The Structural Shifts Of Gut Microbiota In Development Of Chronic Kidney Disease

BackgroundThe human microbial community is huge, which widely distributes the skin, the oral cavity and gastrointestinal tract. Among them, the microbiota which lives in the gastrointestinal tract is referred to the intestinal flora. The gut flora is the most complex part in the human microbial community and plays an important role in human health. The weight of the intestinal flora with a normal adult can reach 1-2 kg, number as many as 1014, including 500-1000 different species, mainly consisting of anaerobic bacteria, facultative anaerobic bacteria and aerobic bacteria etc. All the bacteria is different degree distribution in gut according to certain proportion and quantity. In normal circumstance, the intestinal microbiota keeps dynamic balance inside the human body and it is necessary and beneficial to maintain the human health. The intestinal microbiota participates in digestion nutrition, biosynthesis, biological barriers, and the immune functions promotion. Intestinal mucosa barrier system, consisting of normal intestinal flora, complete intestinal mucosa structure, mucin on the surface of mucosa, sIgA and gut immune cells, which prevents the gut bacteria and endotoxin produced by bacteria to shift outside. In a variety of pathological conditions, microflora imbalance is caused by the damage of the condition which is host to maintain the microbial balance. Flora imbalance includes proportion imbalance and bacteria translocation. The proportion imbalance refers to the dominant origin bacteria with growth suppression and a small number of bacterial with overgrowth, which induces the gut microflora imbalance. Bacterial translocation defines bacteria and bacterial toxin products (such as endotoxin) from the intestine into the mesenteric lymph nodes and other parenteral organs and parts, in most cases, endotoxemia is an important indicator of a reflection of bacterial translocation.At the same time, the intestine which is the biggest bacteria library and endotoxin pool in human is the main source of endogenous infections and endotoxemia. Recent years, increasing studies show that in some pathological conditions such as liver cirrhosis, severe hepatitis and serious burns etc, appearing gut flora imbalance, intestinal barrier function damage, bacteria translocation and enterogenous endotoxemia, which can influence patients’life quality seriously.Chronic kidney disease (CKD) has a broad prevalence, afflicting millions of people worldwide. In China,119.5 million aduls aged 18 years or older have CKD, making it an important public health problem. Renal insufficiency is often a consequence of multisystem disorders, such as hypertension, diabetes, or cardiovascular disease, and it is associated with systemic inflammation. Digestive symptoms and prolonged colonic transit time are frequent concerns for patients with CKD, and each of these factors is closely related to gut microbiota. Several studies have suggested the pathogenic role of gut microbiota in kidney disease. Recently, Vaziri et al. demonstrated that uremia profoundly altered intestinal microbial flora. However, these studies used a small sample size (24 patients and 12 controls) and the diversity of microbiota in CKD patients has not been progressively studied.In current study, fecal microbial communities were profiled using traditional cultivation, quantitative real-time polymerase chain reaction and high-throughput sequencing. The aim was to evaluate and quantify differences in the compositions of gut microbiota during the progression of CKD and to elucidate a possible relationship between gut microbiota profiles and systemic inflammation in CKD.Materials and methodsStudy subjects. Fresh fecal samples were collected into sterile containers from 92 patients diagnosed with CKD and 96 healthy volunteers. CKD was defined as the estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 for 3 months, irrespective of the presence or absence of kidney damage. Individuals with kidney damage were also classified as having CKD, irrespective of eGFR value. CKD patients were divided into end-stage renal disease (ESRD) and Non-ESRD groups.None of the involved individuals used antibiotics and probiotics in the previous 4 weeks.Assessment of clinical parameters. Fasting venous blood samples were collected, and serum was separated in aliquots and immediately frozen at-80℃. A modified kinetic Jaffe method was used to measure serum creatinine, and the CKD Epidemiology Collaboration (CKD-EPI) equation was used to measure eGFR values. Cystatin C and C-reactive protein (CRP) were measured by immunoturbidimetric assays. Lipopolysaccharide (LPS) was detected with the chromogenic end-point Limulus Amebocyte Lysate (LAL) assay. Plasma cholesterol; triglycerides; and high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL) cholesterol levels were