Understanding prehistoric projectile weaponry performance is fundamental to unraveling past humans’ survival and the evolution of technology. One important debate involves how deeply stone-tipped projectiles penetrate a target. Theoretically, all things being equal, projectiles with smaller tip cross-sectional geometries should penetrate deeper into a target than projectiles with larger tip cross-sectional geometries. Yet, previous experiments have both supported and questioned this theoretical premise. Here, under controlled conditions, we experimentally examine fourteen types of stone-tipped projectile each possessing a different cross-sectional geometry. Our results show that both tip cross-sectional area (TCSA) and tip cross-sectional perimeter (TCSP) exhibit a strong, significant inverse relationship with target penetration depth, although TCSP’s relationship is stronger. We discuss why our experimental results support what is mathematically predicted while previous experiments have not. Our results are consistent with the hypothesis that when stone tip cross-sectional geometries become smaller over time in particular contexts, this evolution may be due to the selection of these attributes for increased penetration. A wound that is shallow is more likely to result in hunting failure relative to one that injures a critical internal organ (10: 84). If a projectile has to penetrate roughly into the middle of the preys’ chest cavity to achieve a lethal wound11, then penetration depths of 20–25 cm would be required for large ungulates , and depths of over 50 cm would be required for even larger animals like bison (14: 554). If an animal’s critical organs are not injured, then a more deeply penetrating projectile will possess a better chance of causing severe blood loss or a blood trail that can be tracked16. However, it is important to note that there are cases where projectile tips serve as delivery devices for poison, which may negate the need for deep penetration17. It is also important to note that the penetration lethality estimates reported above need further scientific validation and study. Nevertheless, regardless of the absolute penetration depth needed for a lethal wound, there is agreement that increasing projectile penetration would be a boon to prehistoric people.Killing prey was vital for the survival of prehistoric humans, and projectile penetration depth contributes to the killing of prey26). Following Hughes5, these variables are defined as27, Ashby28, Hughes5, and Kneubuehl29 mathematically demonstrate target penetration is inversely proportional to tip cross-sectional area, then, all things being equal, stone-tipped projectile penetration depth should increase as TCSA and TCSP decrease .Given the importance of increasing projectile penetration depth for ensuring successful hunts when poison is not employed, it is reasonable to conclude that prehistoric people in particular contexts selected for projectile weapon system characteristics that would have improved this factor. While there are many potential variables that can influence stone-tipped projectile penetration depth, two often mentioned are tip cross-sectional area (TCSA) and tip cross-sectional perimeter (TCSP)  was consistent with the mathematical predictions, as was Mika et al.’s6 recent assessment of small triangular stone arrow tips. However, Wood and Fitzhugh’s9 experiments assessing the penetration depths of bone, stone biface, and stone microblade points—all possessing different TCSA and TCSP values—showed no significant difference in penetration depth. Sisk and Shea’s (24: 2044) analysis of triangular  points showed no relationship between TCSA and penetration depth  and only a weak inverse relationship between TCSP and penetration depth . Perhaps most critical of the relationship between TCSA and TCSP with penetration depth is Clarkson3. His ballistics experiments involving stone projectile tips possessing a large range of TCSA and TCSP values led him to suggest that “a very poor correlation exists between these two statistics and penetration depth” (3: 193) such that “TCSA/TCSP also do not provide a valuable measure of the mechanical limits on projectile design” (3: 197).Despite this theoretical foundation, experiments involving prehistoric replica stone tipped projectiles have not consistently supported the inverse relationship between penetration depth and TCSA or TCSP. For example, Salem and Churchill’sGiven the disparate conclusions between theoretical predictions and experimental results, and the contrasting verdicts between different sets of experiments, we conducted a ballistics experiment that explicitly assessed whether TCSA and TCSP were robust indicators of projectile penetration potential.www.neolithics.com) using Texas Fredericksburg chert   ash dowels manufactured by Thunderbird Atlatl . The hafted specimens were shot with a compound bow (29 lbs. draw weight) mounted in the Spot-Hogg “Hooter-Shooter” in a controlled indoor setting.Our experimental procedures followed closely the procedures described in other ballistics studies conducted at The Kent State University Experimental Archaeology Laboratory (31: 39). We fired the hafted projectile specimens into blocks of moist clay containing crystalline silica, which has been used as a substitute for meat and tissue in other studies . The clay was terracotta low-fire earthenware clay, commonly referred to as “potter's clay” .The distance between the stationary target and bow was approximately 180 cm, which allowed adequate room for the specimens to travel once fired without drastically losing speed or dropping (31: 41). Once we removed a specimen from the target, a tape measure was used to measure from the person’s finger mark on the shaft to the tip of the point. The mean penetration depth for each of the fourteen projectile point types was calculated from the 30 shots for that point type , we used a Gamma Master Model Shooting Chronograph throughout the experiment (31: 40–41). The chronograph readings result in “error” if there is a change in sunlight, cloud cover, or some other minor variable. As a result, some of the 30 tests for each point type do not have recorded velocity values. As with penetration depth, we averaged the recorded velocities of each of the fourteen projectile point forms to produce a mean velocity . We reasoned that a prehistoric person—given a particular weapon system—would not have been able simply to muster more energy to achieve a greater velocity with a heavier point, nor would they have necessarily used less energy to achieve a slower velocity with a lighter point. The velocities thus reflect that: given a single hypothetical individual firing all fourteen forms, the more massive projectiles travel slower than smaller ones although we note that the velocities in our experiment fall well within the range of human atlatl throwingeriment 3: 40–41. This mean velocity, along with projectile mass, was used to calculate the average kinetic energy and average momentum of each of the fourteen projectile point types. The kinetic energy of a single point was calculated using:As mentioned above, kinetic energy (KE) was effectively held constant for each individual test through the use of a constant bow draw weight and draw length. KE is thus used as the control for the presented experiments and is expected to remain constant across all tests outside of small experimental errors that may occur; momentum, however, will not be constant and can thus be used as a variable in the presented statistical analyses of penetration depth. There were some instances where either the velocity of the point was unable to be measured or the kinetic energy calculated was a statistical outlier from the population of kinetic energy values. Experiments falling into either of these two categories were removed from the dataset prior to analysis. An outlier was defined as any kinetic energy value outside of 1.5IQR below the first quartile or 1.5IQR above the third quartile, where IQR is the interquartile range of the kinetic energy values. Figure p = 0.61; TCSA W = 0.94, p = 0.48; TCSP W = 0.93, p = 0.29; Momentum W = 0.90, p = 0.13).TCSA and TCSP were calculated for each of the fourteen point forms Table . We use 2 value of 0.69, as shown in Fig. The OLS of TCSA predicting penetration depth of the fourteen point types has a significant negative slope − 0.053) with an r with an 2 = 0.73) as shown in Fig. The OLS of TCSP predicting penetration depth also shows a significant negative relationship  as shown in Fig. Next, we examined the OLS relationship between momentum and penetration depth. The slope for the relationship between momentum and penetration depth is negative − 7.524) and the variables are correlated .Lastly, we entered the three models—TCSA, TCSP, and momentum—into a stepwise multiple regression model. The final model excluded TCSA and momentum as predictors and included only TCSP , recent experiments examining this relationship have yielded mixed results. In this paper, we examined the penetration depths of 14 different projectile point forms, each fired 30 times. These 30 shots per point were used to calculate a mean penetration depth for each point type. We used OLS regression analysis to investigate the relationship between this mean penetration depth and the TCSA and TCSP for each point type. Our results indicate that tip cross-sectional attributes, particularly TCSP, are robust predictors of penetration depth. In other words, these results suggest that—at velocities reasonable for stone tipped projectiles—a stone point’s cross-sectional attributes are good indicators of whether certain types of point would have potentially been more or less successful at injuring a prey’s vital organ.Despite clear mathematical predictions for a strong inverse relationship between penetration depth and tip geometry . To be sure, stochastic mechanisms can also result in the decrease of tip cross-sectional attributes, thus a case for selection depends on context and must remain an inference. However, had our results shown no relationship between penetration depth and TCSA or TCSP, then the case for selection of these attributes for increasing projectile penetration depth—in any context—vanishes.These results have important implications for the interpretation of prehistoric weaponry evolution. When archaeologists document changes in point form such that tip cross-sectional attributes become smaller over time, this evolution may be due to the selection of these attributes for increased penetration , the conclusion that this applies as a general rule independent of other factors cannot be drawn as momentum cannot be assumed to be an independent variable due to its dependence on the tip mass and, thus, tip geometry. As shown in Fig. 3; 24; but see6). We suggest that one reason may be that the target being penetrated may have played an important role. In our experiment we used potter’s clay, a uniform substance that Key et al. (32: 2042) recently examined with experiments that used high-speed video analyses to verify the similarity of clay to meat when using stone points. More experiments are of course necessary to further validate the use of clay, but the use of gelatin for arrow wounds—as was done in the Clarkson3 study—has been questioned by Karger et al. (40: 499). They write “because the penetration depth in gelatin and especially in soap does not come even close to that in nonbone tissues, both media are unsuitable for experimental simulation of arrow wounds.” Furthermore, Karger et al.40 documented different forms of modern arrow tip behaving in opposite ways in gelatin versus meat, indicating that “field tips penetrated deeper in gelatin than in nonbone tissue but broad-heads showed the opposite behavior.” Sisk and Shea’s24 draping of leather over an archery target may have also added an additional variable that resulted in inconsistent penetration from shot to shot.One important question is why our results show a strong relationship between tip cross-sectional attributes while previous results of advocates and critics alike do not .There could be other experimental variables accounting for the differences in the present study versus previous studies examining the relationship between tip cross-sectional attributes and penetration depth. One might be our use of the Spot Hogg Hooter-Shooter to produce consistent aim, draw length, and KE between shots. Another possible factor explaining the different results between this study and previous ones might be our use of ground points possessing only the gross morphology of bifacial points. Sisk and Shea24; Clarkson3) are substantial enough that we advocate more experimental work before the issue is considered settled.In conclusion, our results strongly support the hypothesis that tip cross-sectional attributes predict penetration depth for points given the same kinetic energy. However, in spite of the fact that our results regarding TCSA/TCSP are consistent with mathematical predictions, the differences between our study and those that came before (e.g. Sisk and SheaSupplementary Information


