Analysis of gut microbiota in super donors for fecal microbiota transplantation and isolated gut commensal bacteria of inhibition against Clostridioides difficile
Article information
Abstract
Background/Aims
Fecal microbiota transplantation (FMT) is increasingly recognized as an alternative to antibiotics for treating recurrent Clostridioides difficile infection. The success of FMT heavily depends on the appropriate selection of donors, encompassing factors such as diet patterns, lifestyle, environmental exposures, and intestinal microbiota diversity.
Methods
A potential super donor was identified from 5 healthy adults and provided stool samples periodically over 2 years (2021–2022). The samples underwent 16S rRNA sequencing via the Illumina MiSeq platform, and microbial diversity was analyzed using QIIME 2 in comparison with 152 healthy individuals.
Results
The stool microbiome composition of the potential super donor remained stable without significant changes over a 2-year period. Both alpha and beta diversity analyses revealed significant differences between the super donor and the 152 healthy individuals. The super donor exhibited significantly higher microbial diversity based on alpha diversity metrics (P< 0.0001) and distinct compositional profiles as shown by beta diversity. Linear discriminant analysis effect size (LEfSe) analysis indicated that Faecalibacterium and Prevotella strains comprised a significant proportion, with notable differences in relative abundance patterns (P< 0.05). Furthermore, 7 bacterial species were isolated from the super donor, all of which demonstrated inhibitory effects on the growth of C. difficile in vitro.
Conclusions
These findings suggest that selecting donors with specific microbiota profiles, particularly those exhibiting higher microbial diversity, may potentially contribute to the inhibition of C. difficile, and further clinical studies are warranted to validate these findings.
INTRODUCTION
Clostridioides difficile is a Gram-positive, spore-forming, obligate anaerobic bacterium [1], and produces toxins A and B, which are major virulence factors responsible for the symptoms of C. difficile infection (CDI), causing both organ damage and severe intestinal injury [2]. The misuse of antibiotics is a well-recognized risk factor for CDI, as it induces gut dysbiosis by reducing the diversity and abundance of the commensal gut microbiota, thereby promoting the overgrowth of C. difficile. CDI is clinically characterized by a spectrum of symptoms ranging from mild to profuse diarrhea, accompanied by abdominal pain, fever, nausea, vomiting, and weakness [3]. Firstline treatments with antibiotics such as vancomycin and metronidazole for CDI are recommended; however, recurrence rates are observed to be approximately 25%.
While many researchers suggested that intestinal bacteria play an important role in the pathogenesis and resolution of CDI, it is still not clear which gut bacterial strains constitute the key microbiota that prevents CDI. Several studies have demonstrated that various commensal intestinal microbes isolated from healthy donors can inhibit the growth of C. difficile [4] and one of the main commensal intestinal microbes, Akkermansia muciniphila, has been notably reported to attenuate disease severity in C. difficile-infected mice model [5]. Moreover, the cell-free supernatants of bacterial consortia were sufficient to protect mice from C. difficile challenge [6,7].
Fecal microbiota transplantation (FMT) has been recognized as a highly effective alternative to antibiotics for treating recurrent CDI, achieving primary and secondary cure rates of approximately 80% to 90% [8,9]. However, FMT carries risks, including gastrointestinal adverse events such as abdominal pain, bloating, and diarrhea [10], as well as cases of transmission of antibiotic-resistant pathogens, leading to fatal outcomes [11].
Some evidence indicated that selecting appropriate donors and profiling donor microbiota composition significantly impact the success of FMT, with so-called “super donors” exhibiting particularly higher gut microbiome diversity than other donors within general population of healthy individuals, which increases therapeutic efficacy [12-14]. The variation in the efficacy of FMT depending on donor characteristics has been reported through numerous clinical trials [15], underscoring the need to optimize donor selection criteria for the improvement of FMT efficacy.
Enterotypes have been classified into 3 types: Prevotella, Ruminococcaceae, and Bacteroides. Among these, Prevotella and Bacteroides are predominant in the Korean population, likely reflecting their traditional dietary patterns rich in dietary fiber [16]. However, Enterotype stability may be compromised by exposure to various external factors, such as dietary intake, antibiotic administration, and stress [17], which may influence the long-term therapeutic effectiveness of FMT.
