Introduction

Cystic echinococcosis (CE) is a globally distributed parasitic disease caused by the larval forms of the cestode Echinococcus granulosus sensu lato. CE is a zoonosis that is commonly found in regions where livestock farming is prominent. The disease imposes a heavy burden on the communities living in endemic areas with significant public health and economic consequences1,2. The parasite life cycle involves domestic dogs (and wild canids) as definitive hosts, in which adult tapeworms are harbored in the small intestine. Herbivores, on the other hand, act as intermediate hosts, where the larval stage (hydatid cyst) develops within their internal organs3.

The natural history of human CE presents different outcomes, depending on various host- and/or parasite-related factors. The infection in some individuals is aborted, and in this case, no hydatid cyst developed. On the other hand, some cysts show steady growth over time, whereas others undergo degenerative calcification changes. According to these observations, the World Health Organization classifies echinococcal cysts into active (CE1, CE2), transitional (CE3), and inactive (CE4, CE5) stages4. The factors underlying the different disease outcomes in humans are largely unknown. These factors can be related to either the parasite or host. Among the host-related factors, there is currently a significant focus on the role of the host microbiome in the different outcomes of human communicable and non-communicable diseases. However, little is known about the impact of the host microbiome on the progression of human parasitic infections.

The human microbiome denotes the collection of microorganisms in the human body, including the intestinal, respiratory system, skin, and urogenital systems. The microbiome is believed to affect the health and disease susceptibility of each individual5. A healthy human gut typically harbors a wide variety of bacteria, consisting of approximately 1,000 different species. These bacteria can be classified into two main phyla, Bacteroidetes and Firmicutes6.

The composition of the intestinal microbiome can be influenced by various factors, including the presence of pathogens, antibiotic therapy, diet, and genetics7. The findings of several studies have indicated that the gut microbiota can play an important role in human health. The exact molecular mechanisms by which microbiota influence human health are unknown; however, the regulation of different gene expression and signaling are believed to be involved8,9. Moreover, the gut microbiota significantly affects human metabolism and immunity10 .

Therefore, it is crucial to understand the nature of the relationship between gut microorganisms and the pathogenesis and prognosis of human parasitic diseases11 which is expected to provide new opportunities for therapeutic interventions12. Studies suggest that parasite-induced changes in the host gut microbiota can affect the pathogenesis and pathophysiology of helminth infection by altering the parasite burden13 as well as modulating the immune system14. A recent study15 revealed differences in gut microbiota between patients with chronic and advanced Schistosoma japonicum infections, suggesting a potential role in the pathogenesis and progression of the infection. One study indicated that infection with the liver fluke, Clonorchis sinensis, in mice can alter the composition of the intestinal microbiome and can reduce the risk of immune-mediated diseases16.

Little is known regarding the relationship between the gut microbiome and human CE. Some studies on the intestinal microbiome of mice infected with E. granulosus showed the distinct intestinal microbiota in mice experimentally infected with E. granulosus17,18. Significant differences in the frequency of bacteria were documented in a study comparing the composition of the intestinal microbiome between patients with cystic and alveolar echinococcosis and healthy individuals11. However, no study has investigated the intestinal microbiome of patients with CE at different stages of hydatid cysts by comparing patients with active, transitional, and inactive hydatid cysts. As little evidence is available on the microbiome composition in different types of CE patients, the present study was conducted to identify the intestinal microbiome of CE patients at different stages of hydatid cyst compared to the healthy individuals.

Results

Analysis of the metagenomic sequencing data

After filtering and denoising the raw data based on the quality assessment conducted using Fast QC software (Supplementary file 1: Table S1), a mean sequence quality Phred score of 36 was obtained, indicating an appropriate quality of the readings in all samples The average read counts obtained from the samples were 566,796 per sample (379,934-717,122) (Supplementary file 1: Table S2). The rarefaction curves for each sample approached saturation, indicating that the sequencing depth was sufficient to capture the full diversity of the microbial profiles (Fig. 1).

