Our aim is to recommend the appropriate Chinese medicinals in clinical treatment of insomnia, which are  Based on network pharmacology, the active molecules and mechanism of these four Chinese medicinals treating insomnia were sought and analyzed. The components of the four Chinese medicinals with potential activity were collected and screened. Moreover, the recollected human disease-related targets were correlated through Cytoscape 3.8.2, and the network diagram of drug component disease targets was drawn. Based on the human protein-protein interaction database, the above network diagram was imported to establish the protein-protein interaction (PPI) and composite target pathway (C-T-P) networks. After selecting important information, the pathway analysis was carried out to show the biological process, core target, and core pathway of insomnia treatment.  In this study, 44 active components and 81 drug-disease common targets were obtained; 307 key targets were found in the PPI network; a core cluster composed of 14 nodes and 50 functional associations was found. SuānZăoRénTāng (Sour Jujube Decoction), which is commonly used in insomnia, they have similar effects on the immune system and HPA axis, while the focus of the four Chinese medicinals is mainly on endocrine regulation, and SuānZăoRénTāng (Sour Jujube Decoction) is mainly on anti-inflammatory effect. In summary, the four Chinese medicinals' effective components and main mechanism of in the treatment of insomnia may be related to their participation in the regulation of endocrine. Compared with the existing network pharmacological analysis results of  Insomnia, as a standard clinical symptom associated with neurological or psychological disorders, describes sleep latency or sleep disturbance, as well as the condition of subjective poor sleep quality . It is dSuānZăoRénTāng (Sour Jujube Decoction), GuīPíWán (Spleen-Restoring Pill), and ĀnShénDìngZhìWán  are recommended. Chinese medicinal is more effective in the treatment of insomnia, and there are fewer reports of negative effects.At present, there are three main ways to treat insomnia: cognitive behavior therapy, drug therapy, and traditional Chinese medicine (TCM) intervention . Among tsuānzăorén (Semen Ziziphi Spinosae), zhĭqiào (Fructus Aurantii), báizhú , fúlíng (Poria), báisháo (Radix Paeoniae Alba), chuānxiōng (Rhizoma Chuanxiong), and zhīmŭ (RhizomaAnemarrhenae). According to the domestic literature, the most frequently used drugs for insomnia are suānzăorén (Semen Ziziphi Spinosae), gāncăo (Radix et Rhizoma Glycyrrhizae), yèjiāoténg (Caulis Polygoni Multiflori), cháihú (Radix Bupleuri), fúlíng (Poria), dāngguī (Radix Angelicae Sinensis), chuānxiōng (Rhizoma Chuanxiong), and báisháo (Radix Paeoniae Alba) [SuānZăoRénTāng (Sour Jujube Decoction), which plays a role under the guidance of TCM theory. However, the main components and mechanism of the relevant combinations are still unknown, making it difficult to make a direct comparison with SuānZăoRénTāng (Sour Jujube Decoction). This results in a lack of evidence for clinical choice. Based on the theory of “sour medicinals boost yin,” on which SuānZăoRénTāng (Sour Jujube Decoction) is based, four commonly used Chinese medicinals, namely, suānzăorén (Semen Ziziphi Spinosae), chuānxiōng (Rhizoma Chuanxiong), fúlíng (Poria), and báisháo (Radix Paeoniae Alba), were selected in our study. The network pharmacological flow chart in This study is based on the in-depth discussion of the molecular mechanism of TCM formulas and medicinals for the effective treatment of insomnia. After analyzing 6765 TCM prescriptions for the treatment of insomnia , medicinae Alba) . It is oThe network pharmacology of Chinese medicinals provides a new method to study the protective effect of medicinals during the course of the disease and its possible mechanisms based on network biology and multidirectional pharmacology and makes it possible to explore the medical biological networks and further clarify the human complex network system. The construction of networks of drug and drug targets not only provides a prospective framework for clarifying the relationship between medicinal plants and diseases but also promotes drug research, development, and improvement to a certain extent .Therefore, this study is aimed at analyzing the biomedical targets and mechanism of the four Chinese medicinals for insomnia treatment by network pharmacology and to clarify the components and related properties of them to provide a reference for further pharmacological research and a new perspective for clinical treatment and drug research and development.http://lsp.nwu.edu.cn/tcmsp.php) and PharmMapper [http://www.lilabecust.cn/pharmmapper/), we searched for all chemical components of the four Chinese medicinals. The molecular structures were determined by literature mining and comparison, and the drug compound database was established after screening. Extracting the active target proteins of drugs from the UniProt [http://www.uniprot.org/) database, the target information database was established after processing such as duplicated data elimination.From the platforms of TCMSP  (http://rmMapper  (http:// UniProt  (https:/A), distribution (D), metabolism (M), and excretion (E) to reach the target cells, tissues, and organs to play their roles. Oral bioavailability (OB) and drug-likeness index (DL) are the key parameters of ADME. OB, reflecting drug utilization of oral administration, is the relative amount and rate of absorption of drugs into the blood circulation. As it is the key index to determine the drug characteristic of molecules with pharmacodynamic effects , OB ≥ 30% was taken as one of the screening conditions. DL, a data combing drug compounds and drug database, can be used not only to evaluate whether a compound is suitable for drug design but also to indicate the pharmacodynamic and pharmacokinetic characteristics