Escherichia coli gene expression is influenced more by gut environmental changes from inflammation and microbiota modulation than by colitis severity

Escherichia coli gene expression is influenced more by gut environmental changes from inflammation and microbiota modulation than by colitis severity | 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 Escherichia coli gene expression is influenced more by gut environmental changes from inflammation and microbiota modulation than by colitis severity Jungtak Kim, Karolina Hanna Prazanowska, Merlin Jayalal Lawrence Panchali, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7082467/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 11 You are reading this latest preprint version Abstract Modulation of the gut microbiota has emerged as a promising diagnostic and therapeutic approach for inflammatory bowel disease (IBD), a condition marked by chronic relapse. Analysing gene expression in luminal bacteria helps monitor the gut environment and assess the probiotic effects. However, the complexity of the microbiota poses a challenge. We examined the gene expression of Escherichia coli in the intestines of IBD mouse models in the context of a native gut microbiota. We adopted reporter E. coli expressing reverse transcriptase-Cas1 fusion protein and Cas2 to record transcript data on plasmids as short oligonucleotides. Gene expression profiles differed between IBD models and controls and varied with the type of inflammatory trigger and time point. However, pre-feeding Lactobacillus crispatus before IBD induction yielded E. coli gene expression profiles resembling controls despite worsened colitis. Conclusively, altered E. coli gene expression in the inflamed gut may reflect environmental changes driven by interactions between inflammation and microbiota. These findings suggest that bacterial gene expression adapts dynamically to the gut environment, which is shaped by host inflammatory responses and microbiota interactions. These results have implications for developing non-invasive diagnostic bacteria for gut inflammation. Health sciences/Gastroenterology Biological sciences/Microbiology Inflammatory bowel disease record-seq probiotics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The gut microbiota is involved in the pathogenesis of multiple diseases, including inflammatory bowel disease (IBD), and has emerged as a therapeutic target [ 1 , 2 ]. Alterations in gut microbiota composition have been observed in patients with IBD compared to healthy individuals [ 3 , 4 ]. Transplantation of gut microbiota from patients with IBD, but not from healthy individuals, into germ-free mice induces intestinal inflammation, demonstrating a causative role of the microbiota in IBD [ 5 , 6 ]. The contribution of the microbiota to gut inflammation appears to be related to the interplay between the immune system and the gut microbiota. Metabolites produced by gut microbiota exert regulatory effects on inflammation by modulating immune cell functions [ 7 – 9 ]. Conversely, the immune system influences intestinal commensal microbes through various mechanisms, such as the production of antimicrobial peptides and radicals [ 10 – 12 ]. Recently, strategies targeting the gut microbiota, such as faecal microbial transfer and probiotic administration, have been explored for the treatment of IBD and other diseases [ 13 – 18 ]. IBD is a chronic, recurrent intestinal inflammation that is believed to be elicited by multiple etiological factors. They are considered intractable due to the frequent development of unresponsiveness to current medications [ 19 – 21 ]. The two major types of IBD are ulcerative colitis and Crohn’s disease. Ulcerative colitis is characterised by lesions restricted to the mucosal layer of the colon, whereas Crohn’s may involve all layers of the intestinal wall, potentially resulting in intestinal perforation. Diagnosis relies mainly on an intestinal biopsy via endoscopy, which is inconvenient and invasive [ 22 ]. Owing to the limitations in magnetic resonance imaging (MRI) and faecal calprotectin assessments, the need for a non-invasive, cost-effective, and easily accessible method to monitor gut inflammation remains unresolved [ 23 – 25 ]. IBD is typically managed with anti-inflammatory drugs such as corticosteroids or anti-TNF-α antibodies; however, diminishing therapeutic efficacy and the emergence of drug-related side effects remain major concerns. To address these challenges in both diagnosis and treatment, engineered Escherichia coli that can sense inflammatory markers such as tetrathionate, emit signals such as fluorescent light, and/or produce anti-inflammatory molecules are currently in the preclinical trial stage [ 26 – 29 ]. Although promising, several factors, including the intraluminal persistence of therapeutic E. coli , require improvement [ 30 ]. Understanding E. coli gene expression in a dynamically changing gut environment through the interaction between the microbiota and inflammation may offer new insights for the development of IBD therapeutics. Record-seq is introduced as a method that enables the analysis of gene expression in reporter bacteria within the mouse gut harbouring complex bacterial flora [ 31 ]. Record-seq utilises the Type III clustered regularly interspaced short palindromic repeat (CRISPR)-Cas system [ 32 ], which plays a defensive role in diverse bacteria by inserting DNA fragments originating from invading genetic elements between direct repeat (DR) sequences in the CRISPR array on the genomic DNA. The inserted DNA fragments, known as spacers, serve as records of invasion. Uniquely, the Type III CRISPR-Cas system acquires spacers from mRNA using a complex consisting of reverse transcriptase-Cas1 (RT-Cas1), a naturally occurring fusion protein, and Cas2. To exploit this property, DNA sequences encoding RT-Cas1, Cas2, and CRISPR DRs were cloned into a plasmid under the control of a tetracycline repressor system, and the plasmid was introduced into the reporter E. coli strain [ 33 ]. In the presence of a tetracycline analogue, E. coli expresses RT-Cas1 and Cas2 protein complexes, leading to the acquisition of spacers from the cytoplasmic RNA pool into the CRISPR array sequences on the plasmid. The gene expression profiles were recorded on the plasmid as a series of sequential spacers. These can be analysed by plasmid purification, spacer sequencing, and quantification of spacer-aligned E. coli genes. Notably, these RNA-recorded spacer sequencing (record-seq) profiles correlated well with the conventional RNA sequencing (RNA-seq) profiles [ 34 ]. In this study, we utilised record-seq to investigate the gene expression profiles of E. coli in the guts of two IBD mouse models under conditions that preserved the full spectrum of the gut microbiota. Specifically, we aimed to determine whether E. coli gene expression changes dynamically over the course of gut inflammation, differs between dextran sulfate Sodium (dss)-induced and dinitrobenzene sulfonic acid (dnbs)-induced IBD models, and is altered by the administration of Lactobacillus crispatus , a probiotic known to influence both gut microbiota composition and inflammatory responses. Results Disease activity and intestinal inflammation during the first and second attacks in the dss-induced IBD model When mice were given dss-containing water for 5 days, they manifested IBD-related symptoms such as weight loss, diarrhoea, haematochezia, colon shortening, and inflammation in the colon tissue, beginning on day 5 (D5) (Fig. 1 ). By day 15 (D15), body weight, colon length, and disease activity returned to normal levels; however, inflammation persisted in the colon tissue, which is compatible with the dissociation of clinical remission from intestinal mucosal healing and the progressive nature of ulcerative colitis in some patients [ 35 , 36 ]. To induce a second bout of colitis, dss-containing water was administered again from D15 to day 20. Signs of colitis reappeared more rapidly and with greater severity. The histological scores in IBD mice were significantly higher than those in the corresponding healthy control (HC) (p < 0.05), and those on day 24(D24) were significantly greater than those on D5 (p = 0.0038). Based on these observations, we selected D5, day 9 (D9), and D24 as representative time points for the active phases of the first and second bouts of colitis and D15 as the symptomatic remission phase with persistent intestinal inflammation to analyse gene expression in the reporter E. coli . E. coli gene expression in the gut of dss-induced IBD mice differs from that of healthy control mice The reporter E. coli was administered to mice 11 h prior to each observation time point and subsequently retrieved from faeces for spacer acquisition analysis. Representative results of E. coli recovery, plasmid purification, and spacer acquisition are shown in Supplementary Figs. 2 and 3. Spacer libraries from HC and dss-induced IBD (DSS) mice were sequenced. At least 1.1 x 10 6 reads were obtained per sample, except for one sample (1_8_2, D5 control sample read 2), ranging from 1,172,026 to 30,677,568 total reads. The number of spacers aligned with E. coli genes ranged from 2,144 to 46,178 per sample (Supplementary Table 1). Read quality is shown in Supplementary Fig. 3. The mean spacer length was 49 bp, slightly longer than the 40–42 bp reported by Schmidt et al., whereas the distribution of the spacer GC content was similar to their findings (Supplementary Fig. 4)[ 34 ]. We compared E. coli gene-aligned spacer profiles between the HC and DSS mouse groups to assess gene expression in steady-state versus inflammatory gut environments. Principal component analysis (PCA) plots and heat maps revealed differential gene expression between the HC and DSS groups at all observed time points (Fig. 2 ). Unexpectedly, the greatest difference in gene expression between the DSS and HC mice was observed on D15, corresponding to the symptomatic remission phase of colitis, with a moderate level of colonic inflammation. Heatmaps showing the top 50 significant DEGs and a plot showing the ratio of up- and down-regulated genes in the total DEGs did not exhibit any trend related to histologic inflammatory scores. These results demonstrate that E. coli gene expression in an inflamed gut differs from that in a healthy gut, although the degree of difference does not correlate with the severity of the histological inflammation. Marginal overlap between DEGs in dss-induced colitis at different time points Next, we examined the characteristics of DEGs in dss-induced colitis compared with those in HC. The number of DEGs observed at the four time points ranged from 80 on D24 to 135 on D15 (Fig. 2 ). Interestingly, the lowest number of DEGs was observed on D24, despite the highest disease activity and colonic inflammation. A Venn diagram revealed fewer than five overlapping genes in any pairwise comparison between time points and no shared genes across any combination of three or all four time points. Of the overlapping genes, 13 showed consistent upregulation or downregulation, whereas four exhibited opposite expression patterns across time points (Table 1 ). These common genes included four genes involved in carbohydrate or amino acid degradation, three in stress responses, nine in transcription factor regulation, and four in other functions. KEGG pathway enrichment analysis [ 37 , 38 ] indicated that the downregulated DEGs in the DSS vs. HC on D15 were significantly enriched in the ribosome synthesis pathway (Fig. 2 E, padj < 0.05). In conclusion, the overall overlap of DEGs across the different time courses of gut inflammation was minimal. These findings highlight the dynamic nature of E. coli gene expression in response to temporal changes in an inflamed gut environment. Table 1 The genes differentially expressed in reporter E. coli in a dss-induced IBD model compared to healthy control at two time points Gene ID Gene name Gene Function Log2 fold change D5 D9 D15 D24 EG12416 gatC Carbohydrates and Carboxylates Degradation Alcohol Degradation Sigma Factor Regulons