With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.WikiPathways (  The WikiPathways project was founded in 2007 upon the idea that everyone should be able to participate in the collection and curation of scientific knowledge . No singWikiPathways is a database of biological pathway models collected and curated by the research community. Anyone at any time can contribute their pathway knowledge using freely available pathway editing tools. All edits are attributed to a registered author and screened by at least one other curator by means of organized and distributed community curation. This approach allows WikiPathways to grow at the scale of new discoveries and with input from diverse sources of pathway knowledge.Curation Cafe events focused on selected topics, e.g. improving the quality of existing or creating novel pathways. We also detail some of the latest infrastructure updates, tool development and dissemination work which improve the free exchange of pathway information across platforms and within common analytical workflows.As previously reported, WikiPathways relies on communities of pathway authors and curators, pathway users, and developers to assemble, update and distribute content for myriad research applications ,3–7 In thttp://releases.wikipathways.org). In this section, we report on the content updates between the data releases on 10 September 2017 and 10 September 2020. Overall, WikiPathways currently contains a total of 2857 pathways for all species, out of which 1777 are included in the species-specific analysis collections. In the last three years, the content at WikiPathways has seen 10 079 user contributions, and 122 new contributors joined our community . Additionally, our REST webservice API recorded 27 million requests during this three-year time span. Importantly, in an effort to produce a more accessible and sustainable resource, we regularly disseminate pathway content to third-party tools and databases (see section ‘Connections to other initiatives’), which generate secondary usage statistics not reported here.https://github.com/BiGCAT-UM/WikiPathwaysCurator).New biological knowledge is published every day, and as part of the curation process, pathway models get revised over time with this new information, in addition to other updates and corrections. Quality assurance of the WikiPathways content is accomplished continuously, by a combination of a weekly manual curation by a member of an organized team of curators, computer-assisted curation processes  and montEach edit in a wiki-based system is recorded as a revision in the pathway history. These revisions are a measure for community activity and engagement. Pathway edits cover adding new biological knowledge, annotating the pathways with metadata , improving the layout of a pathway diagram, and any combination thereof. Figure https://gladstone-bioinformatics.shinyapps.io/shiny-25years).Even with the continued growth at WikiPathways, approximately only 50% of protein-coding human genes are represented in pathway models. Despite the availability of free pathway modelling tools, such as PathVisio , CellDeshttp://portals.wikipathways.org). Portal maintenance instructions are provided to enable communities to design and maintain portals themselves, with assistance from the WikiPathways team if needed. Here, we highlight recent community efforts in collaboration with WikiPathways. All pathways receive a tag specifically for their community, allowing for automated downloads of these pathway collections through the REST API (http://webservice.wikipathways.org) with the ‘getCurationTagsByName’ function, and our RDF format with SPARQL queries .http://covid.wikipathways.org, see Figure The COVID-19 Disease Map project aims to understand biological processes relevant to the COVID-19 pandemic . From thhttp://raredisease.wikipathways.org), partnered by EJP-RD (European Joint Programme on Rare Diseases), ELIXIR (European Bioinformatics infrastructure programme) and the Dutch Rett expertise centre . The portal is used to capture knowledge from literature and data to gain a better understanding of these complex disorders. The pathways are created and curated in collaboration with disease experts, currently covering over 60 rare diseases including very different types of diseases, e.g. laminopathies, ciliopathies, disorders of sexual development and fertility, and copy number variation syndromes.Rare diseases affect relatively few people, with the exact definition varying between 5 and 80 individuals per 100 000 for a given rare disease. However, it is estimated that up to 5.9% of the general population is affected by a rare disease. The majority of these disorders are genetic, 4440 of 6172 in total counted by ORPHANET . For manhttp://iem.wikipathways.org), containing molecular pathways connecting clinical biomarkers to disorders. We have started a collaboration with the authors of the book ‘Physician’s Guide to the Diagnosis, Treatment, and Follow-Up of Inherited Metabolic Diseases’  that will not be used for data analysis but can be annotated with a proper identifier for computational processing of the information.Inborn errors of metabolism are a subsection of the rare disease field, which are captured in the ‘IEM portal’ . Annotating individual lipids instead of lipid classes can be quite complicated; this phenomenon is in most cases due to a lack of biological knowledge on individual lipids. Furthermore, several cases are known where homology mapping between different species for proteins is hampered, e.g. for stearoyl-CoA desaturase-1 having four isoforms in mice compared to only two in humans  has been created to capture all pathways relevant to toxicological assessments (https://aopwiki.org).Since the introduction of Adverse Outcome Pathways (AOPs) to support regulatory decision making for risk assessment of chemicals , the priessments . These messments . One chaessments  and therWikiPathways enables anyone to freely share, redistribute, use, and adapt pathway content in the database, by removing any barriers for people to decide to contribute to or use WikiPathways. Furthermore, WikiPathways provides a variety of options to access the data for use, through downloads in various formats for individual pathways or pathway collections, from the pathway editor and analysis software PathVisio , throughManaging molecular pathways requires robust use of database identifiers for all pathway components . Recently, mappings to the EBI Complex Portal  and IUPHhttp://widget.wikipathways.org). The widget unifies identifiers from the data source originally specified by the pathway author, to provide several commonly used data sources for additional exploration. Metabolite identifiers are unified by BridgeDb to include ChEBI, HMDB and Wikidata identifiers. For gene products, the Wikidata API is used for mapping of NCBI Gene, Ensembl, HGNC and Wikidata identifiers. In the future, the mapping for gene products will also be done using BridgeDb.The pathway viewer widget was updated and moved to Wikimedia Toolforge allowing users to integrate the pathways from WikiPathways into their own website with more ease (https://github.com/wikipathways/snorql-extended), a new extended implementation of the SNORQL user interface (UI), to be our go-to semantic web browser (http://sparql.wikipathways.org). WikiPathways SNORQL is a query editor that offers syntax highlighting for writing and executing SPARQL queries directly on our existing SPARQL endpoint (http://sparql.wikipathways.org/sparql). Furthermore, the user interface provides a query examples panel which is auto-populated with SPARQL queries from a customizable GitHub repository (https://github.com/wikipathways/SPARQLQueries). This repository stores queries in folders divided over particular topics, communities, collaborations functionality, or external data sources for federated queries, allowing new users to navigate through our example queries panel with more ease . The WikiPathways content of the human pathway analysis collection in Wikidata gets updated after each monthly release.The CC0 license of WikiPathways also enabled interoperability through Wikidata, the linked data repository of Wikipedia . Like Wihttps://scholia.toolforge.org/wikipathways/WP111. These pages show the participants of the pathway , the literature cited by the pathway, and articles citing the pathway. Moreover, similar to linking to PubMed and Europe PMC, the literature section now links to Scholia pages for the cited articles.Scholia is a graphical user interface that aggregates information from Wikidata around topics ,34, suchhttps://github.com/wikipathways/nanopublications). This results in three types of nanopublications, for three types of facts in WikiPathways: complexes, interactions, and general participation in pathways. Nanopublications are currently only generated if the complex, interaction or participation is linked to a specific literature reference, identified by a PubMed identifier, which is used as part of the provenance of the nanopublication. Nanopublications are findable using semantic web identifiers for genes and proteins  functionality  to Euroionality . This alFunctional enrichment analysis is a popular approach for characterizing differentially expressed genes based on Gene Ontology terms, pathways and other annotated gene sets. We release a standard Gene Matrix Transposed (GMT) file each month that includes the latest set of curated pathways approved for enrichment use cases. Using this file, WikiPathways content can be added to any protocol supporting the GMT standard. A number of R packages and online tools that perform enrichment analysis have incorporated WikiPathways into their methods and vignette examples, including g:Profiler , clusterThe interoperability between the MINERVA platform  and the Several researchers involved in the Biological Expression Language (BEL) project  are harmhttp://ndexbio.org/#/user/363f49e0-4cf0-11e9-9f06-0ac135e8bacf), with over 600 pathways included. Second, as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative, we have led the curation of 28 cancer-specific pathway models and the organization of 87 cancer-related pathways, all of which we have merged into 11 network models representing common cancer hallmark categories (http://cptac.wikipathways.org). We regularly deposit these network models at NDEx. Third, we have extracted gene sets from over 32,000 pathway figures with 10 or more genes and have deposited them at NDEx (http://iquery.ndexbio.org).The Network Data Exchange (NDEx) is a public resource for publishing and sharing biological networks and gene sets , and Wik at NDEx  for manuWith the ever-growing collection of curated and published pathway information, the WikiPathways team is working towards a more sustainable and scalable infrastructure for all pathway knowledge. This work will involve the development of new tools and services, continued integration into community-run resources like Wikidata, and close coordination with other pathway databases and biocuration teams. For example, we are building a pathway knowledge management system using git version control, with automated diff and merge capabilities to synchronize curation efforts across multiple sites. Pathway edits made at Wikidata, NDEx, WikiPathways or Reactome would essentially open a request to merge and redistribute the new information. Utilizing gene, metabolite and disease content extracted from published pathway figures, we will organize focused curation efforts aimed at converting content as pathway models. This process will be facilitated by the addition of a portal highlighting and organizing this content.https://biothings-explorer.readthedocs.io) and Smart API (We are also integrating pathway information into new platforms for biomedical research and discovery. This work includes the continued support for third-party pathway analysis tools online and via R and Python packages, as well as new knowledge bases that connect pathway information to other genomic and phenotypic models of biology. For example, we are contributing curated and published pathway information to the NCATS’ Biomedical Data Translator program  using thmart API .The content of WikiPathways increases every day due to the combined effort of multiple communities, including curators of pathway models and authors of pathway figures. A wide variety of third-party analytical tools utilizes the content distributed by WikiPathways, creating an immeasurable user base. WikiPathways is defined by the people authoring, curating and utilizing pathway knowledge. We invite all researchers interested in pathways to directly participate in the WikiPathways project.All WikiPathways data, including older data releases, are stored on data.wikipathways.org. Scripts and SPARQL queries used to generate the data published here can be found on GitHub .


