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AIM
To directly visualize Helicobacter pylori (H. pylori) by the highly sensitive and specific technique of immunohistochemical staining in colonic tissue from patients newly diagnosed with ulcerative colitis (UC).
METHODS
Colonoscopic biopsies from thirty patients with newly diagnosed UC and thirty controls were stained with Giemsa stain and immu...
Citations
... Bacteria from Helicabacter were found to be predominant in both MP and V groups. There is a known correlation between Helicobacter and UC, however, whether it is a causal relationship remains unclear [32]. The microbial flora from the Weissella genus found to be predominant in the UT group. ...
Background
Several reports demonstrated anti-inflammatory properties of minocycline in various inflammatory disorders including colitis. We have experimental evidence suggesting synergistic anti-inflammatory effect of minocycline with methyl prednisolone in reducing colitis severity in mice, but if this effect is in part related to modulating the composition of colonic microbiota is still unknown.
Methods
the effect of vehicle (V), minocycline (M), methyl prednisolone (MP), or combination (C) regimen on the composition of the microbiota of mice in a state of colon inflammation compared to untreated (UT) healthy mice was determined using 16s metagenomic sequencing, and the taxonomic and functional profiles were summarized.
Results
Overall, the bacterial flora from the phylum Firmicutes followed by Bacteroidota were found to be predominant in all the samples. However, the composition of Firmicutes was decreased relatively in all the treatment groups compared to UT group. A relatively higher percentage of Actinobacteriota was observed in the samples from the C group. At the genus level, Muribaculaceae, Bacteroides, Bifidobacterium, and Lactobacillus were found to be predominant in the samples treated with both drugs (C). Whereas “Lachnospiraceae NK4A136 group” and Helicobacter in the M group, and Helicobacter in the MP group were found to be predominant. But, in the UT group, Weissella and Staphylococcus were found to be predominant. Eubacterium siraeum group, Clostridia vadinBB60 group, Erysipelatoclostridium and Anaeroplasma genera were identified to have a significant (FDR p < 0.05) differential abundance in V compared to C and UT groups. While at the species level, the abundance of Helicobacter mastomyrinus, Massiliomicrobiota timonensis and uncultured Anaeroplasma were identified as significantly low in UT, C, and M compared to V group. Functional categories related to amino acid, carbohydrate, and energy metabolism, cell motility and cell cycle control were dominated overall across all the samples. Methane metabolism was identified as an enriched pathway. For the C group, “Colitis (decrease)” was among the significant (p = 1.81E-6) associations based on the host-intrinsic taxon set.
Conclusion
Combination regimen of minocycline plus methyl prednisolone produces a synergistic anti-inflammatory effect which is part related to alternation in the colonic microbiota composition.
... Bacteria from Helicabacter were found to be predominant in both MP and V groups. There is a known correlation between Helicobacter and UC, however, whether it is a causal relationship remains unclear 30 . The microbial ora from the Weissella genus found to be predominant in the UT group. ...
Background
several reports demonstrated anti-inflammatory properties of minocycline in various inflammatory disorders including colitis. We have experimental evidence suggesting synergistic anti-inflammatory effect of minocycline with methyl prednisolone in reducing colitis severity in mice, but if this effect is in part related to modulating the composition of colonic microbiota is still unknown.
Methods
the effect of vehicle (V), minocycline (M), methyl prednisolone (MP), or combination (C) regimen on the composition of the microbiota of mice in a state of colon inflammation compared to untreated (UT) healthy mice was determined using 16s metagenomic sequencing, and the taxonomic and functional profiles were summarized.
Results
Overall, the bacterial flora from the phylum Firmicutes followed by Bacteroidota were found to be predominant in all the samples. However, the composition of Firmicutes was decreased relatively in all the treatment groups compared to UT group. A relatively higher percentage of Actinobacteriota was observed in the samples from the C group. At the genus level, Muribaculaceae, Bacteroides, Bifidobacterium, and Lactobacillus were found to be predominant in the samples treated with both drugs (C). Whereas “Lachnospiraceae NK4A136 group” and Helicobacter in the M group, and Helicobacter in the MP group were found to be predominant. But, in the UT group, Weissella and Staphylococcus were found to be predominant. Eubacterium siraeum group, Clostridia vadinBB60 group, Erysipelatoclostridium and Anaeroplasma genera were identified to have a significant (FDR p < 0.05) differential abundance in V compared to C and UT groups. While at the species level, the abundance of Helicobacter mastomyrinus, Massiliomicrobiota timonensis and uncultured Anaeroplasma were identified as significantly low in UT, C, and M compared to V group. Functional categories related to amino acid, carbohydrate, and energy metabolism, cell motility and cell cycle control were dominated overall across all the samples. Methane metabolism was identified as an enriched pathway. For the C group, “Colitis (decrease)” was among the significant (p = 1.81E-6) associations based on the host-intrinsic taxon set.