Figure 1
figure 1

Rarefaction curves showing the level of sequencing depth sufficient to capture the full diversity of microbial profiling in patients with different stages of cystic echinococcosis. (A) Active stage; (I) Inactive stage; (T) Transitional stage; (Ctrl) Control.

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Taxonomic profiling of gut microbiome of CE patients

In this study, high-throughput sequencing of the 16S rRNA gene generated a total of 4862 OTUs from three stages of hydatid cysts in CE patients and healthy individuals, with a combined frequency of 2,955,291 across all fecal samples (Supplementary file 2: Sheet 1). The sequences related to the OTUs are listed in Supplementary File 3. Frequency analyses between the groups indicated that the highest frequency was related to the inactive forms of the disease, with a frequency of 851,998, whereas the lowest frequency (662,883) was observed in the group with active forms of the disease (Supplementary file 1: Table S2). Interestingly, the lowest OTU diversity was found in patients with inactive cysts (1372 OTUs) compared to those with active (1742 OTUs) and transitional (1817 OTUs) cyst stages. The highest frequency was observed in one patient with an inactive cyst (318,778(, while the lowest frequency (157,077) was recorded in patient with the active form of CE (Supplementary file 1: Table S2). The genus Agatobacter showed the highest per feature frequency (226,669), with the highest frequency in the inactive group (108,762) and the lowest frequency in the transitional group (33,290). The frequency per OTU is demonstrated in (Supplementary File 2: sheet 2).

The number of identified features at different taxonomic levels in each group is specified in the (Supplementary File 1: Table S3). The number of unique features among the active, transitional, and inactive stages of hydatid cysts was 1141, 1208, and 943, respectively (Fig. 2a). Moreover, 3270 and 906 unique features were found in CE patients and control individuals, respectively (Fig. 2b), indicating a different distribution of features among the groups. The distribution of OTUs among CE patients with three stages of hydatid cyst as well as healthy individuals is shown in Fig. 2c.

Figure 2
figure 2

The OTUs found in metagenomic analysis of gut microbiome in the patients with cystic echinococcosis and healthy individuals. (a) Distribution of OTUs among the three stages of hydatid cysts, (b) Distribution of OTUs between CE patients and healthy individuals, (c) Distribution of OTUs among the patients with three stages of hydatid cyst as well as the healthy individuals.

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The relative abundance of OTUs per group at different taxonomic levels as well as the data for each sample are demonstrated in Fig. 3 and Supplementary Fig. 2, respectively. The identified features were belonged to 33 phyla, 74 classes, 180 orders, 315 families, and 697 genera (Supplementary file 2: sheet 3–7). At the phylum level, the most prevalent bacteria belonged to Firmicutes, Bacteroidota, Actinobacteriota, and Proteobacteria (Fig. 3a,f).

Figure 3
figure 3

Composition of gut microbiome and relative abundance of top 20 OTUs in the patients with cystic echinococcosis (CE) and healthy individuals at five taxonomic levels, phylum (a,f), class (b,g), order (c,h), family (d,i) and genus (e,j).

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The dominant bacterial classes were Clostridia, Bacteroidia, Gammaproteobacteria, Actinobacteria, and Bacilli (Fig. 3b,g). Among the orders, Lachnospirales, Oscillospirales, Bacteroidales, Bifidobacteriales, Veillonellales-Selenomonadales, and Peptostreptococcales-Tissierellales exhibited the highest abundances, respectively (Fig. 3c,h).

The top five families identified were Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, Bifidobacteriaceae, and Prevotellaceae (Fig. 3d,i). The most abundant genera observed were Blautia, Agathobacter, Faecalibacterium, Bacteroides, Bifidobacterium, and Prevotella- 9 (Fig. 3e,j). The relative abundances of the top five bacteria in each group are available in (Supplementary File 1: Table S4).

