Gut microbiota composition and functional prediction in diarrhea-predominant irritable bowel syndrome

doi:10.21203/rs.3.rs-37447/v1

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Gut microbiota composition and functional prediction in diarrhea-predominant irritable bowel syndrome

https://doi.org/10.21203/rs.3.rs-37447/v1

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published 05 Mar, 2021

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Background: Irritable bowel syndrome (IBS) is common and difficult to treat and its pathogenesis is closely related to gut mircobiota. However, differences in gut mircobiota of patients in different regions make it more difficult to elucidate the mechanism of IBS. We performed an analysis of gut microbiota composition and functional prediction in Chinese patients with diarrhea-predominant IBS (IBS-D).

Methods: Fecal samples were obtained from 30 IBS-D patients and 30 healthy controls (HCs) in Nanchang, China. Using 16S gene sequence profiles, we analyzed the abundance of dominant microbiota at different taxonomy levels. Based on 16S information, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predicting the function of gut microbiota.

Results: Compared to HCs, gut microbiota richness but not diversity was decreased in IBS-D patients. The abundant phyla Firmicutes, Fusobacteria and Actinobacteria decreased significantly, and Proteobacteria increased significantly in IBS-D patients. PICRUSt indicated that function expression of gut microbiota in IBS-D patients was up-regulated in metabolism of cofactors and vitamins, xenobiotics biodegradation and metabolism, and down-regulated in environmental adaptation, cell growth and death.

Conclusions: Compared with the normal population in China, IBS-D patients are characterized by complex and unstable gut microbiota, which may influence inflammation and metabolism of the host.

Gastroenterology & Hepatology

diarrhea-predominant irritable bowel syndrome

gut microbiota

functional prediction

Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) disorder that affects 7%-21% of the population worldwide [1] and 5-10% in most European countries, the US and China [2], but it differs depending on regions and diagnostic criterion. It causes significant socioeconomic burden on society [3]. Based on the predominant stool pattern, patients with IBS are categorized 4 subtypes [4]: constipation-predominant IBS (IBS-C), diarrhea-predominant IBS (IBS-D), mixed IBS (IBS-M), and unclassified IBS (IBS-U).

The pathophysiology of IBS has remained elusive and its linked factors include abnormalities of GI motility, visceral hypersensitivity, post-infectious low-grade inflammation, alteration of gut microbiota, brain-gut interactions and genetic factors [5-10]. Interestingly, one of our early studies showed abnormalities in salivary amylase in patients with IBS, suggesting autonomic dysfunction caused by psychological factors may be part of pathogenesis [11].

The past decade has seen considerable studies on gut microbiota due to its key role in human diseases, especially for IBS [12]. Previous studies have found a large number of different kinds of intestinal flora in the human gut, but most of them were limited to the number and abundance of bacteria, which are only the tip of the iceberg compared to the function of these bacteria or the whole bacteria of nature. In addition, gut microbiota represents one source of human genetic and metabolic diversity and differ among human populations [13]. The 5th International Meeting on Inflammatory Bowel Diseases (IBD) pointed out that the increased incidence of IBD among migrants from low-incidence to high-incidence areas within the same generation suggests a strong environmental influence, and added that the importance of gut flora in intestinal homeostasis and inflammation must be reinforced [14]. Therefore, more gut microbiota data of IBS patients from different regions are required.

With the development of next-generation high-throughput sequencing, the investigation of the human gut microbiota has been ever more feasible. Furthermore, bacterial functional profiles can be predicted by inferring the metagenome of the closest available whole genome sequences using 16S gene sequence profiles. Based on 16S information, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty [15]. Using PICRUSt, several differences in such variables that may be of pathophysiological significance, but the findings are challenging to interpret and should be considered with caution [16]. A systematic review of gut microbiota in patients with IBS based on 16S sequencing showed that alterations of gut microbiota exist in patients with IBS and have a significant association with the development of IBS [17]. However, data on specific bacterial groups in IBS are conflicting and still inconclusive according to another systematic review [18].

Through a case-control study, we aimed to address three questions related to gut microbiota in IBS-D patients and health population of Nanchang, China: What are the differences in the composition and abundance of gut microbiota between the two populations? In what ways does the function of these altered bacteria affect the host? What are the probable causes of these differences?

