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Article

The Role of Surgical and Perioperative Factors in Shaping Gut Microbiome Recovery After Colorectal Surgery †

by
Julia Kohn
1,‡,
Alexander Troester
1,‡,
Zachary Ziegert
1,‡,
Julia Frebault
1,2,
Sonja Boatman
1,
Maria Martell
1,
Harika Nalluri-Butz
3,
Matthew C. Bobel
4,
Paolo Goffredo
3,
Abigail J. Johnson
5,
Cyrus Jahansouz
3,*,§,
Christopher Staley
1,6,§ and
Wolfgang B. Gaertner
3,§
1
Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
2
Department of Surgery, Hennepin County Medical Center, Minneapolis, MN 55455, USA
3
Division of Colon and Rectal Surgery, Department of Surgery, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA
4
Granger Medical Center, 1250 E 3900 S #320, Salt Lake City, UT 84124, USA
5
School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
6
BioTechnology Institute, University of Minnesota, St. Paul, MN 55455, USA
*
Author to whom correspondence should be addressed.
†
Presentations: 2024 Annual Scientific Meeting of the American Society of Colon and Rectal Surgery, 2024 Annual Meeting of the Surgical Infection Society, 2025 Digestive Disease Week.
‡
Designates shared co-first authorship.
§
These authors contributed equally to this work.
Antibiotics 2025, 14(9), 881; https://doi.org/10.3390/antibiotics14090881
Submission received: 9 July 2025 / Revised: 23 August 2025 / Accepted: 29 August 2025 / Published: 31 August 2025
(This article belongs to the Special Issue Pioneering the Microbiome—Infections, Inflammation, Inflammatory Bowel Disease and the Role of Antibiotic Therapy)
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Abstract

The gut microbiome is essential for gut health, immune regulation, and metabolism, but pathogenic bacteria like Enterococcus and Streptococcus can disrupt these processes, increasing infection risk after colorectal surgery. Prior studies show that intravenous antibiotics and surgical bowel preparation (SBP, including mechanical preparation with oral antibiotics) significantly disrupt the gut microbiota, potentially delaying postoperative recovery. However, the effects of surgical indication (e.g., diagnosis) and operation type on gut microbiome composition and function remain unclear. This study examines how SBP, resectional and non-resectional surgery, and underlying diagnoses shape the postoperative gut microbiome and microbial recovery. Methods: Fecal samples were collected from patients undergoing colonoscopy (n = 30), non-resectional (ventral mesh rectopexy, transanal surgery; n = 25), or resectional surgery with primary anastomosis (n = 26) at baseline, intraoperatively, and on postoperative days (POD) 10, 30, and 180. Microbial diversity was assessed through 16S rRNA sequencing, and short-chain fatty acid (SCFA) levels were measured to evaluate functional changes. Results: Alpha diversity (Shannon indices) decreased across all groups, recovering by POD10 in colonoscopy patients and by POD180 in non-resectional and resectional cohorts. Beta diversity (community composition) also returned to baseline by POD10 in colonoscopy patients and POD180 in non-resectional patients, but the resectional cohort did not fully recover (p < 0.001). Both surgical cohorts showed substantial losses of commensal bacteria through POD30, with notable increases in Streptococcus in resectional patients (p < 0.0001) and Enterococcus in both surgical cohorts (p < 0.0001). Functionally, only the resectional cohort experienced significant reductions in SCFA levels (p < 0.015) relative to baseline levels. Diagnosis minimally influenced long-term microbiota recovery, although cancer patients tended to have more stable microbiomes compared to patients with diverticulitis. Conclusions: These findings indicate that perioperative factors, especially surgical resection and SBP, significantly impact gut microbial recovery, with pathogenic bacteria persisting up to 6 months post-surgery.

1. Introduction

Intestinal diseases are a significant health burden, with rising prevalence linked to Western diets and an aging population [1]. Surgery is often required for conditions like diverticulitis, colorectal cancer, and inflammatory bowel disease [2,3,4]. Successful bowel resection with primary anastomosis is critical for recovery, yet only half of colon operations achieve a complication-free outcome, leaving room for improvement [5,6]. Delayed return of bowel function [7,8] and infections [9,10,11,12], including anastomotic leaks (AL) and surgical site infections (SSI), are major contributors to poor outcomes, affecting around 20% of operations, despite optimal technique. These complications increase morbidity, mortality, hospital stays, and healthcare costs [13,14]. Despite the need to improve patient outcomes, therapeutic progress in preventing these complications has been hindered by fundamental gaps in understanding their etiology.
The gut microbiome plays a crucial role in maintaining gut health, immune regulation, and metabolic functions [15]. Injured intestinal mucosa is characterized by a microenvironment of cellular migration, proliferation, and tissue remodeling [16]. Certain microbiota subpopulations preferentially colonize damaged mucosa, promoting epithelial wound healing [17]. However, pathogenic bacteria can interfere, increasing the risk of infectious complications. For example, Enterococcus faecalis has been mechanistically linked to AL via the production of matrix metalloproteinase 9, which degrades collagen [18]. Mice intestinally colonized with methicillin-resistant Staphylococcus aureus can develop SSI by the silent migration of bacteria to the wound without bacteremia [19]. The microbiota can also contribute to systemic inflammation through the translocation of microbial products like lipopolysaccharides across the intestinal barrier [20].
Colorectal surgery introduces unique challenges to the gut microbiome. Surgical bowel preparation (SBP), involving mechanical bowel preparation (MBP) and oral antibiotics (OAs), is administered in addition to intravenous (IV) antibiotics at the time of surgery to reduce pathogen load and minimize infection risk [12,21,22,23,24,25,26]. However, SBP leads to significant reductions in beneficial commensal bacteria, including short-chain fatty acid (SCFA)-producing species, which are critical for gut barrier integrity and wound healing [17,27,28,29]. Furthermore, patients undergoing resectional surgery face additional challenges from anatomical disruption, which may exacerbate microbial disturbances and delay recovery. Despite the growing recognition of the microbiome’s role in surgical outcomes, little is known about the distinct contributions of perioperative factors, surgical anatomy, and baseline diagnoses (i.e., diverticulitis versus cancer) in shaping postoperative gut microbial composition.
Building on prior work [30,31], this study aims to explore the relative contributions of SBP, resectional versus non-resectional surgery, and underlying diagnoses on postoperative gut microbial community. This study included a colonoscopy group to model MBP without surgical stress and two surgical groups receiving SBP and either non-resectional or resectional surgery to determine the impact of anatomic disruption. Specifically, we hypothesized that patients undergoing resectional surgery would experience more profound and prolonged disruptions in microbial diversity and community structure than colonoscopy or non-resectional surgery due to anatomical alterations. Additionally, we sought to determine whether baseline diagnoses of diverticulitis or colorectal cancer would lead to distinct patterns of microbial reconstitution, mainly given the higher prevalence of prior antibiotic exposure in diverticulitis patients. Finally, we investigated the functional implications of these microbiome changes by measuring SCFA levels, which are crucial for gut health and healing. By elucidating the interplay between perioperative factors and microbial reconstitution, this study attempted to provide a foundation for developing targeted strategies that minimize microbial disruption and promote faster microbial recovery, ultimately improving surgical outcomes.

2. Results

2.1. Study Cohorts

Fecal samples were obtained from patients who underwent colonoscopy (n = 30), resectional surgery (n = 26), or non-resectional surgery (n = 25). Demographics and clinical data are presented in Table 1.
All surgery patients received perioperative IV antibiotics, though the specific regimen varied based on patient tolerance and allergies (Table 2). Within each cohort, all IV antibiotic groups had similar baseline composition without differences in alpha or beta diversity. Similarly, no significant differences in microbiome composition were observed between samples collected on POD10 and POD30 across the various antibiotic groups within both the non-resectional and resectional cohorts. Compared to colonoscopy patients, individuals undergoing surgery were generally older, and those undergoing resectional procedures were more frequently male. Patients from all three cohorts were similar with regards to comorbidities.

