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. 2021 May 1;10(2):e1181. doi: 10.1002/mbo3.1181

Gut dysbiosis and clinical phases of pancolitis in patients with ulcerative colitis

Brenda Maldonado‐Arriaga 1,2, Sergio Sandoval‐Jiménez 1, Juan Rodríguez‐Silverio 3, Sofía Lizeth Alcaráz‐Estrada 4, Tomás Cortés‐Espinosa 5, Rebeca Pérez‐Cabeza de Vaca 6, Cuauhtémoc Licona‐Cassani 7, July Stephany Gámez‐Valdez 7, Jonathan Shaw 8, Paul Mondragón‐Terán 6, Cecilia Hernández‐Cortez 9, Juan Antonio Suárez‐Cuenca 1,✉, Graciela Castro‐Escarpulli 2,✉
PMCID: PMC8087925  PMID: 33970546

Abstract

Ulcerative colitis (UC) is a frequent type of inflammatory bowel disease, characterized by periods of remission and exacerbation. Gut dysbiosis may influence pathophysiology and clinical response in UC. The purpose of this study was to evaluate whether gut microbiota is related to the active and remission phases of pancolitis in patients with UC as well as in healthy participants. Fecal samples were obtained from 18 patients with UC and clinical‐endoscopic evidenced pancolitis (active phase n = 9 and remission phase n = 9), as well as 15 healthy participants. After fecal DNA extraction, the 16S rRNA gene was amplified and sequenced (Illumina MiSeq), operational taxonomic units were analyzed with the QIIME software. Gut microbiota composition revealed a higher abundance of the phyla Proteobacteria and Fusobacteria in active pancolitis, as compared with remission and healthy participants. Likewise, a marked abundance of the genus Bilophila and Fusobacteria were present in active pancolitis, whereas a higher abundance of Faecalibacterium characterized both remission and healthy participants. LEfSe analysis showed that the genus Roseburia and Faecalibacterium were enriched in remission pancolitis, and genera Bilophila and Fusobacterium were enriched in active pancolitis. The relative abundance of Fecalibacterium and Roseburia showed a higher correlation with fecal calprotectin, while Bilophila and Fusobacterium showed AUCs (area under the curve) of 0.917 and 0.988 for active vs. remission pancolitis. The results of our study highlight the relation of gut dysbiosis with clinically relevant phases of pancolitis in patients with UC. Particularly, Fecalibacterium, Roseburia, Bilophila, and Fusobacterium were identified as genera highly related to the different clinical phases of pancolitis.

Keywords: active and remission phase, gut dysbiosis, gut microbiota, pancolitis

The purpose of this study was to evaluate whether gut microbiota is related to the active and remission phases of pancolitis in patients with ulcerative colitis, as well as in healthy participants.

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1. INTRODUCTION

The intestinal tract houses a large and diverse community of microorganisms collectively referred to as the gut microbiota. These microorganisms contribute to human health by promoting both immune and metabolic functions (Burman et al., 2016; Chassaing et al., 2017). It is widely accepted that the gut microbiota has a crucial role in regulating the function of the intestinal epithelium, the immune system, and its homeostasis within the gut (Imhann et al., 2018). The term “dysbiosis” refers to an imbalance in the composition and function of the microbiota (Danilova et al., 2019; Nishida et al., 2017; Vemuri et al., 2017), whereas gut dysbiosis along with the altered host immune response has been observed in clinically relevant immunological and inflammatory diseases, such as Ulcerative Colitis (UC), which is a frequent type of Inflammatory Bowel Disease, characterized by periods of remission and exacerbation (Nishida et al., 2018). UC has been classified according to its extent and severity in the so‐called Montreal classification, defining the extent as E1 indicates ulcerative proctitis; E2 as UC on the left side; and E3 as extensive UC or pancolitis. Likewise, the severity of the disease is classified into clinical remission (S0), mild disease (S1), moderate disease (S2), and severe disease (S3). Pancolitis is considered the most serious clinical phase of UC, and its involvement represents 10%–15% of all UC (Mohammed Vashist et al., 2018).

Although a causal effect has not been evidenced; nowadays, it is widely accepted that altered interactions between gut dysbiosis and the intestinal immune system promote UC (Imhann et al., 2018), while the precise nature of the intestinal microbiota dysfunction in UC remains to be elucidated. In this sense, the gut microbiota has been considered as a “fingerprint” reflecting the natural history of UC, since it associates with the clinical severity, remission, and flare‐up responses (Marchesi et al., 2016). Gut microbiota from patients with UC has been characterized by a reduced number of bacteria with anti‐inflammatory capacities and a higher proportion of bacteria with pro‐inflammatory properties. Microbiota diversity is also reduced; low abundance of microorganisms like Firmicutes and high abundance of Proteobacteria have been found (Manichanh et al., 2012; Yu, 2018). Rapid development and application of culture‐independent, high throughput DNA‐based sequencing technologies have elicited the recognition of such dysbiotic signatures, which may play a role during the early identification of clinical‐therapeutic phases of UC, and particularly useful in severe clinical manifestations like pancolitis (Peterson et al., 2008; Rintala et al., 2017). Despite this notion, the relation of gut dysbiosis with pancolitis has been poorly characterized. Given the increasing UC prevalence worldwide, including Latin American countries (Bosques‐Padilla et al., 2011; Farrukh & Mayberry, 2014), along with the strong interest to understand the relation of dysbiotic gut microbiota with most serious phases of UC like pancolitis, the present study aimed to characterize gut microbiota from patients with UC and different clinical phases of pancolitis.