Study design

This study included 30 patients with IBS-D and 30 healthy controls (HCs), aged from 20 to 64 and 24 to 65 years, respectively. Inclusion criteria met the Rome Ⅳ diagnostic criteria for IBS-D. Gender, age, BMI and staple food of all participants were collected, which were shown in Table 1 (Details can be found in Supplementary Material Table S1). All the participants lived in Nanchang. Exclusion criteria [19] for all subjects included: 1) taking antibiotics, probiotics, or other treatments, within 2 weeks; 2) Complicated with severe hypertension and organic heart disease; 3) pregnant, menstruating and lactating women; 4) psychopath; 5) excessive physical exercise.

Sample collection and DNA Extraction

A single fecal sample was collected by each participant at home, and immediately stored at -20℃, then transferred to -80℃ for longer-term storage. Fecal bacteria genomic DNA was extracted with Cetyltrimethyl Ammonium Bromide (CTAB).

16S rDNA gene sequencing

The V4 hypervariable region of the 16S rDNA gene was amplified using Phusion ^® High-fidelity PCR Master Mis with GC Buffer (New England Biolabs, US). All PCR products were visualized on agarose gels (2% in TAE buffer), and purified with a DNA gel extraction kit (Qiagen, Germany). Paired-End (PE) amplicon library was constructed using a TruSeq^® DNA PCR-Free Sample Preparaion Kit (Illumina, US) and quantified by Qubit, then sequencing was performed using the Illumina Hiseq platform (APTBIO Technology, Shanghai, China).

Data and bioinformatics analysis

FLASH and Trimmomatic software were used to splice and filter the raw tags, and the effective tags were obtained by comparing in the Gold database. The effective tags were clustered with Uparse software, and the sequences were clustered into operational taxonomic units (OTUs) with 97% consistency. The representative OTUs were selected for species annotation analysis with the method of Mothur and SILVA's SSUrRNA database (The threshold value was set at 0.8-1).

Qiime (Version 1.9.1) and R (Version 2.15.3) were used for data analysis. The indexes, including Observed-species, Chao1, Shannon, Simpson and ACE, were used to describe the Alpha-diversity. By calculating the Unifrac distance, constructing the unweighted pair-group method with arithmetic mean (UPGMA) sample cluster tree and drawing principal component analysis (PCA), principal co-ordinates analysis (PCoA) and non-metric multi-dimensional scaling (NMDS) diagrams, the Beta-diversity was carried out. Based on KEGG database, PICRUSt analysis was applied to predict the functional profiling of microbial communities according to 16S sequencing data. T-test and Wilcox test were used for statistical analysis. T-test test and LDA Effect Size (LEfSe) analysis were used for the analysis of different species between groups. LEfSe analysis using the LEfSe software, the LDA Score screening value is 4.

Sequencing quality analysis

A total of 3,667,351 valid tags sequences were generated from 60 samples, and the average number of high-quality sequences obtained per sample was 61,132. The average length of valid Tags sequences of all samples was between 406-421bp, with an average of 414 bp. Sample sequencing depth was between 99% and 100%, and all samples were fully sequenced.

The number of OTUs

The number of common OTUs between the patients of IBS-D and HCs was 813. Meanwhile, the number of proper OTUs in the patients of IBS-D was 734, and that in HCs was 575. Details can be found in Supplementary Material Table S2.

Table 1 Characteristics and OTUs of two groups

Group		IBS-D	HCs	P-value
Number		30	30	/
Gender	Male	13	15	0.293
Gender	Female	17	15	0.293
Age		40.3±14.7	40.9±14.4	0.874
BMI		19.97±5.58	21.39±3.83	0.014
Staple food	Rice	26	27	1.000
Staple food	Wheat	4	3	1.000
OTUs		1547	1388	0.001

Characterization of fecal microbiota

There is significant difference in gut microbiota composition between IBS-D patients and HCs, details can be found in Supplementary Material Table S3, S4.The phylum level was taken as an example to show a histogram of relative abundance of species. As shown in Figure 1, we found that compared with HCs, Firmicutes (P<0.05), Fusobacteria (P<0.01), Actinobacteria (P<0.01) decreased significantly, and Proteobacteria increased significantly (P<0.01) in IBS-D patients.