2.2. Diversity and Community Composition Within Cohorts

Among all cohorts, there were significant changes in diversity over time. In colonoscopy patients, the mean Shannon index (alpha diversity) decreased significantly from baseline to DOS (mean ± standard deviation: 3.68 ± 0.31 vs. 3.13 ± 0.47; Kruskal–Wallis p = 0.0002; Figure S1, Table S1) but quickly recovered by POD10 (3.55 ± 0.65). In contrast, the mean Shannon index of non-resectional surgery patients decreased significantly from baseline to POD30 (3.65 ± 0.25 vs. 3.11 ± 0.45; p < 0.0001; Figure S1) and did not recover until POD180 (3.39 ± 0.35). Similarly, the mean Shannon index of resectional surgery patients decreased significantly from baseline to POD30 (3.67 ± 0.41 vs. 2.94 ± 0.59; p = 0.001; Figure S1) and then recovered by POD180 (3.30 ± 0.27). Similar to alpha diversity, the community structure (beta diversity) of colonoscopy patients differed significantly from baseline to DOS (ANOSIM R = 0.28, p < 0.001; Figure 1) but recovered by POD10. In non-resectional surgery patients, community structures differed significantly between baseline and both POD10 and POD30 (R = 0.31, 0.35, respectively, p < 0.001, for both comparisons; Figure 1), showing more sustained community disruption. Interestingly, communities for resectional surgery patients differed significantly between baseline and all other time points (R = 0.36, 0.26, 0.24, 0.24, respectively, p < 0.001, for all comparisons; Figure 1).
Several taxa exhibited significant changes in composition over time within the treatment cohorts. Among colonoscopy patients, Ruminococcaceae, Blautia, and Streptococcus significantly decreased from baseline to DOS (p < 0.0001, <0.0001, <0.001, respectively; Figure 2, Table S2), with all three taxa recovering by POD10. In the non-resectional cohort, beneficial commensals Bacteroides, Phocaeicola, and Alistipes decreased significantly from baseline to POD10 (p = 0.001, 0.002, <0.0001, respectively), while Enterococcus increased significantly during the same period (p < 0.0001) and remained elevated through POD30. By POD30, Bacteroides returned to baseline levels, while Phocaeicola, Alistipes, and Enterococcus did not return to baseline until POD180. In the resectional cohort, the relative abundances of Bacteroides and Blautia significantly decreased from baseline to POD10 (p = 0.003, 0.004, respectively), whereas Streptococcus and Enterococcus significantly increased over the same period (p = 0.0001 and 0.0004, respectively). By POD30, Bacteroides, Blautia, and Streptococcus had returned to baseline levels, though Enterococcus remained elevated until POD180.

2.3. Diversity and Community Composition Among Cohorts

Differences in microbial diversity were compared among the colonoscopy, non-resectional, and resectional cohorts throughout the surgery and recovery periods. On POD10, colonoscopy patients exhibited significantly greater Shannon indices (3.55 ± 0.65) compared to both non-resectional (2.73 ± 0.93) and resectional (2.70 ± 1.21) patients (p = 0.0002, 0.006, respectively; Table S1). Similarly, at POD180, colonoscopy patients maintained significantly greater Shannon indices (3.78 ± 0.41) compared to non-resectional (3.39 ± 0.35) and resectional (3.30 ± 0.27) patients (p = 0.011, 0.0005, respectively). Notably, there were no significant differences in Shannon indices between non-resectional and resectional patients at any time point.
When comparing across treatment cohorts at the same time points, several genera showed significant differences in relative abundance. On DOS, Faecalibacterium was more abundant in the colonoscopy group than in the resectional group (p < 0.0001; Figure 2, Table S2), and Streptococcus had significantly greater relative abundances in the non-resectional cohorts compared to the colonoscopy group (p < 0.0001). On POD10, the colonoscopy cohort exhibited a greater relative abundance of Bacteroides and lower levels of Enterococcus compared to the non-resectional cohort (p = 0.0002, 0.0001). The resectional cohort similarly had greater relative abundances of Enterococcus as well as Streptococcus relative to the colonoscopy group (p = 0.003 and 0.0001). By POD30, the colonoscopy group had greater relative abundances of Alistipes, Phocaeicola and Faecalibacterium but lower levels of Enterococcus compared to both non-resectional and resectional patients (p ≤ 0.001). These findings suggest that while microbial diversity generally recovers over time across all cohorts, the resectional and non-resectional groups exhibit sustained shifts in the relative abundances of key genera such as Streptococcus and Enterococcus, particularly in the resectional cohort, which may reflect the differential impacts of surgical interventions on gut microbiota composition.

2.4. Diversity and Community Composition of Right Versus Left Colon Resections

To assess the relative impact of surgical anatomy on microbiome composition, we compared patients undergoing right-sided versus left-sided colon resections within the resectional cohort. There were no significant differences in the mean Shannon index between the two groups at baseline, POD10, or POD30. Parabacteroides was found in lower abundances in left resection patients compared to right resection patients on POD30 (p = 0.030, 0.009, respectively); however, this difference did not remain significant after applying a Bonferroni correction (corrected α = 0.001). Furthermore, no significant differences in overall community structure were observed between the left and right resection groups at any time point (R = 0.05, p > 0.05), suggesting that anatomical differences between left and right-sided resections are unlikely to significantly contribute to microbial communities relative to other perioperative factors.

2.5. Diversity and Community Composition Between Diverticulitis and Cancer

We next aimed to identify microbiome differences between patients with diverticulitis and those with colon cancer (including right- and left-sided together). On POD10, the mean Shannon index for colon cancer patients was significantly greater than that of diverticulitis patients (2.76 ± 0.25 vs. 1.70 ± 0.79; p < 0.0001; Table S3), though no significant differences were observed at other time points. Community structure differed significantly between diverticulitis and colon cancer patients on POD10 (R = 0.30, p = 0.001; Figure S2, Table S4); however, by POD30, the communities were similar. Among diverticulitis patients, community structure differed significantly from baseline to POD10 (R = 0.46, p < 0.001) and from POD10 to POD30 and POD180 (R = 0.47, 0.57, respectively, p < 0.001, for both comparisons). Among cancer patients, overall community structure did not differ at any time points.
In diverticulitis patients, the relative abundances of Enterococcus and Streptococcus significantly increased from baseline to POD10 (p = 0.002, 0.001, respectively; Figure 3, Table S4) before returning to baseline levels by POD30. In contrast, the relative abundance of the gut commensal genus Blautia decreased significantly from baseline to POD10 (p = 0.002) then returned to baseline by POD30. Among cancer patients, Blautia was the only genus that showed a significant change over time, with its relative abundance increasing from DOS to POD180 (p = 0.002). These results suggest that while microbiome diversity and community composition undergo significant shifts in diverticulitis patients during the early postoperative period, they tend to stabilize by POD30, whereas colon cancer patients exhibit a more stable microbial profile.

2.6. Two Resectional Patients with Anastomotic Leaks

Two patients experienced anastomotic leaks following resectional surgery. These patients were matched by age, BMI, and primary diagnosis to two non-leak patients. SourceTracker analysis of fecal samples on POD30 illustrated <0.1% and 3.2% similarity to baseline in leak patients and 54% and 72% similarity to baseline in non-leak patients, likely due to the prolonged course of antibiotics required at the time of AL diagnosis. There were no significant differences in relative abundances of genera, mean Shannon index, or community structure between the two leak patients and non-leak patients who underwent resectional surgery. These findings are limited by the study’s limited statistical power, given that the leak group consisted of only two patients.