of drug-like molecules. A compound with DL ≥ 0.18  is considered similar to the drugs in the DrugBank database. Therefore, OB ≥ 30% and DL ≥ 0.18 were taken as the filter conditions in this study. The target compounds of the four Chinese medicinals were predicted from TCMSP, PharmMapper, and UniProt databases. Then, preliminary screening was carried out, such as removing the compounds whose corresponding targets have not been determined, deleting the repeated targets and diseases, etc. Then, using GeneCards [https://www.genecards.org/) platform, the relevant targets of insomnia were predicted. After processing, association and analysis were made between these relevant targets and the target compounds of the four Chinese medicinals for the drug-disease targets, the basis of later pathway analysis, and topology network establishment.Clinically, these four Chinese medicinals are mostly administered orally. They need to go through absorption (eneCards  (https:/http://www.hprd.org/), the human protein-protein interaction database was obtained. And it was imported into Cytoscape [From HPRD  (http://ytoscape  3.8.2 soytoscape  stands fhttps://metascape.org/gp/indexhtml#/main/step1) containing multiple databases such as GO and KEGG can enrich biological pathways and infer protein complex functions. Confirming the species setting (Homo sapiens), the key targets of the four Chinese medicinals for insomnia were imported into the database, and GO enrichment analysis was performed, including biological process analysis (GO-BP), biological cell component (GO-CC), and MF. Next, key targets and core clusters were introduced for analysis of KEGG metabolic pathway. The signal paths of the top 10-log 10 (P) value were selected as the core path.Metascape  platformsuānzăorén (Semen Ziziphi Spinosae)—American tea acid, carotene, wild Jujuboside A, and phytosterol; 15 in fúlíng (Poria)—Poria neoacid B, Poria acid A, and ivy saponin; 7 in chuānxiōng (Rhizoma Chuanxiong)—Yang Chuanxiong quinone, Chuanxiong indole, and Chuanxiong nafurolactone; and 13 in báisháo (Radix Paeoniae Alba)—paeoniflorin, benzoyl paeoniflorin, and sitosterol. After processing, the predicted targets of the four Chinese medicinals were obtained: 213 in suānzăorén (Semen Ziziphi Spinosae), 31 in fúlíng (Poria), 43 in chuānxiōng (Rhizoma Chuanxiong), and 124 in báisháo (Radix Paeoniae Alba). After deleting the duplicated ones, the aggregate amount of predicted targets was 331.A total of 44 active ingredients were found in the four Chinese medicinals, including 9 in Insomnia-disease targets were retrieved through GeneCards platform, and 528 disease targets were obtained. A total of 81 drug-disease common targets were obtained after the 331 active component targets of the four Chinese medicinals were intersected with 528 disease targets. The drug-disease common target PPI network was derived by Cytoscape software, with 1121 nodes and 6231 action associations, as shown in The top 10 key nodes in the PPI network suggested that the mechanism of the four Chinese medicinals in the treatment of insomnia is related to genes such as ESR1, AKT1, SRC, AR, MAPK1, TP53, NR3C1, CREBBP, and EP300. A core cluster composed of 14 nodes and 50 functional associations was exported through the plug-in of MCODE, and the result suggests that these targets play a key role in PPI network connection, including targets like gene RET, PTK2, ERBB2, PTPN2, MET, SHC1, PDGFRB, STAT5B, INSR, PIK3R1, IRS1, CBL, GRB2, and PTPN6, as shown in According to the HPRD information, drug-disease targets and their neighbor targets were introduced to obtain 1121 nodes. The colors from dark orange to light orange were showing the degree value of nodes in descending order.Selecting nodes above the average degree value, we got 307 key targets. Among the 307 targets, diamonds are the top 10 in regard of the degree value, and the others are circular, of which colors appear from dark green to light green in descending order.The 14 circular nodes represent the core cluster, whose colors appear in green black to azure in descending order of degree value. And the 50 edges are shown in gray.The results are shown in Figures P) values from large to small, and the top 10 were selected as the core path. It was indicated that the key targets notably abounded in the AGE-RAGE signal pathway and cGMP-PKG signal pathway in neuroactive ligand-receptor interaction, cancer pathways, tuberculosis, and diabetes complications.As shown in Figures By introducing core clusters for pathway analysis, it was found that the four Chinese medicinals played a therapeutic role in the ErbB signal pathway, cancer pathway, EGFR tyrosine kinase inhibitor resistance, and insulin signal pathway to treat insomnia.Epidemiological evidence shows that insomnia is not only a trigger factor for many accidents but also a contributing factor to human diseases with high incidence rates such as diabetes, hypertension, and malignant tumors . FurtherChinese medicinals have been reported to have a good curative effect and less side effects in the treatment of insomnia . TherefoIn this study, 44 active components and 81 drug-disease common targets were obtained; 307 key targets were found in PPI network; a core cluster composed of 14 nodes and 50 functional associations was found. In addition, gene function annotation and signal pathway analysis were carried out on them.Referring to the node degree value, ESR1, AKT1, SRC, AR, MAPK1, TP53, etc. were chosen as the key targets. ESR1  gene encIn the KEGG pathway analysis, it was found that the key targets were significantly abounded in the neuroactive ligand-receptor interaction, and the core clusters had a mutual relationship with the ErbB signal pathway. Neuroactive ligand-receptor interaction  is a colsuānzăorén (Semen Ziziphi Spinosae), chuānxiōng (Rhizoma Chuanxiong), fúlíng (Poria), and báisháo (Radix Paeoniae Alba) is related to SuānZăoRénTāng (Sour Jujube Decoction). Yet, compared with SuānZăoRénTāng (Sour Jujube Decoction), the heat-clearing effect of this combination is weaker, and the yin-enriching and blood-nourishing effect is stronger. In other word, the combination of the four Chinese medicinals calms the heart and mind by regulating liver qi and nourishing liver blood. From this perspective, it can relate to the premenopausal syndrome featured by deficient blood and floating yang, which is in accordance with the dysfunction of HPA axis and autonomic nerve [From the perspective of TCM, the combination of the four Chinese medicinals of ic nerve .SuānZăoRénTāng (Sour Jujube Decoction). SuānZăoRénTāng (Sour Jujube Decoction) contains 139 active components, among which jujube kernel saponin is one of the core components, and 1386 targets with 27 significant correlated diseases [SuānZăoRénTāng (Sour Jujube Decoction) plays an effect on cell junction tissue paths like regulation of TRP path, adhesion junction, and vitamin B6 metabolism by inflammatory mediators. What is more, this formula had the regulation of ischemic heart disease, psychiatric diseases, neuropathic pain, and other diseases.Comparison was made between the network pharmacology of these four Chinese medicinals and of diseases . CorrelaSuānZăoRénTāng (Sour Jujube Decoction). For target and mediation center degrees above the mean value, estrogen receptor, calmodulin, androgen receptor, and heat shock protein 90 (HSP90) were associated with more than 70 compounds. It is suggested that the mechanism of SuānZăoRénTāng (Sour Jujube Decoction) lies in the multicomponent and multitarget interactions and has many potential therapeutic effects [Flavonoids such as quercetin, kaempferol, and 7-methoxy-2-methyl isoflavone account for the largest proportion and are predicted to be the core components of  effects . Studies effects . Due to  effects –39.SuānZăoRénTāng (Sour Jujube Decoction), the main inflammatory factors related to sleep are tumor necrosis factor (TNF), interleukin-1 (IL-1), interleukin-2 (IL-2), etc. These inflammatory factors can promote sleep to a certain extent, but their high expression level will affect the sleep quality [SuānZăoRénTāng (Sour Jujube Decoction) can produce an antidepressant effect. The mechanism for this may be to reduce the performance of hippocampal TNF-α and IL-1β to regulate the function of the immune system, restrain the apoptosis of hippocampal neurons, and lower brain injury [SuānZăoRénTāng (Sour Jujube Decoction) may affect the immune system by downregulating the expression of cytokine related pathways to achieve anti-insomnia and antidepression effects.In  quality , 41. The quality , while Sn injury . The secn injury –46. In cSuānZăoRénTāng (Sour Jujube Decoction) shows that they have similarities in downregulating the expression of cytokine related pathways, affecting the immune system and regulating the HPA axis. The combined use of the four Chinese medicinals in this study is more likely to play a role in antitumor effect and endocrine regulation, while SuānZăoRénTāng (Sour Jujube Decoction) may be more related to anti-inflammatory effects and central nervous system regulation. However, the network method has its own limitations, and there are differences in the analysis of its target and interaction relationship, so practical research is still needed to verify the information, such as key nodes.The correlation analysis between the conclusion of this study and suānzăorén (Semen Ziziphi Spinosae), fúlíng (Poria), and báisháo(Radix Paeoniae Alba) and chuānxiōng (Rhizoma Chuanxiong) may entail the regulation of the central nervous system and endocrine system and protection of brain function and immune system in patients with insomnia. Compared with SuānZăoRénTāng (Sour Jujube Decoction), these four Chinese medicinals may be more targeted for insomnia patients complicated by endocrine disorders such as climacteric syndrome and insomnia patients complicated by tumor.In conclusion, the related use  of


Hyperglucagonemia occurs in the pathogenesis of type 2 diabetes mellitus (T2DM). In this meta-analysis, we summarized the effects of DPP4 inhibitors on glucagon levels in patients with T2DM.glucagon) using a meal or oral glucose tolerance test were included. Results were combined using a random-effects model that incorporated potential heterogeneity among the included studies.Randomized controlled trials (RCTs) comparing the influence of DPP4 inhibitors on circulating glucagon levels with placebo or other oral antidiabetic drugs (OADs) in patients with T2DM were identified by searches of Medline (PubMed), Embase (Ovid), and CENTER (Cochrane Library). Only studies reporting changes in glucagon level presented as total area under the curve . Analysis of subgroups revealed that study characteristics had no significant effect on results, such as study design , number of patients, mean patient age, proportion of men, baseline HbA1c, duration of diabetes, background therapy, treatment duration, or methods for glucagon measurement . Moreover, DPP4 inhibitors significantly reduced glucagon levels compared to other OADs , and the reduction in glucagon was greater in comparison with insulin secretagogues than in comparison with non-insulin secretagogues (P for subgroup difference =0.03).A total of 36 RCTs with moderate to high quality were included. Overall, the numbers of T2DM patients included for the meta-analyses comparing DPP4 inhibitors with placebo and other OADs were 4266 and 1652, respectively. Compared to placebo, DPP4 inhibitors significantly reduced circulating glucagon levels (standard mean difference [SMD]: -0.32, 95% CI: -0.40 to -0.24, https://inplasy.com/, identifier INPLASY202280104.DPP4 inhibitors are effective at reducing the circulating postprandial glucagon level in T2DM patients. Currently, type 2 diabetes mellitus (T2DM) is one of the key factors contributing to