The genus level was taken as an example to show the heat map of species abundance clustering. The results were shown in Figure 2 that compared with HCs, Enterobacteriaceae significantly increased (P<0.01), and Alloprevotella (P<0.01), Fusobacterium (P<0.01) significantly decreased in IBS-D patients.

In order to further study the phylogenetic relationship of genus level species, the representative sequences of top100 genus were obtained by multi-sequence alignment and shown in Figure 3.

Statistical analysis was performed to find species with significant differences between the two groups. Using T-test, the phylum level was taken as an example to show the difference of species between the two groups (Figure 4). The result showed that the species that differed significantly between the two groups were Firmicutes and Proteobacteria.

In order to emphasize the statistical significance and biological correlation, we performed LEfSe analysis on two groups of bacteria. Using LEfSe, we can identify the characteristics and related categories of different abundance. As shown in Figure 5, the biomarker of HCs included Ebterobacteriales, Gammaproteobacteria, Proteobacteria, and those of IBS-D included Clostridiales, Clostridia, Firmicutes.

Alpha diversity

Alpha diversity index under 97% consistency threshold for different samples (shannon, simpson, chao1, ACE, goods_coverage, PD_whole_tree) was used for statistics (table 2, data quantity selected during homogenization, cutoff = 48286). There is a significant difference in community diversity between IBS-D and HCs (P<0.05). The result indicated that community diversity of IBS-D is lower than that of HCs. There is no significant difference in community richness and sequencing depth between IBS-D and HCs. The results of difference analysis between groups with Alpha diversity index were shown in Figure 6.

Table 2 Comparison of Alpha diversity index between IBS-D and HCs

Group	species	shannon	simpson	chao1	ACE	goods_coverage	PD_whole_tree
IBS-D	311	4.708	0.890	357.980	374.214	0.998	44.200
HCs	330	5.084	0.921	372.200	385.747	0.999	42.731
P-value	0.191	0.011	0.191	0.374	0.602	0.445	0.343

Beta diversity

PCoA is used to describe the sample distance. We performed PCoA analysis based on weighted unifrac distance and unweighted unifrac distance (Figure 7). The closer the sample distance is, the more similar the species composition. Details can be found in Supplementary Material 4.

PCA can extract two coordinate axes that reflect the differences between samples to the greatest extent, so as to reflect the differences between multidimensional data on the two-dimensional coordinate diagram, and then reveal the simple rules in the background of complex data. The more similar the community composition of the samples, the closer they were in the PCA (Figure 8). In order to overcome the shortcomings of linear models (including PCA and PCoA) and better reflect the nonlinear structure of ecological data, we also conducted NMDS analysis (Figure 8).

In order to study the similarity between different samples, we constructed a cluster tree of samples by cluster analysis of samples. Weighted unifrac distance matrix and unweighted unifrac distance matrix were used for UPGMA clustering analysis, and the clustering results were integrated with the relative abundance of species of each sample at the phylum level, as shown in figure 9.

PICRUSt analysis

Taking the level 1 as an example, according to the database annotation results, we select the functional information of the top 10 in the maximum abundance of each sample or group, and generate a histogram of the relative abundance of functions, so as to visually view the functions and their proportions with the high relative abundance of each sample. As shown in Figure 10, the functional genes of the two groups were mainly involved in metabolism, genetic information processing and environmental information processing. According to the functional annotation and abundance information of the samples in the database, the top 35 functions of the abundance and their abundance information in each sample were selected to draw a heat map, and clustering was carried out from the functional difference level. In IBS-D patients, the function expression of gut microbiota was up-regulated in metabolism of cofactors and vitamins, xenobiotics biodegradation and metabolism, and down-regulated in environmental adaptation, cell growth and death, metabolism of other amino acids (Figure 11). Details can be found in Supplementary Material Table S5, S6, S7, S8.

