DEF4A/hBD2, a non-invasive serum biomarker for detection of Ulcerative colitis

DEF4A/hBD2, a non-invasive serum biomarker for detection of Ulcerative colitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article DEF4A/hBD2, a non-invasive serum biomarker for detection of Ulcerative colitis Soumyabrata Chatterjee, Anannya Chakraborty, Susree Roy, Deeya Roychowdhury, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6544404/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract The ulcerative colitis (UC) is a chronic episodic relapsing, and remitting inflammatory bowel disease with increasing frequency worldwide. Along with colonoscopy, faecal calprotectin (FCP) > 150µg/g, elevated faecal Lactoferrin or elevated CRP are now considered for diagnosis and to take treatment decision. But there are a group of patients showing either symptomatic remission with high biomarkers or active disease having no biomarker. Hence, identification of biomarker with better diagnostic potential is still required for UC patients. To determine the deregulated genes in UC, microarray analysis was employed with colonic tissue of UC and irritable bowel syndrome as control. Pathway enrichment analysis with differentially expressed (DE) genes revealed anti-microbial peptide mediated immune response might play pivotal role in UC. Subsequently, qRT-PCR validation depicted that among the DE genes, Defensins showed highest significant alterations in UC compared to control. Among defensins, DEFB4A, DEFA5, and DEFA6, only DEFB4A/hBD2 showed significant upregulation in the UC patients by qRT-PCR. The data was also validated by Immunohistochemistry, and ELISA. A significantly high level of DEFB4A/hBD2 was noted in the serum of active UC patients (p < 0.001) which disappeared in patients in remission (p < 0.001). ROC analysis showed that DEFB4A/hBD2 with AUROC of 0.94 having cut-off value more than 209pg/ml could differentiate between normal and active UC patient with 89% sensitivity, and 80% specificity and with 95% confidence interval of (0.79–0.98). The positive predictive efficiency was 92% while negative predictive efficiency was 73%. These findings highlight that DEFB4A/hBD2 may be considered as a potential serum diagnostic marker for UC patients, though further validation is necessary in larger number of samples. Health sciences/Gastroenterology/Gastrointestinal diseases Biological sciences/Immunology/Inflammation Ulcerative Colitis UC Defensin Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Inflammatory bowel disease (IBD) is a chronic inflammatory condition that affects more than 3.5 million people worldwide. Ulcerative colitis (UC) is one of the major forms of IBD that causes inflammation in the mucosa lining of the colon and rectum [1]. The episode of the disease is usually chronic with remissions and exacerbations [2, 3]. These patients are susceptible to develop colorectal cancer [4] and it occurs earlier than the sporadic cancer [5]. Albeit microorganisms, genetic factors, environmental factors and immune factors have been known to contribute to the causation of the disease, the exact pathogenesis of UC still remains unresolved [6]. The most commonly accepted aetiology of UC is loss of tolerance to commensal bacteria, an important pathogenic mechanism of the innate immune system response [7]. Despite the availability of multiple imaging techniques to detect UC with high accuracy, requirement of experienced personnel, sophisticated instruments and high costs restricted its application [8, 9]. Hence, non-invasive biomarker is urgently required for early detection and monitoring of disease progression and therapeutic responses. Several inflammatory markers are routinely used in the laboratory for IBD diagnosis such as C-reactive protein (CRP), Erythrocyte sedimentation rate (ESR), Leucine-rich alpha-2 glycoprotein (LRG), Faecal calprotectin (FCP) etc. [10, 11] but CRP and ESR are non-specific and has low sensitivity [12] while LRG has not been tested widely. In this regard, the potential of anti-microbial peptides (AMPs) for e.g. Defensins (DEFA5, DEFA6), Hepcidin, Cathelicidins, Lactoferrin, Elafin, Galectin 1, Galectin 3, FCP in the pathogenesis of IBD has been studied [13–19]. Among these, FCP is the most widely used faecal marker for IBD though it is altered in other colonic and intestinal diseases [20]. As clinical symptoms alone are not sufficient to determine the extent of the disease, transcriptomics and proteomics analysis get more importance which endow data that are more effective and disease specific. But platform variation, variation in studied population, limited sample sizes ultimately lead to incomparable data. In this context, meta-analysis with publicly available datasets of UC transcriptomics have retrieved 6 hub genes Lipocalin 2 (LCN2), C-X-C motif chemokine ligand 1 (CXCL1), matrix metalloproteinase 3 (MMP3), Indoleamine 2,3-dioxygenase 1 (IDO 1), matrix metalloproteinase 1 (MMP1) and S100 calcium binding protein A8 (S1008) for UC [21].While machine learning tools are also being explored to identify the potential genes for therapeutic targets in UC [22] using 10 available dataset in GEO and identified Olfactomedin 4 (OLFM4) and complement component 4 binding protein beta (C4BPβ) may be conducive for identification of UC patients. The latest clinical practice guideline depicts that a combination of multiple markers such as FCP > 150µg/g, elevated Faecal Lactoferrin and CRP should be considered for taking treatment decision instead of colonoscopy [23]. But endoscopy is recommended for a patient with remission and with high biomarker or an active patient with no biomarker. Thus, in combination of the above biomarkers, a diagnostic marker with better potential is still required to reduce grey zone of each marker. We have performed a microarray analysis with colonic tissue of UC patients and control samples to identify the differentially expressed genes in UC patients. The pathway analysis with deregulated genes revealed that anti-microbial peptides (AMPs) driven immune response were over-represented among Indian UC patients. Upon qRT-PCR validation of AMPs, it was indicated that defensin or hBD2 showed significant upregulation among Indian UC patients than IBS, and in remission patients, its level was decreased to the normal level. The ROC analysis showed DEFB4A/hBD2 can differentiate UC from normal with 93% specificity and sensitivity. The positive prediction efficiency is also 92% while 73% is the negative prediction value. Methods Ethical Permission The study was approved by the ethical committee of the Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India [IPGME&R/IEC/2019/529]. Informed consent was obtained from each participant or family member. All methods were performed in accordance with the guidelines and regulations. Inclusion of Human Subjects UC patients within the age group 18–65 years coming to the IBD clinic at Gastroenterology Department of School of Digestive and Liver Diseases, IPGME&R for evaluation of the disease were enrolled in the study. Severe UC patients (n = 43) was diagnosed on the basis of European Crohn’s and Colitis Organisation (ECCO) guidelines i.e., bloody diarrhea with stool frequency ≥ 6/day, pulse rate > 90/minute or temperature > 37.8 0 C or Hemoglobin 30 mm/hour or CRP > 30 mg/L along with verification in colonoscopy and colonic histopathology. Activity and chronicity of the disease were determined by the presence of neutrophil infiltration, crypt abscess, and crypt architectural distortion etc. Patients who recovered were having stool frequency ≤ 3/ day with no blood in stool or no urgency were considered as UC in