathway analysis. Differential expressed genes that are involved in the Regulation of lipolysis in adipocytes [map 04923], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 9: Figure S9. Glycolysis/Gluconeogenesis pathway analysis. Differential expressed genes that are involved in the Glycolysis/Gluconeogenesis [map 00010], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .Additional file 10: Figure S10. Arachidonic acid metabolism pathway analysis. Differential expressed genes that are involved in the arachidonic acid metabolism [map 00590], are highlighted. We thank Kanehisa Laboratories for providing the copyright permission of KEGG pathway maps [way maps .


Amaranthus palmeri is a fast-growing weed, and several populations have evolved resistance to glyphosate through increased EPSPS gene copy number. The main objective of this study was to elucidate the regulation of the shikimate pathway and determine whether the regulatory mechanisms of glyphosate-sensitive and glyphosate-resistant plants were different. Leaf disks of sensitive and resistant (due to EPSPS gene amplification) A. palmeri plants were incubated for 24 h with glyphosate, AAA, glyphosate + AAA, or several intermediates of the pathway: shikimate, quinate, chorismate and anthranilate. In the sensitive population, glyphosate induced shikimate accumulation and induced the gene expression of the shikimate pathway. While AAA alone did not elicit any change, AAA applied with glyphosate abolished the effects of the herbicide on gene expression. It was not possible to fully mimic the effect of glyphosate by incubation with any of the intermediates, but shikimate was the intermediate that induced the highest increase (three-fold) in the expression level of the genes of the shikimate pathway of the sensitive population. These results suggest that, in this population, the lack of end products (AAA) of the shikimate pathway and shikimate accumulation would be the signals inducing gene expression in the AAA pathway after glyphosate application. In general, the effects on gene expression detected after the application of the intermediates were more severe in the sensitive population than in the resistant population. These results suggest that when EPSPS is overexpressed, as in the resistant population, the regulatory mechanisms of the AAA pathway are disrupted or buffered. The mechanisms underlying this behavior remain to be elucidated.The herbicide glyphosate inhibits the plant enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the aromatic amino acid (AAA) biosynthetic pathway, also known as the shikimate pathway.  Besides to their function in protein synthesis, aromatic amino acids (AAA) serve as precursors of a wide variety of natural products that play crucial roles in plant signaling, growth and development, including responses to biotic and abiotic stresses , such asThe AAA biosynthetic pathway, also known as the shikimate pathway, is located in plastids and can be subdivided into two steps: the prechorismate pathway and the postchorismate pathway, which via two different routes can lead from chorismate to the synthesis of Phe and Tyr or Trp . The preAs sessile organisms, plants should regulate and adjust their metabolism to dynamic changes. These changes can be at the level of amino acid metabolic networks , or at tThe major factors limiting the production of essential amino acids in plants are the regulatory factors that control their synthesis by feedback inhibition loops and their efficient catabolism . In planThe EPSPS is the target of glyphosate , the mosAmaranthus palmeri S. Wats  have not been thoroughly investigated. Moreover, the use of a A. palmeri glyphosate-sensitive (GS) and glyphosate-resistant (GR) plants. Finally, the shikimate content, relative gene expression level and protein content of the shikimate pathway were assessed in presence of glyphosate, intermediates and final products of the AAA biosynthetic pathway in leaf disks of GS and GR A. palmeri plants.The aim of this study was to evaluate the role of AAA and its intermediates in the regulation of the shikimate pathway. This evaluation was performed by analyzing whether AAA could revert the effects of glyphosate on the pathway and if the supply of intermediates of the pathway could mimic the glyphosate effects using Amaranthus palmeri GS and GR biotypes were originally collected from North Carolina (United States) (EPSPS gene amplification ( States) . The resfication , with 47fication . Seeds wfication .palmeri GR population was harvested and immediately frozen in liquid nitrogen to determine the EPSPS relative genomic copy number of those individuals. After evaluating the results, 30 out of 48 evaluated plants were selected to obtain a homogeneous population with a similar relative EPSPS genomic copy number (between 60 and 100) to perform the experiments.Before performing the incubation, a leaf of each plant of the A. When the GS and selected GR plants were 21 days old, leaf disks were excised from the leaves using a Harris Uni-Core puncher (4-mm diameter) , avoiding the leaf nerves. From each plant, the two youngest fully expanded leaves were used: one leaf to determine the shikimate content and another leaf to determine transcript levels and enzyme content measurements.