Halorubrum distributum, Halorubrum terrestre, Halorubrum arcis and Halorubrum litoreum was carried out using different approaches, 16S rRNA gene sequence analysis, multilocus sequence analysis (MLSA), phylogenomic analysis based on the comparison of the core genome, orthologous average nucleotide identity (OrthoANI), Genome-to-Genome Distance Calculator (GGDC), synteny plots and polar lipid profile (PLP). The MLSA study, using the five concatenated housekeeping genes atpB, EF-2, glnA, ppsA and rpoB′, and the phylogenomic analysis based on 1347 core translated gene sequences obtained from their genomes showed that Halorubrum distributum JCM 9100T, Halorubrum terrestre JCM 10247T, Halorubrum arcis JCM 13916T and Halorubrum litoreum JCM 13561T formed a robust cluster, clearly separated from the rest of species of the genus Halorubrum. The OrthoANI and digital DDH values, calculated by the GGDC, showed percentages among Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T that ranged from 98.1 to 97.5 %, and 84.0 to 78.0 %, respectively, while these values among those strains and the type strains of their most related species of Halorubrum were equal or lower than 90.8 and 41.2 %, respectively. Moreover, degree of synteny across the four genomes was very high, especially between the genomes of Halorubrum litoreum JCM 13561T and Halorubrum arcis JCM 13916T. In addition, the PLP is quite similar among the four strains studied, showing a common pattern typical of the neutrophilic species of the genus Halorubrum. Overall, these data show that Hrr. distributum, Hrr. terrestre, Hrr. arcis and Hrr. litoreum constitute a single species. Thus, the latter three should be considered as later, heterotypic synonyms of Hrr. distributum based on the rules for priority of names. We propose an emended description of Hrr. distributum, including the features of Hrr. terrestre, Hrr. arcis and Hrr. litoreum.A comparative taxonomic study of  Halorubrum was proposed in 1995 by McGenity and Grant [Halobacterium. Two additional Halorubrum species, reclassified as members of the genus Halorubrobacterium [et al. [Halorubrum is classified within the family Halorubraceae, order Haloferacales, class Halobacteria [Halorubrum saccharovorum as the type species. As of June 2019, the genus Halorubrum, with 39 species with validly published names, includes the largest number of species among all the genera within the haloarchaea [Halorubrum includes rods or pleomorphic cells, Gram-variable, most species are motile and their colonies are red- to orange-pigmented. They are oxidase- and catalase-positive, extremely halophilic archaea, neutrophilic and in some cases alkaliphilic (optimum pH 9–10), such as Halorubrum alkaliphilum, Halorubrum gandharaense, Halorubrum luteum, Halorubrum tibetense and Halorubrum vacuolatum. Their major polar lipids are phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester, phosphatidylglycerol sulfate and a sulfated mannosyl glucosyl diether. However, alkaliphilic species lack phosphatidylglycerol sulfate and glycolipids. Their genomic DNA G+C content range is 60.2–71.2 mol% [rpoB′ gene or a multilocus sequence analysis (MLSA) based on the comparison of several single-copy housekeeping genes have been used [atpB, EF-2, glnA, ppsA and rpoB′ for such phylogenetic analyses as an alternative to the 16S rRNA gene [Halorubrum were not separate species, such as in the case of Halorubrum ezzemoulense and Halorubrum chaoviator [Halorubrum.The genus cterium , were lacterium . This ge [et al. . Currentacteria , with Hoarchaea . These soarchaea . The gen1.2 mol% . In the 1.2 mol% . Alternaeen used . RecentlRNA gene . In thisoviator  and otheHalorubrum, Hrr. distributum and Hrr. terrestre, isolated from saline soils [Hrr. arcis, isolated from a saline lake [Hrr. litoreum, isolated from a saltern [Hrr. distributum being the species name with priority according to the International Code of Nomenclature of Prokaryotes [Halorubrum.Here, we have studied in detail four species of ne soils , Hrr. arine lake  and Hrr. saltern , for whikaryotes , and thuHalobacteria possess highly divergent 16S rRNA genes, we compared the reference 16S rRNA gene sequence  of each of the analysed Halorubrum species with the 16S rRNA gene sequences extracted from the corresponding genome . All the 16S rRNA gene sequences obtained from the genomes showed a similarity between 98.4–100 % with the 16S rRNA gene reference sequence. A more detailed analysis of the pairwise alignments unveiled that lower similarities (i.e. 98.4 %) were due to genome misassembly and, therefore, only microheterogeneities among 16S rRNA gene copies but not highly divergent copies were detected in the species of the genus Halorubrum. The nucleotide sequence of the 16S rRNA gene from the strains was aligned with arb 6.0.5 software package [Halorubrum distributum JCM 9100T, Halorubrum terrestre VKM B-1739T, Halorubrum arcis AJ201T and Halorubrum litoreum Fa-1T with the known sequences of the Halorubrum species showed in Table S1, using the EzBioCloud tool  [arb program package version 6.0.5 [Hrr. distributum JCM 9100T, Hrr. terrestre VKM B-1739T, Hrr. arcis AJ201T and Hrr. litoreum Fa-1T were phylogenetically related but they did not constitute a single cluster  has been recently recommended for this genus [Halorubrum and on the other hand, in this case Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T constituted a well-supported cluster, separated from other species of the genus Halorubrum. The percentage of similarity of the five concatenated genes of Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T ranged between 98.3–99.3 % and those of these four species with respect to the other type strains of Halorubrum were always below 95.6 % (Table S3), below the 96 % cut-off value established for species delineation within the genus Halorubrum [The 16S rRNA gene and MLSA phylogenetic analyses were carried out as described previously . Since s package . The seqeztaxon; ) and arbhaeota) . Howeverhaeota)  and phylon 6.0.5 . Maximumon 6.0.5  using thon 6.0.5 . Base fron 6.0.5 . The phy cluster . The topis genus . Fig. 2 orubrum .Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T and the type strains of other related Halorubrum species (Tables S1 and S4). In brief, the translated gene sequences of all the predicted coding sequences of the genomes under study were compared by blast search in an all-versus-all mode to identify clusters of orthologous genes (OGs), that is, genes with a common ancestor present in two or more species. Those OGs shared among all taxa constituted the core genome of the studied genomes, formed by 1415 genes. From those, only the ones present in single copy per genome  were selected to reconstruct the phylogenomic tree. Enveomics collection toolbox [muscle version 3.8.31 [Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T formed a maximally supported subtree, clearly separated from the rest of species of Halorubrum  using the Genome-to-Genome Distance Calculator (GGDC) version 2.0 as described by Meier-Kolthoff et al. [et al. [et al. [Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T ranged from 84.0 to 78.0 %, but the values among these strains and the type strains of species of Halorubrum was equal or lower than 41.2 %  [m) [m) [m) [in silico and conventional G+C values probably due to the inaccuracies of the applied conventional method and, accordingly, emendation of the species descriptions seems necessary. Although in silico G+C values among the strains belonging to the same species should not vary more than 1 % [Hrr. distributum JCM 9100T, the type strain of the species name with priority.DNA G+C content was calculated from the genomic sequences (Table S1) and compared to the DNA G+C content provided in the species descriptions. For 3.9 (Tm) , 64.4 (T(Tm) [m) , 65.7 (T [m) [m)  and 64.9 [m) [m) . Therefothan 1 % , in thiset al. [Halobacterium salinarum DSM 3754T, Halorubrum saccharovorum DSM 1137T and Halorubrum tibetense JCM 11889T, as representative of the alkaliphilic species, were used as reference in this study.For the polar lipid profile (PLP) analyses, the cell biomass of the strains was obtained after 10 days of aerobic incubation in modified SW20 liquid medium under optimal conditions: 20 % (w/v) NaCl, 37 °C and pH 7.5. The extraction of membrane polar lipids of halophilic archaea and their profile detection were performed following the methods described by Corral et al. . The lipet al. . The lipHrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T possess a similar pattern of polar lipid profile, showing in common the major lipids: phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester, phosphatidylglycerol sulfate and the main polar lipid component of this genus: sulfated mannosyl glucosyl diether. Biphosphatidylglycerol was also found and minor phospholipids were also detected. The polar lipid profile of all these strains possessed all major lipids described for neutrophilic species of the genus Halorubrum [The thin-layer chromatography results of the polar lipids  revealed that orubrum .Hrr. distributum JCM 9100T, Hrr. terrestre JCM 10247T, Hrr. arcis JCM 13916T and Hrr. litoreum JCM 13561T constitute a single species. In accordance to the International Code of Nomenclature of Prokaryotes [Hrr. distributum has priority and thus, Hrr. terrestre, Hrr. arcis and Hrr. litoreum should be considered as later heterotypic synonyms of Hrr. distributum. Based on these corroborations we propose the emended description of the species Hrr. distributum, including the features of Hrr. terrestre, Hrr. arcis and Hrr. litoreum.In summary, the genome-based study shows that karyotes , the namHalorubrum distributum .The description is that of Zvyagintseva and Tarasov  and OrenThe G+C content of the genomic DNA is 67.3–68.9 mol% (genome).T (=VKM B-1733T=ATCC 51197T=CGMCC 1.3491T=CIP 105238T=JCM 9100T=NCIMB 13203T), was isolated from a saline soil sample. The DNA G+C content of this strain is 68.1 mol% (genome).The type strain, 1mHrr. terrestre 4pT (=VKM B-1739T=CIP 108318T=DSM 23453T=JCM 10247T), Hrr. arcis AJ201T (=CGMCC 1.5343T=DSM 23454T=JCM 13916T) and Hrr. litoreum Fa-1T (=CGMCC 1.5336T=DSM 23497T=JCM 13561T) are additional strains of Halorubrum distributum.Click here for additional data file.


