To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.  The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters  were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets. κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.  The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes , lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways  of psoriasis in a “multicompound, multitarget, and multipathway” manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment. Psoriasis is a common and frequently occurring disease in dermatology, which is characterized by easy diagnosis and difficult treatment, as well as recurrent disease course . An epidQubi Formula (QBF) is an experience prescription for treating psoriasis at the Department of Dermatology in our hospital. It consists of Bubali Cornu , Rehmanniae Radix , Paeoniae Radix Rubra , Moutan Cortex , Arnebiae Radix , Lonicerae Japonicae Flos , Forsythiae Fructus , Isatidis Radix , and Glycyrrhizae Radix et Rhizoma . QBF is modified from the classical TCM formula “Xi-Jiao-Di-Huang decoction.” In this formula, Bubali Cornu is used as the sovereign drug (Jun), while Rehmanniae Radix and Isatidis Radix are utilized as the minister herbs (Chen); Paeoniae Radix Rubra, Moutan Cortex, Arnebiae Radix, Lonicerae Japonicae Flos, and Forsythiae Fructus are the assistant herbs (Zuo), whereas Glycyrrhizae Radix et Rhizoma is the messenger herbs (Shi). The mixed application of these drugs exerts the effects of clearing away heat and removing toxicity, cooling blood, and receding speckles. The effects of QBF on psoriasis have been validated by clinical practice in multiple years, especially in patients with blood-heat subtypes of psoriasis. However, the scientific basis as well as potential pharmacological mechanisms of QBF is still unclear, which needs further investigations.Conventional researches on the mechanism of one traditional Chinese medicine mostly follow the model of “one drug-one target-one disease,” which cannot reflect the characteristics of TCM . Herein, in this study, a comprehensive approach  (a combihttp://bionet.Ncpsb.Org/batman-tcm/index.Php/Home/Index/index) is a bioinformatics analysis tool for analyzing pharmacological active ingredients of Chinese medicines. In order to obtain the information about the ingredients of QBF, “SHUI NIU JIAO, DI HUANG, CHI SHAO, MU DAN PI, ZI CAO, JIN YIN HUA, LIAN QIAO, BAN LAN GEN, and GAN CAO” were used as key words to search in the BATMAN database, giving rise to a total of 572 compounds.BATMAN-TCM  (http://http://tcmspw.com/tcmsp.php) is a unique platform which can provide pharmacokinetic properties  and potential targets for natural compounds. We searched the above 572 compounds in the TCMSP platform to obtain their pharmacokinetic parameters. After obtaining the pharmacokinetic information on the 572 compounds, the reference was screened according to oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 , aiming to screen potential pharmacologically active ingredients of QBF [The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database  , or thoApart from assisting in exploring the active ingredients of TCM, TCMSP databases could also predict the potential targets of compounds based on SysDT model, HIT database, reverse molecular docking, etc. .In order to obtain the known psoriasis-related targets, “psoriasis” was used as key word to search in Therapeutic Target Database (TTD) , DrugBanHomo sapiens,” aiming to acquire the single universal gene names. Then, both potential targets of QBF and known psoriasis-related targets were uploaded to the online Wayne diagram tool  for mapping; that is, targets from these two sets were intersected to obtain the candidate targets of QBF for treating psoriasis.First, targets obtained from the above two steps  were standardized in the UniProt database  by selecHomo sapiens.” The topological parameters  of each target (node) in the network were calculated using the cytoHubba plugin [Subsequently, the candidate targets were imported into the String database  to obtaia plugin . The medHomo” in the ClueGO interface. All the core targets were sequentially imported, followed by enrichment analysis. κ value was defaulted at 0.4 and p was set at ≤0.05, which were used as the screening conditions for plotting enrichment analysis.The ClueGO  plugin fThe search through the BATMAN-TCM database revealed a total of 572 compounds of QBF. Accumulative efforts have been made to clarify the therapeutic mechanisms of TCM, however, with sluggish progress on the molecular level. Due to the unavailable effective methods specifically developed for the identification of the active compounds in medicinal herbs, OB screening combined with drug-likeness assessment may be a feasible strategy. In this study, OB ≥ 30% and DL ≥ 0.18 were used as the screening conditions. Then, a total of 202 possible compounds with proper values of above two parameters were collected for potential pharmacologically active ingredients from the herbal constituents of QBF. In addition, among the compounds that were screened out, we found another 35 compounds through the literature research of PubMed. Although these 35 compounds did not meet the screening conditions of OB and DL, they were reported to use a wide range of pharmacological activities and were thus included in potential pharmacologically active ingredients. Finally, the active ingredients of SNJ, GC, CS, DH, MDP, ZC, JYH, LQ, and BLG were 6, 97, 26, 6, 12, 17, 30, 27, and 51, respectively. The proportions of the active ingredients in the sovereign drug (Jun), minister herbs (Chen), assistant herbs (Zuo), and messenger herbs (Shi) in all the 175 ingredients were 3.4%, 30.86%, 53.14%, and 55.43%, respectively. Among them, some compounds were widely present in multiple herbs of QBF, such as oleic acid, methyl linolenate, sitosterol, and paeoniflorin. The basic information of the potential pharmacologically active ingredients of Qubi Formula is shown in Subsequently, we explored the potential targets of the 237 potential pharmacologically active ingredients by excavating TCMSP databases, which yielded to 939 targets shown in . The numIn order to holistically and systemically obtain comprehensive understanding of the ingredient-target network in QBF, a network map was constructed by using Cytoscape, including 6273 edges and 1212 nodes . To be sPsoriasis has been recognized as the polygenic disorder. In addition, the investigation of the interactions between genes as well as gene and environment could be used to reveal the pathogenesis of psoriasis. After the targets with abnormal status from TTD and DrugBank database and DSI < 0.535 (the median of DSI) from DisGeNET database were excluded, we collected 605 targets  associatSubsequently, to further select the core targets of QBF in treating psoriasis, the String database was used to construct the PPI network of the above targets , followeIn order to further understand the “multicompound and multitarget” mechanism of Qubi Formula in treating psoriasis, we searched for the potential ingredients of Qubi Formula which could affect the 27 core targets based on the relationship between the ingredients and their targets, followed by construction of the core network on active ingredients-targets  using Cypvalues of enriched pathways and their correlation with psoriasis, we were most interested in the following five representative signal pathways including VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathway , lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death . Moreove pathway .Qubi Formula is an experience prescription for psoriasis at the Department of Dermatology in our hospital. It is especially suitable for patients with blood-heat subtype of psoriasis, with radiated skin lesions throughout the whole body, redness, obvious scales, itching and burning, red tongue, and yellow fur. In this formula, SNJ, DH, JYH, and BLG can clear heat, cool blood, and remove toxic materials. MDP and CS are responsible for cooling blood and removing blood stasis. GC is in charge of detoxifying and reconciling medicine. The therapeutic effects of Qubi Formula in the clinical treatment of psoriasis are significant. However, the active ingredients and potential targets of Qubi Formula are unclear, which hinders the further development and application of the prescription.Network pharmacology is a new strategy for drug design and development based on the rapid development of systematical biology and multidirectional pharmacology. This concept was first proposed by Hopkins AL in 2007, which was switched from previous “disease-single target-single drug” model of new drug development to the “disease-multitarget-multidrug” model. This idea coincides with the “holistic view” of TCM. Therefore, the application of the network pharmacology method can provide certain research ideas for discovering the mechanism of Qubi Formula in treating psoriasis.In this study, a total of 175 potential active ingredients of Qubi Formula in treating psoriasis were screened through a series of network pharmacological methods, which corresponded to 27 core targets. At present, the widely acknowledged histopathological features of psoriasis include four major aspects: the inflammatory infiltration in dermis and epidermis, the abnormal biological behaviours  of keratinocytes, metabolic disturbance in skin tissue, and the tortuously increased dermal blood vessels and capillaries –41. FirsκB, and lymphocyte differentiation-related pathways. As shown in the κB) regulated by QBF on psoriasis therapy [Enrichment analysis of GO-BP and KEGG on the core targets further suggests that Qubi Formula could intervene in psoriasis through multiple biological processes by acting on several signaling pathways, involving VEGF, JAK-STAT, TLRs, NF- therapy –62.Our team has previously confirmed the safety and effectiveness of Qubi Formula in treating psoriasis through clinical observations. Based on the results of this study, we speculate that the regulatory role of Qubi Formula on psoriasis is not unilateral, but is directly or indirectly involved in the comprehensive treatment of the four major pathological factors of psoriasis through multiple signaling pathways associated with immunoinflammatory response, metabolism, and abnormal angiogenesis. Despite the valuable discoveries, there are still certain limitations. In the present study concerning the network pharmacological analysis on Qubi Formula, only the interactions between the components of QBF and the psoriasis-related targets were considered, but the interactions between the ingredients, the dosage of each ingredient, and the effects of the different processing methods of medicinal materials were neglected. Therefore, the obtained results must be verified by further experiments.The present study illustrates the systemic “multicompound and multitarget” efficacy of QBF against psoriasis. Moreover, this study also provided a theoretical basis to determine the synergistic effects of TCM in treating diseases and the role of systematic network pharmacology in elucidating the potential mechanisms of action of TCMs. However, as this study was based on data mining and data analysis, further validation studies should be undertaken.


