scholarly journals Different analysis strategies of 16S rRNA gene data from rodent studies generate contrasting views of gut bacterial communities associated with diet, health and obesity

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10372
Author(s):  
Jose F. Garcia-Mazcorro ◽  
Jorge R. Kawas ◽  
Cuauhtemoc Licona Cassani ◽  
Susanne Mertens-Talcott ◽  
Giuliana Noratto
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Relative Abundance ◽  
Bacterial Communities ◽  
Future Research ◽  
Rrna Gene ◽  
Microbial Composition ◽  
Analysis Strategies ◽  
The Impact

Background One of the main functions of diet is to nurture the gut microbiota and this relationship affects the health of the host. However, different analysis strategies can generate different views on the relative abundance of each microbial taxon, which can affect our conclusions about the significance of diet to gut health in lean and obese subjects. Here we explored the impact of using different analysis strategies to study the gut microbiota in a context of diet, health and obesity. Methods Over 15 million 16S rRNA gene sequences from published studies involving dietary interventions in obese laboratory rodents were analyzed. Three strategies were used to assign the 16S sequences to Operational Taxonomic Units (OTUs) based on the GreenGenes reference OTU sequence files clustered at 97% and 99% similarity. Results Different strategies to select OTUs influenced the relative abundance of all bacterial taxa, but the magnitude of this phenomenon showed a strong study effect. Different taxa showed up to 20% difference in relative abundance within the same study, depending on the analysis strategy. Very few OTUs were shared among the samples. ANOSIM test on unweighted UniFrac distances showed that study, sequencing technique, animal model, and dietary treatment (in that order) were the most important factors explaining the differences in bacterial communities. Except for obesity status, the contribution of diet and other factors to explain the variability in bacterial communities was lower when using weighted UniFrac distances. Predicted functional profile and high-level phenotypes of the microbiota showed that each study was associated with unique features and patterns. Conclusions The results confirm previous findings showing a strong study effect on gut microbial composition and raise concerns about the impact of analysis strategies on the membership and composition of the gut microbiota. This study may be helpful to guide future research aiming to investigate the relationship between diet, health, and the gut microbiota.

scholarly journals Gut Microbiota Analysis Results Are Highly Dependent on the 16S rRNA Gene Target Region, Whereas the Impact of DNA Extraction Is Minor

2017 ◽  
Vol 28 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Anniina Rintala ◽  
Sami Pietilä ◽  
Eveliina Munukka ◽  
Erkki Eerola ◽  
Juha-Pekka Pursiheimo ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Dna Extraction ◽  
Rrna Gene ◽  
Gene Target ◽  
Target Region ◽  
Microbiota Analysis ◽  
The Impact ◽  
The 16S Rrna Gene

Impact of a probiotic, inulin, or their combination on the piglets’ microbiota at different intestinal locations

2015 ◽  
Vol 6 (4) ◽  
pp. 473-483 ◽  
Author(s):  
V.A. Sattler ◽  
K. Bayer ◽  
G. Schatzmayr ◽  
A.G. Haslberger ◽  
V. Klose
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Real Time ◽  
Relative Abundance ◽  
Real Time Pcr ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Feed Additives ◽  
Rrna Gene ◽  
16S Rrna Gene Pyrosequencing

Natural feed additives are used to maintain health and to promote performance of pigs without antibiotics. Effects of a probiotic, inulin, and their combination (synbiotic), on the microbial diversity and composition at different intestinal locations were analysed using denaturing gradient gel electrophoresis (DGGE), real-time PCR, and 16S rRNA gene pyrosequencing. Bacterial diversity assessed by DGGE and/or pyrosequencing was increased by inulin in all three gut locations and by the synbiotic in the caecum and colon. In contrast, the probiotic did only affect the microbiota diversity in the ileum. Shifts in the DGGE microbiota profiles of the caecum and colon were detected for the pro- and synbiotic fed animals, whereas inulin profiles were more similar to the ones of the control. 16S rRNA gene pyrosequencing revealed that all three additives could reduce Escherichia species in each gut location, indicating a potential beneficial effect on the gut microbiota. An increase of relative abundance of Clostridiaceae in the large intestine was found in the inulin group and of Enterococcaceae in the ileum of probiotic fed pigs. Furthermore, real-time PCR results showed that the probiotic and synbiotic increased bifidobacterial numbers in the ileum, which was supported by sequencing results. The probiotic and inulin, to different extents, changed the diversity, relative abundance of phylotypes, and community profiles of the porcine microbiota. However, alterations of the bacterial community were not uniformly between gut locations, demonstrating that functionality of feed additives is site specific. Therefore, gut sampling from various locations is crucial when investigations aim to identify the composition of a healthy gut microbiota after its manipulation through feed additives.

scholarly journals Microsporidian Infection in Mosquitoes (Culicidae) Is Associated with Gut Microbiome Composition and Predicted Gut Microbiome Functional Content

