Sterols are biologically important molecules that serve as membrane fluidity regulators and precursors of signaling molecules, either endogenous or involved in biotic interactions. There is currently no model of their biosynthesis pathways in brown algae. Here, we benefit from the availability of genome data and gas chromatography-mass spectrometry (GC-MS) sterol profiling using a database of internal standards to build such a model. We expand the set of identified sterols in 11 species of red, brown, and green macroalgae and integrate these new data with genomic data. Our analyses suggest that some metabolic reactions may be conserved despite the loss of canonical eukaryotic enzymes, like the sterol side-chain reductase (SSR). Our findings are consistent with the principle of metabolic pathway drift through enzymatic replacement and show that cholesterol synthesis from cycloartenol may be a widespread but variable pathway among chlorophyllian eukaryotes. Among the factors contributing to this variability, one could be the recruitment of cholesterol biosynthetic intermediates to make signaling molecules, such as the mozukulins. These compounds were found in some brown algae belonging to Ectocarpales, and we here provide a first mozukulin biosynthetic model. Our results demonstrate that integrative approaches can already be used to infer experimentally testable models, which will be useful to further investigate the biological roles of those newly identified algal pathways. Sterols are biologically important molecules in eukaryotes, which function as membrane fluidity regulators and precursors of signaling molecules, either endogenous or involved in biotic interactions. The associated biosynthetic pathways are good models to study metabolic pathway evolution because the enzymes involved are well-known across eukaryotes and relatively conserved despite some variation . RecentlEctocarpus siliculosus  from Roscoff: Saccharina latissima and Laminaria digitata. Furthermore, we analyzed these profiles in three red algae , and one green alga (Ulva sp.), as a basis for comparison and to further investigate the intriguing issue of undetectable cycloartenol that we noticed previously in C. crispus  profiling of sterols present in the two brown algal orders, the Ectocarpales and the Laminariales, for which genome-scale metabolic networks have been recently reconstructed . Specifi crispus .L. digitata, Ulva sp., C. crispus, and M. stellatus were collected at low tides on the shore at Roscoff, France, in front of the Station Biologique  for Ulva sp., C. crispus, and M. stellatus, and at the Bloscon site  for L. digitata. Samples from E. fasciculatus and E. crouaniorum came from frozen samples collected at Roscoff  in 2009. Samples of S. latissima, E. subulatus Ec371 (CCAP accession 1310), E. siliculosus Ec32 (CCAP accession 1310/04), and P. palmata came from cultures and were maintained in 10 L Nalgene flasks in a culture room at 14°C using filtered and autoclave seawater enriched with Provasoli nutrients , squalene, campesterol, brassicasterol, desmosterol, lanosterol, fucosterol, cycloartenol, and 5α-cholestane  were acquired from Sigma-Aldrich , cycloartanol and cycloeucalenol from Chemfaces , and zymosterol from Avanti Polar Lipids . The C7-C40 Saturated Alkanes Standards were acquired from Supelco . Reagents used for extraction, saponification, and derivation steps were n-hexane, ethyl acetate, acetonitrile, methanol , (trimethylsilyl)diazomethane, toluene (Sigma-Aldrich) and N,O-bis(trimethylsilyl)trifluoroacetamide with trimethylcholorosilane .−1. Working solutions were made at a concentration of 1 mg.ml−1, in hexane, by diluting stock solutions. The C7-C40 Saturated Alkanes Standard stock had a concentration of 1 mg.ml−1 and a working solution was prepared at a concentration of 0.1 mg.ml−1. All solutions were stored at −20°C. The 24-alkylsterols from algae are often epimers from their land plant counterparts that can be distinguished by NMR , squalene, campesterol, brassicasterol, desmosterol, lanosterol, fucosterol, cycloartenol, and 5α-cholestane were prepared in hexane at a concentration of 5 mg.mld by NMR , so hereTo avoid biases linked to the variation of the behavior of individual sterols during the ionization step, a quantification method specific to the available standards was developed, according to the recommendations of the community . We choo2, and resuspended with 120 μl of (trimethylsilyl)diazomethane, 50 μl of methanol:toluene [2:1 (v/v)] and 5 μl of 5α-cholestane (1 mg.ml−1) as internal standard. The mixture was vortexed for 30 s and then heated to 37°C for 30 min. After a second evaporation under N2, 50 μl of acetonitrile and 50 μl of BSTFA:TMCS (99:1) were added to the dry residue, vortexed for 30 s and heated to 60°C for 30 min. After final evaporation under N2, the extract was resuspended in 100 μl of hexane, transferred into a sample vial and stored at −80°C until the GC-MS analysis.Algal samples (60 mg) were extracted with 2 ml ethyl acetate by continuous agitation for 1 h at 4°C. After 10 min of centrifugation at 4,000 rpm, the solvent was removed, the extracts were saponified in 3 ml of methanolic potassium hydroxide solution (1 M) during 1 h of incubation at 90°C. The saponification reaction was stopped by plunging samples into an ice bath for 30 min minimum. The unsaponifiable fraction was extracted with 2 ml of hexane and 1.2 ml of water and centrifuged at 2,000 rpm for 5 min. The upper phase was collected, dried under N−1. The temperature of the injector was 280°C and the detector temperature was 315°C. After injection, the oven temperature was kept at 60°C for 1 min. The temperature was increased from 60 to 100°C at a rate of 25°C.min−1, then to 250°C at a rate of 15°C.min−1, then to 315°C at a rate of 3°C.min−1 and then held at 315°C for 2 min, resulting in a total run time of 37 min. Electronic impact mass spectra were measured at 70 eV and an ionization temperature of 250°C. The mass spectra were scanned from m/z 50 to m/z 500. Peaks were identified based on the comparisons with the retention times and the mass spectra . A HP-5MS capillary GC column (30 m × 0.25 mm × 0.25 μm) from J&W Scientific  was used for separation and UHP helium was used as carrier gas at a flow rate to 1 ml.min spectra .Similarity searches for orthologous genes were performed using BLAST either against protein sequences, when available, or directly on translated nucleotidic sequences on the sequence read archive (SRA) or transcriptome shotgun assemblies (TSA). Project accession numbers for SRA and TSA are indicated in Ab-initio inference of biosynthesis pathway was performed using the Pathmodel program  was performed using the curation function of AuReMe  across samples , whereas cholesterol ranged from 0 to 4.4%. Among the other investigated sterols, lanosterol was never found, whereas 24-ethylcholesterol and lathosterol were found only in the three red algal species, and zymosterol was found only in P. palmata and in E. subulatus. The fact that we did not detect ergosterol in any of the analyzed algae was particularly striking because it was recently reported to be present in E. siliculosus, using a different separation technique, HPLC coupled to fluorescence detection  or Δ24(28) double bond (24(25) or Δ24(28) would render impossible a sterol-28-methylation from 24-methylcholest-22-enol. Actually, 24-ethylcholesterol has been reported in other brown algae by different groups -desaturation, has been described in bacteria degrading human sterols in sewage water sediments . In order to make a curation decision as well informed as possible, we decided to integrate our profiling results with the rest of the available knowledge on brown algal sterol profiling, which we gathered into an internal database  do not cluster together, which we interpret as a technical artifact, leading to the overweighting of weak evidence for the late SSR pathway at the ancestral node. Even with this limitation, the probability that the late SSR pathway was present in the common ancestor of Laminariales and Ectocarpales with strong support is 51%. This probability stays identical for the last common ancestor of C. okamuranus and the Punctaria, Striata, and Stilophora genera, all corresponding to the Chordariaceae family (C. okamuranus.The topology of this wn algae . Howevere family . HoweverIn this study, we performed targeted profiling of 12 sterols in 11 algal species and integrated our findings regarding brown algae into biochemical pathway models. We establish a general model for the biosynthesis of the sterols that are widespread across brown algae, but also for some specialized metabolites that are derived from those general pathways. Our analysis therefore provides a starting point for biochemical tests of candidate enzymatic activities.Skeletonema marinoi and Cyclotella criptica, the most abundant sterol in the lipid droplet of the diatom Phaeodactylum tricornutum was found to be brassicasterol (Ectocarpus species.In addition to the biochemical validation, there are still some issues to further clarify from the metabolomic viewpoint. In particular, there are very few data, to date, on the biosynthetic intermediates between cycloartenol and end-pathway products that we reported here. The only robust evidence here is available for the presence of 24-methylenecholesterol, which was reported in at least 11 other brown algal species . Those tcasterol . In addicasterol . This pr8-Δ7 isomerization occurs before the second sterol-4-demethylation, leading to the production of zymosterol. Consistently with this, we inferred the same reaction order for the sterol-Δ8-Δ7 isomerization and sterol-4-demethylation occurring on methylated sterols, in contrast with the inferences in diatoms that keep the plant-like sterol-4-demethylation before the isomerization step. Regarding this, we have to stress that both hypotheses are equally speculative in light of data about the corresponding biosynthetic intermediates. Another difference between diatoms and brown algae is that some diatoms still retain the canonical SSR enzyme that has been lost in brown algae, despite the evidence that steroid side-chain reduction at carbon 24 does occur in brown algae, according to our profiling data and to independent NMR-based data on specialized metabolites in C. okamuranus (Even if important biological variability could impact both quality and quantity of sterols in brown algae, some general patterns, either conserved or not, can be drawn, in relationships with evolution of this pathway. When comparing diatoms and brown algae, which belong both to the stramenopile lineage, it is interesting to note that a pathway identical to the late SSR pathway has been independently proposed , the onlamuranus . This obamuranus .In this paper, we gathered GC-MS data from representatives of two brown algal orders to fuel a first model of sterol biosynthesis pathways and provide a case-study example of how to integrate these data with the literature in an evolutionary context. Specifically, we show that although the most likely way to synthesize cholesterol implies a late SSR pathway, this pathway shows variation either in terms of involved enzymes or in the succession of steps, highlighting the need for experimental data in multiple species to better understand the dynamics of metabolic pathway evolution across the eukaryotic tree of life.https://github.com/pathmodel/pathmodel/blob/master/pathmodel/data/brown_sterols_pwy.lp. Data to construct the mozukulin biosynthesis pathway in C. okamuranus: https://github.com/pathmodel/pathmodel/blob/master/pathmodel/data/mozukulins_pwy.lp. Commands and sources files for ancestral mapping: https://github.com/gabrielmarkov/AncCharMapping. Updated versions of the brown algal genome-scale metabolic networks: http://aureme.genouest.org/wiki.html. All other original contributions presented in the study are included in the article/Data to construct the brown algal biosynthesis pathway: GM, LD, SD, CB, CLb, AS, and JC conceived the project. JGi conducted sterol profiling with help from GM, GL, LD, CLr, and CLb. GM encoded the mozukulin, brown algal biosynthesis pathways into the Pathmodel program with input from AB and AS, and wrote the manuscript with input and edits from all authors. LD performed reciprocal blast searches of orthologs in the sterol pathway. JGo updated the genome-scale metabolic network models on the AuReMe website based on input files prepared by GM. All authors contributed to the article and approved the submitted version.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.