2. METHODS

2.1. Study population

In this cross‐sectional study, groups of 18 patients with UC and clinical‐endoscopic‐evidenced pancolitis (active phase n = 9 and remission phase n = 9) as well as 15 healthy participants, attended the Department of Gastroenterology, Centro Médico Nacional ‘20 de Noviembre’ ISSSTE, Mexico City, Mexico, between July 2017 and January 2019. Patients with concomitant irritable bowel syndrome, pseudomembranous colitis, and antibiotic treatment during the previous 4 weeks were excluded. Pancolitis was defined according to clinical, radiological, endoscopic, and histological criteria (Van Assche et al., 2010). All the patients had experienced at least one previous episode of pancolitis before their recruitment. The study at remission phase of pancolitis received therapy based on pharmacological treatment, a fiber‐rich diet, and the use of probiotics (Owczarek et al., 2016). Some patients with active pancolitis did not receive treatment due to non‐medical reasons, like the inability to attend their follow‐up appointment. Characteristics like age, time since disease onset, affected gastrointestinal location, frequency of bowel movements, and presence of blood in stools were collected from clinical records. Clinical activity was defined as a value of 4 or higher for colitis activity index (CAI [Clinical activity index], used for ulcerative colitis), and clinical remission was defined with CAI value <2 for at least 3 months (Siegel et al., 2018; Van Assche et al., 2010). Healthy participants were volunteers without previous history of chronic disease, belonging to a different family than those with UC, but with a similar diet, as assessed by a 24‐h recall (R24H) survey (Parks et al., 2018).

2.2. Stool samples

Stool samples were collected either during hospitalization (active pancolitis) or prepared at home and collected during programmed medical consultation (remission phase and healthy participants); samples were stored at home between 4 and 8°C for up to 24 h, before hospital collection. Samples were collected with the help of a stool sampling kit, which consisted of a plastic lining to cover the toilet, two stool sample tubes with spoons, two plastic bags, and a clipping system for safe closure of the outer bag. Samples were labeled upon arrival, and one part was processed for fecal calprotectin assay; while the remaining was aliquoted and frozen directly at −80°C for further microbiota analyses (Tedjo et al., 2015).

2.3. DNA extraction of fecal samples

Frozen stool samples were thawed on ice, and approximately 200 mg were added to dry‐bead tubes with lysis buffer (AllPrep PowerFecal DNA, Qiagen). The stool samples were homogenized followed by a combined chemical and mechanical lysis by using prefilled lysis tubes. Inhibitors commonly present in stool samples were then removed before isolation of nucleic acids. DNA isolation was continued by using the AllPrep DNA MiniElute spin column, according to the manufacturer's instructions. DNA was eluted in 30 μl EB‐buffer. Negative control samples (consisted only of PCR grade water) were handled in the same way as the fecal samples, to rule out contamination during the isolation procedure (Tedjo et al., 2015). A Nanodrop ND‐1000 (NanoDrop Technologies), was used to estimate DNA concentrations. DNA concentration was adjusted to a final concentration of 10 ng/µl (Tedjo et al., 2016).

2.4. Amplification and sequencing of bacterial 16S rRNA gene

The V3 and V6 hypervariable regions of the 16S rRNA gene were PCR amplified from microbial genomic DNA with the forward (TAT GGT AAT TGT GTG CCA GCM GCC GCG GTA A) and reverse (GGA CTA CHV GGG TWT CTA AT) primers. The primers were designed with overhanging adapters (Forward: AATGATA CGGC GACC ACCGA GATCT ACAC), (Reverse: GGA CTA CHV GGG TWT CTA AT) for annealing to Illumina universal index sequencing adaptors that were added in a later PCR (Dubinsky & Braun, 2015; Haas et al., 2011). The PCR products were evaluated by 2% agarose gel electrophoresis and purified. After purification, spectrophotometry was used to quantify the PCR products. Samples were normalized to a final concentration of 2 nM.

2.5. Microbial composition and analysis by Illumina

A two‐steps PCR methodology was used to prepare 16S rRNA libraries. For the first‐step, extracted DNA was quantified and samples were diluted to the amount of the least concentrated sample. Then 2 μL were used for the PCR reaction (quadruplicates) at the following conditions: 98°C for 30 s [98°C for 30 s, 52°C for 30 s, 72°C for 30 s] for 20 cycles, 4°C hold. Then, the 4 resulting reactions were amalgamated. The samples were then cleaned by using AmpureXP beads and eluted in 40 μL final volume. For the second step, a 4 μL of the obtained DNA was mixed with primers PE‐PCR‐III‐F and PE‐PCR‐IV‐barcode, in a 25 μL final volume PCR reaction (quadruplicates), at run cycle conditions of 98°C for 30 s [98°C for 30 s, 83°C for 30 s, 72°C for 30 s] for 7 cycles, 4°C hold. Then, the 4 PCR reactions were pooled and the products were cleaned by using 16S Metagenomic Sequencing Purification beads (Caporaso et al., 2012). The DNA library concentrations were quantified and then multiplexed to provide the same amount of DNA in each sample. A single Illumina MiSeq lane set for paired‐end 300‐basepair reads was used to sequence the libraries. Paired‐end reads of 16S rRNA gene libraries were generated with the Illumina, MiSeq platform. A total of 10,629,314 raw sequences were obtained, with further quality filter and binned resulting in 8,349,697 usable sequences, with a sample average of 378,489 per sequence. Sequences were clustered and singletons removed; the data were rarefied to control for variations in sequencing efforts. The datasets supporting the conclusions of this article are available in https://www.ncbi.nlm.nih.gov/bioproject/596546, under the ID PRJNA596549 repository. The analyses of taxonomy and diversity of the samples were performed taking as a reference the SILVA database (Bokulich et al., 2013; Bolyen et al., 2018).