Phellodendron chinense Schneid. played a vital role in the whole formula. Moreover, 7 compounds  and 4 targets  might be the kernel compounds and targets of SMP against GA. According to GOBP and KEGG pathway enrichment analysis and target-pathway network, SMP might exert a therapeutic role in GA by regulating numerous biological processes and pathways, including lipopolysaccharide-mediated signaling pathway, positive regulation of transcription, Toll-like receptor signaling pathway, JAK-STAT signaling pathway, NOD-like receptor signaling pathway, and MAPK signaling pathway. The results of molecular docking showcased that 11 pairs of compound with targets had tight binding strength. Thereinto, 4 compounds of MAPK1 and 5 compounds of NFKB1 possessed a better combination, suggesting that MAPK1 and NFKB1 might be considered as therapeutic targets in treatment of GA. This study verified that SMP had synergistic effect on GA by multicomponents, multitargets, and multipathways.Sanmiao pill (SMP), a Chinese traditional formula, had been used to treat gouty arthritis (GA). However, the active compounds and underlying mechanism remained unclear. Hence, network pharmacology and molecular docking were utilized to explore bioactive compounds and potential mechanism of action of SMP in treating GA. In the study, the compounds of SMP, corresponding targets, and GA-related targets were mined from various pharmacological databases. Then, herb-compound-target, compound-target, PPI, and target-pathway networks were constructed. Ultimately, molecular docking was carried out to verify the predicted results. The results indicated that 47 active compounds, 338 targets, and 144 disease targets were collected. Network analysis implied that  Gouty arthritis (GA) was a common inflammatory arthritis, which affected at least 1% of the population in developed countries and increased obviously over the last few decades . The maiPhellodendron chinense Schneid. , Achyranthes bidentata Blume. , and Atractylodes lancea (Thunb.) DC.  [Achyranthes bidentata Blume. accumulated berberine in the knee joint and enhanced the anti-inflammatory effect under its lower-guiding drug in SMP [Recently, traditional Chinese medicine (TCM) had been regarded as alternative therapies or dietary supplements for treatment of GA. Sanmiao pill (SMP), a basic traditional prescription for treatment of GA since the Ming Dynasty, was composed of three TCMs: zhu, CZ) . One stug in SMP . In addig in SMP . AlthougNetwork pharmacology was emerging discipline which integrated network biology and polypharmacology . Recentlhttp://ibts.hkbu.edu.hk/LSP/tcmsp.php). Then, all compounds in SMP were subjected to ADME screening, which was evaluated by the possibility of each compounds in SMP as drug and carried out by the TCMSP database. In the integrative ADME model, oral bioavailability (OB) and drug-likeness (DL) were significantly pharmacokinetic parameters and regarded as screening criteria. Therefore, active compounds were remained which were satisfactory with criteria of OB ≥ 30% and DL ≥ 0.18 for further study [https://pubchem.ncbi.nlm.nih.gov/) and OSRA (https://cactus.nci.nih.gov/osra/) databases.The compounds in SMP formula were harvested from the Traditional Chinese Medicine Systems Pharmacology Database  by restricting the species to “Homo sapiens” to standardize the gene name for the following data processing.SMP targets were retrieved from three public online databases, namely, TCMSP, Swiss TargetPrediction, and DrugBank databases. Thereinto, Swiss TargetPrediction and DrugBank databases predicted targets per active compound based on the similarity of chemical structure. Therefore, in order to ensure the credibility of the targets, the targets with probability ≥0.5 were retained from the Swiss TargetPrediction database. Meanwhile, the targets which met the criteria of similar drugs approved by FDA with similarity threshold ≥0.7 were selected from the DrugBank database. In addition, the corresponding targets of active ingredients were supplemented through the TCMSP database. Taken together, the SMP targets were imported from UniProt (An herb-compound-target network was established by Cytoscape (3.6.0) software, so as to explore the relationship between herbs in SMP formula, compounds, and corresponding targets. Degree, one of topological parameters, which was calculated by network analysis, was applied to preliminarily identify the mainstream herb, compound, and target.https://www.genecards.org/) and DisGeNet (http://www.disgenet.org/search) with the keywords “gouty arthritis” and “gouty.” The standardized gene names were obtained from UniProt (http://www.uniprot.org/) as well. The compound-related targets and GA-related targets were, respectively, put into the Venn software which was from the OmicShare platform (https://www.omicshare.com/). Ultimately, we could acquire SMP-related targets on GA for the following study.The GA-relevant targets were collected from GeneCards  to acquire protein-protein interactions (PPIs). “Organism” was set option to “Homo sapiens,” and only PPI with a combined score more than 0.4 was selected in the specific operation process. The PPI network of GA-related targets of SMP was built through Cytoscape (3.6.0) software. As previously reported [GA-related targets of SMP were then processed by the String database . The GOBP terms and KEGG pathways with P ≤ 0.05 were screened for further analysis.In order to systematically analyze Gene Ontology (GOBP) and the KEGG pathway of SMP targets related to GA, we imported hub targets into DAVID Bioinformatics Resources 6.8  and PubChem (https://pubchem.ncbi.nlm.nih.gov/) databases before performing the docking progress, respectively. Then, the target proteins were imported into the AutoDock tool for a series of processes including removing H2O, adding hydrogen atoms, and calculating charge and atom type. The grid box was the docking area of the protein and compound which covered as much the protein and ligand as possible. Molecular docking employed the Lamarckian genetic algorithm (LGA). Meanwhile, we ignored all bond rotations of ligand. Ultimately, the docking binding energy of every compound-protein was calculated via the AutoDock tool to assess binding interactions.To validate the significant compounds and targets which were selected from analysis of compound-target and target-pathway networks, the AutoDock tool was applied to perform a molecular docking. First of all, we prepared 3D structure of targets and compounds which were mined from PDB  and interleukin 1β (IL-1β) [The herb-compound-target network was built so as to preliminarily understand the interactions between herbs, compounds, and targets. The network embodied 388 nodes  and 1255 edges . Degree, (IL-1β) . MoreoveWe obtained a total of 144 disease targets from the two pharmacology databases, including 138 targets in the GeneCards database, 37 targets in the DisGeNet database, and 31 redundancies which were excluded  network was constructed. C-T network embodied 71 nodes (38 compounds and 33 SMP targets related to GA) and 130 edges . Based oTo shed light on the hub GA-relevant targets of SMP for the following GO and KEGG pathway enrichment, the PPI network was visualized by Cytoscape (3.6.0) software with 32 nodes and 264 edges based on the protein-protein interactions from the String database . The degβ induced by NLRP3 activation would be blocked, whereas HMOX1 modulated the balance of redox signaling. Consequently, HMOX1 also played an indirect role in the development of GA [It was reported that the pathogenesis of GA was closely related to the NLRP3 inflammasome and inflammatory cytokines . MSU crynt of GA . MMP2 annt of GA . SERPINEnt of GA . CRP, annt of GA . COX2 want of GA . RELA annt of GA . In addint of GA –30.P ≤ 0.05  software. The target-pathway network contained 74 nodes (17 hub targets and 57 pathways) and 311 edges . After nκB was activated. Meanwhile, the MAPK signaling pathway was activated to increase the expression of proinflammatory molecules, for instance, tumor necrosis factor (TNF)-α, IL-6, and IL-8, resulting in inflammatory reaction. From the above analysis, SMP in treatment of GA might inhibit the occurrence of inflammatory reaction via the Toll-like receptor/NF-κB/MAPK pathway [Multiple studies focused that the approaches of treating GA lowered levels of uric acid and reduced inflammation . Althoug pathway . In addi pathway , NOD-lik pathway , 36, and pathway  were actκB family, were involved in the NF-κB signaling pathway and TLR4-NFκB-IL1b signaling pathway, respectively, which regulated the expressing pro-inflammatory cytokines and played a central role in the pathogenesis of acute inflammation in arthritis [These pathways associated with GA-relevant targets of SMP might become multiple inflammatory and immune-related pathways in the development of GA. Besides, we also found that some key targets acted on multiple pathways . NFKB1 and RELA, members of the NF-rthritis –40. MAPKrthritis –43. TakeThe kernel compounds  and targets  were obtained from the result of compound-target and target-pathway network analysis for the binding validation experiment. The binding energy was applied to assess the effects of compound-target interactions. The binding energy was lower, indicating that the molecular docking was more effective. In the molecular docking process, the compound-target interaction with docking energy less than 5 kcal/mol would deem the effective docking . The resIn the present study, network pharmacology was proposed to dissect the molecular mechanism of SMP in treatment of GA. Our study found that HB potentially played a crucial role in the whole SMP formula. We also successfully identified that quercetin, kaempferol, wogonin, rutaecarpine, baicalein, beta-sitosterol, and stigmasterol could exert synergistic or antagonistic effects in treatment of GA through acting on multiple targets. Moreover, SMP took up significant position in anti-inflammatory by regulating multiple pathways. In addition, the results of molecular docking displayed the reliability and accuracy of the prediction results. In summary, the study had provided systematic and comprehensive sight into the mechanism of SMP in treating GA, which might contribute to the further application for SMP development.