The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study.  Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined.  Pathway analysis revealed 18 disturbed pathways related to the CVD. glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound.  It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches. The rate of death due to cardiovascular disease (CVD) e.g., coronary heart disease, stroke, and rheumatic heart disease is mainly increased. This augmented rate is reported about 22% from 1999 to 2005 . High thhttps://www.genome.jp/kegg/compound/) of CVD related metabolites from the previous systematic review study in 2017 by Miguel RuizCanela et al. [The KEGG ID . Except for 3-Methylhistidine (C01152), creatinine (D03600), and TMAO (M00455), the other compounds were found in the MetScape query. A network of compound-reaction-enzyme-genes was retrieved by this application. The obtained network of the query metabolites has 527 nodes and 608 connections. This network is composed of 18 subnetworks (pathways). The list of pathways in this network is tabulated in A number of 34 compounds linked to cardiovascular disease were identified with KEGG IDs from KEGG Website . The fun-hydroxybutanoic acid, citrate, and tryptophan respectively. On the other hand; the glycerophospholipid metabolism pathway as the largest pathway is related to the nine queried metabolites and has common metabolites with nine other pathways. Details of elements and connections in this pathway are presented in et al. (2018) [et al. [The discovery of CVD related biomarkers could be useful in the diagnosis of disease and also for treatment purposes. In a way, it is possible to reduce the mortality rate and approve the life quality of patients. Metabolite biomarkers have attracted the attention of scientists historically . Lipids . (2018) . Direct  [et al. ; hence,  [et al. . Correla Pathway analysis revealed that among 34 reported metabolites, serine could be considered as a potential diagnostic biomarker nominee in CVD patients. It can also be utilized as a biomarker in the follow-up of patients. Yet, more investigation in this regard is suggested for possible application of serine in clinics. The authors declare no conflict of interest. Shahid Beheshti University of Medical Sciences supports this research with the grant number of 15477.


Malt is the main raw material for beer production, which determines not only its taste and aroma profile, but to a large extent its biological value, as well. The aim of the present research was to determine the antioxidant profile of different malt types as a basis for the development of new types of beer with increased antioxidant activity. In the present study the main brewing characteristics, the phenolic profile and the antioxidant potential of 20 malt types used in craft breweries in Bulgaria have been examined. The main brewing characteristics have been determined by the standardized methods of the European Brewing Convention. Malt phenolic content was determined by two methods, and antioxidant potential by five different methods. Based on a statistical factor analysis performed by the principal component analysis, it was confirmed that there was a relationship between malt color and phenolic compounds content. The principal component analysis confirmed that there was a link between the content of the Maillard reaction products and malt biological activity. Malts with the highest degree of heat treatment were characterized by the highest antioxidant activity, which was due to the content of Maillard reaction products with antioxidant capacity. Beer production involves the use of four main raw materials—malt, hops, water and brewer’s yeast. The main sources of starch in brewing are different types of barley malt, wheat malt, corn, unmalted barley, sorghum and others ,2. Malt The main changes in the phenolic profile and antioxidant potential of malt occur during its germination, kilning and eventual roasting. Kilning and roasting lead to changes in the phenolic content and the enzyme activity and cause non-enzymatic browning with a significant effect on the overall malt antioxidant properties ,7.Barley and malt are rich in phenolic compounds—flavan-3-ols, proanthocyanidins, hydroxycinnamic acid derivatives and small amounts of flavanols. These compounds are found in both bound and free form. Flavan-3-ols are most commonly identified from the free phenolic compounds, and the bound forms are most commonly phenolic acids. A number of authors noted that the most common representatives of this group are catechin and ferulic acid ,12,13,14The malt heat treatment during kilning and roasting leads to different types of changes. In some cases, degradation of phenolic compounds is observed, in other cases their polymerization. In most cases, kilning leads to a decrease in the ferulic acid amount, which is associated with the inclusion of phenolic compounds in the structure of the formed melanoidins. The process of reducing the amount of ferulic acid is also associated with the thermal degradation of the esterase responsible for the release of ferulic acid ,18,19.Thermal treatment of malt during kilning is associated with the formation of products of the Maillard reaction ,21,22,23The malt antioxidant potential plays an essential role in beer stability during storage. It has been found that beer sensory properties change during storage as a result of various chemical reactions ,4,26. MaOne of the main difficulties comes from the question: How to distinguish malts in terms of their phenolic capacity and antioxidant potential. As it has already become clear, these two parameters are related and also depend on a number of factors—barley variety, malt production technology, storage conditions and others. In this sense, a complex database, which should be interpreted in an appropriate way, is obtained. Principal Component Analysis (PCA) is a factor analysis method that achieves better interpretation of the data and reduces the number of dimensions in the factor space. This is achieved by creating new variables known as principal components and looking for a relationship between the observed values and the principal components. The method is adaptive and allows the classification of non-numerical variables  into separate groups based on numerical variables, i.e., the method can be adapted to different types and structures of data .The aim of the present work was to analyze the phenolic content and antioxidant activity of different types of malt, used in the brewing industry as a basis for the development of new types of beer with increased biological value. By determining the main brewing characteristics of different malt types, their phenolic complex and their antioxidant potential, malts have been grouped into 3 different groups—basic malts, special malts and functional malts. An approach for the development of new types of beer with increased biological potential will be proposed based on the results obtained.The following malt types produced by BestMalz, Kurfürsten-Anlage 52, D-69115 Heidelberg, Germany: Acidulated malt; Pilsen malt; Pale ale malt; Munich dark malt; Vienna malt; Smoked malt; Rye malt; Red X malt; Caramel amber malt; Caramel hell malt; Black malt; Melanoidine malt; Wheat malt; Caramel pils malt; Munich malt; Special X malt; Special Wheat malt; Caramel Munich I malt; Caramel Munich II malt; Chocolate malt were used in the present research.A standard EBC method was used to determine the malt brewing characteristics. 50 g of the corresponding ground malt were mixed well with 200 mL of water at a temperature of 45–46 °C to obtain a homogeneous mixture. The mixture was placed on a mashing apparatus and mashed for 30 min at 45 °C. After that, the mash temperature was risen to 70 °C with a heating rate of 1 °C/min. Upon reaching 70 °C, 100 mL of water of the same temperature was added to the slurry. The mixture was mashed at 70 °C for 60 min. After that, the samples were cooled for 10–15 min, then diluted to 450 g with water and filtered through Machery-Nagel 614 1/4 filter paper. During filtration, the first 100 mL of the filtrate were returned for re-filtration. Analyzes of the brewing characteristics and the biological value of the malts were performed using the wort obtained . A. Yield of extract—according to method 4.5.1 B. Malt color—according to the catalog data provided on the manufacturer’s website. C. Moisture content of malt—according to method 4.2 D. Starch content in malt.3 volumetric flask, then 25 cm3 of 1% HCl was added. The mixture was stirred by shaking and another 25 cm3 of 1% HCl was added. The sample was placed in a boiling water bath for 15 min, stirring continuously for the first 3–5 min. Under these conditions, the starch turns into a soluble form due to its partial hydrolysis. After removal from the water bath, 30 cm3 of distilled water was added to the flask and, after cooling, 5 cm3 of phosphoric-tungstic acid, which precipitates the proteins, was added. The sample was then diluted to 100 cm3 with distilled water, homogenized and filtered through a dry folded filter. The starch content was calculated according to Equation (1) [The malt was ground and a 3–5 g sample was weighed, transferred quantitatively into a 100 cmtion (1) .(1)St=13 of 80% (v/v) methanol. After an overnight stay, the mixture was diluted to 50 cm3 with the methanol solution. The resulting extract was filtered through a filter (Whattman No.1) and stored at −20 °C until use.10 g of ground malt  was mixed with 40 cmThe wort obtained according to method 2.1 was diluted with methanol (100%) in a ratio of 1:10, left to stand for 30 min and filtered using Whattman No.1 filter paper.The obtained extracts of malt and wort were used to determine the content of phenolic compounds and the antioxidant potential of malt and wort.3 of methanol extract, 4 cm3 of Folin-Ciocalteu working solution, 5 cm3 of sodium carbonate  were mixed in a test tube. The mixture thus obtained was stirred and allowed to stand for 1 h, after which the absorbance of the sample at 765 mn was determined on a Shimadzu UV-VIS1800 spectrophotometer  against a blank prepared with distilled water. The results were presented as gallic acid equivalent in mg GAE/dm3 wort:The content of total phenolic compounds was determined by the Folin-Ciocalteu (FC) method  with the3 of 0.1% HCl in 95% ethanol (v/v), 18.2 cm3 of 2% HCl (v/v) and 1 cm3 of methanol extract were mixed in a tube. The solution was stirred thoroughly and allowed to stand for 15 min, after which the absorbance was measured against a blank prepared with distilled water. The absorbance (A) at 280 nm was used to evaluate the total phenolic content, A320 nm was used to evaluate the content of phenolic acids and A360 nm was used to evaluate the content of flavonoid phenolic compounds. The calibration curves for the total phenolic compounds, phenolic acids and flavonoid phenolic compounds were constructed using gallic acid, caffeic acid and quercetin, respectively:The content of total phenolic compounds, phenolic acids and flavonoid phenolic compounds was determined by a modified method of Glories . 