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Article

Gut Microbiota Associated with Clostridioides difficile Carriage in Three Clinical Groups (Inflammatory Bowel Disease, C. difficile Infection and Healthcare Workers) in Hospital Field

by
Elisa Martinez
1,
Sebastien Crevecoeur
1,
Carine Thirion
2,
Jessica Grandjean
3,
Papa Abdoulaye Fall
4,
Marie-Pierre Hayette
5,
Moutschen Michel
2,
Bernard Taminiau
1,*,
Edouard Louis
3 and
Georges Daube
1
1
Food Microbiology Lab, Fundamental and Applied Research for Animals and Health (FARAH), Department of Food Sciences, Faculty of Veterinary Medicine, University of Liege, 4000 Liège, Belgium
2
Department of Clinical Sciences, Immunopathology—Infectious Diseases and General Internal Medicine, University Hospital CHU of Liege, 4000 Liège, Belgium
3
Department of Gastroenterology, University Hospital CHU of Liege, 4000 Liège, Belgium
4
FoodChain ID, 4040 Herstal, Belgium
5
Department of Biomedical and Preclinical Sciences, Faculty of Medicine, University of Liege, 4000 Liège, Belgium
*
Author to whom correspondence should be addressed.
Microorganisms 2023, 11(10), 2527; https://doi.org/10.3390/microorganisms11102527
Submission received: 5 September 2023 / Revised: 6 October 2023 / Accepted: 7 October 2023 / Published: 10 October 2023
(This article belongs to the Special Issue Effects of Gut Microbiota on Human Health and Disease)
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Abstract

:
Clostridioides difficile is an anaerobic spore-forming Gram-positive bacterium. C. difficile carriage and 16S rDNA profiling were studied in three clinical groups at three different sampling times: inflammatory bowel disease (IBD) patients, C. difficile infection (CDI) patients and healthcare workers (HCWs). Diversity analysis was realized in the three clinical groups, the positive and negative C. difficile carriage groups and the three analysis periods. Concerning the three clinical groups, β-diversity tests showed significant differences between them, especially between the HCW group and IBD group and between IBD patients and CDI patients. The Simpson index (evenness) showed a significant difference between two clinical groups (HCWs and IBD). Several genera were significantly different in the IBD patient group (Sutterella, Agathobacter) and in the CDI patient group (Enterococcus, Clostridioides). Concerning the positive and negative C. difficile carriage groups, β-diversity tests showed significant differences. Shannon, Simpson and InvSimpson indexes showed significant differences between the two groups. Several genera had significantly different relative prevalences in the negative group (Agathobacter, Sutterella, Anaerostipes, Oscillospira) and the positive group (Enterococcus, Enterobacteriaceae_ge and Enterobacterales_ge). A microbiota footprint was detected in C. difficile-positive carriers. More experiments are needed to test this microbiota footprint to see its impact on C. difficile infection.

1. Introduction

Clostridioides difficile is an anaerobic spore-forming Gram-positive bacterium. The intestinal carriage of C. difficile can be asymptomatic, but in cases of infection, it is associated with different clinical signs of disease that can vary from mild diarrhoea to pseudomembranous colitis. The carriage of C. difficile is found in 0–15% in healthy adults [1,2]. Inflammatory bowel disease (IBD) is the chronic inflammation of the gut [3]. The incidence of C. difficile infections (CDI) in hospitalized IBD patients is 5.5–7.6% [4,5]. In 2017, the incidence of CDI in an antibiotic-associated diarrhoea (AAD) population was 10.5–14.1%, and the incidence of CDI in a general hospital population was only 1.1–1.9% [6]. The incidence of C. difficile carriage in healthcare workers (HCWs) is not clear: some studies did not find carriage in any HCWs [7,8], and some studies found a percentage of 4.3% or 13% [7,9].
In the gut, 90% of the normal microbiota is composed of Firmicutes and Bacteroidetes [10,11]. The main genera of Bacteroidetes are Prevotella and Bacteroides, and those of Firmicutes are Lactobacillus, Bacillus, Clostridium, Enterococcus, Faecalibacterium, Roseburia and Ruminococcus [10,11]. The gut microbiota of CDI patients has a signature; studies show an increase in Proteobacteria, Enterobacteriaceae and Escherichia spp. and a decrease in the Lachnospiraceae, Ruminococcaceae, Alistipes, Bacteroides and Prevotella [12,13,14,15,16,17,18,19]. No studies have compared the gut microbiota of healthcare workers with another population of people who do not work at the hospital, and only one compared the gut microbiota between short-term and long-term healthcare workers [20]. The gut microbiota of IBD patients depends on whether they have active inflammation or not. The main changes in their flora are an increase in Enterobacteriaceae, Prevotellaceae, Bacteroidaceae, Ruminococcus torques and R. gnavus and a decrease in Faecalibacterium spp., Roseburia, Butyricicoccus, Phascolarctobacterium and Akkermansia spp. [21].
The first objective of this research was to evaluate the carriage of C. difficile in three groups of persons at risk in the hospital field. The first group was healthcare workers who were in contact with infectious patients and potentially C. difficile-infected patients. The second group was IBD patients who potentially had an inflammatory condition, altered microbiota, and an increased risk of CDI. The hypothesis was that this type of alteration in the microbiota predisposes patients to develop CDI. The last group was CDI patients to characterize the microbiota associated with this group and compare it with IBD patients’ microbiota. The second objective was to compare the bacterial microbiota of the three groups. The third objective was to study the global relationship between C. difficile carriage and the gut microbiota composition. The last objective was to assess the persistence of carriage over time in the three groups. We arbitrarily chose a monitoring time of 3 months to take in a long-term view of the evolution of carriage. The evolution of bacterial microbiota was monitored at three sampling times in CDI patients and in IBD patients.

2. Materials and Methods

2.1. Faeces Acquisition and Ethical Approval

The three groups were persons diagnosed with IBD in clinical remission (group 1, n = 15, 50 ± 15 years, sex ratio men/women: 0.25), persons diagnosed with CDI (group 2, n = 15, 63 ± 12 years, sex ratio men/women: 0.86) and healthcare workers from the Infectious Department in the CHU hospital (group 3 n = 3, 41 ± 15 years, sex ratio men/women: 2). The CDI patients were diagnosed with loose stools, were glutamate dehydrogenase-positive, and provided samples from which C. difficile was isolated in Petri dishes. Faecal samples were obtained anonymously. All information on the patients was obtained in terms of antibiotic or probiotic use in the two months prior to recruitment (see Table S1 for patients’ information). The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Hospital—Faculty Ethics Committee of the University of Liège, Belgium (2020/230) (date of approval, 2020). Written informed consent was obtained from all participants involved in the study. Exclusion criteria were antibiotic and probiotic use for the HCW group, active inflammatory bowel disease for the IBD patient group and no symptoms of diarrhoea for the CDI patient group. Inclusion criteria were the absence of diarrhoea for HCWs, remission status for the IBD patient group and diarrhoea and one faeces sample positive for C. difficile.
Once the faeces were collected in a sterile container, they were transported as quickly as possible, and the samples were stored at 4 °C during transport. The samples were treated as quickly as possible, with a maximum of 6 h between collection and analysis. Thereafter, the samples were stored at −80 °C. For logistical reasons, some samples were stored at −80 °C and analysed later. The samples were classified according to the Bristol stool scale [22] (see Table S2).
Participation at the three sampling times varied across the groups: all HCWs participated every time, but for IBD patient and CDI patient groups, the levels of participation were not the same: IBD-T1 (n = 14); IBD-T2 (n = 8); IBD-T3 (n = 10); CDI-T1 (n = 14); CDI-T2 (n = 12); CDI-T3 (n = 12). For IBD and HCW groups, samples were collected every three months. For the CDI patient group, the first two samples were collected during the disease, and the third one was collected when they were in remission 3 months later.