morbidity and mortality worldwide 1, , 1. It hglucagon) using a meal tolerance test (MTT) or a standard OGTT  or after ingestion of a carbohydrate-rich meal, leading to undesirably high plasma glucagon with hyperglycemia. Typically, the effects of antidiabetic treatment on the postprandial glucagon level are measured by the changes in glucagon total area under the curve  with the registration number of INPLASY202280104. The PRISMA 2020 Checklist has been provided in the This study adhered to PRISMA   and CochIn order to search Medline (PubMed), Embase (Ovid), and CENTER (Cochrane Library), the following strategies were used: (1) “DPP4” OR “DPP-4” OR “dipeptidyl peptidase-4 inhibitors” OR “sitagliptin” OR “vildagliptin” OR “linagliptin” OR “saxagliptin” OR “alogliptin” OR “dutogliptin” OR “aemigliptin” OR “anagliptin” OR “teneligliptin” OR “trelagliptin” OR “omarigliptin” OR “gemigliptin” OR “evogliptin”; (2) “α cell” OR “α-cell” OR “glucagon” OR “alpha cell” OR “islet” OR “hormone” OR “hormonal” OR “meal” OR “prandial” OR “postprandial” OR “Oral Glucose Tolerance Test” OR “OGTT”; and (3) “random” OR “randomized” OR “randomised” OR “randomly”. Only studies including human subjects were considered. As part of the final database search, references to related reviews and original articles were also searched. The final database search was carried out on August 25, 2022.glucagon from baseline after treatment utilizing MTT or OGTT in participants in the interventional and control arms. In this review, we included studies with patients who are drug-nave or with T2DM patients who are receiving background OAD therapy. However, studies including T2DM patients on concurrent antidiabetic injection treatment, such as insulin and GLP-1 receptor agonists (GLP-1RAs) were excluded from the current meta-analysis. Our meta-analysis did not include studies that included patients treated with single-dose or single-day DPP4 inhibitors, since we weren’t planning to evaluate the acute effects of DPP4 inhibitors on circulating glucagon. In addition, non-randomized studies, studies including non-T2DM patients, or those without a measurement of AUCglucagon during MTT or OGTT setting were also excluded.We included studies that met the following criteria: (1) English-language articles with full-length content; (2) RCTs with parallel groups or crossovers; (3) Adults with T2DM were randomly assigned to DPP4 inhibitors or placebo groups, or other OADS, for treatment; and (4) reported the changes of AUCDatabase searches, data collection, and quality assessment were carried out by two authors independently. Discussions with the corresponding author were conducted if disagreements occurred. We collected data of study information , study design , patient information , details of background antidiabetic treatments, drugs and doses of DPP4 inhibitors used, regimens of controls, treatment durations, and methods for circulating glucagon measurement. The Cochrane Risk of Bias Tool was used to determine the quality of the included RCTs  accordinglucagon varied among the included RCTs. Heterogeneity was assessed using Cochrane’s Q test  software.The effects of DPP4 inhibitors on circulating glucagon levels compared to controls in T2DM patients were presented as a standard mean difference (SMD) with 95% confidence interval (CI) because the methods and durations for measuring AUCs Q test . It was  results . Analyse results . Additiotry test . For stutry test . DiffereAn overview of the included studies can be found in Accordingly, a total of 28 RCTs were available for the meta-analysis comparing the influence of DPP4 inhibitors with placebo on postprandial glucagon –47. The A total of 13 studies compared circulating glucagon levels in T2DM patients treated with a DPP4 inhibitor or other OAD , 48–55. 2 = 28%). Pooled results showed that compared to placebo treatment, DPP4 inhibitors significantly reduced the circulating postprandial glucagon level in patients with T2DM . Sensitivity analyses limited to studies with FDA-approved DPP4 inhibitors and dosages showed consistent results . Subgroup analyses showed that compared with either insulin secretagogues or non-insulin secretagogues, DPP4 inhibitors still significantly reduce glucagon levels in T2DM patients, and the reduction of glucagon was more remarkable compared with insulin secretagogues  than with non-insulin secretagogues . Egger’s regression tests also suggested low risk of publication biases .The funnel plots for the meta-analyses comparing DPP4 inhibitors with placebo and other OADs were symmetrical, suggesting low-risk of publication biases , number of patients, mean patient age, proportion of men, baseline HbA1c, duration of diabetes, background therapy, treatment duration, or methods for glucagon measurement. In addition, the pooled results of 13 eligible RCTs showed that DPP4 inhibitors may also significantly reduce the circulating postprandial glucagon level in T2DM patients as compared to other OADs. Researchers have suggested that factors originating from nutrient stimulation of the digestive tract may play a key role in this process, namely, that postprandial glucagon production may be gut-derived, not originating from the pancreas (Based on a pooled analysis of RCT results, this meta-analysis concluded that DPP4 inhibitors are effective at reducing postprandial glucagon levels in T2DM patients compared to placebo, as evidenced by a significantly reduced AUCpancreas , 61. Theglucagon using standard MTT/OGTT as the measurement rather than plasma glucagon level at a certain time to comprehensively reflect the dynamic status of postprandial glucagon (via influencing glucagon levels. As part of its biological function, GLP-1 stimulates insulin secretion and inhibits glucagon action when secreted by the small intestine’s L-cells. By inhibiting the DPP-4–induced degradation of GLP-1, DPP4 inhibitors enhance the inhibitory effect