Recent advancement in integrated multi-omics has significantly contributed to many areas of the biomedical field. Radiation research has also grasped uprising omics technologies in biomarker identification to aid in triage management. Herein, we have used a combinatorial multi-omics approach based on transcriptomics together with metabolomics and lipidomics of blood from murine exposed to 1 Gy (LD) and 7.5 Gy (HD) of total-body irradiation (TBI) for a comprehensive understanding of biological processes through integrated pathways and networking. Both omics displayed demarcation of HD group from controls using multivariate analysis. Dysregulated amino acids, various PC, PE and carnitine were observed along with many dysregulated genes . Joint-Pathway Analysis and STITCH interaction showed radiation exposure resulted in changes in amino acid, carbohydrate, lipid, nucleotide, and fatty acid metabolism. Elicited immune response was also observed by Gene Ontology. BioPAN has predicted Elovl5, Elovl6 and Fads2 for fatty acid pathways, only in HD group. Collectively, the combined omics approach facilitated a better understanding of processes uncovering metabolic pathways. Presumably, this is the first in radiation metabolomics that utilized an integrated omics approach following TBI in mice. Our work showed that omics integration could be a valuable tool for better comprehending the mechanism as well as molecular interactions. Further, there is an urgent need for the identification of universal biomarkers for total or partial body irradiation4. However, uncertainty over the specificity of metabolomic biomarkers for radiation (dose and severity) still looms around. This ambiguity in biomarker identification reinforces the belief that there isn’t a single, ideal technique of evaluation that can account for all dosages and levels of exposure. Therefore, complementary and surrogate approaches are the need of the hour in radiation dose assessment studies.The last decade has shown remarkable research progress towards the development of high throughput biomarkers. Technical advancements, especially in analytical tools and bioinformatics have made remarkable development not only in biomarker research but also in comprehending deep insight into pathophysiological disturbances associated with diseased state. Radiation research has also explored all new dimensions of omics for the development of high throughput biomarkers for radiation triage. Recently published series of studies are direct evidence of the role of a metabolomics-based panel of metabolites as radiation biomarkers for radiation exposure5. On the other hand, metabolomics is described as the study of changes in endogenous metabolites in biological systems (biofluids/cells/tissues) in response to any kind of stressor, whereas lipidomics is a sub-branch of metabolomics.Multi-omics approach could provide a solution through identified and validated biomarkers to some of the complex scenarios like radiation exposure and help in triage management. In line with this, recent literature also supports the applicability of transcriptomics and metabolomics in biomarker identification and pathology elucidation. Transcriptomics is a powerful method for determining the function of important genes and comparing gene expression under various stress circumstances by carrying out qualitative or quantitative analysis of mRNA using high-throughput microarray analysisAlthough metabolic profiling is an effective approach, metabolites/lipid changes may not accurately identify pathways altered by radiation exposure. This gap can be addressed by integrating omics technologies which will offer a more comprehensive view of alterations in complex biological processes induced by any stress. The combined metabolomics and transcriptomics approach will generate extensive data addressing the inter-related metabolic and transcriptomic changes. Further, this data integration will help in identifying the associations between enzymes/proteins and metabolites, uncovering molecular mechanisms based on high throughput data.6. Recently, Meeks et al. have used transcriptomic and metabolomic data to assess radiation-induced damage in parotid salivary glands on the fifth day following 5 Gy targeted exposure to head and neck cancer. Their findings supported the notion of altered energy metabolism as a radiation-induced response7. Another study on HBE cells indicated that integration of metabolomic and transcriptomic dataset has shown that p53 regulates various genes that are associated with nitrogen, glutathione, arachidonic acid metabolism and also with glycolysis or gluconeogenesis in response to ionizing radiation (IR), showing potential targets of p538.The integrated-omics approach will allow simultaneous analysis of mRNAs and metabolites providing whole transcriptome and metabolic changes after radiation exposure which would help in mapping complex relationships9. Similarly, in another study as well, integrative analysis has helped in confirming the correlation of metabolic changes with immunoregulation along with mitochondrial translation in active tuberculosis children patients and has also substantiated the association of various inflammatory pathways and has thus improved the elucidation of biomarkers10. The metabolome and transcriptome together offered credible information on pathological processes and the related metabolic pathways along with possible indicators and targets involved in particular stress conditions, laying the basis for further research on the mechanistic explanation11. To the best of our knowledge, integrated metabolomic and transcriptomic analysis has not been explored so far in total-body irradiation.A recent study on obese breast cancer revealed 7 novel enriched pathways compared to non-obese breast cancer patients after omics integration12. In the present study, we have used a combinatorial multi-omics approach based on transcriptomics analysis along with mass spectrometry-based metabolomics and lipidomics of plasma samples from rodent models exposed to total-body radiation. This has enabled an improved understanding of biological processes in their entirety. The advancement of omics approaches has greatly aided in the identification of critical players and their management in biological and pathological conditions, which is critical for the development of new therapeutic agents. Our study thus not only provides a comprehensive understanding of biological processes after radiation exposure but also enhances the validation of potential diagnostic biomarkers of radiation exposure.We have previously reported comprehensive metabolic profiling in urine using Nuclear Magnetic Resonance (NMR) spectroscopy and Liquid chromatography-mass spectrometry (LC-MS) distinguishing TBI  from PBI  as well as sub-lethal (5 Gy) from lethal (7.5 Gy) doses of IRThe present study was carried out for understanding the impact of ionizing radiation on gene expression (mRNA diversity) and metabolic profile in mice blood after 1 Gy and 7.5 Gy radiation exposure at 24 hours. The workflow used in the present study is represented in Fig. p-value ≤ 0.05 in HD and LD groups post irradiation compared to the control group.RNA sequencing was performed on RNA samples that passed the quality control (QC) indices. QC status and mean quality value across each base position can be observed from Supplementary Table Gene Ontology (GO)-based enrichment analysis mainly showed perturbation in pathways associated with immune response, cell adhesion, and receptor activity and were also found to be more prominent in HD group ; 11 in cellular component (CC); 10 in molecular function (MF) pathways) than LD group  when compared to control  involved in this pathway, H2-Ab1 , Lax1 (lymphocyte transmembrane adaptor 1), Cd22 (CD22 antigen), Cd40  Cd28 (CD28 antigen) Vpreb3 (pre-B lymphocyte gene 3), Cd27 (CD27 antigen) Siglecg , Clcf1 (cardiotrophin-like cytokine factor 1) and Swap70 (SWA-70 protein) are the top 10 DEGs based on adj. Up and down means number of upregulated and downregulated genes in the GO term respectively.https://esbl.nhlbi.nih.gov/) was utilized to find DEGs (obtained from BP) which encode metabolic enzymes. In HD group, 16 DEGs were found to be encoding those metabolic enzymes that were involved in lipid, nucleotide, amino acid, and carbohydrate metabolism   pathway database was also utilized to analyze the biological pathways in which DEGs were involved. Based on DEGs data, 18 KEGG pathways in LD group and 12 pathways in HD group were found to be significantly enriched . Supplementary Table UPLC-MS-based untargeted metabolomics was conducted to identify changes in metabolites and lipids in plasma samples post-radiation exposure. During metabolic analysis of plasma, 3426 peaks in positive and 2548 peaks in the negative ionization mode were detected, whereas lipidomics analysis identified 2315 and 1056 peaks in positive and negative ionization mode respectively. Overall, the univariate analysis identified a total of 1491 significant m/z features  in LD group and 1221 features (424 features downregulated and 797 upregulated) in HD group  in HD group. Whereas, in LD group, only phenylalanine, tyrosine and tryptophan biosynthesis pathway had a significant impact >0.2 , BioPAN software, and STITCH  interaction to understand changes in metabolic pathways after IR exposure.Joint-Pathway Analysis generated 16 and 71 altered pathways in LD and HD groups respectively Tables  and 3. P13. It is apparent that arginine and lysine have shown the maximum number of associations (n = 6) with other metabolites. Among various dysregulated genes present in the Cytoscape network, 3-oxoacid CoA transferase 2 A (Oxct2a) (upregulated), 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2 (Hmgcs2) (upregulated), and isopentenyl-diphosphate delta isomerase (Idi1) (downregulated) displayed high fold change (>10) and were present in the same cluster. Along with them, nitric oxide synthase 2 (Nos2) was also upregulated 17 times and exhibited an association with arginine and histidine. Altogether, from the above elucidation, it is evident that the integration analysis was successful in identifying differential pathway networks and their linked metabolites and DEGs associated with radiation-induced injury.The transcript-metabolite interaction in HD group consists of 114 nodes connected by 81 edges, with a clustering coefficient of 0.889. Whereas no significant interaction was observed in the case of LD group. Here, interactions mean associations between gene-gene, metabolite-gene, and metabolite-metabolite and are clearly shown using Cytoscape Fig. . AssociaIn the present study, lipid pathways based on lipidomic data were analyzed using BioPAN. The network of lipid subclasses and fatty acid (FA) pathways of LD and HD groups is presented in Fig. 4. In this study, we have performed an integrated metabolomic and transcriptomic analysis in the preclinical model exposed to low and high doses of radiation for biomarker identification.In order to facilitate the clinical management of radiation-exposed people during a nuclear accident, several international health and research agencies are looking for high throughput biomarkers as diagnostic tools for immediate use in dose assessment. Multiple potential biomarkers based on genes, proteins and metabolites for radiation injury are being identified and validated across the globe17. The complementing evidence given by transcriptomics and metabolomics on alterations at the genetic level, protein synthesis, metabolism along with cell function represents the changes in genotype and phenotype, respectively7. This study is unique as the integration of transcriptomic and metabolomic data has been used for