2.7. Evaluation of Short-Chain Fatty Acids and IgA

Baseline and POD10 samples were analyzed for SCFA composition (Table 3).
Among POD10 samples, one sample in the resectional group had an SCFA concentration > 3 standard deviations above the mean and was excluded from statistical comparisons due to its extreme magnitude and inconsistency with expected postoperative SCFA levels based on both group trends and known physiological ranges. At baseline, concentrations of SCFAs were similar among groups, except for concentrations of isobutyrate and isovalerate, which were lower in the non-resectional cohort when compared to the resectional cohort (Tukey’s post hoc p = 0.013 and 0.046). In the resectional cohort but not the non-resectional group, all SCFA concentrations were significantly reduced from baseline to POD10 (p < 0.015). Concentrations of IgA normalized to total protein were significantly lower in the colonoscopy group compared to either surgery group (Table S5), but fecal IgA concentrations remained similar in all groups between baseline and POD10 samples.
In the colonoscopy cohort, multiple correlations were observed at baseline and POD10 between predominant genera and SCFA concentrations (Figure 4). Members of the Bacillota (e.g., Ruminococcaceae, Lachnospiraceae, and Faecalibacterium) and Bacteroidota (e.g., Bacteroides, Phocaeicola, and Alistipes) generally positively correlated with abundances of SCFAs (Spearman’s ρ = 0.439–0.779, p ≤ 0.048) at both baseline and POD10, with the exception of a negative correlation between Phocaeicola abundances and the acetic acid concentration at POD10 (ρ = −0.537, p = 0.034). At POD10, relative abundances of Streptococcus among colonoscopy patients negatively correlated with propionic acid and isovaleric acid (ρ = −0.553 and −0.533, p = 0.029 and 0.028). Similarly, Enterococcus negatively correlated with all SCFAs at POD10 (ρ = −0.548 to −0.677, p ≤ 0.030). Phocaeicola was the only genus among the colonoscopy patients that negatively correlated with fecal IgA concentrations at baseline (ρ = −0.440, p = 0.047).
Few correlations were observed among relative abundances of genera and SCFAs in the surgical cohorts. At baseline, Parabacteroides relative abundances were positively correlated with propionic acid in the non-resectional cohort (ρ = 0.567, p = 0.030), while Faecalibacterium abundances correlated with isobutyric acid in the resectional cohort (ρ = 0.520, p = 0.049). At POD10, Lachnospiraceae, Ruminococcaceae, Blautia, and Alistipes were correlated with valeric acid in the non-resectional cohort (ρ = 0.545, 0.780, 0.554, 0.635; p = 0.017, 0.0001, 0.015, 0.004, respectively), while Blautia was inversely correlated with fecal IgA concentrations (ρ = −0.473, p = 0.042).

3. Discussion

The gut microbiota plays a crucial role in influencing complications after intestinal surgery [18,19,32,33], yet the specific contributions of perioperative factors on gut microbiome recovery remain poorly understood. Building on our previous work [30,31], we examined the relative impact of MBP and SBP, type of colorectal operation (resectional versus non-resectional), and baseline diagnosis (diverticulitis versus cancer) on shaping postoperative microbiome composition. MBP, represented by the colonoscopy group, induced transient microbiome shifts, with recovery by POD10 [31]. SBP, surgical intervention, and underlying diagnosis significantly altered microbiome composition, with recovery in surgical cohorts taking up to six months. Given the increasing evidence supporting the enteral translocation of oral pathobionts during periods of gut dysbiosis, strategies to optimize postoperative microbial recovery are paramount to prevent post-surgical complications [34].
Limited knowledge exists regarding factors influencing gut microbiota recovery following surgery, particularly in the context of SBP administration. Considering the variability of surgical stress on the microbiome, we included a non-resectional surgical cohort to compare this impact in patients where anatomical continuity is preserved. Despite this, Shannon diversity indices showed a significant reduction for at least one month postoperatively before returning to baseline. In contrast, resectional patients undergoing partial colectomy exhibited more pronounced and prolonged changes in community structure, which remained altered for up to six months. Interestingly, within the resectional cohort, laterality had less influence on gut composition, as no significant differences were observed between patients undergoing right or left segmental colectomies. This pattern may reflect a more profound disruption of the microbial niche in resectional surgery, which is further explored below.
This prolonged disruption was most evident among patients undergoing resectional surgery, who exhibited more profound and sustained dysbiosis compared to those undergoing non-resectional procedures. This included depletion of key SCFA-producing taxa, such as Blautia and Ruminococcaceae, and prolonged elevation of facultative pathobionts like Enterococcus. We previously reported that Blautia and Faecalibacterium, both important SCFA producers, positively correlated with SCFA levels, while Streptococcus species, which primarily produce lactic acid, negatively correlated with SCFA production [31]. Increased levels of Streptococcus have been linked to dysbiosis and a decline in key butyrate-producing bacteria, including Faecalibacterium and Blautia [35]. Functionally, this correlated with a significant decrease in all measured SCFAs. We similarly observed an increase in Streptococcus in the resectional cohort and a subsequent drop in all SCFA concentrations following surgery. SCFA depletion is associated with gut and systemic inflammation, delayed wound healing, and gut barrier disruption [17,27].
In contrast, non-resectional patients demonstrated more rapid microbial recovery, with partial reconstitution of commensals by POD30. The greater disruption in the resectional group may be explained by several physiologic mechanisms. Ischemia-reperfusion injury during vessel ligation and bowel manipulation can damage mucosal integrity and alter the metabolic environment of the colon [36]. Surgical resection also disrupts enteric neural signaling and colonic transit, both of which influence luminal pH, oxygen tension, and nutrient availability [37,38]. These factors are critical for maintaining anaerobic microbial populations. Further, perioperative antibiotic ablation of microbiota may reduce niche exclusion dynamics allowing the translocation of oral pathobionts like Streptococcus [34]. Additionally, temporary or permanent changes in bowel continuity may result in increased luminal oxygen levels, which can favor the overgrowth of Enterococcus and Streptococcus and further delay the re-establishment of an anaerobic, SCFA-producing community [39]. These interrelated mechanisms likely contribute to the delayed microbial recovery observed in the resectional group and underscore that current perioperative practices may not sufficiently support mucosal healing, particularly in patients with anastomoses where gut recovery is critical to surgical success.
When comparing patients undergoing resection for diverticulitis versus colorectal cancer, we found distinct patterns in microbial recovery. Patients with diverticulitis exhibited a delayed return of commensal SCFA-producing taxa such as Blautia and Faecalibacterium [40], along with sustained elevations in Streptococcus and Enterococcus through POD30. In contrast, cancer patients showed more rapid suppression of pathobionts and the partial reconstitution of commensals by POD180. These differences likely reflect variation in the baseline microbial landscape: diverticulitis patients often have a history of chronic mucosal inflammation, recurrent antibiotic exposure, and altered colonic motility that may impair microbiome resilience [41,42]. In contrast, tumor-associated dysbiosis, such as Fusobacterium and Parabacteroides, may be partially eliminated during oncologic resection, allowing for different reassembly trajectories [43,44,45,46,47,48,49,50,51]. Blautia recovery by POD180 was more robust in cancer patients, while Enterococcus levels remained elevated longer in diverticulitis patients. These dynamics suggest ecological competition, in which the re-emergence of beneficial anaerobes may suppress the persistence of opportunistic pathobionts. These findings support the idea that underlying disease etiology contributes to the pace and pattern of microbial recovery and may have implications for tailoring microbiota-targeted therapies after surgery.
Despite the comprehensive nature of this study, several limitations should be considered. The overall sample size for each subgroup may limit the statistical power of our findings, particularly for specific comparisons, such as cancer versus diverticulitis patients and cancer location. Larger studies are required to confirm these findings and further explore subgroup-specific effects given that these comparisons were exploratory. While we excluded patients who received systemic antibiotics within 30 days prior to enrollment to reduce baseline microbiome variability, we did not account for more remote or cumulative exposures (e.g., multiple antibiotic courses in the preceding year). These unmeasured exposures may have influenced microbial diversity and recovery trajectories [41]. Future studies should also incorporate multiomics approaches to resolve microbial functional pathways and better characterize host–microbiome interactions. These tools will allow for a more comprehensive understanding of how perioperative interventions affect microbial metabolism, antimicrobial resistance, and mucosal immune responses. Furthermore, our study was conducted at a single medical center, which may limit generalizability given potential differences in surgical techniques, bowel preparation protocols, and perioperative practices. Future multicenter studies are needed to validate our results. Although SCFA levels were quantified as functional markers, other metabolites such as bile acids were not assessed and could provide additional insight. Our study accounted for key variables such as age, sex, BMI, and comorbidities, but other factors like diet, lifestyle, and additional perioperative care interventions were not controlled for and may have influenced gut microbiome composition and recovery. Lastly, alternative normalization methods (e.g., cumulative sum scaling and variance-stabilizing transformation) are increasingly used to address uneven sequencing depth. Because all included samples achieved >98% Good’s coverage at our rarefaction threshold, biological inferences are unlikely to differ with these approaches, consistent with prior work [52,53]. We therefore retained rarefaction as our primary strategy while acknowledging these alternatives as complementary options.
Taken together, these findings highlight the need for personalized perioperative strategies that preserve microbial resilience, particularly in patients undergoing resectional surgery. Future research should focus on microbiome restoration protocols and long-term follow-up to better understand how to optimize recovery and reduce postoperative complications through targeted microbial support.