2.2. Isolation and Confirmation of C. difficile Presence

Direct and indirect enrichment proceeded for each faeces sample with the protocol described by Rodriguez et al. (2012) [23]. A C. diff quick check complete® test (Abbott, Wavre, Belgium) for toxins and glutamate dehydrogenase antigen detection (GDH) was performed on each faeces sample (see Table 1). Confirmation with a specific qPCR test using 16S rDNA on DNA extracted from faeces was performed with the protocol described by Mutters et al. (2009) [24]. When a strain was isolated, the entire 16S rDNA was sequenced.

2.3. DNA Extraction and 16s rDNA Amplicon Sequencing

Total DNA extractions from faeces were performed using the QIAamp PowerFecal Pro DNA kit (Qiagen, Antwerp, Belgium) according to the type of sample and the manufacturer’s recommendations.
With total bacterial DNA extracted from 83 samples, 16S rDNA profiling targeting the V1-V3 hypervariable region was performed as described previously [25,26]. All libraries were run with Illumina MiSeq Technology (Illumina, SY—410-1003). The protocol used was the same as that described in the work of Ngo et al. (2018) [26]. Sequence reads were processed using the MOTHUR software package V1.41.1 and Vsearch for chimera detection [27,28]. After cleaning and searching chimeras, 13,314,296 sequence reads were obtained, subsampled at 10,000 reads per sample and clustered into 129,317 OTUs (clustering threshold of 0.03). Reference alignment and taxonomic assignments were based on the SILVA database (v1.38.1) [29] of the full-length 16S rDNA sequences. The file was processed to classify the data into 536 phylotypes at the genus level. Sequencing libraries are available in the GenBank repository under the PRJNA924547 bioproject.

2.4. Confirmation C. difficile Isolates and Toxin Gene Detection

From C. difficile cultures isolated in microbiology tests, DNA extraction was carried out using the “Blood and tissue” kit (Qiagen, Antwerp, Belgium) according to the manufacturer’s recommendations. On the extracted DNA, PCR targeting tcdA and tcdB genes was performed using primers reported by Kouhsari et al., 2019 [30]. Then, PCR targeting 16S rDNA was performed according to the protocol explained by Abdelkader et al. (2021) [31]. The length of the fragment is 1500 bp, and the universal primers are 16Sr (TAC-GGT-TAC-CTT-GTT-ACG-AC) and 16Sf (GAG-TTT-GAT-CMT-GGC-TCA-G). Purification of the PCR product (Wizard® SV Gel and PCR Clean-Up System, Promega, Leiden, Netherlands) and Sanger sequencing (GIGA, Liege, Belgium) were conducted to confirm the C. difficile strain.

2.5. Data Analysis

Three different analyses were performed: group effect (IBD patients n = 32; CDI patients n = 38; HCW n = 9), C. difficile carriage effect (C. difficile-positive n = 28; C. difficile-negative n = 51) and sampling time effect (IBD-T1 n = 14; IBD-T2 n = 8; IBD-T3 n = 10; CDI-T1 n = 14; CDI-T2 n = 12; CDI-T3 n = 12). In this analysis, the patients were integrated as a variable to avoid any bias.
First, a study of β-diversity was realized in R studio (v4.2.2) using adonis2 (global effect) and pairwiseadonis2. In this analysis, the patients were integrated as a variable to avoid any bias. Beta-dispersion was used to visualize the differences between groups using betadisper with the vegan package in R studio v4.2.2. Then, a phylogenetic study was performed on the bacterial genera OTU using Mothur (“tree.shared”) in all samples to evaluate the stability of the microbiota over time.
Second, a study of α-diversity using the InvSimpson index (diversity), Shannon index (diversity), Chao1 index (richness) and Simpsoneven index (evenness) was conducted using R version 4.2.2 with the microbiome package. One-way ANOVAs were performed on each index and each subgroup using R.
Third, linear modelling for differential abundance (linDA) was performed to evaluate the microbiome composition using the groups, times and patients as variables in R studio (V4.2.2) [32].

3. Results

3.1. Isolation and Confirmation of C. difficile Presence

In the HCW group, no samples were C. difficile-positive in the analysis at any of the three time points (see Table 1).
In the IBD group, only two patients were C. difficile-positive in the analysis (IBD 10 T2 and IBD 07 T3), and only one was positive for toxins (IBD 10 T2). This patient was asymptomatic and reported no diarrhoea (see Table S2). Culture and genetic approaches showed the same results.
In the CDI patient group, at sampling time 1, all samples were positive except for CDI 01, which was negative and was excluded from the study. At sampling time 2, seven patients were still positive for C. difficile with the rapid test and qPCR. At sampling time 3, four patients were still positive for C. difficile with the rapid test and qPCR (see Table S2).
The positivity of the samples was always confirmed by qPCR. Of 27 positive samples, only 16 were isolated in culture.

3.2. Confirmation of C. difficile Strains and Toxin Research

Table 2 reports the results of tcdA and tcdB PCR and Sanger sequencing. In the IBD patient group, two isolates of C. difficile carried the tcdA and tcdB genes. In the CDI patient group, seventeen isolates of C. difficile were obtained, and eleven out of the seventeen carried the tcdA and tcdB genes.

3.3. Group Effect on Bacterial Microbiota

Five samples were excluded from the statistical analysis. Samples CDI 01 T1 and T2 were excluded because CDI 01 was not positive at sampling time 1. Patient IBD 01 T1-T2-T3 was removed from the analysis because the amplicon profiling revealed an outlier faeces profile dominated by Lactobacillus spp.
In the three groups, the most abundant phyla were Firmicutes (88.67% ± 8.4 HCW, 81.65% ± 14.3 IBD, 75.96% ± 25.6 CDI), Bacteroidetes (9.04% ± 8.2 HCW, 13.40% ± 11.8 IBD, 10.72% ± 14.4 CDI), Proteobacteria (0.79% ± 0.7 HCW, 2.37% ± 5.8 IBD, 6.81% ± 16.1 CDI), Actinobacteria (0.82% ± 0.8 HCW, 0.44% ± 0.7 IBD, 0.76 ± 1.2 CDI), Verrucomicrobia (0.51% ± 0.8 HCW, 1.08% ± 0.03 IBD, 1.82% ± 6.8 CDI) and Fusobacteria (0.02% ± 0.0 HCW, 0.38% ± 1.3 IBD, 3.64% ± 14.9 CDI).
In the three groups, the most abundant families were Lachnospiraceae (48.8% ± 15.3 HCW, 50.2% ± 23.5 IBD, 39.9% ± 27.3 CDI), Ruminococcaceae (22.8% ± 12.4 HCW, 13.1% ± 14.0 IBD, 7.9% ± 10.7 CDI), Bacteroidaceae (5.6% ± 4.4 HCW, 9.3% ± 11.0 IBD, 9.0% ± 13.6 CDI), Clostridia_fa (2.5% ± 1.5 HCW, 1.3%± 1.3 IBD, 0.6% ± 0.9 CDI), Prevotellaceae (0.4% ± 0.8 HCW, 2.2% ± 6.1 IBD, 0.6% ± 1.5 CDI), Peptostreptococcaceae (3.1% ± 3.0 HCW, 3.1% ± 6.6 IBD, 3.7% ± 6.0 CDI), Streptococcaceae (1.6% ± 4.1 HCW, 3.6% ± 8.7 IBD, 5.8% ± 9.4 CDI) Enterococcaceae (0.1% ± 0.3 HCW, 0.4% ± 1.4 IBD, 7.1% ± 22.5 CDI) and Enterobacteriaceae (0.2% ± 0.3 HCW, 0.8% ± 1.6 IBD, 6.1% ± 14.9 CDI).
In the three groups, the most abundant genera (see Figure 1A) were Lachnospiraceae_ge (17.45% ± 8.1 HCW, 20.58% ± 16.4 IBD, 14.12% ± 12.4 CDI), Faecalibacterium (16.63% ± 13.4 HCW, 9.85%± 10.6 IBD, 5.11% ± 8.8b CDI), Blautia (9.76% ± 2.8 HCW, 13.17% ± 8.9 IBD, 13.31% ± 12.4 CDI), Bacteroides (5.12% ± 4.6 HCW, 9.51% ± 11.0 IBD, 9.00% ± 13.6 CDI), Agathobacter (4.50% ± 4.1 HCW, 1.97% ± 2.9 IBD, 0.32 ± 1.2 CDI), Clostridia_ge (2.24% ± 1.7 HCW, 1.46% ± 1.3 IBD, 0.63% ± 0.85 CDI), Enterococcus (0.00% ± 0.0 HCW, 0.28%± 1.4 IBD, 7.06% ± 9.3 CDI) and Streptococcus (0.24% ± 0.2 HCW, 2.38% ± 6.6 IBD, 5.72% ± 9.5 CDI).
In the study of α-diversity, only the InvSimpson index was significantly different between HCW and CDI (see Figure 1B). In the study of β-diversity, the global group effect was significantly different (p = 0.005). The HCW group vs. IBD patient group (p = 0.002) and IBD patient group vs. CDI patient group (p = 0.001) showed significant differences. In Figure 1C, a visualization of β-dispersion is shown. With linDA models, five genera were associated with the CDI group, and thirteen genera were associated with the IBD patient group. The results are shown in Figure 1D. The results of linDA between the HCW group and the CDI patient group are shown in Supplementary Figure S1.

