Longitudinal study of the gut microbiota in ulcerative colitis patients on anti-integrin biologic therapy

Longitudinal study of the gut microbiota in ulcerative colitis patients on anti-integrin biologic therapy | 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 Research Article Longitudinal study of the gut microbiota in ulcerative colitis patients on anti-integrin biologic therapy Virginia Robles-Alonso, Claudia Herrera-deGuise, Luis Mayorga, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621048/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Disruptions of the gut microbiome are known to play a role in the pathophysiology of inflammatory bowel disease (IBD) and may influence response to biologic therapies. However, defining microbial signatures associated with treatment outcomes remains challenging. In this prospective longitudinal study, we analyzed changes in the intestinal microbiome of patients with active ulcerative colitis starting anti-integrin therapy with vedolizumab. Stool samples and clinical data were obtained at baseline and at weeks 12, 30, and 52 following treatment initiation. Microbial taxonomic composition and functional capacity were assessed using shotgun metagenomic sequencing. Patients who did not achieve clinical remission exhibited substantial interindividual heterogeneity in microbial composition and metabolic pathways throughout follow-up. In contrast, responders showed a more uniform and temporally stable microbiome, particularly during the induction phase. Importantly, none of the patients with markedly reduced baseline alpha diversity reached remission. Long-term responders demonstrated a gradual increase in commensal bacteria with reported anti-inflammatory effects, including Bacteroides uniformis, multiple Roseburia species (R. intestinalis, R. inulinivorans, R. faecis), and members of the Lachnospiraceae family. These microbial shifts were absent in non-responders and in patients who subsequently lost response. Taken together, these results indicate that baseline microbial diversity and longitudinal ecosystem stability may be associated with favorable response to anti-integrin therapy. Gut microbiome features could therefore complement clinical parameters in understanding treatment response and may contribute to future patient stratification strategies in ulcerative colitis. microbiome vedolizumab ulcerative colitis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Crohn´s disease (CD) and Ulcerative colitis (UC) are chronic inflammatory disorders of growing prevalence worldwide. The precise aetiology of the diseases is unknown, but the pathophysiology of the intestinal lesions involves a complex and altered interplay between mucosal immunity and microbial factors that trigger and perpetuate inflammation in individuals with genetic susceptibility( 1 ). Cumulative evidence demonstrates several imbalances of the gut microbial ecosystem at taxonomic and functional level in patients with active mucosal inflammation, characterized by decreased microbial diversity and clade-specific enrichments and depletions( 2 , 3 ). In ulcerative colitis, a decrease in species richness, an underrepresentation of butyrate producers and a gain of potentially harmful bacteria have been consistently found in samples from patients during disease exacerbation or shortly after remission( 4 , 5 ). Abundance of health-promoting bacteria including butyrate producers ( Faecalibacterium prausnitzii and Roseburia species) and other short-chain fatty acid-producers ( Akkermansia muciniphila and Lachnospiraceae species) is usually lower in patients as compared to controls. The depleted species induce regulatory cells and cytokines on the human gut mucosa and have a physiological role in mucosal homeostasis( 6 ). Interestingly, those patients who recover normal counts of butyrate producing species are prone to maintain long-time remission( 7 ). On the other hand, some other species such as Bilophila wadsworthia , Escherichia coli ( 8 ), and Fusobacterium species ( 9 , 10 ), which are known to generate pro-inflammatory stimuli, have been found in higher abundance in patients with UC. Thus, the influence of the intestinal microbiome in the response to immunosuppressors in IBD is plausible but remains largely unexplored. An observational study with Crohn's disease patients suggested that reduced abundance of F. prausnitzii in the intestinal microbiome may predict an early relapse after infliximab discontinuation( 11 ). The study by Ananthakrishnan and coworkers explored whether the gut microbiome may predict responses to vedolizumab biologic therapy. The authors conducted a prospective study both in CD and UC patients initiating anti-integrin therapy (vedolizumab). The study identified an interesting network algorithm integrating microbiome and clinical data with predictive value( 12 ). Longitudinal studies have shown that the microbiomes of IBD subjects fluctuate more than those of healthy individuals, with some correlations between fluctuations and medication intensification due to a flare( 13 ). Temporal microbiome instability has also been demonstrated during periods of remission( 14 ). In this longitudinal study, we followed UC patients who experienced a flare and were eligible for vedoluzimab treatment for induction of remission and subsequent remission maintenance. We used a shotgun metagenomic approach to explore gut microbiome fluctuations associated with response to vedolizumab therapy ( 29 ). Methods Study design, participants, intervention The EPIMUC study was a prospective, observational study of adult patients with left-sided or extensive UC requiring vedolizumab therapy. Vedolizumab is an integrin receptor antagonist indicated for the treatment of adult patients with moderately to severely active UC who have had an inadequate response, loss of response or intolerance to one or more standard therapies (corticosteroids, immunomodulators or tumour necrosis factor-α inhibitor) or demonstrated dependence on corticosteroids. We recruited adult patients (18-75 years) diagnosed with active ulcerative colitis requiring vedolizumab treatment according to the approved indications. Patients with UC diagnosed by standard criteria at least 6 months before inclusion and moderate to severe activity (Simple Colitis Activity Index, SCAI, ³ 6) were eligible(15). Previous anti-TNF therapy with a wash-out period of at least one month was allowed. The exclusion criteria included disease limited to rectum, antibiotics on the previous two months or during the study, pregnancy and breastfeeding. The protocol was approved by the Institutional Review Board of University Hospital Vall de Hebron, study protocol code: IISR-2015-101160 (FGA-VED-2016-01). Twenty-five patients entered the study. There were four dropouts due to clinical criteria (bacterial gastroenteritis, antibiotic treatment, worsening of psoriasis, and COVID infection), and 21 patients completed the study. Eligible patients were treated with vedolizumab, 300 mg on weeks 0, and 2 and 6. After induction, response to vedolizumab was evaluated at week 14 by clinical and endoscopic scoring. Remission was defined by SCAI < 4, faecal calprotectin < 150 µg/g, C-reactive protein < 0.5 mg/L, and Mayo index £ 1. Accordingly, patients were then allocated into two groups, i.e, responders (n=9) and non-responders (n=12). Non-responders at week 14 left the study. These patients were offered intensified treatment with vedolizumab or other alternative therapy and were not suitable for the aim of this study. Responders were kept on vedolizumab every 8 weeks until week 52 for maintenance of remission. Three patients lost response during the follow-up while the remainder 6 patients maintained remission until week 52. Patients provided two faecal samples during the run-in period prior to starting vedolizumab treatment and subsequently at week 12 of vedolizumab treatment. Additional samples were obtained at weeks 30 and 52 from patients who completed the protocol. Metagenomic approach Total DNA was isolated from fecal samples according to standardized procedures aligned with the International Human Microbiome Standards (30). Briefly, approximately 250 mg of frozen stool material was processed using a combination of chemical lysis and mechanical disruption with bead beating to ensure efficient microbial cell breakage. Following clarification of lysates, nucleic acids were recovered by alcohol precipitation. DNA concentration and purity were assessed spectrophotometrically, and DNA integrity was evaluated by microcapillary electrophoresis to confirm suitability for sequencing. Shotgun metagenomic sequencing was performed by an external service provider using an Illumina NovaSeq platform, generating paired-end reads (2 × 150 bp) with a target depth of at least 25 million reads per sample. Raw sequencing data were demultiplexed and adapter sequences were removed. Quality filtering and trimming were applied to exclude low-quality reads and short fragments. To eliminate host-derived sequences, reads were aligned against the human reference genome and matching reads were discarded. High-quality, non-host reads were subsequently used for downstream taxonomic and functional profiling. Taxonomic composition was inferred using an assembly-free approach based on clade-specific marker genes, while functional characterization was performed by mapping reads to reference gene family and metabolic pathway databases (30,31). Gene family and pathway abundances were normalized and expressed as copies per million to allow comparison across samples. All raw metagenomic sequencing data have been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information under BioProject accession number PRJNA1198595. The dataset is currently under controlled access during peer review and is available to editors and reviewers through a secure NCBI reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1198595?reviewer=kkbjgcq15d2lf8qtsdikc2t15n Statistical analysis Statistical analysis compared either two groups, namely, non-responders (No response, NoR, n=12) versus responders (Response, R, n=9), or three groups, namely, non-responders (NoR, n=12), patients on remission until week 52 (Sustained response, SusR, n=6), and patients who lost response after initial clinical remission (Loss of response, LossR, n=3). All statistical analyses were performed using R software (version 4.2.3). Microbiome data were analysed by comparing patients according to treatment outcome, including responders and non-responders at week 14, as well as three outcome-based groups: non-responders, patients with sustained remission up to week 52, and patients who experienced loss of response during follow-up. Within-sample microbial diversity was assessed using alpha diversity indices, while between-sample compositional differences were evaluated using beta diversity metrics based on dissimilarity matrices. Ordination analyses were conducted to visualize differences in microbial community structure across groups. Statistical significance of group-level differences was assessed using permutation-based methods. Differential abundance analyses of microbial taxa and metabolic pathways were conducted