2.6. Bioinformatic analysis

The Illumina Real‐Time Analysis software (version 1.17.28) was used for base calling, image analysis, and error estimation. Sequencing provided read lengths of 300 bp, which were demultiplexed, verifying that the paired ends provided a clear overlap. The paired ends were then linked together with the fastq‐join program (http://code.google.com/p/ea‐utils/). Separate files of each sample (R1 and R2) were entered in fastq format by using the split_libraries_fastq.py pipelines. Sequences that had quality value (QV) scores of ≥20 (Phred score of 20) for no‐less than 99% of the sequence were selected for further study. All sequences with ambiguous base calls were discarded. Subsequently, the sequences were grouped in Operational Taxonomic Units (OTU), where the pick_closed_reference_otus.py pipelines were used. QIIME, which uses the BIOM format, was used to represent OTU tables (Bolyen et al., 2018; Dubinsky & Braun, 2015; Edgar et al., 2011). Analyses of sequence reads were performed by using SILVA multiclassifier tools with a 97% confidence threshold (Navas‐Molina et al., 2013). Subsequent analyses of diversity index were all performed based on this output normalized data (Allali et al., 2017; Aßhauer et al., 2015). To perform the diversity analyses, the core_diversity_analyses.py pipelines were executed with the pipeline alpha_diversity.py. Alpha diversity metrics were calculated with QIIME, that is, the observed OTUs (observed species) and the phylogenetic diversity or complete tree PD (PD_whole_tree) (Bolyen et al., 2018); whereas the weighted distances of UniFrac of the beta diversity were determined with beta_diversity.py pipelines, and the R software v.2.15.3 was used to display the results (Barwell et al., 2015; Chao et al., 2006; Hass et al., 2011). The “Linear discriminant analysis (LDA) effect size (LEfSe)” algorithm was performed with the Galaxy online platform to determine the different relative abundances of bacterial communities among the different groups of patients. The significance thresholds used were those recommended in the program. LEfSe considered statistical significance between the different biological classes with a Kruskal–Wallis test and subsequently analyzed the biological significance with a Wilcoxon test (Segata et al., 2011).

2.7. Fecal calprotectin test

Fecal calprotectin (FC) was measured as a marker of intestinal inflammation by using a commercial ELISA (MyBioSource), following the manufacturer's instructions. Optical densities were read at 405 nm with a microplate ELISA reader. Samples were tested in duplicate, and results were calculated from a standard curve and expressed as μg/g stool (Chang & Cheon, 2018).

2.8. Statistical analysis

Data normality was evaluated with the Shapiro–Wilk test. Quantitative data were compared by non‐paired, two‐tail, t test, or U‐Mann Whitney, as appropriate. Analyses of the sequences were carried out in the QIIME and R software. Multivariate nonparametric ANOVA was used to determine the differences in the abundance of the microbial community between groups, whereas Unifrac was used to compare the abundance of the specific microbiota and the concentration of fecal calprotectin, and it was visualized by principal coordinate analysis. To test whether the clusters of microbiota from the study conditions were different between them, Unifrac p‐values, based on principal coordinate analysis applied to the matrix distance, were performed to allow pairwise comparison of microbiota from clinical phases of pancolitis and healthy controls (Caporaso et al., 2010; Lawley & Tannock, 2017). Finally, the Area Under the Curve (AUC) was calculated to explore whether the relative abundance of the bacterial genus most frequently observed (cutoff value according to ROC analysis) may predict UC severity. The Statistical Package for Social Sciences SPSS v.18.0. was used, and p‐values of ≤0.05 (2‐tailed) were considered to be statistically significant.

3. RESULTS

3.1. Study population

Eighteen patients diagnosed with UC and pancolitis, mean aged 37‐years‐old constituted the study population, who were further divided according to the disease activity, as demonstrated by the CAI and fecal calprotectin values. A cohort of sex‐ and age‐matched, healthy volunteers were included for comparison. Baseline clinical‐demographic characteristics are shown in Table 1. Stools from patients with active pancolitis were characterized by being watery, corresponding to Bristol type 7, and bloody (two points in rectal bleeding of Mayo Clinical Score) in all cases.

TABLE 1.

Demographic and clinical characteristics of the study population (n = 33)

AP

(n = 9)

RP

(n = 9)

HS

(n = 15)

 p‐value
Age (years old) 36.9 ± 1.4 37.9 ± 1.1 36. 4 ± 1.6 NS
Male 7 (77.7) 6 (66.6) 6 (40) NS
Index CAI 11.0 ± 1.3 1.7 ± 0.6 N/A <0.05
Montreal A (age at onset)
A1 (16) None None N/A NS
A2 (17–40) 7 (77.7) 6 (66.6)
A3 (41) 2 (22.2) 3 (33.3)
Montreal Score Extensive
E1 ulcerative proctitis None None N/A NS
E2 left‐sided UC None None
E3 extensive UC 9 (100) 9 (100)
Montreal Score Severity
S0 silent colitis None 9 (100) N/A NS
S1 mild colitis None None
S2 moderate colitis None None
S3 severe colitis 9 (100) None
Endoscopy Mayo Score
0 NONE N/A N/A N/A
1 NONE
2 NONE
3 9 (100)
Frequency of bowel movements ≥ 10 2 to 4 1 to 2 NS
Presence of blood in stools 9 (100) None None NS
Time (years) from diagnosis
≥10 8 (88.8) 6 (66.6) N/A NS
≤10 1 (11.1) 3 (33.3)
Currently smoking 2 (22.2) None None N/A
Medication use
Mesalazine None 6 (66.6) N/A NS
Corticosteroids 2 (22.2) 2 (22.2)
Infliximab none 1 (11.1)
No treatment 7 (77.7) None
Fecal calprotectin (μg/g) 480.1 ± 13.7 99.6 ± 8.9 21.6 ± 4.3 p < 0.05
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Quantitative data were resumed as mean ± SD and qualitative data as n (%). Statistical analysis was performed with a two‐way U‐Mann Whitney and Fisher's test, as appropriate.

Abbreviations: AP, active pancolitis; HS, healthy subjects; N/A, not applicable; NS, non‐significant; RP Remission pancolitis.

3.2. Microbial composition and diversity

The analysis of microbiome from fecal samples showed the relative abundance of OTUs at different taxonomic levels (Figure 1a, b, Table 2). OTUs were created out of the filtered tags and were grouped at a similarity of 97%. This gave a total of 1533 OTUs for the 33 samples used in this study. Taxonomic composition at the level of phyla is summarized in Figure 1a. The bacterial phyla Firmicutes, Bacteroidetes, Proteobacteria, and Fusobacteria were the most common sequences showing 97% of similarity. For remission pancolitis and healthy participants, Firmicutes was the most abundant bacterial phylum. Microbiota abundance in remission pancolitis was very similar to that observed in healthy participants, at the phyla level; whereas, active pancolitis showed phylum Proteobacteria as the most abundant. Genus distribution provided a subjective perception of the difference between the relative abundance of patients with active vs. remission pancolitis and healthy participants (Figure 1b) The most abundant genera in active pancolitis were Fusobacterium and Bilophila. For the group of remission pancolitis and healthy participants, the most abundant genera were Faecalibacterium, Roseburia, and Bacteroides (Appendix: Table A1, A2, and A3). Regarding bacterial alpha diversity comparison, pancolitis activity was related to the lowest community richness (Chao index) and diversity (Shannon index) (Figure 1c, d), whereas community richness and diversity were similar between remission pancolitis and healthy participants. Likewise, significant differences in species dominance of microbiota (Simpson index) (Figure 1e) were found between active vs. remission pancolitis and healthy participants (Appendix: Table A4).