remission (n = 25). Age and sex matched controls were included from the IBS patients (n = 15) attending OPD of IBS Clinic of IPGME&R, and diagnosed according to the Rome IV criteria. Exclusion of Subjects Patients with age 65 years having other pre-existing GI diseases, chronic medical illness viz. Chronic kidney disease, diabetes mellitus etc. were excluded. Biopsy Tissue and Blood Collection Tissue was collected in RNA later and in 10% formalin immediately after biopsy, and washed in sterile saline water. Tissue collected in RNA later was kept at 4 0 C for overnight and then preserved at -80 0 C for future use. Blood was collected in EDTA and non-EDTA vials for separation of plasma and serum by centrifugation and stored at -20 0 C in small aliquots. Total RNA Isolation and Microarray Analysis Total RNA was isolated from 0.5mg of colonic tissue using Trizol (Ambion) following the manufacturer’s protocol. In brief, the colon tissue was homogenized in 500µl of Trizol, followed by addition of 100µl of chloroform and centrifugation. Then 250µl of Isopropanol was added for the precipitation of RNA, and washed with 70% ethanol, air-dried and dissolved in RNase free water. RNA integrity (RIN) was assessed using bioanalyzer and samples from UC and control group (n = 5 each) with RIN value > 7 were subjected to micro array analysis using Illumina platform. The differentially expressed genes were analyzed using the “Limma” package of R Bioconductor (Ritchie et al., 2015). The Benjamini–Hochberg correction was applied to minimize the false discovery rate (FDR). Genes with adjusted p-value below or equals to 0.1 and fold change (± log1) were considered as differentially expressed genes. The “heat mapper” was employed for generation of heatmap with differentially regulated genes. Pathway Analysis Gene Ontology (GO) enrichment analysis was performed with deregulated genes to get information related to biological process (BP), molecular function (MF), and cellular component (CC). We utilized the Biological process component through “cluster Profiler” package of R (version 4.0.3) with the significance threshold of p-value < 0.05 (Yu et al., 2012) cDNA Synthesis and Quantitative Real Time Polymerase Chain Reaction (qRT-PCR) Total RNA (2.5µg) was used to generate cDNA using RevertAid Reverse Transcriptase (Thermo Scientific) following manufacturer’s protocol. cDNA was diluted in 1:30 and subjected to real time PCR using SYBR green PCR master mix (Thermo Scientific) and gene specific primers (Table 1 ) in ABI Quant Studio7 Flex Real time PCR machine in triplicate. Relative expression value [2 – (ΔCt sample-ΔCt control) x10 6 ] was plotted. Each experiment was repeated thrice. Table 1 Sequences of Primers Primers 5’to 3’ direction Primers for gene expression IL6 F IL6 R GGTTGTGGAATCTTGCAGCCTG CTCCAGGCGTCGTGGATGACAC STAT 3 F STAT 3 R CAGCAGCTTGACACACGGTACC CTTGCAGGAAGCGGCTATACTGC TLR4 F TLR4 R AATCTAGAGCACTTGGACCTTTCC GGGTTCAGGGACAGGTCTAAAGA IFN γ F IFN γ R ATTCGGTAACTGACTTGAATGTCC CTCTTCGACCTCGAAACAGC REG 3A F REG3A R CTCATGCTGCTGTCTCAGGTTC CACCAGGGAGGACACGAAGGATC S100A8 F S100A8 R CGCCTTGAACTCTATCATCGAC GCACCAGAATGAGGAACTCCTG HBD2/ DEF4A F HBD2/DEF4A R AAACAACGATGACTCCTGGG AAACAACGATGACTCCTGGG DEFA5 F DEFA5 R GATGAGGCTACAACCCAGAAGC CAGAGTCTGTAGAGGCGGCC DEFA6 F DEFA6 R CCATCCTCACTGCTGTTCTCC TGAAAGCCCTTGTTGAGCCCAA Enzyme Linked Immuno Sorbent Assay (ELISA) DEF4A/HBD2 level was determined in serum of UC and control samples using kit from Elabioscience following manufacturer’s protocol. Statistical Analysis Statistical analysis was performed using GraphPad Prism 5 software (La Jolla, CA,USA). For comparison between the two groups, Mann-Whitney t-test and κ 2 analysis were performed. qRT-PCR data are presented as mean ± standard deviation (SD). Receiver operating characteristic (ROC) analysis was also performed in GraphPad Prism 5. p value ≤ 0.05 was considered statistically significant. Results Clinical, biochemical, and demographic profiles of individuals included in the study The clinical, biochemical and demographic subjects included in the cohort I are presented in the Table 2 . The clinical and biochemical parameters of the UC patients (n = 15) and the control group (n = 15) were compared and found that abdominal pain and weight loss were associated more with the UC patients than the controls (p = 0.003 and 0.05 respectively). The frequency of bloody stool and mucus in stool were also more in the UC patients than the control group (p < 0.001). The UC patients were having higher Erythrocyte sedimentation rate (ESR) (p = 0.009) and lower haemoglobin levels than the controls (p = 0.05). Table 2 Clinical and biochemical parameters of the subjects (cohort1) Variable Control (n = 15) UC (n = 15) P value Baseline characteristics Age (years) Median ( Range) 45 (30–57) 44 (20–65) 0.53 Sex ( Female: Male) 1:14 7:8 0.10 Fever (Yes:No) 1:14 5:10 0.34 Pain abdomen (Yes:No) 3:12 13:2 0.003 Weight loss (Yes:No) 0:15 6:9 0.05 Stool frequency Median (Range) 1 (1–3) 7 (2–12) 3.2x10 -06 Blood in stool (Yes:No) 2:13 15:0 4.15x10 -05 Mucus with stool (Yes:No) 0:15 15:0 1.38x10 -06 Biochemical characteristics Hemoglobin (gram/decilitre) Median (Range) 11 (8.9–14.7) 10.15 (8-12.1) 0.05 ESR (mm per hour) Median (Range) 16 (4–28) 35 (12–150) 0.009 Microarray analysis and qRT-PCR validation to identify differentially expressed genes (DEGs) in UC patients compared to controls The colonic biopsy tissues were stained with Haematoxylin and Eosin (HE) to verify each sample prior to subjecting it for gene expression analysis. To identify the DEGs in the UC patients, four colonic tissue of UC patients (n = 4) were subjected to microarray analysis using Illumina HT platform and compared with control samples (n = 3). After initial normalization of the data, the Principal component analysis (PCA) plot showed UC and control samples were grouped independently (Fig. 1 a). Upon differentially expressed gene (DEG) analysis, 94 significantly up-regulated (log fold change > 1.5, p adj -1.5, p adj <0.05) were obtained as shown in Volcano plot and the heatmap represented the top deregulated genes (Fig. 1 b, 1 c). Pathway analysis with DEGs to select the top deregulated pathways in UC patients The genes altered in microarray analysis were subjected to pathway analysis and the data revealed that anti-microbial humoral response mediated by anti-microbial peptides, anti-microbial immune response and immune response to bacterial lipopolysaccharides (LPS) were among the highest altered pathways [log 2 fold change > 4, p < 0.001) in the UC patients compared to control (Fig. 2 a). This data was further validated by the qRT-PCR verifying a few candidate genes from different pathways such as regulation of tolerance (IL6, TLR4, STAT3 and IFN-γ) and defence response (REG3A, S100A8, hBD2) (Fig. 2 b and 2 c). All the genes were observed to be overexpressed in UC patients compared to control and the data was comparable to the microarray data. Next, the top deregulated pathway genes in anti-microbial response with anti-microbial peptides (AMPs) including DEFB4A/hBD2, REG3A, S100A4 were evaluated in a greater number of patients by qRT-PCR and found that all the three AMPs were significantly upregulated in the UC patients compared to controls. As DEFB4A/hBD2 showed highest alteration in UC patients, we verified two other defensins, DEFA5, and DEFA6. Though microarray analysis displayed higher expression of three defensins in UC patients (Table 3 ), only DEFB4A/hBD2 exhibited significant enhancement in UC samples by qRT-PCR (Fig. 3 a). The overexpression of DEFB4A/hBD2 was also verified in the tissue by immuno-histochemistry in tissue (Fig. 