Conclusion
combination regimen of minocycline plus methyl prednisolone produces a synergistic anti-inflammatory effect which is part related to alternation in the colonic microbiota composition.
... There is evidence that haemophilus is increased in patients with ulcerative colitis [39]. Helicobacter pylori is also involved in the pathogenesis of ulcerative colitis [40]. The abundance of firmicutes in lymph nodes of patients with ulgenerative colitis is very high [41]. ...
Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.
... In the present study, the baseline of mean hemoglobin level was 9.28gm/dl. This agreed with Sabah et al. (2015) in Egypt reported that the epigastric pain and heart burn were represented about 90% of symptoms in positive Helicobacter pylori antigenic patients, and that the incidence was about 70% in the Nile Delta Also, Mansour et al. (2018) in Egypt reporting that the mean hemoglobin level was 9.52gm/dl in the Helicobacter pylori initiated IBD patients. ...
... There is evidence that Haemophilus is increased in patients with Ulcerative colitis [34]. Helicobacter pylori is also involved in the pathogenesis of Ulcerative colitis [35]. The abundance of Firmicutes in lymph nodes of patients with ulgenerative colitis is very high [36]. ...
Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis and treatment of diseases.In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method is effective.
... According to Mansour et al, these bacteria may elicit a chronic systemic inflammatory process, triggering an autoimmune response so that infection by these bacteria is implicated in having a role in the emergence of ulcerative colitis. 11 In theory, H. pylori infection can interfere with the immune system, causing the process of autoimmunity. However, several recent studies have shown that the prevalence of H. pylori infection is actually lower in IBD patients compared to patients without IBD, so it is considered a protective factor for IBD including ulcerative colitis. ...
... 10 From these controversial results, this evidence-based case report will examine whether infection with H. pylori can reduce or increase the risk of ulcerative colitis based on case reports with H. pylori infection and ulcerative colitis. 10,11 Thus allowing the physician to educate the patient in regards to the possible causative or protective factors of ulcerative colitis. ...
Background: Ulcerative colitis (UC), a chronic inflammatory disease that can cause bloody diarrhea, remains a major global disease burden. While Helicobacter pylori infection is postulated to be able to reduce the occurrence of UC, its role in the disease itself remains contentious. Hence, this meta-analysis aims to examine whether H. pylori infection can lower the chance of developing UC.
Method: A systematic search was conducted through three electronic databases, namely Cochrane, PubMed, and Embase, with the addition of individual hand searching to analyze the association between ulcerative colitis and H. pylori infection in the adult population. Relevant articles selected through eligibility criteria were assessed for quality by using the Newcastle-Ottawa Scale. Furthermore, a random-effects meta-analysis was conducted to estimate the pooled odd ratios (ORs) along with their 95% confidence intervals (CIs). Higgins test and funnel plots were also conducted.
Results: A total of 11,498 patients with UC and 356,130 controls from 22 studies were included in the meta-analysis. Included studies showed fair or good quality. Good quality was achieved with the minimum score of 3 stars for selection, 1 star for comparability, and 2 stars for outcome/exposure, while fair quality was achieved with the minimum score of 2 stars for domain, 1 star for comparability, and 2 stars for outcome/exposure. Our findings indicated that H. pylori infection was associated with lower odds of UC [pooled ORs 0.51 (95% CI: 0.46-0.56)]), albeit moderate heterogeneity (I2= 54%, p = 0.002). Furthermore, publication bias was not found.
Conclusion: The present study adds to the growing body of evidence supporting the potential protective effects of H. pylori infection on the occurrence of UC. However, further primary research with prospective study design needs to be conducted to confirm our findings.
... Some studies concluded that H. pylori has a protective role against UC [11][12][13] . Contradictory results have been reported by other investigators who claimed that H. pylori may have a causative role in UC 14,15 . ...
Background/aim:
Ulcerative Colitis (UC) is an inflammatory bowel disease which is common in many areas of the world including Egypt. A lot of controversy regarding the pathogenesis of UC exist. The current study is an attempt to detect some pathogenic bacteria in UC patients.
