Clinicians have long recognized that thyroid hormones have an effect on the gastrointestinal tract. Patients with hyperthyroidism always have a high level of thyroid hormones. But the effect of hyperthyroidism on gut microbiota is still not clear. The purpose of this study was to characterize the gut microbiota in hyperthyroid patients and assess whether there are changes in the diversity and similarity of gut microbiota in the hyperthyroid patients when compared with healthy individuals. Fecal specimens from 14 hyperthyroid patients and 7 healthy individuals were collected and then extracted bacterial DNA was subjected to PCR targeting the 16S rRNA gene using universal primers. All samples were analyzed using PCR-DGGE technology and the selective bands were excised and cloned for sequencing. Enterobacteriaceae, Enterococcus, Bifidobacterium, Clostridium and Lactobacillus genus were also enumerated by real-time quantitative PCR. By comparing bacterial diversity of two groups, it was shown that there were significant difference between hyperthyroid and healthy groups (P=0.025). The results of real-time PCR revealed that Bifidobacterium and Lactobacillus genus decreased dramatically in the hyperthyroid group (Pï¼œ0.05), in contrast, Enterococcus increased significantly (Pï¼œ0.05). In conclusion, this study demonstrates the characterization of gut microbiota in hyperthyroid patients for the first time, and observes that there are some changes on the gut microbiota of the hyperthyroid patients.
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Humans live in symbiosis with a diverse community of microorganisms, howeverï¼Œthe gut microbiota is the biggest and most important, the composition of this microbial community is host specific, evolving throughout an individual's lifetime and susceptible to both exogenous and endogenous modifications. This "microbial organ" has a profound effect on human health and physiology, providing benefits such as modulation of immune development, digestion of recalcitrant dietary nutrients and inhibition of pathogen colonization . Unfortunately, abnormalities in the composition of gut microbiota have been implicated in many disease states, including malnutrition, inflammatory bowel disease (IBD), Crohn's disease and colitis colon, as well as metabolic diseases, including obesity and type II diabetes [2-7]. Thus, better understanding of the composition of the gut microbiota is essential to understand the combination of gut microbiota and disease, and develop new effective therapies.
Hyperthyroidism, often referred to as an 'overactive thyroid', is a condition in which the thyroid gland produces and secretes excessive amounts of the free thyroid hormones. Thyroid hormones have an effect on the gastrointestinal tract at all levels of organization and clinicians have long recognized that the associations exist between gastrointestinal symptoms and thyroid disease. Patients with hyperthyroidism may present a variety of symptoms such as weight loss (often accompanied by an increased appetite), anxiety, irritability, nausea, vomiting and diarrhea. In addition, there is strong evidence that thyroid hormones are necessary to ensure normal maturation in intestinal mucosal cells. The weight of the small intestine, the height of the villi and the total mucosal thickness increase in experimental hyperthyroidism in the rat [8-10]. So we deduce that the environment of bacteria has been changed. This may be benefit or bad for the growth of some bacteria in the gut. Most studies, however, have focused on the therapy and mechanism of hyperthyroidism and its complications, the research about changes in the composition of intestinal microbiota is still in blank. I It is impelling to analyze the changes in intestinal microbiota in patients with hyperthyroidism which can be helpful to restore the ecological balance of the gut microbiota and the mucosal barrier, reduce the complication of gastrointestinal symptoms, and be helpful for the prevention and development of new therapies of hyperthyroidism.
During the last decade, studies employing molecular techniques have elucidated that culture-based approaches may severely underestimate the bacterial diversity in most environments . The use of rRNA gene sequences for the analysis of microbial communities has been widely used in microbial ecology and has been replacing the more-conventional cultural methods gradually . Denaturing gradient gel electrophoresis (DGGE), a rapid and effective molecular method, has been frequently applied in environmental microbiology [13, 14, 15] and food microbiology [16, 17] and in the analysis of microbial communities in the human body [18, 19, 20, 21]. The basis of this technique is that DNA fragments of the same size but with different base pair sequences can be separated. This separation by DGGE relies on the electrophoretic mobility of partially denatured DNA molecules in a polyacrylamide gel, which is encumbered in comparison to the completely helical form of the molecule .While real-time PCR with group-specific primers is considered as a very sensitive and precise technique for an extensive quantitative evaluation of gut microbiota.