FIGURE 1.

FIGURE 1

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Characteristics of the microbial community in pancolitis, remission, and healthy participants. (a) Taxonomic composition distribution in samples of phylum level. (b) The taxonomic composition distribution in samples of genus level. (c) Alpha diversity index boxplot, including community richness (Chao), (d) diversity (Shannon), and (e) Dominance (Simpson). the p‐value indicates the statistical significance of two‐way  ANOVA. Abbreviations: AP, active pancolitis; HS, healthy subjects; RP, Remission pancolitis

TABLE 2.

Comparison of gut dysbiosis in fecal samples from the pancolitis population

Most abundant gut microbiota

AP

(n = 9)

RP

(n = 9)

HS

(n = 15)
Phylum Firmicutes 4.0 ± 1.5*/** 50.0 ± 5.2 54.6 ± 6.4
Bacteroidetes 15.0 ± 0.2*/** 46.0 ± 4.2 45.0 ± 3.4
Proteobacteria 52.5 ± 5.6*/** 0.0 ± 0.0 2.5 ± 1.0
Fusobacteria 30.0 ± 2.5*/** 0.0 ± 0.0 0.0 ± 0.0
Actinobacteria 1.5 ± 0.5 2.5 ± 1.0 2.5 ± 1.0
Verrucomicrobia 0.0 ± 0.0 1.5 ± 0.3 1.5 ± 0.5
Genus Lactobacillus 0.0 ± 0.0*/** 5.6 ± 4.2 8.5 ± 2.4
Faecalibacterium 0.5 ± 1.5*/** 21.0 ± 8.7*** 40.2 ± 4.9
Roseburia 0.0 ± 0.0*/** 5.4 ± 7.2*** 7.3 ± 7.4
Bacteroides 7.6 ± 4.1 11.5 ± 10.8*** 3.5 ± 2.1
Bilophila 12.0 ± 9.1*/** 0.0 ± 0.0 0.0 ± 0.0
Fusobacterium 35.6 ± 15.4*/** 0.0 ± 0.0 0.0 ± 0.0
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Relative abundance is shown as mean ± SD and (†) percentage of the relative abundance in relation to that observed in healthy participants. Statistical analysis was performed with two‐way ANOVA. Significant difference (p < 0.01) between: (*) AP vs. RP; (**) AP vs. HS; (***) RP vs. HS.

Abbreviations: AP, active pancolitis; HS, healthy subjects; RP, Remission pancolitis.

Interestingly, the relative abundance of the most frequent bacterial genus observed in active pancolitis was significantly different from those corresponding to remission pancolitis and healthy participants (Table 2; Figure 2).

FIGURE 2.

FIGURE 2

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Abundance analyses. Whisker‐box plots comparing bacterial genera in the fecal microbiota of pancolitis, remission, and healthy participants. Only the four most relevant bacterial genera, according to abundant taxonomic composition, were analyzed: (a) Fusobacterium; (b) Bilophila; (c) Faecalibacterium, and (d) Roseburia. the p‐value indicates the statistical significance of two‐way ANOVA. Abbreviations: AP, active pancolitis; HS, healthy subjects; RP, Remission pancolitis

The structure of the most abundant microbial species was explored with the biomarker of UC severity, fecal calprotectin; where the clusters of the different phases are significantly separated based on fecal calprotectin (Figure 3).

FIGURE 3.

FIGURE 3

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Gut microbiota abundance and UC severity marker. The plots show the clusters between the bacterial genera in the fecal microbiota with calprotectin, a biomarker of UC severity. The four most abundant bacterial genera, according to abundant taxonomic composition: (a) Fusobacterium; (b) Bilophila; (c) Faecalibacterium, and (d) Roseburia; were analyzed in the subgroups of active pancolitis (green), remission pancolitis (pink), and healthy participants (blue)

The microbial community structure was investigated by using the principal component analysis, which allows splitting particular microbial communities according to their potential relation with clinical scenarios. Highly defined microbiota clusters were distributed according to clinical phases of pancolitis, where the distribution of active pancolitis clusters was significantly separated from those of remission pancolitis and healthy participants, showing these two last clusters a closer distribution between them (Figure 4).

FIGURE 4.

FIGURE 4

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Principal component analysis. The overall structure of the fecal microbiota was plotted according to the different clinical scenarios (active pancolitis [green], remission pancolitis [pink], and healthy participants [blue]). Each data point represents an individual sample

Linear discriminant analysis (LDA) effect size (LEfSe) was used to determine differentially abundant bacterial taxa between active and remission pancolitis. Patients with active pancolitis were related to the phylum Proteobacteria and Fusobacteria, while patients in remission pancolitis were related to the phylum Firmicutes and Bacteroidetes (α = 0.01, LDA score >3.0) (Figure 5; Appendix: Table A5).

FIGURE 5.

FIGURE 5

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The linear discriminant analysis effect size (LEfSe) analysis of fecal microbiota with active pancolitis vs. remission pancolitis. (A) Bar graph showing LDA scores of phylum and cladogram generated by LEfSe indicating differences at phylum among active pancolitis and remission pancolitis. (b) Bar graph showing LDA scores of genus and cladogram generated by LEfSe indicating differences at genus among active pancolitis and remission pancolitis. Each successive circle represents a phylogenetic level. Regions in red indicate taxa enriched in active pancolitis, while regions in green indicate taxa enriched in remission pancolitis. Differing genera are listed on the right side of the cladogram

Finally, we explored whether the relative abundance of the bacterial genus most frequently observed (cutoff values according to ROC analysis: active vs. remission pancolitis Bilophila 10%, Faecalibacterium 40%; pancolitis vs. healthy participants Bilophila 10%, Fusobacterium 10%) were related with active pancolitis. Bilophila and Fusobacterium showed an AUC of 0.917 and 0.988 for active vs. remission pancolitis, while similar AUCs were observed for active pancolitis vs. healthy participants, but not for remission pancolitis vs. healthy participants (Appendix: Table A6).