using established statistical frameworks appropriate for metagenomic data. Microbial features identified as significantly different between groups by any analytical approach were selected for downstream interpretation. To control for multiple comparisons, p-values were adjusted and predefined significance thresholds were applied. Prior to differential analyses, metagenomic data were normalized to account for differences in sequencing depth across samples. Results Table 1 shows patient demographics comparing Response (R) and No Response (NoR) groups at baseline and Table 2 shows clinical findings at week 12 on treatment. There were no differences in baseline demographic and clinical data between patients who attained remission and those who did not. Table 1 Baseline epidemiologic data in responders and no responders Responders (n = 9) No responders (n = 12) p Female, n (%) 3 (33) 5 (42) ns Smoking habit, n (%) ns nonsmoker 5 (55) 5 (41) ex-smoker 3 (33) 7 (58) current smoker 1 ( 11 ) 0 Age, y 48 (32–65) 55 (28–73) ns BMI, Kg/m2 24.4 (27.5–32.0) 25.3 (18.6–28.3) ns Montreal classification, n (%) ns E2 3 (33) 4 (33) E3 6 (66) 8 (66) Disease duration, y 14 ( 2 – 30 ) 9 ( 4 – 16 ) ns Calprotectin, µg/g 1024 (531–4874) 479 (223–3435) ns SCCAI 4 ( 1 – 8 ) 5 ( 2 – 11 ) ns Previous treatment, n (%) ns conventional 1 ( 11 ) 3 ( 25 ) anti-TNF once 6 (66) 5 (41) anti-TNF twice 2 ( 22 ) 4 (33) Qualitative data are n (%); quantitative values are median (range) Table 2 Response to vedolizumab at week 12 Responders (n = 9) No responders (n = 12) p SCCAI 2 ( 1 – 7 ) 10 ( 8 – 12 ) < 0.05 Calprotectin, µg/g 40 (16–302) 383 (349–3032) < 0.05 < 0.05 Mayo endoscopic subscore, n (%) 0 7 (77) 0 1 2 ( 22 ) 0 2 0 8 (66) 3 0 4 (33) C-reactive protein, mg/L 0.07 (0.04–0.71) 0.4 (0.07–11.8) < 0.05 Qualitative data are n (%); quantitative values are median (range) Metagenomic paired-end sequence reads (2x150bp) showing an average minimum of 25 million reads per sample were generated. The number of clean reads after trimming and contamination removal ranged from 2297384 to 86839985. A total 66 metagenome samples passed the quality control corresponding to the following groups of participants: i) No Response, NoR (n = 30), ii) Sustained Response, SusR (n = 26), iii) Loss of Response, LossR (n = 10). Baseline gut microbiome Baseline alpha diversity indices are shown in Fig. 1 . Chao1 and Shannon index data showed a widespread dispersion in patients who did not achieve remission at week 12 compared to responders. No significant differences were found between groups, but it is notable that none of the patients with a low Chao1 index at baseline responded to the biologic treatment. Non-redundant gene count is a straightforward marker of microbial richness in faecal samples, and we observed higher richness in responders (44967 ± 10912 non-redundant genes) than in non-responders (41382 ± 11152 non-redundant genes), although the difference is not significant. Beta diversity is a measure of the dissimilarity between samples from different individuals. Beta diversity within a given group is low when individual samples are similar and cluster together in a graphical representation, whereas beta diversity is high when individual samples show a large difference in composition and are distant in the graphical representation. We found higher beta diversity of metabolic pathways in patients who did not respond to treatment compared to those who did (Fig. 2 A), suggesting greater heterogeneity among microbiomes from patients who did not respond to treatment. Analysis of the principal coordinates of the samples identified statistically different clusters (p < 0.05), showing a higher dispersion of samples in the group of patients who did not respond to treatment than in the others (Fig. 2 B and 2 C). As shown in Tables 3 and 4 , most characteristic metabolic pathways of the SusR group correspond to Bacteroides caccae and B. ovatus and are involved in nucleotide and amino acid metabolism. In the LossR group, most characteristic metabolic pathways correspond to Faecalibacterium prausnitzii , Bifidobacterium longum and Eubacterium eligens and are involved in nucleotide, amino acid and carbohydrate metabolism. Table 3 Number of microbial metabolic pathways showing the highest abundance ( p < 0.05 and p adj < 0.25) in each group of patients. Metabolic pathways are allocated to microbial taxa. NoR: No Response, LossR: Loss of Response, SusR: Sustained Response. Group Taxa Frequency NoR Bifidobacterium longum 3 NoR Faecalibacterium prausnitzii 2 LossR Faecalibacterium prausnitzii 55 LossR Bifidobacterium longum 50 LossR Eubacterium eligens 49 LossR Fusicatenibacter saccharivorans 1 LossR Ruthenibacterium lactatiformans 1 SusR Bacteroides caccae 59 SusR Bacteroides ovatus 38 SusR Parabacteroides merdae 14 Table 4 Number of microbial metabolic pathways showing the highest abundance ( p < 0.05 and p adj < 0.25) in each group of patients (metabolic pathways are defined by function). NoR: No Response, LossR: Loss of Response, SusR: Sustained Response. Group Function Frequency NoR Carbohydrates 2 NoR Other 2 NoR Amino acids 1 LossR Amino acids 52 LossR Nucleotides 38 LossR Carbohydrates 27 LossR Other 21 LossR Vitamins 12 LossR Cell wall 4 LossR Lipids 2 SusR Nucleotides 39 SusR Amino acids 29 SusR Other 22 SusR Vitamins 9 SusR Cell wall 6 SusR Carbohydrates 4 SusR Lipids 2 Our analysis also focussed on distinct taxonomic profiles at baseline that could differentiate gut microbiomes in responders from those of no-responders, and eventually serve as predictive markers of clinical response. Figure 3 shows the bacteria species in which statistical differences between groups were detected. Surprisingly, abundance of Faecalibacterium prausnitzii and Eubacterium eligens at baseline was higher in NoR and LossR than in SusR group. Patients with a sustained long-term response to vedolizumab (SusR) showed higher abundance of Bacteroides caccae , Bacteroides ovatus , Bacteroides uniformis, Fusicatenibacter saccharivorans , Ruthenibacterium lactatiformans , Parabacteroides merdae , Parabacteroides distasonis , Odoribacter splanchnicus and Flavonifractor plautii . Moreover, Bacteroides uniformis , was also highly abundant in LossR as compared with NoR group. Studies have demonstrated that the intestinal abundance of Bacteroides uniformis is significantly higher in healthy controls than that in UC patients and a strain of this species is being investigated as novel probiotic to treat intestinal inflammation( 19 ). The species Fusicatenibacter saccharivorans belongs to the Lachnospiraceae family and is a fermentative short-chain fatty acid producing bacterium. This species has been shown to decrease in colitis patients with active flare and increase during remission. Isolated strains of F. saccharivorans suppressed intestinal inflammation in a murine colitis model( 20 ). Ruthenibacterium lactatiformans is a lactate-producing member of the Ruminococcaceae family. There were no differences for members of the Roseburia genus. Gut microbiome after starting treatment Patients provided a stool sample at week 12 of vedolizumab treatment. Figure 4 shows the genus-level microbiome profiles in the three study groups during remission induction. Bands indicate the relative abundance of the dominant genera and changes over the three time points are shown (2 samples at baseline and 1 at week 12). The microbiome composition in patients who achieved sustained remission appears to be more stable than in the other two patient groups. In patients with sustained remission, obvious changes were only detected at week 12 and consisted of the expansion of the genera Roseburia and a novel Lachnospiraceae genus. In the other two patient groups, changes in microbiome composition are observed at any time point and there is no obvious association with the intervention. Temporal variability appears to be stochastic in these patients. There were several taxonomic differences between groups at week 12 on vedolizumab, as shown in Fig. 5 . First, the abundance of Bacteroides ovatus and Bacteroides uniformis was higher in patients who responded (SusR and LossR groups) compared with no responders (NoR). Interestingly, patients who progressed to long-term remission (SusR) had a higher abundance of several Roseburia species compared with the other two groups. The genus Roseburia consists of obligate Gram-positive anaerobic bacteria and includes several species (e.g. R. intestinalis, R. inulinivorans, R. faecis ) that metabolize dietary components and produce butyrate in the human colon, playing a role in immune cell regulation, cytokine release, and barrier homeostasis( 21 ). In contrast, Faecalibacterium prausnitzii , another well-recognised butyrate producing mutualistic bacterium, was lower in abundance in SusR compared to the other two groups. The patients who maintained remission were followed for one year while on maintenance vedolizumab treatment. A number of taxa changed in abundance over the study period. Figure 6 shows changes in taxa that reached significant difference in this group of patients (SusR). There was a dramatic increase in the abundance of members of the Lachnospiraceae family, including the above mentioned Bacteroides uniformis and other unclassified species. Likewise, abundance of species of the Roseburia genus progressively increased over time, as well as abundance of Eubacterium rectale. There was only little change in the abundance of Faecalibacterium prausnitzii. In contrast, no significant changes in taxonomy were found in patients who lost response to vedolizumab after initial remission at week 14 (LossR). Discussion Diverse interactions between gut microbes, the intestinal epithelium, and gut-associated lymphoid tissues are constantly shaping host immunity and regulate mucosal inflammatory pathways( 22 ). In IBD, mucosal inflammation fluctuates over time and bidirectional interactions between gut microbes and immunocompetent host cells appear to play a key role in the undulating course of these diseases ( 1 , 13 , 23 ). Altered states of the gut microbiome may therefore influence treatment efficacy( 24 ).Our observational study aimed to explore whether pretreatment microbiome signatures could be useful in predicting successful response to anti-integrin biologic therapy in patients with active ulcerative colitis. We also followed-up our patients after clinical and endoscopic remission to further explore whether eventual loss of response was associated with specific microbiome changes. Twenty-five patients with active ulcerative colitis who were candidates for anti-integrin therapy due to previous therapeutic failures were recruited, and twenty-one of them completed the protocol. At the end of the remission induction phase, nine patients had achieved complete clinical and endoscopic remission, and the remaining twelve patients showed disease activity. There were no demographic or clinical differences at baseline between patients who attained remission and the remaining. When comparing microbiomes from patients who responded to those who did not respond, we did not identify any discriminant microbial marker at baseline capable of predicting a satisfactory response to the biologic drug. Nonetheless, we observed some microbiological features of interest for a better understanding of the interplay between intestinal microbes and mucosal inflammation. First, none of our patients with very low Chao1 index (Chao1 > 75) went into remission during vedolizumab treatment. Chao1 is an indicator of alpha-diversity based in species richness, i.e. the total number of species in the sample, that is also sensitive to species with low abundance. Second, microbiomes in the group of patients who did not respond were more heterogeneous in taxonomy and functional metabolic pathways than microbiomes in responders. Additionally, responders exhibited a more stable microbiome compositional profile over the 12 weeks of induction than patients who did not respond. Finally, significant increases in well-recognised anti-inflammatory commensals, such as Bacteroides uniformis , Roseburia intestinalis, R. inulinivorans, R. faecis , Lachnospiraceae species, and others, were observed during vedolizumab treatment only in patients with a sustained remission. Temporal stability and species richness/diversity are important ecological properties of a microbial ecosystem( 25 ). Stability ensures that microbiome functions are maintained over time and relies on its functional attributes to cope with external challenges. High species diversity and functional redundancy are crucial for ecosystem resilience( 26 ). In IBD, periods of disease activity are marked by decreases in gut microbial diversity and increases in temporal variability, with characteristic taxonomic, functional and biochemical shifts( 3 ). Patients may not recover a fully balanced gut microbiota during periods of remission and studies have shown low diversity and temporal variability in patients during remission( 5 , 14 , 27 ). Variability has been associated with a higher risk of a subsequent flare ( 27 ). In our study, we did not detect significantly lower alpha-diversity or higher temporal variability in the baseline microbiome samples of non-responders compared to those who responded. However, there was an obvious trend, and it can be speculated that our sample size was insufficient to show differences at the statistical level. Interestingly, the heterogeneity observed at baseline in the microbiomes of patients who did not respond to treatment (beta diversity data) is also consistent with instability and temporal variability in that patient group. Temporal variability leads to heterogeneity within the group. The study by Ananthakrishnan et al. ( 12 ) detected lower alpha-diversity in baseline microbiome samples from Crohn’s disease patients who did not achieve vedolizumab-induced remission at week 14. As in our study, this indicator did not reach statistical significance in the UC patient cohort. Likewise, beta diversity was higher at baseline in the non-remission group compared to the remission group, but again this finding was only significant for Crohn’s disease and not for UC patients. The divergence between Crohn’s disease and UC may be explained by the fact that dysbiotic changes are typically more pronounced in Crohn’s disease microbiomes than in UC ones ( 3 ). In any case, our findings on core gut microbiome indicators are in line with those previously reported by Ananthakrishnan et al ( 12 ). Our analysis was able to differentiate patients who responded to treatment from those who did not based on microbiome taxonomy at baseline. We identified increased abundance of Bacteroides uniformis, Fusicatenibacter saccharivorans, and others, in patients who would achieve remission. Additionally, Bacteroides uniformis, Lachnospiraceae ssp., and several Roseburia species increased in abundance along with vedolizumab treatment. Ananthakrishnan et al ( 12 ) reported that Roseburia inulinivorans and a Burkholderiales species were significantly more abundant at baseline among Chron's disease patients who achieved remission compared to those who did not. However, these species did not increase in abundance during vedolizumab treatment. As limitations and future perspectives, several aspects of this study should be considered. The present work is a proof-of-concept study involving a moderate sample size but still allows to demonstrate that some descriptive data could preclude response to vedolizumab. The study design includes participants of both genders and covers a broad range of ages and BMI values. Moreover, this is a longitudinal and prospective study with five different timepoints, allowing an assessment of microbiota stability over time. This work contributes to personalized medicine by exploring the role of each participant’s baseline microbiota in shaping the individual response to vedolizumab. On a different note, several microbial biomarkers associated with treatment response were identified in this study; however, further research involving larger cohorts is needed to confirm these findings. In addition, a more comprehensive characterization of these microbial signatures at the strain level will require deeper metagenomic sequencing, together with validation in independent cohorts. Nevertheless, given that metagenomic sequencing is complex and costly, the identification of robust microbial markers of vedolizumab response could facilitate the development of simpler diagnostic tools to stratify patients and guide treatment decisions. Ultimately, further studies are needed to elucidate how vedolizumab modulates the intestinal microbiota and to determine whether microbiome-derived markers can reliably predict treatment outcomes. The ability to predict response to anti-inflammatory treatment in IBD patients using the gut microbiome is an attractive research goal at the hypothesis level. However, a major drawback to the use of microbiome-based biomarkers in diagnosis, prognosis, and precision therapy is that it requires the definition of a healthy microbiome in different populations. Interindividual compositional differences make it difficult to establish normal ranges for taxa that may be present in some healthy individuals but not others, and the abundance of common species is extremely variable between individuals. Furthermore, geography is a surrogate for large-scale variation in the human gut microbiome in terms of ethnicity, culture, diet, lifestyle, etc. ( 28 ). In this study, we did not identify predictive markers that would eventually be applicable or reliable for other patient cohorts. However, based on our findings in a small group of patients with active UC, who were refractory to previous therapies, we speculate that the robustness of the gut microbial ecosystem acts as a predictor, among others, for a successful response to anti-integrin therapy. In patients with a sustained response, microbiomes showed high species richness (alpha diversity), lower heterogeneity (beta diversity), consistent stability over time, and capacity to expand the relative abundance of anti-inflammatory species in parallel with the mucosal anti-inflammatory effect of the drug. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Vall d’Hebron Ethical Committee under code IISR-2015-101160 (FGA-VED-2016-01). All participants provided written informed consent before taking part in the study. Consent for publication Not applicable. Availability of data and materials Raw metagenomic sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1198595. During peer review, the dataset is available to editors and reviewers through the following private reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1198595?reviewer=kkbjgcq15d2lf8qtsdikc2t15n The data will be made publicly available upon acceptance of the manuscript. Competing interests VR, LM, CH: Sponsorship for congress attendance: Pfizer, Janssen, Takeda, Ferring, Lilly, Falk, Abbvie. FG received research grants from Abbvie, Takeda, and AB-Biotics and is a member of the Biocodex Microbiota Institute’s scientific advisory board. EV, FG, CS: no disclosures. AM is Scientific Founder and Member of the Scientific Advisory Board of MicroViable Therapeutics S.L. MDMA has received consulting and/or speaker fees from AbbVie, Pfizer, Takeda, Janssen, Tillotts Pharma, Lilly and Galapagos; and has received research funding from Abbvie, Janssen and Pfizer. NB: consultant and/or speaker with AbbVie, Adacyte, Falk, Galápagos, Janssen, Kern Pharma, MSD, Pfizer, Takeda, Tillotts Pharma. Funding to support research and education from Abbvie, Janssen, MSD, Pfizer and Takeda. Funding Takeda Farmacéutica España, S.A. Authors’ contributions VR, NB: Conceptualization and study design, Methodology, Data curation, Supervision, Data collection, Writing – original draft, Writing – review & editing. CH, LM, FC, MDMA: Data collection, formal analysis. CS, AM, EV: Analysis, Writing – review & editing, Formal analysis. FG: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. Acknowledgments We are grateful to Takeda Farmacéutica España, S.A. for providing funding and resources for this research. References Shanahan F. Inflammatory bowel disease: Immunodiagnostics, immunotherapeutics, and ecotherapeutics. Gastroenterology. 2001 Feb 1;120(3):622–35. Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014 Mar 12;15(3):382–92. Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019 May;569(7758):655–62. Machiels K, Joossens M, Sabino J, De Preter V, Arijs I, Eeckhaut V, et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut. 2014 Aug;63(8):1275–83. Herrera-deGuise C, Varela E, Sarrabayrouse G, Pozuelo Del Río M, Alonso VR, Sainz NB, et al. Gut Microbiota Composition in Long-Remission Ulcerative Colitis is Close to a Healthy Gut Microbiota. Inflamm Bowel Dis. 2023 Sep 1;29(9):1362–9. Manichanh C, Borruel N, Casellas F, Guarner F. The gut microbiota in IBD. Nat Rev Gastroenterol Hepatol. 2012 Oct;9(10):599–608. Varela E, Manichanh C, Gallart M, Torrejón A, Borruel N, Casellas F, et al. Colonisation by Faecalibacterium prausnitzii and maintenance of clinical remission in patients with ulcerative colitis. Aliment Pharmacol Ther. 2013 Jul;38(2):151–61. Sokol H, Lepage P, Seksik P, Doré J, Marteau P. Temperature gradient gel electrophoresis of fecal 16S rRNA reveals active Escherichia coli in the microbiota of patients with ulcerative colitis. J Clin Microbiol. 2006 Sep;44(9):3172–7. Ohkusa T, Sato N, Ogihara T, Morita K, Ogawa M, Okayasu I. Fusobacterium varium localized in the colonic mucosa of patients with ulcerative colitis stimulates species‐specific antibody. J Gastroenterol Hepatol. 2002 Aug 7;17(8):849–53. Ohkusa T, Okayasu I, Ogihara T, Morita K, Ogawa M, Sato N. Induction of experimental ulcerative colitis by Fusobacterium varium isolated from colonic mucosa of patients with ulcerative colitis. Gut. 2003 Jan;52(1):79–83. Rajca S, Grondin V, Louis E, Vernier-Massouille G, Grimaud JC, Bouhnik Y, et al. Alterations in the intestinal microbiome (dysbiosis) as a predictor of relapse after infliximab withdrawal in Crohn’s disease. Inflamm Bowel Dis. 2014 Jun;20(6):978–86. Ananthakrishnan AN, Luo C, Yajnik V, Khalili H, Garber JJ, Stevens BW, et al. Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases. Cell Host Microbe. 