3 b) and in lipopolysaccharide (LPS) treated colon cancer cell line (SW480) and observed higher level of DEFB4A/hBD2 in the tissue of UC patients compared to the control group (Fig. 3 c) Table 3 Microarray analysis data to determine differential expression of the defensin isoforms in the UC vs. control samples GENE ID Log2FoldChange Adjusted p value DEFB4A 4.69 0.005 DEFA5 3.87 0.003 Verification of serum level of DEFB4A/hBD2 to classify its potential as diagnostic biomarker for UC As DEFB4A/hBD2 was the highest altered AMP among UC patients, this gene was further explored to verify its potential as a biomarker for UC. The expression pattern of DEFB4A/hBD2 was verified in a new cohort of UC patients denoted as cohort II. The clinical, and biochemical data of each subject is presented in Table 4 . In this cohort, both active UC patients and UC patients under remission were included. No new control was included. The data was analyzed with the previous control group. The clinical, and biochemical parameters of the remission and the control group were compared and it was found that abdominal pain and weight loss were more associated with the active UC (p = 0.003) while the UC in remission patients showed opposite (p = 0.05). The daily frequency of stool with blood and mucus was lower in the remission group compared to the active UC patients. Hemoglobin level was significantly increased in the remission group (Table 4 ). Table 4 The clinical, and biochemical parameters of the subjects include in the cohort II Variable Control a (n = 15) UC Exacerbation b (n = 28) UC Remission c (n = 25) p value a vs. b p value b vs. c Baseline characteristics Age (years) (Median range) 45 (30–57) 49 (27–65) 43.5 (32–65) 0.1414 0.568 Sex (Female : Male) 3:12 13:16 14:11 0.0875 0.4865 Fever (Yes : No) 3:12 23:5 1:24 < 0.0001 < 0.0001 Pain abdomen (Yes : No) 3:12 26:2 4:21 < 0.0001 < 0.0001 Weight loss (Yes : No) 0:15 24:4 7:18 < 0.0001 < 0.0001 Stool frequency Median (Range) 1 (1–3) 5 (2–12) 2 (1–2) < 0.0001 < 0.0001 Blood with stool (Yes : No) 2:13 25:3 6:19 < 0.0001 < 0.0001 Biochemical characteristics Hemoglobin (gram/decilitre) Median (range) 11 (8.9–14.7) 9.2 (7.6–15.1) 11.45 (8.3–15.5) < 0.0001 < 0.0001 ESR (mm/hour) Median (range) 16 (4–28) 35 (12–47) 27 (2–36) < 0.0001 0.0002 Next, the expression of DEFB4A/hBD2 was verified by qRT-PCR using tissue samples from cohort-II and observed that DEFB4A/hBD2 expression was higher in active UC patients compared to control and it was significantly dropped after remission (Fig. 4 a). The level of DEFB4A/hBD2 was also quantified in the serum and observed a significant enrichment of DEFB4A/hBD2 in the serum of the active UC patients than normal individual and it was decreased in the UC patients under remission (Fig. 4 b). Receiver operating curve (ROC) analysis was performed to verify its potential to be used as a biomarker for classification of UC patients from normal. The area under curve (AUC) was 0.94. The predictive cut-off value for DEFB4A/hBD2 in detecting UC was more than 209 pg/mL. It showed sensitivity and specificity of 93% and accuracy of 87% in differentiating active UC patients with 95% confidence interval (CI) of (0.84-1). The positive predictive efficiency of UC by serum DEFB4A/hBD2 level was 92% while negative predictive efficiency was 73% (Fig. 4 c). Thus, the overall data depicted that serum DEFB4A/hBD2 may be considered as a non-invasive biomarker for detection of UC patients. Discussion In this study we have identified and validated a secretary protein DEFB4A/hBD2 in the blood of UC patients and compared with IBS individuals. In order to identify tissue specific genes to classify this patient group, a microarray analysis was performed with colonic tissue using Illumina platform. After pathway analysis with the significantly upregulated genes, anti-microbial response by antimicrobial peptides (AMP) was the highest hit pathway and validation with qRT-PCR revealed DEFB4A/hBD2 exhibited comparable data with microarray. Thus, we verified this protein in the blood of exacerbated UC patients and patients under remission. The ELISA data suggests that this protein can distinguish both the groups. Thus, this marker showed high efficiency in classifying UC group from normal with 89% sensitivity and 80% specificity, PPV 92% and NPV 73% and cut-off 209pg/mL while 82% sensitivity, 73% specificity, 77% PPV and 79% NPV between active UC and remission with cut-off 235pg/mL. Despite availability of transcriptomics, proteomics, metabolomics data of IBD patients, clinicians are still considering the level of CRP and FCP in routine clinical practice though CRP is non-specific and FCP has major limitations [24]. FCP varies over a few days [25] and even diet and exercise has profound impact on it. It can differentiate inflammatory and non-inflammatory gastrointestinal diseases [26]. Thus, elevated level of FCP can be seen in various inflammatory diseases [27]. FCP might also be increased in non-intestinal inflammatory diseases when microbiota is altered such as decompensated liver cirrhosis [28], pneumonia [29] etc. Proton pump inhibitors [30], glucocorticoids [31], and NSAIDs [32] also induce FCP expression. Thus, along with FCP, colonoscopic confirmation is required before therapy. Moreover, FCP is costlier than DEFB4A/hBD2 estimation. A significantly higher expression of S100A8 compared to control has been noted in our cohort of UC patients in both microarray and qRT-PCR. The monomer S100A8 forms heterodimer with S100A9 in a calcium dependent manner to form FCP. As DEFB4A/hBD2 showed highest significant alteration in expression in our UC cohort compared to S100A8, we verified its serum level and observed significant enrichment in active UC patients while reduced upon remission. A longitudinal study is required to follow the DEFB4A/hBD2 trajectories with patients from diagnosis to exacerbation, initiation of biologic therapy and remission. Though this study has identified a non-invasive marker to follow the disease prognosis of an UC patient, larger sample size in all the groups may allow us to interpret the results in a better way. Further, molecular analysis is also required to understand the impact of enhanced DEFB4A/hBD2 mediated disease progression. Declarations Acknowledgement Authors acknowledged all the participants of this study who agreed to donate blood and colon tissue. Multidisciplinary Research Unit (MRU) of Institute of Post Graduate Medical Education and Research, Kolkata had provided funds for the pilot experiment and also the instrument facilities as required. S Chatterjee and A Karmakar are the recipients of fellowship from University Grant Commission, Government of India and Council of Scientific and Industrial Research, Government of India respectively. Authors contribution A Banerjee and S Banerjee conceived the idea, designed experiments, and drafted the manuscript. S Roy, D Roychowdhury, A Karmakar and A. Saha collected samples. S Chatterjee also collected samples, isolated and quantified RNAs, analyzed data, prepared figures and performed ELISA with A Chakraborty. GK Dhali read the manuscript critically. S. Banerjee supervised all experiments and examined each data. A Banerjee finalized the manuscript. Conflict of interest All authors disclosed to have no competing interests. Data Availability Data may be shared upon reasonable request to the corresponding author with proper documentation. Funding The MRU, IPGME&R supported this study through research grant [MRU/PG/01/2019]. 