The relative abundance of the gut microbiome was compared between CE patients and healthy individuals. No significant differences were observed between the two groups at phylum and class levels. A significantly higher frequency of order Enterobacterals was found in the healthy group, compared to the patients (P = 0.009). At the family level, Enterobacteriaceae were significantly more frequent in the healthy group than in the patients (P = 0.036). In CE patients, the frequency of the genus Anaerostipes was found to be significantly higher (P = 0.036), while the control group displayed a higher frequency for the genus Candidatus arthromitus (P = 0.036).

In this study, we also investigated the differential frequency of OTUs among the three different stages of cystic echinococcosis, i.e., active, transitional, and inactive stages. There were no significant differences between the three groups of patients at the phylum and class levels. However, at the genus level, the abundance of Pseudomonas was significantly higher in CE patients with active hydatid cysts than in those with other stages (P = 0.027). Likewise, the abundance of the order Pseudomonadals (P = 0.027) and family Pseudomonadaceae (P = 0.027) was higher in the active stage of the disease. However, a significantly higher frequency of the genus Gemella (P = 0.035), as well as the Gemellaceae family, was found in the transitional group of patients than in the active and inactive stages (P = 0.035). In addition, CE patients with inactive stage displayed a higher frequency of Ligilactobacillus (P = 0.027).

Diversity analysis of the gut microbiome

Alpha diversity analysis of the gut microbiome in CE patients with three different stages of hydatid cysts, as well as healthy individuals, revealed no statistically significant differences within these groups (Fig. 4). The average values for alpha diversity indices in CE patients with the three stages and healthy individuals are shown in (Supplementary File 1: Table S5).

Figure 4
figure 4

Alpha diversity analysis of gut microbial community in patients with cystic echinococcosis (CE) as well as healthy individuals, based on Shannon, Chao1, Pielou’s evenness and Simpson indices. (a) CE patients and healthy individuals. (b) CE patients with three different stages of CE i.e. active, inactive and transitional hydatid cysts. (c) CE patients with three different stages of CE as well as the healthy individuals.

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Beta-diversity analysis of the gut microbiome among CE patients with active, transitional, and inactive hydatid cyst stages, as well as healthy individuals, presented no significant differences among the four groups (Supplementary File 1: Table S6, Fig. 5).

Figure 5
figure 5

Beta diversity analysis of gut microbial community in patients with cystic echinococcosis (CE) as well as the healthy individuals, based on Jaccard, Bray–Curtis, weighted and unweighted UniFrac indices. (a) CE patients compared with the healthy individuals. (b) CE patients with three different stages of CE, comparing active, inactive and transitional hydatid cysts. (c) CE patients with three different stages of CE compared with the healthy individuals.

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Additionally, PCOA was used to visualize the similarity of microbial communities among different samples and groups. The results showed no distinct clustering within or between the groups (Supplementary Fig. 1). Figure 6 presents the phylogenetic relationships of the gut microbiome in CE patients and healthy individuals in the top 20 genera based on their relative abundance. In the phylogenetic analyses, Pseudomonas was clustered among the top 20 most abundant genera in patients with active CE; however, in inactive CE patients as well as in healthy individuals, Escherichia-Shigella were clustered among the top 20 genera. Also, the presence of two genera, Alistipes and Delftia was prominent in the healthy group (Fig. 6).

Figure 6
figure 6

The phylogenetic relationships of gut microbiome in the patients with cystic echinococcosis (CE) and healthy individuals at the top 20 genera based on the relative abundance. Phylogenetic analysis was performed in Mega7 software by maximum likelihood method based on the Kimura 2-parameter model with 1000 bootstraps (a) CE Patients. (b) Healthy individuals. (c) Active CE patients. (d) Patients with transitional CE stage. (e) Inactive CE patients.

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Discussion

During the past decade, significant focus has been placed on the role of the host microbiome in various outcomes of infectious diseases in humans and animals. Very little is known about the impact of the host microbiome on the development and natural history of parasitic infections19. However, our knowledge of the factors involved in the natural history of cystic echinococcosis is limited. Several studies have demonstrated that intestinal microbiota are associated with helminth infections20,21. The purpose of this study was to identify the intestinal microbiome of cystic echinococcosis patients of different stages with the healthy individuals.

