Abstract
Background:
The role of microbiota in the pathogenesis of ulcerative colitis (UC) has been increasingly recognized. However, most of the reports are from Western populations. In Middle Eastern countries, including Saudi Arabia, little is known about the role of microbiota. Therefore, our aim was to describe the bacterial microbiota profile and signature in pediatric UC in Saudi Arabia.
Methods:
Twenty children with UC and 20 healthy controls enrolled in the study gave stool samples. Twenty rectal mucosal samples were taken from UC and 20 from non-UC controls. Inclusion criteria included newly diagnosed and untreated children and lack of antibiotic exposure for at least 6 months before stool collection was required for children with UC and controls. Bacterial deoxyribonucleic acid was extracted and sequenced using shotgun metagenomic analysis. Statistical analysis included Shannon alpha diversity metrics, Bray-Curtis dissimilarity, DESeq2, and biomarker discovery.
Results:
The demographic characteristics were similar in children with UC and controls. There was a significant reduction in alpha diversity (P = 0.037) and beta diversity in samples from children with UC (P = 0.001). Many taxa were identified with log2 abundance analysis, revealing 110 and 102 species significantly depleted and enriched in UC, respectively. Eleven bacterial species’ signatures were identified.
Conclusions:
In Saudi Arabian children with UC, we demonstrate a dysbiosis similar to reports from Western populations, possibly related to changes of lifestyle. Microbial signature discovery in this report is an important contribution to research, leading to the development of adjunctive non-invasive diagnostic options in unusual cases of UC.
Keywords: IBD, microbiome, Saudi children, ulcerative colitis
INTRODUCTION
Inflammatory bowel diseases (IBDs), which include ulcerative colitis (UC), are immune-mediated chronic diseases. The incidence is the highest in Western populations, but the trend is increasing globally.[1,2,3] Although the cause of IBDs, including UC, remains unknown, a multi-factorial etiology involving an interaction between genetics, host immunity, the mucosal barrier, and the gut microbiome is highly suspected.[4,5,6] The role of microbiota has been extensively reported with strong evidence of association with UC, indicating possible “beneficial” or “harmful” bacterial species.[7,8] However, most of the literature has described a strong association between microbiota and UC in Western populations.
In Saudi Arabia, a Middle Eastern developing country, the incidence and clinical patterns of UC have been reported.[9,10,11,12] In addition, the microbiota profile in Crohn’s disease in Saudi children showed significant dysbiosis.[13] However, to our knowledge, no studies on the microbiota profile in Saudi children with UC have been reported thus far. Therefore, the objective of this study was to describe the bacterial microbiota profile in a cohort of Saudi children with new-onset untreated UC.
PATIENTS AND METHODS
The study population
Children with a confirmed diagnosis of UC were enrolled in the study. Inclusion criteria included newly diagnosed and untreated disease and no antibiotic exposure at least 6 months before sample collection. Forty-one samples (20 rectal tissue and 21 stool) were obtained from 30 children with UC. Forty-one samples, 21 rectal and 20 stool, were obtained from 21 non-UC and healthy controls, respectively. Mucosal and stool samples from UC cases and non-UC controls were collected before bowel preparation using biopsy forceps. All samples were stored in cryovials without fixatives or stabilizers and immediately stored at − 80°C until analysis. Dietary lifestyle was assessed by calculation of the average frequency of food items consumed by children in the UC and control groups.
DNA extraction and quantification
Bacterial DNA was isolated from samples using the QIAGEN DNeasy PowerSoil Pro Kit according to the manufacturer’s protocol. DNA samples were quantified using the GloMax Plate Reader System (Promega) using QuantiFluor® dsDNA System (Promega) chemistry.
Library preparation and sequencing methods
DNA libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina) and IDT Unique Dual Indices with a total DNA input of 1 ng. Genomic DNA was fragmented using a proportional amount of Illumina Nextera XT fragmentation enzyme. Unique dual indices were added to each sample, followed by 12 cycles of polymerase chain reaction (PCR) to construct libraries. DNA libraries were purified using AMpure magnetic beads (Beckman Coulter) and eluted in QIAGEN EB buffer. DNA libraries were quantified using a Qubit 4 fluorometer and Qubit dsDNA HS Assay Kit. Libraries were then sequenced on an Illumina NovaSeq S4 platform (2 × 150 bp).