4. DISCUSSION

Our main finding was the significant differences of fecal microbiota composition from patients with active vs. remission pancolitis, with potential clinical application. Our study population was constituted of young aged patients with UC and severe stage of pancolitis. Scarce studies have explored gut microbiota in such population, probably due to the low prevalence of pancolitis between cases with UC. However, gut dysbiosis observed in patients with active vs. remission pancolitis in the present study is comparable with other reports (Alam et al., 2020; Danilova et al., 2019; Franzosa et al., 2019; Halfvarson et al., 2017; Imhann et al., 2018; Kumari et al., 2013; Sha et al., 2013). Our results were further validated by comparison with gut microbiota from healthy participant controls from a family who shares a similar diet and they are expected to exert a lower influence on the gut microbiota composition.

Our results showed an increased proportion of the phylum Proteobacteria and the genera Fusobacterium and Bilophila in active pancolitis, which was significantly different from the group of remission pancolitis and healthy participants, who shared a microbiota profile of higher proportion of phylum Firmicutes, and genera Faecalibacterium and Roseburia (Vester‐Andersen et al., 2019). These results are similar to those obtained by Franzosa et al., 2019, Kumari et al., 2013; Sha et al., 2013. Particularly, the findings of a reduced proportion of the genera Faecalibacterium and Roseburia in active pancolitis, and their restoration in remission pancolitis, has also been observed in previous reports (Khan et al., 2019; Man et al., 2011; Palmela et al., 2018; Vigsnæs et al., 2012). Such characterization is relevant due to scanty information regarding microbiota abundance in the remission phase of pancolitis, whereas consistent identification of specific genus in the remission phase may be useful to design more efficient therapeutic strategies, prompted to reduce UC severity. Interestingly, a particular bacterial composition like Faecalibacterium was shared by remission pancolitis and healthy participants. These bacteria have been reported to metabolize dietary components that promote colonic motility, maintain the intestinal immune system, and anti‐inflammatory properties (Dicks et al., 2018). Consistently, reduced abundance of these microorganisms has been associated with a higher rate of recurrence of UC (Alam et al., 2020; Al‐Bayati et al., 2018; Ferreira‐Halder et al., 2017; Kinross et al., 2011; Lopez‐Siles et al., 2017; Machiels et al., 2014) although increased levels of Faecalibacterium in stool samples have been associated with a lower activity index, supporting their role as potential biomarkers of disease severity and outcome, as suggested in other studies (Paramsothy et al., 2019; Wang et al., 2018).

Other findings were the higher abundance of the phylum Proteobacteria, and particularly the expansion of the genus Bilophila in active pancolitis. It is known that the relative abundance of Bilophila is promoted by diets enriched in saturated fats, which increase bacterial resistance to bile elimination. Furthermore, a change in the type of fat consumed affects the composition of gut microbiota, which may modify the onset and severity of UC (Devkota & Chang, 2015; Pittayanon et al., 2020; Torres et al., 2018). Dietary modifications involving excessive consumption of fried food, dairy products, and wheat flour are associated with the development of severe diarrhea in patients with active pancolitis (Keshteli et al., 2019). In the present study, we consider that there is no significant effect derived from the modification of the diet, since the population consumed a soft diet with abundant hydration; without a specific recommendation for dietary restrictions, even during active pancolitis.

Certain species of Fusobacterium show pro‐inflammatory, invasive, and adherent capacity to the intestinal mucosa, while the increased proportion of Bilophila in the gut promotes an immune response mediated by Th1, resulting in the development of colitis in experimental mice models (Bashir et al., 2016; Chen et al., 2020; Hirano et al., 2018; Liu et al., 2019; Ohkusa et al., 2002; Tahara et al., 2015; Wright et al., 2015). Although a direct pathophysiological mechanism is not possible to elucidate from the present study, we can propose that the relative abundance of some species is associated with the degree of inflammation and pancolitis, derived from the inverse relationship observed between the abundance of Fecalibacterium and Roseburia with calprotectin, a biomarker of severity of UC, which was consistent with a recent report (Björkqvist et al., 2019; Yu et al., 2019). Likewise, differences in bacterial richness, diversity, and dominance were highly related to the clinical scenarios studied. Remarkably, remission pancolitis and healthy participants showed the highest relative abundance of the phylum Firmicutes, which contributed to most of the bacterial diversity and richness (Björkqvist et al., 2019; Ganji‐Arjenaki & Rafieian‐Kopaei, 2018; Jandhyala et al., 2015). Further analyses of cluster distribution of bacterial communities showed differences in active pancolitis, as compared to remission pancolitis and healthy participants, which was consistent with previous studies showing a difference in the structure of microbiota between active pancolitis and healthy participants (Forbes et al., 2016; Havenaar, 2011; Louis & Flint, 2017).

Furthermore, studies characterizing gut microbiota composition and its modification during pancolitis are relevant, since (a) pancolitis provides a higher risk for colorectal cancer, whereas gut dysbiosis is thought to facilitate colorectal cancer development; (b) the study of gut microbial communities during clinical phases of pancolitis contributes to a better understanding of potential interactions with the host immune response; (c) characterization of a specific genus of gut microbial communities may own potential clinical application derived from their association with pancolitis or remission phases; and (d) specific microbial manipulation, concomitant to antibiotic use, is currently used as a therapeutic approach for UC (Alard et al., 2018; Devkota & Chang, 2015; Galazzo et al., 2019).

Finally, gut dysbiosis has been proposed as an important contributing factor to the increasing prevalence of pancolitis, with a potential role for the related clinical‐therapeutic phases (Halfvarson et al., 2017; Miyoshi et al., 2018; Petrof et al., 2013). Consistently, we found a significant ability of the genus Bilophila and Fusobacterium to selectively associate with cases of activity/remission pancolitis (Fukuda & Fujita, 2014; Guo et al., 2019).