To address this concern, we conducted a longitudinal study to monitor temporal changes in the gut microbiota of potential donors with the aim of identifying stable and effective donors for FMT. Through a rigorous screening process and 16S rRNA-based next-generation sequencing analysis, we longitudinally tracked the gut microbiome profiles of carefully selected healthy Korean donors. Additionally, we identified specific microbial strains isolated from healthy donors that exhibited potential antimicrobial activity against C. difficile. We propose that monitoring individual microbiome stability over time may be critical for optimizing donor selection, ensuring consistency and efficacy of FMT therapy.
METHODS
1. Donor Screening and Analysis
Healthy donors were recruited with the approval of the Seoul National University Bundang Hospital Institutional Review Board (IRB No. B-1810/497-308). Written informed consent was obtained from all donors. The selection of healthy donors was based on criteria such as regular questionnaires, body mass index (BMI), age, presence of medical conditions, personal history, and dietary habits. Only donors who pass the screening tests listed in Table 1 are eligible for donation [18]. Finally, 5 donors can be selected through screening tests, including blood tests and stool tests. We defined the criteria for selecting a potential super donor among the 5 candidates as an individual who maintained a regular lifestyle with consistent physical activity and followed a dietary pattern rich in dietary fiber, with a BMI within the normal range. In addition, 16S rRNA sequencing analysis revealed a high level of gut microbial diversity and a relatively high abundance of beneficial taxa such as Faecalibacterium and Prevotella, which have been previously studied [19-22]. Fecal samples from the super donor were collected at intervals of 5 to 10 days over a 2-year period, with 27 samples collected in 2021 and 31 samples in 2022, resulting in a total of 58 samples. To monitor the super donor’s health status and dietary habits, a Food Frequency Questionnaire (FFQ) was administered every 3 months, accompanied by individual interviews. Fecal samples collected in 2021 are referred to as potential super donor 21, whereas those collected in 2022 are designated as the potential super donor 22, in order to demonstrate the temporal stability of the super donor gut microbiota across the 2 years. Throughout this period, the donor’s health status and dietary habits were closely assessed, and the gut microbial diversity was systematically analyzed. Furthermore, the gut microbiome data of 152 healthy individuals who were enrolled in the Korean gut microbiome bank study (No. B-1701-380-304), whose purpose is to collect stool samples from healthy Korean adults and analyze their gut microbiome, were used for comparative analysis with those of the super donor 21 and the super donor 22.
Donor Screening Criteria
2. DNA Extraction
Genomic DNA from the 5 healthy donors was extracted using the Maxwell RSC PureFood GMO and Authentication Kit (Promega, Madison, WI, USA). Initially, 800 μL of DNA/RNA Shield solution was added to Zymo Research Bashing Bead Tubes (0.1 & 0.5 mm; Zymo Research, Irvine, CA, USA). Subsequently, 30–200 mg of feces sample was added to the tubes. The tubes were then mounted onto a Mini-Bead Beater 24 (Biospec Products, Bartlesville, OK, USA), ensuring they were balanced. Bead beating was performed at 3,400 rpm for 5 minutes to homogenize the samples. After bead beating, the tubes were centrifuged at 12,000 rpm for 3 minutes. Next, 500 μL of the supernatant from each tube was transferred into well 1 of the Maxwell RSC PureFood Pathogen Cartridge. Prior to this, the cartridges were placed onto the Deck Tray of the Maxwell RSC 48 Instrument (Promega). Following the transfer of the supernatant, 300 μL of Lysis Buffer and 30 μL of RNase were added to well 1, and a plunger was placed in well 8. Additionally, a 0.5 mL elution tube was placed in the Deck Tray of the Maxwell RSC 48 Instrument, and 50 μL of Elution Buffer was added to it. The PureFood Pathogen method was selected on the Maxwell RSC 48 Instrument, and the run was initiated. Upon completion of the run, the elution tube was centrifuged at 12,000 rpm for 1 minute. The supernatant was then transferred to a new 1.5 mL tube. The eluted DNA was stored at –20°C until used for library preparation.