2017 May 10;21(5):603-610.e3. Halfvarson J, Brislawn CJ, Lamendella R, Vázquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017 Feb 13;2:17004. Martinez C, Antolin M, Santos J, Torrejon A, Casellas F, Borruel N, et al. Unstable composition of the fecal microbiota in ulcerative colitis during clinical remission. American Journal of Gastroenterology. 2008;103(3). Walmsley RS, Ayres RC, Pounder RE, Allan RN. A simple clinical colitis activity index. Gut. 1998 Jul;43(1):29–32. Lahti, L., Shetty, S. Tools for Microbiome Analysis in R. Version 2.1.24.. 2017; Available online at: https://microbiome.github.io/tutorials/. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. Cao Y, DQ, WD, ZP, LY, & NC. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics, 2022, 38(16), 4027-4029. Doi: 10.1093/bioinformatics/btac438. Dai W, Zhang J, Chen L, Yu J, Zhang J, Yin H, et al. Discovery of Bacteroides uniformis F18-22 as a Safe and Novel Probiotic Bacterium for the Treatment of Ulcerative Colitis from the Healthy Human Colon. Int J Mol Sci. 2023 Sep 28;24(19). Takeshita K, Mizuno S, Mikami Y, Sujino T, Saigusa K, Matsuoka K, et al. A Single Species of Clostridium Subcluster XIVa Decreased in Ulcerative Colitis Patients. Inflamm Bowel Dis. 2016 Dec;22(12):2802–10. Nie K, Ma K, Luo W, Shen Z, Yang Z, Xiao M, et al. Roseburia intestinalis: A Beneficial Gut Organism From the Discoveries in Genus and Species. Front Cell Infect Microbiol. 2021;11:757718. Macdonald TT, Monteleone G. Immunity, inflammation, and allergy in the gut. Science. 2005 Mar 25;307(5717):1920–5. Borruel N, Casellas F, Antolín M, Llopis M, Carol M, Espíin E, et al. Effects of nonpathogenic bacteria on cytokine secretion by human intestinal mucosa. Am J Gastroenterol. 2003 Apr;98(4):865–70. van de Guchte M, Mondot S, Doré J. Dynamic Properties of the Intestinal Ecosystem Call for Combination Therapies, Targeting Inflammation and Microbiota, in Ulcerative Colitis. Gastroenterology. 2021 Dec;161(6):1969-1981.e12. Konopka A. What is microbial community ecology? ISME J. 2009 Nov;3(11):1223–30. Larsen OFA, Claassen E. The mechanistic link between health and gut microbiota diversity. Sci Rep. 2018 Feb 1;8(1):2183. Braun T, Di Segni A, BenShoshan M, Neuman S, Levhar N, Bubis M, et al. Individualized Dynamics in the Gut Microbiota Precede Crohn’s Disease Flares. Am J Gastroenterol. 2019 Jul;114(7):1142–51. Shanahan F, Ghosh TS, O’Toole PW. The Healthy Microbiome-What Is the Definition of a Healthy Gut Microbiome? Gastroenterology. 2021 Jan;160(2):483–94. Robles AV, Herrera-deGuise C, Mayorga L, Varela E, Martín AM, Margolles A, et al. Longitudinal study of the gut microbiome in patients with ulcerative colitis on biological treatment with vedolizumab. J Crohns Colitis. 2025;19(Suppl 1):i2393. Pascal V, Pozuelo M, Borruel N, Casellas F, Campos D, Santiago A, et al. A microbial signature for Crohn’s disease. Gut. 2017;66(5):813–822. doi:10.1136/gutjnl-2016-313235 Sabater C, Calvete I, Vázquez X, Ruiz L, Margolles A. Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning. Int J Food Microbiol. 2024;421:110789. doi:10.1016/j.ijfoodmicro.2024.110789 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor invited by journal 17 Feb, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 26 Jan, 2026 First submitted to journal 26 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8621048","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604359444,"identity":"10d856e0-a606-4a99-9c55-7772a328654b","order_by":0,"name":"Virginia Robles-Alonso","email":"","orcid":"","institution":"Crohn’s and Colitis Attention Unit, Digestive System Service, Universitary Hospital Vall d’Hebron","correspondingAuthor":false,"prefix":"","firstName":"Virginia","middleName":"","lastName":"Robles-Alonso","suffix":""},{"id":604359445,"identity":"c1de6833-70a5-4d5d-8c29-98e20b7bfba6","order_by":1,"name":"Claudia Herrera-deGuise","email":"","orcid":"","institution":"Crohn’s and Colitis Attention Unit, Digestive System Service, Universitary Hospital Vall d’Hebron","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Herrera-deGuise","suffix":""},{"id":604359446,"identity":"176bf9f2-2fb2-4290-99e9-961e64fc303f","order_by":2,"name":"Luis Mayorga","email":"","orcid":"","institution":"Crohn’s and Colitis Attention Unit, Digestive System Service, Universitary Hospital Vall d’Hebron","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Mayorga","suffix":""},{"id":604359447,"identity":"56f3ce84-9398-4825-86bc-58bd418b9a66","order_by":3,"name":"Encarna Varela","email":"","orcid":"","institution":"Microbiome Lab, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Barcelona Hospital Campus","correspondingAuthor":false,"prefix":"","firstName":"Encarna","middleName":"","lastName":"Varela","suffix":""},{"id":604359448,"identity":"b31336c1-feab-4355-a7a2-0f1d93183695","order_by":4,"name":"María Dolores Martín Arranz","email":"","orcid":"","institution":"Department of Gastroenterology, Hospital Universitario La Paz","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Dolores Martín","lastName":"Arranz","suffix":""},{"id":604359449,"identity":"a12e1484-43a6-414c-9d6f-ca6f88b2338e","order_by":5,"name":"Abelardo Margolles","email":"","orcid":"","institution":"Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA), Consejo Superior de Investigaciones Científicas (CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Abelardo","middleName":"","lastName":"Margolles","suffix":""},{"id":604359450,"identity":"24aab670-a91b-4d15-8dd4-c494a52bed05","order_by":6,"name":"Carlos Sabater","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie2Qv6rCMBTGTyh0qnTtZF4h3UVfJSLo4uIbKIHb5V5cFV+i7g5HAk7+WQt1iEtd6+bQwUPBwaHxujnkNyScj3yc7wuAw/GdMKyvqD47AN4/PE8LmwIMP7fo98/D5GCwrKAXrtTV3DYnzhPPACmNRPuR2C5/oL847+LZosjjVPuCkdKIoPS6NQUJmYxVgLkUXgAeKc2WsAAdUDCejW6qwqPkiiyVJZiIaEvgA0uzcawAUdJIn+ZbumQFUJeovz7vJ8tfHNRdtn+WLuF8yExZdXrtPEnLO3Y5n+uLuVuCPZe9jvjW4HA4HA4rDz2MVRLrQcrRAAAAAElFTkSuQmCC","orcid":"","institution":"Rowett Institute, University of Aberdeen","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Sabater","suffix":""},{"id":604359451,"identity":"7e2bf421-7fce-4993-adf3-8219090e1c37","order_by":7,"name":"Francesc Casellas","email":"","orcid":"","institution":"Crohn’s and Colitis Attention Unit, Digestive System Service, Universitary Hospital Vall d’Hebron","correspondingAuthor":false,"prefix":"","firstName":"Francesc","middleName":"","lastName":"Casellas","suffix":""},{"id":604359452,"identity":"0a065392-b0b6-4744-bd1d-0e9f47beca88","order_by":8,"name":"Francisco Guarner","email":"","orcid":"","institution":"Gastroenterología, Centro médico Teknon","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Guarner","suffix":""},{"id":604359453,"identity":"1061ceeb-a5d7-4806-995b-c711514d9128","order_by":9,"name":"Natalia Borruel","email":"","orcid":"","institution":"Crohn’s and Colitis Attention Unit, Digestive System Service, Universitary Hospital Vall d’Hebron","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Borruel","suffix":""}],"badges":[],"createdAt":"2026-01-16 16:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8621048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8621048/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104668258,"identity":"1025c366-bc7c-4065-8abf-af9f864d8fa3","added_by":"auto","created_at":"2026-03-15 16:52:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116696,"visible":true,"origin":"","legend":"\u003cp\u003eChao1 and Shannon alpha-diversity indexes of the microbial communities in samples at baseline. \u003csup\u003ea\u003c/sup\u003eNo statistically significant (p \u0026gt; 0.05) difference was found between groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/8e1273486052b364f9cdc8b6.png"},{"id":104668259,"identity":"3c8484f9-5a41-4767-8a96-5c9203cd41c9","added_by":"auto","created_at":"2026-03-15 16:52:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003eBeta-diversity was higher in no responders than in responders, indicating greater heterogeneity of microbiomes within NoR as compared with R patients. Bray-Curtis method was selected for the calculation. \u003cstrong\u003eB and C:\u003c/strong\u003e Graphical representation of Bray-Curtis distances as principal coordinates analysis (PCoA). The percentage (%) of variance explained by each PC is indicated in the axis. Differences between groups are statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/a37e57468d555ab38e952b88.png"},{"id":104668261,"identity":"4234d9fd-ed53-4f70-ba40-2136dc21c756","added_by":"auto","created_at":"2026-03-15 16:52:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153774,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant bacteria species in patient samples at baseline. Data are expressed as abundance percentages (%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/03d853a767a5a1d5729bd04d.png"},{"id":104782340,"identity":"68bfe192-fce6-459d-980f-f998aac3d4aa","added_by":"auto","created_at":"2026-03-17 07:57:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207518,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobiome profiles at genus level in the three study groups during induction of remission. The microbiome in patients who evolved to a sustained response showed consistent stability in composition over time and expansion of \u003cem\u003eRoseburia\u003c/em\u003e and a novel \u003cem\u003eLachnospiraceae\u003c/em\u003e genus (arrow).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/3bdcecfae48ab9ee665c106c.png"},{"id":104668264,"identity":"ea65e5b6-5d60-4b0a-9657-8869a2905c9e","added_by":"auto","created_at":"2026-03-15 16:52:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89948,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant taxonomic clades in patient samples at week 12 on vedolizumab treatment. Data are expressed as abundance percentages (%).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/4d5e2e991bd074bfffc81080.png"},{"id":104782407,"identity":"55b02396-e232-4dcd-9729-4ad80f79708b","added_by":"auto","created_at":"2026-03-17 07:57:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102985,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant taxonomic clades found in patients with sustained response over the one-year follow-up period. Data are expressed as abundance percentage (%).