In the present study, a total of 4,862 OTUs with a total frequency of 2,955,291 were obtained from the active, transitional, and inactive stages of hydatid cysts in CE patients and healthy individuals. Compared to the healthy individuals, a reduced OTU diversity was found in CE patients, as the average number of OTUs in CE patients was 439.6, compared to 530.7 for healthy individuals. Diverse microbiota in the human gut plays a key role in intestinal stability and its normal function. A reduction and disturbance in the diversity of the gut microbiome can lead to intestinal dysfunction and inflammatory disorders22. Dysbiosis of the human gut microbiome and disruptions in the balance of gastrointestinal microorganisms can lead to serious health problems, including liver diseases23 and irritable bowel disease24. Gut microbial diversity can be reduced due to various factors including antibiotic therapy25,26, low-fiber diet27, immune enhancement28, and metabolic syndrome29. Decreased diversity in the gut microbiome has been documented in many diseases, including inflammatory bowel disease30, autoimmune hepatitis31, and type 1 diabetes32.

Assessment of the identified OTUs among the three patient groups indicated lower OTU diversity in the patients with inactive cysts (1372 OTUs) than in those with active (1742 OTUs) and transitional (1817 OTUs) cyst stages. This is in line with the finding of a recent study showing a decreased diversity of the gut microbiome in patients with acute schistosomiasis compared to those with chronic ones15. Several studies have reported lower gut microbial diversity in chronic diseases such as ulcerative colitis, rheumatoid arthritis33, chronic kidney disease34, and non-alcoholic fatty liver disease35. Changes in bacterial metabolic pathways following the reduction in gut microbial diversity have a potential impact on the function of the human immune system36. Higher intestinal microbial diversity is documented in severe acute ulcerative colitis than in mild and moderate forms37. In rheumatoid arthritis, alternations in the composition of the gut microbiota have been demonstrated between the early and active stages of the disease38.

Analysis of alpha diversity in different groups of CE patients as well as healthy individuals showed no significant differences. Although more diversity was found in the healthy individuals than in the CE patients, beta diversity analysis showed no significant differences among the groups. In addition, based on the PCOA analysis, no distinct clustering was observed within or between the groups. The diversity analysis in the three groups of hydatid cyst patients showed no significant difference within and between the groups; however, the active and transitional groups showed higher diversity than the inactive group.

Our findings indicated that the most frequent bacteria in our subjects belonged to Firmicutes, Bacteroidota, Actinobacteriota, and Proteobacteria. In a study on the gut microbiome of sheep infected with E. granulosus, the most dominant bacteria in the healthy and infected groups were belonged to the phyla Firmicutes and Proteobacteria39. The highest and lowest OTU frequencies were observed in the inactive and active groups, respectively. Based on the frequency per OTUs, the highest frequency was related to the bacteria of the genus Agathobacter. The highest and lowest frequencies of this OTU were observed in the inactive and active groups, respectively. Agathobacter species are gram-positive anaerobic bacteria that belong to the Lachnospiraceae family. They produce butyrate, which is a short-chain fatty acid (SCFA)40, and play a positive key role in intestinal physiology as well as in preventing the invasion of pathogens by modulating the immune system41. A decrease in the frequency of Agathobacter has been reported in sleep disorders of children with autism40, as well as in patients with atrial fibrillation42, and recurrent spontaneous abortions43.

The genus Anaerostipes was present at a significantly higher frequency in the CE patients’ group. Anaerostipes species are gram-variable, obligates anaerobes bacteria that produce acetate and butyrate, leading to modulating the host response to inflammation44,45. Therefore, it can be speculated that Anaerostipes species microbial richness might be associated with the pathophysiology of cystic echinococcosis.

