A comprehensive strategy based on several Chinese herb databases and chemical compound databases was established to screen active compounds of ZSYTP and predict target genes. For network pharmacological analysis, network construction and gene enrichment analysis were conducted and further verified by molecular docking.  A total of 476 target genes of ZSYTP were obtained from 205 active compounds. 13 herbs of ZSYTP overlapped on 8 active compounds based on the compound-target-disease network (C-T network). 20 biological processes and 9 pathways were strongly connected to the targets of ZSYTP in treating POI, including negative regulation of gene expression, mRNA metabolic process, hypoxia-inducible factor 1 (HIF-1) signaling pathway, and gluconeogenesis. Finally, molecular docking was visualized.  Intriguingly, the signal pathways and biological processes uncovered in this study implicate inflamm-aging and glucose metabolism as potential pathological mechanisms of POI. The therapeutic effect of ZSYTP could be mediated by regulating glucose metabolism and HIF-1 signal pathway. Collectively, this study sheds light on the therapeutic potential of ZSYTP on POI. Premature ovarian insufficiency (POI) is a common reproductive endocrine disorder in females of childbearing age. POI is characterized by amenorrhea before the age of 40, with high gonadotropin and low estrogen . EndocriComplementary and alternative medicine (CAM) refers to a group of diverse medical and healthcare systems, practices, and products that are not generally considered a part of conventional medicine  and inclNetwork pharmacology is an approach to drug design that improves clinical efficacy and understands side effects and toxicity . It enabhttp://lsp.nwu.edu.cn/tcmsp.php, updated in March 2014) [http://tcm.cmu.edu.tw) [http://bionet.ncpsb.org/batman-tcm/) [Chemical compounds from all 15 herbs in ZSYTP were obtained from the Traditional Chinese Medicine Systems Pharmacology  , TCM Datan-tcm/) . The TCMThe screening filters used in this research to maximize drug discovery included oral bioavailability (OB) ≥ 30%  and drugOB is the percentage value that measures the fractional extent of an orally administered drug that reaches the systemic circulation after ADME. A higher OB value indicates that a lesser amount of drug must reach the intended pharmacological effect, thereby reducing the risk of drug toxicity and potential side effects. A chemical compound with a low OB value indicates a poor drug effect and a higher intersubject variability. Thus, the OB value is one of the most commonly used pharmacokinetic properties in drug screening.DL is a network pharmacological concept that indicates the similarity or likeness of the compound in question compared with known compounds. The concept originates from Lipinski's Rule of Five, which is used to estimate the possibility of obtaining a pharmacologically active compound, thereby reducing failure rates and increasing efficacy in pharmacological studies .http://pubchem.ncbi.nlm.nih.gov/) [The canonical SMILES and IUPAC International Chemical Identifier (InChI) of each of the chemical compounds were collated from PubChem Database (ih.gov/)  to ensurhttp://stitch.embl.de) [http://www.swisstargetprediction.ch/index.php) [http://pubchem.ncbi.nlm.nih.gov/), an online chemistry database providing drug-target identification; and (4) DrugBank (https://go.drugbank.com) [We input all molecular information of the chemical compounds of ZSYTP into the following: (1) STITCH (embl.de) , a databdex.php) , an onliank.com) , an onliThe canonical SMILES of each compound were input into the “chemical structure(s)” search engine of STITCH, and “Homo sapiens” was selected as the organism. Similarly, the canonical SMILES of each active compound were input into the search engine of Swiss Target Prediction; “Homo sapiens” was selected as the species, and target genes with a probability of >70%  were incBoth the canonical SMILES and InChI key of the compounds were searched through the PubChem database, and target genes were subsequently obtained from the “Biological Test Results” panel, only including target genes with known bioactive outcomes.The InChI key of each chemical compound was searched at DrugBank and consequent target genes were obtained.European Society of Human Reproduction and Embryology (ESHRE) in 2015 [With reference to the POI guideline published by the https://www.omim.org) [https://www.ncbi.nlm.nih.gov/gene) [http://db.idrblab.net/ttd/) [https://www.malacards.org) [POI targets were collected through searches using databases such as Online Mendelian Inheritance in Man (OMIM) (mim.org) , NCBI Geov/gene) , Therapeet/ttd/) , and Malrds.org) .The network construction was built as follows: (1) ZSYTP chemical compounds-potential target network (C-T network), (2) PPI network of POI disease targets, (3) PPI network of ZSYTP target genes, and (4) PPI network of interaction between POI disease targets and ZSYTP target genes.https://cytoscape.org, version 3.8.2) [The network analysis software Cytoscape (n 3.8.2)  was usedThe C-T network was constructed with Cytoscape software, and the network analyzer tool was used to evaluate the network of chemical compounds and their corresponding gene targets. PPI networks were visualized using BisoGenet plugin for the Cytoscape software.p-value <0.05 was set as statistically significant.Cytoscape ClueGO  applicathttp://www.rcsb.org/) [Molecular docking simulations were used to verify the binding of the target and the corresponding compound. 8 active compounds of the highest degree and 5 potential targets of the highest degree were obtained from the C-T network . Data onsb.org/) , and datsb.org/)  and TCMSsb.org/) . The expsb.org/) , and forsb.org/) . The resRehmanniae Radix Praeparata (SDH), 188 from Lycii Fructus (GQZ), 29 from Cuscutae Semen (TSZ), 174 from Morindae Officinalis Radix (BJT), 134 from Codonopsis Radix (DS), 55 from Atractylodes macrocephala Koidz (BZ), 30 from Dipsaci Radix (XD), 119 from Eucommiae Cortex (DZ), 165 from Amomum aurantiacum (SR), 190 from Panax Ginseng (RS), 46 from Herba Taxilli (SJS), 135 from Folium Artemisiae Argyi (AY), 17 from Fallopia multiflora (HSW), 4 from Colla Corii Asini (EJ), and 2 from Cornu Cervi Degelatinatum (LJS).A total of 1364 chemical compounds were found in the process, 76 from In accordance with the ADME thresholds of OB ≥ 30% and DL ≥ 0.18, 187 chemical compounds were obtained as follows: 2 from SDH, 45 from GQZ, 11 from TSZ, 20 from BJT, 21 from DS, 9 from BZ, 6 from XD, 28 from DZ, 10 from SR, 22 from RS, 2 from SJS, 9 from AY, and 2 from HSW. EJ and LJS were not found in any of the TCM databases. However, a further 18 compounds with lower OB or DL values were consolidated as they hold extensive pharmacological activities: 10 from HSW, 4 from EJ, and 2 from LJS. The final list of 205 active compounds with their parameters and sources are provided in Supplementary Target gene prediction of the ZSYTP active compounds based on molecular similarity was conducted by entering each unique molecular data into STITCH, PubChem, Swiss Target Prediction, and DrugBank. 481 target genes were obtained from ZSYTP active compounds upon eliminating duplication. The target genes of each herb of ZSYTP are listed in Supplementary The keyword “premature ovarian insufficiency” was used in the search through OMIM, TTD, NCBI Gene Database, and MalaCards as a POI disease target. OMIM and TTD yielded no results, and MalaCards provided potential disease targets including premature ovarian failure (POF), which were not included in this study. A total of 119 disease targets were obtained ,” “closeness (CC),” and “eigenvector (EC),” deriving a total of 8511 nodes and 190,297 edges . Based op-value was set at >1.0 × 10−21, and 20 processes were selected for further analysis. The full list of enriched GO-BP terms is presented in Supplementary The enrichment analysis was completed with the Cytoscape ClueGO plugin for visualization. 221 biological processes were retrieved; the The 20 biological processes selected were mainly involved in the negative regulation of gene expression (15.46%), telomere maintenance (10.57%), viral process (8.76%), regulation of protein localization of chromosome, telomeric region (5.41%), mRNA metabolic process (5.41%), and establishment of protein localization to organelles (5.15%). Percentage values were calculated based on the number of processes in a group out of the 221 chart records retrieved.For negative regulation of the gene expression group, biological processes, such as negative regulation of nucleobase-containing compound metabolic process, negative regulation of RNA metabolic process, negative regulation of macromolecule biosynthetic process, negative regulation of biosynthetic process, negative regulation of nucleic acid templated transcription, and negative regulation of gene expression (epigenetic), were identified. For the mRNA metabolic process group, biological processes, such as RNA processing, mRNA metabolic process, RNA splicing, protein-containing complex subunit organization, and mRNA catabolic process, were identified.The other biological processes selected for further analysis were as follows: chromosome organization, cellular protein-containing complex assembly, ATP-dependent chromatin remodeling, telomere maintenance, CENP-A containing nucleosome assembly, posttranscriptional regulation of gene expression, protein localization to organelles, SRP-dependent cotranslational protein targeting to membrane, cellular response to DNA damage stimulus, chromatin assembly and disassembly, DNA repair, and double-strand break repair.p values.The selected processes of GO-BP were visualized  and rankp values >1.0 × 10−7 were selected and visualized; the results are shown in p values. The list of enriched GO-MF terms is presented in Supplementary Gene ontology analysis of MF identified 45 functions. The results indicated that the active target genes of ZSYTP that act on POI have functions, including transcription factor binding (15.56%), rRNA-binding (8.89%), helicase activity (8.89%), and nucleosome binding (6.67%). 