–2 s–1 photosynthetic photon flux) at 24°C. Solutions were freshly prepared, and the pH was adjusted to 7.0 with NaOH in all the treatments. Technical glyphosate  was used. All other reagents were purchased from Sigma–Aldrich Chemical, Co. .The same treatments and doses were applied in both populations . Leaf diThe incubation system is shown in After incubation, disks incubated in 96-well plants were washed thoroughly before freezing, and the plates were placed in a freezer (−20°C). Disks incubated in 6-well microplates were removed from the incubating medium, immediately frozen in liquid nitrogen and stored at −80°C. The experiment was repeated twice.The concentration of shikimate in each leaf disk located in each cell was measured according to the procedure described previously . BrieflyProtein extraction was performed using 0.1 g of ground leaf tissue in 0.2 mL of extraction buffer . Proteins were separated by 12.5% SDS-PAGE and immunoblots were produced according to standard techniques. The protein amount loaded per well for each antibody used is specified in the figure legends. EPSPS and DAHPS antibody dilutions were 1:2000 and 1:1000, respectively . Bands wA. palmeri was not sequenced, primers were designed using a related sequenced species of the Amaranthaceous family and crossed with Arabidopsis thaliana. Most primers were used previously . Statistical analyses were performed using IBM SPSS statistics 24.0 .Analysis was performed using 10 biological replicates for shikimate content determination and three or four biological replicates for enzyme content or nucleic acid determination, respectively. Replicates from both experiments were used. In the study of glyphosate and AAAs, differences between treatments for each population were evaluated by one-way ANOVA with a multiple-comparison adjustment (Tukey) at –1) among untreated plants in both populations .Shikimate content was low and similar  . In relaDAHPS, DHQS, DQSD, SK, EPSPS, and CS) and only an increase of ADHα transcript level in the postchorismate pathway . In contrast, in the GR population, glyphosate did not induce the relative expression level of these genes in this pathway  in the relative expression level of all the genes of the prechorismate part of the AAA biosynthetic pathway , with the post-chorismate part being the more affected. Interestingly, this pattern was not observed in the GR population, where only DQSD and the ADHα isoform increased their relative mRNA levels after shikimate  were upregulated after quinate incubation. The incorporation of quinate in the shikimate pathway can occur through two different pathways: through the reversible quinate dehydrogenase to 3-dehydroquinate and through the quinate hydrolyase to shikimate  and ADH (1.6-fold) genes in GS and ADH (9.4-fold) in GR after quinate would confirm that quinate would direct the flux to the synthesis of Tyr and Phe, as has been described recently , the enzyme that uses chorismate as a substrate. It seems that as happens with glyphosate (The incubation with chorismate did not alter the transcription level of pulation . In the yphosate , the fluyphosate , which dyphosate , in whicyphosate .SK expression was 1.75-fold upregulated. In the GS population, a different response was observed on the two isoenzymes of CM: the plastidics 1 and 3 isoenzymes but not the cytosolic isoform 2 would be downregulated by anthranilate. In the same way, the two isoenzymes of ADH also showed different responses, and ADHα expression was the only downregulated 1.5-fold by anthranilate. In the same way, differences in the regulation of the isoforms by other metabolites of the pathway has been reported in Beta vulgaris, where only the activity of ADHα exhibited relaxed sensitivity to Tyr and ADHβ was strongly inhibited (Anthranilate was the intermediate that less affected the expression level of the shikimate pathway. In the GR population, only nhibited .AAA biosynthesis is subjected to complex posttranscriptional and allosteric regulations . How carEPSPS overexpressing population, consistent with its resistance to glyphosate, as has been reported before in susceptible and resistant soybeans (EPSPS would have an effect in the regulation of the pathway, buffering or attenuating the transcriptional changes induced by the intermediates.The results showed that in sensitive plants glyphosate affects the shikimate pathway, and no such changes were observed in the soybeans . Beside The exogenous supply of AAA did not induce any notable change in the transcriptome of the shikimate pathway. Nevertheless, when applied in combination with glyphosate, the upregulation of gene expression detected after glyphosate was abolished, suggesting that the effect of glyphosate on relative expression level of pre-chorismate genes is mediated by a transitory lack of the final products, as detected in the GS population. Shikimate