1 cm3 o3 of DPPH solution in methanol (6 × 10−5 M); the mixture was left for 15 min (kept in the dark at room temperature) to allow the reaction to proceed and then the absorbance at 517 nm against a blank of purified water was determined. The control sample was prepared with methanol. The antioxidant activity was determined by a standard curve using Trolox as standard, and the results were expressed as μM Trolox equivalents per 1 dm3 for wort:The antioxidant activity of malt and wort was measured by the DPPH method  with som2H3NaO2 • 3H2O and 16 cm3 of C2H4O2) with a pH of 3.6, 10 mM TPTZ solution prepared in 40 mM HCl and 20 mM FeCl3 • 6H2O solution. The working solution was prepared by mixing the acetate buffer, TPTZ solution and FeCl3 • 6H2O solution in a ratio of 10:1:1. 150 μL of the methanol extract was allowed to react with 2850 μL of FRAP solution for 4 min in the dark. The absorbance of the sample was then measured at 593 nm against a blank prepared with methanol. The antioxidant activity was determined by a standard curve using Trolox as standard, and the results were expressed as μM Trolox equivalents per 1 dm3 for wort:The FRAP analysis was performed according to the method of Benzie and Strain (1996)  with the−3 M ABTS solution and 2.45 × 10−3 M potassium persulfate solution were prepared. The two solutions were mixed in a ratio of 1:1 and the mixture was left in the dark for 12–16 h. The resulting solution was stable for 2 days. It was then diluted with methanol in a ratio of 1:30 until an absorbance of 1.1 ± 0.1 was reached, which was measured against methanol at λ = 734 nm. The methanolic extracts of malt and wort were diluted with deionized water in an extract: water ratio of 0.5 cm3 + 9.5 cm3 or 1 cm3 + 9 cm3, or other suitable dilution to achieve A734 in the range of 0.2–0.9. A blank sample (methanol), a control sample—0.15 cm3 MeOH + 2.85 cm3 ABTS and a working sample—0.15 cm3 of diluted extract + 2.85 cm3 ABTS were prepared. The measurements were made at λ = 734 nm and the results were calculated based on the dependence:1—the absorbance recorded when measuring the control against the blank sample; A2—the absorption recorded when measuring the working sample against the blank sample;The ABTS analysis was performed according to the method of Iqbel et al. (2015)  with theTrolox) by the equation of the standard curve was determined. The antiradical activity, expressed as mM Trolox for L extract (mM TE/dm3) was determined after 120 min:p—dilution coefficient of the extract (13).Depending on the percentage of inhibition , the concentration of Trolox solution  . The fol3 of CuCl2·2H2O + 1 cm3 of acetate buffer + 1 cm3 of neocuproin + 0.5 cm3 of extract + 0.6 cm3 of distilled water; a blank sample—1 cm3 of CuCl2·2H2O + 1 cm3 of acetate buffer + 1 cm3 of neocuproin + 1.1 cm3 of distilled water. The samples were homogenized and allowed to stand for 30 min at room temperature. Measurement of absorbance was made at λ = 450 nm in semi-micro cuvettes against the blank sample.The following samples are prepared: a working sample—1 cmp—dilution coefficient of the extract (13).Antioxidant activity was calculated as μM Trolox according to the following formula:The method developed by Ou et al. (2001)  with somData from triplicate experiments were processed using MS Office Excel 2013 software, using statistical functions to determine the standard deviation and maximum estimation error at significance levels of α < 0.05. The statistical analysis was performed using Statsoft Statistica 10 according to an algorithm set in the software itself. The grouping of malts was accomplished by the principal component analysis with the help of Statsoft Statistica 10  according to an algorithm set in the software itself. The results for the main brewing characteristics of the studied malt types are summarized in All basic malt types  had goodAn important characteristic of basic malts is their starch content . For theIn the present article, it was chosen to compare malts by the total content of phenolic components, without taking into account the individual components in malts. The reasons for this are complex. First of all, different malt types are produced from different varieties of barley. In addition, for the production of the three malt groups—basic, special and functional, different technological regimes are used, which quite naturally leads to changes in the individual components. Last but not least, the wort component composition with respect to the phenolic compounds depends on the chosen method and mode of mashing, the type and method of hopping and the mode of brewing of the wort. In this sense, the determination of the individual components of the phenolic compounds can be made by comparing production regimes or different batches of the same malt. This publication aims to compare different malt types, which requires the use of more global indicators, summarized in The content of phenolic compounds in malt  and wort determined by the Folin-Ciocalteu reagent are shown in 3). The amount of phenolic compounds in malt increased with increasing the malt color intensity. This was due to the fact that some of the phenolic compounds polymerize and are preserved to a greater extent at moderate kilning regimes. They were usually included in the matrix of the products of the Maillard reaction and were released in the process of extraction and/or mashing, due to which a higher value of the content of phenolic compounds in darker colored malts was reported [The content of total phenolic compounds in the basic malt types varied in the range from 1 mg/g to 1.5 mg/g . The substances that gave malt its antioxidant potential are of particular interest. The data show that antioxidants that determined the observed activity against the DPPH radical were extracted from the malt to the least extent. In most cases, the extraction rate did not exceed 100%, although there were malts with increased extraction—mostly caramel malt type. This gave us reason to say that these substances were probably localized in the husk of the grain and during mashing remained associated with its components and were not extracted in the wort.The established difficulty in the extraction of phenolic compounds in wort also led to lower biological potential of worts obtained from the basic malt types, but this should not be considered a problem, as the phenolic content in malt is responsible for the colloidal stability of beer and, therefore, for these malt types, it is better (because of other considerations) to keep the phenolic content low. In contrast to the basic malt types, there was increased antioxidant capacity in the special malt types. It was due to the increased extractability of melanoidins, other products of the Maillard reaction and phenolic compounds in wort. If a comparison of the results was made, a correlation between the content of phenolic compounds, their increased extractability in the wort and the wort AOA, can be clearly seen. In general, the degree of heat treatment also affected the AOA of the malt and its corresponding wort. The data show that highly roasted malts lost their AOA, while caramel malts with a high degree of roasting had the highest AOA  , the conThe development of new types of beer and wort-based beverages should be subject to knowledge of both the malt brewing characteristics and the knowledge of the biological capacity of the malt that is included in the preparation of the beverage. The development of new types of beverages should be based on the development of mixtures, using the methods of mathematical modeling of mixtures and the composition of mixtures should be determined on the following principles: at least 50% of the composition of the meal should be occupied by one of the basic malt types; the remaining 50% should be distributed between special and functional malt types. The reasons for this distribution are due to the fact that special malt types, especially those with higher degree of heat treatment, cannot provide the necessary enzyme systems responsible for hydrolysis during wort production. Even though special malts provide higher antioxidant potential, the lack of enzyme systems is leading in terms of the formulation of the wort composition.Our results showed that there was a relationship between malt color and the content of phenolic compounds, as well as between the content of the Maillard reaction products and malt biological activity. The malts with the highest degree of heat treatment were characterized by the highest antioxidant activity, which was due to the Maillard reaction products with antioxidant capacity formed in them. These results, especially in the grouping of malts in different groups in terms of phenolic content and antioxidant potential, were the basis for the process of modeling the composition of the malt meal. They allow, on the basis of knowledge of the malt biological value and its brewing characteristics, including the organoleptic characteristics of the malt and the resulting wort, to develop mixtures intended for the preparation of a specific type of beverage, i.e., to apply the so-called tailor-made concept for the production of the beverage.The established correlation between the malt color and its biological value could be applied to assess the malt biological potential and is a quick method for selection of malts in the process of modeling the composition of the beverage. The obtained results confirm the observations of other authors. The purpose of the publication is for the results to serve as a scientific basis for modeling the wort composition, using the phenolic content and the antioxidant capacity of malt as target functions. Naturally, these results will be the subject of other future publications.


Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge.Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant–product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks.https://github.com/EPFL-LCSB/nicepath.Bioinformatics online. Extracting meaningful metabolic pathways from large metabolic networks is essential for the computational design of bioproduction pathways, for the elucidation of biosynthesis of natural products, and for the fundamental understanding of metabolism. Metabolic pathways describe the transformation from one or several source molecules over consecutive reaction steps into one or several target molecules and provide the roadmap guiding these various applications . Traditicore atoms. Cofactors and co-metabolites are considered boundary compounds and their metabolic provenance and fate are not further detailed in the linear pathway. In reality however, pathways are not always linear. Anabolic pathways may involve the concatenation of two or more metabolites to form a bigger compound [e.g. (S)-norcoclaurine synthase], and catabolic pathways may break down a compound into two or more smaller molecules. However, every branched pathway can also be described as a combination of linear pathways, and we therefore only consider linear pathways in this work. Here, we define biologically relevant, linear pathways as biochemical routes that fulfill the following criteria: (i) Core atoms are conserved throughout the pathway, (ii) loops are not allowed, meaning that no metabolite appears twice and (iii) other metabolites that contribute to the main biotransformation route in a lesser degree are considered as cofactors or co-metabolites.Pathway search tools aim to provide meaningful, easily interpretable metabolic pathways as they are shown in textbooks, but through an automated approach. A typical, linear pathway starts with a precursor molecule that is chemically modified by subsequent enzymatic steps until a target molecule is obtained. The atoms of the precursor compounds that are conserved throughout the pathway can be defined as 2, ATP, H2O)  , defininTP, H2O) .et al. to calculate network properties of the metabolism of Escherichia coli  or from metabolic networks specific to an organism . The method can be further applied to efficiently search large biochemical networks, as they are generated by reaction prediction tool such as BNICE.ch (Our approach can be divided into four steps : (i) TheAtom-mapped reactions are the prerequisite for calculating weighted reactant–product pairs. Here, we use the computational tool BNICE.ch, developed to predict hypothetical biochemical networks, to calculate biochemically correct atom mappings of enzymatic reactions. The core of BNICE.ch consists of 442 bidirectional, generalized biochemical reaction rules that describe the biochemical reaction mechanisms of enzymatic reactions. The reaction rules are applied to a molecular structure to (i) reconstruct atom-mapped, known biochemical reactions; and (ii) to predict all possible biochemical transformations that a given compound can undergo along with the product compounds generated in the process. Here, BNICE.ch calculates atom maps for metabolic reactions using the mechanistic knowledge stored in the reaction rules, as described by cn) between reactant and product is calculated along with the total number of atoms in the reactant (rn) and the total number of atoms in the product (pn). Hydrogen atoms are omitted from the calculation. (iii) For each pair, the ratio of conserved atoms  is calculated with respect to the reactant (rCAR) and with respect to the product (pCAR).The following steps are applied to each reaction in the network to generate atom-weighted reactant–product pairs: (i) Each reaction is split into all possible reactant–product pairs. (ii) For each pair of reactant and product, the number of common atoms ((iv) To calculate a bidirectional CAR, the mean CAR is multiplied with a correction factor that decreases with the difference between the number of common atoms and the total number of atoms in the molecule.The only exception to this approach is made for reactions involving the cofactor Coenzyme A (CoA). In a molecule, CoA is treated as a single atom when it occurs in both the reactant and in the product, mainly because the high number of conserved atoms between the comparably big CoA leads to high CARs, thus masking the biochemically more interesting connections between the smaller metabolites that are attached to and detached from CoA during metabolic transformations. The final CAR value is used to weight reactant–product pairs in the network.https://github.com/EPFL-LCSB/nicepath.We used KEGG as a reference database for enzymatic reactions, from which we extracted all reactions that have an associated mechanism in BNICE.ch. If a given reaction from KEGG could be reconstructed with BNICE.ch, it was assigned a reaction mechanism that allowed us to retrieve the number of conserved atoms between each reactant–product pair. The set of KEGG reactions with assigned reaction mechanisms and pre-calculated CAR values was used for further validation and as an example network for network analysis and pathway search. The set of BNICE.ch curated KEGG reactions is available from the GitHub repository at For a given reaction network, NICEpath loads all the reactant–product pairs to generate a weighted, undirected graph, where metabolites are nodes connected by edges, representing the reactant–product relationship. Edges are assigned a weight that defines the relation between two connected nodes. To use state-of-the-art shortest-path graph search algorithms, highly atom-conserving reactants should be close to each other, and pairs that only share a few atoms should be further away. Hence, we convert the CAR into a distance:NICEpath accepts two alternative ways to calculate the distance, which can be used to modulate the influence of the atom conservation on the weight of the reactant–product pair.The type of transformation can be changed to square root or exponential depending on the nature of the pathway search problem, i.e. the structures of source and target molecules as well as the estimated number of biotransformation used to convert one into the other. The distance measure is used to reconstruct a directed graph whose edge weights represent the atomic distance between reactants and products. The different transformations as a function of the CAR are visualized in For this study, we grouped duplicate KEGG compounds into one node. Duplicates were identified based on the first fourteen letters of the InChIKey that describe the atom connectivity of a compound, but that do not contain additional information  In practice, this means that different stereoisomers of the same molecular structure were merged into one node.NICEpath applies a Yen’s k-shortest loop-less path search  to extraThe run time of NICEpath depends on the structure of the network, the distance between the source and target compound in the graph, the number of pathways to be found and the maximum pathway length allowed. As an example, to find 10 000 pathways of maximum length 100, the algorithm runs for about 15 min on a standard desktop computer using a single core. If there are several source compounds given as input, NICEpath can run path searches in parallel for different source compounds using all available cores.NICEpath first calculates standard network statistics, such as the number of nodes and edges, and then extracts an undirected, unweighted network from the original network by only considering edges with a CAR higher than a given threshold. For this new network, the number of components, or disjoint graphs, is extracted, and the biggest component is further analyzed regarding its size relative to the previous network as well as its diameter. Since searching for pathways between two compounds belonging to different disconnected graphs will not yield any good pathways, NICEpath will warn the user in this case.https://networkx.github.io/) was used to implement and search the reaction graph. An extensive list of libraries used can be found in the specification file on GitHub.The NICEpath code can be executed with any python version up to 3.7. The NetworkX python library . To take the alcohol dehydrogenase reaction as an example, the main pair would be the transformation of the primary alcohol to the aldehyde, and the conversion of the cofactor NAD+ to NADH would be of type ‘cofac’ . ‘Main’ We validated the NICEpath method by predicting KEGG RPAIRS of type ‘main’ using the concept of the Conserved Atom Ratio. We used BNICE.ch to calculate CAR values for a test set of 6546 KEGG reactions for which the exact reaction mechanism is known, and which are, therefore, reconstructed by BNICE.ch . From thFor 100 CAR cutoff values between zero and one we calculated the number of good predictions  and bad predictions . By drawing true positives versus false positives, we found an Area Under Curve (AUC) of 0.88. An AUC above 0.8 is considered an ‘excellent discrimination’ . We furtsuch as the number of components or the network diameter. For calculating these properties, a simple, non-directed graph was generated by removing reactant–product pairs with a CAR lower than the previously calculated optimal threshold of 0.34 and by removing the weights on the remaining reactant–product pairs. The unweighted graph contains 5518 nodes and 5541 edges, which are distributed over 813 smaller disjoint graphs, or so-called components. The biggest component contains 2663 nodes (48%) and 3422 edges (62%), and it has a network diameter of 40. In other words, the longest shortest pathway connecting two compounds counts 40 biotransformation steps in the main component of the KEGG network. This means that our KEGG network is dominated by one big component, or ‘island’, that includes half of the metabolites in KEGG and represents the core metabolism plus connected secondary metabolism. The remaining metabolites are organized in small, disconnected subnetworks, which we hypothesize to be mostly secondary metabolites without defined biosynthesis pathways.Characterizing biochemical networks from a graph-theoretical point of view can be used to evaluate the quality and connectivity of the represented network, and also bring new insights into the overall organization of metabolism. Furthermore, knowing the graph-theoretical properties of a biochemical network can be crucial for anticipating potential problems in the pathway search. NICEpath provides basic network statistics that allow us to assess the quality of the data. Here, the weighted graph of the KEGG network used for validation initially contained 5578 compounds, or nodes, and 20 911 directed edges representing reactant–product pairs. Certain graph properties are not defined for weighted directed graphs, To illustrate the output of NICEpath, we discuss two example pathway searches. In the first example, we tried to biochemically connect tyrosine to caffeate, and we allowed a maximum number of ten pathways to be found. The pathway search resulted in ten pathways with lengths ranging from two to six consecutive reaction steps . The quaIn a second example, we searched for pathways connecting the compounds tyrosine and syringin. The number of pathways to be found was restricted to five, and we used three different transformations to calculate the distance between reactant–product pairs: The default transformation 1/CAR, the square root transformation, and the exponential transformation. Using the default option, NICEpath first listed three short pathways with a low average CAR (∼0.5), followed by two longer pathways with high average CAR (∼0.8) . The squTwo pathways were chosen to understand in detail the influence of the type of transformation used for calculating the distances between reactants and products: one was short with a low CAR (A) and one was long with a high CAR (D*) . PathwayTo demonstrate the universality of our approach, we performed a systematic validation on 50 pathways collected from KEGG consisting of 40 biosynthesis and 10 biodegradation routes and involving between 3 and 15 reactions steps . S2. We For each of the reference pathways, we performed a k-shortest path search within each network between the source and the end compound of the pathway. To evaluate the performance, we retrieved the rank of the reference pathway within the top 100 pathways produced by the algorithm, and we measured the runtime of the search algorithm . We coulInterestingly, the ranking performance of the exponentially transformed network and the unweighted CAR > 0.34 were very similar. This indicates that when the algorithm runtime becomes a limiting factor, the search can be performed within an unweighted graph (CAR > 0.34) to reduce the runtime, allowing a two-sided search starting simultaneously from the source and the target compound and thus speeding up the search.There are cases in which NICEpath will not find satisfactory solutions. Possible reasons for suboptimal results are (i) the network does not contain the necessary reactions to connect the starting compound to the target compound, and (ii) the source and the target compound initially have only a few atoms in common. The first issue can be solved by adding the missing reactant–product pairs to the network. Missing steps can be hypothesized manually or predicted using reaction prediction tools such as BNICE.ch. The second issue is more complex, since it depends on the molecular structure of the source and target compound, as well as on the real number of biochemical transformations needed to transform one into the other. Possible solutions to improve the output include breaking down the search into several sub-searches by identifying intermediates and increasing the penalty on atom loss by using an exponential transformation of the CAR into the distance between reactants and products. While our algorithm successfully circumvents the recurrent problem of shortcuts through small hub metabolites, it does not satisfactorily avoid shortcuts through big hub metabolites such as Coenzyme A (CoA). In fact, reactant pairs involving CoA structures on both sides have a lot of atoms in common, and therefore a high CAR value. For this reason, NICEpath excludes CoA by default from the reactant pair network.We introduce a new pathway search method based on weighted reactant–product pairs. To our best knowledge, this is the first to use automatically generated atom-weighted reactant–product pairs in combination with a k-shortest graph search approach. We benchmark our method for reactant-pair weighting against the KEGG RPAIR database, and we evaluated the performance of the proposed pathway search on 50 pathways obtained from KEGG. The strong point of NICEpath is that it is suitable for big biochemical networks, spanning more than hundreds of thousands biochemical reactions, such as hypothetical reaction networks generated by retrobiosynthesis tools and predictive biochemistry . As all We estimate that the future development of reaction prediction tools, based on biochemical reaction rules or machine learning methods, will yield big hypothetical reaction networks that require optimized search tools to efficiently extract biochemical pathways. Furthermore, the presented method to translate metabolic networks into a graph structure can be used in the future to analyze the global characteristics of biochemical networks, such as the diameter of a network or its connectivity, and finally to detect and map knowledge gaps in metabolic databases.https://github.com/EPFL-LCSB/nicepath), and it comes with a collection of 5434 known metabolic reactions with pre-calculated atom-weighted reactant pairs.Finally, the herein proposed framework will lay the foundation for further developments. Other types of weights, such as kinetic and thermodynamic considerations, can be integrated into the weighting of substrate-product pairs to steer the pathway search toward biochemically feasible pathways, and a set of user-defined parameters will make it easy to fine-tune the pathway search. Additionaly, metrics other than the here proposed CAR could be utilized to quantify atom conservation within substrate-product pairs, such as the Jaccard index . The NICbtab368_Supplementary_DataClick here for additional data file.