of GLP-1 on glucagon, which may be more remarkable in a hyperglucagonaemic state in T2DM patients (As far as we know, this is the first meta-analysis examining how DPP4 inhibitors affect circulating postprandial glucagon levels in people with T2DM. Unlike pharmacological studies in healthy volunteers with a single dose of the medication, we only included studies with T2DM patients who were treated for a stable period with DPP4 inhibitors, aiming to show that the possible glucagon-lowering efficacy of DPP4 inhibitors is clinically relevant. Because the relevant data on glucagon levels after fasting is limited and the circulating level of glucagon is variable according to feeding status, we chose AUCglucagon . Studiesglucagon , 64. Thepatients –67. Thispatients , 69. Howpatients , 71.This meta-analysis has several strengths, including a rigorous literature review, strict inclusion and exclusion criteria, and robust results, comprehensive full-text review to include available related RCTs, and performance of multiple sensitivity analyses to confirm the stability and robustness of the findings. However, this study also has limitations. First, the optimal assessment method for circulating glucagon remains to be developed . TherefoOverall, the results of this meta-analysis showed that DPP4 inhibitors are effective at reducing circulating postprandial glucagon levels in T2DM patients. In view of the importance of hyperglucagonemia in the pathogenesis of T2DM, these results highlight the additional effect of DPP4 inhibitors in patients with T2DM.In conclusion, this meta-analysis revealed the effectiveness of DPP4 inhibitors for reducing circulating postprandial glucagon levels in T2DM patients in comparison with placebo or other OADs. The results confirmed that DPP4 inhibitors improve glycemic control, in part, by affecting glucagon levels.The original contributions presented in the study are included in the article/SC, YZ, and RC conceived, designed, or planned the study; SC collected data and conducted research; RZ and LJ performed or supervised analyses; RZ, SR and YMZ interpreted the results; SC wrote the initial paper; All authors provided substantive suggestions for revision or critically reviewed subsequent iterations of the manuscript, reviewed and approved final version of the paper, and for all aspects of the work in ensuring that questions related to the accuracy.This study received funding from MSD China. The funder had the following involvement in the study: study design, data collection and analysis, and preparation of the manuscript.Jingya Chen of MSD China Holding Co., Ltd., Shanghai, China, assisted literature research of the manuscript. Administrative assistance was provided by Li Qi of MSD China Holding Co., Ltd., Shanghai, China. Medical writing and editorial assistance were provided by Medjaden, Inc. This assistance was funded by MSD China.SC, RZ, YZ and YMZ are employees of MSD China. SR is an employee of Merck Sharp & Dohme LLC., a subsidiary of Merck & Co., Inc., Rahway, NJ, US.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In the Methods-Histology and immunostaining section, “anti-PSA antibody ” should be corrected to “anti-PSA antibody ”. Both antibodies worked well in immunofluorescence and western blot experiments and rendered consistent results. In this paper, AF0246 was used for Figs. The word “PSA” mentioned in the article should be changed to “PSA homologous KLK protein” (or “KLK*” for short in the figures) and annotated “*mouse KLK homologous protein recognized by the PSA/KLK3 antibody” upon the first occurrence. We noticed that PSA (prostate-specific antigen), also called KLK3 , is a human-specific gene, and mice express other KLK homologs. Blast results of the immunogens suggested that the PSA antibodies used in this paper might bind to other KLK family members in the mouse. In this paper, we used the PSA/KLK3 antibodies to label prostate luminal cells, as PSA is a marker of human prostate luminal cells. To verify the antibody specificity, we performed immunostaining on mouse prostate tissue cryosection and found that the antibody indeed labeled the luminal cells in the mouse prostate tissue ” to “anti-PSA antibody ”Page 3, left column, lines 4 and 8Page 5, right column, line 5Page 7, left column, line 1,Page 8, right column, line 1Page 9, left column, line 1, right column, line 10Page 10, left column, line 3Page 11, right column, line 2 from bottomPage 12, left column, line 8Change “PSA” to “PSA homologous KLK protein *” in the following sentences:Specifically, we want to make the following changes:The corrected figures:Figures These corrections will not affect the result and conclusion of the article. We sincerely apologize for any inconvenience caused.Additional file 1.Supplementary Fig. 1. Immunostaining of the prostate tissue cryosection of wild-type C57BL/6J mice. The antibody was against PSA/KLK3 from Affinity (Cat#AF0246). DAPI stained nuclei. Scale bars, 100 μm.


FDXR stood out as the most sensitive and responsive to the low dose of radiation received from the localised radiation treatment by the circulating white blood cells. The activation of FDXR was found to depend on the volume of the body exposed with a peak of expression around 8–9 hours after irradiation was delivered. Finally results obtained ex vivo confirmed the results obtained in vivo.In this study, the transcriptional response of a panel of radiation responsive genes was monitored over time in blood samples after radiation exposure in vivo. For this aim, cancer patients treated by radiotherapy were recruited after consent forms were obtained. Following the first fraction of radiotherapy, 2 mL blood samples were collected at different time points during the first 24h hours (before the second fraction was delivered) and at mid and end of treatment. Amongst the 9 genes studied, the gene CDKN1A, GADD45, CCNG1, FDXR, DDB2, MDM2, PHPT1, SESN1, and