the identification of blood-based biomarkers for radiation exposure.A total of 2837 and 143 DEGs in HD and LD groups respectively, were identified between irradiated groups and controls, 93 of which were common to both the irradiated groups showing that they are sensitive to even 1 Gy of radiation dose [LD dose group]  radiation dose . We also observed a significant change in L-histidine in both the radiation groups, which is an important metabolite of “histidine metabolism” pathway and acts as a proton buffer, scavenger of nitric oxide (NO) and reactive oxygen species (ROS), and is also involved in anti-inflammatory function21. Increased histidine in both the radiation groups in the present study also aligns with an earlier study22.On the other hand, untargeted metabolomics identified a total of 13 and 15 dysregulated circulating metabolites in LD and HD groups respectively when compared to controls that were involved in “lipid metabolism and amino acid metabolism” in metabolic pathway analysis. Moreover, KEGG pathway analysis of metabolites revealed “phenylalanine, tyrosine and tryptophan biosynthesis” as the common dysregulated pathway in both the radiation groups  in LD and HD exposed groups respectively when compared with controls. Differential networking by Joint-Pathway Analysis and STITCH presented a good metabolite–gene interaction network in high dose group  in our study. Upregulation of Nos is reported in the inflammatory and hypoxic condition in an earlier study25. A dose-dependent increase of plasma Nos has also been observed at 24 h post irradiation in rats26. We also utilized Joint-Pathway Analysis for finding differential networking between LD and HD groups as well. Differential networking was able to differentiate these group based on “Arginine biosynthesis” pathway  and Oxct2a (FC 14) observed in our study can be linked to dysregulation of “synthesis and degradation of ketone bodies”. Interestingly, Hmgcs2 is also involved in the rate-limiting step of ketogenesis27. These two DEGs have also shown association in STITCH analysis predicting elevated energy demand at the systemic level after radiation exposure. Similarly perturbed ketogenesis after radiation exposure has also been shown in earlier studies28. Likewise, another pathway “butanoate metabolism” can also be linked to energy metabolism as it can get involved directly in the citric acid cycle via succinate or indirectly via pyruvate and acetyl-CoA29. The above observations, by and large, infer changes in energy metabolism post TBI.In our earlier studies, we found changes in energy metabolism post radiation exposure in urine31. Lipids play different roles apart from cell membrane formation and storage of energy including cellular signaling and inflammatory response32. The influence of IR on apoptosis has been reported widely. Some of the lipids reported in this study like PCs, LysoPCs (LPC), and LysoPEs (LPE), have been linked to apoptosis in previous studies33. On a similar note, PCs play a major role in cell signaling through the generation of LPCs, phosphatidic acids (PA) and diacylglycerol (DG)22. Changes observed in LPCs, and PCs in this study indicates radiation-induced cell membrane damage as well as dysregulation at molecular level. Linoleic acid metabolism, alpha-Linolenic acid metabolism and glycerophospholipid metabolism have also been observed in both rats and mice after radiation exposure in a recent study34. It is known that linoleic acid is involved in the regulation of cholesterol synthesis and hence affect lipid metabolism as well. Dysregulated linoleic acid metabolism may also be involved in apoptosis and inflammation34. Lipid pathways identified through lipidomic data analysis have been correlated well with pathways  identified through KEGG based on transcriptomic data  ->FA(18:1) (Oleic acid)->FA (18:2) (Linoleic acid)” pathway. Joint-Pathway Analysis has also shown significant involvement of these genes in “fatty acid elongation” denoting complementarity of lipidomics and transcriptomics data. Furthermore, these 3 DEGs have also shown confidence in STITCH interaction networking with glycerophosphocholine , CAR 16:2 -hexadecadienoylcarnitine) or ST 23:1;O4, CAR 16:0  and CAR 14:1 (tetradecenoylcarnitine) which were significantly decreased in both radiation groups and seen to be linked with increased energy demand through fatty acid oxidation/degradation.Our findings have also been able to delineate different pathways associated with lipids based on lipidomic data. Pathway analysis using BioPAN showed “biosynthesis of PC” as the most active pathway in both radiation groups whereas “biosynthesis of PE” was the most suppressed pathway observed in HD group only Fig. A, B. Thiine Fig. . Althoug38. Decreased PC 30:0, PC 34:4, PC 36:2, PC 36:3, PC 36:6, PC 36:4 and PC 38:5 in our study is also concurrent with the study by Pannkuk et al.39. They showed that these polyunsaturated PCs were significantly decreased following different radiation dose exposure in non-human primate (NHP)39. Similarly, a recent study by Crook et al. group has illustrated the downregulation of six carnitine metabolites even at day 4 compared to baseline. CAR 18:1 and CAR 14:2 were also found to be downregulated in NHPs at 24 h post-radiation40. Changes in acylcarnitines along with phospholipids and amino acids indicate an alteration in the metabolism of proteins, beta-oxidation and changes in inflammatory pathways39. Increased LPCs at day 2 have also been observed in an earlier study in NHP by Pannkuk et al. indicating the potentialities of these compounds as possible markers for biodosimetry36. The plasma lipidome thus reported changes in specific lipid concentrations after radiation exposure.The findings are also in support of already identified metabolite markers available in the literature for radiation exposure. Earlier research suggested that radiation exposure causes alterations in the lipidome39. Recently, changes in histidine metabolism have also been observed by Sato et al., though the changes observed were in contrast to our findings41. Some of these changes complement pathways based on transcriptomic data for their validation. Largely, the integrated omics has helped us in understanding the dysregulated pathways and ameliorating inter-linking between them to gain a better insight into molecular mechanisms during radiation injury.In the present study, we have observed upregulation of amino acid at 24 h post radiation exposure which is in concurrence with the previous studies that had also shown changes in amino acids like valine, proline, glutamate, histidine, alanine, arginine, alanine, and asparagine, tyrosine in NHP serum after IROverall, the combination of transcriptomics and metabolomics approaches have brought better insight into radiation-induced changes at the metabolic level. Primarily, the present study identified interactions among DEGs and dysregulated metabolites/lipids based on extensive pathway analysis. Overall, radiation exposure resulted in changes in immunological processes, carbohydrate metabolism , lipid metabolism , amino acid metabolism  and nucleotide metabolism . To the best of our knowledge, the approach used in the present study has not been applied so far after total body radiation exposure in mice. Biomolecules crosstalk synergistically for signal perception and derive biological functions. A single “omics” field cannot adequately capture biological complexity. The present study supports the drive for an integrated multi-omics approach that would not only help in looking for deeper insights into crosstalk between different biomolecules but also in substantiating the validation of the identified biomarkers mechanistically for radiation exposure.13C2) from Cambridge isotope laboratories, Inc. whereas caffeine, nitrobenzoic acid, ammonium formate (AF), formic acid and tert-butyl methyl ether (MTBE) were purchased from Sigma-Aldrich .All solvents, isopropyl alcohol, acetonitrile, water, and methanol were of LC-MS grade (Fisher Scientific). Lipid internal standards including lysophosphatidylethanolamine 17:1, lysophosphatidylcholine 13:0, phosphatidylethanolamine 15:0, and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine (C18(plasm)-18:1PC) were purchased from Avanti polar lipids, Inc. . Taurine : control, low dose, and high-dose group. Animals were exposed to 1 Gy  and 7.5 Gy  of total-body gamma radiation through a 60Co source. Blood was collected through retro-orbital sampling at 24 hours post-radiation exposure.C57BL6 male mice (8–10 weeks old) were obtained from the animal facility of the institute followed by one-week acclimatization before irradiation. Temperature and humidity conditions were properly maintained. Animals were randomly allocated to three groups , DRDO, India with approval number 8/GO/RBI/S/99/CPCSEA/INM/IAEC/2017/09.oC. Water containing 0.1% formic acid (100 μL) was added for phase separation followed by vortexing and centrifugation at 13,000 g for 20 minutes at 4 °C. The upper organic layer containing lipids was collected in a separate tube. For polar metabolites, 400 μL of cold methanol was added to the remaining phase, followed by centrifugation at 13,000 g, 20 minutes at 4 °C. The supernatant containing polar metabolites was separated. Both organic and polar phases (supernatant) were evaporated in a nitrogen gas evaporator .Whole blood collection was done using BD Vacutainer EDTA tubes. Plasma samples were processed for extraction of lipids and polar metabolites using MTBE and methanol. In brief, 50 μL of plasma was mixed with 370 μL cold MTBE and 125 μL methanol followed by vortexing and incubation for 15 minutes at 413C2 labeled taurine . Prepared samples were then filtered through 3 kDa filters. The evaporated lipid samples were reconstituted in 500 μL of 1:2 v/v of mobile phase A: B containing all IS i.e., lysophosphatidylcholine 13:0 (0.5 μg/ml), C18(plasm) (2.5 μg /ml), phosphatidylethanolamine 15:0 (6 μg/ml) and lysophosphatidylethanolamine 17:1 (2 μg/ml). By combining equal aliquots from each of the prepared samples, a quality control (QC) sample was also prepared.For the metabolomics study, the evaporated extract was resuspended in 500 μL of mobile phase (1:1 v/v), containing internal standards (IS) caffeine (0.5 μg/ml), nitrobenzoic acid (0.5 μg/ml) and 1,2-LC-MS/MS analysis was performed using a Sciex 5600 + UHPLC MS-Q-TOF system  using an acquity bridged ethylene hybrid (BEH) amide   and charged surface hybrid (CSH) C18   column with their compatible vanguard pre-column for metabolomics and lipidomics respectively. A multistep gradient elution method was used for the metabolomics and lipidomics study.For metabolomics analysis, mobile phase A (water + 0.1% formic acid) and mobile phase B (acetonitrile + 0.1% formic acid) were used as follows: the initial mobile phase was 95% B for 3 mins; followed by 70% B from 3–4 min, 45% B from 4–11 min, 45% B from 11–15 min, 95% B from 15–15.1 min followed by equilibration at 95% B for one minute.For lipidomics, mobile phase A consisting of water:acetonitrile (40:60 v/v) having 10 mM AF and 0.1% formic acid and mobile phase B consisting of isopropanol:acetonitrile (80:20 v/v) having 10 mM AF and 0.1% formic acid was used as follows for chromatographic separation: the initial mobile phase was 15% B followed by 30% B from 0–2 min, 48% B from 2–2.1 min, 55% B from 2.1–2.5 min; 70% B from 2.5–5 min, 75% B from 5–6.5 min; 82% B from 6.5–8 min; 99% B from 8–14 min, 99% B for 3 min, 15% B from 17–17.1 followed by equilibration at 15% B from17.1–19 min. A flow rate of 0.2 mL/min was used for both metabolomics and lipidomics analysis, whereas injection volume of 10 μL (in both positive and negative mode of ionization) for metabolomics analysis