Materials and methods:
Endoscopic colonic biopsies obtained from 40 patients with ulcerative colitis and 20 controls were analyzed by means of real-time PCR technique for the presence of Clostridium difficile, Helicobacter Pylori (H. pylori) and pathogenic Escherichia Coli (E. coli) which are positive for KPC and/or OXA-48.
Results:
All patients and control samples were negative for Clostridium difficile. Three of the 40 patient samples (7.5%) and none of the 20 controls were positive for H. pylori with no significant difference between the two groups. KPC-positive E. coli were detected in 11 of the 40 patients (27.5%) and in none of the controls with a significant difference between the two groups (P=0.01). All patients and control samples were negative for OXA-48 positive E. coli.
Conclusion:
Although this study does not support the claim that Clostridium difficile and/or H. pylori have a role in UC, it greatly suggests that pathogenic E. coli may be involved in one way or another in the course of UC.
... H. pylori also contributes to non-GI disorders, such as irondeficient anemia, vitamin B12 deficiency, and idiopathic thrombocytopenic purpura. Extragastric diseases that were previously considered to be independent of H. pylori have recently been found to be related to H. pylori infection [2,3]. Multiple liver diseases, such as chronic viral hepatitis and nonalcoholic fatty liver disease (NAFLD), are also known to be associated with H. pylori infection [4][5][6]. ...
Despite the importance of Helicobacter pylori infection and portal hypertension (PH)-associated gastrointestinal (GI) diseases, such as esophageal varices and portal hypertensive gastropathy (PHG), the impact of H . pylori infection on PH-related GI complications has not yet been elucidated. This meta-analysis investigated the association between H . pylori infection and the risk of PH-related GI complications. An electronic search for original articles published before May 2020 was performed using PubMed, EMBASE, and the Cochrane Library. Independent reviewers conducted the article screening and data extraction. We used the generic inverse variance method for the meta-analysis, and Begg’s rank correlation test and Egger’s regression test to assess publication bias. A total of 1,148 cases of H . pylori infection and 1,231 uninfected controls were included from 13 studies. H . pylori infection had no significant association with esophageal varices [relative risk (RR) = 0.96, 95% confidence interval (CI) = 0.87–1.06 for all selected studies; RR = 0.95, 95% CI = 0.84–1.07 for cohort studies; odds ratio (OR) = 0.96, 95% CI = 0.60–1.54 for case-control studies]. Although H . pylori infection was significantly associated with PHG in case-control studies [OR = 1.86, 95% CI = 1.17–2.96], no significant differences were found in the cohort studies [RR = 0.98, 95% CI = 0.91–1.05] or all studies combined [RR = 1.18, 95% CI = 0.93–1.52]. In conclusion, H . pylori infection was not associated with the risk of PH-related GI complications. Clinicians should carefully treat cirrhotic patients with PH-related GI complications, regardless of H . pylori infection.
... For example, Mansour et al. analyzed colonic biopsies from 30 patients newly diagnosed with UC and 30 controls and found H. pylori in 56.6% of cases and 20% of controls using polyclonal anti-H. pylori antibodies [35]. ...
Background
Epidemiological studies suggest an inverse association between H. pylori infection/exposure and inflammatory bowel disease prevalence/incidence, however, there are no reports of individual patients who developed a "non-transient” ulcerative colitis (UC) following H. pylori eradication.
Case presentation
We report a case of a 72-year-old female with an elderly-onset UC developed upon H. pylori eradication and a 3-year follow-up of the progression to steroid-dependent colitis complicated with enteropathic arthritis and final containment of the disease with golimumab. In our patient, H. pylori eradication was associated with the development of pancolitis that evolved into clinically, endoscopically, and pathohistologically confirmed UC.
Conclusions
The case of our patient provides a unique clinical context for a growing body of literature suggesting molecular mechanisms involved in the interaction of genes, environment, and microbiota to be of critical importance in the etiopathogenesis of UC, and thus, provides a valuable set of complementary translational information for preclinical and epidemiological research on the topic.
... A large number of murine experiments and clinical tests have been performed to identify the bright prospects for the treatment of UC by curcuma, and TNF-α and STAT3 are two research hotspots. It was reported that curcumin and semibionic extraction of compound turmeric can inhibit the proinflammatory signaling by STAT3 and TNF-α in Evidence-Based Complementary and Alternative Medicine experimental colitis [30,31]. Similarly, clinical research studies verified that curcumin inhibits NF-κB expression by regulating tumor necrosis factor-α (TNF-α) in humans [5]. ...