PUMA), eight of them regulated by p53 in circulating blood leukocytes at different time points  in cancer patients  undergoing radiotherapy. We discovered that, although the calculated mean physical dose to the blood was very low (0.038–0.169 Gy), an upregulation of Ferredoxin reductase (FDXR) gene transcription was detectable 2 h after exposure and was dose dependent from the lowest irradiated percentage of the body (3.5% whole brain) to the highest,  reaching a peak at 6–8 h. The radiation response of the other genes was not strong enough after such low doses to provide meaningful information. Following multiple fractions, the expression level increased further and was still significantly up-regulated by the end of the treatment. Moreover, we compared FDXR transcriptional responses to ionizing radiation (IR) in vivo with healthy donors’ blood cells exposed ex vivo and found a good correlation in the kinetics of expression from the 8-hours time-point onward, suggesting that a molecular transcriptional regulation mechanism yet to be identified is involved. To conclude, we provided the first in vivo human report of IR-induced gene transcription temporal response of a panel of p53-dependant genes. FDXR was demonstrated to be the most responsive gene, able to reliably inform on the low doses following partial body irradiation of the patients, and providing an expression pattern corresponding to the % of body exposed. An extended study would provide individual biological dosimetry information and may reveal inter-individual variability to predict radiotherapy-associated adverse health outcomes.External beam radiation therapy leads to cellular activation of the DNA damage response (DDR). DNA double-strand breaks (DSBs) activate the ATM/CHEK2/p53 pathway, inducing the transcription of stress genes. The dynamic nature of this transcriptional response has not been directly observed in vivo in humans. In this study we monitored the messenger RNA transcript abundances of nine DNA damage-responsive genes ( Radiotherapy (RT) represents a cost-effective tool in cancer treatment alongside surgery and chemotherapy, with roughly half of patients undergoing RT as part of their treatment; this represents 134,000 radiotherapy “episodes” delivered every year for England alone . While tFDXR, Cyclin G1 (CCNG1), cyclin-dependent kinase inhibitor 1A (CDKN1), Sestrin 1 (SESN1), p53-upregulated modulator of apoptosis (PUMA), damage-specific DNA-binding protein 2 (DDB2), MDM2 proto-oncogene (MDM2), the growth arrest, and DNA damage-inducible 45 (GADD45); importantly, these upregulations are dose-dependent after IR exposure [Radiation-induced cellular damage rapidly activates multiple signal pathways; amongst them the DDR which contribute to cell survival and therapeutic outcome at cellular and patient levels, respectively. The cellular response is coordinated by kinase-signaling cascades; amongst them, the ATM/CHEK2/p53 is activated by induced DNA DSBs  leading exposure ,24,25,26exposure ,31,32,33exposure ,35,36 anexposure . The detexposure . Clinicaexposure , and a bexposure , which pexposure .Biologically, our current knowledge and understanding of underlying radiobiological processes is based mainly on in vitro and animal model studies. The kinetics of transcriptional response after exposure to IR have been studied in different cell types, including blood cells; however, in vivo data are sparse. A first study was performed in patients undergoing total-body irradiation where gene expression was monitored 6 h after the first 1.5-Gy fraction in patients undergoing total-body irradiation (TBI) in preparation for hematopoietic stem cell transplantation . Data poVenous blood from three healthy donors was taken at the Centre for Radiation, Chemical and Environmental Hazards, UKHSA  with informed consent and the ethical approval of the West Midlands–Solihull Research Ethics Committee (REC 14/WM/1182) for these experiments. g at 22 °C for 20 min, the cloudy PBMC layer between a top layer of plasma and a bottom layer of polymorphonuclear cells was collected and transferred to a new tube to proceed with DPBS washes (300× g at room temperature for 10 min). After the isolation, 1.5–2 × 106 PBMCs were seeded in T25 flasks and maintained in LGM-3 culture medium  for 0–72 h at 37 °C in a humidified 5% CO2 atmosphere.From whole blood samples (20 mL per donor), human peripheral blood mononuclear cells (PBMCs) were isolated using a density gradient centrifugation of peripheral whole blood using Lymphoprep . Briefly, blood was diluted with 1:1 volume of Dulbecco’s phosphate-buffered saline (DPBS) and layered on top of the Lymphoprep solution (15 mL of Lymphoprep for 10 mL of diluted blood). After a centrifugation at 800× PBMCs were exposed to 2 Gy of X-rays at room temperature (22 °C) in air on top of a Perspex platform, with a dose rate of 0.5 Gy/min, using the self-contained 250 kVp X-ray unit  with aluminum and copper filtration (~1 mm) containing a Varian NDI-320 source . Dosimetry was performed with a calibrated reference ionization chamber for the exact exposure setup used. Exposures were always monitored using a calibrated UNIDOS E electrometer and “in-beam” monitor ionisation chamber  located at source. Correction factors are used to calculate exact dose. Spatial dose uniformity was checked using Gafchromic EBT2 films  to ensure a homogenous dose was delivered. Once irradiated, the samples were incubated for either 1, 2, 3, 4, 5, 6, 12, 16, 18, 20, or 24 h at 37 °C with 5% CO2. Viability was measured with a Luna FL Automated Fluorescence Cell Counter . Viability values ranged between 0.79 and 0.80% at the time of harvest. Following incubation, PBMCs were mixed with 700 µL of QIAzol Lysis Reagent  and stored at −80 °C. miRNeasy Mini Kit  was used to isolate RNA from blood cells, according to the manufacturer’s instructions. The quantity of isolated RNA was subsequently