3.4. C. difficile Carriage Effect on Bacterial Microbiota

The more abundant genera in the C. difficile-negative group compared to the C. difficile-positive group (see Figure 2A) were Lachnospiraceae_ge (18.5% versus 14.4%), Blautia (13.7% versus 11.3%) and Faecalibacterium (10.3% versus 4.6%). The more abundant genera in the C. difficile-positive group compared to the C. difficile-negative group were Bacteroides (9.5% versus 8.4%), Enterococcus (9.8% versus 0.3%), Streptococcus (5.7%, 2.7%), Dorea (4.9% versus 1.7%), Lachnoclostridium (5.0% versus 1.8%) and Escherichia-Shigella (4.2% versus 0.4%).
In the study of α-diversity, only the InvSimpson index and Shannon index were significantly different between the two groups (see Figure 2B). In the study of β-diversity, the global group effect was significantly different (p = 0.04). In Figure 2C, a visualization of β-dispersion is shown. With linDA models, four genera were associated with the C. difficile-positive group, and twenty-one genera were associated with the C. difficile-negative group. The results in Table 3 are shown in Figure 2D. In Table 3, the mean relative abundance of the main genera (>0.5%) in C. difficile-positive and C. difficile-negative groups is presented. In Supplementary Table S3, the details of the linDA analysis are shown.

3.5. Sampling Time Effect on Bacterial Microbiota

In the study of bacterial microbiota evolution over time (See Figure 3A), the α-diversity study showed statistical differences in InvSimpson between T2 and T3 in the CDI group (See Figure 3B1) and in Chao1 between T1 and T3 and in evenness between T1 and T2 in the IBD group (See Figure 3B2). There were no statistical differences in β-diversity or in differential abundance in the CDI group. In the study of β-diversity, there were statistical differences between T1 and T2 (p = 0.01172) and between T1 and T3 (p = 0.003) in the IBD group. The differential abundance analysis of the IBD group is shown in Supplementary Figure S2.
In Figure 4, a representation of the stability of each patient is shown in a dendrogram using the “tree.shared” formula. No phylogenetic groups were marked by this representation. Some samples are clustered as a function of the group, but most of the time, the samples are grouped as a function of the patient. In Supplementary Figures S3–S5, the details of gut microbiota profiling of the nine HCW samples, IBD patient samples and CDI patient samples are shown.