Our aim was to find chlorine-substituted antidotes against organophosphate poisoning and compare their pharmacokinetics to their parent compound, K-203. White male Wistar rats were intramuscularly injected with K-203, K-867 or K-870. Serum, brain, kidneys, liver, lung, eyes, and testes tissues were taken after 5, 15, 30, 60, and 120 min and analyzed using reversed-phase high-performance liquid chromatography. K-203, K-867, or K-870 was present in every tissue that was analyzed, including the serum, the eyes, testes, liver, kidneys, lungs, and the brain. The serum levels of K-867 and K-870 (chlorine-substituted derivatives of K-203) were nearly constant between 15 and 30 min, while their parent compound (K-203) showed peak level at 15 min after the administration of 30 µmol/rat. Neither K-203, nor K-867 or K-870 were toxic at a dose of 100 µmol/200 g in rats. Chlorine-substitution of K-867 and K-870 produced limited absorbance and distribution compared to their parent compound, K203. Sarin and certain other phospho-organic compounds have been used by terrorists. The use of sarin ,2,3 prodThe determination of pyridinium aldoximes-type cholinesterase reactivators in tissues can be done using either one of the detection such as UV absorbance ,6,7,8,9,Kuca et al. ,21,22,23Pharmacokinetics of K-203 were studied in our previous works ,14 as weZorbaz et al.  determinA search for reactivators with an adequately long level in the blood may be required. This paper demonstrates the success in finding chlorine-substituted compounds, simply by the use of an adequately increased dose.Reversed-phase HPLC separation of either K-867 or K-870 was carried out using a relatively short C-18 column and ultraviolet detection. A demonstrative chromatogram of separation of K-347 and K-867 is given in The elimination of K-867 mainly takes place through the kidneys; the lungs have a minor elimination rate, and the level eliminated through the liver is negligible. Administration of 3 µmol of K-870 resulted in low level in various tissues of the rats. This was the reason that 100 µmol the compound was injected i.m.−1K-203 (i.m. dose: 30 µmol) 21,086 µmol min L−1K-867 (i.m. dose: 3 µmol) 1,287 µmol min L−1K-867 (i.m. dose: 30 µmol) 12,199 µmol min L−1K-870 (i.m. dose: 100 µmol) 27,227 µmol min LArea under the curve (AUC) values were as follow:K-203, K-347, K-867, and K-870 were produced in the Department of Chemistry at the Faculty of Sciences, University of Hradec Kralove, Hradec Kralove, Czech Republic. The chemical structures of K-347 , K-203, K-867, and K-870 are given in Solvents and other chemicals: Acetonitrile  and methanol  were part of mobile phase for HPLC separations. The applied water was ultrapure, produced by a Millipore Ultrapure (Type 1) equipment, Merck Kft., Budapest, Hungary.Animals: White male rats (180-199 g) were supplied by Toxicoop Co., Budapest, Hungary. They were intramuscularly injected with the freshly dissolved aqueous solution of either K-203, K-867 or K-870 using an adequate dose of the active compound. After 5, 15, 30, 60, and 120 min of administration, the anaesthetized animals were sacrificed. Animal treatment was done according to the ethical regulation of Semmelweis University and permitted by the Government Office of Pest County (No. PE/EA/385-7/2018).Various tissues and body fluids were taken. The samples were immediately placed and kept on ice, then homogenized using 0.3 M of perchloric acid . A nine-fold excess of PCA was used to rat serum, while four-fold excesses were used for homogenization of all other tissue samples. Centrifugation was done with the homogenized samples and the supernatants were directly used for RP-HPLC as detailed earlier .The employed HPLC system included a JASCO AS-4050 automatic injector, a JASCO PU-4180 pump with built-in degasser, and an MD-4010 photodiode array detector set at 300 nm and a JASCO ChromNav 2.0 software . The analytes were baseline-separated on a Phenomenex Kinetex EVO-C18 100 × 3mm (5 µm)  column (kept at 40 ℃) using reversed phase ion-pair chromatography in an isocratic run lasting 40 min. The mobile phase consisted of sodium acetate (5.44 g/L) , citric acid (4.72 g/L)  and 1-octanesulfonic acid sodium salt (2.0 g/L) .https://www.ema.europa.eu/documents/scientific-guideline/guideline-bioanalytical-method-validation_en.pdf. Accessed: 27 November 2019). Validation is detailed in our recent publication [K347 was employed as the internal standard (IS). IS was applied at the construction of the calibration curve, as well as to each sample before homogenization. Five (5) parallel injections were administered, 20 microliters each. The means of their results are used. Validation corresponds to the guideline of the European Medicine Agency (EMEA/CHMP/EWP/192217/2009 Rev. 1 Corr. 1**). European Medicine Agency, 2012. Available from: The fast elimination of antidotes is a general but not exclusive requirement. This is the case in a terrorist attack when the source of a toxic agent cannot always be eliminated. In general, the victims of a terrorist attack are immediately transferred to a safe place, however, rescue persons should take care of the victims while they are on the site of the attack. For the completion of their task, a long-lasting reactivator may be advantageous. Similarly to the pharmacokinetics of K-203 ,14, chloSubstitution with chloride does not only reduce the solubility of K-867 and K-870, but also their absorption and distribution in circulation after intramuscular injection. This is evident from the fact that the AUC of the serum level of K-203 is 1.7 times more than that of K-867 when compared to the parent compound. In addition to the reduced absorption and distribution of K-867 in the body, its elimination is also impaired. An increase in the dose (from 3 to 30 micromole) of K-867 results in a protracted serum level of the compound.The serum level of any xenobiotic compound influences the amount that gets into the central compartment of the body. It gives a possibility to the drug to penetrate into other compartments, such as body fluids, the central nervous system as well as the organs responsible for the elimination of the drug from the body. Both K203 and many other bis-pyridinium aldoximes  show definite penetration into the brain . K-867 a


A model-based approach for the assessment of pathway dynamics is explored to characterize metabolic and signaling pathway activity changes characteristic of the dosing-dependent differences in response to methylprednisolone in muscle. To consistently compare dosing-induced changes we extend the principles of pharmacokinetics and pharmacodynamics and introduce a novel representation of pathway-level dynamic models of activity regulation. We hypothesize the emergence of dosing-dependent regulatory interactions is critical to understanding the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways, including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism are dependent on dosing, exhibiting diverse dynamics depending on whether the drug is administered acutely of continuously. Therefore, the dynamics of drug presentation offer the possibility for the emergence of dosing-dependent models of regulation. Finally, we compared acute and chronic MPL response in muscle with liver. The comparison revealed systematic response differences between the two tissues, notably that muscle appears more prone to adapt to MPL. Methylprednisolone (MPL) is a synthetic glucocorticoid (GC) widely used to treat a multitude of conditions including arthritis, blood disorders, severe allergic reactions, certain cancers, eye conditions, skin/kidney/intestinal/lung diseases, and immune system disorders. MPL, a typical corticosteroid, manages symptoms such as swelling, pain, and allergic-type reactions by decreasing the immune system’s response . Mechaniin vivo high-throughput transcriptomics to capture the tissue and dosing effects of MPL  to characterize pathway activity. We hypothesize the emergence of dosing-dependent regulatory interactions to understand the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism, are all dependent on dosing. Finally, we compare acute and chronic MPL response across muscle with liver and observe systematic response differences between the two tissues. Notably, we observe that muscle appears more prone to developing tolerance to MPL.n = 2–4) from 0 to 72 h post dosing and isolated RNA were hybridized with Affymetrix GeneChips Rat Genome U34A containing 8799 probes. Chronic MPL administration response data in muscle tissue was obtained from a longitudinal study in which 40 ADX male Wistar rats were administered 0.3 mg/kg⋅hr of MPL intravenously for 7 days  software . Differefc) of each pathway  is determined using the 1-tail Fisher’s Exact test such that the total rat genome is the set of unique rat genes in all KEGG’s rat-relevant pathways   and Reac pathway . This stpathways . Pathwayfp) captured by the corresponding PAL. In order to characterize the actual significance of a corresponding PAL, an associated fpp-value is evaluated using bootstrapping of the original gene set  bootstrapped pathway gene sets are generated from the original pathway gene set. These bootstrapped sets are decomposed with SVD, significant PAL profiles identified, and their corresponding fp and fpp-value statistics retained for each significant pathway. All PAL profiles extracted from these bootstrapped gene sets are assumed likely system behavior that would emerge if the rat experiments were repeated. Bootstrapped PAL within a pathway are subsequently clustered for the identification of common activity patterns. The MATLAB® function evalclusters.m is applied to assess optimal cluster number using the gap statistic and applying kmeans clustering  (Rf. CMPL corresponds to the concentration of free receptor in the cytosol and is given by MPL action is receptor-mediated as described in equations 3 through 6 and depicted in  nucleus . The connucleus) . The fra.43ApVp .Pharmacogenomic models have been extensively used to model complex transcriptional dynamics , whereasks indicates the activation rate of pathway activity; IC50PAL indicates the concentration of DRN responsible for 50% inhibition of the pathway activity activation rate; and kd indicates the deactivation rate of pathway activity). This model captures the pathway activity response to intravenous MPL administration for both acute and chronic dosing, reflecting transient or persistent response types depending on dosing. Receptor-mediated response is expected to taper-off under acute administration since the dynamics of PAL should follow the dynamics of DRN. Under chronic dosing, and as DRN accumulates the influence should persist . Biosignal-mediated responses could either lead to “rebound” like behavior under acute dosing or, under chronic dosing, could lead to “tolerance”-like response or “rebound”-like eventually reaching a new steady state  were considered active. For the acute dosing, 49 pathways emerged as significant while all 61 pathways emerged as significant for the chronic dosing . Alternatively, a PAL profile can be thought of as the expression of a metagene over time, where the metagene is a representation of common trends in gene expression within a pathway. The emergence of multiple significant PALs indicates a codominance of activity patterns, and complex regulatory structures, within the pathway. To characterize these activity dynamics consistently, we extended the PD concept to describe the dynamics of a signaling or metabolic pathway’s corticosteroid impact in muscle tissue. Models that describe receptor and biosignal-mediated regulation by MPL were fitted to PAL profiles. This step served to hypothesize likely modes of pathway regulation and is essential to enable comparison of drug response across experimental platforms, animals, tissues, dosing regimens, or time horizons. Overall, we observed strikingly consistent response profiles across pathways and within the same dosing regimen. We further observed that chronic administration yielded more complex pathway dynamics for most pathways, than did acute administration.Pathway activity analysis was explored to identify pathways that are significantly active in response to MPL. We view a  pathway as a high-dimensional system whose decomposition identifies intrinsic trends. Using singular value decomposition (SVD), a pathway’s dynamic is decomposed into constituent singular values and singular vectors. The singular vectors are linear combinations of the longitudinal transcriptional expression over time, thus capture trends in the activity of the pathway, defined as Pathway Activity Level (PAL). The fraction of variability of each PAL profile is calculated from the singular values and is defined as the fraction of pathway activity  response to chronic MPL. The HIF-1 signaling pathway, Regulation of actin cytoskeleton, Oxytocin signaling pathway, PI3K-Akt signaling pathway, Apoptosis, Cellular senescence, Peroxisome, Insulin signaling pathway, Rap1 signaling pathway, Glucagon signaling pathway, cGMP-PKG signaling pathway, Arginine and Proline metabolism, Glutathione metabolism, PPAR signaling pathways exhibit both receptor, as well as biosignal-mediated responses to chronic MPL administration.The chronic administration explored the Affymetrix microarray platform 230A and yielded 61 pathways as significantly active . As in tChronic dosing appears to, disproportionately, impact metabolic processes, as indicated in However, chronic MPL administration has the potential of yielding more complex behaviors. The Fox-O signaling pathway consists of a series of transcription factors that regulate multiple events within the cell including “apoptosis, cell-cycle control, glucose metabolism, oxidative stress resistance, and longevity .” Fox-O intravenous).The most striking characteristic of muscle, compared to liver, pathway dynamics in response to acute MPL administration, is that muscle was found to be driven primarily by receptor mediated regulation, as opposed to liver which appears to reflect a balance between receptor and biosignal-mediated regulation . HoweverDRN, equilibrates to its new steady state  administration respectively.The datasets for this study can be found in the GEO data base AA developed the computational framework, conducted the studies, analyzed the results, co-wrote, and edited the manuscript. DD, RA, and WJ provided the experimental data, contributed to the analysis, and reviewed the manuscript. IA conceived the studies, contributed to the analysis, co-wrote, and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Teucrium polium L. has been used as a folk medicine for the treatment of T2DM in Anatolia, Turkey. The antihyperglycemic effect of the plant was reported previously. However, there was no detailed study on the underlying molecular mechanisms. In this study, we generated a research plan to clarify the active constituents of the extract and uncover the molecular mechanisms using network pharmacology analysis.Type 2 diabetes mellitus (T2DM) is a metabolic disease affecting a huge population worldwide. T. polium. Drug-likeness of the compounds was evaluated, and 52 compounds were selected for further investigation. A total of 252 T2DM related targets hit by selected compounds were subjected to DAVID database.For this purpose, we composed a dataset of 126 compounds for the phytochemical profile of the aerial parts of T. polium. Various compounds of T. polium were shown to interact with the key targets activating PI3K-AKT and insulin signaling pathways.The KEGG pathway analysis showed enrichment for the TNF signaling pathway, insulin resistance, the HIF-1 signaling pathway, apoptosis, the PI3K-AKT signaling pathway, the FOXO signaling pathway, the insulin signaling pathway, and type 2 diabetes mellitus which are related to T2DM . AKT1, IL6, STAT3, TP53, INS, and VEGFA were found to be key targets in protein-protein interaction. Besides these key targets, with this study the role of GSK3β, GLUT4, and PDX1 were also discussed through literature and considered as important targets in the antidiabetic effect of T. polium. This study reveals the relationship of the compounds in T. polium with the targets of T2DM in human. Our findings suggested the use of T. polium as an effective herbal drug in the treatment of T2DM and provides new insights for further research on the antidiabetic effect of T. polium.According to these findings, mainly phenolic compounds were identified as the active components and IRS1/PI3K/AKT signaling and insulin resistance were identified as the main pathways regulated by  Diabetes mellitus is a metabolic disease characterized by high blood glucose levels. According to the reports of WHO, about 422 million people live with diabetes. Diabetes was a direct cause of about 1.6 million deaths only in 2016. Among adults, 90% of the patients have type 2 diabetes mellitus (T2DM) . In T2DMTeucrium L., a member of Lamiaceae family (Subfamily Ajugoideaea), has a cosmopolitan distribution and including about 250 species spread worldwide  network of common targets was constructed. Key targets were determined and the role of targets in T2DM pathways was discussed.In this study, the underlying mechanisms of T. polium were reviewed and compounds that were isolated or determined listed. Literature search was performed using ‘Scopus’ and ‘Web of Science–Clarivate’ databases with the keyword ‘Teucrium polium’ upto June 2020. After the review process, it was found that 126 compounds were reported from T. polium by several reports. Due to their structure, compounds were listed under 3 groups . All the compounds were converted to Canonical SMILES format using PubChem (https://pubchem.ncbi.nlm.nih.gov/) or CS Chemdraw Ultra.Previous phytochemical studies on The absorption and permeation abilities of the compounds in the extract play a critical role in the biological activity observed. In this study, Lipinski’s rule of five was used to filter compounds which possess good absorption and permeation so that could be a new drug candidate. According to this criteria compounds which have; (i) molecular weight (MW) greater than 500, (ii) the calculated logP value above 5, (iii) more than 5 hydrogen bond donors (HBD) and (iv) more than 10 hydrogen bond acceptors (HBA) were filtered . All 126Homo sapiens. To avoid confusion, targets obtained were screened for UniprotKB ID for their unique identifiers (http://www.uniprot.org/) , and SYMMAP databases . Compounot.org/) . Duplicaot.org/) . Cytoscaot.org/) .https://www.disgenet.org/). ‘Type 2 diabetes mellitus’ was used as a keyword. DisGeNET is a platform with collections of genes associated with diseases  was used. STRING is a database that helps understanding associations between expressed proteins in a cellular function . The results were listed based on their p values. Top 20 results with lower p value were selected. The results of gene ontology (GO) function and (Kyoto Encyclopedia of Genes and Genomes) KEGG pathway analysis were evaluated and discussed . For further investigation, the common targets for  targets . These tT. polium , negative regulation of apoptotic process (GO:0043066) and positive regulation of transcription from RNA polymerase II promoter (GO:0045944) showed higher target numbers in count (GO:0006954), positive regulation of cell proliferation (GO:0008284), glucose homeostasis (GO:0042593) and glucose transport (GO:0015758) were found to be related with at least one of the KEGG pathways such as PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), insulin resistance (hsa04931), insulin signaling pathway (hsa04910), FoxO signaling pathway (hsa04068), adipocytokine signaling pathway (hsa04920), AMPK signaling pathway (hsa04152) and type 2 diabetes mellitus (hsa04930). Molecular function results with higher target numbers were protein binding (GO:0005515), protein homodimerization activity (GO:0042803), and identical protein binding (GO:0042802) (GO:0016301) and insulin receptor substrate binding (GO:0043560) were found to be related to at least one of the KEGG pathways such as insulin resistance, insulin signaling pathway, FoxO signaling pathway, PI3K-Akt signaling pathway, and type 2 diabetes mellitus.The results for GO analysis were evaluated through related terms option of DAVID database. According to biological process results; response to drug (in count . Negativ0042802) . Kinase p values suggested that, compounds reported from T. polium might also leads new insights for the treatment of cancer (KEGG enrichment results supported these findings. Results of 252 common targets were listed as (related with T2DM); TNF signaling pathway, insulin resistance, apoptosis, HIF-1 signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, insulin signaling pathway and type 2 diabetes mellitus . The topf cancer .T. polium extract has been reported by several reports , 6-hydroxy luteolin (TP3), apigenin (TP5), isoscutellarein (TP11), jaceosidin (TP12), luteolin (TP13), quercetin (TP14), 20-O-acetyl teucrasiatin (TP24), and capitatin (TP25), 4β,5α-epoxy-7 αH-germacr-10(14)-en,1β-hydroperoxyl,6β-ol (TP36), 4α,5β-epoxy-7αH-germacr-10(14)-en,1β-hydroperoxyl,6α-ol (TP38), 5,3′,4′- trihydroxy-3,7-dimethoxyflavone (TP47), jaranol (TP48) showed interaction with the target AKT1 that could have a role in the antidiabetic effect of T. polium  pathway provoked with oxidative stress leads to the inactivation of pancreas/duodenum homeobox protein 1 (PDX1) via FoxO1. In the same study, T. polium extract increased FoxO1 phosphorylation and, promoted the expression of PDX1 , 4′-O-methyl luteolin (TP2), 6-hydroxy luteolin (TP3), acacetin (TP4), apigenin (TP5), cirsilineol (TP7), cirsiliol (TP8), cirsimaritin (TP9), eupatorin (TP10), isoscutellarein (TP11), jaceosidin (TP12), luteolin (TP13), quercetin (TP14), 20-O-acetyl teucrasiatin (TP24), capitatin (TP25), clerodane-6,7-dione (TP26), -1,6-dihydroxy-4,11-dimethyl-germacran-4(5), 10(14)-dien-8,12-olide (TP31), prunasin (TP33), teucladiol (TP40), 4β,6β-dihydroxy-1α,5β(H)-guai-9-ene (TP41), oplopanone (TP42), ladanein (TP45), salvigenin (TP46), 5,3′,4′- trihydroxy-3,7-dimethoxyflavone (TP47), jaranol (TP48)  . In skell (TP48) . Both phT. polium increased translocation of GLUT4 in L6 muscle cells in the absence and presence of insulin when compared with the control. These findings indicated that insulin-like activity of T. polium is a result of increase in GLUT4 translocation (p-coumaric acid (TP17) ameliorate insulin shortage and insulin resistance  induction through insulin-stimulated PI3K/AKT has an important role in whole-body glucose homeostasis by glucose intake at adipose tissue, cardiomyocytes and skeletal muscle cells . Previoulocation . Quercetlocation . Furthersistance . A previsistance . Kaempfesistance . Similarsistance .t-ferulic acid (TP18), 4α-[(β-D-glucopyranosyloxy)methyl]-5α-(2-hydroxyethyl)-3-methylcyclopent-2-en-1-one (TP22), 5α-[2-(β-D-glucopyranosyloxy)ethyl]-4α-hydroxymethyl-3-methylcyclopent-2-en-1-one (TP23), and teupolin VIII (TP29) showed interaction with the target STAT3 , quercetin (TP14) and teupolin VIII (TP29) showed interaction with the target IL6 . Severalet STAT3 .VEGFA is a growth factor that has an important role in angiogenesis, vasculogenesis and endothelial cell growth. Apigenin (TP5), luteolin (TP13), quercetin (TP14) and prunasin (TP33) showed interaction with the target VEGFA . HyperglT. polium in the treatment of T2DM were evaluated with the help of bioinformatics. Though there were studies on T. polium extract’s antihyperglycemic effect via in vitro and in vivo assays, the underlying molecular mechanisms has not totally determined yet. We constructed a comprehensive dataset of compounds reported in T. polium. 126 compounds previously isolated or determined from the aerial parts of the plant were listed through a detailed literature search. In this view, this study serves the most detailed data on the content of the phytochemical profile of T. polium so far.In the last decade, network pharmacology driven omics methods play an important role to understand the role of the herbal prescriptions used as a folk medicine in various diseases. In this study, molecular mechanisms of T. polium. T. polium is an insulin-sensitizing plant. Even though insulin resistance has an important role in the pathophysiology of T2DM, insulin resistance does not result in T2DM in all cases. It turns to T2DM with a loss in β-cell mass in pancreatic islets. The results of the present network pharmacology studies taken together with the previously reported data, suggested that T. polium could be a promising herb for the treatment of T2DM through ameliorating insulin resistance and enhancing β-cell mass.In the present network pharmacological analysis, insulin resistance and PI3K-AKT signaling pathway were shown to take place in the center of the mechanism of action of 10.7717/peerj.10111/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10111/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10111/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10111/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10111/supp-5Supplemental Information 5Click here for additional data file.


Zhibai Dihuang Wan (ZDW) has frequently been used for treating SLE in China and been proved to have a prominent role in decreasing SLE patients' morality rate. However, the active substances in ZDW and the molecular mechanisms of ZDW in SLE remain unclear. This study identified the bioactive compounds and delineated the molecular targets and potential pathways of ZDW by using a network biology approach. First, we collected putative targets of ZDW based on TCMSP, GeneCards, and STITCH databases and built a network containing the interactions between the putative targets of ZDW and known therapeutic targets of SLE. Then, the key hubs were imported to DAVID Bioinformatics Resources 6.7 to perform gene ontology biological process (GOBP) and pathway enrichment analysis. A total of 95 nodes including 73 putative targets of ZDW were determined as major hubs in terms of their node degree. The results of GOBP and pathway enrichment analysis indicated that putative targets of ZDW mostly were involved in various pathways associated with inflammatory response and apoptosis. More importantly, eleven putative targets of ZDW  were recognized as active factors involved in the main biological functions of treatment, implying the underlying mechanisms of ZDW acting on SLE. This study provides novel insights into the mechanisms of ZDW in SLE, from the molecular level to the pathway level.Systemic lupus erythematosus (SLE) is a refractory autoimmune disease.  Zhibai Dihuang Wan  has frequently been used for treating SLE in China and been proved to have a prominent role in decreasing SLE patients' morality rate [zhī mŭ), Phellodendri Chinensis Cortex , Rehmanniae Radix Praeparata , Rhizoma Dioscoreae , Cornus officinalis , poria cocos , Alisma orientale , and Cortex Moutan . ZDW exerts many effects, such as enriching yin, subduing fire, and returning fire to its source. Meanwhile, patterns of yin deficiency and fire exuberant are the most typical in TCM's view on SLE [Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component, characterized by hyperactive T and B cells, autoantibody production, immune complex deposition, and multiorgan damage . Given tity rate –8. ZDW iw on SLE . Due to w on SLE .In the present study, we, respectively, collected the information of targets from active ingredients in ZDW and related targets of SLE from several databases for the first time. Network construction and topological structural analysis were established, which may provide a basis for a more comprehensive understanding of the action mechanisms of ZDW in the treatment of SLE.http://ibts.hkbu.edu.hk/LSP/tcmsp.php) [The entire compound data of ZDW were retrieved from the TCM Systems Pharmacology Database and Analysis Platform  . The acthttps://www.genecards.org, v4.10.0) and the STITCH database  with the species limited as “Homo sapiens” and the condition of high confidence (0.700) [The integrative efficacy of the compounds in ZDW was determined by analyzing the compounds and target interactions obtained from the GeneCards database ( (0.700) , 13. Genhttp://www.omim.org/, updated in May, 2019), which provides over 1,500 relevant genes assigned to the known diseases [There are 3 sources used for predicting SLE-associated targets. The first batch of genes associated with SLE were collected from the Online Mendelian Inheritance in Man (OMIM) database  [The gene ontology (GO) biological process (BP) was analyzed with the limitation of “rf.gov/) . The terhttp://www.kegg.jp). Then, we analyzed the top 10 significant pathways and their related targets to elucidate the molecular mechanisms.To comprehensively understand the molecular mechanisms of ZDW, the dominating target-pathway network was constructed using Cytoscape 3.7.1. The significant pathways were identified by performing enrichment analysis of the proteins by using DAVID Bioinformatics Resources 6.7 and were extracted from KEGG  network was built  first. TAmong 73 hub targets, ZDW exhibited great action on CASP3 (degree = 16), which has been identified as the executioner of apoptosis and the key enzyme of the apoptosis cascade. Similarly, ZDW also strongly affected CASP9 (degree = 11) and CASP8 (degree = 10), suggestive of its contribution to apoptosis. Beyond that ZDW could influence BCL2 (degree = 12) and BAX (degree = 12) to regulate apoptosis. Meanwhile, ZDW also has the potential to act on the MAP kinase family, which participates in the cellular processes like proliferation, differentiation, and development, such as MAPK1 (degree = 12), MAPK14 (degree = 11), and MAPK8 (degree = 9).The top 5 of the 22 central compounds in ZDW are quercetin (degree = 228), ethyl oleate (degree = 134), (+)-catechin (degree = 129), berberine (degree = 96), and kaempferol (degree = 76).p ≤ 0.05). The results revealed that numerous targets are involved in the regulation of apoptotic process, lipopolysaccharide (LPS), and inflammation.A GO enrichment analysis was performed using DAVID Bioinformatics Resources 6.7 to clarify the mechanism of ZDW's main action in SLE. 73 hub targets obtained from the CT network were included in the GO enrichment analysis. P value ≤ 0.05 are presented in The KEGG pathway enrichment analysis was performed with the 73 hub targets by using the functional annotation tool of DAVID Bioinformatics Resources 6.7. The pathways with κB pathway, the hypoxia inducible factor (HIF)-1 signaling pathway, and the mitogen-activated protein kinase (MAPK) signaling pathway. The medium module is related to apoptosis, including the TNF signaling pathway, apoptosis, and the Forkhead Box O (FoxO) signaling pathway. The minimum module concerned prolactin (PRL).Modularity is a critical measurement for analyzing a network. Nodes highly interconnected within a network usually participate in the same biological modules. In terms of functional distribution, the interaction network of the top 10 significant signaling pathways and corresponding targets was divided into 3 modules. The groups of the main pathways and modules are shown in κB and MAPK signaling pathway. Suppression of the NF-κB and MAPK signaling pathway in SLE could reduce proinflammatory cytokine production [κB pathway. The HIF pathway has been identified as a possible therapeutic target for diseases including chronic inflammation, infection, and autoimmunity [According to oduction , 21. Hypimmunity .