determined by spectrophotometry using a ND-1000 NanoDrop  and quality was assessed using a Tapestation 2200 . The collection of blood samples from cancer patients for in vivo and ex vivo investigation study was performed at S. P. Grigoriev Institute for Medical Radiology and Oncology of the National Academy of Medical Science of Ukraine. Each patient gave written informed consent to participate to the study. Patient recruitment, including the volume of blood to be taken and the sampling scheme .Patients were irradiated using a Linac Clinac 600c , 6 MeV photon beam, at the dose rate 3072 Gy/min.Extra blood was taken prior to the beginning of the radiotherapy treatment into EDTA tubes and exposed ex vivo to 2 or 3 Gy depending on the patient’s RT dose per fraction . Ex vivoWithin <2 h, PAXgene tubes with patients’ in vivo or ex vivo blood samples were passed to a biobank , where they were cooled down, frozen at −20 °C, and stored at −70 °C until shipment on dry ice to UKHSA RCE, UK, for transcriptional analyses.Total RNA was extracted with the PAXgene Blood miRNA kit  using a robotic workstation Qiacube . The quantity of isolated RNA was determined by spectrophotometry with a ND-1000 NanoDrop and quality was assessed using a Tapestation 220 . A high-capacity cDNA reverse transcription kit  was used to prepare cDNA from 700 ng for the in vivo irradiated samples or 200 ng for the ex vivo irradiated samples of total RNA according to the manufacturer’s protocol.A Rotor-Gene Q  with PerfeCTa MultiPlex qPCR SuperMix  was used to perform the QRT-PCR. The samples were run in triplicate in 10 μL reactions with 1 μL of the cDNA synthesis reaction, together with seven different sets of primers and fluorescent probes at 500 nM concentration each. 3′6-Carboxyfluorescein (FAM), 6-Hexachlorofluorescein (HEX), Texas red (TEX), CY5, Atto 680, and Atto 390  were used as fluorochrome reporters for the probes analysed in multiplexed reactions with different genes per run.Primer sequences:HPRT1 F: 5′ TCAGGCAGTATAATCCAAAGATGGT 3′, R: 5′ AGTCTGGCTTATATCCAACACTTCG 3′, probe: 5′ CGCAAGCTTGCTGGTGAAAAGGACCC 3′;CCNG1 F: 5′ GGAGCTGCAGTCTCTGTCAAG 3′, R: 5′ TGACATCTAGACTCCTGTTCCAA 3′, probe: 5′ AACTGCTACACCAGCTGAATGCCC 3′; PHPT1 F: 5′ TCGCTCTCATTCCTGATGTG 3′, R: 5′ TCGTAGATGTCCGCATGGTA 3′, probe: 5′ CTTGTAGCCGCGCACGATCTCCTT 3′;FDXR F: 5′ GTACAACGGGCTTCCTGAGA 3′, R: 5′ CTCAGGTGGGGTCAGTAGGA 3′, probe: 5′ CGGGCCACGTCCAGAGCCA 3′;GADD45 F: 5′ CTGCGAGAACGACATCAAC 3′, R: 5′ AGCGTCGGTCTCCAAGAG 3′, probe: 5′ ATCCTGCGCGTCAGCAACCCG 3′;DDB2 F: 5′ GTCACTTCCAGCACCTCACA 3′, R: ACGTCGATCGTCCTCAATTC 3′, probe: 5′ AGCCTGGCATCCTCGCTACAACC 3′;MDM2: 5′ CCATGATCTACAGGAACTTGGTAGTA 3′, R: ACACCTGTTCTCACTCACAGATG 3′, probe: 5′ CAATCAGCAGGAATCATCGGACTCAG 3′;SESN1: 5′ GCTGTCTTGTGCATTACTTGTG 3′, R: CTGCGCAGCAGTCTACAG 3′, probe: 5′ ACATGTCCCACAACTTTGGTGCTGG 3′;CDKN1A: 5′ GCAGACCAGCATGACAG 3′, R: TAGGGCTTCCTCTTGGA 3′, probe: 5′ TTTCTACCACTCCAAACGCCGGCT 3′;PUMA: 5′ CGGAGACAAGAGGAGCAG 3′, R: GGAGTCCCATGATGAGATTG 3′, probe: 5′ CCCTCACCCTGGAGGGTCCTGT 3′;HPRT1) internal control was used to normalise the gene target Ct (cycle threshold) values. For each gene, standard curves obtained from a serial dilution of PCR-amplified DNA fragments were used to convert Ct values to transcript quantity.During all our experiments, the reactions were performed with the same cycling conditions: 2 min at 95 °C followed by 45 cycles of 10 s at 95 °C, then 60 s at 60 °C. Data were collected and analysed using Rotor-Gene Q Series software. Hypoxanthine phosphoribosyltransferase 1 . Briefly, when we considered the time needed for gantry rotation, the delivery of one treatment fraction took about 2 minutes. Although the treatment planning systems are able to calculate 3D dose distribution in the human body very accurately, currently-used algorithms are not able to take blood flow into account. Thus, we used 3D dose distribution calculated by the treatment planning system Eclipse version 15.6  to estimate the mean blood dose.Our calculation method assumes that most of the blood flows through the irradiated volume during the 40–60 s-long effective irradiation period (2 min treatment period) and thus radiation dose is delivered to full blood volume.IV) defined as a volume surrounded by 5% isodose and mean dose (D) in this volume. Five percent Isodose is the surface that connects points in 3D dose distribution where the dose is equal to 5% of the prescribed dose. Doses lower than 5% of the prescribed dose can be neglected. The reason for cutting off lower doses is the consistency of calculation with respect to different ranges of CT scans and treatment sites.The Eclipse software displays the geometry of the patient acquired by computed tomography and computed dose distribution. Using these data for each patient, we determined irradiated volume (MBD) was calculated according to the relation 3 of human body weighs approximately 1 kg.We assumed that blood in the patient’s body is irradiated and stored homogeneously, and that 1 dmFDXR gene expression with ex vivo exposures. In vivo samples reflect a partial body irradiation with a dose received to the blood calculated in In vivo sample values were corrected to 2 Gy with blood dose calculations  in ordert test (student’s t test). One-way ANOVA followed by Tukey’s tests was applied for multiple comparisons. A significance p ≤ 0.05 was applied for all tests.Minitab software was used to perform statistical analyses. Data are presented as mean ± standard deviation (SD) and comparisons were analysed by Six patients representing four types of cancer were selected to cover a range of volumes exposed. The detailed description of the patients’ radiotherapy treatments and blood collection time points is provided in Blood dose calculations were obtained and described in FDXR, CCNG1, PHPT1, CDKN1, SESN1, PUMA, DDB2, MDM2, GADD45 was measured by quantitative PCR using RNA extracted from the RT