4. Materials and Methods

4.1. Study Design

Eighty-one adult patients were recruited from March 2019 to April 2024 and underwent colonoscopy, elective resectional, or elective non-resectional colorectal surgery at the University of Minnesota Medical Center. This was a prospective observational cohort study. Participants were enrolled and followed longitudinally at predefined postoperative time points. Fecal samples were obtained pre-procedurally or preoperatively (within 30 days), intra-procedurally or on the day of surgery (DOS), post-procedurally or postoperatively within 10 days (POD10), within 3–6 weeks (POD30), and at 6 months (POD180).
Colonoscopy patients underwent MBP alone, while surgery patients underwent SBP composed of MBP with OAs and perioperative IV antibiotics. MBP included either MiraLAX (238 g) plus magnesium citrate (296 mL), GoLYTELY, or MiraLAX plus Gatorade. Oral antibiotics included neomycin (1 g q12 h for 2 doses) plus metronidazole (500 mg q6 h for 3 doses) given with prophylactic Zofran (4 mg q6 h for 3 doses) as an anti-nausea measure. All patients undergoing surgery received perioperative antibiotic prophylaxis in accordance with SCIP guidelines [54] and were initiated 1–2 h before incision and continued selectively for up to 24 h postoperative. Regimens included ciprofloxacin (400 mg q12 h) plus metronidazole (500 mg q8 h), cefazolin (2 g if <120 kg or 3 g if ≥120 kg) plus metronidazole, ertapenem (1 g daily), and cefotetan (1–2 g q12 h) at the discretion of the surgeon. Postoperative diet consisted of liquids until evidence of return of bowel function. The choice of laparoscopic versus open surgery was at the discretion of the attending colorectal surgeon, consistent with standard clinical practice and patient anatomy/pathology. Patients were advanced to a low-fiber diet for four weeks, after which dietary restrictions were lifted and a high-fiber diet was recommended. Exclusion criteria included history of organ transplant, active immunosuppression, chronic steroid use, any chemotherapy 12 months before procedure, history of intra-abdominal or pelvic radiation, inflammatory bowel disease, previous colorectal resection, planned diverting ileostomy, and pregnancy. Patients were excluded if they had received systemic antibiotic therapy within 30 days prior to enrollment, except for antibiotics administered as part of standard perioperative prophylaxis. This criterion was applied to reduce potential variability in baseline microbiota composition related to recent antimicrobial exposure. Human fecal specimens were obtained under approval by the Institutional Review Board of the University of Minnesota. Informed written consent was obtained from each study participant before enrollment.

4.2. Sample Collection

Fecal samples were collected by patients in single-use specimen pans and transferred to 30 mL polystyrene fecal specimen containers (Globe Scientific, Inc., Paramus, NJ, USA), as previously described [31]. In brief, patients stored samples in their home freezers prior to their clinic, procedure, or surgery visit. Intra-procedural or intraoperative samples were collected by the treating surgeon via endoscopic suction of colonic effluent. Specimens were placed in 30 mL polystyrene containers and immediately transferred to a −80 °C freezer until processing.

4.3. DNA Extraction and Sequencing

DNA was extracted from 250 mg thawed fecal samples using the DNeasy PowerSoil Pro kit (QIAGEN, Hilden, Germany) on the automated QIAcube platform following the inhibitor removal technology (IRT) protocol. The V4 hypervariable region of the 16S rRNA gene was amplified using the 515F/806R primer set [55] by the University of Minnesota Genomics Center (UMGC, Minneapolis, MN, USA), as previously described [56]. Paired-end, dual-indexed sequencing at a read length of 300 nucleotides (nt) was performed on the Illumina MiSeq platform (Illumina, Inc., San Diego, CA, USA) by UMGC [56]. Negative sterile water controls were included in all sequencing runs and did not produce amplicons. Raw data were stored in the Sequence Read Archive under BioProject accession number SRP250717.

4.4. Bioinformatics

Amplicon sequence data were processed and analyzed using mothur software ver. 1.41.1 [57] with our previously published pipeline for quality screening and taxonomic annotation [58]. Briefly, reads were paired-end joined, quality trimmed, and aligned against the SILVA database ver. 138 [59]. Sequences were further cleaned using a 2% pre-clustering step [60], and chimeric sequences were identified and removed using UCHIMER ver. 4.2.40 [61]. Operational taxonomic units (OTUs) were binned at 99% similarity using the OptiClust algorithm [62], and taxonomic assignment was performed against the Ribosomal Database Project ver. 16 [63]. Across all samples, the lowest sequencing depth was 10 reads, and the highest was 117,425 reads (median 19,826; interquartile range 10,046–28,870).
For unbiased statistical comparisons [64], samples were rarefied to 5000 sequence reads by random subsampling, and samples with fewer sequence reads were removed from the dataset. At a rarefaction depth of 5000 reads, 23 samples of 323 (7%) were excluded. A mean estimated Good’s coverage of 98.5 ± 1.3% and a mean of 176 ± 99 OTUs were observed among all samples. The number of fecal samples included in comparisons among sample groups at each time point after normalization by rarefaction are shown in Table S1.
SourceTracker was used with default parameters to assess the similarity of samples from leak and matched patients to their own preoperative microbiota [65]. This software uses a Bayesian inference approach to determine what percent of the community of sink samples (postoperative samples) is derived from source (preoperative) samples taking an OTU table as input.

4.5. Statistical Analysis

Alpha and beta diversity statistics were calculated in mothur. Alpha diversity was evaluated with the Shannon index [66], which accounts for richness and evenness. Beta diversity was calculated using Bray–Curtis distances [67] and was visualized by ordination using principal coordinate analysis (PCoA) [68]. Spearman correlations were calculated to determine abundant genera that were significantly associated with PCoA axis position using the corr.axes command in mothur. Spearman correlations were also performed to determine associations between relative abundances of genera and SCFAs and IgA concentrations. Canonical correspondence analysis (CCA) was used for multivariate analysis. Patient demographic and clinical data were compared using Chi-squared analysis for categorical variables and ANOVA test for continuous variables. Differences in diversity indices were determined using ANOVA with Tukey’s post hoc test. Community compositions at different time points were compared using analysis of similarity (ANOSIM) [69] with Bonferroni correction for pairwise comparisons. Analyses were conducted with XLSTAT (version 2020.2.3; Addinsoft, Belmont, MA, USA). All statistics were evaluated at α = 0.05.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/antibiotics14090881/s1, Table S1: Mean (±standard deviation) Good’s coverage, observed richness (Sobs), Shannon, and Chao1 indices for colonoscopy, non-resectional, and resectional patients over time. Table S2: Mean (±standard deviation) relative abundance for colonoscopy, non-resectional, and resectional patients over tim; Table S3: Mean (±standard deviation) Good’s coverage, observed richness (Sobs), Shannon, and Chao1 indices for diverticulitis and cancer patients who underwent resectional surgery over time; Table S4: Mean (±standard deviation) relative abundance for diverticulitis and cancer patients who underwent resectional surgery over time: Table S5: Percent fecal IgA concentrations relative to total protein at baseline and POD10 in colonoscopy, non-resectional surgery, and resectional surgery. Figure S1: Box plots of Shannon indices for (A) colonoscopy, (B) non-resectional surgery, and (C) resectional surgery patients at baseline, DOS, POD10, POD30, and POD180. Figure S2: (A) Predominant genera for diverticulitis and cancer patients who underwent resectional surgery at baseline, DOS, POD10, POD30, and POD180. (f) denotes classification beyond family-level was not possible. (B) Principal coordinate analysis of Bray-Curtis distances in diverticulitis and cancer patients who underwent resectional surgery at baseline, DOS, POD10, POD30, and POD180.

Author Contributions

Conceptualization: J.K., A.T., S.B., W.B.G., C.J. and C.S.; Methodology: J.K., A.T., Z.Z., J.F., M.M., H.N.-B. and M.C.B.; Validation: S.B., H.N.-B. and M.C.B.; Formal Analysis: J.K., A.T., Z.Z., S.B., M.M. and H.N.-B.; Investigation: J.K., A.T., Z.Z., C.J. and C.S.; Data curation: A.J.J., C.S., C.J. and W.B.G.; Visualization: J.K., A.T., Z.Z., J.F., P.G., C.J. and C.S.; Mentorship: H.N.-B., M.C.B., P.G. and A.J.J.; Project Administration: W.B.G.; Writing—Original Draft: J.K., A.T., Z.Z., C.J. and C.S.; Writing—Review and Editing: All authors. All authors have read and agreed to the published version of the manuscript.