Transcription Factor Regulons Plasma Membrane Proteins 1.58 -0.91 EG11005 tnaA Amino Acid Degradation Sigma Factor Regulons Transcription Factor Regulons 0.72 0.74 EG10874 rplM Translation Proteins Protein Metabolism Transcription Factor Regulons -1.40 -1.65 EG11527 narP RNA Metabolism Signal transduction pathways Sigma Factor Regulons Transcription Factors 2.59 1.09 EG10241 dnaK DNA and RNA Metabolism Protein Metabolism Protein Folding Sigma Factor Regulons Heat stress Plasma Membrane Proteins 0.73 0.65 G0-10657 yshB uncharacterised protein -1.67 -0.74 EG11396 ubiD Plasma Membrane Proteins 2.72 1.29 EG11605 smg Transcription Factor Regulons 2.44 1.06 EG11507 rlmE RNA Metabolism Sigma Factor Regulons Transcription Factor Regulons 2.45 0.89 EG10922 coaA Cofactor Synthetic pathway 2.17 2.29 G7683 yhcN Transcription Factor Regulons pH stress/Oxidation stress Proteins Involved in Biofilm Formation Periplasmic Proteins 1.52 -0.94 EG10399 glpQ Carbohydrates and Carboxylates Degradation Alcohol Degradation Sigma Factor Regulons Transcription Factor Regulons Periplasmic Proteins 3.03 1.29 EG10394 glpD Carbohydrates and Carboxylates Degradation Alcohol Degradation Inorganic Nutrient Metabolism Sigma Factor Regulons Transcription Factor Regulons Plasma Membrane Proteins -2.30 -2.08 EG10246 mltD Transcription Factor Regulons Cell Wall Biogenesis/Organization Proteins Plasma Membrane Proteins 1.57 -2.05 G7513 yqfB Enzymes not in Pathways -1.61 -1.57 EG11724 yieG Transcription Factor Regulons Transport Proteins Plasma Membrane Proteins 1.09 -1.25 EG11276 tnaC RNA Metabolism Sigma Factor Regulons Transcription Factor Regulons 1.20 -1.00 Differential gene expression in E. coli within the gut of dnbs-treated mice compared to vehicle-treated controls Next, we examined the E. coli gene expression in the gut of another mouse model of IBD induced by dnbs. Dnbs was dissolved in 50% ethyl alcohol and administered rectally. The mice were monitored for changes in body weight, faecal consistency, survival, and intestinal histology. The observed symptoms included weight loss, diarrhoea, haematochezia, melena, and intestinal inflammation. Unlike mice in the dss-induced IBD model, approximately 30% of the dnb-treated mice died by day 2 (Fig. 3 ), and autopsies revealed small intestinal perforation or obstruction in the deceased mice (data not shown). The reporter E. coli was orally administered to the surviving mice on day 3 (D3). Faecal samples were collected 11 h later for the analysis of the acquired spacers. Spacer analysis aligned to E. coli genes revealed significant differential expression of 848 genes; 440 were upregulated and 408 were downregulated in E. coli from dnb-treated mice (DNBS) compared with those from vehicle-treated mice (VehC) (Fig. 3 ). PCA plots and heatmaps confirmed the differential gene expression between the DNBS and VehC groups. Significantly upregulated genes were enriched in pathways related to the biosynthesis of secondary metabolites; amino acid biosynthesis; carbon metabolism; cationic antimicrobial resistance; and glycine, serine, and threonine metabolism. We analysed the E. coli genes associated with these pathways using a KEGG database and displayed them in the tree map plot. DEGs in dnbs-induced IBD differ from those in dss-induced IBD Next, we examined the similarity of DEGs in E. coli between dnbs- and dss-induced IBD mouse models. A total of 28 shared genes were identified between the 848 DEGs in the DNBS vs. VehC comparison and 97 DEGs in the DSS vs. HC comparison on D5 (Fig. 4 ). Similarly, 28 and 20 shared genes were observed between the DEGs of DNBS and DSS on D9 and between the DEGs of DNBS and DSS on D15, respectively. Some of these genes showed commonly up- and downregulated in both the DNBS- and DSS-induced IBD models, but majorities of these genes exhibited opposite regulation between the two models. In addition, the upregulated genes involved in stimulus responses differed between the two models [analysed using EcoCyc database, 39]. These findings indicate that E. coli gene expression in the inflammatory gut differs depending on the IBD model used. Administration of L. crispatus prior to dss exposure exacerbates colitis without significant changes in E. coli gene expression compared to healthy or L. crispatus -only controls To evaluate the effect of oral gavage of L. crispatus on intraluminal gene expression in E. coli , mice were administered L. crispatus once daily for five days, beginning one day prior to the administration of dss-containing water (Fig. 5 ). During the five-day dss administration period, body weights were similar across all groups: untreated controls (Con), L. crispatus -treated group (Lc), dss-treated group (DSS), and the group receiving both L. crispatus and dss (DSLc). However, the IBD disease activity index significantly increased on D5 in both the DSS and DSLc groups compared to the Con or Lc groups, and was significantly higher in the DSLc group than in the DSS group(p = 0.0002, ANOVA with a Bonferroni correction). Colon lengths were similar between the Lc and Con groups but significantly decreased in the DSS and DSLc groups compared to the Lc group (p = 0.035, ANOVA with a Bonferroni correction). In line with this, the histological scores were significantly higher in the DSS and DSLc groups than in the Con group (p = 0.0496 and p = 0.0001, Kruskal-Wallis test with Dunn's correction). Histological scores were higher in DSLc than in DSS, although the difference between these groups was not statistically significant, probably because of the small number of samples. To examine gene expression, reporter E. coli was administered on D5, and faecal samples were collected 11 h later for analysis. PCA plots of the record-seq profiles revealed a clear separation between the Con and DSS samples (Fig. 5 ). However, the Con cluster was positioned near the Lc and DSLc clusters and did not form distinct clusters. Volcano plots consistently showed significantly different gene expression between Con and DSS and between Lc and DSS, but not between Con and Lc or between Con and DSLc. However, the small sample size of the DSLc group may have limited the statistical power to detect significance. Collectively, although oral administration of L . crispatus prior to the induction of gut inflammation exacerbated colitis, E. coli gene expression profiles under these conditions remained similar to those of healthy controls or L. crispatus -only treatment, rather than resembling those of the dss-only treatment. Because the Lc and DSLc samples were not clearly separated in the PCA plot, we analysed the DEGs of Con vs. the combined profiles of Lc and DSLc (LcDSLc) and DEGs of DSS vs. LcDSLc (Fig. 6 ). Consistent with the PCA results, more individual DEGs were identified in the DSS vs. LcDSLc comparison than in the Con vs. LcDSLc comparison (66 vs. five genes). Five genes were shared by DEGs of Con vs. DSS and DEGs of Lc vs. DSS, which increased in the dss-treatment condition, whereas 22 genes were common in DEGs of Lc vs. DSS and DEGs of LcDSLc vs. DSS. The upregulated genes in the control vs. DSS and LcDSLc vs. DSS groups were also enriched in the ribosome synthesis pathway. Enrichment of downregulated genes in the phosphotransferase system was identified in LcDSLc compared to DSS. Additionally, one of the downregulated DEGs of Con vs. LcDSLc was significantly enriched in several pathways related to metabolism, such as tryptophan metabolism, degradation of several amino acids, and the TCA cycle, suggesting an alteration of the gut environment by the administration of L. crispatus . Collectively, oral administration of L. crispatus affected the gut environment by altering E. coli gene expression profiles in both steady and inflammatory states. Discussion We investigated E. coli gene expression in the gut of two IBD mouse models using record-seq, building on Schimdt’s study showing that record-seq profiles correlated with RNA-sequencing profiles [ 34 ]. We observed three main findings: (1) E. coli gene expression in the inflamed gut differed from that in the healthy gut and changed dynamically during the first and second colitis episodes; (2) DEGs in E. coli from dss- or dnbs-induced IBD models minimally overlapped; and (3) Administration of L. crispatus prior to dss exposure resulted in E. coli gene expression profiles resembling those of healthy controls, despite greater colitis severity compared to dss treatment alone. Notably, these findings were obtained from mice harbouring the full spectrum of gut microbiota. Compared with previous reports analysing E. coli gene expression in germ-free mice colonised solely with E. coli , our results showed similarities and differences. Consistent with the findings of Dacquay and Schmidt, who used germ-free mice colonised with E. coli and subsequently exposed to DSS [ 31 , 40 ], we observed differential E. coli gene expression in inflamed guts containing diverse microbiota. According to Dacqauay et al., the upregulated pathways were associated with flagellar biosynthesis and motility. These genes were absent from our DEGs, likely because of the differences in E. coli strains. Dacqauay et al. used the Nissle strain, known for its probiotic and colonisation ability in the human gut, whereas we used the BL21(DE3) strain, commonly employed in recombinant protein production and is known to lack flagellar genes due to IS1 element insertion [ 41 – 43 ]. When comparing our dss-induced DEGs with those from Schmidt’s study using the BL21 (DE3) or K-12 MG1655 strains, only 22 of the 184 DEGs overlapped in the expression direction. This suggests that E. coli gene expression is influenced not only by gut inflammation but also by the composition of the gut microbiota. An alternative to invasive histological evaluation of intestinal inflammation in IBD is needed. Schmidt et al. proposed that record-sequencing of sentinel bacteria could serve as a stand-alone method for monitoring gut inflammation [ 31 ]. Although our findings partially supported this hypothesis, they did not fully align with Schmidt’s conclusions. Like their results, our record-seq profiles distinguished between inflamed and healthy gut conditions in dss-induced colitis. However, unlike Schmidt’s study, we found that the record-seq profiles did not clearly reflect the severity of inflammation [ 34 ]. Schmidt observed that severe inflammation (induced by 3% dss) caused record-seq profiles to shift further from the control group compared to milder inflammation (induced by 1% dss). In contrast, in our study, the profiles of severe inflammation (D24 and dss + L. crispatus ) were not more distant from the controls than those of milder inflammation (D5 and dss alone). This discrepancy may be explained by differences in microbial environments and E. coli colonisation dynamics. In our study, the reporter E. coli was transiently present for 11 h in the gut containing the entire microbiota, whereas in Schmidt’s study, the bacteria were long-term colonisers in a germ-free environment. Furthermore, we identified very few genes that were consistently upregulated or downregulated across the inflammatory time points. The key genes frequently identified by Schmidt, such as alaE, narH, hdeB , and glgS , did not appear in the DEG lists. Given the complexity of the human gut microbiota, our findings suggest that record-seq may not always reflect inflammation or its severity, particularly in microbiota-rich environments. In our study, record-seq revealed distinct DEG patterns between dss- and dnbs-induced IBD models. Only 1.2% of the 848 DEGs identified in the DNBS-induced model overlapped with those in the dss-induced model on D5. The enriched pathways significantly differed. For example, genes involved in the oxidative stress response were more abundant in the dnbs model (35 genes) than in the dss model (two genes). Given the characteristic histopathology of dnbs-induced colitis—autolytic epithelial cell death, it is likely that the dnbs-treated gut presented a different environment from the dss-treated gut. Thus, record-seq effectively captured the E. coli