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Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 02 May, 2025 First submitted to journal 02 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6544404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471539823,"identity":"fb87c4d5-e6d1-45c8-835a-f6b3785858c3","order_by":0,"name":"Soumyabrata Chatterjee","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Soumyabrata","middleName":"","lastName":"Chatterjee","suffix":""},{"id":471539824,"identity":"8fbb936e-adc5-47ff-a4ff-babc73bbddd8","order_by":1,"name":"Anannya Chakraborty","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Anannya","middleName":"","lastName":"Chakraborty","suffix":""},{"id":471539825,"identity":"2c487969-b97f-4520-9d46-5232115ce221","order_by":2,"name":"Susree Roy","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Susree","middleName":"","lastName":"Roy","suffix":""},{"id":471539826,"identity":"38cc288b-4c13-4248-b2bf-e0b5bc3c4a10","order_by":3,"name":"Deeya Roychowdhury","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Deeya","middleName":"","lastName":"Roychowdhury","suffix":""},{"id":471539827,"identity":"2a861750-1dfb-4464-a23d-6e691eb95c9f","order_by":4,"name":"Ankita Karmakar","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Ankita","middleName":"","lastName":"Karmakar","suffix":""},{"id":471539828,"identity":"42a21d27-5b4f-464f-99a4-1f160be83730","order_by":5,"name":"Antara Saha","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Antara","middleName":"","lastName":"Saha","suffix":""},{"id":471539829,"identity":"f005f187-70e4-42bb-ae7d-b3b42b498484","order_by":6,"name":"Gopal Krishna Dhali","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Gopal","middleName":"Krishna","lastName":"Dhali","suffix":""},{"id":471539830,"identity":"2870add2-528e-4c02-9c8b-9d075caf9c46","order_by":7,"name":"Soma Banerjee","email":"","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Soma","middleName":"","lastName":"Banerjee","suffix":""},{"id":471539831,"identity":"d63410f1-4992-40a8-b770-c3862b1e6b60","order_by":8,"name":"Arka Banerjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDCCAxAiAcyRYGCQA/MfkKLFGMxPIFoLECQ2gEh8WvhuH2D8XHHGJo9/dvOxDxYVNunzww4/BNpiJ6fbgF2L5LkEZskzN9KKJe4cS54hcSYtd+PtNAOglmRjswPYtRicAWpr+HA4seFGjjGDZNvh3I2zE0BaDiRuw62F+SdIy/wb+Z8ZJP/9Tzecnf6BkBY2yYYbhxM33MhhBtp3IEFeOge/LZJnGNssG86kFRveSDNmkDiWbLhBOqfgQIIBbr/wnWE+fLPhmE2e3I3kx8wSNXby8rPTN3/4UGEnh0sLAwNjA5zJLAFyKlilAS7l6Lo/AAn5BkLKRsEoGAWjYKQBAAtDaRbM2GKSAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Post Graduate Medical Education and Research","correspondingAuthor":true,"prefix":"","firstName":"Arka","middleName":"","lastName":"Banerjee","suffix":""}],"badges":[],"createdAt":"2025-04-28 06:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6544404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6544404/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22893-4","type":"published","date":"2025-11-06T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84869410,"identity":"69f1be3b-3578-4486-b32b-9e95537c00fc","added_by":"auto","created_at":"2025-06-18 08:49:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":625942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eHematoxylin and Eosin staining of colonic biopsy tissue for Control and Ulcerative colitis patients. \u003cstrong\u003e(b)\u003c/strong\u003ePrincipal Component Analysis (PCA) to determine the relatedness between samples, \u003cstrong\u003e(c)\u003c/strong\u003e Volcano plot shows the statistical significance and magnitude of alteration in data, and \u003cstrong\u003e(d)\u003c/strong\u003e Unsupervised Hierarchical clustering heatmap with top upregulated genes and 15 downregulated genes shows the expression variation of genes among control and UC patients. Red to green represents low to high expression. P\u0026lt;0.05 was considered as significance.\u003c/p\u003e","description":"","filename":"Fig1.hBD2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544404/v1/381f5dad05d8371c63c9c83d.jpg"},{"id":84869407,"identity":"e42a1ae2-92d5-4b5f-be0e-28de5b8ddd5b","added_by":"auto","created_at":"2025-06-18 08:49:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":787922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003ePathway analysis with deregulated genes in UC patients vs. control group using KEGG pathway database. Validation of genes by qRT-PCR analysis from top two altered pathways (b) regulation of tolerance (IL6, TLR4, STAT3 and IFN-γ) and (c) defence response (REG3A, S100A8, hBD2). *, ** and ns mean p\u0026lt;0.05, 0.01 and not significant respectively.\u003c/p\u003e","description":"","filename":"fig2.hBD2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544404/v1/8d4fbee87923eed75bdda780.jpg"},{"id":84871227,"identity":"0ae9f13b-bb55-4b07-8a1b-92d65caeb75c","added_by":"auto","created_at":"2025-06-18 09:05:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":501467,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of expression of defensin isoforms DEFB4A, DEFA6 and DEFA5 by (a) qRT-PCR in colonic tissue of UC patients and control. (b) Immune staining with anti-DEFB4A/hBD2 antibody using control and UC colonic tissue and (c) expression analysis of DEFB4A/hBD2 gene in SW480 cell line treated with or without LPS by qRT-PCR. ** and *** indicate p\u0026lt;0.01 and 0.001 respectively.\u003c/p\u003e","description":"","filename":"Fig3.hBD2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544404/v1/323b612c39ad7ec2c4efedfd.jpg"},{"id":84870566,"identity":"e7313c4d-5bbf-4302-8ae9-45bb22a764c3","added_by":"auto","created_at":"2025-06-18 08:57:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":569680,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of DEFB4A/hBD2 level in control, active UC, UC under remission subjects using (a) colonic tissue by qRT-PCR, and (b) serum by ELISA. (c) \u0026amp; (d) Receiver operating curve (ROC) analysis to reveal its function as biomarker for detection of UC patients. *** and ns indicate p\u0026lt;0.0001 and not significant respectively.\u003c/p\u003e","description":"","filename":"Fig.4.hBD2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6544404/v1/53dbe5792262515f191e86f7.jpg"},{"id":95563894,"identity":"d6026cbb-de92-4a2c-bdd9-6a43773a68d8","added_by":"auto","created_at":"2025-11-10 16:00:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3511341,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6544404/v1/f47f6dd0-514d-4ba1-b0b2-962457e9a1da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DEF4A/hBD2, a non-invasive serum biomarker for detection of Ulcerative colitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD) is a chronic inflammatory condition that affects more than 3.5\u0026nbsp;million people worldwide. Ulcerative colitis (UC) is one of the major forms of IBD that causes inflammation in the mucosa lining of the colon and rectum [1]. The episode of the disease is usually chronic with remissions and exacerbations [2, 3]. These patients are susceptible to develop colorectal cancer [4] and it occurs earlier than the sporadic cancer [5]. Albeit microorganisms, genetic factors, environmental factors and immune factors have been known to contribute to the causation of the disease, the exact pathogenesis of UC still remains unresolved [6]. The most commonly accepted aetiology of UC is loss of tolerance to commensal bacteria, an important pathogenic mechanism of the innate immune system response [7].