Funding

Funding was provided by the Hubbard Family private fund for advancing treatment practices to improve patient outcomes in colorectal surgery (fund 21692—University of Minnesota).

Institutional Review Board Statement

Approval for this study was given by the University of Minnesota Institutional Review Board (Protocol Number: 00005429). All subjects provided written informed consent for participation in the study and collection and analysis of data. All study procedures were in accordance with the ethical standards of the national research ethics committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The dataset supporting the conclusions of this article is available in the Sequence Read Archive repository under BioProject accession number SRP250717. It is available at https://www.ncbi.nlm.nih.gov/sra/?term=SRP250717 (accessed on 8 July 2025).

Acknowledgments

We would like to thank Kelli Tourand for administrative assistance. Sequence data were generated by the University of Minnesota Genomics Center and processed and analyzed using resources of the Minnesota Supercomputing Institute.

Conflicts of Interest

W.B.G. is a consultant and advisory board member for Intuitive Surgical, Coloplast, Steel Therapeutics, BD, and Applied Medical. The remaining authors declare that they have no competing interests.

References

  1. Everhart, J.E.; Ruhl, C.E. Burden of Digestive Diseases in the United States Part II: Lower Gastrointestinal Diseases. Gastroenterology 2009, 136, 741–754. [Google Scholar] [CrossRef]
  2. Morris, A.M.; Regenbogen, S.E.; Hardiman, K.M.; Hendren, S. Sigmoid diverticulitis: A systematic review. JAMA 2014, 311, 287–297. [Google Scholar] [CrossRef] [PubMed]
  3. Anaya, D.A.; Flum, D.R. Risk of emergency colectomy and colostomy in patients with diverticular disease. Arch. Surg. 2005, 140, 681–685. [Google Scholar] [CrossRef] [PubMed]
  4. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 145–164. [Google Scholar] [CrossRef] [PubMed]
  5. Kolfschoten, N.E.; Kievit, J.; Gooiker, G.A.; van Leersum, N.J.; Snijders, H.S.; Eddes, E.H.; Tollenaar, R.A.; Wouters, M.W.; Marang-van de Mheen, P.J. Focusing on desired outcomes of care after colon cancer resections; hospital variations in ‘textbook outcome’. Eur. J. Surg. Oncol. 2013, 39, 156–163. [Google Scholar] [CrossRef]
  6. Hyer, J.M.; Tsilimigras, D.I.; Diaz, A.; Mirdad, R.S.; Azap, R.A.; Cloyd, J.; Dillhoff, M.; Ejaz, A.; Tsung, A.; Pawlik, T.M. High Social Vulnerability and “Textbook Outcomes” after Cancer Operation. J. Am. Coll. Surg. 2021, 232, 351–359. [Google Scholar] [CrossRef]
  7. Abernethy, E.K.; Aly, E.H. Postoperative Ileus after Minimally Invasive Colorectal Surgery: A Summary of Current Strategies for Prevention and Management. Dig. Surg. 2024, 41, 79–91. [Google Scholar] [CrossRef]
  8. Barbieux, J.; Hamy, A.; Talbot, M.F.; Casa, C.; Mucci, S.; Lermite, E.; Venara, A. Does enhanced recovery reduce postoperative ileus after colorectal surgery? J. Visc. Surg. 2017, 154, 79–85. [Google Scholar] [CrossRef]
  9. Wick, E.C.; Hobson, D.B.; Bennett, J.L.; Demski, R.; Maragakis, L.; Gearhart, S.L.; Efron, J.; Berenholtz, S.M.; Makary, M.A. Implementation of a surgical comprehensive unit-based safety program to reduce surgical site infections. J. Am. Coll. Surg. 2012, 215, 193–200. [Google Scholar] [CrossRef]
  10. Young, H.; Knepper, B.; Moore, E.E.; Johnson, J.L.; Mehler, P.; Price, C.S. Surgical site infection after colon surgery: National healthcare safety network risk factors and modeled rates compared with published risk factors and rates. J. Am. Coll. Surg. 2012, 214, 852–859. [Google Scholar] [CrossRef]
  11. Kamboj, M.; Childers, T.; Sugalski, J.; Antonelli, D.; Bingener-Casey, J.; Cannon, J.; Cluff, K.; Davis, K.A.; Dellinger, E.P.; Dowdy, S.C.; et al. Risk of Surgical Site Infection (SSI) following Colorectal Resection Is Higher in Patients With Disseminated Cancer: An NCCN Member Cohort Study. Infect. Control Hosp. Epidemiol. 2018, 39, 555–562. [Google Scholar] [CrossRef]
  12. Woodfield, J.C.; Clifford, K.; Schmidt, B.; Turner, G.A.; Amer, M.A.; McCall, J.L. Strategies for Antibiotic Administration for Bowel Preparation Among Patients Undergoing Elective Colorectal Surgery: A Network Meta-analysis. JAMA Surg. 2022, 157, 34–41. [Google Scholar] [CrossRef]
  13. Snijders, H.S.; Wouters, M.W.J.M.; Van Leersum, N.J.; Kolfschoten, N.E.; Henneman, D.; De Vries, A.C.; Tollenaar, R.A.E.M.; Bonsing, B.A. Meta-analysis of the risk for anastomotic leakage, the postoperative mortality caused by leakage in relation to the overall postoperative mortality. Eur. J. Surg. Oncol. 2012, 38, 1013–1019. [Google Scholar] [CrossRef]
  14. Turrentine, F.E.; Denlinger, C.E.; Simpson, V.B.; Garwood, R.A.; Guerlain, S.; Agrawal, A.; Friel, C.M.; Lapar, D.J.; Stukenborg, G.J.; Jones, R.S. Morbidity, mortality, cost, and survival estimates of gastrointestinal anastomotic leaks. J. Am. Coll. Surg. 2015, 220, 195–206. [Google Scholar] [CrossRef]
  15. Mulders, R.J.; de Git, K.C.G.; Schéle, E.; Dickson, S.L.; Sanz, Y.; Adan, R.A.H. Microbiota in obesity: Interactions with enteroendocrine, immune and central nervous systems. Obes. Rev. 2018, 19, 435–451. [Google Scholar] [CrossRef] [PubMed]
  16. Leoni, G.; Neumann, P.A.; Sumagin, R.; Denning, T.L.; Nusrat, A. Wound repair: Role of immune-epithelial interactions. Mucosal Immunol. 2015, 8, 959–968. [Google Scholar] [CrossRef] [PubMed]
  17. Alam, A.; Leoni, G.; Quiros, M.; Wu, H.; Desai, C.; Nishio, H.; Jones, R.M.; Nusrat, A.; Neish, A.S. The microenvironment of injured murine gut elicits a local pro-restitutive microbiota. Nat. Microbiol. 2016, 1, 15021. [Google Scholar] [CrossRef] [PubMed]
  18. Shogan, B.D.; Belogortseva, N.; Luong, P.M.; Zaborin, A.; Lax, S.; Bethel, C.; Ward, M.; Muldoon, J.P.; Singer, M.; An, G.; et al. Collagen degradation and MMP9 activation by Enterococcus faecalis contribute to intestinal anastomotic leak. Sci. Transl. Med. 2015, 7, 286ra268. [Google Scholar] [CrossRef]
  19. Krezalek, M.A.; Hyoju, S.; Zaborin, A.; Okafor, E.; Chandrasekar, L.; Bindokas, V.; Guyton, K.; Montgomery, C.P.; Daum, R.S.; Zaborina, O.; et al. Can Methicillin-resistant Staphylococcus aureus Silently Travel from the Gut to the Wound and Cause Postoperative Infection? Modeling the “trojan Horse Hypothesis”. Ann. Surg. 2018, 267, 749–758. [Google Scholar] [CrossRef]
  20. Cani, P.D.; Bibiloni, R.; Knauf, C.; Aurelie, W.; Neyrinck, A.M.; Delzenne, N.M.; Burcelin, R. Changes in Gut Microbiota Control Metabolic Diet—Induced Obesity and Diabetes in Mice. Diabetes 2008, 57, 1470–1481. [Google Scholar] [CrossRef]
  21. Nichols, R.L.; Broido, P.; Condon, R.E.; Gorbach, S.L.; Nyhus, L.M. Effect of preoperative neomycin erythromycin intestinal preparation on the incidence of infectious complications following colon surgery. Ann. Surg. 1973, 178, 453–462. [Google Scholar] [CrossRef] [PubMed]
  22. Koskenvuo, L.; Lunkka, P.; Varpe, P.; Hyoty, M.; Satokari, R.; Haapamaki, C.; Lepisto, A.; Sallinen, V. Morbidity After Mechanical Bowel Preparation and Oral Antibiotics Prior to Rectal Resection: The MOBILE2 Randomized Clinical Trial. JAMA Surg. 2024, 159, 606–614. [Google Scholar] [CrossRef] [PubMed]
  23. Scarborough, J.E.; Mantyh, C.R.; Sun, Z.; Migaly, J. Combined mechanical and oral antibiotic bowel preparation reduces incisional surgical site infection and anastomotic leak rates after elective colorectal resection: An analysis of colectomy-targeted ACS NSQIP. Ann. Surg. 2015, 262, 331–337. [Google Scholar] [CrossRef]
  24. Midura, E.F.; Jung, A.D.; Hanseman, D.J.; Dhar, V.; Shah, S.A.; Rafferty, J.F.; Davis, B.R.; Paquette, I.M. Combination oral and mechanical bowel preparations decreases complications in both right and left colectomy. Surgery 2018, 163, 528–534. [Google Scholar] [CrossRef]
  25. McSorley, S.T.; Steele, C.W.; McMahon, A.J. Meta-analysis of oral antibiotics, in combination with preoperative intravenous antibiotics and mechanical bowel preparation the day before surgery, compared with intravenous antibiotics and mechanical bowel preparation alone to reduce surgical-site infec. BJS Open 2018, 2, 185–194. [Google Scholar] [CrossRef]
  26. Moghadamyeghaneh, Z.; Hanna, M.H.; Carmichael, J.C.; Mills, S.D.; Pigazzi, A.; Nguyen, N.T.; Stamos, M.J. Nationwide analysis of outcomes of bowel preparation in colon surgery. J. Am. Coll. Surg. 2015, 220, 912–920. [Google Scholar] [CrossRef]