patients’ blood samples collected at 2, 6–8, 16–18, and 24 h  were presented individually in decreasing order of irradiated body %  to the highest,  reaching a peak at 6–8 h for all patients except P1 and P4  where the maximum upregulation was at 24 h. After multiple fractions (mid-RT), the expression level increased further or reached a plateau phase and was still significantly up-regulated at the end of RT. Therefore, the d body % ; a schemTo compare transcriptional dynamics in vitro and in vivo, we performed experiments where PBMCs from healthy donors’ blood were incubated for either 1, 2, 3, 4, 5, 6, 12, 16, 18, 20, or 24 h after irradiation with a 2 Gy dose, used classically as the dose per fraction . FDXR RNA expression increase linearly with the dose in this dose range both in vitro and in vivo [FDXR was conserved across patients. One exception was for the oropharyngeal zone and neck lymph nodes  . This wa in vivo . The datph nodes  where thFDXR transcriptional responses to radiation in vivo and ex vivo from same-donor samples , irradiation time increases and radiation damage is expected to become more normally distributed amongst cells, resulting in a lower mean dose per leukocyte. A novel promising radiotherapy treatment, FLASH radiotherapy, delivers ionising radiation at ultra-high dose rates (≥40 Gy/s), reducing treatment times and radiation-induced damage to healthy tissues ,53. In tIt may be hypothesized that the dose received by immune cells and their response is determined by their localisation during RT. Cells in the tumour area do receive a higher dose, therefore affecting the stress gene response . FDXR is the gene generating the strongest response; FDXR is an established radio-responsive gene in the field of biodosimetry [FDXR responses.Transcriptional upregulation reported after one fraction of radiotherapy is very low. osimetry ,41, recoosimetry . We therFDXR is a mitochondrial flavoprotein which transfers electrons from NADPH to ferredoxin. p53 is a mediator of FDXR-dependent iron metabolism [FDXR variants lead to sensorial neuropathies, damaging optic and auditory neurons [FDXR variant expression [tabolism ,56, crittabolism . FDXR va neurons . Moreovepression . FDXR temporal transcriptional expression dynamics following IR exposure is particularly intriguing; it was previously reported in vitro in white blood cells [od cells  where trod cells . The turod cells  and shouCDKN1A, SESN1, ATF3, MDM2, PUMA, and GADD45A). However, DDB2, FDXR, and CCNG1 responded with slower kinetics, reaching peak expression between 5 and 24 h after exposure. Melanson et al. [FDXR mRNA in a stable transcript cluster with a half-life of 4–6 h, which may explain the constant increase of the FDXR mRNA, as mRNA is synthesized but not degraded rapidly. Nevertheless, blood leukocytes are normally not dividing, and the cell cycle is not playing a role here.Cell cycle affects gene expression following IR. The temporal transcriptional response has previously been assessed in stimulated T lymphocytes . Exposedn et al.  have plaFDXR transcriptional expression fluctuations we observed both in vitro and in vivo as it does not take place in vitro. Similar time-effect patterns were observed in cytogenetic studies involving multiple blood samplings after the RT fraction ,68,69,70FDXR expression were found between WBC and PBMCs at 24 h following a 2 Gy dose [The kinetics of apoptotic death induced by IR in human leukocytes has been studied extensively. Apoptosis in lymphocytes is activated 12–14 h after IR either in vivo or ex vivo; it reaches a maximum at 18–24 h and reduces after 48 h; ,84,85,86 Gy dose . FDXR gene expression at the middle and end of the RT course can be attributed to the features of patients’ individual RT schemes. Cytogenetic studies in vivo during the RT course reported an increase in lymphocytes yields carrying radiation markers proportional to the size of the irradiated target area and exposed body volume [The patterns of y volume ,90,91,92y volume .FDXR transcript variants in vitro and in vivo in RT patients [FDXR gene promoter could lead to increased nascent mRNA levels following IR exposure as a response to this genotoxic stress; it could be a mechanism not yet reported, used by p53 to boost gene expression if necessary. The overall shape of the time course may be the result of the activation of several transient biological processes, (gene expression/nuclear speckles bodies activation) with intense transcription leading to a peak of expression rapidly reached (around 8 h) followed by a decrease (degradation/slower transcription rate) reaching a lower level by 24 h post-exposure. There is another potential cause for the shape of the time course response. There is an extensive co-regulation between splicing and transcription ,95; we rpatients . It is ppatients . These “patients . In the patients . It can FDXR expression in leukocytes during radiotherapy may therefore represent a biomarker informing on the level of ATM/CHEK2/p53 activity [The ATM gene is a sensor of DNA damage , more spactivity , which mFDXR RNA expression as an endpoint. The low dose of ionising radiation exposure reported leads to limited genotoxic damage and the effects of such exposures are dominated by the consequences of cell stress and damage to circulating immune cells, possibly informing inflammation-related late normal tissue toxicities. Confirmation on a larger number of patients is required to retrospectively identify patients that develop radiation toxicity (acute or late) and possibly validate this assay as a predictive biomarker of radiotoxicity. In this study, we demonstrated the possibility to monitor biological response kinetics of circulating immune cells to IR treatment using radiation-dependent Monitoring inter-individual IR responses based on leukocyte RNA expression after the first RT fraction is rapid, affordable, and easy to automate . Such an