To our knowledge, this is the first study that investigated the composition of fecal microbiota in Mexican patients with active and remission pancolitis. Our study faces some limitations. First, 16S rRNA analysis provides the taxonomic composition of the microbes present in the community and does not provide an analysis of the role of the microbiota in the disease. Second, data analysis may show limitations regarding the specific characterization of microbiota composition as an isolated endpoint; however, we think that the analysis performed yields an adequate interpretation within a translational context, highlighting the role of microbiota diversity in the clinical phases of pancolitis. Third, larger sample size may be required to confirm our data and further research is required to better characterize the role of gut microbiota in patients with pancolitis.

Here, we provide a broad investigation of the fecal microbial community in Mexican patients presenting pancolitis. We demonstrate differences in the microbiota communities in patients with active pancolitis, remission pancolitis, and healthy participants. Selective association of gut dysbiosis with active/remission pancolitis may set the basis for further applications of non‐invasive methods, clinically useful for early identification of disease severity.

CONFLICTS OF INTEREST

None declared.

AUTHOR CONTRIBUTIONS

Brenda Maldonado Arriaga: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Sergio Sandoval‐Jimenez: Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Juan Rodriguez ‐Silverio: Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Sofía Lizeth Alcaráz‐Estrada: Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Tomás Cortés‐Espinosa: Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Rebeca Pérez‐Cabeza de Vaca: Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Cuauhtémoc Licona‐Cassani: Data curation (equal); Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). July Stephany Gámez‐Valdez: Data curation (equal); Formal analysis (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Jonathan Shaw: Formal analysis (equal); Investigation (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Paul Mondragón‐Terán: Investigation (equal); Writing‐original draft (equal); Writing‐review & editing (equal). Cecilia Hernández‐Cortez: Writing‐original draft (equal); Writing‐review & editing (equal). Juan Antonio Suárez‐Cuenca: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Validation (equal); Writing‐original draft (equal); Writing‐review & editing (equal). GRACIELA CASTRO‐ESCARPULLI: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Supervision (equal); Writing‐original draft (equal); Writing‐review & editing (equal).

ETHICS STATEMENT

The study was carried out according to the 1975 ethical guidelines of the Declaration of Helsinki. All participants provided written informed consent. The study was approved by the Local Committees of Research, Ethics in Research and Biosafety of the Centro Médico Nacional ‘20 de Noviembre’ ISSSTE, Mexico City (Protocol ID No. 358.2017).

ACKNOWLEDGMENTS

This study was funded by the E‐015 institutional program and Secretaría de Investigación y Posgrado del Instituto Politécnico Nacional (SIP 20200675 and 20194936) (IPN). BMA held a scholarship from CONACyT. GCE received support from Estímulos al Desempeño en Investigación, Comisión y Fomento de Actividades Académicas (Instituto Politécnico Nacional), and Sistema Nacional de Investigadores (SNI, CONACyT), CHC received support from Sistema Nacional de Investigadores (SNI, CONACyT). We would like to thank Sofia Mulia for kindly correcting the style of the manuscript.

1.

TABLE A1.

List of bacterial taxa with their OTUs ID in active pancolitis

No. OTUs ID Bacteria No. OTUs ID Bacteria
1 925 Brenneria 49 471278 Buttiauxella
2 1006 Curtobacterium 50 479863 Shimwellia
3 1163 Microbacteriaceae 51 484145 Enterobacteriaceae
4 4981 Rothia 52 489853 Cronobacter
5 5294 Micrococcaceae 53 498961 Klebsiella
6 5605 Trueperella 54 501249 Enterobacteriaceae
7 6509 Actinomycetaceae 55 504890 Phocoenobacter
8 7967 Actinomyces 56 526378 Pasteurellaceae
9 8584 Actinomycetaceae 57 528975 Pasteurellaceae
10 9376 Anaerofilum 58 542569 Mannheimia
11 10096 Ruminococcaceae 59 554567 Actinobacillus
12 10256 Sporosarcina 60 561469 Aggregatibacter
13 10458 Planococcaceae 61 566893 Pasteurellaceae
14 10725 Gemella 62 576178 Haemophilus
15 11203 Bacillales 63 580361 Pasteurellaceae
16 14506 Pediococcus 64 603577 Fusobacteriaceae
17 14625 Lactobacillaceae 65 640658 Fusobacteriaceae
18 15213 Tetragenococcus 66 648973 Ilyobacter
19 16721 Enterococcaceae 67 689746 Propionigenium
20 19563 Delftia 68 704128 Fusobacteriaceae
21 125300 Comamonadaceae 69 728910 Bacteroides
22 128695 Burkholderiales 70 731897 Bacteroidaceae
23 132065 Neisseriaceae 71 735981 Blautia
24 136219 Rhodospirillum 72 765640 Lachnospiraceae
25 139865 Rhodospirillaceae 73 777038 Aeromonas
26 174563 Rhodospirillales 74 796328 Aeromonadaceae
27 176398 Gemmiger 75 857896 Coprococcus
28 182397 Hyphomicrobiaceae 76 871259 Lachnospiraceae
29 187569 Hyphomicrobiaceae 77 901257 Ruminococcaceae
30 195637 Rhizobiales 78 943078 Ruminococcus
31 198759 Campylobacterales 79 963365 Ruminococcaceae
32 201289 Campylobacter 80 1005973 Pseudomonas
33 206986 Campylobacteraceae 81 1019639 Pseudomonadaceae
34 217893 Campylobacterales 82 1025697 Enterococcus
35 241479 Pseudomonadales 83 1056986 Enterococcaceae
36 274893 Aeromonadaceae 84 1078963 Bacilli
37 289963 Shewanella 85 1086935 Escherichia
38 301329 Shewanellaceae 86 1087789 Fusobacterium
39 325698 Alteromonadales 87 1096348 Fusobacteriaceae
40 341449 Enterobacteriaceae 88 1098986 Fuobacteria
41 371893 Yersinia 89 1118963 Bilophila
42 378963 Providencia 90 1116035 Desulfovibrionaceae
43 390132 Pantoea 91 1131789 Desulfovibrionales
44 405698 Enterobacteriales 92 1135348 Deltaproteobacteria
45 424147 Citrobacter 93 1137986 Enterobacteriaceae
46 444893 Raoultella 94 1142963 Gammaproteobacteria
47 459993 Kluyvera 95 1145350 Enterobacteriaceae
48 461329 Enterobacteriaceae 96 1147789 Betaproteobacteria
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TABLE A2.