Bioinformatics analysis methods
The system utilizes a high-performance data-mining k-mer algorithm that rapidly disambiguates millions of short sequence reads into discrete genomes encompassing particular sequences. The pipeline has two separable comparators: The first consists of a pre-computation phase for reference databases, and the second is a per-sample computation. The inputs to the pre-computation phase are databases of reference genomes, virulence markers, and antimicrobial resistance markers that are continuously curated by CosmosID scientists. The output of the pre-computational phase is a phylogenetic tree of microbes, together with sets of variable length k-mer fingerprints (biomarkers) uniquely associated with distinct branches and leaves of the tree. The second per-sample computational phase searches hundreds of millions of short sequence reads or, alternatively, contigs from draft de novo assemblies against the fingerprint sets. This query enables the sensitive yet highly precise detection and taxonomic classification of microbial next generation sequencing (NGS) reads. The resulting statistics are analyzed to return the fine-grain taxonomic and relative abundance estimates for the microbial NGS datasets. To exclude false-positive identifications, the results are filtered using a filtering threshold derived based on internal statistical scores that are determined by analyzing a large number of diverse metagenomes. The same approach is applied to enable the sensitive and accurate detection of genetic markers for virulence and for resistance to antibiotics.
Alpha Diversity Boxplots: Alpha diversity boxplots were calculated from the phylum, genus, species, and strain-level abundance score matrices from CosmosID-HUB analysis. Shannon alpha diversity metrics were calculated in R using the R package Vegan.[14,15] Wilcoxon rank-sum tests were performed between groups using the R package ggsignif.[16] Boxplots with overlaid significance in P value format were generated using the R package ggpubr.[15]
Beta Diversity PCoA: Beta diversity principal coordinate analyses were calculated from phylum-, genus-, species-, and strain-level matrices for bacteria from CosmosID-HUB. Bray-Curtis dissimilarity was calculated in R using the vegan package with the function vegdist, and PCoA tables were generated using the ape function pcoa.[17,18] PERMANOVA tests for each distance matrix were generated using vegan’s6 function adonis2, and beta dispersion was calculated and compared using the ANOVA method for the betadisper function from vegan.[15] Plots were visualized using the R package ggpubr.[17]
DESeq2: DESeq2 uses a negative binomial distribution model to estimate differential abundance between cohorts based on count data.[19] The algorithm assumes that most features in microbiome data should not vary greatly between conditions, so it preferentially highlights features that are highly expressed/prevalent, have large fold changes in prevalence, and are statistically significantly different. The figures presented are annotated with some of the most significant features.
Biomarker discovery: The Boruta algorithm was implemented in R. In brief, the Boruta algorithm is a wrapper built around the random forest classification algorithm implemented in the R package random forest.[20] Species-level relative abundance data were used to generate shadow variables to predict taxa that may be important in distinguishing UC from controls.
Ethical consideration: This study was approved by the Institutional Review Board of the College of Medicine, King Saud University in Riyadh, Kingdom of Saudi Arabia [No: 10/2647/IRB,26/6/2010]. Informed consent: Guardians and/or children signed informed consent and/or assent before enrollment in the study.
RESULTS
The study population: The age median and range were 14 (0.5-21) and 12.9 (6.8-16.3) years for children with UC and controls, respectively. There were 8 (40%) and 7 (35%) males for UC and controls, respectively. At diagnosis, UC extent was E4 (38%); E3 (25%); E2 (37%) and 35% had a PUCAI ≥ 65. The details of the dietary lifestyle are shown in Table 1, indicating that 83% and 17% of the children with UC consume rice daily and twice weekly, respectively.
Table 1.