\u003c/p\u003e \u003cp\u003eDespite the availability of multiple imaging techniques to detect UC with high accuracy, requirement of experienced personnel, sophisticated instruments and high costs restricted its application [8, 9]. Hence, non-invasive biomarker is urgently required for early detection and monitoring of disease progression and therapeutic responses. Several inflammatory markers are routinely used in the laboratory for IBD diagnosis such as C-reactive protein (CRP), Erythrocyte sedimentation rate (ESR), Leucine-rich alpha-2 glycoprotein (LRG), Faecal calprotectin (FCP) etc. [10, 11] but CRP and ESR are non-specific and has low sensitivity [12] while LRG has not been tested widely. In this regard, the potential of anti-microbial peptides (AMPs) for e.g. Defensins (DEFA5, DEFA6), Hepcidin, Cathelicidins, Lactoferrin, Elafin, Galectin 1, Galectin 3, FCP in the pathogenesis of IBD has been studied [13\u0026ndash;19]. Among these, FCP is the most widely used faecal marker for IBD though it is altered in other colonic and intestinal diseases [20]. As clinical symptoms alone are not sufficient to determine the extent of the disease, transcriptomics and proteomics analysis get more importance which endow data that are more effective and disease specific. But platform variation, variation in studied population, limited sample sizes ultimately lead to incomparable data. In this context, meta-analysis with publicly available datasets of UC transcriptomics have retrieved 6 hub genes Lipocalin 2 (LCN2), C-X-C motif chemokine ligand 1 (CXCL1), matrix metalloproteinase 3 (MMP3), Indoleamine 2,3-dioxygenase 1 (IDO 1), matrix metalloproteinase 1 (MMP1) and S100 calcium binding protein A8 (S1008) for UC [21].While machine learning tools are also being explored to identify the potential genes for therapeutic targets in UC [22] using 10 available dataset in GEO and identified Olfactomedin 4 (OLFM4) and complement component 4 binding protein beta (C4BPβ) may be conducive for identification of UC patients. The latest clinical practice guideline depicts that a combination of multiple markers such as FCP\u0026thinsp;\u0026gt;\u0026thinsp;150\u0026micro;g/g, elevated Faecal Lactoferrin and CRP should be considered for taking treatment decision instead of colonoscopy [23]. But endoscopy is recommended for a patient with remission and with high biomarker or an active patient with no biomarker. Thus, in combination of the above biomarkers, a diagnostic marker with better potential is still required to reduce grey zone of each marker.\u003c/p\u003e \u003cp\u003eWe have performed a microarray analysis with colonic tissue of UC patients and control samples to identify the differentially expressed genes in UC patients. The pathway analysis with deregulated genes revealed that anti-microbial peptides (AMPs) driven immune response were over-represented among Indian UC patients. Upon qRT-PCR validation of AMPs, it was indicated that defensin or hBD2 showed significant upregulation among Indian UC patients than IBS, and in remission patients, its level was decreased to the normal level. The ROC analysis showed DEFB4A/hBD2 can differentiate UC from normal with 93% specificity and sensitivity. The positive prediction efficiency is also 92% while 73% is the negative prediction value.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical Permission\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study was approved by the ethical committee of the Institute of Post Graduate Medical Education and Research (IPGME\u0026amp;R), Kolkata, India [IPGME\u0026amp;R/IEC/2019/529]. Informed consent was obtained from each participant or family member. All methods were performed in accordance with the guidelines and regulations.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion of Human Subjects\u003c/h3\u003e\n\u003cp\u003eUC patients within the age group 18\u0026ndash;65 years coming to the IBD clinic at Gastroenterology Department of School of Digestive and Liver Diseases, IPGME\u0026amp;R for evaluation of the disease were enrolled in the study. Severe UC patients (n\u0026thinsp;=\u0026thinsp;43) was diagnosed on the basis of European Crohn\u0026rsquo;s and Colitis Organisation (ECCO) guidelines i.e., bloody diarrhea with stool frequency\u0026thinsp;\u0026ge;\u0026thinsp;6/day, pulse rate\u0026thinsp;\u0026gt;\u0026thinsp;90/minute or temperature\u0026thinsp;\u0026gt;\u0026thinsp;37.8\u003csup\u003e0\u003c/sup\u003eC or Hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;10.5 gm/dl or ESR\u0026thinsp;\u0026gt;\u0026thinsp;30 mm/hour or CRP\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/L along with verification in colonoscopy and colonic histopathology. Activity and chronicity of the disease were determined by the presence of neutrophil infiltration, crypt abscess, and crypt architectural distortion etc. Patients who recovered were having stool frequency\u0026thinsp;\u0026le;\u0026thinsp;3/ day with no blood in stool or no urgency were considered as UC in remission (n\u0026thinsp;=\u0026thinsp;25). Age and sex matched controls were included from the IBS patients (n\u0026thinsp;=\u0026thinsp;15) attending OPD of IBS Clinic of IPGME\u0026amp;R, and diagnosed according to the Rome IV criteria.\u003c/p\u003e\n\u003ch3\u003eExclusion of Subjects\u003c/h3\u003e\n\u003cp\u003ePatients with age\u0026thinsp;\u0026lt;\u0026thinsp;18 years or \u0026gt;\u0026thinsp;65 years having other pre-existing GI diseases, chronic medical illness viz. Chronic kidney disease, diabetes mellitus etc. were excluded.\u003c/p\u003e\n\u003ch3\u003eBiopsy Tissue and Blood Collection\u003c/h3\u003e\n\u003cp\u003eTissue was collected in RNA later and in 10% formalin immediately after biopsy, and washed in sterile saline water. Tissue collected in RNA later was kept at 4\u003csup\u003e0\u003c/sup\u003eC for overnight and then preserved at -80\u003csup\u003e0\u003c/sup\u003eC for future use. Blood was collected in EDTA and non-EDTA vials for separation of plasma and serum by centrifugation and stored at -20\u003csup\u003e0\u003c/sup\u003eC in small aliquots.\u003c/p\u003e\n\u003ch3\u003eTotal RNA Isolation and Microarray Analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTotal RNA was isolated from 0.5mg of colonic tissue using Trizol (Ambion) following the manufacturer\u0026rsquo;s protocol. In brief, the colon tissue was homogenized in 500\u0026micro;l of Trizol, followed by addition of 100\u0026micro;l of chloroform and centrifugation. Then 250\u0026micro;l of Isopropanol was added for the precipitation of RNA, and washed with 70% ethanol, air-dried and dissolved in RNase free water. RNA integrity (RIN) was assessed using bioanalyzer and samples from UC and control group (n\u0026thinsp;=\u0026thinsp;5 each) with RIN value\u0026thinsp;\u0026gt;\u0026thinsp;7 were subjected to micro array analysis using Illumina platform. The differentially expressed genes were analyzed using the \u0026ldquo;Limma\u0026rdquo; package of R Bioconductor (Ritchie et al., 2015). The Benjamini\u0026ndash;Hochberg correction was applied to minimize the false discovery rate (FDR). Genes with adjusted p-value below or equals to 0.1 and fold change (\u0026plusmn;\u0026thinsp;log1) were considered as differentially expressed genes. The \u0026ldquo;heat mapper\u0026rdquo; was employed for generation of heatmap with differentially regulated genes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePathway Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGene Ontology (GO) enrichment analysis was performed with deregulated genes to get information related to biological process (BP), molecular function (MF), and cellular component (CC). We utilized the Biological process component through \u0026ldquo;cluster Profiler\u0026rdquo; package of R (version 4.0.3) with the significance threshold of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Yu et al., 2012)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ecDNA Synthesis and Quantitative Real Time Polymerase Chain Reaction (qRT-PCR)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTotal RNA (2.5\u0026micro;g) was used to generate cDNA using RevertAid Reverse Transcriptase (Thermo Scientific) following manufacturer\u0026rsquo;s protocol. cDNA was diluted in 1:30 and subjected to real time PCR using SYBR green PCR master mix (Thermo Scientific) and gene specific primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in ABI Quant Studio7 Flex Real time PCR machine in triplicate. Relative expression value [2 \u003csup\u003e\u0026ndash; (ΔCt sample-ΔCt control)\u003c/sup\u003e x10\u003csup\u003e6\u003c/sup\u003e] was plotted. \u003cb\u003eEach experiment was repeated thrice.