adaptation to these model-specific environments, highlighting its utility in studying bacterial gene expression in diverse inflammatory contexts. Our record-seq findings following L. crispatus administration further underscore the complexity of the gut environment. IBD is characterized by epithelial damage, increased inflammatory mediators, altered metabolites, and microbial dysbiosis—interconnected factors that contribute to disease pathology [ 44 ]. These complex environmental factors, in addition to inflammation, likely influence E. coli gene expression. This may explain the unexpected observation that record-seq profiles in the DSLc group resembled those of healthy controls more closely than those of dss-only treated mice, despite more severe inflammation in the former. Given L. crispatus ' known effects on vaginal microbiota and innate immunity [ 45 – 48 ], it is plausible that it also modulates gastrointestinal microbiota and immune responses. In fact, L. crispatus inhibits the adhesion of pathogenic bacteria to the gastric cells and reduces inflammatory cytokine expression in these cells [ 49 ]. In IBD mouse models, the regulatory effects of L. crispatus on inflammation have been reported to depend on the strain [ 50 – 53 ]. Moreover, L. crispatus may influence the host microbiota through a putative bacteriocin encoded by a mobile genetic element (Tn7088), whose sequence varied between the beneficial strain M247 and the pathogenic strain vpi3199 [ 54 ]. We used vpi3199. Consistent with our findings, L. crispatus strain M206119 has been reported to worsen colitis [ 52 ], though the mechanisms underlying strain-specific effects remain unclear. One of the limitations of our study is the small sample size of the dss-induced model, particularly in the group treated with both dss and L. crispatus . It is possible that a larger sample size would yield more significant DEG differences between DSS and DSLc. Further studies incorporating analyses of the gut microbiota composition, metabolites, and inflammatory mediators during colitis would provide deeper insights. In conclusion, our study demonstrates that E. coli gene expression in the gut is influenced by the type of IBD-inducing agent, temporal course of inflammation, and administration of L. crispatus. These findings suggest that bacterial gene expression adapts dynamically to the gut environment, which is shaped by host inflammatory responses and microbiota interactions. Our results have implications for understanding the role of microbiota in diseases and for developing effective bacterial therapeutics and noninvasive diagnostic tools for gut inflammation. Materials and Methods Induction of Inflammatory Bowel Disease in Mice Six-week-old male C57BL/6 mice were purchased from Orient Bio (South Korea). The mice were housed in groups of 3 to 5 per cage and maintained in the specific pathogen-free area of the Laboratory Animal Research Center at Ajou University Medical Center. To induce inflammatory bowel disease, mice were given drinking water containing 2% dss (MPbio, 9011-18-1) for five days. Alternatively, mice received a single rectal administration of 33.3 mg/mL dnbs (Thermo, A18493) in 50% ethyl alcohol. To modulate the gut microbiota, we fed mice 200 µL Lactobacillus crispatus (ATCC No 33820, vpi3199, 2 × 10 9 CFU/mL in phosphate buffered saline (PBS)) daily for 5 days. Mouse body weight, faecal consistency, and the presence of blood in stools were monitored. We also observed the intestinal appearance and length of the mice after sacrifice. The intestinal tissue was fixed in 10% formaldehyde solution, embedded in paraffin block, cut into 5 µm thick sections and then stained with haematoxylin and eosin. Histological scoring was performed using Remke’s method [ 55 ]. Three individuals assessed the inflammation in the colon tissue. One of them was a pathologist blinded to the sample information. The experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Ajou University Medical Center (authorization number 2023-0024) and performed in accordance with the IACUC guidelines and regulations. All procedures were conducted in accordance with ARRIVE guidelines [ 56 ] Vectors and Bacteria For record sequencing, we used reporter Escherichia coli BL21(de3) (Dynebio. DYO1360) transformed with pFS_0453 [ 31 ], an expression plasmid encoding RT-Cas1 and Cas2 and containing a CRISPR array. Three colonies of reporter E. coli grown on the LB agar plate containing 50 µg/mL of Kanamycin were inoculated in 20 mL LB broth with 50 µg /mL of Kanamycin and cultured for 14 hours at 37℃ with shaking at 200 rpm. The culture was transferred to the 80 mL of new LB broth containing 50 µg/mL of Kanamycin and 30 ng/mL of anhydrous tetracycline (ATC, Cayman,10009542) and then 2 h later, the bacteria were harvested. The reporter E. coli cells were washed and resuspended in PBS containing 30 ng/mL ATC and then orally administered to the mice. E.coli , DH5α transformed with pTRKH3-ermGFP (a gift from Michela Lizier (Addgene plasmid # 27169 ; http://n2t.net/addgene:27169 ; RRID:Addgene_27169) [ 57 ] was used to assess the faecal recovery rate after oral gavage of mice. Lactobacillus crispatus was grown in MRS media in anaerobic chamber at 35℃. When the absorbance of the culture at 600 nm reached 1.7 ~ 2.0, the bacilli were harvested, washed with PBS, and resuspended in PBS. Harvest of reporter E. coli from mice and purification of plasmid from faeces Prior to oral gavage of reporter E. coli into mice, we provided the mice with water containing 30 ng/mL ATC for 3 days. Eleven hours after oral gavage, faeces (up to 100 mg per mouse) were collected. The particles in the faeces were eliminated by centrifuging the diluted faecal solution in PBS containing 10 mM EDTA for 1 min at 200 × g. Faecal bacteria were precipitated by centrifugation for 10 min at 6800 × g and washed once with PBS containing 1 mM EDTA. The bacterial pellet was purified using a QIAprep spin Miniprep kit (QIAGEN). Construction of spacer libraries The purified plasmid pFS_0453 was digested with FastDigest FaqI (ThermoFisher, FD1814) and annealed with an adapter composed of two complementary oligonucleotides (AAAGCCAAATCTTCCACTTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC and GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCAAGTGGAAGATTTGG, Bioneer) using T7 DNA ligase (NEB, M0318S). The 99 cycles of digestion and annealing reactions were done at 37°C for 3 min and at 20°C 3 min, respectively. The plasmid DNA annealed with the adaptor was subjected to PCR to amplify the spacers, with an extension time of 13 s for 16 cycles using the first-round PCR primer set [ 33 ]. The PCR product was separated using AMPure XP beads (Beckman, A63880) and one-fifth of the PCR product was used in consecutive PCR using the second-round primer set [ 33 ]. Second-round PCR products with sizes of approximately 240 and 300 bp were purified by agarose gel electrophoresis using the QIAQuick Gel Extraction Kit. Next-generation sequencing of spacer libraries Libraries were quantified using qPCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantification kits for Illumina Sequencing platforms) and qualified using a TapeStation HSD5000 ScreenTape (Agilent Technologies, Waldbronn, Germany). The indexed libraries were sequenced on a NovaSeqX Plus platform (Illumina, San Diego, CA, USA by the Macrogen Incorporated). Briefly, Illumina utilises a unique "bridged" amplification reaction that occurs on the surface of the flow cell. A flow cell containing the prepared libraries was loaded onto a NovaSeq X Plus sequencer (Illumina) for automated extension and imaging cycles. The sequencing-by-synthesis cycle was repeated to obtain a paired-end read length of 2 X 150bp (10B). Data Analysis of Spacer Libraries Raw fastq files were processed according to the ETH Zurich protocol “Recording transcriptional histories using record-seq” ( https://doi.org/10.3929/ethz-b-000396475 ). We used a Linux desktop terminal as the first part of the pipeline. The appropriate environment was created using the necessary software. The tools and their versions are listed in the Protocol section. The input fastq files were processed using the Snakemake workflow provided by the authors of the protocol. Briefly, the workflow included quality control of the input fastq files by fastqc ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ), read trimming by trimmomatic [ 58 ], fastq to fasta conversion using the FASTX-toolkit ( http://hannonlab.cshl.edu/fastx_toolkit ), extraction of spacer sequences using the Python script, sequence alignment to reference genomes by bowtie2 [ 59 ], and count matrix generation using Subread FeatureCounts [ 60 ] (Supplementary Fig. 1). For Snakemake workflow execution, a supplementary configuration file is required with information about directory pathways, genome names, library barcodes, and direct repeat sequences. The configuration file used in this study is available as supplementary data (config. ymL). The second stage of the computational pipeline was performed using RStudio (v4.4.1) [ 61 ]. For spacer-level quality control, we generated spacer length and GC content plots using the recordseq R package (v0.2) [ 33 ] SpacerStats function, and spacer information files obtained during the snakemake workflow (Supplementary Fig. 1). Differential expression analysis was conducted using Escherichia coli gene count matrices and the DESeq2 R package (v1.44.0) [ 62 ]. The raw counts and metadata of the samples of interest were extracted from the original matrix and used to create a DESeq object (dds) using the DESeqDataSetFromMatrix function in the DESeq2 package. To exclude genes with low expression, rows with a sum of counts less than 15 were removed from the object. Differential expression was analysed using the DESeq function. Result tables for the comparison of specific samples were extracted from the DDS object using the results function. Significantly differentially expressed genes (DEGs) were identified based on the following criteria: padj 0.6/ < -0.6. To visualise the results of differential expression analysis, the output tables were directly used to generate volcano plots using the EnhancedVolcano (v.1.22.0) package ( https://bioconductor.org/packages/EnhancedVolcano ). For more advanced visualisation purposes, a variance-stabilising transformation was applied to the DDS object via the varianceStabilizingTransformation function with the blind = FALSE option, which is more suitable for datasets with a lower number of genes. Transformed object (vsd) was used to generate PCA plots via plotPCA function with intgroup = “Condition” option, to group samples by condition/treatment. The percentage variance for the first two principal components (PCs) was automatically calculated using the top 500 genes (by variance). To generate heatmaps, the top 50 significant genes (by p-value) were chosen from among all the significant DEGs. Normalised counts from the vsd object were extracted for the selected genes, and heat maps were created using the pheatmap function (v1.0.12) ( https://github.com/raivokolde/pheatmap ). To investigate the involvement of the identified DEGs in known pathways, we conducted gene set enrichment analysis using the enrichKEGG function in the clusterProfiler R package (v4.12.6) [ 63 ]. Gene names of the significantly up- and downregulated DEGs were converted to KEGG IDs via the bitr_kegg function, with E. coli (eco) as a supported organism. Significant pathways from the KEGG database were determined using p-values (p-value cutoff = 0.05). Results from the enrichment analysis were visualised as bar plots using the plotEnrichAdv function from the Genekitr R package (v1.2.8) [ 64 ] and tree map plots using the treemap R package (v2.4-4) ( https://CRAN.R-project.org/package=treemap ). Abbreviations ATC : anhydrous tetracycline; DEGs : differentially expressed genes; dnbs: 2,4-dinitrobenzene sulfonic acid; DR : direct repeat; dss : dextran sulfate sodium; HC: healthy control; IBD : inflammatory bowel disease; MRI : magnetic resonance imaging; PCs : principal components; record-seq : rna-recorded spacer sequencing; RT-Cas1 : reverse transcriptase-cas1; VehC: vehicle-treated control. Declarations Acknowledgement The authors thank Dr. YB Kim (previously in the Department of Pathology, Ajou University Hospital, currently in GC Labs) for scoring colon tissue inflammation. Funding Declaration This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. RS-2023-00244783). Competing interests The author(s) declare no competing interests Data Availability Statement The datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository, [The accession numbers are GSE303195 and GSE303196]. Authors’ contributions S. B. L.and S.P. : the conception and design of the study, and interpretation of data, writing the article; J. K. and K. H. P. : acquisition and analysis of data, drafting part of the article and revising it critically, M. J. L. P. and C. M. : acquisition of data, K. K. : revising the article critically. References Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nature Reviews Microbiology 19 , 55-71 (2021). https://doi.org:10.1038/s41579-020-0433-9 Iliev, I. D., Ananthakrishnan, A. N. & Guo, C.