  27. Ilhan, Z.E.; DiBaise, J.K.; Isern, N.G.; Hoyt, D.W.; Marcus, A.K.; Kang, D.-W.; Crowell, M.D.; Rittmann, B.E.; Krajmalnik-Brown, R. Distinctive microbiomes and metabolites linked with weight loss after gastric bypass, but not gastric banding. ISME J. 2017, 11, 2047–2058. [Google Scholar] [CrossRef]
  28. Zheng, L.; Kelly, C.J.; Battista, K.D.; Schaefer, R.; Lanis, J.M.; Alexeev, E.E.; Wang, R.X.; Onyiah, J.C.; Kominsky, D.J.; Colgan, S.P. Microbial-Derived Butyrate Promotes Epithelial Barrier Function through IL-10 Receptor-Dependent Repression of Claudin-2. J. Immunol. 2017, 199, 2976–2984. [Google Scholar] [CrossRef]
  29. Chen, G.; Ran, X.; Li, B.; Li, Y.; He, D.; Huang, B.; Fu, S.; Liu, J.; Wang, W. Sodium Butyrate Inhibits Inflammation and Maintains Epithelium Barrier Integrity in a TNBS-induced Inflammatory Bowel Disease Mice Model. EBioMedicine 2018, 30, 317–325. [Google Scholar] [CrossRef]
  30. Nalluri, H.; Kizy, S.; Ewing, K.; Luthra, G.; Leslie, D.B.; Bernlohr, D.A.; Sadowsky, M.J.; Ikramuddin, S.; Khoruts, A.; Staley, C.; et al. Peri-operative antibiotics acutely and significantly impact intestinal microbiota following bariatric surgery. Sci. Rep. 2020, 10, 20340. [Google Scholar] [CrossRef]
  31. Nalluri-Butz, H.; Bobel, M.C.; Nugent, J.; Boatman, S.; Emanuelson, R.; Melton-Meaux, G.; Madoff, R.D.; Jahansouz, C.; Staley, C.; Gaertner, W.B. A pilot study demonstrating the impact of surgical bowel preparation on intestinal microbiota composition following colon and rectal surgery. Sci. Rep. 2022, 12, 10559. [Google Scholar] [CrossRef] [PubMed]
  32. Boatman, S.; Kaiser, T.; Nalluri-Butz, H.; Khan, M.H.; Dietz, M.; Kohn, J.; Johnson, A.J.; Gaertner, W.B.; Staley, C.; Jahansouz, C. Diet-induced shifts in the gut microbiota influence anastomotic healing in a murine model of colonic surgery. Gut Microbes 2023, 15, 2283147. [Google Scholar] [CrossRef] [PubMed]
  33. Boatman, S.; Khan, M.H.; Ganesan, N.; Nalluri-Butz, H.; Kohn, J.; Troester, A.; Ziegert, Z.; Madoff, R.; Gaertner, W.B.; Jahansouz, C.; et al. Anastomotic leak occurs independently from microbiota shifts associated with surgical bowel preparation. Sci. Rep. 2024, 14, 21711. [Google Scholar] [CrossRef] [PubMed]
  34. Kunath, B.J.; De Rudder, C.; Laczny, C.C.; Letellier, E.; Wilmes, P. The oral-gut microbiome axis in health and disease. Nat. Rev. Microbiol. 2024, 22, 791–805. [Google Scholar] [CrossRef]
  35. Wang, S.; Rubio, L.; Duncan, S.; Donachie, G.; Holtrop, G.; Lo, G.; Farquharson, F.; Wagner, J.; Parkhill, J.; Louis, P.; et al. Pivotal Roles for pH, Lactate, and Lactate-Utilizing Bacteria in the Stability of a Human Colonic Microbial Ecosystem. mSystems 2020, 5, e00645-20. [Google Scholar] [CrossRef]
  36. Litvak, Y.; Byndloss, M.X.; Baumler, A.J. Colonocyte metabolism shapes the gut microbiota. Science 2018, 362, eaat9076. [Google Scholar] [CrossRef]
  37. Rivera-Chavez, F.; Zhang, L.F.; Faber, F.; Lopez, C.A.; Byndloss, M.X.; Olsan, E.E.; Xu, G.; Velazquez, E.M.; Lebrilla, C.B.; Winter, S.E.; et al. Depletion of Butyrate-Producing Clostridia from the Gut Microbiota Drives an Aerobic Luminal Expansion of Salmonella. Cell Host Microbe 2016, 19, 443–454. [Google Scholar] [CrossRef]
  38. Fung, T.C.; Olson, C.A.; Hsiao, E.Y. Interactions between the microbiota, immune and nervous systems in health and disease. Nat. Neurosci. 2017, 20, 145–155. [Google Scholar] [CrossRef]
  39. Parada Venegas, D.; De la Fuente, M.K.; Landskron, G.; Gonzalez, M.J.; Quera, R.; Dijkstra, G.; Harmsen, H.J.M.; Faber, K.N.; Hermoso, M.A. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front. Immunol. 2019, 10, 277. [Google Scholar] [CrossRef]
  40. Mj, O.; Turner, G.A.; Sulit, A.; Frizelle, F.; Purcell, R. Distinct changes in the colonic microbiome associated with acute diverticulitis. Colorectal Dis. 2022, 24, 1591–1601. [Google Scholar] [CrossRef]
  41. Anthony, W.E.; Wang, B.; Sukhum, K.V.; D’Souza, A.W.; Hink, T.; Cass, C.; Seiler, S.; Reske, K.A.; Coon, C.; Dubberke, E.R.; et al. Acute and persistent effects of commonly used antibiotics on the gut microbiome and resistome in healthy adults. Cell Rep. 2022, 39, 110649. [Google Scholar] [CrossRef]
  42. Palleja, A.; Mikkelsen, K.H.; Forslund, S.K.; Kashani, A.; Allin, K.H.; Nielsen, T.; Hansen, T.H.; Liang, S.; Feng, Q.; Zhang, C.; et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 2018, 3, 1255–1265. [Google Scholar] [CrossRef]
  43. Ahn, J.; Sinha, R.; Pei, Z.; Dominianni, C.; Wu, J.; Shi, J.; Goedert, J.J.; Hayes, R.B.; Yang, L. Human gut microbiome and risk for colorectal cancer. J. Natl. Cancer Inst. 2013, 105, 1907–1911. [Google Scholar] [CrossRef] [PubMed]
  44. Zepeda-Rivera, M.; Minot, S.S.; Bouzek, H.; Wu, H.; Blanco-Miguez, A.; Manghi, P.; Jones, D.S.; LaCourse, K.D.; Wu, Y.; McMahon, E.F.; et al. A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche. Nature 2024, 628, 424–432. [Google Scholar] [CrossRef] [PubMed]
  45. Conde-Perez, K.; Aja-Macaya, P.; Buetas, E.; Trigo-Tasende, N.; Nasser-Ali, M.; Rumbo-Feal, S.; Nion, P.; Arribas, E.M.; Estevez, L.S.; Otero-Alen, B.; et al. The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis. Mol. Oncol. 2024, 18, 1093–1122. [Google Scholar] [CrossRef] [PubMed]
  46. Liao, C.; Rolling, T.; Djukovic, A.; Fei, T.; Mishra, V.; Liu, H.; Lindberg, C.; Dai, L.; Zhai, B.; Peled, J.U.; et al. Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes. Nat. Microbiol. 2024, 9, 1555–1565. [Google Scholar] [CrossRef]
  47. Abbas, M.; Gaia, N.; Buchs, N.C.; Delaune, V.; Girard, M.; Andrey, D.O.; Meyer, J.; Schrenzel, J.; Ris, F.; Harbarth, S.; et al. Changes in the gut bacterial communities in colon cancer surgery patients: An observational study. Gut Pathog. 2022, 14, 2. [Google Scholar] [CrossRef]
  48. Sánchez-Alcoholado, L.; Ramos-Molina, B.; Otero, A.; Laborda-Illanes, A.; Ordóñez, R.; Medina, J.A.; Gómez-Millán, J.; Queipo-Ortuño, M.I. The role of the gut microbiome in colorectal cancer development and therapy response. Cancers 2020, 12, 1406. [Google Scholar] [CrossRef]
  49. Thomas, A.M.; Manghi, P.; Asnicar, F.; Pasolli, E.; Armanini, F.; Zolfo, M.; Beghini, F.; Manara, S.; Karcher, N.; Pozzi, C.; et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 2019, 25, 667–678. [Google Scholar] [CrossRef]
  50. Wirbel, J.; Pyl, P.T.; Kartal, E.; Zych, K.; Kashani, A.; Milanese, A.; Fleck, J.S.; Voigt, A.Y.; Palleja, A.; Ponnudurai, R.; et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 2019, 25, 679–689. [Google Scholar] [CrossRef]
  51. Bullman, S.; Pedamallu, C.S.; Sicinska, E.; Clancy, T.E.; Zhang, X.; Cai, D.; Neuberg, D.; Huang, K.; Guevara, F.; Nelson, T.; et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 2017, 358, 1443–1448. [Google Scholar] [CrossRef] [PubMed]
  52. Weiss, S.; Xu, Z.Z.; Peddada, S.; Amir, A.; Bittinger, K.; Gonzalez, A.; Lozupone, C.; Zaneveld, J.R.; Vazquez-Baeza, Y.; Birmingham, A.; et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 2017, 5, 27. [Google Scholar] [CrossRef] [PubMed]
  53. McMurdie, P.J.; Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 2014, 10, e1003531. [Google Scholar] [CrossRef] [PubMed]
  54. Bratzler, D.W.; Dellinger, E.P.; Olsen, K.M.; Perl, T.M.; Auwaerter, P.G.; Bolon, M.K.; Fish, D.N.; Napolitano, L.M.; Sawyer, R.G.; Slain, D.; et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am. J. Health Syst. Pharm. 2013, 70, 195–283. [Google Scholar] [CrossRef]
  55. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Huntley, J.; Fierer, N.; Owens, S.M.; Betley, J.; Fraser, L.; Bauer, M.; et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012, 6, 1621–1624. [Google Scholar] [CrossRef]
  56. Gohl, D.M.; Vangay, P.; Garbe, J.; MacLean, A.; Hauge, A.; Becker, A.; Gould, T.J.; Clayton, J.B.; Johnson, T.J.; Hunter, R.; et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 2016, 34, 942–949. [Google Scholar] [CrossRef]
  57. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef]
  58. Staley, C.; Kaiser, T.; Vaugh, B.P.; Graiziger, C.T.; Hamilton, M.J.; Khoruts, A.; Sadowsky, M.J. Predicting recurrence of Clostridium difficile infection following encapsulated fecal microbiota transplantation. Microbiome 2018, 6, 166. [Google Scholar] [CrossRef]
  59. Pruesse, E.; Quast, C.; Knittel, K.; Fuchs, B.M.; Ludwig, W.G.; Peplies, J.; Glockner, F.O. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007, 35, 7188–7196. [Google Scholar] [CrossRef]
  60. Huse, S.M.; Welch, D.M.; Morrison, H.G.; Sogin, M.L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 2010, 12, 1889–1898. [Google Scholar] [CrossRef]
  61. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef]