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of Primers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;to 3\u0026rsquo; direction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimers for gene expression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6 F\u003c/p\u003e \u003cp\u003eIL6 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTTGTGGAATCTTGCAGCCTG\u003c/p\u003e \u003cp\u003eCTCCAGGCGTCGTGGATGACAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAT 3 F\u003c/p\u003e \u003cp\u003eSTAT 3 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGCAGCTTGACACACGGTACC\u003c/p\u003e \u003cp\u003eCTTGCAGGAAGCGGCTATACTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4 F\u003c/p\u003e \u003cp\u003eTLR4 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAATCTAGAGCACTTGGACCTTTCC\u003c/p\u003e \u003cp\u003eGGGTTCAGGGACAGGTCTAAAGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFN γ F\u003c/p\u003e \u003cp\u003eIFN γ R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATTCGGTAACTGACTTGAATGTCC\u003c/p\u003e \u003cp\u003eCTCTTCGACCTCGAAACAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREG 3A F\u003c/p\u003e \u003cp\u003eREG3A R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCATGCTGCTGTCTCAGGTTC\u003c/p\u003e \u003cp\u003eCACCAGGGAGGACACGAAGGATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100A8 F\u003c/p\u003e \u003cp\u003eS100A8 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGCCTTGAACTCTATCATCGAC\u003c/p\u003e \u003cp\u003eGCACCAGAATGAGGAACTCCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBD2/ DEF4A F\u003c/p\u003e \u003cp\u003eHBD2/DEF4A R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAACAACGATGACTCCTGGG\u003c/p\u003e \u003cp\u003eAAACAACGATGACTCCTGGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFA5 F\u003c/p\u003e \u003cp\u003eDEFA5 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATGAGGCTACAACCCAGAAGC\u003c/p\u003e \u003cp\u003eCAGAGTCTGTAGAGGCGGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFA6 F\u003c/p\u003e \u003cp\u003eDEFA6 R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCATCCTCACTGCTGTTCTCC\u003c/p\u003e \u003cp\u003eTGAAAGCCCTTGTTGAGCCCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eEnzyme Linked Immuno Sorbent Assay (ELISA)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDEF4A/HBD2 level was determined in serum of UC and control samples using kit from Elabioscience following manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStatistical analysis was performed using GraphPad Prism 5 software (La Jolla, CA,USA). For comparison between the two groups, Mann-Whitney t-test and κ\u003csup\u003e2\u003c/sup\u003e analysis were performed. qRT-PCR data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Receiver operating characteristic (ROC) analysis was also performed in GraphPad Prism 5. p value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical, biochemical, and demographic profiles of individuals included in the study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe clinical, biochemical and demographic subjects included in the cohort I are presented in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The clinical and biochemical parameters of the UC patients (n\u0026thinsp;=\u0026thinsp;15) and the control group (n\u0026thinsp;=\u0026thinsp;15) were compared and found that abdominal pain and weight loss were associated more with the UC patients than the controls (p\u0026thinsp;=\u0026thinsp;0.003 and 0.05 respectively). The frequency of bloody stool and mucus in stool were also more in the UC patients than the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The UC patients were having higher Erythrocyte sedimentation rate (ESR) (p\u0026thinsp;=\u0026thinsp;0.009) and lower haemoglobin levels than the controls (p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and biochemical parameters of the subjects (cohort1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003cp\u003eMedian ( Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e(30\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003cp\u003e(20\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex ( Female: Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (Yes:No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain abdomen (Yes:No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss (Yes:No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6:9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool frequency\u003c/p\u003e \u003cp\u003eMedian (Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(2\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2x10\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood in stool (Yes:No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15x10\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucus with stool (Yes:No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38x10\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (gram/decilitre)\u003c/p\u003e \u003cp\u003eMedian (Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(8.9\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.15\u003c/p\u003e \u003cp\u003e(8-12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm per hour) Median (Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(4\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e(12\u0026ndash;150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroarray analysis and qRT-PCR validation to identify differentially expressed genes (DEGs) in UC patients compared to controls\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe colonic biopsy tissues were stained with Haematoxylin and Eosin (HE) to verify each sample prior to subjecting it for gene expression analysis. To identify the DEGs in the UC patients, four colonic tissue of UC patients (n\u0026thinsp;=\u0026thinsp;4) were subjected to microarray analysis using Illumina HT platform and compared with control samples (n\u0026thinsp;=\u0026thinsp;3). After initial normalization of the data, the Principal component analysis (PCA) plot showed UC and control samples were grouped independently (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Upon differentially expressed gene (DEG) analysis, 94 significantly up-regulated (log fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5, p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05) and 14 down-regulated genes (log fold change \u0026gt;-1.5, p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05) were obtained as shown in Volcano plot and the heatmap represented the top deregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePathway analysis with DEGs to select the top deregulated pathways in UC patients\u003c/h2\u003e \u003cp\u003eThe genes altered in microarray analysis were subjected to pathway analysis and the data revealed that anti-microbial humoral response mediated by anti-microbial peptides, anti-microbial immune response and immune response to bacterial lipopolysaccharides (LPS) were among the highest altered