20 molecular functions with For the transcription factor binding group, the identified functions included transcription factor binding, DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and transcription coactivator activity.For the group of rRNA binding, the identified functions included RNA binding, mRNA binding, rRNA binding, and double stranded RNA binding.Other identified functions included cadherin binding, protein domain-specific binding, cell adhesion molecule binding, kinase binding, nucleosome binding, ubiquitin-like protein binding, protein kinase binding, hormone receptor binding, helicase activity, ATPase activity, steroid hormone receptor binding, and chromatin DNA binding.p values >1.0 × 10−10 were selected for further analysis. The full list of enriched GO-BP terms is presented in Supplementary Gene ontology analysis of CC identified 82 components, including spliceosomal complex (18.07%), cytosolic ribosomes (10.84%), nuclear chromosome, telomeric region (10.84%), and SWI/SNF superfamily-type complex (7.23%). Twenty cellular components with GO-CC analysis identified the following cellular components as enriched, extracellular vesicle, spliceosome complex, catalytic step 2 spliceosome, nuclear chromosome (telomeric region), chromosome (telomeric region), cytosolic ribosome, chromosomal region, nuclear nucleosome, focal adhesion, nucleosome, nuclear body, ribosomal subunit, ribosome, nuclear speck, nucleolus, U2-type spliceosomal complex, anchoring junction, host cell, and cytosolic large ribosomal subunit.p values.The 20 selected components of GO-CC analysis are visualized in The results of KEGG pathway analysis highlighted nine pathways, in which target genes of ZSYTP acting on POI are enriched significantly; 33.33% of the enriched target genes act on viral carcinogenesis and 22.22% on the hypoxia-inducible factor 1 (HIF-1) signaling pathway; hepatocellular carcinoma, cell cycle, spliceosome, and ribosome pathways were equally divided at 11.11%.p values  prescription derived from the clinical experience of Professor Luo Yuankai, a nationally acclaimed TCM scholar of Guangzhou University of Chinese Medicine. Luo received a Class 1 award from China's National Health Commission for his contributions with the ZSYTP.ZSYTP was originally derived as treatment to prevent recurrent and early pregnancy loss. In accordance with the concept of TCM that states that different diseases can be treated with the same therapeutic principle, and on the basis of the TCM theory that kidneys are closely linked with reproduction, it is postulated that ZSYTP could also be used clinically for irregular periods and infertility.It has been found that ZSYTP can improve ovarian function in patients with POI –14; as aThe nine pathways highlighted by KEGG analysis were studied extensively for further corroboration with existing literature that would provide insights and possible hypotheses of the pharmacological mechanisms of ZSYTP in the context of POI. In particular, the HIF-1 signaling pathway was highlighted by KEGG analysis due to its multiple roles in ovarian function.An overview of the C-T network combined with GO and KEGG analysis led us to postulate that the potential mechanism of ZSYTP on POI is likely connected to the anti-inflammatory and antioxidant properties of ZSYTP, as well as participating in glucose metabolism. GO and KEGG analysis also highlighted a change in cell and gene expression as a possible pathological mechanism of POI.A term first coined by Franceschi et al.  in 2000,Chronic inflammation has been closely linked with oxidative stress , cytokinIn reference to the C-T network, active compounds, such as quercetin, kaempferol, luteolin, beta-sitosterol, and emodin, were found to be key hub compounds of ZSYTP, in descending degree, respectively.Quercetin, kaempferol, and luteolin are natural flavonoids that demonstrate anti-inflammatory, antioxidant, anticarcinogenic properties and gene expression-modulating potential . A recenThe C-T network also highlighted NFE2L2 and NOX4 as key targets of ZSYTP. NFE2L2 encodes a transcription factor that regulates genes containing antioxidant response element (ARE) in their promoters; these genes encode proteins involved in the production of free radicals. NOX4 encodes a family of enzymes that catalyzes the reduction of molecular oxygen to various ROS.It is, therefore, possible that the potential anti-inflammatory and antioxidant effect of ZSYTP could be achieved by the combined actions of quercetin, kaempferol, and luteolin via key gene targets; together, these actions could reduce ovarian inflammation and possibly slow ovarian degradation in POI.POI describes a hypoestrogenic state in women that is associated with metabolic changes . As estrKEGG analysis implicated the HIF-1 signaling pathway and glycolysis as being associated with the underlying therapeutic mechanisms of ZSYTP in POI. HIF-1 is a transcription factor of two subunits, HIF-1a and HIF-1b . The actInhibition of HIF-1a expression was found to have triggered atresia in large follicles of mice with polycystic ovary syndrome (PCOS) and, therefore, prevented ovulation . One stuHyperglycemia augments oxidative stress and contributes to the overproduction of ROS , which, In ZSYTP, active compounds that act on glucose metabolism include quercetin, kaempferol, emodin , luteoliIt is possible that the active compounds of ZSYTP collectively act on regulating glucose metabolism and HIF-1 expression, therefore improving ovarian follicle development, slowing the rate of ovarian degradation in POI.p-value was “mRNA metabolic process,” which refers to the process of carrying messages transcribed from DNA to the protein assembly at ribosomes. Moreover, “negative regulation of gene expression” was the group of biological processes highlighted in the GO-BP analysis, implying abnormal regulation of gene expression in POI.Subsequent GO and KEGG analysis aided in the elucidation of another possible pharmacological mechanism of ZSYTP on POI. The enriched GO term of BP with the smallest The most enriched GO-CC was “extracellular vesicles (EVs).” Recent studies have suggested the ability of EVs to transfer functional RNA from cell to cell , 64 and The most significant enriched GO-MF was “RNA binding,” which further implies the importance of RNA in the mechanism of ZSYTP on POI.KEGG analysis indicated “viral carcinogenesis” as the most enriched process. The mechanisms of viral carcinogenesis include direct transformation through the expression of viral genes and indirect transformational activities in cells. These activities increase translation of modified proteins with altered cell function, which corroborate with the GO-BP analysis of multiple negative regulation of gene expression processes identified.Multiple studies have shown decreased protein expression in POI –72, signAccording to previous research, we considered the binding activity of molecular docking simulations to be practical when the binding energy was < −1.2 kcal/mol (−5.0 kJ/mol) and dynamite when the binding energy was < −5.0 kcal/mol. In our study, all of the binding energies were < −1.2 kcal/mol, and 12.5% of binding energies were < −5.0 kcal/mol. Furthermore, it was found that beta-sitosterol could stably dock to the F2 protein structure, while the H-bond plays a critical role at residue ASP-170. These active compounds may provide a foundation for treating POI, and the therapeutic action could be performed by correlative pathways in ZSYTP.POI remains a debilitating disease for women with no fixed treatment protocol currently. ZSYTP has previously been shown to have a clinical effect on POI and warrants further research –14. Our Our results provide future research directions for the therapeutic use of ZSYTP in POI into three aspects: (1) the anti-inflammatory and antioxidant effect, (2) regulation of glucose metabolism, and (3) negative regulation on mRNA metabolic process. Our results also indicated that quercetin and kaempferol, as the two major active compounds found in ZSYTP, have potential pharmacological effects on POI. Future studies should aim to validate the effect of quercetin and kaempferol on POI to elucidate the underlying mechanism. In addition, future studies should also ascertain the status of glucose metabolism in patients with POI.In conclusion, it is possible that the therapeutic potential of ZSYTP on POI is a multipathway effect, and therefore, more research is warranted to fully elucidate this relationship. The results of this study brought focus to potential pharmacological mechanisms of ZSYTP and its effect on POI. Multiple hypotheses of the pathological mechanism of POI were thereby formulated. Here, we provide a preliminary platform showcasing a comprehensive study of a TCM formula for POI, establishing a protocol enhancing TCM drug discovery to be more systematic and efficient.


Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases , led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics. Metabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biological status  on cellular biochemistry, and has wide-ranging applications, from biomarker discovery and personalised medicine in healthcare to crop protection and food security in agriculture. Pathway analysis helps to understand which biological pathways, representing collections of molecules performing a particular function, may be involved in response to a disease phenotype, or drug treatment, for example. Over-representation analysis (ORA) is perhaps the most common pathway analysis method used in the metabolomics community. However, ORA can give drastically different results depending on the input data and parameters used. Here, we have established the effects of these factors on ORA results using computational modifications applied to five real-world datasets. Based on our results, we offer the research community a set of best-practice recommendations applicable not only to ORA but also to other pathway analysis methods to help ensure the reliability and reproducibility of results. Pathway analysis (PA) plays a vital role in the interpretation of high-dimensional molecular data. It is used to find associations between pathways, which represent collections of molecular entities sharing a biological function, and a phenotype of interest . Based oThere are several methods for PA, which can be classed into three broad categories: over-representation analysis (ORA), functional class scoring (FCS), and topology-based methods . In thisp-values) to filter the list. The background set contains all molecules which can be detected in the experiment. For example, in transcriptomic studies, this consists of all genes or transcripts which can be quantified. In targeted metabolomics, the background set would contain all compounds assayed; in untargeted metabolomics, all annotatable metabolites . P-values for each pathway are calculated using a right-tailed Fisher’s exact test based on the hypergeometric distribution. The probability of observing at least k metabolites of interest in a pathway by chance is given by (1):N is the size of background set, n denotes the number of metabolites of interest, M is the number of metabolites in the background set mapping to the ith pathway, and k gives the number of metabolites of interest which map to the ith pathway. A visual representation of ORA is shown in To perform ORA, three essential inputs are required: a collection of pathways (or custom metabolite sets), a list of metabolites of interest, and a background or reference set of compounds. Pathway sets can be obtained from several databases, for example, the Kyoto Encyclopaedia of Genes and Genomes (KEGG) , ReactomDespite the widespread use of ORA in metabolomics  the commFirst, we examined several factors which are common to all ORA applications, beginning with the background set. Five publicly available metabolomics datasets have been used throughout this work . These dThe term background set , not pathways (second step of ORA). The list of compounds of interest is a key parameter of ORA, as any compound falling below the significance threshold will not be able to contribute to the enrichment of a pathway. Methods used to select DA metabolites typically rely on p-values or q-values derived from a statistical test, for example when comparing metabolite abundances between study groups, or regression-based approaches for continuous outcomes. A threshold such as q ≤ 0.05 is often used to select DA metabolites, however, as with all hypothesis testing this is an arbitrary choice. Furthermore, in untargeted metabolomics, hundreds or thousands of metabolites are often profiled and therefore multiple testing correction is essential. We investigated the effect of using varying significance levels and different multiple correction testing approaches to select metabolites of interest on ORA results. To this end, DA compound lists of increasing length were constructed by adding compounds, from lowest t-test An important consideration when conducting any type of pathway analysis is the nature of the pathway sets used. Pathway sets can differ between databases in many ways, including the number of pathways present, the size of pathways, how pathways are curated , pathway boundaries, and the organisms supported. We compared several properties of three pathway databases: KEGG, Reactome, and BioCyc. As this work focuses on metabolomics, only pathways which contain at least three metabolites were considered for the purposes of this paper, and genes and proteins were excluded from the pathway definition. Using human pathways as an example, as of December 2020, Reactome contained the highest number of pathways (1631), followed by HumanCyc (390) (part of the BioCyc collection) and KEGG, containing 261 pathways. A comparison of pathway sizes across the three databases can be seen in We next investigated the similarity of metabolite composition for KEGG and Reactome pathways. Identifiers for metabolites in each pathway were first converted to KEGG IDs and the ComPath  resourceet al. dataset based on KEGG, Reactome, and HumanCyc pathways  and larger pathways (mean pathway size 45 vs. 30 compounds). The two pathway sets had a median OC of 0.92 (IQR = 0.83–0.97) for pathways with a common ID , indicating a high level of similarity between the pairs but that analogous pathways are not identical. We performed ORA for each example dataset using both the organism-specific and reference pathway sets and compared the SEPs obtained . While tA final consideration when selecting a pathway database is the version of the database one will use. Not all ORA tools will use the latest version of a certain pathway database available. The vast majority of pathway databases will undergo at least yearly updates, with some such as Reactome providing four major releases per year. To investigate how much impact pathway database updates can have on ORA results, we obtained four years’ worth of Reactome pathway sets spanning the period from June 2017 to December 2020. We compared three aspects of the Reactome human pathway sets (R-HSA) between each release: the number of pathways, the number of unique compounds in the database, and the mean pathway size . As expeNext, we investigated some factors which are specific to metabolomics data, such as metabolite misidentification and assay chemical bias. A major bottleneck in untargeted metabolomics is the identification of compounds. In untargeted metabolomics, it is commonplace to putatively identify (“annotate”) metabolites based on their physicochemical properties  and similarity to compounds in spectral databases, and then confirm the identities of compounds of interest using chemical reference standards. Consequently, a large proportion of compounds in untargeted metabolomics assays are expected to have a degree of uncertainty in their identification, ranging from Metabolomics Standards Initiative (MSI) confidence levels 2–4 . These lf % misidentification, and the pathway gain rate refers to the number of SEPs not originally present at 0% misidentification which become significant at f % misidentification.To compare the effects of metabolite misidentification on the number and identity of significant pathways detected using ORA, we introduce two new statistics: the pathway loss rate and the pathway gain rate see . The forWe simulated the effects of metabolite misidentification on ORA using KEGG pathways by replacing the true metabolites with false ones in two different ways: a) by similar molecular weight (20ppm window), and b) by identical chemical formula see . For botThe analytical platform and specific assay used for a metabolomics study can be expected to introduce bias into the pathways which might be detected by ORA. Assays typically differ in their ability to detect compounds with different physico-chemical properties . While it is increasingly common for metabolomics experiments to incorporate multiple assays, most studies will still be biased in the compounds they can detect. We would expect to be able to access different pathways depending on the compounds assayed, resulting in disparate ORA results.Using the Stevens et al. dataset as an example, which contains compounds identified using four different assay types, we mapped these compounds onto the KEGG pathway network using iPath 3.0  Fig 6A)Fig 6A). in-silico simulations based on five untargeted metabolomics datasets.As metabolomics continues to grow as a field of study with a multitude of applications within various disciplines, deriving meaningful conclusions from such data becomes increasingly important. ORA is one of the most popular approaches used to draw functional interpretations from metabolomics data. However, to date, there have been no published investigations of the consequences of varying input parameters on ORA results derived using metabolomics data. Understanding the sensitivity of ORA to tuning parameters, especially how it is influenced by metabolomics-specific factors, will play a crucial role in its successful application. In the present study, we sought to investigate the effects of varying inputs on ORA results, which we demonstrated using One of the most salient findings was the difference in the number of SEPs detected when using an assay-specific versus a nonspecific background set. The use of a nonspecific background set, such as all compounds present in the KEGG reference or human pathway set, for example, resulted in a drastic increase in the number of SEPs. In many ORA tools, use of a nonspecific background is typically the default option, and one that may lead users to believe that this is the ‘correct’ procedure. It is crucial however to understand that the consequence of not specifying a background set, which should contain all compounds that are realistically observable, is that an assumption is being made that the compounds in the default background set are all equally likely to be detected in the experiment . Such anThe list of compounds of interest  is an essential input for ORA and we have demonstrated that the way these compounds are selected greatly impacts PA results. It is important to select a threshold that strikes a balance between selecting too few compounds, therefore resulting in low power for the detection of significant pathways, or selecting compounds too liberally and losing power by introducing noise into the analysis. Visualisation of the curve of number of significant pathways vs. the number of compounds of interest  can be aUnlike other fields , the level of uncertainty surrounding compound identities remains a critical issue in metabolomics studies. While it is not possible to find a benchmark level of metabolite misidentification typically found in metabolomics studies, most studies will contain at least some misidentified compounds . The levAnother essential input of ORA is the pathway database or list of metabolite sets used. The inherent differences between pathway databases will undoubtedly impact the PA results, regardless of the method used . In the A further important consideration for pathway database evaluation is the type of compound identifiers used in the pathway. KEGG and BioCyc use database-specific identifiers, whereas Reactome uses