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This study analyzed the DGGE profiles of the complex microbial diversity sampled from the patient with hyperthyroidism and healthy individuals. PCR-DGGE was combined with image analysis to give insights into the microbial diversity of fecal samples, while UPGMA dendrogram construction and sequencing were done to test for disease-associated DGGE motifs and taxa. Furthermore, real-time PCR was used to quantify Enterobacteriaceae, Enterococcus, Bifidobacterium, Clostridium and Lactobacillus genus to observe changes at specific genus level.
Materials and Methods
Samples Collection and Processing
Fecal samples were collected from 14 patients with hyperthyroidism (5 male and 9 female, ranging from 45-65 years old) and 7 healthy volunteers (3 male and 4 female, ranging from 48-60 years old). All the patients and volunteers were free from gastrointestinal diseases and did not receive antibiotics, probiotics and prebiotics within one month prior to sampling. All the samples were collected using sterile tube and then were immediately stored at -80â„ƒ until used.
Total DNA Extraction
Total DNA was extracted separately by using E.Z.N.A. Â® Stool DNA Kit (OMEGA, USA) according to the manufacturer's instructions. The integrity of the nucleic acids was determined visually by electrophoresis on 1% agarose gel (BIOWEST) containing ethidium bromide. The DNA concentration was determined using Bio-Photometer plus (Nano Vue, USA). All the DNA samples were pooled, and used as template in PCR to amplify 16S rDNA.
The variable V3 regions of 16S rRNA genes were amplified by using the universal bacterial primer 341F-GC clamp and reverse primer 518R. Their nucleotide sequences are listed in Table 1. GC-rich sequences were incorporated into one of the primers to modify the melting behavior of the fragment of interest to the extent to which close to 100% of all possible sequence variations can be detected [27, 28].
A total of 50 Î¼l of reaction mixture consisted of 20 pmol of each primer, 20 mM of dNTP mixture, 5 Î¼l 10Ã- Ex Taq buffer (Mg2ï¼‹ plus), 5 Î¼l 1 % BSA, 2.5 U of Ex Taq DNA polymerase (TakaRa, Japan), and 100-150 ng of template DNA. The PCR amplification was performed by Thermo Cycler (Thermo USA) using the following program: denaturing at 94â„ƒ for 5 min, followed by 30 cycles of 30 s of denaturing at 94â„ƒ, 30 s of annealing at 54â„ƒ, 30 s of elongation at 72â„ƒ and final extension at 72â„ƒfor 7 min. The PCR products were checked by electrophoresis in 1 % (w/v) agarose gel and stained with ethidium bromide.
Table 1: Primer used in PCR-DGGE
Denaturing Gradient Gel Electrophoresis
The DGGE analysis was performed by a Universal Mutation Detection System (Bio-Rad, USA). The electrophoresis was performed on an 8.0 % acrylamide/bis-acrylamide (37.5:1) gel with a 35-55 % denaturant gradient (100 % denaturant solution contained 7 mM urea and 40 % [v/v] formamide) and repeated at least three times. Comparison of DGGE profiles in different gels was performed by employing a standard reference (DNA Marker: DL2000). All DGGE analysis was performed at a constant temperature of 60â„ƒ at 90 V for 10 min, followed by 65 V for 7 h. Gels were stained for 40 min with 0.5 Î¼g/ml ethidium bromide solution, then washed by deionized water, and viewed by Gel Documentation System (Bio-Rad, USA) .