Schisandrae Sphenantherae Fructus (SSF) and Schisandrae Chinensis Fructus (SCF). First, the antioxidant activity of SSF and SCF was evaluated through the DPPH free radical scavenging method and compared with the half maximal inhibitory concentration (IC50) value. Next, components of SSF and SCF were detected by employing ultrahigh-performance liquid chromatography-Q-Exactive Orbitrap mass spectrometry (UPLC-QEO/MS) technology, and differential compounds were screened out as potential antioxidant compounds by using Compound Discover 3.1 Software. After that step, in order to verify the antioxidant compounds, the network method was applied. Biological targets were searched in the GeneCards database, and that related to antioxidant ability were selected in the Comparative Toxicogenomics Database (CTD). Finally, the pharmacology network was constructed. Results showed that SSF and SCF possessed different compounds and antioxidant abilities. A total of 14 differential compounds such as γ-schizandrin, schisandrin B, schisandrin, and tigloylgomisin H between them were screened out and identified. Twenty targets associated with antioxidant activity contained MAP2K1, MAPK8, RPS6KB1, PRKCB, HIF1A, and so on were investigated. Thirty-six pathways contained HIF-1 signaling pathways, choline metabolism in cancer, serotonergic synapse, Fc epsilon RI signaling pathway, GnRH signaling pathway, and so on related to the above twenty targets were identified. The pharmacology network analysis indicated that the differential components may be helpful in treating various diseases, especially cancer, by exerting antioxidant activity. In conclusion, this study provided a novel method for identifying active components with antioxidant activity in SSF and SCF, and this method may be applicable for the filtration of bioactive components in other herbs.This study attempted to filter active components with antioxidant activities based on the differing antioxidant abilities of  Antioxidation is a process that can effectively inhibit the oxidation reaction caused by free radicals. The mechanism of antioxidation involves either an action on free radicals or the consumption of substances that can easily generate free radicals.Free radicals are constantly produced in the body due to exposure to the environment through processes such as respiration (oxidation reaction), air pollution, and radiation. Free hydroxyl radicals are associated with aging stress , autoimmSchisandrae Sphenantherae Fructus (SSF) and Schisandrae Chinensis Fructus (SCF) are both well-known Chinese herbal medicines, and SSF is primarily produced in the central and southern part of China, whereas SCF is primarily produced in northern China. Although these medicines possess some similar pharmacological actions, such as antioxidant, anti-inflammatory, and anxiolytic effects +, [M + H-H2O-CH3]+, [M + H-H2O-OCH3]+, [M + H-H2O-OCH3-CH3]+, [M + H-H2O-C4H8]+, and [M + H-H2O-OCH3-OCH3]+ based on high-resolution mass spectrometry. Hence, it was deduced to schisandrin initially. Further, the schisandrin standard was detected by the same UPLC/MS condition for verifying the deduction, which showed that their retention time and MS2 spectrum were matched completely. Therefore, the ion of 455.2040 was identified as schisandrin. The proposed fragmentation pathways of schisandrin are shown in Among these compounds, espatulenol was proposed by comparing the MSferences –17, and ragments . SchisanA total of 441 targets on which the abovementioned potential antioxidant compounds acted were obtained. At the same time, 4151 targets associated with antioxidant activity were obtained by searching the GeneCards database, and 3946 of them were retained after UniProt processing. Furthermore, 4531 targets related to antioxidant activity were obtained by searching the CTD database, and 150 of them were retained after UniProt processing. There were 147 common targets in the GeneCards database and CTD database. Furthermore, the 147 targets were compared to the abovementioned 441 targets, and it was found that 20 targets on which the antioxidant compounds acted were related to antioxidant activity; these targets are listed in The protein-protein interaction (PPI) network diagram was constructed using Cytoscape software . Figure pGO (gene ontology) enrichment analysis of potential targets was performed using the Metascape database, and the functions were enriched under the conditions of value <0.01 and enrichment factor >1.5. The top 36 enriched pathways of the above 20 targets were obtained from the DAVID  database, which is shown in The compound-target-pathway network was constructed by connecting 14 differential compounds, 20 targets, and 36 pathways. SSF and SCF, which serve as typical “medicine-food homology” herbal medicines, showed similar pharmacological effects, such as antioxidant activity. Nevertheless, SCF was generally more popular than SSF because it was said that SCF possessed higher quality than SSF. Regarding SSF and SCF, because both of these medicines had multipharmacological effects, the main effects that were essential for quality differences were not clear. The antioxidant activity or components of medicines have long been compared by medicinal scholars [γ-schizandrin, espatulenol, angeloylgomisin H, tigloylgomisin H, schisantherin B, gomisin K1, and gomisin M1, which were colored yellow, were unique components in SCF that were not observed in SSF. In addition, five compounds, namely, angeloylgomisin H, tigloylgomisin H, angeloylgomisin P, schisantherin C, and schisantherin, showed larger nodes than others, which indicated that they were the main antioxidant components. Among the 20 targets, four, namely, MAP2K1, MAPK8, PRKCB, and CCND1, showed larger nodes than the others, which indicated that they were the main targets of antioxidant functions. Among the 36 pathways, pathways in cancer, choline metabolism in cancer, and proteoglycans in cancer showed larger nodes than others, which indicated that they were the main pathways through which the abovementioned bioactive compounds exerted antioxidant function.scholars , 29, butAs a dual-specific kinase, mitogen-activated protein kinase kinase 1 (MAP2K1) participates in the extracellular regulated protein kinase (ERK) pathway by activating ERK1 and ERK2. Through the identification of transforming genes for refractory diseases in the sclerosing subtypes of highly invasive tumors of gastric cancer, it was determined that cancer cells are dependent on the increased proliferation activity of MAP2K1, and MAP2K1 could be utilized as a cancer inhibitor target .MAPK8 (mitogen-activated protein kinase 8), which is a protein-coding gene, was observed to participate in the abovementioned 16 pathways and to connect to 9 targets in the PPI network diagram, which indicated its importance. MAPK8-associated diseases include fatty liver disease and hepatitis C. The pathways related to MAPK8 include the ATM (serine/threonine kinase) pathway and the link between physicochemical characteristics and toxicity-related pathways. Previous studies showed that MAPK8 is involved in the gene expression process of anterior papillary hypertension caused by hypoxia .PRKCB (protein kinase C) has been identified as a drug treatment target for specific diseases involved in various cellular processes, such as the regulation of B-cell receptor (BCR) signaling bodies, oxidative stress-induced apoptosis, male hormone receptor-dependent transcriptional regulation, insulin signaling, and endothelial cell proliferation. PRKCB, as the locus of systemic lupus erythematosus , was obsMAPK8 and HIF1A were important targets in choline metabolism. Abnormal choline metabolism may serve as an indicator of tumorigenesis and tumor progression. Increased levels of MAPK8 and HIF1A could induce the expression of related genes and subsequently affect the cell growth and proliferation . TherefoAs a cyclin, CCND1 participates in a variety of gene expression and protein pathways. CCND1-associated chromosomal aberrations could cause multiple occurrences of lymphocyte malignant tumors, rack mounting, and multiple occurrences. CCND1 and its catalytic partner cyclin-dependent kinase 4 (cdk4) play important roles in the G1/S checkpoint of the cell cycle. Takano et al.  hypothesSSF and SCF were closely related to the HIF-1 (hypoxia-inducible factor 1) signaling pathway, pathways in cancer, and choline metabolism in cancer. HIF-1 is a transcription factor and a major regulator of oxygen homeostasis. This protein consists of an inducible HIF-1-alpha subunit and a constitutively expressed HIF-1beta subunit. Under normoxia and hypoxia, the conversion of the two subunits regulates their transcriptional activity. Under hypoxia, HIF-1 is the main regulator of many hypoxia-induced genes. HIF-1-related factors may cause such diseases as diabetic retinopathy, glucocorticoid-induced osteonecrosis, and malignant paraganglioma [Through pathway analysis, it was observed