ChEBI identifiers. It is necessary to convert the identifiers present in a metabolomics dataset to their database-specific equivalent, which often results in loss of information as not all identifiers will necessarily map directly to a database compound or be mapped to a pathway . For exaThe incompleteness of pathway databases, together with the evolution of pathway definitions between releases, are key factors highlighting the necessity of using an up-to-date resource; not doing so can have a detrimental effect on PA results . FurtherIn this work we have focused on ORA, but many other PA methods exist ,38,39. WThis study is limited by the lack of availability of a ground-truth dataset where the identities of enriched pathways are known. Possible sources of ground-truth data include simulations based on genome-scale metabolic models, in which enzymes in specific pathways are knocked out or the flux through reactions altered. Alternatively, one could insert artificial pathway signals into simulated or real data by altering the relative abundance levels of metabolites involved in the target pathways. Experimental datasets such as gene knockouts or knock-downs offer more realistic forms of ground truth datasets, which more accurately reflect the complexity of a biological system. Both simulated and experimental ground-truth datasets have limitations, however, such as the former being too simplistic, or the inability to pinpoint the exact pathway(s) affected by a perturbation in the latter. Nevertheless, such datasets might enable quantification of a wider variety of performance metrics than available here. Another limitation is that in the majority of examples, a p-value threshold of P ≤ 0.1 was used without multiple testing correction to select SEPs. As metabolomics experiments usually identify far fewer compounds than transcriptomic experiments identify genes, ORA based on metabolites appears to have much lower power to identify significant pathways and as such in the example datasets few, if any, pathways remained significant after multiple testing correction was applied.in-silico simulations based on experimental datasets, all of the aforementioned variables have been shown to introduce varying levels of bias and uncertainty into ORA results, which has significant implications for those using ORA to analyse metabolomics data. In particular, use of an assay-specific background set is often ignored, yet has a critical effect on the output. Overall, this study has been the first detailed investigation into the application of ORA to metabolomics data, with wide-ranging findings that have implications not only to ORA but also a variety of other PA methods in metabolomics.The purpose of the present research was to evaluate the suitability of ORA for metabolomics PA and assess the effects of varying input data and parameters. We have investigated the three main input parameters: the background set, the list of compounds of interest, and the pathway database, as well as metabolomics-specific considerations such as metabolite misidentification and assay chemical bias. By means of We therefore offer the community a set of recommendations for application, as well as suggested minimal reporting criteria, which may contribute to the future development of best-practice guidelines for the application of ORA to metabolomics data.Specify a realistic background set based on the analytical platform used in the experiment. A conservative yet practical approach is to use all the metabolites that have been identified in the assay.Use an organism-specific pathway set if the organism is supported by the pathway database.Perform ORA using multiple pathway databases and derive a consensus pathway signature using the results if possible.Use multiple-testing correction to select both DA metabolites and, where feasible, significant pathways.The statistical test/approach used for pathway analysis The tool (and version) used to perform ORA.The pathway database used, the corresponding compound identifier type , its release number, and which organism-specific pathway set was used (if any).Which compounds form the background set.The multiple testing correction methods applied for i) selection of DA metabolites and ii) selection of SEP, alongside the adjusted p-value thresholds used.Mus musculus tissue samples  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Kiran Raosaheb Patil, Ph.D.Deputy EditorPLOS Computational BiologyJason PapinEditor-in-ChiefPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Wieder et al examine how different parameters for pathway over-representation analysis (ORA) influence the results of metabolomics data analysis. They use five experimental metabolomics data sets  to test the relationship between ORA parameters and ORA results. The study is relevant for the field because it nicely illustrates the strong influence of ORA parameters on the outcome of the analysis. However, my main concern is that the authors cannot identify the best parameter configuration because they lack a proper reference of what is true . Specific comments are below:1) The authors claim in the abstract that they used in-silico simulations, thus I expected that they simulated metabolic changes (e.g. with a dynamic model) and used ORA to recover the true in silico perturbation. Instead they use real data, which is great, but it is difficult to judge which parameter configuration is best. The authors mention themselves that the study lacks a “ground-truth dataset”. The authors should at least better describe the nature of such a ground-truth dataset/ result. They should also describe better what they mean with in silico simulation.2) The authors selected 5 experimental data sets for their study. They could better describe in the main text (instead of Table 1) why they selected these data and which conditions/organisms are investigated. For example, why did they select (only) two strains out of 3800 E. coli strains in Fuhrer et al? In fact, these data contain some information about the "ground truth", because in many cases the deleted gene can be assigned to a metabolic pathway.3) A main concern is the selection of Databases. Obviously, the best choice is a genome-scale reconstruction of metabolism of the respective organism and I wonder why the authors did not consider them; at least for the E. coli data, mouse and HeLa cells.4) The authors could give a better overview about the parameters tested and better quantify their relevance relative to each other. The recommendations in the discussion are not specific enough. For example, how could one derive a “consensus” pathway signature.Reviewer #2: The authors assess parameters used in pathway enrichment analysis using 5 publicly available MS-based metabolomics datasets. While those dealing with these tools have surely identified inconsistencies in results according to the tools and parameters used, the exercise of testing the boundaries and consequences in results of mis-use of the tools is interestingly quantified by the authors. In addition it is of value the section on recommendations on the best practice to use over-representation analysis (ORA) in the metabolomics field.The manuscript is well-written but requires improvement in certain sections.Title/introduction – It is worth mentioning that ORA is also known as metabolite enrichment analysis, that might even be a more common name used within the metabolomics community.MethodsL501 – for dataset MTBLS135 the text mentions that the sample type is plasma, while Table 1 mentions tissue. So here it is important to rectify and harmonize. In addition, the files of the uploaded dataset mention ‘serum’ and not plasma. This might sound like a detail, but one should be precise, as the two sample types (serum and plasma) are not interchangeable.L502 – dataset MTBLS136: I could not retrieve any data files in the Metabolights repository for this study! Supplementary Materials of the associated publication do not contain the metabolomics data itself per sample. So I could also not confirm the number of samples (controls and estrogen-users).L506 – as for all the other datasets, it is important to mention the number of samples for the last dataset Fuhrer et al. And was the negative mode subdataset used or the positive mode or both? In the results, it is then mentioned 2 subsets from this particular study, so this needs to be clarified in the Methods.Table 1 – where does the total number of metabolites mapping to KEGG compounds was extracted from? Analysis within this manuscript or extracted from the original datasets?L525 – metabolite ID conversionThis is a stress point of identification and according to the algorithms used, it can over-identify and thus overestimate metabolite coverage or if too conservative, it can assign only a part of possible metabolites.For example: when one measures an amino acid, will it be immediately assigned to L-amino acid? An amino acid can be also D-amino acid in a biological environment, however this type of assignment is hardly assessed and possibly not even feasible to know using regular LC/CE-MS techniques . And in the likely even of not knowing, will it be assigned to D/L-amino acid or assumed to be L-amino acid?Another example are acids and salts and ions ): will these be assigned to the same metabolite ID or to different ones?As different metabolite ID convertors  were used, it is likely that these will produce different results!! This aspect deserves some explanation and words of caution in the manuscript. Will the IDs be back-converted to the same list of IDs when using convertors from other databases? This would be good to check.L567 – metabolite misidentificationResultsL169 – NMR is not relevant in this study, as none of the studies chosen have used it, so please remove it.L293 – if the authors want to mention MSI levels 2-4, then they need to explain what these are, as the readers might not know…Fig5 – A and B figures are actually quite similar. So it might be worth mentioning in the text that the misidentification is probably from molecular formula to metabolite and not so much from mass to molecular