In our study, Candidatus arthromitus showed a significantly lower frequency in patients with CE. C. arthromitus is a gram-positive segmented filamentous bacteria (SFB) attaching to the host's intestinal epithelium. They are immune-modulating bacteria present in the small intestine of a variety of vertebrates, with a known role in IgA production and T-cell maturation46. One study showed the presence of a special variant of C. arthromitus in human ileostomy samples46. A decrease in SFBs has been documented in mice infected with the rodent nematode Nippostrongylus brasiliensis, suggesting a role for these bacteria in the induction of T helper 17 cells and the expression of genes related to IL-1714. In immunodeficient C57BL/6 Rag1-knock out mice, FSBs showed a protective role against rotaviruses independent of immune-mediated mechanisms47.

Also, findings of the differential analysis of the frequency of OTUs among the three active, transitional, and inactive stages of CE indicated that the active stage of CE exhibited a higher frequency of the family Pseudomonadaceae, including Pseudomonas species. Bacteria in this genus are opportunistic, gram-negative pathogens. There are no comparative microbiome studies on CE patients with different disease stages; however, one study showed that the frequency of Pseudomonas sp._C27_2019 was higher in the healthy group than in the CE patients11. Similar findings have been reported for other helminth infections. In an intestinal microbiome study on schistosomiasis, the order Pseudomonadales was absent in uninfected children compared with schistosomiasis patients48. Additionally, during experimental opisthorchiasis in hamsters, Pseudomonas spp. have been detected in the biliary tree20. Also, a higher frequency of Pseudomonas spp. has been reported in multiple sclerosis49 and diabetic patients50. In patients with diabetes, Pseudomonas probably participates in autoimmune diseases through pro-inflammatory responses. Immune cells associated with pro-inflammatory responses include Th1, Th17, B cells, inflammatory dendritic cells, and monocytes51. LPS, a compound in the cell wall of gram-negative bacteria, causes a local immune response, inflammation, and insulin resistance50. The immune system can play an important role in cystic echinococcosis and the evolution of hydatid cysts from CE1 to CE5 types. Nevertheless, little is known about how immunological responses attribute to different cyst types52. Different immunological processes have been documented in different cyst stages. Normally, in CE1 patients, a proven Th-2 immune response and production of IgG antibodies to a multitude of antigens have been observed. On the other hand, in case of CE4 cysts there are significant leukocyte infiltrations as well as high levels of antibodies, while CE5 cysts have demonstrated decreased antibody profiles52.

In patients with CE1 stage cyst, there is a significant increase in inflammatory cytokines (IL- 1β and IL-1Rα) and chemokines (MIP-1α and MIP-1β) expression compared to the other stages53. It is also reported that TH9 cells increase significantly in the liver and blood of CE1, CE2 and CE3b stages compared to the inactive forms and healthy individuals54.Moreover some specific immunodominant epitopes of hydatid cyst fluid are involved in disease progression from CE1 to CE255.

Three microRNAs derived from the metacestode in CE patients, have presented different expression patterns in the active and inactive stages of CE, of which egr-let-7-5p and egr-miR-9-5p can be considered as potential biomarkers for differentiating active and inactive cysts56.

It is believed that the complex inter-relations of the host immune system and gut microbiome with E. granulosus can modulate the host immune mechanisms and affect the evolution of different stages of hydatid cyst. However, further in-depth studies are necessary to fully understand these processes.

In our study, in patients with the transitional stage of hydatid cyst, a higher frequency of the family Gemellaceae was recorded compared to the active and inactive stages. Remarkably, Gemella was not present in the inactive stage. Gemella is a facultative anaerobic gram-positive bacteria57. An increase in the frequency of this genus has been documented in patients with colorectal cancer58 and diabetes50,59. In animal studies on oral infections in mice, Gemella was associated with decreased IL-12 levels60, but its role in the intestinal microbiome is unclear61. The specific role of different cytokines including IL-12 has not yet been well-documented in CE. The molecular immunological basis of the variations in the natural history of hydatid cyst is poorly understood, however there are several studies investigating factors and determinants of this processes both in in vitro and in vivo settings52,53,54,55,56,62. Studies have shown that Th1 and Th2 responses play an essential role in CE infection, and the disruption of the balance of Th1/Th2 related cytokines has an important role in the immunopathogenic changes of the disease53. IL-12, mainly produced by activated macrophages/monocytes, is important to initiate and regulate the innate cellular immune responses. It also triggers a protective T helper type 1 immune response in hydatid disease63. Further studies are required to understand the role of different cytokine-related processes on the evolution of hydatid cyst in human and different intermediate hosts.