through BEH amide column and injection volume of 2 μL in positive mode and 4 μL in negative ionization mode for lipidomics analysis through CSH column.3. Mass spectrometry was performed with the following conditions: nebulizer gas (GS1) = 50 psi, desolvation gas (GS2) = 50 psi, curtain gas flow = 30 psi, capillary voltages (ISVF) = 5500 V for positive and 4500 V for negative ionization modes with source temperature = 400 °C for metabolomics and 500 °C for lipids with flow rate of 200 μL/min. Parameters used for MS/MS fragmentation include collision energy (CE) of 20 eV for metabolites and 35 eV for lipids with a CE spread of 15 eV. The mass range was set at 60–1000 m/z and 100–1600 m/z for metabolomics and lipidomics respectively.The mass spectrum was acquired in data-dependent acquisition (DDA) in both positive and negative ionization modes. The different MS parameters used in the study are outlined in our earlier studyTM software (Sciex Applied Biosystems) for the generation of the m/z features list with their intensities. Features having relative standard deviation (RSD) > 25% based on QC samples were excluded from the analysis. Metabolite identification was done based on m/z having mass accuracy (error<5 ppm), isotope pattern matching (error <10%) and MS/MS fragmentation matching (library score >70) by utilizing Accurate Mass Metabolite Spectral Library from Sciex which contains a highly curated collection of high-resolution accurate mass spectra. LIPIDMAPS® (www.lipidmaps.org), LipidBlast (www.fiehnlab.ucdavis.edu/projects/LipidBlast)42 and LipidViewTM (AB Sciex) software were used for the assignment of lipid class (delta < 0.002)43.The pretreatment of the acquired raw data was performed which includes peak finding, alignment, filtering, and retention time correction by MarkerviewTM Blood RNA tubes , as per the manufacturer’s instruction. Briefly, blood samples were mixed instantly by inverting few times and were kept at room temperature for 4 hours and stored at −80 °C in PAXgenesTM Blood RNA tubes until further analysis.For transcriptome sequencing, 1 mL of whole blood was drawn into PAXgenesTM Blood RNA Kit protocol followed by elution in nuclease-free water (20–30 μL). Assessment of RNA quality was performed by the RNA ScreenTape system (Agilent). The RNA integrity was ascertained by the RNA integrity number (RINe). All the samples showed RINe > 7 . Further, Qubit® 3.0 Fluorometer (ThermoFisher Scientific) was used to measure the RNA concentration.Total RNA extraction from whole blood was done by using the PAXgeneThe RNA-seq experiment was conducted by utilizing the next-generation sequencing technologies to assess mRNA in blood 24 h after radiation exposure. The mRNA enrichment and library preparation were performed by NEBNext® Ultra™ II RNA Library Prep Kit for Illumina (New England BioLabs). Enriched mRNA was fragmented to achieve ~200 nucleotide-long inserts. Further, complementary DNA (cDNA) libraries were constructed, and the end was repaired following the manufacturer’s protocol. Amplification and purification were also performed for facilitating the multiplexing in sequencing. The library concentration was assayed by Qubit3 Fluorometer (Life Technologies) by utilizing the Qubit dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific) as per protocol whereas quality assessment of the cDNA library was measured by Agilent D1000 ScreenTape System in 4150 TapeStation System (Agilent).The library sequencing was performed using Illumina HiSeq. FastQC and MultiQC softwares were used for checking the data quality of sequencing. The processing of the raw sequence reads was performed using fastp to filter out adapter sequences along with low-quality bases. STAR v2 aligner was used for aligning the QC passed reads to the Mus musculus genome (GRCm38.90). On average, 99.91% of the reads were matched to the reference genome. Gene level expression value (read-count) of each gene was attained using feature-counts software.44. In order to compare differentially expressed genes (DEGs) among the samples, the TMM normalization approach is widely utilized to normalize total RNA output. Furthermore, PCA was performed on normalized read counts for visualizing differences between groups. Those features which did not have one count-per-million in at least three samples were removed. Differential gene expression analysis was performed using edgeR software . DEGs were presented as genes having absolute log2 fold change ≥ 2 and adj. p-value ≤ 0.05. Normalization by the total area, log-transformation and pareto-scaling was done on the metabolomic and lipidomic dataset using MetaboAnalyst5.0 . In addition to this, univariate analysis (two-tailed student’s t test and fold change) was also performed using MetaboAnalyst software. Identified metabolite/lipids having p-value < 0.05 were considered significant.Normalization of the transcriptomic data was done using a trimmed mean of M (TMM) value. TMM is defined as the weighted trimmed mean of the log expression ratios. TMM are gene-wise log-fold change quantities originally defined by Robinson and Oshlackhttp://geneontology.org/) focusing on BP, MF, and CC, were conducted using the clusterProfiler R package for determining functions of the DEGs. Pathways and GO having adj. p-value ≤ 0.05 were regarded as significant. GOplot R package and Pathview package were used for GO pathway visualization.KEGG pathway, as well GO (http://www.kegg.jp/). Pathway analysis for metabolites was performed using MetaboAnalyst whereas BioPAN software (www.lipidmaps.org/biopan) was used for analyzing lipid pathways. Metabolic pathways having a p-value < 0.05 and impact >0.2 were regarded as significant. For lipid pathways, we have taken the most active and most suppressed lipid reaction as significant . The BioPAN software workflow utilizes Z-score, which takes into account both the mean as well as the standard deviation assuming a normally distributed data of lipid subclasses. Z-score was calculated as detailed in Gaud et al.45.Metabolic pathway enrichment was done through KEGG . Both metabolic  and transcriptomic  datasets were used for Joint-Pathway Analysis. For that, official gene symbols of mRNA (or DEGs) and compound names of metabolites along with their corresponding log2Fold Change were uploaded for assessing the potential importance of individual molecules within a network. In the Joint Pathway Analysis module, various selected parameters were (i) in pathway database- metabolic pathways (integrated); and (ii) in algorithm selection: enrichment analysis- hypergeometric test, topology measure-degree centrality, integration method- combine queries46. Pathways having a p-value < 0.05 with impact >0.2 were considered significant. The pathway impact score summarizes the normalized topological measure of altered genes or metabolites present in each metabolic pathway whereas, the −log 10(p) value shows enrichment analysis results47.For differential networking, integrated transcriptomic and metabolic analysis was carried out using the Joint-Pathway Analysis module of MetaboAnalyst software (www.stitch.embl.de)49. The subgraph generated from STITCH is bipartite due to the existence of edges only between proteins and chemicals. The obtained interactions were then constructed by Cytoscape software (3.9.1) to create the final differential network image.Furthermore, the interaction network between the key metabolites and DEGs involved in the Joint-Pathway Analysis was also established using STITCH - an online webtool for visualizing biological relationships between metabolites-genes, metabolites-metabolites and genes-genes (Further information on research design is available in the Supplementary MaterialReporting Summary


Recurrent respiratory tract infections (RRTIs) are one of the most common diseases in children and adolescents. The causes of RRTIs are various. In addition to the factors related to infection, basic diseases such as respiratory system, immune system, and digestive system are also involved. The cost of patients' frequent medical treatment and hospitalization has been deemed to be a heavy burden to the society and family. In China, traditional Chinese medicine (TCM) is commonly used to treat RRTIs. TCM treatment has been appraised to be effective, for reducing the number of hospital stays. Illustrious senior TCM practitioners of pediatrics are recognized as a group of outstanding physicians with significantly better patient outcomes. However, different illustrious senior TCM practitioners can lead to differences in treatment strategies due to factors such as region, prescription theory, and individual differences of patients. This makes it difficult for the experience of illustrious senior TCM practitioners to be popularized. However, there have been no prescription mining studies for the treatment of RRTIs based on different and multiple illustrious senior TCM practitioners. We explored the core prescriptions and drug mechanisms through data mining based on the prescriptions of illustrious senior TCM practitioners treating RRTIs from different clinical settings. This is important to promote the effective treatment of RRTIs with TCM. The objective of this study is to reveal the strategies (core prescriptions) from the prescriptions of multiple illustrious senior TCM practitioners for the treatment of RRTIs. We hope that this core prescription can help all TCM pediatricians to improve RRTIs children's outcome. Meanwhile, it could provide a new way for researchers to study the treatment of RRTIs. R. The execution of all methods was completed in May 2022.  In this study, we prospectively collected 400 children's prescriptions with RRTIs receiving TCM treatment from four illustrious senior TCM practitioners in different hospitals. We described and analyzed the characteristics of TCM prescriptions. The prescription regularity was analyzed by hierarchical clustering and association rules. Network pharmacology methods has been used to reveal the pathway mechanism of core prescriptions which have been mined and visualized with the help of SymMap, Genecards, KEGG, Metascape databases, and Scutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong) is the core strategy for the treatment of RRTIs. Cold herbs and heat herbs in the core prescription are approximately equal. Scutellariae radix (Huangqin) was dominant, and other herbs exert synergistic effects. The core prescription covered 76 pathways and 226 herb-disease genes. It promotes the differentiation of Th1, Th2, and Th17 cells and the secretion of inflammatory factors through toll-like receptor signaling pathway in the immune system, T cell receptor signaling pathway, and PPAR signaling pathway in the endocrine system, thereby exerting immune regulation and anti-inflammation.  