formula. Some words on the similarly / differences between these two graphs are worth mentioning.L334 - 349 – one needs to be careful with these type of statements. Reversed phase is used in combination with ion pairing for detecting polar metabolites, of a similar nature to the ones that are detected by HILIC. HILIC can also detect a lot of apolar metabolites, because it can act in a mixed mode type of chromatography. In addition GC-MS with a prior derivatisation step in the sample preparation has been used a lot for detecting polar metabolites! So being that there is a lot of variety in analytical and sample prep methods for metabolomics, this whole section should be rephrased and adapted.The authors should stick to polarity of compounds to make their point, irrespective of the technique used, as clearly the reality is not this simple, as it does not only depend on chromatography!! in fact one of the datasets does not use chromatography but capillary electrophoresis!!Then none of the datasets aimed at lipid metabolism, this would then lead to completely different result. So this whole section is very circumstantial and simply not informative.DiscussionL395 – mis-identification is abundant in all analytical platforms!L397 – not relevant to mention NMR as it was not used in this study. To add to this: maybe NMR provides less coverage but maybe better identification…?Reviewer #3: Wieder and colleagues performed an interesting study on the application of ORA to metabolomics data. The paper is well-written and proposes, for the first time, the guidelines to perform ORA analysis in metabolomics. I especially enjoyed reading the pathway comparison part, it is a nice addition to the paper. However, some of the observations or conclusions were somewhat trivial to me. Still, I find the paper suitable for publication and I suggest the following changes to improve the paper:- The authors state: "To perform ORA, three essential inputs are required: a collection of pathways (or custom metabolite sets), a list of metabolites of interest, and a background or reference set." By definition, all annotatable metabolites in untargeted metabolomics are all those in the collection of pathways. How do all annotatable metabolites and all metabolites in the pathway differ?- Pg 12, section "increasing the number...". It is not needed to do all that to demonstrate this trivial aspect. It is expected. The authors could perform a similar approach but instead of considering all pathways, considering only those pathways that have at least 2  DA, and then randomly add new DA to the pathways to see how the overall ranking fluctuates. Otherwise, adding DA by p-value is arbitrary and, considering the nature of untargeted metabolomics data, these observations are expected.- "Pathway sets can be obtained freely from several databases..". . KEGG is partially commercial so should not be included. For BioCyc, I would like to know how the authors obtained that information as I believe it's partially commercial. MetExplore uses others databases so it should be removed as well. Is Ingenuity still on business?- Pg 26: "Suggested recommendations...". The paper discusses the ambiguity of the composition of the background set in untargeted metabolomics, but the recommendations are not clear on how this background set should be built in untargeted metabolomics. It would be worthwhile to break down the first recommendation into untargeted and targeted.- Discussion, how using topology-based or FCS do/could naturally overcome some of the ORA limitations, or introduce different biases. Could the recommendations be instead: do not use ORA, but FCS/Topology-based? What could the limitations of FCS/Topology-based in untargeted metabolomics be? I believe a brief discussion about this is necessary.- Pg 7 lines 139; "consisting of all compounds annotated to at least one KEGG pathway", could you define this better?Minor:- Change: Firstly -> first, secondly -> second- Pg 5 line 95: p-value, P should be capitalized.- Pg 14 line 225. "Pathway database is key" I suggest using a more informative sentence.- I did not find the supplementary materials.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at  29 Jul 2021AttachmentResponse_to_reviewers_v1.pdfSubmitted filename: Click here for additional data file. 12 Aug 2021Dear Dr Ebbels,Thank you very much for submitting your manuscript "Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the editorial recommendation below.Before formal acceptance, I would like to suggest a change in the title: replacing "pitfalls and best practice" by "recommendations". 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Before formal acceptance, I would like to suggest a change in the title: replacing "pitfalls and best practice" by "recommendations". The reason being that the term "best" in the computational context often implies optimisation / rigorous analytical basis. I would therefore like to encourage you to consider this change .Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: I thank the authors for addressing all of my points, and I have no further comments.Reviewer #2: The authors improved the study by addressing the reviewers concerns to a level that in my opinion makes this manuscript worthy of publication.Reviewer #3: The authors have addressed all my concerns.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). 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Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at References:Review your reference list to ensure that it is complete and correct. 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R. Scrophulariae, a male SD rat (180–220 g) model of hyperthyroidism induced by Euthyrox was used. Thirty rats were randomly distributed into three groups: the Model group, the R. Scrophulariae treatment group (RS group) and the healthy Control group. Using the UHPLC/Q-TOF-MS metabolomics approach, 44 metabolites were found to be profoundly altered in the model group, and the levels of these biomarkers were significantly decreased after treatment with R. Scrophulariae. Forty-four metabolites and 13 signaling pathways related to R. Scrophulariae, including the biosynthesis of unsaturated fatty acids, primary bile acid biosynthesis and sphingolipid metabolism, were explored, and linoleic acid metabolism and sphingolipid metabolism were identified as the most relevant metabolic pathways. In addition, the system pharmacology paradigm revealed that R. Scrophulariae contains 83 active ingredients and is related to 795 genes, and 804 disease genes are related to hyperthyroidism. The construction of the R. Scrophulariaceae-chemical composition-target-hyperthyroidism network identified a total of 112 intersection genes. The enriched gene targets were analyzed, and five pathways were found to be enriched. Among them pathways, the HIF signaling pathway had the highest enrichment score, which indicated that this pathway might be the main signaling pathway related to the treatment of hyperthyroidism by R. Scrophulariae.The integrated approach involving metabolomics and network pharmacology revealed that R. Scrophulariae might play a role in the treatment of hyperthyroidism by regulating the “IL6-APOA1-cholesterol” pathway and disturbing the HIF signaling pathway. The results demonstrate that the combination of metabolomics and network pharmacology could be used to reflect the effects of R. Scrophulariae on the biological network and metabolic state of hyperthyroidism and to evaluate the drug efficacy of R. Scrophulariaceae and its related mechanisms.Radix Scrophulariae (Chinese name: Xuanshen), a traditional Chinese herb, is used for the treatment of hyperthyroidism, and in this study, its mechanisms were evaluated by metabonomics and system pharmacology. To study the anti-hyperthyroidism effects of  Hyperthyroidism, a common and frequently occurring disease of the endocrine system, is characterized by thyrotoxicosis caused by the excessive production of thyroid hormones by the thyroid gland itself . HyperthRadix Scrophulariae, the dried root of Scrophularia ningpoensis Hemsl., which belongs to the Scrophulariaceae family, has been used in TCM for thousands of years. As detailed in the Pharmacopoeia of the People’s Republic of China (2020 Edition), the traditional functions of this species include the treatment of febrile diseases, constipation, hot eyes, pharyngalgia, diphtheria, and scrofula , and the voucher specimen (hlj-20120623012) of the herb was authenticated by Professor Zhenyue Wang of the Department of Resources and Development of TCM at Heilongjiang University of Traditional Chinese Medicine, who found that the specimen met the standards detailed in the Pharmacopoeia of the People’s Republic of China  for the aqueous extract of R. Scrophulariae  was purchased from Merck KGaA. UPLC-grade acetonitrile and formic acid were obtained from Dikma Technologies Inc. (United States), and leucine-enkephalin (No. L9133) was obtained from Sigma Aldrich .EuthyroxELISA kit for rat triiodothyronine , thyroxine  and norepinephrine  were purchased from Nanjing Jiancheng Biotechnology Co., Ltd.,(China).Healthy male Sprague-Dawley rats weighing 180–220 g each were purchased from Liaoning Changsheng Biotechnology Co., Ltd. (P. R. China) . The rats were fed a standard diet, had free access to water and were housed one per metabolic cage in controlled rooms with a temperature of 21–23°C, 40–50% humidity and a 12-h light/12-h dark cycle. This study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the College of Pharmacy of Heilongjiang University of Chinese Medicine (No. DWLL20151108001).R. Scrophulariae treatment group (RS group) and the healthy Control group. The rats in the Control group were fed normally every day and given 0.9% saline twice daily at an interval of 90 min for 15 consecutive days. The rats in the model group were i.g., administered 120 mg/kg/d Euthyrox suspension  and i.g., administered saline 90 min after the Euthyrox administration; the Euthyrox suspension and saline were administered once a day for 15 days  once daily for 15 consecutive days, and the rats in the control group received the same volume of 0.9% saline once daily for 15 days; the other interventions administered to the rats in the RS group were the same as those administered to the rats in the model group.After acclimation for 1 week, 30 rats were randomly divided into three groups (n = 10 per group): the Model group, the  15 days . The ratAll of the animals survived until the end of the treatment period, after rats were anaesthetized with 1% sodium pentobarbital (0.15 ml/100 g) via intraperitoneal injection, blood samples were collected from the abdominal aorta with pro-coagulation tubes. The serum was obtained by centrifuged at 4000 rpm for 10 min, and then T3, T4 and NE were measured by enzyme-linked immunosorbent assay.For the metabolomic analysis, feces were collected from all the rats in each group for 24 h on the 15th day. After freeze-drying, 2.7 ml of methanol was added to 300 mg of feces, and the mixture was vortexed for 3 min, mixed by ultrasound for 15 min and centrifuged at 15,000 rpm and 4°C for 15 min. Two microliters of the supernatant was injected into the UPLC/TOF-MS instrument for analysis.