Development of a real-time quantitative PCR method for detection and quantification of Prevotella copri

Comparative analysis of human gut bacterial microbiota between shallow shotgun metagenomic sequencing and full-length 16S rDNA amplicon sequencing

The human gut microbiome is increasingly recognized as important to health and disease, influencing immune function, metabolism, mental health, and chronic illnesses. Two widely used, cost-effective, and fast approaches for analyzing gut microbial communities are shallow shotgun metagenomic sequencing (SSMS) and full-length 16S rDNA sequencing. This study compares these methods across 43 stool samples, revealing notable differences in taxonomic and species-level detection. At the genus level, Bacteroides was most abundant in both methods, with Faecalibacterium showing similar trends but Prevotella was more abundant in full-length 16S rDNA. Genera such as Alistipes and Akkermansia were more frequently detected by full-length 16S rDNA, whereas Eubacterium and Roseburia were more prevalent in SSMS. At the species level, Faecalibacterium prausnitzii, a key indicator of gut health, was abundant across both datasets, while Bacteroides vulgatus was more frequently detected by SSMS. Species within Parabacteroides and Bacteroides were primarily detected by 16S rDNA, contrasting with higher SSMS detection of Prevotella copri and Oscillibacter valericigenes. LEfSe analysis identified 18 species (9 species in each method) with significantly different detection between methods, underscoring the impact of methodological choice on microbial diversity and abundance. Differences in classification databases, such as Ribosomal Database Project (RDP) for 16S rDNA and Kraken2 for SSMS, further highlight the influence of database selection on outcomes. These findings emphasize the importance of carefully selecting sequencing methods and bioinformatics tools in microbiome research, as each approach demonstrates unique strengths and limitations in capturing microbial diversity and relative abundances.

Abundance of Prevotella copri in gut microbiota is inversely related to a healthy diet in patients with type 2 diabetes

While the gut microbiota is known to be influenced by habitual food intake, this relationship is seldom explored in type 2 diabetes patients. This study aims to investigate the relationship between dietary patterns and gut microbial species abundance in 113 type 2 diabetes patients (mean age, 58 years; body mass index, 29.1; glycohemoglobin [HbA1c], 8.1%). We analyzed the gut microbiota using 16S amplicon sequencing, and all patients were categorized into either the Bacteroides enterotype (57.5%, n = 65) or the Prevotella enterotype (42.5%, n = 48) using the partitioning around medoids clustering algorithm, based on the most representative genera. Patients with the Bacteroides enterotype showed better glycemic control with a 2.71 odds of HbA1c ≤ 7.0% compared to the Prevotella enterotype (95% confidence interval, 1.02-7.87; P, 0.034). Dietary habits and the nutrient composition of all patients were assessed using a validated food frequency questionnaire. It was observed that the amounts of dietary fiber consumed were suboptimal, with an average intake of 16 g per day. Additionally, we extracted four dietary patterns through factor analysis: eating-out, high-sugar foods, fish-vegetable, and fermented foods patterns. Patients with the Bacteroides enterotype had higher scores for the fish-vegetable pattern compared to the Prevotella enterotype (0.17 ± 0.13 versus -0.23 ± 0.09; P, 0.010). We further investigated the relationship between the microbiota and the four dietary patterns and found that only the fish-vegetable dietary pattern scores were correlated with principal coordinate values. A lower pattern score was associated with the accumulated abundance of the 31 significant microbial features. Among these features, Prevotella copri was identified as the most significant by using a random forest model, with an area under the receiver operating characteristic of 0.93 (95% confidence interval, 0.88-0.98). To validate these results, we conducted a custom quantitative polymerase chain reaction assay. This assay confirmed the presence of P. copri (sensitivity, 0.96; specificity, 0.97) in our cohort, with a prevalence of 47.8%, and a mean relative abundance of 21.0% in subjects harboring P. copri. In summary, type 2 diabetes patients with the Prevotella enterotype demonstrated poorer glycemic control and deviations from a healthy dietary pattern. The abundance of P. copri, as a major contributing microbial feature, was associated with the severity in the deficiency in dietary fish and vegetables. Emphasis should be placed on promoting a healthy dietary pattern and understanding the microbial correlations.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

Correlation between Targeted qPCR Assays and Untargeted DNA Shotgun Metagenomic Sequencing for Assessing the Fecal Microbiota in Dogs

DNA shotgun sequencing is an untargeted approach for identifying changes in relative abundances, while qPCR allows reproducible quantification of specific bacteria. The canine dysbiosis index (DI) assesses the canine fecal microbiota by using a mathematical algorithm based on qPCR results. We evaluated the correlation between qPCR and shotgun sequencing using fecal samples from 296 dogs with different clinical phenotypes. While significant correlations were found between qPCR and sequencing, certain taxa were only detectable by qPCR and not by sequencing. Based on sequencing, less than 2% of bacterial species (17/1190) were consistently present in all healthy dogs (n = 76). Dogs with an abnormal DI had lower alpha-diversity compared to dogs with normal DI. Increases in the DI correctly predicted the gradual shifts in microbiota observed by sequencing: minor changes (R = 0.19, DI < 0 with any targeted taxa outside the reference interval, RI), mild-moderate changes (R = 0.24, 0 < DI < 2), and significant dysbiosis (R = 0.54, 0.73, and 0.91 for DI > 2, DI > 5, and DI > 8, respectively), compared to dogs with a normal DI (DI < 0, all targets within the RI), as higher R-values indicated larger dissimilarities. In conclusion, the qPCR-based DI is an effective indicator of overall microbiota shifts observed by shotgun sequencing in dogs.

Evaluation of hypoglycemic effect, safety and immunomodulation of Prevotella copri in mice

The gut bacterium Prevotella copri (P. copri) has been shown to lower blood glucose levels in mice as well as in healthy humans, and is a promising candidate for a next generation probiotic aiming at prevention or treatment of obesity and type 2 diabetes. In this study the hypoglycemic effect of live P. copri was confirmed in mice and pasteurization of P. copri was shown to further enhance its capacity to improve glucose tolerance. The safety of live and pasteurized P. copri was evaluated by a 29-day oral toxicity study in mice. P. copri did not induce any adverse effects on body growth. General examination of the mice, gross pathological and histological analysis showed no abnormalities of the vital organs. Though relative liver weights were lower in the pasteurized (4.574 g ± 0.096) and live (4.347 g ± 0.197) P. copri fed groups than in the control mice (5.005 g ± 0.103) (p = 0.0441 and p = 0.0147 respectively), no liver biochemical marker aberrations were detected. Creatinine serum levels were significantly lower in mice fed with live (p = 0.001) but not pasteurized (p = 0.163) P. copri compared to those of control mice. Haematological parameter analysis and low plasma Lipopolysaccharide Binding Protein (LBP) levels ruled out systemic infection and inflammation. Immunomodulation capacity by P. copri as determined by blood plasma cytokine analysis was limited and gut colonisation occurred in only one of the 10 mice tested. Taken together, no major adverse effects were detected in P. copri treated groups compared to controls.