Dietary lifestyle: Frequency of food consumption
| Food items | Ulcerative colitis | Controls | ||
|---|---|---|---|---|
|
|
|
|||
| Daily (%) | Twice weekly (%) | Daily (%) | Twice weekly (%) | |
| Rice | 83 | 17 | 71 | 18 |
| Bread | 54 | 25 | 41 | 29 |
| Red meat | 25 | 38 | 12 | 41 |
| Chicken | 17 | 54 | 59 | 29 |
| Dairy items | 50 | 36 | 85 | 15 |
| Fast food | 42 | 21 | 35 | 50 |
| Gaseous drinks | 46 | 29 | 55 | 25 |
| Fruits | 33 | 25 | 5 | 30 |
| Vegetables | 38 | 38 | 15 | 30 |
Shannon alpha diversity: The Shannon diversity index of species in fecal samples is illustrated in Figure 1, indicating a significant reduction in children with UC (P = 0.037), whereas in mucosal, no significant difference was found (P = 0.71).
Figure 1.
Shannon alpha diversity. Alpha diversity is significantly lower in ulcerative colitis compared to control groups. Mean Shannon alpha diversity per group, P = 0.037
Bray-Curtis beta diversity: The analysis is illustrated in Figure 2, indicating a significant dissimilarity of species in fecal samples from children with UC and controls (P = 0.004). However, no significant dissimilarity of mucosal samples (P = 0.954) was noted.
Figure 2.
Bray-Curtis beta diversity. Bacteria species level beta diversity is significantly different between ulcerative colitis and control groups. PERMANOVA P = 0.004
Log2 differential abundance analysis: A positive log2-fold change indicates enrichment in controls (depletion in UC samples), and a negative log2-fold change indicates depletion in controls (enrichment in UC samples). In fecal samples, the details of the top 25 genera and 25 species are presented in Table 2, and the most common significantly associated with UC are illustrated in Figure 3. However, in mucosal samples, fewer taxa than in fecal samples were identified. Table 3 shows that Escherichia and Parabacteroides were the only significantly enriched genera in samples of the control group (depleted in the UC group) and Parabacteroides_u_s was the only significantly enriched species in the control group (depleted in the UC group). Table 4 shows a comparison of the microbiota profile in this study with the data of some European countries and the USA.
Table 2.
Log2 fold change abundance of the top 25 genera and 25 species
| Taxon | Base mean | Log2 fold change | Statistics | P | *P adj |
|---|---|---|---|---|---|
| Genera | |||||
| Abiotrophia | 130.30 | -6.5397 | -7.8446 | 4.34E-15 | 8.89E-14 |
| Akkermansia | 5244.13 | 5.0769 | 4.9902 | 6.03E-07 | 3.70E-06 |
| Allisonella | 75.78 | 6.6131 | 7.3555 | 1.90E-13 | 2.80E-12 |
| Atopobium | 105.85 | -6.6688 | -7.8422 | 4.42E-15 | 8.89E-14 |
| Campylobacter | 60.82 | -6.4357 | -7.6208 | 2.52E-14 | 4.28E-13 |
| Catenibacterium | 2057.34 | 11.3867 | 11.2360 | 2.71E-29 | 3.00E-27 |
| Christensenella | 643.11 | 5.3938 | 5.8458 | 5.04E-09 | 4.28E-08 |
| Coprobacter | 29.20 | 5.2190 | 6.4934 | 8.39E-11 | 8.42E-10 |
| Desulfovibrio | 530.99 | 8.7802 | 9.9695 | 2.07E-23 | 1.53E-21 |
| Enterococcus | 1808.07 | -5.4176 | -5.6954 | 1.23E-08 | 9.38E-08 |