that the antioxidant activities of anglioma .SCF and SSF might serve as cancer treatments by exerting antioxidant activity.Antioxidants were frequently used for cancer treatment in previous studies , and angSSF and SCF was assessed and compared by DPPH free radical scavenging experiments, and the differential compounds, as well as their associated biological functions, were obtained by coupling UPLC/Q-Exactive Orbitrap MS technology with network pharmacological analysis. The results showed that the antioxidant ability of SCF was stronger than that of SSF, and 14 differential compounds were identified between SCF and SSF, which indicated that these compounds were potential active components with antioxidant activity. Further analysis showed that there were a total of 20 predicted targets and 36 pathways related to these active components with antioxidant activity, and most of these targets and pathways were associated with cancer regulation. The pharmacology network predicted that the differential components might exert antioxidant effects on the 20 targets by regulating the 36 pathways. These medicines may be helpful for treating various diseases, especially cancer, by exerting antioxidant activity. Therefore, this study provided a novel method for identifying active components with antioxidant activity, and this technique may be applicable for the filtration of bioactive components in other herbs.In this study, the antioxidant activity of


The purpose of this study was to demonstrate the pharmacodynamic material basis and molecular mechanism of pilose antler (PA) in the prevention and treatment of osteoporosis (OP) by the method of network pharmacology.  First, the active components of PA were screened by BATMAN-TCM database, and the component targets were obtained from the SwissTargetPrediction online tool. Moreover, the relevant target genes of OP were obtained by searching the DisGeNET database. Second, the Venn diagram was drawn to obtain the PA-OP common targets, and the protein-protein interaction (PPI) network and drug-component-target (D-C-T) network were constructed by Cytoscape software. Finally, the GO functional annotation and KEGG pathway enrichment analysis of common targets were performed using the Metascape online tool.  82 common targets were identified by generating a Venn diagram. The PPI network of 82 common targets indicated that the top 5 nodal targets, including PIK3CA, MAPK1, ESR1, AKT1, and SRC, were strongly associated with other proteins. The D-C-T network suggested that the active components with high degree of connectivity include Prostaglandin E1, 17-Beta-Estradiol, Alpha-Estradiol, and Estrone. Furthermore, the GO enrichment analysis revealed that the biological process categories were dominated by response to peptide, cellular response to lipid, regulation of MAPK cascade, and so on. Additionally, the KEGG pathway analysis indicated the estrogen signaling pathway, osteoclast differentiation, and HIF-1 signaling pathway might have critical effects on the development of OP.  The study shows that PA has the characteristics of multi-component, multi-target, and multi-pathway in treating osteoporosis. Osteoporosis (OP) is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture , 2. Osteα-mediated suppression of osteoblastogenesis in vitro via the NF-kB/p65 pathway as well as inhibit osteoclastogenesis in vitro [Chinese traditional medicine has a good effect in treating OP, and it has few side effects. Pilose antler (PA) is the only completely regenerable tissue found in mammals and has extremely rapid growth rates (∼1.7 cm/day in red deer) , 5. PA iin vitro . HoweverIn 2007, the concept of “network pharmacology” was systematically expounded by pharmacologist Hopkins in Nature Biotechnology for the first time, which integrates many new cross-disciplines such as systems biology and bioinformatics . Networkhttps://bionet.ncpsb.org/batman-tcm/index.php) [https://pubchem.ncbi.nlm.nih.gov/) [The active components of PA were acquired from BATMAN-TCM (dex.php)  by inputih.gov/)  to collehttps://www.swisstargetprediction.ch) [Homo sapiens” and high probability targets (probability > 0).In the present study, the component targets were obtained from SwissTargetPrediction (tion.ch) , which ihttps://www.disgenet.org/) [OP-related targets were obtained by entering the keyword “Osteoporosis” from the DisGeNet database (et.org/) . Then, ahttps://www.bioinformatics.com.cn/). The interaction of “PA-active compounds-target genes-OP” was constructed by Cytoscape software (version 3.7.2) [Candidate targets of PA against OP were obtained by entered PA-related targets and OP-related targets into the Venn online tool (n 3.7.2) .https://string-db.org/) [The above candidate targets were submitted to STRING (db.org/)  for prothttps://metascape.org/gp/) [P value cutoff = 0.01, and min enrichment = 1.5).Last but not least, Metascape (org/gp/) , an onliA total of 14 candidate components were collected from BATMAN-TCM for further analysis as shown in To predict possible targets for PA, we imported the SMILES format of active components into SwissTargetPrediction, an online predictive tool, and a total of 320 targets of the 14 ingredients were identified. Then, the keyword “osteoporosis” was entered into DisGeNet database to predict 1098 targets associated with OP. Subsequently, 82 common targets were identified by generating a Venn diagram from the component-associated targets and OP-related targets, which were the potential therapeutic targets of PA against OP . Using tFurther searching for a key gene in PA against OP, the PPI network of 82 common targets was constructed  and the In order to indicate the biological mechanisms of PA against OP, GO and KEGG pathway enrichment analyses of the 82 common targets were performed by using Metascape online tool. To gain insight into the specific functional characteristics, the 82 targets were categorized based on the following GO classes: biological process, molecular function, and cellular component. First, as depicted in In addition, the KEGG pathway analysis revealed that the 82 targets were mostly enriched in pathways in cancer and endocrine resistance , such asOP is mainly caused by the imbalance of bone formation and bone resorption , 2. Tradα-mediated suppression of osteoblastogenesis via the NF-kB/p65 pathway as well as inhibit osteoclastogenesis in vitro [For instance, Liu et al. found that PA can promote osteoblast differentiation and block TNF-in vitro . Anotherin vitro . Both thIn the present study, network pharmacology based on various bioinformatics methods was used to examine the network of the molecular mechanisms of PA, and we first identified 14 active components of PA. Several of these components have been confirmed to have effect on bone metabolism. For example, estrogens, including estrone, Estrone, and 17-Beta-Estradiol, are key regulators of bone homeostasis. It is a consensus that estrogen deficiency has become an independent risk factor of postmenopausal osteoporosis (PMOP) –25. ClinThe 82 common targets predicted from SwissTargetPrediction and DisGeNet database were found to be involved in both osteogenic and osteoclastic differentiation. Meanwhile, most of these common targets were hit by multiple ingredients at the same time, indicating that individual ingredients could act on the same targets, which finally played a synergistic effect in the treatment of osteoporosis. We carried out PPI network analysis, and the results show that the target proteins interact and regulate each other, rather than acting independently. The hub proteins, key targets for drugs to exert biological effects, especially PIK3CA, MAPK1, ESR1, AKT1, SRC, etc., have been confirmed to play an important role in the pathogenesis of OP –38. TherGO analysis suggests that these common targets may play the function of lipid binding through subcellular structures such as cell body and membrane raft, thus participating in biological processes such as cellular response to lipid and regulation of MAPK cascade. As we know, when estrogen spreads to the nucleus, it binds to its nuclear receptor and regulates the transcription of downstream genes, which is closely related to behaviors such as lipid binding . MoreoveOf course, there are limitations to our work, such as the lack of experimental validation of the results. In addition, the form and dose of PA depends on the doctor's experience and patient's situation, and there may be differences in efficacy and mechanism of action.In general, this study revealed that mechanism of the anti-OP effect of PA is associated with the binding of estrogen. Predicting the effective components and action targets of PA through network pharmacology will help us to understand how PA affects bone metabolism and the potential molecular mechanism. Hence, it not only provides a pharmacological basis for clinical application of PA in treating OP but also gives us insight and inspiration for further research.