κB and MAPK signaling pathway and induce apoptosis and necroptosis [TNF signaling pathway, which has a leading position among the 10 pathways, plays a crucial role in SLE immunopathogenesis, as it can activate the prosurvival NF-roptosis . Apoptosroptosis .The minimum module is centered on the PRL signaling pathway. PRL has an antiapoptotic effect, enhances proliferative response to antigens and mitogens, and enhances the production of immunoglobulins and autoantibodies . It has κB, NFKBIA , IL(interleukin)-6, TLR4, IL-1β, PTGS2 (prostaglandin-endoperoxide synthase 2), CCL2(C-C motif chemokine ligand 2), and TNF-α . The main biological processes related with certain ZDW regulation are shown in SLE is an autoimmune disease mediated by pathogenic autoantibodies directed against nucleoprotein complexes . TherapeκB signaling way [κB expressions. This suggests that the TLR4/NF-κB signaling pathway could regulate apoptosis [Patients with SLE often display a deficiency in clearing apoptotic cells. The accumulation of postapoptotic remnants and fragments derived from secondary necrotic cells in the presence of autoantibodies against apoptotic cells or adaptor molecules obliges their pathological elimination and maintains autoinflammation, which is responsible for the initiation of SLE . Thus, aling way . Extractling way . Extractling way . More impoptosis . Interespoptosis . HoweverκB, as a key transcription factor involved in the regulation of immune responses and apoptosis, could promote the inflammatory responses in the body. NFKBIA, known as I-kappa-B-alpha (I-κBα), which inhibits the activity of dimeric NF-kappa-B/REL complexes, has been reported as a target for glucocorticoid-mediated immunosuppression [κB signaling pathway [κB activation, we found that ZDW might be more sensitive to an LPS-induced TLR4/NF-κB signaling pathway. LPS could bind the CD14/TLR4/MD2 receptor complex in many cell types, especially in monocytes, dendritic cells, macrophages, and B cells, so as to promote the secretion of proinflammatory cytokines [κB signaling pathway is activated during the progression of a direct autoantibody-mediated tissue injury and the deposition of complement-fixing immune complexes, inducing chronic inflammatory response. Importantly, the activation of NF-κB is critically responsible for the secretion of cytokines including, IL-6, IL-1β, PTGS2, CCL2, and TNF-α [κB signaling pathway [κBα and the activation of NF-κB [α, can not only be activated by canonical NF-κB and MAPK signaling playing a role in inflammation, but can also participate in the derivation of inflammatory responses [α-induced NF-κB p65 translocation and suppress the expression levels of MCP-1 induced by TNF-α [κB signaling pathway and decreasing proinflammatory cytokines in SLE.NF-pression . It has  pathway . Althougytokines . As a reytokines , 48. Theytokines . Cytokinnd TNF-α , 51. IL-nd TNF-α . Extract pathway . Furtherof NF-κB . CCL2, kof NF-κB . The comof NF-κB . TNFα, cesponses . The extby TNF-α . In summTCM, one of the most important parts of complementary and alternative medicine, markedly contributes to the therapeutic action of autoimmune diseases. This study uses a scientific approach to holistically elucidate that the pharmacological mechanisms of ZDW in the treatment of SLE may be associated with its involvement in apoptosis suppression and anti-inflammation. Among these crucial biological functions, eleven targets were identified as key active factors involved in the main biological processes with validated evidence. However, to comprehensively understand the mechanism of ZDW, further experimental research needs to be undertaken to validate if ZDW treats SLE through this mechanism as a formula.


A common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters. Placing omic data in a network context allows for the discovery of key members of a process that may be missed from a single data source and functional summarization for hypothesis generation and other downstream analyses.Network analysis can integrate and analyze large amounts of biological “omic” data from genomic, transcriptomic, proteomic, or metabolomic assays3. Thus, it is often preferable to infer a customized subnetwork specific to an experimental data set starting from all known protein interactions, referred to as the interactome. We refer to this problem as pathway reconstruction: using condition-specific input omic data to select nodes and edges that represent some process or cellular state from a generic background network. Pathway reconstruction differs from module detection4, which divides a network into functional units or clusters. It is also distinct from network propagation5, which identifies relevant regions of a larger network but typically does not select specific edges within that region.Although biological pathway enrichment can be used to interpret omic data, pathways in curated databases are incomplete and also contain proteins or genes that are not involved in a particular biological context8, shortest paths9, enrichment analysis10, network flow13, and other graph theory algorithms. These methods also take in a variety of inputs. Some, such as the Prize-Collecting Steiner Forest (PCSF) algorithm14, accept scores for the biological entities of interest. Other methods, such as PathLinker9, require the inputs to be split into starting points (sources) and end points (targets) for the pathway. Despite the different optimization strategies and inputs, pathway reconstruction algorithms almost always require the user to set parameters. Adjusting the parameters can produce pathways with drastically different topological properties and biological interpretations. For instance, both pathways in Fig. Existing pathway reconstruction methods are based on diverse strategies such as combinatorial optimization problems17, which requires multiple parameter settings.An open challenge is how to configure these critical pathway reconstruction parameters in a manner that is objective and applicable across diverse types of algorithms. Existing approaches tend to be informal and ad hoc, and most best practices in parameter tuning are not applicable to pathway reconstruction. The simplest way to choose parameters would be to use default values. However, a single parameter setting cannot work for all data sets. The number of input proteins, genes, or metabolites varies based on the experiment, and the effects of input size can be unpredictable for different pathway reconstruction algorithms. For instance, if PathLinker is run with fixed parameters, increasing the number of source and target nodes can actually result in a smaller reconstructed pathway, which is not necessarily what a user would intend. In addition, some methods are commonly run repeatedly and combined into an ensemble network18 and the Bayesian information criterion19 are not applicable. For instance, the PCSF objective function value can be arbitrarily increased by changing the parameter values.Parameter tuning methods from supervised learning are also a poor match. There is no ground truth for supervised parameter tuning, and unsupervised cross-validation (CV) is ineffective (see “Results”). In addition, the objective functions of pathway reconstruction methods only approximate biologically meaningful graph topologies and typically have no probabilistic likelihood. Thus, their values cannot be compared between different parameter settings, and statistical model selection criteria such as the Akaike information criterion20. PCSF recommends choosing pathways robust to small random input variation or matching the average degree of input nodes and non-input nodes14. We show that these heuristics can perform poorly in practice.Given the lack of objective, quantitative methods for tuning, parameter settings are often chosen by manual inspection or informal heuristics. ResponseNet recommends choosing parameters that recover at least 30% of inputs while minimizing low confidence edgesBiologists often have intuition about which pathways are unrealistic or impractical for downstream analysis, such as the 7000 node pathway in Fig. 23, which was originally developed for multiple sequence alignment. Parameter advising can be used to adapt the parameter tuning framework in settings where no ground truth tuning set exists. However, parameter advising requires a means to estimate the accuracy of a model with a given set of parameters. Because pathway reconstruction is an exploratory analysis, there is no formal notion of accuracy. We overcome this limitation by leveraging background knowledge to create a score that prefers pathways in topological agreement with reference pathways. Our parameter tuning method, pathway parameter advising, uses the parameter advising framework in combination with a distance metric based on graphlet decomposition to measure similarity between reconstructed pathways and pathways from curated databases. Pathway databases may be imperfect and incomplete, but they reflect models that the expert curators consider to be biologically plausible. Only measuring the topology of a reconstructed pathway means that pathway parameter advising is also method agnostic. It can tune the parameters of any pathway reconstruction method.One framework for finding optimal parameters in an uncertain setting is parameter advisingFigure Pathway parameter advising ranks parameter settings for pathway reconstruction methods. Because pathway reconstruction is an exploratory analysis and our goal is to maximize downstream biological utility, which cannot be directly quantified, we resort to multiple indirect approaches to evaluate pathway parameter advising Fig. . We firsPathway parameter advising is a framework to score the pathways produced by a pathway reconstruction algorithm run with multiple parameter settings. The scored pathways are ranked so that the top-scoring pathway(s) can be used for downstream analyses and poor-scoring pathways can be ignored. We define a pathway quality score for the ranking that compares the graph structure of a reconstructed pathway with the graph structures found in reference pathways from a curated database. Specifically, the score computes the topological distances between the reconstructed pathway and reference pathways. Reconstructed pathways with small distances are preferred.24. We calculate the graphlet frequencies in the reconstructed pathway and the reference pathways. This graphlet frequency decomposition summarizes the topology of a pathway in a vector of 17 values, one per graphlet type  to reconstruct the 15 NetPath pathways. We selected the NetPath pathways Wnt, TNF alpha, and TGF beta as validation pathways to evaluate different graphlet distances and develop pathway parameter advising. These three validation pathways are excluded from all aggregate results. The other 12 NetPath pathways were designated as test pathways. More details on the four pathway reconstruction methods, NetBox, PathLinker, PCSF, and min-cost flow, can be found in section “Pathway reconstruction methods.” Overall, we considered 60 parameter tuning tasks across the 15 NetPath pathways and 4 pathway reconstruction methods.In these experiments, we used the implausibility criteria defined in section “Evaluating reconstructed pathway plausibility” to label reconstructed pathways as plausible or implausible without reference to Reactome or other reference pathways. Reconstructed pathways that meet all of the plausibility criteria are treated as the positive set, while reconstructed pathways defined as implausible are the negative set. These reconstructed pathway labels of plausible and implausible are considered alongside the ranking produced by a parameter selection method. We then used precision–recall (PR) curves . Figure Of the 36 cases where AUPRs could be compared (both plausible and implausible pathways were present), pathway parameter advising had the highest AUPR in 30. CV had the highest AUPR in the