Aim. The incidence of ulcerative colitis (UC) is increasing steadily in developed countries, it is plaguing nearly 1 million people in the United States and European countries, while developing countries have had a rapidly increased incidence over the past decades. Curcuma is widely used in treating malaria, UC, Crohn’s disease, and colon cancer, which lead to diarrhea and bloody stool. However, the systemic mechanism of curcuma in treating UC is still unclear. Our work was supposed to expound how does curcuma alleviate UC in a comprehensive and systematic way by network pharmacology, molecular docking, and experiment verification. Methods. Traditional Chinese Medicine System Pharmacology Database (TCMSP), Shanghai Chemistry & Chemical Industry Data Platform (SGST), and papers published in Chinese Network Knowledge Infrastructure (CNKI) and PubMed were used to collect the chemical constituents of curcuma based on ADME (absorption, distribution, metabolism, and excretion). And effective targets were predicted by Swiss Target Prediction to establish the curcuma-related database. The disease targets of UC were screened by GeneCards and DrugBank databases, and Wayne (Venn) analysis was carried out with curcuma targets to determine the intersection targets. AutoDock software and TCMNPAS system were used to dock the core chemical components of curcuma with key UC targets. Protein interaction (PPI) network was constructed based on the STRING database and Cytoscape software. Gene function GO analysis and KEGG pathway enrichment analysis were carried out by using Metascape database. Finally, HE staining was performed to identify the inflammatory infiltration and expression difference in TNF-α and STAT3 before and after the treatment of curcuma which was verified by immunoblotting. Results. Twelve active components containing 148 target genes were selected from curcuma. Potential therapeutic targets of curcuma in the treatment of UC were acquired from 54 overlapped targets from UC and curcuma. Molecular docking was used to filter the exact 24 core proteins interacting with compounds whose docking energy is lower than −5.5 and stronger than that of 5-aminosalicylic acid (5-ASA). GO and KEGG analyses showed that these targets were highly correlated with EGFR tyrosine kinase inhibitor resistance, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and inflammatory bowel disease (IBD). Experiments verified curcuma relieved pathological manifestation and decreased the expression of TNF-α and STAT3. Conclusion. Curcuma relieved the colon inflammation of ulcerative colitis via inactivating TNF pathway, inflammatory bowel disease pathway, and epithelial cell signaling in Helicobacter pylori infection pathway, probably by binding to STAT3 and TNF-α.
1. Background
UC is characterized by abdominal pain, diarrhea, and bloody stool [1]. Globally, the annual incidence of UC is about 9–20/100,000 and the prevalence is 156–291/100,000 [2]. However, the exact pathogenesis is still not fully clear for the etiology is prototypically diverse. It is known that many factors are involved in the development of UC, interacting environmental, genomic, microbial, and immunological elements [3]. With the deterioration of UC, it will eventually lead to colorectal cancer. Therefore, to prevent before the occurrence of UC, to avoid the complications of UC, and to prevent the recurrence of UC after recovery, a new treatment is urgently needed.
TCM is gaining its popularity in the ameliorating subhealth state and treating disease. Much more attention has been focused on the treatment of UC by Chinese herb; curcuma is one of the popular ones. In ancient China, curcuma was widely used to invigorating the circulation of blood in clinical applications; nowadays, the function of the positive regulation of inflammatory cytokines in inflammatory diseases [4] and its safety [5] attracted many more clinical trials and experimental verifications.
It is well known that TCM is guided by the theory of TCM and characteristics of being multicomponents, multitargets, and multipathways in the treatment of diseases, which meets the requirements of systematically tackling complex diseases such as colorectal cancer. Guo et al. established the model of colorectal cancer and predicted the traditional Chinese medicine components of inhibiting inflammation-induced tumorigenesis by using network pharmacology method [6]. Gupta et al. gathered curcumin’s pleiotropic activities from many research studies to conclude its ability to modulate numerous signaling molecules such as proinflammatory cytokines, apoptotic proteins, cyclooxygenases, and C-reactive protein in human participants [7]. To improve the “one target and one drug” mode to “network targeting multicomponent” mode and to discover traditional Chinese medicine from the perspective of system and molecular level [8], a systemic overall approach of curcuma in the treatment of UC is still needed to verify the previous results and broaden the mechanism of curcuma in the treatment of UC.