Puerariae lobatae Radix (known as Gegen in Chinese) is one of the most prevalent Chinese herbs applied to treat these diseases. The underlying mechanism by which Gegen simultaneously treats diabetes and hyperlipidemia, however, has not been clearly elucidated to date. Therefore, we systematically explored the potential mechanism of Gegen in the treatment of T2DM complicated with hyperlipidemia based on network pharmacology. We screened the potential targets of Gegen, T2DM, and hyperlipidemia in several online databases. Then, the hub targets were analyzed by performing protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment assays, and finally, the complicated connections among compounds, targets, and pathways were visualized in Cytoscape. We found that isoflavones, including daidzein, genistein, and puerarin, as well as β-sitosterol, are the key active ingredients of Gegen responsible for its antidiabetic and antihyperlipidemia effects, which mainly target AKR1B1, EGFR, ESR, TNF, NOS3, MAPK3, PPAR, CYP19A1, INS, IL6, and SORD and multiple pathways, such as the PI3K-Akt signaling pathway; the AGE-RAGE signaling pathway in diabetic complications, fluid shear stress, and atherosclerosis; the PPAR signaling pathway; insulin resistance; the HIF-1 signaling pathway; the TNF signaling pathway; and others. These active ingredients also target multiple biological processes, including the regulation of glucose and lipid metabolism, the maintenance of metabolic homeostasis, and anti-inflammatory and antioxidant pathways. In conclusion, Gegen is a promising therapeutic phytomedicine for T2DM with hyperlipidemia that targets multiple proteins, biological processes, and pathways.Rapid increases in metabolic disorders, such as type 2 diabetes mellitus (T2DM) and hyperlipidemia, are becoming a substantial challenge to worldwide public health. Traditional Chinese medicine has a long history and abundant experience in the treatment of diabetes and hyperlipidemia, and  The rapidly increasing incidence of chronic metabolic diseases has become a global health threat, among which diabetes mellitus (DM) and hyperlipidemia must be mentioned. First, the latest global estimate from the International Diabetes Federation is that 463 million people suffered from diabetes mellitus in 2019 and that the number will be 700 million by 2045 . In ChinIn fact, the two groups often overlap; namely, someone may suffer from both diabetes and hyperlipidemia. Compared with individuals without dyslipidemia, the risk for diabetes in subjects with hyperlipidemia increases approximately 2- to 3-fold ; more thAlthough many drugs have been successfully applied to treat T2DM and hyperlipidemia, safe and effective medicines targeting both diseases at the same time are still lacking. 3-Hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors , the most widely used lipid-lowering drugs in the clinic, have consistently been reported to cause new-onset diabetes mellitus . In addiPueraria lobata (Willd.) Ohwi or Pueraria thomsonii Benth., is a very popular Chinese herb that has been used as a medicine and food. From the perspective of TCM theory, Gegen has the pharmacological functions of clearing heat and promoting the secretion of saliva and body fluid. In clinical practice, Gegen is one of the commonly used herbs for the treatment of metabolic and cardiovascular diseases, such as diabetes mellitus and hyperlipidemia [As an effective supplementary and alternative medicine, traditional Chinese medicine (TCM) has attracted increasing attention. Chinese medicinal herbs are regarded as a rich source for natural drug development. Gegen, the dried root of the leguminous plant ipidemia , 23. Somipidemia , 24, butIn addition, the rapid development of computer technology enables the identification of the targets and mechanisms of multicomponent natural herbs, accelerating the process of drug development and application because of its low cost and high efficiency , 26. Acchttps://tcmspw.com/tcmsp.php) according to oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, two parameters of ADME  properties for evaluating compounds. Furthermore, ingredients not meeting the screening criteria but reported as metabolic regulators were also included by performing text mining.Active components of Gegen were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database (https://pubchem.ncbi.nlm.nih.gov/) containing the chemical structures of small organic molecules and information on their biological activities. Then, targets of active ingredients were searched in Binding DB (http://bindingdb.org/bind/index.jsp), DrugBank (https://go.drugbank.com/), STITCH (http://stitch.embl.de/), and Swiss Targets Prediction (http://www.swisstargetprediction.ch/) according to the SMILES formula. The target prediction algorithms of these databases are mainly based on the structural features of small-molecule ligands, namely, the chemical structure similarity of compounds.The canonical simplified molecular input line entry specification (SMILES) of each compound was retrieved from the PubChem database (https://www.omim.org/) and GeneCards (https://www.genecards.org/), respectively, to obtain targets of the diseases. The higher the relevance score of the target predicted in GeneCards, the closer the target to the disease. If too many targets are forecasted, those with scores greater than the median score are empirically considered potential targets. Notably, most proteins and genes have multiple names, such as official names and generic names, and thus their names need to be converted uniformly. The protein targets of compounds were checked in UniProt (https://www.uniprot.org/), an online database that collects protein functional information with accurate, consistent, and rich annotations and was represented as the names of the genes encoding them. Next, all gene names were rechecked in the NCBI gene database (https://www.ncbi.nlm.nih.gov/gene) and converted into Entrez IDs and gene symbols. Repeated targets identified due to nonstandard naming were eliminated. The species of the acquired and checked targets was limited to “Homo sapiens.”“Type 2 diabetes mellitus” and “hyperlipidemia” were entered into OMIM (https://hiplot.com.cn/), and the intersections were regarded as hub genes, namely, the potential targets of Gegen working on type 2 diabetes with hyperlipidemia. The hub genes were analyzed using String , an online database that integrates experiments, databases, and text mining data for PPI prediction and extraction, with the organism restricted to “Homo sapiens” and a confidence score >0.4. Isolated nodes were hidden, namely, proteins without any interaction. The result was exported as a “TSV” format file and imported into Cytoscape, which is an open-source software project for visualizing any network of molecular components and interactions to construct a PPI network [A protein-protein interaction (PPI) analysis of targets was performed to explore the relationship among the targets and the biological processes involved. A Venn diagram of targets of Gegen, T2DM, and hyperlipidemia was drawn using an online visualization tool  are both common approaches used to find shared functions among genes based on biological ontologies . Brieflyrichment –34.Cytoscape was used to construct and analyze a three-layer network in order to understand the complex relationships among compounds, targets, and pathways. Taking advantage of another built-in network analyzer , the topTwelve active ingredients of Gegen were finally included based on ADME attributes and text mining. They are shown in Targets of active ingredients retrieved from Binding DB, DrugBank, STITCH, and Swiss Targets Prediction (only targets with probability >0 included) were merged by deduplication. Ultimately, we obtained 304 targets of the 12 compounds , inflammation , and oxidative stress .A Venn diagram  was drawModules were extracted from the PPI network using MCODE, and the 2 modules with the highest scores are displayed in Figures P < 0.05, P values were corrected by the Benjamini-Hochberg procedure). The largest number of BP terms was enriched at 1286. Almost all of the top 20 BPs are involved in the regulation of metabolic processes. In addition, regulation of the inflammatory response is also noteworthy. MF terms are second in number (79) and are mainly related to the activity of various receptors and enzymes, as well as molecular binding. CC terms are minimal (38), and the action sites of gene products are mainly located in various types of vesicles, lumens, membranes, and lipoprotein particles or complexes , cellular component , and molecular function  enrichment analyses of 65 common targets were performed using the ClusterProfiler package in R. The top 20 terms significantly enriched in BP, CC, and MF are shown in β-sitosterol . The top three targets with the highest degree value in the network were AKR1B1 , EGFR , and ESR1 , suggesting that they are crucial genes involved in the effect of Gegen on targeting T2DM with hyperlipidemia, and TNF, NOS3, MAPK3, PPAR-γ, PPAR-α, ESR2, CYP19A1, INS, IL6, and SORD are also relatively important targets , AGE-RAGE signaling pathway (degree = 11), fluid shear stress and atherosclerosis (degree = 11), endocrine resistance (degree = 9), HIF-1 signaling pathway (degree = 9), PPAR signaling pathway (degree = 8), insulin resistance (degree = 8), TNF signaling pathway (degree = 8), nonalcoholic fatty liver disease (degree = 8), prolactin signaling pathway (degree = 6), type II diabetes mellitus (degree = 5), and ovarian steroidogenesis (degree = 5), and much more information is available in Supplementary Moreover, signaling pathways are an essential part of system pharmacology and associate receptor-ligand interactions with pharmacodynamic outputs. Sixty of all targets were mapped to 96 KEGG pathways  and hyperlipidemia (similar to blood stasis or phlegm retention syndrome in TCM) for many years. In addition, Gegen has the advantage of long-term use with security due to its edibility. Based on these results, Gegen is one of the optimized candidates for diabetic and hyperlipidemic drug development.In the present study, we screened 12 active components of Gegen and 65 hub targets using network pharmacology. The PPI analysis shows that INS, encoding the insulin protein that plays a vital role in regulating carbohydrate and lipid metabolism, is the gene with the highest degree. Thus, glucose and lipid metabolic disorders are the key to the occurrence of the two diseases discussed, and one of the main benefits of Gegen is to ameliorate this disorder. In addition, cholesterol metabolism, inflammation, and oxidative stress may also be potential mechanisms by which Gegen regulates metabolic disorders. Moreover, module 1 extracted from the PPI network suggests that the excessive accumulation of oxides (GO: 1901700), such as reactive oxygen species, may be one of the common pathological features of metabolic dysfunction, consistent with previous studies that are well summarized in the literature . Module The GO enrichment analysis further revealed that Gegen may regulate lipid metabolism, especially cholesterol metabolism, steroid metabolism, response to nutrient levels, and the inflammatory response through ligand binding, signal transduction, and fatty acid binding in blood, vesicle lumen, caveolae, cell membranes, cytoplasm, and other sites to improve metabolic disorders and exert anti-inflammatory effects. Interestingly, a large number of statistically significant BP terms, such as response to insulin, regulation of insulin secretion, insulin secretion, cellular response to insulin stimulus, glucose metabolic process, glucose homeostasis, and response to glucose, were not at the top of the list , obesity, and cardiovascular disease, as well as in relieving postmenopausal symptoms [α concentrations and preventing oxidative damage [Consistently, the major bioactive components of Gegen have been widely shown to regulate these targets. Thus, the rich isoflavones in Gegen are the dominant active ingredients responsible for the antidiabetes and antihyperlipidemia effects, including daidzein and genistein that are commonly found in soybeans, as well as puerarin, formononetin, and 3′-methoxydaidzein. Isoflavones are phytoestrogens with potent estrogenic activity that have structural similarity with the human female hormone 17-symptoms –59. In tsymptoms . Clinicasymptoms . With ree damage . In addie damage .α and NOS3, and the release of TNF-α and NO to inhibit inflammatory signaling in RAW264.7 macrophages treated with high concentrations of free fatty acids, which are often increased in patients with T2DM and MS [As the most abundant secondary metabolite, puerarin is a unique isoflavone of Gegen. Due to its multiple pharmacological functions, such as vasodilation, cardioprotection, and antioxidant and anti-inflammatory effects, along with the attenuation of insulin resistance, puerarin has been widely used to treat cardiovascular and cerebrovascular diseases, diabetes, and diabetic complications , as provM and MS .