pathways [log\u003csub\u003e2\u003c/sub\u003efold change\u0026thinsp;\u0026gt;\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the UC patients compared to control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This data was further validated by the qRT-PCR verifying a few candidate genes from different pathways such as regulation of tolerance (IL6, TLR4, STAT3 and IFN-γ) and defence response (REG3A, S100A8, hBD2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). All the genes were observed to be overexpressed in UC patients compared to control and the data was comparable to the microarray data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, the top deregulated pathway genes in anti-microbial response with anti-microbial peptides (AMPs) including DEFB4A/hBD2, REG3A, S100A4 were evaluated in a greater number of patients by qRT-PCR and found that all the three AMPs were significantly upregulated in the UC patients compared to controls. As DEFB4A/hBD2 showed highest alteration in UC patients, we verified two other defensins, DEFA5, and DEFA6. Though microarray analysis displayed higher expression of three defensins in UC patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), only DEFB4A/hBD2 exhibited significant enhancement in UC samples by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The overexpression of DEFB4A/hBD2 was also verified in the tissue by immuno-histochemistry in tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and in lipopolysaccharide (LPS) treated colon cancer cell line (SW480) and observed higher level of DEFB4A/hBD2 in the tissue of UC patients compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroarray analysis data to determine differential expression of the defensin isoforms in the UC vs. control samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGENE ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog2FoldChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFB4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eVerification of serum level of DEFB4A/hBD2 to classify its potential as diagnostic biomarker for UC\u003c/h2\u003e \u003cp\u003eAs DEFB4A/hBD2 was the highest altered AMP among UC patients, this gene was further explored to verify its potential as a biomarker for UC. The expression pattern of DEFB4A/hBD2 was verified in a new cohort of UC patients denoted as cohort II. The clinical, and biochemical data of each subject is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In this cohort, both active UC patients and UC patients under remission were included. No new control was included. The data was analyzed with the previous control group. The clinical, and biochemical parameters of the remission and the control group were compared and it was found that abdominal pain and weight loss were more associated with the active UC (p\u0026thinsp;=\u0026thinsp;0.003) while the UC in remission patients showed opposite (p\u0026thinsp;=\u0026thinsp;0.05). The daily frequency of stool with blood and mucus was lower in the remission group compared to the active UC patients. Hemoglobin level was significantly increased in the remission group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical, and biochemical parameters of the subjects include in the \u003cb\u003ecohort II\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUC Exacerbation\u003csup\u003eb\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUC\u003c/p\u003e \u003cp\u003eRemission\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003evs. \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003evs.\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003cp\u003e(Median range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e(30\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003cp\u003e(27\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003cp\u003e(32\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female : Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14:11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (Yes : No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23:5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain abdomen\u003c/p\u003e \u003cp\u003e(Yes : No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4:21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003cp\u003e(Yes : No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStool frequency\u003c/p\u003e \u003cp\u003eMedian (Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(2\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood with stool (Yes : No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25:3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003cp\u003e(gram/decilitre)\u003c/p\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(8.9\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003cp\u003e(7.6\u0026ndash;15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.45\u003c/p\u003e \u003cp\u003e(8.3\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/hour)\u003c/p\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(4\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e(12\u0026ndash;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(2\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, the expression of DEFB4A/hBD2 was verified by qRT-PCR using tissue samples from cohort-II and observed that DEFB4A/hBD2 expression was higher in active UC patients compared to control and it was significantly dropped after remission (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The level of DEFB4A/hBD2 was also quantified in the serum and observed a significant enrichment of DEFB4A/hBD2 in the serum of the active UC patients than normal individual and it was decreased in the UC patients under remission (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReceiver operating curve (ROC) analysis was performed to verify its potential to be used as a biomarker for classification of UC patients from normal. The area under curve (AUC) was 0.94. The predictive cut-off value for DEFB4A/hBD2 in detecting UC was more than 209 pg/mL. It showed sensitivity and specificity of 93% and accuracy of 87% in differentiating active UC patients with 95% confidence interval (CI) of (0.84-1). The positive predictive efficiency of UC by serum DEFB4A/hBD2 level was 92% while negative predictive efficiency was 73% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThus, the overall data depicted that serum DEFB4A/hBD2 may be considered as a non-invasive biomarker for detection of UC patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we have identified and validated a secretary protein DEFB4A/hBD2 in the blood of UC patients and compared with IBS individuals. In order to identify tissue specific genes to classify this patient group, a microarray analysis was performed with colonic tissue using Illumina platform. After pathway analysis with the significantly upregulated genes, anti-microbial response by antimicrobial peptides (AMP) was the highest hit pathway and validation with qRT-PCR revealed DEFB4A/hBD2 exhibited comparable data with microarray. Thus, we verified this protein in the blood of exacerbated UC patients and patients under remission. The ELISA data suggests that this protein can distinguish both the groups. Thus, this marker showed high efficiency in classifying UC group from normal with 89% sensitivity and 80% specificity, PPV 92% and NPV 73% and cut-off 209pg/mL while 82% sensitivity, 73% specificity, 77% PPV and 79% NPV between active UC and remission with cut-off 235pg/mL.