DGGE Fingerprints Analysis
In this study, the software Phoretix 1D (Phoretix International, Newcastle upon Tyne, UK) was used to analyze the abundance and relative intensity in the DGGE fingerprints . The abundance was expressed as the number of bands in the DGGE fingerprints. The relative intensity referred to the peak area of a particular band as a percentage of the total peak area of all the bands in that sample . The Shannon-Wiener index of diversity (HÂ´) [42, 43] was used to determine the diversity of taxa present in fecal sampled from the hyperthyroid and healthy group [44, 45]. This index was calculated by the following equation:
Shannon-Wiener index (HÂ´) = ï¼
where s is the number of bands in the sample and is the relative intensity value of band i. Generally, the data were nonuniformly distributed, therefore, a nonparametric analysis was performed using a Mann-Whitney U test, where a Pï¼œ0.05 was considered as statistically significant. The nonparametric statistical analysis was performed using SPSS (version 11.5). The evenness (E) which reflected uniformity of bacterial species distribution was calculated by the following equation:
Evenness (E) = HÂ´/In s
Excision of bands from the gel was performed using sterilized scalpel under UV illumination. Gel slices were washed in 200 Î¼l sterile deionized water, kept in 30 Î¼l sterile water overnight at 4â„ƒ for diffusion, and then heated at 90â„ƒ for 10 min. The DNA extracted from the gel slices was used as a template for the re-amplification by PCR using the primers 341F (without GC clamp) and 518R. The PCR program was the same as described previously. The PCR products were recovered after gel electrophoresis, cloned into the PMD18-T Easy vector (TakaRa, Japan) and used to transform Escherichia coli Nova blue cell. Positive clones were selected, and another round of amplification was performed with these clones to confirm that the DNA fragment inserted in the plasmid was from a single band. Positive clones that met the DGGE criteria were selected, and their cultures were subjected to sequencing (TakaRa, Japan). Then we use Blast search (NCBI) to determine similarities between sequences and those deposited at GenBank .
Real-Time PCR Analysis
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The PCR amplification and detection were performed with a real-time PCR detection system (Agilent, USA). Each reaction mixture (25 Î¼l) was composed of 12.5 Î¼l of 2Ã-SYBR Green PCR Mix (TakaRa, Japan), 1Î¼l of each specific primer (20 Î¼M) (Table 2), 2 Î¼l of sample DNA and 8.5 sterile deionized water. For Enterococcus, Escherichia coli, Clostridium and Lactobacillus, the PCR reaction program was one cycle of 95â„ƒ for 1 min, then 40 cycles of 95â„ƒ for 5 s, 55â„ƒ for 30 s, and 72â„ƒ for 30 s. For Bifidobacterium, the procedure was one cycle at 95â„ƒ for 1 min, 40 cycles at 95â„ƒ for 10 s, 55â„ƒ for 15 s, and 72â„ƒ for 50 s. A melting curve analysis was done after amplification to distinguish targeted products from nontargeted products . The melting curves were obtained by an extra cycle of 95â„ƒ for 1 min, 55â„ƒ for 30 s, and 95â„ƒ for 30 s. Plasmid DNAS were obtained by PCR cloning with the specific primer sets , and then the PCR products were purified, cloned and sequenced as previously described. A tenfold dilution series of plasmid DNA was used as the standard DNA in each real-time PCR.
Table 2: Specific 16S rRNA primers for real-time PCR
DGGE Fingerprints Analysis of Hyperthyroid Group and Healthy Group
DGGE analysis was performed with the PCR products of the universal primers targeting the V3 region of the 16S rRNA gene from the hyperthyroid and healthy group (Fig. 1, right). The number, position and intensity of the bands were different among samples, which indicated the complex fingerprints of gut microbiota. The cluster analysis generated an UPGMA dendrogram (Fig. 1, left) which based on analysis of similarity score and cluster from DGGE profiles by Phoretix 1D (Single Gel Dendrogram) software. There were two main clusters in each dendrogram, the healthy group and hyperthyroid group are in the different cluster. This demonstrates statistically that the dominant microbiota of the hyperthyroid group was different from that of the healthy group. In order to analyze the diversity of intestinal microbiota in hyperthyroid and healthy group, the Mann-Whitney U test was used to compare the Shannon-Weaver indexes of diversity (HÂ´) of the bands from DGGE profiles. The results revealed that there was a significantly greater biological diversity in the two groups (P < 0.05). The band number of viral hyperthyroid group was richer as compared with healthy group (P=0.065), but it's not significantly (Table 3).
Fig. 1 The fingerprint of and dendrogram cluster analysis of intestinal microbiota of hyperthyroid and healthy group.
H1-14: the hyperthyroid group; C1-7: the healthy group, M: DL2000 marker.