β-Sitosterol, a prevalent plant cholesterol derivative (phytosterol) known as the “key of life”, is another pivotal active ingredient in Gegen. As a natural PPAR-γ agonist, β-sitosterol could be used as a potential therapeutic phytomedicine for the management of metabolic disorders, and overwhelming evidence has been obtained from basic experiments. For example, Gumede et al. discovered that β-sitosterol prevents high-fructose diet-induced metabolic dysfunction in female rats, including visceral obesity, hypertriglyceridemia, and hypoadiponectinemia [β-sitosterol significantly increased the levels of insulin, hemoglobin, and the PPAR-γ and GLUT4 proteins, alleviated insulin resistance, and decreased the plasma glucose levels in high-fat diet- and streptozotocin-induced diabetic rats [α levels in both subjects with and without diabetes, suggesting the anti-inflammatory potential of β-sitosterol [ctinemia . Anothertic rats . In additosterol .Interacting targets form signaling pathways that may reveal the mechanism of a disease. The PI3K-Akt signaling pathway, PPAR signaling pathway, and insulin resistance play pivotal roles in insulin secretion, glucose uptake, and lipid metabolism to maintain glucose and lipid homeostasis , 72. Theβ-sitosterol, a natural triterpenoid. To some extent, these components have consistent multitarget effects. Briefly, the efficacy of Gegen is mainly attributed to regulating insulin secretion and sensitivity, glucose metabolism, and fatty acid and cholesterol synthesis and decomposition to maintain metabolic homeostasis . On the other hand and more importantly, it also regulates the expression of proteins that mediate complications and risk factors for diabetes and hyperlipidemia, including diabetic cataracts, diabetic retinopathy, diabetic neuropathies, inflammation, atherosclerosis, liver steatosis, and obesity , to alleviate pathological progression and improve the prognosis. Thus, we speculated that the abundant bioactive ingredients of Gegen may work simultaneously on multiple targets in the PI3K-Akt signaling pathway, AGE-RAGE signaling pathway, fluid shear stress, and atherosclerosis to exert a synergistic therapeutic effect on T2DM and hyperlipidemia, which not only regulates glucose and lipid metabolism directly but also prevents and improves complications secondary to glucose and lipid metabolic disorders. Of course, this study has some limitations. We did not perform in vivo or in vitro experiments to validate the results. We would be willing to supplement the research in the future if the conditions are available and even try to design a clinical trial for verification.In summary, the main active ingredients of Gegen are isoflavones, such as daidzein, genistein, and puerarin, as well as β-sitosterol are the critical active components of Gegen responsible for its antidiabetic and antihyperlipidemia effects, which involve multiple mechanisms, such as ameliorating insulin resistance, increasing glucose uptake, promoting adipocyte differentiation, reducing lipid accumulation, and resisting inflammation and oxidative stress. These results are consistent with the main findings of previous studies, providing valuable information for the further development of Gegen. Specifically, Gegen or its bioactive extract has the potential to be applied to the treatment of T2DM and hyperlipidemia independently as a new medicine. After all, Gegen has been used only in the form of a compound prescription (a prescription consisting of 2 or more different Chinese medicines) for clinical treatment to date. Compared with Chinese herbal compounds, the mechanism of action of single Chinese herbs and monomeric compounds is easier to explain and apply in the clinic. Overall, Gegen is a safe, effective, and promising natural phytomedicine for the treatment of type 2 diabetes accompanied by hyperlipidemia with the advantages of multiple ingredients, targets, biological processes, and pathways.Taken together, we have discovered that isoflavones and


Nephrotic syndrome (NS) is a common glomerular disease caused by a variety of causes and is the second most common kidney disease. Guizhi is the key drug of Wulingsan in the treatment of NS. However, the action mechanism remains unclear. In this study, network pharmacology and molecular docking were used to explore the underlying molecular mechanism of Guizhi in treating NS.  The active components and targets of Guizhi were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Hitpick, SEA, and Swiss Target Prediction database. The targets related to NS were obtained from the DisGeNET, GeneCards, and OMIM database, and the intersected targets were obtained by Venny2.1.0. Then, active component-target network was constructed using Cytoscape software. And the protein-protein interaction (PPI) network was drawn through the String database and Cytoscape software. Next, Gene Ontology (GO) and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. And overall network was constructed through Cytoscape. Finally, molecular docking was conducted using Autodock Vina.  According to the screening criteria, a total of 8 active compounds and 317 potential targets of Guizhi were chosen. Through the online database, 2125 NS-related targets were identified, and 93 overlapping targets were obtained. In active component-target network, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were the important active components. In PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 were the core targets. GO and KEGG analyses showed that the main pathways of Guizhi in treating NS involved VEGF, Toll-like receptor, and MAPK signaling pathway. In molecular docking, the active compounds of Guizhi had good affinity with the core targets.  In this study, we preliminarily predicted the main active components, targets, and signaling pathways of Guizhi to treat NS, which could provide new ideas for further research on the protective mechanism and clinical application of Guizhi against NS. Nephrotic syndrome (NS) is a clinical syndrome defined as massive proteinuria, hypoalbuminemia, hyperlipidemia, and edema . AccordiAccording to traditional Chinese medicine (TCM), NS belongs to “edema,” and the pathogenesis of NS lies in the dysfunction of the lung, spleen, and kidney . TCM hasNetwork pharmacology integrates diseases and drugs into the biomolecular network, to predict the active components and the action mechanism . As a neIn the present study, network pharmacology and molecular docking were used to explore the potential action mechanism of Guizhi to treat NS. It is hoped to provide theoretical foundation and scientific evidence for the clinical treatment of NS, and the workflow of our study is shown in https://tcmspw.com/tcmsp.php). The PubChem ID, molecular formula, and canonical SMILES of each component were collected through PubChem database (https://pubchem.ncbi.nlm.nih.gov/). And the main active components were obtained by the screening criteria of oral bioavailability (OB) ≥ 30%, drug − like (DL) ≥ 0.18, and cell permeability (Caco − 2) ≥ −0.4.The active compounds of Guizhi were searched by TCMSP database (http://mips.helmholtz-muenchen.de/hitpick/cgi-bin/index.cgi?content=help.html), SEA (http://sea.bkslab.org/), and Swiss Target Prediction database (http://swisstargetprediction.ch/). The duplicates were deleted after the predicted targets of the 3 databases were merged.The targets of active compounds of Guizhi were predicted by Hitpick , cell component (CC), and molecular function (MF), at a cutoff of The active compounds of Guizhi, the top 30 KEGG signaling pathways, and the corresponding targets were used to construct the drug-compound-target-pathway-disease network through Cytoscape.The top 5 important targets with high network connection degrees were selected for molecular docking analysis using Autodock Vina. The smaller the binding energy (affinity) was, the more stable the interaction between the target protein and the active ingredient was.A total of 220 compounds were collected from the TCMSP database, with OB ≥ 30%, DL ≥ 0.18, and Caco − 2 ≥ −0.4, and 7 active compounds were identified. Cinnamaldehyde, OB = 31.99%, DL = 0.02, and Caco − 2 = 1.35, was not included in the results of TCMSP screening. However, our previous study found that cinnamaldehyde might be an important active compound of Guizhi . Therefo8 active components of Guizhi were input into Hitpick, SEA, and Swiss Target Prediction databases. After combining the predicted targets from the 3 databases, the duplicates were deleted, and a total of 317 potential targets were selected.The keywords “Nephrotic syndrome” and “Adriamycin Nephropathy” were searched in DisGeNET, GeneCards, and OMIM databases. A total of 2125 targets were identified. Venny 2.1.0 was used to intersect disease targets with drug targets. Finally, 93 potential targets  were selThe 93 potential targets were analyzed by Cytoscape to construct the active component-target interaction network . The resA total of 93 nodes and 1478 edges were involved in the PPI network . The bar93 potential targets were analyzed using DAVID 6.8, and the GO terms  and KEGG signaling pathway were selected. Targets in the BP were closely related to response to organic substance and positive regulation of molecular function and response to hormone stimulus. In the CC, Guizhi had great effect on cell surface, cell fraction, and plasma membrane part. At the MF level, drug components of Guizhi were mainly related to steroid binding, heme binding, and tetrapyrrole binding . A totalTo further investigate the molecular mechanism of Guizhi against NS, overall network was constructed based on the top 30 significant KEGG signaling pathways and their corresponding targets . 90 nodeThe top 5 targets, including VEGFA, MAPK3, SRC, PTGS2, and MAPK8  in the PNS is a common glomerular disease caused by various etiologies and is the second most common kidney disease after acute glomerulonephritis. At present, Western medicine mainly focuses on glucocorticoids, cytotoxic drugs, and immunosuppressants for NS , such asWulingsan is a classic prescription for NS, which has obvious advantages in improving urinary system diseases. Guizhi is an important component of Wulingsan, which has the pharmacological activities of diuresis, improving blood circulation and dilating blood vessels. Our preliminary study found that Guizhi is indeed an important drug of Wulingsan with the protective effect on rats with adriamycin-induced nephropathy . HoweverIn order to further explore the potential mechanism of Guizhi in treating NS and provide more evidence for clinical treatment, the main active components and targets of Guizhi, as well as the possible signaling pathways of Guizhi to treat NS, were predicted through network pharmacology and molecular docking.In the active component-target network, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were much higher than those of other components, which were 37, 37, 36, and 29, respectively, suggesting that they were the key active components in the treatment of NS.Among them, beta-sitosterol and sitosterol belong to plant sterols. Studies have found that beta-sitosterol has antihyperlipemia, anti-inflammatory, and immunomodulatory effects and can treat cholesterol, proteinuria, and edema . CinnamaIn the PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 had higher degrees than others, indicating that Guizhi might achieve the protective effect on the kidney through the above targets.VEGFA is a receptor in vascular endothelial cells that can induce endothelial cell differentiation and proliferation . It is aIn KEGG and overall network analysis, the key targets were mainly involved in VEGF, Toll-like receptor, and MAPK signaling pathway, which were highly correlated with renal disease, suggesting that the action mechanism of Guizhi to treat NS might be related to VEGF, Toll-like receptor, and MAPK signaling pathways.VEGF signaling pathway plays an irreplaceable role in the whole process of angiogenesis and is directly related to the occurrence of hypertension. When the endothelial function of patients with hypertensive is impaired, blood pressure will significantly increase, and proteinuria is aggravated. Studies have found that the inhibition of VEGF signaling pathway can lead to large amounts of proteinuria accompanied by irreversible renal impairment . In receBased on the network pharmacology results, molecular docking was performed to verify the interactions between the 8 active components of Guizhi and the key targets. The docking results showed that all the compounds had good binding activities with the targets, indicating that they may play an important role in Guizhi to treat NS.In conclusion, the potential mechanism of Guizhi to treat NS was predicted based on the network pharmacology and molecular docking. The results indicated that the underlying mechanism of Guizhi in treating NS may be related to VEGF, Toll-like receptor, and MAPK signaling pathway. However, the transmission of each signal pathway is complicated, and the final mechanism needs to be further verified by subsequent experiments.