Homogeneity of the vaginal microbiome at the cervix, posterior fornix, and vaginal canal in pregnant Chinese women.

The vaginal microbiome is an emerging concern in prenatal health. Because the sampling process of vaginal microbiota may pose potential risks for pregnant women, the choice of sampling site should be carefully considered. However, whether the microbial diversity is different across various sampling sites has been controversial. In the present study, three repeated swabs were collected at the cervix (C), posterior fornix (P), and vaginal canal (V) from 34 Chinese women during different pregnancy stages, and vaginal species were determined using the Illumina sequencing of 16S rRNA tag sequences. The identified microbiomes were classified into four community state types (CSTs): CST I (dominated by L. crispatus), CST II (dominated by L. gasseri), CST III (dominated by L. iners), and CST IV-A (characterized by a low abundance of Lactobacillus, but with proportions of various species previously shown to be associated with bacterial vaginosis). All individuals had consistent CST at the three sampling sites regardless of pregnancy stage and CST group. In addition, there was little heterogeneity across community structures within each individual, as determined by LEfSe, indicating high vaginal microbiome homogeneity at the three sampling sites. The present study also revealed different beta diversity during pregnancy stages. The vaginal microbiome variation among women during trimester T1 (9 ± 2.6 weeks) is larger than that of non-pregnant women and women from other trimesters, as demonstrated by the UniFrac distance (P < 0.05). In particular, the present study is the first one that demonstrates the notably difference of vaginal microbiome of postpartum women compare to women in gestation. These results will be useful for future studies of the vaginal microbiota during pregnancy.

Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial communities in a mangrove wetland.

The microbial community plays an essential role in the high productivity in mangrove wetlands. A proper understanding of the spatial variations of microbial communities will provide clues about the underline mechanisms that structure microbial groups and the isolation of bacterial strains of interest. In the present study, the diversity and composition of the bacterial community in sediments collected from four locations, namely mudflat, edge, bulk, and rhizosphere, within the Mai Po Ramsar Wetland in Hong Kong, SAR, China were compared using the barcoded Illumina paired-end sequencing technique. Rarefaction results showed that the bulk sediment inside the mature mangrove forest had the highest bacterial α-diversity, while the mudflat sediment without vegetation had the lowest. The comparison of ß-diversity using principal component analysis and principal coordinate analysis with UniFrac metrics both showed that the spatial effects on bacterial communities were significant. All sediment samples could be clustered into two major groups, inner (bulk and rhizosphere sediments collected inside the mangrove forest) and outer mangrove sediments (the sediments collected at the mudflat and the edge of the mangrove forest). With the linear discriminate analysis scores larger than 3, four phyla, namely Actinobacteria, Acidobacteria, Nitrospirae, and Verrucomicrobia, were enriched in the nutrient-rich inner mangrove sediments, while abundances of Proteobacteria and Deferribacterias were higher in outer mangrove sediments. The rhizosphere effect of mangrove plants was also significant, which had a lower α-diversity, a higher amount of Nitrospirae, and a lower abundance of Proteobacteria than the bulk sediment nearby.

Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018.

Objective: To determine differences in DM in the U.S. population according to demographic characteristics, physical indicators and living habits. Methods: 23 546 participants in the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) who were 20 year of age or older and not pregnant. All analyses used weighted samples and considered the stratification and clustering of the design. Specific indicators include length of leg (cm), BMI (kg/cm2), TCHOL (mg/dL), fasting plasma glucose (mg/dL) and comparison of means and the proportion of participants with DM. Results: The prevalence of DM in the USA has been rising modestly in the past decade, and were consistent and robust for the observed differences in age, sex, and ethnicity. Compared with white participants, black participants and Mexican-American were both more likely (P<0.001) to have diabetes: 14.6% (CI, 13.6% to 15.6%) among black participants, 10.6% (CI, 9.9% to 11.3%) among white participants, and 13.5% (CI, 11.9% to 15.2%) among Mexican-American participants. The prevalence of diabetes is increasing with age, males peaked around the 60s, and women around the 70s. The overall mean leg length and TCHOL was lower in diabetics than in non-diabetics (1.07 cm, 18.67 mg/dL, respectively), while mean BMI were higher in diabetics than in non-diabetics (4.27 kg/cm2). DM had the greatest effect on decline of TCHOL in white participants (23.6 mg/dL), less of an effect in black participants (9.67 mg/dL), and the least effect in Mexican-American participants (8.25 mg/dL). Notably, smoking had great effect on percent increment of DM in whites (0.2%), and have little effect on black and Mexican-Americans. Conclusions: DM is more common in the general population than might be clinically recognized, and the prevalence of DM was associated to varying degrees with many indicators of demographic characteristics, physical indicators, and living habits. These indicators should be linked with medical resource allocation and scientific treatment methods to comprehensively implement the treatment of DM.

Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumina tags.

Sediment, a special realm in aquatic environments, has high microbial diversity. While there are numerous reports about the microbial community in marine sediment, freshwater and intertidal sediment communities have been overlooked. The present study determined millions of Illumina reads for a comparison of bacterial communities in freshwater, intertidal wetland, and marine sediments along Pearl River, China, using a technically consistent approach. Our results show that both taxon richness and evenness were the highest in freshwater sediment, medium in intertidal sediment, and lowest in marine sediment. The high number of sequences allowed the determination of a wide variety of bacterial lineages in all sediments for reliable statistical analyses. Principal component analysis showed that the three types of communities could be well separated from phylum to operational taxonomy unit (OTU) levels, and the OTUs from abundant to rare showed satisfactory resolutions. Statistical analysis (LEfSe) demonstrated that the freshwater sediment was enriched with Acidobacteria, Nitrospira, Verrucomicrobia, Alphaproteobacteria, and Betaproteobacteria. The intertidal sediment had a unique community with diverse primary producers (such as Chloroflexi, Bacillariophyta, Gammaproteobacteria, and Epsilonproteobacteria) as well as saprophytic microbes (such as Actinomycetales, Bacteroidetes, and Firmicutes). The marine sediment had a higher abundance of Gammaproteobacteria and Deltaproteobacteria, which were mainly involved in sulfate reduction in anaerobic conditions. These results are helpful for a systematic understanding of bacterial communities in natural sediment environments.

Analysis of endometrial microbiota in intrauterine adhesion by high-throughput sequencing.

BACKGROUND: Intrauterine adhesions (IUA) arise from scar tissue formation between the endometrial surfaces in response to mechanical or infectious injuries. However, the potential role of endometrial microbiota in IUA remains unclear. We aimed to explore the composition of endometrial microbiota and its potential role in IUA. METHODS: We retrospectively enrolled 46 patients diagnosed with IUA and 21 infertility patients without intrauterine lesions, as control subjects. All cases were diagnosed with hysteroscopy and endometrial tissues were taken from the intrauterine cavity using a hysteroscopic cutting ring without electricity study. After endometrial samples were collected, DNA was extracted and amplified for barcoded Illumina high-throughput next-generation sequencing targeted to the 16S rRNA V4 region for microbiota. Microbiota data were compared between two groups using α-diversity, ß-diversity and Nonmetric Multidimensional Scaling based on Weighted Unifrac distance. RESULTS: At the phyla level, the dominant bacteria included Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria. Proteobacteria accounted for more than 64.48%. At the genus level, the proportions of Klebsiella, Shewanella, and Lactobacillus were higher in patients with IUA than in non- IUA participants (20.67% and 8.77%, P=0.006, 13.37% and 4.53%, P=0.175, 12.74% and 6.95%, P=0.882; respectively). The proportion of Acinetobacter was significantly lower in patients with IUA than in non- IUA participants (P=0.005). CONCLUSIONS: Endometrial microbiota differ between patients with IUA and infertility patients without intrauterine lesions, and the potential variation of endometrial microbiota might cause IUA.

Large-scale characterisation of the pregnancy vaginal microbiome and sialidase activity in a low-risk Chinese population.

Vaginal microbiota-host interactions are linked to preterm birth (PTB), which continues to be the primary cause of global childhood mortality. Due to population size, the majority of PTB occurs in Asia, yet there have been few studies of the pregnancy vaginal microbiota in Asian populations. Here, we characterized the vaginal microbiome of 2689 pregnant Chinese women using metataxonomics and in a subset (n = 819), the relationship between vaginal microbiota composition, sialidase activity and leukocyte presence and pregnancy outcomes. Vaginal microbiota were most frequently dominated by Lactobacillus crispatus or L. iners, with the latter associated with vaginal leukocyte presence. Women with high sialidase activity were enriched for bacterial vaginosis-associated genera including Gardnerella, Atopobium and Prevotella. Vaginal microbiota composition, high sialidase activity and/or leukocyte presence was not associated with PTB risk suggesting underlying differences in the vaginal microbiota and/or host immune responses of Chinese women, possibly accounting for low PTB rates in this population.

Regional variation limits applications of healthy gut microbiome reference ranges and disease models.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1-3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis.

BACKGROUND: The metabolic syndrome (MetS) epidemic is associated with economic development, lifestyle transition and dysbiosis of gut microbiota, but these associations are rarely studied at the population scale. Here, we utilised the Guangdong Gut Microbiome Project (GGMP), the largest Eastern population-based gut microbiome dataset covering individuals with different economic statuses, to investigate the relationships between the gut microbiome and host physiology, diet, geography, physical activity and socioeconomic status. RESULTS: At the population level, 529 OTUs were significantly associated with MetS. OTUs from Proteobacteria and Firmicutes (other than Ruminococcaceae) were mainly positively associated with MetS, whereas those from Bacteroidetes and Ruminococcaceae were negatively associated with MetS. Two hundred fourteen OTUs were significantly associated with host economic status (140 positive and 74 negative associations), and 157 of these OTUs were also MetS associated. A microbial MetS index was formulated to represent the overall gut dysbiosis of MetS. The values of this index were significantly higher in MetS subjects regardless of their economic status or geographical location. The index values did not increase with increasing personal economic status, although the prevalence of MetS was significantly higher in people of higher economic status. With increased economic status, the study population tended to consume more fruits and vegetables and fewer grains, whereas meat consumption was unchanged. Sedentary time was significantly and positively associated with higher economic status. The MetS index showed an additive effect with sedentary lifestyle, as the prevalence of MetS in individuals with high MetS index values and unhealthy lifestyles was significantly higher than that in the rest of the population. CONCLUSIONS: The gut microbiome is associated with MetS and economic status. A prolonged sedentary lifestyle, rather than Westernised dietary patterns, was the most notable lifestyle change in our Eastern population along with economic development. Moreover, gut dysbiosis and a Western lifestyle had an additive effect on increasing MetS prevalence.

Author Correction: Regional variation limits applications of healthy gut microbiome reference ranges and disease models.

In the version of this article originally published, in the sentence "Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results," two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.

[A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption].

OBJECTIVE: To establish a machine learning model based on gut microbiota for predicting the level of trimethylamine N-oxide (TMAO) metabolism in vivo after choline intake to provide guidance of individualized precision diet and evidence for screening population at high risks of cardiovascular disease. METHODS: We quantified plasma levels of TMAO in 18 healthy volunteers before and 8 h after a choline challenge (ingestion of two boiled eggs). The volunteers were divided into two groups with increased or decreased TMAO level following choline challenge. Fresh fecal samples were collected before taking fasting blood samples for amplifying 16S rRNA V4 tags, and the PCR products were sequenced using the platform of Illumina HiSeq 2000. The differences in gut microbiata between subjects with increased and decreased plasma TMAO were analyzed using QIIME. Based on the gut microbiota data and TMAO levels in the two groups, the prediction model was established using the machine learning random forest algorithm, and the validity of the model was tested using a verified dataset. RESULTS: An obvious difference was found in beta diversity of the gut microbota between the subjects with increased and decreased plasma TMAO level following choline challenge. The area under the curve (AUC) of the model was 86.39% (95% CI: 72.7%-100%). Using the verified dataset, the model showed a much higher probability for correctly predicting TMAO variation following choline challenge. CONCLUSION: The model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.

Fructooligosaccharide (FOS) and Galactooligosaccharide (GOS) Increase Bifidobacterium but Reduce Butyrate Producing Bacteria with Adverse Glycemic Metabolism in healthy young population.

The gut microbiota has been implicated in glucose intolerance and its progression towards type-2 diabetes mellitus (T2DM). Relevant randomized clinical trial with prebiotic intervention was inadequate. We sought to evaluate the impact of fructooligosaccharides (FOS) and galactooligosaccharides (GOS) on glycemia during oral glucose tolerance test (OGTT) and intestinal microbiota. A randomized double-blind cross-over study was performed with 35 adults treated with FOS and GOS for 14 days (16 g/day). Faeces sampling, OGTT and anthropometric parameters were performed. Short-term intake of high-dose prebiotics had adverse effect on glucose metabolism, as in FOS intervention demonstrated by OGTT (P < 0.001), and in GOS intervention demonstrated by fasting glucose (P < 0.05). A significant increase in the relative abundance of Bifidobacterium was observed both in FOS and GOS group, while the butyrate-producing bacteria like Phascolarctobacterium in FOS group and Ruminococcus in GOS group were decreased. A random forest model using the initial microbiota was developed to predict OGTT levels after prebiotic intervention with relative success (R = 0.726). Our study alerted even though FOS and GOS increased Bifidobacterium, they might have adverse effect on glucose metabolism by reducing butyrate-producing microbes. Individualized prebiotics intervention based on gut microbiome needs to be evaluated in future.

The Potential Effect of Oral Microbiota in the Prediction of Mucositis During Radiotherapy for Nasopharyngeal Carcinoma.

BACKGROUND: Oral mucositis is probably the most debilitating complication that can arise in treating a patient with head and neck cancer. Little is known about the impacts of oral microbiota on the initiation and progression of mucositis. METHODS: Based on 16S rRNA gene sequencing, dynamic changes in oral bacterial profile as well as correlations between the severity of mucositis and bacterial shifts during radiotherapy were investigated. FINDINGS: Our results revealed that bacterial community structure altered progressively during radiation therapy, in parallel with a marked increase in the relative abundance of some Gram-negative bacteria. Patients who eventually developed severe mucositis harbored a significantly lower bacterial alpha diversity and higher abundance of Actinobacillus during the phase of erythema - patchy mucositis. Accordingly, a random forest model for predicting exacerbation of mucositis was generated, which achieved a high predictive accuracy (AUC) of 0.89. INTERPRETATION: Oral microbiota changes correlate with the progression and aggravation of radiotherapy-induced mucositis in patients with nasopharyngeal carcinoma. Microbiota-based strategies can be used for the early prediction and prevention of the incidence of severe mucositis during radiotherapy.

[Distribution characteristics of trimethylamine N-oxide and its association with gut microbiota].

OBJECTIVE: To analyze the distribution of trimethylamine N-oxide (TMAO) in healthy adults with different risk factors and explore its association with gut microbiota. METHODS: We collected fasting blood samples and fresh fecal samples from 181 subjects without atherogenesis in the carotid arteries. Plasma TMAO levels of the subjects were determined using stable isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS). The fecal DNA was extracted, and the 16S rRNA V4 tags were amplified and sequenced by Illumina HiSeq 2000. The association between TMAO and classical cardiovascular risk factors were analyzed. Gut microbial community structure was analyzed with QIIME, and LEfSe was used to identify the biomarkers. RESULTS: The median (IQR) TMAO level was 2.66 (1.96-4.91) µmol/L in the subjects. TMAO level was significantly correlated with body mass index and operational taxonomic units (OTU). Individuals with high TMAO levels were found to have abundant Clostridiales, Phascolarctobacterium, Oscillibacter, and Alistipes but less abundant Anaerosprobacter. CONCLUSION: Chinese subjects have in general low levels of TMAO. TMAO levels are not significantly correlated with the classical cardiovascular risk factors or the gut microbial structures.

[Methods, challenges and opportunities for big data analyses of microbiome].

Microbiome is a novel research field related with a variety of chronic inflamatory diseases. Technically, there are two major approaches to analysis of microbiome: metataxonome by sequencing the 16S rRNA variable tags, and metagenome by shot-gun sequencing of the total microbial (mainly bacterial) genome mixture. The 16S rRNA sequencing analyses pipeline includes sequence quality control, diversity analyses, taxonomy and statistics; metagenome analyses further includes gene annotation and functional analyses. With the development of the sequencing techniques, the cost of sequencing will decrease, and big data analyses will become the central task. Data standardization, accumulation, modeling and disease prediction are crucial for future exploit of these data. Meanwhile, the information property in these data, and the functional verification with culture-dependent and culture-independent experiments remain the focus in future research. Studies of human microbiome will bring a better understanding of the relations between the human body and the microbiome, especially in the context of disease diagnosis and therapy, which promise rich research opportunities.

[Bacterial community structure and diversity in soils of different forest ages and types in Bao- tianman forest, Henan Province, China].

To compare the microbial compositions and diversities in soils of different forest ages and types in Baotianman forest, Henan Province, China, genomic DNA of forest soils was extracted for amplifying the 16S rRNA V4 hyper variable region by PCR and sequencing by Illumina MiSeq. The BIPES, UCHIME and QIIME were employed to analyze the soil bacterial community. It was shown that 60 phyla were identified, with Proteobacteria, Acidobacteria, and Verrucomicrobia representing the most dominant lineages and accounting for 29%, 18.5% and 10% of all sequences, respectively. At the genus level, 1209 genera were identified, the most abundant phylotypes were DA101 (6.3%), Acidobacteria-2 (5.9%), Candidatus Solibacter (2.9%) and Candidatus Nitrososphaera (2.6%). Different forest age and type soil samples had unique compositions and specific high and rare genus. Forest type and age both impacted the soil microbial community structure, and the influence of the former was stronger than the latter. The soil microbial diversity of the 80-year-old Quercus aliena forest was the lowest among all age and type forest soil samples. Soil pH, soil nitrogen and organic carbon contents were the most important factors affecting soil bacterial community structure.

Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity.

BACKGROUND: The operational taxonomic unit (OTU) is widely used in microbial ecology. Reproducibility in microbial ecology research depends on the reliability of OTU-based 16S ribosomal subunit RNA (rRNA) analyses. RESULTS: Here, we report that many hierarchical and greedy clustering methods produce unstable OTUs, with membership that depends on the number of sequences clustered. If OTUs are regenerated with additional sequences or samples, sequences originally assigned to a given OTU can be split into different OTUs. Alternatively, sequences assigned to different OTUs can be merged into a single OTU. This OTU instability affects alpha-diversity analyses such as rarefaction curves, beta-diversity analyses such as distance-based ordination (for example, Principal Coordinate Analysis (PCoA)), and the identification of differentially represented OTUs. Our results show that the proportion of unstable OTUs varies for different clustering methods. We found that the closed-reference method is the only one that produces completely stable OTUs, with the caveat that sequences that do not match a pre-existing reference sequence collection are discarded. CONCLUSIONS: As a compromise to the factors listed above, we propose using an open-reference method to enhance OTU stability. This type of method clusters sequences against a database and includes unmatched sequences by clustering them via a relatively stable de novo clustering method. OTU stability is an important consideration when analyzing microbial diversity and is a feature that should be taken into account during the development of novel OTU clustering methods.

Erratum to: Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity.

[This corrects the article DOI: 10.1186/s40168-015-0081-x.].

Diverse vaginal microbiomes in reproductive-age women with vulvovaginal candidiasis.

Vulvovaginal candidiasis (VVC) is one of the most prevalent vaginal infectious diseases, and there are controversial reports regarding the diversity of the associated vaginal microbiota. We determined the vaginal microbial community in patients with VVC, bacterial vaginosis (BV), and mixed infection of VVC and BV using Illumina sequencing of 16S rRNA tags. Our results revealed for the first time the highly variable patterns of the vaginal microbiome from VVC patients. In general, the alpha-diversity results of species richness and evenness showed the following order: normal control < VVC only < mixed BV and VVC infection < BV only. The beta-diversity comparison of community structures also showed an intermediate composition of VVC between the control and BV samples. A detailed comparison showed that, although the control and BV communities had typical patterns, the vaginal microbiota of VVC is complex. The mixed BV and VVC infection group showed a unique pattern, with a relatively higher abundance of Lactobacillus than the BV group and higher abundance of Prevotella, Gardnerella, and Atopobium than the normal control. In contrast, the VVC-only group could not be described by any single profile, ranging from a community structure similar to the normal control (predominated with Lactobacillus) to BV-like community structures (abundant with Gardnerella and Atopobium). Treatment of VVC resulted in inconsistent changes of the vaginal microbiota, with four BV/VVC samples recovering to a higher Lactobacillus level, whereas many VVC-only patients did not. These results will be useful for future studies on the role of vaginal microbiota in VVC and related infectious diseases.

Two-stage clustering (TSC): a pipeline for selecting operational taxonomic units for the high-throughput sequencing of PCR amplicons.

Clustering 16S/18S rRNA amplicon sequences into operational taxonomic units (OTUs) is a critical step for the bioinformatic analysis of microbial diversity. Here, we report a pipeline for selecting OTUs with a relatively low computational demand and a high degree of accuracy. This pipeline is referred to as two-stage clustering (TSC) because it divides tags into two groups according to their abundance and clusters them sequentially. The more abundant group is clustered using a hierarchical algorithm similar to that in ESPRIT, which has a high degree of accuracy but is computationally costly for large datasets. The rarer group, which includes the majority of tags, is then heuristically clustered to improve efficiency. To further improve the computational efficiency and accuracy, two preclustering steps are implemented. To maintain clustering accuracy, all tags are grouped into an OTU depending on their pairwise Needleman-Wunsch distance. This method not only improved the computational efficiency but also mitigated the spurious OTU estimation from 'noise' sequences. In addition, OTUs clustered using TSC showed comparable or improved performance in beta-diversity comparisons compared to existing OTU selection methods. This study suggests that the distribution of sequencing datasets is a useful property for improving the computational efficiency and increasing the clustering accuracy of the high-throughput sequencing of PCR amplicons. The software and user guide are freely available at http://hwzhoulab.smu.edu.cn/paperdata/.

BIPES, a cost-effective high-throughput method for assessing microbial diversity.

Pyrosequencing of 16S rRNA (16S) variable tags has become the most popular method for assessing microbial diversity, but the method remains costly for the evaluation of large numbers of environmental samples with high sequencing depths. We developed a barcoded Illumina paired-end (PE) sequencing (BIPES) method that sequences each 16S V6 tag from both ends on the Illumina HiSeq 2000, and the PE reads are then overlapped to obtain the V6 tag. The average accuracy of Illumina single-end (SE) reads was only 97.9%, which decreased from ∼99.9% at the start of the read to less than 85% at the end of the read; nevertheless, overlapping of the PE reads significantly increased the sequencing accuracy to 99.65% by verifying the 3' end of each SE in which the sequencing quality was degraded. After the removal of tags with two or more mismatches within the medial 40-70 bases of the reads and of tags with any primer errors, the overall base sequencing accuracy of the BIPES reads was further increased to 99.93%. The BIPES reads reflected the amounts of the various tags in the initial template, but long tags and high GC tags were underestimated. The BIPES method yields 20-50 times more 16S V6 tags than does pyrosequencing in a single-flow cell run, and each of the BIPES reads costs less than 1/40 of a pyrosequencing read. As a laborsaving and cost-effective method, BIPES can be routinely used to analyze the microbial ecology of both environmental and human microbiomes.