Bands H2a, H2b, H4a, H5a, H5b, H6a, H6b, C4a, C5a, C6a, C6b, C7a, H9a, H11a were cut for sequencing
Table 3: Microbiota diversity index analysis of hyperthyroid and healthy group
Sequence Analysis of Selected Bands
We selected 14 bands and cut them from the DGGE gel based on quantity analysis. The obtained sequences were further analyzed through GeneBank (NCBI) by Blast search (Table 4). In order to certify the resolution capability of DGGE, bands in the same position but in different lanes (4A and 6A, 13A and 14A) were selected and sequenced, the result were identical, which indicates that DGGE can separated V3 16S rRNA genes from different bacteria effectively. Band H4a, H5b, H6a, H6b, C6b, C7a, H9a were belonging to Firmicutes; Band H2a, H5a, C4a, C5a, C6a were belonging to Bacteroidetes; and band H11a and H2b were belonging to Proteobacteria and Fusobacteria.
Table4: Sequence identities of PCR amplicons derived from DGGE gels
Real-Time PCR Analysis
Real-time PCR analyses were performed to quantify Enterococcus, Enterobacteriaceae, Bifidobacterium, Lactobacillus, and Clostridium genus in fecal samples of hyperthyroid group and healthy group. The standard plasmid with six serial dilutions was simultaneously used for each detection. The results indicated that the hyperthyroid group had less copy numbers of Bifidobacterium and Lactobacillus in the fecal microbiota than healthy group (Pï¼œ0.05). However, Clostridium and Enterococcus in hyperthyroid group was higher in copy number than that in healthy group (Pï¼œ0.05). Enterobacteriaceae in hyperthyroid group and healthy group in copy number had no significant difference (Table 5). The data were presented as the means of triplicate determinations (Fig.2 ).
Table 5: Quantitation analysis of bacterial copy numbers in fecal samples by real-time PCR
Data were reported as the average estimate of Logarithms of fecal PCR target genetic amplicon copy numbers present in 1 g of feces.
Results which are significantly different (Mann-Whitney U test). * Pï¼œ0.05 and ** Pï¼œ0.01.
Fig. 2 Real-time PCR detection for Enterococcus, Enterobacteriaceae, Bifidobacterium, Lactobacillus, Clostridium were presented to bacterial populations in the hyperthyroid group and healthy group
In this manuscript, we demonstrate that the fecal microbial composition was different between the hyperthyroid group and healthy group by molecular profiling, cloned sequencing and quantitative PCR (qPCR) analysis. Most strikingly, there was a significant difference in the similarity of bacterial community and the number of Enterococcus, Bifidobacterium, and Lactobacillus between hyperthyroid group and healthy group. Here, for the first time, we provide basic knowledge about the intestinal bacterial communities in hyperthyroid patients and we show that the fecal microbial has the characteristics in the hyperthyroid patients which can affect human immunity, physiology and the development of thyreoidism.
One of the major challenges in micro ecology is the assessment of the bacterial diversity or community structure present in a defined environment. Recent advances in microbial ecology and culture-independent methods including Clone library, Denaturing Gradient Gel Electrophoresis (DGGE), Temperature Gradient Gel Electrophoresis (TGGE), Fluorescence In-situ Hybridization (FISH), 454 Pyrosequencing etc. have proved useful to allow us understand the polymicrobial processes and have highlighted the limitations of culture dependent methods. Still DGGE has its advantages as a means of studying microbial ecology among all because it is easy to perform, rapid to get results, and it provides a more accurate means of visualizing whole microbial communities. In our study, the bacterial diversity of the gut microbiota in hyperthyroid group and healthy group was analyzed by combining DGGE of the 16S rRNA gene with imaging and sequencing of key PCR amplicons, together with statistical analyses [6, 22, 31].
When considering ecological diversity and community structure, it is believed that species diversity is an important feature in maintaining a degree of stability within that community. There are a variety of ecological diversity measures, nevertheless, previous workers  have demonstrated that the diversity index Shannon-Weaver index (HÂ´) can be applied to complex microbial communities and is well suited for comparing large sets of samples. To assess the diversity of intestinal microbiota in our study, we calculated the number of bands and HÂ´ from DGGE profiles. The results demonstrated that there was a greater biological diversity in the fecal sample group with the hyperthyroidism than non-hyperthyroidism (P = 0.025). This might indicate that an increase in bacterial diversity may be associated with the shift from health to hyperthyroidism and it might be related to the bacterial overgrowth in hypothyroid patients [23, 24].