The analysis of the relationship between gut microbiota and IBS by 16SrDNA sequencing is not new, but this study firstly analyzed the differences of gut microbiota between IBS-D patients and health population in Nanchang, China. Furthermore, we performed PICRUSt analysis to predict the function of gut microbiota.

The composition of gut microbiota in IBS-D patients was abnormal

In this study, we found that there was significant difference in gut microbiota of IBS-D patients compared to those of HCs in China. At phylum level, Firmicutes and Fusobacteria decreased significantly, and Proteobacteria increased significantly in IBS-D patients. In reviewing the literature, decreased levels of Firmicutes and increased levels of Bacteroidetes were found in 2 studies [20, 21], one in China, but 4 studies [22, 23, 24, 25] reported opposite results, and 3 studies [24, 25, 26] found that Proteobacteria were increased. In assessing genus level, Enterobacteriaceae significantly increased, and Alloprevotella, Fusobacterium significantly decreased. The results indicated that IBS-D might be related to potential inflammation. Two studies confirmed the hypothesis that Enterobacteriaceae increases in IBS-D patients and had significantly lower relative abundance after Rafaximin treatment [27, 28]. Three studies assessing Bacteroides demonstrated significant increase in IBS-D patients, whereas 2 studies showed insignificant results compared to controls [21, 29, 30, 31, 32]. Moreover, 3 studies assessing genus Bifidobacterium showed a significant decrease and 3 studies evaluating genus Faecalibacterium showed a significant decrease in IBS-D patients [21, 29, 30, 31, 32, 33]. In addition, the species-specific alterations of gut microbiota, such as Bifidobacteria and Bacteroides, were different between IBS patients from China and other regions [27], this discovery was similar with our study. However, there were significant differences in the results of the studies on the changes of gut microbiota in IBS-D patients, which may be caused by regional and dietary factors.

Predictive functional profiling of gut microbiota

There is ever-growing evidence supporting the role of microbes in the pathophysiology of IBS. Alterations in the gut microbiome may lead to impaired gut barrier function and potentially contribute to IBS symptoms [34]. Firmicutes makes up the largest portion of the human gut microbiota and has been shown to be involved in energy extraction and potentially related to obesity and diabetes [35]. Bacteroidetes, the second most abundant phylum in the human gut, generally produces butyrate, which is suggested to reduce inflammation and plays a role in the normal development of the gut [36]. Proteobacteria are one of the most abundant phyla, an increasing amount of data identifies it as a possible microbial signature of disease, and major pieces of evidence currently involve metabolic disorders and inflammatory bowel disease [37]. Fusobacteria is rare constituents of the fecal microbiota, but have been cultured previously from biopsies of inflamed gut mucosa [38]. In our study, decreased Firmicutes, Fusobacteria and increased Proteobacteria were found in IBS-D patients, which may be related to inflammation and metabolic disorder.

Effect of staple food on gut microbiota and IBS-D

There are many factors affecting gut microbiota, such as dietary patterns (alcohol, caffeine, spicy food, elimination diets, fat and fluid intakes and dietary habits [39]), environment (temperature and humidity [40]) and pressure, etc. In this study, the influence of staple food on gut microbiota was observed.

China had undergone significant transitions in dietary patterns during the last several decades, one of the changes was that the shift of staple food consumption towards refined cereals (e.g., polished rice, white wheat) and away from traditional coarse staple foods (e.g., millet, sorghum) [41]. In China, people in different regions have different eating habits. The general staple food for people living south of Yangtze River is rice; in the area north of the Yangtze River, people subsist chiefly on wheat; whereas people in the north of the Yellow River, one of the major staple food is naked oats [42]. These differences led to the different incidence of IBS-D in China. An epidemiological study indicated that 277 (1.72%) had IBS-D in 16078 respondents from 5 cities of China [43]. The incidence of IBS-D in Guangzhou and Wuhan were higher than that in Beijing [44, 45, 46]. A Japanese study reported that dietary interventions did not make any significant difference in the abundance of Bacteroidetes and Firmicutes, but the abundance of Actinobacteria was significantly increased after 7 days intake of white bread [47]. But in our study, at phylum level, there is no significant difference in abundance of gut microbiota between peoples who consume rice as a staple food (samples of A1, A5, A7, A10, B5, B7, B17) and peoples who consume wheat as a staple food. But the 7 samples have decreased Firmicutes (0.498±0.179 v.s. 0.61±0.164, P=0.09) and increased Bacteroidetes (0.299±0.223 v.s. 0.212±0.167, P=0.22) compared with other samples. Overall, these results indicated that effect of staple food on gut microbiota is not related to IBS-D.