determined using enzymatic methods.Bacterial culture.500 mg of the samples was homogenized and 10-fold serial dilutions were made in nutrient broth. The dilutions(in triplicates,20μl each) were plated on different selective medium in an anoxic workstation(Shanghai, P.R. China) which contained a gas atmosphere of N2-C02-H2(80:10:10,by vol) for anaerobic bacteria. Selective medium used for culture 6 types of bacteria, including 4 anaerobes (Bacterioides, Lactobacillus, Bifidobacterium and Clostridium) and 2 aerobes (Enterobacter and Enterococcus). Plates for anaerobes were incubated in the anaerobic workstation, which was maintained at 37℃. Plates for aerobes were incubated aerobically at 37℃. The anaerobic colonies were counted after 72h while the aerobes were counted after 24h.The results are given as colony forming unit (CFU) per gram of fecal wet weight.DNA extraction. Immediately after collection, fecal samples were stored at-80℃ until they were analyzed. DNA extraction from stools was performed with TIANamp Stool DNA Kits (TIANGEN Biotech, Beijing, China) according to the manufacturer’s instructions. All DNA samples were stored at-80℃ until further processing.Quantitative real-time PCR (qPCR). qPCR assays were performed in 96-well optical plates on the LightCycler(?) 480 Real-Time PCR System (Roche Diagnostics, Basel, Switzerland). All assays were carried out in duplicate and performed in a total volume of 20μl LightCycler(?) 480 SYBR Green I Master solution (Roche Diagnostics). The reaction mixture consisted of 0.5μM concentrations of each of the specific primer pairs for quantification and 5μl DNA template. Amplifications were performed as follows:initial denaturation at 95℃ for 5 min, followed by 45 cycles of denaturation at 95℃ for 10s, annealing at 50-61℃ (primer dependent) for 20s, and extension at 72℃ for 30s. Amplification specificity was assessed with melting curve analysis. The efficiency of amplification for each primer pair was estimated from the standard curves.Pyrosequencing and bioinformatics analysis. The PCR cycle conditions were performed as follows:an initial denaturation at 94℃ for 2 min,25 cycles of 94℃ for 30s,59℃ for 30s, and 72℃ for 30s, and a final extension at 72℃ for 5 min. Each 20μl reaction mixture consisted of 10μl TaKaRa Premix Taq,1μl template DNA, 0.5μl 10μM barcode forward primer,0.5μl 10μM reverse primer, and 8μl double-distilled H2O. The barcode-tagged 16S rRNA gene PCR products were pooled with the other samples and sequenced using Illumina GAII (Illumina, San Diego, CA, USA) at the Beijing Genomic Institute (Shenzhen, China). All reads were sorted into different samples according to their barcodes. The sequences were clustered by the program UCLUST using default parameters with identity parameter set to 0.97. The Ribosomal Database Project (RDP) classifier was used to determine the phylogeny of the operational taxonomic unit (OTU). Principal coordinate analysis (PCoA) based on UniFrac distance was performed with the UniFrac metric. A relatively short UniFrac distance implies the similar composition between two communities. The linear discriminant analysis (LDA) effect size (LEfSe) was used to identify indicator bacterial groups specialized within the three groups.Statistical analysis. Data are presented as mean±standard deviation (SD) or median and interquartile range for quantitative variables and as ratio for qualitative variables. Given the non-normal distribution of the analyzed data, non-parametric tests were used to assess differences in bacterial number and biochemical variables. Spearman correlation coefficients were calculated to estimate the linear correlations between variables. Statistical analyses were performed with the statistical software package SPSS13.0 (SPSS Inc., Chicago, IL, USA). Two-tailed P values less than 0.05 were considered statistically significant.ResultsClinical characteristics and biochemical indicators. Samples were collected from 92 patients diagnosed with CKD and 96 healthy volunteers. The anthropometric and biochemical variables of the healthy individuals and those with CKD were measured. Apart from cystatin C levels, no other significant differences were observed. LPS and bacterial DNA in serum were also assessed. Compared to healthy controls, a marked increase in LPS level was observed in Non-ESRD patients, and the increase was more significant in ESRD patients. A similar change was observed for serum bacterial DNA levels. Consistent with other studies, we found the patients exhibited elevated CRP levels. The increase was significant in ESRD patients compared with either Non-ESRD patients or healthy volunteers, while the different between Non-ESRD patients and healthy controls was not significant.Bacterial cultivation. Patients with chronic kidney disease had significantly lower numbers of Enterobacteriaceae and Enterococcus than those in healthy control. There were no significant