-J. 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Experimental scheme. Mice were provided with water containing dss or not for 5 days twice as shown and then sacrificed on the day indicated with an arrow to obtain intestine. \u003cstrong\u003eb\u003c/strong\u003e. Body weight kinetics (D5 n≥25, D9 n=15, D15 n=10, D24 n=5 for both control (HC) and IBD model (DSS) group) \u003cstrong\u003ec\u003c/strong\u003e. Disease activity kinetics (D5 n≥18, D9 n≥11, D15 n≥9, D24 n≥4 for both HC and DSS) \u003cstrong\u003ed\u003c/strong\u003e. The representative colon photographs on the indicated day and a plot of colon lengths of mice in DSS and HC groups. \u003cstrong\u003ee\u003c/strong\u003e. The representative images of colon sections stained with Hematoxylin and Eosin (left). Histologic scores of colon samples from the mice in DSS and HC groups (right). (D5 and D9, n=6, respectively for both HC and DSS, D15 n=5 for both HC and DSS, D24 HC n=5, DSS n=4) Data of four independent experiments. *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001 using Mann-Whitney U analysis For the comparisons of D5 with D9, D5 with D15, and D5 with D24 within the DSS group were statistically tested using the Kruskal-Wallis test with Dunn’s correction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/558fc9ba4044a888c0d6975a.png"},{"id":87918738,"identity":"65ec9e75-5a3a-4abd-9d71-250968ad04b3","added_by":"auto","created_at":"2025-07-30 11:26:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462420,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of \u003cem\u003eE. coli\u003c/em\u003e record-seq profiles in a dss-induced IBD Model. Record-seq profiles were obtained from reporter \u003cem\u003eE. coli\u003c/em\u003e harvested from faeces of the control healthy (HC) mice or dss-induced IBD model (DSS) mice on the indicated day. \u003cstrong\u003ea\u003c/strong\u003e. Principal component analysis (PCA) plot showing the first 2 PCs for HC and DSS on the indicated day. PCA was conducted for HC and DSS samples, separately at each time point, using plotPCA function from DESeq2 R package. The percentage of variance was calculated based on standard deviations of each PC. \u003cstrong\u003eb\u003c/strong\u003e. Heatmap of the top 50 differentially expressed genes on the indicated day. DEGs were identified via DESeq function from DESeq2 R package utilizing Wald test. Significance was indicated by padj \u0026lt; 0.1 and log2FoldChange \u0026gt; 0.6/ \u0026lt; -0.6. The top 50 genes were chosen based on the lowest p value. \u003cstrong\u003ec\u003c/strong\u003e. Percentages of up- and down-regulated genes in DEGs \u003cstrong\u003ed\u003c/strong\u003e. Venn diagram of DEGs on the indicated day \u003cstrong\u003ee\u003c/strong\u003e. Results of KEGG pathway enrichment analysis for genes significantly downregulated in DSS D15 vs control. Significantly enriched pathways were identified using enrichKEGG function from clusterProfiler R package, utilizing Fisher’s exact test (significant pathways indicated by p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/83ea53d4f997debca1fcd481.png"},{"id":87921070,"identity":"4827d371-2073-4956-8291-57b38d732c3c","added_by":"auto","created_at":"2025-07-30 11:42:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":390994,"visible":true,"origin":"","legend":"\u003cp\u003eMonitoring the Intestinal Environment of Mice in a dnbs-induced IBD Model. Body weights (\u003cstrong\u003ea\u003c/strong\u003e) and survival (\u003cstrong\u003eb\u003c/strong\u003e) of mice after administration of dnbs (DNBS, n=12 for A n=29 for B) or vehicle (VehC, n=8 for \u003cstrong\u003ea\u003c/strong\u003e n=9 for \u003cstrong\u003eb\u003c/strong\u003e). \u003cstrong\u003ec\u003c/strong\u003e. The representative intestinal histologic images stained with H \u0026amp; E. \u003cstrong\u003ed\u003c/strong\u003e. (n=6 for VehC n=5 for DNBS). Principal component analysis (PCA) plot showing the first 2 PCs for VehC and DNBS groups. PCA was conducted for record-seq profiles obtained from reporter \u003cem\u003eE. coli\u003c/em\u003e of the VehC and DNBS groups. The percentage of variance was calculated based on standard deviations of each PC. \u003cstrong\u003ee\u003c/strong\u003e. Heatmap of the top 50 differentially expressed genes in the DNBS samples. DEGs were identified via DESeq function from DESeq2 R package utilizing Wald test. Significance was indicated by padj \u0026lt; 0.1 and log2 fold change \u0026gt; 0.6/ \u0026lt; -0.6. The top 50 genes were chosen based on the lowest p value. \u003cstrong\u003ef\u003c/strong\u003e. Results of KEGG pathway enrichment analysis for genes significantly upregulated in DNBS vs VehC. Significantly enriched pathways were identified using enrichKEGG function from clusterProfiler R package, utilizing Fisher’s exact test (significant pathways indicated by p \u0026lt; 0.05). \u003cstrong\u003eg\u003c/strong\u003e. Treemap representing the enriched KEGG pathways. The size of the squares was scaled to the p-value.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/953752e762308dc7e727e195.png"},{"id":87919885,"identity":"37a6a3d5-8962-4220-9d51-8825185cb130","added_by":"auto","created_at":"2025-07-30 11:34:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185416,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences between differentially expressed genes (DEGs) in DSS- and DNBS-induced IBD models. \u003cstrong\u003ea\u003c/strong\u003e. Venn diagrams showing the number of genes shared by DEGs of dss-induced IBD model (DSS) vs healthy control (HC) on the indicated day and by DEGs of dnbs-induced IBD model (DNBS) vs vehicle control (VehC). \u003cstrong\u003eb\u003c/strong\u003e. Plots showing log2 fold changes of shared genes by the indicated DEGs. \u003cstrong\u003ec\u003c/strong\u003e. A plots showing log2 fold changes of genes involved in stimulus response (analysed using EcoCyc) in the indicated DEGs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/e90eae954abb84f6d55cfcda.png"},{"id":87918749,"identity":"0381eae2-8667-4c82-9916-4603f25445d5","added_by":"auto","created_at":"2025-07-30 11:26:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":361358,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of oral administration of \u003cem\u003eL. crispatus\u003c/em\u003e on the intestinal inflammation and reporter \u003cem\u003eE. coli\u003c/em\u003e gene expression. \u003cstrong\u003ea\u003c/strong\u003e. Experimental scheme. Mice were allocated four groups: control (Con) administered with PBS alone, Lc administered with \u003cem\u003eL. crispatus\u003c/em\u003e alone, DSS administered with dss alone, and DSLc administered with both \u003cem\u003eL. crispatus\u003c/em\u003eand dss. The oral gavage of \u003cem\u003eL. crispatus\u003c/em\u003eor PBS is indicated by arrowheads and the supply of water containing dss or not by syringes. Anhydrous tetracycline (ATC) was added to the drinking water to induce spacer acquisition in reporter \u003cem\u003eE. coli\u003c/em\u003e. \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e. Kinetics of body weights and disease activity score. (Con n=28, Lc n=15, DSS n=35 DSLc n=20 for \u003cstrong\u003eb\u003c/strong\u003e Con n=13, Lc n=15, DSS n=15 DSLc n=20 for \u003cstrong\u003ec\u003c/strong\u003e) \u003cstrong\u003ed\u003c/strong\u003e. The representative image of colons obtained from mice of each group (left). A plot of colon lengths of all the mice (right). (Con n=12, Lc n=11, DSS n=8, DSLc n=17) \u003cstrong\u003ee\u003c/strong\u003e. The representative images of colon tissue (left) A plot showing histologic scores of colonic inflammation (right) (n=6 per group) \u003cstrong\u003ef\u003c/strong\u003e. Principal component analysis (PCA) of record-seq profiles obtained from the indicated mouse groups. PCA plot shows the first 2 PCs. The percentage of variance was calculated based on standard deviations of each PC. \u003cstrong\u003eg\u003c/strong\u003e. Volcano plots showing statistical significance of genes differentially expressed in the indicated group comparison. DEGs were identified via DESeq function from DESeq2 R package utilizing Wald test. Significance was indicated by padj \u0026lt; 0.1 and log2 fold change (FC) \u0026gt; 0.6/ \u0026lt; -0.6. Genes with significant log2 FC are marked in green. Genes with significant p value are marked in blue. Genes with both significant p value and log2 FC are marked in red.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/b0be1441e721264899f51606.png"},{"id":87919888,"identity":"48ae93fc-b067-4592-95b5-5734e15ed754","added_by":"auto","created_at":"2025-07-30 11:34:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258387,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of intestinal \u003cem\u003eE. coli\u003c/em\u003e gene expression in the condition of \u003cem\u003eL. crispatus \u003c/em\u003egavage. \u003cstrong\u003ea\u003c/strong\u003e. Volcano plots showing statistical significance of genes differentially expressed in the indicated comparison. DEGs were identified via DESeq function from DESeq2 R package utilizing Wald test. Significance was indicated by padj \u0026lt; 0.1 and log2 fold change (FC) \u0026gt; 0.6/ \u0026lt; -0.6. Genes with significant log2fold change(FC) are marked in green. Genes with significant p value are marked in blue. Genes with both significant p value and log2FC are marked in red. Con, samples from control mice; LcDSLc, combined samples from mice gavaged with \u003cem\u003eL. crispatus\u003c/em\u003e and samples from mice treated with both dss and \u003cem\u003eL. crispatus\u003c/em\u003e; DSS, samples of dss-treated mice. \u003cstrong\u003eb\u003c/strong\u003e. Venn diagram showing gene numbers in DEGs of the indicated comparison and plots showing expression fold changes of the shared genes in the upper Venn diagrams. \u003cstrong\u003ec\u003c/strong\u003e. Results of KEGG pathway enrichment analysis for genes significantly up- or downregulated in the indicated comparison. Significantly enriched pathways were identified using enrichKEGG function from clusterProfiler R package, utilizing Fisher’s exact test (significant pathways indicated by p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/25af635ba8d5ddf1b1842b03.png"},{"id":95564699,"identity":"40bdbc2a-6893-49eb-8434-6b9b38d9ef4b","added_by":"auto","created_at":"2025-11-10 16:10:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2983659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/756e3733-a123-4665-8931-591ee711f659.pdf"},{"id":87919886,"identity":"e89beffd-a7f7-4735-8339-4cff2644f640","added_by":"auto","created_at":"2025-07-30 11:34:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":885841,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedSupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7082467/v1/c31b3b26db1784b9e93888bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Escherichia coli gene expression is influenced more by gut environmental changes from inflammation and microbiota modulation than