accumulation was a dose-response direct effect of EPSPS inhibition detected in both populations and cannot be abolished by increased AAA availability.In conclusion, this study suggests that any perturbation in the shikimate pathway would provoke changes in the relative transcript level of the genes and confirm a complex regulation of this pathway with mechanisms interacting at different levels and behaving differently in each population. No intermediate fully mimicked the effect of the herbicide. Nevertheless, although the toxic effect of the herbicide could be due to a combination of different factors, shikimate incubation, similar to glyphosate, elicited upregulation of most of the shikimate pathway genes.EPSPS is overexpressed.The effects detected after the application of the final products or the intermediates of the shikimate pathway were more severe in the GS than in the GR population, suggesting that the regulatory mechanisms that operate in the GS population are disrupted or buffered when The datasets generated for this study are available on request to the corresponding author.MR and AZ conceived and designed the research. AZ-G, MB-A, and MG-M conducted the research. AZ-G and MG-M analyzed the data. AZ-G and AZ wrote the manuscript. All authors have read and approved the manuscript. All authors were involved in the implementation of the experimental design and have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Harmonisation is the process whereby standardised uptake values from different scanners can be made comparable. This PET/CT pilot study aimed to evaluate the effectiveness of harmonisation of a modern scanner with image reconstruction incorporating resolution recovery (RR) with another vendor older scanner operated in two-dimensional (2D) mode, and for both against a European standard (EARL). The vendor-proprietary software EQ•PET was used, which achieves harmonisation with a Gaussian smoothing. A substudy investigated effect of RR on harmonisation.Phantom studies on each scanner were performed to optimise the smoothing parameters required to achieve successful harmonisation. 80 patients were retrospectively selected; half were imaged on each scanner. As proof of principle, a cohort of 10 patients was selected from the modern scanner subjects to study the effects of RR on harmonisation.p = 0.78). Bland-Altman analysis showed that harmonisation reduced the SUVmax ratio between RR and no RR to 1.07 (95% CI 0.96–1.18) with no outliers.Before harmonisation, the modern scanner without RR adhered to EARL specification. Using the phantom data, filters were derived for optimal harmonisation between scanners and with and without RR as applicable, to the EARL standard. The 80-patient cohort did not reveal any statistically significant differences. In the 10-patient cohort SUVmax for RR > no RR irrespective of harmonisation but differences lacked statistical significance (one-way ANOVA F(3.36) = 0.37, EQ•PET successfully enabled harmonisation between modern and older scanners and against the EARL standard. Harmonisation reduces SUVmax and dependence on the use of RR in the modern scanner.EQ•PET is feasible to harmonise different PET/CT scanners and reduces the effect of RR on SUVmax. However, advanced image reconstruction algorithms including resolution recovery (RR) with point spread function modelling or Bayesian reconstruction4 are also in widespread clinical use today.The recent rate of advance of PET/CT equipment technology has led to the everyday use worldwide of many variants of detector and software.5 Ideally, standardised protocols would be used to ensure comparability between the SUVs; however, it is frequently necessary to compare SUVs acquired in 2D and 3D,6 or reconstructed with different algorithms. Harmonisation is the process by which it is assured that SUVs acquired using different scanners or protocols can be compared; if harmonisation is not performed there is a risk of misclassification of lesion response.It is frequently necessary to compare standardised uptake values (SUV) of a lesion between two scans, for example in assessing response to therapy using PERCIST.7 Such variations are problematic both in clinical work and in multicentre clinical trials, where multiple imaging systems are used to recruit sufficient patients for statistical power. The process of deriving comparable SUVs from different equipment and protocols is termed harmonisation. Controversy exists in comparing clinical scans acquired with different advanced reconstruction software8–11 that has stimulated debate regarding the use of harmonisation schemes.12SUV variations can arise from factors like patient preparation, scan protocol, data acquisition/processing and image reconstruction/analysis.13,14 and the Radiological Society of North America’s (RSNA) North American Quantitative Imaging Biomarkers Alliance (QIBA). Guidelines generally recommend scanning a fillable phantom consisting of hot spheres in a colder background, such as that designed by the Association of Electrical Equipment and Medical Imaging Manufacturers (NEMA) and the International Electrotechnical Commission (IEC). EARL recommends a value and permissible range for the recovery coefficient (RC) for each sphere diameter in the phantom.15 Such approaches have proved helpful for achieving harmonisation in clinical trials.Attempts to minimise SUV inconsistencies through harmonisation strategies have been recommended by various internationally recognised programmes, for example European Association of Nuclear Medicine (EANM) Research Ltd (EARL) accreditationsyngo.MM Oncology, Siemens Medical Solutions USA Inc., Knoxville, TN, United States)16.One challenge with such methodology is the potential generation of a separate image set optimised for harmonisation, distinct from the other datasets usually acquired at a centre. This can have consequences for scan labelling, identifying which scan to report, disk storage/data backup and manpower resources. A solution to the challenge of multiple dataset production and associated management has been proposed as the “EQ•PET” proprietary harmonisation software developed by Siemens . The EQ•PET factor is usually derived using the NEMA / IEC phantom and is entered directly into the vendor’s software. Smoothing is performed in the background as the original image is displayed, and the harmonised SUV is displayed without additional image creation. Detectability and harmonisation are thus achieved simultaneously. The EQ·PET has been shown to product similar results as EARL,17 but without requiring a separate reconstruction.EQ•PET allows SUV to be harmonised to baseline scans obtained with less spatial resolution . EQ•PET applies a 3D Gaussian filter to images, using a filter kernel width (“EQ•PET factor”) derived specifically for the particular scanner and protocol in use, that optimally harmonises that scanner and protocol to an agreed standard  crystals, including the increased axial field of view (Siemens “TrueV”) which operates without septa in “3D mode” (“scanner A”), and General Electric (GE)  Discovery VCT with BGO crystals (“scanner B”), operated with septa extended between imaging planes in “2D mode”. Harmonisation was performed both between each scanner to the EARL standard, and also between the two scanners, harmonising the newer mCT to the older VCT. A NEMA phantom was used to derive EQ•PET filter parameters. The SUVs measured on clinical images from both scanners were then compared, with and without harmonisation.This study does not constitute clinical research according to the National Health Service England and was therefore conducted as an audit without the need for patient consent or ethical approval.The NEMA phantom was prepared without the lung insert as scatter correction was not a prime concern. Spheres and background were filled with F-18 FDG solution concentrations of 45 kBq/ml and 5 kBq/ml respectively, at the time of the first imaging, giving a ratio of 9:1 between these phantom compartments.18The phantom was scanned on both scanners using standard clinical imaging protocols. The scanner A protocol consisted of a 3D PET acquisition, with 3 min/bed, reconstructed using ordered subsets expectation maximisation using time-of-flight information (OSEM + TOF), both with and without RR, with two iterations, 21 subsets, a 200×200 matrix and a Gaussian 5 mm post-filter (referred to hereafter as OSEM + TOF+ RR and OSEM + TOF). The scanner B protocol consisted of a 2D PET acquisition with 4 min/bed, reconstructed using OSEM VuePoint with two iterations, 28 subsets, a 128×128 matrix and a 5.14 mm post-filter. Standard clinical low-dose CT protocols were used for attenuation correction on both scanners. The phantom was scanned six times, three times alternately on each scanner, starting with scanner B. Both scanners had passed manufacturer and nationally approved QA regimes and were serviced at recommended periods.The following harmonisations were attempted using EQ•PET filtering:To harmonise scanner A OSEM + TOF+ RR data to EARLTo harmonise scanner A OSEM + TOF data to EARLTo harmonise scanner B data to EARLTo harmonise scanner A OSEM + TOF+ RR data to scanner B data18 and entered into the vendor’s dedicated viewing and analysis workstation .For each harmonisation, an optimised EQ•PET filter parameter was calculatedThe EARL standard specifies a range in which the RC should reside for each phantom sphere diameter. However, in the case of scanner A harmonisation to scanner B (case four above), no standard upper or lower limits are available to validate the harmonisation success. Therefore for consistency we used the permitted range for each sphere in the EARL recommendations, as a percentage of the RC for that sphere, and applied the same percentage to the scanner B mean RCs to generate a comparable window of acceptance for harmonisation in this case. The phantom-derived EQ•PET filters were then used in the next section with clinical data.n = 10), lung (n = 10), oesophagus (n = 5), colorectum (n = 8) or lymphatic system (n = 7) were consecutively selected from the scanner A database to investigate the influence of different