differences between the CKD and control groups in the numbers of Clostridium, Lactobacillus, Bacteroides and Bifidobacterium. As facultative anaerobic organisms, Enterobacteriaceae and Enterococcus were obtained in more than 85% individuals. However, the positive rate of obligate anaerobes was 20～49% (Clostridium 20%; Lactobacillus 26%; Bacteroides 49%; Bifidobacterium 38%).Quantification of bacterial gene copies in feces. qPCR was used to assess the changes in the bacterial population abundance in the fecal samples of the three groups. The bacterial copy number values were converted into logarithmic values before analysis. The quantities of total 16S rRNA gene copies of Bacteroides, Clostridium, Enterococcus, Bifidobacterium, and Escherichia were significantly decreased in CKD patients compared with the healthy group, but the number of Lactobacillus was similar among all three groups. The reduction was more significant in patients with ESRD than in the Non-ESRD group. In the ESRD patients, the numbers of total 16S rRNA gene copies of Clostridium were significantly reduced with respect to the Non-ESRD patients. Similar trends were observed for Bacteroides, Bifidobacterium, and Escherichia, though the differences were not statistically significant.The correlation coefficients for bacterial gene copy numbers and kidney function biomarkers were calculated for each individual. In patients with CKD, we found a significant negative correlation between serum creatinine level and the amount of specific bacterial groups (total bacteria, Bacteroides, Clostridium, Enterococcus, Bifidobacterium, and Escherichia). A negative correlation was also observed between the level of cystatin C and the quantities of total bacteria, Bacteroides, Clostridium, Lactobacillus, and Escherichia. We further observed that patients with higher CRP levels had marked reductions in bacterial gene copy numbers.Metagenomic analysis.PCoA based on the UniFrac metric revealed a clear separation of healthy volunteers and CKD patients. The difference between healthy controls and ESRD patients was much more significant than that between healthy controls and Non-ESRD patients.The ratios of different phyla and genera were assessed by taxonomic assignment of all sequences using the RDP classifier. Among all bacterial groups revealed by the interpretable sequences, Bacteroidetes and Firmicutes were the most predominant phyla in both healthy individuals and patients. Bacteroidetes accounted for 41.7%, 40.7%, and 42.8% of the gut microbiota in Non-ESRD patients, ESRD patients, and healthy volunteers, respectively, followed by Firmicutes, which contributed 40.6%, 38.7%, and 41.1%, respectively. Proteobacteria and Actinobacteria were the next most dominant phyla at 15.6% and 0.4% in Non-ESRD patients,13.4% and 0.9% in ESRD patients, and 10.2% and 2.9% in healthy controls, respectively. High inter-individual variability was observed in microbial compositions. For example, Bacteroidetes and Firmicutes accounted for 2-86% and 6-93%, respectively, among all individuals.LEfSe showed indicator microbial groups between patients and healthy volunteers. Compared to Non-ESRD patients, four groups in Bacteroidetes (Prevotellaceae, Prevotella, Paraprevotella, and Butyricimonas), nine groups in Firmicutes (Ruminococcaceae, Dialister, Clostridiaceae, Catenibacterium, Roseburia, Ruminococcus, Eubacterium, Coprococcus, and Dorea) and one genus in Actinobacteria (Collinsella) were enriched in healthy individuals. Compared to ESRD patients, three groups in Bacteroidetes (Prevotellaceae, Prevotella, and Hallella), eight groups in Firmicutes (Roseburia, Faecalibacterium, Dialister, Coprococcus, Dorea, Catenibacterium, Butyricicoccus, and Oribacterium) and eight groups in Proteobacteria (Pseudomonadaceae, Pseudomonas, Pasteurellales, Pasteurellaceae, Pseudomonadales, Haemophilus, Parasutterella, and Alcallgenaceae) were enriched in healthy individuals. Two groups in Bacteroidetes, Bacteroidaceae (family), and Bacteroides (genus), were enriched in both ESRD and Non-ESRD patients, whereas Parebacteroides (genus), belonging to Bacteroidetes, was only enriched in ESRD patients.Conclusion1. The gut microbiota disorder with different degrees is prevalent in patients with chronic kidney disease。The disorder started from the low abundance OTUs and butyrate-producing bacteria significantly reduced. What’s more, the higher deterioration of renal function, the more serious disorder in gut microbiota.2. Patients with chronic kidney disease performed damage of intestinal mucosa barrier resulting in bacterial translocation and intestinal endotoxemia, which may be the important reason for micro-inflammatory state in chronic kidney disease.5. Traditional bacterial cultivation with low cost could suggest the disorder of gut microbiota, and it could be used for living bacterium analysis. Therefor, traditional bacterial cultivation still have cinical value.