® system coupled with a time-of-flight mass spectrometer. Chromatography was performed using an Acquity SDS ACQUITY UPLC® BEH C18 column  The column and sample temperatures were set to 40.0°C and 4.0°C, respectively. The gradient mobile phase consisted of solvent A (0.05% FA-ACN) and solvent B (0.05% FA-H2O). The fecal sample gradient was as follows: 0–15 min, 2.0–100% A; 15–17 min, 100% A; 17–18 min, 100–2% A; and 18–20 min, 2% A. The flow rate was 0.400 ml min−1. To guarantee system stability and repeatability, quality control (QC) samples were inserted every 10 samples; these samples consisted of feces obtained by mixing 100–200 ml of 10 samples from group.The metabolomic analysis was performed using a Waters ACQUITY UPLC−1 and 750.0 L h−1, respectively. The capillary voltage was set to 1300.0 V in the positive-ion (ESI+) mode and to 1500.0 V in the negative-ion (ESI-) mode. The sample cone voltage was set to 60.0 V (ESI+) and 70.0 V (ESI-). The ion energy voltage was set to 35.0 V (ESI+) and 34.0 V (ESI-). The scan duration time and interscan delay were set to 0.200 and 0.010 s, respectively. Leucine-enkephalin was used as the lock-mass compound (556.2771 [M + H]+ and 554.2615 [M-H]-).The MS parameters were established as follows. The mass range was from 100 to 1500 in the full-scan mode. The desolvation temperature and source temperature were set to 350.0°C and 110.0°C, respectively. The cone and desolvation gas flow rates were maintained at 20.0 L hp ≤ 0.05 and variable importance in the projection (VIP) > 1, potential biomarkers were selected and compared using HMDB (http://www.hmdb.ca/) and Progenesis MetaScope. Metabolic pathway analysis was performed using KEGG (http://www.kegg.jp/), and Cytoscape 3.7.1 software was used to visualize the obtained network diagrams of compounds and pathways.The raw data acquired by the UPLC-TOF-MS system were exported to a Progenesis QI v3.0.3  workstation for peak alignment, peak picking, and deconvolution . The datR. Scrophulariae obtained through a search of various databases, such as TCMSP (http://lsp.nwu.edu.cn/tcmspsearch.php), TCM-ID (http://bidd.nus.edu.sg/group/TCMsite/Default.aspx), BATMAN-TCM (http://bionet.cpsb.org/batman-tcm/), and TCMGeneDIT (http://tcm.lifescience.ntu.edu.tw/index.html), was extensively studied to retrieve all chemical ingredients and their protein targets. Second, the molecular targets and genes associated with hyperthyroidism were ascertained from databases such as MalaCards , GenBank (https://www.ncbi.nlm.nih.gov/genbank/), GeneCards (https://www.genecards.org) and OMIM (https://www.omim.org).First, the literature on R. Scrophulariae that intersected with hyperthyroidism were identified. Fourth, using the DAVID (http://david.nifcrf.gov/) database, the obtained intersecting genes were subjected to a GO annotation analysis (Gene-Ontology) and a KEGG pathway analysis (Kyoto Encyclopedia of Genes and Genomes). The intersection genes could be directly mapped onto the pathways. Finally, according to the target prediction results obtained for the above-mentioned chemical constituents of R. Scrophularia, a network model of the chemical constituents, intersections, and target pathways was constructed using Cytoscape 3.7.1. If a target is a potential target of a compound, it is connected by an edge. If a target participates in a certain pathway, the target is also connected to the action pathway by an edge. Through the construction of the network, the characteristics of multiple components, multiple targets, and multiple pathways associated with the treatment of hyperthyroidism by R. Scrophularia were revealed   , indirecThe basal peak intensity (BPI) chromatograms of the fecal supernatants of the Control, Model and RS groups are shown in R. Scrophularia and Euthyrox; instead, a variety of metabolites formed a biomarker group for fecal supernatants.This study found a significant difference in the metabolic spectra of fecal supernatants between the feces of the Control rats and those of the Model rats, which indicated that the rats belonging to the Model group had unique characteristics of fecal metabolites. This finding suggested that a changes in single substances did not serve as biomarkers of R. Scrophulariae, which indicated that R. Scrophulariae exerts an improved therapeutic effect. The results from the metabonomics analysis are more intuitive, clear and comprehensive and thus reflect the advantages of model induction and R. Scrophulariae intervention on hyperthyroidism.EZinfo was used to perform a 3D-PCA analysis of the Control and Model groups . Good seIn the PLS-DA analysis, each point in the PLS-DA score plot of the Control, Model and RS groups  represenhttp://www.hmdb.ca/), METLIN (http://www.metlin.scipps.edu/), KEGG (http://www.genome.jp/kegg/) and SMPD (http://www.smpdb.ca/) databases. A total of 44 potential biomarkers were identified through comparisons with the literature , dipropyl disulfide,phenylalanyl-valine, and valyl-phenylalanine were upregulated in the fecal supernatants of the Model group compared the supernatants of the Control group, whereas linoleic acid, linoelaidic acid, sphingosine,3-dehydrosphinganine, palmitoylethanolamide, nutriacholic acid, lithocholic acid glycine conjugate, leucyl-phenylalanine,aspartyl-leucine, glycyl-tryptophan,isoleucyl-aspartate,isoleucyl-phenylalanine,phenylalanyl-isoleucine, tryptophylglycine, cyclohexanecarboxylic acid,2,3-heptanedione,glutamylvaline, 5beta-coprostanol, 5alpha-cholestanol,epi-coprostanol,cholesterol, nervonic acid, docosapentaenoic acid,docosa-4,7,10,13,16-pentaenoic acid,l-leucyl-l-proline, leucyl-proline, deoxycholic acid glycine conjugate, chenodeoxycholic acid glycine conjugate, glycoursodeoxycholic acid, and l-phenylalanyl-l-proline were downregulated. After treatment with R. Scrophulariae, the levels of these biomarkers changed to the levels found in the Control group, which indicated that R. Scrophulariae extract can regulate the metabolism of hyperthyroid rats.Based on tandem mass spectrometry data, potential biomarkers were retrieved and confirmed in the HMDB  to construct and analyze the related metabolic pathways. The critical value of the metabolic pathway impact value was set to 0.10. If the value was higher than this threshold, the pathway would be selected as a potential target path.The biomarkers shown in R. Scrophulariaceae exerts a good therapeutic effect on the hyperthyroidism model induced by Euthyrox.As shown in R. Scrophulariaceae can play a therapeutic role by regulating the metabolic network and metabolic pathways involved in Euthyrox-induced hyperthyroidism in rats.Of the 44 markers identified, nervonic acid, docosapentaenoic acid, palmitic acid and linoleic acid are mainly involved in unsaturated fatty acid biosynthesis, and cholesterol, chenodeoxycholic acid glycine conjugate and cholic acid are mainly involved in primary bile acid biosynthesis.3-Dehydrosphinganine and sphingosine mainly participate in sphingolipid metabolism, and spermidine is mainly involved in β-alanine metabolism, glutathione metabolism, and arginine and proline metabolism. Linoleic acid is mainly involved in linoleic acid metabolism. Palmitic acid is mostly involved in mitochondrial fatty acid elongation, fatty acid metabolism and fatty acid biosynthesis, and cholesterol is mainly involved in steroid biosynthesis and steroid hormone biosynthesis. Guanine is mainly involved in purine metabolism. The results showed that hyperthyroidism can induce a series of complex metabolic pathway disorders, such as lipid metabolism, amino acid metabolism and nucleic acid metabolism. R. Scrophulariae were retrieved from TCMSP, TCM-ID, BATMAN-TCM, and TCMGeneDIT, respectively, and 83 chemical constituents of R. Scrophulariae were obtained by deleting the intersection. These constituents were divided into 11 categories:iridoids, phenylpropanoid glycosides, phytosterols, organic acids, flavonoids, triterpene saponins, volatile oils, sugars, amino acids, alkaloids and monoterpene and diterpene components and other compounds. The chemical composition of R. Scrophulariae matched 795 genes, such as PTGS2, MAOB, and MAOA  pathway, the NOS3-LYPLA1-palmitic acid pathway and the NOS3-LYPLA1-hexadecanoate (n-C16:0) pathway to play a role in the treatment of hyperthyroidism, and among these pathways, the IL6-APOA1-cholesterol pathway was the most strongly related to the treatment of hyperthyroidism by R. Scrophulariae , and the administration of R. Scrophulariae significantly reduced the cholesterol level of the rats (p ≤ 0.01), which indicated that R. Scrophulariae might treat hyperthyroidism by adjusting the cholesterol level via the lipid metabolism pathway. Cholesterol is involved in the primary bile acid biosynthesis, steroid biosynthesis and steroid hormone biosynthesis pathways in lipid metabolism. However, the primary bile acid biosynthesis, steroid biosynthesis, and steroid hormone biosynthesis pathways are affected by upstream disorders, and these effects are mainly