other six. The impact of the choice of parameter ranking strategy is most stark for PCSF, where pathway parameter advising has perfect AUPR in almost all pathways and the other approaches struggle. Not only did pathway parameter advising have the highest median AUPR overall, but its performance was the most consistent; it had the lowest variance in AUPR across all parameter tuning tasks.In order to make sure that performance was not overly influenced by our specific choice of criteria for plausible and implausible pathways, we varied the threshold for each of the four plausibility topological properties. We considered all combinations of thresholds for all four proprieties in a grid search, resulting in a total of 10,000 plausibility configurations. Figure Having achieved our primarily goal of accurately prioritizing parameters that generate plausible pathways, we also evaluated the quality of the reconstructed pathways themselves. We compared pathway parameter advising to the alternative ranking methods and the default parameters. In these experiments, we define all pathway edges in the NetPath pathway as a positive set and all other edges as a negative set. We then compared the ability of pathway parameter advising and other parameter ranking methods to promote reconstructed pathways that closely resemble their NetPath equivalent.Figure Figure 25 (see “Data sets”). These host factors were given as input to PCSF using the same range of parameter settings as in the other experiments, creating a candidate host factor pathway for each parameter setting.To illustrate how pathway parameter advising can guide the biological interpretation of omic data, we reconstructed an influenza host factor pathway. Our aim was to create a pathway that represents aspects of influenza’s infectious activities and could lead to the discovery of new host factors or host factor regulators. We created an influenza host factor network using the 1257 host factors from a meta-analysis of eight RNAi screensAfter creating the candidate host factor pathways, we ranked the 1000 parameter settings using pathway parameter advising. Figure We also created three ensemble networks from the resultant pathways from the top, middle, and bottom 50 parameter settings ranked by pathway parameter advising. Ensembling the reconstructed pathways is a common way to use PCSF. We expect the top 50 ensemble pathway to be best for downstream analysis and interpreting the input host factor data. Of the three ensemble pathways, the ensemble made from the 50 highest ranked parameters contains 86 nodes. The middle-ranked and low-ranked ensemble pathways have 7337 and 15 nodes, respectively . We tested both Gene Ontology (GO) biological process terms  in “Pathway parameter advising”) across all pathway reconstruction methods, Reactome pathways, and Reactome pathways with added noise. E(G) is the score used by pathway parameter advising to rank pathways. The E(G) distribution for each pathway reconstruction algorithm summarizes reconstructed pathways from every parameter setting applied to the sampled inputs from all 15 NetPath pathways. We added noise to Reactome pathways by randomly removing a percentage of all edges in the pathway and adding back that number of edges that did not appear in the original pathway. We also calculated the graphlet distances between each Reactome pathway and all of the other Reactome pathways. Figure To further assess the validity of our pathway ranking metric, we explored several properties of the graphlet distance score. We first examined the overall distribution of graphlet distances  we selected for pathway parameter advising with two alternatives: normalized graphlet frequency distance (NGFD) and graphlet correlation distance (GCD). The choice of distance calculation affects how parameter settings are ranked. When we run multiple pathway reconstruction algorithms to reconstruct the three NetPath validation pathways, GFD outperforms NGFD and GCD .Our parameter tuning approach requires the inputs to the pathway reconstruction algorithm, a set of candidate parameter settings, and a set of pathways from a reference pathway database. Pathway reconstruction algorithms’ input typically consists of an interactome, such as STRINGPathway parameter advising is designed to be method agnostic. It can be run with any pathway reconstruction algorithm that outputs pathways and has user-specified parameters. Currently, pathway parameter advising is designed to examine undirected graphs, and directed graphs are converted to be undirected.39. However, network motifs typically refer to graphlets that appear in a network significantly more often than expected by chance.In order to topologically compare reconstructed and reference pathways, we first decompose all pathways into their graphlet distributions. A graphlet is a subgraph of a particular size within a network. The concept of graphlets is similar to that of network motifs24. However, we use both connected and disconnected subgraphs, thus allowing all possible combinations of nodes in a pathway to be considered a graphlet. This allows our parameter ranking to capture global topological properties such as pathway size in addition to local topology. One disadvantage of disconnected graphlet counts is that the counts of disconnected graphlets, such as the graphlet containing four unconnected nodes, grow at a much faster rate than those of connected graphlets in sparse networks. However, this does not adversely affect our ranking metric .Original work with graphlets only considered connected graphlets to better capture local topology40 to calculate counts of all graphlets up to size 4 in a pathway. This constitutes 17 possible graphlets  and Fi(H), scalars between 0 and 1. We then define the GFD D as follows:24. We considered other graphlet-based metrics such as a variation of relative GFD and GCD41 but found that they performed worse in our preliminary analyses  is calculated as:Rtop is the set of the 20% closest reference pathways to G. The pathways, or equivalently the parameters used to generate those pathways, are sorted by E(G) in ascending order. Once the final ranking is created, the top reconstructed pathway can be used for downstream analysis. Alternatively, the top n pathways can be merged into an ensemble pathway.After calculating the graphlet frequency distribution for each reconstructed and reference pathway, we can calculate their mean GFD to the reference pathways to get Pathway reconstruction algorithms were chosen to have a wide range of methodologies, from NetBox’s statistical test to PathLinker’s weighted shortest paths algorithm. We used the following four methods for our implausible pathway detection and NetPath reconstruction experiments. These methods and the parameters tested are summarized in Table 9 reconstructs pathways based on a weighted k-shortest paths algorithm. It finds paths between sets of receptors and transcriptional regulators, similar to the source and target nodes in minimum-cost flow. It is controlled by the parameter k, which defines how many paths to return in the final network. We varied k from 1 to 1000 in increments of 1. We used PathLinker version 1.1 for all analyses.PathLinker10 hierarchically constructs networks from a set of input nodes. At each iteration, it searches for linker nodes that connect two nodes in the current network. It then chooses to add these linker nodes to the network based on the results of a hypergeometric statistical test comparing the degree of the linker node to how many nodes in the pathway it connects. NetBox is controlled by the parameter p, a p value threshold, which sets the threshold for whether or not linker nodes should be included. We varied p from 0 to 1 on a log scale from 1 × 10−30 to 1 in increments of half an order of magnitude, giving a total of 60 steps. We used NetBox version 1.0 for all analyses.NetBox42, nodes are assigned prizes and edges are given costs. The optimal subnetwork, which is found via a message-passing algorithm43, is the pathway F consisting of nodes NF and edges EF that best balances collected prizes versus cumulative edge costs according to the following function:p is the positive prize for each node, d is a node’s degree, c is the cost of each edge, and κ is the number of connected components in the pathway. The optimal subnetwork is always a tree- or forest-structured graph. We varied three PCSF parameters: β, which controls the relative weight of the node prizes versus edge costs, was varied from 0 to 5 in increments of 0.5; μ, which affects the penalty for high-degree nodes, was varied from 0 to 1 in increments of 0.1; and ω, which controls the cost of adding an additional tree to the solution network, was varied from 0 to 10 in increments of 1. We used version 1.3 of the msgsteiner message-passing algorithm and version 0.3.1 of OmicsIntegrator for all analyses.In PCSF12. We implemented a version of min-cost flow using the solver provided in Google’s OR-Tools (https://developers.google.com/optimization/flow/mincostflow), which solves the min-cost flow problem using the algorithm outlined in 44. This is a generic version of the algorithm used in ResponseNet11. Two parameters control the min-cost flow solution: the total flow through the network, which we vary from 1 to 50 in increments of 1, and the edge flow capacity, which we vary from 1 to 25 in increments of 1. We used Google’s OR-Tools version 7.1.6720 for all analyses.The minimum-cost flow problem assigns certain nodes in the network to be “sources” and others to be “targets.” Edges, which transport the flow from node to node, have a cost associated with using them and a capacity of how much flow they can hold. The solution is the network that satisfies the flow requirements of the source and target nodes while using the lowest cost in edgesWe compare our pathway parameter advising approach to the following parameter selection strategies from the literature.CV involves splitting the input data, the biological nodes of interest provided to the pathway reconstruction algorithm, into training and testing sets multiple times for each parameter setting. For example, the input data could be sampled nodes from a NetPath pathway. A pathway reconstruction method is then run on each training set and evaluated on each respective testing set. In this problem setting, we do not have external ground truth with which to evaluate the predictions on test set data. Instead, we perform fivefold CV on subsets of the input data, producing a pathway from the training set nodes. The parameter values that produce pathways that recover the highest proportion of the test set nodes are chosen.20. The criterion is to select parameters that result in a pathway whose nodes include at least 30% of the input data, while having the lowest proportion of low confidence edges. We extend this to rank the pathways