2021 ◽  
Author(s):  
Artur Trzebny ◽  
Anna Slodkowicz-Kowalska ◽  
Johanna Björkroth ◽  
Miroslawa Dabert
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Bacterial Communities ◽  
Bacterial Species ◽  
Rrna Gene ◽  
Microbial Composition ◽  
Microbiome Composition ◽  
Microsporidian Infection ◽  
Functional Content

AbstractThe animal gut microbiota consist of many different microorganisms, mainly bacteria, but archaea, fungi, protozoans, and viruses may also be present. This complex and dynamic community of microorganisms may change during parasitic infection. In the present study, we investigated the effect of the presence of microsporidians on the composition of the mosquito gut microbiota and linked some microbiome taxa and functionalities to infections caused by these parasites. We characterised bacterial communities of 188 mosquito females, of which 108 were positive for microsporidian DNA. To assess how bacterial communities change during microsporidian infection, microbiome structures were identified using 16S rRNA microbial profiling. In total, we identified 46 families and four higher taxa, of which Comamonadaceae, Enterobacteriaceae, Flavobacteriaceae and Pseudomonadaceae were the most abundant mosquito-associated bacterial families. Our data suggest that the mosquito gut microbial composition varies among host species. In addition, we found a correlation between the microbiome composition and the presence of microsporidians. The prediction of metagenome functional content from the 16S rRNA gene sequencing suggests that microsporidian infection is characterised by some bacterial species capable of specific metabolic functions, especially the biosynthesis of ansamycins and vancomycin antibiotics and the pentose phosphate pathway. Moreover, we detected a positive correlation between the presence of microsporidian DNA and bacteria belonging to Spiroplasmataceae and Leuconostocaceae, each represented by a single species, Spiroplasma sp. PL03 and Weissella cf. viridescens, respectively. Additionally, W. cf. viridescens was observed only in microsporidian-infected mosquitoes. More extensive research, including intensive and varied host sampling, as well as determination of metabolic activities based on quantitative methods, should be carried out to confirm our results.

scholarly journals Identification and Removal of Potential Contaminants in 16S rRNA Gene Sequence Datasets from Low Microbial Biomass Samples: An Example from the Mosquito Gut

2020 ◽  
Author(s):  
Sebastián Diaz ◽  
Juan Sebastián Escobar ◽  
Frank William Avila
Keyword(s):  
Microbial Biomass ◽  
16S Rrna ◽  
Gut Microbiota ◽  
Relative Abundance ◽  
Vector Competence ◽  
Negative Control ◽  
Rrna Gene ◽  
Physiological Processes ◽  
Physiological Relevance ◽  
The Impact

Abstract Background: The bacterial gut microbiota of the female mosquito influences numerous physiological processes, including vector competence. As a low-microbial-biomass ecosystem, mosquito gut tissue is prone to contamination from the laboratory environment and from reagents commonly used to dissect and/or isolate DNA from gut tissue. In this report, we analyze five 16S rRNA datasets, including new data obtained by us, to gain insight into the impact of potential contaminating sequences on the composition, diversity, and structure of the mosquito gut microbial community. Results: We present a clustering-free approach that, based on the relative abundance of amplicon sequence variants (ASVs) in gut and negative control samples , allowed for the identification of candidate contaminating sequences. Some of these sequences belong to bacterial taxa previously identified as common contaminants in metagenomic studies; they have also been identified as part of the mosquito core gut microbiota, with putative physiological relevance for the host. By using different relative abundance cutoffs, we show that contaminating sequences have a significant impact on gut microbiota diversity and structure.Conclusions: The approach presented here allows the identification and removal of purported contaminating sequences in datasets obtained from low-microbial biomass samples. While it was exemplified with the analysis of gut microbiota from mosquitos, it can easily extend to other datasets dealing with similar technical artifacts.

scholarly journals The impact of persistent bacterial bronchitis on the pulmonary microbiome of children

2017 ◽  
Author(s):  
Leah Cuthbertson ◽  
Vanessa Craven ◽  
Lynne Bingle ◽  
William O.C.M. Cookson ◽  
Mark L. Everard ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Bacterial Diversity ◽  
Community Composition ◽  
Bacterial Communities ◽  
Microbial Community Analysis ◽  
Rrna Gene ◽  
Healthy Children ◽  
Significant Difference ◽  
The Impact