Osteoarthritis (OA) is the most common arthritic disease in humans, affecting the majority of individuals over 65 years of age. The aim of this study is to identify the gene expression profile specific to subchondral bone in OA by comparing the different expression profiles in experimental and sham-operation groups.Gene expression profile GSE30322 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by limma package. And Database for Annotation, Visualization and Integrated Discovery (DAVID) databases were further used to identify the potential gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, a protein–protein interaction (PPI) network was constructed and significant modules were extracted.Totally, 588 DEGs were identified including 199 upregulated DEGs and 389 downregulated DEGs screened in OA and sham-operation. GO showed that DEGs were significantly enhanced for ribosomal subunit export from nucleus and molting cycle. KEGG pathway analysis revealed that target genes were enriched in thiamine metabolism.These key candidate DEGs that affect the progression of OA, and these genes might serve as potential therapeutic targets for OA. Osteoarthritis (OA) is a degenerative disease characterized by the gradual degeneration of articular cartilage, joint stiffness, and loss of function . It was Subchondral bone consists tripartite: subchondral bone plate, trabecular bone, and bone marrow space . It has Microarray technology has been used to obtain information on the genetic alteration that occurs during many diseases , 7. Herehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30322) was downloaded from the Gene Expression Omnibus . GSE30322 was based on Agilent-014879 Whole Rat Genome Microarray 4x44K G4131F (Probe Name version) platform. GSE30322 dataset contained ten samples, including five bone 4 weeks post-surgery samples (E-group), and five sham-operated group (S-group) 4 weeks post-surgery samples.The gene expression profile GSE30322 (P < .05 and [log fold change (FC)] > 2 were defined as DEGs. The DEGs data were then processed by R software (pheatmap package) to draw a heatmap and volcano plot.The raw data files were downloaded and then python scripts for matrix transformation were used. The analysis was carried out using Limma package from Bioconductor project. In this study, genes with https://david.ncifcrf.gov/) to analyze candidate DEG functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) of the overlapping genes. DEG functions, also named as Gene ontology (GO), mainly including biological process (BP), molecular function (MF), and cellular component (CC). P value less than 0.05 was considered as cut-off criterion [Target genes list were submitted to the DAVID 6.8 (riterion .https://string-db.org/) to better illustrate the potential interactive relationships among the DEGs [http://www.cytoscape.org/). Finally, the plug-in Molecular Complex Detection (MCODE) was used to filter the significant modules from the PPI network for the selection of hub genes  [We used the online database STRING  .After analyzing, differentially expression gene profiles were obtained. Totally, 588 DEGs were identified including 199 upregulated DEGs and 389 downregulated DEGs screened in OA and sham-operation. Top 10 up-DEGs and down-DEGs were listed in Table Gene Ontology (GO) showed that up-DEGs were significantly enhanced for ribosomal subunit export from nucleus, ribosome localization, regulation of hemopoiesis, negative regulation of hemopoiesis, and rRNA-containing ribonucleoprotein complex export from nucleus. Downregulated DEGs were enriched for the molting cycle, hair cycle, molting cycle process, hair cycle process, and the skin epidermis development Table .Table 3The result of KEGG pathway analysis revealed that target genes were enriched in thiamine metabolism, regulation of lipolysis in adipocytes, central carbon metabolism in cancer, estrogen signaling pathway, collecting duct acid secretion, Rap1 signaling pathway, measles, sphingolipid metabolism, drug metabolism-other enzymes, and circadian rhythm. These key pathways were showed in Table Using data from the Cytoscape and STRING databases, the 10 hub nodes with the greatest degree of network connection were determined. The top 10 hub genes identified were RASL2-9, PSMD6, CPOX, FTH1, PYGL, GNAI1, PTPN1, RIC8A, RAB7A, LOC680441, USP4, and HIST2H2BE Fig. . We listSubchondral bone remodeling is regulated by the bone resorption and bone formation, which mainly regulated by osteoclast and osteoblast respectively. Despite advancements in the understanding of the mechanism of OA, an effective method for accelerating the process remains to be identified. In this analysis, we found that 588 DEGs between E-group and S-group.Bone remodeling is regulated by the balanced processes of osteoclast-mediated bone resorption and osteoblast-mediated bone formation , 14. DisWe further performed Kegg pathways to identify the potential pathways that involving in the progress of OA. We found that Rap1 signaling pathway was the most obvious different signaling pathways. For Rap1 signaling pathway, we found nine DEGs (GNAI3/RAPGEF5/CSF1/PIK3CD/MK1/LPAR2/P2RY1/GNAS/PRKD1/RASSF5). Another potential pathway was estrogen signaling pathway. Ren et al.  used genWe also found that Rap1 signaling pathway participated into the pathological process of OA. Zhang et al.  performeIn summary, totally, 588 DEGs were identified including 199 upregulated DEGs and 389 downregulated DEGs screened in OA and sham-operation. We identified 12 core genes, including RASL2-9, PSMD6, CPOX, FTH1, PYGL, GNAI1, PTPN1, RIC8A, RAB7A, LOC680441, USP4, and HIST2H2BE. Additional experimental studies will be needed to validate our findings.


The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SwissTargetPrediction, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-target network, and herb-target-pathway network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification. Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-sitosterol, kaempferol, stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets.  A total of 235 putative targets were obtained from 59 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved in TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that  Our study demonstrated that BHD exerted the effect of treating IS by regulating multitargets and multichannels with multicomponents through the method of network pharmacology and molecular docking. The incidence of ischemic stroke (IS) is particularly high, and survivors have more or less neurological function deficits, which brings about a large burden on society and patients' families. With high mortality and morbidity rate, stroke has been the third most common cause of death following by coronary heart disease and cancer in the world . IS accoRadix Astragali), Honghua (Carthami flos), Taoren (Persicae Semen), Chishao (Radix Paeoniae Rubra), Danggui (Angelicae sinensis Radix), Chuangxiong (Chuanxiong Rhizoma), and Dilong (Pheretima). Among them, Radix Astragali is the most widely used in the original prescription, which is the monarch drug in BHD. A systematic review and meta-analysis of nineteen RCTs with 1580 individuals showed that BHD could significantly improve the neurological deficit score and the ability of self-care of patients with IS [Radix Astragali has the function of dilating blood vessels and improving microcirculation, which can significantly increase the brain's ability to withstand periods of severe hypoxia and/or ischemia [Carthami flos, Persicae Semen, Radix Paeoniae Rubra, Angelicae sinensis Radix, and Chuanxiong Rhizoma can effectively improve the microcirculation of the body, significantly inhibit the proliferation of fibrous tissue, and reduce the inflammatory response [Buyang Huanwu decoction (BHD) is a classical prescription for the treatment of IS. This prescription is mainly composed of Huangqi  [http://bionet.ncpsb.org/batman-tcm/index.php/Home/Index/index) [http://www.tcmip.cn/ETCM/index.php/Home/index/index.html), and SymMap (https://www.symmap.org/) [Traditional Chinese Medicine Systems Pharmacology Database  , BATMAN-x/index) , ETCM (hap.org/)  were useap.org/) . Throughhttp://stitch.embl.de/) [http://www.Swisstargetprediction.ch/) [http://sea.bkslab.org/) [TCMSP, BATMAN-TCM, ETCM, and SymMap databases were used to screen the targets of all the components of BHD. If related targets of components in BHD could not be found from the above databases, further target prediction was carried out in the STITCH (mbl.de/) , SwissTaion.ch/) , and Simab.org/)  databasehttp://www.disgenet.org/) [https://omim.org/) [http://bidd.nus.edu.sg/group/cjttd/), DrugBank (https://www.drugbank.ca/), PharmGKB (https://www.pharmgkb.org/), and MalaCards  [Targets related to IS were derived from six public databases, including DisGeNET (et.org/) , OMIM (him.org/) , TTD (htds.org/) , with kehttps://www.uniprot.org/) [Homo sapiens.” Finally, the intersection targets of BHD active components and IS were obtained and drawn using a Venn diagram.The targets were normalized to the official gene symbols using UniProt database (ot.org/)  with thehttps://string-db.org/) [String 11.0 (db.org/)  is a datHomo sapiens” and a confidence score >0.7. The protein interaction data were imported into Cytoscape 3.6.0 (https://cytoscape.org/) to construct the PPI network.The selected intersection targets were imported into String for protein interaction analysis, and the protein interaction network was obtained with the species limited to “https://david.ncifcrf.gov/) [P value and false discovery rate (FDR) < 0.05 were used to indicate statistical significance. GO annotation and KEGG PATHWAY analysis were carried out for the intersection genes. Finally, we could