3. Library Preparation and 16S rRNA Sequencing
The DNA library preparation was performed according to Illumina guide protocol #15044223 Rev.B. For the polymerase chain reaction (PCR) mixture, 2.5 μL of microbial DNA (5 ng/μL), 5 μL of Amplicon PCR Forward Primer (1 μM), 5 μL of Amplicon PCR Reverse Primer (1 μM), and 12.5 μL of 2x KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Roche, Pleasanton, CA, USA) were combined to a total volume of 25 μL. The PCR was performed according to Illumina guide protocol #15044223 Rev.B, using the following primers: 341F (5’ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG) and 805R (5’ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC).
The PCR conditions were as follows: initial denaturation at 95°C for 3 minutes, followed by 25 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, the final extension at 72°C for 5 minutes and a hold at 4°C. After the PCR reaction, the PCR product was cleaned up using AMPure XP beads (Beckman Coulter Inc., Brea, CA, USA). The final elution volume was adjusted to 50 μL, which was then transferred to a new 8-strip PCR tube or a 96-well PCR plate. Gel electrophoresis was performed to ensure that the DNA was amplified to the desired size (approximately 550 bp).
Subsequent to the completion of the amplicon PCR, an index PCR was performed. The PCR mixture consisted of 5 μL of template DNA, 5 μL of Nextera XT Index Primer 1 (N7xx), 5 μL of Nextera XT Index Primer 2 (S5xx), 25 μL of 2x KAPA HiFi HotStart ReadyMix, and 10 μL of PCR-grade water, resulting in a total volume of 50 μL. According to Illumina guide protocol #15044223 Rev.B, the Nextera XT Index Kit (Illumina, San Diego, CA, USA) was used to index the 16S amplicons.
After the PCR reaction, the product was cleaned up using AMPure XP beads. The final elution volume was set to 25 μL, which was then transferred to a new 8-strip PCR tube or a 96-well PCR plate. The concentration of the DNA library was measured using the Qubit 1X dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) with a Qubit Flex Fluorometer (Life Technologies Ltd., Paisley, UK). The size of the DNA library was determined using the High Sensitivity D1000 ScreenTape Assay (Agilent Technologies, Santa Clara, CA, USA) on the TapeStation 4150 platform (Agilent Technologies).
The library was normalized to 4 nM and pooled. The 4 nM library was denatured using 0.2 N NaOH and diluted to 8 pM with prechilled HT1 buffer (included in the MiSeq Reagent Kit v2 500-cycle Kit, Illumina). Similarly, the PhiX CONTROL V3 Kit (Illumina) was diluted to 4 nM, denatured with 0.2 N NaOH, and then diluted to 8 pM with prechilled HT1 buffer. 10% of denatured PhiX library, adjusted to the final concentration, was combined with the denatured and indexed library. This mixture was loaded into the Illumina MiSeq V2 reagent cartridge and sequenced on the Illumina MiSeq platform with a 500-cycle run.
4. Sequencing Data Analysis
Raw sequence reads from the FASTQ files generated by the MiSeq platform were denoised by filtering out low-quality and ambiguous bases. The Amplicon Sequence Variants method was applied using the DADA2 (v2022.8.0). Quality control ensured reads exceeded a Q30 quality score. For downstream analyses, QIIME 2 (v2022.8.3, https://github.com/qiime2/qiime2) was used for alpha diversity analysis, beta diversity analysis, and taxonomic analysis, with visualizations performed using Python scripts.