AbstractPersistent bacterial bronchitis is a leading cause of chronic wet cough in young children. This study aimed to characterise the respiratory bacterial microbiota of healthy children and to assess the impact of the changes associated with the development of persistent bacterial bronchitis.Blind, protected brushings were obtained from 20 healthy controls and 24 children with persistent bacterial bronchitis, with an additional directed sample obtained from persistent bacterial bronchitis patients. DNA was extracted, quantified using a 16S rRNA gene quantitative PCR assay prior to microbial community analysis by 16S rRNA gene sequencing.No significant difference in bacterial diversity or community composition (R2 = 0.01, P = 0.36) was observed between paired blind and non-blind brushes, showing that blind brushings are a valid means of accessing the airway microbiota. This has important implications for collecting lower respiratory samples from healthy children. A significant decrease in bacterial diversity (P < 0.001) and change in community composition (R2 = 0.08, P = 0.004) was observed between controls and patients. Bacterial communities within patients with PBB were dominated by Proteobacteria, and indicator species analysis showed that Haemophilus and Neisseria were significantly associated with the patient group. In 15 (52.9%) cases the dominant organism by sequencing was not identified by standard routine clinical culture.The bacteria present in the lungs of patients with persistent bacterial bronchitis were less diverse in terms of richness and evenness. The results validate the clinical diagnosis, and suggest that more attention to bacterial communities in children with chronic cough may lead to more rapid recognition of this condition with earlier treatment and reduction in disease burden.

scholarly journals Monosodium Glutamate Induces Changes in Hepatic and Renal Metabolic Profiles and Gut Microbiome of Wistar Rats

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1865
Author(s):  
Kanokwan Nahok ◽  
Jutarop Phetcharaburanin ◽  
Jia V. Li ◽  
Atit Silsirivanit ◽  
Raynoo Thanan ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Wistar Rats ◽  
Monosodium Glutamate ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Male Wistar Rats ◽  
The Impact

The short- and long-term consumption of monosodium glutamate (MSG) increases urinary pH but the effects on the metabolic pathways in the liver, kidney and the gut microbiota remain unknown. To address this issue, we investigated adult male Wistar rats allocated to receive drinking water with or without 1 g% MSG for 2 weeks (n = 10, each). We performed a Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomic study of the jejunum, liver, and kidneys, while faecal samples were collected for bacterial DNA extraction to investigate the gut microbiota using 16S rRNA gene sequencing. We observed significant changes in the liver of MSG-treated rats compared to controls in the levels of glucose, pyridoxine, leucine, isoleucine, valine, alanine, kynurenate, and nicotinamide. Among kidney metabolites, the level of trimethylamine (TMA) was increased, and pyridoxine was decreased after MSG-treatment. Sequencing of the 16S rRNA gene revealed that MSG-treated rats had increased Firmicutes, the gut bacteria associated with TMA metabolism, along with decreased Bifidobacterium species. Our data support the impact of MSG consumption on liver and kidney metabolism. Based on the gut microbiome changes, we speculate that TMA and its metabolites such as trimethylamine-N-oxide (TMAO) may be mediators of the effects of MSG on the kidney health.

scholarly journals Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling

2020 ◽  
Author(s):  
Sandra Reitmeier ◽  
Thomas CA Hitch ◽  
Nikolaos Fikas ◽  
Bela Hausmann ◽  
Amanda E Ramer-Tait ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Data Processing ◽  
Relative Abundance ◽  
Large Scale ◽  
Peat Soil ◽  
Rrna Gene ◽  
The Impact

Abstract Background: 16S rRNA gene amplicon sequencing is a very popular approach for studying microbiomes. However, varying standards exist for sample and data processing and some basic concepts such as the occurrence of spurious sequences have not been investigated in a comprehensive manner, which was done in the present study. Methods: Using defined communities of bacteria in vitro and in vivo , we searched for sequences not matching the expected species ( i.e. , spurious taxa) and determine a threshold of occurrence relevant for adequate data analysis. The origin of spurious taxa was then investigated via large-scale amplicon queries. We also assessed the impact of varying sequence filtering stringency on diversity readouts in human fecal and peat soil communities. Results: 16S rRNA gene amplicon data processing based on Operational Taxonomic Units (OTUs) clustering and singleton removal, a commonly used approach that discards any taxa represented by only one sequence across all samples, delivered approx. 50% (mock communities) to 80% (gnotobiotic mice) spurious taxa on average. This spurious fraction of taxa was lower based on amplicon sequence variants (ASVs) analysis but varied depending on the gene region targeted and the barcoding system used. A relative abundance of 0.25% was identified as a threshold below which the analysis of spurious taxa can be prevented to a large extent. Most spurious taxa (approx. 70%) detected in simplified communities occurred in samples multiplexed in the same sequencing run and were present in only one of ten runs. Use of the 0.25% relative abundance threshold decreased the coefficient of variations calculated on richness in the same six human fecal samples across seven sequencing runs by 38% compared with singleton filtering. The output of beta -diversity analyses of human fecal communities was markedly affected by both the filtering strategy and the type of phylogenetic distances used for comparing samples. Importantly, major findings were confirmed by using data generated in a second sequencing facility. Conclusions: Handling of artifact sequences during bioinformatic processing of 16S rRNA gene amplicon data requires careful attention to avoid the generation of misleading findings. A threshold of relative abundance of 0.25% is more appropriate than singleton removal, although study-specific analysis strategies are mandatory. We propose the concept of effective richness, which will help comparing results across studies.

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