According to RRTIs multiple clinical syndromes, five new prescriptions were obtained based on illustrious senior TCM practitioners. Among them, the prescription composed of  In this study, we revealed the prescription regularity of TCM in the treatment of RRTIs and analyzed the mechanism of core prescriptions, which provided new ideas for the treatment of RRTIs. Recurrent respiratory infections (RRTIs) refer to the upper and lower respiratory tract infections that occur frequently and exceed the normal range within one year . RRTIs aAs an ancient medicine, traditional Chinese medicine (TCM) has accumulated precious experience for clinical treatment and medical research in various fields for a long time . TCM hasData mining has been widely used in disease diagnosis, syndrome differentiation, and prescription analysis . It is aStep 1 presents the source and the basic characteristics of the data. Step 2 shows how to use data mining  to get the core prescriptions. Step 3 introduces the mechanism of core prescriptions by using network pharmacology (multisource database). The workflow chart is shown in Previous studies mainly focused on the case report of one doctor in a single hospital center. In this study, we prospectively collected clinical cases of RRTIs in children in multiple hospitals based on real-world records. The four pediatric illustrious senior TCM practitioners who participated in the study were renowned nationwide. They come from three regions of China  and four provinces . They have been engaged in the treatment of pediatric diseases for more than forty years. The patients can achieve a better outcome from their treatment, and they have high reputations in this field , 14. We In four clinical centers , we prospectively collected the prescriptions of four pediatric illustrious senior TCM practitioners for RRTIs from November 2019 to October 2021, with 100 cases in each center.The respiratory group of the Pediatric Society of Chinese Medical Association formulated the diagnostic criteria of RRTIs .The inclusion criteria include the following: (1) children meeting the above diagnostic criteria for RRTIs; (2) the age of children is between 1 and 17 years; (3) the children or the guardian of the children sign the informed consent form.The exclusion criteria include the following: (1) children suffer from severe primary disease; (2) children are participating in other clinical trials.We collected the general clinical data of these cases, including the patient's gender, age, diagnosis, and TCM prescriptions. All data were entered into Microsoft Excel 2016 to establish a database for the treatment of RRTIs by four physicians. The prescription herbs were subsequently further standardized in the following manner. In accordance with the Chinese Pharmacopoeia of the People's Republic of China (2020 edition) and “Chinese Materia Medica,” the properties, flavours, and meridian tropism of the medicinal herbs in these prescriptions were further supplemented. Three researchers were assigned to complete the information entry work separately. The research protocol was reviewed and approved by the Ethics Committee of Beijing University of Chinese Medicine (ethics batch no. 2019BZHYLL0204). The researchers followed the protocol strictly.We used Microsoft Office Excel 2016 for statistical analysis of the patient's age and gender distribution. GraphPad Prism9 and Adobe Illustrator are used for graphics. Firstly, we analyzed the property, flavours, and meridian tropism of the prescription herbs, and then used hierarchical clustering to find new prescriptions. Finally, we used association rules to screen out the core herbs. RStudio 1.4.1717 due to the visualization of the above method.Descriptive analysis of herbs' characteristics, including three parts: properties, flavours, and meridian tropism. The properties consist of cold, cool, mild, warm, and hot. The flavours consist of sour, astringent, salty, sweet, bitter, pungent, and light. There are twelve items of meridians: spleen, liver, heart, kidney, pericardium, stomach, urinary bladder, large intestine, small intestine, trijiao, gallbladder, and lung. The top 10 high-frequency herbs of each physician were selected for visual analysis of herbal properties and flavours.x and y in the following algorithm formula represent different herbs, respectively. The similarity between herbs was calculated by using the Euclidean distance. Draw the tree diagram, different colors labeled with different categories.The hierarchical clustering algorithm was used to cluster each herb. According to the similarity measurement among the objects, stable and regular new categories could be obtained in many individualized prescriptions. In order to reduce the possible bias of the difference of each physician on the core prescription, we selected the top 10 herbs in each physician's prescription and deleted the duplicate values, finally obtaining 33 Chinese herbs. The X and Y in the following formula represent the frequent itemsets of one or more herbs. “Support” refers to the proportion of transactions containing X and Y (P (X∪Y)) in all transactions (P (I)), that is, the probability of simultaneous occurrence of X and Y. “Confidence” refers to proportion of transactions with X that contain both Y transactions, that is, the probability of Y occurring at the same time when X. “Lift” refers to the ratio of “proportion of transactions with X that include Y at the same time” to “proportion of transactions with Y.” “Lift” reflects the correlation between X and Y. “Lift” greater than 1 and higher indicates a higher positive correlation; “Lift” less than 1 and lower indicates a higher negative correlation, and “Lift” equal to 1 indicates no correlation.Apriori algorithm is an association rule algorithm for frequent itemsets. We used the apriori algorithm to analyze the prescriptions and found the core herbs. Each prescription is equivalent to a transaction, and each herb is equivalent to an itemset. Through calculating frequent itemsets, we screened the association rules between herbs and finally obtained the core herbs. Core prescriptions were obtained by filtering the rules with top “Support.” http://www.symmap.org/), to acquire the molecular targets of each herb in the core prescriptions.In this study, we used the SymMap database (http://www.genecards.org/), to acquire relevant disease targets for RRTIs.In this study, we used the GeneCards database (http://metascape.org/gp/index.html#/main/step1) was used to analyze the targets obtained, the species for selection was “Homo sapiens,” and custom analysis mode was used, set P value < 0.01 and min overlap > 3, the KEGG pathway result was obtained. Then, only items that also have −log  > 5 and belong to more than 5% of the target of the category were kept. RStudio's ggplot2 was used to draw the remaining items into a bubble chart. Finally, the bubble graphs of each herb was combined.Metascape  was 248 times at most, and the frequency of Armeniacae semen amarum (Kuxingren) was 234 times.We analyzed the top 10 herbs in the prescription of each physician, including 33 herbs in total. Herbal frequency, properties and flavours characteristics, and affiliated physicians of the herbs are shown in Scutellariae radix (Huangqin) appeared 248 times most frequently, and Armeniacae semen amarum (Kuxingren) appeared 234 times. The frequency of other herbs in descending order were Glycyrrhizae radix et rhizoma (Gancao), Peucedani radix (Qianhu), Magnoliae flos (Xinyi), Xanthii fructus (Cang'erzi), Ephedrae herba (Mahuang), and Pheretima (Dilong). These herbs are commonly used to treat RRTIs.Among them, Ephedrae herba (Mahuang), Lonicerae japonicae flos (Jinyinhua), Lysimachiae herba (Jinqiancao), Ziziphi spinosae semen (Suanzaoren), Gastrodiae rhizoma (Tianma), Crataegi fructus (Shanzha), Hordei fructus germinatus (Maiya), Massa medicata fermentata (Shenqu), Glycyrrhizae radix et rhizoma (Gancao), and Magnoliae flos (Xinyi). Prescription 2 was assigned to Physician2 and included Xanthii fructus (Cang'erzi), Belamcandae rhizoma (Shegan), Eriobotryae folium (Pipaye), Arisaema cum bile (Dannanxing), and Descurainiae semen lepidii semen (Tinglizi). Prescription 3 was divided into a separate category, without any particular preference for any physicians, and included Scutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong). Prescription 4 was assigned to Physician 4 and included Saposhnikoviae radix (Fangfeng), Cicadaeperiostracum (Chantui), Liquidambaris fructus (Lulutong), Fritillariae thunbergii bulbus (Zhebeimu), Cynanchi paniculati radix et rhizoma (Xuchangqing), Arctii fructus (Niubangzi), and Platycodonis radix (Jiegeng). Prescription 5 was assigned to Physician 3 and included Trichosanthis fructus , Pinelliae rhizoma (Banxia), Asteris radix et rhizoma (Ziwan), Farfarae flos (Kuandonghua), Perillae fructus (Zisuzi), Cynanchi stauntonii rhizoma et radix (Baiqian), and Bupleuri radix (Chaihu).We divided these herbs into five new prescriptions according to the TCM theory. There were four prescriptions corresponding to different physicians, respectively . PrescriScutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Glycyrrhizae radix et rhizoma (Gancao), Peucedani radix (Qianhu), Magnoliae flos (Xinyi), Xanthii fructus (Cang'erzi), Ephedrae herba (Mahuang), Pheretima (Dilong), Trichosanthis fructus , and Cicadaeperiostracum (Chantui) (Scutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong) were the herbs with the strongest association and were in the core position. At the same time, we found that the core herbs were highly overlapped with New Prescription 3, and could become the core prescription. We could also refer to those herbs as the core prescription (CP) for the treatment of RRTIs.First, we transform the prescriptions data into frequent itemsets, and the top 10 frequent items were Chantui) . Set supChantui) , see TabP < 0.001) of 4 herbs in CP from the SymMap database. We obtained a total of 7878 targets related to RRTIs from GeneCards database. We intersected the targets of the herbs with the targets of RRTIs to obtain a total of 226 interactive targets, including 51 targets of Scutellariae radix (Huangqin), 113 targets of Armeniacae semen amarum (Kuxingren), 106 targets of Peucedani radix (Qianhu), and 51 targets of Pheretima (Dilong) (We obtained 283 targets ((Dilong) , see TabP value > 0.01, min overlap > 3) and kept more than 5% of the paths with -log  > 5, and got 76 KEGG paths. GO process: 177 BP , 63 CC (cell component), and 93 MF (molecular function). Among KEGG pathways, 16 are related to the immune system and 14 are related to the endocrine system. Immune system pathways mainly involve receptor signaling pathways of T and B cells, Th1, Th2, and Th17 cell differentiation. Endocrine system pathways mainly involve the activation of peroxisome proliferator-activated receptors (PPARs), as well as the synthesis and secretion of hormones. From Scutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), and Peucedani radix (Qianhu) had more pathways for the immune system and endocrine system intervention, while Pheretima (Dilong) only had an effect on the endocrine system. BP was mainly enriched in the regulation of external stimulation and small molecule metabolism, involving the cellular response to inorganic substances, nitrides, lipids, and lipopolysaccharides; MF was mainly enriched in kinase activity, enzyme, and transcription factor binding; CC was mainly enriched in membrane rafts and mitochondrial membranes With the help of Metascape database, we selected , Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong) had the closest relationship. They are the core herbs for the treatment of RRTIs. The core herbs were fully consistent with the new prescription 3 found by hierarchical clustering. Therefore, we believed that Prescription 3 may be the core prescription (CP) for the treatment of RRTIs. The mechanism of core prescription for the treatment of RRTIs was studied by KEGG and GO analysis. The four herbs in the core prescription acted synergistically and treat RRTIs in a variety of ways. In China, Scutellariae radix (Huangqin) is widely used to treat respiratory tract infections [Scutellariae radix can promote T lymphocyte differentiation and Th1 (IFN-γ and IL-12), Th2 , and Th17 (IL-17) cytokine secretion through Th1 and Th2 cell differentiation, Th17 cell differentiation, and T cell receptor signaling pathways in the immune system. Baicalin and wogonin can also exert immunomodulatory and trickling effects by downregulating toll-like receptors through the toll-like receptor signaling pathway, as well as activating the PPAR signaling pathway of the endocrine system [Scutellariae radix