Another family with a pronounced presentation among patients with inactive hydatid cysts was Lactobacillaceae. Members of the Lactobacillaceae family are among the most common probiotic bacteria64. In this family, the genus Ligilactobacillus was found to have a significantly higher frequency in the inactive stage than in the active and transitional stages. Ligilactobacillus spp. are lactic acid, gram-positive bacteria previously known as Lactobacillus65. A recent study on the gut microbiome of mice infected with E. granulosus exhibited an enriched frequency of the Lactobacillaceae family in the healthy group17. In a comparative study on the intestinal microbiome of mice with acute and chronic Schistosoma japonicum infection, a decreasing trend was found in the frequency of Lactobacillaceae in healthy individuals in the acute and chronic phases of the disease66. Several studies have shown that Ligilactobacillus salivarius is related to reducing inflammation-induced colitis in murine models67. These bacteria exert anti-inflammatory activity by reducing the production of TNF-α , IL-6 and IL1ß68. In contrast to the Lactobacillaceae, the genus Megamonas belonging to the family Selenomonadaceae was totally absent in the patients with inactive CE compared with the other groups. One study showed that Megamonas is a differential biomarker of acute and chronic Schistosoma japonicum infection and presented a significantly higher frequency of chronic Schistosoma infection15. Further comparative studies are required on animal and human echinococcosis to document the role of the microbiome in the natural history of hydatid cysts.

According to the various studies, the composition of the gut microbiome can be changed by factors such as age, diet, genetics69,70, and mode of birth71. In our study, to minimize the genetic and dietary differences among the individuals, one family member from each group was selected to serve as the control; however, these effects cannot be eliminated from the study, and various host-related factors should be considered in gut microbiome studies.

Limited metagenomic data are available on the probable role of the intestinal microbiome in the natural history of cystic echinococcosis. In this study, we identified the gut microbial communities in CE patients as well as healthy individuals. In addition, for the first time, in this study, the gut microbiome of patients at different stages of hydatid disease was compared. The inactive forms of CE showed the highest microbiome frequency, compared to the active forms, with the lowest frequency. Also, a reduced diversity of the microbial community in CE patients was found compared to the healthy individuals.

Conclusion

Our findings indicated that several bacterial genera including Pseudomonas, Gemella, and Ligilactobacillus can play a role in the fate of the disease in patients with different stages of hydatid cyst. Also, Anaerostipes and Candidatus showed significantly different reads in the CE patients compared to the healthy individuals. Our understanding of the nature and significance of microbiome in cystic echinococcosis is poor and obviously, further large-scale multi-center studies across the different stages of hydatid cysts in different endemic countries are required to improve our knowledge of the management and control of the disease.

Materials and methods

The present study was designed to investigate the microbial communities in CE patients and healthy individuals. Informed consent was obtained from all participants in this study. The study was approved by the Research Ethics Review Committee of Kerman University of Medical Sciences (Approval code: IR.KMU.AH.REC.1400.349) and all the methods performed in accordance with relevant guidelines and protocols of this committee.

Participant inclusion and sample collection

CE patients with a confirmed history of hepatic cystic echinococcosis with different cyst classifications of hydatid disease (CE1-CE5) were included in the study. The patients were either surgically confirmed as having known liver hydatid cysts or confirmed by abdominal ultrasonography in the case of the CE4 and CE5 stages. A 24 h dietary recall questionnaire was used to assess the dietary habits and overall health status of each participant, and individuals with comparable dietary habits and socioeconomic status were recruited for the study. The exclusion criteria for the study included: individuals with a past medical history of other parasitic infections; patients with any personal history of significant health problems such as cancer, gastrointestinal syndromes, and other systemic diseases; individuals with an undefined cyst classification according to the WHO-IWGE ultrasound classification; and history of antibiotic therapy within the past three months.