by colitis severity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut microbiota is involved in the pathogenesis of multiple diseases, including inflammatory bowel disease (IBD), and has emerged as a therapeutic target [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Alterations in gut microbiota composition have been observed in patients with IBD compared to healthy individuals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Transplantation of gut microbiota from patients with IBD, but not from healthy individuals, into germ-free mice induces intestinal inflammation, demonstrating a causative role of the microbiota in IBD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The contribution of the microbiota to gut inflammation appears to be related to the interplay between the immune system and the gut microbiota. Metabolites produced by gut microbiota exert regulatory effects on inflammation by modulating immune cell functions [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conversely, the immune system influences intestinal commensal microbes through various mechanisms, such as the production of antimicrobial peptides and radicals [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, strategies targeting the gut microbiota, such as faecal microbial transfer and probiotic administration, have been explored for the treatment of IBD and other diseases [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIBD is a chronic, recurrent intestinal inflammation that is believed to be elicited by multiple etiological factors. They are considered intractable due to the frequent development of unresponsiveness to current medications [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The two major types of IBD are ulcerative colitis and Crohn\u0026rsquo;s disease. Ulcerative colitis is characterised by lesions restricted to the mucosal layer of the colon, whereas Crohn\u0026rsquo;s may involve all layers of the intestinal wall, potentially resulting in intestinal perforation. Diagnosis relies mainly on an intestinal biopsy via endoscopy, which is inconvenient and invasive [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Owing to the limitations in magnetic resonance imaging (MRI) and faecal calprotectin assessments, the need for a non-invasive, cost-effective, and easily accessible method to monitor gut inflammation remains unresolved [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. IBD is typically managed with anti-inflammatory drugs such as corticosteroids or anti-TNF-α antibodies; however, diminishing therapeutic efficacy and the emergence of drug-related side effects remain major concerns. To address these challenges in both diagnosis and treatment, engineered \u003cem\u003eEscherichia coli\u003c/em\u003e that can sense inflammatory markers such as tetrathionate, emit signals such as fluorescent light, and/or produce anti-inflammatory molecules are currently in the preclinical trial stage [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although promising, several factors, including the intraluminal persistence of therapeutic \u003cem\u003eE. coli\u003c/em\u003e, require improvement [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Understanding \u003cem\u003eE. coli\u003c/em\u003e gene expression in a dynamically changing gut environment through the interaction between the microbiota and inflammation may offer new insights for the development of IBD therapeutics.\u003c/p\u003e\u003cp\u003eRecord-seq is introduced as a method that enables the analysis of gene expression in reporter bacteria within the mouse gut harbouring complex bacterial flora [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Record-seq utilises the Type III clustered regularly interspaced short palindromic repeat (CRISPR)-Cas system [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which plays a defensive role in diverse bacteria by inserting DNA fragments originating from invading genetic elements between direct repeat (DR) sequences in the CRISPR array on the genomic DNA. The inserted DNA fragments, known as spacers, serve as records of invasion. Uniquely, the Type III CRISPR-Cas system acquires spacers from mRNA using a complex consisting of reverse transcriptase-Cas1 (RT-Cas1), a naturally occurring fusion protein, and Cas2. To exploit this property, DNA sequences encoding RT-Cas1, Cas2, and CRISPR DRs were cloned into a plasmid under the control of a tetracycline repressor system, and the plasmid was introduced into the reporter \u003cem\u003eE. coli\u003c/em\u003e strain [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the presence of a tetracycline analogue, \u003cem\u003eE. coli\u003c/em\u003e expresses RT-Cas1 and Cas2 protein complexes, leading to the acquisition of spacers from the cytoplasmic RNA pool into the CRISPR array sequences on the plasmid. The gene expression profiles were recorded on the plasmid as a series of sequential spacers. These can be analysed by plasmid purification, spacer sequencing, and quantification of spacer-aligned \u003cem\u003eE. coli\u003c/em\u003e genes. Notably, these RNA-recorded spacer sequencing (record-seq) profiles correlated well with the conventional RNA sequencing (RNA-seq) profiles [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we utilised record-seq to investigate the gene expression profiles of \u003cem\u003eE. coli\u003c/em\u003e in the guts of two IBD mouse models under conditions that preserved the full spectrum of the gut microbiota. Specifically, we aimed to determine whether \u003cem\u003eE. coli\u003c/em\u003e gene expression changes dynamically over the course of gut inflammation, differs between dextran sulfate Sodium (dss)-induced and dinitrobenzene sulfonic acid (dnbs)-induced IBD models, and is altered by the administration of \u003cem\u003eLactobacillus crispatus\u003c/em\u003e, a probiotic known to influence both gut microbiota composition and inflammatory responses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDisease activity and intestinal inflammation during the first and second attacks in the dss-induced IBD model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen mice were given dss-containing water for 5 days, they manifested IBD-related symptoms such as weight loss, diarrhoea, haematochezia, colon shortening, and inflammation in the colon tissue, beginning on day 5 (D5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By day 15 (D15), body weight, colon length, and disease activity returned to normal levels; however, inflammation persisted in the colon tissue, which is compatible with the dissociation of clinical remission from intestinal mucosal healing and the progressive nature of ulcerative colitis in some patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To induce a second bout of colitis, dss-containing water was administered again from D15 to day 20. Signs of colitis reappeared more rapidly and with greater severity. The histological scores in IBD mice were significantly higher than those in the corresponding healthy control (HC) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and those on day 24(D24) were significantly greater than those on D5 (p\u0026thinsp;=\u0026thinsp;0.0038). Based on these observations, we selected D5, day 9 (D9), and D24 as representative time points for the active phases of the first and second bouts of colitis and D15 as the symptomatic remission phase with persistent intestinal inflammation to analyse gene expression in the reporter \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003egene expression in the gut of dss-induced IBD mice differs from that of healthy control mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe reporter \u003cem\u003eE. coli\u003c/em\u003e was administered to mice 11 h prior to each observation time point and subsequently retrieved from faeces for spacer acquisition analysis. Representative results of \u003cem\u003eE. coli\u003c/em\u003e recovery, plasmid purification, and spacer acquisition are shown in Supplementary Figs.\u0026nbsp;2 and 3. Spacer libraries from HC and dss-induced IBD (DSS) mice were sequenced. At least 1.1 x 10\u003csup\u003e6\u003c/sup\u003e reads were obtained per sample, except for one sample (1_8_2, D5 control sample read 2), ranging from 1,172,026 to 30,677,568 total reads. The number of spacers aligned with \u003cem\u003eE. coli\u003c/em\u003e genes ranged from 2,144 to 46,178 per sample (Supplementary Table\u0026nbsp;1). Read quality is shown in Supplementary Fig.\u0026nbsp;3. The mean spacer length was 49 bp, slightly longer than the 40\u0026ndash;42 bp reported by Schmidt et al., whereas the distribution of the spacer GC content was similar to their findings (Supplementary Fig.\u0026nbsp;4)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe compared \u003cem\u003eE. coli\u003c/em\u003e gene-aligned spacer profiles between the HC and DSS mouse groups to assess gene expression in steady-state versus inflammatory gut environments. Principal component analysis (PCA) plots and heat maps revealed differential gene expression between the HC and DSS groups at all observed time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Unexpectedly, the greatest difference in gene expression between the DSS and HC mice was observed on D15, corresponding to the symptomatic remission phase of colitis, with a moderate level of colonic inflammation. Heatmaps showing the top 50 significant DEGs and a plot showing the ratio of up- and down-regulated genes in the total DEGs did not exhibit any trend related to histologic inflammatory scores. These results demonstrate that \u003cem\u003eE. coli\u003c/em\u003e gene expression in an inflamed gut differs from that in a healthy gut, although the degree of difference does not correlate with the severity of the histological inflammation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMarginal overlap between DEGs in dss-induced colitis at different time points\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we examined the characteristics of DEGs in dss-induced colitis compared with those in HC. The number of DEGs observed at the four time points ranged from 80 on D24 to 135 on D15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, the lowest number of DEGs was observed on D24, despite the highest disease activity and colonic inflammation. A Venn diagram revealed fewer than five overlapping genes in any pairwise comparison between time points and no shared genes across any combination of three or all four time points. Of the overlapping genes, 13 showed consistent upregulation or downregulation, whereas four exhibited opposite expression patterns across time points (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These common genes included four genes involved in carbohydrate or amino acid degradation, three in stress responses, nine in transcription factor regulation, and four in other functions. KEGG pathway enrichment analysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] indicated that the downregulated DEGs in the DSS vs. HC on D15 were significantly enriched in the ribosome synthesis pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In conclusion, the overall overlap of DEGs across the different time courses of gut inflammation was minimal. These findings highlight the dynamic nature of \u003cem\u003eE. coli\u003c/em\u003e gene expression in response to temporal changes in an inflamed gut environment.