has antiviral and antibacterial effects. For example, Baicalein can inhibit the replication of COVID-19 virus [Peucedani radix (Qianhu) can significantly inhibit ovalbumin (OVA)-induced airway inflammation, airway hyperresponsiveness, and Th2 major responses in mice [Peucedani radix is commonly used to treat allergic asthma in clinic [Armeniacae semen amarum (Kuxingren) was the most common single herb used to treat asthma symptoms in children [Scutellariae radix and Armeniacae semen amarum could alleviate the symptoms of respiratory tract infection in children and reduce the incidence of RRTIs [Pheretima's targets was PPAR signaling pathway. Pheretima could assist Scutellariae radix in activating PPAR signaling pathway and exerting immune regulation. In addition, the prescription of TCM containing Pheretima could regulate T cell function and attenuate anaphylactic rhinitis-asthma symptoms [Scutellariae radix has the effect of removing lung heat and was the monarch herb in the prescription for the treatment of the most common inflammatory fever symptoms of RRTIs. Armeniacae semen amarum, Peucedani radix, and Pheretima are mainly used to relieve cough, transform phlegm, and relieve asthma. These herbs are used to relieve other syndromes of RRTIs, such as asthma and catarrhal symptoms in the upper respiratory tract. Together with Peucedani radix, another herbs also exert the effects of anti-inflammation and immune regulation on the pathway mechanism. It reflects the principle of compatibility (Peiwu) in TCM. This is consistent with the modern medical theory: enhance the efficacy and reduce toxicity [Association rule analysis indicated that fections . The acte system . In addi19 virus . The act in mice , 28. Peun clinic . A clinichildren . Studiesof RRTIs . Figure symptoms . Accorditoxicity . Attentitoxicity . For exaCombined with data mining and systems pharmacology, we comprehensively analyzed the prescription patterns of TCM and explained the mechanism of traditional prescription, providing useful new enlightenment for the treatment of RRTIs. In this study, the clinical data and prescriptions of patients were prospectively collected in multiple hospitals in order to ensure the quality of research data and minimize research bias caused by single physician. Through data mining approaches, unique core prescriptions can be discovered from large-scale medical records. In this study, we found that core prescription mainly affects the immune system and endocrine system, which have some significance for further understanding the pathomechanism of RRTIs. However, it has also been suggested that herbal selects for future RRTIs treatment could cover not only the immune system but also the neuroendocrine-digestive system in humans for better clinical outcomes . To someScutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong) for the treatment of RRTIs. It mainly promotes the differentiation of Th1, Th2, and Th17 cells and the secretion of inflammatory factors through the toll-like receptor signaling pathway in the immune system, T cell receptor signaling pathway, and PPAR signaling pathway in the endocrine system, to exert immune regulation and anti-inflammatory effects. The research analysis and results indicated that the method used in this study can effectively analyze the mechanism and regularity of TCM prescriptions. In the future, we can further conduct clinical research studies to explore the effectiveness and safety of core prescriptions. At the same time, we can try to find specific monomer compounds in the core prescriptions and study the microscopic mechanism of dual regulation in the immune and endocrine system.Five new prescriptions were found by hierarchical clustering algorithm, and then core herbs were found by the apriori algorithm of association rules. Combining the analysis of the results of the two algorithms, we confirmed the core prescription of


Based on analysis of 950 mainly metagenome-derived dsrAB-carrying genomes, we redefine the global diversity of microorganisms with the potential for dissimilatory sulfate/sulfite reduction and uncover genetic repertoires that challenge earlier generalizations regarding their mode of energy metabolism. We show: (i) 19 out of 23 bacterial and 2 out of 4 archaeal phyla harbor uncharacterized SRM, (ii) four phyla including the Desulfobacterota harbor microorganisms with the genetic potential to switch between sulfate/sulfite reduction and sulfur oxidation, and (iii) the combination as well as presence/absence of different dsrAB-types, dsrL-types and dsrD provides guidance on the inferred direction of dissimilatory sulfur metabolism. We further provide an updated dsrAB database including > 60% taxonomically resolved, uncultured family-level lineages and recommendations on existing dsrAB-targeted primers for environmental surveys. Our work summarizes insights into the inferred ecophysiology of newly discovered SRM, puts SRM diversity into context of the major recent changes in bacterial and archaeal taxonomy, and provides an up-to-date framework to study SRM in a global context.Sulfate/sulfite-reducing microorganisms (SRM) are ubiquitous in nature, driving the global sulfur cycle. A hallmark of SRM is the dissimilatory sulfite reductase encoded by the genes  Sulfate/sulfite reducing microorganisms are shaping Earth's interconnected sulfur and carbon cycles since the Archaean: this legacy unfolds in 27 archaeal and bacterial phyla encountered in diverse marine, terrestrial, and deep-subsurface environments. Lambdaproteobacteria), Ca. Zixibacteriota and Ca. Hydrothermarchaeota contained Dsr-pathway genes to perform sulfate/sulfite reduction  from a mine drainage with pH ∼2  and Thermoproteota (Ca. Methanodesulfokores washburnensis) encode both a methanogenesis pathway and the capability to perform sulfate or sulfite reduction, respectively, suggests that sulfur-dependent AOM can be carried out in a single organism, independent from syntrophic interactions , were recovered from a large variety of environments . This resulted in a total of 902 bacterial and 48 archaeal genomes distributed across 27 and 4 phyla according to the GTDB-Tk taxonomy  and 936 species in total according to the GTDB-Tk taxonomy (Verrucomicrobiota MAG with early diverging DsrAB (Verrucomicrobia bacterium SbV1) as well as representatives of the Schekmanbacteria (Schekmanbacteria bacterium RBG_13_48_7) and Chloroflexota (Chloroflexi bacteria RBG_13_60_13 and RBG_13_52_14) described previously in the literature  to search for genes encoding proteins known to be involved in dissimilatory sulfur metabolism , Bacillota (11%), Nitrospirota (5%), and Thermodesulfobiota (0.2%), which together represent phyla encompassing all cultured and well-characterized bacterial SRM  represented cultured thiosulfate-reducing bacteria that employ the Dsr-pathway , including the classes Alphaproteobacteria, Gammaproteobacteria, and Magnetococcia, and to the Bacteroidota, class Chlorobia (2%). The two phyla represent cultured and well-characterized SOM with an oxidatively-operating Dsr-pathway   and Actinomycetota (14 MAGs) representing the most prominent groups , SZUA-79, OLB16, and AABM5-125-24 were all characterized by the full set of Dsr-pathway genes, including dsrD as indicator for a reductively operating metabolism and dsrL of type 2  or DsrL-2 (f_9FT-COMBO-42–15) indicating an oxidatively operating Dsr-pathway , which represent canonical SOM and carried gene combinations of the Dsr-pathway typical for SOM . However, five representatives of the Bacteroidota family UBA2268 (class Kapabacteria), encoded both DsrD and DsrL-2 (which was clearly distinct from DsrL of the Chlorobiaceae) indicating a reductively operating Dsr-pathway. In situ transcriptional profiles of UBA2268 MAGs recovered from hot spring microbial mats clearly supported a reductively operating Dsr-pathway activated under anoxic conditions  or oxidative sulfur metabolism (no dsrD but dsrL-1 or dsrL-2). Furthermore, our analysis recovered six SAR324 members with different gene combinations of the Dsr-pathway. Two SAR324 MAGs, which affiliated to the provisional family XYD2_FULL-50–16, were recovered from the terrestrial subsurface with an indicated reductive sulfur metabolism . The remaining four MAGs (f_NAC60-12) were recovered from marine environments with an indicated oxidative sulfur metabolism . The latter coincides well with reports of Dsr-pathway encoding SAR324 from oxygenated deep ocean waters  and the Desulfobacterota (five MAGs within the family Desulfocapsaceae). Here, at least two different bacterial-type DsrAB, including always one reductive and one oxidative one, were encoded on the same genome  encoded oxidative DsrAB as well but was missing further genes indicative of a reductive or oxidative metabolism. Yet another unusual representative of the terrestrial deep subsurface was the single MAG representing candidate phylum CG2-30-53–67  and estimated completeness ranging from 77% to 99% . The combination of this high binning quality with the recovery of such unusual dsrAB gene combinations in multiple studies from various environments and several phylogenetic lineages makes the likelihood quite high that these MAGs represent real microorganisms awaiting discovery using cultivation approaches.The five unusual MAGs from the family ila Fig. . A unitiL-2 Fig. , which cium Fig. . Both dsium Fig. . Since oBacteroidota and Pseudomonadota, (ii) Nitrospinota and Nitrospirota MAGs with an indicated oxidative sulfur metabolism, (iii) and MAGs encoding oxidative and reductive DsrAB on the same genome. For the latter group, DsrL-2A and DsrL-2B were always encoded just downstream of oxidative DsrAB, with the respective genes being part of the same operon  canonical SOM of the ron Fig. . Therefoism Fig. . Howevere genome , making -pathway are currently characterized by a solely reductively operating Dsr-pathway. Among the 48 archaeal genomes studied, 67% belonged to cultured representatives with a known sulfate-, sulfite-, or thiosulfate-reducing metabolism within the phyla Thermoproteota  and Halobacteriota (Archaeoglobus spp.). The remaining MAGs expanded the diversity of archaea encoding a Dsr-pathway to include two additional phyla (Hydrothermarchaeota and Thermoplasmatota) and four additional families in the Thermoproteota (three families) and Halobacterota (one family). Most of the DsrAB-encoding archaea either represented cultured thermophiles or their MAGs were retrieved from high-temperature environments  including their deposits . The only exceptions were two Halobacteriota MAGs  and one Thermoplasmatota MAG (GCA_002503985) assembled from marine sediment or soil metagenomes, respectively  were represented by MAGs , bacterial reductively-operating DsrAB were spread between these two clusters in a bush-like manner  as an organism capable of growth by thiosulfate reduction  recovered from deep marine subsurface sediments . Chloroflexota representing uncultured family lineage 5 were recovered from hydrothermal sediments and a bioreactor  and DsrAB sequences of uncultured lineages 8 and 9 were found in MAGs of terrestrial and marine Acidobacterota, respectively genome targeted approaches. However, the affiliation of uncultured family lineages 2, 4, 7, and 12 still awaits its discovery.Previous PCR-based dsrAB genes among extant microorganisms is represented by a combination of divergence through speciation, functional diversification and lateral gene transfer (LGT)  from Desulfobacterota   and Korarchaeaceae (Ca. Methanodesulfokores washburnensis) , which will be useful for dsrAB gene amplicon sequencing analyses  and DSR2107r mix (88%). This covers the great majority of (putative) SRM B amplicons  and rDSR4r mix (95%), which can be used