Using data from the Iranian national hydatid disease registry (HydatidReg), 12 participants living in Kerman Province were enrolled in the study. The participants included nine patients with CE divided into three groups representing various stages of hepatic hydatid cysts: active (CE1 and CE2), transitional (CE3), and inactive (CE4 and CE5). To minimize genetic and dietary differences among individuals, one family member from each group was selected as the control. Prior to the collection of stool samples, a comprehensive checklist of dietary habits and overall health status was administered to each participant.

Stool samples were obtained from each individual, and the specimens were transferred to the departments’ laboratory. Thereafter, microscopic examinations were performed on direct wet as well as preparations from formalin ether concentration, to exclude any intestinal parasitic infection. All specimens were promptly stored at −70 ℃ until DNA extraction.

DNA extraction and amplicon sequencing

Total genomic DNA was extracted from fecal samples using a QIAamp PowerFecal Pro DNA Kit (Qiagen, Germany), following the manufacturer’s instructions. The concentration of the extracted DNA was determined by nanodrop spectrophotometry (NANODROP 2000C, Thermo SCIENTIFIC). To assess the quality and purity of the DNA sample, the 260/280 ratio of the samples was evaluated. Additionally, DNA samples were subjected to 1.5% agarose gel electrophoresis. The DNA samples were delivered to the Beijing Genomics Institute (BGI), where the V3–V4 hypervariable regions of bacteria were used to amplify a 466-bp fragment of the 16S rRNA gene using primers 341F (5′- CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′)72. Metagenomic DNA libraries were prepared, and next-generation amplicon sequencing was performed on the Illumina MiSeq platform with error rates of less than one, according to the instructions provided elsewhere73. Further analyses and investigations were conducted using raw data acquired from sequencing.

Metagenomic data analysis

The raw data were analyzed according to the standard guidelines for paired-end data using QIIME2 v. 2023.5 pipeline. Initially, the FastQC program version 0.12.1 was utilized to verify the quality of the raw sequences74. In the next stage, the DADA2 plugin was utilized to perform sequence trimming, denoising, and removal of adapters and primer sequences to eliminate any low-quality reads74. To ensure accurate taxonomic assignment, the Rescript plugin was used to train the Silva database75 using the specific primer pair (341F and 806R) employed in our study to amplifying the V3-V4 region of the 16S rRNA gene. The OTU clustering was performed using the Silva database (v.123) with a 99% sequence similarity threshold76. Alignment was performed using the Mafft plugin. Subsequently, the samples were rarified to a sampling depth of 157,000 sequences per sample, followed by phylogenetic analysis using Fasttree plugin.

Bacterial composition, frequency and diversity analysis

QIIME2 v.2023.5 was employed to examine the relative abundance and composition of OTU features for the top 20 taxa at different taxonomic levels. To determine the significance of differences in frequency, Kruskal–Wallis one-way ANOVA and Mann–Whitney U tests were performed using SPSS software. Alpha diversity analysis was carried out using various indices, including Shannon, Pielou’s evenness, Chao1, and Simpson, using the non-parametric Kruskal–Wallis test in QIIME2. Beta diversity analysis was assessed using Jaccard, Bray–Curtis, and unweighted/weighted UniFrac indices using PERMANOVA test in QIIME2. In addition, Principal Coordinates Analysis (PCoA) was utilized to investigate the similarities in bacterial community structures among individuals or groups based on the Bray–Curtis index. Shared and unique gut microbiota in each group were demonstrated by Venn diagrams using an online tool (bioinformatics.psb.ugent.be/webtools/Venn/). Maximum likelihood method was applied for phylogenetic analysis at the genus level in each group for the top 20 genera.

Ethics approval and consent to participate

Informed consent was obtained from the patients before participating in the study. The study protocol was reviewed and approved by the Research Ethics Review Committee of Kerman University of Medical Sciences under ethical approval code, IR.KMU.AH.REC.1400.349.