4. Discussion

The objectives of this research were to evaluate the carriage of C. difficile in three groups of persons at risk in the hospital field, to evaluate the persistence of carriage over time, to study the relationship between C. difficile carriage and the gut microbiota composition and to compare the bacterial microbiota in the three clinical groups.
In the HCW group, no patients were C. difficile-positive at any sampling time. This population is in contact with C. difficile cases in the hospital. Unfortunately, the level of involvement was below the desired level. With only three people at three different times (n = 9), these results cannot be considered representative of the carriage of C. difficile in HCWs in the hospital field. In a hospital in Sweden, 0% of HCWs had C. difficile-positive stools and were in contact with C. difficile patients [33]. Even though it was a small number of subjects, it is the first preliminary study that examines the gut microbiota of HCWs of the Department of Infectiology of our hospital over time. In Supplementary Figure S3, the details of gut microbiota profiling of the nine samples are shown.
In the IBD patient group, two patients were C. difficile-positive during the study. This asymptomatic carriage was transient over time. Patient IBD 07 was positive at the third time point, and patient IBD 10 was positive at the second time point. The sample of IBD 10 T1 (sampling time before positivity) had a microbiota similar to CDI patients (see dendrogram). In patient IBD 10, the abundance of Enterococcus (7.78% T1; 0.10% T2; 0.10% T3), Escherichia/Shigella (1.48% T1; 0.53% T2; 1.23% T3), Erysipelatoclostridium (42.22% T1; 0.67% T2; 0.53% T3) and Erysipelotrichaceae (35.52% T1; 0.04% T2; 0.95% T3) was similar to CDI patient microbiota. Before the asymptomatic carriage of C. difficile (T1), the gut microbiota was modified, with an increase in certain bacteria that are present in C. difficile-positive patients. Unfortunately, for patient IBD 07, only two samples were given: sampling time 1 (before positivity) and sampling time 3 (during positivity). In patient IBD 07, the abundance of Faecalibacterium (15.42% T1; 1.91% T3), Lachnospiraceae_ge (12.35% T1; 45.70% T3), Streptococcus (1.64% T1; 0.10% T3), Blautia (25.03% T1; 6.00% T3) and Dorea (2.3% T1; 4.96% T3) was concordant with CDI patient microbiota. During asymptomatic carriage (T3), some genera were reduced compared to T1 (Blautia, Faecalibacterium), as described by Martinez et al., 2022. Before asymptomatic carriage (T1), some genera were increased compared to T3 (Streptococcus spp.), and some were reduced (Lachnospiraceae_ge and Dorea). These differences were observed during the C. difficile infection [34]. The IBD patient microbiota is dynamic, and depending on sampling, the microbiota may vary [35]. Our results are valid only for microbiota studied from faeces.
In the CDI patient group, at the three sampling times, seven patients were positive once, three patients were positive twice, and four patients were positive all three times. Several genera were statistically different between the IBD and CDI groups. Some genera were significantly decreased in the CDI patient group in comparison with the IBD patient group: GCA.900066575, Lachnospirales_ge, Oscillospira, UCG.003, Paraprevotella, Clostridiales_ge, Colidextribacter, Holdemanella, Sutterella, Monoglobus, Agathobacter, Alistipes, Clostridia_vadinBB60_group_ge. These modifications were already described for Agathobacter, Alistipes, Clostridiales_ge, Paraprevotella and Sutterella in a recent study that identified the main significantly different genera of the gut microbiota of CDI patients versus healthy people [34]. Some genera were significantly decreased in the CDI patient group in comparison with the HCW group and IBD patient group: Sutterella and Agathobacter. Some genera were significantly increased in the CDI patient group in comparison with the IBD patient group: Clostridioides, Enterococcus, Enterobacteriaceae_ge, Enterobacterales_ge and Lactobacillales_ge. Martinez et al. (2022) described similar modifications in the gut microbiota of CDI patients versus healthy people [34].
This work has limitations. The level of involvement in the HCW group was below the expectation, which prevents a real statistical analysis for this population.
In the positive C. difficile carriage group, several genera were significantly decreased: Lachnospiraceae_NK4A136, Colidextribacter, Monoglobus, UCG.003, Oscillibacter, Oscillospiraceae_ge, Oscillospira, Coriobacteriales_ge, Marvinbryantia, Romboutsia, Ruminococcaceae_ge, Fusicatenibacter, Butyricicoccus, Clostridia_ge, Sutterella, Lachnospiraceae_UCG.004, Agathobacter, Clostridiales_ge, Lachnospira, Anaerostipes, Rhodospirillales_fa_ge. These modifications were described for Ruminococcaceae_ge, Oscillospira, Fusicatenibacter and Anaerostipes in a recent study that identified the main significantly different genera of the gut microbiota of CDI patients versus healthy people [34]. In the positive C. difficile carriage group, several genera were significantly increased: Enterococcus, Enterobacteriaceae_ge, Enterobacterales_ge, Clostridioides. These modifications were described by Martinez et al., 2022 [34].
In positive CDI samples, several genera were increased: Veillonella spp.; Enterococcus spp.; Streptococcus spp.; Escherichia-Shigella; Enterobacteriaceae_ge. Veillonella spp. is associated in the literature with CDI [34], irritable bowel syndrome [36] and colorectal cancer [36]. Enterococcus spp. are present in human vaginal secretions, human milk, fermented foods and dairy products [37,38,39,40]. In the species analysis, Enterococcus faecalis (29.6% of positive samples vs. 3.8% of negative samples) and Enterococcus faecium (22.2% of positive samples vs. 13.5% of negative samples) were the two species present in the samples. E. faecium and E. faecalis are controversial bacteria. They are commensal bacteria of the intestinal flora. Recently, it was shown that E. faecalis and E. faecium are potentially pathogenic bacteria due to their ability to adapt to new environments [34,37,40]. Additionally, resistance to vancomycin has emerged in this genus [34,37,40]. Romyasamit et al. (2020) reported that six E. faecalis strains have a probiotic effect and anti-C. difficile activity [38]. Streptococcus spp. are associated in the literature with CDI [34] and irritable bowel syndrome [36]. In the species analysis, Streptococcus parasanguinis and Streptococcus mutant were present in 63% and 25%, respectively, of positive CDI samples. Streptococcus parasanguinis is a common human commensal bacteria present in the oral cavity and plays an active role in dental plaque formation. Streptococcus mutant is a bacterium involved in oral infections [41,42]. In other research, Streptococcus parasanguinis was associated with the asymptomatic carriage of C. difficile [43]. Escherichia-Shigella was present in 77.8% of positive CDI samples and 46.1% of negative CDI samples. Intestinibacter bartlettii was present in 48.1% of both types of samples. This novel species showed decreased abundance with the use of a diabetes type 2 drug (Metformin) [44]. Klebsiella oxytoca (29.6% of positive samples vs. 1.9% of negative samples) and Klebsiella pneumoniae (25.9% of positive samples vs. 17.3% of negative samples) are pathogenic bacteria in humans. They are responsible for bronchopneumonia, urinary tract infections and septicaemia [45].
In the negative C. difficile carriage group, several genera were significantly increased: Lachnospiraceae_NK4A136, Colidextribacter, Monoglobus, UCG.003, Oscillibacter, Oscillospiraceae_ge, Oscillospira, Coriobacteriales_ge, Marvinbryantia, Romboutsia, Ruminococcaceae_ge, Fusicatenibacter, Butyricicoccus, Clostridia_ge, Sutterella, Lachnospiraceae_UCG.004, Agathobacter, Clostridiales_ge, Lachnospira, Anaerostipes, Rhodospirillales_fa_ge. Lachnospiraceae are bacterial producers of short-chain fatty acids [46]. Blautia, Coprococcus, Dorea, Lachnospira, Oribacterium and Roseburia are the main genera of the Lachnospiraceae family. Eubacterium spp., Roseburia spp. and Faecalibacterium spp. are butyrate producers and considered beneficial for human health [47]. Eubacterium spp. are present in the oral cavity, the intestinal tract and the environment [47]. Eubacterium (and also Agathobacter rectalis [48,49]) rectale was present in 57.7% of negative CDI samples and 14.8% of positive CDI samples. Anaerostipes spp. are also butyrate producers and lactate consumers [50]. In the species analysis, Anaerostipes hadrus was present in 71% of negative CDI samples and 18.5% of positive CDI samples. Anaerostipes hadrus is a dominant species of human colonic microbiota and is a butyrate-producing bacterium. This bacterium was associated with asymptomatic carriage of C. difficile in a study by Fishbein et al. [43]. One species from the Oscillospira genus is associated with leanness and health [51], and Oscillospira spp. are reduced in patients suffering from Crohn’s disease [52]. Lachnospiraceae, Coprococcus spp., Faecalibacterium spp. Fusicatenibacter spp. and Anaerostipes spp. are associated with CDI in the literature [34].
The effect of antibiotics (ATB) was studied (see Supplementary Figure S6). Fourteen samples were collected during antibiotic use. In this group, a statistical difference in beta-diversity was found between the antibiotic group and the non-antibiotic group (p value < 0.05). Using the linDA model with antibiotics and patients as variables, only one bacterial genus was identified as associated with the ATB group (Enterococcus), and four bacterial genera were identified as associated with the non-ATB group (Flavonifractor, Colidextribacter, Lachnospiraceae_UCG.004, Dorea, Lachnospiraceae_FCS020_group) (see Supplementary Figure S6). The modifications observed with antibiotic use had little impact on the bacterial microbiota. This may be due to the small sample size taken when the antibiotic was taken or to the fact that we did not consider the temporal impact on the bacterial microbiota [53]. Enterococcus was associated with the ATB group, as well as positive C. difficile carriage and the CDI patient group. Colidextribacter and Lachnospiraceae_UCG.004 were associated with negative C. difficile carriage and the non-ATB group. Dorea was associated with healthy people in a recent study [34].

5. Conclusions

These results confirm previous studies of the gut microbiota of CDI patients. Changes in the microbiota in IBD patients with C. difficile seem to occur in the same direction. This is the first work that studies carriage over time in three different populations: healthcare workers, inflammatory bowel disease patients and C. difficile patients. A microbiota footprint was detected in C. difficile-positive carriers, and some genera could be beneficial by protecting against the colonization of C. difficile. More experiments are needed to test this microbiota footprint to see its impact on C. difficile infection. If an impact is confirmed, an early screening of the gut microbiota at hospital admission could identify patients at higher risk of CDI.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms11102527/s1: Figure S1—Group effect on bacterial microbiota; Figure S2—Time effect on bacterial microbiota; Figure S3—Evolution of bacterial genera in gut microbiota (mean cumulative relative abundance) for clinical group over time in HCW group; Figure S4—Evolution of bacterial genera in gut microbiota (mean cumulative relative abundance) for clinical group over time in CDI group; Figure S5—Evolution of bacterial genera in gut microbiota (mean cumulative relative abundance) for clinical group over time in IBD group; Figure S6—Antibiotic effect on bacterial microbiota; Table S1: Identification of patients and patients’ information: CDI, IBD and HCW; Table S2: Classification of faeces with Bristol scale stool and means of preserving the samples; Table S3: Significant taxa using linDA models in R studio v4.2.2 between C. difficile-positive and C. difficile-negative groups.

Author Contributions

Conceptualization, E.M., G.D. and E.L.; methodology, E.M., B.T. and G.D.; software, E.M., B.T. and P.A.F.; validation, B.T., G.D. and E.L.; formal analysis, E.M. and B.T.; investigation, E.M.; resources, E.M., S.C., M.-P.H., C.T., E.L., M.M. and J.G.; data curation, E.M.; writing—original draft preparation, E.M.; writing—review and editing, E.M., G.D., B.T. and E.L.; visualization, E.M.; supervision, G.D. and E.L.; funding acquisition, E.M. and G.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by “FSR 2020—crédit sectoriel de Recherche en Sciences de la Santé” granted by the University of Liege for the Genx-CDI project.