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/fcb1cd39a1089b4ec284cb9a.png"},{"id":104784941,"identity":"faced099-0f1b-4a38-9de8-ab7f5104987f","added_by":"auto","created_at":"2026-03-17 08:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1428173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8621048/v1/ea908ee7-f01e-4c67-9164-9edea97958fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal study of the gut microbiota in ulcerative colitis patients on anti-integrin biologic therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCrohn\u0026acute;s disease (CD) and Ulcerative colitis (UC) are chronic inflammatory disorders of growing prevalence worldwide. The precise aetiology of the diseases is unknown, but the pathophysiology of the intestinal lesions involves a complex and altered interplay between mucosal immunity and microbial factors that trigger and perpetuate inflammation in individuals with genetic susceptibility(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Cumulative evidence demonstrates several imbalances of the gut microbial ecosystem at taxonomic and functional level in patients with active mucosal inflammation, characterized by decreased microbial diversity and clade-specific enrichments and depletions(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In ulcerative colitis, a decrease in species richness, an underrepresentation of butyrate producers and a gain of potentially harmful bacteria have been consistently found in samples from patients during disease exacerbation or shortly after remission(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbundance of health-promoting bacteria including butyrate producers (\u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e species) and other short-chain fatty acid-producers (\u003cem\u003eAkkermansia muciniphila\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e species) is usually lower in patients as compared to controls. The depleted species induce regulatory cells and cytokines on the human gut mucosa and have a physiological role in mucosal homeostasis(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Interestingly, those patients who recover normal counts of butyrate producing species are prone to maintain long-time remission(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). On the other hand, some other species such as \u003cem\u003eBilophila wadsworthia\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and \u003cem\u003eFusobacterium\u003c/em\u003e species (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), which are known to generate pro-inflammatory stimuli, have been found in higher abundance in patients with UC. Thus, the influence of the intestinal microbiome in the response to immunosuppressors in IBD is plausible but remains largely unexplored. An observational study with Crohn's disease patients suggested that reduced abundance of \u003cem\u003eF. prausnitzii\u003c/em\u003e in the intestinal microbiome may predict an early relapse after infliximab discontinuation(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The study by Ananthakrishnan and coworkers explored whether the gut microbiome may predict responses to vedolizumab biologic therapy. The authors conducted a prospective study both in CD and UC patients initiating anti-integrin therapy (vedolizumab). The study identified an interesting network algorithm integrating microbiome and clinical data with predictive value(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLongitudinal studies have shown that the microbiomes of IBD subjects fluctuate more than those of healthy individuals, with some correlations between fluctuations and medication intensification due to a flare(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Temporal microbiome instability has also been demonstrated during periods of remission(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In this longitudinal study, we followed UC patients who experienced a flare and were eligible for vedoluzimab treatment for induction of remission and subsequent remission maintenance. We used a shotgun metagenomic approach to explore gut microbiome fluctuations associated with response to vedolizumab therapy (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design, participants, intervention\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe EPIMUC study was a prospective, observational study of adult patients with left-sided or extensive UC requiring vedolizumab therapy. Vedolizumab is an integrin receptor antagonist indicated for the treatment of adult patients with moderately to severely active UC who have had an inadequate response, loss of response or intolerance to one or more standard therapies (corticosteroids, immunomodulators or tumour necrosis factor-α inhibitor) or demonstrated dependence on corticosteroids. We recruited adult patients (18-75 years) diagnosed with active ulcerative colitis requiring vedolizumab treatment according to the approved indications. Patients with UC diagnosed by standard criteria at least 6 months before inclusion and moderate to severe activity (Simple Colitis Activity Index, SCAI,\u0026nbsp;³ 6) were eligible(15). Previous anti-TNF therapy with a wash-out period of at least one month was allowed. The exclusion criteria included disease limited to rectum, antibiotics on the previous two months or during the study, pregnancy and breastfeeding. The protocol was approved by the Institutional Review Board of University Hospital Vall de Hebron, study protocol code: IISR-2015-101160 (FGA-VED-2016-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwenty-five patients entered the study. There were four dropouts due to clinical criteria (bacterial gastroenteritis, antibiotic treatment, worsening of psoriasis, and COVID infection), and 21 patients completed the study. Eligible patients were treated with vedolizumab, 300 mg on weeks 0, and 2 and 6. After induction, response to vedolizumab was evaluated at week 14 by clinical and endoscopic scoring. Remission was defined by SCAI \u0026lt; 4, faecal calprotectin \u0026lt; 150 µg/g, C-reactive protein \u0026lt; 0.5 mg/L, and Mayo index\u0026nbsp;£\u0026nbsp;1. Accordingly, patients were then allocated into two groups, i.e, responders (n=9) and non-responders (n=12). Non-responders at week 14 left the study. These patients were offered intensified treatment with vedolizumab or other alternative therapy and were not suitable for the aim of this study. Responders were kept on vedolizumab every 8 weeks until week 52 for maintenance of remission. Three patients lost response during the follow-up while the remainder 6 patients maintained remission until week 52.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients provided two faecal samples during the run-in period prior to starting vedolizumab treatment and subsequently at week 12 of vedolizumab treatment. Additional samples were obtained at weeks 30 and 52 from patients who completed the protocol.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetagenomic approach\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal DNA was isolated from fecal samples according to standardized procedures aligned with\u0026nbsp;the\u0026nbsp;International Human Microbiome Standards (30). Briefly, approximately 250 mg\u0026nbsp;of\u0026nbsp;frozen stool material was processed using a combination\u0026nbsp;of\u0026nbsp;chemical lysis and mechanical disruption with bead beating to ensure efficient microbial cell breakage. Following clarification\u0026nbsp;of\u0026nbsp;lysates, nucleic acids were recovered by alcohol precipitation. DNA concentration and purity were assessed spectrophotometrically, and DNA\u0026nbsp;integrity was evaluated by microcapillary electrophoresis to confirm suitability for sequencing.\u003c/p\u003e\n\u003cp\u003eShotgun metagenomic sequencing was performed by an external service provider using an Illumina NovaSeq platform, generating paired-end reads (2 × 150 bp) with a target depth\u0026nbsp;of\u0026nbsp;at least 25 million reads per sample. Raw sequencing data were demultiplexed and adapter sequences were removed. Quality filtering and trimming were applied to exclude low-quality reads and short fragments. To eliminate host-derived sequences, reads were aligned against\u0026nbsp;the\u0026nbsp;human reference genome and matching reads were discarded.\u003c/p\u003e\n\u003cp\u003eHigh-quality, non-host reads were subsequently used for downstream taxonomic and functional profiling. Taxonomic composition was\u0026nbsp;inferred using an assembly-free approach based on clade-specific marker genes, while functional characterization was performed by mapping reads to reference gene family and metabolic pathway databases (30,31). Gene family and pathway abundances were normalized and expressed as copies per million to allow comparison across samples.\u003c/p\u003e\n\u003cp\u003eAll raw metagenomic sequencing data have been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information under BioProject accession number PRJNA1198595. The dataset is currently under controlled access during peer review and is available to editors and reviewers through a secure NCBI reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1198595?reviewer=kkbjgcq15d2lf8qtsdikc2t15n\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis compared either two groups, namely, non-responders (No response, NoR, n=12) versus responders (Response, R, n=9), or three groups, namely, non-responders (NoR, n=12), patients on remission until week 52 (Sustained response, SusR, n=6), and patients who lost response after initial clinical remission (Loss of response, LossR, n=3).\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.2.3). Microbiome data were analysed by comparing patients according to treatment outcome,\u0026nbsp;including responders and non-responders at week 14, as well as three outcome-based groups: non-responders, patients with sustained remission up to week 52, and patients who experienced loss\u0026nbsp;of\u0026nbsp;response during follow-up.\u003c/p\u003e\n\u003cp\u003eWithin-sample microbial diversity was assessed using alpha diversity\u0026nbsp;indices, while between-sample compositional differences were evaluated using beta diversity metrics based on dissimilarity matrices. Ordination analyses were conducted to visualize differences\u0026nbsp;in\u0026nbsp;microbial community structure across groups. Statistical significance\u0026nbsp;of\u0026nbsp;group-level differences was assessed using permutation-based methods.\u003c/p\u003e\n\u003cp\u003eDifferential abundance analyses of microbial taxa and metabolic pathways were conducted using established statistical frameworks appropriate for metagenomic data. Microbial features identified as significantly different between groups by any analytical approach were selected for downstream interpretation. To control for multiple comparisons, p-values were adjusted and predefined significance thresholds were applied. Prior to differential analyses, metagenomic data were normalized to account for differences in sequencing depth across samples.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows patient demographics comparing Response (R) and No Response (NoR) groups at baseline and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows clinical findings at week 12 on treatment. There were no differences in baseline demographic and clinical data between patients who attained remission and those who did not.