5. Bacterial Strains and Culture Conditions
Roseburia faecis BBH024, Clostridium leptum BBH026, Coprococcus eutactus BBH027, Dorea longicatena BBH029, Roseburia intestinalis BBH031, Agathobacter rectalis BBH034, and Faecalibacterium prausnitzii BBH036 isolated from feces of the potential super donor were used in this study. As previously reported, these bacterial genera are associated with recurrent CDI patients [23,24]. R. faecis KCTC 15739, R. intestinalis KCTC 15746, A. rectalis KCTC 5835, C. leptum KCTC 5155, D. longicatena KCTC 25294, C. difficile KCTC 5009 were purchased from Korea Collection for Type Cultures (KCTC). F. prausnitzii ATCC 27768 and C. eutactus ATCC 27759 were purchased from the American Type Culture Collection (ATCC). C. difficile KCTC 5009 were cultured in brain heart infusion (BHI) medium (BD, Franklin Lakes, NJ, USA) supplemented with 0.05% L-cysteine. R. faecis BBH024, C. leptum BBH026, D. longicatena BBH029 and A. rectalis BBH034 were cultured in DSM 104 (DSMZ; Leibniz Institute, Braunschweig, Germany) medium. R. intestinalis BBH031, and F. prausnitzii BBH036 were cultured in DSM 1611 (DSMZ; Leibniz Institute) medium. All bacterial cultures were incubated in an anaerobic chamber (5% CO2, 5% H2, 90% N2 condition) maintained at 37°C for 1–3 days separately.
6. Preparation of Cell-Free Supernatant
After incubation, bacterial cultures were washed three times in 1x phosphate-buffered saline (PBS) containing 0.05% L-cysteine (Sigma, St. Louis, MO, USA) by centrifugation, discarding only the supernatant after each wash. The cell pellets were resuspended in 1xPBS with 0.05% L-cysteine, and the optical density (OD) was measured using a spectrophotometer (ThermoFisher, Waltham, MA, USA). The suspensions were then adjusted to OD600 of 0.1 using 1xPBS. Each suspension (1% v/v) was inoculated into 30 mL of appropriate media in serum bottles and cultured anaerobically for 24–72 hours. Cell-free supernatants were collected by centrifugation at 5,000× g for 20 minutes at 4℃ under anaerobic conditions. The cell-free supernatants were divided into two 15 mL aliquots: one maintained at its original pH, and the other adjusted to pH 7 using NaOH. The supernatant was isolated from both types using 100 kDa centrifugal filter unit (Amicon Ultra-15 centrifugal filter unit; Millipore, Burlington, MA, USA), followed by filtration through 0.2 μm membrane filter (Dismic Disposable membrane filter unit; Advantec, Tokyo, Japan). The concentrated vesicles (approximately 800 μL) were stored at –80°C until use.
7. C. difficile Co-culture Assay Treated with Cells
Seven bacterial strains in the stationary phase were harvested by centrifugation, washed, resuspended, and adjusted to an OD 600 nm of 0.5 using 1xPBS. Each strain was then inoculated at a concentration of 1% (v/v) into 30 mL of BHI medium (BD) supplemented with 0.05% cysteine and 5% Yeast extract (BD) in a serum bottle. The experimental groups included C. difficile alone (control) and co-cultures with the following strains: R. faecis BBH024, C. leptum BBH026, C. eutactus BBH027, D. longicatena BBH029, R. intestinalis BBH031, A. rectalis BBH034, F. prausnitzii BBH036. After 24–48 hours of incubation anaerobically, the co-culture broths were serially diluted from 10–5 to 10–6, and plated in triplicate on BHI agar plates. After incubation, viable C. difficile colonies were enumerated and presented as colony forming unit (CFU)/mL.
8. C. difficile Co-culture Assay Treated with Cell-Free Supernatants
Following the previously described methods, after culture suspensions were centrifuged, washed, re-homogenized, and adjusted to OD 0.5 at 600 nm. The cell-free supernatants of the 7 selected strains prepared before needed to be thawed at room temperature. A 70 μL of the C. difficile sample followed by 700 μL of the supernatant each (1:10 ratio) was dispensed to 1.5 mL microcentrifuge tube and co-incubated for 24 hours under anaerobic conditions. Subsequently, 200 μL of each culture medium was transferred to a 96-well plate in triplicate wells. Antimicrobial activity of supernatant of the test strains against C. difficile was determined by measurement of the absorbance at OD 600 nm.