\u003c/p\u003e \u003cp\u003eDespite availability of transcriptomics, proteomics, metabolomics data of IBD patients, clinicians are still considering the level of CRP and FCP in routine clinical practice though CRP is non-specific and FCP has major limitations [24]. FCP varies over a few days [25] and even diet and exercise has profound impact on it. It can differentiate inflammatory and non-inflammatory gastrointestinal diseases [26]. Thus, elevated level of FCP can be seen in various inflammatory diseases [27]. FCP might also be increased in non-intestinal inflammatory diseases when microbiota is altered such as decompensated liver cirrhosis [28], pneumonia [29] etc. Proton pump inhibitors [30], glucocorticoids [31], and NSAIDs [32] also induce FCP expression. Thus, along with FCP, colonoscopic confirmation is required before therapy. Moreover, FCP is costlier than DEFB4A/hBD2 estimation. A significantly higher expression of S100A8 compared to control has been noted in our cohort of UC patients in both microarray and qRT-PCR. The monomer S100A8 forms heterodimer with S100A9 in a calcium dependent manner to form FCP. As DEFB4A/hBD2 showed highest significant alteration in expression in our UC cohort compared to S100A8, we verified its serum level and observed significant enrichment in active UC patients while reduced upon remission. A longitudinal study is required to follow the DEFB4A/hBD2 trajectories with patients from diagnosis to exacerbation, initiation of biologic therapy and remission.\u003c/p\u003e \u003cp\u003eThough this study has identified a non-invasive marker to follow the disease prognosis of an UC patient, larger sample size in all the groups may allow us to interpret the results in a better way. Further, molecular analysis is also required to understand the impact of enhanced DEFB4A/hBD2 mediated disease progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledged all the participants of this study who agreed to donate blood and colon tissue. Multidisciplinary Research Unit (MRU) of Institute of Post Graduate Medical Education and Research, Kolkata had provided funds for the pilot experiment and also the instrument facilities as required. S Chatterjee and A Karmakar are the recipients of fellowship from University Grant Commission, Government of India and Council of Scientific and Industrial Research, Government of India respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Banerjee and S Banerjee conceived the idea, designed experiments, and drafted the manuscript. S Roy, D Roychowdhury, A Karmakar and A. Saha collected samples. S Chatterjee also collected samples, isolated and quantified RNAs, analyzed data, prepared figures and performed ELISA with A Chakraborty. \u0026nbsp;GK Dhali read the manuscript critically. S. Banerjee supervised all experiments and examined each data. A Banerjee finalized the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors disclosed to have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData may be shared upon reasonable request to the corresponding author with proper documentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MRU, IPGME\u0026amp;R supported this study through research grant [MRU/PG/01/2019].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrd\u0026aacute;s, I., Eckmann, L., Talamini, M., Baumgart, D. C. \u0026amp; Sandborn, W. J. 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Crohn\u0026rsquo;s Colitis\u003c/em\u003e\u003cstrong\u003e18\u003c/strong\u003e (Suppl. 1), i1226\u0026ndash;i1227 (2024).\u003c/li\u003e\n\u003cli\u003eKlingberg, E., \u003cem\u003eet al.,\u003c/em\u003e A longitudinal study of fecal calprotectin and the development of inflammatory bowel disease in ankylosing spondylitis. \u003cem\u003eArthritis Res Ther\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 21 (2017).\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ulcerative Colitis, UC, Defensin, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6544404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6544404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ulcerative colitis (UC) is a chronic episodic relapsing, and remitting inflammatory bowel disease with increasing frequency worldwide. Along with colonoscopy, faecal calprotectin (FCP)\u0026thinsp;\u0026gt;\u0026thinsp;150\u0026micro;g/g, elevated faecal Lactoferrin or elevated CRP are now considered for diagnosis and to take treatment decision. But there are a group of patients showing either symptomatic remission with high biomarkers or active disease having no biomarker. Hence, identification of biomarker with better diagnostic potential is still required for UC patients.\u003c/p\u003e \u003cp\u003eTo determine the deregulated genes in UC, microarray analysis was employed with colonic tissue of UC and irritable bowel syndrome as control. Pathway enrichment analysis with differentially expressed (DE) genes revealed anti-microbial peptide mediated immune response might play pivotal role in UC. Subsequently, qRT-PCR validation depicted that among the DE genes, Defensins showed highest significant alterations in UC compared to control. Among defensins, DEFB4A, DEFA5, and DEFA6, only DEFB4A/hBD2 showed significant upregulation in the UC patients by qRT-PCR. The data was also validated by Immunohistochemistry, and ELISA. A significantly high level of DEFB4A/hBD2 was noted in the serum of active UC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) which disappeared in patients in remission (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis showed that DEFB4A/hBD2 with AUROC of 0.94 having cut-off value more than 209pg/ml could differentiate between normal and active UC patient with 89% sensitivity, and 80% specificity and with 95% confidence interval of (0.79\u0026ndash;0.98). The positive predictive efficiency was 92% while negative predictive efficiency was 73%. These findings highlight that DEFB4A/hBD2 may be considered as a potential serum diagnostic marker for UC patients, though further validation is necessary in larger number of samples.\u003c/p\u003e","manuscriptTitle":"DEF4A/hBD2, a non-invasive serum biomarker for detection of Ulcerative colitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 08:49:27","doi":"10.21203/rs.3.rs-6544404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T11:38:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T13:40:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T12:10:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223485727601549568490191338269577061673","date":"2025-06-15T11:41:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57161935297330679799035207641784616277","date":"2025-06-13T12:22:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T10:40:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T07:45:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-02T06:45:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-02T06:44:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ec2bff7-4d87-4bdd-b7d3-912fde8e34e0","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50073924,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases"},{"id":50073925,"name":"Biological sciences/Immunology/Inflammation"}],"tags":[],"updatedAt":"2025-11-10T15:58:34+00:00","versionOfRecord":{"articleIdentity":"rs-6544404","link":"https://doi.org/10.1038/s41598-025-22893-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-06 15:56:53","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-06-18 08:49:27","video":"","vorDoi":"10.1038/s41598-025-22893-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22893-4","workflowStages":[]},"version":"v1","identity":"rs-6544404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6544404","identity":"rs-6544404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}