Data Availability Statement

The data presented in this study are openly available in the Genbank repository under the PRJNA924547 Bioproject.

Acknowledgments

We want to thank Denis Disse and Diffimalone Kavunka, who worked with us on these experiments. We want to thank Frédéric Farnir for his help with the statistical design.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Furuya-Kanamori, L.; Marquess, J.; Yakob, L.; Riley, T.V.; Paterson, D.L.; Foster, N.F.; Huber, C.A.; Clements, A.C.A. Asymptomatic Clostridium difficile colonization: Epidemiology and clinical implications. BMC Infect. Dis. 2015, 15, 516. [Google Scholar] [CrossRef]
  2. Crobach, M.J.T.; Vernon, J.J.; Loo, V.G.; Kong, L.Y.; Péchiné, S.; Wilcox, M.H.; Kuijper, E.J. Understanding Clostridium difficile colonization. Clin. Microbiol. Rev. 2018, 31, e00021-17. [Google Scholar] [CrossRef]
  3. Khanna, S. Management of Clostridioides difficile infection in patients with inflammatory bowel disease. Intest. Res. 2021, 19, 265–274. [Google Scholar] [CrossRef]
  4. Sokol, H.; Jegou, S.; McQuitty, C.; Straub, M.; Leducq, V.; Landman, C.; Kirchgesner, J.; Le Gall, G.; Bourrier, A.; Nion-Larmurier, I.; et al. Specificities of the intestinal microbiota in patients with inflammatory bowel disease and Clostridium difficile infection. Gut Microbes 2017, 9, 55–60. [Google Scholar] [CrossRef]
  5. Singh, H.; Nugent, Z.; Yu, B.N.; Lix, L.M.; Targownik, L.E.; Bernstein, C.N. Higher incidence of Clostridium difficile infection among individuals with inflammatory bowel disease. Gastroenterology 2017, 153, 430–438.e2. [Google Scholar] [CrossRef]
  6. The ANTICIPATE Study Group; van Werkhoven, C.H.; Ducher, A.; Berkell, M.; Mysara, M.; Lammens, C.; Torre-Cisneros, J.; Rodríguez-Baño, J.; Herghea, D.; Cornely, O.A.; et al. Incidence and predictive biomarkers of Clostridioides difficile infection in hospitalized patients receiving broad-spectrum antibiotics. Nat. Commun. 2021, 12, 2240. [Google Scholar] [CrossRef]
  7. Friedman, N.D.; Pollard, J.; Stupart, D.; Knight, D.R.; Khajehnoori, M.; Davey, E.K.; Parry, L.; Riley, T.V. Prevalence of Clostridium difficile colonization among healthcare workers. BMC Infect. Dis. 2013, 13, 459. [Google Scholar] [CrossRef]
  8. Hell, M.; Sickau, K.; Chmelizek, G.; Kern, J.M.; Maass, M.; Huhulescu, S.; Allerberger, F. Absence of Clostridium difficile in asymptomatic hospital staff. Am. J. Infect. Control 2012, 40, 1023–1024. [Google Scholar] [CrossRef]
  9. Kato, H.; Kita, H.; Karasawa, T.; Maegawa, T.; Koino, Y.; Takakuwa, H.; Saikai, T.; Kobayashi, K.; Yamagishi, T.; Nakamura, S. Colonisation and transmission of Clostridium difficile in healthy individuals examined by PCR ribotyping and pulsed-field gel electrophoresis. J. Med. Microbiol. 2001, 50, 720–727. [Google Scholar] [CrossRef]
  10. Rinninella, E.; Raoul, P.; Cintoni, M.; Franceschi, F.; Miggiano, G.; Gasbarrini, A.; Mele, M. What is the healthy gut microbiota composition? a changing ecosystem across age, environment, diet, and diseases. Microorganisms 2019, 7, 14. [Google Scholar] [CrossRef]
  11. MetaHIT Consortium; Arumugam, M.; Raes, J.; Pelletier, E.; Le Paslier, D.; Yamada, T.; Mende, D.R.; Fernandes, G.R.; Tap, J.; Bruls, T.; et al. Enterotypes of the human gut microbiome. Nature 2011, 473, 174–180. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, L.; Dong, D.; Jiang, C.; Li, Z.; Wang, X.; Peng, Y. Insight into alteration of gut microbiota in clostridium difficile infection and asymptomatic C. difficile colonization. Anaerobe 2015, 34, 1–7. [Google Scholar] [CrossRef] [PubMed]
  13. Han, S.-H.; Yi, J.; Kim, J.-H.; Lee, S.; Moon, H.-W. Composition of gut microbiota in patients with toxigenic Clostridioides (Clostridium) difficile: Comparison between subgroups according to clinical criteria and toxin gene load. PLoS ONE 2019, 14, e0212626. [Google Scholar] [CrossRef]
  14. Jeon, Y.D.; Ann, H.W.; Lee, W.J.; Kim, J.H.; Seong, H.; Kim, J.H.; Ahn, J.Y.; Jeong, S.J.; Ku, N.S.; Yeom, J.S.; et al. Characteristics of faecal microbiota in korean patients with Clostridioides difficile-associated diarrhea. Infect. Chemother. 2019, 51, 365–375. [Google Scholar] [CrossRef]
  15. Milani, C.; Ticinesi, A.; Gerritsen, J.; Nouvenne, A.; Lugli, G.A.; Mancabelli, L.; Turroni, F.; Duranti, S.; Mangifesta, M.; Viappiani, A.; et al. Gut microbiota composition and Clostridium difficile infection in hospitalized elderly individuals: A metagenomic study. Sci. Rep. 2016, 6, 25945. [Google Scholar] [CrossRef]
  16. Amrane, S.; Hocquart, M.; Afouda, P.; Kuete, E.; Pham, T.-P.-T.; Dione, N.; Ngom, I.I.; Valles, C.; Bachar, D.; Raoult, D.; et al. Metagenomic and culturomic analysis of gut Microbiota dysbiosis during Clostridium difficile infection. Sci. Rep. 2019, 9, 12807. [Google Scholar] [CrossRef] [PubMed]
  17. Gu, S. Identification of key taxa that favor intestinal colonization of Clostridium difficile in an adult chinese population. Microbes Infect. 2016, 9, 30–38. [Google Scholar] [CrossRef] [PubMed]
  18. Sangster, W.; Hegarty, J.P.; Schieffer, K.M.; Wright, J.R.; Hackman, J.; Toole, D.R.; Lamendella, R.; Stewart, D.B. Bacterial and fungal microbiota changes distinguish C. difficile infection from other forms of diarrhea: Results of a prospective inpatient study. Front. Microbiol. 2016, 7, 789. [Google Scholar] [CrossRef]
  19. Stewart, D.B.; Wright, J.R.; Fowler, M.; McLimans, C.J.; Tokarev, V.; Amaniera, I.; Baker, O.; Wong, H.-T.; Brabec, J.; Drucker, R.; et al. Integrated meta-omics reveals a fungus-associated bacteriome and distinct functional pathways in Clostridioides difficile infection. mSphere 2019, 4, e00454-19. [Google Scholar] [CrossRef]
  20. Zheng, N.; Li, S.-H.; Dong, B.; Sun, W.; Li, H.-R.; Zhang, Y.-L.; Li, P.; Fang, Z.-W.; Chen, C.-M.; Han, X.-Y.; et al. Comparison of the gut microbiota of short-term and long-term medical workers and non-medical controls: A cross-sectional analysis. Clin. Microbiol. Infect. 2021, 27, 1285–1292. [Google Scholar] [CrossRef]
  21. Rodríguez, C.; Romero, E.; Garrido-Sanchez, L.; Alcaín-Martínez, G.; Andrade, R.; Taminiau, B.; Daube, G.; García-Fuentes, E. Microbiota insights in Clostridium difficile infection and inflammatory bowel disease. Gut Microbes 2020, 12, 1725220. [Google Scholar] [CrossRef]
  22. Lewis, S.J.; Heaton, K.W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 1997, 32, 920–924. [Google Scholar] [CrossRef]
  23. Rodriguez, C.; Taminiau, B.; Van Broeck, J.; Avesani, V.; Delmée, M.; Daube, G. Clostridium difficile in young farm animals and slaughter animals in Belgium. Anaerobe 2012, 18, 621–625. [Google Scholar] [CrossRef]
  24. Mutters, R.; Nonnenmacher, C.; Susin, C.; Albrecht, U.; Kropatsch, R.; Schumacher, S. Quantitative detection of Clostridium difficile in hospital environmental samples by real-time polymerase chain reaction. J. Hosp. Infect. 2009, 71, 43–48. [Google Scholar] [CrossRef]
  25. Gérard, A.; El-Hajjaji, S.; Burteau, S.; Fall, P.A.; Pirard, B.; Taminiau, B.; Daube, G.; Sindic, M. Study of the microbial diversity of a panel of belgian artisanal cheeses associated with challenge studies for Listeria monocytogenes. Food Microbiol. 2021, 100, 103861. [Google Scholar] [CrossRef]
  26. Ngo, J.; Taminiau, B.; Fall, P.A.; Daube, G.; Fontaine, J. Ear canal microbiota—A comparison between healthy dogs and atopic dogs without clinical signs of otitis externa. Vet. Dermatol. 2018, 29, 425-e140. [Google Scholar] [CrossRef]
  27. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A Versatile open source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef]
  28. 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. AEM 2009, 75, 7537–7541. [Google Scholar] [CrossRef]
  29. Tatusova, T.; DiCuccio, M.; Badretdin, A.; Chetvernin, V.; Nawrocki, E.P.; Zaslavsky, L.; Lomsadze, A.; Pruitt, K.D.; Borodovsky, M.; Ostell, J. NCBI Prokaryotic Genome Annotation Pipeline. Nucl. Acids Res. 2016, 44, 6614–6624. [Google Scholar] [CrossRef]
  30. Kouhsari, E.; Douraghi, M.; Barati, M.; Yaseri, H.; Talebi, M.; Abbasian, S.; Moqarabzadeh, V.; Amirmozafari, N. Rapid simultaneous molecular stool-based detection of toxigenic Clostridioides difficile by quantitative TaqMan Real-Time PCR assay. Clin. Lab. 2019, 65, 461–469. [Google Scholar] [CrossRef]