\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\u003eThe genes differentially expressed in reporter \u003cem\u003eE. coli\u003c/em\u003e in a dss-induced IBD model compared to healthy control at two time points\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGene Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eLog2 fold change\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eD15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eD24\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG12416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egatC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCarbohydrates and Carboxylates Degradation\u003c/p\u003e\u003cp\u003eAlcohol Degradation\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etnaA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmino Acid Degradation\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erplM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranslation Proteins\u003c/p\u003e\u003cp\u003eProtein Metabolism\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enarP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA Metabolism\u003c/p\u003e\u003cp\u003eSignal transduction pathways\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ednaK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDNA and RNA Metabolism\u003c/p\u003e\u003cp\u003eProtein Metabolism\u003c/p\u003e\u003cp\u003eProtein Folding\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eHeat stress\u003c/p\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG0-10657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyshB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003euncharacterised protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eubiD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esmg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erlmE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA Metabolism\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoaA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCofactor Synthetic pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG7683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyhcN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003epH stress/Oxidation stress\u003c/p\u003e\u003cp\u003eProteins Involved in Biofilm Formation\u003c/p\u003e\u003cp\u003ePeriplasmic Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eglpQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCarbohydrates and Carboxylates Degradation\u003c/p\u003e\u003cp\u003eAlcohol Degradation\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003ePeriplasmic Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eglpD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCarbohydrates and Carboxylates Degradation\u003c/p\u003e\u003cp\u003eAlcohol Degradation\u003c/p\u003e\u003cp\u003eInorganic Nutrient Metabolism\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG10246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emltD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003eCell Wall Biogenesis/Organization Proteins\u003c/p\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-2.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG7513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyqfB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnzymes not in Pathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyieG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003cp\u003eTransport Proteins\u003c/p\u003e\u003cp\u003ePlasma Membrane Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEG11276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etnaC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA Metabolism\u003c/p\u003e\u003cp\u003eSigma Factor Regulons\u003c/p\u003e\u003cp\u003eTranscription Factor Regulons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.00\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\u003eDifferential gene expression in\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003ewithin the gut of dnbs-treated mice compared to vehicle-treated controls\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we examined \u003cem\u003ethe E. coli\u003c/em\u003e gene expression in the gut of another mouse model of IBD induced by dnbs. Dnbs was dissolved in 50% ethyl alcohol and administered rectally. The mice were monitored for changes in body weight, faecal consistency, survival, and intestinal histology. The observed symptoms included weight loss, diarrhoea, haematochezia, melena, and intestinal inflammation. Unlike mice in the dss-induced IBD model, approximately 30% of the dnb-treated mice died by day 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and autopsies revealed small intestinal perforation or obstruction in the deceased mice (data not shown). The reporter \u003cem\u003eE. coli\u003c/em\u003e was orally administered to the surviving mice on day 3 (D3). Faecal samples were collected 11 h later for the analysis of the acquired spacers. Spacer analysis aligned to \u003cem\u003eE. coli\u003c/em\u003e genes revealed significant differential expression of 848 genes; 440 were upregulated and 408 were downregulated in \u003cem\u003eE. coli\u003c/em\u003e from dnb-treated mice (DNBS) compared with those from vehicle-treated mice (VehC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PCA plots and heatmaps confirmed the differential gene expression between the DNBS and VehC groups. Significantly upregulated genes were enriched in pathways related to the biosynthesis of secondary metabolites; amino acid biosynthesis; carbon metabolism; cationic antimicrobial resistance; and glycine, serine, and threonine metabolism. We analysed the \u003cem\u003eE. coli\u003c/em\u003e genes associated with these pathways using a KEGG database and displayed them in the tree map plot.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDEGs in dnbs-induced IBD differ from those in dss-induced IBD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we examined the similarity of DEGs in \u003cem\u003eE. coli\u003c/em\u003e between dnbs- and dss-induced IBD mouse models. A total of 28 shared genes were identified between the 848 DEGs in the DNBS vs. VehC comparison and 97 DEGs in the DSS vs. HC comparison on D5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, 28 and 20 shared genes were observed between the DEGs of DNBS and DSS on D9 and between the DEGs of DNBS and DSS on D15, respectively. Some of these genes showed commonly up- and downregulated in both the DNBS- and DSS-induced IBD models, but majorities of these genes exhibited opposite regulation between the two models. In addition, the upregulated genes involved in stimulus responses differed between the two models [analysed using EcoCyc database, 39]. These findings indicate that \u003cem\u003eE. coli\u003c/em\u003e gene expression in the inflammatory gut differs depending on the IBD model used.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdministration of\u003c/b\u003e \u003cb\u003eL. crispatus\u003c/b\u003e \u003cb\u003eprior to dss exposure exacerbates colitis without significant changes in\u003c/b\u003e \u003cb\u003eE. coli\u003c/b\u003e \u003cb\u003egene expression compared to healthy or\u003c/b\u003e \u003cb\u003eL. crispatus\u003c/b\u003e\u003cb\u003e-only controls\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the effect of oral gavage of \u003cem\u003eL. crispatus\u003c/em\u003e on intraluminal gene expression in \u003cem\u003eE. coli\u003c/em\u003e, mice were administered \u003cem\u003eL. crispatus\u003c/em\u003e once daily for five days, beginning one day prior to the administration of dss-containing water (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). During the five-day dss administration period, body weights were similar across all groups: untreated controls (Con), \u003cem\u003eL. crispatus\u003c/em\u003e-treated group (Lc), dss-treated group (DSS), and the group receiving both \u003cem\u003eL. crispatus\u003c/em\u003e and dss (DSLc). However, the IBD disease activity index significantly increased on D5 in both the DSS and DSLc groups compared to the Con or Lc groups, and was significantly higher in the DSLc group than in the DSS group(p\u0026thinsp;=\u0026thinsp;0.0002, ANOVA with a Bonferroni correction). Colon lengths were similar between the Lc and Con groups but significantly decreased in the DSS and DSLc groups compared to the Lc group (p\u0026thinsp;=\u0026thinsp;0.035, ANOVA with a Bonferroni correction). In line with this, the histological scores were significantly higher in the DSS and DSLc groups than in the Con group (p\u0026thinsp;=\u0026thinsp;0.0496 and p\u0026thinsp;=\u0026thinsp;0.0001, Kruskal-Wallis test with Dunn's correction). Histological scores were higher in DSLc than in DSS, although the difference between these groups was not statistically significant, probably because of the small number of samples.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo examine gene expression, reporter \u003cem\u003eE. coli\u003c/em\u003e was administered on D5, and faecal samples were collected 11 h later for analysis. PCA plots of the record-seq profiles revealed a clear separation between the Con and DSS samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the Con cluster was positioned near the Lc and DSLc clusters and did not form distinct clusters. Volcano plots consistently showed significantly different gene expression between Con and DSS and between Lc and DSS, but not between Con and Lc or between Con and DSLc. However, the small sample size of the DSLc group may have limited the statistical power to detect significance. Collectively, although oral administration of \u003cem\u003eL\u003c/em\u003e. \u003cem\u003ecrispatus\u003c/em\u003e prior to the induction of gut inflammation exacerbated colitis, \u003cem\u003eE. coli\u003c/em\u003e gene expression profiles under these conditions remained similar to those of healthy controls or \u003cem\u003eL. crispatus\u003c/em\u003e-only treatment, rather than resembling those of the dss-only treatment.\u003c/p\u003e\u003cp\u003eBecause the Lc and DSLc samples were not clearly separated in the PCA plot, we analysed the DEGs of Con vs. the combined profiles of Lc and DSLc (LcDSLc) and DEGs of DSS vs. LcDSLc (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Consistent with the PCA results, more individual DEGs were identified in the DSS vs. LcDSLc comparison than in the Con vs. LcDSLc comparison (66 vs. five genes). Five genes were shared by DEGs of Con vs. DSS and DEGs of Lc vs. DSS, which increased in the dss-treatment condition, whereas 22 genes were common in DEGs of Lc vs. DSS and DEGs of LcDSLc vs. DSS. The upregulated genes in the control vs. DSS and LcDSLc vs. DSS groups were also enriched in the ribosome synthesis pathway. Enrichment of downregulated genes in the phosphotransferase system was identified in LcDSLc compared to DSS. Additionally, one of the downregulated DEGs of Con vs. LcDSLc was significantly enriched in several pathways related to metabolism, such as tryptophan metabolism, degradation of several amino acids, and the TCA cycle, suggesting an alteration of the gut environment by the administration of \u003cem\u003eL. crispatus\u003c/em\u003e. Collectively, oral administration of \u003cem\u003eL. crispatus\u003c/em\u003e affected the gut environment by altering \u003cem\u003eE. coli\u003c/em\u003e gene expression profiles in both steady and inflammatory states.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated \u003cem\u003eE. coli\u003c/em\u003e gene expression in the gut of two IBD mouse models using record-seq, building on Schimdt\u0026rsquo;s study showing that record-seq profiles correlated with RNA-sequencing profiles [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We observed three main findings: (1) \u003cem\u003eE. coli\u003c/em\u003e gene expression in the inflamed gut differed from that in the healthy gut and changed dynamically during the first and second colitis episodes; (2) DEGs in \u003cem\u003eE. coli\u003c/em\u003e from dss- or dnbs-induced IBD models minimally overlapped; and (3) Administration of \u003cem\u003eL. crispatus\u003c/em\u003e prior to dss exposure resulted in \u003cem\u003eE. coli\u003c/em\u003e gene expression profiles resembling those of healthy controls, despite greater colitis severity compared to dss treatment alone. Notably, these findings were obtained from mice harbouring the full spectrum of gut microbiota.