for short-read amplicon sequencing  genome sequence is available so far. Even though their genomic context provides clues about a reductively or oxidatively operating dissimilatory sulfur metabolism, we still miss a large part of their actual physiology. Enriching and isolating these microorganisms into culture will remain the best approach to understand their biology but will also take time. An alternative to cultivation is to understand the ecophysiology of these newly discovered SRM (and SOM) in their natural setting using controlled experimental setups along with studying their activity responses at the transcriptome and/or proteome level or using isotope labeling techniques at the population or single-cell level. In the following, we describe several examples, where this has been achieved.Acidobacteriota encoding a Dsr-pathway were first discovered in pristine low-sulfate environments including peatlands  within the class Terriglobia (comprising former uncultured dsrAB family-level lineage 8) with some recovered MAGs encoding the full canonical pathway of sulfate reduction while others harboring only genes for sulfite reduction. The latter encoded in addition enzymes that can liberate sulfite from organosulfonates, implying organic sulfur compounds as complementary energy sources. Interestingly, these Acidobacteriota encoded also the full respiratory chain for aerobic respiration including low and high affinity terminal oxidases as well as a large enzymatic repertoire for polysaccharide degradation and sugar utilization. In addition, capabilities for a fermentative lifestyle and hydrogen oxidation were encoded as well. This “Swiss army knife”-array of potential energy metabolism variants opens up a lot of possibilities how these Dsr-pathway encoding Acidobacteriota may cope with the fluctuating redox conditions in peat soils. The redox state of these typically water-saturated soils can change dramatically and is mainly driven by changes in the water table through rainfall and droughts. In addition, lateral flow of water can heavily influence the topography of redox gradients in peat soils through space and time  with and without externally supplied sulfate, Dsr-pathway encoding Acidobacteriota showed a steady transcriptional activity including all genes of the Dsr-pathway. However, there was no significant increase of transcriptional activity triggered by either one of the individually supplied low-molecular weight compounds indicating that the activity of the respective Acidobacteriota rather relied on organic substances already present in the peat itself  and represented 4% of dsrB transcripts, emphasizing their in situ activity. When expanded to a global marine dsrAB gene dataset, acidobacterial dsrAB genes averaged 15% in marine sediments worldwide  than peatland Dsr-pathway encoding Acidobacteriota and comprise former uncultured dsrAB family-level lineage 9. Detailed annotation of their genomes revealed the metabolic potential for various respiratory pathways based on oxygen, nitrous oxide, metal-oxide, tetrathionate, sulfur and sulfate/sulfite as terminal electron acceptor. Potential electron donors comprised cellulose, proteins, cyanophycin, hydrogen, and acetate . In contrast to their well-studied phototrophic and sulfur-oxidizing relatives within the Chlorobiaceae, UBA2268-related MAGs retrieved from microbial mats of hot springs or groundwater encode reductive DsrAB as well as DsrD and DsrL-2C. For one of these MAGs (Candidatus Thermonerobacter thiotrophicus), the genome was annotated in greater detail and its transcriptional profile characterized during the diel cycle in the microbial mat of the thermal outflow of Mushroom Spring in Yellowstone National Park, USA  during the night, which cease during daytime because of oxygen production by cyanobacteria-driven photosynthesis , the partial genome of Dsr-pathway encoding Candidatus Sulfobium mesophilum (Nitrospirota family UBA6898) could be recovered. Parallel metaproteomics revealed active expression of its Dsr-pathway under gypsum amendment in support of a sulfate-reducing lifestyle. Interestingly, Ca. S. mesophilum also encoded the full pathway of dissimilatory nitrate reduction to ammonia, which was expressed in the treatment without gypsum amendment. The relative abundance of Ca. S. mesophilum was similar under both treatments, indicating that it maintains a stable population in rice paddy soils while shifting its primary energy metabolism. In contrast to the Acidobacteriota described above, Ca. S. mesophilum was rather adapted to the breakdown of classical substrates of SRM covering the metabolic potential to utilize butyrate, formate, H2, and acetate as an electron donor  encoded only the genetic potential to reduce sulfite to sulfide including DsrD, lacked the dsrL gene, and were so far isolated or encountered in intestines of humans and animals including pig, chicken, and termites , meta-omics based studies under constrained environmental conditions could provide strong evidence of a sulfate-reducing lifestyle. However, the large majority of novel, putative SRM still await experimental confirmation of their physiology. Furthermore, we could show that the primers used in dsrAB gene-based approaches cover a large fraction of the novel diversity of SRM, with many of the previously taxonomically unresolved DsrAB lineages now anchored by DsrAB-encoding MAGs. As such, dsrAB gene-based surveys can be used with confidence in the future to explore the enigmatic world of a functional microbial guild that has shaped biogeochemical cycling on Earth since the Archaean (Shen et al. Metagenome-driven discoveries have opened a new window into the hidden diversity of SRM. We can now start to appreciate that besides the four bacterial and two archaeal phyla harboring cultured SRM, the potential to perform dissimilatory sulfate/sulfite reduction extends to a total of 23 bacterial and 4 archaeal phyla. Many of the phyla now recognized to play a role in sulfur cycling were represented by DsrAB-encoding MAGs recovered from low-sulfate environments, supporting the notion that hidden or cryptic sulfur cycling in low-sulfate environments is an understudied area. For a few of these potential SRM, such as members of the fuad058_Supplemental_FileClick here for additional data file.


Drosophila melanogaster underlies some major advances in the understanding of metazoan development and biology. To examine whether the publication record for signalling pathway genes can indicate which factors have shaped pathway research, the publication history of selected genes is used to compare differences in research output over time. This is used to discuss how research trends may be shaped by a variety of factors such as advances in technology, ease of study and importance to human health.The development and application of genetic techniques in the fruit fly Drosophilahas been central to the discovery and elucidation of many key signalling pathwaysDrosophilamay provide a good starting point to compare and examine research trends of genes with a common functional role but with different histories. To do this, a defining/prototypic gene was selected as a representative of eleven different pathways (Notch(N) gene for the Notch signalling pathway andwg for the canonical Wnt signalling pathway. The research output over time was compared by plotting the number of research papers published/year for each gene by using gene publication data in FlyBase . The most intensely studied genes, which were all first described between 1916 and 1960 ; the gene encoding the ligand of sev,boss, is not found outside of a limited set of insect speciesThe genestorandsev, publications forTl,egr and Pvr , there is a profound difference. As discussed above, publication rate fortorandsev roseimmediately after discovery, peaking in the 1990s and waning by the early 2000s. However,InRhad very little study until the mid-2000s – pointing to other influences beyond technological limitations. Prior to that time,Drosophilawas not regarded as a good model for human insulin signalling and therefore was largely ignored by the fieldDrosophilagained popularity as a model for the role of the insulin pathway in obesity and ageing - theInRresearch publication rate rose rapidly between 2005 and 2015, reaching similar levels to the more highly studied pathway genes such asNandwg.In common withctively ) show actively . Howeve levels . Howevethcentury, such asNandwg, were discovered because of the visible phenotypic effects of their mutants. The impact of molecular genetics on study of these genes and their cognate pathways is reflected in the steep rise in publication output during the 1980s. Their continued prominence in research reflects the importance of these signalling pathways in development and human health. For other genes described later, technological limitations may have a smaller influence on study and other factors, such as their popularity as tractable model systems and/or relevance to human health, may be more dominant factors in shaping their study.In conclusion, comparison of the publication profile of Drosophila pathway genes shows that for some the research output follow common trajectories, while for others, very different trends are evident. Genes described early in the 20wgfor the canonical Wnt signalling pathway. These choices were aided by examining number of associated pathway papers from the FlyBase Signalling Pathway resource (http://flybase.org/lists/FBgg/pathwaysGenes were selected as ‘representative’ of a pathway such that research on the gene and research of its cognate pathway would be regarded as intimately associated. In this way the number of papers may serve as a proxy measure (but by no means an exact measure) for the pathway’s research and the discovery of the representative gene would be considered by most to coincide with the beginning of research on that pathway. Several criteria were used to select genes: (1) the gene encodes a ligand or receptor (other classes of gene such as transcription factor or kinase are often shared by multiple processes and would therefore not be suitable); (2) the gene must have no or extremely limited functions beyond its associated signalling pathway; (3) ideally, the gene should have no functional redundancy in the pathway e.g. Egfr is the sole receptor of the EFGR pathway. If this is not possible, the dominant form employed by the pathway should be selected e.g.,For assessing the number of research papers published/year for each representative gene, research papers were exported from the reference section of the corresponding gene report in FlyBase (release FB2022_06) to a HitList. The year of publication for each paper was obtained using FlyBase Batch Download and the number of publications/year collated using the PivotTable function in Microsoft Excel.https://www.ou.edu/journals/dis/). This was particularly important for finding early reports describing mutant flies. For more detailed information on the FlyBase bibliography see Marygold et al., 2012.Note: For this work, data from FlyBase was deemed more reliable for finding a complete list of research publications on Drosophila genes over other available research paper resources. First, the Drosophila genes studied in research papers have been associated with papers by expert curators. Second, FlyBase has assembled publications that have not been indexed by PubMed and/or do not have a DOI such as the Drosophila Information Service (