| Fusobacterium | 45.05 | -5.9971 | -7.3405 | 2.13E-13 | 2.94E-12 |
| Holdemanella | 1144.44 | 10.5445 | 11.7366 | 8.27E-32 | 1.83E-29 |
| Lacticaseibacillus | 370.19 | -9.0272 | -9.0441 | 1.51E-19 | 4.16E-18 |
| Lactiplantibacillus | 42.56 | -5.8902 | -6.6977 | 2.12E-11 | 2.34E-10 |
| Lactococcus | 241.63 | -4.2584 | -4.3553 | 1.33E-05 | 6.67E-05 |
| Longibaculum | 77.11 | -6.7587 | -7.2250 | 5.01E-13 | 6.51E-12 |
| Longicatena | 59.87 | -6.3926 | -7.0694 | 1.56E-12 | 1.91E-11 |
| Merdimonas | 26.31 | 5.1317 | 6.0462 | 1.48E-09 | 1.31E-08 |
| Methanobrevibacter | 380.14 | 8.9511 | 9.6192 | 6.63E-22 | 2.93E-20 |
| Peptoniphilus | 24.68 | -5.1116 | -6.6497 | 2.94E-11 | 3.09E-10 |
| Peptostreptococcus | 961.98 | -8.7049 | -9.6526 | 4.79E-22 | 2.65E-20 |
| Prevotellamassilia | 260.01 | 8.4795 | 9.1594 | 5.21E-20 | 1.65E-18 |
| Solobacterium | 24.95 | -5.1277 | -6.2137 | 5.17E-10 | 4.76E-09 |
| Tannerella | 60.26 | 6.2823 | 7.4705 | 7.98E-14 | 1.26E-12 |
| Tidjanibacter | 96.97 | 6.9720 | 7.6266 | 2.41E-14 | 4.28E-13 |
| Species | |||||
| Akkermansia_muciniphila | 891.4806 | 8.5683 | 9.0684 | 1.21E-19 | 3.02E-18 |
| Alistipes_dispar | 73.9743 | 6.8199 | 7.5090 | 5.95E-14 | 6.30E-13 |
| Alistipes_senegalensis | 321.4091 | 6.6611 | 7.2498 | 4.17E-13 | 3.89E-12 |
| Allisonella_histaminiformans | 142.7299 | 7.7021 | 8.3692 | 5.80E-17 | 9.56E-16 |
| Anaerostipes_caccae | 31.4770 | -5.5309 | -6.4994 | 8.06E-11 | 6.09E-10 |
| Bacteroides_cellulosilyticus | 738.1187 | 5.5147 | 5.4879 | 4.07E-08 | 2.08E-07 |
| Bacteroides_clarus | 216.3052 | 8.3045 | 9.0738 | 1.15E-19 | 3.02E-18 |
| Bacteroides_faecis | 265.4703 | 7.8664 | 9.0041 | 2.17E-19 | 5.17E-18 |
| Bacteroides_finegoldii | 61.9934 | 6.4900 | 7.4992 | 6.42E-14 | 6.50E-13 |
| Bacteroides_finegoldii | 61.9934 | 6.4900 | 7.4992 | 6.42E-14 | 6.50E-13 |
| Bifidobacterium_angulatum | 8906.1851 | 13.6732 | 16.3930 | 2.14E-60 | 1.02E-57 |
| Bifidobacterium_merycicum | 299.1998 | 6.1188 | 6.9128 | 4.75E-12 | 3.83E-11 |
| Butyricimonas_faecalis | 25.9483 | 5.2099 | 6.2246 | 4.83E-10 | 3.06E-09 |
| Catenibacterium_mitsuokai | 460.5272 | 9.3970 | 9.9333 | 2.98E-23 | 1.77E-21 |
| Clostridium_fessum | 400.5663 | 5.8940 | 6.4834 | 8.97E-11 | 6.67E-10 |
| Clostridium_perfringens | 169.9046 | -7.9858 | -8.2613 | 1.44E-16 | 2.29E-15 |
| Coprococcus_eutactus | 123.8391 | 7.4965 | 8.5443 | 1.29E-17 | 2.56E-16 |
| Desulfovibrio_piger | 88.6132 | 7.0101 | 7.7149 | 1.21E-14 | 1.42E-13 |
| Dialister_pneumosintes | 48.1496 | -6.1533 | -7.6754 | 1.65E-14 | 1.83E-13 |
| Dialister_succinatiphilus | 348.6811 | 8.9951 | 10.0138 | 1.32E-23 | 1.05E-21 |
| Eisenbergiella_tayi | 61.4437 | 6.4770 | 7.5046 | 6.16E-14 | 6.37E-13 |
| Enterococcus_avium | 756.8379 | -10.1451 | -9.6993 | 3.04E-22 | 1.44E-20 |
| Enterococcus_casseliflavus | 33.2646 | -5.6114 | -6.3990 | 1.56E-10 | 1.11E-09 |
| Enterococcus_faecalis | 397.4125 | -9.2147 | -8.8349 | 1.00E-18 | 2.17E-17 |
| Enterococcus_faecium | 515.7677 | -5.6110 | -5.8375 | 5.30E-09 | 3.00E-08 |
Figure 3.