  31. Abekhti, A.; Taminau, B.; Djebbar, A.; Crevecoeur, S.; Daube, G. Identification of bacterial strains isolated from the traditional date product “Btana” produced in south regions of Algeria. Apppl. Biol. Sahar. Aereas 2021, 3, 11–26. [Google Scholar]
  32. Zhou, H.; He, K.; Chen, J.; Zhang, X. LinDA: Linear Models for Differential Abundance Analysis of Microbiome Compositional Data. Genome Biol. 2022, 23, 95. [Google Scholar] [CrossRef]
  33. Säll, O.; Johansson, K.; Norén, T. Low Colonization Rates of Clostridium Difficile among Patients and Healthcare Workers at Örebro university hospital in Sweden. APMIS 2015, 123, 240–244. [Google Scholar] [CrossRef]
  34. Martinez, E.; Taminiau, B.; Rodriguez, C.; Daube, G. Gut microbiota composition associated with Clostridioides difficile colonization and infection. Pathogens 2022, 11, 781. [Google Scholar] [CrossRef]
  35. Halfvarson, J.; Brislawn, C.J.; Lamendella, R.; Vázquez-Baeza, Y.; Walters, W.A.; Bramer, L.M.; D’Amato, M.; Bonfiglio, F.; McDonald, D.; Gonzalez, A.; et al. Dynamics of the Human Gut Microbiome in Inflammatory Bowel Disease. Nat. Microbiol. 2017, 2, 17004. [Google Scholar] [CrossRef]
  36. Kitamoto, S.; Nagao-Kitamoto, H.; Hein, R.; Schmidt, T.M.; Kamada, N. The bacterial connection between the oral cavity and the gut diseases. J. Dent. Res. 2020, 99, 1021–1029. [Google Scholar] [CrossRef]
  37. Krawczyk, B.; Wityk, P.; Gałęcka, M.; Michalik, M. The many faces of Enterococcus Spp.—Commensal, probiotic and opportunistic pathogen. Microorganisms 2021, 9, 1900. [Google Scholar] [CrossRef]
  38. Romyasamit, C.; Thatrimontrichai, A.; Aroonkesorn, A.; Chanket, W.; Ingviya, N.; Saengsuwan, P.; Singkhamanan, K. Enterococcus Faecalis isolated from infant feces inhibits toxigenic Clostridioides (Clostridium) difficile. Front. Pediatr. 2020, 8, 572633. [Google Scholar] [CrossRef]
  39. García-Díez, J.; Saraiva, C. Use of starter cultures in foods from animal origin to improve their safety. IJERPH 2021, 18, 2544. [Google Scholar] [CrossRef]
  40. Gaca, A.O.; Lemos, J.A. Adaptation to Adversity: The intermingling of stress tolerance and pathogenesis in enterococci. Microbiol. Mol. Biol. Rev. 2019, 83, e00008-19. [Google Scholar] [CrossRef]
  41. Chen, Q.; Wu, G.; Chen, H.; Li, H.; Li, S.; Zhang, C.; Pang, X.; Wang, L.; Zhao, L.; Shen, J. Quantification of human oral and fecal Streptococcus parasanguinis by use of quantitative real-time PCR targeting the groel gene. Front. Microbiol. 2019, 10, 2910. [Google Scholar] [CrossRef] [PubMed]
  42. Factors associated with Streptococcus mutans pathogenicity in the oral cavity. Biointerface Res. Appl. Chem. 2022, 13, 368. [CrossRef]
  43. Fishbein, S.R.; Robinson, J.I.; Hink, T.; Reske, K.A.; Newcomer, E.P.; Burnham, C.-A.D.; Henderson, J.P.; Dubberke, E.R.; Dantas, G. Multi-omics investigation of Clostridioides difficile-colonized patients reveals pathogen and commensal correlates of C. difficile pathogenesis. eLife 2022, 11, e72801. [Google Scholar] [CrossRef] [PubMed]
  44. Russell, J.T.; Roesch, L.F.W.; Ördberg, M.; Ilonen, J.; Atkinson, M.A.; Schatz, D.A.; Triplett, E.W.; Ludvigsson, J. Genetic risk for autoimmunity is associated with distinct changes in the human gut microbiome. Nat. Commun. 2019, 10, 3621. [Google Scholar] [CrossRef] [PubMed]
  45. Singh, L.; Cariappa, M.P.; Kaur, M. Klebsiella Oxytoca: An emerging pathogen? Med. J. Armed Forces India 2016, 72, S59–S61. [Google Scholar] [CrossRef]
  46. Vacca, M.; Celano, G.; Calabrese, F.M.; Portincasa, P.; Gobbetti, M.; De Angelis, M. The controversial role of human gut Lachnospiraceae. Microorganisms 2020, 8, 573. [Google Scholar] [CrossRef]
  47. Mukherjee, A.; Lordan, C.; Ross, R.P.; Cotter, P.D. Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health. Gut Microbes 2020, 12, 1802866. [Google Scholar] [CrossRef]
  48. Rosero, J.A.; Killer, J.; Sechovcová, H.; Mrázek, J.; Benada, O.; Fliegerová, K.; Havlík, J.; Kopečný, J. Reclassification of Eubacterium rectale (hauduroy et al. 1937) prévot 1938 in a new genus Agathobacter gen. nov. as Agathobacter rectalis comb. nov., and description of Agathobacter ruminis sp. nov., isolated from the rumen contents of sheep and cows. Int. J. Syst. Evol. Microbiol. 2016, 66, 768–773. [Google Scholar] [CrossRef]
  49. Zuo, G.; Hao, B. Whole-genome-based phylogeny supports the objections against the reclassification of Eubacterium rectale to Agathobacter rectalis. Int. J. Syst. Evol. Microbiol. 2016, 66, 2451. [Google Scholar] [CrossRef]
  50. Bui, T.P.N.; Mannerås-Holm, L.; Puschmann, R.; Wu, H.; Troise, A.D.; Nijsse, B.; Boeren, S.; Bäckhed, F.; Fiedler, D.; deVos, W.M. Conversion of dietary inositol into propionate and acetate by commensal Anaerostipes associates with host health. Nat. Commun. 2021, 12, 4798. [Google Scholar] [CrossRef]
  51. Konikoff, T.; Gophna, U. Oscillospira: A Central, Enigmatic Component of the Human Gut Microbiota. Trends Microbiol. 2016, 24, 523–524. [Google Scholar] [CrossRef] [PubMed]
  52. Walters, W.A.; Xu, Z.; Knight, R. Meta-Analyses of Human Gut Microbes Associated with Obesity and IBD. FEBS Lett. 2014, 588, 4223–4233. [Google Scholar] [CrossRef] [PubMed]
  53. Jernberg, C.; Löfmark, S.; Edlund, C.; Jansson, J.K. Long-Term Ecological Impacts of Antibiotic Administration on the Human Intestinal Microbiota. ISME J. 2007, 1, 56–66. [Google Scholar] [CrossRef] [PubMed]
Microorganisms 11 02527 g001
Figure 1. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: HCW group (n = 9), IBD patient group (n = 32) and CDI patient group (n = 38) assessed by 16S rDNA profiling using Prism v9.1.1. (B) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes in three clinical groups in R studio v4.2.2. Graphs were created in Prism v9.1.1. The repetitive data from one patient are expressed as one mean value. (C) Visualization of β-dispersion using betadisper in Rstudio v4.2.2. Betadisper analysed beta-dispersion on the basis of the Bray–Curtis dissimilarity matrix. Dissimilarity (beta-dispersion) between groups was assessed using the Adonis2 test. The HCW group is represented in red, the IBD group is represented in green, and the CDI group is represented in black. (D) Linear model for differential abundance in Rstudio v4.2.2 (linDA) using groups and patients as variables. Here, data are presented as log2fold change in the IBD group vs. CDI group. Two thresholds are present: the first one is fixed at -log10(padj > 0.05), and the second one is fixed at −1 and +1 log2fold change. The grey dots are genera with padj > 0.05 and with an effect size ≤ 1; the green dots are genera with padj > 0.05 and with an effect size > 1; the blue dots are genera with padj ≤ 0.05 and with an effect size > 1. The bacterial genera associated with the IBD patient group are on the right side of the graph (positive axis), and the bacterial genera associated with the CDI patient group are on the left (negative axis).
Figure 1. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: HCW group (n = 9), IBD patient group (n = 32) and CDI patient group (n = 38) assessed by 16S rDNA profiling using Prism v9.1.1. (B) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes in three clinical groups in R studio v4.2.2. Graphs were created in Prism v9.1.1. The repetitive data from one patient are expressed as one mean value. (C) Visualization of β-dispersion using betadisper in Rstudio v4.2.2. Betadisper analysed beta-dispersion on the basis of the Bray–Curtis dissimilarity matrix. Dissimilarity (beta-dispersion) between groups was assessed using the Adonis2 test. The HCW group is represented in red, the IBD group is represented in green, and the CDI group is represented in black. (D) Linear model for differential abundance in Rstudio v4.2.2 (linDA) using groups and patients as variables. Here, data are presented as log2fold change in the IBD group vs. CDI group. Two thresholds are present: the first one is fixed at -log10(padj > 0.05), and the second one is fixed at −1 and +1 log2fold change. The grey dots are genera with padj > 0.05 and with an effect size ≤ 1; the green dots are genera with padj > 0.05 and with an effect size > 1; the blue dots are genera with padj ≤ 0.05 and with an effect size > 1. The bacterial genera associated with the IBD patient group are on the right side of the graph (positive axis), and the bacterial genera associated with the CDI patient group are on the left (negative axis).