clinical acquisition parameters on the ability of EQ•PET to harmonise suspected lesion SUVmax. Similarly a second independent group of 40 patients with the same number of clinical indications were also consecutively selected from the scanner B database over the same time period. All 80 patients were imaged using routine protocols over a period of approximately 7 months. The tracer used was F-18 FDG throughout. Patients were imaged 90 min after injection.40 patients with suspected cancer in the head and neck  for weight, age, injected activity and blood glucose was 78.22 kg ±22.98, 64.30 year ±11.66, 347.5 MBq ±97.5 and 6.25 mmol ln = 1), lung (n = 5) and lymphoma (n = 4) cancer with a range of suspected lesion SUVmax was also arbitrarily selected to investigate the effect of OSEM + TOF+ RR versus OSEM + TOF reconstruction with and without EARL harmonisation.A subset of 10 scanner A patients comprising head and neck . Lesions were identified by inspection of the images during reporting; clinicians drew VOIs and derived lesion SUVmax accordingly. Comparisons were made of SUVmax pre-harmonisation and post-harmonisation.Radiologists reported PET/CT scans under usual standard-of-care conditions on dedicated vendor-specific workstations  to determine statistical significance between distributions. Because distributions were tested for normality, non-parametric testing was not used in this analysis. Comparison of paired weight, age, injected activity and blood glucose datasets between scanners for non-normal patient demographic data was evaluated using Wilcoxin-signed ranked test. StatsDirect statistical software package  was used for all analysis with a p-value of <0.05 considered statistically significant.SUVmax distributions were evaluated for normality using Shapiro-Wilk test and if necessary logRespective phantom RCs harmonised to the EARL criteria are displayed in In the case of scanner A harmonised to scanner B , it is s2. clinical datap = 0.89), weight (p = 0.96), injected activity (p = 0.72) or blood glucose (p = 0.34). There is therefore a low probability of bias in results presented here arising from variations in patient demographics between scanners.40 different patients were imaged on each scanner. For each scanner, demographic parameters were investigated, some known to influence SUVmax: no significant differences were found between paired patient age (p = 0.51).The clinical effect of scanner A RR with or without harmonisation with respect to EARL or scanner B can be seen in the box and whisker plot  depictinp = 0.78).2 = 0.99) or without (r2 = 0.97) harmonisation. The correlation coefficient must be interpreted bearing in mind that reconstructions with and without RR derive from the same raw data; note although that the level of correlation remains high when harmonisation is applied. The equality line shows that SUVmax with RR is greater than that without RR, irrespective of harmonisation status; however, the slope for harmonised scans (0.89) is greater than that for non-harmonised scans (0.76), indicating that with harmonisation there is less effect on SUVmax when RR is included in the reconstruction.For this subcohort 19 and have clinician-approved image quality. During scanner downtime, consistency in patient SUV may be preserved by scanning on different equipment and harmonising SUVs. In addition, harmonisation using modern equipment can play a role immediately following replacement of older technology, where one may wish to ensure consistency of SUV between scanners. EQ•PET harmonisation assists without additional dataset creation in such cases.Harmonising scanners in PET/CT centres with different imaging equipment may be of value when scanners have not or cannot be harmonised to recommended guidelines like EARL, but pass all required QA regimes for safe use20 We found that datasets reconstructed with RR yielded higher mean SUVmax values than datasets reconstructed without RR, but that this difference was less when harmonisation was applied. However, no statistically significant differences in SUVmax were observed between scanner A RR, scanner A RR harmonised to EARL, scanner A RR harmonised to scanner B, scanner B or scanner B harmonised to EARL scanned datasets.We successfully harmonised scanner A data with and without RR to EARL and scanner B data to EARL at a centre that had PET/CT national certification but had not applied for EARL accreditation, It should be noted that the phantom used for these results did not have the lung insert in place, and this might affect the relationship of 2D to 3D results because the scatter conditions are significantly different between the two modalities. This was not investigated in this work and is a limitation of the study.SUVmax comparison of clinical datasets from scanner A, with or without RR, and with or without harmonisation was investigated in a subgroup of 10 patients . Mean SUThe findings in  demonstr21 heterogeneous lesions and variable background activity distributions including respiratory motion effects. Other filtering techniques and measurement metrics