Log2 fold change abundance at species level. Positive changes represent significantly enriched species in controls (depleted in the UC group) and negative changes represent enriched species in the UC group
Table 3.
Log2 fold change abundance of mucosal genera and species
| Taxon | Base mean | Log2 fold change | Statistics | P | *P adj |
|---|---|---|---|---|---|
| Genera | |||||
| Alterileibacterium | 1.85 | -0.6121 | -0.6865 | 0.492 | 0.880 |
| Bacteroides | 685.0 | 3.550 | 1.8791 | 0.060 | 0.200 |
| Bilophila | 1.78 | -0.4439 | -0.5013 | 0.616 | 0.880 |
| Dialister | 1.64 | 0.0734 | 0.0824 | 0.934 | 0.983 |
| Dorea | 1.86 | -0.6366 | -0.7130 | 0.475 | 0.880 |
| Escherichia | 40.39 | 5.8903 | 3.5932 | 3.27E-04 | 0.001 |
| Lachnospiraceae_u_g | 510.8 | -0.0407 | -0.0212 | 0.983 | 0.983 |
| Parabacteroides | 51.01 | 6.2417 | 3.9337 | 8.36E-05 | 8.36E-04 |
| Phascolarctobacterium | 1.73 | -0.2867 | -0.3247 | 0.745 | 0.931 |
| Porphyromonas | 1.82 | -0.5411 | -0.6090 | 0.542 | 0.880 |
| Species | |||||
| Alterileibacterium_massiliense | 1.81 | -0.6388 | -0.7222 | 0.470 | 0.999 |
| Bacteroides_caccae | 1.78 | -0.5755 | -0.6529 | 0.513 | 0.999 |
| Bacteroides_faecis | 1.86 | -0.7351 | -0.8258 | 0.408 | 0.999 |
| Bacteroides_fragilis | 138.1 | 0.8238 | 0.4251 | 0.670 | 0.999 |
| Bacteroides_stercoris | 1.61 | 3.93E-04 | 4.48E-04 | 0.999 | 0.999 |
| Bilophila_u_s | 1.74 | -0.4701 | -0.5358 | 0.592 | 0.999 |
| Dialister_pneumosintes | 1.60 | 0.0548 | 0.0623 | 0.950 | 0.999 |
| Dorea_longicatena | 1.82 | -0.6633 | -0.7487 | 0.453 | 0.999 |
| Lachnospiraceae_u_s | 505.4 | -0.0505 | -0.0269 | 0.978 | 0.999 |
| Parabacteroides_u_s | 35.11 | 5.7243 | 3.4781 | 5.05E-04 | 0.003 |
| Phascolarctobacterium_succinatutens | 1.69 | -0.3116 | -0.3566 | 0.721 | 0.999 |
| Porphyromonas_asaccharolytica | 1.65 | -0.1721 | -0.1969 | 0.843 | 0.999 |
| Porphyromonas_uenonis | 1.60 | 0.0738 | 0.0838 | 0.933 | 0.999 |
| Ruminococcus_lactaris | 1.89 | -0.7859 | -0.8796 | 0.379 | 0.999 |
Table 4.
Comparative microbiota profile by geography
| Taxa | Saudi Arabia | Europe | United states |
|---|---|---|---|
| Shannon Alpha diversity | Decreased | Decreased | Decreased |
| Beta diversity | Dissimilarity | Dissimilarity | Dissimilarity |
| Firmicutes | Decreased | Decreased | Decreased |
| Verrucomicrobia | Decreased | Decreased | Decreased |
| Proteobacteria | Increased | Increased | Increased |
| Fusobacteria | Increased | Increased | Increased |
| Veillonellaceae | increased | Decreased | Decreased |
| Ruminococcaceae | Decreased | Decreased | Decreased |
| Escherichia | Increased | Increased | Increased |
| Peptostreptococcus | Increased | Increased | Increased |
| Faecalibacterium | Decreased | Decreased | Decreased |
| Prevotella | Decreased | Increased | Decreased |
| Subdoligranulum | Decreased | Decreased | Decreased |
| Escherichia coli | Increased | Increased | Increased |
| Faecalibacterium prausnitzii | Decreased | Decreased | Decreased |
| Alistipes finegoldii | Decreased | Decreased | |
| Alistipes putredinis | Decreased | Decreased | |
| Roseburia intestinalis | Reduced | Increased | Increased |
| Predictive power | 84 – 97.6 | 0.83-0.92 | 86.5 |
Microbiota biomarkers: Random forest classification algorithm identified 11 bacterial species’ important signature for predicting UC. Alistipes communis, Alistipes putredinis, Bacteroides caccae, Bifidobacterium adolescentis, Bifidobacterium angulatum, Bifidobacterium bifidum, Bifidobacterium catenulatum, and Dialister succinatiphilus were depleted, whereas Peptostreptococcus stomatis, Prevotella copri, and Streptococcus_u_s were enriched in the UC group. Figure 4 illustrates the details of these bacterial signatures.