get the pathway maps from KEGG PATHWAY Database (https://www.kegg.jp/) [DAVID (rf.gov/)  databaseegg.jp/) .Network construction was performed as follows: (1) BHD-IS PPI network; (2) compound-target network; (3) herb-target-pathway network.All networks can be constructed via utilizing the network visualization software Cytoscape, which displays network graphically. Cytoscape makes it possible for data integration, analysis, and visualization of complicated networks. In the network diagram, “node” represents the active component and target in BHD, and “edge” represents the relationship between the active component and target. The “degree” parameter, presenting the number of connections between the nodes in the network, was used to evaluate important targets .The closely related regions in protein-protein interaction networks are defined as topological modules or clusters. These clusters or functional modules can put nodes of similar or related function together in the same network. By the help of MCODE, a plug-in of Cytoscape, we can get clusters.http://www.rcsb.org/) [https://pubchem.ncbi.nlm.nih.gov/) [To validate the compound-target associations, the AutoDock software (version 4.2) was used to perform the molecular docking program . RCSB PDsb.org/)  was usedih.gov/) . Finallyih.gov/) . The binih.gov/) .Radix Astragali, 189 in Chuanxiong Rhizoma, 119 in Radix Paeoniae Rubra, 125 in Angelicae sinensis Radix, 66 in Persicae Semen, 189 in Carthami flos, and 4 in Pheretima. After ADME screening, 78 potential compounds  of seven herbal medicines in BHD were identified, including 16 from Radix Astragali, 6 from Chuanxiong Rhizoma, 20 from Radix Paeoniae Rubra, 2 from Angelicae sinensis Radix, 13 from Persicae Semen, 21 from Carthami flos, and 0 from Pheretima. The details of candidate compounds are described in Radix Astragali, Chuanxiong Rhizoma, Radix Paeoniae Rubra, Angelicae sinensis Radix, Persicae Semen, and Carthami flos are simplified as RA, CR, RPR, ASR, PS, and CF, respectively.775 compounds were ultimately reserved: 87 in As 19 compounds of BHD had no targets in TCMSP, BATMAN-TCM, ETCM, and SymMap, Canonical SMILES of these compounds were found in PubChem. Based on chemical structural similarity, we used databases like STITCH, SwissTargetPrediction, and SEA, to predict their targets. These compounds were excluded because of the targets score less than 50% eventually. In brief, 235 targets were adopted in this research.By means of the six available resources, namely, DisGeNET, OMIM, TTD, DrugBank, PharmGKB and MalaCards databases, we obtained 460 IS-related targets.Based on targets of the candidate ingredients and IS, intersection targets were got by R software. 62 intersection genes were found eventually, shown in GO analysis of 62 candidate targets for BHD against IS was performed using the DAVID database to understand the relationship between functional units and their underlying significance in the biological system networks. The results were divided into three parts including biological processes, cellular component, and molecular function, as shown in Figures P value as shown in Through comprehensive analysis, we obtained an integrated IS pathway based on our current knowledge of IS pathogenesis to illuminate the integral role of BHD in treating IS. TOP 10 KEGG signaling pathways of BHD were obtained and constructed based on 62 intersection targets were imported into the String database, and TSV text showing the interaction relationship was obtained, as shown in The active components and their targets were constructed to establish the compound-target network using Cytoscape. This network was composed of 294 nodes (235 compound-target nodes and 59 compound nodes) and 1192 edges. In this network, we could find that one target could be hit by several compounds , but some were modulated by only one compound in this network. Furthermore, one potential active compound could correspond to multiple targets. Top twelve compounds with high degree were quercetin, beta-sitosterol, kaempferol, stigmasterol, baicalein, luteolin, hederagenin, 7-o-methylis omicron ulatol, formononetin, isorhamnetin, dimethoxy, and myricanone, shown in Radix Astragali and Carthami flos had the highest degree, which means that the two herbs might be the main herbs in treating IS. Meanwhile, TNF signaling pathway showed the highest degree, followed by PI3K-Akt signaling pathway, MAPK signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and T cell receptor signaling pathway, respectively. The herb-target-pathway network is shown in By importing 62 targets which overlapped with IS disease genes into DAVID, we could get 20 IS-related pathways. Compound-target interactions with binding energy less than −5.0 kcal/mol are shown in κB signaling pathway, and PI3K/AKT signaling pathway.The pathway map of BHD in treating IS was obtained from KEGG PATHWAY Database, as shown in In our study, we found the molecular mechanism of BHD's neuroprotection effect against IS using network pharmacology strategy. The network pharmacology strategy is helpful to clarify the mechanism of TCM's function from a systematic viewpoint , 41. FurRadix Astragali and Carthami flos. The results of network topology analysis showed that the degrees of Radix Astragali and Carthami flos were the highest in BHD. As the core herb in BHD, the dosage of Radix Astragali is the highest, indicating that the results of network pharmacology are consistent with the clinical application of Chinese medicine. Quercetin and kaempferol are common components of Radix Astragali and Carthami flos. It was found that quercetin could pass through the blood-brain barrier with the highest passage rate [α, IL-1 β, and IL6 by reducing the production of NF–κB in elderly mice [Carthami flos and Radix Paeoniae Rubra. As an important flavonoid compound, baicalein has many pharmacological effects, such as antioxidant stress, anti-inflammatory, antiexcitatory toxicity, antiapoptosis, stimulating neurogenesis, and promoting the expression of neuroprotective factors [Carthami flos, luteolin could downregulate the expression of TLR4, TLR5, NF-κ B, and P-P38MAPK, upregulate the expression of p-ERK, and protect cerebral ischemia in rats [Carthami flos, Persicae Semen, Radix Paeoniae Rubra, and Angelicae sinensis Radix. They are both sterol compounds, mainly having the functions of reducing blood fat, antioxidation, and anti-inflammation [Core ingredients with the highest degree in compound-target network were considered to be responsible for neuroprotection, including quercetin, beta-Sitosterol, kaempferol, stigmasterol, baicalein, luteolin, hederagenin, 7-O-methylisomucronulatol, formononetin, isorhamnetin, Dimethoxy, and myricanone. Six of these components belong to age rate . A reseaage rate , 44. In age rate . Lu et arly mice . Kaempferly mice , 48. Onerly mice . These f factors –52. Liu  factors . As an i in rats . Experim in rats . Beta-siammation .α, IL1B, and IL6) and anti-inflammatory cytokines (IL10 and TGF-β) [PPI analysis showed that IL6, TNF, VEGFA, AKT1, MMP-9, IL1B, MAPK1, ICAM1, PTGS2, and IL10 were the top ten targets with high degrees. Followed by cluster of the PPI network, the network could be divided into four modules, which were mainly related to angiogenesis, inflammation, coagulation, and blood-brain barrier. Inflammation plays a critical role in the pathological process of stroke . IL1B, Id TGF-β) . MMP-9 id TGF-β) . VEGF isd TGF-β) . AKT1 isd TGF-β) .κB and MAPK signaling pathways would downregulate the expression of proinflammatory factors. The MAPK signaling pathway could be a promising candidate for future applications in CNS injury treatment [β/Smads and MAPKs signaling activated fibrosis [Pathway enrichment analysis results showed that TNF signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and NF-kappa B signaling pathway were the main pathways. TNF signaling pathway is an important inflammatory pathway. As an important cytokine, TNF can induce apoptosis, cell survival, inflammation, immunity, and other intracellular signaling pathways. TNFR1 signal transduction can induce the activation of many genes, which are mainly controlled by NF-kappa B and MAPK cascade. In this present study, TNF, IL-1B, MYC, and TGFB1 are potential targets of BHD, suggesting that BHD plays a neuroprotective role against ischemia-reperfusion injury through TNF signaling pathway. PI3K/Akt signaling pathway is one of the important pathways of cerebral ischemia and neuronal apoptosis. A study found that activating PI3K/Akt signal pathway could inhibit the apoptosis of nerve and reduce the occurrence of blood-borne brain edema. A series of studies have shown that many Chinese herbal extracts play a protective role in IS through this pathway , 63. Anoreatment . BHD allfibrosis .However, our research also has some limitations. For example, the accuracy and integrity of existing databases need further verification. Higher quality databases of traditional Chinese medicine and more accurate background network databases are needed. Moreover, the results of network pharmacology need experimental support. The application of network pharmacology in the study of Chinese medicine is just in its start-up step. We need to promote the interdisciplinary researches integrating network science, bioinformatics, computer science, mathematics, and pharmacology in the future.In this study, we explored and discussed the characteristic of “multicomponents, multitargets, and multichannels” of BHD-mediated IS treatment through the method of network pharmacology and molecular docking. In the future, we should provide experimental evidence for the neuroprotective effect of BHD against IS according to the results of network pharmacology research.


Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets , and four key pathways  which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease. Inflammation is regarded as a kind of congenital immunity as well as the basis of various physiological and pathological processes, and it can affect human health and living quality in many respects –4. Five Traditional Chinese medicines (TCMs) have been used to treat various diseases including inflammation for a long time. For example, Reduning Injection , Bi-Qi cLonicera japonica Thunb. , Gardenia jasminoides Ellis. , and Artemisia annua L.  [Network pharmacology provides frameworks to understand how regulation arises from the interactions between cellular components, and it is considered the next paradigm in drug development . By usinQinghao) –31. In oQinghao) , 22, 27.http://pubchem.ncbi.nlm.nih.gov) [Nine ingredients with measurable content of RDNI have been identified in previous works –38. Fournih.gov) , a chemihttp://lsp.nwu.edu.cn/tcmsp.php) [http://www.bindingdb.org/bind/index.jsp) [http://www.drugbank.ca) [The targets of RDNI compounds and metabolites were collected by database searching and molecular docking. Four databases were used in this step, namely, PubChem, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  , Bindingdex.jsp) , 45, andbank.ca) . TCMSP ihttp://www.rcsb.org). Molecular docking was adopted to evaluate the binding affinity between each compound and target by Autodock 4.2.6 [6, 2.7 × 104, and 0.02, respectively. Other parameters were set to default. The docking results were sorted according to the binding energy, and the proteins with binding energy lower than -8.18 kcal/mol (the threshold for inhibition constant was 1 μM) were regarded as a target of the corresponding compound.The DrugBank database can provide detailed drug data and comprehensive drug target information. There are 126 FDA-approved anti-inflammatory drugs in DrugBank . Their tck 4.2.6 . The enehttp://www.uniprot.org) [On the basis of compound-target interaction obtained in the previous step, the compound-target network (CTN) was constructed and visualized by Cytoscape version 3.6.1 . Key tarrot.org) .http://www.kegg.jp) [The related pathways (Supplementary Table kegg.jp) . The tarA cross-talk network was constructed to visualize the cross-talks among key pathways and other three pathways, which were closely associated with both the four key pathways and inflammatory processes.The CAS entry, PubChem CID, molecular weight, and content in RDNI of the RDNI compounds are recorded in These compounds and their metabolites have complex interactions with cellular targets. First, caffeic acid (CAA) and its metabolite ferulic acid (FA) have wide influences on inflammatory processes. CAA has 36 targets, and FA has 32 targets, with 20 targets in common. Many targets are related to the regulation of inflammatory process such as arachidonate 5-lipoxygenase , heat shock protein HSP 90-alpha , and prostaglandin G/H synthase 2 . Second, isochlorogenic acid A (IsoA), isochlorogenic acid B (IsoB), and isochlorogenic acid C (IsoC) have 13, 11, and 9 targets, respectively. Most proteins have explicit relationship with inflammation because they are targets of FDA-approved anti-inflammatory drugs. Geniposide (Gen) and its metabolite genipin (Gep) have 12 and 9 targets, respectively, while only one target has affinity with both of them. Third, secoxyloganin (Sec), chlorogenic acid (CGA), cryptochlorogenic acid (4CQA), and neochlorogenic acid (5CQA) have 2, 4, 6, and 6 targets, respectively. They are also important to the therapeutic effect of RDNI as the high content.Eight targets, namely, thiopurine S-methyl transferase , carbonic anhydrase 2 , amyloid-beta A4 protein , progesterone receptor , membrane associated phospholipase A2 , 5′-AMP-activated protein kinase subunit beta-2 , aldose reductase , and PTGS2 would bind with more than 3 compounds of RDNI. Four proteins, namely, histamine H1 receptor , peroxisome proliferator-activated receptor gamma , prostaglandin G/H synthase 1 , and FAD-linked sulfhydryl oxidase ALR , can be targeted by 3 compounds of RDNI. These top twelve targets screened by degree centrality have high betweenness centralities . Seven of them have high closeness centralities . Other targets can interact with one or two compounds of RDNI. Many of these targets would play significant roles in regulating inflammatory processes.α, UniProt: P01375) [For example, PTGS2 is the target of CGA, 4CQA, CAA, FA, and Gep. It is the major enzyme responsible for the production of inflammatory prostaglandins and expresses in the inflammatory process only . First,  P01375) . Second, P01375) . Third,  P01375)  and airf P01375) . Fourth, P01375) , 58. Fin P01375) .β, UniProt: P01584) [Apoptosis regulator Bcl-2  is the target of Gen. After binding with Bcl-2-like protein 1 , Bcl-2 can regulate the expression of caspase 1  by inhibiting NLR family protein LRR and PYD domain-containing protein 1 . CASP1 participates in the dissociation of substrates involved in cell apoptosis and inflammation, and it is capable of catalyzing the maturation and secretion of interleukin-1 beta (IL-1 P01584) , 61. NAL P01584) , 63.FA and IsoA can bind with eNOS and then regulate the production of nitric oxide (NO), which plays an important role in inflammatory processes . The oveα and therefore participate in regulation of inflammatory processes [Peroxisome proliferator-activated receptor alpha  is a member of nuclear hormone receptor superfamily and a target of IsoB. It participates in the regulation of lipid metabolism, adipocyte differentiation, and inflammatory process by activating related transcription factors . Interstrocesses . TherefoEighty targets of RDNI are involved in 201 pathways according to KEGG . The compounds of RDNI can regulate the inflammatory process by regulating the estrogen signaling pathway in many ways.The estrogen signaling pathway  contains3. PIP3, as well as the complex of HSP90 and Hsp90 cochaperone Cdc37 (UniProt: Q16543), can activate AKT [κBα) and nuclear factor kappa-B (NF-κB), and then, NF-κB is released [The PI3K-AKT signaling pathway  is respovate AKT , 79. Actreleased . The phoγ by inhibiting EGFR in the calcium signaling pathway. Second, HRH1, a subtype of G-protein coupled receptor (GPCR), is the target of FA, IsoA, 4CQA, and IsoC and activates PLCβ. Third, cAMP is the activator of PLCε and also regulates PLCδ by regulating Ca2+ concentration. PLC produces D-myo-inositol 1,4,5-trisphosphate (IP3) and diacylglycerol (DAG); then, IP3 and DAG activate protein kinase C (PKC). PKC can regulate lipopolysaccharide-induced macrophage functions involved in inflammation [κB-induced gene expression through the IL-1α-dependent induction of IκBα [The calcium signaling pathway  maintainammation . It can  of IκBα .αARs, P08913, P18089, P18825, and P35348) and beta adrenergic receptors . αARs activate guanine nucleotide-binding protein subunit alpha-11  and guanine nucleotide-binding protein G(q) subunit alpha . GAN11 and GANQ can further mediate the generation of Ca2+ by activating IP3R. βARs would activate guanine nucleotide-binding protein G(i) subunit alpha (Gi). Gi can conduct the stimulus signal to eNOS and then mediate the generation of NO, while FA and IsoA can bind with eNOS directly. NO regulates the generation of cGMP by activating s-GC. cGMP can activate PKG which regulates multitudinous targets, including inflammation-related targets  and targets responsible for Ca2+ concentrate in the body, such as PLCβ, protein MRVI1 (UniProt: Q9Y6F6), and cardiac phospholamban .The cGMP-PKG signaling pathway  regulateκB signaling pathway, and arachidonic acid metabolism pathway. It is worth mentioning that these pathways are closely relevant to the former four pathways.The other two pathways are fluid shear stress and atherosclerosis pathway (hsa05418) and prostate cancer pathway (hsa05215). The fluid shear stress and atherosclerosis pathway regulates the progress of atherosclerosis and correlates with the activation of proinflammatory gene expression as well as early atherogenic inflammation , 89. RDNκB signaling pathway (hsa04064). The cross-talk network , MAPK signaling pathway (hsa04010), and NF- network  shows thas IL-1β , TNFα [9IL-1β [α , PTGS2, IL-1β [α , MMPs, aκB through the Ras pathway. In the calcium signaling pathway, the generation of Ca2+ is regulated by cAMP through multiple approaches. Ca2+ can regulate the expression of cAMP in the form of feedback by Ca2+-dependent adenylate cyclase (AC) and PKCθ. The generation of cGMP is regulated by NO whose concentrate is regulated by eNOS, a downstream enzyme of AKT. It can also interact with regulatory targets of Ca2+ just like cAMP. The expression product of NF-κB such as IL-1β, TNFα, and IκBα regulates both the expression of IKK and the dissociation of the IκBα-NF-κB complex. The generation of IL-6, PTGS2, and MMPs also has feedback regulation on upstream pathways. These feedback regulation and the mediation of the MAPK signaling pathway increase the complexity and robustness of the regulatory effects of RDNI on the inflammatory process.The MAPK signaling pathway connects the estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and cAMP signaling pathway to the expression of NF-In this work, the anti-inflammatory mechanism of RDNI was explored by network pharmacological methods. Eighty-four targets of RDNI were collected by data mining and molecular docking to construct a compound-target network. Key targets  were found to be responsible for regulating the inflammatory process by RDNI compounds and metabolites. Two hundred and one pathways were found to be connected with RDNI targets. Four key pathways, namely, estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway, would play important roles. The cross-talks among four key pathways and another three related pathways were further identified. Results demonstrate that RDNI, an injection formed by multiple ingredients, can interact with multifarious inflammation-related targets. The interactions make RDNI capable of regulating multiple biological processes and treat inflammation at the systems level. Moreover, TCM is a complicated drug system; thus, complex interactions between multicomponents and multitargets make it possible to regulate multipathways and biological processes. Although the conclusions obtained in this research require to be verified by further experiments, network pharmacology provides a promising approach to investigate the MOA of TCM.