  62. Westcott, S.L.; Schloss, P.D. OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units. mSphere 2017, 2, e00073-17. [Google Scholar] [CrossRef] [PubMed]
  63. Cole, J.R.; Wang, Q.; Cardenas, E.; Fish, J.; Chai, B.; Farris, R.J.; Kulam-Syed-Mohideen, A.S.; McGarrell, D.M.; Marsh, T.; Garrity, G.M.; et al. The Ribosomal Database Project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009, 37, D141–D145. [Google Scholar] [CrossRef] [PubMed]
  64. Gihring, T.M.; Green, S.J.; Schadt, C.W. Massively parallel rRNA gene sequencing exacerbates the potential for biased community diversity comparisons due to variable library sizes. Environ. Microbiol. 2012, 14, 285–290. [Google Scholar] [CrossRef] [PubMed]
  65. Knights, D.; Kuczynski, J.; Charlson, E.S.; Zaneveld, J.; Mozer, M.C.; Collman, R.G.; Bushman, F.D.; Knight, R.; Kelley, S.T. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 2011, 8, 761–763. [Google Scholar] [CrossRef]
  66. Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication; The University of Illinois Press: Urbana, IL, USA, 1949; 117p. [Google Scholar]
  67. Bray, J.R.; Curtis, J.T. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol. Monogr. 1957, 27, 325. [Google Scholar] [CrossRef]
  68. Anderson, M.J.; Willis, T.J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 2003, 84, 511–525. [Google Scholar] [CrossRef]
  69. Clarke, K.R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 1993, 18, 117–143. [Google Scholar] [CrossRef]
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Figure 1. (A) Distribution of predominant genera (or most resolved classification) among patients receiving colonoscopy, non-resectional surgery, or resectional surgery over the course of the study; (f) denotes classification beyond family level was not possible. (B–D) Principal coordinate analysis of Bray–Curtis distances in colonoscopy (B), non-resectional (C), and resectional (D) cohorts at baseline, POD0, POD10, POD30, and POD180. Panels (B–D) highlight functionally distinct bacterial groups and are separated to emphasize specific trends in microbial disruption and recovery.
Figure 1. (A) Distribution of predominant genera (or most resolved classification) among patients receiving colonoscopy, non-resectional surgery, or resectional surgery over the course of the study; (f) denotes classification beyond family level was not possible. (B–D) Principal coordinate analysis of Bray–Curtis distances in colonoscopy (B), non-resectional (C), and resectional (D) cohorts at baseline, POD0, POD10, POD30, and POD180. Panels (B–D) highlight functionally distinct bacterial groups and are separated to emphasize specific trends in microbial disruption and recovery.
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Figure 2. Abundances of predominant genera of patients prior to and up to POD180 following colonoscopy, non-resectional surgery, or resectional surgery. (A) Relative abundances of Bacteroides, Phocaeicola, and Alistipes. (B) Relative abundances of Ruminococcaceae, Blautia, and Faecalibacterium. (C) Relative abundances of Streptococcus and Enterococcus.
Figure 2. Abundances of predominant genera of patients prior to and up to POD180 following colonoscopy, non-resectional surgery, or resectional surgery. (A) Relative abundances of Bacteroides, Phocaeicola, and Alistipes. (B) Relative abundances of Ruminococcaceae, Blautia, and Faecalibacterium. (C) Relative abundances of Streptococcus and Enterococcus.
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Figure 3. Distribution of predominant genera in diverticulitis and cancer patients who underwent resectional surgery at baseline, intraoperative (POD0), and postoperative (POD10, POD30, and POD180) time points.
Figure 3. Distribution of predominant genera in diverticulitis and cancer patients who underwent resectional surgery at baseline, intraoperative (POD0), and postoperative (POD10, POD30, and POD180) time points.
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Figure 4. Canonical correspondence analysis relating genera, SCFAs, and IgA among samples from the colonoscopy cohort at baseline and POD10. Time points are shown as circles, SCFAs as diamonds, and IgA as a square. Taxa are colored by phyla: green—Bacillota; blue—Bacteroidota; red—Pseudomonadota.
Figure 4. Canonical correspondence analysis relating genera, SCFAs, and IgA among samples from the colonoscopy cohort at baseline and POD10. Time points are shown as circles, SCFAs as diamonds, and IgA as a square. Taxa are colored by phyla: green—Bacillota; blue—Bacteroidota; red—Pseudomonadota.
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Table 1. Patient demographic and clinical data. Continuous data presented as mean ± standard deviation.
Table 1. Patient demographic and clinical data. Continuous data presented as mean ± standard deviation.
Colonoscopy (n = 30)Resectional (n = 26)Non-Resectional (n = 25)p-Value
Male sex, n (%)14 (47)18 (69)7 (28)0.011
Age (years)52.8 ± 15.457.2 ± 11.062.9 ± 12.20.017
BMI (kg/m2)26.5 ± 5.128.9 ± 5.529.2 ± 7.20.178
Albumin4.0 ± 0.43.9 ± 0.44.1 ± 0.50.164
Charlson comorbidity index1.9 ± 2.32.6 ± 2.62.7 ± 2.00.423
Smoking status, n (%) 0.037
Active smoker1 (3)5 (19)5 (20)-
Former smoker7 (23)9 (35)11 (44)-
Non-smoker22 (73)12 (46)9 (36)-
Chronic narcotics, n (%)0 (0)1 (4)1 (4)0.390
History of diabetes mellitus, n (%)0 (0)5 (19)4 (16)0.011
Metformin0 (0)4 (15)3 (12)-
Preoperative bowel prep, n (%) <0.0001
MiraLAX and Mag Citrate15 (50)25 (96)10 (40)-
GoLYTELY13 (43)1 (4)0 (0)-
MiraLAX and Gatorade2 (7)0 (0)14 (56)
Fleet enemas0 (0)0 (0)1 (4)-
Postoperative complications, n (%) 0.124
None30 (100)20 (77)23 (92)-
Anastomotic leak/sepsis-2 (8)--
Urinary tract infection-2 (8)1 (4)-
Surgical site infection----
Deep organ space infection-1 (4)--
Pneumonia----
Ileus requiring nasogastric tube-1 (4)--
Anemia requiring transfusion----
Acute renal failure-1 (4)--
Deep venous thrombosis----
30-day hospital readmission, n (%)1 (3)2 (8)1 (4)0.742
Table 2. Perioperative details and clinical history of surgery patients.
Table 2. Perioperative details and clinical history of surgery patients.
Group B (Resectional)
n (%)		Group C (Non-Resectional)
n (%)
Preoperative enteral antibiotics
Neomycin plus metronidazole26 (100)25 (100)
Perioperative intravenous antibiotics
Ciprofloxacin plus metronidazole10 (38)7 (28)
Cefazolin plus metronidazole7 (27)8 (32)
Cefotetan5 (19)-
Ertapenem4 (16)10 (40)
Operative procedure
Right colectomy7 (27)-
Left colectomy1 (4)-
Sigmoid colectomy9 (35)-
Sigmoid colectomy, diverting loop ileostomy1 (4)-
Low anterior resection5 (19)-
Ventral rectopexy-13 (52)
Transanal excision-12 (48)
History of colorectal cancer12 (46)12 (48)
Pathology
Adenocarcinoma11 (92)8 (67)
Neuroendocrine tumor1 (8)-
Serrated adenoma with high-grade dysplasia-2 (16)
Tubulovillous adenoma with high-grade dysplasia-2 (16)
Pathologic Stage
0-4 (33)
I2 (17)4 (33)
IIA4 (33)-
IIIA3 (25)-
IIIB3 (30)-
Not staged-4 (33)
Diverticulitis History
Recurrent, uncomplicated5 (50)-
Complicated5 (50)-
Table 3. Fecal SCFA concentrations (µM/g) in patients undergoing colonoscopy, resectional, or non-resectional surgery at baseline and POD10.
Table 3. Fecal SCFA concentrations (µM/g) in patients undergoing colonoscopy, resectional, or non-resectional surgery at baseline and POD10.
ProcedureTime Point (n)AcetatePropionateIsobutyrateButyrateIsovalerateValerate2-Methylbutyrate
ColonoscopyBaseline (22)23.19 (16.61) AB20.19 (10.81) B3.40 (2.22) AB17.52 (8.86) A2.32 (1.24) AB3.30 (1.59) A1.90 (1.15) A
POD10 (16)23.37 (25.40) AB15.71 (10.25) AB3.29 (2.39) ABC15.26 (10.11) A2.05 (1.70) ABC2.04 (1.84) AB1.58 (1.41) AB
ResectionBaseline (16)26.41 (17.94) A25.68 (13.34) A3.54 (3.49) A18.89 (12.06) A3.40 (1.76) A3.70 (2.21) A2.18 (0.79) A
POD10 (14)5.06 (4.90) B5.31 (4.81) B2.21 (4.23) B2.21 (4.23) B0.68 (1.17) C0.57 (0.79) C0.39 (0.69) C
Non-resectionBaseline (16)16.78 (13.96) AB15.65 (11.18) AB2.90 (2.89) BC12.06 (6.56) A1.66 (1.11) BC2.53 (1.85) BC1.16 (0.69) AB
POD10 (19)17.83 (20.42) AB7.77 (8.57) B2.65 (2.98) BC9.35 (10.11) AB1.55 (1.68) BC0.71 (1.17) BC0.78 (1.33) BC
Blanks(5)1.25 (0.12)0.04 (0.01)0.03 (0.01)0.01 (0.01)0.01 (0.01)0.01 (0.01)0.01 (0.01)
p-value 0.024<0.0001<0.001<0.001<0.0001<0.0001<0.0001
Concentrations are mean (SD) of SCFA (µM/g stool). Values sharing the same superscript letter do not differ significantly (Tukey’s post hoc test, p > 0.05). Values with different letters indicate statistically significant differences.
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MDPI and ACS Style