List of bacterial taxa with their OTUs ID in remission pancolitis

No. OTUs ID Bacteria No. OTUs ID Bacteria
1 9012 Peptococcus 49 502589 Pediococcus
2 9378 Trueperella 50 527810 Lactobacillus
3 9646 Actinomycetaceae 51 530182 Pilibacter
4 9899 Peptostreptococcaceae 52 537891 Enterococcaceae
5 10463 Dialister 53 540128 Lactobacillales
6 10896 Adlercreutzia 54 562189 Anaerococcus
7 11302 Bacteroides 55 567812 Peptoniphilus
8 14567 Bacteroidaceae 56 572143 Parvimonas
9 14996 Clostridiales 57 577810 Clostridiales
10 15027 Ruminococcaceae 58 583071 Pseudoramibacter
11 15201 Atopobium 59 594777 Eubacterium
12 16457 Lactobacillus 60 617802 Eubacteriaceae
13 16830 Collinsella 61 631492 Clostridiales
14 18473 Bradyrhizobium 62 637490 Peptococcus
15 18990 Rikenellaceae 63 658714 Peptococcaceae
16 117963 Clostridiaceae 64 669748 Acetanaerobacterium
17 118634 Bifidobacterium 65 689816 Ruminococcus
18 119012 Bifidobacteriaceae 66 705933 Butyricicoccus
19 119986 Peptostreptococcaceae 67 729801 Flavonifractor
20 122479 Catenibacterium 68 742137 Clostridium IV
21 126239 Bacteroidales 69 775910 Ruminococcaceae
22 129301 Barnesiellaceae 70 778426 Parasporobacterium
23 134203 Erysipelotrichaceae 71 798763 Lachnospiraceae
24 137748 Prevotella 72 825479 Dorea
25 150193 Phascolarctobacterium 73 876900 Coprococcus
26 189760 Parabacteroides 74 889861 Lachnospiraceae
27 199127 Porphyromonas 75 918733 Ruminococcaceae
28 215079 Odoribacter 76 934759 Peptostreptococcus
29 215630 Butyricimonas 77 989744 Peptostreptococcaceae
30 217998 Barnesiella 78 1012989 Anaerostipes
31 260327 Porphyromonadaceae 79 1026597 Lachnospiraceae
32 269160 Prevotella 80 1044390 Pediococcus
33 285496 Prevotellaceae 81 1073619 Lactobacillaceae
34 290112 Alistipes 82 1079801 Enterococcus
35 301028 Rikenellaceae 83 1093607 Bacteroides
36 303241 Staphylococcus 84 1107218 Bacteroidaceae
37 339875 Staphylococcaceae 85 1128970 Bacteroidales
38 340178 Bacillales 86 1140126 Enterococcaceae
39 346920 Aerococcus 87 1149566 Clostridiales
40 375984 Aerococcaceae 88 1151490 Lachnospiraceae
41 379617 Granulicatella 89 1156301 Blautia
42 398863 Carnobacteriaceae 90 1189612 Ruminococcaceae
43 420159 Weissella 91 1211437 Roseburia
44 426713 Leuconostocaceae 92 1237019 Lachnospiraceae
45 456906 Enterococcaceae 93 1240789 Clostridiales
46 479802 Streptococcus 94 1246772 Faecalibacterium
47 481590 Streptococcaceae 95 1250116 Ruminococcaceae
48 499301 Lactobacillales 96 1257301 Clostridiales
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TABLE A3.

List of bacterial taxa with their OTUs ID in healthy participants

No. OTUs ID Bacteria No. OTUs ID Bacteria
1 11456 Rhodospirillum 49 583010 Dorea
2 11895 Duodenibacillus 50 588322 Roseburia
3 11634 Lactobacillales 51 593701 Lachnospiraceae
4 12581 Collinsella 52 601086 Faecalibacterium
5 12988 Atopobium 53 605865 Clostridiales
6 16217 Ruminococcaceae 54 612789 Bifidobacterium
7 18759 Bacteroidales 55 645277 Lachnospiraceae
8 24663 Enterococcus 56 648712 Blautia
9 169384 Leuconostocaceae 57 657899 Ruminococcaceae
10 183691 Bacteroidaceae 58 678970 Sutterella
11 183968 Enterococcaceae 59 698782 Rikenellaceae
12 214569 Streptococcus 60 719017 Prevotella
13 221447 Bacteroides 61 733265 Lactobacillales
14 225896 Streptococcaceae 62 739100 Staphylococcus
15 237562 Enterococcaceae 63 753101 Staphylococcaceae
16 268741 Porphyromonas 64 772368 Odoribacter
17 271458 Ruminococcaceae 65 794890 Porphyromonadaceae
18 276398 Phascolarctobacterium 66 827745 Eubacteriaceae
19 281145 Butyricimonas 67 875214 Eubacterium
20 287482 Parvimonas 68 889803 Clostridiales
21 301189 Pseudoramibacter 69 914732 Prevotella
22 308756 Clostridiales 70 963365 Prevotellaceae
23 324470 Peptococcus 71 980217 Butyricicoccus
24 328621 Peptococcaceae 72 998510 Ruminococcaceae
25 331398 Butyricicoccus 73 1038960 Clostridium
26 335563 Parabacteroides 74 1041517 Parasporobacterium
27 346308 Barnesiella 75 1048796 Dorea
28 362391 Flavonifractor 76 1057893 Lachnospiraceae
29 367522 Peptostreptococcus 77 1096780 Clostridiales
30 371145 Peptostreptococcaceae 78 1101203 Mobiluncus
31 376598 Acetanaerobacterium 79 1106891 Actinomyces
32 379856 Lachnospiraceae 80 1110289 Actinomycetaceae
33 392350 Pediococcus 81 1115981 Prevotella
34 398571 Anaerostipes 82 1145780 Dialister
35 402265 Enterococcus 83 1148765 Clostridiales
36 428796 Lactobacillaceae 84 1150127 Lachnospiraceae
37 441278 Ruminococcaceae 85 1159812 Bacteroides
38 449127 Ruminococcaceae 86 1163970 Bacteroidales
39 451281 Ruminococcus 87 1167095 Ruminococcus
40 456580 Clostridiaceae 88 1208976 Bacteroidaceae
41 459102 Bifidobacterium 89 1220178 Blautia
42 467458 Bifidobacteriaceae 90 1227141 Ruminococcaceae
43 481097 Peptostreptococcaceae 91 1240139 Roseburia
44 489810 Catenibacterium 92 1247890 Lachnospiraceae
45 501433 Bacteroidales 93 1261372 Faecalibacterium
46 510129 Bacteroides 94 1266716 Ruminococcaceae
47 514780 Barnesiellaceae 95 1284820 Clostridiales
48 568894 Erysipelotrichaceae 96 1289362 Clostridiales
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TABLE A4.