Chuanxiong rhizoma (CX) has been utilized for centuries as a traditional herb to treat blood stasis syndromes. However, the pharmacological mechanisms are still not completely revealed. This research was aimed at exploring the molecular mechanisms of CX treatment for thrombosis. Network pharmacology was used to predict the potential anti-thrombosis mechanism after correlating the targets of active components with targets of thrombosis. Furthermore, we verified the mechanism of using CX to treat thrombosis via molecular docking and in vitro experiments. Network pharmacology results showed that a total of 18 active ingredients and 65 targets of CX treatment for thrombosis were collected, including 8 core compounds and 6 core targets. We revealed for the first time that tissue factor (TF) had a close relationship with most core targets of CX in the treatment of thrombosis. TF is a primary coagulation factor in physiological hemostasis and pathological thrombosis. Furthermore, core components of CX have strong affinity for core targets and TF according to molecular docking analysis. The in vitro experiments indicated that Ligustilide (LIG), the representative component of CX, could inhibit TF procoagulant activity, TF mRNA and protein over-expression in a dose-dependent manner in EA.hy926 cells through the PI3K/Akt/NF-κB signaling pathway. This work demonstrated that hemostasis or blood coagulation was one of the important biological processes in the treatment of thrombosis with CX, and TF also might be a central target of CX when used for treating thrombosis. The inhibition of TF might be a novel mechanism of CX in the treatment of thrombosis. Thrombosis is one of the leading causes of death worldwide, because it is the frequent etiology of myocardial ischemia, stroke and vascular embolism . ThrombuUp to now, the basic framework of hemostasis and thrombosis is still partially understood. Clinicians have attempted to explain hemostasis and thrombosis in light of exogenous cascade activation triggered by tissue factor (TF)-FVIIa complexes for more than half a century . Tissue Ligusticum chuanxiong Hort , a medicinal and food homologous herb [ous herb ,14, is oous herb ,16. For ous herb ,18,19. Rous herb . HoweverThis study comprehensively employed network pharmacology, molecular docking and pharmacological experiments in vitro to evaluate the molecular mechanism of CX thrombosis treatment. The findings from the study provides new insights into the CX antithrombotic disease targets and promotes the rational application of CX. The workflow diagram is shown in A total of 189 chemical components of CX were retrieved from TCMSP database, but only 7 of them met the screening conditions . We further consult literature widely and found that 11 ingredients of CX reported in the literature that have been beneficial in the treatment of cardiovascular disease, such as (Z)-ligustilide, ferulic acid, senkyunolide A, tetramethylpyrazinte, Cnidilide, Butylidene phthalide, Senkyunolide I, Levistolide A, Senkyunolide J, Senkyunolide L, 3-Butylphthalide, etc. Therefore, 18 compounds with potential biological activity were screened out based on network pharmacology combined with literature study . AdditioTo identify the mechanism of CX anti-thrombosis, we constructed a components–disease–targets network using Cytoscape software B. The neThe 65 intersection targets of CX in thrombosis treatment were incorporated into STRING to construct the PPI network, which contained 65 nodes and 488 edges C. Using Aiming to obtain further insights into the potential effects of CX in the treatment of thrombosis on a systematic level, we proceeded with an enrichment analysis of the 65 targets of CX anti-thrombosis in terms of BP, CC, MF and signaling pathways through using the DAVID database. As shown in Although TF was not the core target of thrombosis-treating CX predicted with previous network pharmacology, this might be related to the limitation of network pharmacology. Therefore, in view of the important role of TF in the process of hemostasis and thrombosis, we further explored whether TF was the core target of CX treating thrombosis. The targets of CX in the treatment of thrombosis and TF were extracted from thrombotic disease targets A, and thAutodock-vina further confirmed the degree of interaction between the core targets and components of CX. Binding energy below 0 suggests that the targets and ingredients combine spontaneously, and energy ≤ −5.0 kcal/mol suggests that the components and targets interact adequately, whereas energy ≤ −7.0 kcal/mol shows a robust association . The sixMoreover, the results of network pharmacology demonstrated that TF was strongly linked to the majority of the principal targets of CX treatment of thrombosis. Therefore, we examined the molecular docking of TF (PDB ID: 4YLQ) and the active components of CX . The finThe above results of network pharmacological research and molecular docking showed that TF might be a key target in CX anti-thrombosis. Therefore, in the next step, we used pharmacological experiments to verify the prediction. The above results of network pharmacology and molecular docking also showed that ligustilide was one of the core components of CX in the treatment of thrombosis and had a strong binding affinity to core targets and TF. Ligustilide (LIG), a phthalide derivative, whose ratio is the highest in CX volatile oil , is the Firstly, we studied the cytotoxicity of LIG in EA.hy926 cells, and the cells’ viability was tested using an MTT assay. Compared to the normal group, the concentration of LIG (0.1–100 µM) had no cytotoxic effect on EA.hy926 cells . Then, wThe results of GO and KEGG enrichment analysis found that the PI3K-Akt signaling pathway is the key pathway of thrombosis treatment with CX out of the top 20 signaling pathways. To further confirm the mechanism of LIG in regulating the expression of TF by the PI3K/Akt signaling pathway in EA.hy926 cells, we next evaluated the protein expression level of PI3K/Akt and the downstream pathway (NF-κB pathway). Firstly, the PI3K inhibitor wortmannin was used to investigate whether LIG could inhibit TF through PI3K-Akt signaling. The results of the Western blot assay showed that wortmannin was able to reverse the blocking effect of TF expression induced by LIG, leading to an increase in the expression of the TF protein A. AdditiCX is an extensively used and indispensable herb in traditional Chinese medicine for promoting blood circulation and removing blood stasis (PBCRBS). It has been demonstrated to possess promising efficacy in protecting against cardiovascular diseases  and reduNetwork pharmacology offers a new strategy to search for TCM’s biologically active ingredients, targets and pharmacological mechanisms. On the other hand, this approach also has many limitations. One important disadvantage of network pharmacology is the reliability of the data source. In a general way, network pharmacological studies use public databases to screen out active phytochemicals and related disease targets. However, many of the current databases are not complete in all aspects, because they are built on existing research data. Therefore, the active ingredients and targets predicted using network pharmacology are not necessarily comprehensive and accurate.Salvia miltiorrhiza Bge.), Chuanxiong (Ligusticum chuanxiong Hort.) and Honghua (Carthamustinctorius L.). Their anti-thrombotic mechanism analysis revealed that 25 ingredients had an effect on 29 thrombosis-related molecules, of which 23 molecules are related to inflammation response [For example, a recent report showed that the most commonly used herbs for PBCRBS are Danshen  and other cytokines in endothelial cells and monocytes . In thisThe present study indicated that LIG could inhibit TF procoagulation activity, TF mRNA and protein expression induced by TNF-α in a dose-dependent manner in EA.hy926 cells. Some studies have revealed that TF pathway inhibition is an effective strategy for thrombosis treatment, and experiments in vitro and in vivo have proved that TF inhibition could effectively inhibit thrombosis ,39. ConsKEGG enrichment analysis indicated that the PI3K-Akt signaling pathway was the core pathway among the top 20 pathways involved in the therapy of thrombosis using CX. This pathway network has an effect on multiple bodily functions, including metabolism, cell growth, and angiogenesis. Studies have revealed that the PI3K/Akt pathway regulates myocardial ischemia, atherosclerosis and vasodilation and other processes, which can effectively prevent cardiovascular disease ,41,42. IOur data suggested that hemostasis or blood coagulation was one of the important biological processes in treating thrombosis with CX, and TF might also be a central target of CX when treating thrombosis. The results of pharmacological experiments in this study confirmed that the representative component of CX could actually inhibit TF procoagulation activity and the over-expression of TF so as to suppress thrombosis.12H14O2, molecular weight 190.24, purity ≥ 98%). TNF-α and primer were purchased from Thermo Fisher Biochemical Products . TF antibodies were purchased from Abcam  and Proteintech Group, Inc. . The antibodies for p65, phosphor-p65, phosphor-IκBα, Akt, phosphor-Akt and β-actin were purchased from Affinity Biosciences . Horseradish peroxidase (HRP)-conjugated secondary antibody and goat anti-rabbit IgG H&L (Alexa Fluor® 488) were acquired from ImmunoWay Biotechnology . Lysis buffers and DAPI were purchased from Solarbio . The PI3K inhibitor wortmannin was purchased from Beyotime Biotechnology . The RNA extraction kit and BCA kit were bought from CWBIO . The TF activity assay kit was acquired from Abnova . The PVDF membrane was purchased from Millipore Corporation . The FastKing gDNA Dispelling RT Super Mix kit was purchased from TIANGEN .Aladdin  supplied Ligustilide  and relevant literature were consulted to acquire CX compounds. The active compounds of CX were obtained through setting parameters of oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18. In addition, we also selected chemical components of CX with cardiovascular pharmacological activity found in previous literature through consulting PubMed, Web of Science and the CNKI database. From the Swiss Target Prediction, PubChem and Pharmmapper databases, we obtained the targets of CX and converted these targets into gene symbols through the UniProt database. The OMIM, GEO and Gene Cards databases were used to collect thrombus disease targets. A Venn diagram of the CX targets and thrombus targets was visualized to obtain the overlapping targets.The “components-targets-disease” network of CX was created using Cytoscape 3.7 software. The STRING database is used to determine the protein–protein interaction (PPI) and obtain information on protein connections. The results were imported into Cytoscape 3.7 software to simulate the PPI network for network topology analysis.p-value < 0.05 was regarded statistically significant.