\u003c/p\u003e\u003cp\u003eCompared with previous reports analysing \u003cem\u003eE. coli\u003c/em\u003e gene expression in germ-free mice colonised solely with \u003cem\u003eE. coli\u003c/em\u003e, our results showed similarities and differences. Consistent with the findings of Dacquay and Schmidt, who used germ-free mice colonised with \u003cem\u003eE. coli\u003c/em\u003e and subsequently exposed to DSS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], we observed differential \u003cem\u003eE. coli\u003c/em\u003e gene expression in inflamed guts containing diverse microbiota. According to Dacqauay et al., the upregulated pathways were associated with flagellar biosynthesis and motility. These genes were absent from our DEGs, likely because of the differences in \u003cem\u003eE. coli\u003c/em\u003e strains. Dacqauay et al. used the Nissle strain, known for its probiotic and colonisation ability in the human gut, whereas we used the BL21(DE3) strain, commonly employed in recombinant protein production and is known to lack flagellar genes due to IS1 element insertion [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. When comparing our dss-induced DEGs with those from Schmidt\u0026rsquo;s study using the BL21 (DE3) or K-12 MG1655 strains, only 22 of the 184 DEGs overlapped in the expression direction. This suggests that \u003cem\u003eE. coli\u003c/em\u003e gene expression is influenced not only by gut inflammation but also by the composition of the gut microbiota.\u003c/p\u003e\u003cp\u003eAn alternative to invasive histological evaluation of intestinal inflammation in IBD is needed. Schmidt et al. proposed that record-sequencing of sentinel bacteria could serve as a stand-alone method for monitoring gut inflammation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Although our findings partially supported this hypothesis, they did not fully align with Schmidt\u0026rsquo;s conclusions. Like their results, our record-seq profiles distinguished between inflamed and healthy gut conditions in dss-induced colitis. However, unlike Schmidt\u0026rsquo;s study, we found that the record-seq profiles did not clearly reflect the severity of inflammation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Schmidt observed that severe inflammation (induced by 3% dss) caused record-seq profiles to shift further from the control group compared to milder inflammation (induced by 1% dss). In contrast, in our study, the profiles of severe inflammation (D24 and dss\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. crispatus\u003c/em\u003e) were not more distant from the controls than those of milder inflammation (D5 and dss alone). This discrepancy may be explained by differences in microbial environments and \u003cem\u003eE. coli\u003c/em\u003e colonisation dynamics. In our study, the reporter \u003cem\u003eE. coli\u003c/em\u003e was transiently present for 11 h in the gut containing the entire microbiota, whereas in Schmidt\u0026rsquo;s study, the bacteria were long-term colonisers in a germ-free environment. Furthermore, we identified very few genes that were consistently upregulated or downregulated across the inflammatory time points. The key genes frequently identified by Schmidt, such as \u003cem\u003ealaE, narH, hdeB\u003c/em\u003e, and \u003cem\u003eglgS\u003c/em\u003e, did not appear in the DEG lists. Given the complexity of the human gut microbiota, our findings suggest that record-seq may not always reflect inflammation or its severity, particularly in microbiota-rich environments.\u003c/p\u003e\u003cp\u003eIn our study, record-seq revealed distinct DEG patterns between dss- and dnbs-induced IBD models. Only 1.2% of the 848 DEGs identified in the DNBS-induced model overlapped with those in the dss-induced model on D5. The enriched pathways significantly differed. For example, genes involved in the oxidative stress response were more abundant in the dnbs model (35 genes) than in the dss model (two genes). Given the characteristic histopathology of dnbs-induced colitis\u0026mdash;autolytic epithelial cell death, it is likely that the dnbs-treated gut presented a different environment from the dss-treated gut. Thus, record-seq effectively captured the \u003cem\u003eE. coli\u003c/em\u003e adaptation to these model-specific environments, highlighting its utility in studying bacterial gene expression in diverse inflammatory contexts.\u003c/p\u003e\u003cp\u003eOur record-seq findings following \u003cem\u003eL. crispatus\u003c/em\u003e administration further underscore the complexity of the gut environment. IBD is characterized by epithelial damage, increased inflammatory mediators, altered metabolites, and microbial dysbiosis\u0026mdash;interconnected factors that contribute to disease pathology [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These complex environmental factors, in addition to inflammation, likely influence \u003cem\u003eE. coli\u003c/em\u003e gene expression. This may explain the unexpected observation that record-seq profiles in the DSLc group resembled those of healthy controls more closely than those of dss-only treated mice, despite more severe inflammation in the former. Given \u003cem\u003eL. crispatus\u003c/em\u003e' known effects on vaginal microbiota and innate immunity [\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], it is plausible that it also modulates gastrointestinal microbiota and immune responses. In fact, \u003cem\u003eL. crispatus\u003c/em\u003e inhibits the adhesion of pathogenic bacteria to the gastric cells and reduces inflammatory cytokine expression in these cells [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In IBD mouse models, the regulatory effects of \u003cem\u003eL. crispatus\u003c/em\u003e on inflammation have been reported to depend on the strain [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Moreover, \u003cem\u003eL. crispatus\u003c/em\u003e may influence the host microbiota through a putative bacteriocin encoded by a mobile genetic element (Tn7088), whose sequence varied between the beneficial strain M247 and the pathogenic strain vpi3199 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. We used vpi3199. Consistent with our findings, \u003cem\u003eL. crispatus\u003c/em\u003e strain M206119 has been reported to worsen colitis [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], though the mechanisms underlying strain-specific effects remain unclear.\u003c/p\u003e\u003cp\u003eOne of the limitations of our study is the small sample size of the dss-induced model, particularly in the group treated with both dss and \u003cem\u003eL. crispatus\u003c/em\u003e. It is possible that a larger sample size would yield more significant DEG differences between DSS and DSLc. Further studies incorporating analyses of the gut microbiota composition, metabolites, and inflammatory mediators during colitis would provide deeper insights.\u003c/p\u003e\u003cp\u003eIn conclusion, our study demonstrates that \u003cem\u003eE. coli\u003c/em\u003e gene expression in the gut is influenced by the type of IBD-inducing agent, temporal course of inflammation, and administration of \u003cem\u003eL. crispatus.\u003c/em\u003e These findings suggest that bacterial gene expression adapts dynamically to the gut environment, which is shaped by host inflammatory responses and microbiota interactions. Our results have implications for understanding the role of microbiota in diseases and for developing effective bacterial therapeutics and noninvasive diagnostic tools for gut inflammation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eInduction of Inflammatory Bowel Disease in Mice\u003c/p\u003e\u003cp\u003eSix-week-old male C57BL/6 mice were purchased from Orient Bio (South Korea). The mice were housed in groups of 3 to 5 per cage and maintained in the specific pathogen-free area of the Laboratory Animal Research Center at Ajou University Medical Center. To induce inflammatory bowel disease, mice were given drinking water containing 2% dss (MPbio, 9011-18-1) for five days. Alternatively, mice received a single rectal administration of 33.3 mg/mL dnbs (Thermo, A18493) in 50% ethyl alcohol. To modulate the gut microbiota, we fed mice 200 \u0026micro;L \u003cem\u003eLactobacillus crispatus\u003c/em\u003e (ATCC No 33820, vpi3199, 2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e CFU/mL in phosphate buffered saline (PBS)) daily for 5 days. Mouse body weight, faecal consistency, and the presence of blood in stools were monitored. We also observed the intestinal appearance and length of the mice after sacrifice. The intestinal tissue was fixed in 10% formaldehyde solution, embedded in paraffin block, cut into 5 \u0026micro;m thick sections and then stained with haematoxylin and eosin. Histological scoring was performed using Remke\u0026rsquo;s method [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Three individuals assessed the inflammation in the colon tissue. One of them was a pathologist blinded to the sample information. The experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Ajou University Medical Center (authorization number 2023-0024) and performed in accordance with the IACUC guidelines and regulations. All procedures were conducted in accordance with ARRIVE guidelines [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eVectors and Bacteria\u003c/p\u003e\u003cp\u003eFor record sequencing, we used reporter \u003cem\u003eEscherichia coli\u003c/em\u003e BL21(de3) (Dynebio. DYO1360) transformed with pFS_0453 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], an expression plasmid encoding RT-Cas1 and Cas2 and containing a CRISPR array. Three colonies of reporter \u003cem\u003eE. coli\u003c/em\u003e grown on the LB agar plate containing 50 \u0026micro;g/mL of Kanamycin were inoculated in 20 mL LB broth with 50 \u0026micro;g /mL of Kanamycin and cultured for 14 hours at 37℃ with shaking at 200 rpm. The culture was transferred to the 80 mL of new LB broth containing 50 \u0026micro;g/mL of Kanamycin and 30 ng/mL of anhydrous tetracycline (ATC, Cayman,10009542) and then 2 h later, the bacteria were harvested. The reporter \u003cem\u003eE. coli\u003c/em\u003e cells were washed and resuspended in PBS containing 30 ng/mL ATC and then orally administered to the mice. \u003cem\u003eE.coli\u003c/em\u003e, DH5α transformed with pTRKH3-ermGFP (a gift from Michela Lizier (Addgene plasmid # 27169 ; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:27169\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:27169\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ; RRID:Addgene_27169) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] was used to assess the faecal recovery rate after oral gavage of mice. \u003cem\u003eLactobacillus crispatus\u003c/em\u003e was grown in MRS media in anaerobic chamber at 35℃. When the absorbance of the culture at 600 nm reached 1.7\u0026thinsp;~\u0026thinsp;2.0, the bacilli were harvested, washed with PBS, and resuspended in PBS.\u003c/p\u003e\u003cp\u003eHarvest of reporter \u003cem\u003eE. coli\u003c/em\u003e from mice and purification of plasmid from faeces\u003c/p\u003e\u003cp\u003ePrior to oral gavage of reporter \u003cem\u003eE. coli\u003c/em\u003e into mice, we provided the mice with water containing 30 ng/mL ATC for 3 days. Eleven hours after oral gavage, faeces (up to 100 mg per mouse) were collected. The particles in the faeces were eliminated by centrifuging the diluted faecal solution in PBS containing 10 mM EDTA for 1 min at 200 \u0026times; g. Faecal bacteria were precipitated by centrifugation for 10 min at 6800 \u0026times; g and washed once with PBS containing 1 mM EDTA. The bacterial pellet was purified using a QIAprep spin Miniprep kit (QIAGEN).\u003c/p\u003e\u003cp\u003eConstruction of spacer libraries\u003c/p\u003e\u003cp\u003eThe purified plasmid pFS_0453 was digested with FastDigest FaqI (ThermoFisher, FD1814) and annealed with an adapter composed of two complementary oligonucleotides (AAAGCCAAATCTTCCACTTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC and GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCAAGTGGAAGATTTGG, Bioneer) using T7 DNA ligase (NEB, M0318S). The 99 cycles of digestion and annealing reactions were done at 37\u0026deg;C for 3 min and at 20\u0026deg;C 3 min, respectively. The plasmid DNA annealed with the adaptor was subjected to PCR to amplify the spacers, with an extension time of 13 s for 16 cycles using the first-round PCR primer set [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The PCR product was separated using AMPure XP beads (Beckman, A63880) and one-fifth of the PCR product was used in consecutive PCR using the second-round primer set [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Second-round PCR products with sizes of approximately 240 and 300 bp were purified by agarose gel electrophoresis using the QIAQuick Gel Extraction Kit.