manifested due to an abnormal expression of IL6 in the HIF-1 signaling pathway, which results in disordered lipid metabolism.Patients with hyperthyroidism exhibit hypermetabolism, accelerated breakdown of proteins and fats in the body, and low cholesterol levels . In thisIn vitro studies have confirmed that the binding of the complex of IL6 and the soluble IL6 receptor (sIL6-R) complex to gp130 induces formation of an IL6 signal transducer, which can transmit the IL6 signal. In the presence of sufficient sIL6-R, IL6 dose-dependently inhibits thyroid function and the release of T3 and T4 (Interleukin-6 (IL6) is a cytokine that can be synthesized by a variety of cells. The biological effects of this cytokine are very complex, and its target cells include macrophages, hepatocytes, resting T cells, activated B cells and plasma cells . Hyperth3 and T4 . IL6 is 3 and T4 .ApoA1 is the main cholesterol-related gene, and apolipoprotein A-I is the main protein component of high-density lipoprotein in the blood circulation and the main receptor for cholesterol in extrahepatic tissues. This protein plays an important role in the transport and metabolism of cholesterol because it can remove excess lipid deposits on the vascular walls to protect blood vessels .R. Scrophulariae might treat hyperthyroidism by upregulating the APOA1 and cholesterol levels while simultaneously downregulating the IL6 levels and regulating the IL6-APOA1-cholesterol pathway via the HIF signaling pathway.The content of APOA1 in the serum of pregnant women with hyperthyroidism at the early stage of pregnancy does not significantly differ from that found in normal pregnant women. The serum APOA1 content observed in pregnant women with hyperthyroidism at the middle and late stages of pregnancy is significantly lower than that found in normal pregnant women. The possible mechanism for this difference is that the liver is the main site for the synthesis of APOA1, and thyroid hormones can promote APOA1 gene transcription, but long-term thyroid hormone stimulation would inhibit the synthesis of APOA1 mRNA. This negative feedback regulation mechanism might lead to continuous increases in the APOA1 levels in the serum of normal pregnant women but would result in no significant changes in the levels found in pregnant women with hyperthyroidism; as a result, pregnant women with hyperthyroidism have a lower serum APOA1 content than normal pregnant women at the second and third trimesters . TherefoR. Scrophulariaceae exerts a callback effect on the 13 metabolic pathways affected by Euthyrox:unsaturated fatty acid biosynthesis, primary bile acid biosynthesis, sphingolipid metabolism, β-alanine metabolism, glutathione metabolism, arginine and proline metabolism, linoleic acid metabolism, mitochondrial fatty acid elongation, fatty acid metabolism, and fatty acid biosynthesis, steroid biosynthesis, steroid hormone biosynthesis, and purine metabolism. The R. Scrophulariae-active ingredient-target-disease-related network was constructed using network pharmacology approaches. A total of 73 interaction targets and 13 related pathways were found, and among these pathways, the HIF-1 signaling pathway was the main pathway associated with the treatment of hyperthyroidism by R. Scrophulariae. The integration of metabolomics and network pharmacology revealed that R. Scrophulariae might mainly regulate the IL6-APOA1-cholesterol pathway, AKT1-THEM4-palmitic acid pathway, AKT1-THEM4-hexadecanoate (n-C16:0) pathway, NOS3-LYPLA1-palmitic acid pathway and NOS3-LYPLA1-hexadecanoate (n-C16:0) pathway to play a role in the treatment of hyperthyroidism. This study of hyperthyroidism disease using an approach that integrates metabolomics with network pharmacology provides new ideas for further research on the pharmacological mechanism of R. Scrophulariae and its material basis.


Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder with complex pathogenesis. Currently, the pathogenesis of ASD is not fully understood. Moreover, current treatments do not effectively alleviate the primary symptoms of ASD social disorder (SCDA). Jiawei Yinhuo Tang (JWYHT) is an improved version of the classic prescription Yinhuo Tang. Although this medication has been shown to improve social behavior in ASD patients, the mechanism by which it works remains unknown.  In this study, network pharmacology bioinformatics analysis was used to identify the key targets, biological functions, and signal pathways of JWYHT in SCDA. Then, molecular docking and molecular dynamic simulation were used to validate the activity and stability of the active ingredient and the target protein during the binding process.  The analysis identified 157 key targets and 9 core targets of JWYHT (including proto-oncogene (FOS), caspase 3 (CASP3), mitogen-activated protein kinase-3 (MAPK3), interleukin-6 (IL6), mitogen-activated protein kinase-1 (MAPK1), tumor necrosis factor (TNF), mitogen-activated protein kinase-8 (MAPK8), AKT serine/threonine kinase 1 (AKT1), and 5-hydroxytryptamine receptor 1B (5HT1B)) in SCDA. In addition, the Kyoto Encyclopedia of Gene and Genome results, as well as the staggering network analyses, revealed 20 biological processes and 20 signal pathways targeted by JWYHT in SCDA. Finally, molecular docking analysis was used to determine the binding activity of the main active components of JWYHT to the key targets. The binding activity and stability of methyl arachidonate and MAPK8 were demonstrated using molecular dynamics simulation.  This study demonstrates that JWYHT regulates neuronal development, synaptic transmission, intestinal and cerebral inflammatory response, and other processes in SCDA. ASD is a clinical condition characterized by a set of neurodevelopmental symptoms. ASD is defined by three main characteristics: persistent impairment in social communication and interaction, repetitive behavior, and restricted interests . The mosSchisandrae chinensis Fructus, Poria cocos, Ophiopogonis Radix, Rehmanniae Radix Praeparata, Asparagi Radix, and Mume Fructus. JWYHT was first mentioned in the famous ancient medicine treatise, “Bian-Zheng-Qi-Wen,” of the Qing dynasty. Morindae Officinalis Radix has the function of strengthening sinew and bone, tonifying kidney yang, and dispelling wind-dampness in this Chinese formula. Rehmanniae Radix Praeparata is used to tonify blood, essence, and marrow as well as to enrich yin. S. chinensis Fructus is responsible for enriching qi, generating fluid, securing and tonifying the kidney, and nourishing the heart to tranquilize. P. cocos is primarily used to induce diuresis to drain dampness, fortify the spleen, and nourish the heart to tranquilize. Ophiopogonis Radix is frequently used to enrich yin, generate fluid, moisten the lung, and clear heartburn. Asparagi Radix is used to enrich yin, moisten dryness, clear lung fire, and generate fluid. Asparagi Radix is used to enrich yin, moisten dryness, clear lung fire, and generate fluid. Mume Fructus is used to astringe the lung, and intestine, as well as to calm Ascaris and stimulate fluid production. The whole formula feeds yin while tonifying yang. Pharmacologic studies have shown the positive therapeutic effects of certain components of JWYHT on ASD patients. S. chinensis Fructus and its active ingredients, for example, have demonstrated anti-oxidation, apoptosis suppression, anti-inflammation, and neurotransmitter properties. These modifications made modulating brain-derived neurotrophic factor (BDNF) related pathways easier [Rehmannia glutinosa can alleviate scopolamine-induced neuronal damage and memory dysfunction in rats, demonstrating a significant neuroprotective effect [JWYHT is a traditional Chinese formula made up of Morindae officinalis Radix, s easier . Additioe effect . CurrentTraditional Chinese medicine (TCM) is a valuable medication that has been passed down through Chinese culture. It has evolved into a significant player in mainstream medicine. TCM evaluates a patient's symptoms and restores the balance of life and bodily functions by prescribing therapeutic herbs based on “King, Vassal, Assistant and Delivery servant” compatibility . In receIn recent years, there has been an increase in the number of studies focusing on the advancement of network pharmacology in Chinese medicine. Based on these biological databases and clinical trial results, researchers may use systems biology to investigate the network connection between “herbs-compounds-proteins/genes-disease” and scientifically explain the mechanism of action of herbal medicines on specific diseases. The significance of network pharmacology is that it allows for the development of new ideas and approaches to drug research. As a result, biological systems, drugs, and diseases may be linked in a reliable network, benefiting the modernization and internationalization of TCM .Herein, we used a network pharmacology-based approach, followed by molecular docking analysis, to identify the active ingredients, potential targets, and pharmacological mechanism of JWYHT in SCDA treatment. First, we searched for information on bioactive compounds in JWYHT. Candidate target genes for such compounds and SCDA were identified and collected from relevant databases. The drug ingredients-target network was constructed using the target sets of JWYHT and SCDA. The overlapped genes were examined using comparative analysis. Following that, protein-protein interaction (PPI) and module networks were built using overlapping genes and hub nodes, followed by GO and KEGG pathway enrichment analysis. Therefore, this study aimed to explore and validate the molecular mechanisms and pathways of the major bioactive components of JWYHT in SCDA treatment. This would provide a necessary and fundamental basis for further research into the pharmacological mechanism of JWYHT.http://lsp.nwu.edu.cn/) and Traditional Chinese Medicine Integrated Database  were used to find well-reported pharmacological targets of JWYHT such as Morinda officinalis, Fructus schisandra, P. cocos, Praeparata Radix Rehmanniae, asparagus, black plum, and Ophiopogon japonicus. According to the pharmacokinetic