2. Methods
2.1. Network Pharmacology
2.1.1. Active Compound Screening
Traditional Chinese Medicine System Pharmacology Database [9] (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php), Shanghai Chemistry & Chemical Industry Data Platform (SGST, http://www.organchem.csdb.cn), and papers published in Chinese Network Knowledge Infrastructure (CNKI, https://www.cnki.net/) and PubMed were used to collect the chemical constituents of curcuma. We screened curcuma compounds based on absorption, distribution, metabolism, and excretion (ADME) [10], and pharmacokinetic information retrieval filters were used to retrieve bioactive compounds for further analysis under the conditions of OB ≥ 30% and DL ≥ 0.18 in TCMSP [9]. We further screen the active ingredients by their effects on the human body. However, the compounds were searched from CNKI, PubMed, and SGST without ADME parameters, so we obtained chemical formula of those components from PubChem (https://pubchem.ncbi.nlm.nih.gov/) to finish Swiss ADME prediction [11], which was requested that OB degree was equal to HIGH and at least two terms of druglikeness were YES [12].
2.1.2. Screening of Possible Targets for Curcuma
PubChem was used to search the chemical structures of the active compounds. Potential targets of curcuma were predicted by Swiss Target Prediction (STP, http://www.swisstargetprediction.) [11]. Probability was used to balance the connection between compounds and targets, which was closer to 1, and it was more connective. We screened targets by the median of probability to establish potential target database related to curcuma.
2.1.3. Predicting the Possible Targets of UC
Data of UC-associated target genes were gathered from GeneCards (https://www.genecards.org/) [13] and DrugBank [14] (https://www.drugbank.ca/) with the keyword “ulcerative colitis.” In addition, articles published in CNKI and PubMed about the known targets of its active compounds were counted [15]. Genes from GeneCards were provided with scores, and genes were selected as UC-related ones whose scores were above the median degree [16].
2.1.4. Gathering Compound-Disease Overlapped Targets
The screened curcuma targets and UC targets were imported into Bioinformatics [17] (http://www.bioinformatics.com.cn/), and the overlapped targets of compound-disease were obtained as the potential targets for further analysis.
2.1.5. PPI Network of Compound-Disease Overlapped Targets
Protein-protein interaction (PPI) network was derived based on the STRING database (https://string-db.org/), which covered almost all functional interactions between the expressed proteins [18]. Species were set as “Homo sapiens,” and the target interaction information was obtained according to the results of analysis.
2.1.6. Gene Ontology (GO) and KEGG Pathway Enrichment Analysis
The biological process (BP), molecular function (MF), cell component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out using Metascape system (https://metascape.org/) [19]. In this research, GO functional annotation and KEGG pathway enrichment analyses were performed using the value less than 0.05.
2.1.7. Construction of Active Component-Target-Pathway Network
A visual network was constructed through Cytoscape software to reflect the complex relationship between active compounds, filtrated targets [17], and pathways based on KEGG pathway enrichment analysis to reflect the relationship between top pathways, included targets, and active compounds. Nodes represented the compounds, targets, and pathways, while edges indicated the interactions between pathways, targets, and components potentially included in the treatment of UC by curcuma.
2.2. Molecular Docking
Using TCMNPAS system [20] and AutoDock [21] software, the docking energy between the overlapped proteins and chemical ingredients of curcuma was calculated. Between the component and the target by docking score value the binding activity should meet two standards: bind tighter than 5-ASA and binding energy was lower than −5.5 to further filtrate the targets related to the treatment of UC by curcuma.
2.3. Experiment Verification
2.3.1. Drugs and Reagents
Curcuma (TCM Pharmacy of Longhua Hospital of Shanghai University of Traditional Chinese Medicine), DSS (MP Biomedicals, USA), absolute ethyl alcohol, Tween-20, xylene substitute (Sinopharm Group Chemical Reagent Co. Ltd.), RIPA Lysis buffer, PMSF, BSA, BCA Protein Quantitation Kit (Beyotime), PAGE gel rapid preparation kit, Multicolor Restrained Protein Ladder (Shanghai EpiZyme Biotechnology Co., Ltd.), β-actin, anti-STAT3 antibody, anti-TNF-α antibody (Abcam Company, England), HE dyeing (Shanghai Yixin Biotechnology Co., Ltd.), and neutral gum (Shanghai Yiyang Instrument Co., Ltd.) were used.