Diversity Indexes for pancolitis phases and healthy participants

Diversity index HS AP RP p‐value
Observed 7122.82 2321.01 4138.14 0.001
Chao1 4215.58 1112.32 3241.56 0.001
Shannon 4.1 1.0 3.6 0.001
Simpson 0.48 1.2 0.52 0.001
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p‐value indicates the statistical significance of 2‐way ANOVA.

Abbreviations: AP, active pancolitis; HS, healthy participants; RP Remission pancolitis.

TABLE A5.

Linear discriminant analysis (LDA) effect size (LEfSe) analysis for active pancolitis and remission pancolitis

Taxa Group LDA score p‐value
p_Proteobacteria;c_Betaproteobacteria Active Pancolitis 4.8999 0.042
p_Proteobacteria;c_Gammaproteobacteria Active Pancolitis 4.8963 0.0210
p_Proteobacteria;c_Deltaproteobacteria;o_Desulfovibrionales Active Pancolitis 4.8912 0.0160
p_Proteobacteria;c_Deltaproteobacteria;o_Desulfovibrionales;f_Desulfovibrionaceae Active Pancolitis 4.8836 0.0309
p_Proteobacteria;c_Deltaproteobacteria;o_Desulfovibrionales;f_Desulfovibrionaceae;g_Bilophila Active Pancolitis 4.8792 0.0436
p_Fusobacteria;c_Fusobacteria Active Pancolitis 4.8569 0.0122
p_Fusobacteria;c_Fusobacteria;o_Fusobacteriales;f_Fusobacteriaceae Active Pancolitis 4.8498 0.0132
p_Fusobacteria;c_Fusobacteria;o_Fusobacteriales;f_Fusobacteriaceae;g_Fusobacterium Active Pancolitis 4.8475 0.0145
p_Firmicutes;c_Clostridia Remission Pancolitis 4.8961 0.0394
p_Firmicutes;c_Clostridia;o_Clostridiales Remission Pancolitis 4.8926 0.0058
p_Firmicutes;c_Clostridia;o_Clostridiales;f_Ruminococcaceae;g_Faecalibacterium Remission Pancolitis 4.8912 0.0132
p_Firmicutes;c_Clostridia;o_Clostridiales;f_Lachnospiraceae Remission Pancolitis 4.8859 0.0246
p_Firmicutes;c_Clostridia;o_Clostridiales;f_Lachnospiraceae;g_Roseburia Remission Pancolitis 4.6898 0.0322
p_Bacteroidetes;c_Bacteroidia;o_Bacteroidales;f_Bacteroidaceae Remission Pancolitis 4.6889 0.0125
p_Bacteroidetes;c_Bacteroidia;o_Bacteroidales Remission Pancolitis 4.6885 0.0203
p_Bacteroidetes;c_Bacteroidia;o_Bacteroidales;f_Bacteroidaceae;g_Bacteroides Remission Pancolitis 4.6880 0.0209
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The threshold on the logarithmic LDA score for discriminative features was set to 2.0. The name of a higher taxon level was added before its taxon abbreviation. “p”, phylum; “c”, class; “o”, order; “f”, family; “g”, genus. “LDA” Linear discriminant analysis. p < 0.05 are considered statistically significant.

TABLE A6.

Diagnostic performance of Bilophila, Fusobacterium, Faecalibacterium, and Roseburia in discriminating Pancolitis‐related conditions

AUC Se (%) Sp (%) PPV (%) NPV (%)
AP vs. HS
Bilophila 0.917 80 100 100 83
Fusobacterium 0.955 90 100 100 90
Faecalibacterium 0.222 0 80 0 44
Roseburia 0.237 0 90 0 47
AP vs. RP
Bilophila 0.917 80 100 100 83
Fusobacterium 0.955 90 100 100 90
Faecalibacterium 0.206 10 10 10 10
Roseburia 0.237 0 90 0 47
RP vs. HS
Bilophila 0.000 N/A 100 N/A 50
Fusobacterium 0.000 N/A 100 N/A 50
Faecalibacterium 0.237 90 0 47 0
Roseburia 0.400 40 60 50 50
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Cutoffs. HS vs. AP discrimination: Bilophila (10%), Fusobacterium (10%), Faecalibacterium (45%), Roseburia (20%). AP vs RP discrimination: Bilophila (10%), Faecalibacterium (40%), Roseburia (20%). HS vs RP discrimination: Bilophila (5%), Fusobacterium (5%), Faecalibacterium (20%), Roseburia (10%).

Abbreviatures: AP, Active Pancolitis; AUC, Area Under the Curve; HS, Healthy Subjects; N/A, non‐applicable; NPV, Negative Predictive Value; PPV, Positive Predictive Value; RP, Remission Pancolitis; Se, Sensitivity; Sp, Specificity. Bold letters and values indicates the most abundant and have statistical significance.

Contributor Information

Juan Antonio Suárez‐Cuenca, Email: suarej05@gmail.com.

Graciela Castro‐Escarpulli, Email: chelacastro@hotmail.com.

DATA AVAILABILITY STATEMENT

Sequence data are available in the NCBI repository under BioProject accession number PRJNA596546: https://www.ncbi.nlm.nih.gov/bioproject/596546

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Sequence data are available in the NCBI repository under BioProject accession number PRJNA596546: https://www.ncbi.nlm.nih.gov/bioproject/596546

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