\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\u003eBaseline epidemiologic data in responders and no responders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponders (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo responders (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habit, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enonsmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eex-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (32\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (28\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4 (27.5\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3 (18.6\u0026ndash;28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMontreal classification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalprotectin, \u0026micro;g/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1024 (531\u0026ndash;4874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479 (223\u0026ndash;3435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanti-TNF once\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanti-TNF twice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQualitative data are n (%); quantitative values are median (range)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse to vedolizumab at week 12\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponders (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo responders (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalprotectin, \u0026micro;g/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (16\u0026ndash;302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383 (349\u0026ndash;3032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayo endoscopic subscore, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.04\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 (0.07\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\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\u003eQualitative data are n (%); quantitative values are median (range)\u003c/p\u003e \u003cp\u003eMetagenomic paired-end sequence reads (2x150bp) showing an average minimum of 25\u0026nbsp;million reads per sample were generated. The number of clean reads after trimming and contamination removal ranged from 2297384 to 86839985. A total 66 metagenome samples passed the quality control corresponding to the following groups of participants: i) No Response, NoR (n\u0026thinsp;=\u0026thinsp;30), ii) Sustained Response, SusR (n\u0026thinsp;=\u0026thinsp;26), iii) Loss of Response, LossR (n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\n\u003ch3\u003eBaseline gut microbiome\u003c/h3\u003e\n\u003cp\u003eBaseline alpha diversity indices are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Chao1 and Shannon index data showed a widespread dispersion in patients who did not achieve remission at week 12 compared to responders. No significant differences were found between groups, but it is notable that none of the patients with a low Chao1 index at baseline responded to the biologic treatment. Non-redundant gene count is a straightforward marker of microbial richness in faecal samples, and we observed higher richness in responders (44967 \u0026plusmn; 10912 non-redundant genes) than in non-responders (41382 \u0026plusmn; 11152 non-redundant genes), although the difference is not significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeta diversity is a measure of the dissimilarity between samples from different individuals. Beta diversity within a given group is low when individual samples are similar and cluster together in a graphical representation, whereas beta diversity is high when individual samples show a large difference in composition and are distant in the graphical representation. We found higher beta diversity of metabolic pathways in patients who did not respond to treatment compared to those who did (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), suggesting greater heterogeneity among microbiomes from patients who did not respond to treatment. Analysis of the principal coordinates of the samples identified statistically different clusters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing a higher dispersion of samples in the group of patients who did not respond to treatment than in the others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Tables \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, most characteristic metabolic pathways of the SusR group correspond to \u003cem\u003eBacteroides caccae\u003c/em\u003e and \u003cem\u003eB. ovatus\u003c/em\u003e and are involved in nucleotide and amino acid metabolism. In the LossR group, most characteristic metabolic pathways correspond to \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e, \u003cem\u003eBifidobacterium longum\u003c/em\u003e and \u003cem\u003eEubacterium eligens\u003c/em\u003e and are involved in nucleotide, amino acid and carbohydrate metabolism.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of microbial metabolic pathways showing the highest abundance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.25) in each group of patients. Metabolic pathways are allocated to microbial taxa. NoR: No Response, LossR: Loss of Response, SusR: Sustained Response.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium longum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium longum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEubacterium eligens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFusicatenibacter saccharivorans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacteroides caccae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBacteroides ovatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParabacteroides merdae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of microbial metabolic pathways showing the highest abundance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.25) in each group of patients (metabolic pathways are defined by function). NoR: No Response, LossR: Loss of Response, SusR: Sustained Response.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLossR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSusR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\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\u003eOur analysis also focussed on distinct taxonomic profiles at baseline that could differentiate gut microbiomes in responders from those of no-responders, and eventually serve as predictive markers of clinical response. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the bacteria species in which statistical differences between groups were detected. Surprisingly, abundance of \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e and \u003cem\u003eEubacterium eligens\u003c/em\u003e at baseline was higher in NoR and LossR than in SusR group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients with a sustained long-term response to vedolizumab (SusR) showed higher abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e, \u003cem\u003eBacteroides ovatus\u003c/em\u003e, \u003cem\u003eBacteroides uniformis, Fusicatenibacter saccharivorans\u003c/em\u003e, \u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e, \u003cem\u003eParabacteroides merdae\u003c/em\u003e, \u003cem\u003eParabacteroides distasonis\u003c/em\u003e, \u003cem\u003eOdoribacter splanchnicus\u003c/em\u003e and \u003cem\u003eFlavonifractor plautii\u003c/em\u003e. Moreover, \u003cem\u003eBacteroides uniformis\u003c/em\u003e, was also highly abundant in LossR as compared with NoR group. Studies have demonstrated that the intestinal abundance of \u003cem\u003eBacteroides uniformis\u003c/em\u003e is significantly higher in healthy controls than that in UC patients and a strain of this species is being investigated as novel probiotic to treat intestinal inflammation(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe species \u003cem\u003eFusicatenibacter saccharivorans\u003c/em\u003e belongs to the \u003cem\u003eLachnospiraceae\u003c/em\u003e family and is a fermentative short-chain fatty acid producing bacterium. This species has been shown to decrease in colitis patients with active flare and increase during remission. Isolated strains of \u003cem\u003eF. saccharivorans\u003c/em\u003e suppressed intestinal inflammation in a murine colitis model(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). \u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e is a lactate-producing member of the Ruminococcaceae family. There were no differences for members of the \u003cem\u003eRoseburia\u003c/em\u003e genus.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGut microbiome after starting treatment\u003c/h2\u003e \u003cp\u003ePatients provided a stool sample at week 12 of vedolizumab treatment. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the genus-level microbiome profiles in the three study groups during remission induction. Bands indicate the relative abundance of the dominant genera and changes over the three time points are shown (2 samples at baseline and 1 at week 12). The microbiome composition in patients who achieved sustained remission appears to be more stable than in the other two patient groups. In patients with sustained remission, obvious changes were only detected at week 12 and consisted of the expansion of the genera \u003cem\u003eRoseburia\u003c/em\u003e and a novel \u003cem\u003eLachnospiraceae\u003c/em\u003e genus. In the other two patient groups, changes in microbiome composition are observed at any time point and there is no obvious association with the intervention. Temporal variability appears to be stochastic in these patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were several taxonomic differences between groups at week 12 on vedolizumab, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. First, the abundance of \u003cem\u003eBacteroides ovatus\u003c/em\u003e and \u003cem\u003eBacteroides uniformis\u003c/em\u003e was higher in patients who responded (SusR and LossR groups) compared with no responders (NoR). Interestingly, patients who progressed to long-term remission (SusR) had a higher abundance of several \u003cem\u003eRoseburia\u003c/em\u003e species compared with the other two groups. The genus \u003cem\u003eRoseburia\u003c/em\u003e consists of obligate Gram-positive anaerobic bacteria and includes several species (e.g. \u003cem\u003eR. intestinalis, R. inulinivorans, R. faecis\u003c/em\u003e) that metabolize dietary components and produce butyrate in the human colon, playing a role in immune cell regulation, cytokine release, and barrier homeostasis(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In contrast, \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e, another well-recognised butyrate producing mutualistic bacterium, was lower in abundance in SusR compared to the other two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe patients who maintained remission were followed for one year while on maintenance vedolizumab treatment. A number of taxa changed in abundance over the study period. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows changes in taxa that reached significant difference in this group of patients (SusR). There was a dramatic increase in the abundance of members of the \u003cem\u003eLachnospiraceae\u003c/em\u003e family, including the above mentioned \u003cem\u003eBacteroides uniformis\u003c/em\u003e and other unclassified species. Likewise, abundance of species of the \u003cem\u003eRoseburia\u003c/em\u003e genus progressively increased over time, as well as abundance of \u003cem\u003eEubacterium rectale.\u003c/em\u003e There was only little change in the abundance of \u003cem\u003eFaecalibacterium prausnitzii.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, no significant changes in taxonomy were found in patients who lost response to vedolizumab after initial remission at week 14 (LossR).