9. Statistical Analysis
Alpha diversity was assessed using the Kruskal–Wallis H test to evaluate significant differences among groups. Beta diversity, including a variation of abundant taxa between the samples was analyzed using pairwise permutational multivariate analysis of variance (PERMANOVA). Taxonomic classification was conducted using the SILVA database (version 132; https://www.arb-silva.de/). Differentially abundant genera between groups were identified using linear discriminant analysis effect size (LEfSe; version 1.1.2). Antibacterial analysis was independently conducted in triplicate, and the results were expressed as the mean±standard deviation. Intergroup differences were analyzed using one-way analysis of variance, followed by Tukey’s multiple comparisons test, utilizing Graph-Pad Prism version 10 (GraphPad Software, San Diego, CA, USA). Statistical significance was defined as P<0.05.
RESULTS
1. Donor Healthy Condition
A total of 5 healthy volunteers underwent a rigorous screening process to assess their eligibility as FMT donors. All candidates met the predefined inclusion criteria, including optimal health status, appropriate age range, and BMI within the recommended limits. Comprehensive stool and blood analyses confirmed the absence of infectious agents and systemic abnormalities, further supporting their suitability for donation. Additionally, all selected donors reported adherence to a structured lifestyle, characterized by the absence of smoking and alcohol consumption, alongside a nutritionally balanced diet. Among the 5 donors, the individual identified as the potential super donor was a 31-year-old male fitness trainer with a BMI of 24.4 kg/m2, who reported no history of smoking or alcohol consumption.
2. Diversity and Abundance of Microbiota
Among the 5 donors, Donor 5 was identified as a potential super donor, exhibiting the greatest level of gut microbiota diversity (Supplementary Fig. 1). Remarkably, the super donor consistently maintained a relatively stable gut microbiota composition over the 2 years, except for a few minor fluctuations (Supplementary Fig. 2). The alpha diversity of the gut microbiome was compared between fecal samples from the super donor, collected in 2021 and 2022, and those from other 152 healthy donors.
Shannon entropy, observed features, and Faith phylogenetic diversity indices were calculated, and group-wise comparisons were performed using the Kruskal–Wallis test implemented in QIIME 2. Alpha diversities of microbiota were significantly different between the longitudinal samples from the super donor and those from the healthy donors, which means that both species richness and evenness of the super donor’s samples were higher than those of the healthy donors’ samples (Fig. 1).
The super donor alpha and beta diversity compared with healthy individuals. (A) Shannon diversity, (B) observed features, and (C) Faith phylogenetic diversity. NS, not significant. Statistically significant; ***P<0.0005, ****P<0.00005.
The microbial structure of the super donor was compared to that of 152 healthy individuals using PERMANOVA tests. In beta diversity, principal coordinate analysis plot of the weighted UniFrac distance matrix based on operational taxonomic unit abundance indicated that while the gut microbiome composition between the super donor’s samples in 2021 and 2022 showed that there was no significant difference in gut microbiome composition between the super donor’s samples from 2021 and 2022, whereas a significant difference was observed between the super donor’s samples and those of the healthy individuals (PERMANOVA: P=0.133 for potential super donor 2021 vs. 2022; P=0.001, R2 =0.1771 for potential super donor 2021 and 2022 vs. healthy individuals) (Fig. 2). We confirmed that the dispersed distance of the super donor’s samples was significantly different from that of the healthy donors’ samples. In the case of the super donor in this study, sequencing of 16S rRNA reveals unique distributions of intestinal microorganisms compared to other healthy donors.
Principal coordinate analysis was performed on the microbiota using (A) weighted UniFrac and (B) unweighted UniFrac distances. Color coding represents differences among lines, while point shapes correspond to distinct temperature conditions. Red circles represent samples from the super donor (n=58), while blue circles indicate samples from healthy individuals (n=152). Greater distances between points reflect more divergent microbiota compositions, whereas closer proximity suggests more similar microbial profiles.
3. Taxa Comparison
There were distinct differences in relative abundance between a set of fecal samples from the potential super donor and the healthy donors at the phylum and genus levels. At the phylum level, Actinobacteria were more abundant in the healthy donors, while Bacteroidota were more abundant in the super donor (Fig. 3A). At the genus level, the super donor compared to the healthy donor was comprised of a higher relative abundance of Prevotella and Faecalibacterium but a lower relative abundance of Streptococcus, Romboutsia, Bifidobacterium, Bacteroides and Blautia. In the super donor, Prevotella and Faecalibacterium were accounted for more than 40% of the whole sequences, whereas Streptococcus, Romboutsia, Bifidobacterium, Bacteroides, and Blautia covered less than 20%. On the contrary, the 152 healthy individuals showed a total of 50% abundance of the latter group of genera but less than 10% abundance of Prevotella and Faecalibacterium.