for example SUVpeak, tumour lesion glycolysis (TLG) or metabolic tumour volume (MTV) may provide additional information in this setting and be less sensitive to reconstruction algorithms,22 and it is a specific limitation of the study that only SUVmax is considered. This paper does not consider the frequency with which EQ•PET filter parameters should be checked; however, it is reasonable to assume that this should be done after significant modification to the data acquisition, reconstruction or image processing.20The EANM/EARL and EQ•PET harmonisation process applied in this pilot study has limitations. Internationally not all PET/CT scanning centres fully conform to EANM-endorsed imaging guidelines but will maintain national QA standards for routine and research scans. Here EARL RCs were based on an average of three scans. Furthermore, RR reconstruction may be differently characterised in simple geometry like the NEMA phantom rather than clinically with variable lesion sizes,23 used EQ•PET on images from a single scanner, reconstructed using RR and 3D OSEM, of 50 NSCLC patients. Bland-Altman analysis showed that the impact on SUV of the choice of reconstruction algorithm could be reduced without adversely affecting throughput of reconstruction and image interpretation. In another study,24 86 non-small cell lung cancer (NSCLC) patients were imaged for treatment response assessment using three 3D scanners from the same vendor. The authors showed that choice of reconstruction algorithm introduced variability in PERCIST (PET response criteria in solid tumours) response classification, which was minimised by the use of harmonisation to EARL with EQ•PET using lean body mass-based SUVpeak.There is growing awareness of PET harmonisation techniques, particularly involving modern technology operated under EANM guidelines against the EARL standard. A recent harmonisation study25 describing different generation scanners, different reconstruction parameters were used to confirm successful EARL harmonisation via Bland-Altman analysis. In a different study,26 517 oncology patients with NSCLC, non-Hodgkin lymphoma and metastatic colorectal cancer were scanned on three different 3D scanner models but from the same manufacturer. Bland-Altman results showed EARL harmonisation with EQ•PET was successful for RR, TOF and OSEM algorithms using SUVmax, SUVmean and SUVpeak.In a further NSCLC study27 endorsed PET/CT EARL harmonisation methodology using EQ•PET in the oncology therapy setting whilst validating accreditation methodology in 51 participating centres. In a new publication,20 four modern TOF-enabled and RR-enabled 3D PET/CT scanners from three different vendors were used to demonstrate that harmonisation of phantom studies to EARL specifications could be achieved. However, different RCs were advocated and a modification to multicentre accreditation was recommended as a consequence of advanced reconstruction algorithms.Another review28 investigated adapting harmonisation to other radiopharmaceuticals for example Ga-68-PSMA in prostate cancer for lymph node detection. Literature also exists relating to advanced image analysis techniques like textural analysis29 with PET/CT30 which require consistency between SUV acquired under different conditions. In a single 3D scanner study31 with 71 lesions in 60 NSCLC patients comparable SUV and heterogeneity information was achieved using RR and harmonising with Gaussian smoothing, and then compared to simpler OSEM reconstructed scans.A recent forward-facing reviewThe majority of clinical studies using EQ•PET have been based on NSCLC, with Siemens modern 3D scanners using LSO-based crystal detector technology. There is little reported about harmonisation between truly different generations of PET/CT scanner, especially across different vendors.The retrospective cohort of 80 patients in this study demonstrated the clinical feasibility of harmonisation between two scanners and against the EARL standard. Future studies are suggested with larger patient numbers combined with varied scanners from different vendors for further analysis of the clinical consequences of harmonisation. We encourage the adoption of the EQ•PET concept to promote harmonisation without creation of multiple datasets.As a PET/CT centre approved for national research studies, we have shown in a pilot study that different PET/CT scanners can be harmonised using EQ•PET to current EARL specifications. Our results are in accordance with others. No statistically significant differences among 80 suspected oncology patient lesion SUVmax with or without harmonisation between scanners was observed. Standard clinical protocols with or without RR reconstruction had lower SUVmax mean and sd following respective harmonisation. A subset of 10 patients reconstructed with or without RR and with or without harmonisation revealed smaller mean and sd in harmonised scans, but again differences were not statistically significant. Harmonisation with EQ•PET is shown to reduce dependence on reconstruction algorithm in both older and modern scanners.