According to our study, in Nanchang, China, IBS-D patients are characterized by a thin body and complex and unstable gut microbiota compared with the normal population. Decreased Firmicutes, Fusobacteria, Actinobacteria, and increased Proteobacteria may be contributed to these differences by influencing inflammation and metabolism of host.

Gut microbiota composition is quite different between Chinese and Western IBS-D patients and the reasons need to be further studied. Effect of dietary patterns, such as staple food, on gut microbiota does not increase the incidence of IBS-D.

The sample size of this study is small, so multi-center, large sample size and long-term clinical observation are still needed to confirm this conclusion. However, the cause of gut microbiota change in IBS-D patients is still unknown and needed further study.

GI: gastrointestinal; HCs: healthy controls; IBD: inflammatory bowel diseases; IBS-D: diarrhea-predominant irritable bowel syndrome; KEGG: Kyoto Encyclopedia of Genes and Genomes; LDA: linear discriminant analysis; NMDS: non-metric multi-dimensional scaling; OTUs: operational taxonomic units; PCA: principal co-ordinates; PCoA: principal compant analysis; PCR: polymerase chain reaction; PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; UPGMA: unweighted pair-group method with arithmetic mean.

Ethics approval and consent to participate

All procedures complied with the principles of the Declaration of Helsinki and were approved by the institutional review board (No. 2019L001) nd ethics committee at our institution. Healthy controls were recruited among students and volunteers. All participants had signed informed consent.

Consent for publication

All authors have agreed to publish this manuscript.

Availability of data and material

All available data are in the manuscript and Supplementary Material.

Table S1 Characteristic of two groups

Table S2 OTUs of two groups

Table S3 Class top10 of two groups

Table S4 Comparison of genus 35 clustering between two groups

Table S5 KEGG metagenome predictions

Table S6 Predicted metagenomes KEGG at L1

Table S7 Predicted metagenomes KEGG at L2

Table S8 Predicted metagenomes KEGG at L3

Competing interests

All authors declare no conflict of interest of this work.

Funding

LJ Mei, L He and JL Zou are supported by Jiangxi Provincial Health Committee, and we thank their funding provided to this study (No. 2018A043).

Authors' contributions

L He and Y Cui conceived the original idea of the study. LJ Mei and JL Zhou analyzed data and wrote this paper. YM Su, KH Mao, J WU and CC Zhu collected cases and samples. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

Acknowledgments

We thank Jiangxi University of Traditional Chinese Medicine (Nanchang, China) and APTBIO Technology (Shanghai, China) for its help.

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published 05 Mar, 2021

Read the published version in BMC Gastroenterology →

Review #1 received at journal
03 Aug, 2020
Editorial decision: Minor revision
03 Aug, 2020
Reviewer #2 agreed at journal
23 Jul, 2020
Review #2 received at journal
23 Jul, 2020
Reviewers invited by journal
22 Jul, 2020
Reviewer #1 agreed at journal
22 Jul, 2020
Editor assigned by journal
14 Jul, 2020
Submission checks completed at journal
13 Jul, 2020
Editor invited by journal
13 Jul, 2020

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Gut microbiota composition and functional prediction in diarrhea-predominant irritable bowel syndrome

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Study design

Sample collection and DNA Extraction

16S rDNA gene sequencing

Data and bioinformatics analysis

Results

Sequencing quality analysis

The number of OTUs

Characterization of fecal microbiota

Alpha diversity

Beta diversity

PICRUSt analysis

Discussion

The composition of gut microbiota in IBS-D patients was abnormal

Predictive functional profiling of gut microbiota

Effect of staple food on gut microbiota and IBS-D

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Authors' contributions

Acknowledgments

References

Supplementary Files

Status:

Journal Publication

Version 1