Nowadays, acute intracerebral hemorrhage stroke (AICH) still causes higher mortality. Liangxue Tongyu Formula (LXTYF), originating from a traditional Chinese medicine (TCM) prescription, is widely used as auxiliary treatment for AICH.  To dig into the multicomponent, multitarget, and multipathway mechanism of LXTYF on treating AICH via network pharmacology and RNA-seq.  Network pharmacology analysis was used by ingredient collection, target exploration and prediction, network construction, and Gene Ontology (GO) and KEGG analysis, with the Cytoscape software and ClusterProfiler package in R. The RNA-seq data of the AICH-rats were analyzed for differential expression and functional enrichments. Herb-Compound-Target-Pathway (H-C-T-P) network was shown to clarify the mechanism of LXTYF for AICH.  76 active ingredients  of LXTYF and 376 putative targets to alleviate AICH  were successfully identified. The protein-protein interaction (PPI) network indicated the important role of STAT3. The functional enrichment of GO and KEGG pathway showed that LXTYF is most likely to influence MAPK and PI3K-Akt signaling pathways for AICH treatment. From the RNA-seq of AICH-rats, 583 differential mRNAs were identified and 14 of them were consistent with the putative targets of LXTYF for AICH treatment. The KEGG pathway enrichment also implied that the MAPK signaling pathway was the most correlated one among all the related signaling pathways. Many important targets with expression changes of LXTYF for AICH treatment and their related pathways are great markers of antioxidation, anti-inflammatory, antiapoptosis, and lowering blood pressure, which indicated that LXTYF may play mutiroles in the mechanisms for AICH treatment.  The LXTYF attenuates AICH partially by antioxidation, anti-inflammatory, and antiapoptosis and lowers blood pressure roles through regulating the targets involved MAPK, calcium, apoptosis, and TNF signaling pathway, which provide notable clues for further experimental validation. Nowadays, stroke is still a major threat to people's health. Statistics from the World Health Organization show that 15 million people suffer the disease worldwide each year , 3. ICH Rheum palmatum L. , Rehmannia glutinosa Libosch. , Panax notoginseng (Burk.) F.H.Chen , Paeonia lactiflora Pall. , Paeonia suffruticosa Andr , and Acori Tatarinowii Schott  and two animal medicines like Bubali Cornu , Pheretima aspergillum (E. Perrier) . This formula was adapted from Xijiao Dihuang Decoction, an ancient TCM classical prescriptions reserved in one of the classic works of ancient Chinese medicine called Essential Recipes for Emergent Use Worth A Thousand Gold, and only replaced Rhinoceri Asiatici Cornu (rhinoceros horn) with SNJ for the similar medicinal effects and compliance with animal protection and regulations [In the last two thousand years, traditional Chinese medicines (TCMs) have been applied in treating various kinds of diseases in China, including stroke , 8. Compulations . Accordiulations , 12. Forulations . It atteulations . Li's prulations .In our research, network pharmacology method is applied to predict active components, targets, and pathways of LXTYF to alleviate AICH. TCMs provide multiple components, targets, and pathways, also with similar characteristics with systems biology and network pharmacology. This indicates that we can use the approach of the network pharmacology to deeply investigate the mechanisms of TCMs to alleviate AICH . FurtherThis study is aimed at digging into the system pharmacological mechanism of Liangxue Tongyu Formula (LXTYF) on treating intracerebral hemorrhage via network pharmacology method and RNA sequencing data and providing notable clues for further experimental validation.http://lsp. http://nwu.edu.cn/tcmsp.php) [https://bionet.ncpsb.org/batman-tcm) [The chemical ingredients of six botanical medicinal materials in LXTYF were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (msp.php) . TCMSP iman-tcm) . BATMAN-The active ingredients from herbs of LXTYF were filtered by OB and DL. OB represents the relative amount of a drug absorbed into the blood circulation, and DL is used to describe pharmaceutical properties of compounds , 19. Bashttp://www.uniprot.org/), which is specialized for protein information [TCMSP and BATMAN-TCM databases are integrated to collect known targets of active ingredients in LXTYF. The target names were obtained from the TCMSP and then converted into Uniprot ID in the “Retrieve/ID Mapping” tool of Uniprot  database , a public genetic association database serving for human genetic diseases based on integrated datas of more than 5000 published studies [https://www.genecards.org/), an automatically mined database of human genes and a very wide range of resources for gene-centric information in the human genome [http://www.disgenet.org/web/DisGeNET), which integrates data from animal models, GWAS catalogues, expertly curated repositories, and the scientific literature, constructing a comprehensive platform studying human disease-associated genes and variants [Different genes linked to ICH were collected from three existing resources with Medical Subject Heading (MeSH) “Cerebral Hemorrhage”. (1) GAD ( studies . (2) Genn genome , 30. (3)variants .Putative targets of LXTYF for the treatment of AICH were obtained based on the interaction data between active ingredient-associated targets and ICH-associated targets, and Cytoscape software (Version 3.2.1) was utilized to visualize the network for AICH-associated-active ingredients and targets. Degree and betweenness centrality are important topological parameters of networks and were calculated in Cytoscape to measure the topological importance of target and compound nodes –34. The https://string-db.org/) is a database with integrated data from several active interaction sources such as text mining, experiments, databases, and coexpression. It uploads gene datasets as input and visualizes them in a network for the next analysis [Proteins are rare to achieve specific functions alone and tend to form macromolecular complexes by interaction in a single cell to complete biological functions . So, it analysis . In thisgroupGO method to classify genes [enrichKEGG function is provided to proceed with enrichment test for KEGG pathways. p.adjust values are also estimated to prevent a high false discovery rate (FDR). Pathways that the p.adjust ≤ 0.05 were reserved to analyze.The ClusterProfiler package installed in R offers the fy genes . There aEight crude herbs of LXTYF were purchased from Tongrentang Chinese Medicine Company (Since 1669). Spontaneous hypertension rats (SHRs) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd., and animal license number was SCXK (Beijing) 2016-0006. Collagenase type VII was purchased from Sigma-Aldrich . Brain stereotaxic instruments and electric drills were bought from Yuyan Instruments Co., Ltd. . TJ-1A microinjection pump was purchased from Lange Constant Flow Pump Co., Ltd. . Microneedle was purchased from Gaoge Industry & Trade Co., Ltd. . Chloral hydrate and penicillin were bought from Sinopharm Chemical Reagent Co., Ltd. . TRIzol® Reagent and TBS380 Picogreen were purchased from Invitrogen . Magnetic frame Ribo-Zero Magnetic kit was bought from EpiCentre (USA). TruSeqTM Stranded Total RNA Library Prep Kit, UNG enzyme, HiSeq 4000 PE Cluster Kit, and HiSeq 4000 SBS Kit (300cycles) were purchased from Illumina . Certified Low Range Ultra Agarose was purchased from Bio-Rad .Cornu Bubali (SNJ), Rheum palmatum L. (DaH), Rehmannia glutinosa Libosch. (DiH), Panax notoginseng (Burk.) F.H.Chen (SQ), Radix paeoniae rubra (CS), Moutan Cortex (MDP), Pheretima aspergillum (DL), and Acori Tatarinowii Rhizoma (SCP). The crude herbs mixed in the weight ratio of 30 : 10 : 20 : 5 : 15 : 10 : 10 : 10 and extracted by means of reflux in ethanol and water for 3 hours. LXTYF decoction was prepared with 8 : 1 water/oil.LXTYF consisted of μl and brain stereotaxic instrument. According to our previous study, the middle dose of 11.55 g/kg of LXTYF has the best efficiency on survival time and rate for rats with AICH. So, all the dosage of the following administration was 11.55 g/kg. After 24 hours of modeling, rats were sacrificed by decapitation, and brain tissue of the rat was quickly obtained from where the microinjector was inserted. The specimens of brain tissues were frozen in liquid nitrogen and stored in -80°C refrigerator for RNA-seq.All procedures concerning animals in this study were approved by the ethics committee of Nanjing University of Chinese Medicine. Spontaneous hypertension rats (SHRs) were randomly assigned into cages and adapted for experiment environment in the SPF-room for one week, with food and water regularly and temperature at 20-25°C. 20 male SHR rats (230-260 g) were divided into four groups randomly for modeling and experiments: control group (CG), control group with LXTYF treatment (CGT), ICH-model group (MG), and AICH model with LXTYF-treatment group (MGT), with 5 SHR rats in each group. Rats in CG and MG were given physiological saline, while CGT and MGT rats received intragastric administration of LXTYF decoction for 5 days. 12 h-fasting-rats in MG and MGT were made AICH models by using 0.12 U collagenase of 2 μg of rat total RNA was started, and the concentration was ≥250 ng/ml. OD260/280 was between 1.8 and 2.2. After removing the rRNA, indexed libraries were prepared from purified RNA with TruSeqTM Stranded Total RNA Library Prep Kit (Illumina) according to the manufacturer's instructions. Randomly interrupted with metal ions, 2000 bp mRNA fragments were obtained. The first strand cDNA synthesis was performed according to the mRNA fragments as described in the Illumina kit. The second strand cDNA synthesis was modified by exchanging dTTP with dUTP. After adding end repair mix to double-stranded cDNA, blunt ends were performed, and A bases were added to attach the Y-shaped adapters. The UNG enzyme digested the second-strand cDNA, and the library only contained the first-strand cDNA. Bridge PCR amplification was performed on cBot, and 2∗150 bp sequencing was performed on Illumina Hiseq. Further details can be found in the references [The specimens of brain tissues were obtained from the rats (4 groups with 3 replicates). Total RNA was extracted from brain tissue specimens and tested for its concentration and purity. 