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Microorganisms 11 02527 g002
Figure 2. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: C. difficile-negative (n = 51) and C. difficile-positive (n = 28), assessed by 16S rDNA profiling using Prism v9.1.1. (B) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes in two clinical groups in R studio v4.2.2. Graphs were performed in Prism v9.1.1. The repetitive data from one patient are expressed as one mean value. (C) Visualization of β-dispersion using betadisper in Rstudio v4.2.2. Betadisper analysed beta-dispersion on the basis of the Bray–Curtis dissimilarity matrix. Dissimilarity (beta-dispersion) between groups was assessed using the Adonis2 test. The C. difficile-positive group is represented in red; the C. difficile-negative group is represented in black. (D) Linear model for differential abundance in Rstudio v4.2.2 (linDA) using C. difficile carriage and patients as variables. Here, data are presented as log2fold change in the C. difficile-positive group vs. C. difficile-negative group. Two thresholds are present: the first one is fixed at -log10(padj > 0.05), and the second one is fixed at −1 and +1 log2fold change. The grey dots are genera with padj > 0.05 and with an effect size ≤ 1; the green dots are genera with padj > 0.05 and with an effect size > 1; the blue dots are genera with padj ≤ 0.05 and with an effect size ≤ 1 and the red dot are genera with padj ≤ 0.05 and with an effect size > 1. The bacterial genera associated with positive C. difficile carriage are on the right side of the graph (positive axis), and the bacterial genera associated with negative C. difficile carriage are on the left (negative axis).
Figure 2. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: C. difficile-negative (n = 51) and C. difficile-positive (n = 28), assessed by 16S rDNA profiling using Prism v9.1.1. (B) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes in two clinical groups in R studio v4.2.2. Graphs were performed in Prism v9.1.1. The repetitive data from one patient are expressed as one mean value. (C) Visualization of β-dispersion using betadisper in Rstudio v4.2.2. Betadisper analysed beta-dispersion on the basis of the Bray–Curtis dissimilarity matrix. Dissimilarity (beta-dispersion) between groups was assessed using the Adonis2 test. The C. difficile-positive group is represented in red; the C. difficile-negative group is represented in black. (D) Linear model for differential abundance in Rstudio v4.2.2 (linDA) using C. difficile carriage and patients as variables. Here, data are presented as log2fold change in the C. difficile-positive group vs. C. difficile-negative group. Two thresholds are present: the first one is fixed at -log10(padj > 0.05), and the second one is fixed at −1 and +1 log2fold change. The grey dots are genera with padj > 0.05 and with an effect size ≤ 1; the green dots are genera with padj > 0.05 and with an effect size > 1; the blue dots are genera with padj ≤ 0.05 and with an effect size ≤ 1 and the red dot are genera with padj ≤ 0.05 and with an effect size > 1. The bacterial genera associated with positive C. difficile carriage are on the right side of the graph (positive axis), and the bacterial genera associated with negative C. difficile carriage are on the left (negative axis).
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Microorganisms 11 02527 g003
Figure 3. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: IBD patient group (IBD-T1, n = 14; IBD-T2, n = 8; IBD-T3, n = 10) and CDI patient group (CDI-T1, n = 14; CDI-T2, n = 12; CDI-T3, n = 12), assessed by 16S rDNA profiling using Prism v9.1.1. (B1) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes over time in CDI patient group in R studio v4.2.2. Graphs were created in Prism v9.1.1. (B2) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes over time in IBD patient group in R studio v4.2.2. Graphs were created in Prism v9.1.1.
Figure 3. (A) Mean cumulative relative abundance of bacterial genera for three clinical groups: IBD patient group (IBD-T1, n = 14; IBD-T2, n = 8; IBD-T3, n = 10) and CDI patient group (CDI-T1, n = 14; CDI-T2, n = 12; CDI-T3, n = 12), assessed by 16S rDNA profiling using Prism v9.1.1. (B1) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes over time in CDI patient group in R studio v4.2.2. Graphs were created in Prism v9.1.1. (B2) Analysis of α-diversity using Chao1, Shannon, InvSimpson and Simpson indexes over time in IBD patient group in R studio v4.2.2. Graphs were created in Prism v9.1.1.
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Microorganisms 11 02527 g004
Figure 4. Dendrogram for three clinical groups at the three sampling times for bacterial genera using Mothur (version 1.48.0) using “tree.shared” formula. Samples from the same patient are stained with the same colour.
Figure 4. Dendrogram for three clinical groups at the three sampling times for bacterial genera using Mothur (version 1.48.0) using “tree.shared” formula. Samples from the same patient are stained with the same colour.
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Table 1. Results of microbiological and genetic analyses of stool samples.
Table 1. Results of microbiological and genetic analyses of stool samples.
GDH aToxins aqPCR C. diffDirect bIndirect b
IBD patients
T10/150/150/150/150/15
T21/91/91/91/91/9
T31/110/111/110/111/11
CDI patients
T114/158/1514/159/159/15
T27/132/137/134/133/13
T34/121/124/121/121/12
HCWs
T10/30/30/30/30/3
T20/30/30/30/30/3
T30/30/30/30/30/3
a C. diff quick check complete (Techlab). b Microbiological cultures.
Table 2. Main characteristics of C. difficile strains isolated from faeces.
Table 2. Main characteristics of C. difficile strains isolated from faeces.
SamplesSanger SequencingtcdBtcdA
IBD 10 T2C. difficileYY
IBD 07 T3C. difficileYY
CDI 02 T1C. difficileNN
CDI 02 T2C. difficileNN
CDI 03 T1C. difficileNN
CDI 04 T1C. difficileYY
CDI 05 T3C. difficileNN
CDI 06 T1C. difficileYY
CDI 07 T1C. difficileYY
CDI 07 T2C. difficileYY
CDI 07 T3C. difficileYY
CDI 08 T1C. difficileYY
CDI 08 T2C. difficileYY
CDI 09 T1C. difficileNN
CDI 10 T1C. difficileNN
CDI 11 T2C. difficileYY
CDI 15 T1C. difficileYY
Legend: Y: Yes; N: No.
Table 3. Mean relative abundance of main genera (>0.5%) in C. difficile-positive and C. difficile-negative groups.
Table 3. Mean relative abundance of main genera (>0.5%) in C. difficile-positive and C. difficile-negative groups.
GeneraC. difficile-NegativeC. difficile-PositiveP adj (LinDA Models)
Lachnospiraceae_ge18.5% ± 13.214.4% ± 15.4%NS (0.1478)
Blautia13.7% ± 8.8%11.3% ± 12.8%NS (0.0922)
Bacteroides8.4% ± 9.7%9.5% ± 15.3%NS (0.55)
Faecalibacterium10.3% ± 11.1%4.6% ± 8.9%NS (0.2120)
Enterococcus0.3% ± 1.1%9.8% ± 26.4%(0.0037) **
Streptococcus2.7% ± 7.1%5.7% ± 9.2%NS (0.6089)
Lachnoclostridium1.8% ± 2.9%5.0% ± 15.7%NS (0.9720)
Dorea1.7% ± 1.9%4.9% ± 12.7%NS (0.1287)
Escherichia-Shigella0.4% ± 1.0%4.2% ± 10.5%NS (0.0681)
Fusobacterium1.9% ± 11.8%2.0% ± 7.4%NS (0.1309)
Enterobacteriaceae_ge0.1% ± 0.2%3.5% ± 13.7%(0.0223) *
Intestinibacter1.1% ± 2.8%2.2% ± 5.1%NS (0.4192)
Lactobacillus1.9% ± 8.0%1.4% ± 3.2%NS (0.2338)
Akkermansia1.3% ± 4.8%1.3% ± 5.9%NS (0.2690)
Clostridium_sensu_stricto_10.8% ± 1.3%1.7% ± 3.8%NS (0.5143)
Fusicatenibacter1.9% ± 3.3%0.5% ± 1.3%(0.0171) *
Ruminococcus1.6% ± 2.6%0.7% ± 1.6%NS (0.0533)
Agathobacter2.1% ± 3.2%0.2% ± 0.6%(0.0302) *
Ruminococcaceae_ge1.1% ± 1.2%1.1% ± 4.8%(0.0348) *
Prevotella1.5% ± 5.5%0.6% ± 1.4%NS (0.9842)
Romboutsia1.6% ± 3.4%0.3% ± 0.9%NS (0.0148)
Clostridia_ge1.5% ± 1.4%0.4% ± 0.5%(0.0131) *
Anaerostipes1.6% ± 1.6%0.3% ± 0.5%(0.0032) **
Oscillospirales_ge1.2% ± 1.8%0.6% ± 1.3%NS (0.4386)
Coprococcus1.5% ± 1.8%0.4% ± 0.8%NS (0.1309)
Erysipelatoclostridium1.1% ± 5.8%0.7% ± 1.2%NS (0.9156)
Erysipelotrichaceae_ge0.8% ± 4.9%0.8% ± 2.3%NS (0.6146)
Subdoligranulum1.0% ± 1.7%0.4% ± 1.0%NS (0.0719)
Clostridia_UCG-014_ge0.8% ± 2.3%0.5% ± 2.6%NS (0.9156)
Lachnospiraceae_NK4A136_group1.2% ± 5.4%0.1% ± 0.5%(0.0085) **
Veillonella0.4% ± 0.9%0.9% ± 2.3%NS (0.8942)
Legend: *: p < 0.05; **: p < 0.01; NS: Not significant.
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MDPI and ACS Style