Relative abundances of bacterial taxa at the genus and species levels were compared among the super donors 21 and 22 and the healthy individuals. (A) Phylum-level comparisons were conducted between the super donors and the healthy group, and (B) genus-level comparisons were also performed. The most abundant taxa across all samples are shown in the legend.
4. Genus Taxa Comparison of LEfSe Analysis
LEfSe was conducted to evaluate differences in microbial composition between the healthy control samples and the potential super donor 21 and potential super donor 22 independently. This analysis identified differentially abundant genera by comparing their relative abundances between the defined samples. At the genus level, the super donor 21 was characterized by increased abundances of Prevotella, Faecalibacterium, Dialister, and Coprococcus, whereas the healthy donors’ samples showed higher abundances of Bifidobacterium, Blautia, Romboutsia, and Anaerostipes (Fig. 4A). Also, a comparison of the super donor 22 showed a distinct enrichment profile compared to those of the healthy donors. Specifically, the former were enriched in Prevotella, Faecalibacterium, Dialister, and UCG-002, in contrast to the latter enriched in Blautia, Romboutsia, Collinsella, and Streptococcus (Fig. 4B). All taxa presented had a linear discriminant analysis score greater than 3.0 and were statistically significant (P<0.001).
Comparative analysis of LDA scores and significantly enriched taxa between the super donor and the healthy individuals. (A) The super donor 21 versus healthy group, (B) the super donor 22 versus healthy individuals. The analysis was conducted using the LEfSe method. LEfSe was limited to P<0.05 for genus analysis and LDA score >3.0. LDA, linear discriminant analysis; LEfSe, linear discriminant analysis effective size.
5. In vitro Inhibition Against C. difficile
When 7 selected strains from the potential super donor or their produced supernatants were added to an actively growing C. difficile, the inhibition of the growth of C. difficile was observed. The inhibitory activities of the tested bacterial isolates as well as their antimicrobial metabolites against C. difficile were expressed as CFU rate and absorbance relative to the growth of C. difficile, respectively. In particular, R. intestinalis BBH031 demonstrated significantly higher antimicrobial activity compared to the control group, whereas C. leptum BBH026, D. longicatena BBH029, A. rectalis BBH034, and the control group all exhibited antimicrobial activity against C. difficile (Fig. 5A). In the natural supernatant, BBH strains and their type strains demonstrated antimicrobial activity against C. difficile. However, following pH neutralization to 7.0, all the strains except Faecalibacterium prausnitzii maintained their inhibitory effects (Fig. 5B and C).
In vitro inhibition of Clostridioides difficile by isolated species, determined by CFU and ABS assays. (A) CFU counts of C. difficile were graphically represented for each individual co-culture experiment conducted in triplicate for 7 different test species. (B) Inhibition of C. difficile was graphically represented for each individual supernatant treatment of C. difficile experiment conducted in triplicate for each of the 7 species. (C) Five species supernatant-neutralized. Error bars depict the standard deviations from 3 independent experiments for each inhibition assay of individual species and were analyzed by the ANOVA at a value of 95% (P<0.05). CFU, colony forming unit; ABS, absorbance; ANOVA, analysis of variance; NS, not significant. Statistically significant; *P<0.05.