\u003c/p\u003e\u003cp\u003eNext-generation sequencing of spacer libraries\u003c/p\u003e\u003cp\u003eLibraries were quantified using qPCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantification kits for Illumina Sequencing platforms) and qualified using a TapeStation HSD5000 ScreenTape (Agilent Technologies, Waldbronn, Germany). The indexed libraries were sequenced on a NovaSeqX Plus platform (Illumina, San Diego, CA, USA by the Macrogen Incorporated). Briefly, Illumina utilises a unique \"bridged\" amplification reaction that occurs on the surface of the flow cell. A flow cell containing the prepared libraries was loaded onto a NovaSeq X Plus sequencer (Illumina) for automated extension and imaging cycles. The sequencing-by-synthesis cycle was repeated to obtain a paired-end read length of 2 X 150bp (10B).\u003c/p\u003e\u003cp\u003eData Analysis of Spacer Libraries\u003c/p\u003e\u003cp\u003eRaw fastq files were processed according to the ETH Zurich protocol \u0026ldquo;Recording transcriptional histories using record-seq\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3929/ethz-b-000396475\u003c/span\u003e\u003cspan address=\"10.3929/ethz-b-000396475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used a Linux desktop terminal as the first part of the pipeline. The appropriate environment was created using the necessary software. The tools and their versions are listed in the Protocol section. The input fastq files were processed using the Snakemake workflow provided by the authors of the protocol. Briefly, the workflow included quality control of the input fastq files by fastqc (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), read trimming by trimmomatic [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], fastq to fasta conversion using the FASTX-toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hannonlab.cshl.edu/fastx_toolkit\u003c/span\u003e\u003cspan address=\"http://hannonlab.cshl.edu/fastx_toolkit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), extraction of spacer sequences using the Python script, sequence alignment to reference genomes by bowtie2 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and count matrix generation using Subread FeatureCounts [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] (Supplementary Fig.\u0026nbsp;1). For Snakemake workflow execution, a supplementary configuration file is required with information about directory pathways, genome names, library barcodes, and direct repeat sequences. The configuration file used in this study is available as supplementary data (config. ymL). The second stage of the computational pipeline was performed using RStudio (v4.4.1) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. For spacer-level quality control, we generated spacer length and GC content plots using the recordseq R package (v0.2) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] SpacerStats function, and spacer information files obtained during the snakemake workflow (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eDifferential expression analysis was conducted using \u003cem\u003eEscherichia coli\u003c/em\u003e gene count matrices and the DESeq2 R package (v1.44.0) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The raw counts and metadata of the samples of interest were extracted from the original matrix and used to create a DESeq object (dds) using the DESeqDataSetFromMatrix function in the DESeq2 package. To exclude genes with low expression, rows with a sum of counts less than 15 were removed from the object. Differential expression was analysed using the DESeq function. Result tables for the comparison of specific samples were extracted from the DDS object using the results function. Significantly differentially expressed genes (DEGs) were identified based on the following criteria: padj\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and log2FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;0.6/ \u0026lt; -0.6. To visualise the results of differential expression analysis, the output tables were directly used to generate volcano plots using the EnhancedVolcano (v.1.22.0) package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/EnhancedVolcano\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/EnhancedVolcano\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For more advanced visualisation purposes, a variance-stabilising transformation was applied to the DDS object via the varianceStabilizingTransformation function with the blind\u0026thinsp;=\u0026thinsp;FALSE option, which is more suitable for datasets with a lower number of genes. Transformed object (vsd) was used to generate PCA plots via plotPCA function with intgroup = \u0026ldquo;Condition\u0026rdquo; option, to group samples by condition/treatment. The percentage variance for the first two principal components (PCs) was automatically calculated using the top 500 genes (by variance). To generate heatmaps, the top 50 significant genes (by p-value) were chosen from among all the significant DEGs. Normalised counts from the vsd object were extracted for the selected genes, and heat maps were created using the pheatmap function (v1.0.12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/raivokolde/pheatmap\u003c/span\u003e\u003cspan address=\"https://github.com/raivokolde/pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo investigate the involvement of the identified DEGs in known pathways, we conducted gene set enrichment analysis using the enrichKEGG function in the clusterProfiler R package (v4.12.6) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Gene names of the significantly up- and downregulated DEGs were converted to KEGG IDs via the bitr_kegg function, with E. coli (eco) as a supported organism. Significant pathways from the KEGG database were determined using p-values (p-value cutoff\u0026thinsp;=\u0026thinsp;0.05). Results from the enrichment analysis were visualised as bar plots using the plotEnrichAdv function from the Genekitr R package (v1.2.8) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and tree map plots using the treemap R package (v2.4-4) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=treemap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=treemap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATC : anhydrous tetracycline; DEGs : differentially expressed genes; dnbs: 2,4-dinitrobenzene sulfonic acid; DR : direct repeat; dss : dextran sulfate sodium; HC: healthy control; IBD : inflammatory bowel disease; MRI : magnetic resonance imaging; PCs : principal components; record-seq : rna-recorded spacer sequencing; RT-Cas1 : reverse transcriptase-cas1; VehC: vehicle-treated control.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. YB Kim (previously in the Department of Pathology, Ajou University Hospital, currently in GC Labs) for scoring colon tissue inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. RS-2023-00244783).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository, [The accession numbers are GSE303195 and GSE303196].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. B. L.and S.P. : the conception and design of the study, and interpretation of data, writing the article; J. K. and K. H. P. : acquisition and analysis of data, drafting part of the article and revising it critically, M. J. L. P. and C. M. : acquisition of data, K. K. : revising the article critically.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFan, Y. \u0026amp; Pedersen, O. Gut microbiota in human metabolic health and disease. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e, 55-71 (2021). https://doi.org:10.1038/s41579-020-0433-9\u003c/li\u003e\n \u003cli\u003eIliev, I. D., Ananthakrishnan, A. N. \u0026amp; Guo, C.-J. 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Empowering biologists to decode omics data: the Genekitr R package and web server. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 214 (2023). https://doi.org:10.1186/s12859-023-05342-9\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":"Inflammatory bowel disease, record-seq, probiotics","lastPublishedDoi":"10.21203/rs.3.rs-7082467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7082467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eModulation of the gut microbiota has emerged as a promising diagnostic and therapeutic approach for inflammatory bowel disease (IBD), a condition marked by chronic relapse. Analysing gene expression in luminal bacteria helps monitor the gut environment and assess the probiotic effects. However, the complexity of the microbiota poses a challenge. We examined the gene expression of \u003cem\u003eEscherichia coli\u003c/em\u003e in the intestines of IBD mouse models in the context of a native gut microbiota. We adopted reporter \u003cem\u003eE. coli\u003c/em\u003e expressing reverse transcriptase-Cas1 fusion protein and Cas2 to record transcript data on plasmids as short oligonucleotides. Gene expression profiles differed between IBD models and controls and varied with the type of inflammatory trigger and time point. However, pre-feeding \u003cem\u003eLactobacillus crispatus\u003c/em\u003e before IBD induction yielded \u003cem\u003eE. coli\u003c/em\u003e gene expression profiles resembling controls despite worsened colitis. Conclusively, altered \u003cem\u003eE. coli\u003c/em\u003e gene expression in the inflamed gut may reflect environmental changes driven by interactions between inflammation and microbiota. These findings suggest that bacterial gene expression adapts dynamically to the gut environment, which is shaped by host inflammatory responses and microbiota interactions. These results have implications for developing non-invasive diagnostic bacteria for gut inflammation.\u003c/p\u003e","manuscriptTitle":"Escherichia coli gene expression is influenced more by gut environmental changes from inflammation and microbiota modulation than by colitis severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 11:26:32","doi":"10.21203/rs.3.rs-7082467/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-09T06:13:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T17:42:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T01:30:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127944302785941825954532468683926980502","date":"2025-08-19T00:32:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73986394458796242298376130993383964056","date":"2025-08-15T15:15:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199877810468945227385051587583930075902","date":"2025-08-13T12:36:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T15:23:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T15:20:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T09:45:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T02:07:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-22T02:04:04+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":"b4ae5deb-81cc-4ab6-a062-692cce80357d","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52253472,"name":"Health sciences/Gastroenterology"},{"id":52253473,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-11-10T16:08:39+00:00","versionOfRecord":{"articleIdentity":"rs-7082467","link":"https://doi.org/10.1038/s41598-025-22697-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-06 15:58:06","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-07-30 11:26:32","video":"","vorDoi":"10.1038/s41598-025-22697-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22697-6","workflowStages":[]},"version":"v1","identity":"rs-7082467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7082467","identity":"rs-7082467","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}