5 ferences , 38.p value of ≤0.05 via edgeR  software, differentially expressed mRNAs were identified especially the MGT and MG. Rat genes were converted into human genes based on homology mapping through the Biomart package in R. Compared with the putative targets that LXTYF treats for AICH, PPI network of verified genes and other genes was constructed in STRING and Cytoscape software.With the thresholds of ≥2-fold and the http://kobas.cbi.pku.edu.cn/anno_iden.php). In the KEGG database, Fisher's exact test was used as a statistical method, and Benjamini and Hochberg was used as an FDR correction method. Enriched pathways that p value≦0.05 were identified compared with former pathway enrichment prediction.KEGG orthology-based annotation system (KOBAS) is a web server for annotation and identification of enriched pathways and diseases . In our Compared the pathways enriched by the network pharmacology method with those enriched by RNA-seq data, the common and most likely pathways are obtained. Differentially expressed genes related to common pathways are achieved from the RNA-seq data. The verified gene-related compounds and herbs are found from the collected network pharmacology datas, connections among them were shown in the Herb-Compound-Target-Pathway(H-C-T-P) network and visualized in Cytoscape software.A total of 517 compounds of 8 herbs in LXTYF were collected from TCMSP and BATMAN-TCM database after removing duplicate items, including 119 compounds from CS, 92 compounds from DaH, 76 compounds from DiH, 119 compounds from SQ, 55 compounds from MDP, 105 compounds from SCP, 6 compounds from SNJ, and 8 compounds from DL  Table.Considering the drug formation of the compound and metabolism after being absorbed into the human body, 81 active ingredients of LXTYF filtered by OB and DL were screened out from the 517 compounds with ADME parameters as shown in On the basis of the 81 active ingredients of LXTYF above, 965 targets were collected from two databases with no duplicate values. Among them, 874 targets of the active ingredients were obtained from the TCMSP  Table, aTopological analysis is an effective approach to evaluate the above-mentioned component-target network based on the degree and betweenness centrality parameters  whose degrees and betweeness centralities are both larger than the average ones were identified as key targets and chemical ingredients. The average degree of the nodes is 3.938, and 122 nodes are larger than this number. The average betweenness centrality of nodes is 0.006549 and 85 nodes are larger than it. In general, 74 key nodes (35 key components from 76 components and 39 key targets from 376 targets) were obtained, which was shown in For analysis of the proteins-proteins interaction network of LXTYF for AICH, STRING 11.0 was used to analyze the targets of LXTYF , the analysis of differently expressed genes among four groups from the RNA-seq data was shown in With a fold-change cutoff of 2  verified in RNA-seq were shown in The results visually clarify the mechanisms of the multicomponent, multitarget, and multipathway of LXTYF on the treatment of AICH. Further studies can be made and deeply investigate mechanisms upon the connections among the H-C-T-P network of LXTYF to treat AICH in our study.TCM has been developed and applied in China for more than 2000 years to prevent and treat diseases. To explore active ingredients, predict the targets, and reveal compounds action and drug-gene-disease associations in depth, TCM network pharmacology approach was constructed through “network target, multicomponents” mode . The metNetwork pharmacology and transcriptomics data were applied in this study to understand the underlying different mechanisms and actions of LXTYF for AICH treatment. We successfully identified 76 active ingredients  of LXTYF and 376 putative targets to alleviate AICH . PPI network indicated the important role of STAT3 in the proteins-proteins interaction. Gene Ontology and KEGG pathway enrichment analysis indicated that LXTYF is most likely to influence the MAPK signaling pathway and PI3K-Akt signaling pathway. From the RNA-seq data of AICH-rats, 583 differentially expressed mRNAs were identified, and 14 of them were consistent with the putative targets of LXTYF treatment of AICH. KEGG pathway enrichment results showed that the MAPK signaling pathway was the most correlated one among the pathways.Ingredient-target-pathway analysis on herb prescriptions is helpful to uncover the complicated mechanisms underlying diseases and the therapy. Network pharmacology is an effective method to deeply investigate the mechanisms of TCMs to treat AICH through muti-ingredients and mutitargets. In this study, 76 active ingredients and 376 targets of LXTYF for treating AICH were obtained at the help of 2315 ICH targets, the deeper results than the 34 active ingredients, 146 targets, and the 1436 ICH targets in our former study [κB and STAT3 in stroke rats [The active ingredients involved in eight herbs were screened by OB and DL values, which represent the vast majority of the ingredients absorbed into the blood circulation and pharmaceutical properties of compounds in the prescription, and form a relatively complete chemical component library. To some extent, they can reflect the pharmacodynamic ingredients of LXTYF that may play a role in the clinical medication. Results of the active component-target network of LXTYF to treat AICH showed that most components affected multiple targets. Quercetin, alanine, and kaempferol from MDP/SQ, SNJ, and MDP/SCP acted on 112, 104, and 39 targets, respectively. They are of high OB and DL values and regulated most targets for AICH treatment, also with anti-inflammatory, antioxidant, and antiapoptotic properties. The findings were consistent with previous studies as follows. Alanine was reported to have antioxidant and procoagulant efficacy . D-alanioke rats . So, queRhinoceri Asiatici Cornu (a highly endangered species) since the 1970s because it is abundant, has low price and with similar inorganic, amino acid constituents, and comparable pharmacological properties [Rhinoceri Asiatici Cornu in TCM. The research and discovery of the active ingredients in this study will contribute to find alternative medicines of endangered animals to better protect animals and treat diseases.Numerous constituents make up the complexed TCM system, and combinations of them play a role in the treatment of diseases. From the H-C-T-P network, the compounds of animal herbs like xanthine and guanidine in DL and guanidine, arginine and alanine in SNJ play vital role in the putative mechanisms of LXTYF to treat AICH. SNJ has been a good substitute for operties . Thus, iVia evaluating topological paramters, among all the putative targets of LXTYF associated with AICH, PTGS2, Bax, TNF, JUN, IL1B, and NOS2 are examples of relatively important targets which are involved in MAPK, PI3K-Akt, TNF, and apoptosis signaling pathways, while RNA-seq analysis showed that the LXTYF administration significantly regulated 583 gene transcriptions in AICH rat models. Combined with putative targets in network pharmacology, a total of 14 genes were verified. PTEN, CTH, PTGS2, PTAFR, FOS, and NOS1 are six upregulated genes, and NOS2, PGR, MMP3, PON1, NEDD4, ACADSB, KL, and CASP3 are eight downregulated genes. Especially CTH, PTAFR, PGR, PON1, NEDD4, ACADSB, and KL are 7 new genes compared with our previous study . And PPIPTGS2 and PTGS1  are genes that code for the COX-2 and COX-1 proteins, respectively . COX enzPTEN conditional knockouts have become a potentially approach targeting cancer and obesity treatment [Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a tumor suppressor, a proapoptotic gene mainly via inactivation of PI3K/AKT signaling pathway, and reatment . But in reatment . The prereatment , 54. Whireatment . So, uprNitric oxide synthases (NOS), with 3 isoforms called neuronal NOS (nNOS/NOS1), inducible NOS (iNOS/NOS2), and endothelial NOS (eNOS/NOS3), form NO and play dual roles of damage and protection in ICH . NO formFOS is one of the components of AP1, the inducer of cell proliferation. It can activate nerve growth factor and has gene repair activities after neuronal death in the stroke model. Knockdown of c-fos enhances necrosis significantly . But serCaspase 3, a member of caspases family involved in cell death, has been recognized as a crucial mediator of neuronal apoptosis . Stroke β), tumor necrosis factor-alpha (TNF-α)) and transforming growth factor-beta (TGF-β) activate p38 MAPK, and its specific inhibitors were used as the treatment of peripheral inflammatory diseases [Multicomponent, multitarget, and multipathway are general features of TCMs, and LXTYF has therapeutic effects by making contributions to several pathways. In this present work, putative targets were enriched significantly in 72 KEGG pathways, and the two most important enriched pathways were the MAPK signaling pathway and PI3K-Akt signaling pathway. Our previous study has verified that LXTYF inhibited the apoptosis to treat AICH based on the PI3K-AKT pathway on the level of transcription and protein . Meanwhidiseases . It is kdiseases , 68, whidiseases . MEF2C, diseases , 71. Prodiseases , 72. Mosdiseases . FOS anddiseases , 73. Dowdiseases , 73, 74.2+ signals to regulate physiological processes [Moreover, the other pathways such as the calcium signaling pathway, apoptosis, TNF signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, and HIF-1 signaling pathway are the common signaling pathways of differentially expressed genes and putative targets in LXTYF. The calcium signaling pathway generates Carocesses . Researcrocesses . Blood crocesses . Activatrocesses . Bcl-2 frocesses . DIABLO rocesses . So, wherocesses . Tumor nrocesses . Observarocesses . Thereforocesses . BAG4/SOCompared to the previous two papers published in Frontiers in Pharmacology and Journal of Ethnopharmacology, all studies used reasonable models and reliable validation data to detect the network effect of LXTYF to treat AICH and verified some components, targets, and pathways , 78. ButThere are still some limitations to our study. The insufficient research on medicine composition of this prescription, especially the animal herbs, cannot completely dig into all the effective components. Further progress of chemical component separation technology is in great need to fully clarify all chemical components in the prescription. Also, results of PPI network prediction showed that STAT3 may play an important role of the bridge to connect other proteins among the pharmacological actions, but it did not verify in RNA-seq and needs to investigate in the next future. More subsequent experiments on differently expressed mRNA and enriched pathways are necessary to be investigated deeply. Moreover, it is common to see that some genes have dual effects and play opposite roles in different physical conditions, especially some are unsure and need further study. Critical insight for targets and pathways are necessary for mutual promotion between pharmacological research and physiological/pathology research.Taken together, network pharmacology method and enrichment analysis in R were applied in our study to identify the 76 active components and 376 putative targets of LXTYF alleviating AICH. Compared with RNA-seq results of AICH rat models and enrichment analysis, we found that LXTYF attenuates AICH partially by regulating the targets involved MAPK, calcium, apoptosis, and TNF signaling pathway and plays antioxidation, anti-inflammatory, antiapoptosis and lowers blood pressure roles in the treatment mechanisms.