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiverse interactions between gut microbes, the intestinal epithelium, and gut-associated lymphoid tissues are constantly shaping host immunity and regulate mucosal inflammatory pathways(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In IBD, mucosal inflammation fluctuates over time and bidirectional interactions between gut microbes and immunocompetent host cells appear to play a key role in the undulating course of these diseases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Altered states of the gut microbiome may therefore influence treatment efficacy(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).Our observational study aimed to explore whether pretreatment microbiome signatures could be useful in predicting successful response to anti-integrin biologic therapy in patients with active ulcerative colitis. We also followed-up our patients after clinical and endoscopic remission to further explore whether eventual loss of response was associated with specific microbiome changes.\u003c/p\u003e \u003cp\u003eTwenty-five patients with active ulcerative colitis who were candidates for anti-integrin therapy due to previous therapeutic failures were recruited, and twenty-one of them completed the protocol. At the end of the remission induction phase, nine patients had achieved complete clinical and endoscopic remission, and the remaining twelve patients showed disease activity. There were no demographic or clinical differences at baseline between patients who attained remission and the remaining. When comparing microbiomes from patients who responded to those who did not respond, we did not identify any discriminant microbial marker at baseline capable of predicting a satisfactory response to the biologic drug. Nonetheless, we observed some microbiological features of interest for a better understanding of the interplay between intestinal microbes and mucosal inflammation.\u003c/p\u003e \u003cp\u003eFirst, none of our patients with very low Chao1 index (Chao1\u0026thinsp;\u0026gt;\u0026thinsp;75) went into remission during vedolizumab treatment. Chao1 is an indicator of alpha-diversity based in species richness, i.e. the total number of species in the sample, that is also sensitive to species with low abundance. Second, microbiomes in the group of patients who did not respond were more heterogeneous in taxonomy and functional metabolic pathways than microbiomes in responders. Additionally, responders exhibited a more stable microbiome compositional profile over the 12 weeks of induction than patients who did not respond. Finally, significant increases in well-recognised anti-inflammatory commensals, such as \u003cem\u003eBacteroides uniformis\u003c/em\u003e, \u003cem\u003eRoseburia intestinalis, R. inulinivorans, R. faecis\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e species, and others, were observed during vedolizumab treatment only in patients with a sustained remission.\u003c/p\u003e \u003cp\u003eTemporal stability and species richness/diversity are important ecological properties of a microbial ecosystem(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Stability ensures that microbiome functions are maintained over time and relies on its functional attributes to cope with external challenges. High species diversity and functional redundancy are crucial for ecosystem resilience(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In IBD, periods of disease activity are marked by decreases in gut microbial diversity and increases in temporal variability, with characteristic taxonomic, functional and biochemical shifts(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Patients may not recover a fully balanced gut microbiota during periods of remission and studies have shown low diversity and temporal variability in patients during remission(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Variability has been associated with a higher risk of a subsequent flare (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In our study, we did not detect significantly lower alpha-diversity or higher temporal variability in the baseline microbiome samples of non-responders compared to those who responded. However, there was an obvious trend, and it can be speculated that our sample size was insufficient to show differences at the statistical level. Interestingly, the heterogeneity observed at baseline in the microbiomes of patients who did not respond to treatment (beta diversity data) is also consistent with instability and temporal variability in that patient group. Temporal variability leads to heterogeneity within the group.\u003c/p\u003e \u003cp\u003eThe study by Ananthakrishnan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) detected lower alpha-diversity in baseline microbiome samples from Crohn\u0026rsquo;s disease patients who did not achieve vedolizumab-induced remission at week 14. As in our study, this indicator did not reach statistical significance in the UC patient cohort. Likewise, beta diversity was higher at baseline in the non-remission group compared to the remission group, but again this finding was only significant for Crohn\u0026rsquo;s disease and not for UC patients. The divergence between Crohn\u0026rsquo;s disease and UC may be explained by the fact that dysbiotic changes are typically more pronounced in Crohn\u0026rsquo;s disease microbiomes than in UC ones (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In any case, our findings on core gut microbiome indicators are in line with those previously reported by Ananthakrishnan et al (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur analysis was able to differentiate patients who responded to treatment from those\u003c/p\u003e \u003cp\u003ewho did not based on microbiome taxonomy at baseline. We identified increased\u003c/p\u003e \u003cp\u003eabundance of Bacteroides uniformis, Fusicatenibacter saccharivorans, and others, in\u003c/p\u003e \u003cp\u003epatients who would achieve remission. Additionally, Bacteroides uniformis,\u003c/p\u003e \u003cp\u003eLachnospiraceae ssp., and several Roseburia species increased in abundance along with vedolizumab treatment. Ananthakrishnan et al (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) reported that Roseburia inulinivorans and a Burkholderiales species were significantly more abundant at baseline among Chron's disease patients who achieved remission compared to those who did not. However, these species did not increase in abundance during vedolizumab treatment.\u003c/p\u003e \u003cp\u003eAs limitations and future perspectives, several aspects of this study should be considered. The present work is a proof-of-concept study involving a moderate sample size but still allows to demonstrate that some descriptive data could preclude response to vedolizumab. The study design includes participants of both genders and covers a broad range of ages and BMI values. Moreover, this is a longitudinal and prospective study with five different timepoints, allowing an assessment of microbiota stability over time. This work contributes to personalized medicine by exploring the role of each participant\u0026rsquo;s baseline microbiota in shaping the individual response to vedolizumab.\u003c/p\u003e \u003cp\u003eOn a different note, several microbial biomarkers associated with treatment response were identified in this study; however, further research involving larger cohorts is needed to confirm these findings.\u003c/p\u003e \u003cp\u003eIn addition, a more comprehensive characterization of these microbial signatures at the strain level will require deeper metagenomic sequencing, together with validation in independent cohorts.\u003c/p\u003e \u003cp\u003eNevertheless, given that metagenomic sequencing is complex and costly, the identification of robust microbial markers of vedolizumab response could facilitate the development of simpler diagnostic tools to stratify patients and guide treatment decisions. Ultimately, further studies are needed to elucidate how vedolizumab modulates the intestinal microbiota and to determine whether microbiome-derived markers can reliably predict treatment outcomes.\u003c/p\u003e \u003cp\u003eThe ability to predict response to anti-inflammatory treatment in IBD patients using the gut microbiome is an attractive research goal at the hypothesis level. However, a major drawback to the use of microbiome-based biomarkers in diagnosis, prognosis, and precision therapy is that it requires the definition of a healthy microbiome in different populations. Interindividual compositional differences make it difficult to establish normal ranges for taxa that may be present in some healthy individuals but not others, and the abundance of common species is extremely variable between individuals. Furthermore, geography is a surrogate for large-scale variation in the human gut microbiome in terms of ethnicity, culture, diet, lifestyle, etc. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In this study, we did not identify predictive markers that would eventually be applicable or reliable for other patient cohorts. However, based on our findings in a small group of patients with active UC, who were refractory to previous therapies, we speculate that the robustness of the gut microbial ecosystem acts as a predictor, among others, for a successful response to anti-integrin therapy. In patients with a sustained response, microbiomes showed high species richness (alpha diversity), lower heterogeneity (beta diversity), consistent stability over time, and capacity to expand the relative abundance of anti-inflammatory species in parallel with the mucosal anti-inflammatory effect of the drug.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Vall d’Hebron Ethical Committee under code IISR-2015-101160 (FGA-VED-2016-01). All participants provided written informed consent before taking part in the study.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eRaw metagenomic sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1198595. During peer review, the dataset is available to editors and reviewers through the following private reviewer link:\u003cbr\u003e\u0026nbsp;https://dataview.ncbi.nlm.nih.gov/object/PRJNA1198595?reviewer=kkbjgcq15d2lf8qtsdikc2t15n\u003cbr\u003e\u0026nbsp;The data will be made publicly available upon acceptance of the manuscript.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eVR, LM, CH: Sponsorship for congress attendance: Pfizer, Janssen, Takeda, Ferring, Lilly, Falk, Abbvie.\u003cbr\u003e\u0026nbsp;FG received research grants from Abbvie, Takeda, and AB-Biotics and is a member of the Biocodex Microbiota Institute’s scientific advisory board.\u003cbr\u003e\u0026nbsp;EV, FG, CS: no disclosures.\u003cbr\u003e\u0026nbsp;AM is Scientific Founder and Member of the Scientific Advisory Board of MicroViable Therapeutics S.L.\u003cbr\u003e\u0026nbsp;MDMA has received consulting and/or speaker fees from AbbVie, Pfizer, Takeda, Janssen, Tillotts Pharma, Lilly and Galapagos; and has received research funding from Abbvie, Janssen and Pfizer.\u003cbr\u003e\u0026nbsp;NB: consultant and/or speaker with AbbVie, Adacyte, Falk, Galápagos, Janssen, Kern Pharma, MSD, Pfizer, Takeda, Tillotts Pharma. Funding to support research and education from Abbvie, Janssen, MSD, Pfizer and Takeda.