Kohn, J.; Troester, A.; Ziegert, Z.; Frebault, J.; Boatman, S.; Martell, M.; Nalluri-Butz, H.; Bobel, M.C.; Goffredo, P.; Johnson, A.J.; et al. The Role of Surgical and Perioperative Factors in Shaping Gut Microbiome Recovery After Colorectal Surgery. Antibiotics 2025, 14, 881. https://doi.org/10.3390/antibiotics14090881

AMA Style

Kohn J, Troester A, Ziegert Z, Frebault J, Boatman S, Martell M, Nalluri-Butz H, Bobel MC, Goffredo P, Johnson AJ, et al. The Role of Surgical and Perioperative Factors in Shaping Gut Microbiome Recovery After Colorectal Surgery. Antibiotics. 2025; 14(9):881. https://doi.org/10.3390/antibiotics14090881

Chicago/Turabian Style

Kohn, Julia, Alexander Troester, Zachary Ziegert, Julia Frebault, Sonja Boatman, Maria Martell, Harika Nalluri-Butz, Matthew C. Bobel, Paolo Goffredo, Abigail J. Johnson, and et al. 2025. "The Role of Surgical and Perioperative Factors in Shaping Gut Microbiome Recovery After Colorectal Surgery" Antibiotics 14, no. 9: 881. https://doi.org/10.3390/antibiotics14090881

APA Style

Kohn, J., Troester, A., Ziegert, Z., Frebault, J., Boatman, S., Martell, M., Nalluri-Butz, H., Bobel, M. C., Goffredo, P., Johnson, A. J., Jahansouz, C., Staley, C., & Gaertner, W. B. (2025). The Role of Surgical and Perioperative Factors in Shaping Gut Microbiome Recovery After Colorectal Surgery. Antibiotics, 14(9), 881. https://doi.org/10.3390/antibiotics14090881

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