that do include 30% of the inputs by their proportion of low confidence edges, followed by the pathways that include <30% of the inputs to form a full ranking.We also tested a parameter selection heuristic used by ResponseNet14, for PCSF we can also rank pathways by their robustness. Robustness is measured by how often nodes appeared in multiple runs with small random perturbations to the scores on the input nodes. We only applied this strategy to reconstructed pathways from PCSF. Although it could be adapted to other pathway reconstruction methods, we decided to use it only in the method for which it was directly implemented.As suggested by In order to examine the ability of pathway parameter advising to avoid parameter settings that lead to impractical pathways, we created topological criteria that we use to define pathways as plausible or implausible. These criteria are based on the literature where possible and were created without considering the topology of pathways from pathway databases. However, given that curated pathway databases are also based on information from the literature, these plausibility criteria should not be considered completely independent from the pathway databases. We use these criteria as a heuristic to label pathways as positive (plausible) or negative (implausible). The labels enable us to evaluate pathway rankings as a classification problem, determining if a method can correctly rank plausible reconstructed pathways before implausible pathways. These criteria are based on previous analyses of biological networks and are as follows.We allowed pathways that had between 10 and 1000 nodes. Pathways whose size was outside this range are not practical for hypothesis generation and downstream analysis.14. We score hub node dependence using the ratio of the degree of the highest degree node to the average node degree of the entire pathway. If the maximum degree is more than 20 times greater than the average degree, we consider the pathway implausible.A common issue with pathway reconstruction is an over-reliance on high-degree or hub nodes. Dominant hub nodes can create pathways consisting almost entirely of a single node and its neighbors with few to no connections between those neighbors45; communities within the network exist at multiple scales and are often nested within each other. Thus, it would be reasonable to expect a plausible biological pathway to have at least a moderate level of community structure. We calculate the average clustering coefficient of all nodes in the pathway, a common metric for measuring community structure46. The clustering coefficient of a node is the proportion of its neighbors that are also neighbors of each other. This can be averaged over all nodes in the pathway as a measure of the overall level of clustering. We require pathways to have a mean clustering coefficient of at least 0.05, as we expect at least some small level of clustering to exist. Because this requirement eliminated all PCSF pathways in 25% of parameter tuning tasks, when evaluating PCSF we excluded this metric in all cases.Biological networks have been found to have clustering or community structure that is hierarchical48. Assortativity is measured between −1 and 1, where assortative networks have positive values and disassortative networks have negative values. This value can be viewed as the correlation between a node’s degree and its neighbors’ degrees within the pathway. We consider pathways with assortativity between −1 and 0.1 plausible to allow for some leeway in pathways being slightly assortative.A network’s level of assortative mixing is defined as the tendency of high-degree nodes to be connected to other high-degree nodes. Biological networks have been found to be generally disassortative, meaning that high-degree nodes tend to be connected to low-degree nodesWe selected these criteria based on attributes it would be reasonable to expect a biological pathway to have, with values supported by the literature where possible. If any of these criteria are not met, we consider the pathway to be implausible. These thresholds were not influenced by the graph topologies in the reference pathway database in order to minimize circularity between the reference pathway-based rankings and the plausibility criteria used to evaluate those rankings. However, most of the reference pathways we considered happen to be plausible. Seventy-seven percent of the Reactome pathways we used as reference pathways are plausible, though it should be noted that these Reactome pathways have already been filtered by size (see “Data sets”).While the criteria for defining a plausible network are useful for comparing networks created by the same method with different parameter settings, they should not be considered as a metric for comparing pathways across pathway reconstruction methods. Different pathway reconstruction methods are able to use different sources of information and have complex strengths and weaknesses beyond the local topologies they return. For instance, NetBox, which had the highest proportion of plausible pathways, cannot take into account information such as edge confidence or scores on proteins of interest that other methods such as PCSF can. Furthermore, the four plausibility properties are a binary way to determine if a pathway is reasonable or unreasonable. They cannot be used to rank pathway reconstruction parameters.In order to make sure that our experimental results are not overly sensitive to the specific choice of thresholds for pathway plausibility, we tested other thresholds in a grid search. We varied the maximum network size threshold from 200 to 2000, the hub node dependence measure from 5 to 50, the clustering coefficient threshold from 0.0 to 0.1, and the assortativity threshold from −0.5 to 0.5. Each range was divided into ten intervals, for a total of 10,000 sets of plausibility thresholds. Figure 49. MCC is a metric used in binary classification that ranges between −1 and 1, where 1 indicates a perfect binary classification and −1 indicates a completely incorrect classification. It can be viewed as the correlation between the predicted and true labels in a classification task. MCC has been shown to be well suited to evaluate classification in imbalanced settings50. In order to treat comparing a reconstructed and a NetPath pathway as a classification task, we consider all edges in a NetPath pathway as the positive set and all other edges as the negative net. MCC is then defined as follows:When comparing reconstructed pathways to the original NetPath pathways in section “Quality of NetPath pathway reconstruction,” we used MCC to quantify the reconstruction quality3 that merged protein interactions from the iRefIndex database v1351 and kinase–substrate interactions from PhosphoSitePlus52. The interactions from the iRefIndex database include confidence scores, while confidence scores for kinase–substrate interactions were inferred from the number of interactions for each kinase–substrate pair and the type of experiment that detected the interaction. If an interaction was included in both databases, the PhosphoSitePlus interaction was used. This resulted in a network with 161,901 weighted edges.For both PathLinker and NetBox, we used the interactome included as a part of their software packages. For PCSF and min-cost flow, we used an interactome from 36 is a database of manually curated pathways, including 2287 human pathways. Reactome is open-source, where all contributions must provide literature evidence and are reviewed by an external domain expert before being added. Pathways smaller than 15 nodes were excluded, as they were too small for meaningful interpretation. Reactome pathways were retrieved using Pathway Commons53. Pathway Commons converted from the Reactome data model to a binary interaction model using a set of rules for each interaction type (https://www.pathwaycommons.org/pc/sif_interaction_rules.do).All parameter tuning was performed with Reactome as the set of reference pathways. Reactome37. We used 15 NetPath pathways that contain at least 1 receptor and transcriptional regulator and are sufficiently connected, as described by 9. We designated three of these NetPath pathways as validation pathways: Wnt, TGF beta, and TNF alpha. Validation pathways were used to guide the choice of distance measure. The remaining 12 pathways were reserved as test pathways for quantitative evaluations. We sampled the NetPath pathways in different ways for each pathway reconstruction algorithm to provide inputs in their expected formats, generally following the node sampling protocol that PathLinker9 used to reconstruct NetPath pathways. PCSF and NetBox do not require sources and targets, so we randomly sampled 30% of the pathway nodes as input. We also assigned each input a random prize sampled uniformly between 0 and 5 for PCSF. For PathLinker and min-cost flow, which require sources and targets, we selected all transcription factors and receptors for each pathway as outlined by 9.The implausible pathway detection and NetPath reconstruction experiments were performed on pathways from the NetPath database. NetPath is a collection of 36 manually curated human signal transduction pathways25. These screens used RNAi to measure the effect of each screened gene’s knockdown on influenza infection. For instance, .54 assessed influenza viral replication 48 h after infection. They identified host genes whose knockdown substantially impacted viral titers relative to a negative control .25 merged the hits from the eight RNAi screens to obtain 1257 pro-viral host genes and separately calculated a consolidated Z-score from the normalized activities scores in the four genome-wide screens. We selected these 1257 host factors as inputs for pathway reconstruction and set the PCSF node prize to the absolute value of the consolidated Z-score.Influenza host factors were gathered from a meta-analysis of eight genome-wide and targeted RNAi screens55 and KEGG pathway35 enrichment was carried out with DAVID v6.726. Enrichment was performed using GO biological process terms and all KEGG pathways. Thresholds for term inclusion were set to a count of 2 and an EASE score of 0.1.GOhttps://github.com/gitter-lab/pathway-parameter-advising under the MIT license. While v0.1.0 and later versions of the pathway parameter advising software support Python v3.6, the results here used Python v2.7.16 and Anaconda v2019.03. The following package versions were used: pandas v0.24.2, networkx v2.2, numpy v1.16.2, matplotlib v2.2.3, and seaborn v0.9.0. The Parallel Graphlet Decomposition library was pulled from GitHub on April 30, 2019.A Python implementation of pathway parameter advising is available at 56.The article was previously published as a preprintFurther information on research design is available in the Supplementary InformationReporting Summary