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eTakeda Farmacéutica España, S.A.\u003c/p\u003e\n\u003ch3\u003eAuthors’ contributions\u003c/h3\u003e\n\u003cp\u003eVR, NB: Conceptualization and study design, Methodology, Data curation, Supervision, Data collection, Writing – original draft, Writing – review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;CH, LM, FC, MDMA: Data collection, formal analysis.\u003cbr\u003e\u0026nbsp;CS, AM, EV: Analysis, Writing – review \u0026amp; editing, Formal analysis.\u003cbr\u003e\u0026nbsp;FG: Conceptualization, Supervision, Writing – original draft, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eWe are grateful to Takeda Farmacéutica España, S.A. for providing funding and resources for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShanahan F. Inflammatory bowel disease: Immunodiagnostics, immunotherapeutics, and ecotherapeutics. Gastroenterology. 2001 Feb 1;120(3):622\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eGevers D, Kugathasan S, Denson LA, V\u0026aacute;zquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn\u0026rsquo;s disease. Cell Host Microbe. 2014 Mar 12;15(3):382\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eLloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019 May;569(7758):655\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eMachiels K, Joossens M, Sabino J, De Preter V, Arijs I, Eeckhaut V, et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut. 2014 Aug;63(8):1275\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eHerrera-deGuise C, Varela E, Sarrabayrouse G, Pozuelo Del R\u0026iacute;o M, Alonso VR, Sainz NB, et al. Gut Microbiota Composition in Long-Remission Ulcerative Colitis is Close to a Healthy Gut Microbiota. Inflamm Bowel Dis. 2023 Sep 1;29(9):1362\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eManichanh C, Borruel N, Casellas F, Guarner F. The gut microbiota in IBD. Nat Rev Gastroenterol Hepatol. 2012 Oct;9(10):599\u0026ndash;608. \u003c/li\u003e\n\u003cli\u003eVarela E, Manichanh C, Gallart M, Torrej\u0026oacute;n A, Borruel N, Casellas F, et al. Colonisation by Faecalibacterium prausnitzii and maintenance of clinical remission in patients with ulcerative colitis. Aliment Pharmacol Ther. 2013 Jul;38(2):151\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eSokol H, Lepage P, Seksik P, Dor\u0026eacute; J, Marteau P. Temperature gradient gel electrophoresis of fecal 16S rRNA reveals active Escherichia coli in the microbiota of patients with ulcerative colitis. J Clin Microbiol. 2006 Sep;44(9):3172\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eOhkusa T, Sato N, Ogihara T, Morita K, Ogawa M, Okayasu I. \u003cem\u003eFusobacterium varium\u003c/em\u003e localized in the colonic mucosa of patients with ulcerative colitis stimulates species‐specific antibody. J Gastroenterol Hepatol. 2002 Aug 7;17(8):849\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eOhkusa T, Okayasu I, Ogihara T, Morita K, Ogawa M, Sato N. Induction of experimental ulcerative colitis by Fusobacterium varium isolated from colonic mucosa of patients with ulcerative colitis. Gut. 2003 Jan;52(1):79\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eRajca S, Grondin V, Louis E, Vernier-Massouille G, Grimaud JC, Bouhnik Y, et al. Alterations in the intestinal microbiome (dysbiosis) as a predictor of relapse after infliximab withdrawal in Crohn\u0026rsquo;s disease. Inflamm Bowel Dis. 2014 Jun;20(6):978\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eAnanthakrishnan AN, Luo C, Yajnik V, Khalili H, Garber JJ, Stevens BW, et al. Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases. Cell Host Microbe. 2017 May 10;21(5):603-610.e3. \u003c/li\u003e\n\u003cli\u003eHalfvarson J, Brislawn CJ, Lamendella R, V\u0026aacute;zquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017 Feb 13;2:17004. \u003c/li\u003e\n\u003cli\u003eMartinez C, Antolin M, Santos J, Torrejon A, Casellas F, Borruel N, et al. Unstable composition of the fecal microbiota in ulcerative colitis during clinical remission. American Journal of Gastroenterology. 2008;103(3). \u003c/li\u003e\n\u003cli\u003eWalmsley RS, Ayres RC, Pounder RE, Allan RN. A simple clinical colitis activity index. Gut. 1998 Jul;43(1):29\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eLahti, L., Shetty, S. Tools for Microbiome Analysis in R. Version 2.1.24.. 2017; Available online at: https://microbiome.github.io/tutorials/. \u003c/li\u003e\n\u003cli\u003eMcMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. \u003c/li\u003e\n\u003cli\u003eCao Y, DQ, WD, ZP, LY, \u0026amp; NC. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics, 2022, 38(16), 4027-4029. Doi: 10.1093/bioinformatics/btac438. \u003c/li\u003e\n\u003cli\u003eDai W, Zhang J, Chen L, Yu J, Zhang J, Yin H, et al. Discovery of Bacteroides uniformis F18-22 as a Safe and Novel Probiotic Bacterium for the Treatment of Ulcerative Colitis from the Healthy Human Colon. Int J Mol Sci. 2023 Sep 28;24(19). \u003c/li\u003e\n\u003cli\u003eTakeshita K, Mizuno S, Mikami Y, Sujino T, Saigusa K, Matsuoka K, et al. A Single Species of Clostridium Subcluster XIVa Decreased in Ulcerative Colitis Patients. Inflamm Bowel Dis. 2016 Dec;22(12):2802\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eNie K, Ma K, Luo W, Shen Z, Yang Z, Xiao M, et al. Roseburia intestinalis: A Beneficial Gut Organism From the Discoveries in Genus and Species. Front Cell Infect Microbiol. 2021;11:757718. \u003c/li\u003e\n\u003cli\u003eMacdonald TT, Monteleone G. Immunity, inflammation, and allergy in the gut. Science. 2005 Mar 25;307(5717):1920\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eBorruel N, Casellas F, Antol\u0026iacute;n M, Llopis M, Carol M, Esp\u0026iacute;in E, et al. Effects of nonpathogenic bacteria on cytokine secretion by human intestinal mucosa. Am J Gastroenterol. 2003 Apr;98(4):865\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003evan de Guchte M, Mondot S, Dor\u0026eacute; J. Dynamic Properties of the Intestinal Ecosystem Call for Combination Therapies, Targeting Inflammation and Microbiota, in Ulcerative Colitis. Gastroenterology. 2021 Dec;161(6):1969-1981.e12. \u003c/li\u003e\n\u003cli\u003eKonopka A. What is microbial community ecology? ISME J. 2009 Nov;3(11):1223\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eLarsen OFA, Claassen E. The mechanistic link between health and gut microbiota diversity. Sci Rep. 2018 Feb 1;8(1):2183. \u003c/li\u003e\n\u003cli\u003eBraun T, Di Segni A, BenShoshan M, Neuman S, Levhar N, Bubis M, et al. Individualized Dynamics in the Gut Microbiota Precede Crohn\u0026rsquo;s Disease Flares. Am J Gastroenterol. 2019 Jul;114(7):1142\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eShanahan F, Ghosh TS, O\u0026rsquo;Toole PW. The Healthy Microbiome-What Is the Definition of a Healthy Gut Microbiome? Gastroenterology. 2021 Jan;160(2):483\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eRobles AV, Herrera-deGuise C, Mayorga L, Varela E, Mart\u0026iacute;n AM, Margolles A, et al. \u003cstrong\u003eLongitudinal study of the gut microbiome in patients with ulcerative colitis on biological treatment with vedolizumab.\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eJ Crohns Colitis.\u003c/em\u003e 2025;19(Suppl 1):i2393.\u003c/li\u003e\n\u003cli\u003ePascal V, Pozuelo M, Borruel N, Casellas F, Campos D, Santiago A, et al.\u003cbr\u003e\u003cstrong\u003eA microbial signature for Crohn\u0026rsquo;s disease.\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003cem\u003eGut.\u003c/em\u003e 2017;66(5):813\u0026ndash;822. doi:10.1136/gutjnl-2016-313235\u003c/li\u003e\n\u003cli\u003eSabater C, Calvete I, V\u0026aacute;zquez X, Ruiz L, Margolles A.\u003cbr\u003e\u003cstrong\u003eTracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning.\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eInt J Food Microbiol.\u003c/em\u003e 2024;421:110789. doi:10.1016/j.ijfoodmicro.2024.110789\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microbiome, vedolizumab, ulcerative colitis","lastPublishedDoi":"10.21203/rs.3.rs-8621048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisruptions of the gut microbiome are known to play a role in the pathophysiology of inflammatory bowel disease (IBD) and may influence response to biologic therapies. However, defining microbial signatures associated with treatment outcomes remains challenging. In this prospective longitudinal study, we analyzed changes in the intestinal microbiome of patients with active ulcerative colitis starting anti-integrin therapy with vedolizumab. Stool samples and clinical data were obtained at baseline and at weeks 12, 30, and 52 following treatment initiation. Microbial taxonomic composition and functional capacity were assessed using shotgun metagenomic sequencing.\u003c/p\u003e \u003cp\u003ePatients who did not achieve clinical remission exhibited substantial interindividual heterogeneity in microbial composition and metabolic pathways throughout follow-up. In contrast, responders showed a more uniform and temporally stable microbiome, particularly during the induction phase. Importantly, none of the patients with markedly reduced baseline alpha diversity reached remission. Long-term responders demonstrated a gradual increase in commensal bacteria with reported anti-inflammatory effects, including Bacteroides uniformis, multiple Roseburia species (R. intestinalis, R. inulinivorans, R. faecis), and members of the Lachnospiraceae family. These microbial shifts were absent in non-responders and in patients who subsequently lost response.\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that baseline microbial diversity and longitudinal ecosystem stability may be associated with favorable response to anti-integrin therapy. Gut microbiome features could therefore complement clinical parameters in understanding treatment response and may contribute to future patient stratification strategies in ulcerative colitis.\u003c/p\u003e","manuscriptTitle":"Longitudinal study of the gut microbiota in ulcerative colitis patients on anti-integrin biologic therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-15 16:52:50","doi":"10.21203/rs.3.rs-8621048/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-18T15:31:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111964205551929213372675622125215475963","date":"2026-03-11T06:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T04:43:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-17T09:19:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T12:12:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-26T11:27:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-01-26T11:21:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc2e7573-fe14-4988-b9ba-7438e66b4ade","owner":[],"postedDate":"March 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T16:52:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-15 16:52:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8621048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8621048","identity":"rs-8621048","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}