absorption, distribution, metabolism, and excretion parameters, the criteria for screening out the active ingredients from the TCMSP were oral bioavailability (OB) ≥30% and drug-likeness (DL) quality ≥0.18 [https://www.genecards.org/) and Online Mendelian Inheritance in Man . Using JWYHT drug targets and SCDA pathological targets as two independent sets, the intersection genes were obtained using the jvenn online software mapping tool platform. A Venn diagram was used to evaluate all of the collected targets and identified the ones that overlapped as potential therapeutic targets.The Traditional Chinese Medicine Systems Pharmacology  database was used to investigate the relationship between the targets. The species was set to “Homo sapiens,” and the lowest interaction threshold was set to 0.4. All other parameters were set to default. The protein-protein interactions of the JWTHT- and SCDA-related targets were exported, following which the tsv data was extracted. Cytoscape3.8.2 software was used to develop a medicine–component–disease-target network model based on the common targets for a better visual comprehension of their interrelationships. The major active targets of JWYHT were analyzed by degree value, revealing the number of components and action targets. The higher the degree value, the more powerful the biological function and effect of the ingredient.The Search Tool for the Retrieval of Interacting Genes/Proteins , closeness (CC), degree (DC), eigenvector (EC), local average connectivity-based method (LAC), and network (NC). Genes with scores higher than the median of the aforementioned items were successively selected to construct the final key subnetwork. The top 10 genes in PPIs and important subnodes that were independent by phase one were identified using cytoHubba chrome. MCODE can also be used to filter subsets of the entire PPI network.R software packages “DOSE,”“clusterProfiler,”“EnrichPlot,” and “PathView” were installed via the “BiocManager” and used to identify biological processes (BP), molecular functions, and KEGG pathways with P-values <0.05 and Q-value <0.05. This data was used to generate bubble graphs, histograms, and related route graphs.The KEGG-GENE visualization was performed in Cytoscape 3.8.2 software to identify potential targets and JWYHT-associated enrichment pathways for SCDA treatment. The connection between them could be easily demonstrated.https://www.uniprot.org/) was used to obtain the PDB files of the three-dimensional structure of the receptor protein of the key genes. The files containing the two-dimensional structure of the main active ingredients were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The ChemBio3D software was then used to calculate and export the three-dimensional structure of the ingredients as mol files, which were subsequently imported into the SYBYL-X 2.0 software for dehydration and hydrogenation. Minimize module was used for molecular energy optimization. The results were saved as mol2 files. The PDB files were imported into SYBYL-X2.0 software, where we exposed the active site of the ligand, removed the crystal water, hydrogenated, removed the side chain residues, and optimized the energy using the AMBER7 FF99 force field. The remaining parameters were set to default. Next, the docking files containing active pockets were generated. Finally, we used the Surflex-Dock Screen docking mode of SYBYL-X2.0 software to batch dock nine JWYHT compounds and nine SCDA key target proteins and obtained the corresponding total score value. The matching method of this docking tool is primarily based on structural and shape similarity, and the benefits include high accuracy, a high positive rate, and efficient matching. Total score is a scoring function that uses negative logarithms to simulate the binding capacity. It converts the binding free energy formula . It is widely assumed that the lower the free binding energy of the receptor and ligand, the greater the stability, and the higher the total score value. When total score is >5, we considered it to have well binding activity, and when total score is >7, we considered it to have superactivity due to the combination of the receptor protein and molecular ligand [The most significant targets and active components with a degree >25.2 (twice the mean) were selected from the aforementioned essential subnetworks using a combination of literature mining and network analysis. The UniProt database ). Furthermore, using the OMIM and GeneCards databases, 546 and 789 GENES associated with SCDA were obtained, respectively. A total of 1,314 SCDA-related gene sets were obtained after removing duplicates and consolidating search results  for details), for a better visual understanding of their interrelationships . TopologP < 0.05and Q < 0.05, respectively. In total, we found 2,008 important biological processes involving 178 molecular functions. Of note, according to P-value, 9 of the top 20 biological processes were involved in synaptic regulation. GO nomenclatures such as neurotransmitter receptor activity, ion channel activity, substrate-specific channel activity, channel activity, and passive transmembrane transporter activity were primarily involved in molecular function  and molecular functions (MF); the significance levels were set to function . These tP < 0.05 and Q < 0.05. Finally, 150 metabolic pathways were enriched, and the results demonstrated that these genes were primarily involved in neuroactive ligand-receptor interaction, pathways of neurodegeneration – multiple diseases, Alzheimer's disease, signal transduction-related pathways, proteoglycans in cancer, serotonergic synapse, dopaminergic synapse, alcoholism, viral infection, and other pathways. Additionally, we linked KEGG-enriched pathways and cross-target genes and imported them into Cytoscape 3.8.2 to create visualizations of the major targets and pathways of JWYHT in SCDA treatment , demonstrated ASD-related gene binding activities. AA is an unsaturated fatty acid that aids in the improvement of cognition and the esterification of cholesterol. Monoacylglycerol lipase (MAGL) can hydrolyze 2-arachidonic acid glycerol (2-AG) into AA and glycerol, thereby regulating the endocannabinoid (ECB) system signal transduction y effect . Experimy effect . Orchinoy effect . ASD risseverity , and orc pathway . Cerevis pathway . LPS is  pathway , suggestoperties , 33. The pathway . Its antnd blood . Recent nd blood . Cephalofunction . Valproipression , implyinα and caspase-3 expression [+ T cells regulate oxidative stress by releasing inflammatory factors such as IL-6. Transduction of the IL-6 receptor promotes the release of IL-17A and polarizes Th0 cells to Th17 cells. Also, the upregulation of Th17 cell development is related to ASD pathogenesis [Following PPI network topology and cluster analysis, nine important gene target proteins, including FOS, CASP3, MAPK3, IL-6, MAPK1, TNF, MAPK8, AKT1, and 5HT1B, were identified, and AKT1 was the most targeted gene of JWYHT. AKT1 is a protein kinase B that regulates multiple cell functions, including cell growth, apoptosis, nutritional metabolism, and transcription factor activity . Studiespression . MAPK3, pression . Animal pression . ERK2 knpression . CD4+ T ogenesis . TNF is ogenesis .+ and Ca2+ channels as well as the ERK signal pathway, thereby improving GABA neurotransmitter transmission [The biological regulatory mechanism of GO enrichment includes neurotransmitter receptor activity, ion channel activity, substrate-specific channel activity, channel activity, passive transmembrane transporter activity, and so on. The molecular process of synaptic transmission, which includes the coupling of electrical and chemical signals, is required for the integration of learning and memory functions. Membrane potential is essential in nerve cell communication. The movement of important ions across the membrane is required for the generation of membrane potential. CaV2 and NaV1.2 channels are related to the action potential of cortical neurons, which may tune dendritic excitability and synaptic plasticity. Both mutations and polymorphisms can be used as abnormal ASD targets, whereas CaV2 channel abnormalities can cause ASD learning or memory loss , 60. Incsmission . The GABsmission . By modusmission –69, and smission –72.The KEGG enrichment pathway is primarily made up of neuroactive ligand-receptor interactions, serotonergic synapse, and other elements. In the early stages, the fetus/cortex is the most significant developmental stage/brain area, and the interaction between neuroactive ligand-receptor and chr16q22 is the main pathway of ASD . Finnishin vivo studies.Therefore, JWYHT can regulate ASD social communication disorders through multiple targets and pathways, primarily by regulating signal pathways such as neuroactive ligand-receptor interaction, MAPK, AKT, and synaptic transmission of 5-HT and GABA, as well as the transmission process of gut-brain axis-related neurons and inflammatory response. These regulatory changes alter brain neuronal development, synaptic transmission, intestinal, and cerebral inflammatory response to improve ASD social behavior and related memory, emotion, and language ability either directly or indirectly. The results of molecular docking revealed that methyl arachidonate had a high affinity for MAPK8, MAPK3, and MAPK1. The MAPK pathway is involved in the regulation of autistic brain neurodevelopmental processes such as the formation of hippocampal neuron dendritic spines and synapses , 86, socThe main targets of JWYHT on social communication disorders in autism, including MAPK, AKT1, 5HT1B, and IL6, were investigated using network pharmacology and molecular docking technology in this study. The most important MAPK pathway in autism is involved in neurodevelopmental disorders. Combined with molecular dynamics simulation research, MAPK8 may be one of the key targets of JWYHT in SCDA treatment. The findings of this study demonstrated the potential efficacy and molecular mechanism of traditional Chinese medicine in the treatment of autism-related social communication disorders.