Characteristic profiles of microbial communities or DNA fingerprints can be produced by DGGE. To analyze the changes of gut microbiota of hypothyroid patients, the similarity of gut microbiota were compared by cluster and diversity analysis. Cluster analysis, also known as "classification," has been defined as the search for a natural grouping . UPGMA, one of Cluster analysis, applied in this study, can be used to identify samples that generate similar patterns [51, 52]. The UPGMA dendrogram showed that samples from hyperthyroid group and the healthy group was clustered to two different clades respectively. This implied that the two groups had different DGGE fingerprints, demonstrating the different composition of gut microbiota communities. It also showed that the hypothyroid group displayed a relative high homology, indicating characteristics of thyreoidism.
In order to get more information from the DGGE fingerprints, sequencing was used in this experiments. First, to verify the accuracy and reliability of the DGGE technique, the test was detected in this study by excising bands of same migration positions and carried out sequence analysis to ensure that the same identification was obtained. The results show that same bacteria share same migration, and indicate that DGGE can separate V3 16S rRNA genes from different bacteria effectively. Then we use sequencing to identify dominant bacteria. We selected dominant bands from different positions of DGGE profile to perform cloning and sequence. The results in our study showed that most the excised bands from the two groups were belonging to Firmicutes or Bacteroidetes. Published studies including ours have also reported that Firmicutes and Bacteroidetes were the dominant bacterial phyla in the identified fecal microbiota [25, 26, 47]. Same with the previous studies, the Prevotella genus was found associated with healthy group. Surprisingly, we found that another bacterium Fusobacterium nucleatum, belonging to Fusobacterium, in the hyperthyroid group. Fusobacterium is a gram-negative anaerobe. Individual cells are rod-shaped bacilli with pointed ends. Strains of Fusobacterium contribute to several human diseases, including periodontal diseases, Lemierre's syndrome, ulcerative colitis, and topical skin ulcers . In addition, Aleksandar and Dirk  have recently revealed that Fusobacterium species were enriched in colorectal carcinomas, especially the phylotype of F. nucleatum. We discovered this bacterium in the hypothyroid patients' stool, which means that the hypothyroid patients have the high ratio to infect with the above-mentioned diseases.
DGGE with its advantages also have some limitations as DGGE is not a truly quantitative technique, its band density does not accurately relate to target abundance. Therefore, it is possible that subtle associations between species abundance and diseases would not necessarily be identified . In our study, we designed the specific primer pairs and performed real-time quantitative PCR using SYBR Green I to detect the quantitative estimation of the five main gut bacterium including Enterococcus, Enterobacteriaceae, Bifidobacterium, Lactobacillus, and Clostridium genus. The result of our study indicates that Enterococcus is remarkably higher in the hypothyroid group, and the Bifidobacterium and Lactobacillus have a significant decrease. The Enterobacteriaceae and Clostridium both increased, but there is no significant difference. This is the first quantitative investigation on alternation of gut microbiota. Enterococcus, as we known, is a Gram-positive, facultative anaerobic genus of the phylum Firmicutes. From a medical standpoint, an important feature of this genus is the high level of intrinsic antibiotic resistance. Some enterococci are intrinsically resistant to Î²-lactam-based antibiotics, as well as many aminoglycosides . We speculated the increase of Enterococcus of the patients with thyreoidism might be a risk factor for acquiring infections. However, the probiotics, especially Lactic acid bacterium and Bifidobacterium, may confer a health benefit on the host. Metchnikoff et al.  reported that probiotics such as Bifidobacterium and Lactobacillus may beneficially affect the host by augmenting its intestinal microbial population beyond the amount already existing, thus possibly inhibiting pathogens. The decrease of Bifidobacterium and Lactobacillus may lead to poor immune function. This puts them at increased risk for food-borne illness, and goes against the rehabilitation of diseases.
Gut microbiota may play an even more important role in maintaining human health than previously thought. In our study, we observed the characterization of intestinal microbiota in hypothyroid patients for the first time, which suggests that the intestinal microbiota of hypothyroid patients have some changes compared with healthy group. More studies are necessary to reflect the mechanism between thyreoidism and gut microbiota. A combined approach of DGGE and real-time PCR can directly reflect the alternation of the gut microbiota, but they cannot accurately or truly reproduce bacterial community composition. Therefore, we still need to improving study methods and offsetting experimental errors as much as possible through the integration of other techniques, which will allow experimental technologies to better serve scientific studies and provide guidance for the diagnosis, treatment and rehabilitation of thyreoidism.