DISCUSSION
The exploration of potential donors with a robust and highly diverse gut microbiota is a critical factor for ensuring successful outcomes of FMT [15]. We defined the donor with the highest microbial diversity among 5 selected healthy donors as a potential “super donor.” In comparison to the other donors, the super donor maintained a more stable and evenly distributed abundance of Prevotella and Bifidobacterium (Supplementary Fig. 1). To further elucidate the distinctive characteristics of the super donor’s gut microbiota, we conducted a comparative analysis against a publicly available dataset of healthy Korean individuals. The super donor’s microbial profile was unique, characterized by a significantly enriched Prevotella population (Fig. 3). Prevotella species have been generally associated with high-carbohydrate and high-fiber diets, whereas Bacteroides species have been linked to high-protein and high-fat diets [25]. Furthermore, groups consuming diets associated with a high Prevotella/Bacteroides ratio have been shown to experience greater reductions in body weight and fat mass than those with a low Prevotella/Bacteroides ratio, suggesting potential metabolic benefits [26]. Additionally, Faecalibacterium was also present at a relatively high abundance. Previous studies have consistently reported reduced abundances of Prevotella, Roseburia, and Faecalibacterium spp. in CDI groups compared to controls. Also, butyrate-producing Faecalibacterium spp. correlate with a plant-based diet [27] and play pivotal roles in promoting intestinal health by the production of short-chain fatty acids (SCFAs) and maintenance of intestinal homeostasis and anti-inflammatory effect [28].
To evaluate the dietary habits of the potential super donor, FFQs were administered in December 2022 and April 2023, coinciding with the final fecal sample collections. Each FFQ assessed dietary intake over the preceding 3 months. Analysis of the follow-up FFQs revealed that the donor adhered to a balanced diet with a high intake of dietary fiber (Supplementary Data 1). However, due to the temporal gap between the FFQ assessments and the microbiome analyses, further studies are warranted to verify the potential associations between dietary patterns and the observed microbial profiles. This observation aligns with previous research demonstrating associations between dietary patterns and gut microbiota composition, highlighting the potential role of a balanced diet in promoting a beneficial gut microbial ecosystem [29]. High microbial diversity is generally considered a key indicator of a healthy gut environment, and elevated proportions of beneficial genera such as Prevotella and Faecalibacterium have been linked to various positive health effects. Nevertheless, several studies have indicated that increased gut microbial diversity does not inherently imply an improved microbial ecosystem [30]. Thus, careful interpretation that accounts for multiple influencing factors is warranted.
The human intestine harbors hundreds of thousands of types of bacterial species, and their symbiotic relationships in accordance with balance in the gut bacterial composition are significantly considered as a pivotal role for human health and diseases. Likewise, microbial compounds derived from the gut bacteria may represent a link to metabolic health and disease pathogenesis [31]. The potential mechanism of the high therapeutic efficacy of FMT could be antibacterial activities exerted by specific intestinal microbes transplanted during FMT that are known as producers of main and anti-clostridial metabolites, such as SCFAs and secondary bile acids [32-34].
In this study, we isolated 7 bacterial strains from a potential “super donor”. A subset of BBH strains and their type strains used as controls exhibited in vitro antimicrobial effects against C. difficile in the form of live cells as well as supernatants (Fig. 5). In vitro findings should be framed as a preliminary screening step, necessitating progression to more complex systems, such as animal models. This approach is crucial for translational research to bridge the significant gap between the simplified in vitro lab conditions and the dynamic in vivo intestinal environment. Failing to address these discrepancies can lead to flawed conclusions, such as overestimating their efficacies or overlooking indirect mechanisms of anticlostridial activity. Critically, the in vivo models incorporate factors absent in vitro, including the resident microbiome, the host immune response, physiological oxygen gradients, and other chemical and physical dynamics, which can play a critical role in controlling CDI.