Figure 4.
Eleven bacterial species’ signatures are potentially important in predicting UC in stool samples. The Boruta algorithm was used to select features potentially important in distinguishing UC from stool samples
DISCUSSION
In Saudi Arabia, as in many developing countries, IBDs, which were unknown several decades ago, are now increasingly reported, implying a strong role of environmental factors such as diet, hygiene, and microbiota in the pathogenesis of IBDs. A significant association between microbiota and UC is well described in Western and some Asian populations, identifying dysbiosis in patients with IBDs, including UC.[21,22,23,24] However, in other populations, such as the Middle East, where IBDs are an emerging condition, information on microbiota associated with IBDs is still scarce.
In this report, we characterized the bacterial microbiota profile in a cohort of Saudi children with newly diagnosed treatment-naïve UC. The finding of bacterial dysbiosis in the form of significantly reduced alpha diversity, beta diversity dissimilarity, and many taxa associated with UC is in accordance with the Western literature in both adults and children.[25,26,27,28,29] In a pediatric study from USA with similar numbers of children with UC and controls and shotgun metagenomic methodology as the present study, the 24 microbial families associated with UC were smaller than the 30 families in our study (10 were enriched and 20 depleted in children with UC). The finding of six species belonging to Enterococcaceae is less than the ten species associated with UC. Finally, the three enriched Bifidobacteriaceae species and the three depleted species were similar to the four and two in our study.[30] In another pediatric study from Italy, Alistipes putredinis, Alistipes finegoldii, and many Prevotella spp and Bacteroides spp. were depleted in children with UC, but Faecalibacterium prausnitzii, although reduced, was not significantly different between UC and controls, which was similar to our findings.[31] Taken together, the similarity of dysbiosis in this Saudi pediatric population with UC to Western populations was not surprising to us, since Saudi Arabia is considered a country in transition between “developing” and “developed” countries. Studies from communities with similar lifestyle reported similar microbiota profiles.[32,33] The comparison of the microbiota profile reported in this study with reports from Europe and USA supports this similarity. Socioeconomic improvement over the last few decades led to a change toward a more Western lifestyle. The overall pattern of lifestyle of the children with UC and controls indicates that apart from the high frequency of rice consumption, the dietary pattern in this study is consistent with Western diet, explaining, at least in part, the similarity of microbiota profiles. Accordingly, the microbiota profile in Saudi children may be applicable to other Middle Eastern countries with similar lifestyle.
The main limitations of this study were the relatively small sample size, which may be partially balanced using shotgun technology in samples from newly diagnosed treatment-naïve children with UC. The cross-sectional design lacked longitudinal data.
As is well known, depleted microbiota species in any condition suggest a “beneficial” role, and enriched species suggest a “harmful” role. It is also important to note that microbiota associations do not imply causality. However, the significant dysbiosis in children with new onset treatment-naïve UC and the discovery of microbial signature in this study suggest a stronger association.
In conclusion, in Saudi Arabia, a fast-developing country, we demonstrate a dysbiosis in children with UC, similar to reports from Western populations, possibly related to changes in lifestyle, expanding the global knowledge of the role of microbiota in the pathogenesis of UC. Microbial signature discovery in this report is an important contribution to research, potentially leading to adjunctive non-invasive diagnostic options in unusual cases of UC. We regard this report as preliminary, and further studies from different populations with larger sample sizes are needed.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
This work was funded by the Researchers Supporting Project no (RSPD2023R864), King Saud University, Riyadh, Saudi Arabia.
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