2.3.2. Consumables
Homogenized tube, ceramic beads, frozen storage tube (Shanghai Yike Biotechnology Co., Ltd.), centrifuge tube (Axygen Company, USA), PVDF membrane (Millipore Company, USA), and 96-well plate (Eppendorf Life Sciences Corporation) were used.
2.3.3. Instruments
H2050r high-speed refrigerated centrifuge (Hunan Xiangyi Company), MIX-S vortex mixer, shaker oscillator (Shiloh, USA), TGear mini centrifuge (Tiangen), heating magnetic agitator (Dalong, Beijing), SIM-F140 ice maker (Sanyo, Japan), electronic balance (Sartorius, Germany), enzyme labeling instrument (BioTek, USA), tissue grinding homogenizer (MP Biomedicals, USA), electric constant temperature blast drying oven (Jinghong, Shanghai), electrophoretic system, transfer system, glue rack, ultralow-temperature freezer (SANYO, Japan), microtome (Laika, Germany), and TKY-BMB, electrothermostatic water bath (Hualida).
2.3.4. Animals
Healthy male Sprague Dawley (SD) rats, weighing 180 ± 20 g, were provided by Charles River Experiment Technology Co., Ltd., and the certificate number is SCXK (Hu) 2017-0005. The rats were housed in the animal room of Shanghai University of Traditional Chinese Medicine.
2.3.5. Preparation of Curcuma
The native herb was selected, and standard decoction pieces were prepared with reference to the Chinese Pharmacopoeia (2015 edition). Extract was prepared by boiling the samples in 8 times amount of water for 30 min. The procedure was repeated 3 times.
2.3.6. Groups and the Construction of the UC Model
SD rats were accepted to the laboratory for 7 days before the experiments. According to the random number table, the rats were divided into 3 groups of 4 rats each: control, model, and curcuma. Except for the control group, the UC model was prepared with 5% DSS, and the intervention was given according to the group after 7 days.
2.3.7. Drug Administration
The curcuma group was administered continuously by gavage with 2 ml 0.1 g/ml curcuma suspension for 7 days. The other two groups were given saline 1.08 g/kg.
2.3.8. HE Staining and Western Blot
The steps to stain the samples are as follows: xylene I and xylene II, 10 min for each; 100% alcohol I, 100% alcohol II, 95% alcohol, and 85% alcohol, 5 min for each; water washing for 20 s, hematoxylin for 7 min, water washing for 1 min, 1% hydrochloric acid alcohol for 10 s, 50°C water washing for 5 min, eosin stain for 2 min, and water washing for 10 s; 85% alcohol, 95% alcohol, 100% alcohol II, and 100% alcohol I, each for 2 min; and xylene II and xylene I, 3 min for each. Neutral balsam was added after xylene was passerillaged.
For protein extraction, tissues were placed in homogenized tubes; 5 porcelain beads, 500 μL of RIPA, and 5 μL PMSF were added to each tube. After five times’ homogenization, the colon tissue was basically broken. The supernatant was extracted after centrifugation to test the protein concentration and to collocate protein solution. Equivalent amounts of protein (200 μl) were denatured at 98°C for 10 min in sample loading buffer, then separated by electrophoresis in 15% gel, and electrotransferred onto 0.45 μm polyvinylidene difluoride membranes for 60 min at 350 mA. Subsequently, the membranes were blocked in blocking buffer (0.01 M phosphate-buffered saline, 0.05% Tween-20 with 5% skim milk) at 25 ± 5°C, followed by incubation with primary antibodies against STAT3 (1 : 5000) and TNF-α (1 : 5000) at 4°C overnight. After being washed with Tris-buffered saline containing Tween-20 (TBST) for 5 min three times, the membranes were incubated with a horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. After the membranes were washed three times in TBST for 20 min each time, the bands were visualized on X-ray film using an enhanced chemiluminescence western blotting (WB) detection system. The Image Lab™ software was used for quantitative analysis.
2.3.9. The Whole Workflow of Network Pharmacology Strategy
The workflow of this study is summarized in Figure 1. We built the ingredient-target collection of curcuma and UC-related genes, respectively. The overlapping targets of curcuma and UC were subsequently identified using molecular docking. And the PPI network was constructed according to the targets. Further, the gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed and also compound-target-pathway network was executed. Next, we performed animal experiments to verify the remission of UC by admitting curcuma through HE staining and immunoblotting. Last, key pathways were analyzed to elucidate the mechanism of curcuma in the treatment of UC.