The antimicrobial activity of microorganisms is often evaluated by distinguishing between live cells and cell-free supernatants [35,36]. This distinction is crucial because the underlying mechanisms of action are fundamentally different. The antimicrobial effects of the live cells are primarily attributed to contact-dependent effects and colonization-related competition. These mechanisms involve direct interaction, where the live microorganisms actively compete with pathogens for nutrients and adhesion sites on a host surface, a process known as competitive exclusion. In contrast, the antimicrobial activity of cell-free supernatants is mediated by various secreted antimicrobial compounds without the direct influence of live cell behavior. These compounds are metabolic byproducts that include bacteriocins, proteinaceous toxins that inhibit the growth of similar or closely related bacterial strains, and organic acids such as lactic acid and SCFAs, which lower the environmental pH and can penetrate the cell membrane of pathogens, disrupting their metabolic functions. Therefore, separating the two allows researchers to determine whether the observed antimicrobial effect is due to the direct contact and competitive actions of the living microorganisms or to the inhibitory properties of the byproducts they produce. This approach provides a clearer understanding of the specific bioactive components and their potential applications. While R. intestinalis BBH031 displayed superior antimicrobial activity over its type strain when co-cultured, the remaining BBH strains exhibited lower or comparable levels of activity relative to their type strains, with differences observed between their live cell and cell-free supernatant forms (Fig. 5A and B). Even when the pH of the supernatant was neutralized to 7.0, six of the seven strains retained their inhibitory effects against C. difficile (Fig. 5C), suggesting that the suppression of C. difficile growth was not solely attributable to acidification of the culture medium.
Members of the Roseburia, Agathobacter, and Faecalibacterium genera are well-documented producers of butyrate, a key metabolite known to regulate immune responses and reinforce mucosal barrier integrity, thereby mitigating intestinal inflammation and preventing the translocation of pathogenic bacteria [37,38]. However, in our study, not all butyrate-producing strains exhibited inhibitory activity against C. difficile. These results imply that the observed inhibition is likely a consequence of complex metabolic interactions rather than the action of butyrate alone. In particular, other metabolites or synergistic effects between microbial metabolites may play a crucial role in C. difficile suppression. This observation highlights the necessity of exploring alternative antimicrobial mechanisms beyond butyrate-mediated inhibition.
Our study has several limitations. First, it focused primarily on microbiota data comparison between the super donor and 152 healthy individuals, yet the super donor was selected from only 5 healthy individuals. To strengthen the validity of the findings, future studies should recruit a larger cohort of donors and conduct comparative analyses with the super donor. Moreover, clinical research on the transfer of the fecal microbiota from a super or healthy donor to specific patients is required to enhance the translational potential of FMT. It should be noted that the gut microbiome is highly individualized and dynamic, influenced by both internal and external factors such as genetic components, environment, and dietary life. Hence, monitoring the dominant species in donor stool samples at each donation is essential to facilitate optimal donor-recipient matching and augment therapeutic efficacy. Second, the study lacked direct quantification of butyrate levels in the bacterial culture supernatants. Future studies should focus on metabolomic profiling of the supernatants to identify key inhibitory compounds and further elucidate their roles in C. difficile suppression.
In conclusion, our study suggests that selecting donors with specific microbiota profiles, particularly those exhibiting higher microbial diversity, may potentially contribute to the inhibition of C. difficile, and further clinical studies are warranted to validate these findings.
Notes
Funding Source
This study was supported by the Seoul National University Bundang Hospital Research Fund (grant number: 02-2018-0016) and the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MIST) of the Republic of Korea (Project number: 2016M3A9F3947027).
Conflict of Interest
Kang KS, Choi GH, Kim YJ, Lee WS, Lee DE, and Kim MY are currently employees of BioBankHealing Corp. Except for that, no potential conflict of interest relevant to this article was reported.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Author Contributions
Conceptualization: Lee DH, Kang KS. Investigation: Kang KS, Kim YJ. Methodology: Kang KS, Kim YJ. Project administration: Lee DH, Lee WS. Supervision: Lee DH, Yoon H. Validation: Lee DE, Kim MY. Writing original draft: Kang KS, Choi GH, Kim YJ, Lee WK, Lee DE, Kim MY. Writing review & editing: Lee DH, Yoon H. Approval of final manuscript: all authors.
Supplementary Material
Supplementary materials are available at the Intestinal Research website (https://www.irjournal.org).
Supplementary Fig. 1.
Alpha diversity and bacterial taxa at the genus level were compared among samples from 5 individual donors. (A) Alpha rarefaction analysis of Shannon diversity and (B) bacterial taxa at the genus level.
Supplementary Fig. 2.
Weekly relative abundances of predominant genera in the super donors 21 (n=27) and 22 (n=31) across the sampling periods. (A) Genus-level taxonomic profiles the super donor 21 and (B) genus-level taxonomic profiles of the super donor 22.