Martinez, E.; Crevecoeur, S.; Thirion, C.; Grandjean, J.; Fall, P.A.; Hayette, M.-P.; Michel, M.; Taminiau, B.; Louis, E.; Daube, G. Gut Microbiota Associated with Clostridioides difficile Carriage in Three Clinical Groups (Inflammatory Bowel Disease, C. difficile Infection and Healthcare Workers) in Hospital Field. Microorganisms 2023, 11, 2527. https://doi.org/10.3390/microorganisms11102527

AMA Style

Martinez E, Crevecoeur S, Thirion C, Grandjean J, Fall PA, Hayette M-P, Michel M, Taminiau B, Louis E, Daube G. Gut Microbiota Associated with Clostridioides difficile Carriage in Three Clinical Groups (Inflammatory Bowel Disease, C. difficile Infection and Healthcare Workers) in Hospital Field. Microorganisms. 2023; 11(10):2527. https://doi.org/10.3390/microorganisms11102527

Chicago/Turabian Style

Martinez, Elisa, Sebastien Crevecoeur, Carine Thirion, Jessica Grandjean, Papa Abdoulaye Fall, Marie-Pierre Hayette, Moutschen Michel, Bernard Taminiau, Edouard Louis, and Georges Daube. 2023. "Gut Microbiota Associated with Clostridioides difficile Carriage in Three Clinical Groups (Inflammatory Bowel Disease, C. difficile Infection and Healthcare Workers) in Hospital Field" Microorganisms 11, no. 10: 2527. https://doi.org/10.3390/microorganisms11102527

APA Style

Martinez, E., Crevecoeur, S., Thirion, C., Grandjean, J., Fall, P. A., Hayette, M. -P., Michel, M., Taminiau, B., Louis, E., & Daube, G. (2023). Gut Microbiota Associated with Clostridioides difficile Carriage in Three Clinical Groups (Inflammatory Bowel Disease, C. difficile Infection and Healthcare Workers) in Hospital Field. Microorganisms, 11(10), 2527. https://doi.org/10.3390/microorganisms11102527

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