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID database. GO analysis takes into account biological processes (BP), molecular functions (MF) and cellular components (CC). A The 3D arrangement of central targets was obtained from RCSB’s Protein Data Bank, and the 3D shapes of the ingredients in CX were generated with Chemoffice-2014 and stored as mol2 files. AutoDockVina1.1.2 was used to replicate the interactions between molecules and proteins. The TF receptor (PDB id: 4YLQ) underwent a process of water and ion removal, hydrogen addition and miscellaneous compound elimination; then, it was saved as a pdbqt file in Autodock Tools. A 20 × 20 × 20 point auto grid was placed around the active site to form the catalytic cavity, with its center located at center-x_1.588, center-y_−6.319, center-z_−52.186, thus creating the binding pocket . The Vin2.The EA.hy926 cells, derived from the fusion of human umbilical vein endothelial cells with A549 human lung cancer cells, were purchased from the Shanghai Institute of Cell Biology . Cells were grown in a medium of DMEM F-12 with 10% FBS and 1% penicillin-streptomycin, incubated at 37 °C with 5% CO5 cells/well and incubated for 48 h. Afterward, 10 μL of various levels of Ligustilide (ranging from 0.1 μM to 100 μM) were added to the growth medium. After 24 h, 20 μL of MTT solution was added and incubated for 4 h. Subsequently, 100 μL of DMSO was added to each well, shaken in a shaker for 10 min, and the amethyst formaldehyde was dissolved. Used a microplate reader to obtain absorbance at 490 nm wavelength.The cell survival rate was measured using MTT . EA.hy926 cells were put in 96-well plates at a concentration of 1 × 105 cells/well and cultivated over 24 h. Cells were treated with Ligustilide for 1 h, and then we added 0.01 μg/mL TNF-α into the medium for 4 h. Cultured cells were dissolved with 15 mM octyl-beta-D-glucopyranoside for 15 min, and we obtained the supernatant. According to the activity kit instructions, the cell lysates (10 μL) were left to sit at 37 °C for 30 min in combination with a mixed solution, including the assay diluent, human FVII and human FX. Finally, the Factor Xa chromogenic substrate was added to measure the absorbance at 405 nm every 5 min over 30 min.EA.hy926 cells were introduced into 96-well plates at a concentration of 1.5 × 105 per well for 24 h. Cells were treated as previously described. Cells were collected and placed in the darkness at 4 °C for 40 min with anti-TF antibody. Following PBS rinsing, cells were incubated with goat anti-rabbit IgG H&L secondary antibody in the dark for 40 min. Cells were suspended in 400~500 μL PBS to analyze TF expression with FACS .EA.hy926 cells were grown at a concentration of 2.5 × 10EA.hy926 cells were treated with different concentrations of Ligustilide for 1 h; then, we added 0.01 μg/mL TNF-α into the medium for 2 h. Total RNA was extracted using the RNA Extraction Kit. RNA quantification was quantified using an ND-2000  micro quantitative spectrophotometer. We synthesized cDNA using the FastKing gDNA Dispelling RT Super Mix kit. The GenStar Q-RCR mix was used to test mRNA levels of β-actin and TF. The specific primers were as follows: β-actin, 5′-AGCGGGAAATCGTGCGTGAC-3′, 5′-AGTTTCGTGGATGCCACAGGAC-3′, TF:5′-GAACCCAAACCCGTCAAT-3′, 5′-TCTCATACAGAGGCTCCC-3′.5 cells/well) and treatment proceeded in accordance with the above methods. The cells were disrupted with lysis buffers to extract the total protein. The BCA assay kit was utilized to measure the concentration of protein in the samples according to the specification. The prepared protein samples were separated via SDS-PAGE gel electrophoresis and via the wet transfer membrane method and transferred to the PVDF membrane. Subsequently, the membrane was sealed off with 5% BSA for 2 h and left to incubate with the primary antibodies TF, p65, phosphor-p65, phosphor-IκBα, Akt and phosphorylated Akt overnight at 4 °C. After washing the membrane three cycles with TBST, secondary antibodies were added at a dilution of 1:10,000 and left to incubate for one hour at ambient temperature. β-actin was utilized as an internal reference.EA.hy926 cells were introduced into 6-well plates  was used to capture images.p-value below 0.05 was viewed as having statistically significant difference.The same experiment was conducted independently three or more times. The results are expressed in terms of the mean ± SD. GraphPad Prism 7.0 was utilized for the analysis of the data. A To sum up, the present study comprehensively employed network pharmacology, molecular docking and in vitro experiments to evaluate the possible active constituents and molecular mechanism of CX’s thrombosis-treating properties. The results of the network pharmacology analysis indicated that 18 active ingredients and 65 targets of CX in the treatment of thrombosis were collected. Furthermore, this study first discovered that TF was a crucial target for CX in the treatment of thrombosis. The molecular docking results revealed a strong affinity between the active components of CX and core targets treating thrombosis, including the TF target. As a representative component of CX, LIG could inhibit TF procoagulation activity and the over-expression of TF by the PI3K/Akt/NF-κB signaling pathway. Therefore, the inhibition of TF over-expression might be a novel mechanism of CX anti-thrombosis.


Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.To address this issue, we in this work present MAVEN , an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.MAVEN is available as a fully open-source tool at  The discovery of the Mechanism of Action (MoA) of a small molecule, which describes the biochemical interactions a molecule makes to produce a pharmacological effect, is an important aspect of drug discovery for a wide range of reasons, from repurposing for a new indication to anticipating potential side effects and rationalising phenotypic findings . AdvanceHowever, such approaches often require proficiency in programming languages such as R and Python as well as the command-line, and output computer-readable data which can be difficult to convey to non-specialists, which can hinder scientific communication in multi-disciplinary groups. R/Shiny apps allow for the implementation of R code and the visualisation of results in an interactive GUI, and have been widely used, e.g., also for the integration of multi-omics  data with bioinformatics tools such as COSMOS  and CARNhttps://github.com/laylagerami/MAVEN. To run direct target prediction  the app also invokes PIDGINv4  daminsize’ VIPER parameter which indicates the minimum number of genes per TF regulon, set to 5 by default.To perform causal reasoning on a protein–protein interaction network, the gene expression data must be converted from the “gene-level” to the “protein-level” by inferring upstream TFs driving the expression changes. DoRothEA (Bioconductor dorothea v1.8.0) describes curated TF regulons, so known TF-gene interactions . Each inPre-weighting proteins on the prior knowledge network has shown to improve the causal reasoning results by CARNIVAL . PROGENyThe last analysis tool implemented in MAVEN is causal reasoning to infer dysregulated signalling networks. CARNIVAL  . To put the inferred signalling network into biological context it is possible to perform functional enrichment. To this end, 11 MSigDB  gene setTo demonstrate the app’s utility for generating hypotheses for compound mechanism of action in practice, and to give an overview of the UI and app functionalities, we will now present a case study using the EGFR and ERBB2 (HER2) inhibitor lapatinib .The differential gene expression data used in this case study is derived from lapatinib-treated  HER2-positive BT474 breast cancer cells, from a publication by Sun et al  , Data, Targets, Analysis and Visualisation Fig. . The lanHere the gene expression data and prior knowledge are uploaded and stored in local memory for use in the Analysis tab. The user can browse for their files or use the toggle to load the Omnipath network and the lapatinib gene expression data used in this case study Fig. . As wellAfter checking that the data is in the correct format , the GUThe Targets page is split into four sub-tabs Fig.  and is aOnce the PIDGIN run is complete, the results are saved and also displayed in the third tab, (C) Results, as a data table Fig. , with onhttps://www.uniprot.org/uniprot/P00533 for EGFR). Alternatively, the analysis can be run without targets, and then re-run with selected targets based on these findings to investigate specific target hypotheses. For example, if the final network outputs nodes from a particular signalling pathway, a highly-predicted target upstream of this pathway can be used to refine the final network. The information provided in Fig. Targets can be chosen from the PIDGIN output (by selecting rows) based on the predicted probabilities as well as Tanimoto similarities , or by We took the three targets with highest predicted probability; ERBB4 (0.790), EGFR (0.784) and ERBB2/HER2 (0.724) all known to be expressed in HER2 + breast cancer , 45, to The analysis page is split into three sub-tabs for the three bioinformatics analysis methodologies; DoRothEA, PROGENy and CARNIVAL. The settings for each can be set on the left-hand side of the page Fig. .Fig. 5SeFor the case study, DoRothEA Fig. A was runFollowing DoRothEA analysis, the resulting normalised enrichment scores (NES) for each TF are displayed as a bar chart Fig.  and a coFollowing PROGENy analysis, the results are visualised in the same way—a bar chart of predicted pathway activity score (from -1 to 1 indicating inhibition and activation) Fig.  and a coFollowing DoRothEA and PROGENy analysis, CARNIVAL can be run, taking as input the DoRothEA enriched TFs and pathway weights from PROGENy, as well as the prior knowledge network uploaded in the first Data step. Once complete, the resulting CARNIVAL consensus network is visualised on the visualisation page Fig. . Files fp-value = 2.26e−9) is visualised on the network with participating proteins highlighted in green , the bottom layer consists of the input TFs , and they are connected by signalling proteins with inferred directionality (indicated with blue for up-regulation and red for down-regulation), which along with their interactions have been optimised from the input prior knowledge network. As well as visualising the resulting network and deriving hypotheses from individual nodes, it is possible to perform pathway enrichment using the network proteins in an over-representation analysis. To illustrate this, we ran the enrichment analysis using the BioCarta  gene seteen Fig. . Hence, The enrichment results are also displayed in the GUI as a data table Fig.  and if oThrough the case study, we have demonstrated the ability of the MAVEN R/Shiny app and its constituent tools to produce and report correct target prediction results (predicting the lapatinib targets EGFR and ERBB2), infer both down- and up-regulated transcription factors induced by lapatinib (including FOXO3 and ESRRA), infer pathways known to be modulated by lapatinib , and finally construct and visualise a signalling network which is highly enriched for the HER2 signalling pathway known to be modulated by lapatinib. This demonstrates the detailed insights into compound MoA that can be obtained using MAVEN’s user-friendly interface, and without requiring extensive coding knowledge.https://github.com/laylagerami/MAVEN.Future additions to the app will include a batch upload option to analyse multiple compounds at once and compare their results, options to use other causal reasoning algorithms, and the ability to upload and analyse other data types . Supplementary files such as the MSigDB gene sets will be continuously updated to reflect any major version changes. Suggestions for new features can also be requested on the GitHub page We have developed an R/Shiny app called MAVEN  a novel, feature-rich tool integrating chemical-structure-based target prediction with gene expression-based causal reasoning analysis, coupled with visualisation and pathway enrichment analysis. A case study, using the chemical structure of lapatinib and gene expression data derived from lapatinib-treated HER2 + positive cells, has demonstrated the ease of inferring detailed insights into compound MoA using MAVEN.https://github.com/laylagerami/MAVEN. Operating system(s): Platform independent. Programming language: R, Python. Other requirements: R v.4.1 or higher, Python v.2 or higher. License: GNU General Public License. Any restrictions to use by non-academics: A license is required to use the IBM solver which is only freely available for academics.Project name: MAVEN. Project home page: