scholarly journals Choice of 16S ribosomal RNA primers affects the microbiome analysis in chicken ceca

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nadia Darwish ◽  
Jonathan Shao ◽  
Lori L. Schreier ◽  
Monika Proszkowiec-Weglarz
Keyword(s):  
16S Rrna ◽  
Dna Sequences ◽  
Bacterial Communities ◽  
Beta Diversity ◽  
Taxonomic Composition ◽  
Rrna Gene ◽  
Unifrac Distance ◽  
Variable Regions ◽  
Alpha And Beta Diversity ◽  
Predicted Function

AbstractWe evaluated the effect of applying different sets of 16S rRNA primers on bacterial composition, diversity, and predicted function in chicken ceca. Cecal contents from Ross 708 birds at 1, 3, and 5 weeks of age were collected for DNA isolation. Eight different primer pairs targeting different variable regions of the 16S rRNA gene were employed. DNA sequences were analyzed using open-source platform QIIME2 and the Greengenes database. PICRUSt2 was used to determine the predicted function of bacterial communities. Changes in bacterial relative abundance due to 16S primers were determined by GLMs. The average PCR amplicon size ranged from 315 bp (V3) to 769 bp (V4–V6). Alpha- and beta-diversity, taxonomic composition, and predicted functions were significantly affected by the primer choice. Beta diversity analysis based on Unweighted UniFrac distance matrix showed separation of microbiota with four different clusters of bacterial communities. Based on the alpha- and beta-diversity and taxonomic composition, variable regions V1–V3(1) and (2), and V3–V4 and V3–V5 were in most consensus. Our data strongly suggest that selection of particular sets of the 16S rRNA primers can impact microbiota analysis and interpretation of results in chicken as was shown previously for humans and other animal species.

scholarly journals AB1232 ORAL DYSBIOSIS REFLECTS THE IMMUNOLOGICAL ALTERATION OF RA REGARDING TO ACPA AND HLA DRB1*SE: NAGASAKI ISLAND STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1907.2-1907
Author(s):  
Y. Tsuji ◽  
M. Tamai ◽  
S. Morimoto ◽  
D. Sasaki ◽  
M. Nagayoshi ◽  
...  
Keyword(s):  
Beta Diversity ◽  
Healthy Subjects ◽  
Microbial Community Composition ◽  
Alpha Diversity ◽  
Enzyme Linked Immunosorbent Assay ◽  
Rrna Gene ◽  
Oral Microbiota ◽  
Unifrac Distance ◽  
Alpha And Beta Diversity ◽  
The Difference

Background:Anti-citrullinated protein antibody (ACPA) production is observed in several organs even prior to the onset of rheumatoid arthritis (RA), and oral mucosa is considered to be one of the important tissues. The presence of HLA-DRB1*SE closely associates with ACPA production. Saliva is considered to reflect the oral microbiota including periodontal disease. Alteration of oral microbiota of RA becomes to be normalized by DMARDs treatment, however, the interaction of HLA-DRB1*SE, ACPA and oral microbiota of RA patients remains to be elucidated.Objectives:The Nagasaki Island Study, which had started in 2014 collaborating with Goto City, is intended for research of the preclinical stage of RA, including ACPA/HLA genotype screening and ultrasound and magnetic resonance imaging examinations in high-risk subjects. Using the samples accumulated in this cohort, we have tried to investigate the difference of oral microbiota among RA patients and healthy subjects regarding to ACPA and HLA-DRB1*SE.Methods:Blood and salivary samples were obtained from 1422 subjects out of 4276 who have participated in the Nagasaki Island Study from 2016 to 2018. ACPA positivity was 1.7 % in total. Some of RA patients resided in Goto City participated in the Nagasaki Island Study. At this point, we selected 291 subjects, who were ACPA positive non-RA healthy subjects (n=22) and patients with RA (n=33, 11 subjects were ACPA positive and 22 ACPA negative respectively) as the case, age and gender matched ACPA negative non-RA healthy subjects (n=236) as the control. ACPA was measured by an enzyme-linked immunosorbent assay, and HLA genotyping was quantified by next-generation sequencing (Ref.1). The operational taxonomic unit (OUT) analysis using 16S rRNA gene sequencing were performed. The richness of microbial diversity within-subject (alpha diversity) was scaled via Shannon entropy. The dissimilarity between microbial community composition was calculated using Bray-Curtis distance as a scale, and differences between groups (beta diversity) were tested by permutational multivariate analysis of variance (PERMANOVA). In addition, UniFrac distance calculated in consideration of the distance on the phylogenetic tree were performed.Results:Median age 70 y.o., % Female 58.8 %. Among RA and non-RA subjects, not alpha diversity but beta diversity was statistically significance (p=0.022, small in RA). In RA subjects, both alpha and beta diversity is small (p<0.0001), especially significant in ACPA positive RA (Figure 1). Amongt RA subjects, presence of HLA-DRB1*SE did not show the difference but the tendency of being small of alpha diversity (p=0.29).Conclusion:Our study has suggested for the first time the association of oral microbiota alteration with the presence of ACPA and HLA-DRB1*SE. Oral dysbiosis may reflect the immunological status of patients with RA.References:[1]Kawaguchi S, et al. Methods Mol Biol 2018;1802: 22Disclosure of Interests:None declared

scholarly journals Gut microbiota composition is associated with narcolepsy type 1

2020 ◽  
Vol 7 (6) ◽  
pp. e896
Author(s):  
Alexandre Lecomte ◽  
Lucie Barateau ◽  
Pedro Pereira ◽  
Lars Paulin ◽  
Petri Auvinen ◽  
...  
Keyword(s):  
Community Structure ◽  
Gut Microbiota ◽  
Bacterial Communities ◽  
Beta Diversity ◽  
Alpha Diversity ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Differential Abundance ◽  
Alpha And Beta Diversity

ObjectiveTo test the hypothesis that narcolepsy type 1 (NT1) is related to the gut microbiota, we compared the microbiota bacterial communities of patients with NT1 and control subjects.MethodsThirty-five patients with NT1 (51.43% women, mean age 38.29 ± 19.98 years) and 41 controls (57.14% women, mean age 36.14 ± 12.68 years) were included. Stool samples were collected, and the fecal microbiota bacterial communities were compared between patients and controls using the well-standardized 16S rRNA gene amplicon sequencing approach. We studied alpha and beta diversity and differential abundance analysis between patients and controls, and between subgroups of patients with NT1.ResultsWe found no between-group differences for alpha diversity, but we discovered in NT1 a link with NT1 disease duration. We highlighted differences in the global bacterial community structure as assessed by beta diversity metrics even after adjustments for potential confounders as body mass index (BMI), often increased in NT1. Our results revealed differential abundance of several operational taxonomic units within Bacteroidetes, Bacteroides, and Flavonifractor between patients and controls, but not after adjusting for BMI.ConclusionWe provide evidence of gut microbial community structure alterations in NT1. However, further larger and longitudinal multiomics studies are required to replicate and elucidate the relationship between the gut microbiota, immunity dysregulation and NT1.

scholarly journals Accounting for 16S rRNA copy number prediction uncertainty and its implications in microbial diversity analyses

2021 ◽  
Author(s):  
Yingnan Gao ◽  
Martin Wu
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Diversity ◽  
Bacterial Communities ◽  
Beta Diversity ◽  
Copy Number ◽  
Rrna Gene ◽  
Prediction Uncertainty ◽  
Functional Profiles ◽  
Diversity Studies

Background: 16S rRNA gene has been widely used in microbial diversity studies to determine the community composition and structure. 16S rRNA gene copy number (16S GCN) varies among microbial species and this variation introduces biases to the relative cell abundance estimated using 16S rRNA read counts. To correct the biases, methods (e.g., PICRUST2) have been developed to predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored in the downstream analyses. However, a recent study suggests that the uncertainty can be so great that copy number correction is not justified in practice. Despite the significant implications in 16S rRNA based microbial diversity studies, the uncertainty associated with 16S GCN predictions has not been well characterized and its impact on microbial diversity studies needs to be investigated. Results: Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S rRNA GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms PICRUST2 in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, PERMANOVA and random forest test. Conclusion: We have developed a method to accurately account for uncertainty in 16S rRNA GCN predictions and the downstream analyses. For almost all 16S rRNA surveyed bacterial communities, correction of 16S GCN should improve the results when estimating their compositional and functional profiles. However, such correction is not necessary for beta-diversity analyses.

scholarly journals Molecular characterization of bacterial communities in sheep cheese through 16S rRNA gene sequencing

2019 ◽  
Author(s):  
Creciana Maria Endres ◽  
Ícaro Maia Santos de Castro ◽  
Laura Delpino Trevisol ◽  
Michele Bertoni Mann ◽  
Ana Paula Muterle Varela ◽  
...  
Keyword(s):  
Public Health ◽  
16S Rrna ◽  
High Throughput ◽  
Bacterial Communities ◽  
Value Added ◽  
Rrna Gene ◽  
Unifrac Distance ◽  
Organoleptic Characteristics ◽  
Significant Difference

AbstractThe production of sheep’s milk cheese has grown in recent years since it is a high value-added product with excellent properties. As such, it is necessary to provide data on the microbiota and organoleptic characteristics of this product, as well as the influence of these microorganisms on public health. Thus, the aim of the present study was to characterize the microbial community of different types of sheep cheeses using high-throughput sequencing of the 16S rRNA gene. The study was conducted with four groups of cheese: colonial, fresh, feta, and pecorino (n = 5 samples per group). The high-throughput 16S rRNA amplicon sequencing revealed 55 operational taxonomic units in the 20 samples, representing 9 genera of the two bacterial phyla Firmicutes and Proteobacteria. The predominant genera in the samples were Streptococcus and Lactobacillus. When evaluating alpha diversity by the indexes of Simpson, Chao1, Shannon, and Skew no significant differences were observed between the groups. Evaluating of the beta diversity using Bray-Curtis dissimilarity, the group of colonial cheeses presented a significant difference when compared to the feta (q = 0.030) and pecorino groups (q = 0.030). Additionally, the fresh group differed from the pecorino group (q = 0.030). The unweighted Unifrac distance suggests that the colonial cheese group differed from the others. Moreover, the feta cheese group differed from the fresh group. The distance-weighted Unifrac suggests that no significance exists between the groups. According to this information, the microbiota characterization of these cheese groups was useful in demonstrating the bacterial communities belonging to each group, its effects on processing, elaboration, maturation, and public health.

scholarly journals Seasonal changes in bacterial communities associated with healthy and diseasedPoritescoral in southern Taiwan

2016 ◽  
Vol 62 (12) ◽  
pp. 1021-1033 ◽  
Author(s):  
Chorng-Horng Lin ◽  
Chih-Hsiang Chuang ◽  
Wen-Hung Twan ◽  
Shu-Fen Chiou ◽  
Tit-Yee Wong ◽  
...  
Keyword(s):  
Health Status ◽  
Bacterial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Dna Sequences ◽  
Bacterial Communities ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Rrna Gene ◽  
Southern Taiwan ◽  
The 16S Rrna Gene

We compared the bacterial communities associated with healthy scleractinian coral Porites sp. with those associated with coral infected with pink spot syndrome harvested during summer and winter from waters off the coast of southern Taiwan. Members of the bacterial community associated with the coral were characterized by means of denaturing gradient gel electrophoresis (DGGE) of a short region of the 16S rRNA gene and clone library analysis. Of 5 different areas of the 16S rRNA gene, we demonstrated that the V3 hypervariable region is most suited to represent the coral-associated bacterial community. The DNA sequences of 26 distinct bands extracted from DGGE gels and 269 sequences of the 16S rRNA gene from clone libraries were determined. We found that the communities present in diseased coral were more heterogeneous than the bacterial communities of uninfected coral. In addition, bacterial communities associated with coral harvested in the summer were more diverse than those associated with coral collected in winter, regardless of the health status of the coral. Our study suggested that the compositions of coral-associated bacteria communities are complex, and the population of bacteria varies greatly between seasons and in coral of differing health status.

scholarly journals Inconsistent Patterns of Microbial Diversity and Composition Between Highly Similar Sequencing Protocols: A Case Study With Reef-Building Corals

2021 ◽  
Vol 12 ◽  
Author(s):  
Hannah E. Epstein ◽  
Alejandra Hernandez-Agreda ◽  
Samuel Starko ◽  
Julia K. Baum ◽  
Rebecca Vega Thurber
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Beta Diversity ◽  
Sequence Data ◽  
Alpha Diversity ◽  
Gene Profiling ◽  
Rrna Gene ◽  
Sequencing Platform ◽  
Alpha And Beta Diversity ◽  
Diversity Metrics

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata, we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences.

Comparative analysis of microbiome of bladder cancer patients

2020 ◽  
Author(s):  
Ayesha Ishaq ◽  
Saman Zahid ◽  
Salma Mukhtar ◽  
Muhammad Kamran Azim ◽  
Saleem Akhtar ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
16S Rrna ◽  
Bacterial Diversity ◽  
Cancer Patients ◽  
Beta Diversity ◽  
Control Sample ◽  
Diagnostic Tools ◽  
Rrna Gene ◽  
Male And Female ◽  
Alpha And Beta Diversity

Abstract Background: It has been studied that the urinary tract, which was once considered to be sterile retains a unique microbiome. The current study was performed to explore the microbiome of male and female cancerous bladder tissue, including 01 control sample and 09 cancer samples using 16S rRNA gene sequencing. In previous studies, the significance of microbiome has been found to be associated with the bladder cancer. For diversity analysis, V3-V4 regions of 16S rRNA were used for PCR and later sequencing was carried out through Illumina, Miseq platform. The metadata generated was analyzed on QIIME 1.9.1.Result: The bacterial diversity detected showed that five of the phyla; namely Proteobacteria (38.1%), Firmicutes (37.8%), Actinobacteria (5.9%), Thermi (4.9%) and Tenericutes (2.5%) were more abundant in all samples as compared to other phyla. The genera found in all samples were Enterobacter (18.3%), Bacillus (13.9%), Meiothermus (4.9%), Methylotenera (1.2%), Ralstonia (3.6%) and Streptocococcus (1.4%). Ralstonia and Streptococcus were absent in BLC 10 and BLC 2, respectively, while present in the rest. The results of alpha and beta diversity showed that female samples had more bacterial diversity and uniformity as compared to male samples. Conclusion: The present study used biopsy samples of newly diagnosed cancer patients without taking into account any treatment given to the cancer patients. Our analysis showed insignificant differences in alpha and beta diversity of male and female samples. The genus Meiothermus detected in this study was firstly reported in a bladder microbiome analysis. The data generated from such preliminary futuristic study can help in devising new diagnostic tools and therapies.

scholarly journals Microbial community composition and diversity via 16S rRNA gene amplicons: evaluating the Illumina platform

2014 ◽  
Author(s):  
Lucas Sinclair ◽  
Omneya Ahmed Osman ◽  
Stefan Bertilsson ◽  
Alexander Eiler
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Beta Diversity ◽  
Sequence Data ◽  
Soda Lakes ◽  
Microbial Community Composition ◽  
Rrna Gene ◽  
Clustering Methods ◽  
Illumina Platform ◽  
Alpha And Beta Diversity

As new sequencing technologies become cheaper and older ones disappear, laboratories switch vendors and platforms. Validating the new setups is a crucial part of conducting rigorous scientific research. Here we report on the reliability and biases of performing bacterial 16S rRNA gene amplicon paired-end sequencing on the MiSeq Illumina platform. We designed a protocol using 50 barcode pairs to run samples in parallel and coded a pipeline to process the data. Sequencing the same sediment sample in 248 replicates as well as 70 samples from alkaline soda lakes, we evaluated the performance of the method with regards to estimates of alpha and beta diversity. Using different purification and DNA quantification procedures we always found up to 5-fold differences in the yield of sequences between individually barcodes samples. Using either a one-step or a two-step PCR preparation resulted in significantly different estimates in both alpha and beta diversity. Comparing with a previous method based on 454 pyrosequencing, we found that our Illumina protocol performed in a similar manner -- with the exception for evenness estimates where correspondence between the methods was low. We further quantified the data loss at every processing step eventually accumulating to 50\% of the raw reads. When evaluating different OTU clustering methods, we observed a stark contrast between the results of QIIME with default settings and the more recent UPARSE algorithm when it comes to the number of OTUs generated. Still, overall trends in alpha and beta diversity corresponded highly using both clustering methods. Our procedure performed well considering the precisions of alpha and beta diversity estimates, with insignificant effects of individual barcodes. Comparative analyses suggest that 454 and Illumina sequence data can be combined if the same PCR protocol and bioinformatic workflows are used for describing patterns in richness, beta-diversity and taxonomic composition. (version 1.1 resubmitted to PLOS one 2014-Sept-08)

scholarly journals The Effect of Common Viral Inactivation Techniques on 16S rRNA Amplicon-Based Analysis of the Gut Microbiota

2021 ◽  
Vol 9 (8) ◽  
pp. 1755
Author(s):  
Zachary McAdams ◽  
Kevin Gustafson ◽  
Aaron Ericsson
Keyword(s):  
16S Rrna ◽  
Gut Microbiome ◽  
Beta Diversity ◽  
Sodium Dodecyl ◽  
Rrna Gene ◽  
Fecal Dna ◽  
Viral Inactivation ◽  
Fecal Samples ◽  
Alpha And Beta Diversity ◽  
Microbiome Analysis

Research investigating the gut microbiome (GM) during a viral infection may necessitate inactivation of the fecal viral load. Here, we assess how common viral inactivation techniques affect 16S rRNA-based analysis of the gut microbiome. Five common viral inactivation methods were applied to cross-matched fecal samples from sixteen female CD-1 mice of the same GM background prior to fecal DNA extraction. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA. Treatment-dependent effects on DNA yield, genus-level taxonomic abundance, and alpha and beta diversity metrics were assessed. A sodium dodecyl sulfate (SDS)-based inactivation method and Holder pasteurization had no effect on measures of microbial richness, while two Buffer AVL-based inactivation methods resulted in a decrease in detected richness. SDS inactivation, Holder pasteurization, and the AVL-based inactivation methods had no effect on measures of alpha diversity within samples or beta diversity between samples. Fecal DNA extracted with TRIzol-treated samples failed to amplify and sequence, making it unsuitable for microbiome analysis. These results provide guidance in the 16S rRNA microbiome analysis of fecal samples requiring viral inactivation.

scholarly journals Ranking the Biases: The Choice of OTUs vs. ASVs in 16S rRNA Amplicon Data Analysis Has Stronger Effects On Diversity Measures Than Rarefaction and Similarity Threshold

2021 ◽  
Author(s):  
Marlène Chiarello ◽  
Mark McCauley ◽  
Sébastien Villéger ◽  
Colin R Jackson
Keyword(s):  
16S Rrna ◽  
Beta Diversity ◽  
Alpha Diversity ◽  
Diversity Indices ◽  
Taxonomic Composition ◽  
Shannon Index ◽  
Minimal Impact ◽  
Alpha And Beta Diversity ◽  
Methodological Choices ◽  
Similarity Threshold

Abstract BackgroundAdvances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from the classification and downstream analyses of traditional operational taxonomic units (OTUs), and towards the usage of amplicon sequence variants (ASVs). While ASVs have several inherent properties that make them desirable compared to OTUs, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e.g. rarefaction) and computing diversity indices. ResultsWe compared the respective influences of using ASVs vs. OTU-based pipelines, rarefaction of the community table, and OTU similarity threshold (97% vs. 99%) on the ecological signals detected in freshwater invertebrate and environmental (sediment, seston) 16S rRNA data sets, determining the effects on alpha diversity, beta diversity and taxonomic composition. While the choice of OTU vs. ASV pipeline significantly influenced unweighted alpha and beta diversities and changed the ecological signal detected, weighted indices such as the Shannon index, Bray-Curtis dissimilarity, and weighted Unifrac scores were not impacted by the pipeline followed. By comparison, OTU threshold and rarefaction had a minimal impact effect on all measurements, although rarefaction improved overall signals, especially in OTU-based datasets. The identification of major classes and genera identified revealed significant discrepancies across methodologies. ConclusionWe provide a list of recommendations for the analysis of 16S rRNA amplicon data. We notably recommend the use of ASVs when analyzing alpha-diversity patterns, especially in species-rich or environmental samples. Abundance weighted alpha- and beta-diversity indices should also be preferred compared to ones based on the presence-absence of biological units.

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